project: activate
TRANSCRIPT
This project has received funding from the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 229)
Document Properties
Dissemination level Public
Author(s) G. Kryonidis, L. Kontis, A. Nousdilis, K. Pippi, A.
Boubaris
Reviewed by T. Papadopoulos, N. Papanikolaou
Checked by PI 17/06/2020
Submission due date 17/06/2020
Actual submission date 17/06/2020
Project: ACTIVATE
Deliverable Number: 1.1
Deliverable Name: Review of state-of-the-art
and technical solutions
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Document History
Version Date Contributor(s) Description
1.0 01/03/2020 Eleftherios Kontis First Draft
1.1 10/03/2020 Georgios Kryonidis Second Draft
1.2 16/03/2020 Angelos Nousdilis Third Draft
1.4 14/06/2020 Kalliopi Piipi Fourth Draft
1.5 15/06/2020 Alexandros Boubaris Final Draft
1.6 16/06/2020 Dimosthenis Peftitsis Comments to Final Draft
2.0 17/06/2020 Nick Papanikolaou Comments to Final Draft
3.0 17/06/2020 Theofilos Papadopoulos Final
List of Acronyms
Acronym Meaning
ADC Analog to Digital Converters
ADN Active Distribution Network
ANN Artificial Neural Network
APC Active Power Curtailment
ARMA Autoregressive Moving Average Model
ARMAX Autoregressive–moving-average Model with Exogenous Inputs
Model
AS Ancillary Services
BESS Battery Energy Storage Systems
CHB Cascade Half Bridge Inverter
COI Center of Inertia
DG Distributed generation
DRES Distributed Renewable Energy Sources
DMD Dynamic Mode Decomposition
DSC Digital Signal Processor-Based Controllers
DSP Digital signal processor
D-STATCOMs Distribution Static Compensators
DSM Demand Side Management
DSO Distribution System Operator
EFCC Enhanced Frequency Control Capability
EKF Extended Kalman Filter
ERA Eigenvalue Realization Algorithm
ES Exponential Smoothing
ESS Energy Storage System
ECKF Extended Complex KF
FACTS Flexible AC Transmission Systems
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FBEP Forward and Backward Extended Prony
FD Frquency-Domain
FFT Fast Fourier Transform
FLL Frequency locked-loop
FPGA Field Programmable Gate Arrays
GUI Graphical User Interface
HMI Human Machine Interface
HV High Voltage
IoT Internet of Things
LPF Low Pass Filter
LV Low Voltage
MCU Micro controller unit
MFLOPS Million Floating Point Operations
MIPS Million Instruction per Second
MP Matrix Pencil
MPC Model Predictive Control
MPP Maximum Power Point
MPPT Maximum Power Point Tracking
MV Medium Voltage
N4SID Subspace State Space System Identification
OLEC Oveload Emergency State Control
OLTC On Load Tap Changers
PCC Point of Common Coupling
PDC Phasor Data Concentrators
PEM Prediction Error Method
PEV Plug-in Electric Vehicles
PLC Programmable Logic Controller
PMU Phasor Measurement Unit
PR Public Report
PSO Particle Swarm Optimization
PLL Phase-Locked Loop
PV Photovoltaic
PWM Pulse Width Modulation
RES Renewable Energy Sources
RGA Real Coded Genetic Algorithm
RoCoF Rate of Change of Frequency
RPC Reactive Power Control
RT Real-time
RT-DTLR Real-Time Dynamic Thermal Line Rating
SiC Silicon carbite
SMD Surface Mounted Device
SOC State-of-charge
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STS Static Transfer Switches
SVD Singular Value Decomposition
TCSC Thyristor Controlled Series Capacitor
TD Time-Domain
TSO Transmission System Operator
UPFC Unified Power Flow Controller
VF Vector Fitting
VISMA Virtual Synchronous Machine
VPP Virtual Power Plant
VSG Virtual Synchronous Generators
VSM Virtual Synchronous Machines
WAMS Wide-Area Monitoring Systems
WoC Web of Cells
WP Work Package
ZCS Zero Current Switch
ZVS Zero Voltage Switch
ZIP Constant impedance, current and power load model
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Disclaimer: “This document has been prepared in the context of ACTIVATE project, funded by
the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I.
Research Projects to support Faculty members and Researchers and the procurement of high-
cost research equipment grant” (Project Number: 229). This document reflects only the
authors’ views and H.F.R.I. are not responsible for any use that may be made of the
information it contains.”
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TABLE OF CONTENTS
EXECUTIVE SUMMARY ................................................................................................................. 9
INTRODUCTION ................................................................................................................... 10
ANCILLARY SERVICES SOLUTIONS FOR DSOS AND TSOS ....................................................... 11
2.1. VOLTAGE REGULATION ................................................................................................................................. 11 2.1.1. Reactive power control for voltage regulation................................................................................................. 11 2.1.2. On load tap changers combined with reactive power control for voltage regulation ..................................... 12 2.1.3. Active power curtailment for voltage regulation ............................................................................................. 12 2.1.4. Energy storage systems and voltage regulation .............................................................................................. 13
2.2. VOLTAGE UNBALANCE MITIGATION ................................................................................................................. 15 2.2.1. Utilization of DG inverters ................................................................................................................................ 15 2.2.2. Utilization of flexible loads – demand response ............................................................................................... 16 2.2.3. Utilization of ESSs ............................................................................................................................................. 16 2.2.4. Utilization of static transfer switches ............................................................................................................... 17
2.3. OVERLOAD ALLEVIATION ............................................................................................................................... 17
2.4. POWER SMOOTHING .................................................................................................................................... 20
2.5. VIRTUAL INERTIAL RESPONSE ......................................................................................................................... 23
2.6. ESS SIZING FOR INERTIAL RESPONSE ............................................................................................................... 25
2.7. COORDINATED PRIMARY FREQUENCY RESPONSE ............................................................................................... 26
NETWORK MONITORING TECHNOLOGIES AND TECHNIQUES................................................ 28
3.1. NETWORK MONITORING TECHNOLOGIES AND TECHNIQUES ................................................................................. 28
3.2. IDENTIFICATION TECHNIQUES FOR MODAL ANALYSIS OF POWER SYSTEMS .............................................................. 30 3.2.1. Single-signal identification techniques ............................................................................................................. 30 3.2.2. Multi-signal identification techniques .............................................................................................................. 32
3.3. REAL-TIME ESTIMATION OF INERTIA TIME CONSTANTS ....................................................................................... 33
3.4. EQUIVALENT MODELS FOR ADN ANALYSIS ....................................................................................................... 34 3.4.1. Static equivalent models for ADN analysis ....................................................................................................... 35 3.4.2. Dynamic equivalent models for ADN analysis .................................................................................................. 37
POWER CONVERTER IMPLEMENTATIONS ............................................................................ 39
4.1. THREE-PHASE INVERTER REVIEW .................................................................................................................... 39
4.2. CONVERTER TOPOLOGIES .............................................................................................................................. 39 4.2.1. Three phase two level inverter topology .......................................................................................................... 40 4.2.2. CHB topology .................................................................................................................................................... 41
4.3. MICROPROCESSORS ..................................................................................................................................... 41 4.3.1. Real-Time digital control .................................................................................................................................. 41 4.3.2. Project evaluation cycle ................................................................................................................................... 42
4.4. DIGITAL-CONTROL AND ANCILLARY SERVICES ................................................................................................... 42 4.4.1. RoCoF measurement ........................................................................................................................................ 42 4.4.2. Power Smoothing ............................................................................................................................................. 42 4.4.3. Voltage Unbalance Mitigation ......................................................................................................................... 43 4.4.4. Voltage regulation ........................................................................................................................................... 43
4.5. ESS INTEGRATION ....................................................................................................................................... 44 4.5.1. DC/DC power converter for ESS integration ..................................................................................................... 44
4.6. COMMUNICATION ....................................................................................................................................... 45
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REFERENCES ............................................................................................................................... 47
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Table of figures
FIGURE 1: FLOWCHART OF OLEC SCHEME. .................................................................................................... 18
FIGURE 2: FLOWCHART OF THE MULTI-LEVEL METHOD [72]............................................................................... 19
FIGURE 3: FLOWCHART OF THE STEP-RATE CONTROL STRATEGY [9]. .................................................................... 22
FIGURE 4: A) THREE PHASE TWO LEVEL INVERTER, B) THREE PHASE TWO LEVEL INVERTER WITH ZVS [298]. ................ 40
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Executive summary Scope of this deliverable (D1.1) is to summarize the state-of-the-art, challenges and possible technical
solutions regarding the three key objectives of ACTIVATE. Specifically, the deliverable will be
published as a public report in the project website and provide details and possible solutions in the
following main areas:
• operation control and stability issues in active distribution networks (ADNs); incorporation of
new emerging technologies and ancillary services for control and operation applications
• modelling and analysis techniques of the dynamic performance of power systems; novel
architecture technologies for network monitoring in ADNs
• state-of-the-art solutions for power converter operation and control in ADNs
The deliverable concludes the work carried out in work package 1 (WP1) “Requirements engineering
and state-of-the-art”.
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Introduction The structure of this deliverable is divided into three chapters to cover the state-of-the-art and
requirements related to the three project key objectives, respectively.
Chapter 2: Ancillary services solutions
Within this chapter, state-of-the-art solutions for ADNs, including operation control and stability
issues, are examined. Of main importance is the incorporation of the new emerging technologies in
control applications. Scope of this chapter is to address the resulting needs and possible solutions for
tools to better observe, understand and operate ADNs.
Chapter 3: Network monitoring technologies and techniques
All state-of-the-art techniques and technologies considering network monitoring, measuring,
modelling and analysis with special emphasis to ADNs are examined.
Chapter 4: Power converter technologies
State-of-the-art for power converter operation and control in ADNs. The technical specifications
regarding the power control, communication, recording capabilities and implementation
requirements are examined.
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Ancillary services solutions for DSOs and TSOs A key objective of ACTIVATE is to develop novel control strategies, incorporating energy storage
systems (ESSs), in order to address technical issues related to the steady state operation of ADNs
(overvoltages, voltage unbalances, and overloading). Main target is to optimize the steady-state
operation of ADNs by exploiting coordinated, generalized, and straightforward control strategies,
with low communication requirements. Moreover, novel dynamic control functionalities, such as
virtual inertia and power smoothing techniques, are planned to be developed to ensure the stable
and reliable network operation of ADNs.
Therefore, state-of-the-art solutions for ADNs, including operation control and stability issues, must
be examined. Of main importance is the incorporation of new emerging technologies in control
applications. Scope of this Chapter is to address the resulting needs and possible solutions for tools
to better observe, understand and operate ADNs. Specifically, voltage regulation and voltage
unbalance mitigation techniques in low- and medium- voltage networks, already proposed in the
literature, are reviewed and discussed in Sections 2.1 and 2.2, respectively. A review of methods
regarding ESS sizing, overload alleviation, power smoothing and virtual inertial response solutions is
presented in Sections 2.3, 2.4, 2.5 and 2.6, respectively.
2.1. Voltage regulation The conventional distribution grids were designed to operate under a unidirectional power flow,
i.e. from the HV/MV transformer towards end users connected either at the medium voltage (MV)
or the low voltage (LV) level. However, the penetration of distributed generation (DG) units in
distribution grids resulted in a bidirectional power flow, causing overvoltages along the network and
affecting conventional voltage regulation methods. Several solutions have been proposed in the
literature to tackle voltage limit violations in distribution grids. Grid reinforcement is an effective
solution; however, the associated investment cost may be restrictive [1]. Alternative methods utilize
various controllable components of the grid to tackle overvoltages, including the inverters of DG
units. These strategies include the utilization of active transformers with on load tap changers (OLTC),
active power curtailment (APC) of DG production, reactive power control (RPC) employing
photovoltaic (PV) inverters capabilities and distribution static compensators (D-STATCOMs), ESSs and
demand side management (DSM) [1] - [4]. The most important methods are described below, using
the abovementioned categories.
2.1.1. Reactive power control for voltage regulation
Network voltage can be controlled by injecting/absorbing reactive power into/from the grid. Several
methods have been proposed for the exploitation of RPC capabilities of DG inverters and
D-STATCOMs. A simple RPC method is introduced in [5] for DG inverters aiming to mitigate voltage
rise caused by active power injections. A coordinated active power dependent RPC method (Q(P)) is
developed in [6] based on the sensitivity matrix of the network and the local active power injection.
The Q(V) droop of distributed DG units is optimally adjusted for the voltage regulation of radial
feeders through the strategy developed in [7], aiming to line losses minimization, by utilizing voltage
sensitivities of radial feeders. In [8], a two-level control strategy for the voltage control is proposed
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exploiting the reactive power of DG units. At local level fast disturbances of voltage can be mitigated,
while the centralized level is utilized to allocate the reactive power contribution of each DG unit.
A nearly decentralized voltage control is introduced in [9] for the optimal allocation of reactive power
contribution among generation units. The proposed strategy targets to the minimization of MV
network losses, while it can be combined with the operation of OLTC transformers for further
reduction of losses. The authors in [10] propose a distributed RPC provided by DG inverters and
assisted by shunt reactors in case of DGs sharing a common point of connection with the grid. A
control for D-STATCOM is proposed in [11] to mitigate voltage fluctuations of both positive and
negative sequence voltage.
It has to be mentioned that although RPC methods are designed for both LV and MV distribution
grids, the effectiveness of reactive power in voltage regulation is lower in LV networks [12]. This
happens due to the highly resistive line characteristics of LV networks [1].
2.1.2. On load tap changers combined with reactive power control for voltage
regulation
The utilization of active distribution transformers equipped with OLTC is an effective tool for voltage
regulation, thus various methods have been proposed. Note that usually HV/MV transformers are
equipped with OLTC, while off load tap changer transformers are installed in the LV network [1].
Therefore, most of the methods refer to the MV grid, while they usually combine the utilization of
RPC capabilities of DG units along with OLTC control.
In [13], a two-stage methodology is introduced to regulate voltage in unbalanced radial distribution
grids, by optimally coordinating OLTC and static VAr compensator. A coordinated control for OLTC,
voltage regulator and schedulable DG units minimizing the number of regulating actions is developed
in [14], for the voltage regulation of distribution feeders. Furthermore, in [15], the use of
transformers with OLTC and wind turbine RPC capabilities is exploited; scope of this system is to
regulate voltages of distribution grids. A coordinated control of OLTC and reactive power of
distributed PV inverters is proposed in [16], suitable to handle different voltage conditions in multi-
feeder networks. Moreover, in [17], optimal voltage regulation is achieved by coordinating the RPC
of DG units combined with OLTC operations. Specifically, a methodology is proposed to solve the bi-
objective optimization problem of two conflicting objectives, i.e. minimization of power losses and
tap actions. Moreover, authors in [18] propose a scheme for voltage control in distribution networks,
through coordinated control schemes that prioritize the use of OLTC, the reactive and finally the
active power of inverters. In [19], a consensus-based distributed voltage regulation strategy is
introduced aiming to utilize effectively the active and reactive power of DG inverters and the OLTC.
2.1.3. Active power curtailment for voltage regulation
Even though RPC is an efficient tool for voltage regulation, network losses are inevitably increased
under the use of RPC methods, due to the increased current flow in distribution lines [20]. Moreover,
to exploit the reactive power capabilities of DG inverters, either a part of the active power has to be
lost, or the inverter has to be oversized [21]. For this reason, APC methods for distributed renewable
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energy sources (RES) units have been also proposed in the literature for voltage regulation of
distribution grids.
An active power capping method is developed in [22] to prevent voltage violations caused by
increased PV penetration, using local voltage and power measurements. The authors in [20]
proposed a local voltage regulation strategy through a droop-based APC for distributed PV inverters.
A similar APC droop-control is introduced in [23]. The methods exploit the voltage sensitivities to
uniformly allocate the curtailment of active power injections among the PV owners. In [24], two
coordinated PV control strategies are proposed aiming to enhance the fairness of APC schemes.
APC methods have been combined with RPC strategies to enhance the voltage regulation of
distribution grids. An optimal control of active and reactive power of PV inverters is presented in [25]
for the voltage regulation of LV distribution feeders. In [26], a distributed control scheme for voltage
regulation of LV feeders is introduced that prioritizes the use of reactive power before the use of
APC. The method requires only a limited communication among PV inverters. A local voltage
regulation method is developed in [27] through APC and RPC of PV inverters, based on short-term PV
power forecasts. Authors in [28] developed a local voltage regulation methodology for distributed
PV microinverters. The proposed method is based on the correction of voltage measurements at the
ac-side of the microinverters, aiming to ensure that unnecessary curtailment of active power and use
or reactive power is avoided.
It has to be noticed that although APC methods can efficiently regulate voltage in distribution
networks, especially in LV feeders, they result into a loss of renewable energy.
2.1.4. Energy storage systems and voltage regulation
Proposed RPC and APC techniques for voltage regulation are effective and can be applied on the
already existing DG units, however they result in increased network losses or green energy
curtailment. On the other hand, electrical ESSs can be utilized to efficiently control the voltage of
distribution grids, avoiding the above-mentioned problems. Based on [1] and [21], ESSs constitute
the most reliable and efficient solution for voltage regulation compared with APC and RPC
techniques, that are based on DG inverters and grid equipment (OLTC, D-STATCOM, etc). Several
methods for voltage regulation by the use of ESSs have been proposed in the literature. The methods
can be divided in two main categories based on the voltage level of the distribution network, i.e. MV
and LV oriented control methods. Note that, ESSs can be either operated by their owners (e.g.
prosumers), an aggregator, or the distribution system operator (DSO).
2.1.4.1. MV distribution network
2.1.4.1.1. Central (community level) ESS
The use of central ESS units for voltage regulation has been tested in various works in the literature.
An optimal scheduling and sizing method has been proposed in [29] for community based DSO-
operated battery ESS, providing among others voltage regulation services at the distribution feeder.
Authors in [30] proposed a coordinated control for PV inverters and battery ESSs for voltage
regulation, ensuring that deep discharge of battery is prevented.
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2.1.4.1.2. Distributed ESSs
Additionally, distributed ESS units may be connected in various points of a distribution grid to
efficiently aid voltage control. Voltage regulation is achieved by the utilization of reactive power of
distributed ESSs in [31], through a localized and a consensus-based control. Network loading
management is also achieved by a distributed control of the active power of the ESSs. Authors in [32]
proposed a coordinated control for voltage regulation (based on three fuzzy controllers managing
the OLTC of the transformer) as well as the reactive and active power of DG units and/or ESSs.
Moreover, in [33] a coordinated control of distributed battery ESSs for voltage regulation and
mitigation of frequency deviations is introduced. The control groups neighbor ESSs and prioritizes
the use of the largest ESS. In [34], a distributed control of heterogeneous ESSs is designed to provide
voltage/frequency support, while synchronizing the active/reactive power sharing and energy levels
of the participating ESSs.
2.1.4.2. LV distribution network
2.1.4.2.1. Central (community level) ESS
A methodology for the siting and sizing of central battery ESS is developed in [35]; the active and
reactive power contribution of the central ESS is utilized to maintain voltage into permissible limits
and thus increase the hosting capacity of the grid. In [36], the optimal sizing and placement of a
central battery is defined based on multiple objectives, such as the voltage regulation of the LV
network.
2.1.4.2.2. Distributed ESSs
The use of distributed ESSs for the voltage control of LV grids has been investigated in numerous
studies. The developed strategies of ESSs can be divided into centralized, distributed and localized
control.
Centralized control of distributed ESSs
A centralized control approach for distributed ESSs is introduced in [37] for the mitigation of
overvoltages in LV feeders, comprising also the reactive power capabilities of PV inverters. The
centralized coordination controller of [38] decides the charging/discharging profiles of distributed
ESSs and the tap changes of active distribution transformers for overvoltage mitigation. The main
target is to relieve the tap changer stress, by limiting the required voltage control actions. Authors in
[39] developed a centralized control for the cooperation of both utility- and prosumer-owned
batteries, that exploit the real and reactive power of the inverters to improve the voltage profile.
The coordination of PV inverters reactive power capability and droop-based controlled battery
storage systems is proposed in [40] and [41], to maintain the voltage into permissible limits. The
paper assesses the impact of line R/X characteristics on the performance of the proposed control and
the required size of distributed ESSs. Moreover, in [42] a coordination control scheme for distributed
utility-scale ESSs was proposed, aiming at voltage regulation. The control utilizes both reactive and
active power capabilities of ESS inverters, and efficiently selects the most appropriate ESSs to
regulate voltage, taking into consideration the lengthening of battery life span.
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Distributed control of distributed ESSs
Whereas centralized controls are capable of maintaining the voltage into the permissible limits, they
are based on the uninterruptable communication of the central and ESS controllers. In case that the
communication with the central controller is lost, the objective of the centralized strategy cannot be
guaranteed. To overcome this issue, distributed controls have been developed; such methods need
only a communication link between neighbor installations, while the control decision does not
require global information of the grid [43].
Authors in [44] designed a distributed control for battery ESSs based on the consensus algorithm to
regulate the voltage of LV feeders. A localized control is also utilized to maintain the battery state of
charge (SoC) into the operational limits. Authors in [45] developed two consensus-based controls for
voltage regulation, utilizing distributed battery ESSs connected with PVs. The controls ensure that
batteries contribute fairly to the voltage regulation taking into consideration the capacity and the
SoC of ESS. A similar control approach for voltage regulation is proposed by the same authors where
plug-in electric vehicles (PEV) are utilized for voltage regulation in [46]. In case the connected PEV
are not capable to mitigate overvoltages, APC of PV generation takes place.
Localized control of distributed ESSs
Both centralized and distributed methods for voltage regulation rely on the reliable and
uninterruptible communication between storage controllers. Therefore, they may not ensure
successful voltage regulation under communication errors. To avoid the dependence on
communication systems, several localized controls have been developed. The most common ESS
local control strategy, integrated in prosumer-owned storage systems, charges the battery as soon
as PV surplus is realized, while it discharges the storage when PV power in not sufficient to supply
the consumption [47]. Under the previous strategy, battery capacity is fully charged before the peak
PV generation. Hence, increased PV injections during noon are not avoided, resulting in overvoltage
incidents. To tackle this issue, a peak shaving control of ESS is introduced in [48] aiming to mitigate
overvoltages. The same work incorporates several local controls of ESS combining also the
capabilities of PV inverter for voltage regulation. Similarly, [49] proposed a battery management
strategy that activates the charging process, after PV production exceeds a predefined power
threshold, aiming to alleviate overvoltages. This threshold-based charging process was combined
with a discharging strategy by [50], to mitigate also undervoltages occurring during peak load periods.
Voltage limits violations are alleviated by efficiently controlling the ESS power and SoC level in the
localized control strategy of [51]. Furthermore, an adaptive battery charging and discharging
scheduling strategy is developed in [52] to alleviate voltage and thermal issues of distribution feeders
by efficiently utilizing the capacity of the ESSs.
2.2. Voltage unbalance mitigation
2.2.1. Utilization of DG inverters
The voltage unbalance can be efficiently mitigated in distribution grids by using the appropriate
equipment, such as series active power filters, shunt power filters, series-parallel compensators, and
static synchronous compensators [53]. Nevertheless, the installation of such equipment increases
the investment cost of the DSO. On the other side, the inverters of distributed DGs provide the
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capability to balance the power flow among the three phases of a LV distribution grid, and thus
numerous inverter-based controls have been developed. The authors in [54] proposed a scheme for
voltage unbalance mitigation through a sophisticated control of inverter injected currents. The
scheme is based on a damping control strategy that efficiently mitigates the voltage unbalance by
injecting higher currents in the phase with lower voltage and lower current in the phase with higher
voltage. This is achieved by varying the damping conductance of the inverter. A similar approach was
used by [55] and was tested both in single- and three-phase inverters.
The approach of the variable damping conductance of [54] and [55] was also combined with voltage
regulation methods. A two-layer control method is developed in [56] to mitigate overvoltages and
voltage unbalance in LV feeders. A local P(V) droop is utilized to mitigate voltage unbalance based on
the idea of the damping conductance, while a centralized control coordinates the OLTC and DG unit
actions for voltage magnitude regulation. The damping control strategy was combined with a voltage-
based droop in [57] to simultaneously mitigate overvoltage and voltage unbalance incidents.
Another approach to mitigate voltage unbalance is presented in [53], where the proposed method
aims to minimize the grid negative sequence voltage, by setting the negative sequence current of the
inverter to be inphase with the negative sequence current of the grid. Moreover, a two-layer reactive
power control is developed in [58] for voltage regulation, aiming to mitigate voltage imbalance
between phases. The control utilizes the single-phase PV inverters that are connected to the grid.
2.2.2. Utilization of flexible loads – demand response
Demand response has been also proposed as a measure to tackle voltage unbalance. Specifically,
thermostatically controlled loads (TCLs) are exploited in [59] to mitigate voltage unbalance in
microgrids through a control algorithm that is based on voltage sensitivity coefficients. The control is
designed to deploy the minimum required TCLs of the microgrid. In [60], the cooperation of PV
inverters and TCLs is proposed as a voltage unbalance mitigation method based on demand side
management for islanded microgrids. Authors in [61] proposed a combined voltage regulation
strategy using DR and OLTC management for low-voltage distribution grids. The integrated control
scheme manages residential appliances and transformer’s taps to mitigate voltage unbalance and
voltage magnitude violations.
2.2.3. Utilization of ESSs
Furthermore, ESSs may be utilized to improve voltage profile with respect to voltage unbalance in LV
distribution networks. A control of ESSs is proposed in [62] to reduce voltage unbalance of LV feeders.
The strategy aims to balance the net power of a PV prosumer exchanged with the grid using the
minimum power of ESS. The use of distributed single-phase batteries connected at the three phases
of a network is optimized in [39] to improve the voltage profile in terms of voltage magnitude and
voltage unbalance. Authors in [63] designed three voltage unbalance mitigation techniques for the
cooperation of distributed single-phase ESSs coupled with PVs, aiming to reduce voltage unbalance
in LV grids. A community energy storage (CES) connected in a common dc-link with all single-phase
PV dc/dc converters is used in [64] to alleviate voltage unbalances of radial LV feeders. The authors
proposed a charging/discharging control that mitigates the current flowing over the neutral
conductor, by offering an active power balance between phases. In [65] the capability of electric
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vehicles to mitigate voltage unbalance in LV feeders was investigated, under a droop-based charging
control.
2.2.4. Utilization of static transfer switches
Moreover, static transfer switches (STSs) can be utilized to mitigate the power unbalance of a feeder,
by rearranging the consumers between the phases [66]. A centralized control scheme for STSs is
developed in [67] for alleviating voltage unbalance in a distribution feeder with PV prosumers by the
management of the prosumers’ load between the three phases.
2.3. Overload alleviation
One of the most crucial incidents that power systems may experience is thermal limit violations,
typically owing to overloads. Thermal limit violations can damage the network equipment and limit
the number of DG units that can be connected to the grid. Hence, the absence of network overloads
is one of the most serious requirements of performance standards [68]-[79]. Due to the fact that
various methods can be adopted in order to alleviate overloads in network branches. In the relevant
literature several methods have been proposed such as [68], [70]:
• Generation rescheduling
• Load shedding
• Control using phase shifting transformers or FACTS devices
• Generation curtailment
• Control through HVDC links
• Line switching
In [68] an expert real-time system for overload alleviation is proposed, which combines the use of
phase shifting transformers with generation rescheduling. The power flow problem is solved in the
off-line analysis, where sensitivity factors and linear programming optimization are applied, in order
to create a knowledge base. If the proposed system detects an overload event, it will recommend an
appropriate change to the phase shifter angle. If the overload isn’t completely eliminated, the
generation will be rescheduled.
In [69] two different kinds of generation curtailment are examined as methods to alleviate overloads.
Specifically, these methods are utilized to permit the installation of more DG units in the grid without
impacting other consumers since the hosting capacity of a distribution network is limited by technical
criteria, e.g. overvoltage or overcurrent limitations. Firstly, the hard curtailment case is presented in
which all the DG units must be disconnected from the grid if an overload incident occurs. Then, the
soft curtailment case is described in which the generation is decreased enough to eliminate the
overload. It is worth mentioning that the annual energy yield might be decreased when the installed
capacity of RES increases, if the hard curtailment method is utilized.
In [70] an overload emergency state control (OLEC) scheme is presented, which uses generation
curtailment. According to OLEC there are three different types of overloads that should be alleviated;
severe, light and residual overloads. Severe overloads must be automatically alleviated by OLEC
before network elements are tripped. When the overload for emergency thermal rating equals to
zero and the one for normal thermal rating is greater than zero, it is considered as light overload.
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Light overloads can be alleviated by quick or slow generation curtailment. As residual overloads are
defined the remaining overloads after the alleviation of severe or light overloads. The proposed
scheme is depicted in the flowchart of Fig. 1.
In [71] an algorithm for optimum load shed is proposed for overload alleviation, employing teaching
learning-based optimization, which requires less computational efforts since it needs no algorithm-
specific parameters. The algorithm considers both the next interval predicted load and the present
loading condition, as well as line flows and voltage limits. In order to determine the optimum load
shedding of a bus the sensitivity of a severity index is utilized. The algorithm is also validated by using
another evolutionary technique.
In [72] a multi-level method is proposed, which utilizes the basic capability of ADNs to coordinate
Distributed Renewable Energy Sources (DRES) and flexible loads, in order to relief a transmission
network from overload conditions. According to the overload relief that is requested from the
transmission network, the method is divided in three layers; the active reconfiguration scheme,
which reduces the power losses, the load transferring scheme and the demand response scheme. All
of them are considered as load curtailment. The largest capacity of load curtailment is accomplished
when the demand response scheme is utilized. The proposed method is depicted in the flowchart
of Fig. 2.
Figure 1: Flowchart of OLEC scheme.
Data and measurements update
Network overload incident?
No Yes
Timer counting from the occurrence
Type of overload?
Severe
LightAlleviation
Overloadremains?
No Yes
Quick generation curtailment
Generation curtailment
Prediction of residual overload
Alleviation of light overloadElimination of residual overload
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Figure 2: Flowchart of the multi-level method [72].
In [73] a multi-objective method is proposed to manage network congestion via load shedding and
generation rescheduling. Specifically, a combination of different objective functions is utilized in
order to obtain the best solution. These functions are coupled with the main objective function, i.e.
the generation and load shedding cost minimization function, and they are presented below:
• Social welfare maximization function including demand response offers
• Load served error minimization function
• Load shedding minimization function
• Load served maximization function.
The proposed method can be utilized when the load is modelled as voltage dependent.
In [74] a control scheme based on model predictive control is presented to maintain the current rate
between preferable limits. In general, at each time step a new optimization problem is solved using
new measurement data. The control scheme utilizes a closed loop operation. Hence, the strategy is
generally stable to modelling errors and measurement noises, but at the same time becomes slower.
An algorithm is proposed in [75], utilizing bus sensitivities and cost in order to sort system buses. This
is a useful tool for the system operators since they can choose only the most “attractive” buses to
adjust their generation and consumption, and apply optimal overload alleviation strategies.
In [76] FACTs devices, unified power flow controllers (UPFC) and thyristor-controlled series capacitors
(TCSC) are utilized in order to alleviate overloads in a power system. Computational methods are
proposed to determine the most proper location and the most suitable settings for the installation
of these components. Also, two different algorithms are implemented, i.e. the real coded genetic
algorithm (RGA) and the particle swarm optimization algorithm (PSO). It is concluded that
overloading decreases with by increasing the use of FACTs. Overloading incidents remain almost
constant beyond a certain number of FACTs. When UPFC are utilized, the security margin is wider
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than in the case when TCSC are utilized. Also, RGA increases the security margin but demands more
computational time than PSO.
A congestion management approach is presented in [77], which adjusts the active power flow in the
transmission lines by using FACT and D-FACT devices. Specifically, the proposed control algorithm
determines the proper changes in the line reactance that should be applied in order to alleviate
overloads. From simulations, it is concluded that the automatic change of line impedance can relieve
the overloaded lines, hence, the power flow approaches the desirable limits.
In [78] a modified optimal power flow problem with relaxation of restrictions is solved in order to
determine the optimal amount of load that should be shed to alleviate overloads. The actions, which
are made, are:
• relaxation of the minimum voltage limits and the maximum power flow limits through
transformers.
• introduction of limits for the permitted load curtailment based on the importance of the load.
This way, if an overload incident with small duration occurs, only a small amount of load will be shed
in order to avoid unnecessary load cuts. On the contrary, if the duration of the incident is longer and
there are residual overloads after the first curtailment, the necessary load cuts are made to provide
congestion relief.
A flexible load shedding strategy is presented in [79], which takes into account the real-time dynamic
thermal line rating (RT-DTLR). RT-DTLR is an indirect method to relieve the network congestion since
the line capacity is increased when RT-DTLR is utilized. Nevertheless, if the line rating is not calculated
correctly, system instability risks are increased and other types of limits, e.g. voltage limits, can be
violated. The proposed strategy combines two different objective functions; one based on the system
risk increment and another based on load shedding variation, in order to deal with the benefits and
risks of RT-DTLR. Thus, an optimal compromise solution arises.
2.4. Power smoothing
The probabilistic nature of solar irradiance, the environmental temperature or the location, where a
PV system is installed, are some of the main reasons of output power fluctuations of PV systems that
affect the amount and the quality of the produced energy [80]-[90]. The variability of the output
power results in significant voltage fluctuations and the injection of high frequency components into
the distribution grid. In addition, if the duration of the power fluctuations is less than 10 min and the
grid is stable, they can be absorbed as frequency fluctuations without any risk by the grid [82]. On
the other hand, when their duration is higher, it is necessary to take measures in order to ensure the
proper operation of the utility grid via smoother power output of DG units. For this purpose, various
methods have been proposed in the literature [86], [90], such as:
• Generation curtailment via operation below the maximum power point (MPP) of the PV
system
• Utilization of dump loads (resistors that are used for dumping power when it is not needed)
• Utilization of ESS, such as:
o Electric double layer capacitors
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o Fuel cell system
o Flywheel energy system
o Superconducting magnetic energy system
o Battery energy storage system (BESS)
There are two main smoothing strategies of ESS, adopted in a series of papers; they are presented
below:
• Ramp-Rate Control: When a fluctuation occurs and its value exceeds a maximum allowable
ramp-rate value rmax, the control scheme is enabled, as described by (1)
( ) ( ) − + − − max maxΔ Δ Δ ΔG G GP t t t r P P t t t r (1)
where PG is the power that should be injected into the grid in order to smooth the fluctuation and t
is the time step [82], [83], [85].
• Moving Average (MA) Control: The basic idea of this control scheme is that the amount of
the power PG(t) that should be injected into the grid in order to smooth the power of the PV
system PPV(t), is calculated as the mean production value in a time window with duration T,
and it is described by (2) [82], [83], [85].
( ) ( )1
−
= T
G PV
t T
P t P t dtT
(2)
Based on the above concepts, a natural gas engine generator and a BESS are utilized in [80] in order
to smooth the PV output power with MA method. It is concluded that the generator is too slow to
mitigate higher ramp rates and the installation of BESS is essential. It is worth noticing that the
utilization of a gas engine increases the lifetime of the BESS because the burden of the BESS for power
smoothing is decreased.
In [81] an electric double layer capacitor is used to smooth PV output power fluctuations. MA method
is utilized for the calculation of the inverter output. The MA method is modified by adding a voltage
control unit in order to maintain the capacitor voltage constant. It seems that when the ramp rate is
reduced, the required capacitance decreases but the energy loss of the control scheme increases.
In [82] a method is proposed to calculate the minimum storage requirements and the maximum
power to mitigate the worst fluctuations at any PV plant size and maximum allowable ramp-rate.
Also, the “worst fluctuation model” is defined and three different battery recharging strategies are
examined. It is concluded that the storage system manages a small amount of energy; PV peak power
aggregation causes a decrease in capacity and power requirements of BESS. Hence, it seems wiser
the installation of a single storage system that manages the energy of multiple PV power plants.
A step-rate control strategy is proposed in [83] and it is compared with the ramp-rate control and
MA control scheme. It is based on strict compliance with the maximum ramp constraint rmax for a
defined time window Δt. The proposed strategy is depicted in the flowchart of Fig. 3. It is concluded
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that the main advantage of the MA method is its minimum storage capacity requirements. On the
contrary, this method presents higher losses (2-3 times) and the life span of BESS is degraded.
Figure 3: Flowchart of the step-rate control strategy [9].
In [84] a new ramp-rate control strategy for BESS is presented. In order to mitigate fluctuations and
improve the performance of the conventional ramp-rate method, an inverse characteristic of the
desired ramp-rate with the PV output ramp-rate is proposed. This control scheme in contrast to the
MA method, is independent of past PV data. The exported results of this method are comparable to
those of the MA method and the life degradation of BESS is improved since the BESS is not utilized
all the time. In addition, when severe fluctuations occur, the proposed method can provide tighter
control than the traditional ramp-rate scheme.
Two management strategies based on ramp-rate control are presented in [85]. The ESS sizing
requirements are lower by 50% than the conventional ramp-rate control. The first strategy utilizes
the PV inverters in order to limit ramping-up fluctuation incidents. The second strategy is a BESS SoC
control scheme that is based on the actual power of the PV system and the production limits. Due to
inverter limitation, the first strategy has higher losses than the second one.
A SOC based control scheme for output power fluctuation mitigation of PV and WP system is
proposed in [86]. The proposed scheme is divided in four stages:
• Determination of the initial target power of the BESS by a dynamic filtering controller or a
dynamic rate limiter.
• Determination of the target power of each power converter system.
• Determination of the modified target power of each power converter system.
• Determination of the target power for each unit.
The utilization of this control scheme delays the life degradation of BESS.
Fluctuation mitigation using MA method and a first-order low-pass-filter (LPF) is presented in [87],
where the SoC of BESS is maintained within a range by modifying the power output if BESS uses a
tuned gain parameter.
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In [88] a power control scheme for BESS is proposed, considering the SOC and charge-discharge
depth of BESS based on the LPF principle. This strategy can smooth the power output fluctuations.
As far as the BESS is concerned, the control scheme guarantees the proper operation and extends
the lifetime of the BESS since the storage system in not overcharged or overdischarged.
In [89] the utilization of three different methods to smooth the output power of PVs is presented.
The methods, which are examined and compared, are the utilization of BESS, dump load, and
generation curtailment by operating below MPP. The economic aspects of using these methods are
examined and 10-min radiation data are analyzed. It has been found that combining a BESS and
power curtailment is the most economical solution.
In [90] a new ramp-rate control strategy based on the exponential smoothing (ES) method is
proposed and it is compared with the MA method and the conventional ES method. The proposed
strategy limits the PV ramp rate within desirable limits (preventing over-smooth) and has some
advantages over the other two control schemes. Firstly, it utilizes all data points of the system. In
addition, the phenomenon of memory effect, which is found in the MA and the conventional ES
method is removed from the proposed control scheme, since the weights distributed in PV data
points are not equal and are based on PV ramp-rate. Also, the smoothing parameter “σ” is varied
according to PV ramp-rate. Finally, BESS does not operate all the time. Due to the fact that the size
of BESS is decreased and its lifespan is increased.
2.5. Virtual inertial response The majority of the DRESs are connected to the grid using grid-interfaced converters. As long as DRESs
constitute a small portion of the total installed generation capacity of the power system, stability
problems can be effectively addressed by conventional generators. Nevertheless, the ever-increasing
penetration of DRESs over the last years has led to the gradual decommission of conventional
generators, causing a series of technical problems related to safe and secure operation of the power
system. One of the most important problems is the reduction of the system inertia [91]. More
specifically, contrary to the conventional power plants which are connected to the grid via
synchronous generators, converter-interfaced DRESs lack of rotating inertia (rotor) and damping
mechanisms (mechanical friction and damper windings). As a result, power systems are more
vulnerable to power dynamics and system faults.
A promising solution to this problem is to modify the control and operation of the grid-interfaced
converters in order to imitate the behavior of synchronous generators. This idea is firstly proposed
in [92] and [93] by introducing the virtual synchronous machine (VISMA) concept. According to this
concept, the traditional synchronous machine model is employed to control the output currents of
the grid-interfaced converter. Therefore, by implementing the VISMA concept, each converter-
interfaced DRES can virtually provide inertia to the grid. It can be shown that, under certain
conditions, the dynamic performance of the VISMA model resembles the use of frequency droop
curves for converter-based microgrids [94]. A similar approach has been recently presented in [95],
where an algorithm is proposed to determine the rate of change of frequency (RoCoF) at the point
of interconnection with the grid based on the data received from a phase-locked loop (PLL). The
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variation of the output power (ΔP) in p.u. with respect to the RoCoF (in p.u.), is calculated by using
the well-established swing equation of synchronous machines, as follows:
=2P HRoCoF (3)
where H is the inertia constant, which usually varies from 0.1 up to 9 s. Note that in (3) the damping
windings that exist in a synchronous machine are neglected. Finally, this variation is forwarded to a
current control loop to build the desired output currents. However, a common drawback of the
above-mentioned methods lies on the use of the current control loop, making the DRESs to behave
as variable current sources, from a power systems perspective. Consequently, contrary to the
synchronous generators that operate in grid-forming mode, the grid-interfaced converters of the
DRESs operate in grid-following mode.
A similar idea to the VISMA model is proposed by Q.-C. Zhong and G. Weiss in [96], [97], and [98],
introducing the synchronverter concept. According to this concept, the detailed mathematical model
of the synchronous machine is incorporated into the grid-interfaced converter of the DRES. More
specifically, the converter operates as an ideal controllable voltage source is series with an
impedance. The angle and the magnitude of the voltage source are determined by the following four
factors: (a) the voltage at the point of interconnection with the grid, (b) the active power of the
primary source connected at the dc-link of the grid-interfaced converter, (c) the output reactive
power of the DRES, and (d) the dynamic behavior of the synchronverter as mathematically expressed
by the detailed model of the synchronous machine. Additionally, contrary to the VISMA model, the
synchronverter concept offers a grid-forming capability, since it operates as a controllable voltage
source. Improved versions of the synchronverter model are proposed in [99] and [100]. More
specifically, a new synchronization process of the synchronverter with the grid is presented in [99].
Its distinct feature is the lack of any dedicated synchronization unit. Additionally, the dynamic
performance of the synchronverter towards voltage and frequency deviations at the point of
interconnection with the grid is improved in [100] by developing a bounded dynamic controller.
Nevertheless, in all the above-mentioned implementations, the synchronverter cannot actively
control the output currents, since it operates as controllable voltage source. Thus, in case of a fault
close to the point of interconnection with the grid, the output currents of the synchronverter will be
uncontrollably increased, exceeding the nominal values. Contrary to the synchronous generators,
converters present a limited overloading capability. Therefore, these uncontrollable currents can
destroy the converter.
To overcome this problem, a restraining method of fast transient inrush fault currents is presented
in [101]. The main idea behind this method is that when a fault occurs the converter controller
activates an emergency condition. During this condition, the control of the grid-interfaced converter
switches from a voltage control loop, i.e., used in the synchronverter concept, to a current control
loop using a hysteresis algorithm. Nevertheless, according to the control theory [100], the switching
between different control schemes should be generally avoided to prevent oscillations and repeated
activation-deactivation cycles.
An alternative implementation for providing virtual inertia is presented in [102], [103], and [104].
This method builds on the existing control algorithms used for commercial PV plants. In particular,
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the converter of a PV plant usually operates as a current source with unit power factor. This means
that a change at the magnitude of the output current will affect the injected power to the grid. The
magnitude of the output current is determined by a controller aiming to keep the voltage at the dc-
link close to a reference value. A constant voltage at the dc-link indicates that no energy is stored at
the capacitor of the dc-link, i.e., the power generated by the primary source is also provided to the
grid. The authors in [102] propose a modified version of the above-mentioned scheme by dynamically
changing the voltage reference at the dc-link with respect to frequency variations. In this way, a
mismatch between the generated power of the primary source and the power provided to the grid
is created, forcing the capacitor at the dc-link to store or provide excess energy to grid, similarly to a
conventional synchronous generator. However, the capacitor used for commercial applications is
very small, leading to limited inertia response. Thus, additional short-term storage systems need to
be connected at the dc-link, e.g. ultracapacitors. Furthermore, these converters operate as a
controllable current source in grid-following mode.
All the above-mentioned drawbacks have been addressed in [105] by employing a sophisticated
control scheme. The proposed control scheme consists of two cascaded control loops: (a) current
and (b) voltage control loop. The former is the inner control loop and is employed to actively control
the output currents of the DRES. Thus, in case of a large disturbance, e.g. a fault, the output currents
can be actively controlled, avoiding this way the overloading of the converter. The later is the outer
control loop and is used to actively control the voltage of the DRES. Therefore, the DRES is seen from
the power system as a controllable voltage source providing grid-forming capabilities.
In [106], a new algorithm is proposed to provide virtual inertia under unbalanced operation
conditions. Finally, an improved damping strategy for virtual synchronous machines is proposed in
[107], operating as an enhanced power system stabilizer which is employed in synchronous
generators.
2.6. ESS sizing for inertial response The sizing of the energy storage systems to provide inertial response is a relatively new research topic
[108]. Currently, all the solutions that have been proposed in the research community are based on
deterministic and analytical approaches. A simple and straightforward approach for ESS in presented
in [109]. The required ESS size is calculated with respect to the worst-case scenario, i.e., a power
mismatch around 4% to 10%. The authors in [110] propose an exhaustive search to determine the
optimal size of the ESS under the worst-case scenario. The worst-case scenario corresponds to case
where the largest generation unit of the power system trips, since it is the event where the greatest
RoCoF and the deepest frequency nadir are likely to occur. According to this approach, a large DRES
is considered at the power system capable of providing virtual inertia. Afterward, an iterative process
is implemented by changing the inertia constant of the DRES to evaluate how the dynamic
performance of the power system is affected. The optimal value of the inertia constant, which
indirectly determines the ESS size, is searched among the feasible solutions that preserve the
frequency quality. The main advantage of this exhaustive search is the fact that it enables the
inclusion of nonlinear frequency dynamics to the optimization problem.
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In [111], an analysis is performed to estimate the inertia deficiency of a low-inertia microgrid. This
inertia deficiency directly determines the ESS sizing, since there is a strong coupling between inertia
constant and ESS size. Although this method focuses on low-inertia microgrids, it can be also applied
to large power systems as presented in [112]. More specifically, an analytical methodology is
developed for ESS sizing towards the provision of inertial and primary frequency response in [112].
The proposed method uses the preliminary knowledge of the power system without the ESS, i.e.,
system size, inertia constant, droop characteristics of the primary frequency response. By comparing
the actual system characteristics with the desired ones, the required ESS size is calculated. Finally, it
is demonstrated that the same ESS can be used to provide both inertial and primary frequency
response.
In [113], an analytical methodology based on the frequency characteristics of the power system is
proposed for sizing the ESS. Towards this objective, a simplified model of the power system is initially
derived to represent rotor and speed-governor dynamics. Afterwards, a parametric analysis is
performed where the size and the control parameters of the ESS are modified to evaluate their
impact on the dynamic performance of the simplified network. Based on the results of this analysis,
the most suitable ESS size is selected.
A frequency-based sizing methodology is developed in [114] to optimize a hybrid ESS consisting of
ESS with fast and slow response. By employing the Fourier analysis to the power imbalance between
generation and demand, the low and high frequency power fluctuations are supplied to slow and fast
ESS, respectively. It is worth mentioning that the analysis is performed in an isolated power system
with high penetration of wind generation. A similar approach is presented in [115], where a
methodology based on the equivalent inertia calculations is proposed for sizing a hybrid ESS to
provide both inertia and primary frequency response.
An optimization process is proposed in [116] to determine the optimal ESS sizing for inertial response.
Initially, a simplified dynamic model of the system is derived taking as important parameters, the
damping, and the inertia constant. Next, an optimization problem is solved to minimize the overall
installed capacity of the storage system, while satisfying technical constraints, e.g., permissible limits
of network frequency, etc. A similar approach is proposed in [117], where the ESS sizing problem is
formulated as a standard, cost-based optimization problem and solved using metaheuristic
optimization algorithm.
2.7. Coordinated primary frequency response Due to the advent of DRESs, the portion of the conventional power plants to the generation mix is
decreasing. As a result, DRESs should be actively involved in the frequency regulation of the power
system to ensure the stable and reliable grid operation.
The incorporation of the primary frequency regulation functionality to the DRESs constitutes a newly
emerging research topic. Currently, the main research effort has been devoted to the development
of methods for the provision of primary frequency regulation from large wind farms. More
specifically, in [118], a centralized, optimization-based control algorithm of the wind farm is proposed
to maintain a certain reserve of power to be utilized during primary frequency control. The algorithm
is designed to minimize the power losses within the wind farm by optimizing the share of evert
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individual wind turbine. An improved solution to the optimization process of [118] is presented in
[119]. The wind turbines of the wind farm are first grouped by means of clustering analysis according
to their wind profiles. In this way, the same control commands apply to each wind turbine belonging
to the same group. Consequently, each group can be modeled as a single wind turbine, reducing the
number of control variables and the computational complexity of the optimization problem. In [121],
an optimization method is applied to a wind farm to support three operation strategies: (a)
maximization of the wind farm power while maintaining a constant rotational kinetic energy, (b)
maximization of the wind farm kinetic energy while maintaining a constant output power, and (c) a
de-loaded strategy where the wind farm rotational kinetic energy is maximized for a fixed de-loading
margin. The three operation strategies are formulated as nonlinear optimization problems solved
separately by the central controller of the wind farm.
Contrary to the centralized methods presented above, a distributed approach is adopted in [120].
The authors developed a distributed Newton method to optimally distribute the power among the
wind turbines of a wind farm. Although the proposed method requires the wind turbines to exchange
limited information with their neighbors over a sparse communication network, it presents a super-
linear convergence rate.
The authors in [122] and [123] adopt the concept of the virtual power plant (VPP) to provide primary
frequency response to the grid. The VPP plant acts as an aggregator of resources of demand response
and wind farms, which are optimally controlled by employing an intraday and a close to real-time
scheduling algorithm.
In [124], a two-stage method is proposed for coordinating the droop controls between the wind
turbines and the associated energy storage systems for supporting the primary frequency control in
power systems. In the first stage, the available power reserve from the de-loaded wind turbines is
estimated in an efficient manner. In the second stage, the coordinated droop control between the
wind turbines and the energy storage systems is redesigned based on the available power reserve of
the wind turbines.
The provision of primary frequency response from distributed BESS is proposed in [125]. In particular,
the distributed BESS are optimally coordinated to maximize their profit by adopting a two-stage
approach. The first stage is related to the frequency regulation, where the regulation failure penalty
is minimized by optimally coordinating the operation of multiple storage systems in case of frequency
events. In the second stage, the state of charge of the participating storage systems is recovered to
a proper range to avoid regulation failure in the next frequency event.
Finally, the authors in [126] propose a framework for DRESs located at distribution networks to
provide primary frequency response. More specifically, a methodology is presented which
determines the parameters of the power/frequency droop curves that should be applied to each
DRES to guarantee a specific frequency regulation characteristic at the point of interconnection of
the distribution grid with the transmission system.
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Network monitoring technologies and techniques In the framework of ACTIVATE, a novel network monitoring architecture will be proposed to enhance
the observability and controllability of modern power systems. The proposed architecture combines
both distributed online network monitoring and analysis techniques [127] – [129] as well as
centralized monitoring techniques [130] – [132] to evaluate close to real-time the stability margins
of power systems [133]. Towards this objective, methods and tools are proposed to:
• Compute power system modes and mode shapes
• Estimate the inertia capability (i.e., inertia time constants) of individual active distribution
networks (ADNs) as well as the overall inertia of the power system
• Derive static and dynamic equivalent models for ADNs
Mode estimates are used to assess close to real-time the stability margins of the power system [133],
[134] and to provide alarm signals for potential instability events [135], [136]. Mode shapes are used
to identify coherent generators at a TSO level [132], [137] in order to develop simplified
representations of the transmission system [138] as well as to identify centers of inertia at the
transmission grid [139], [140]. Dynamic equivalent models are used to represent extended
distribution grids in stability studies [141] - [145]. Finally, network equivalents are used to represent
extended distribution grids or parts of them; static equivalents are used for the analysis of the normal
operation (mainly for power flow analysis [146], [147] and grid optimization [148]), while dynamic
equivalents for the analysis of the system dynamic performance (mainly stability).
Network monitoring techniques, that already proposed in the literature, are reviewed and discussed
in Section 3.1. Conventional single- and multi-signal identification techniques for the evaluation of
modal estimates are presented in Sections 3.2.1 and 3.2.2, respectively. A review of methods for the
online estimation of inertia time constants is provided in Section 3.3. Derivation of static and dynamic
equivalent models is discussed in Sections 3.4.1 and 3.4.2, respectively.
3.1. Network monitoring technologies and techniques During the last few decades, power systems undergo significant changes due to the integration of
several new technologies both on the transmission and the distribution side [149], e.g. electric
vehicles, DRESs, new types of power electronic interfaced loads, etc. Due to these new technologies
along with the ever-increasing power demand, power systems are now operating closer to their
operation limits [150]. In this context, new methodologies and techniques must be developed to
evaluate the power system health in close to real-time [149], [151]. Το enhance power system
security and reliability, synchrophasor measurements can be exploited to develop advanced wide-
area monitoring systems (WAMS); the vast deployment of synchrophasor technology offers a diverse
range of applications that are becoming increasingly useful for close to real-time grid operations,
such as disturbance detection, modal estimation of electromechanical oscillations, coherency
detection, voltage stability monitoring, etc. [152] – [154]. Recently, various techniques have been
developed based on measured data; a literature review regarding some of the network monitoring
technologies and techniques applied during the last years is presented below.
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In [137], a multichannel continuous wavelet transform based modal analysis was proposed; correct
estimation of the modal properties, i.e., frequency, damping, mode shapes and coherency is of major
importance for power grid operators. The adopted approach can successfully identify the dominant
modes using the information contained in multichannel measurements; the proposed approach is
also capable of estimating the mode shapes and coherent groups of generators. Similarly to [137], in
[132], a holistic framework is adopted to reveal the inherent electromechanical dynamics of the
system. Specifically, an eigensystem realization algorithm is developed to capture the dominant
modes, mode shapes, participation factors and coherent groups of generators using synchrophasor
measurements. Moreover, to identify several modes simultaneously and to estimate the
corresponding mode shapes, a stochastic subspace method using ambient data is proposed in [155].
Additionally, to identify in real-time coherent groups of generators following the appearance of a
disturbance, a novel methodology based on the correlation coefficients of rotor angle/speed
oscillations of generators is presented in [156]; in this work, to classify a number of generators into
coherent groups a clustering algorithm based on the correlation coefficients of generators
oscillations is employed. To estimate the frequencies, damping ratios, mode shapes and participation
factors, a new mode identification method was also introduced in [157], using ambient
synchrophasor data. The proposed method consists a hybrid measurement- and model-based
methodology to estimate the system state matrix in ambient conditions and provide accurate
estimation of modal knowledge for all modes.
Furthermore, in [158], a robust method is presented to locate disturbances using synchrophasor
measurements from the wide-area frequency monitoring network– FNET/GridEye [159], [160]; by
applying this method, the real-time distribution of electromechanical wave propagation speed can
also be calculated. In [161], a centralized Prony-based algorithm was extended to a distributed
estimation problem to compute inter-area oscillations of large power system networks using
synchrophasor data. The proposed architecture demonstrates how dispersed PMUs and phasor data
concentrators (PDCs) can communicate with each other cooperatively for wide-area oscillation
monitoring applications. Reference [129] extends the work of [161] by presenting two additional
distributed algorithms presented for estimating electromechanical oscillation modes, whereas [162]
shows that these methods can be effectively selected to eliminate asynchrony in wide-area
estimation problems. An unprecedented wide-area monitoring and control system for fast frequency
response, namely Enhanced Frequency Control Capability (EFCC), was proposed and validated in
[163]. The EFCC is able to detect and analyze the regional impact of disturbances, and deploy fast
and coordinated responses in consideration of the characteristics and capabilities of a range of
different resources.
In [164], a two-stage methodology was proposed to identify power system dynamic signature using
synchrophasor measurements and data mining. The first step is to predict the transient stability
status following the clearance of a transient disturbance in real-time, whereas at the second stage, a
new methodology based on data mining was proposed to predict detailed generator dynamic
behavior, provided that the system is determined to be unstable. Reference [165] enhances the
method introduced in [164]. In [165], the problem of online identification of generator grouping
patterns consists a one-stage multiclass classification problem compared to the two-stage process
described in [164]. Moreover, the order in which generators lose synchronism is identified as a
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supplementary procedure compared to the previous developed methodology. In [166], a
comprehensive data-driven methodology is proposed to identify and analyze power systems
oscillatory behavior in close to real-time. The proposed approach can identify critical groups of
generators that exhibit poorly or negatively damped oscillations in systems with renewable
generation, providing vital information to system operators. It is worth noticing that the results of
this work also revealed that the probability of the appearance of negatively damped oscillations for
certain generators in a power system might increase when RES are connected. A methodology for
the online identification of power systems dynamic behavior with an increased amount of power
electronics interface units is also presented in [149]. The study demonstrated that power electronics
interface units must be taken into account while developing online identification algorithms and also
while investigating the dynamic behavior of individual generators.
3.2. Identification techniques for modal analysis of power systems
3.2.1. Single-signal identification techniques
Vital information regarding grid oscillations and consequently the stability margins of the power
system can be provided by mode estimation [167]. Traditionally, eigenvalue analysis approaches are
applied on linearized dynamic power system models to obtain power system modes [168].
Nevertheless, the applicability of eigenanalysis is limited [169], e.g. for real-time applications or large
power system configurations. As an alternative, measurement-based system identification methods
are proposed to compute power system modes. Nowadays, measurement-based identification
techniques are favored due to the increasing deployment of synchronized measurement technology
at power systems, enabling the close to real-time estimation of oscillatory modes [170]. In this
context, novel control and monitoring applications can be performed.
Power system analysis is performed using measurements either from ambient, transient (ringdown)
or forced oscillations [171]. Ambient data is obtained when a system is working under an equilibrium
condition, and the major disturbance results from small-amplitude load variations [172], whereas
ringdown data are obtained from the system during transient operation following a major
disturbance or a fault [171], [172]. External mechanisms, e.g. cyclic loads or mechanical aspects of
generators, are typically associated with the introduction of forced oscillations into power systems
[173] – [175].
In the literature, several system identification techniques have been proposed to perform modal
analysis of power systems using ringdown responses. Linear measurement-based system
identification techniques can be classified into time-domain (TD) and frequency-domain (FD). To
perform modal analysis and study power system electromechanical oscillations the Prony method
was initially proposed by J.F. Hauer et al. [176]. To this date, Prony method is probably the most
well-established method [168], which has been extensively investigated in power system applications
[177] – [182]. Prony method has also been modified to include transfer function applications, e.g. to
obtain reduced-order transfer functions of large-scale systems [183], whereas a stepwise regression-
based Prony was further developed by Zhou et al. [167]. Other well-known identification techniques
are the eigenvalue realization algorithm (ERA) [184], the matrix pencil (MP) method [185], the
subspace state-space system identification (N4SID) [186], the prediction error method (PEM) [187]
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and the minimal realization approach [188]. Dominant modes can also be extracted in the FD. For
example, in [189], [190], the dominant modes are extracted using the fast Fourier transform (FFT),
combined also with the sliding window method for the estimation of mode damping. Furthermore,
in [191], a new hybrid FD/TD method was proposed to automatically identify the dominant modes
contained in ringdown responses.
To estimate power-system electromechanical modes, recursive methods have been also introduced.
In [172], a regularized robust recursive least squares method is presented based on PMU data,
whereas in [192], an online recursive algorithm that can be applied to both ringdown and ambient
data was proposed and evaluated. In addition, the application of an extended Kalman filter (EKF) was
discussed by Yazdanian et al. [193], whereas Peng and Nair based on the EKF, suggested an extended
complex KF (ECKF) and ECKF-based smoother (ECKFs) [194]. An efficient dynamic mode
decomposition technique for modal analysis of large data sets was introduced in [195]. Moreover,
identification of low-frequency electromechanical modes in power systems is performed using
Zoloratev polynomials and a digital Taylor-Fourier transform in [196] and [197], respectively. A
method for identifying interarea modes by using curve-fitting of the Laplace transform of the modal
transient response, obtained from a difference sequence between two sets of ringdown frequency
data was proposed in [198]. In [199], the vector fitting (VF) technique is proposed as an identification
method for the estimation of the dominant modes contained in ringdown responses of power
system. The application of VF was extended in [200], where a novel method, called ringdown time-
domain vector fitting, for the estimation of electromechanical modes in interconnected power
systems was introduced. Additionally, the applicability of the variational mode decomposition
technique to extract electromechanical oscillatory modes in power systems considering the time-
frequency analysis of nonlinear signals which arise after a large disturbance was demonstrated
in [201]. To estimate the modal parameters, an improved stochastic subspace identification method
using a combination of stationary wavelet transform and exact model order algorithm was proposed
in [202].
Although the above-mentioned methods present very good performance, they require the existence
of transient responses in the system, which makes mode estimation in continuous (near real-time)
difficult [168], [203]. For this purpose, ambient data are often employed, where information
regarding system transfer functions, and, consequently, system modes, are contained in the
spectrum of ambient responses [204]. Electromechanical mode identification from ambient data was
first considered in [205], where an autoregressive model of ambient data was used; this method was
later extended to include the autoregressive moving average model (ARMA) [206]. A stochastic
subspace identification technique was proposed by Ghasemi et al. [207]. Additionally, frequency-
domain decomposition [39] has been also applied for mode estimation in power systems. Motivated
by [208], the concept of extracting the time-domain exponential decay response of dominant modes
from ambient PMU data was extended to wavelet formulation in [209]. A distributed frequency
domain algorithm for real-time modal estimation of large power systems using ambient
synchrophasor data [210]. To enable ambient oscillation monitoring a two fast SVD (singular value
decomposition) computation was proposed by Wu et al. in [211]. Furthermore, to systematically
identify the dominant modes from both ringdown and ambient data, in [212], a novel dominant mode
estimation method for monitoring inter-area oscillations using PMU measurements was presented.
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3.2.2. Multi-signal identification techniques
Methods based on single inputs are related mainly with the section of the power system where the
corresponding signal was measured. To obtain better estimations of the system modes for the entire
system, multichannel techniques that include information from multiple measured signals can be
employed [213]. Therefore, it is imperative to generate one set of modal estimates based on multiple
data channels, e.g. from PMUs [214]. Several papers have been published in the literature regarding
multi-signal identification techniques.
Trudnowski et al. first proposed a simple extension to obtain a unique set of mode estimates by
simultaneously analyzing multiple signals using Prony analysis [215]. To estimate electromechanical
modes the multi-channel Prony method has been employed in several other works, e.g. [216]–[218].
Moreover, multi-channel Prony analysis was extended in [219], where a recursive solution was
proposed to make it more suitable for near to real-time applications. Furthermore, in [220], a
distributed multi-signal Prony analysis algorithm using consensus and subgradient updates was
proposed. Additionally, a multi-signal approach using the Forward and Backward Extended Prony
(FBEP) method and sliding window analysis on power system small-signal stability was investigated
in [221].
To obtain modal estimates in close to real-time based on multiple synchronized PMUs an algorithm
namely Frequency-Domain Optimization was developed in [222], whereas a distributed frequency
domain algorithm to monitor modes from multiple PMUs based on ambient data was presented in
[210]. Recently, in [170], the accuracy of the mode estimates was investigated using multi-signal
analysis, by extending the applicability of eight widely-known system identification techniques,
following the average energy approach originally proposed in [210], [223]. The same method was
adopted by [201], where the use of a variational mode decomposition technique for extracting modal
components was examined.
To estimate the damping ratio of power system modes directly from the Fourier Transform using
PMU data a multi-dimensional Fourier ringdown analyzer was proposed in [224], while reference
[225] extended this approach by applying SVD to the power spectrum before calculating the damping
estimates. As mentioned in Section 3.2.1, in [197], the modal estimates related to ringdown analysis
were computed through the digital Taylor–Fourier transform; the authors in [226] and consequently
in [227] extended the capability of the suggested method to process multiple signals. Additionally,
the Taylor-Kalman-Fourier and Alternating Kalman filters are proposed to extract modal information
and where also modified to enable multi-signal analysis; moreover, the performance of all methods
is compared to Prony multi-signal analysis.
The advances in PMU infrastructure have also enabled the application of other techniques based on
multi-channel signals. Recently, a spectral fitting approach that combines the numerical Laplace
transform with the VF method was presented to estimate electromechanical oscillatory modes and
mode shapes [228], whereas in [214] a multi-signal ringdown TD vector fitting method that can
analyze multiple ringdown signals simultaneously, was introduced. In [229], it was derived that the
linearized power system can be modelled using a multi-channel ARMAX structure; therefore, the
transfer function of the system and consequently the modes and mode shapes can be characterized
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using the parameters of the ARMAX model. It should be mentioned that this multi-signal
methodology provided also promising results when applied to ambient data. A new approach based
on multi-dimensional wavelets was presented in [230] and was used to identify the dominant modal
frequencies, damping ratios and to estimate the mode shapes of dominant oscillatory modes. In
addition, in [231], a dynamic mode decomposition (DMD) framework was successfully implemented
to analyze large datasets from multiple sources. More information regarding the abovementioned or
other relevant techniques is also discussed in [168].
3.3. Real-time estimation of inertia time constants In the literature, several approaches have been proposed for the estimation of inertia time constants.
A comprehensive literature review is provided below.
In [232], a method for the inertia estimation of synchronous generators is proposed. The method
requires measurements for frequency, mechanical and electrical power of every generator of the
examined power system. In practical applications, it is impossible to obtain all these measurements.
Therefore, this method is not suitable for real applications. To reduce the number of required
measurements, the same authors have proposed in [233], a method for the online estimation of
inertia time constants. The method requires as inputs real power and frequency measurements
recorded during system disturbances. The inertia time constant of each generator is determined
using the swing equation and the sliding window technique, which is used to determine the
frequency and the real power change due to a system disturbance. The overall system inertia is
computed based on the inertia estimates of the individual generators. The method requires accurate
identification of the time of disturbance, i.e. the exact time that the disturbance occurs. Therefore,
in [234], a method for the identification of the time of disturbance is proposed. The method is based
on the use of sequential window data and it is validated using simulated responses and laboratory
measurements. The presented results reveal that the performance of the method is considerably
affected by the definition of the sequential windows. Additionally, another type that may affect the
performance of the method is the type of the PMU [235].
In [236], a fifth order polynomial was fitted to measured frequency transients using a least squares
approximation. The purpose of the fitting procedure was to restrain the influence of oscillatory
components, that are superimposed on frequency signals due to intra- and inter-area oscillations.
The method is tested only on field measurements. Hence, its performance and accuracy cannot be
fully quantified. A similar approach is also presented in [237]. In this case, a linear model is used to
fit frequency signals and determine RoCoF. The method is further tested in [238], to estimate the
overall system inertia of the Great Britain power system. However, to perform satisfactorily, the
method requires the monitoring of the most critical power system nodes as well as the use of probing
signals.
In [239] a method which uses historical data to correlate generator capacity with RoCoF values and
inertia time constants is proposed. With this knowledge, the typical RoCoFs of a power system with
any amount of system capacity can be roughly estimated. However, the performance of the method
has been tested only on field measurements. Therefore, its accuracy cannot be fully quantified. A
Gaussian Markov Model is proposed in [240] to correlate frequency responses with inertia time
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constants. However, the model requires the fine-tuning of several parameters to provide satisfactory
results.
In [241] the relationship between power system eigenvalues and inertia time constants of
synchronous generators is derived. Additionally, a methodology based on the eigenvalue sensitivity
matrix is proposed to estimate in real-time inertia time constants of synchronous generators using
wide area measurements. In [138] and [242], the use of inter-area oscillations for the estimation of
inertia time constants is investigated. The implementation of these methods requires the
identification of pilot buses, i.e. buses that represent the center of inertia (COI). Therefore, in [139]
a method to determine COI and to estimate the overall power system inertial response is proposed.
The method initially identifies clusters of generators that form aggregate sources of inertial response.
Then, the overall dynamics of each cluster are synthesized using PMUs placed at selected buses that
represent the COI.
Finally, several methods have been proposed in the literature [243] – [247], to estimate the inertia
time constant as perceived by particular buses. These approaches are very useful to quantify the
virtual inertia of DRESs at the PCC with the transmission grid [244]. In [243]- [245] the use of ARMAX
models is discussed, in [246] the use of dynamic regressor is investigated, while in [247]the
micropetrubation method is proposed. The performance of the above-mentioned methods has not
been tested under both ringdown and ambient data. Additionally, it is worth noticing that only the
method proposed in [245] has been validated using field measurements. All other approaches have
been tested only in simulation environments.
3.4. Equivalent models for ADN analysis Traditionally, for power flow simulations and dynamic analysis, distribution grids were modeled as
aggregated loads, containing different types of individual components such as motors, lighting and
electronic devices [145] - [147]. However, the increased penetration of DRESs into distribution grids
will eventually alter the properties of these systems [142], [248], [249]. Therefore, during the last
years, power system operators and academia have initiated serious efforts to improve modeling
practices and to develop new aggregate equivalent models that can simulate more accurately the
complex behavior of future ADNs [141], [142], [248].
Towards this objective, several static and dynamic aggregated models have been proposed in the
literature. Static models express the real and reactive power at any time instant as algebraic
functions of the bus voltage magnitude and frequency at that instant [145], [146]. Static equivalent
models can be used for power flow analysis [146], [249] as well as to optimize grid operation [148].
However, static models do not take into account system dynamics and therefore are not suitable for
voltage and angular stability studies [145], [146]. For this reason, dynamic equivalent models are
adopted. Dynamic equivalents express the real and reactive power at any time as functions of the
voltage and frequency related to previous time instants, taking also into account the present instant
[145], [146]. Difference or differential equations are used to describe this type of models [146].
The main challenge concerning the development of equivalent models is to determine an aggregated
representation for the different types of loads and DRESs that are connected to the same distribution
grid [250]. Generally, the equivalencing procedure consists of two main stages [251]. In the first stage,
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a suitable model structure is specified, while in the second stage the model parameters are
derived [251]. Depending on the available information and insight of the true system, three model
structures can be used, namely white-box, black-box, and grey-box.
In the white-box approach [249], the topology of the distribution grid, the type and the control of the
DRESs as well as the exact composition of the load are assumed a priori known. The aim of white-box
modeling is to derive an exact mathematical model of the true system [249]. In many cases, the
development of white-box models is a very complex and hard-to-achieve procedure, since several
data are required [143]. In these situations, black- and grey-box equivalents are developed.
In the black-box approach [143], [249], as the other extreme case, the topology of the distribution
grid, the location and the control of the DRESs as well as the load composition are not known. Only
the input – output data of the true system are available [143]. The aim of black-box modeling is to
map the input data set to the output data set by adjusting free model parameters in order to force
the output of the equivalent model to become as similar as possible to the output of the true
system [249].
In grey-box approach [143], [144] basic information concerning the grid topology, the control systems
of the DRESs and the composition of the load are known. However, the exact components and their
rates are not available. Therefore, grey-box models are developed using the known structure of the
system with unknown parameters [143]. The parameters are then identified in a similar way to black-
box modeling [143], [144].
The estimation of model parameters can be performed using either the component- or the
measurement-based approach [251]. The component-based approach requires reliable data
concerning the load class mix, the load components, and a priori knowledge of typical characteristics
of individual devices. Therefore, the application of this method requires accurate data, which usually
cannot be determined in distribution networks due to their size and confidentiality issues [252]. On
the other hand, in the measurement-based approach, the model parameters are estimated from in-
situ measurements, using system identification techniques [252], [253]. This approach can be
especially favored in smart grid environments, where synchronophasor data can provide the required
measurements. This way, parameters are continuously updated, and accurate equivalent models are
derived close to real-time [253], [254].
3.4.1. Static equivalent models for ADN analysis
Traditionally, for power flow analysis, distribution grids are represented by constant power load
models (PQ model), exponential load models, or a combination of constant impedance, constant
current and constant power load models (ZIP model) [146]. The PQ model is the simplest equivalent
model and is adopted by 84 % of the system operators worldwide for steady state analysis [46]. The
exponential and the ZIP models consider the nonlinear characteristics of loads with respect to voltage
changes [146]. However, all above-mentioned models are oriented to the analysis of passive
distribution grids and fail to provide consistent results for the analysis of ADNs, since they completely
neglect control strategies and operational constraints of DRESs [249], [256].
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Therefore, new static equivalent models are required to simulate more accurately the steady state
behavior of modern ADNs and to determine with higher accuracy the flexibility margins of ADNs
[257], [258]. Towards this objective, during the last years, several white-, black-, and grey-box models
have been proposed in the literature. White-box models are mainly used to assess flexibility margins
of ADNs, while black- and grey-box models to facilitate steady state analysis, e.g. power flow analysis,
grid optimization. A comprehensive review of these models is provided in the next three paragraphs.
In [257], a white-box model is developed to determine the time-dependent flexibility of ADNs and to
control their TSO-DSO interconnection power flow. Specifically, a nonlinear set of algebraic equations
is developed to represent the steady state behavior of the ADN. Grid operational constraints are also
incorporated into the model. However, in this approach, real and reactive power flowing through the
interconnection point are estimated using sequential Monte Carlo simulations, resulting in increased
computational burden and increased execution times. A more sophisticated white-box model is
presented in [259]. Also, in this work, the grid is represented by nonlinear algebraic equations. The
model receives as input the real power of the ADN at the interconnection point and estimates the
capability chart of the reactive power by conducting optimal power flow analysis and by considering
grid operational constraints; the model proved to be accurate. However, it cannot be used to
evaluate the flexibility margin of real power at the interconnection point. To tackle this issue, in [258]
and [260] two white-box models, with similar concepts, are presented. In both approaches the grid
is represented by nonlinear sets of algebraic equations, while in both formulations grid operational
constraints are incorporated to the model. The real and reactive power at the interconnection point
are estimated by solving a sequence of optimal power flows. In both cases, simulation results are
used to demonstrate the accuracy of the methods. However, the derivation of both models requires
detailed data concerning grid topology, the location of DRESs and load composition. Therefore, these
models cannot be easily developed for extended ADNs.
To overcome the need of detailed data, a black-box equivalent model based on radial basis functions
and artificial neural networks (ANNs) has been developed in [261]. This model receives as inputs the
grid voltage at the interconnection point and estimates real and reactive power. The model has been
tested on a simulation environment for the analysis of passive grids and found to be accurate and
reliable. However, the performance of the model for the analysis of active grids is still an open issue.
In [262] a black-box static model, based on Ward equivalents, is proposed and further extended
in [263]. Model parameters are estimated via a least-square approach. The model is used to replace
extended parts of grids in power flow analysis. The model was tested on a simulation environment
for both passive and active grids containing synchronous generators. Validation results show that the
model is robust and accurate. However, the performance of the model has not been tested under
real field conditions and for different types of DRESs.
Static equivalent models for the analysis of ADNs can also be developed using the grey-box approach.
For instance, in [256] a grey-box equivalent model consisting of a ZIP load, an equivalent generator
and an equivalent branch is proposed. The model receives as input the grid voltage and estimates
real and reactive power at the interconnection point. Model parameters are estimated using a
least-square approach. The model has been tested using simulation results and proved to be more
accurate compared to conventional approaches, i.e. ZIP and exponential models. However, the
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model does not take into account the control strategies of DRESs. In [264], a grey-box modeling
approach based on enhanced reinforcement learning is proposed; the model considers spatial
uncertainties and reactive power control of DRESs. The model receives as inputs the wind speed, the
total active and reactive power of distributed wind turbines, the global irradiation, the ambient
temperature, the total active and reactive power of distributed PVs, and the voltage at the
interconnection point. Using these inputs, the model estimates the real and reactive power at the
interconnection point. However, the large amount of input data prevents the implementation of the
model to real-field applications. A simpler, but still accurate static grey-box model is proposed
in [249]. The model consists of a ZIP load and a DRES operated under several voltage control schemes.
Model parameters are estimated via nonlinear optimization. The model receives as input the voltage
at the interconnection point and estimates the real and reactive power. However, the performance
of the model has been tested only using simulation results. Additionally, another drawback of the
model is that completely neglects frequency control schemes that may apply to DRESs [265].
3.4.2. Dynamic equivalent models for ADN analysis
Traditionally, for dynamic studies, distribution grids are represented by exponential load models, the
ZIP model, and the composite model (ZIP model augmented with induction machine) [145].
Specifically, even nowadays, 72 % of power system operators use the exponential and the ZIP model
for stability studies [255], while 26 % use some form of the composite model [255]. Concerning the
modeling of distributed generation, 40 % of system operators neglect its influence on system stability
studies, 28 % simulate DRESs simply as negative loads, while 23 % use dynamic load models to analyze
its behavior [255]. Only 3 % of system operators have developed and use detailed dynamic models
for the analysis of DRESs [255].
Nowadays, in most of the modern distribution grids, the penetration of DRESs is reasonably low. This
may justify neglecting its influence on system stability studies [255]. However, anticipated increase
of DRESs, particularly those based on power electronic-interfaced units, will effectively change the
nature of power system dynamic responses [141], [142] and will inevitably affect the overall stability
margins [266], [267]. Therefore, new methods and modeling approaches are required [39]. Towards
this objective, during the last years, several dynamic equivalent models have been proposed in the
literature. A comprehensive review of the available methods and approaches is presented in the next
paragraphs.
In [268] and [269], white box equivalents for the dynamic analysis of ADNs are proposed. In these
approaches detailed information concerning the grid topology, the location of loads and DRESs as
well as detailed data concerning the control systems of the DRESs are required. Additionally, all
power system components are modelled in full detail. However, the use of detailed models for the
analysis of distribution grids can increase considerably the computational burden of dynamic
simulations [270]. Therefore, this type of models is generally avoided, and black- or grey-box
equivalents are mainly used for dynamic simulations [270].
In this context, in [271] - [273] black-box equivalent models based on the Hankel method are
proposed, while in [274] - [276] models based on the Prony method are derived. Moreover, in [277]
and [278] the Ν4SID method is used, whereas in [279] black-box equivalents based on the PEM are
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developed. In [280] and [281] black-box equivalents consisting of nonlinear equations and linear
transfer functions are developed. The parameters of these two models are estimated using the VF
technique. In [282] and [283] black-box equivalents based on ANNs are proposed. All the above-
mentioned models can simulate accurately several dynamic phenomena, e.g. small and large
disturbances. However, their main limitation is that the model parameters depend on a significant
degree on the operational and loading conditions of the grid [280] and [284]. Therefore,
methodologies to derive robust sets of parameters should be developed [284], [285].
In [143] and [144], a grey-box equivalent model is proposed. However, this model is not
observable [286]. Thus, its parameter cannot be fully estimated using only input / output data [286].
Therefore, a further insight concerning the grid topology is required, while accurate initial estimates
for the model parameters are also needed. To overcome this issue, in [78], a reduced representation
of the model presented in [143] and [144] is proposed and rules of thumb are derived to determine
the initial condition of the model parameters. A strong disadvantage of these models is that their
parameters are strongly affected from grid operational conditions [143]. Additionally, all the above-
mentioned grey-box models neglect the control strategies of DRESs. As a solution to this issue, several
grey-box equivalents which incorporate different control strategies for the DRESs have been
proposed in the literature. Among them, the most representative are the models of [288] - [290].
These models were tested in simulation environments and found to be accurate. However, their
performance has not been tested under real-field conditions. Additionally, it is worth noticing that
for the development of these equivalents, a large number of parameters must be identified. For
instance the models of [289] and [290] require the identification of 40 and 42 parameters,
respectively. This fact, in conjunction with the variability of model parameters under different
operational conditions, poses serious concerns regarding their implementation for real-time
applications.
To derive robust sets of parameters, i.e., parameters applicable for a wide range of operational
conditions, several approaches have been proposed. In [143] and [144] the use of statistical analysis
is proposed to derive mean and/or median values for the model parameters. In [291], multi-signal
parameter identification is proposed. The use of ANNs is investigated in [284] and [285] to generalize
model parameters. A comparative assessment of the above approaches is required to determine the
most accurate and reliable method.
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Power converter implementations During the last few years, several converter configurations have been proposed that incorporate new
dynamic functionalities, e.g. virtual inertia [126]. The main parts of a typical configuration of a three-
phase converter are the dc/ac part, the dc/dc converter, the ESS, and the output filter. The ESS can
be used for implementing either virtual inertia or power smoothing functionalities. Two pulse width
modulation (PWM) control signals are needed to control the dc/dc and the dc/ac converters. The
derivation of the PWM signals is carried out from a single processor unit, i.e., a microcontroller, based
on the current and voltage measurements acquired at the output of the dc/ac converter and the dc-
link. In the framework of ACTIVATE, a lab-scale three-phase converter prototype will be implemented
incorporating all functionalities to be developed within ACTIVATE. The prototype will correspond to
a DRES grid-interfaced converter with the ability to connect an ESS at the dc-link though a dc/dc
converter. Towards this objective, the prototype converter will support:
• concurrently implement all the control functionalities that will be developed within ACTIVATE
to provide ancillary services to the grid
• the grid-interfaced converter will neglect possible interactions with the electrical network
• monitoring capabilities in terms of applying power system identification methods
4.1. Three-phase inverter review While the grid-tied RESs are beneficial in improving voltage profile of power systems, the increased
penetration level of them is challenging regarding grid stability. Frequency instabilities and reverse
power flows along with high RoCoF value, arise critical issues for grid reliability. Depending on the
nature of RES, many power electronic converter topologies have been developed, in order to
facilitate the connection of RES in the grid and to jeopardize their adverse effects; total harmonic
distortion, DC current and uncertainty in power production. While in the field of wind power
production the referred problem has been solved [292], in PVs there are plenty of issues which need
to be addressed, e.g. DC harmonic injection due to DC-Link, dangerous inrush current, and adequate
power efficiency after two to three stages of power converter [293]. The advent of new
semiconductor technologies and microcontroller internet communication features pave the way for
new more advanced grids with higher penetration of RES and reduced hazard emissions.
4.2. Converter topologies In the field of power generation by PVs, the inversion process is constituted in multiple stages due to
the DC production of PV [294]. In this type of inversion, the last stage performs the DC to AC
conversion while the starting one (or the intermediate) stages achieve the voltage amplification
and/or the galvanic isolation. ESS such as batteries and ultracapacitors are connected in the same
way in the common DC-Link [295].
Recent technological achievements lead to focus on transform-less inverter topologies, which offer
a high degree of flexibility and plenty of inversion advantages; lower volume compared with the
conventional transformer-based topologies, cost-effective structures, reduced total harmonic
content etc. Nevertheless, for addressing the issue of DC current injection, they require extra circuits
to be installed. Furthermore, the lack of galvanic isolation induces hazardous charges between the
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surface of PV and the installed ground that may be dangerous for the personnel. However, several
transform-less inverter topologies have been proposed in the literature that can eliminate the
aforementioned problem [295] - [297].
Multilevel inverters result in staircase sinusoidal waveform that is closer to an actual pure sinusoidal
wave with low total harmonic distortion. Several DC voltage levels can be easily produced due to the
modular structure of PV arrays; therefore, multilevel topologies are fundamentally suitable for PV
systems. One of the most promising solution in the field of multilevel inverters is the cascade half
bridge inverter (CHB); the modular and scalable feature of the cascade inverter are the key
advantages of CHBs, as they may be extended to achieve even more number of levels [294].
4.2.1. Three phase two level inverter topology
The two level three phase inverter topology is the most reliable and well-established structure that
supports virtual inertia implementation either in virtual synchronous generators (VSG) or virtual
synchronous machines (VSM) [292]. Its well-known control logic and flexible design offers invaluable
solutions for test-bench development, which is intended for new virtual inertia algorithms testing.
While this topology is the simpler one among the synchronous transform-less inverters, advanced
circuity techniques can improve its performance dramatically; soft switching techniques, zero voltage
switch (ZVS) or zero current switch (ZCS) [298]. The classic one and the ZVS two level three phase
inverter topologies are illustrated in [298], respectively. In addition, the incorporation of new SiC
semiconductors leads inverter stage efficiency slightly over 99%, as referred in [299]. Thus, there is a
scheme with high efficiency that can support almost any new-coming virtual inertia algorithm with
robust control and with no high complexity.
Figure 4: a) Three phase two level inverter, b) Three phase two level inverter with ZVS [298].
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4.2.2. CHB topology
Recently, in the field of virtual inertia development from PV panels, CHB inverter topologies are
usually referred in the literature [297], [301]. Their quite straightforward structure along with their
modular and flexible nature offer unique aspects in terms of power quality and near actual sinusoidal
waveforms. The separate cell per cell maximum power point tracking (MPPT) that implement,
exceeds in terms of power efficiency even the well-established multi-string inverters. While the need
for numerus switches and power converters is tent to reduce the overall system efficiency, the
advent of new sophisticated Silicon carbite (SiC) semiconductor technology seems to preserve their
enhanced performance.
4.3. Microprocessors Several microcontrollers, digital signal processors (DSPs) or computer including dSpace, Microchip,
Texas Instrument or STMicroelectronics are applicable to implement ancillary services with suitable
sensors, interface and configuration [298]. New generation of microcontrollers like STM32 provide
useful tools based on human machine interface (HMI) of things, which enable engineers to easily
develop graphical user interface (GUI), supporting real-time supervision and reconfiguration [302].
Their relatively small size allows compact design, which MCU and converter units are enclosed in the
same package, reducing thus the overall system complexity. The Internet of Things (IoT) enables grid
to leverage the word network and pave the way for the future grid forms [303], where multiple
converters are locally connected and cooperate, such as Microgrid or even the upcoming Web of
Cells (WoC) [304].
4.3.1. Real-Time digital control
The real-time (RT) digital control can be implemented using several technologies, such as:
• Microcontroller units (MCU)
• Digital Signal Processor-Based Controllers (DSC)
• Field Programmable Gate Arrays (FPGA)
• RT Rapid Prototyping systems (i.e. DSpace platform)
• Programmable Logic Controllers (PLC)
While PLCs are used mainly in industrial environment, MCUs, FPGAs and Prototyping platforms are
dedicated for testing energy system applications such as RES, electrical vehicles etc.
Nowadays, modern MCUs include high-performance dual-core processors with 32-bit architecture,
sufficient amount of FLASH memory and plenty of peripherals: many analog to digital converters
(ADC), several PWM modules and a large variety of communication protocols [305]. Their very high
computational capabilities, translated into execution of million instruction per second (MIPS) and in
million floating point operations (MFLOPS). The second one is very useful for enhancing
computational performance in power converter applications which operate on complex numbers
represented by trigonometric functions or using complicated algorithms (e.g. Kalman filter or
recursive least square algorithm), where floating points are essential in terms of high precision [306].
Modern devices mainly use Harvard architectures that support two distinct paths for instructions and
data flows. Saving, thus, time because the buses operate independently and simultaneously.
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Therefore, the control system plays a vital role in any converter design. Today MCUs that support
DSP logics and FPGAs are the predominant hardware for power electronic converters. The MCU
executes all instructions in a sequential way using the CPU. In terms of multi-carrier processing needs,
FPGA based control schemes offer plenty of advantages because support parallel execution, unlike
DSPs [307].
4.3.2. Project evaluation cycle
Nowadays, power electronic converter control design is characterized by high complexity, thus high-
level design environments should be used. The sequence of power electronic converter development
starts with system-level simulation using programs like Matlab/Simulink, PSim etc; Next, hardware
development can be analyzed using circuit simulators like PSpice; they allow precise estimations,
optimizing power converter circuit performance [306]. Then, digital control can be developed and
tested using real-time prototyping methods, firstly in hardware-in-the-loop platforms such as DSpace
and next by MCU or FPGA, depend on system requirements.
GUIs of prototyping platforms offer valuable solutions for quick testing and development of
optimized control algorithms. Their main function is to generate codes in low level programming
languages automatically from structured block diagrams [307]. Thus, the engineers are allowed to
implement different control algorithms rapidly, using functionalities and peripherals which only MCU
and FPGA units can support.
4.4. Digital-Control and Ancillary Services
4.4.1. RoCoF measurement
One of the key elements that critically determines the performance of every virtual inertia support
scheme, is the ability of fast and accurate measurement of RoCoF. Since now this need was decent
covered by closed loop methods, where the most popular and mature technique is the PLL. While
the PLL is a well-established technique with robust design and trusted operation, it presents noise
sensitivity that leads to cumulative errors [308]. These kinds of divergence from the real measured
value may deteriorate the virtual inertia response and can be crucial for system reliability and
stability. Several studies attempt to address the later issue with the use of a double SOGI based
Frequency locked-loop (FLL) either in single phase [309], [310] or three phase applications [311].
Besides that, the implementation of FLL in the frame makes their analysis and tuning procedure more
complicated [312]. However, in the literature [310], [311] the FLL method is presented as the most
promising solution in terms of fast and accurate measurement of RoCoF.
4.4.2. Power Smoothing
While PV generation is clean and environmentally friendly, its uncertain power production is
inevitably its most important drawback. Natural conditions, such as light, temperature, climate
change and so on affect PV power fluctuations, resulting into significant challenges to the integration
of PVs in power grid systems. In recent years, the advent of ESS technologies provides new innovative
ways to overcome the inherent issues of PV power production. Integrating ESS or taking the
advantage from already installed storage units (i.e large UPS banks etc.), the active power output
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characteristic of the new hybrid PV-ESS systems may improve large power grids credibility and
stability, by increasing also the penetration level of PVs.
Not only the advantage storage units’ capabilities, but also the sophisticated control algorithms that
are employed by power converters, would facilitate PVs integration. In the relevant literature there
are plenty of proposed algorithms that are deployed based on advanced predictive control schemes,
such as model predictive control MPC [313]. The multi-parameter nature of PVs power smoothing
lead us to employ holistic monitoring techniques, which are continuously recording all factors
(voltage, current, SoC etc.) of power production, in every stage of production (ESS condition, PV type,
light condition, season etc.) [314]. Thus, good approximate estimation of hybrid PV-ESS system total
response is achieved. Parameters such as SoC of ESS and the PVs momentary power generation
capability, are of high importance in terms of a proper grid-tied operation [315]. As illustrated in Fig.
4a, the voltage and current from the ESS and the PV are collected by MCU, whereas instantaneous
calculations are forming the control sequence that converters would employ, as depicted in Fig. 4b.
The benefits from the incorporation of IoT in low level real-time control schemes are obvious,
weather forecast along with GPS/GIS sensors for better location accuracy, would help to achieve
higher level of PV integration with less negative impact. While MCU is calculating SoC, special
attention is paying on certain storage limits, avoiding over-charge or over-discharge as far as possible.
Thus, depending on PV anticipated active power curve and SoC level, the control algorithm
determines the proper injected active power from the ESS in order to smooth hybrid system
output [316]. Fig. 4b illustrates the multi-parameter control scheme which the MCU is called to
support.
4.4.3. Voltage Unbalance Mitigation
Conventional electrical networks are mainly designed to accommodate unidirectional power flow
rather than bidirectional. The reverse power flow from the upcoming grids with high penetration
level of PVs, has the potential to cause several issues in terms of grid stability; overvoltage, cable
overheating, often voltage unbalances and reduce network overall efficiency. Specifically, voltage
unbalances are likely to become a significant barrier in terms of increased penetration level of PVs in
European electrical systems, due to restricted limit of 2% in EU [317]. There are several proposed
strategies that are employed to address voltage unbalances; double droop control method, active
power filters [318]. Most of them take the advantage of the forthcoming widely installed ESS,
compensating local voltage unbalances and keeping high grid system efficiency. The growth of
rooftop PV systems along with home installed ESS remodel the conventional LV networks, rising the
need for new decentralized voltage-control strategies [319].
4.4.4. Voltage regulation
The mass integration of DERs in power networks has arisen several problems related with network
stability. From the other hand, a cooperative operation of hybrid DERs under scalable control
architecture can enhance network reliability against their uncertain power production. In the
relevant literature [320]-[322] there are many proposed strategies which focus on the local voltage
control, employing PVs storage systems. The desirable result is achieved through voltage dependent
battery charging, followed by reactive power provision and PV power curtailment. Nevertheless, the
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applicability of these methods under different SOC levels is not sufficiently covered. Moreover,
coordinated control strategies based on layered local communication system can suppress voltage
fluctuations in a critical node, employing droop-based or distributed control methods [323]. The
incorporation of voltage profile estimation in distributed control algorithms can enhance voltage
regulation locally.
It is regarded that behind a typical Microgrid PCC there is a DER that consist of a PV with an ESS and
a dc/ac inverter, and PCC can be regarded as a node. The primary control effort to regulate the
voltage in this specific node, this could be implemented with the proper cooperation of node
components. Its parts could be controlled by means of an FPGA that generates all the needed control
pulses and implements the related control algorithms for the dc/ac inverter and for PV and ESS
converters in parallel. In this case there are obviously not synchronization issues among node parts.
In this node, while the voltage exceeds the predetermined limits, it is necessary to change the power
injection that provided by ESS. The power control of the ESS adjusts the output power depend on
exceeded voltage limit; with active power curtailment mainly during peak PV generation or power
injection during load demand.
From the other hand, if the use of the same computational unit are not allowed, then the
employment of several communication protocols are inevitable. Ethernet, WiFi, ZigBee or CAN bus
protocols are used in terms of accurate and effective cooperation without latencies.
4.5. ESS integration The integration of the ESS and PV in a common DC-Link by DC/DC power converters, which is
connected to AC grid through aν DC/AC inverter would pave the way for the implementation of more
effective voltage-control strategies. Thus, ESS can provide enchantment to grid stability and ensure
the safe and normal LV grid operation, during undesired weather conditions. In addition, the
cooperative operation of multiple locally installed PV-ESS system under a proper decentralized
control, can alleviate overload and overvoltage in residential LV network.
4.5.1. DC/DC power converter for ESS integration
The most preferred type of DC/DC power converters that integrate ESS in electrical power systems,
is the step-up topology. Since the majority of DRESs require step-up converters for grid interfacing.
In the literature several step-up DC/DC converter topologies are proposed; two-inductor boost
converter, interleaved boost converter with two or three levels, synchronous rectification boost
converters [324].
One of the key features that render a DC/DC step-up converter topology is its bidirectional design.
While some DC/DC step-up converter topologies are presenting unique bidirectional capabilities,
their high control complexity render them inappropriate for multi-parameter control architectures.
Interleaved boost converter with two or more levels and synchronous rectification incorporation is
the most promising solution. In the literature the multi-level dc/dc converters are often refereed as
multi-phase [325]. Using this technique, the power stage of the dc/dc converter is divided into several
smaller power stages (two, three or more). However, the size and the current stress are greatly
reduced, leading to higher efficiency levels. In multi-stage power schemes, such as in our case, where
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the ESS and the PV are connected to a common DC-Link through dc/dc converters and next to AC grid
through DC/AC inverter, maintaining the overall system efficiency as high as possible is of high
importance. This design has several advantages such as filter reduction, enhanced dynamic response,
improved thermal management and so on. The power components can be surface mounted devices
(SMDs) and the inductor can be integrated in the PCB, increasing thus its flexibility and applicability
(i.e. integrated in electric vehicles [326]). In terms of control implementation, there are many signals
to generate, depending on desired level design, all of them can be generated by an advanced MCU
or cooperative by an MCU and an FPGA in a Master-Slave control scheme.
4.6. Communication Future power distribution systems can be benefited from the systematic coordination of DERs. More
specifically, DERs can be cooperated to control the main quantities across a grid locally, such as power
line flows, frequency deviations and voltage magnitudes by controlling both their real and reactive
power injection in real time. In the literature, this is referred as optimal power flow (OPF), whereas
many distributed optimization algorithms are proposed to ensure network reliability [327], [328]. The
most of them are requiring a preexisting strong connected communication network [329], whereas
the local information can be transmitted across all DERs, despite that, the related communication
technologies are still under-deployed with limited available capabilities. The widely known methods
are summarized as follows:
• Fiber optic cables, through Ethernet protocols
• Power line carrier, through the industrial well-established PLC
• Multi-point microwave antennas, the classic WiFi protocols
• Combination of the latter techniques
Every communication protocol is characterized by its own inherent drawbacks, whereas in general
terms are strongly dependent with a cost/latency factor [330]. In home area network, where the
solar PV the ESS and the inverter are located, low-power wireless personal area network (LoWPAN),
narrow or board band PLC are used. The neighborhood area networks could be interconnected with
WiFi ad-hoc (mobile network), board-band WiFi (WiMax) or Ethernet cables, whereas the wide area
networks are employed high-speed Ethernet. Obviously, the multilayered communication
architectures suffer from unacceptable time delays, bandwidth restrictions and high installation cost.
For these reasons, the practical implementation of the distribution system management algorithms
has been challenged, even in decentralized low communication-based systems [331]. In recent
literature, there are a lot of works that attempt to address this communication barrier, incorporating
sophisticated algorithms based on advanced control theory or communication-less techniques; for
instance, hybrid voltage strategies that depend on neighborhood nodes support [332], [333]. Local
control techniques that require not sharing information among DERs based on local voltage
measurements [334] or game theory control [335] (regarded every DER as a player that try to
maintain its stability), decentralized stochastic control methods that are based on actuator type
systems [336]. However, it has been proven that these local schemes with a weak or no
communication bonds, are prone to make fail decisions and lead to loss of DSM performance.
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The involvement of advanced estimation techniques strongly related with the geographical position
and historical data of the intermitted source, appears to be one promising solution in this way [337].
Since the well-established 4G communication network or the upcoming 5G are not incorporated in
real time power system control yet, new probabilistic methods have to be found in order to facilitate
the advent of DER dominated network. In addition, the implementation of communication networks
lacks of realistic scenarios such as radio frequency disturbances, noise spikes or communication loss
due to weather conditions.
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Principal Investigator Research Team Theofilos Papadopoulos 1) Nikolaos Papanikolaou 2) Dimosthenis Peftitsis
3) Eleftherios Kontis 4) Georgios Kryonidis
5) Angelos Nousdilis
6) Kalliopi Pippi