dynamic modelling, analysis and design of smart hybrid ... · “smart hybrid energy storage for...

Dynamic Modelling, Analysis and Design of Smart

Hybrid Energy Storage System for Off-grid

Photovoltaic Power Systems

by

Wenlong JING

A dissertation submitted to the Swinburne University of Technology in support of an

application for the degree of Doctor of Philosophy in Engineering

Faculty of Engineering, Computing and Science

Swinburne University of Technology

Sarawak Campus

January 2019

Dedicated to…

my beloved Parents, Teachers, Partner, Family & Friends

Abstract

Battery technology has been widely utilized in different energy storage

applications. However, limitations and challenges such as heat dissipation, low power

density, unsatisfactory lifetime characteristics, environmental impacts, and high cost

hinder its development in many key areas, for instance, the residential energy storage

applications. To address the issue of the short service life of the battery, hybrid energy

storage system (HESS) of various designs have been reported in the literature. However

the limited focus has been put on the case of the stand-alone residential energy system,

especially for remote rural electrification. This thesis aims at proposing suitable battery-

supercapacitor HESS designs and control strategies that can effectively extend the

battery service lifetime via mitigating its operation stress, thereby realizing the cost

reduction on the installing construction and operating costs of the stand-alone

photovoltaic (PV) power system. For such systems that is planned to be installed in

rural areas, potential suitable battery-supercapacitor HESSs are designed and discussed.

To leverage on existing infrastructure in installed standalone PV-battery power system,

novel smart supercapacitor/Li-ion HESS plug-in module (SHESS) is proposed to relieve

the main battery operation stress. Theoretical analysis and numerical simulations for the

designed HESSs and the SHESS are conducted, and their effectiveness in mitigating

battery stress are investigated and compared via pulse load testing and case studies with

actual data of solar irradiance and load profile from a remote community in Sarawak,

Malaysia. A battery health cost model is formulated to qualitatively evaluate the impact

of battery current on battery health, thus enabling the estimation of service life

improvement as well as the assessment on the economic impact of the remote stand-

alone microgrid. A down-scaled prototype of the proposed HESS is designed and

developed to verify the theoretical analysis and analytical findings. Experiments have

been carried out to test the feasibility and performance of the proposed system in terms

of power-sharing capability in stand-alone PV power system operations. The

experimental results demonstrate the feasibility of the proposed HESSs in retrofitting

existing installed PV power systems and support the theoretical analysis and simulation

outcomes.

Acknowledgments

The journey to achieve the Ph.D. is full of obstacles, challenges, and happiness, which

will always remind me of the importance of humility, exploration and lifelong learning.

The completion of the dissertation marks the end of such an eventful journey and many

people I am willing to offer my sincerest thanks. Firstly, I would like to express my

sincere gratitude to my principal supervisor, Dr. Chean Hung Lai, for his invaluable

inspiration, continuous guidance, constructive criticism and support throughout this

research. This dissertation would not have been possible without his help, support,

resolute dedication, and patience. His expert advice and unsurpassed knowledge of the

various fields of electrical energy systems has always provided me an endless supply of

idea to move to the next step and complete this dissertation.

I also feel privileged that my Ph.D. study was conducted under the supervision of co-

supervisors, Prof. Wallace Wong Shung Hui and Prof. M. L. Dennis Wong. Their

constant encouragement, illuminating guidance, and research suggestions were indeed

essential for the completion of this study and dissertation. Meanwhile, I would like to

thank all colleagues at the Research Laboratory of Swinburne University of Technology

Sarawak Campus for their various forms of help and support so that I can achieve my

research goals. To my best friends and research partners in Malaysia, Dr. Pan, Ms. Jong

Bih Fei, Dr. Liew Lin Shen, Mr. Tay Jia Jun, Abdul Kadir Muhammad Lawan, Mr.

Derrick Ling Kuo Xiong, Mr. Chang Zhi Hao, Dr. Wong Wei Jing and Dr. Chong

Nguan Soon, the memorable times, laughs, countless discussions, sharing, etc that we

have will be the lifelong treasures and I would never forget.

To my dear life partner, Yun Wang, appreciate and value her everlasting love and

understanding so that I could fully focus on my study. I also extend my utmost gratitude

to my dearest teachers, family, and friends, for their encouragement and assistance all

these years. Last but the most important, I would like to give special thanks to my

mother and father for their unconditional support both financially and morally, the grace

of raising me up and deep love, which is the most precious thing in my life.

Declaration

I hereby declare that, to the best of my knowledge, this thesis contains no material

which has been accepted for the award to the candidate of any other degree or

qualification in this, or any other University and contains no material previously

published or written by another person except where due reference is made in the text of

this thesis. Furthermore, any idea, technique, quotation, or any other material from other

people’s work included in this thesis, published or otherwise, are fully acknowledged in

accordance with the standard referencing practices.

----------------------------------

Wenlong JING

January 2019

Publications arising from this study

Journal Papers

Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, "A comprehensive study of Battery-SC hybrid ESS for stand-alone PV power system in rural electrification." Applied Energy, Volume 224, May 2018, Pages 340-356.

Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Dynamic Power Allocation of Battery-SC Hybrid Energy Storage for Stand-alone PV microgrid Applications”, Sustainable Energy Technologies and Assessments, Volume 22, August 2017, Pages 55-64, ISSN 2213-1388.

Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Battery-SC Hybrid ESS in Stand-alone DC Microgrids: A Review”, in IET Renewable Power Generation, vol. 11, no. 4, pp. 461-469, 2017.

Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “A Comprehensive Review of Energy Storage Technologies and Case Study for PV Residential Energy Storage Applications”, (submitted to Journal of Energy Storage on Sept 2018)

Wenlong Jing, Chean Hung Lai, Derrick K.X. Ling, Wallace SH Wong, and ML Dennis Wong, “Battery Lifetime Enhancement via Smart Hybrid Energy Storage Plug-in Module in Stand-alone Photovoltaic Power System”, Journal of Energy Storage, Volume 21, February 2019, Pages 586-598.

Conference Papers

Wenlong Jing, Derrick K.X. Ling, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Hybrid Energy Storage Retrofit for Stand-alone Photovoltaic-Battery Residential Energy System”, The 7th Innovative Smart-grid Technologies (ISGT Asia 2017), Auckland, New Zealand, December 4 – 7, 2017.

Wenlong Jing, Chean Hung Lai, Wallace Wong Shung Hui, M.L. Dennis Wong, “Cost Analysis of Battery-SC Hybrid ESS for Stand-alone PV Systems”, 4th IET International Conference on Clean Energy and Technology, November 2016.

Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Theoretical Analysis and Software Modeling of Composite Energy Storage Based on Battery and SC in Microgrid Photovoltaic Power System”, (ICISCA-2015) International Conference on Information, System and Convergence Applications, Kuala Lumpur, 2015.

Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “Smart Hybrid Energy Storage for Stand-alone PV Microgrid: Optimization of battery lifespan through dynamic power allocation”, IEEE PES Asia-Pacific Power and Energy Engineering Conference, pp. 1-5, Brisbane, 2015.

Wenlong Jing, Chean Hung Lai, Wallace SH Wong, and ML Dennis Wong, “The Comparison between Two Types of Bidirectional Dual Active Bridge DC/DC Converter for Photovoltaic Application”, (In progress)

I

Table of Contents

List of Figures .............................................................................................................................. V

List of Tables ................................................................................................................................ X

Nomenclature .............................................................................................................................. XI

Chapter 1 Introduction ........................................................................................................... - 1 -

1.1 Research background ................................................................................................ - 1 -

1.2 Problem statement ..................................................................................................... - 3 -

1.3 Research contributions .............................................................................................. - 4 -

1.4 Outline of the thesis................................................................................................... - 5 -

Chapter 2 Review of Energy Storage Technologies .............................................................. - 8 -

2.1 Introduction ............................................................................................................... - 8 -

2.2 Modern power system with energy storage ............................................................... - 8 -

2.3 Energy storage technologies .................................................................................... - 13 -

2.3.1 Mechanical energy storage .................................................................................. - 13 -

2.3.2 Electrical energy storage ..................................................................................... - 18 -

2.3.3 Electrochemical battery ....................................................................................... - 20 -

2.3.4 Thermal energy storage (TES) ............................................................................ - 27 -

2.3.5 Chemical energy storage ..................................................................................... - 28 -

2.4 ESS characteristics .................................................................................................. - 30 -

2.4.1 Technical characteristics comparison of energy storage technologies ................ - 30 -

2.4.2 Summary table of energy storage characteristics ................................................ - 37 -

Table of Contents

II

2.4.3 The selection of energy storage technologies ...................................................... - 38 -

2.5 General discussion on energy storage technology and future development ............ - 41 -

2.6 Conclusion ............................................................................................................... - 45 -

Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System .......... - 47 -

3.1 Introduction ............................................................................................................. - 47 -

3.2 Overview of solar PV technology ........................................................................... - 48 -

3.2.1 Background of solar cell ..................................................................................... - 48 -

3.2.2 Solar cell model ................................................................................................... - 49 -

3.2.3 Overview of the PV power system ...................................................................... - 52 -

3.3 Stand-alone PV power system ................................................................................. - 54 -

3.3.1 Components and system structure ....................................................................... - 54 -

3.3.2 Solar power generation ........................................................................................ - 55 -

3.3.3 Load demand estimation ..................................................................................... - 56 -

3.4 Energy storage in stand-alone PV power system .................................................... - 58 -

3.4.1 Discussion about the selection of ESS ................................................................ - 58 -

3.4.2 Lead-acid battery and its stress factors ................................................................ - 60 -

3.5 Case study of stand-alone PV-Battery power system.............................................. - 61 -

3.5.1 Scaled prototype of stand-alone PV power system ............................................. - 63 -

3.5.2 The concept of SC/Lead-acid battery HESS ....................................................... - 64 -

3.6 Conclusion ............................................................................................................... - 65 -

Chapter 4 Study on Battery-SC HESS: Topology, Control Strategy, and Application ....... - 66 -

4.1 Introduction ............................................................................................................. - 66 -

4.2 Battery-SC HESS topologies .................................................................................. - 67 -

4.2.1 Passive Battery-SC HESS ................................................................................... - 68 -

4.2.2 Semi-Active Battery-SC HESS ........................................................................... - 69 -

4.2.3 Full Active Battery-SC HESS ............................................................................. - 70 -

4.2.4 Multi-level Battery-SC HESS ............................................................................. - 71 -

4.3 Energy management system (EMS) ........................................................................ - 72 -

Table of Contents

III

4.3.1 Overview of EMS ................................................................................................ - 72 -

4.3.2 The design considerations of EMS ...................................................................... - 74 -

4.3.3 Advanced algorithm in EMS ............................................................................... - 77 -

4.4 Analysis and discussion .......................................................................................... - 81 -

4.4.1 HESS topologies and EMS ................................................................................. - 82 -

4.4.2 Summary of the advantages of Battery-SC HESS .............................................. - 83 -

4.4.3 AC coupled and hybrid AC/DC PV-HESS power system .................................. - 84 -

4.4.4 HESS topologies and EMS in rural electrification .............................................. - 85 -

4.5 Conclusion ............................................................................................................... - 85 -

Chapter 5 Battery-SC HESS for Stand-alone PV Power System in Rural Electrification ... - 86 -

5.1 Introduction ............................................................................................................. - 86 -

5.2 Selected HESSs for stand-alone PV power system in rural electrification ............. - 87 -

5.3 Stand-alone PV power system with selected HESSs .............................................. - 88 -

5.3.1 With Passive HESS ............................................................................................. - 88 -

5.3.2 With SC Semi-Active HESS ............................................................................... - 93 -

5.3.3 SC/Lithium-ion/Lead-acid battery Multi-level HESS ......................................... - 97 -

5.4 Numerical simulations in stand-alone PV power system ...................................... - 102 -

5.4.1 Solar irradiance and load profiles for case studies ............................................ - 102 -

5.4.2 Results of simulations ....................................................................................... - 104 -

5.4.3 Battery health assessment and financial analysis .............................................. - 111 -

5.5 Experiment verification ......................................................................................... - 119 -

5.5.1 Testbed setup ..................................................................................................... - 119 -

5.5.2 Pulse load response ........................................................................................... - 120 -

5.5.3 Daily operation in stand-alone PV power system ............................................. - 122 -

5.6 Conclusion ............................................................................................................. - 124 -

Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System ......... - 125 -

6.1 Introduction ........................................................................................................... - 125 -

6.2 SHESS Plug-in module ......................................................................................... - 127 -

Table of Contents

IV

6.2.1 System structure ................................................................................................ - 127 -

6.2.2 Power allocation and control strategy ............................................................... - 129 -

6.3 Simulation analysis ............................................................................................... - 131 -


6.3.2 Operation in stand-alone PV-battery power system .......................................... - 134 -

6.4 Experiment verification ......................................................................................... - 140 -


6.4.2 Operation in the lab-scale stand-alone PV-battery power system ..................... - 143 -

6.5 Conclusion ............................................................................................................. - 148 -

Chapter 7 Conclusion and Future Work ............................................................................ - 149 -

7.1 Summary ............................................................................................................... - 149 -

7.2 Future work ........................................................................................................... - 151 -

Reference ............................................................................................................................... - 154 -

Appendix ............................................................................................................................... - 172 -

A.1. Case study ............................................................................................................. - 172 -

A.1.1 Existing power generation and distribution system in BANP ....................... - 172 -

A.1.2 Proposed solar-battery-generator hybrid power system ................................ - 174 -

A.2. Buck-boost DC/DC converter control and codes .................................................. - 178 -

A.2.1 The hardware structure .................................................................................. - 178 -

A.2.2 Control code in Arduino microprocessor ...................................................... - 179 -

V

List of Figures

Fig. 1.1 Typical PV power generation and load profile in Sarawak, Malaysia ......................... - 2 -

Fig. 1.2 Content summary of the study in this thesis ................................................................ - 4 -

Fig. 2.1 Power system and energy storage applications ............................................................ - 9 -

Fig. 2.2 Load levelling and capacity firming in fluctuating load profile using ESS ............... - 10 -

Fig. 2.3 The detailed components of the power system .......................................................... - 12 -

Fig. 2.4 Energy storage technology classification ................................................................... - 13 -

Fig. 2.5 Pumped hydro storage (PHS) in renewable power system ........................................ - 14 -

Fig. 2.6 Three types of compressed air energy storage (CAES) ............................................. - 15 -

Fig. 2.7 Schematic layout of diabatic CAES system ............................................................... - 16 -

Fig. 2.8 Schematic layout of adiabatic CAES system ............................................................. - 16 -

Fig. 2.9 The flywheel ESS ...................................................................................................... - 17 -

Fig. 2.10 Simplified cross-section of SC cell .......................................................................... - 18 -

Fig. 2.11 The superconducting magnetic energy storage (SMES) .......................................... - 20 -

Fig. 2.12 General structure of electrochemical cell batteries .................................................. - 21 -

Fig. 2.13 Simplified layout of NaS cell/battery....................................................................... - 23 -

Fig. 2.14 Simplified layout of the metal-air battery ................................................................ - 25 -

Fig. 2.15 Simplified layout of flow battery ............................................................................. - 26 -

Fig. 2.16 Simple layout of thermal ESS .................................................................................. - 28 -

Fig. 2.17 Hydrogen fuel cells .................................................................................................. - 29 -

Fig. 2.18 Energy storage characteristics taxonomy ................................................................. - 30 -

Fig. 2.19 Comparison of energy density and power density ................................................... - 31 -

Fig. 2.20 Comparison of specific energy and specific power ................................................. - 32 -

Fig. 2.21 Comparison of energy rating and discharge time duration in power rating ............. - 33 -

Fig. 2.22 Comparison of storage duration and self-discharge rate .......................................... - 34 -

Fig. 2.23 Comparison of energy capital cost and maturity level ............................................. - 35 -

Fig. 2.24 Comparison of energy capital cost and M&O cost .................................................. - 36 -

Fig. 2.25 The framework of the energy storage technology selection .................................... - 40 -

List of Figures

VI

Fig. 3.1 Solar cell classification .............................................................................................. - 48 -

Fig. 3.2 The equivalent circuit of solar cell ............................................................................. - 49 -

Fig. 3.3 The I-V and P-V characteristics of the solar cell ....................................................... - 51 -

Fig. 3.4 V-I and P-V curves in different intensity ................................................................... - 51 -

Fig. 3.5 Solar cells, PV modules, PV panels and PV arrays ................................................... - 52 -

Fig. 3.6 The classification of PV power system ...................................................................... - 53 -

Fig. 3.7 A typical stand-alone PV-battery power system ........................................................ - 54 -

Fig. 3.8 The 7-day solar irradiance in Kuching, Sarawak, Malaysia ...................................... - 56 -

Fig. 3.9 Assessment of load profile for a non-electrified rural community ............................ - 57 -

Fig. 3.10 Estimated load profiles of the target rural site in Sarawak, Malaysia ...................... - 57 -

Fig. 3.11 Actual load profiles of the target rural site in Sarawak, Malaysia ........................... - 58 -

Fig. 3.12 Electrochemical reaction of the Lead-acid battery................................................... - 60 -

Fig. 3.13 The curves of the Lead-acid battery healthy and stress factors ............................... - 61 -

Fig. 3.14 Matlab Simulink model of stand-alone PV-Lead-acid battery system .................... - 62 -

Fig. 3.15 Simulation current of stand-alone PV-battery power system (sunny) ..................... - 62 -

Fig. 3.16 Simulation current of stand-alone PV-battery power system (cloudy) .................... - 62 -

Fig. 3.17 Experimental current of stand-alone PV-battery power system (one day) .............. - 63 -

Fig. 3.18 Experimental testing of stand-alone PV-battery power system (2 hours) ................ - 64 -

Fig. 4.1 Classification of the battery-SC HESS topologies ..................................................... - 68 -

Fig. 4.2 Passive HESS topology.............................................................................................. - 68 -

Fig. 4.3 Semi-Active HESS topologies ................................................................................... - 69 -

Fig. 4.4 Active HESS topologies ............................................................................................ - 71 -

Fig. 4.5 Multi-level Battery-SC HESS topology ..................................................................... - 72 -

Fig. 4.6 The EMS with the multi-level control structure ........................................................ - 73 -

Fig. 4.7 Features and function of an efficient EMS design ..................................................... - 74 -

Fig. 4.8 Typical EMS for stand-alone PV power system with parallel active HESS .............. - 75 -

Fig. 4.9 Classification of intelligent algorithms for EMS supervisory control ....................... - 78 -

Fig. 4.10 Fuzzy logic flowchart .............................................................................................. - 79 -

Fig. 4.11 Genetic algorithm flowchart .................................................................................... - 80 -

Fig. 4.12 Dynamic programming flowchart ............................................................................ - 81 -

Fig. 4.13 The stand-alone PV-HESS power system expansion............................................... - 84 -

Fig. 5.1 Classification of the Battery-SC HESS topologies .................................................... - 87 -

Fig. 5.2 Multi-level HESS topology ........................................................................................ - 88 -

Fig. 5.3 The equivalent circuit of the Passive HESS ............................................................... - 89 -

Fig. 5.4 Matlab Simulink model of Passive HESS.................................................................. - 92 -

Fig. 5.5 Pulse load response of Passive HESS ........................................................................ - 92 -

List of Figures

VII

Fig. 5.6 Stand-alone PV power system with Passive HESS ................................................... - 93 -

Fig. 5.7 The equivalent circuit of the SC Semi-Active HESS................................................. - 93 -

Fig. 5.8 The Matlab Simulink model of the SC Semi-Active HESS ...................................... - 95 -

Fig. 5.9 Pulse load response of the SC Semi-Active HESS .................................................... - 95 -

Fig. 5.10 The stand-alone PV power system with SC Semi-Active HESS ............................. - 96 -

Fig. 5.11 Power allocation strategy for the SC Semi-Active HESS ........................................ - 97 -

Fig. 5.12 The equivalent circuit model of the Multi-level HESS ............................................ - 97 -

Fig. 5.13 The Matlab Simulink model of the Multi-level active HESS .................................. - 99 -

Fig. 5.14 Pulse load response of the Multi-level active HESS .............................................. - 100 -

Fig. 5.15 Stand-alone PV power system with Multi-level HESS.......................................... - 101 -

Fig. 5.16 Power allocation strategy for the Multi-level HESS ............................................. - 101 -

Fig. 5.17 The PV output in sunny day ................................................................................... - 102 -

Fig. 5.18 The PV output in cloudy day ................................................................................. - 103 -

Fig. 5.19 Estimated load profiles of the target rural site in Sarawak, Malaysia .................... - 103 -

Fig. 5.20 Matlab Simulink model of Passive HESS .............................................................. - 104 -

Fig. 5.21 Current profiles of sunny day ................................................................................ - 105 -

Fig. 5.22 Current profiles of cloudy day ............................................................................... - 105 -

Fig. 5.23 Matlab Simulink model of SC Semi-Active HESS ............................................... - 106 -



Fig. 5.26 Matlab Simulink model of Multi-level HESS ........................................................ - 108 -



Fig. 5.29 SoC variation of SC in three different scenarios (sunny day) ................................ - 110 -

Fig. 5.30 SoC variation of SC in three different scenarios (cloud day) ................................ - 110 -

Fig. 5.31 The results of the charge/discharge rate (sunny day) ............................................. - 113 -

Fig. 5.32 The results of the dynamicity (sunny day) ............................................................. - 113 -

Fig. 5.33 The results of the SoC (sunny day) ........................................................................ - 114 -

Fig. 5.34 The results of the charge/discharge transition (sunny day) .................................... - 114 -

Fig. 5.35 Normalized results of cost function throughout the sunny day .............................. - 115 -

Fig. 5.36 The results of the charge/discharge rate (cloudy day) ........................................... - 115 -

Fig. 5.37 The results of the dynamicity (cloudy day) ........................................................... - 116 -

Fig. 5.38 The results of the SOC (cloudy day) ...................................................................... - 116 -

Fig. 5.39 The results of the charge/discharge transition (cloudy day) .................................. - 117 -

Fig. 5.40 Normalized results of cost function throughout the cloudy day ............................ - 117 -

Fig. 5.41 Experiment setup of selected HESS topologies ..................................................... - 119 -

List of Figures

VIII

Fig. 5.42 Experimental responses to pulsed load of Passive HESS ...................................... - 120 -

Fig. 5.43 Experimental responses to pulsed loads of SC Semi-Active HESS ...................... - 121 -

Fig. 5.44 Experimental responses to pulsed loads of Multi-level HESS ............................... - 121 -

Fig. 5.45 Experimental currents of battery-only in stand-alone PV power system ............... - 122 -

Fig. 5.46 Experimental currents of Passive HESS in stand-alone PV power system............ - 123 -

Fig. 5.47 Experimental currents of Semi-Active HESS in stand-alone PV power system ... - 123 -

Fig. 5.48 Experimental currents of Multi-level HESS in stand-alone PV power system...... - 124 -

Fig. 6.1 The stand-alone PV power system with Lead-acid battery ...................................... - 125 -

Fig. 6.2 SHESS plug-in module in stand-alone PV-battery power system ........................... - 128 -

Fig. 6.3 Mode selection process of the proposed SHESS plug-in module ............................ - 129 -

Fig. 6.4 Power allocation strategy for the SHESS plug-in mode .......................................... - 130 -

Fig. 6.5 Matlab Simulink model of SHESS used in pulse load testing ................................. - 132 -

Fig. 6.6 Simulated results in pulse load testing in light mode ............................................... - 133 -

Fig. 6.7 Simulated results in pulse load testing in heavy mode ............................................ - 133 -

Fig. 6.8 Matlab Simulink model of stand-alone PV power system with SHESS .................. - 134 -

Fig. 6.9 Simulation results of plug-in testing in light mode .................................................. - 136 -

Fig. 6.10 Simulation results of plug-in testing in heavy mode .............................................. - 136 -

Fig. 6.11 Lead-acid battery currents profile in light mode .................................................... - 137 -

Fig. 6.12 Lead-acid battery currents profile in heavy mode.................................................. - 138 -

Fig. 6.13 Normalised cumulative battery health cost in light mode ...................................... - 139 -

Fig. 6.14 Normalised cumulative battery health cost in heavy mode ................................... - 139 -

Fig. 6.15 Prototype of the SHESS plug-in module ............................................................... - 140 -

Fig. 6.16 Experimental setup and circuit diagram for the pulse load testing ........................ - 142 -

Fig. 6.17 Experiment results of the pulse load testing (light mode)...................................... - 142 -

Fig. 6.18 Experiment results of the pulse load testing (heavy mode) ................................... - 143 -

Fig. 6.19 Experimental setup and circuit diagram for the case study.................................... - 144 -

Fig. 6.20 Experiment results of the stand-alone PV-battery power system .......................... - 144 -

Fig. 6.21 Experimental plug-in testing of SHESS plug-in module (light mode) .................. - 145 -

Fig. 6.22 Experimental plug-in testing of SHESS plug-in module (heavy mode) ................ - 146 -

Fig. 6.23 Experimental one day testing of SHESS plug-in module (light mode) ................. - 147 -

Fig. 6.24 Experimental one day testing of SHESS plug-in module (heavy mode) ............... - 147 -

Fig. A.1 Layout of BANP and current electricity supply system .......................................... - 172 -

Fig. A.2 Existing power consumption pattern of BANP ....................................................... - 173 -

Fig. A.3 Physical settings of the proposed system ................................................................ - 174 -

Fig. A.4 Connection diagram of the proposed system .......................................................... - 175 -

Fig. A.5 PV panels installation and research team ................................................................ - 176 -

List of Figures

IX

Fig. A.6 Control system installation (MPPT + OPTI-Solar Controller) ............................... - 176 -

Fig. A.7 Main switches installation (Load+PV+Battery+Controller) ................................... - 177 -

Fig. A.8 2400Ah PK200-12 Lead-acid battery bank Installation .......................................... - 177 -

Fig. A.9 Actual load profile collection .................................................................................. - 178 -

Fig. A.10 The system architecture diagram of DC/DC converter ......................................... - 178 -

Fig. A.11 The bidirectional buck-boost DC/DC converter ................................................... - 179 -

X

List of Tables

Table 2.1 Applications of energy storage in the power system ............................................... - 11 -

Table 2.2 Comparison of battery technologies ........................................................................ - 27 -

Table 2.3 Summary of energy storage characteristics ............................................................. - 37 -

Table 3.1 Three main types of solar cells in the market .......................................................... - 49 -

Table 5.1 Model parameters for different HESS under simulation ....................................... - 104 -

Table 5.2 Financial analysis under different modes .............................................................. - 118 -

Table 5.3 Experimental testbed parameters .......................................................................... - 120 -

Table 6.1 Comparison of energy storage technologies in SHESS ........................................ - 126 -

Table 6.2 Financial analysis of SHESS under different modes............................................. - 140 -

Table 6.3 Experiment testbed parameters ............................................................................. - 141 -

Table A.1 Electrical appliances (currently in-use) of main office and barrack A ................. - 173 -

Table A.2 Solar installation checklist ................................................................................... - 175 -

XI

Nomenclature

Abbreviations Description

ADC Analog to Digital Converter

BANP Batang Ai National Park Headquarter

C-Rate Charge and Discharge Rate

CAES Compressed Air Energy Storage

DoD Depth-of-Discharge

DAC Digital to Analog Converter

EMS Energy Management System

EMU Energy Management Unit

ESS Energy Storage System

FBC Filtration-Based Controller

FES Flywheel Energy Storage

GPMES Gravity Power Module Energy Storage

HVAC Heating, Ventilation, Air-Conditioning

HESS Hybrid Energy Storage System

IEA International Energy Agency

LA Lead-Acid

LCOE Levelized Cost Of Energy

Nomenclature

XII

LCOS Levelized Cost Of Storage

LCIA Life Cycle Impact Assessment

LPF Low-Pass Filter

MPP Maximum Power Point

MPPT Maximum Power Point Tracking

PV Photovoltaic

PSB Polysulphide Bromide

PAC Power Allocation Controller

PCU Power Conditioning Unit

PI Proportional-Integral

PWM Pulse Width Modulation

PHS Pumped Hydro Storage

RES Renewable Energy Sources

SHESS Smart Hybrid Energy Storage System

NaS Sodium-Sulphur Battery

SoC State-of-Charge

SC Supercapacitor

SMES Superconducting Magnetic Energy Storage

TES Thermal Energy Storage

VRB Vanadium Redox

VAR Voltage-Ampere Reactive

ZnBr Zinc Bromine

- 1 -

Chapter 1

Introduction

1.1 Research background

Modern electrical grid is required to provide reliable power supply that matches the

varying electricity demand from all sectors at all times. Aligning different sources of

power generation and varying load demand in an electricity network is one of the

biggest challenges for the utility company [1]–[3]. The unbalanced generation-demand

would potentially cause serious negative impacts on power quality such as voltage

variation, random frequency difference or sudden blackouts [4]–[7]. In addition, the

surplus electricity generated cannot be practically and economically stored which

requires it to be consumed at the instant when it is generated [8].

Ideally, energy storage system (ESS) as an intermediate buffer could potentially

alleviate some of the existing challenges such as frequency regulation, load levelling,

peak shaving and spinning reserve [9]. The key idea of energy storage integration is to

absorb excess energy when available, and to release the stored energy during peak

demand, which improves the flexibility and resilience of the electrical grid [10], [11].

There are numerous energy storage technologies available nowadays, which can be

broadly classified based on the form of energy stored: (1) mechanical (pumped hydro

storage, compressed air energy storage and flywheels), (2) electrical (supercapacitor and

superconducting magnetic energy storage), (3) electrochemical or battery (conventional

rechargeable batteries and flow batteries), thermal (latent heat storage and sensible heat

storage), (4) chemical (hydrogen fuel cells and thermochemical energy storage) and (5)

thermal energy storage (sensible heat storage and latent heat storage) [12].

Chapter 1 Introduction

- 2 -

ESS is also a vital tool for enabling integration of Renewable Energy System (RES) in

the electrical grid and distributed residential energy storage solution [13], [14]. Typical

renewable sources, such as solar, wind, biomass, geothermal, and tidal, are highly

intermittent in nature and often generate unstable and unpredictable electricity over time.

The undesirable fluctuating power generation from RES creates several issues such as

power safety, power quality, and reliability as well as islanding protection. Over recent

decades, solar photovoltaic (PV) technology has become one of the most prominent

RES due to many advantages such as modular, easy to install, mature technology and

most importantly low operating cost [15].

PV based power generation system can be categorized into grid-connected PV power

system and stand-alone PV power system. Low capacity stand-alone PV systems are

primarily used to supply electricity in off-grid communities such as remote rural areas,

to provide basic electricity needs for example lighting, food refrigeration and other

basic electrical appliances [16], [17]. Fig. 1.1 illustrates a typical profile of PV power

generation in tropical rainforest climate (red line) and the estimated load demand (blue

line) of rural communities. The nonlinear electrical characteristic of PV cells and

intermittency of solar irradiance require integration of intermediate ESS in order to

provide stable electricity supply, especially in stand-alone PV power system. ESS

absorbs the generation-demand mismatch and fluctuating power exchange in the system

through charge and discharge processes.

Fig. 1.1 Typical PV power generation and load profile in Sarawak, Malaysia

0 4 8 12 16 20 00

1

2

3

4

5

6

7

Pow

er (k

W)

Hours of Day

PV PowerLoad Profile


- 3 -

Lithium-ion and Lead-acid batteries are the two most commonly used energy storage

technologies in residential ESS [18]. The Lithium-ion battery has a higher energy

density, round-trip efficiency, and longer cycle lifetime compared to the Lead-acid

battery, but is relatively more expensive and immature in large-scale packaging. In

contrast, the Lead-acid battery is more suitable for off-grid PV power systems,

particularly in rural electrification due to its lower initial cost and excellent thermal

stability. Despite many advantages of integrating battery in stand-alone PV power

system, the highly dynamic fluctuations in generation and demand from the low-

capacity energy accelerate the battery aging process, which will significantly increase

the operating cost of the system [19], [20].

Hybridization of different energy storage technologies turns out to be one of the

promising solutions to mitigate the battery charge-discharge stress by directing the short

term power fluctuation to another form of energy storage such as the supercapacitor (SC)

[21]–[25]. Compared to electrochemical batteries, SC has very high power density, fast

response time and nearly infinite cycle life, which make it a good complement to the

Lead-acid battery bank in absorbing fluctuating power exchange [26]. Thus the concept

of Battery-SC Hybrid Energy Storage System (HESS) has been proposed by many

researchers aiming to mitigate the impact of fluctuating power on battery’s lifespan

[27]–[29]. In Battery-SC HESS, the net current flowing in and out of the HESS will be

divided into two or more components with varies frequencies. The SC absorbs the high

frequency and surge power exchange, while the battery bank responses to the smoothed

average power demand. The combination of battery and SC can provide a wide range

of power and energy requirements in stand-alone PV power systems. Besides having

correct combination and appropriate sizing of energy storage devices, the design of

Energy Management System (EMS) is another key to achieve better efficiency,

operation and maintenance costs reduction, and most importantly prolonged battery

service life [30]–[34].

1.2 Problem statement

In the perspective of rural electrification, the stand-alone PV power system is usually

installed to the places that are geographical dispersal, decentralized, low population


- 4 -

density and geographically isolated from the national grid, thus simple, stable and

inexpensive design of HESS will be of crucial useful than the high efficiency but a

costly and complex system [35]–[37]. Many research studies focused mainly on

proposing advanced and innovated HESS in aspects of novel topologies or complicated

EMS with hierarchical control, supervisory monitoring, adaptive droop moderate

scheme or control strategy based on the artificial intelligence algorithms [38]–[41].

These HESS designs are generally intended to serve large-scale utilities, smart-grid,

electric vehicles and other high power applications that require extensive sensing,

computation, and communication, this leads to increased system complexity and

implementation, and are not suitable in the rural area. However, the study on HESS for

off-grid residential energy system especially in rural electrification application is limited,

and their design considerations have not been systematically discussed. To address this

gap in the literature, this thesis conducted a study on the applications of HESS in stand-

alone PV power system for rural electrification.

1.3 Research contributions

The primary objective of this study is to enhance the service life of the Lead-acid

battery in stand-alone PV-battery power systems by mitigating life-limiting factors such

as current fluctuations and surge current. Thus, improving the system reliability and

power quality and most importantly reducing the system operating cost. To achieve this

aim, the research major works, methodology, contributions and outcomes are

summarised in Fig. 1.2 and as follows:

Fig. 1.2 Content summary of the study in this thesis

Major Work

• Battery-SC HESS study

• Lead-acid battery lifetime

enhancement in standalone PV

power system

Methodology

• Propose selection methodology

based on the overview of ESS

• Standalone PV power system

study and application of lead-

acid battery

• Discuss HESS topology and

select suitable topologies for

standalone PV Power system

• Propose Smart HESS as a plug-

in module

Contribution

• ESS selection methodology

• Load profile estimation

• HESS study for rural

applications

• Novel smart HESS plug-in

module is proposed

• Battery healthy assessment

system is proposed


- 5 -

Comprehensively review the existing energy storage technologies regarding

their technical merit, characteristic, economic feasibility and development trends.

Technical analysis, comparison and potential applications in modern power

system networks are presented. In particular, the methodology to help decision-

makers in identifying optimal energy storage technologies for specific

applications has been developed and discussed.

Investigate the lifetime characteristics of Lead-acid battery bank in power

system applications and identify the life-limiting factors that accelerate the

performance deterioration and aging process.

Investigate the effectiveness of hybrid ESSs in enhancing battery’s service and

feasibility in stand-alone PV-based power systems. This includes the

development status, operation principles, characteristics, advantages and

disadvantages, energy management and control strategies, and possible

application scenarios.

Complete the literature gap of the Battery-SC hybrid ESS in remote rural

applications.

Propose a novel smart hybrid ESS plug-in module that enhances the lifetime

characteristics of the primary Lead-acid battery in existing installed stand-alone

PV-battery power systems by mitigating life-limiting factors such as current

fluctuations and surge power demand.

Formulate a battery health cost function to quantitatively evaluate the negative

impact of charge/discharge current profile, follow with a series of battery health

cost analysis to systematically evaluate the effectiveness of different hybrid ESS

topologies and control schemes in mitigating battery stress, and perform

economic analysis and financial feasibility study.

Provide a complete analytical methodology for the research of the Battery-SC

HESS technology, including mathematical modelling, numerical simulation and

analysis, prototype design and corresponding experimental testing strategy.

1.4 Outline of the thesis

Chapter 2 presents an updated review of the currently available ESS technologies. The

following energy storage technologies, Pumped Hydro Storage (PHS), Compressed Air


- 6 -

Energy Storage (CAES), Flywheel Energy Storage (FES), Superconducting Magnetic

Energy Storage (SMES), Supercapacitor (SC), cell battery, flow battery, latent and

sensible Thermal Energy Storage (TES), hydrogen fuel cell energy storage and

thermochemical energy storage, will be present in terms of their operation, functionality,

distinct characteristics, limitations and advantages.

Chapter 3 introduces solar cell technology and PV power systems. A series of

considerations for the design of stand-alone PV power systems, such as solar irradiance

measurements, load profile estimation, and energy storage devices selection, are

discussed and presented. By using local solar irradiance and an estimated load profile, a

typical stand-alone PV-battery power system is modelled and simulated to show the

profiles of PV output power and battery currents. A lab-scale prototype of the stand-

alone PV-battery power system is constructed to show the performance experimentally.

Chapter 4 provides a study on the latest works related to Battery-SC HESS regarding

topologies, control algorithms and EMS, and the applications in PV based stand-alone

power system.

Chapter 5 discusses the potential Battery-SC HESS topologies that are suitable for the

stand-alone PV power systems in rural areas. Theoretical analysis and numerical

simulation in Matlab Simulink for the selected HESS topologies have been carried out,

and their effectiveness in mitigating battery stress are compared. The battery health cost

and financial analyses are presented to evaluate the life-extending capability of different

HESSs and their cost reduction in the overall system. The lab-scale prototypes of them

are developed, and their performances in stand-alone PV system are emulated to

validate the simulation analysis.

Chapter 6 proposes a smart SC/Lithium-ion HESS plug-in module that aims to extend

the Lead-acid battery lifetime in installed stand-alone PV-battery power systems

without reconstructing the system structure. It supposes to remove the severely current

fluctuations from the Lead-acid battery current profile and direct it passively operating

with a smoothing current profile. The proposed module is validated using numerical

simulation and prototype experiment.


- 7 -

Finally, in Chapter 7, the conclusion concludes this study with a summary and provides

suggestions for future research in this area.

- 8 -

Chapter 2

Review of Energy Storage Technologies

2.1 Introduction

Energy storage technologies store electrical, thermal, chemical and mechanical energy

and release them in the form of electricity when needed. Energy storage devices have

been widely used in various applications ranging from large-scale power systems to

distributed generation in microgrids, to improve the operational capability of power

systems, enhance power quality and reliability, and optimize power generation cost.

Besides, energy storage technology is also one of the vital components that enable the

adoption of RESs in the electrical power networks. Renewable energy sources, such as

solar, wind, biomass, geothermal and tidal, are often intermittent in nature that tends to

produce fluctuating and unstable electricity over time. The intermittent power

generation from RES creates issues such as power quality and reliability, power safety

and the needs for islanding protection. Energy storage technologies can be classified

into five major categories based on the form of energy when stored: (1) mechanical

energy, (2) electrical energy, (3) electrochemical energy, (4) thermal energy and (5)

chemical energy. The following sub-sections will present an overview of the modern

power system and a comprehensive review of energy storage technologies, including

their electrical and lifetime characteristics, technical analysis, and comparison.

2.2 Modern power system with energy storage

Fig. 2.1 shows a typical structure of modern power system consisting of subsystems

such as power generation, transmission, distribution, and end-users. The figure also

Chapter 2 Review of Energy Storage Technologies

- 9 -

summarizes the major challenges that exist in each subsystem, and the potential

applications of ESS across modern power system network [42]–[46].

Centralized Storage(>50MW)

ESS Substation(Up to 10MW)

Container ESS(Up to 2MW)

Residential(Up to 100 kW)

Industrial & Commercial

Generation Transmission Distribution

RES system

Load levelingFrequency regulation

Integration of RESVoltage SupportCapacity firming

Voltage regulationLoad shifting

Spinning ReserveNetwork stabilization

Upgrade power qualityCongestion relief

Micro-generation feed-in

End-users

Power Factor Optimize Time Shifting/ Peak Shaving

Stabilise PricesEmergency power back-up/ UPS

Applicationsummary

ESS sizereference

ESS Location

TransmissionSubstation

Step-upTransformer

Step-downTransformer

Residential

ESSLoad leveling• (~100MW, 4h)Frequency regulation• (1~50MW, 0.25~4h)

ESSIntegration of RES• (1~100MW, 1~10h)Voltage Support• (0.5~50 MVAr, 0.25~4h)

ESS

Spinning Reserve• (10~100MW, 0.25~10h)Load leveling for postponement of grid upgrade• (1~10MW, 6h)

ESSUpgrade power quality• (1~100MW, 0.25~6h)

ESS

Time ShiftUPS• (5~10 kW, 2~24h)

ESS

Peak Shaving• (5~10 MW, <1h)Power Factor Optimize Voltage Support• (0.5~50 MVAr, 0.25~4h)Emergency power back-up• (10~50 MW, 0.25~10h)

Fig. 2.1 Power system and energy storage applications

The primary role of the power system is to generate electricity that matches the

continually varying load demand as close as possible to avoid over-generation of

electricity and power deficit which requires load shedding remedy. Secondly, the power

system is required to maintain the system voltage and frequency at all times despite

random failure and/or variations at the generation side. ESS is one of the promising

solutions to enhance the reliability of the power system by acting as a contingency

reserve to provide immediate supply or spinning reserve during supply-demand

mismatch [47].

For load levelling application, ESS stores the excess energy at off-peak periods and

supply the stored energy during peak demand periods as shown in Fig.2.2 [48]. Besides,

the adoption of ESS can effectively counterbalance the power fluctuations from

generators to ensure frequency regulation [49]. The integration of ESS at generation

side can minimize the usage of costly load following power plant as well as wastage

during off-peak hours. In the case of renewable energy based generation, ESS plays an

important role to smooth (capacity firming) the intermittent RESs output power [50].


- 10 -

Serious voltage collapse in power system due to excessive reactive power loss has been

one of the causes in major blackouts and voltage instability such as voltage sags,

voltage swells, frequency harmonics and flickers [51]–[53]. A stable electricity supply

requires a well-balanced real power and reactive power. The centralized power

generation supplies the real power and the reactive power can be generated either by

local Voltage-Ampere Reactive (VAR) supply or remote generators [54]. The local

VAR supply, also known as static compensation, can be the transformer tap changers,

switched capacitor banks and large rotating machines which are typically located within

the transmission and distribution sub-systems [55]–[57].

Fig. 2.2 Load levelling and capacity firming in fluctuating load profile using ESS

As an alternative technology, ESS can be used to effectively perform voltage regulation

and VAR support function [58]. Instead of the conventional methods where VAR (up to

10MW) is installed in transmission sub-system to control the voltage dynamic behaviors

and balance phase-angle difference between generation and demand sides, ESS

container (up to 2MW) can be installed at the end of distribution network that acts as an

energy buffer to provide stabilized high quality electricity to the loads [59].

In small-scale residential power applications, ESS can be used as a back-up power

supply or uninterrupted power supply. In addition, the ESS can be controlled to simulate

the time-shifting processes for which cheaper electricity at off-peak is stored and

provide electricity supply during peak hours [60]. Assuming well-coordinated time-

shifting processes at the demand side, the ESSs in distributed residential power system

networks not only reduce the electricity cost for the households, but the cumulative

Discharge energy storage

Smooth the peak power

Daily average load demand

Charge energy storage Load levellingUnstable

generationSupplement the

intermittent power


- 11 -

effect of time-shifting could also be an attractive alternative to replace huge centralized

ESS at the generation side for load levelling.

With the rapid development of information technology and matured power electronic

technology, the traditional power system is undergoing a revolutionary transformation

in order to address the fast-growing global electricity demand, large-scale renewable

energy penetration, and large-scale multi-regional grid integration. The concept of

smart-grid has been widely accepted as one of the promising solutions for next-

generation power systems, for which the ESS is one of the critical components in the

modern power system network. Smart-grid incorporates the state-of-the-art technologies

in power electronics, sensors, control systems, communications and networking that

significantly improve the intelligence of the power system, including self-healing,

consumer-friendly, optimized asset utilization, eco-friendly, improved efficiency,

reliability, and safety [61].

Unlike the traditional power system, smart-grid allows seamless integration of

microgrids that contain distributed generations and loads. In general, sustainable energy

technologies such as solar PV, wind turbine and small hydro are often being integrated

into microgrids. Microgrid works as an independent small-scale, localized power system

that includes transmission, distribution, and EMS and energy storage [62]. Because of

the intermittency, variation and instability of renewable energy power generation, the

installation of ESS in the system will be essential to ensure power stability with

acceptable power balance, power quality, and reliability between power generations and

user demands.

Table 2.1 summarizes the applications of ESS in modern power system including

generation, transmission, power distribution, and demand side. In general, the

integration of ESS in the power system can effectively improve the electric grid

flexibility, resilience, technical efficiency and economic performance [42], [63]–[69].

Table 2.1 Applications of energy storage in the power system

Generation (Centralised and Distributed)

Transmission and Distribution

Load demand side


- 12 -

Renewable energy integration Grid fluctuation suppression Capacity firming Ramping and Load Following Frequency regulation Seasonal energy storage Contingency reserve Black-start Spinning reserve Load Levelling / Peak Shaving

Voltage regulation Voltage-ampere reactive Transmission curtailment Transmission deferral Distribution deferral Transient stability Power quality Outage mitigation

Energy management Time shifting Demand side management Energy arbitrage Uninterrupted power supply Vehicle-to-grid Microgrid

The detailed components of the modern power system are summarized in Fig. 2.3 [70]–

[73]. The power system can be grid-connected or stand-alone or interchangeably with

advanced control strategy and switching. The power system contains energy sources,

interface components, power conversion system, control, and monitoring system, ESS

and loads. The power conversion system is the interconnection between systems of

different electrical characteristics, for example, the conversion from AC to DC or vice

versa. The loads include the end-use customer in different sectors which can be either

AC or DC.

Energy Source• Utility Grid• RES Microgrid• Hybrid Grid

Interface

Power Conversion System• Interconnection of AC/DC• AC/DC switchgear • Rectifier/inverter/converter• Control unit• Protection devices

(overvoltage, cooling, etc)

Loads• DC/AC• Industry• Residential

ESSs

Central Control System• Source/ESS/Interface/PCS/Load• Voltage/Frequency/VAR• Thermal management• Power conversion• Power dispatching• Protection devices • Subsystems control

Interface• Transmission Networks• HVDC/FACTS• Voltage transformers• Distribution Networks • Protection devices

Construction facilities• Electrical infrastructure• Telecommunication infrastructure• Lighting system• Grounding/cabling • Heating, ventilation, air-conditioning

(HVAC)

EMS

System Monitors • Sources/ESS/Interface/PCS/Load• Voltage/Frequency/VAR• Power flow • Power quality & Efficiency

Charge / discharge Power allocation SOC & Temperature Protection & SOH

Fig. 2.3 The detailed components of the power system

The power system with ESS integrated allows the surplus electricity to be stored in

energy storage devices and dispatched during high electricity demand for improved

efficiency. The ESS contains three main components that are charge/discharge module,

energy storage devices, and energy management system. The charge/discharge module


- 13 -

manages the energy that flows in and out of the energy storage devices, while the EMS

monitors, controls and manages the ESS to ensure safe operation.

2.3 Energy storage technologies

Energy storage technology can be classified into five categories based on the form of

stored energy, which are (1) Mechanical, (2) Electrical, (3) Electrochemical, (4)

Thermal and (5) Chemical. Fig. 2.4 shows the classifications of energy storage

technologies available today. The following subsections present a comprehensive

review and discussion on each ESS, including their electrical and lifetime characteristics,

operating principles, system components, advantages as well as limitations.

Energy Storage Technologies

Mechanical Electrical Electro-chemical Thermal Chemical

Potential EnergyPumped Hydro Storage (PHS)

Compressed Air Energy Storage (CAES)

Kinetic EnergyFlywheel Energy Storage (FES)

ElectrostaticSupercapacitor (SC)

MagneticSuperconducting Magnetic Energy Storage (SMES)

Cell Batteryeg. Lead Acid / Sodium-Sulfur / Lithium-Ion / Nickel / Metal-Air

Flow Batteryeg. Vanadium Redox / Zinc Bromine / Cerium Zinc / Polysulfide Bromide

Latent ThermalSolid-Liquid

Liquid-Gaseous

(Phase Change)

Sensible ThermalLiquids-Liquids

Solids-Solids

(Non-phase Change)

Fuel Celleg. Proton exchange membrane / Phosphoric acid / Solid oxide / Molten carbonate

Thermochemicaleg. Ammoniates / Hydrate / Metal Hydrate

(Sorption Process)

eg. Hydration / Carbonation

(Chemical Reactions)

Fig. 2.4 Energy storage technology classification

2.3.1 Mechanical energy storage

Electrical energy can be converted and stored in the form of potential energy and kinetic

energy. Pumped hydro energy storage (PHS) and compressed air energy storage (CAES)

are two common potential ESSs. While flywheel technology converts and stores energy

in the form of rotational energy.

a. Pumped hydro storage (PHS)


- 14 -

Low DemandCharging (Pumping up)

Lower ReservoirEnd-User

Pump/Turbine for Charging Motor/Generator for Discharging

Tunnels

Renewable Energy Generation

High DemandDischarging (Generation)

Higher Reservoir

Generator

Turbine

HeightGravitational potential energy ≈

Electricity

Fig. 2.5 Pumped hydro storage (PHS) in renewable power system

Fig. 2.5 shows the structure of PHS, where two reservoirs at different altitudes are used

to achieve electrical energy storage and conversion to gravitational potential energy [74].

PHS was first implemented in Switzerland and Italy in 1890 and currently accounts for

95% of global energy storage capacity [75], [76]. There are currently more than 300

PHS plants worldwide with a total installed capacity of 169 GW. The largest PHS plant

is rated at 3 GW and can last 10 hours at rated power [77]. Compared to other energy

storage technologies, PHS is considered as a large-scale energy storage and is

commonly used for daily load levelling and seasonal energy storage applications.

The storage capacity of PHS depends on the elevation difference between the two

reservoirs and the maximum amount of water that can be stored in the higher reservoir.

During off-peak hours, excess power is used to run electric pumps to transfer water

from the lower reservoir to the upper reservoir. During the high power demand period,

the stored water in the higher reservoir is released to run turbines to generate electricity.

The round-trip efficiency of PHS is reported to be between 70% and 85%, mainly due to

energy losses during pumping and generation processes. PHS is one of the most cost-

effective ways to store large amounts of energy. However, the implementation of PHS

is limited by high initial cost, complex geographic location requirements, long

construction periods, and potential negative ecological and environmental impacts [78].


- 15 -

b. Compressed air energy storage (CAES)

Air Storage Vessel

Air Shaft

Water

Compressed air

Impermeable Caprock

Porous and Permeable LayerLower

Water Tank

WaterCompensation

tunnel

Upper Water Tank

Caverns in Hard Rock Formation

Typical CAES Hard-Rock Cavern CAES

Aquifer Reservoir CAES

(a) (b) (c)

Wells

Fig. 2.6 Three types of compressed air energy storage (CAES)

CAES technology is highly equivalent to PHS in terms of their operation principle,

application scenarios, input/output form, and storage capacity. But unlike PHS, which

moves water from lower reservoirs to higher reservoirs, CAES stores energy in the form

of compressed air and is usually stored in large underground caverns (aquifers, hard

rock caverns, underground natural gas storage, or salt caverns) with air pressure ranging

from 40 to 70 bars, as shown in Fig. 2.6 [79]–[81]. The excess power generated during

off-peak hours is used to compress air, which can be released to drive the gas turbine

generator to meet higher power demand during peak hours. CAES has high energy and

power density, which is considered as long-term energy storage technology. CAES has

been widely used in load shifting and power smoothing applications [82], [83].

However, similar to PHS, its development is mainly limited by geographical location

and large initial cost. There are two common types of CAES based on how heat is

handled during the compression and storage processes, namely diabatic CAES and

adiabatic CAES, as shown in Fig. 2.7 and Fig. 2.8 respectively [84].


- 16 -

Fig. 2.7 Schematic layout of diabatic CAES system

M

Heat Storage

Compressed Air

G

Compressor Train

Air Inlet

Air Outlet

Power In

Power Out

Air Turbine

Fig. 2.8 Schematic layout of adiabatic CAES system

Heat will be generated during the compression process and dissipated during the

expansion process. The diabatic CAES removes heat produced in the compression with

intercoolers. A reheating process with a gas-fired burner is often needed before

expansion in the turbines, which decreases the round-trip efficiency to about 40% to 50%

[85]. If the heat generated during the compression process (charging process) can be

stored and used during the expansion process (discharge process), the round-trip

efficiency can be significantly improved. The adiabatic CAES storage retains the heat

during compression with well-insulated heat storage and returns the heat during

expansion process while running the turbine generators. The round-trip of adiabatic

CAES power plant has been reported to be approximately 70% [86]–[88].

Compressor train Expander/generator train

Fuel (e.g. natural gas, distillate)

IntercoolersHeat recuperator

PC PG

Air Exhaust

AirStorage

Aquifer,salt cavern,

or hard mine

hS = Hours ofStorage (at PC)

(power in) (power out)


- 17 -

c. Flywheel energy storage (FES)

FES stores electrical energy in the form of kinetic energy by rotating mass, as shown in

Fig. 2.9. It comprises of a flywheel (steel or carbon composite) coupled with a high-

speed motor-generator and magnetic bearings that are mounted in a vacuum box in a

suspended state that aims to reduce self-discharge loss [89]. The stored energy is

calculated as E = Jω2/2, where ω is the angular velocity and J is the moment of inertia

[90]. The faster the flywheel rotates the more energy it stores. FES is considered as

short-term energy storage since the discharge time is from few mins to hours.

Fig. 2.9 The flywheel ESS

There are two types of FES based on the flywheel rotating speed: (1) low speed FES (up

to 6000 rpm), and (2) high speed FES (up to 60,000 rpm). The low-speed FES has a

specific energy close to 10-30 Wh/kg and they are made of steel rotors and conventional

bearings. High-speed FES can achieve a specific energy of 100 Wh/kg because of its

lightweight and high strength composite rotors. Low-speed FES is suitable for

applications that require short-term energy storage with high power capacity. On the

other hand, high-speed FES aims to serve applications that require medium-term energy

storage with a relatively lower power rating. During the charging process, the flywheel

is accelerated by a high-speed motor to convert the electrical energy to kinetic energy of

the rotating mass. When electrical energy is needed (discharging process), the rotating

flywheel is used to drive the generator in order to generate electricity. FES system

performs a series of good characteristics such as high power density, long cycle life (up

TopBearing

Motor/Generator

Rotor

BottomBearing

Shaft

Control


- 18 -

to 100,000 cycles), high round-trip efficiency (80% to 90%), no Depth-Of-Discharge

(DoD) effect, wide operating temperature range, and environment-friendly.

FES technology is theoretically suitable for energy storage applications that experience

frequent charge-discharge cycle, require short to medium term energy storage and fast

response. For example, uninterrupted power supply, ancillary services, peak power

buffer, solar or wind power system and aerospace applications [90], [91]. The main

shortcomings of FES are the high energy cost (up to 1400 US$/kW) and high self-

discharge rate.

2.3.2 Electrical energy storage

Electrical ESS stores energy in the form of electrostatic or electromagnetic energy that

includes SCs and superconducting magnetic energy storage.

a. Supercapacitor (SC)

SC, also known as ultracapacitor or electrochemical double-layer capacitor, stores

electrical energy in the form of the static electric field. As shown in Fig. 2.10, SC

consists of metal plates, polarized electrode, electrolyte, and separator (ion-permeable

membrane) [26]. Due to the porous and large specific surface area, the highly activated

porous carbon is used as the polarized electrodes.

Fig. 2.10 Simplified cross-section of SC cell

The two electrodes are separated by the separator and electrically connected to each

other by the electrolyte. The separator serves as a physical separation between the two

electrodes, which provides insulation and allows ion conduction between the two

Current Collector

Positive Electrode

-- - - - -

++ + + + +Negative Electrode

Current Collector

Separator


- 19 -

oppositely polarized electrodes. During the charging process, ions in the electrolyte

migrate to the opposite polarity of the electrode, and the electrostatic field are formed at

the interface between electrolyte and electrode on both sided of the separator that

creating an electric double layer. The double layer increases the surface area that allows

a relatively large amount of electrical energy to be stored and thus having a capacitance

that is thousands of times larger than a conventional electrolytic capacitor. Compared to

other energy storage technologies such as electrochemical batteries, SC has higher

power density ranging from 800-2000 W/kg. Similar to the conventional electrolytic

capacitor, SC has nearly infinite charge/discharge cycles without degradation, with an

efficiency as high as 95%.

Typical single SC cell operates in the low voltage range and can be connected in series

or in parallel to form modules and arrays to suit different application requirements. The

relatively higher self-discharge rate and low energy density of SC limit it to only short-

term applications. Short-term energy storage applications such as regenerative braking

in electric vehicles have become one of the main application areas of SC arrays, where

the SC array is designed to absorb and supply surge power demands during acceleration

and braking events. Research and development works for the next-generation SC have

been on-going and are mainly focusing on novel capacitive materials with larger

capacitance, such as graphene.

b. Superconducting magnetic energy storage (SMES)

The concept of SMES was first proposed by M. Ferrier in 1969 [92]. SMES stores

energy in superconducting magnetic conductors in the form of electromagnetic energy.

Fig. 2.11 shows a schematic of SMES with the superconducting coils as a core

component that placed in a helium vessel. The coils form a conductor that is made of

superconducting materials, such as Niobium-Titane (NbTi) wire operating at an

extremely low temperature (-270°C). The low-temperature environment allows the

superconducting materials to carry the electrical current as a large inductor and generate

a magnetic field with almost no resistive losses. The conductor inductance determines

the capacity of SMES and the corresponding maximum allowed current. The stored

energy in a typical SMES can be formulated as [93]:


- 20 -

𝑊 = 1

2𝐿 ∙ 𝑖2 (2.1)

where W is the stored energy inside the superconducting magnet conductor, L is its

inductance, and i is the current flows in the coil. Under the superconducting state, the

current density inside the SMES is ten to hundred times higher than conventional coil

and therefore higher power density (up to 105 kW/kg), and energy density (1-10 Wh/kg)

can be achieved. SMES has a series of advantages such as high efficiency of 90-95%, a

large number of charge-discharge cycles, environmentally friendly, and fast response

during charge-discharge processes (less than 100 milliseconds).

Fig. 2.11 The superconducting magnetic energy storage (SMES)

The installed capacity of typical SMES is generally within 10 MW and are mainly used

for power quality improvement such as transient stability and spinning reserve in grid-

scale applications [94]. Moreover, SMES allows the stored energy to be fully

discharged, which is useful for applications that require continuously complete charge-

discharge operations. Currently, the SMES is still under development stage, and some

major shortcomings hinder the development, such as a complex structure in the

refrigeration system and control system, and most importantly the high cost in

superconducting materials.

2.3.3 Electrochemical battery

Electrochemical battery technology uses chemical reactions to exchange electrical

energy and chemical energy. There are two types of electrochemical storage system: (1)

cell battery and (2) flow battery.

a. Cell Battery

RefrigerationSystem

Controller

Load

Superconducting Coils

Helium Vessel

+

-


- 21 -

Cell battery stores electrical energy in the electrode materials, while the flow battery

stores energy in the electrolyte. Fig. 2.12 illustrates the typical structure of the

electrochemical cell battery. Cell battery is a classical solution for storing electricity in

direct current form. It normally comprises of anode, cathode and the electrolyte that acts

as the medium allowing electrons exchange between two electrodes. It has the

rechargeable function of storing and releasing electricity via chemical reactions in

alternating the charge-discharge phases. Examples of cell battery technologies include

Lead-acid batteries, Lithium-ion batteries, sodium-sulphur batteries, nickel-based

batteries, and metal-air batteries.

Fig. 2.12 General structure of electrochemical cell batteries

LEAD-ACID BATTERY

The Lead-acid battery was first proposed in 1859 and is considered to be the oldest and

most matured cell battery technology. It composes of the lead-dioxide cathode, sponge

metallic lead anode and an electrolyte in sulphuric acid. The Lead-acid battery has an

energy density of up to 30 Wh/kg. As a rechargeable cell battery, it can tolerate

approximately 75% DoD and has approximately 1000-2000 cycles lifetime at 72-78%

efficiency. The Lead-acid battery has many advantages such as low cost, rapid

electrochemical reaction, simple power system installation, low maintenance, low self-

discharge rate (about 2-5% per month), and excellent thermal stability. Thus, the Lead-

Inverter/Converter

Load/Power system

ChargingDischarging

Electrolyte

+ -

Controller

Eg.Positive Electrode

PbSO4Li1-xMnO2

Metal (Mn, Co, Ni)…

Negative ElectrodePb

LixSiHydrogen-absorbing alloy

…


- 22 -

acid battery is still one of the most widely used ESSs in various applications such as

uninterruptible power supplies, residential energy storage, backup power supplies,

automotive batteries, etc [95], [96]. Although the Lead-acid battery is the most matured

cell battery technology, the relatively short service life and poor lifetime characteristics

hinder its development in many application areas, such as power system and residential

energy storage [97], [98].

LITHIUM-ION BATTERY

The Lithium-ion battery has been widely used in consumer electronics (smartphones,

laptops, portable devices), and electric vehicles. It uses an intercalation lithium

compound as an electrode material, allowing lithium ions to move between electrodes

for charging and discharging processes. The advantages of Lithium-ion battery include

negligible memory effect, long lifetime (3000 cycles at 80% depth of discharge), high

power density (500-2000 W/kg), high energy density (80-190 Wh/kg), lightweight,

stable, low self-discharge rate (1–3% per month), abundant, high efficiency (90-100%),

and low cost cathode material [99], [100]. However, Lithium-ion batteries are more

expensive compared to other electrochemical energy storage devices, ranging from

$900/kWh to $1300/kWh. Other drawbacks such as sensitive to overcharge and deep-

discharge, for which additional State-of-Charge (SoC) monitoring and protection system

are required for safe operation [101]. The Lithium-ion battery is one of the most

promising energy storage solutions for the modern power system. However, the

weaknesses mentioned above must be addressed before it can be widely adopted in

large-scale energy storage applications as well as residential energy storage solution.

SODIUM-SULPHUR BATTERY (NAS)

The sodium-sulphur battery (NaS) was introduced in the 1960s and first commercially

available by the TEPCO (Tokyo Electric Power Co.) and NGK (NGK Insulators Ltd.) in

2000 [102]. NaS operates at a high temperature (300°C-350°C) and uses liquid sulphur

as the positive electrode and liquid molten sodium as the negative electrode, as depicted

in Fig. 2.13. The high temperature ensures the electrodes (Na and S) are in liquid state

with a high reactivity. The solid beta alumina ceramic electrolyte separates the two

electrodes. During charging process, the sulphur forming sodium polysulfide (Na2S4)


- 23 -

release the Na+ ions to the electrolyte and combine with elemental sodium, for which

electrical energy is converted to chemical energy, and vice versa during the discharging

process. The free electrons flow to the current collector that is connected to the external

circuit. During the operation, heat produced by the chemical reaction is sufficient to

maintain the required high operating temperature. The NaS battery has an energy

density of about 100 Wh/kg and allows 100% depth of discharge, performing 2,500

cycles at a high roundtrip efficiency of up to 89% [103]. High operating temperature

and highly corrosive sodium polysulfide in the electrode material limit the NaS

application to only large-scale energy storage solutions. NaS can be applied in the

transmission and distribution sections of the power system for power quality

improvement, transient stability, Transmission, and distribution deferral, and outage

mitigation [104]. Another major limitation of NaS is that it requires additional auxiliary

equipment to maintain the high operating temperature and to ensure safe operation.

Fig. 2.13 Simplified layout of NaS cell/battery

NICKEL-BASED BATTERY

The nickel-based battery uses the active material of nickel oxide hydroxide as the

positive electrode and varies metal elements as the cathode. Typical nickel-based

batteries include nickel-zinc (Ni-Zn), nickel-cadmium (Ni-Cd), nickel-metal hydride

(Ni-MH) and nickel-iron (NiFe). The Ni-Cd and Ni-MH batteries are relatively mature

Na+

BetaAluminaTube

SNa2S4

Sulfur

Volts

MoltenNa


- 24 -

technologies and widely used in consumer electronics [105]. Ni-Cd battery use nickel

oxy-hydroxide and metallic cadmium as the electrodes. The energy density can reach up

to 75Wh/kg with a cycle life of approximately 2000-2500 [106]. The shortcomings are

the high self-discharge rate (5-20% per month) and the use of rare and highly toxic

electrode materials. Therefore, the Ni-Cd batteries have gradually lost their dominant

position in the consumer electronics market since the 1990s, and it was replaced by Ni-

MH and Lithium-ion batteries.

The Battle-Geneva Research Center around 1970 first invented the Ni-MH battery, that

uses metal hydride as the cathode [107]. Ni-MH battery has an energy density of up to

50 Wh/kg, the power density of up to 1000 W/kg and recorded 500 cycle life at 100%

depth of discharge. Compared to Ni-Cd battery of similar size, Ni–MH battery offers

30-40% more energy capacity and power capabilities, thus making it a more affordable

option for hybrid fuel-electric vehicles. Ni-MH batteries are widely used in hybrid fuel-

electric vehicles, contributing to over 95% of all market shares [108]. Other advantages

of Ni-MH include low maintenance, high power and high energy density,

environmentally friendly, safe operation during the charging-discharging transition at

high voltages.

METAL-AIR BATTERY

Metal-air battery uses pure metal as the anode and ambient air as the cathode from an

external system, its layout is shown in Fig. 2.14. The chemical reaction occurs in a tank

containing the aqueous electrolyte. Unlike the usage of aqueous in previous cell battery

technologies, the metal-air battery uses the ionic liquids as the electrolyte. Many metal

elements can be used as anodes, such as aluminum, calcium, barium, iron, lithium,

magnesium, potassium, silicon, sodium, tin, and zinc [109]. The most mature metal-air

batteries among them are the lithium–air and zinc-air batteries [110], [111], the latter is

more suitable due to cheap pure metal materials and low environmental impact. Metal-

air batteries are of interest to electric car companies because of their free cathode

materials and simple structures that do not require heavy enclosures to hold batteries

[112]. It can theoretically be designed to be ultra-lightweight and have a long-lasting

regenerative cathode, which is an attractive advantage in EV applications. For future

development, major limitations should be further considered, such as poor life cycle,


- 25 -

low roundtrip efficiency of about 50%, unstable operating environment, and relative

safety issues [113], [114].

Fig. 2.14 Simplified layout of the metal-air battery

b. Flow battery

Flow battery, or redox flow battery, is a kind of electrochemical energy storage that

stores energy via two chemically electroactive materials. It converts electrical energy

into chemical potential energy by charging two liquid electrolytes and releasing stored

energy in reverse process. As shown in Fig. 2.15, the catholyte and anolyte are

externally stored in two separate electrolysis tanks/reservoirs, and the electrolyte is

pumped through the ion-exchange membrane cell. The reversible electrochemical redox

reaction occurs here and generates the electricity simultaneously. During charging, one

electrolyte is oxidized at the anode and the other electrolyte is reduced at the cathode.

The reverse process occurs during discharging. Flow battery has a roundtrip efficiency

of 70% to 85%. Since the battery capacity depends on the electrolyte volume and the

electrode surface area, the external electrolyte tank concept allows the battery to be

independently sized from different power or energy requirements in varies applications.

Moreover, it also results in that the flow battery can be recharged almost

instantaneously by recovering the spent electrolyte liquid for re-energization. In

addition, it has the advantages of low self-discharge percentage, fast response time,

tolerance to overcharge/over discharge, long lifetime (without phase transition during

Air : H2O, CO2, O2

Metal Anode

Temperature

Electrolyte Porous Carbon


- 26 -

the process), low maintenance requirements and environment-friendly [115]. Currently,

the available flow batteries that has been developed over the past years contains

vanadium redox (VRB), polysulphide bromide (PSB), zinc bromine (ZnBr), and cerium

zinc (CeZn) [116]. They are generally considered to be an attractive technology for

relatively large scale energy storage applications in the 1 kW-10 MW range.

Fig. 2.15 Simplified layout of flow battery

c. Summary of electrochemical battery

Table 2.2 presents the technical comparison among these types of electrochemical

energy storage discussed above [73], [95], [101], [109], [117]–[120]. As a matured

energy storage technology and lowest cost per unit energy stored, the Lead-acid battery

is one of the preferred choices for small to medium scaled renewable and backup power

systems. Lithium-ion battery, with superior electrical characteristics, is currently

utilized in portable consumer electronic devices, the adoption in power system

applications are still low due to the safety issues and higher initial cost. On the other

hand, despite the excellent properties of NaS such as high energy density and small self-

discharge, the technology is still in the developmental stage due to the challenges in

maintaining the optimal operating temperature above 300°C. Unlike other

electrochemical batteries, the flow battery allows instant recharge by refilling the spent

Anolytereservoir

ElectrolyteA

Catholytereservoir

ElectrolyteB

Electrode

Ion-SelectiveMembrane

Flow Cell

LoadPump A Pump B


- 27 -

electrolyte liquid. The flexible capacity, long service life, and low initial cost make the

flow battery an up-and-coming ESS technology in large-scale renewable power

generation plants.

Table 2.2 Comparison of battery technologies

Battery type Advantages Limitations

Lead-acid

Low costs Low self-discharge (2–5%per month) Low maintenance Most maturity technology

Short cycle life (1200–1800 cycles) Aging due to deep depth of charge Environment unfriendly (Pb & acid) Low power density

Lithium-ion

High efficiency 90–100% High energy density Low self-discharge rate Low maintenance No priming required Variety of types available

Overcharge protection is required Aging due to deep depth of charge Very high cost ($900–1300/kW h)

Nickel-based

Can be fully charged (3000 cycles) Higher energy density (50–

80 Wh/kg) Lowest cost/cycle Good low-temperature performance Available in a wide range of sizes

and performance options

High cost, 10 times of Lead-acid battery High self-discharge (10% per month) Sensitive to overcharge Memory effect Generates heat at high C-rate

Sodium Sulphur (NaS)

High efficiency 85–92% High energy density (90-120 Wh/kg) No degradation to deep charge No self-discharge Insensitivity to ambient conditions

Be heated in standby mode at 325 °C (Heating consumes 14% of battery energy per day)

High price in battery sealing to avoid leak

Metal-air High capacity low cost Removal of seal enables airflow

Sensitive to cold heat, humidity, and air pollution one-time use

Flow battery

Capacity can be modified via set tank size

Long service life (10,000 cycles) No degradation for deep charge Negligible self-discharge

Medium energy density (40–70 Wh/kg) High corrosion

2.3.4 Thermal energy storage (TES)

As shown in Fig. 2.16, TES technology stores energy via cooling or heating storage

medium materials in the insulated containers and the stored thermal energy can be used

directly in the domestic buildings, district heating, the industrial process needs where

consumes thermal energy, or in power generation applications via gas generator [121],

[122]. There are two types of TES technologies, latent heat storage and sensible heat

storage [123]. Latent TES uses the liquid-solid transition of phase change materials


- 28 -

(PCMs) to exchange the thermal energy. During charging, the PCMs will shift from the

low internal energy state to higher internal energy state, such as solid to liquid and

liquid to gaseous. During retrieval, the higher internal energy state will transit back to

former state and release energy.

Fig. 2.16 Simple layout of thermal ESS

Unlike Latent TES, sensible TES stores the energy via high heat capacity of bulk

medium materials such as sodium, molten salt, pressurized water, etc. When the system

is charging, the bulk medium materials will be heated to a higher temperature. The heat

is then released to generate water vapour that drives turbo-generator system for

electricity. The main drawbacks include the low efficiency due to the energy losses

during heat process, weather sensitivity and require large volume or surface to get the

rated power.

2.3.5 Chemical energy storage

Chemical energy storage technologies include hydrogen fuel cells and thermal-chemical

energy storage. Hydrogen fuel cell as depicted in Fig. 2.17, comprises of electrolyze

unit, electrode storage tanks, and energy conversion cell. It uses the chemical reaction

between oxygen and hydrogen to generate electricity, the reaction results are water and

therefore offers zero-emission [124]. Up to 65% efficiency in hydrogen fuel cell has

been reported in the literatures [125]. Its benefits contain high energy density (300–

Heat Source

Heat Sink

Thermal energy storage (TES)

Charging Discharging

Heat Transfer Fluid

Pump


- 29 -

1200 Wh/kg), low maintenance requirements, easy installation and environment friendly

with low toxic emissions. The main drawbacks of hydrogen fuel cells are the high cost

of hydrogen containers, stringent transport requirements, and the highly flammable

nature of hydrogen. Hydrogen fuel cell performs more than 20,000 lifecycles with about

15 years lifetime. Its initial cost mainly from the storage method ($2–15/kWh) and

storage equipment ($500–10,000/kW) [65][126][127]. While for the hydrogen

production, hydrogen production with costs in the range of 1.34~2.27 $/kg, using

available electrolysers [128][129]. Another form of chemical energy storage is the

thermos-chemical ESS. Similar to flow battery, thermos-chemical ESS has two separate

tanks to accommodate two different electrode materials. The heat energy is stored in the

endothermic chemical reaction in molecular level. The stored energy is used to break

the chemical bond (dissociation reaction) during the charging process, and then the

reverse reaction will release energy in the discharging process.

Fig. 2.17 Hydrogen fuel cells

Based on the type of reaction within the energy conservation cell, it can be classified

into sorption systems and pure chemical reaction system [130]. The thermos-chemical

energy storage has a relatively high energy density, which allows a large amount of

energy to be stored using small amounts of storage medium materials. The heat loss in

thermos-chemical energy storage is relatively low due to two separate storage tanks at

Oxygen

e-

Anode Cathode

Hydrogen


- 30 -

ambient temperature. The technology is still under research and development stage,

with emphasis on reactor design, heat exchangers, and vacuum control.

2.4 ESS characteristics

A comprehensive understanding of ESS characteristics is essential in the consideration

and selection of suitable energy storage technologies for different application scenarios.

Fig. 2.18 presents the ESS characteristics in four different aspects, which are capacity,

efficiency, response, and product features.

ESS Characteristics

Capacity Efficiency

Power

Power Density (W/m3)

Specific Power Density (W/kg)

Energy

Efficiency

Round-trip Efficiency (%/cycle)

Discharge Efficiency (%)

Self-discharge (%/day)

Energy Density (Wh/m3)

Specific Energy Density (Wh/kg)

Response

Operation

Storage Duration

Discharge Time

Response time

Life

Calendar Life

Cycle Life

Product

Property

Memory Effect

Environmental Impact

Cost

Cost per unit Energy

Technology

Maturity

Applications

Fig. 2.18 Energy storage characteristics taxonomy

2.4.1 Technical characteristics comparison of energy storage technologies

a. Volumetric: power density and energy density

The volumetric property of ESS is an important factor in many applications, such as

power transmission, portable devices, automotive, and remote applications. For a given

energy capacity of ESS, the higher power and energy density is often desirable. Fig.

2.19 depicts the volumetric properties of different ESSs, with the nominal values of

1000W/L and 100Wh/L set for power density and energy density respectively. The

bottom left corner indicates the ESS with low energy density and power density, and

vice versa for ESS located at the top right corner.


- 31 -

PHS and CAES are low in energy density and power density by nature, which are

normally implemented in large-scale and stationary energy storage project. In the top

left corner, the SC, SMES, and flywheel are having high power density but relatively

low in energy density. SC has the largest power density beyond 10,000 W/L, while its

energy density is in the range of 10-30 Wh/L. In general, SC, Flywheel, and SMES have

speedy response times, which are typically in the range of milliseconds. As one of the

oldest electrochemical battery, the Lead-acid battery has a relatively higher energy

density in the range of 50-90 Wh/L among the ESS grouped in the bottom left region.

Lithium-ion battery exhibits high power density and energy density among all energy

storage technologies. In the bottom right corner, the Zn–air battery as an emerging

energy storage technology has demonstrated superior energy density, making it one of

the promising energy storage solutions in power transmission systems.

Fig. 2.19 Comparison of energy density and power density

b. Gravimetric: specific power and specific energy

Specific energy and specific power are gravimetric characteristics that indicate the

energy or power per unit weight. Fig. 2.20 illustrates the technical comparison of

different ESS regarding specific power and specific energy. The VRB flow cell is

High100Low

1000

High

Energy Density (Wh/L)

Pow

er D

ensi

ty (W

/L)

Low

Lead-acid ZnBr VRB

Nickle-metal PHS CAES

SC SMES

Flywheel

NaS Fuel Cell

Zn-air

Li-ion


- 32 -

located in the lower left area and performs the lowest specific power and specific energy,

160-170 W/kg and 10-30 Wh/kg, respectively. In the upper right corner, Lithium-ion

and Zn-air batteries are the lightest energy storage technologies. Compared to Lithium-

ion battery, the Zn-air battery has a relatively lower power density, which makes them

less suitable for consumer electronic devices. On the other hand, SC, SMES, and

Flywheel energy storages demonstrate the highest specific power but lower specific

energy due to the electrical characteristics and energy conversion mechanism. SMES

has the highest specific power of 500-2000 W/kg, while SC exhibits an exceptionally

high power density in the range of 40,000–120,000 kW/m3. Due to their fast response

time, they are broadly applied to power quality management systems. The flow batteries

of VRB, ZnBr and Lead-acid battery have moderate specific energy and specific power,

which are suitable for many medium-sized stationary residential energy storage

solutions. The TES and fuel cell show high specific energy but low specific power

(lower right area), that are usually used for large-scale ESS projects.

High30Low

500

High

Specific Energy (Wh/kg)

Spec

ific

Pow

er (W

/kg)

Low

TES Fuel Cell

NaS Nickle-metal

SC SMES

Flywheel

Li-ion

Zn-airLead-acid

VRB

ZnBr

Fig. 2.20 Comparison of specific energy and specific power

c. Capacity: power rating and energy rating


- 33 -

The power rating indicates that the amount of power in kW can flow in or out of the

energy storage at any given moment. The energy rating, or energy storage capacity,

describes the amount of energy or electricity that energy storage can be stored and

measured in kWh.

Based on the individual application scenarios of each energy storage technologies, Fig.

2.21 presents a comparison in energy rating and nominal discharge time duration at the

power rating. The SC, SMES, and flywheel, with the highest power density and lowest

energy density, are at the bottom of the chart. Their discharge time at rated power is

generally in the range of minutes. The cell battery and flow battery are middle range

energy storage devices with energy rating ranges from 1kWh to 1MWh, and the

discharge time can reach 10 hours at the rated power. The PHS, CAES, NaS, VRB,

hydrogen fuel cell and TES are ordinarily large-scale energy storage technologies with a

capacity of more than 10 MWh, and their discharge time at rated power can up to days.

Fig. 2.21 Comparison of energy rating and discharge time duration in power rating

d. Efficiency: discharge efficiency and roundtrip efficiency

Discharge efficiency and round-trip efficiency are two of the standard performance

indicators for energy storage technology. The roundtrip efficiency, or cycle efficiency,

is a ratio of output energy to input energy in one complete charge-discharge process.

The discharge efficiency represents the ability to transmit stored energy in the case of

full discharge. Most of the energy storage technologies perform medium to high cycle

days

10 hrs

1 hr

min

PHS CAES NaSVRB Hydrogen TES

Lead-acid Li-ion NiCd NiMH ZnBr

Flywheel SCSMES

Discharge Time duration (at power rating)

> 10MWh

1k-10M Wh

0.1-1 kWh

Energy rating


- 34 -

efficiency of above 70%, except for CAES, TES, metal-air battery and hydrogen fuel

cell that generally exhibit lower efficiency below 60%. Efficiency enhancement has

been one of the main focuses in ESS research, aiming to minimize system cost.

e. Self-discharge rate and storage duration

The self-discharge rate (%), or idling losses rate, is a measure of how fast one energy

storage unit will lose its stored energy without being connected to load. The

phenomenon of self-discharge in energy storage device is mainly caused by undesirable

internal mechanical or chemical effects such as energy dissipation via heat transfer

losses in TES, the chemical reaction in batteries, friction energy loss in the flywheel, air

leakage loss in CAES, etc. The storage duration refers the time period that ESS can

provide from full charge to zero. Fig. 2.22 presents a comparison chart based on self-

discharge rate and storage duration for ESSs. The SC, SMES, and flywheel have the

highest self-discharge rate, and their storage duration can only be maintained within

hours. Therefore, they are limited to short-term storage applications. Large-scale energy

storage devices such as PHS, CAES, NaS, hydrogen fuel cell, and VRB, and ZnBr flow

battery have low self-discharge rates, which is capable of keeping the stored energy for

months. While the TES and conventional cell batteries such as Lead-acid, Lithium-ion,

Nickel-based batteries exhibit intermediate self-discharge rate of 0.05-5%. They are

commonly used for medium storage applications such as RES distributed power

systems, electric vehicles, and consumer electronics.

Fig. 2.22 Comparison of storage duration and self-discharge rate

months

days

hrs

mins

PHS CAES NaSVRB ZnBr Hydrogen

Lead-acid Li-ionNiCd NiMH TES

Flywheel SCSMES

Storage Duration

<0.1

0.03-5

15 – 40

Self-Discharge (%/day)


- 35 -

f. Calendar Life and Cycle Life

The lifespan of ESS can be expressed regarding calendar life or cycle life. The calendar

life of an ESS indicates the period before an ESS deteriorates to an unacceptable state.

Cycle life represents the number of full charge-discharge cycles before significant

performance degradation. The lifespan of energy storage is an essential factor that

determines the system operating cost. In general, non-chemical based energy storage

technologies including PHS, CAES, flywheel, SC, SMES, and TES typically have much

longer lifetime compared to chemical energy storage technologies such as cell batteries,

flow batteries, and hydrogen fuel cell, due to the chemical deterioration and corrosion

phenomenon.

g. Maturity and economic cost

The technical maturity, memory effect, financial cost and environmental impact of

energy storage products are also important evaluation factors for selecting suitable

energy storage in specific projects. The maturity of energy storage technology can be

classified into five levels which are (1) research and development (R&D), (2)

proven/commercializing, (3) mature/commercialized, and (4) very mature/fully

commercialized.

SMES Flywheel

SC

Hydrogen fuel cellZnBr

CAESVRB

Latent TES

NaSLi-ionZn-air

Sensible TES

PHSNiMHNiCd

Lead-acid

R&D Proven/Commercializing

Mature/Commercialized

Very Mature/Fully commercialized

80,000

10,000

2,000

10

Energy Capital Cost ($/kWh)

Fig. 2.23 Comparison of energy capital cost and maturity level


- 36 -

Fig. 2.23 presents the technology maturity versus energy capital cost of the different

energy storage technologies [131]. The cost of energy storage includes the energy

capital cost (initial cost) and maintenance and operation (M&O) cost. The former one is

usually expressed in cost/per unit ($/kWh). Well-developed energy storage technologies,

such as PHS and Lead-acid battery are classified as very mature, with a history of more

than 100 years. The sensible TES, Lithium-ion, Zn-air and NaS battery are mature

technologies that have been commercialized on the market for decades. They are

technically developed and commercially available in the industry, but stability,

reliability, and cost competitiveness are still evolving and need further improvement.

CAES, VRB flow battery, flywheel, and latent TES are on the commercializing stage

with some significant improvement before commercialization. Hydrogen fuel cell,

SMES, and ZnBr flow battery are still in their early stage of development.

SMESFlywheel

SC

Hydrogen fuel cellPHS CAES Li-ionNickel-based, TES

Lead-acid Flow batteries NaS

5 40 60 90

Energy Capital Cost ($/kWh)

M&O Cost ($/KW-year)

80,000

10,000

2,000

10

Fig. 2.24 Comparison of energy capital cost and M&O cost

Fig. 2.24 shows the comparison of M&O cost versus energy capital cost of energy

storage technologies under consideration. The energy capital cost and M&O cost are the

crucial aspects to consider when selecting energy storage for any project to ensure long-

term financial sustainability. For example, the Lead-acid battery has a relatively low

energy capital cost, but it requires a relatively higher M&O cost, which makes it

inappropriate for the large-scale energy storage applications. More financial analyses of


- 37 -

different energy storage technologies in various application scenarios such as internal

rate of return, tariff reduction, price arbitrage evaluation, are reported in [132]–[134].

h. Other characteristics: memory effect, aging mechanism, environment impact

Other non-quantifiable energy storage characteristics are critical aspects to consider

when selecting an energy storage device. The memory effect is a unique feature of

nickel-based rechargeable batteries. It is an effect where the battery loses its maximum

energy capacity when it is repeatedly not completely discharged before being recharged,

which seems that the battery remembers the latest smaller capacity. While the aging

mechanism refers to the process of performance deterioration due to the physical and

chemical changes during operation. In general, the aging mechanism is commonly

found in chemical-based batteries due to the corrosion, and in mechanical-based energy

storage due to physical wear and tear.

Environmental impact is another issue to be considered when choosing energy storage

products or design ESS. The PHS and CASE require large-scale infrastructure which

may disturb the local ecosystem. The flywheel, SMES, and hydrogen fuel cell are

considered to have the least impact on the environment because there is no apparent

toxic emission or waste by-product that causes pollution. Chemical-based energy

storage technologies are using potential hazardous toxifying materials such as lead,

cadmium, bromine, lithium, etc. Thus the recycling process of the aged chemical battery

becomes extremely important, and additional attention needs to be paid to before their

implementation.

2.4.2 Summary table of energy storage characteristics

Table 2.3 presents the summary of characteristics, parameters and product features for

each of the energy storage technologies [63], [65], [75], [135], [136]. Inside the table,

the item of Energy capital costs means the rough estimation about the cost of an ESS

project to a commercially operable status.

Table 2.3 Summary of energy storage characteristics

ESS Technology

Power Density [W/L]

Specific Power [W/kg]

Self-discharge [%/day]

Discharge time

Cycle time (Cycles)

Energy capital costs ($/kWh)

Application

Scale


- 38 -

Energy Density [Wh/L]

Specific Energy [Wh/kg]

Round-trip efficiency [%]

Response time

Lifetime (Years)

Environment impact Maturity

Mechanical

CAES 0.5~2 Not appl. Small

hours~ days 8000~12000 2-50 Very large

2~6 30~60 40~50 s-min 20~40 Medium Proven

PHS 0.5~1.5 Not appl. Very small hours–

days 10000~30000 10~12 Very large

0.5~2 0.5~1.5 70~85 s-mins 30+ High Very Mature

FES 1000~5000 400~1600 20~100 mins~

hours 20000+ 1000~14000 Medium

20~80 5~130 85~95 10-20 ms 15~20 Very low Proven

Electrical

SC 40000~1200

00 500~15000 20~40 mins~ hours 100000+ 300~2000 Small/

Medium 10~30 0.1~15 85~98 10-20 ms 5~20 Very low Mature

SMES 1000~2500 500~2000 10~15 mins~

hours 100000+ 1000~72000 Large/Medium

2~6 0.5~5 >95 <100 ms 5~20 Low R&D

Battery

Lead-acid

10~400 75~300 0.1~0.3 hours 500~1000 200~400 Small/ Medium

50~90 30~50 80~90 ms 3~10 High Very Mature

Lithium-ion

1500~10000 230~340 0.1~0.3 hours 1000-20000 900~1300 Small/ Medium

150~500 100~250 90~98 10-20 ms 5~15 High/Medium Mature

NiCd 80~600 150~300 0.2~0.6 hours 2000~3500 500–1500

Small/ Medium

<200 45~80 70~75 ms 10~20 High Very Mature

NiMH 500~3000 70~800 0.4~1.2 hours 300~3000 270~530 Small

<350 60~120 70~75 ms 5~10 High Very Mature

NaS 120~180 90~230 ~20 hours 2000~4500 300~500 Large/Me

dium <400 150~240 85~90 10-20 ms 12~20 Medium/Low Mature

VRB 0.5~2 Not appl. 0~10 hours 12000+ 600–1500 Large/Me

dium 20~35 10~30 75~82 ms 10~20 Medium/Low Proven

ZnBr ~25 50~150 Small hours 2000+ 700~2500 Large

30~65 30~80 60~70 ms 5~20 Medium R&D

Zn-air 50~100 ~1350 Not appl. hours 2000+ 10~1000 Small

~800 ~400 60 ms 30+ Very low Mature

Thermal

Latent Heat

Not appl. 10~30 0.5~1 Not appl. Not appl. 3~80 Large/Medium

25~120 150~250 75~90 Not appl. 20~40 Low Proven Sensible Heat

Not appl. Not appl. 0.5 Not appl. Not appl. 0.04~50 Medium 100~370 10~120 70~90 Not appl. 10~20 Low Mature

Chemical

Hydrogen

Fuel-cell

>500 >500 0.5~2 hours-days 1000+ 500~3000 Large/Medium

500~3000 800~10000 30~50 s-mins 5~15 Very low R&D

2.4.3 The selection of energy storage technologies

In power system, the application of ESS can be divided into five categories: large-scale

energy services, transmission, and distribution infrastructure services, auxiliary services,


- 39 -

and residential energy storage solutions. The auxiliary service contains emergency

power back-up, peak shaving, time shifting and etc. Since each type of energy storage

technology has its unique characteristics, it is essential to select the optimal energy

storage technology for specific applications [137]–[139]. In general, the characteristics

that can be used to assess the applicability of energy storage technology for specific

applications include:

Energy rating and power rating; Energy density and power density; Response time; Self-discharge rate; Cycle efficiency; Rated voltage and current; Charge/discharge duration and ampere-hour; Operating temperature; Service life; Environmental impact; Cost, weight, and size.

The above characteristics can be sorted into three main categories: technical, economic

performance and environmental standards. In most cases, the rated power and duration

of discharge are the critical characteristics in selecting appropriate energy storage

technology for various applicationsm; this is mainly because that the functionality is a

prerequisite and to meet the technical requirements of the targeted system is the priority.

At the same time, ESS economic performance and environmental standards are also

important factors that influence the final decision. A typical method of analyzing the

economic performance of an ESS is the Levelized Cost Of Energy (LCOE), sometimes

referred to as Levelized Cost Of Storage (LCOS) [140]–[142].

The environmental standards mainly include the impact on the environment and the life

of the ESS itself. The Life Cycle Impact Assessment (LCIA) is a systematic and

commonly used method to assess the environmental impact of a product or process

system throughout its life cycle [143], [144]. With the various characteristics, no single

energy storage technology can fulfill all desirable requirements in different energy

storage applications. For example, the Lithium-ion battery is one of the best options for

small-scale portable devices because of its high power density, high energy density,


- 40 -

high efficiency, and reasonable cycle life. However, the higher initial cost and thermal

instability hinder its usage in residential energy storage applications. As an alternative,

the Lead-acid battery is a mature technology with relatively low cost and superior

electrical stability. It is a practical option for residential energy storage solution,

especially in stand-alone power systems. At the same time, however, in large-scale

situations (several kWh), the Lead-acid battery is not suitable because of its high M&O

cost and inefficiency.

Fig. 2.25 The framework of the energy storage technology selection

With the growth in ESS applications and options of energy storage technologies, it is

efficient for decision-makers to provide a framework that can facilitate the selection of

suitable ESSs. Fig. 2.25 shows the typical framework of the energy storage selection.

Technical, economic performance and environmental standards are evaluated using sub-

items that need to be balanced by decision-makers. The selection aims at finding the

most suitable energy storage technology that can not only meet the technical constraints

imposed by the target applications but also has the best overall performance in the main

criteria, for example, the one with high technology maturity, low total cost, and small

environmental impact.

Target ESS

Technical

Required installation

Duration

Maturity

Complexity

Economic performance

Initial cost (Budget)

Maintenance

Environmental standard

Lifecycles

Waste treatment

Environmental friendliness

Main-criteria Sub-criteria


- 41 -

The main criteria in the three domains for selection normally include qualitative and

quantitative features. The quantitative considerations include efficiency, lifetime, cost,

power and energy densities, electrical response time, etc. A comprehensive quantitative

assessment can eliminate the effects of human factors and make the selection more

realistic. The qualitative considerations include the evaluation of energy storage

technologies attributes such as device properties, technology maturity, reliability and

safety, environmental impact, etc. Qualitative analysis will mainly base on the user

features, expert experience, and government policies to meet the environmental

requirements, cost requirements, and stability requirements for specific projects. Once

the appropriate energy storage product has been determined, the design of ESS will

follow the procedures:

1. Determination of the required size of the energy storage, such as voltage and

storage duration requirements (large, medium or small);

2. Determination of the energy storage structure that is required to meet the total

ampere-hour capacity (single, parallel or series);

3. Design the control system;

4. Design of construction facility at the installed site, including protection system,

monitoring system, cabling and etc.

The above procedures are the main steps in most ESS design process, and the detailed

design and installation steps will be sequentially expanded according to the different

application scenarios. As a case study to illustrate how to select and design an ESS in a

specific application, a stand-alone PV power system with energy storage will be

discussed in the next chapter.

2.5 General discussion on energy storage technology and future development

The design of energy storage technology normally involves multiple forms of energy,

multiple devices, multiple substances, and multiple processes. Over the past few

decades, researchers and industrial practitioners are putting great efforts in developing

advanced energy storage technologies to reduce economic costs while ensuring

longevity, good load regulation performance, high efficiency, and long-term reliability.


- 42 -

The ESS plays a crucial role in many aspects, including electric power transmission,

large-scale renewable energy based power plant, large-scale grid flexible

interconnection, residential RES based microgrid, and one of the necessary support

technologies for the smart-grid. The main applications of various types of energy

storage technologies are as follows:

1. Large-scale, long-term energy storage facilities, such as PHS and CAES, can be

used for large-scale power grid peaking shaving and seasonal storage. The flow

battery and thermal storage with the second large energy storage capacity, a large

number of cycles, and long service life can be used as load levelling devices in the

grid. While Hydrogen storage can be used to store surplus wind and solar energy

to power fuel cell vehicles.

2. The SC, SMES, flywheel energy storage, sodium-sulfur batteries, and other high

power density ESS are mainly operated in combination with large-scale RESs.

They can quickly respond to surge power generation and stabilize the fluctuations

from the RESs.

3. Lithium-based batteries, Lead-acid batteries, metal-air batteries, and other battery

ESSs are less suitable for large-scale power plants and are mainly used for

distributed residential energy storage applications and electric vehicles.

With the continuous innovation and development of new energy storage materials, it is

expected to make significant breakthroughs in extending service life, increasing energy

density, fast charging time, and reducing costs. Meanwhile, the deployment of ESS in

large-scale power grids and/or microgrids is still facing significant challenges, and they

shape the direction of research and development roadmap of energy storage technology,

which contains three key areas:

1. To enhance energy storage performance and reliability, effective supply chain and

fabrication processes to enable mass production, thus overall cost reduction;

2. Energy storage technology selection and performance optimization complementary

technologies for specific applications;

3. Formalization of a systematic approach in evaluating energy storage technologies

for different applications.


- 43 -

For traditional large-scale energy storage technologies, PHS, CAES, and TES, their

main challenges are low roundtrip efficiency and higher implementation costs. The

refinements of the PHS system are mainly focusing on upscaling the hydroelectric

turbo-machinery, optimizing the operation efficiency via installing enhanced monitoring

system and advanced intelligent energy management system. In addition to

improvements in existing PHS technology, similar ideas are being actively studied such

as seawater usage as a reservoir, tidal barrages, and Gravity Power Module Energy

Storage (GPMES) [145]. The GPMES uses two different sized water shafts to store

energy. The electricity is consumed to pump water in the smaller shaft and raises the

position of a heavyweight piston of the more massive shaft. When the electricity is

needed, the piston is set free, and the water flow rotates the turbine to generator power.

Instead of large-scale CAES, researchers have developed mini-CAES and aboveground

CAES to increase their usability in many applications and to overcome the dependency

of the large-scale CAES on the geographical site selection [146]. Rapid development

has been found in small-scale ground CAES as an alternative to electrochemical energy

storage for the applications such as uninterrupted power supply or backup power supply.

For flywheel energy storage, the core development area should aim to investigate novel

materials for the rotor, high performance and low-loss bearings, a robust control system

and the cost of precision manufacturing processes. Currently, the strength composite

fiber materials are applied in rotor fabrication, which may have significant

improvements in rotation speed, storage capacity, power density, and reliability. The

latest bearing technology is using the high-temperature superconductor, which

significantly increases the efficiency. However, the high production cost, operational

safety issues, and high self-discharge rate still limit its usage in long-term storage

applications.

The research and development in SC mainly focus on enhancing the chemical capacitive

materials to improve the lifespan and storage capability. The latest in SC development

includes the use of carbon graphene-based electrodes and novel nanostructured

materials that can significantly improve the SC surface area and therefore storage

capacity [147], [148]. For SMES, the cost reduction of superconducting coils, the

development of novel low cryogenically sensitive coil materials, and EMS optimization


- 44 -

should be the primary issues to be addressed in order to penetrate the market share in

energy storage industry.

Electrochemical energy storages or battery technologies as one of the mainstream

energy storages for stationary energy storage applications require technology

breakthrough in enhancing the reliability and lifespan, electrode materials, fast charging

and energy management system. Among the battery energy storage technologies, Lead-

acid battery are still one of the most widely used energy storage technologies. For

research and development of new-generation Lead-acid batteries, it should mainly focus

on electrode materials for performance improvement, extending cycling times,

enhancing the deep discharge capability as well as the recycling processes of electrode

materials. At present, one of the advanced Lead-acid battery is developed by adding up

to 40% of activated carbon to the negative electrode composition, wherein the cycle life

is significantly increased by up to 2,000 times [149]–[151].

For lithium-based battery, many research works have been initiated on the innovation of

electrode and electrolyte materials to increase their power capability, energy density and

lifetime. The usage of graphene in replacing the conventional electrolyte materials has

claimed to revolutionary improves the storage capacity and charging time. The

graphene-based lithium battery is still on the early stage of research and development,

but the superior characteristics of graphene have attracted attentions from researchers

and industry [152].

NaS battery is a relatively mature technology and is often used for large-scale energy

storage projects. However, the high temperature operating requirements and eliminating

the corresponding limitations still need to be addressed [153]. The main limitations of

flow battery are the capital cost associated with electrolyte sources, battery stack

manufacture and high maintenance costs. The next generation of flow batteries could be

the structure with hybrid redox fuel cells. Shortly, it is possible to apply in electric

vehicles which makes it easier for electric vehicles to recharge energy by refueling

[118]. The hydrogen energy storage technology is still in its early stages. It requires

extensive testing and validation before it can be fully commercialized. The

improvements in system power conversion efficiency, safe operation, low-cost


- 45 -

hydrogen production, storage, and transportation processes will become the leading

research and development directions for future development [154].

In general, energy storage technology provides more flexibility and optimizes the power

system operations. In the macro view of future energy storage deployment, the trend

that forcing energy storage technologies to become a central element of the future

power system will mainly depend on the penetration level of RES, the emergence of

smart-grid development and the advancement in low-cost environment-friendly

materials [155]–[157]. Thus its future development could mainly focus on the following

areas.

Firstly, energy storage technology needs to achieve more grid integration to improve its

safety, stability, high efficiency, and reliability. With ESS integration, power electronics,

and information technologies, the electric grid is anticipated to transforming into the

decentralized structure and achieving two-way interaction in every node in the power

system including generation, transmission, distribution, and consumers’ end. This will

eventually realize the upgrade to smart-grid and form the powerful internet-style global

energy grid in the future [158], [159]. Secondly, the longevity and cost of ESS must be

improved, while having both high power density and high energy density, to provide the

auxiliary services such as backup energy, power smoothing and peak shaving for the

renewable power systems that have intermittent and unpredictable output power. Multi-

variate hybrid ESS with complementary characteristics and its optimized supporting

control system will become the enabler in achieving both high power and energy

density requirements. Finally, energy storage will be more widely used in electric

powered transportation systems such as electric vehicles, hybrid vehicles, electric-

driven trains, drones, and aerospace applications, etc. [160]–[162]. This is the highest

technical requirement for ESSs, and it usually requires them to have higher security,

longer duration, shorter charging time, faster response, lighter and cheaper.

2.6 Conclusion

The potential applications of energy storage in modern power system and residential

ESS are presented in this chapter. The desired characteristics of energy storage at a


- 46 -

different part of the power systems are discussed to ease the selection of energy storage

for optimal performance and sustainability. The energy storage technologies in various

forms were comprehensively reviewed and compared, including the electrical

characteristics, storage capability, efficiency, and product features. The introduced

energy storage technologies include CAES, FES, PHS SC, SMES, cell battery, flow

battery, TES, and chemical energy storage. The comparative analysis of available

energy storage technologies was carried out based on storage and electrical properties,

strengths and limitations, technology maturity as well as the potential for future

development. It can be concluded that the wide spectrum of technical characteristics of

current ESS technologies can meet the requirements of different power system

operations, with a suitable combination of different ESS technologies. Finally, this

chapter also summarizes the limitations that exist in various types of energy storage

technologies and the possible future development trends. The integration of ESS

technologies in modern large-scale power system and residential applications are critical

for providing flexibility and ancillary services in smart-grid to handle the increasing

supply-demand challenges.

- 47 -

Chapter 3

Solar PV Technology and Energy Storage

in Stand-alone Power System

3.1 Introduction

The global climate change caused by greenhouse effect has motivated the development

of renewable energy based power system to replace the traditional fossil fuel energy

[163]–[165]. Among various RES technologies, solar PV technology has become one of

the most prominent and widely used technologies due to its modularity, ease of

installation, matured technology, and low operating costs [166]. The modularity of solar

panel allows large-scale centralized PV farm (up to 50MW) to be set up, and at the

same time enables decentralized PV power system such as residential building and

remote area electrification. The main disadvantages of PV power generation are: (1) the

intermittency of output power due to clouds, and (2) variability due to the day-night

cycles as well as seasonal variation. Therefore, the PV power systems, especially for the

stand-alone systems, are generally equipped with ESS to ensure stable and reliable

electricity. Over the past decades, ESSs have been widely used and indispensable in

stand-alone solar PV power generation systems.

This chapter presents an overview of PV technology and its application in power

systems, including the considerations of PV system design, solar irradiance

measurement, load profile estimation and the selection of energy storage technology. A

detailed discussion and analysis of the stand-alone PV-battery power systems will also

be provided that contains an introduction to the system topology, the design of its ESS,

Chapter 3 Solar PV Technology and Energy Storage in Stand-alone Power System

- 48 -

system modelling, numerical simulation and experimental demonstration based on a

lab-scale prototype.

3.2 Overview of solar PV technology

3.2.1 Background of solar cell

Solar cell, also known as PV cell, is a solid state device that converts sunlight directly

into electricity. The name of PV comes from the process of converting light (photons)

into electricity (voltage), which is the so-called PV effect. The PV effect was discovered

by French physicist, Becquerel, in 1839 and was first applied in silicon cell by Bell

Laboratories until 1954. The silicon cell is soon gained applications in U.S space

programs as the power system of earth-orbiting satellites owing to its high power-

generating capacity per unit weight. The space applications inspired the solar cell

development and continued to spread into varies applications ranging from powering

rural villages to feeding national grids globally. Fig. 3.1 shows the classification of solar

cell based on the material property [167]. The family of solar cells in different primary

active materials are divided into silicon solar cell, multi-compound thin film solar cell,

polymer solar cell, modified electrode layers, nano-crystalline solar cell, organic solar

cell and other solar cells with novel materials.

Fig. 3.1 Solar cell classification

Table 3.1 summarizes the efficiency of relatively mature and dominant solar cells in the

market. Mono-crystalline silicon solar cells have been widely used in the large-scale PV

Solar Cell Technologies

Wafer-based Cells

Mono-crystalline

Cells

Poly-crystalline

CellsGaInP,

GaAs, etc.

Thin Film Cells

Amorphous Silicon (a-Si)

Cells

CdTe, CZTS, CIGS, CIS, Condenser

Organic-Materials, Dye Sensitized, etc.


- 49 -

power system. It possesses the highest conversion efficiency and performs significantly

better compared to its counterparts under medium temperature zones such as the

subtropical area [168]. Polycrystalline silicon PV cells have similar properties to

monocrystalline silicon PV cells but suffer from higher temperature sensitivity, which

reduces conversion efficiency. Although amorphous silicon PV cells are a cheaper

option, conversion efficiency is lower, in the range of 5-7%.

Table 3.1 Three main types of solar cells in the market

Solar cells Efficiency Features

Mono-crystalline Silicon 14-17% Higher efficiency but higher price

Poly-crystalline Silicon 11-14% Cheaper than mono-crystalline but less efficient

Amorphous Silicon (a-Si) 5-7% Flexible shape, cheaper, but low efficiency

3.2.2 Solar cell model

In solid-state physic, solar cell works as a classical diode with P-N junction. The

complex PV process in solar cell can be simplified as an equivalent electrical circuit

model as shown in Fig. 3.2. The summation of the output current I, the diode current ID

and the shunt-leakage current 𝐼𝑆𝐻 is equal to the photo-current 𝐼𝐿 . The series resistor

𝑅𝑆 represents the internal resistance which depends on P-N junction characteristics such

as depth, impurities and contact resistance. Due to the material imperfections of solar

cell, there is leakage current at the edge of each solar cell and it is characterized by the

shunt resistor 𝑅𝑆𝐻 and leakage current 𝐼𝑆𝐻. For an ideal solar cell, there is no series loss

(𝑅𝑆 = 0) and no leakage current (𝑅𝑆𝐻 = 𝐼𝑛𝑓𝑖𝑛𝑖𝑡𝑦 ). The efficiency of solar cell is

sensitive to 𝑅𝑆 variation and therefore, minimizing the resistance from P-N junction is

one of the research directions in solar cell to improve the conversion efficiency.

Fig. 3.2 The equivalent circuit of solar cell


- 50 -

The output current of solar cell is given by the following equation:

𝐼 = 𝐼𝐿 − 𝐼𝑑 − 𝐼𝑆𝐻 (3.1)

The diode current 𝐼𝑑 can be calculated by the classical diode current expression:

𝐼𝑑 = 𝐼𝐷[𝑒𝑄𝑉𝑜𝑐𝐴𝑘𝑇 − 1] (3.2)

Where

𝐼𝐷 is the diode saturation current

Q is the electron charge which equals to 1.6 ∗ 10−19𝐶

A is the curve-fitting constant

k is the Boltzmann constant which equals to 1.38 ∗ 10−23J/°K

T is the temperature in °K

The shunt leakage current 𝐼𝑆𝐻 is calculated by open-circuit voltage 𝑉𝑜𝑐 and shut

resistance,

ISH =Voc

RSH

(3.3)

Where 𝑉𝑜𝑐 is obtained when there is zero load. Thus the load current is given by the

expression:

I = IL − ID[eQVocAkT − 1] −

Voc

RSH

(3.4)

The electrical characteristics of solar cell can be graphically represented by the current

versus voltage (I-V) curve and power versus voltage (P-V) curve, as shown in Fig. 3.3.

On the vertical axis, the highest current in I-V curve occurs at zero voltage when the

output terminals are connected. This is called the short-circuit current 𝐼𝑠𝑐 . On the

horizontal axis, the highest terminal voltage occurs when no load is connected, which is

called the open-circuit voltage 𝑉𝑜𝑐. These two parameters are usually given on the solar

cell datasheet. The Maximum Power Point (MPP) occurs at the knee point on the I-V

curve (marked in red circle). The I-V curve in one solar cell will vary based on the sun

irradiation.


- 51 -

Fig. 3.3 The I-V and P-V characteristics of the solar cell

Fig. 3.4 illustrates the different I-V curves of typical solar panel. It can be observed that

the knee points of the I-V curves are shifting to the right and the corresponding MPP on

P-V curves increases as the sun irradiation increases. Therefore, keeping the solar cell to

generate electricity at MPP continually can significantly increase the overall power

efficiency. There are many MPP tracking algorithms available nowadays to maximize

the efficiency of solar PV power systems.

Fig. 3.4 V-I and P-V curves in different intensity

*

P

(W)

0 20 40 60 80 100 120 1400

5

10

1 kW/m2

Cur

rent

(A)

Voltage (V)

Array type: SunPower SPR-305-WHT; 2 series modules; 2 parallel strings

0.75 kW/m2

0.5 kW/m2

0.25 kW/m2

0 20 40 60 80 100 120 1400

500

10001 kW/m2

Pow

er (W

)

Voltage (V)

0.75 kW/m2

0.5 kW/m2

0.25 kW/m2


- 52 -

Fig. 3.5 Solar cells, PV modules, PV panels and PV arrays

A single solar cell typically produces 1W of power with approximately 0.5 volts. A

number of them are interconnected in series and/or parallel to generate the appropriate

voltage and current levels that can be integrated to the power systems. The PV module

is formed by solar cells that are typically sealed in a protective laminate as depicted in

Fig. 3.5. The PV modules are then assembled as a PV panel to achieve the desired

voltage and power. In a large-scale PV power system, PV panels as the fundamental

unit are connected to form PV array in order to generate the desired power generation

capacity. The flexibility and modularity of solar PV allows different size of PV power

systems to be implemented, ranging from large-scale solar farm down to residential PV

power system.

3.2.3 Overview of the PV power system

The typical PV power system consists of core components such as PV array, charge

controller, ESS, power inverter and loads. Modern PV power system also includes smart

meters, AC/DC isolator, sensors, and monitoring systems to ensure system reliability

and performance optimization. In order to achieve specific goals from powering basic

electrical appliances to feeding electricity to the national grid, the PV power system can

be designed in many different topologies. PV power system can be configured either as

a grid-connected PV system or stand-alone PV power system, with and without ESS

and/or other distributed generation sources, as categorized in Fig. 3.6. Grid-connected

PV power systems operate as distributed generation networks that are interconnected

with the utility grid. It uses power electronic inverters, or Power Conditioning Unit

Solar Cell PV Module PV Panel PV Array


- 53 -

(PCU), to convert the DC power produced by the solar cells into AC power that are

synchronized with grid voltage and frequency. With ESS integrated, grid-connected PV

power systems can operate in either grid-connected mode or islanded mode based on the

real-time generation-demand conditions.

Fig. 3.6 The classification of PV power system

Conversely, stand-alone PV power system, or off-grid PV power system, is designed to

operate autonomously and independently that are generally used to supply electricity in

isolated or remote areas. The simplest and primitive form of the off-grid PV power

system is the direct-coupled system, where the PV panel is directly connected to a DC

load, and it usually is in small power capacity. Since there is no energy storage in the

system, it only operates when sunlight is available and commonly applied in ventilation

fans, water pumping system for agriculture, etc. A complete stand-alone PV power

system contains PV panels, charge controller, power inverter, ESS and loads. Stand-

alone PV power systems are widely used for rural electrification, especially in remote

rural areas where grid connection is not economically viable. Hybrid PV power system

is one of the solutions to compensate for the variability of PV generation, where other

distributed generations such as diesel generator, wind power generator, and mini-hydro

generator are integrated.

PV Power System

Grid-connected

Without ESS

Directly connected to Grid

With ESS

Bimodal PV System

Stand-alone

Without ESS

Direct-Couple System

With ESS

DC SystemSelf-Regulating

AC System

Hybrid PV System

With other RES based system

With Diesel Generation


- 54 -

3.3 Stand-alone PV power system

Reliable and affordable electricity is of crucial importance to ensure the quality of life.

However, according to the International Energy Agency (IEA), in 2015, nearly 1.3

billion people are not connected to the utility grid, and over 95% of them live in the

developing countries or remote rural areas [169]. Off-grid rural communities are

generally geographical dispersal, decentralized, low population density and

geographically isolated from the national grid. Due to the resource and financial

constraints, it is difficult to extend the national grid to these areas through the

conventional power transmission and distribution approach. Thus, a typical solution to

provide electricity to the remote and inaccessible area is achieved by using petrol/diesel

generators which have low initial costs but high running costs, and most importantly,

not environmental friendly. Therefore, using renewable energy based power system in

these areas is one of the practical solutions that will bring significant benefits to the

communities, especially in reducing dependence on the expensive petrol/diesel fuel and

the difficulty in transporting them. Solar PV is one of the preferred choices of RES for

remote rural electrification due to its excellent properties such as modular, easy to

install, eco-friendly, and low operating cost [170].

3.3.1 Components and system structure

Fig. 3.7 A typical stand-alone PV-battery power system

A typical configuration of stand-alone PV power system is illustrated in Fig. 3.7. It

consists of PV arrays, charge controller, energy storage, power inverter and loads. The

charge controller with Maximum Power Point Tracking (MPPT) or the one without will

Energy Storage

Charge Controller

Inverter AC Load

DC LoadDC/DC(MPPT)


- 55 -

regulate and control the charging process from the PV arrays to energy storage [171]. In

most cases, a DC/AC power inverter is needed to convert the DC power of ESS to AC

electricity to power up basic electrical appliances such as lighting, television, washing

machines, freezers, etc. In addition, energy storage devices are needed because of the

intermittent nature of the PV output and changeable load demand. It acts as an

intermediate energy buffer compensating the generation-demand mismatch.

To design an effective stand-alone PV power system, engineers need to carry out the

on-site assessment. For example, solar irradiance measurement, user behaviour,

electricity consumption estimation, topographical and meteorological constraints.

Alongside these aspects, the social acceptance, maintenance requirement, and

environmental impacts also need to be taken into consideration before installing PV

power systems.

3.3.2 Solar power generation

Solar irradiance refers to the electromagnetic radiation that reaches the earth surface

after being absorbed, scattered, and reflected by the atmosphere in power per unit area.

The daily solar irradiance profile (sun hours) is the key parameter that determines the

total output power of the PV panels, and it is used to calculate the photo-current

generated from PV panels, as shown in Eq. (3.5) [172]:

𝐼𝐿 = 𝐼𝑠𝑐𝑅𝐺

𝐺𝑅

[1 + 𝛼𝑇(𝑇𝑐 − 𝑇𝑐𝑅)] (3.5)

where 𝐺𝑅 and 𝐺 are the reference solar irradiation and real-time solar irradiation; 𝑇𝑐𝑅

and 𝑇𝑐 is the reference solar cell temperature and real-time solar cell temperature; 𝐼𝑠𝑐𝑅 is

the solar cell short circuit current at 𝐺𝑅 and 𝑇𝑐𝑅; 𝛼𝑇 is temperature coefficient of photo

current. In the case where the ambient temperature variation is relatively small, the real-

time output current of the PV panel can be approximated from the solar radiation.

In order to evaluate and design a sustainable PV power system, it is necessary to

understand the availability and quality of solar irradiance at the targeted site. Sarawak is

the largest state in Malaysia with the total land area of 124,450 km2, with a daily

average of ambient temperature of 26.14 ⁰C and 12 hours average sunlight throughout


- 56 -

the years [173], [174]. The tropical climate with mostly cloudy weather condition

provides average daily sun hours of about 4 to 5 hours. In most cases, the solar input

fluctuates due to the shelter of clouds. Fig. 3.8 illustrates a 7-day solar radiation profile

collected in Kuching, Sarawak, Malaysia.

Fig. 3.8 The 7-day solar irradiance in Kuching, Sarawak, Malaysia

3.3.3 Load demand estimation

In order to design a cost-effective off-grid residential PV power system, an accurate

estimation of the load profile is vital to ensure that the system can generate sufficient

energy and to prevent short battery cycling. Fig. 3.9 depicts a simple load profile

estimation method used in this work. This method is based on the demographics of local

households, the quantity, and characteristics of household appliances and users’

behaviour on daily energy consumption.

Firstly, a site survey was conducted to collect basic information about the population in

the community, living area, public space, daily work routines and behaviours, and the

types of electrical appliances. These appliances include lighting, television, refrigerator,

electric fan, and consumer electronic devices. The characteristics and usage frequency

of each electrical appliance are recorded during the site assessments. Secondly,

aggregate the collected information into a checklist, and assign values to each item

using the appropriate data. The estimated load curve will then be formed by the sum of

12.08 12.09 12.10 12.11 12.12 12.13 12.14 12.150

400

800

1200

1600

2000

2400

Days

Sola

r Rad

iatio

n (W

/m2)


- 57 -

the values of both items. Finally, add a degree of redundancy to the results to form the

final load profile estimation.

Fig. 3.9 Assessment of load profile for a non-electrified rural community

Fig. 3.10 illustrates an example of the estimated load profile for a rural community,

Batang-Ai (1°14’20.5”N, 112°02’10.7”E), in the inner part of Sarawak using the

abovementioned method. The target site has 6 households. It is noticed that peak energy

demand occurs during noon time (between 11 AM to 2 PM) and night time (between 7

PM to 10 PM). This is because most of the power-demanding activities such as cooking

and entertainment are scheduled during these times. The estimated total load demand

per day is around 15kWh.

0 6 12 18 00

0.25

0.5

0.75

Hours of Day

Pow

er (k

W)

Additional Appliances

Community Hall

Total ConsumptionCommon Lighting

Household Use

Fig. 3.10 Estimated load profiles of the target rural site in Sarawak, Malaysia

Load profile

Households

Population Public area

Appliances

Daily activity profile

Standby consumption

Use frequency Types

Appliances list

Load curve for the appliancesTotal load curve

Step 1: Survey

Step 2: Estimation


- 58 -

A 2kW stand-alone PV-battery-genset hybrid power system was designed and installed

at the targeted site during this research work (Appendix 1). The actual load profiles

were measured as shown in Fig. 3.11, and it demonstrates very similar electricity usage

pattern as the one estimated using the abovementioned method.

(a) 2016/10/21 (b) 2016/10/22

Fig. 3.11 Actual load profiles of the target rural site in Sarawak, Malaysia

3.4 Energy storage in stand-alone PV power system

ESS is the key component in stand-alone PV power system to balance the generation-

demand mismatch [175]. It absorbs excess energy generated by solar panels during the

off-peak period and supplies energy when PV generation is insufficient during peak

time or night time. The selection and design of ESS in stand-alone PV power system is

essential as it is one of the major cost components in a typical installation.

3.4.1 Discussion about the selection of ESS

As discussed in Chapter 2, no single energy storage technology can have all the desired

features to fulfill the criteria in any specific application scenarios. The determination for

selecting an appropriate energy storage product normally requires a series of trade-offs

and considerations. Thus the selection of suitable energy storage device for stand-alone

PV power system in remote areas normally needs to consider the following criteria:

capacity, efficiency, availability and cost, stability, and lifespan. Ideally, in remote

applications, energy storage device with small size, high efficiency, fast response,


- 59 -

mature technology, good stability, longevity, clean and low maintenance cost will be the

preferred choice. Based on the analysis in Table 2.3, SMES and SC have relatively high

efficiency, fast response, clean, and longevity, but their energy density is low and with

high self-discharge rate. In addition, SMES is a relatively new technology and

extremely expensive due to superconductive wiring coil usage.

On the other hand, the voltage and capacity of a single SC are minimal, and it usually

requires a lot of them to connect in series and in parallel to reach the rated voltage and

rated capacity. Therefore, the entire system requires power electronic converters as the

controller to maintain the voltage balance and manage the power flow, which increases

the complexity. Thus, neither SMES nor SC is suitable as the primary energy storage in

this application. Similarly, in the hydrogen and flywheel energy storage, the issues of

relatively high cost, immature technology, high self-discharge rate (Flywheel), and the

difficulty in transporting the hydrogen to remote areas make them unsuitable for remote

off-grid applications.

Among all ESS technologies discussed above, electrochemical energy storage turns out

to be the most appropriate ESS for stand-alone PV power system. Firstly, the fluctuating

and unpredictable nature of the PV output cannot allow the nickel-based battery to

operate effectively without losing its capacity due to memory effects. Thus Nickel-

based battery also is not suitable in PV power system. While NaS battery and flow

battery provide robust lifespan and relatively low cost, but NaS battery requires high

maintenance and complicated system to ensure to the high-temperature operating

environment. Flow battery and metal-air battery are still under research and

development stage.

Till now, the Lead-acid battery and Lithium-ion battery are the two remaining ESS

options. They are both suitable for the remote applications, but Lithium-ion battery is

thermally less stable than the Lead-acid battery (safety reason) and higher initial cost,

which needs reliable SoC monitoring and control to avoid overcharging. Conversely,

the Lead-acid battery is much stable (electrically and thermally) and robust. Thus, the

Lead-acid battery is chosen as the most suitable energy storage technology for stand-

alone PV energy system as its ESS.


- 60 -

3.4.2 Lead-acid battery and its stress factors

In a stand-alone PV-battery power system, the battery bank is often one of the most

expensive components regarding initial cost and operating cost [176]–[178]. This is

because the aging mechanism of Lead-acid batteries results from various stress factors

caused by the intrinsic characteristics of charging and discharging processes. The life-

limiting factors in the Lead-acid battery are listed below [95], [179]–[181]:

1. Prolonged low SoC;

2. Overcharging;

3. High operating temperature;

4. Frequent deep or full discharge, high depth of discharge;

5. Charging/discharging with a high C-rate;

6. Frequent charge-discharge transitions;

7. Partial cycling in low SoC.

Most of these life-limiting factors are closely related to SoC, which is the amount of

remaining available energy in the battery and expressed as a percentage of rated energy

[182], [183].

Fig. 3.12 Electrochemical reaction of the Lead-acid battery

Fig. 3.12 shows the electrochemical reaction in a typical Lead-acid battery [180].

During the discharging process, sponge lead (𝑃𝑏) is converted to lead sulfate (𝑃𝑏𝑆𝑂4)

on the negative plate and generate electrons simultaneously. The lead dioxide (𝑃𝑏𝑂2) is

converted into 𝑃𝑏𝑆𝑂4 on the positive plate and sulfuric acid (𝐻2𝑆𝑂4) is consumed in the

electrolyte. The 𝑃𝑏𝑆𝑂4 in the process of becoming the 𝑃𝑏𝑆𝑂4 will cause an increase of

the battery volume. While during the charging process, the 𝑃𝑏𝑆𝑂4 is converted back to

Porous LeadSulfuric

AcidPorous lead

DioxideLead sulfate Water Lead sulfate

Active material of

negativeplate

Electrolyte

Active material of

positive plate

Active material of

negative plate

Electrolyte

Active material of

positiveplate

Discharging

Charging


- 61 -

𝑃𝑏𝑂2 again, and the battery volume will shrink. When the battery undergoes repeatedly

shrinkage and expansion, the inter-bonding between the 𝑃𝑏𝑂2 particles will be

gradually loosened, and eventually leading to shortened battery life. If the DoD can be

reduced, the degree of such shrinkage and expansion will be significantly reduced, and

the binding force between the lead dioxide particles will continue to be maintained, thus

increasing the lifetime of the battery [184].

Another stress factor that accelerates the performance deterioration of the Lead-acid

battery is the Charge and Discharge Rate (C-rate). High C-rate will cause the removal of

active materials on the plate and reduces the battery rated capacity and lifetime [185]. In

Fig. 3.13, the relation of the C-rate, DoD, battery rated capacity and lifecycles are

presented [186], [187]. The graphs show that the performance and cycle life of Lead-

acid battery be improved by carefully controlling the DoD and C-rate. Therefore,

mitigating the stress factors can effectively prolong the service life of the Lead-acid

battery.

(a) LA battery lifecycles vs. DoD (b) LA battery rated capacity vs. Discharge time

34

s

Fig. 3.13 The curves of the Lead-acid battery healthy and stress factors

3.5 Case study of stand-alone PV-Battery power system

A Matlab Simulink model of a 5 kW stand-alone PV-battery power system is developed

as shown in Fig. 3.14. Actual solar irradiance data recorded on typical sunny days and

partially cloudy days were used to simulate PV power generation. While the estimated

load profile (as shown in Fig. 3.10) is considered in the simulation and analysis.


- 62 -

Fig. 3.14 Matlab Simulink model of stand-alone PV-Lead-acid battery system

0 4 8 12 16 20 0-100

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)

(a) PV-Load

(b) LA Battery

Fig. 3.15 Simulation current of stand-alone PV-battery power system (sunny)

0 4 8 12 16 20 0

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)

(a) PV-Load

(b) LA Battery

Fig. 3.16 Simulation current of stand-alone PV-battery power system (cloudy)


- 63 -

The net power flow to and from the battery bank is illustrated in Fig. 3.15 (for sunny

day condition) and Fig. 3.16 (for partly cloudy day condition). The Lead-acid battery

bank (4000Ah) absorbs or supplies the net difference between the PV output and the

load demand. When its current is negative, the Lead-acid battery will be charged and

being discharged when net current is positive. as can be observed from the battery

current profiles, severe fluctuations occur during day time where PV generation is

available, even during sunny day condition.

3.5.1 Scaled prototype of stand-alone PV power system

A down-scaled prototype of stand-alone PV-battery power system was constructed to

demonstrate the operating characteristics of the Lead-acid battery experimentally. The

voltage on the DC bus is 12V, and the capacity of the Lead-acid battery is 30Ah. A

typical commercial charge controller is used to regulate the charging process from PV

to the Lead-acid battery. The experimental results in Fig. 3.17 demonstrate the battery

current profiles measured during partly cloudy weather at Swinburne University

Sarawak Campus, Kuching, Malaysia. The PV output current changes abruptly and

fluctuates randomly. The Lead-acid battery starts to charge at around 7:00 a.m. and

discharge at around 4:00 PM.

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Cur

rent

(A)

(a) LA Battery (b) PV-Load

Fig. 3.17 Experimental current of stand-alone PV-battery power system (one day)


- 64 -

In Fig. 3.18, the 2 hours operating current can indicate that the battery closely follows

the net difference in PV and load currents. There are rapid charge/ discharge conditions,

from -1.5A to 1.5A, as well as high current charging or discharging requirements, which

has a significant adverse effect on battery life.

8

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Hours of Day

Cur

rent

(A)

(a) LA Battery (b) PV-Load

Fig. 3.18 Experimental testing of stand-alone PV-battery power system (2 hours)

3.5.2 The concept of SC/Lead-acid battery HESS

In stand-alone PV power system, the Lead-acid battery has to continuously charge and

discharge based on the fluctuating power requirement as a result of solar intermittency

and load variability. This highly dynamic charge/discharge processes are putting heavy

operation stress on the battery bank, thus accelerating performance deterioration and

aging process. Over the past decades, Battery-SC hybrid ESSs has been proposed by

many researchers for mitigating charge-discharge stress on the Lead-acid [188]–[190].

SC as an energy storage device with excellent power density, fast response time and

most importantly long cycle life, has turned out to be a great complementary energy

storage device to compensate the limitations of the electrochemical battery bank. The

primary idea of Battery-SC hybrid ESS is to allow the SC to absorb/supply the dynamic

power exchange while letting the primary battery bank in responding to the average


- 65 -

power demand. Although the concept of HESS sounds promising, special consideration

must be taken into accounts such as topology design, control system, and energy

management strategy. The next chapter will introduce the Battery-SC HESS in details.

3.6 Conclusion

This chapter introduced solar cell technology and PV power generation systems,

including literature review, working principles, classification, and typical system

architecture. In rural electrification, stand-alone PV power systems have been widely

installed and applied. This chapter provided an overview and discussion of system

design, solar irradiance measurement, load estimation methods, system modelling, and

analysis. Based on the energy storage technology discussed in Chapter 2, the selection

of energy storage technology in the stand-alone PV power system is critically discussed

and concluded that the Lead-acid battery is still one of the most suitable ESS for remote

applications.

- 66 -

Chapter 4

Study on Battery-SC HESS: Topology,

Control Strategy, and Application

4.1 Introduction

Lead-acid battery as one of the mainstream energy storage devices used in stand-alone

PV power system suffers from short service life, despite the excellent electrical

characteristics and lower initial cost. The ESS acts as an intermediate buffer to absorb

excess energy and supply the stored energy when the power deficit. In addition, it also

plays a vital role in regulating instantaneous power variations, power quality, and

system reliability. Especially for a stand-alone PV power system, it relies heavily on

energy storage to balance the unmatched generation and power consumption profiles

[191]–[193]. For example, surge demand power to start motors in appliances can be 7 to

10 times larger than normal operation current [194], and the PV output fluctuates

dynamically due to cloud shelter on PV arrays. The fluctuating and variable power flow

could potentially accelerate the aging process of the Lead-acid battery. Thus, without

proper control, the Lead-acid battery normally can only last 3-5 years which leads to

higher operating cost [195]. Therefore, effectively prolonging the service life of Lead-

acid battery bank will have a significant economic impact on the market of the off-grid

PV power system.

Hybridization of different ESS technologies turns out to be one of the promising ways

to mitigate the battery charge-discharge stress by directing the short term power

fluctuation to another form of ESS such as the SC [196]–[198]. The SC stores electricity

Chapter 4 Study on Battery-SC HESS: Topology, Control Strategy, and Application

- 67 -

via electrons in the static electric field and possesses high power density, has short

charging-discharging time, and nearly unlimited cycle life. The battery and SC

combination has been considered in most HESS developments because of their

availability, similarity in working principle, relatively low cost and most importantly.

They complement each other’s limitations very effectively. Recently, the development

of Battery-SC HESS for residential energy storage applications is beginning to generate

positive outcomes, and it typically is connected to the power system via AC or DC

coupling [199]–[204]. Power electronic converters are used to control the power flow

among different ESS elements [205]–[207]. Depending on the complexity of the control

strategies, the use of power converters and microcontrollers can be costly [208]. Hence,

the trade-off between economic feasibility and technical advantages exist, and it is

crucial in determining the financial and technical sustainability of the system.

Various Battery-SC HESS topologies had been proposed in microgrid applications

aiming to optimally utilize the benefits of different ESS elements [209], [210]. Besides

having correct HESS topology and appropriate sizing, energy management and control

strategy of HESS is another key to improve system efficiency, maximize energy

throughput and prolong the lifetime of HESS [211]–[213]. This chapter presents a

comprehensive review and discussion of Battery-SC HESS, including their system

topologies design, electrical characteristics, energy management system, control

algorithms and applications in stand-alone PV power system.

4.2 Battery-SC HESS topologies

In Battery-SC HESS, the two ESS elements can be coupled to either a common DC or

AC bus. For stand-alone microgrid, common DC bus is the preferred choice due to

various reasons [214], [215]. First, most ESS elements and RES based generators

operate in DC voltage. Therefore, maintaining a DC bus minimizes the needs of power

converter [216]. Second, DC bus does not require synchronization which greatly

reduces the complexity of the overall system [217], [218]. As a result, DC coupling is

more efficient and lower cost than equivalent AC bus systems [219]–[222]. In general,

the topologies of the Battery-SC HESS can be classified based on the interfacing

approach, as shown in Fig. 4.1. For passive connection, the terminals of energy storage


- 68 -

are directly connected to the DC bus for which the power-sharing mechanism and

response is purely determined by the electrical characteristic of the energy storage

devices. On the other hand, active HESS topologies employ active components such as

bi-directional DC/DC power converter to interface the energy storage elements from

DC bus and to actively control their power flow.

Battery-SC HESS

Passive HESS Semi-Active HESS

Full Active HESS

SC Semi-Active

Battery Semi-Active

Parallel

Cascaded

Multi-LevelHESS

Fig. 4.1 Classification of the battery-SC HESS topologies

4.2.1 Passive Battery-SC HESS

The passive connection of battery and SC modules to DC bus offers the simplest and

cheapest form of HESS [223]. It has been reported to effectively suppress transient

current under pulse load conditions, increase the peak power and reduce internal losses

[224]–[226].

Battery SC

DC Bus

Fig. 4.2 Passive HESS topology

As shown in Fig. 4.2, the battery and SC are connected to the DC bus directly, thus

sharing the same terminal voltage. The DC bus voltage is purely depending on the

battery SoC, and therefore it is highly influenced by the charge-discharge characteristic


- 69 -

of the battery. In some rural microgrid applications, the battery capacity is sized up to

five days as a reserve without any external source of energy [227]. Consequently, most

of the time the battery will be cycled with relatively low DoD and charged/discharged in

a relatively low C-rate. As a result, the fluctuation in DC bus voltage will be minimal,

ensuring a relatively stable system voltage.

However, the system current will be drawn from or feed into the battery and SC based

on their respective internal resistances. Therefore, the transient power handling

capability of the SC is not optimally utilized. In addition, as the voltage variation of the

battery terminal is small, the SC will not be operating at its full SoC range which results

in poor volumetric efficiency [228].

4.2.2 Semi-Active Battery-SC HESS

To make better use of the ESS elements in Passive HESS, power electronic converters

are included between the ESS elements and DC bus. This allows the power flow to be

actively controlled [229]. In Semi-Active HESS topology, only one of the two energy

storage elements is actively controlled as illustrated in Fig. 4.3.

Battery

SC

DC Bus

DC/DC

Battery

SC

DC Bus

DC/DC

(a) SC Semi-active HESS (b) Battery Semi-active HESS

Fig. 4.3 Semi-Active HESS topologies

Fig. 4.3(a) shows the SC Semi-Active HESS topology where the SC is connected to the

DC bus through a bidirectional DC/DC converter and the battery is directly connected

to the DC bus [230]. In this topology, the bidirectional DC/DC converter isolates the SC


- 70 -

from the DC bus and battery terminal. In this setting, the SC can be operated within a

wider range of voltages, which significantly improves the volumetric efficiency. The

direct connection of the battery also ensures stable DC bus voltage [231]. However, the

passive connection of the battery inevitably exposes the battery to fluctuating

charge/discharge current that has a negative effect on battery lifetime [232].

Conversely, the battery is interfaced by a DC/DC converter, while the SC is directly

connected to DC Bus in the battery Semi-Active HESS configuration as shown in Fig.

4.3(b) [199], [233]–[235]. Unlike passive and SC Semi-Active HESS topologies, the

battery current can be controlled at a relatively gentler manner regardless of the

fluctuation in the power demand. The battery terminal voltage is not required to match

the DC bus voltage, allowing flexible and efficient sizing and configuration of battery

bank. However, the volumetric efficiency of the SC is low. The linear charge/discharge

characteristic of the SC also causes large variation in the DC bus, which may result in

poor power quality and system stability. To maintain a relatively stable DC bus voltage,

the capacity of SC must be extremely large, which leads to high cost.

4.2.3 Full Active Battery-SC HESS

In Full-Active Battery-SC HESS topologies, the power flow of battery and SC are both

actively controlled via bidirectional DC/DC converters. This enhances the flexibility of

the HESS and improves the overall system performance and cycle life. Fig. 4.4 shows

two of the most common Full-Active HESS topologies, namely parallel active HESS

and cascaded active HESS. In Parallel Full-Active HESS, both battery and SC are

isolated from the DC bus by bidirectional DC/DC converters, as shown in Fig. 4.4(a). It

is one of the most common topologies in grid-scale storage applications, allowing full

control of both energy storage elements [194], [236]–[238]. With this topology, the

performance, battery life, and DC bus stability can be improved through a carefully

designed control strategy [239]. For instance, the battery, as a high energy density ESS,

can be programmed to meet the low-frequency power exchanges. The SC can be

programmed to respond to the high-frequency power surges and regulate the DC bus

voltage. The decoupling of battery and SC allows both ESS elements to operate at a

wider range of SoC that can greatly improve the volumetric efficiency of the HESS.


- 71 -

Battery

SC

DC Bus

DC/DC

DC/DC

SC

DC Bus

DC/DC DC/DC

(a) Parallel Active HESS Topology

(b) Cascaded Active HESS Topology

Battery

Fig. 4.4 Active HESS topologies

In cascaded active HESS topology, two bidirectional DC/DC converters are cascaded to

isolate the battery and SC from the DC bus, as illustrated in the Fig. 4.4(b) [240]. The

bidirectional DC/DC converter that isolates the battery is normally current controlled to

provide smooth power exchange with the battery. This releases the battery from harsh

charge/discharge process due to the intermittency of renewable power generation and

load. The bidirectional DC/DC converter that isolates the SC from the DC bus is

normally voltage controlled to regulate the DC bus voltage while absorbing the high-

frequency power exchanges [241]. Since the SC has wide operating voltage, a large

voltage swing between the SC and DC bus is expected. As a result, the power losses in

the DC/DC converter will be higher as it is difficult to maintain efficiency across a wide

range of operating voltages.

4.2.4 Multi-level Battery-SC HESS

In Fig. 4.5, assuming a basic module includes either or both batteries and SC

with/without DC/DC converters, hybridization beyond two energy storage modules can

be configured in different combinations of passive, active, cascaded and/or parallel with

unique power management system and control strategy.


- 72 -

ESS module can be passively/actively series/parallel connected

DC Bus

Module1

ModuleN

Module1 ModuleN

orBatt

SCDC/DC

orPassive

Basic module

Fig. 4.5 Multi-level Battery-SC HESS topology

The SC or battery can be modularized into a different voltage or power levels and

performs a good adaptability to different system requirements. This enables a more

flexible HESS in terms of system configuration and the adaptability to more

sophisticated energy control algorithms for broader power and energy applications. As

the number of power converters increases, the overall coulombic efficiency of the HESS

will decrease due to losses from the switches among the MOSFET devices. The

performance of full active HESS system is also extremely reliant on the reliability of the

DC/DC converters and their control system.

4.3 Energy management system (EMS)

4.3.1 Overview of EMS

Although the hybridization of battery and SC with different characteristics have great

potential in complementing the lifetime limitations of Lead-acid battery in stand-alone

PV power system, it creates energy management and control problems. Especially for

HESS with actively controlled DC/DC converter(s), a properly designed EMS is the key

to ensuring harmonized operation while achieving the objectives and control goals of

HESS implementation. As shown in Fig. 4.6, a complete EMS may include long-term


- 73 -

energy management, medium-term energy management, and real-time energy

management. In the long-term energy management (beyond 24 hours), information such

as weather conditions, load demand status, and battery SoC are collected and analyzed

in order to generate reference control signals for the medium-term controller. Then the

medium-term EMS will focus on monitoring hourly parameter variations and operation

mode selection, as well as generating reference signals for a low-level control system

for DC/DC converters.

Fig. 4.6 The EMS with the multi-level control structure

Generally, the objectives of HESS implementation in stand-alone microgrid can be

grouped into three main categories: (i) optimizing system performance, (ii) enhancing

system reliability and (iii) lowering set-up and operating cost. Fig. 4.7 summarises the

objectives. Active HESS topology enables each ESS elements to be optimized through

an EMS.

Long-term Supervision

Medium-term Supervision

Real-time Supervision

Electrical Grid

ES1 ES2 ESnHybrid Energy Storage System

(HESS)

Real Time

1h – 1/2h

24h or Longer

Overall Cost Evaluation Storage Capacity

Load ForecastWeather Forecast

Weather Forecast (hr-Level)Mode Selection by UsersStorage Capacity (hr-Level)

Based on Energy Index Determine Suitable Mode Mode 1,2, … , n

Availability of ESS Units


- 74 -

Good EMS

Performance System Reliability

HESS EfficiencyPower Quality

ESS Lifespan ExtensionDevice Protection

System Stability

Economic Feasibility

Reduce Power Conversion FrequencyEnergy Saving (Load Management) Simplicity (Structure and algorithm)

Fig. 4.7 Features and function of an efficient EMS design

The role of EMS is to maximize the benefits of HESS. Volumetric and coulombic

efficiencies have to be maximized while maintaining system stability and power quality

at the DC bus. In terms of system reliability, EMS must ensure robust system operation

in all possible loading conditions, protect the ESSs from extreme conditions and extend

the useful lifetime of the ESS elements. EMS also needs to ensure that cost of

implementation, operation and maintenance are kept low. For instance, scheduling of

diesel generator and load can be integrated into the EMS to lower the operating cost.

4.3.2 The design considerations of EMS

Fig. 4.8 depicts a typical EMS structure for a Battery-SC HESS in microgrid

applications [242]. In general, the EMS can be divided into two levels: (i) the low-level

control system regulates the DC bus voltage and controls the current flowing in and out

of ESS elements based on the reference signal generated by the high-level control

system. (ii) The high-level control system performs the power allocation strategy, SoC

monitoring and control, and other sophisticated energy management strategies to

achieve the set control goals.

Zhou et al. adopted the parallel active topology and proposed a modular HESS scheme

that splits the single battery bank into multiple smaller battery modules [243]. The SC

module and battery bank modules are interfaced to DC bus using dual-active-bridge

bidirectional DC/DC converters. The authors employed a linear filtering approach to

remove high-frequency power fluctuations and distribute the smooth power demands to

each battery modules based on their SoC level. The SC module will respond to the high-


- 75 -

frequency power exchange through cascaded inner current control loop and outer

voltage control loop. A simple SoC management scheme for SC module is implemented

where the battery modules will charge the SC when the SoC level is lower than a pre-set

threshold. The EMS mainly focuses on balancing the charge/ discharge current among

different battery modules. However, it does not consider the impacts of battery SoC

variation in long-term operation, which may affect the system stability and longevity of

the battery. Moreover, the proposed modular HESS topology requires a large number of

DC/DC converters, leading to a significant increase in power loss and set-up cost.

Energy Management System (EMS)

HESS

Alg

orith

m

DC Bus

MPPT

MM

M Load

SCM

M

PV

PHESS

Current Control Voltage Control

SoC Management

MDC/DC Converter

DC/AC Inverter

Measurement

Control Signal

Measurement Signal

Batt

Power Allocation

PBATT(ref) PSC(ref)

Generation ForecastLoad PredictionWeather ForecastEnergy Market Data

Low-level ControlHigh-level Control

+-

PPV

PLoad

PPV

PLoad

Fig. 4.8 Typical EMS for stand-alone PV power system with parallel active HESS

To address the issue of high charge/discharge rate and possible response delay of the

DC/DC converter, Kollimalla et. al. adopted the linear filtering approach to decompose


- 76 -

the power demand into high-frequency and low-frequency components and added a rate

limiter to prevent the battery from being damaged by the high charge/discharge rate

[194]. An additional compensator is implemented to compensate the slow response of

battery charge/discharge. The proposed EMS mainly focuses on regulating the DC bus

voltage and mitigating battery stress by limiting the battery current. The technique

assumes that all ESS elements work within the acceptable limits throughout the

operation. It does not take into consideration the SoC control of the battery. This may

cause the battery to experience deep discharge under extreme conditions, which may

lead to shorter battery lifetime.

Choi et al. presented an EMS scheme in Battery-SC HESS to achieve two objectives: (1)

to minimize the energy loss due to SC internal resistance and (2) to mitigate the

fluctuations of current flowing in/out of the battery pack [30]. The author

mathematically formulated the two objectives in order to obtain the optimal solution to

control the current flow in each ESS elements. The two objectives were formulated into

convex optimization problems, which are norm approximation and penalty function

approximation, respectively. The two problems are then combined into a single multi-

objectives function. In order to obtain the optimal solution, boundary parameters were

determined through multiplicative-increase-additive-decrease principle. The values of

the boundary parameters critically determine the feasibility and optimality of the

solution. This control strategy only considers the characteristics of ESS elements. It

does not consider the interactions between the elements and other components within

the microgrid. Thus, the resulting optimal EMS scheme tends to be for one particular

system only.

The above EMS strategies for HESS mainly focus on solving the short-term power

demand variations and power-sharing between SC and battery. However, the impact of

SoC drift in the battery is not addressed. Specifically, in a microgrid, seasonal variations

in renewable energy generation and load demand must be carefully addressed to ensure

reliable operation in all possible loading conditions. To accurately monitor the battery

SoC and address its long-term variations, Xue et al. proposed an actively controlled,

parallel connected Battery-SC HESS in PV power system that employs a multimode

fuzzy-logic power allocator to address the mismatch between supply and demand [244].


- 77 -

Based on the SoC conditions of battery and SC, the fuzzy-logic controller selects the

appropriate operating mode to allocate the demand power to the energy storage devices.

To avoid overcharging or discharging the energy storage, the controller allows the

power exchange between the battery and SC and their individual power contribution to

be optimally adjusted. The EMS control strategy guarantees all energy storage devices

operate within their safe operating range and compensates transient mismatches

between power generation and load demand.

Most EMS relies on a centralized controller to manage the power flow among different

ESS elements and DC bus. Therefore, a robust communication link between

components is needed. A disruption in the communication link will lead to catastrophic

system failure. A hierarchical controller for HESS was proposed in [212] to address this

risk. In the normal operating mode, the centralized controller allocates power to ESS

modules based on their ramp rate. ESS with the highest ramp rate usually is assigned to

regulate bus voltage. In the situation where the centralized controller fails, distributed

control at individual ESS elements will be activated to ensure power system continuous

operation.

4.3.3 Advanced algorithm in EMS

Due to the complex and non-linear characteristics of battery and SC during the

charging/discharging operation, simple power allocation method such as linear filtering

may not be sufficient to effectively allocate the power demand among the energy

storage elements in HESS. Therefore, advance supervisory control algorithms for EMS

have been developed. A number of different intelligent and sophisticated control

algorithms, such as deterministic rule-based strategy, fuzzy logic control, linear

programming, genetic algorithms, dynamic programming, neural network, self-adaptive

algorithms, etc. have been reported in the literature [155], [245]–[247]. Fig. 4.9

illustrates an overview of the classification of existing EMS algorithms in HESS. The

EMS algorithms are generally categorized into two main classes which are rule-based

approaches and optimization-based approaches [248]–[250].


- 78 -

Intelligent Algorithms for EMS Control

Rule-Based Optimization-Based

Fuzzy Logic Global Optimization (Off-line)

Real-time Optimization (On-Line)

Power FollowerState Machine

Typical Fuzzy LogicFuzzy Predictive

Deterministic

Fuzzy Adaptive

Linear ProgrammingDynamic ProgrammingStochastic DPGenetic AlgorithmAdaptive Fuzzy Rule BasedEvolutionary MethodsSimulated Annealing

Frequency DecouplingRobust ControlOptimal Predictive

Thermostat (On-Off)

Supervised Learning Machine (SLM)

Fig. 4.9 Classification of intelligent algorithms for EMS supervisory control

(1) Rules-Based Approach

Rule-based approach controls the power exchange of HESS based on rules that are

derived from mathematical models and experiences [251]–[253]. The rule-based

approach is an effective method for real-time energy management widely used in HESS

applications. In thermostat control strategy, the battery operates with constant power at

its optimal efficiency point, and it will be turned on or off according to the lower and

upper SoC limits. In power follower control strategy, the battery is set as the primary

energy storage, and the EMS will adjust the battery charge/ discharge power that

follows the power demand. As a secondary ESS, the SC covers the difference between

the power demand and battery response.

Unlike thermostat and power follower control strategy, the state machine control

strategy uses multiple rules to control the power flow in HESS. The pre-defined rules

can be designed based on the allowable upper and lower SoC limits of SC and battery,

maximum charge and discharge rates, load and generation powers, etc. Based on the

real-time operational states of the HESS, power generation and power demand, the

algorithm selects the appropriate operation mode to optimize the power distribution

between SC and battery.


- 79 -

The deterministic rule-based control strategies are widely adopted due to its simplicity,

less computationally intensive and great reliability [251], [254]–[257]. However, the

rules are generally designed based on the initial state of the ESS elements. This may not

accurately reflect the actual conditions of the elements in the long run. Therefore, the

fuzzy logic control strategy as shown in Fig. 4.10 is introduced. Similar to the

deterministic approaches, the rules in fuzzy control are also pre-defined based on

mathematical model and empirical data. The transition between different rules is

determined by the fuzzy-rules and membership functions which results in smoother,

more flexible and logical operation compared with deterministic rule-based approaches

[244], [258]–[260]. Fuzzy rule-based control algorithms can be integrated with other

intelligent algorithms to form a hybrid control strategy which further improves the

performance of EMS in HESS [236], [261]–[263].

Start

Measure and Collect Data

Fuzzification

InferenceEngine

Defuzzification

Results

Output Membership

Functions

Fuzzy Rules(Relation between

Input&Output)

Solar Irradiance Weather Condition Load Profile SoC & Power Flow Voltage & Currentetc.

Input Membership

Functions

(Eg.)

Fig. 4.10 Fuzzy logic flowchart

(2) Optimization-Based Approach

Optimisation-based EMS employs modern optimization algorithms, such as linear

programming (if the system is convex and could be mathematically represented via a set


- 80 -

of linear functions), dynamic programming (both deterministic and stochastic),

evolutionary methods such as genetic algorithm, simulated annealing, and particle

swarm optimisation [264]–[269]. These algorithms can be classified into global

optimization (offline) and real-time optimization (on-line). Unlike the rule-based

approaches, modern optimization algorithms and their associated optimization processes

are often sophisticated that require heavy computation capability [248]. In genetic

algorithms, optimal power allocation in HESS can be achieved by analyzing the genetic

processes as shown in Fig. 4.11. Based on the pre-defined fitness functions, the

determined percentage and mutation rate, the genetic algorithms controller will

iteratively search for optimal solutions until the number of iteration exceeds the pre-set

values.

Meteorological Conditions(Solar Irradiance,

Weather Conditions, etc.)

Economical Data (Electricity Tariff, Price of

PV, ESSs, Load Profile, etc.)

Optimization Constraints and Termination Criteria

Pre-Defined Fitness Function and

GA Parameters (Selection Percentage, Mutation Rate, etc )

Create Random Initial Population

Evaluate Fitness of Each Solution

Selection

Crossover

Mutation

Select Next Generation

Stopping Criteria Check

Return the Best Solution

Iterative Procedure

NOYES

OUTPUT

INPUT

Fig. 4.11 Genetic algorithm flowchart

Linear programming optimization approach is commonly used for systems that are

convex and could be mathematically represented via a set of linear functions.

Conversely, dynamic programming employs multiple iteration loops that are designed


- 81 -

to execute the backward iteration from time step N to 1, as shown in Fig. 4.12 [252].

Every loop is employed to calculate the corresponding value as shown in the flowchart.

The cost-to-go function will be updated at each time step, and the iterative processes

will end when optimal solution of the objective function is obtained. One computation

cycle will generate one state, and the whole process can be called state iterations.

Finally, the dynamic programming approach will generate a look-up table that contains

the optimal decision variable at each state. In general, trade-offs in technical

requirements and economic feasibility need to be carefully considered in the design and

development of HESS control algorithms. Advanced EMS can provide high power

quality and improve system stability. However, system complexity and hardware

requirements will add to the overall system cost.

Start

Iteration from Time Step N to 1

Determine Load Demand and System Parameters

Iteration of State Variables

Determine Constraints for Control Variable

Iteration of Control Variable

Evaluate Cost-To-Go Function

Finish Iteration of Control Variable

Determine Optimal Control Decision at Current Time

Step and States

Finish Iteration of All States at Time Step k

Finish Iteration of All Time Steps from N to 1

End

INPUT

OUTPUT

NO

NO

NO

YES

YES

YES

Fig. 4.12 Dynamic programming flowchart

4.4 Analysis and discussion

There are varieties of HESS topologies and the associated energy management and

control strategies used in the microgrid applications aiming to improve the system


- 82 -

operation from multiple aspects. However, there is no single unique HESS solution

suitable for all microgrid applications. To determine an ideal HESS design and control

strategy, a detailed analysis of the system requirements, end-user expectations,

physical/environmental constraints, and both technical and economic feasibilities have

to be considered.

4.4.1 HESS topologies and EMS

The development of HESS is expected to progress in two directions: (i) robust and

reliable HESS in small-scale stand-alone microgrids specifically for remote or isolated

sites, (ii) independent and intelligent medium in the large-scale grid-connected power

system that is part of the smart-grid architecture. Most researches on HESS are focused

at reducing the stress on the batteries while maintaining power quality, improving HESS

efficiency and lowering set-up cost. For greater controllability, active HESS topology is

commonly used. However, this increases the system complexity and most importantly,

creating additional uncertainty and making the power system more vulnerable to

components failures [270].

On the contrary, as the most straightforward HESS structure, passively connected

Battery-SC HESS provides a simple and robust way to relieve battery operating

pressure, but at the same time reduces system efficiency and controllability. In this

topology, the SC acts as a Low-Pass Filter (LPF) for battery, and the filtering effect is

inherently dependent on the internal resistance of the Battery-SC HESS as well as the

installed SC capacitance, which also implies the low volumetric efficiency of the SC.

Between the Full-Active and Passive HESS topologies, Semi-Active HESS enables

active control of power flow with less active components used.

For the Multi-level HESS topologies, there exist many sophisticated designs and control

strategies in the literature. In [271], the battery bank is made up of micro-bank modules,

each with its own DC/DC converter and EMS. A control strategy that dynamically

configures the battery modules is put in place to optimize the use of the modules and for

greater system efficiency. A similar concept was also proposed in [272], where banks of

varied energy storage elements and battery types were used with a global charge


- 83 -

allocation algorithm that controls the power flow between the storage banks. With the

careful use of power electronic converters, the configurable and modular HESS could

be one of the future trends in the development of EMS in microgrid applications.

4.4.2 Summary of the advantages of Battery-SC HESS

The Battery-SC HESS was initially designed to take the benefits of SC such as fast

response, long life, and high power density and make it absorb high-frequency currents,

thereby protecting the battery from the surge, intermittent, variable currents and

effectively increasing its service lifetime. The advantages are listed below:

a) Battery lifetime extension

The integration of SC helps to reduce transient fluctuations in the battery power profile

which releases its operation stress. This enables the battery lifetime extension owing to

the reduction in the peak power requirement. In the battery-only system, it needs to

cover peak power demand, leading to increased temperature which also results in

lifetime reduction.

b) Higher energy efficiency

The relatively low ramp rate of traditional chemical battery could result in the mismatch

in surge power demands, which can have a negative impact on power quality and

energy efficiency. In HESS, the SC acts as a load balancing device for the battery when

the SC transmits or receives energy at peak power. The battery current profile will

become closer to the average or smooth power, with small RMS value and fewer peaks.

Consequently, the HESS satisfies all power demands with higher energy efficiency,

while the battery operates in a much healthy current profile.

c) Size reduction

For the same power requirements, the battery-only system needs to oversize the battery

capacity to cater for the short term surge demand [273].

d) Less environmental impact and cost saving


- 84 -

As the lifespan of the battery increases, the operating cost of a stand-alone PV-battery

system can be significantly reduced, thus minimizing the needs to recycle the battery

frequently.

4.4.3 AC coupled and hybrid AC/DC PV-HESS power system

The aforementioned HESS configurations in this chapter are mainly based on the DC

bus. In the rural area, the remote distance for connection to the grid and lack of

advanced control hardware maintenance requires the power system to have a robust

control solution [274]–[276]. DC coupled stand-alone PV-battery power system is

ideally suited for remote rural electrification, and they are generally small-scale in the

range of 2-10kW [277]–[280]. In the large-scale microgrid, AC coupling is common to

allow seamless integration with utility grid and minimize losses in power transmission

effectively [217].

AC Load

DC Distributed Generation (PV)AC/DC

AC Distributed Generation

(Wind, Hydro)AC/DC/AC

AC Bus

Battery

HESS

SC

Controller

DC/AC

DC/AC

PVCharge

Controller(MPPT)

DC Load

DC Bus

AC Load

DC/AC

Battery

DC/DC

Passive/Semi/Active HESS

SC

Controller

DC/DC

DC/AC

DC/AC

Interlinking Converters(ILCs)

AC Bus

AC Load

Passive/Semi/Active HESSAC/DC

DC Distributed Generation (PV)AC/DC

AC Distributed Generation (Wind)AC/DC/AC

Utility Grid

DC-Coupled AC-Coupled

Utility Grid

(a) AC Coupled PV Power System With other RES or Gird Connection

(b) Hybrid AC/DC PV Power System With other RES or Gird Connection

Fig. 4.13 The stand-alone PV-HESS power system expansion


- 85 -

Fig. 4.13 demonstrates a hybrid AC-DC PV power system that consists of both DC and

AC buses [202], [267]. For possible future requirement, the hybrid AC-DC bus coupling

architecture allows integration of AC-based renewable generation technologies (such as

wind turbines and hydropower), distributed generations as well as the utility grid. The

connection of two buses is the interlinking converters. In the DC-coupled part, the

control system will be designed to regulate the voltage and manage the power flow

within HESS. The situation is different in AC bus, where the HESS is required to

control the frequency and voltage simultaneously and transfer the energy between the

DC and AC in a bidirectional way.

4.4.4 HESS topologies and EMS in rural electrification

Rural communities are usually located far from the grid where technical support is

usually limited. Therefore, the implementation of power systems in these areas

prioritizes simplicity, stability, robustness, and low maintenance requirements, rather

than efficiency, intelligence, and functionality. Thus simple, inexpensive, and robust

HESS are desirable and shall be prioritized in the design process for remote application.

Therefore, the Battery-SC HESS structure with fewer DC/DC converters will be more

favorable, such as Passive HESS and SC Semi-Active HESS configurations. The next

chapter will provide a comprehensive discussion and analysis on Battery-SC HESS in

stand-alone PV power systems, including system topology, control system,

mathematical modelling, numerical simulation, and experimental verification.

4.5 Conclusion

This chapter introduced the concept of Battery-SC HESS, including the system

topologies, control strategies and possible applications in the stand-alone power system.

The existing HESS topologies are categorized into four main categories, which are

Passive HESS, Semi-Active HESS, Full-Active HESS, and Multi-level HESS. Their

corresponding characteristics, strengths, weaknesses and possible applications were

discussed and compared. The availability of actively controlled components enabled the

use of EMS to manage the power exchange within the HESS. The operation stress of the

battery can be reduced while maintaining a high level of power quality and reliability.

- 86 -

Chapter 5

Battery-SC HESS for Stand-alone PV

Power System in Rural Electrification

5.1 Introduction

In Battery-SC HESS, the net current exchange is decomposed into two or more

frequency components. Ideally, the primary large battery bank will supply the low-

frequency component which often represents the nominal current profile. As a high

power density ESS, the SC absorbs the high-frequency power exchange from the

intermittency of solar power generation and sudden change in load demand. As

discussed in Chapter 4, many variations of Battery-SC HESS designs and their

associated EMS have been proposed by researchers over the past decades. These HESS

designs are generally intended to serve large-scale utilities, smart-grid, electric vehicles,

and other high power applications, and they normally require extensive sensing,

computation, and communication, which significantly increase the system complexity

and implementation.

This chapter presents a comprehensive study and discussion on the potential Battery-SC

HESS topologies and energy management strategies in stand-alone PV power system,

especially for rural electrification purposes. The targeted HESS topologies are presented

and discussed in sequence from the simplest passive HESS structure to complex active

topology. Theoretical analysis and numerical simulation of the HESS under

consideration are presented to demonstrate the feasibility and effectiveness of mitigating

battery stresses in stand-alone PV power system based on case studies of a remote

Chapter 5 Battery-SC HESS for Stand-alone PV Power System in Rural Electrification

- 87 -

community in Sarawak, Malaysia. Novel evaluation method based on a battery healthy

cost model is proposed to perform technical comparison and to demonstrate the

improvement in primary battery health, thus the economic impact study. Experiments

have been carried out and presented to verify the theoretical analysis.

5.2 Selected HESSs for stand-alone PV power system in rural electrification

Based on the discussion in Chapter 4, HESS topologies can be categorized regarding the

number of ESS elements, the strategies of power-sharing among ESS elements and the

interfacing methods, as summarised in Fig. 5.1.

Multi-level HESS

Battery-Supercapacitor HESS

Passive HESS

Batt SC

DC Bus

Bi-directional power electronic converter

(a)

Active Power Allocation

Supercapacitor Semi-Active HESSParallel Full Active HESS

Batt

SC

DC Bus

DC/DC

Batt

SC

DC Bus

DC/DC

DC/DC

Cascaded Full Active HESS

Batt SC

DC Bus

DC/DC DC/DC

Battery Semi-Active HESS

Batt

SC

DC Bus

DC/DC

(b) (d)

(e)

2 ESS elements > 2 ESS elements

DC Bus

DC/DC DC/DCSC Batt

(c)

(f) ESS module can be passively/actively series/parallel connected

DC Bus

Module1

ModuleN

Module1 ModuleN

orBatt

SCDC/DC

orPassive

Basic module

DC/DC

Passive Power Allocation

Fully Active Semi Active

Fig. 5.1 Classification of the Battery-SC HESS topologies

In rural electrification, most stand-alone PV power systems are geographically isolated

[281], [282]. As a result of the high maintenance cost and limited technical support,

system robustness turns out to be one of the most important considerations when

designing HESS. Therefore, fully active HESS topologies may not be suitable for this

rural electrification application. Conversely, HESS that can perform basic power

management with minimal active components interfacing secondary ESS module(s) will


- 88 -

be the preferred choice. Among all HESS topologies presented above, the Passive HESS

(Fig. 5.1(a)) and SC Semi-Active HESS (Fig. 5.1(d)) are two potential configurations for

stand-alone PV power system in rural electrification. However, the two topologies have a

common weakness that both of them require installing a large capacitance of SC to

effectively absorb the fluctuating current, which leads to higher costs. Thus, based on Fig.

5.1(f), a novel Multi-level HESS configuration with passively connected primary ESS

(extended from the SC Semi-Active HESS) is proposed, as illustrated in Fig. 5.2. This

topology can have both benefits of passive HESS and semi-active HESS, the installed 2nd

battery can replace part of SC which contribute an economic save. Their operation

characteristics and control strategy in stand-alone PV power system will be discussed

and compared in the following subsections.

2nd Battery

SC

DC Bus

DC/DC

DC/DC

1st Battery

Fig. 5.2 Multi-level HESS topology

5.3 Stand-alone PV power system with selected HESSs

To evaluate and compare the power-sharing capability of the selected HESS designs for

rural applications, the responses of different ESS elements are investigated with pulse

current loads. Matlab Simulink models of the respective HESS topologies and their

control algorithm are developed, and the integration of these HESSs in stand-alone PV

power system are discussed and demonstrated.

5.3.1 With Passive HESS

As the simplest topology of HESS, the Passive Battery-SC HESS can help to smooth

the original current curve of Lead-acid battery directly and is the easy to install in


- 89 -

standalone PV power system. The Passive HESS can be modelled by an equivalent

circuit model as shown in Fig. 5.3 [283]. The SC is modelled as a large capacitance C

with an equivalent series resistance 𝑅𝑠𝑐 with a finite initial voltage 𝑉𝑠𝑐𝑖 in SC. The

Lead-acid battery is modeled as a constant voltage source VLA with an equivalent series

resistance RLA, where Lead-acid is its short name.

+

-

vBus(t)RLARsc

C

vLA

iLA(t)

iBus(t)

ILA(s)

Rsc

VLA /s

Vsc /C

1 /sC

+

-

IBus(s)

VBus(s)

isc(t) Isc(s)

RLA

+

-

ZTh(s)

VTh(s)

IBus(s)

VBus(s)

vsci : SC initial voltage

(a) Time Domain (b) Frequency Domain (c) Equivalent Circuit

Fig. 5.3 The equivalent circuit of the Passive HESS

The Thevenin equivalent voltage VTh(s) and impedance ZTh(s) in frequency domain are

[222], [228]:

𝑉𝑇ℎ(s)=RLA

s+

RLA

RLA+R𝑠𝑐

*𝑣𝑠𝑐𝑖 − 𝑣𝐿𝐴

s+ 1(RLA+R𝑠𝑐)C

(5.1)

𝑍𝑇ℎ(𝑠) = RLA𝑅𝑠𝑐

RLA + 𝑅𝑠𝑐

∗𝑠 +

1𝑅𝑠𝑐𝐶

𝑠 +1

(RLA + 𝑅𝑠𝑐)𝐶

(5.2)

where s is the complex variable. In order to study the behavior of the HESS when there

is a source/load, and suddenly fluctuate from one level to another, this study assumes a

pulse source. The analysis method is as follows. For repetitive pulse load input, the

periodic load current, 𝑖𝐵𝑢𝑠(𝑡) can be expressed as:

𝑖𝐵𝑢𝑠(𝑡) = 𝐼𝐵𝑢𝑠 ∑[∅(𝑡 − 𝑘𝑇) − ∅(𝑡 − (𝑘 + 𝐷)𝑇)]

𝑁−1

𝑘=0

(𝑘 = 0,1,2, … ) (5.3)

where D is the duty ratio of the input signal, ∅(𝑡) is the step function. To transfer the

function with variable time to its frequency domain with complex variable s, the

corresponding Laplace transform of 𝑖𝐵𝑢𝑠(𝑡) is:


- 90 -

𝐼𝐵𝑢𝑠(𝑠) = 𝐼𝐵𝑢𝑠 ∑ [𝑒−𝑠𝑘𝑇

𝑠−

𝑒−𝑠(𝑘+𝐷)𝑇

𝑠]

𝑁−1

𝑘=0

(𝑘 = 0,1,2, … ) (5.4)

Then the voltage drop across the impedance 𝑍𝑇ℎ(𝑠) for the given current 𝐼𝐵𝑢𝑠(𝑠):

𝑉𝑍(𝑠) = 𝐼𝐵𝑢𝑠(𝑠) ∗ 𝑍𝑇ℎ(𝑠) = RLAR𝑠𝑐𝐼𝐵𝑢𝑠

RLA + 𝑅𝑠𝑐

∑ [𝑠 +

1𝑅𝑠𝑐𝐶

𝑠 +1

(𝑅𝐿𝐴 + 𝑅𝑠𝑐)

∗𝑒−𝑠𝑘𝑇 − 𝑒−𝑠(𝑘+𝐷)𝑇

𝑠]

𝑁−1

𝑘=0

(5.5)

The terminal voltage in the frequency domain can be expressed as:

𝑉𝐵𝑢𝑠(𝑠) = 𝑉𝑇ℎ(s) − 𝑉𝑍(𝑠) (5.6)

𝑉𝐵𝑢𝑠(𝑠) =𝑉𝐿𝐴

𝑠+

RLA𝑅𝑠𝑐

RLA + 𝑅𝑠𝑐

∗𝑣𝑠𝑐𝑖 − 𝑣𝐿𝐴

s+ 1(RLA+R𝑠𝑐)C

− 𝑉𝑍(𝑠) (5.7)

and its expression in the time domain via inverse Laplace transforms:

𝑣𝐵𝑢𝑠(𝑡) = 𝑣𝐿𝐴 +RLA

RLA + 𝑅𝑠𝑐

∗ (𝑣𝑠𝑐𝑖 − 𝑣𝐿𝐴) ∗ 𝑒−

𝑡(RLA+𝑅𝑠𝑐)𝐶

− RLA𝐼𝐵𝑢𝑠 ∑

[ (1 −

RLA

RLA + 𝑅𝑠𝑐

𝑒−

𝑡−𝑘𝑇(RLA+𝑅𝑠𝑐)𝐶) ∅(𝑡 − 𝑘𝑇)

−(1 −RLA

RLA + 𝑅𝑠𝑐

𝑒−

𝑡−(𝑘+𝐷)𝑇(RLA+𝑅𝑠𝑐)𝐶)∅(𝑡 − (𝑘 + 𝐷)𝑇)

] 𝑁−1

𝑘=0

(5.8)

Finally, the battery current 𝑖𝐿𝐴(𝑡) and SC current 𝑖𝑠𝑐(𝑡) can be obtained as:

𝑖𝐿𝐴(𝑡) =𝑣𝐿𝐴 − 𝑣𝐵𝑢𝑠(𝑡)

𝑅𝑏

= −(𝑣𝑠𝑐𝑖 − 𝑣𝐿𝐴)

RLA + 𝑅𝑠𝑐∗ 𝑒

−𝑡

(RLA+𝑅𝑠𝑐)𝐶

+ 𝐼𝐵𝑢𝑠 ∑

[ (1 −

RLA

RLA + 𝑅𝑠𝑐𝑒

−𝑡−𝑘𝑇

(RLA+𝑅𝑠𝑐)𝐶)∅(𝑡 − 𝑘𝑇) −

(1 −RLA

RLA + 𝑅𝑠𝑐𝑒

−𝑡−(𝑘+𝐷)𝑇(RLA+𝑅𝑠𝑐)𝐶)∅(𝑡 − (𝑘 + 𝐷)𝑇)

]

𝑁−1

𝑘=0

(5.9)

𝑖𝑠𝑐(𝑡) = 𝑖𝐵𝑢𝑠(𝑡) − 𝑖𝐿𝐴(𝑡) (5.10)

Since the SC and battery will share the same terminal voltage with DC bus at steady

states, where 𝑣𝑠𝑐𝑖 = 𝑣𝐿𝐴, then their current in steady state are:

𝑖𝐿𝐴𝑠𝑠(𝑡) = 𝐼𝐵𝑢𝑠 ∑

[ (1 −

RLA

RLA + 𝑅𝑠𝑐

𝑒−

𝑡−𝑘𝑇(RLA+𝑅𝑠𝑐)𝐶)∅(𝑡 − 𝑘𝑇) −

(1 −RLA

RLA + 𝑅𝑠𝑐

𝑒−

𝑡−(𝑘+𝐷)𝑇(RLA+𝑅𝑠𝑐)𝐶)∅(𝑡 − (𝑘 + 𝐷)𝑇)

] 𝑁−1

𝑘=0

(5.11)


- 91 -

𝑖𝑆𝐶𝑠𝑠(𝑡) =RLA𝐼𝐵𝑢𝑠

RLA + 𝑅𝑠𝑐

∑ [𝑒

−𝑡−𝑘𝑇

(RLA+𝑅𝑠𝑐)𝐶 ∗ ∅(𝑡 − 𝑘𝑇) −

− 𝑒−

𝑡−(𝑘+𝐷)𝑇(RLA+𝑅𝑠𝑐)𝐶 ∗ ∅(𝑡 − (𝑘 + 𝐷)𝑇)

]

𝑁−1

𝑘=0

(5.12)

At time 𝑡 = (𝐷 + 𝑘)𝑇, the pulse load current steps from low to high resulting in a net

change in HESS current. Assume the maximum current occurs at N approaching infinity,

the battery peak current can be simplified as:

𝑖𝐿𝐴𝑝 = 𝐼𝐵𝑢𝑠(1 − 𝜀) (5.13)

The parameter 𝜀 defines the current allocation relationship between the battery and the

SC at the peak current, and its expression is:

𝜀 =RLA

RLA + 𝑅𝑠𝑐

∗ 𝑒−

𝐷𝑇(RLA+𝑅𝑠𝑐)𝐶 ∗

1 − 𝑒−

(1−𝐷)𝑇(RLA+𝑅𝑠𝑐)𝐶

1 − 𝑒−

𝑇(RLA+𝑅𝑠𝑐)𝐶

(5.14)

The parameter also indicates that the peak current from the battery will always be less

than 𝐼𝐵𝑢𝑠 when SC is connected. If there is no SC, 𝜀 = 0, and then 𝑖𝐿𝐴𝑝 = 𝐼𝐵𝑢𝑠 , which

means that there is no enhancement and the battery alone absorbs all the charging

current. Eq. (5.13) also expresses the relation between the battery current and DC bus

current at specific time. In the case where the Lead-acid battery operates under a rated

current, the DC bus current can be calculated as:

𝐼𝐵𝑢𝑠 =1

(1 − 𝜀)𝐼𝐿𝐴𝑅𝑎𝑡𝑒𝑑 = 𝜕 ∗ 𝐼𝐿𝐴𝑅𝑎𝑡𝑒𝑑 (5.15)

To evaluate the peak power enhancement in HESS, assuming the instantaneous HESS

peak power occurs at rated current:

𝑃𝐻𝐸𝑆𝑆𝑝 = 𝐼𝐵𝑢𝑠 ∗ 𝑉𝐵𝑢𝑠 = 𝜕 ∗ 𝐼𝐿𝐴𝑅𝑎𝑡𝑒𝑑 ∗ 𝑉𝐵𝑢𝑠 = 𝜕 ∗ 𝑃𝐿𝐴𝑅𝑎𝑡𝑒𝑑 (5.16)

Eq. (5.16) shows the peak power in the HESS increases as the parameter ∂ is greater

than 1, while the SC passively connects in parallel.

The computation model of Passive HESS in Matlab Simulink is shown in Fig. 5.4. A

12Ah Lead-acid battery and 10 Farad SC are connected in parallel to the pulsed current

load. The input pulsed current load is set to 5 amps, 50% duty cycle and cycling period

of 10s. Fig. 5.5 presents the simulation results of the capability of power sharing between

the Lead-acid battery and SC for Passive HESS topology. At the rising edge, the SC


- 92 -

responses rapidly due to the relatively low time constant. The SC will be discharged

immediately once the step current drop to zero and it allows the battery discharge in

relatively smooth curve. On the other hand, the battery slowly picks up over time and

supply the demanded current. The results show minor effect on the power-sharing in the

passively connected Battery-SC HESS for which the power-sharing only occurs within

fractions of seconds. Additionally, the power-sharing capability of Passive HESS is fixed

based on the internal parameters of the two ESS elements.

Fig. 5.4 Matlab Simulink model of Passive HESS

15 20 25 30 35 40

-4

-2

0

2

4

6

8

Time (s)

Cur

rent

(A)

(a) LA Battery(b) SC(c) Pulse Load

Fig. 5.5 Pulse load response of Passive HESS


- 93 -

A schematic diagram of the integration of a passive Battery-SC HESS in a typical stand-

alone PV power system is illustrated in Fig. 5.6. The Lead-acid battery is usually the

primary energy storage. The charge controller is modeled as a unidirectional DC/DC

converter with a MPPT system and two control switches for interfacing the HESS and

the load. A diesel/petrol generator usually is integrated as a backup and dispatchable

power source in case of system failure or when additional energy is required.

Fig. 5.6 Stand-alone PV power system with Passive HESS

5.3.2 With SC Semi-Active HESS

For SC Semi-Active HESS (Fig. 5.1(d)), the equivalent circuit model in the time domain

and frequency domain are presented in Fig. 5.7.

+

-

vBusRLARsc

CvLA

ic iLA

iBus

Ic(s) Ib(s)

Rc

VLA /s

Vc /C

1 /sC

IBus(s)

VBus(s)

DC/DC DC/DCisc Isc(s)

RLA


Vsc

vc

vsc

(a) Time Domain (b) Frequency Domain

ηsc η

sc

Fig. 5.7 The equivalent circuit of the SC Semi-Active HESS

DC/DC(MPPT)

Charge ControllerDC Bus DC

Load

ACLoad

Inverter

Supercapacitor

Diesel Generator

Lead-acid battery


- 94 -

By neglecting the dynamic characteristics, the DC/DC converter is simplified and

represented as parameters of efficiency 𝜂𝑠𝑐 and voltage transfer rate Ƙsc [222]. Thus the

SC terminal voltage and current in real time before DC/DC converter is expressed as:

𝑖𝑐(𝑡) =Ƙ𝑠𝑐

𝜂𝑠𝑐

𝑖𝑠𝑐(𝑡) = 𝑖𝐵𝑢𝑠(𝑡) − 𝑖𝐵𝑢𝑠(𝑡)𝐿𝑃𝐹 (5.17)

𝑣𝑐 =𝑣𝑠𝑐

Ƙ𝑠𝑐

=𝑣𝑏𝑢𝑠

Ƙ𝑠𝑐

(5.18)

where the 𝑖𝐵𝑢𝑠(𝑡)𝐿𝑃𝐹 is the filtered 𝑖𝐵𝑢𝑠(𝑡). When pulse signal 𝑖𝐵𝑢𝑠(𝑡) is loaded, the SC

current and the battery current on DC bus are:


𝑁−1

𝑘=0

= 𝑖𝑠𝑐(𝑡) + 𝑖𝐿𝐴(𝑡) (5.19)

𝑖𝑠𝑐(𝑡) =𝜂𝑠𝑐

Ƙ𝑠𝑐

𝑖𝑐(𝑡) =𝜂𝑠𝑐

Ƙ𝑠𝑐

[𝑖𝐵𝑢𝑠(𝑡) − 𝑖𝐵𝑢𝑠(𝑡)𝐿𝑃𝐹] (5.20)

𝑖𝐿𝐴(𝑡) = 𝐼𝐵𝑢𝑠 ∑[∅(𝑡 − 𝑘𝑇) − ∅(𝑡 − (𝑘 + 𝐷)𝑇)]

𝑁−1

𝑘=0

−𝜂𝑠𝑐

Ƙ𝑠𝑐


Assuming the peak current will occur at the end of the pulse duty cycle, when 𝑡 =

(𝑘 + 𝐷)𝑇 . Considering the efficiency factor of DC/DC converter, 𝜂𝑠𝑐 , then the

maximum current drawn from the battery can calculated by the following expression,

𝑖𝐿𝐴𝑝 = 𝐼𝐵𝑢𝑠 −𝜂

𝑠𝑐

Ƙ𝑠𝑐


Thus the peak current of battery is reduced by the SC current which is actively

controlled by DC/DC converter. As a result, mitigation in battery stress due to surge

current can be achieved.

The Matlab Simulink model for SC Semi-Active HESS was constructed as shown in Fig.

5.8. The parameters for SC, Lead-acid battery and the input pulsed current are set

identical to those in Passive HESS (Fig. 5.4). A LPF is used to decompose the pulse

load into two different frequency components. The generated signal from the higher one

is used to control the DC / DC converter. Inside the grey box, the Proportional-Integral

(PI) controller is implemented to track the reference signal, while the remaining of the

current demand will be responded by the passively controlled Lead-acid battery. The


- 95 -

advantage of actively controlled SC module is that the time constant of the response can

be adjusted to utilize the SC module better. Also, the isolation of the SC module from

the DC bus allows wider variation in SoC that significantly improves the volumetric

efficiency.

Fig. 5.8 The Matlab Simulink model of the SC Semi-Active HESS

15 20 25 30 35 40-6

-4

-2

0

2

4

6

8

Time (s)

Cur

rent

(A)

LA BatterySCPulse Load

Fig. 5.9 Pulse load response of the SC Semi-Active HESS


- 96 -

Fig. 5.9 shows the simulation results for power-sharing between Lead-acid battery and

SC in the Semi-Active setting. The pulse load is successfully decomposed into two

different frequencies by the LPF. Since the introduction of DC/DC converter and

simulation limitation, the current profile is changed into the shape with minor high

frequency oscillation, which is different in Passive HESS. When the pulse load becomes

5A, the SC is discharged immediately to meet the pulse requirement. When the pulse

becomes 0A, then the SC is quickly charged to absorb excess power. Throughout the

process, the battery can be slowly discharged and charged at its own rate. The sum of

the charge and discharge from the two energy storage elements is equal to the value of

the pulse load. However, due to the unavoidable time delay of the active component and

controller, there is an inrush current when there is a step change in load current.

Fig. 5.10 depicts the same stand-alone PV power system with SC Semi-Active HESS as

the energy storage. The Lead-acid battery is directly connected to the DC bus, while the

SC is interfaced with a bi-directional DC/DC converter. The net change in current is

measured and a Pulse Width Modulation (PWM) signal with the appropriate duty cycle

DSC is generated by the controller to control the power flow of SC.

Fig. 5.10 The stand-alone PV power system with SC Semi-Active HESS

DC/DC(MPPT)

Charge Controller

DC/DC

Inverter

DC Bus

ACLoad

-

DCLoad

Lead-acid Battery Storage

SupercapacitorDiesel

Generator

Energy Management Unit

(EMU)Filtration-Based Controller (FBC)


- 97 -

Ppv

Pload

+-

PHESS Decomposition Methods(eg. LPF)

+-

PSC(ref)

PSC(ref)1/VSC

ISC(ref)PI PWM

D+-

ISC

Switching Signal(SC converter)

(a) Power Allocation Strategy

(b) Linearized Model of the Current Control Loop

Fig. 5.11 Power allocation strategy for the SC Semi-Active HESS

Its control scheme is illustrated in Fig. 5.11 where the demanded power PHESS is

resolved by using decomposition methods such as LPF, and the high-frequency

component of the power exchange will be used as the reference signal PSC(ref) for SC. A

carefully tuned current tracker (PI controller) is used to control the power flow from SC.

When selecting the bandwidth of the LPF, a trade-off exists between the smoothness of

battery current IBatt and the capacity of SC. For instance, a relatively low cut-off

frequency in LPF will generate a smoother IBatt but requires larger SC capacity as well

as higher power rating of active components in DC/DC converter.

5.3.3 SC/Lithium-ion/Lead-acid battery Multi-level HESS

+

-

RLi-ion

CvLi-ion

isc iLi-ion iBus

DC/DCDC/DC

RLA

iLA

vLA

RLi-ion

VLi-ion/s

DC/DCDC/DC

RLA

Ic(s)

Rc

Vc /C

1 /sC

IBus(s)

VLA/s

ILA(s)

ILi-ion(s)

vBus VBus(s)

Rsc

η

ic ir

Isc(s)

Ir(s)

ηsc Li-ion

vc

vsc Vsc VLi-ionvLi-ion


(a) Time Domain (b) Frequency Domain

η ηsc Li-ion

Fig. 5.12 The equivalent circuit model of the Multi-level HESS


- 98 -

To maximize the advantages of the previous two topologies, this study proposed the

novel Multi-level HESS topology, and the equivalent circuits are illustrated in the Fig.

5.12. The three types of ESS elements are Lead-acid battery, Lithium-ion battery, and

SC. The Lead-acid battery is the main energy storage component, Lithium-ion battery is

the intermediate battery, and SC is the third one. The terminal voltage of SC and its

current can be expressed as:

𝑣𝑠𝑐 =𝑣𝑠𝑐

Ƙ𝑠𝑐

=𝑣𝑏𝑢𝑠

Ƙ𝑠𝑐

(5.23)

𝑖𝑐(𝑡) =Ƙ𝑠𝑐

𝜂𝑠𝑐

𝑖𝑠𝑐(𝑡) = 𝑖𝐵𝑢𝑠(𝑡) − 𝑊1 ∗ 𝑖𝐵𝑢𝑠(𝑡)𝐿𝑃𝐹1 − {[𝑖𝐵𝑢𝑠(𝑡) − 𝑤1 ∗ 𝑖𝐵𝑢𝑠(𝑡)𝐿𝑃𝐹1]}𝐿𝑃𝐹2 (5.24)

and for the Lithium-ion battery,

𝑣𝐿𝑖−𝑖𝑜𝑛 =𝑣𝑟

Ƙ𝐿𝑖−𝑖𝑜𝑛

=𝑣𝑏𝑢𝑠


(5.25)

𝑖𝑟(𝑡) =Ƙ𝐿𝑖−𝑖𝑜𝑛

𝜂𝐿𝑖−𝑖𝑜𝑛

𝑖𝐿𝑖−𝑖𝑜𝑛(𝑡) = {[𝑖𝐵𝑢𝑠(𝑡) − 𝑤1 ∗ 𝑖𝐵𝑢𝑠(𝑡)𝐿𝑃𝐹1]}𝐿𝑃𝐹2 (5.26)

where the 𝜂𝑠𝑐, 𝜂𝐿𝑖−𝑖𝑜𝑛, 𝐾𝑠𝑐 and 𝐾𝐿𝑖−𝑖𝑜𝑛 are the efficiencies and voltage transfer rates of

the corresponding DC/DC converters respectively, W1 is the scaling factor that set the

proportion of Lithium-ion battery capacity in total power demand. For pulse load

response, the pulsed input current, Lead-acid battery, Lithium-ion battery and SC

currents on are:


𝑁−1

𝑘=0

= 𝑖𝐿𝐴(𝑡) + 𝑖𝐿𝑖−𝑖𝑜𝑛(𝑡) + 𝑖𝑠𝑐(𝑡) (5.27)

𝑖𝑠𝑐(𝑡) =𝜂𝑠𝑐

Ƙ𝑠𝑐

𝑖𝑐(𝑡) (5.28)

𝑖𝐿𝑖−𝑖𝑜𝑛(𝑡) =𝜂𝐿𝑖−𝑖𝑜𝑛


𝑖𝑟(𝑡) (5.29)

𝑖𝐿𝐴(𝑡) = 𝐼𝑜 ∑[∅(𝑡 − 𝑘𝑇) − ∅(𝑡 − (𝑘 + 𝐷)𝑇)]

𝑁−1

𝑘=0

−𝜂𝑠𝑐

Ƙ𝑠𝑐

𝑖𝑐(𝑡) −𝜂𝐿𝑖−𝑖𝑜𝑛


𝑖𝑟(𝑡) (5.30)

Similarly, assuming the peak power demand still occurs at the moment 𝑡 = (𝑘 + 𝐷)𝑇,

and the maximum current drawn from the Lead-acid battery can be expressed in Eq.

(5.31) when N tends to infinity,


- 99 -

𝑖𝐿𝐴𝑝 = 𝐼𝐵𝑢𝑠 −𝜂𝑠𝑐

Ƙ𝑠𝑐

𝑖𝑐(𝑡) −𝜂𝐿𝑖−𝑖𝑜𝑛


𝑖𝑟(𝑡) (5.31)

Fig. 5.13 depicts the Matlab Simulink model of Multi-level HESS and its EMS. The

parameters for Lead-acid battery module, SC module, and pulse load are kept identical

as the one in Passive HESS and SC Semi-Active HESS. The additional Lithium-ion

battery module has a capacity of 2 Ah, and the scaling factor W1 is set to 0.85. This

value is set as an example in this section, but in different applications, it can be

dynamically changed according to specific needs.

Fig. 5.13 The Matlab Simulink model of the Multi-level active HESS

The responses of the Lithium-ion battery, Lead-acid battery, and SC are depicted in Fig.

5.14. SC is programmed to respond to transient currents, while the Lithium-ion battery

responses to the medium frequency component during current variation and supplying a

small portion of the current demand. Due to the discrete simulation steps, high

frequency ripples are observed in simulation results. The DC/DC converters controls


- 100 -

current variation and allow the SC/Lithium-ion operate at their corresponding curve.

When the pulse load suddenly rises to 5A, the SC is rapidly discharged to 5A to provide

this instantaneous power, and then slowly reduce the discharge rate until 0A. At this

time, the Lithium-ion battery starts to discharge and faster than the Lead-acid battery.

After the Lead-acid battery is slowly discharged to a certain value, the Lithium-ion

battery begins to reduce the discharge rate. When the pulse load suddenly drops to 0A,

the SC instantaneously absorbs the 5A currents from the Lead-acid battery and Lithium-

ion battery. Meanwhile, the Lithium-ion battery changes its status from the discharged

state to the charged state. The Lead-acid battery slowly reduces the discharge rate until

the next pulse period. The series of above actions enable the reduction in peak current

for the passive Lead-acid battery bank which contributes to the cycle life of the battery.

Since the DC/DC converter controls the Lithium-ion battery and the SC, the inrush

current still exists as the one in the Semi-Active HESS. However, in the pulse load test,

the instantaneous increase of the current is the extreme conditions and rare in the actual

system. In a stand-alone PV power system, such a situation is not going to occur, so the

problem of inrush current will not affect the operation of the HESS.

0 5 10 15 20 25 30-6

-4

-2

0

2

4

6

8

Time (s)

Cur

rent

(A)

Li-ion BatteryLA BatteryPulse LoadSC

Fig. 5.14 Pulse load response of the Multi-level active HESS

Fig. 5.15 illustrates the stand-alone PV power system with Multi-level Battery-SC HESS

in which three different energy storage devices are utilized for better stress mitigation. In

this setting, two actively controlled complementary ESS elements (SC and Lithium-ion

battery) are connected in parallel with the passively connected primary Lead-acid battery.


- 101 -

The combination of SC and Lithium-ion modules enhances the stress mitigation

capability by covering a wider spectrum of current fluctuation as well as supply part of

the nominal current demand, which enables a more stable charge-discharge process and

peak current reduction in the primary Lead-acid battery bank.

Fig. 5.15 Stand-alone PV power system with Multi-level HESS

Ppv

Pload

+-

PHESS LPF(Primary)

+ -

+-

PSC (ref)

LPF (Secondary)

W1

PLi-ion (ref)

(a) Power Allocation Strategy

PSC(ref) 1/VSCISC(ref)

PI PWMD+

-

ISC

Switching Signal(SC Converter)

PLi-ion(ref) 1/VLi-ionILi-ion (ref)

PI PWMD+

-

ILi-ion

Switching Signal (Li-ion Battery Converter)

(b) Linearized Model of the Current Control Loop

Fig. 5.16 Power allocation strategy for the Multi-level HESS

DC/DC(MPPT)

Charge ControllerDC Bus

-

Supercapacitor DC/DC

Li-ion Battery DC/DC

Inverter ACLoad

DCLoad

Lead-acid Battery Storage

Diesel Generator

Energy Management Unit

(EMU)Filtration-Based Controller (FBC)


- 102 -

The associated power allocation strategy is shown in Fig. 5.16. Two LPFs are cascaded

to decompose the net power demand into three different frequency ranges. The highest

frequency PSC(ref) will be used as the reference signal to control the power flow of the SC

module. While the medium frequency component PLithium-ion(ref) will be the reference for

Lithium-ion battery module. A scaling factor W1 is proposed to set the proportion of

Lithium-ion battery load in total power demand.

5.4 Numerical simulations in stand-alone PV power system

To evaluate the effectiveness of the selected HESS in mitigating battery stress in stand-

alone PV power system, Matlab Simulink models of 5kW stand-alone PV power system

with different HESS are developed, and simulations have been carried out with actual

solar irradiance data and estimated load profile from the targeted site.

5.4.1 Solar irradiance and load profiles for case studies

Fig. 5.17 and Fig. 5.18 show the PV output based on the 24-hour solar irradiance data

recorded for a typical (a) sunny day and (b) cloudy day at Kuching Sarawak.

0 4 8 12 16 20 00

1

2

3

4

5

6

Hours of Day

Pow

er (k

W)

PV output in Sunny Day

Fig. 5.17 The PV output in sunny day


- 103 -

0 4 8 12 16 20 00

1

2

3

4

5

6

Hours of Day

Pow

er (k

W)

PV output in Cloudy Day

Fig. 5.18 The PV output in cloudy day

This section uses the forecasted daily scaled-up load profile that using the methodology

in Chapter 3 as load demand in the following numerical simulations. The simulation is

executed in 5kW stand-alone PV power system, and thus the estimated load profile is

scaled up into Fig. 5.19 where will have 12 households for 30 people with similar

electricity usage pattern, same place in Batang-Ai (1°14’20.5”N, 112°02’10.7”E). The

peak power demand is around 2.7kW and total load demand per day is around 30kWh.

0 6 12 18 00

0.5

1

1.5

2

2.5

3

Hours of Day

Pow

er (k

W)

Community HallTotal ConsumptionHousehold UseCommon Lighting

Fig. 5.19 Estimated load profiles of the target rural site in Sarawak, Malaysia


- 104 -

5.4.2 Results of simulations

Chapter 3 has introduced the simulation results of the battery-only in a stand-alone PV

power system, showing the PV output characteristics and current profiles of the battery

in sunny and cloudy weather conditions. With the same stand-alone PV power system,

the selected HESS topologies will be simulated and analyzed individually under the

same PV output and load profile in the following section.

Table 5.1 lists the parameters of the Matlab Simulink model used in the simulation. For

a fair comparison, the primary Lead-acid battery was kept identical for the four different

cases, and the rated voltage is 12 volts, and the installed Lithium-ion battery module is

400Ah with 12V rated voltage.

Table 5.1 Model parameters for different HESS under simulation

HESS System Topology Primary Battery Capacity (Ah) Complimentary Energy Storage Capacity

Battery-only 4000Ah (Lead-acid) - Passive HESS 4000Ah (Lead-acid) 1000F (SC)

SC Semi-Active HESS 4000Ah (Lead-acid) 1000F (SC) Multi-level HESS 4000Ah (Lead-acid) 400Ah (Lithium-ion) + 200F (SC)

a. Passive HESS

Fig. 5.20 Matlab Simulink model of Passive HESS


- 105 -

Fig. 5.20 illustrates the Matlab Simulink model of stand-alone PV power system with

Passive HESS. An ideal PV array with MPPT charge controller is considered in the

simulation of PV power generation. The simulation results for sunny and cloudy

weather conditions are shown in Fig. 5.21 and Fig. 5.22, respectively. The SC in sunny

day condition shares a small portion and normally operates between -5A to 5A. While

in cloudy day condition, the SC operates within a larger range between -20A to 20A.

With SC integrated, the Lead-acid battery current profiles show insignificant

improvement compared to the battery-only system (Fig. 3.15 and Fig. 3.16).

0 4 8 12 16 20 0-100

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)

(a) PV-Load(b) LA Battery (c) SC

Fig. 5.21 Current profiles of sunny day

0 4 8 12 16 20 0-100

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)


Fig. 5.22 Current profiles of cloudy day


- 106 -

b. Semi-Active HESS

The Matlab Simulink model for the SC Semi-Active HESS is developed as illustrated in

Fig. 5.23. Identical SC with 1000F is connected to DC bus with a bidirectional

buck/boost DC/DC converter. For power allocation, the LPF with a time constant

T=300s is employed to decompose the power demand from the DC bus. The LPF

divides the net power demand into high and low-frequency components. The low-

frequency parts are passively covered by the primary Lead-acid battery, while the SC is

programmed to respond to the high frequency fluctuating current actively.

Fig. 5.23 Matlab Simulink model of SC Semi-Active HESS

Compares to results in Passive HESS, the current profiles in Fig. 5.24 and Fig. 5.25 are

significantly smoothed. In sunny day, the SC operation range is extended to ±20A. The

effectiveness of actively controlled SC module is even more apparent in cloudy day

condition, as shown in Fig. 5.25. The sharp change in solar irradiance due to cloud

shelters caused the PV generated current to vary severely throughout the day. The

operating range of SC current is about ±60A under cloudy day condition. This

significantly improved the SC utilization rate compared to Passive HESS.


- 107 -

0 4 8 12 16 20 0

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)



0 4 8 12 16 20 0

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)



c. Multi-level HESS

The Matlab Simulink model with Multi-level HESS is shown in Fig. 5.26. The Lead-

acid battery remains identical as in Passive HESS and Semi-Active HESS. The SC

capacity is reduced to 200F, and the additional Lithium-ion battery module is sized at


- 108 -

400Ah. The net power demand is split into three frequency components with two

cascaded LPF (see Fig. 5.16). The time constants are set to 300s and 150s, respectively.

The scaling factor W1 is set to 0.85 which indicates that the Lithium-ion battery module

is designed to cover 15% of the average power demand, while absorbing the medium

frequency fluctuation.

The SC module is programmed to absorb the highest frequency component, while the

primary Lead-acid battery bank will passively cover the remaining power demand.

Significantly smoothed primary Lead-acid battery current profiles can be observed in

Fig. 5.27 and Fig. 5.28. The SC handles the rapid oscillations and Li-ion battery handles

great oscillations. While the Lead-acid battery takes care of the averaged nominal

current demand. The SC operates within ±60A during the daytime, while the Lithium-

ion battery absorbs part of the fluctuations (±20A) and at the same time shares a portion

of the nominal load. The Lead-acid battery is significantly smoothed with a reduction in

peak current as a result of the power-sharing capability of the Lithium-ion battery

module.

Fig. 5.26 Matlab Simulink model of Multi-level HESS


- 109 -

0 4 8 12 16 20 0

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)

(a) PV-Load(b) LA Battery (c) SC(d) Li-ion Battery


0 4 8 12 16 20 0

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)

(a) PV-Load(b) LA Battery (c) SC(d) Li-ion Battery


d. SoC variation in SC

Fig. 5.29 and Fig. 5.30 present the SoC variation in SC for Passive HESS, Semi-Active

HESS, and Multi-level HESS, respectively. With the capacity of 1000F in Passive HESS,

only less than 10% of the SoC is utilized because of the shared terminal voltage with


- 110 -

battery. Conversely, with the same 1000F capacitance, the decoupled SC terminal voltage

with DC/DC interface in Semi-Active topology, approximately 55% of the SoC is

utilized for both sunny and cloudy conditions.

0 4 8 12 16 20 0

40

60

80

100

Hours of Day

SC S

tate

of C

harg

e (%

)

(a) Passive HESS(b) Semi-active HESS(c) Multi-level HESS

Fig. 5.29 SoC variation of SC in three different scenarios (sunny day)

0 4 8 12 16 20 0

40

60

80

100

Hours of Day

SC S

tate

of C

harg

e (%

)

(a) Passive HESS(b) Semi-active HESS(c) Multi-level HESS

Fig. 5.30 SoC variation of SC in three different scenarios (cloud day)


- 111 -

As for the Multi-level HESS, the SoC variation ranges from 25% for the sunny day and

55% for the cloudy day, with a much-reduced capacity of 200F. Since the lifetime of SC

usually is much longer than Lithium-ion battery, and its initial cost is also higher, there is

a tradeoff about the installation of SC between Semi-Active HESS and Multi-level HESS.

In long-term applications, the Multi-level HESS needs to replace Lithium-ion battery

regularly while Semi-Active HESS would not need it. On the other hand, in short-term

applications, the lifetime of Lithium-ion battery would satisfy the requirements and

effectively prolongs the primary battery lifetime similar to Semi-Active HESS which

requires 5 times more SC installation with a much higher price.

5.4.3 Battery health assessment and financial analysis

a. Cost function

The simulation results presented above demonstrate the effectiveness of different HESSs

in mitigating the Lead-acid battery stress compared to the conventional battery-only

system. Based on the phenomenon in Lead-acid battery current profile, the

charge/discharge rate, surge and fluctuating current, deep discharge effect and

charge/discharge transition rate will be the main life-limiting factors. To quantify the

effectiveness and extend the improvements of the selected HESS topologies for the

Lead-acid battery operation environment under different weather conditions, a battery

health cost function is proposed in this study as shown in Eq. (5.32), including the sum

of real-time current square, the differential of current, square of DoD, times count of

charge/discharge transition and the calendrer lifetime of battery:

𝐶𝑜𝑠𝑡(𝑇) = ∑ 𝑛1[𝑖𝑏(𝑡)]2

𝑇

𝑡=0

+ ∑ 𝑛2 |𝑑𝑖𝑏(𝑡)

𝑑𝑡|

𝑇

𝑡=0

+ 𝑛3[𝑚𝑎𝑥(𝑏(𝑡)) − 𝑚𝑖𝑛(𝑏(𝑡))]2

+ ∑𝑛4 |1 ; 𝑖𝑓 [𝑖𝑏(𝑡) ∙ 𝑖𝑏(𝑡 − 1) < 0]

0 ; 𝑖𝑓 [𝑖𝑏(𝑡) ∙ 𝑖𝑏(𝑡 − 1) ≥ 0] +

𝑇

𝑡=0

𝑛5𝑇𝑦𝑒𝑎𝑟 (5.32)

where T is the total operating time, 𝑖𝑏(𝑡) is the battery current, 𝑏(𝑡) is the SoC of

battery, while the 𝑛1, 𝑛2, 𝑛3, 𝑛4 and 𝑛5are positive constants. Five life-limiting factors

are considered based on the lifetime characteristics of Lead-acid battery. The first term

quantifies the negative impact of charge/discharge rate (C rate). The second term


- 112 -

evaluates the accumulation of the negative extent on Lead-acid battery when subjected

to surge and fluctuating current. The third term quantifies the impact of deep discharge.

During the day, due to unpredictable power demand from PV output or load users,

Lead-acid battery may frequently switch between charging state and discharging state,

which accelerates the cycle life of Lead-acid battery. Therefore, the fourth term

penalizes the impact of the charge/discharge transition. The last term indicates the

calendar life of the battery, which is assumed to be constant over time. The aging

process of Lead-acid battery is a fairly complex chemical phenomenon caused by many

factors. It is very difficult to establish an accurate mathematical model and quantify the

extent of their impact. The formulated cost function intends to relatively measure the

impact on battery health for comparison among the HESS under consideration.

b. Battery health assessment in different weather conditions

This section will conduct a health assessment of the Lead-acid battery based on the

current profiles in different HESSs. The coefficient n2 and n4 are set to 0.3 indicating

the strong negative impact of current fluctuation and frequent charge-discharge

transitions due to the intermittent solar power. While n1 and n3 are set to 0.15 and 0.2

respectively to quantify the damaging impacts of the charge/discharge rate and deep-

discharge. The effect of calendar life n5 is usually much lower compared to the other

factors, and therefore it is set to 0.05. The assessment is executed under two different

weather conditions, which thoroughly presents the improvement of battery health with

different HESS topologies. Different load profile will be used during these two days.

1. Sunny day condition

The cumulative impacts (24 hours) of the life-limiting factors for different HESS are

shown in Figs. 5.31-5.33, respectively. The impact of calendar life is not presented

because they are assumed to be identical for the Lead-acid batteries of similar size. In Fig.

5.31, the Multi-level HESS performs the lowest value while the other three systems have

almost the same value. This is because the Lithium-ion battery shares a part of the

current so that the Lead-acid battery can work under the lower charge/discharge rate. Fig.

5.32 shows the extent to which HESS reduces drastic current fluctuations, or dynamicity,

in Lead-acid battery throughout the day. In general, a significant reduction in battery


- 113 -

health cost can be observed in Semi-Active HESS and Multi-level HESS. The Multi-

level HESS with 200F SC performs equally well compared to SC Semi-Active HESS

with 1000F SC. In the case of Passive HESS, since the terminal voltage of the SC is

limited by the terminal voltage of the Lead-acid battery, the power-sharing capability is

insignificant. As for the penalty on DoD (Fig. 5.33), Multi-level HESS demonstrates the

least impact on battery health cost due to the power-sharing capability of Lithium-ion

battery module.

0 4 8 12 16 20 00

2

4

6

8

10

12

14

16

18

Hours of Day

LA b

atte

ry c

harg

e/di

scha

rge

rate

(a) Battery-Only(b) Passive HESS(c) Semi-active HESS(d) Multi-level HESS

n1 : 0.15

Fig. 5.31 The results of the charge/discharge rate (sunny day)

0 4 8 12 16 20 00

200

400

600

800

1000

Hours of Day

Dyna

mic

ity o

f LA

batte

ry c

urre

nt


n2 : 0.3

Fig. 5.32 The results of the dynamicity (sunny day)


- 114 -

0 4 8 12 16 20 050

60

70

80

90

Hours of Day

Stat

e of

Cha

rge

(%)

(a) Battery-Only [DoD : 30.78%](b) Passive HESS [DoD : 30.67%](c) Semi-active HESS [DoD : 30.74%](d) Multi-level HESS [DoD : 27.58%]

Fig. 5.33 The results of the SoC (sunny day)

Fig. 5.34 shows the impact of charge-discharge transitions on battery health. It can be

seen that SC Semi-Active performs slightly better than the Multi-level HESS. Generally,

both Semi-Active HESS and Multi-level HESS significantly reduce the impact of

fluctuating current compared to systems with battery-only and Passive HESS.

0 4 8 12 16 20 00

0.5

1

1.5

2

2.5

3

Hours of Day

LA b

atte

ry c

harg

e/di

scha

rge

tran

sitio

n


n3 : 0.2

Fig. 5.34 The results of the charge/discharge transition (sunny day)

The normalized cumulative battery health costs of the different HESS settings in sunny

day condition are shown in Fig. 5.35. The results indicate that the Multi-level HESS

reduces about 50.1% of the battery health cost compared to conventional battery-only PV


- 115 -

power system in sunny day condition. Followed by the SC Semi-Active HESS, it

demonstrates 43.4% reduction in sunny day, while only 6.2% reduction in battery health

cost for Passive HESS.

0 4 8 12 16 20 00.2

0.4

0.6

0.8

1

Hours of Day

Cos

t (T)


Max : 0.499

Max : 0.566

Max : 0.938

Max : 1.000

Fig. 5.35 Normalized results of cost function throughout the sunny day

2. Cloudy day condition

The results of the first four individual factors in cloudy day condition are presented in

Figs. 5.36-5.39.

0 4 8 12 16 20 00

2

4

6

8

10

12

14

16

18

Hours of Day

LA b

atte

ry c

harg

e/di

scha

rge

rate


n1 : 0.15

Fig. 5.36 The results of the charge/discharge rate (cloudy day)


- 116 -

Similar to results in sunny day, the Multi-level HESS recorded the lowest cost due to the power-sharing capability of Lithium-ion battery module.

0 4 8 12 16 20 00

200

400

600

800

1000

Hours of Day

Dyn

amic

ity o

f LA

bat

tery

cur

rent


n2 : 0.3

Fig. 5.37 The results of the dynamicity (cloudy day)

Similar patterns are observed on dynamicity, DoD and charge-discharge transitions.

0 4 8 12 16 20 050

60

70

80

Hours of Day

LA b

atte

ry S

tate

of C

harg

e (%

)

(a) Battery-Only [DoD : 26.28%](b) Passive HESS [DoD : 26.16%](c) Semi-active HESS [DoD : 26.08%](d) Multi-level HESS [DoD : 23.37%]

Fig. 5.38 The results of the SOC (cloudy day)


- 117 -

0 4 8 12 16 20 00

2

4

6

8

10

12

14

Hours of Day

LA b

atte

ry c

harg

e/di

scha

rge

tran

sitio

n


n3 : 0.2

Fig. 5.39 The results of the charge/discharge transition (cloudy day)

The normalized cumulative battery health costs of the different HESS settings in cloudy

day condition are shown in Fig. 5.40.

0 4 8 12 16 20 0

0.2

0.4

0.6

0.8

1

1.2

1.4

Hours of Day

Cos

t (T)


Max : 1.164

Max : 0.491

Max : 1.308

Max : 0.529

Fig. 5.40 Normalized results of cost function throughout the cloudy day

Setting the battery health cost of battery-only setting in the sunny day as the reference,

the battery-only system in cloudy day condition is 1.308 which is 30.8% more than the

same setting in sunny day. This is because the relatively heavier PV power fluctuation


- 118 -

in cloudy day has a larger impact on battery health, thus higher battery health cost. By

comparing the battery health cost in cloudy day condition, the Multi-level HESS

manages to reduce the battery health cost by nearly 62.5%, followed by a reduction of

59.6% in SC Semi-Active HESS, and 11% reduction in Passive HESS setting.

c. Financial analysis

The estimated annual battery cost (365 days) for different systems and their

corresponding cost reduction are calculated and presented in Table 5.2.

Table 5.2 Financial analysis under different modes

Operation mode

Weather condition

Battery Capacity

(kWh)

Initial Cost

1

($)

Battery Health Cost

Cost(T) 2

Estimated Life

Cycle

Cost / cycle ($)

Estimated Annual Battery Cost ($)

Cost Reduction

4

LA Battery-only

Sunny 48 (LA) 12288 1.000 500 24.58 8971.70 0 Cloudy 48 (LA) 12288 1.308 382 32.17 11742.05 0

Passive HESS

(SC 1000F)

Sunny 48 (LA) 12288 0.938 533 23.50 8577.5 4.4%

Cloudy 48 (LA) 12288 1.164 429 28.64 10453.6 11%

SC Semi-Active HESS (SC 1000F)

Sunny 48 (LA) 12288 0.566 883 13.92 5080.8 43.4%

Cloudy 48 (LA) 12288 0.529 945 13.00 4745.00 59.6%

Multi-level HESS

(SC 200F)

Sunny 48 (LA) 12288 0.499 1002 13.26 4839.9 +

255.5(Lithium-ion) = 5095.4

43.2% 4.8 (Li-ion) 1392 - 2000

3 0.70

Cloudy 48 (LA) 12288 0.491 1018 12.07 4405.55 + 255.5(Li-

ion) = 4661.05 60.3% 4.8 (Li-ion) 1392 - 2000

3 0.70

#Note 1 – Initial cost of Lead-acid battery ($256/kWh) and Lithium-ion battery ($290/kWh) are considered.[269] #Note 2 – Typical life cycle / Cost of battery utilization; (typical life cycle for Lead-acid – 500 cycles, Lithium-ion – 4000 cycles and SC >100,000 cycles)[269] #Note 3 – Estimated to perform 50% of the expected lifecycles of the Lithium-ion battery when Lead-acid battery is replaced #Note 4 – Percentage cost reduction is calculated based on battery-only system.

Since the SC lifetime is nearly infinite, it is not considered in the annual operating cost

of the ESS in stand-alone PV power system. The Passive HESS reduces the battery cost

by 6.3% and 10.8% respectively for sunny and cloudy days, while both SC Semi-Active

HESS and Multi-level HESS demonstrate significant ESS cost reduction of about 43%

on a sunny day and 60% on a cloudy day. Despite the higher upfront battery cost (Lead-

acid and Lithium-ion) in Multi-level HESS, it only requires 20% of the SC capacity

compared to other HESSs.


- 119 -

5.5 Experiment verification

5.5.1 Testbed setup

To demonstrate the feasibility of the selected HESS topologies and to verify the

simulation analysis presented in the previous section, scale-down prototypes of stand-

alone PV-battery power system with the selected HESSs were designed and developed

as illustrated in Fig. 5.41. The test-bed is tested in campus of Swinburne University.

MPPT

Lead-acid Battery

Solar Irradiance

ipv

vpv

Charge Controller

Dpv

SC

SC

Ls

Cbus

Sw1 Sw2

ISC(ref)

EMS(Fig.5.11)

Sw1 Sw2

SC

Ls

Cbus

Sw1 Sw2

ISC(ref)

IBus

Li-ion

Ls

Cbus

Sw3 Sw4

EMS(Fig.5.16)

ILi-ion(ref)

Sw1 Sw2 Sw4Sw3

Pulse Load

IBus

IBus

DC/DC

InstalledPV Panel

EMS + HESS

SC Semi-active HESS

Multi-level HESS Passive HESS

Estimated load

0 6 12 18 00

0.5

1

1.5

2

2.5

3

Hours of Day

Pow

er (k

W)

Community HallTotal ConsumptionHousehold UseCommon Lighting

BK8500

Fig. 5.41 Experiment setup of selected HESS topologies

Inside the system, the Lead-acid battery is connected to the DC Bus through a charge

controller. The three HESS modules, marked with red dashed lines, can be individually

connected to the DC bus and tested. A programmable DC electronic load (BK Precision

BK8500) is used to emulate the pulsed load as well as the estimated load profiles. A

15W solar panel is used to generate the PV power. The current flows in energy storage

devices are measured by current sensors (ACS712) and logged by using the NI USB-

6008 data acquisition device. The power allocation algorithm is controlled by Arduino

(ATMEGA328P). The design and implementation of bidirectional buck-boost DC/DC

converter and its control system are presented in Appendix 2.1. The parameters of the

experiment testbed are summarized in Table 5.3.


- 120 -

Table 5.3 Experimental testbed parameters

System Parameters Passive HESS SC Semi-Active HESS Multi-level HESS

PV panel peak power 15W

Daily load energy consumption 0.4 kWh

Lead-acid battery nominal voltage 12V

Lead-acid battery capacity 30Ah

SC Capacitance - 50F 25F

SC Equivalent Series Resistance - 0.001Ω 0.001Ω

Lithium-ion Battery nominal voltage - - 12V

Lithium-ion Battery capacity - - 6Ah

5.5.2 Pulse load response

A repetitive pulsed current profile with amplitude of 1 Ampere, period of 120s, and 50

percent in duty ratio is generated by using BK8500. Figs. 5.42-5.44 show the responses

of battery and SC currents in each selected HESSs under pulse load test.

300 350 400 450

-0.5

0

0.5

1

1.5

Time (s)

Curr

ent (

A)


Fig. 5.42 Experimental responses to pulsed load of Passive HESS

In Passive HESS (Fig. 5.42), the SC responded instantaneously to the step change in

current, while the battery picked up slowly with a time constant of about 1.5s. In SC

Semi-Active HESS, an Arduino controlled bi-directional buck/boost DC/DC converter

is used to control the current flow in SC module with a simple digital LPF to allocation


- 121 -

current among SC and battery modules. As can be seen from Fig. 5.43, the SC

responded quickly to the step change in current and allowed the battery to gently

supply/absorb the current change.

100 200 300 400

-1

-0.5

0

0.5

1

1.5

Time (s)

Cur

rent

(A)


Fig. 5.43 Experimental responses to pulsed loads of SC Semi-Active HESS

800 900 1000 1100

-1

-0.5

0

0.5

1

1.5

2

Time (s)

Cur

rent

(A)

(a) LA Battery(b) Li-ion Battery(c) SC(d) Pulse Load

Fig. 5.44 Experimental responses to pulsed loads of Multi-level HESS

The current response of the Multi-level HESS is shown in Fig. 5.44. The structure

enables the primary battery to gently supply/absorb the changes but with notably lower


- 122 -

peak current because the Lithium-ion battery module shares part of the current demand

that can be determined by setting the scaling factor (set at 0.85 for this experiment). The

experimental results of pulsed load responses agree to the simulation results presented

in section 5.3.

5.5.3 Daily operation in stand-alone PV power system

The daily operational test with 15W stand-alone PV power system was carried out on

HESSs under test separately on four different partly cloudy days in Swinburne

University of Technology Sarawak Campus, Kuching, Malaysia. The net current demand

(𝐼𝑃𝑉 − 𝐼𝐿𝑜𝑎𝑑, black line), primary battery current (red line) and SC current (blue line) are

depicted in Figs. 5.45 – 5.48, respectively for battery-only system, system with Passive

HESS, system with SC Semi-Active HESS and system with Multi-level HESS.

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Curr

ent (

A)

(a) LA Battery (b) SC(c) PV-Load

Fig. 5.45 Experimental currents of battery-only in stand-alone PV power system

Minimal mitigation of current fluctuation is demonstrated in Passive HESS (Fig. 5.46),

while SC Semi-Active HESS and Multi-level HESS remove majority of the primary

battery current fluctuation as can be observed from Fig. 5.47 for SC Semi-Active HESS

and Fig. 5.48 for Multi-level HESS. In addition, the Multi-level HESS shares part of the

current demand with Lithium-ion battery module with a pre-determined scaling factor.

The experimental results demonstrate the feasibility of the HESS under test in stand-

alone PV power system and validate the simulation analysis presented in Section 5.4.2.


- 123 -

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Curr

ent (

A)

(a) LA Battery (b) SC(c) PV-Load

Fig. 5.46 Experimental currents of Passive HESS in stand-alone PV power system

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Cur

rent

(A)

(a) LA Battery(b) SC(c) PV-Load

Fig. 5.47 Experimental currents of Semi-Active HESS in stand-alone PV power system


- 124 -

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Curr

ent (

A)

(a) LA Battery(b) Li-ion Battery(c) SC(d) PV-Load

Fig. 5.48 Experimental currents of Multi-level HESS in stand-alone PV power system

5.6 Conclusion

Stand-alone PV power system with Lead-acid battery has been one of the preferred

choices in off-grid rural electrification. However, the nature of solar energy is causing

the additional impact on the battery which accelerates the deterioration of battery

performance and cycle life. This chapter presented a comprehensive study of Battery-

SC HESS and their feasibility in stand-alone PV power system. Three potential HESS

topologies and their associated power allocation strategy, and control system had been

discussed in this chapter, followed by numerical simulation and experimental

verification. The Matlab Simulink models of the selected HESSs were developed and

simulated with actual solar irradiance data and estimated load profile to evaluate the

effectiveness in mitigating battery stress. The simulation analysis and results had been

verified by experiments with the developed lab-scale prototype of HESS under

consideration. Simulation results, battery health cost and financial analyses, and

empirical outcomes suggest that the combination of active secondary energy storage

with the passive primary battery could be the optimal setting for stand-alone PV power

system applications.

- 125 -

Chapter 6

Smart HESS Plug-in Module for Stand-

alone PV-Battery Power System

6.1 Introduction

The structure of stand-alone PV power system, illustrated in Fig. 6.1, consists of PV

arrays, charge controller, inverter, the Lead-acid battery, and AC/DC loads. This

conventional stand-alone PV-battery power system has been widely installed in off-grid

rural communities. Chapter 5 discussed and compared three HESS topologies that are

suitable for stand-alone PV power system and the technical analysis and simulation

outcomes showed that the proposed Multi-level HESS could effectively mitigate battery

stress and thus leading to enhanced lifetime characteristic of the Lead-acid battery bank.

However, to achieve the Multi-level Battery-SC HESS as demonstrated in Chapter 5, a

complete redesign of the existing installed ESS structure is required. This may not be

financially viable in most cases, especially in remote applications.

Fig. 6.1 The stand-alone PV power system with Lead-acid battery

Lead-acid Battery

Charge Controller

Inverter AC Load

DC LoadDC/DC(MPPT)

Plug-inModule

Chapter 6 Smart HESS Plug-in Module for Stand-alone PV-Battery Power System

- 126 -

Typical stand-alone PV-battery power system consists of PV arrays, charge controller,

inverter, ESS and loads. Typical solar charge controller features MPPT that maximizes

the power generation, and regulates the battery charging process by monitoring the

battery SoC to prevent overcharging and overly discharged [284]–[286]. However, other

life-limiting factors that accelerate the deterioration of battery performance such as high

C-rate, fluctuating power exchange, frequent charge-discharge transition, deep-

discharging, and other external factors are often not considered in the research of the

charge controllers [287]–[289].

In this chapter, a novel Smart Hybrid Energy Storage System (SHESS) plug-in module is

proposed that is retrofittable on typical stand-alone PV-battery power systems, as shown

in Fig. 6.1. The proposed module is designed as a plug-in that can be adopted directly in

existing installed infrastructure in the installed stand-alone PV-battery power system. By

design, it mitigates the principal Lead-acid battery operation stress from current

fluctuations and surge demand without changing the structure of the original system.

Such a scheme is simple, effective and will have a significant economic impact on the

market of the installed PV system.

The proposed SHESS plug-in module consists of SC and Lithium-ion battery modules

that operate in two different modes based on weather conditions: (1) light mode for

operation under sunny day condition and (2) heavy mode for operation under cloudy day

condition. The performance metric of the three energy storage technologies is tabulated

in Table 6.1 [176], [290], [291].

Table 6.1 Comparison of energy storage technologies in SHESS

Lead-acid Battery SC Lithium-ion Battery

Specific Energy Density 30 -50 Wh/kg 0.1 – 15 Wh/kg 100 - 250 Wh/kg

Specific Power Density 75 - 300 W/kg 500-15,000 W/kg 230– 340 W/kg

Life Cycle 500-1000 100,000+ 1,000-20,000

Charge/Discharge Efficiency 80 – 90 % >90% >90%

Shortest Response Time 1 min 0.1 – 1 s 30 s

Self-Discharge/ Day 0.1-0.3% 20-40% 0.1-0.3%

Unit Cost ($/kWh) 300 2000 500


- 127 -

In sunny day condition, PV power is relatively stable for which only SC module will be

activated (light mode) to complement the battery operation. In light mode, the SC will be

interfaced to battery terminal by using a bidirectional DC/DC converter that is actively

controlled by the microcontroller. While in cloudy day condition, the PV generates less

power and fluctuates more frequently. Therefore, the heavy mode will be activated

where an additional Lithium-ion battery module is activated to provide the required

capacity in absorbing the current fluctuation. The current exchange will be decomposed

into three frequency components (high, middle and low) and allocated reasonably to the

SHESS plug-in modules of different lifetime characteristics (SC and Lithium-ion) and

the primary Lead-acid battery bank.

Computational model of the proposed SHESS plug-in module is developed and

evaluated in Matlab Simulink. Dynamic modelling and numerical simulations are carried

out to examine the effectiveness of the SHESS plug-in module in mitigating battery

stresses under different operating conditions. A scaled-down prototype of the proposed

SHESS plug-in module is examined experimentally to verify the simulation outcomes

and demonstrate the feasibility of the proposed system. To evaluate the technical and

financial improvements of the stand-alone PV power system with SHESS, a financial

analysis is presented by quantitatively investigating the lifetime improvement with

battery health cost model proposed in Chapter 5.

6.2 SHESS Plug-in module

6.2.1 System structure

The structure of the proposed SHESS plug-in module is illustrated in Fig. 6.2. The

SHESS is connected to the terminal of the Lead-acid battery. A mode controller

interfacing the Lead-acid battery and the SHESS plug-in module is implemented to

control the mode of operation. In light mode, only the SC module is interfaced to the

Lead-acid battery terminal via an actively controlled bidirectional DC/DC converter. The

active connection ensures DC bus stability while allowing the SC to operate in a wide

range of terminal voltages. In this mode, high-frequency current fluctuation will be

directed to the SC module that is controlled by the power allocator.


- 128 -

Differently, both SC and Lithium-ion modules will be activated in heavy mode, where

both SC and Lithium-ion modules are interfaced with actively controlled bidirectional

DC/DC converters. This configuration will ensure sufficient capacity in handling severe

fluctuation in current without requiring a large amount of costly SC to be installed. In

this setting, the SC module will absorb/supply the high-frequency current fluctuation,

while the Lithium-ion module is controlled to absorb/supply the medium frequency

component of the fluctuating current, and at the same time to supply a portion of the total

power demand. A power-sharing factor W1 determines the proportion of power-sharing

within the power allocator. The power allocator adopts the low pass filtering approach in

decomposing the power demand into multiple frequency components. A power

controller is used to coordinate the operation of mode controller and power allocator

while generating appropriate PWM signals to operate the individual bidirectional DC/DC

converters.

Fig. 6.2 SHESS plug-in module in stand-alone PV-battery power system


- 129 -

6.2.2 Power allocation and control strategy

The signal of power difference is collected and process via the central control system

that contains Power Allocation Controller (PAC) and Energy Management Unit (EMU).

The EMU is used to process the signals, determine the corresponding operating mode,

and send the signal to the PAC, as well as the DC/DC converters and Mode controller.

According to different working modes, the system will divide the signal into different

frequency parts and send it to the control unit. Fig. 6.3 shows the mode selection process

when the SHESS is operational. The mode controller can be configured as the automatic

mode or manual mode. In automatic mode, the selection of mode will be made

automatically based on real-time or predicted (from solar irradiance data) weather

conditions. While in manual mode, the operating mode will be manually set by the user.

This simple method will be attractive for the applications in remote areas.

Solar Irradiance

Manual setup

WeatherConditions

Sunny

Cloudy

Plug-in/off

Mode Selection

SC Li-ion

Off Mode(LA-Only) Off Off

Light Mode(Sunny) On Off

Heavy Mode(Cloudy) On On

Controller signal

Automatic setup

Fig. 6.3 Mode selection process of the proposed SHESS plug-in module

There are three modes of operation, namely off mode, light mode and heavy mode. In

light mode, SC module will be configured to respond actively to the high-frequency

power exchange while the average power demand (low-frequency components) will be

supplied passively by the primary Lead-acid battery. In the heavy mode, both the

Lithium-ion battery module and SC module will be parallel connected to the Lead-acid

battery. The power demand will be decomposed into three frequency components, (1)

high-frequency, (2) intermediate frequency, and (3) low-frequency. The high-frequency

power exchange will be responded by the SC module, while the Lithium-ion battery

module will cover the intermediate frequency, and lastly, the average power demand

(low-frequency) will be powered by the primary Lead-acid battery passively.


- 130 -

Heavy Mode

Ppv

Pload

+-

PESS

LPF (Primary)

+ -

+-

PSC (ref)

LPF (Secondary)

W1

PLi-ion (ref)

(a) Power Allocator for SHESS

PSC(ref) 1/VSCISC(ref)

PI PWMD+

-

ISC

Switching Signal(SC Converter)

PLi-ion(ref) 1/VLi-ionILi-ion (ref)

PI PWMD+

-

ILi-ion

Switching Signal (Li-ion Converter)

(b) Linearized Model of the Li-ion and SC Current Control Loop

Mode Control

Light Mode

Fig. 6.4 Power allocation strategy for the SHESS plug-in mode

(1) Light mode

In the light mode, the power allocation strategy is shown in Fig. 6.4(a). High-frequency

component PSC(ref) is extracted from the net demand power PESS (PPV - Pload) using the

secondary LPF. The SC actively handles the high-frequency components, and the battery

responsible for the low-frequency ones in a passively way. The signal of PSC(ref) will be

the input reference signal for the controller to generate appropriate PWM signals that

operates the bidirectional DC/DC converter for the SC module as shown in Fig. 6.4(b).

While the remaining smoothed power demand (low-frequency component) will be

supplied by the passively connected Lead-acid battery.

(2) Heavy mode

In the heavy mode, the SHESS utilizes two LPFs to decompose the power demand into

three different frequency components as shown in Fig. 6.4(a). The highest frequency

component PSC(ref) will be supplied by the SC module. While the middle frequency

component PLithium-ion(ref) will be covered by the Lithium-ion battery module. A weight

factor W1 is implemented to set the load proportion of Lithium-ion battery module with

reference to the total power demand PESS. In this way, the load of the Lithium-ion battery


- 131 -

module can be flexibly adjusted according to the installed capacity, to achieve optimal

power-sharing among different modules. Moreover, the W1 can be set manually or

modified dynamically by advanced control algorithms to satisfy different operating

conditions. In Fig. 6.4(b), it depicts the control systems for generating appropriate PWM

signals to operate bidirectional DC/DC converters of both SC and Lithium-ion battery

modules. Finally, the original Lead-acid battery passively absorbs/supplies the low-

frequency currents.

In addition to the hybrid integration of the Lead-acid battery and SHESS, it is also

critical to determine the cutoff frequency of LPF in both operation modes. In practice,

the cut-off frequency needs to be carefully considered based on the capacity of the Lead-

acid battery itself and the installed capacity of the SHESS. The cut-off frequencies

determine the smoothness of the fluctuating current in the Lead-acid battery and can be

calculated in real-time using advanced algorithms, or be manually adjusted by preset

optimization values. Researchers have made considerable efforts to optimize cutoff

frequency determination to maximize the benefits of HESS. The main research of this

chapter is to propose the SHESS plug-in module that can extend the lifetime of the main

battery in the installed PV system, and use simulation test and experimental verification

to demonstrate its operating characteristics and verify its feasibility. Therefore, the

algorithms for the mode selection, cut-off frequency determination, and the dynamic

adjustment of weight factor will not be discussed in details in the following subsections.

6.3 Simulation analysis

In this subsection, the Matlab Simulink model and simulation results of the proposed

SHESS plug-in module are presented. The model is tested with standard pulsed load and

actual PV-battery PV system operational.


The pulsed load used in simulation has a period of 10s and 5A of amplitude with a fixed

50% duty cycle. In light mode, only the SC module is activated to absorb the fluctuating

current. While in heavy mode, both the SC module and Lithium-ion battery module are


- 132 -

activated to perform power-sharing. Fig. 6.5 shows the SHESS model developed in

Matlab Simulink for pulse load test. Two switch breakers are implemented to simulate

the mode controller. The EMS simulates real-time power allocation and control

algorithms. The capacity of Lead-acid Battery and Li-ion Battery is set at 300Ah (12V) and

15Ah (12V), respectively. While the installed capacitance of SC is 50F.

Fig. 6.5 Matlab Simulink model of SHESS used in pulse load testing

(1) Light mode

Fig. 6.6 shows the simulation result for power-sharing between the Lead-acid battery and

the SC module under pulsed current load. During step change in current (from 0A to 5A),

the SC module is activated and discharges rapidly to fulfill the sudden change in current,

while the Lead-acid battery discharges gradually towards the current demand. In the OFF

state (from 5A to 0A), the SC module absorbs the excess current and gradually reduces

the Lead-acid battery current towards zero. Throughout the process, the battery can be

slowly discharged and charged at its own rate. The sum of the charge and discharge from

the two energy storage elements is equal to the value of the pulse load.


- 133 -

15 20 25 30 35 40 45 50-6

-4

-2

0

2

4

6

8

10

Time (s)

Cur

rent

(A)

LA BatterySCPulse Load

SC ModuleActivated

Fig. 6.6 Simulated results in pulse load testing in light mode

(2) Heavy mode

On the other hand, both of the SC module and Lithium-ion battery module are parallel

connected (both switch breakers are activated) with Lead-acid battery terminal in heavy

mode. Fig. 6.7 depicts the current profiles for Lead-acid battery, Lithium-ion battery

module, and SC module under pulsed current load.

0 5 10 15 20 25 30 35 40 50-6

-4

-2

0

2

4

6

8

10

Time (s)

Cur

rent

(A)

Li-ion BatteryLA BatteryPulse LoadSC

SC/Li-ion HESSModule Activated

Fig. 6.7 Simulated results in pulse load testing in heavy mode


- 134 -

The pulsed current was split into three frequency components, where the SC module

responded quickly to step change, while the Lithium-ion battery module took a small

portion of the overall capacity and at the same time responded to the middle frequency

component. As can be observed from the figure, the peak current of the Lead-acid

battery is reduced due to the W1 factor where the Lithium-ion battery module supplies a

small portion of the current demand.

6.3.2 Operation in stand-alone PV-battery power system

Fig. 6.8 illustrates the Matlab Simulink model of the SHESS plug-in module integrated

into a five kilowatts stand-alone PV-battery power system. The system model contains

5kW (peak) PV arrays, 4000Ah Lead-acid battery with 12V rated voltage, AC/DC loads,

and SHESS plug-in module interfaced with two individual bidirectional buck-boost

DC/DC converters. In SHESS plug-in module, the initial weight factor is set as 0.95, and

the installed Lithium-ion battery module is 200Ah with 12V rated voltage. The SC

capacitance is 100F. The simulations are conducted under the same daily solar irradiance

data profiles of the sunny and cloudy days as shown in Figs.5.17 and 5.18 (Chapter 5)

and the estimated load profile as presented in Fig. 5.19 (Chapter 5).

Fig. 6.8 Matlab Simulink model of stand-alone PV power system with SHESS


- 135 -

Multi-level LPFs are used to perform power decomposition in different operating modes.

In light mode, the power allocator generates reference signal PSC(ref) for the controller to

generate PWM signal to execute the SC module. The LPF time constant is set to 300s. In

the heavy mode, the power allocator splits the power demand PESS into reference signals

for Lithium-ion battery module PLithium-ion(ref) and SC module PSC(ref) by using Multi-level

LPFs (as shown in Fig.6.4(a)) with time constants of 600s and 300s, respectively. In both

operating modes, the Lead-acid battery responds passively to the remaining power

demand (low-frequency component).

(1) Retrofittability test

To examine the retrofittable feature of the SHESS plug-in module on the typical stand-

alone PV-battery power system, the SHESS was activated halfway during the 24-hour

simulation for both operating modes. Figs. 6.9 and 6.10 illustrate the 24-hour simulation

results of SHESS plug-in module light mode (Fig. 6.9) and heavy mode (Fig. 6.10).

In both modes, the PV-battery power system operated without the SHESS plug-in

module during the first half of the simulation time. The SHESS plug-in module was

activated at 12:00 PM to perform the power-sharing for the second half of the simulation

time. The plug-in action occurs at around 12:00 PM, the Lead-acid battery continuously

starts to run with a smooth current curve, and the negative impact of the peak current is

significantly diminished. The SHESS can adequately compensate the rapid current

variation either from PV generation or load demand. When the power demand is positive

(Pload > PPV), the SHESS plug-in module supplies power to the DC bus, while the

SHESS plug-in module absorbs excess power from the DC bus when the power demand

is negative (Pload < PPV).

It can be observed that the Lead-acid battery current (red line) absorbs all the current

fluctuations due to intermittent PV power without the SHESS (first half of simulation

time), whereas the Lead-acid battery current is significantly smoothed after the SHESS

was activated (second half of simulation time). The SC current (blue line) responded

effectively to absorb/supply the high-frequency current. In the light mode, the SC current

varies from -20A to 20A, which covers the fastest current fluctuations and is sufficient to

smooth the Lead-acid battery operating current profile. In the heavy mode, Lithium-ion


- 136 -

battery is utilized to mitigate power fluctuations in the middle frequency range and

extend the SC current range to -50A to 50A. The Lead-acid battery still can operate

under a smooth current curve. It is noted that the SC needs to reduce the current variation

from approximately -80A to 80A without the integration of Lithium-ion battery module.

This means that larger SC capacity is required, leading to a substantial increase in cost.

0 4 8 12 16 20 0-100

-60

-20

20

60

100

Hours of Day

Cur

rent

(A)

(a) PV-Load(b) LA Battery(c) SC

SC Module Activatied

Fig. 6.9 Simulation results of plug-in testing in light mode

0 4 8 12 16 20 0-80

-40

0

40

80

Hours of Day

Cur

rent

(A)

(a) PV-Load(b) LA Battery(c) Li-ion Battery(d) SC


Fig. 6.10 Simulation results of plug-in testing in heavy mode


- 137 -

(2) SHESS under 24-hour operation

The results in retrofittability test presented above demonstrate integrateability of the

proposed SHESS to the existing PV system and the capability to effectively reduce the

current fluctuations of the primary Lead-acid battery. To further evaluate the

performance of SHESS plug-in module in mitigating battery stresses under continuous

operation, a daily PV-battery power system operation was simulated for both modes. The

SHESS model parameters (PV and Lead-acid battery capacity) and simulation conditions

(solar irradiance and load profile) were set identical. Figs. 6.11 and 6.12 show the daily

Lead-acid battery current profiles under light mode (Fig. 6.11) and heavy mode (Fig.

6.12). It can be observed that the Lead-acid battery current profiles under both operating

modes have been significantly smoothed with the SHESS plug-in module installed.

4 8 12 16 20

-60

-20

20

60

100

0 4 8 12 16 20 0

-80

-40

0

40

80

Hours of Day

Cur

rent

(A)

(a) PV- Load(b) LA Battery

With SC Module

Without SC Module

Fig. 6.11 Lead-acid battery currents profile in light mode

On cloudy days, the solar irradiance changes too violently which requires enormous SC

capacity to effectively mitigate the severe current fluctuations, if SC-only HESS is

implemented. In order to balance the smoothness of the Lead-acid battery current and the

overall system cost, the capacity of the SHESS has to be designed to make the Lead-acid

battery operates with a relatively smooth curve instead of a remarkably smooth curve.


- 138 -

This is also why the SC/Lithium-ion HESS mode was introduced under cloudy

conditions to replace the SC-only mode. As an inexpensive ESS technology compared to

SC, the Lithium-ion battery absorbs/supplies a part of the high-frequency components in

the overall demand power and corporate with SC to maximally remove the Lead-acid

battery current fluctuations. This approach effectively extends the lifetime of the Lead-

acid battery while also ensures that the system is maintained at a lower cost range.

4 8 12 16 20

-60

-20

20

60

100

0 4 8 12 16 20 0-80

-40

0

40

80

Hours of Day

Cur

rent

(A)

(a) PV- Load(b) LA Battery

With SC/Li-ion HESS Module

Without SC/Li-ion HESS Module

Fig. 6.12 Lead-acid battery currents profile in heavy mode

(3) Battery healthy cost analysis

Insert the simulation results to the battery health cost function Cost(T) which was

presented as Eq.(5.32) in Chapter 5, Figs. 6.13 and 6.14 show the normalized cumulative

battery health cost in light mode (Fig. 6.13) and heavy modes (Fig. 6.14) respectively. In

sunny day condition, the simulation results show that the SHESS plug-in module under

light mode reduces the battery health cost by 26.2% compared to the Lead-acid battery-

only system. In the case of cloudy day condition, the SHESS under heavy mode achieved

the battery health cost of about 43.8% reduction compared to battery-only settings.


- 139 -

0 4 8 12 16 20 0

0.4

0.6

0.8

1

Hours of Day

Cos

t (T)

(a) LA Battery-Only(b) With SC Module

Max : 1.000

Max : 0.738

Fig. 6.13 Normalised cumulative battery health cost in light mode

0 4 8 12 16 20 00.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Hours of Day

Cos

t (T)

(a) LA Battery-Only(b) With SC/Li-ion HESS Module Max : 1.830

Max : 0.806

Fig. 6.14 Normalised cumulative battery health cost in heavy mode

Based on the battery health cost analysis presented above, a financial analysis is

presented in Table 6.2 to estimate the annual operating cost improvement with SHESS

plug-in module installed. The analysis assumes that the LA battery’s life cycle is linearly

proportional to the reciprocal of battery health cost. Based on the energy storage review

in [85] [106], the estimated life cycle of battery for different topologies are calculated

based on the typical life expectancy of 500 cycles for LA battery and 4000 cycles for Li-

ion battery. In [135][292], the power capacity cost of LA battery is around 150 ~ 500

$/kWh and the Li-ion battery is around 600 ~ 2500 $/kWh for small-scale power system.


- 140 -

The Li-ion battery is estimated to perform 50% of the expected cycle life under

fluctuating current condition. Assuming the LA battery is cycled once a day within the

entire year, the estimated cost per cycle is calculated. The capacity of SC module in both

light and heavy modes is set identical. Due to the extremely long service life of the SC,

normally higher than 100,000 times, this paper neglects the performance deterioration of

SC and assumes infinite lifetime in the economic analysis. Under these assumptions,

25.4%~25.8% annual operating cost reduction is projected under sunny day conditions,

while a higher 52.4%~52.7% cost reduction is estimated under highly dynamic power

fluctuation on cloudy condition.

Table 6.2 Financial analysis of SHESS under different modes

Operation mode Weather condition

Battery Capacity

(kWh)

Initial Cost1

($) Cost(T)

2

Estimated Life Cycle

3

Cost / Cycle ($)

Estimated Annual

Battery Cost ($)

Cost Reduction

4

(%)

Battery-only Sunny 48 (LA)

5 7200~24000 1.000 500 14.4~48.0 5256~17520 0

Cloudy 48 (LA) 7200~24000 1.830 273 26.3~87.9 9600~32084 0

Light Mode (with SC) Sunny 48 (LA) 7200~24000 0.738 677 10.6~35.5

(3869~12958) + 50 (SC)

6

25.4 ~ 25.8%

Heavy Mode (with SC/Li-ion) Cloudy

48 (LA) 7200~24000 0.806 620 11.6~38.7 (4490~15221) + 50 (SC)

6

52.4~52.7% 2.4 (Li-ion) 1440~6000 - 2000

3 0.7~3.0

#Note 1 – Initial cost of Lead-acid battery ($256/kWh) and Lithium-ion battery ($290/kWh) are considered.[269] #Note 2 – Typical life cycle / Cost of battery utilization; (typical life cycle for Lead-acid – 500 cycles, Li-ion – 4000 cycles and SC >100,000 cycles)[269] #Note 3 – Estimated to perform 50% of the expected lifecycles of the Li-ion battery when LA battery is replaced; #Note 4 – Percentage cost reduction is calculated based on battery-only system; #Note 5 – Assume the LA Battery: 4000 Ah with 12 volts; Li-ion Battery: 200Ah with 12 Volts (Weight factor: 0.5); #Note 6 – Cost of 500F SC is around $50 https://item.taobao.com/item.htm?ft=t&spm=a21m2.8958473.0.0.668e3663aIH9be&id=529538036640,

6.4 Experiment verification

Fig. 6.15 Prototype of the SHESS plug-in module

DC/DC for SC

DC/DC for Li-ionMicroprocessor

Supercapacitor Current sensors

NI USB-6008 data acquisition Li-ion Battery

Primary Lead-acid Battery

https://item.taobao.com/item.htm?ft=t&spm=a21m2.8958473.0.0.668e3663aIH9be&id=529538036640


- 141 -

In order to evaluate the viability of the proposed SHESS plug-in module in retrofitting

the typical Lead-acid battery-only stand-alone PV power system, a down-scaled

prototype, as shown in Fig. 6.15, has been developed and tested in laboratory settings.

Experiments were carried out to examine the pulsed load response and operation in

scaled stand-alone PV-battery power system. The parameters of main components are

summarized in Table 6.3. The currents of the Lead-acid battery and SC and Lithium-ion

battery were measured and recorded using current sensors (ACS712) and microcontroller

(ATMEGA328P).

Table 6.3 Experiment testbed parameters

Parameters Lead-acid Only Light Mode Heavy Mode

PV panel peak power 15W

Daily load energy consumption 0.4 kWh

Lead-acid Battery nominal voltage 12V

Lead-acid Battery capacity 30Ah

SC Capacitance - 25F

SC Equivalent Series Resistance - 0.001Ω

Lithium-ion Battery nominal voltage - - 12V

Lithium-ion Battery capacity - - 2Ah


Fig. 6.16 depicts the experimental setup and circuit diagram for pulsed load response.

Two bidirectional buck-boost DC/DC converters interfacing SC and Lithium-ion battery

modules are implemented and parallel connected with the Lead-acid battery through DC

bus. The periodic pulsed load is generated using a programmable DC electronic load

(BK Precision BK8500). The period, amplitude and duty ratio of the pulsed load are set

to 120s, 1 ampere, and 50%, respectively. The EMS uses the strategies in Figs. 6.3 and

6.4 which determine the operation mode, plug-in status and controls the signals to the

DC/DC converters in SHESS plug-in module. The testing is executed in the campus of

Swinburne University, Sarawak, Malaysia.


- 142 -

Fig. 6.16 Experimental setup and circuit diagram for the pulse load testing

Figs. 6.17 and 6.18 show the pulsed load responses of SHESS plug-in module under

light mode (Fig. 6.17) and heavy mode (Fig. 6.18). In light mode (Fig. 6.17), the SC

current responses rapidly to supply the step change in load, while the Lead-acid battery

current responses gradually towards the demanded current.

100 200 300 400

-1

-0.5

0

0.5

1

1.5

2

Time (s)

Cur

rent

(A)



Fig. 6.17 Experiment results of the pulse load testing (light mode)

Lead-acid Battery

SC

Pulse Load

Ls

Cbus

Sw1 Sw2Plug-in Control

ISC(ref)

IBatt

Li-ion

Ls

Cbus

Sw3 Sw4Mode

Control

EMS(Figs.6.3~6.4)

ILi-ion(ref)

Sw1 Sw2 Sw4Sw3

Plug-in Control

ModeControl


- 143 -

In heavy mode (Fig. 6.18), the SC picked up the step current load as expected, while the

Lithium-ion battery module supplies a portion of the remaining load demand. The Lead-

acid battery current responses gradually and a portion of the demanded current is shared

the Lithium-ion battery module. Thus, the experimental results of the pulse load response

demonstrate the power-sharing capability of the SHESS plug-in module and verify the

simulation outcomes as presented in Figs. 6.6 and 6.7.

700 800 900 1000 1100

-1

-0.5

0

0.5

1

1.5

2

Time (s)

Curr

ent (

A)

(a) LA Battery(b) Li-ion Battery(c) SC(d) Pulse Load


Fig. 6.18 Experiment results of the pulse load testing (heavy mode)

6.4.2 Operation in the lab-scale stand-alone PV-battery power system

To further verify the feasibility of the SHESS plug-in module, a down-scaled stand-alone

PV-battery power system with SHESS plug-in module prototype was developed and

tested under actual operating conditions. The experimental setup is shown in Fig. 6.19.

The PV-battery power system includes a 15Wp PV panel, a charge controller with MPPT,

a 12V 30Ah Lead-acid battery, and a programmable DC electronic load to emulate the

load profile. The charge controller is kept connected the PV panels and Lead-acid

battery by default. The plug-in module with two buck/boost DC/DC converters is

connected, and EMS controls the operation mode according to the weather conditions.

The currents of the PV panel, load, Lead-acid battery, SC and Lithium-ion battery were

measured and logged by using the NI USB-6008 data acquisition device for 24-hours.


- 144 -

Fig. 6.19 Experimental setup and circuit diagram for the case study

The experiment was carried out on five different days in Swinburne University (Sarawak

Campus), Kuching, Malaysia, one day for battery only testing, two days for plug-in

testing and two more days for one-day testing.

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Curr

ent (

A)

(a) LA Battery(b) Plug-in Module(c) PV-Load

Fig. 6.20 Experiment results of the stand-alone PV-battery power system

MPPT

Lead-acid Battery AC Loads

Solar Irradiance

ipv

vpv

Charge Controller

Dpv

SC

Ls

Cbus

Sw1 Sw2

Plug-in Control

ISC(ref)

IBatt

Li-ion

Ls

Cbus

Sw3 Sw4Mode

Control

EMS(Figs.6.3~6.4)

ILi-ion(ref)

Sw1 Sw2 Sw4Sw3

inverter

DC Loads

Plug-in Control

Mode Control

Weather Condition


- 145 -

As a reference, the stand-alone PV-battery power system is operated independently in 24

hours in a partly cloudy day. The Lead-acid battery current profile and power demand

PPV-Load are illustrated in Fig. 6.20, and solar irradiance is available from 8:00 AM- 17:00

PM It is seen that high transient currents, varies from ± 1.5A, are drawn from the Lead-

acid battery, which considerably reduces lifetime.

(1) Retrofittability test

In order to demonstrate the ease of being retrofit on existing installed PV-battery power

system, the SHESS plug-in module was activated at 12 PM during the experiment. Figs.

6.21 and 6.22 show the current profiles of the Lead-acid battery and SHESS plug-in

module. It is noted that the power-sharing process is initiated when the SHESS plug-in

module was activated after 12 pm, and the Lead-acid battery current is significantly less

current fluctuation. In the sunny day (Fig. 6.21), the SHESS plug-in module operated

under light mode with SC current fluctuated between -0.5A to 1A. The SC module

responded to the fluctuating current between 12 PM to 3 PM, while remain inactivated

when the current demand was relatively stable during night time.

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Cur

rent

(A)



Fig. 6.21 Experimental plug-in testing of SHESS plug-in module (light mode)


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On the cloudy day, as shown in Fig. 6.22, the current demand (IPV - ILoad) varied heavily

with rich harmonic. Despite the massive current fluctuation, the SC maintained a current

variation of within 1A, while the Lithium-ion battery module shared part of the

intermediate frequency current fluctuation. This allows the SHESS plug-in module to

perform equally well with the same SC capacity installed. In addition, the Lithium-ion

battery module shared 5% of total power demand with the weight factor W1 set to 0.95,

which reduces the C-rate of the Lead-acid battery. The experimental results show that the

SHESS plug-in module can reduce the Lead-acid battery stress through properly

controlled power allocation.

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Cur

rent

(A)



Fig. 6.22 Experimental plug-in testing of SHESS plug-in module (heavy mode)

(2) Daily operation in stand-alone PV-battery power system

The SHESS plug-in module is tested while connecting to the DC bus by running 24

hours in both operations, sunny and cloudy, as shown in Figs. 6.23 and 6.24 respectively.

With the integration of the SHESS plug-in module, the Lead-acid battery current

fluctuations are suppressed noticeably in both weather scenarios. In Fig. 6.23, SC

module absorbed most of the high peak currents variations in a range of -0.5 A to 1A. In

Fig. 6.24, the solar irradiance varies much heavy than in sunny mode and leads more

power demand fluctuations with rich surge peak current components. The Lithium-ion


- 147 -

battery shared a portion of total power demand in a moderate variation frequency, and

the SC still operated in the same range as in sunny mode. Accordingly, it is evident that

the SHESS plug-in module works appropriately and efficiently releases the Lead-acid

battery operate stress all over the day.

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Cur

rent

(A)


Fig. 6.23 Experimental one day testing of SHESS plug-in module (light mode)

0 4 8 12 16 20 0-1.5

-1

-0.5

0

0.5

1

1.5

Hours of Day

Cur

rent

(A)


Fig. 6.24 Experimental one day testing of SHESS plug-in module (heavy mode)


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6.5 Conclusion

Typical stand-alone PV power systems with the Lead-acid battery are widely installed in

remote areas. Typical charge controllers lack the considerations the life-limiting factors

of the battery such as C-rate, DoD and current fluctuations, which are commonly

encountered in stand-alone power systems. This chapter proposed a SHESS plug-in

module that is retrofittable to existing installed PV-battery power systems to mitigate

battery stress by absorbing the damaging current profile. Two operation modes are

designed to face the sunny and cloudy weather conditions. Matlab Simulink model of the

SHESS plug-in module has been developed and simulated to investigate the power-

sharing capability. Battery health cost analysis is presented to qualitatively evaluate the

improvement in battery health and reduction in system operating cost. The analysis

shows that after installing the SHESS plug-in module, the annual operating cost can be

reduced up to 53.7%. A lab-scale prototype of the SHESS plug-in HESS was constructed

and tested under the same lab-scaled stand-alone PV-battery power system in Chapter 3,

including the pulse load test, plug in/off test, and one-day test. The experiment results

demonstrated the ease of being integrated into existing installed PV-battery power

system and were in good agreement with the simulation results.

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Chapter 7

Conclusion and Future Work

7.1 Summary

Energy storage technology provides a way to increase grid flexibility and enable the

integration of intermittent, non-distributable renewable power generations. In Chapter

2, an overview of the state-of-the-art in energy storage technologies was presented,

including CAES, FES, PHS SC, SMES, cell battery, flow battery, TES, and chemical

energy storage. Their operation principles, technical performance, and economic aspects

and the current research and development status were discussed, classified, compared

and analyzed. Following this, a comprehensive comparison and potential applications

analysis of the reviewed technologies were presented systematically to illustrate the

advantages and limitations of various energy storage technologies in the view of

different perspectives. Selection criteria and consideration that intends to help decision

makers and researchers to select suitable energy storage technologies in specific

applications was presented. The future development trend of different energy storage

technologies was discussed.

The PV-based power system is seen to be a promising renewable energy technology in

the off-grid applications in remote areas. Chapter 3 presents an introduction of solar

cells and the core components in a typical PV power system. The design methodology,

including site evaluation, solar irradiance measurement, load profile estimation, and

system analysis were presented. Based on the collected data of solar irradiance and the

estimated load profile, dynamic modelling and simulation of a stand-alone PV-battery

power system were conducted in Matlab Simulink. The results show the typical

Chapter 7 Conclusion and Future Work

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behaviour of the battery charge-discharge process under off-grid operation. With actual

data of solar irradiance and estimated load profiles, the battery current is demonstrated

to behave under severely fluctuating conditions, which tends to accelerate the

performance deterioration and aging process of the Lead-acid battery bank.

Hybridization of battery and supercapacitor has been reported to be one of the effective

ways to mitigate the operational stress on the Lead-acid battery, leading to enhanced

lifetime characteristics. Energy storage elements of different electrical and lifetime

characteristics complement each other up and down during the operation and thus

optimize the operation and longevity. Chapter 4 reviews different HESSs, including the

system topology, control strategies, and the associated energy management system.

Their corresponding characteristics, advantages, disadvantages and possible

applications in stand-alone PV power system were discussed and compared.

A feasibility study is presented in Chapter 5 and HESS topologies, and associated

power allocation strategy that is practically applied to remote microgrid were identified,

considering their system complexity, technical merits, and limitations. Theoretical

analysis and numerical simulation in Matlab Simulink for the selected Battery-SC

HESS were carried out, and their effectiveness in mitigating battery stress was

investigated and compared. The simulation analysis and results have been verified by

experiments with the developed prototype of hybrid energy storage system under

consideration. A battery healthy cost function that quantitatively evaluates the impact of

the damaging factors on the Lead-acid battery was proposed in this work. This allows

the effectiveness of different HESSs in mitigating battery operation stress to be

evaluated and compared. Furthermore, the proposed battery health cost model can be

used to analyze the economic improvement of the stand-alone PV power system by

estimating the extension in battery service life as a result of stress mitigation. Their

performances and effectiveness in stress mitigation were examined with a lab-scaled

prototype of HESS under consideration. The experimental outcomes verify the

theoretical analysis and simulation results done in this work. Simulation results, battery

health cost and financial analyses, and empirical outcomes suggest that the combination

of active secondary energy storage with the passive primary battery could be the

optimal setting for standalone PV power system applications.


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To leverage on existing infrastructure in installed stand-alone PV-battery power system,

a smart HESS plug-in module (SHESS) was proposed in Chapter 6 to complement the

ESS operation. The design and development of the retrofittable SHESS module were

systematically presented, including the control system and power allocation strategy

used. The effectiveness of the proposed SHESS plug-in module in mitigating battery

operation stress was simulated in Matlab Simulink with actual solar irradiance data and

estimated load profile for a rural community in Sarawak Malaysia. Based on the

simulation results, the battery healthy cost analysis suggests that the battery lifetime can

be enhanced through mitigating damaging loading current to the primary battery bank,

hence reduction in overall system operating cost can be achieved. The analysis shows

that after installing the SHESS plug-in module, the annual operating cost can be reduced

up to 53.7%. The prototype of the proposed SHESS was constructed, and the

experimental results demonstrate the feasibility of being integrated into existing

installed PV-battery power system and the effectiveness of the proposed SHESS are in

good agreement with the simulation outcomes.

7.2 Future work

In this study, Battery-SC HESSs were implemented in stand-alone PV power system to

mitigate the primary Lead-acid battery operating stress factors. Throughout the research

and development work, progress was made in discovering promising directions which

could not be achieved because of time and/or scope considerations. Based on the

presented research work in this thesis, the following suggestions are provided for further

explorations:

(1) EMS optimization with SoC and temperature management

In this work, the design of EMS mainly focuses on power allocation strategy and

power-sharing capability without accurately evaluating and managing the real-time SoC

of energy storage devices and considering the effect of battery temperature into the

optimization processes. In EMS, the estimation and management of battery SoC, as well

as temperature monitoring, are also two of the essential aspects to be addressed. These

two parameters should be accurately monitored and controlled for performance


- 152 -

optimization. SoC estimation can be achieved by complementing sophisticated

approximation algorithms, empirical and statistical data. Therefore, one of the main

focuses of the extension of this work will be to devise an accurate and reliable battery

SoC estimation method that will serve as a useful parameter for the battery performance

optimization process. Moreover, at the same time, take into consideration of battery

temperature into the EMS and performance optimization process.

(2) HESS prototype in actual sized PV-battery power system

The proof-of-concept prototypes of the proposed SHESS plug-in module were

developed and tested in the lab-scaled facility. Research and development work can be

extended to design and develop an actual working prototype of the proposed SHESS

and to be tested in the real PV-battery power system to verify the feasibility of the

proposed system further. In addition, proof-of-concept to commercialization will be one

of the main aims of this research work.

(3) Demand-side management and load prediction

In this study, energy storage in HESS is designed to absorb/supply the power mismatch

between renewable energy sources and load in real-time, without considering demand-

side management such as load forecasting and demand response. If the load forecast can

be included in the energy management scheme, the power allocation strategy among

energy storage elements can be pre-determined (in a more optimal way) rather than

passively responding to it. For HESS design, future works include integration of

demand-side management into the optimization process, and daily/hourly load

prediction algorithm shall be considered in order to improve the efficiency and

sustainability of the overall system further.

(4) Battery healthy cost function extension

The battery health cost function proposed in this study considered five major life-

limiting factors that would have a negative impact on battery life. Exploration of other

internal or external factors that contribute to the aging process of battery should be one

of the major direction of the extension of this work. This would significantly improve


- 153 -

the model accuracy of the complex lifetime characteristic of battery and thus, providing

a more realistic battery state-of-health assessment.

(5) Battery lifetime characteristic study and evaluation

The Battery-SC HESSs in this study are designed to mitigate the operating pressure of

Lead-acid battery in stand-alone PV power systems and thus extend battery lifetime.

However, the exact extended lifetime of the Lead-acid battery under HESS is not given

in this study. Future work could involve organizing a series of experiments to assess the

Lead-acid battery operation lifetime in different HESS topologies, which provides

reliable evidence on battery lifetime extension and economic improvement with HESS

and thus commercialization can be realized.

- 154 -

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Appendix

A.1. Case study

Stand-alone PV-battery Power System for Batang Ai National Park Headquarter

(BANP)

A.1.1 Existing power generation and distribution system in BANP

Two major buildings are supplied with electricity namely the main office and Barrack A

(staff quarter) as shown in Fig. A.1.

Barrack A(User 1 - 6)

Main

Office

Main Office Distribution box

Barrack A Distribution Box

N

Genset Room

Main Distribution box

Fig. A.1 Layout of BANP and current electricity supply system

Barrack A is a longhouse-liked terrace that houses 6 families in total. Currently, a

5.6KVA petrol generator is used to supply electricity in BANP. In normal day, the

generator will be operated on average 3 hours in between 9am-1pm to carry out routine

Appendix

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work on PC, and 6pm-11pm for basic electricity needs for the residents in Barrack A.

Their primary source of power is diesel generators and can only be started at specific

times. Based on the power consumption data collected during site visit on 28 March

2016, the power consumption pattern of the 2 main buildings is shown in Fig. A.2. The

barrack A consumes on average 1.4KW at night while the office consumes an additional

300W-500W on top of the Barrack A. A summary of electrical appliances that are

currently in use are tabulated in Table A.1.

Table A.1 Electrical appliances (currently in-use) of main office and barrack A

User Lighting

(20W) Fan Entertainment

Portable

Device

Refrigerator

/ Freezer

PC

workstation

Office 5 1 - - - 3

1 5 1 1 Radio 5 2-door -

2 5 1 1 TV + Astro 5 Freezer -

3 4 - - - - -

4 5 1 1 TV + Astro 3 - -

5 4 - - 1 - -

6 4 1 - 1 1-door -

As can be seen from the graph in Fig. A.2, the average load consumption is

approximately half the rated power of the petrol generator.

Time (hour)

Pow

er (k

W)

0 4 8 12 16 20 24

1

2

5.6KVA Petrol GensetEstimated fuel consumption (½ load) = 2.1L/hr Estimated fuel consumption (rated load)= 2.9L/hrEstimated fuel price = RM 2.80 / L (including transportation)

~4 Rated Load (Genset)

GENSET OFF

GENSET ON

Estimated load Power

Estimated daily fuel consumption ~= 16L (RM45)

Lite

r (L)

5

10

15

20

Estimated Fuel Consumption

BarrackA Barrack

A

Office

Fig. A.2 Existing power consumption pattern of BANP

Appendix

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Based on the estimated fuel consumption of 2.1L/hour at 50% of rated load, the site is

consuming on average 16L of petrol which cost about RM45 for one ordinary day (not

including the transportation cost of petrol).

A.1.2 Proposed solar-battery-generator hybrid power system

(a) Stand-alone PV power system deign

Based on the power usage survey conducted, the site is estimated to consume on average

10kWh of energy per day. This power consumption estimation includes basic electricity

needs for barrack A and lighting consumption of the main office only. To provide

sufficient energy to this demand, a 2KWp solar array is proposed to generate on average

90% of the demand (based on average 4.5 sun hours), and the existing generator will be

used to top up the remaining 10% of the energy demand. Deep cycle Lead-acid battery

bank rated at 48V 600Ah will be used as the intermediate buffer for imbalance

generation and load. Fig. A.3 shows the design of the proposed stand-alone PV based

microgrid system to be implemented in BANP.

Barrack A

Main

Office

Proposed 2kWp Solar array Installation Site (S-1)

MPPT Charge Controller (48V 45A ~2.2KW)Battery (Lead Acid 48V 600Ah)Inverter/charger ~3KVA (D-1)

Main Office Distribution box (D-2)

Barrack A Distribution Box (D-3)

N

B

Genset Room

G

5.6KVA Petrol Generator (S-2)

(C-1)

(C-2)

(C-3)

Fig. A.3 Physical settings of the proposed system

Table A.2 summarized the required equipment and cables. Fig. A.4 presents the

connection diagram of the stand-alone PV power system.

Appendix

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Table A.2 Solar installation checklist

S-2

D-1S-1

D-3

Solar Array8 X 250Wp (4S 2P)Peak Power: 2kWVmp: 121.36VImp: 16.48A

+-

Charge ControllerSystem Voltage: 48VMax Current: 45A

PV+

PV- BAT-BAT+

Lead Acid Battery 48V 600Ah24 X (12V 100Ah)Configuration: 4S 6P

Inverter / ChargerRated Power: 3KVASurge Power: 6KVACharge Current: ??

DC+

DC-

AC Out+

AC Out

-

D-2

+-

Combiner Block

2P MC

B

(20A)

Ground Busbar

1P MC

B

(50A)

1P MC

B

(50A)

Neutral LinkNeutral Line

13A

5A 5A

RC

D

20A

Barrack A

LightingB

arrack A Socket

Main O

ffice Lighting

AC In+

AC In-

C-2

C-1

Petrol GeneratorRated Power: 5.6kVA

+ -

C-3

13AM

ain Office Equipm

ent Fig. A.4 Connection diagram of the proposed system

Sub-system Device / Equipment Parameters / Specifications / Configuration

S-1

Promelight Polycrystalline solar panel (PT-

P660250BB)

8 x 250Wp

Array configuration: 4S 2P

Vmp = 121.36V

Imp = 16.48A

S-2 Petrol Genset (existing) Rated Power: 5.6KVA

C-1 DC cable Cable length: 25m

Cable size: 5AWG (16mm2)

C-2 AC Cable Cable length: 80m

C-3 AC Cable Cable length: 30m

D-1

Morning Star TriStar MPPT Charge

Controller

Max Current: 45A

Max Solar Voltage: 150V

Max solar power: 2.2kW (48V System)

Lead-acid Battery 24 x (12V 100Ah) Lead-acid Battery (4S 2P)

Total Capacity: 28.8kWh

OPTI Inverter / Charger

MCB 2-pole DC MCB:

1-pole DC MCB:

D-2

Main circuit breaker

RCD

Line circuit breaker

D-3 Line circuit breaker

Appendix

- 176 -

(b) Photos of on-site installation

Fig. A.5 PV panels installation and research team

Fig. A.6 Control system installation (MPPT + OPTI-Solar Controller)

Appendix

- 177 -

Fig. A.7 Main switches installation (Load+PV+Battery+Controller)

Fig. A.8 2400Ah PK200-12 Lead-acid battery bank Installation

Appendix

- 178 -

Fig. A.9 Actual load profile collection

A.2. Buck-boost DC/DC converter control and codes

A.2.1 The hardware structure

Fig. A.10 illustrates the system architecture diagram of the DC-DC converter system

that is based on a digital control strategy. The system includes the main power circuit,

the interface circuit, and the control circuit which incorporates the microprocessor and

external devices.

Peripheral(Power Supply,Computer, etc.)

DSPTMS320F28335

DAC or IO Peripheral Circuit

ADCSampling

Circuit

PWMPeripheral Circuit

Driver Circuit

Main Power Circuit

Interface Circuit

Fig. A.10 The system architecture diagram of DC/DC converter

Appendix

- 179 -

The main power circuit is composed of DC/DC converters and energy storage devices.

The interface circuit consists of three main parts. The first part is the Digital to Analog

Converter (DAC) and a peripheral circuit which contain IO ports. The second part

contains the external circuits for receiving the PWM signal and the corresponding drive

circuits. The third part is the feedback loop which consists of an Analog to Digital

Converter (ADC) and its sampling circuits. The interface circuit acts as a bridge to

connect the main power circuit to the microprocessor. The digital signal from the

microprocessor will be converted into an analog signal, and then it is used to control the

main power circuit. In turn, the analog signal from the main power circuit will be

sampled and converted into a digital signal, which is then fed back to the

microprocessor. The microprocessor will adjust the output control signal automatically

and finally improve the stability of the entire system. The prototype of the buck-boost

DC/DC converter is shown in Fig. A.11.

Fig. A.11 The bidirectional buck-boost DC/DC converter

A.2.2 Control code in Arduino microprocessor

(a) Code for Semi-Active SC HESS Control

#include <PID_v1.h>

#include <PID_buck.h>

#include <PID_boost.h>

const int pwm_in = 5;

const int pwm_sd = 6;

const int current_pin = 0;

Appendix

- 180 -

const int current1_pin = 1;

double amps_raw = 0; // value from acs712

double amps_actual = 0; // actual current

double amps_desired = 0; // desired current

double amps_desired_last = 0;

double amps_diff = 0;

double pwm_dc = 0; // dc = duty cycle

double pwm_dc_last = 0;

int i = 0;

int a = 0;

int b = 0;

const int sampleTime = 1;//1714*2

const int numReadings = 20 ;

double readings[numReadings];

int readIndex = 0;

double total = 0;

double average = 0;

double aggKp = 0.5, aggKi = 50, aggKd = 0;

double consKp = 0.5, consKi = 50, consKd = 0;

PIDBUCK buckPID(&amps_actual, &pwm_dc, &amps_desired, consKp, consKi, consKd,

DIRECT);

PIDBOOST boostPID(&amps_actual, &pwm_dc, &amps_desired, consKp, consKi, consKd,

DIRECT);

void setup(){

// Change prescale to 1 for max PWM frequency - 62.5kHz

TCCR0B = (TCCR0B & 0b11111000) | 0x01;

// Declaring pin to be input or output

pinMode(current_pin, INPUT);

pinMode(current1_pin, INPUT);

amps_actual = 0;

Appendix

- 181 -

amps_desired = 0;

pwm_dc = 0;

i = 0;

Serial.begin(9600);

buckPID.SetMode(AUTOMATIC);

boostPID.SetMode(AUTOMATIC);

buckPID.SetOutputLimits(125,255);

boostPID.SetOutputLimits(105,255);

for (int thisReading = 0; thisReading < numReadings; thisReading++){

readings[thisReading] = 0;

}

}

void loop(){

amps_actual = ((510 - analogRead(current_pin)) * (5/0.185) / 1024)*(-1);

amps_diff = ((509 - analogRead(current1_pin)) * (5/0.1) / 1024);

if (i < sampleTime){

i++;

} else if (i == sampleTime){

total = total - readings[readIndex];

readings[readIndex] = amps_diff;

total = total + readings[readIndex];

readIndex = readIndex + 1;

if (readIndex >= numReadings){

readIndex = 0;

}

i = 0;

}

amps_desired = amps_diff - (total / numReadings);

amps_desired = (amps_desired + amps_desired_last) / 2;

Appendix

- 182 -

amps_desired_last = amps_desired;

double gap = abs(amps_desired - amps_actual);

if (gap < 0.5){

buckPID.SetTunings(consKp, consKi, consKd);

boostPID.SetTunings(consKp, consKi, consKd);

} else{

buckPID.SetTunings(aggKp, aggKi, aggKd);

boostPID.SetTunings(aggKp, aggKi, aggKd);

}

if (amps_desired > 0){

if (amps_desired <= 0.04 && a == 0){

a = 1;

} else if (amps_desired <= 0.1 && a == 1){

if (amps_desired > 0.07){

a = 0;

}

} else {

TCCR0A = TCCR0A & 0b11101111;

buckPID.Compute();

Serial.print("buck ");

}

} else if (amps_desired < 0){

if (amps_desired >= -0.04 && b == 0){

b = 1;

} else if (amps_desired >= -0.1 && b == 1){

if (amps_desired < -0.07){

b = 0;

}

} else {

TCCR0A = TCCR0A | 0b00110000;

boostPID.Compute();

Serial.print("boost ");

}

}

Appendix

- 183 -

if (pwm_dc < 0){

pwm_dc = 0;

}

if (pwm_dc > 253){

pwm_dc = 254;

}

analogWrite(pwm_in, pwm_dc);

analogWrite(pwm_sd, pwm_dc);

Serial.print(amps_desired,5);

Serial.print(",");

Serial.print(amps_actual,5);

Serial.print(",");

Serial.print(amps_diff,5);

Serial.print(",");

Serial.println(pwm_dc);

}

(b) Code for SC/Lithium-ion HESS Control

#include <PID_buck.h>

#include <PID_boost.h>

// Inverted signal on IN of gate driver

// Non-inverter signal on SD of gate driver

// COMnA1 | COMnA0 | COMnB1 | COMnB0

const int pwm_6 = 6; // OC0A

const int pwm_5 = 5; // OC0B





const int current_converter_sc_pin = 0;

const int current_converter_batt_2_pin = 2;

const int current_hess_pin = 1;

Appendix

- 184 -

double amps_hess = 0; // pv - load current

double amps_batt_1 = 0;

float wf = 0.9; // weight factor

// For supercap converter

double amps_converter_sc = 0; // actual current amps_actual

double amps_sc = 0; // desired current amps_desired

double amps_sc_last = 0;

double pwm_sc = 0; // dc = duty cycle

int a = 0;

int b = 0;

// For battery 2 converter

double amps_converter_batt_2 = 0; // actual current in converter battery 2

double amps_batt_2 = 0; // desired battery 2 current

double amps_batt_2_last = 0;

double pwm_batt_2 = 0; // pwm signal for converter battery 2

int c = 0;

int d = 0;

// For battery 1 averaging (primary)

const int sampleTime_batt_1 = 1/0.055*180; // 1/0.055 = 1s

const int numReadings_batt_1 = 20 ;

double readings_batt_1[numReadings_batt_1];

int readIndex_batt_1 = 0;

double total_batt_1 = 0;

int i = 0;

// For battery 2 averaging (secondary)

const int sampleTime_batt_2 = 1/0.055*60;// 1/0.055 = 1s

const int numReadings_batt_2 = 20 ;

double readings_batt_2[numReadings_batt_2];

int readIndex_batt_2 = 0;

double total_batt_2 = 0;

int j = 0;

Appendix

- 185 -

unsigned int time = 0;

double aggKp = 0.5, aggKi = 50, aggKd = 0;

double consKp = 0.5, consKi = 50, consKd = 0;

PIDBUCK buck_sc_PID(&amps_converter_sc, &pwm_sc, &amps_sc, consKp, consKi, consKd,

DIRECT);

PIDBOOST boost_sc_PID(&amps_converter_sc, &pwm_sc, &amps_sc, consKp, consKi,

consKd, DIRECT);

PIDBUCK buck_batt_2_PID(&amps_converter_batt_2, &pwm_batt_2, &amps_batt_2, consKp,

consKi, consKd, DIRECT);

PIDBOOST boost_batt_2_PID(&amps_converter_batt_2, &pwm_batt_2, &amps_batt_2,

consKp, consKi, consKd, DIRECT);

void setup() {

// Change prescale to 1 for max PWM frequency - 62.5kHz

// TCCR0B = (TCCR0B & 0b11111000) | 0x01;

TCCR1A = (TCCR1A & 0b11111100) | 0b00000001; // _BV(WGM10)

TCCR1B = (TCCR1B & 0b11100000) | 0b00001001; // _BV(WGM12) | _BV(CS10)

TCCR2A = (TCCR2A & 0b11111100) | 0b00000011; // _BV(WGM21) | _BV(WGM20)

TCCR2B = (TCCR2B & 0b11110000) | 0b00000001; // _BV(CS20)

// Declaring pin to be input or output

pinMode(current_converter_sc_pin, INPUT);

pinMode(current_converter_batt_2_pin, INPUT);

pinMode(current_hess_pin, INPUT);

amps_converter_sc = 0;

amps_sc = 0;

pwm_sc = 0;

i = 0;

Serial.begin(9600);

buck_sc_PID.SetMode(AUTOMATIC);

boost_sc_PID.SetMode(AUTOMATIC);

buck_batt_2_PID.SetMode(AUTOMATIC);

Appendix

- 186 -

boost_batt_2_PID.SetMode(AUTOMATIC);

buck_sc_PID.SetOutputLimits(0, 255);

boost_sc_PID.SetOutputLimits(0, 255);

buck_batt_2_PID.SetOutputLimits(0, 255);

boost_batt_2_PID.SetOutputLimits(0, 255);

buck_sc_PID.SetTunings(consKp, consKi, consKd);

boost_sc_PID.SetTunings(consKp, consKi, consKd);

buck_batt_2_PID.SetTunings(consKp, consKi, consKd);

boost_batt_2_PID.SetTunings(consKp, consKi, consKd);

for (int thisReading_batt_1 = 0; thisReading_batt_1 < numReadings_batt_1;

thisReading_batt_1++) {

readings_batt_1[thisReading_batt_1] = 0;

}

for (int thisReading_batt_2 = 0; thisReading_batt_2 < numReadings_batt_2;

thisReading_batt_2++) {

readings_batt_2[thisReading_batt_2] = 0;

}

}

void loop() {

time = micros();

amps_converter_sc = ((512 - analogRead(current_converter_sc_pin)) * (5 / 0.185) / 1024) * (-

1);

amps_converter_batt_2 = ((512 - analogRead(current_converter_batt_2_pin)) * (5 / 0.185) /

1024) * (-1);

amps_hess = ((509 - analogRead(current_hess_pin)) * (5 / 0.1) / 1024);

if (i < sampleTime_batt_1) {

i++;

} else if (i == sampleTime_batt_1) {

total_batt_1 = total_batt_1 - readings_batt_1[readIndex_batt_1];

readings_batt_1[readIndex_batt_1] = amps_hess;

total_batt_1 = total_batt_1 + readings_batt_1[readIndex_batt_1];

readIndex_batt_1 = readIndex_batt_1 + 1;

Appendix

- 187 -

if (readIndex_batt_1 >= numReadings_batt_1) {

readIndex_batt_1 = 0;

}

i = 0;

}

amps_batt_1 = (total_batt_1/numReadings_batt_1) * wf;

if (j < sampleTime_batt_2) {

j++;

} else if (j == sampleTime_batt_2) {

total_batt_2 = total_batt_2 - readings_batt_2[readIndex_batt_2];

readings_batt_2[readIndex_batt_2] = amps_hess - amps_batt_1;

total_batt_2 = total_batt_2 + readings_batt_2[readIndex_batt_2];

readIndex_batt_2 = readIndex_batt_2 + 1;

if (readIndex_batt_2 >= numReadings_batt_2) {

readIndex_batt_2 = 0;

}

j = 0;

}

amps_batt_2 = (total_batt_2/numReadings_batt_2);

amps_sc = amps_hess - amps_batt_1 - amps_batt_2;

amps_sc = (amps_sc + amps_sc_last) / 2;

amps_sc_last = amps_sc;

// PI

if (amps_sc > 0) {

if (amps_sc <= 0.04 && a == 0) {

a = 1;

} else if (amps_sc <= 0.1 && a == 1) {

if (amps_sc > 0.07) {

a = 0;

}

Appendix

- 188 -

} else {

buck_sc_PID.Compute();

output_pwm_sc();

TCCR1A = TCCR1A & 0b10111111;

}

} else if (amps_sc < 0) {

if (amps_sc >= -0.04 && b == 0) {

b = 1;

} else if (amps_sc >= -0.1 && b == 1) {

if (amps_sc < -0.07) {

b = 0;

}

} else {

boost_sc_PID.Compute();

output_pwm_sc();

TCCR1A = TCCR1A | 0b11000000;

}

}

if (amps_batt_2 > 0) {

if (amps_batt_2 <= 0.04 && c == 0) {

c = 1;

} else if (amps_batt_2 <= 0.1 && c == 1) {

if (amps_batt_2 > 0.07) {

c = 0;

}

} else {

buck_batt_2_PID.Compute();

output_pwm_batt_2();

TCCR2A = TCCR2A & 0b10111111;

}

} else if (amps_batt_2 < 0) {

if (amps_batt_2 >= -0.04 && d == 0) {

d = 1;

} else if (amps_batt_2 >= -0.1 && d == 1) {

if (amps_batt_2 < -0.07) {

d = 0;

Appendix

- 189 -

}

} else {

boost_batt_2_PID.Compute();

output_pwm_batt_2();

TCCR2A = TCCR2A | 0b11000000;

}

}

Serial.print(amps_sc, 5);

Serial.print(" ");

Serial.print(amps_converter_sc, 5);

Serial.print(" ");

Serial.print(amps_batt_2, 5);

Serial.print(" ");

Serial.print(amps_converter_batt_2, 5);

Serial.print(" ");

Serial.println(amps_hess, 5);

Serial.print(" ");

Serial.print(pwm_sc);

Serial.print(" ");

Serial.println(pwm_batt_2);

}

void output_pwm_sc(){

if (pwm_sc < 0) {

pwm_sc = 0;

}

if (pwm_sc > 253) {

pwm_sc = 254;

}

analogWrite(pwm_9, pwm_sc); // inverted

analogWrite(pwm_10, pwm_sc);

}

void output_pwm_batt_2(){

Appendix

- 190 -

if (pwm_batt_2 < 0) {

pwm_batt_2 = 0;

}

if (pwm_batt_2 > 253) {

pwm_batt_2 = 254;

}

analogWrite(pwm_3, pwm_batt_2);

analogWrite(pwm_11, pwm_batt_2); // inverted

}

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