Research ArticleAn Advanced Multicarrier Residential Energy Hub SystemBased on Mixed Integer Linear Programming

Yue Yuan,1 Angel A. Bayod-Rújula ,2 Huanxin Chen,3 Amaya Martínez-Gracia,4

Jiangyu Wang,3 and Anna Pinnarelli5

1China-Eu Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China2Department of Electrical Engineering, University of Zaragoza, Spain3Department of Refrigeration & Cryogenics, Huazhong University of Science and Technology, 1037 Luoyu Road,430074 Wuhan, China4Department of Mechanical Engineering, University of Zaragoza, Spain5Department of Mechanical, Energy and Management Engineering, University of Calabria,Arcavacata of Rende-Cosenza 87036, Italy

Correspondence should be addressed to Angel A. Bayod-Rújula; aabayod@unizar.es

Received 27 February 2019; Accepted 14 May 2019; Published 9 June 2019

Academic Editor: Francesco Riganti-Fulginei

Copyright © 2019 Yue Yuan et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This work proposes a multicarrier energy hub system with the objective of minimizing the economy cost and the CO2 emissions of aresidential building without sacrificing the household comfort and increasing the exploitation of renewable energy in daily life. Theenergy hub combines the electrical grid and natural gas network, a gas boiler, a heat pump, a photovoltaic plant, and aphotovoltaic/thermal (PV/T) system. In addition, to increase the overall performance of the system, a battery-based energystorage system is integrated. To evaluate the optimal capacity of each energy hub component, an optimization schedulingprocess and the optimization problem have been solved with the YALMIP platform in the MATLAB environment. The resultshowed that this advanced system not only can decrease the economic cost and CO2 emissions but also reduce the impact toelectrical grid.

1. Introduction

As the climate becomes increasingly polarized today, thedemand for electricity and heating load of buildings alsoincreases daily, sometimes even accounting for half of thetotal building energy consumption. Relying only on the elec-trical grid and natural gas network input to meet customerdemand, energy utilization efficiency will be extremely low.Not only can the grid meet a huge pressure during peak loadperiod, but it will also produce large amounts of CO2 whichwill aggravate urban heat island effect, which makes thedemand of polarization phenomenon more serious. In recentyears, although renewable energy generation has been greatlydeveloped, the constraint in terms of transport capacity of the

electrical grid still becomes a bottleneck of its participation inenergy supply. In last years, in order to provide a solution tothese limits, a large number of small renewable energy gener-ators have been connected to the distribution networks [1].

An energy hub is a multicarrier energy system consistingof multiple energy conversion, storage, and/or network tech-nologies and is characterized by some degree of local control.Conception of an energy hub (EH) system has been firstproposed on the research project “Vision of Future EnergyNetwork” by ETH, Zurich, in 2007 [2]. In this future visionof energy networks, an energy hub system is a highly abstractunit structure, providing a great opportunity for systemschedulers and operation workers to create a more efficientsystem [3]. An energy hub is an initial part of a multienergy

HindawiInternational Journal of PhotoenergyVolume 2019, Article ID 1384985, 12 pageshttps://doi.org/10.1155/2019/1384985

system, which can accommodate various forms of energyinput and diversified load types [4]. Because of this diversi-fied form of energy carrier, the energy hub system can realizecollaborative optimization for various forms of energy [5]. Inorder to ensure that the energy hub is operated under asecure and economic and environmentally friendly condi-tion, the research for energy hub systems mainly focuses onthe component type or the capacity of an energy hub andthe energy hub optimal dispatch [6–9]. Different from usingtraditional flat traffic, Le Blond et al. [10] introduced“dynamic traffic” to drive energy storage system operationto control energy hub system operation under minimum costand CO2 emissions. Beigvand et al. [11] proposed a new algo-rithm named SAL-TVAC-GSA to dispatch energy hub eco-nomic cost. In [9], Setlhaolo et al. present a residentialenergy hub model for a smart home as a modified frameworkof conventional energy hubs in a smart grid with consider-ation of heat pump water heater, coordination of sources,and carbon emission. Lingang Industrial Park is an engineer-ing example [12], whose results provide a technical supportfor the construction of a resource-saving and environment-friendly harbour. In [13], a residential building and its elec-trical equipment are modelled as an energy hub system,including washing machine, dryer, HVAC system, refrigera-tor, and lighting equipment, to minimize energy cost, carbonemission, and peak load for optimal scheduling under thepremise of maintaining the comfort of the users. In addition,a cloud computing framework is present to achieve the effec-tive management of data and information. Ma et al. [8] pre-sented a community micro energy grid in four differentscenarios on a typical summer day, and the roles of renewableenergy, energy storage devices, and demand response are dis-cussed separately.

Due to the fact that an energy hub is an efficient meansfor the optimal exploitation of renewable energy generation,research focuses are generally concentrated on variablekinds of energy inputs [14–16]. Sharif et al. [17] presenteda simulation model for an energy hub which major energysource is renewable energy (wind and solar energy) and nat-ural gas. Ha et al. [18] proposed an energy hub system forresidential buildings with solar energy and battery-basedenergy storage systems, and the results showed that thisenergy system structure conforms to the characteristics ofresidential buildings’ energy consumption. Furthermore, itcan reduce costs and save energy consumption at the sametime. However, little research considers the integration ofPV/T systems into an energy hub. PV/T can generate waterheating and electricity at the same time [19], being an idealcomponent for residential energy hub system. This paperproposes a multicarrier energy hub (EH) system based onsolar PV and PVT systems and a battery-based energy stor-age system. Moreover, an optimal scheduling strategy isproposed to calculate the optimum capacity of each compo-nent in order to minimize the energy cost and the CO2emission. The optimization problem has been solved withthe YALMIP platform in the MATLAB environment. Theproposed EH system is tested for a residential applicationaccording to the characteristics of buildings of apartmentsin Zaragoza, Spain.

2. Description of the Building SimulationModeland Optimization Process

A residential building located in Zaragoza, in the centre ofSpain, with a simple but typical architecture as shown inFigure 1 has been simulated in EnergyPlus software. Cli-matic parameters, including ambient temperature, solar irra-diation, and wind speed, have been taken from the Zaragozalocal dataset. The simulation building is a 3-storey resi-dential building, 1660.73m2, with five 3-bedroom apart-ments per floor. It has been considered that 4 people live ineach apartment.

Solar irradiation in Zaragoza is abundant (see Figure 2),so it is reasonable to introduce photovoltaic (PV) systemsor PV/T systems into energy hubs to decrease the energycosts and the CO2 emissions.

The electricity price has been calculated from hourly localreal-time price in Spain in 2017, as it is shown in Figure 3.The considered gas prices were 0.0667 euro/kWh and0.0865 euro/kWh according to Spain gas prices for house-hold consumers, all taxes and levies included in the firstand second semester of 2017, respectively [20].

The daily energy consumption of the simulation buildingfor four seasons is shown in Figure 4; blue bars represent elec-tricity (including air conditioning) consumption and redcurve represents heating demand (including heating and hotwater demand). The highest electricity demand can approach220 kWh per day (sum of the blue bar in Figure 4 for summer)and the highest heating demand is approximately 55 kWhper day (sum of grey values in Figure 4, winter).

Relying only on the electrical grid and natural gasnetwork to satisfy electricity and heating demands of thisbuilding, the economic cost is 11713 euro/year and the CO2emission is 21711 kg/year (see Table 1).

An EH system has been so developed with the aim tominimize the energy cost and the CO2 emissions but alsofor relief grid’s pressure. Firstly, in accordance with the build-ing’s energy consumption pattern, an EH system with only agas boiler (GB), a heat pump (HP), a PV plant (PV), and aPV/T system has been designed. A capital cost of 800euro/kW has been considered for the GB and 1000 euro/kWfor the HP, both with an estimated lifetime of 20 years. In thecase of the PV plant, a capital cost of 1500 euro/kWp hasbeen considered (including inverter, wire, and protections)and 700 euro/m2 for the PVT systems and their lifetime hasbeen considered to be 30 years. Later, the EH system has beenimproved with the integration of a BESS. The EH systemdesign has been optimized by the optimization schedulingprocess as shown in Figure 5. The optimization problem issolved by using Mixed Integer Linear Programming (MILP)on the YALMIP platform in the MATLAB environment.Since there are too many elements included in the EH sys-tem, it is difficult to get the optimal solution directly througha single-layer optimization process. Therefore, the EH systemdesign is threefold. Firstly, only the HP and the PV (with var-iable efficiency) are considered as elements involved in theoperation of the EH system and for this configuration theiroptimum capacity is obtained. Then, operating the EH sys-tem under this optimum capacity, a PV/T (with variable

2 International Journal of Photoenergy

thermal efficiency) is introduced into the EH system and theoptimization schedule is solved again to calculate the optimalPV/T capacity. Finally, the BESS is integrated and the finaloptimal capacity of each element of the EH system isobtained. The entire process is modelled taking into accountthe dynamic price of electricity and natural gas.

The optimal operation model is formulated with the mul-tiple objective of minimizing the entire cost, CO2 emission,and peak load:

min 〠T−8760

t−1Pele t pele t Δt + Pgas t × pgas t Δt + Finstall + f O&M,

min 〠T−8760

t−1Pele t Eele t Δt + 〠

T

t−1Pgas t Egas t Δt,

min Pmaxele t ,

1

where Pele t and Pgas t represent the price of electricity andgas from the main grid, respectively. Eele t and Egas trepresent the CO2 emission index of electricity and gas,respectively. Finstall represents the installation fee of allcomponents, and FO&M represents the operation and man-agement fee of all components.

Electricity balance constraints and heat balance con-straints can be formulated as equations (2) and (4),respectively.

Lele t = eele,grid t + eele,PV t + eele,PV/T t

− eele,hp t − Sele,B t ,2

eele,grid t = Pele,grid t ηele,grid t ,

eele,PV t = APVG t ηele,PV t ,

eele,PV/T t = APV/TG t ηele,PV/T t ,

3

where eele,grid t is the electricity supplied from the main grid,eele,PV t is the electricity supplied from the PV panel, andeele,PV/T t is the electricity supplied from the PV/T panel;APV and APV/T are the surface areas of the PV panel andthe PV/T panels. G t is the solar irradiance. eele,hp t repre-sents the electrical input of the heat pump at time, which isdetermined by heating balance constraints. And Sele,B t isthe energy input or output from the battery.

Lheat t = hheat,grid t + hheat,PV/T t + hele,hp t , 4

hheat,grid t = Pheat,grid t ηheat,grid t ,

hheat,hp t = COPhp t eele,hp t ,5

where hheat,grid t and hheat,hp t represent the heating sup-plied from natural gas net and heat pump, respectively, andhheat,PV/T t is the heat supplied from the PV/T panel.

Solving the layer-by-layer optimization is helpful to knowthe impact of each element on the EH system and simplifythe solving process at the same time. The building perfor-mance throughout the year is valuated with all the compo-nents at its optimum capacity.

3. The EH Structure and Matrix Representation

The first structure of the EH system proposed for the build-ing is shown in Figure 6. Initially, a gas boiler (GB), a heatpump (HP), a PV plant (PV), and a PV/T system are consid-ered besides the electrical grid and the natural gas network.

Figure 1: Building structure simulated in EnergyPlus software.

0

Time (hour)0 2 4 6 8 10 12 14 16 18 20 22 24

200

400

Irra

diat

ion

(W/m

2 )

600

800

1000

JanFebMarApr

MayJunJulAug

SepOctNovDec

Figure 2: Average daily solar irradiation in Zaragoza for eachmonth.

60

0 2 4 6Month

8 10 12

80

100

120

Cos

t (eu

ro/M

Wh)

140

160

180

200

Figure 3: Actual hourly real-time electricity price in 2007.

3International Journal of Photoenergy

The basic mathematical model of the EH system is [21]

L = f P , 6

where L expresses the energy (electricity and heating)demand and considers the power supply.

The basic matrix function of the EH system can bewritten as

L1

L2

⋮

Ln n×1

=

c11 c12 ⋯ c1m

c21 c22 ⋯ c2m

⋯

cn1 cn2 ⋯ cnm n×m

P1

P2

⋮

Pm m×1

7

In this function, cijth is the coupling factor, which rep-

resents the conversion efficiency between the ith energyinput and the jth energy output.

In this framework, the relationship between the demandside and the supply side of the EH system is formulated witha coupling matrix indicated as follows:

L = C1 C2P

R, 8

L = C1P + C2R 9

Energy demand matrix L is equal to coupling matrix Ctimes the installed generating capacity of P and the renewableenergy R.

C1P can be developed as

C1P = ηgrid ηheatpump ηboiler

Pgrid

Pheatpump

Pboiler

, 10

141210

86420

1:00 4:00 7:00

Spring electricity loadSpring heating load

10:00 13:00

Spring

16:00 19:00 22:00

1614121086420

(a)

141210

86420

1:00 4:00 7:00

Summer electricity loadSummer heating load

10:00 13:00

Summer

16:00 19:00 22:00

1614121086420

(b)

141210

86420

1:00 4:00 7:00

Autumn electricity loadAutumn heating load

10:00 13:00

Autumn

16:00 19:00 22:00

1614121086420

(c)

141210

86420

1:00 4:00 7:00

Electricity loadHeating load

10:00 13:00

Winter

16:00 19:00 22:00

1614121086420

(d)

Figure 4: Daily energy consumption of the simulation building for different seasons.

Table 1: Cost, energy consumption, and CO2 emissions for the traditional supply.

Cost(euro/year)

CO2 emission(kg/year)

Gas consumption(kWh/year)

Electricity consumption(kWh/year)

11713 21711 20518 62589

4 International Journal of Photoenergy

Design an initial energy hub system with heatpump and PV system

Select multiple objectives for optimization

Is it theoptimalvalue?

Yes

No

Simulation software

Input data (whole year)

Main grid electricity and natural gas feeComponents install, operation and fixed fee

Meteorological data

Occupant schedule

Electricity demand

HVAC system demand

Heating demand

Final optimization schedule

Each components operation condition

Annual economic cost Annual CO2 emission

Real data input

Typical meteorological year data

Carbon dioxide emission index

Electricity & natural gas price

Components install, operation andmanagement fee

Design multiple-carrier energy hub system

Building energy consumption form analysis

Energy generation and conversion componentselection

Energy hub optimization scheduling withminimize cost, emission and peak load.

Operating energy hub under electricity and heatinginput and output conservation

Introduce PV/T system into the original system

PV/T systemmodel establish

PV/T systemcapacity validation

Variable proportion ofPV/T system to entire PV

system

Select multiple objectives for optimization

Is it theoptimalvalue?

No

Yes

Introduce energy storage system into the originalsystem

Battery systemmodel establishHot water tankmodel establish

Select multiple objectives for optimization

Is it theoptimalvalue?

Yes

No

Each component design with capacity limitation andcost calculation

Components contact with input-output balancelimitation

Heat pumpmodel establish

PVmodel establish

Heat pumpcapacity

validationPV capacityvalidation

Battery systemcapacity validation

Hot water tankcapacity validation

Figure 5: EH system optimization scheduling process.

5International Journal of Photoenergy

C1P = ηgridPgrid + ηheatpumpPheatpump + ηboilerPboiler , 11

ηgridPgrid =ηgrid,ele

ηgrid,heat

Pgrid,ele

Pgrid,heat,

ηheatpumpPheatpump =ηheatpump,ele

ηheatpump,heat

Pheatpump,ele

Pheatpump,heat,

ηboilerPboiler =ηboiler,ele

ηboiler,heat

Pboiler,ele

Pboiler,heat

12

In (7), due to the value of ηboiler,elePboiler,ele which is equalto 0, the equation can be represented as

C1P =ηgrid,elePgrid,ele + ηheatpump,elePheatpump,ele

ηgrid,heatPgrid,heat + ηheatpump,heatPheatpump,heat + ηboiler,heatPboiler,heat

13

C2R can be represented as

C2R =ηPV

ηPV/T

PPV

PPV/T

, 14

C2R =ηPV,elePPV,ele + ηPV/T,elePPV/T,ele

ηPV,heatPPV,heat + ηPV/T,heatPPV/T,heat15

Because the value of ηPV,heatPPV,heat is equal to 0, C2R canbe represented as

C2R =ηPV,elePPV,ele + ηPV/T,elePPV/T,ele

ηPV/T,heatPPV/T,heat16

For each EH system component, the following parame-ters are included: capacity, efficiency, capital cost, fixed cost,variable cost, and lifetime as reported in Table 2. The electric-

ity net emission factor considered is 0.28 kg/kWh and0.204 kg/kWh for natural gas [22].

In real-life application, the PV efficiency is not a constantvalue; it varies with such parameters as solar irradiation,environmental temperature, and PV panel surface tempera-ture. The working temperature of the cells (Tc) dependsexclusively on the solar irradiation G and the ambient tem-perature (Ta) according to the linear function

Tc − Ta = C2G, 17

where C2 is represented by

C2 =NOCT °C − 20

800W/m2 18

NOCT in equation (18) is the nominal operating celltemperature defined as the temperature reached by opencircuited cells in a module under 800W/m2 cell surface irra-diance and ambient temperature of 20°C and so on. NOCT ofa typical commercial module is approximately 45 ± 2°C,accordingly, C2 is approximately equal to 0.3°C/(W/m2).

Therefore, if the PV nominal power value under STCcondition Pn is known, it will be possible to figure outthe PV power value at any time via

Pm = PnGGSTC

1 − γ Tc − 25 , 19

where GSTC is the irradiation under standard test conditions(STC), which is equal to 1000W/m2.

In a conventional PV plant of crystalline silicon, the con-version efficiency is in the range 15%-20% so 75% to 80% ofthe solar energy is not being used effectively [22]. Besides, theunused solar energy will heat up the panel, causing a decreasein the efficiency of the electricity generation.

Transferring the thermal energy to a heat transfer fluid,we obtain useful thermal energy and refrigerate the photo-voltaic cells at the same time. This system, which can makeuse of solar light and thermal simultaneously, is calledphotovoltaic/thermal integrated system (PV/T system)(Figure 7).

Electricity grid

Heatpump

Photovoltaic/thermal

planeElectricitydemand

Heatingdemand

Photovoltaic

Nature gas grid

Gas boiler

Figure 6: EH system structure for the building.

6 International Journal of Photoenergy

The calculation about the thermal part of the PV/Tsystem is shown as follows:

QS =G × AC , 20

QC = ηh ×QS, 21

QC = cpw ×mwC × twe c − tws c , 22

Qd = AC ×Ud × twe c − t0 , 23

Qcons = cpw ×mr × td ini − tpipe , 24

inct

0QC −Qd −Qcons = cpw ×mwd × td fin − td ini

= cpw ×mr × tconsig − td fin

25

QS represents the radiation projected onto the PV plane, QCis the heat absorbed by the panel, ηh is the heating efficiencyof the PV/T systems, mwC is the fluid mass under the panel,twe c and tws c are output and input temperature of underpanel fluid, respectively, Qd is the heat loss of natural con-vection heat transfer between the tube and the environment,and t0 represents the ambient temperature. Qcons refers tothe heat loss of direct heat transfer between the fluid inthe pipe and the pipe wall. td ini and td fin are fluid inputand output pipe temperature, respectively. tpipe is the tem-perature of pipe shall and tconsig is the final demand fluidtemperature. Qa is the auxiliary energy supply. The thermo-dynamic relationship characterizing the PV/T is shown inFigure 8.

In addition, the thermal part of the PV/T systemefficiency ηh is influenced by many factors, such as

solar irradiation and pipe fluid temperature, as repre-sented in

ηh = η0 − a1 ×Te − Ta

G− a2 ×

Te − Ta

G26

In this equation, η0 means the optical performance of thePV/T system, a1 and a2 are the thermal loss coefficients, in thispaper set as 3.3 (W/m2)/K and 0.018 (W/m2)/K2, respectively,and I represents solar irradiation. Te is the average value ofpipe fluid temperature and Ta is the ambient temperature.

First of all, the scale of the PV/T system water tank isshown in Table 3.

Vd = πDd

2

2Hd , 27

Ad = 2πDd

2

2+ πDdHd 28

The temperature of the PV and PV/T panels is shown inFigure 9. Obviously, the PV/T surface temperature is higher

Table 2: EH system component parameters.

Component name Efficiency (%) Capital cost (€/kW or €/m2) Fixed cost (€/kW) Variable cost (€/kWh) Lifetime (years)

Gas boiler 70 800 10 0.02 20

Heat pump 3.2 (COP) 1000 8.7 0 20

PV Variable 250 2 0 30

PV/T Variable 700 10 0 30

Photoelectrictransformation

Electricityoutput

Opto-thermaltransformationSolar

irradiation

HeattransferPV/T

plan

e

Auxiliary heating

Heatingoutput

Figure 7: The schematic of the PV/T system.

Energydirection

Qs Qc

Qa

Qu

Qcons

Qd

Figure 8: Thermodynamic relationship in the PV/T systems.

7International Journal of Photoenergy

than the PV panel in summer season. In order to decrease thePV/T surface temperature, the hot water tank size should beenlarged. In this case, the hot water tank size is related topanel area, when Vd/Ac value is increased from 0.1 to 0.2,the hot water tank will become bigger gradually. Figure 9 pre-sents the panel temperatures for Vd/Ac = 0 2.

The thermal efficiency of the PV/T system is shown inFigure 10.

The average daily electricity generation for 1m2 PV andPV/T panel with an 18% efficiency is shown in Figure 11for every month. A PV/T system can generate more electric-ity in winter months and part of spring and autumn.

4. Energy Hub Results

The optimum values of the EH system components’ capacityfor the building under analysis were calculated in a previouspaper [22] for a 20 kW HP and 65m2 of PV systems. Withthe combination of PV+PVT, the best results were obtainedfor a HP of 20 kW, 36m2 of PV modules, and 30m2 ofPV/T. With the operating condition of 20 kW HP and65m2 of PV systems, the cost reaches the minimum value10727 euro/year. The CO2 emission achieved 13560 kg/yearat the meantime. Comparing these values with the total sup-ply of electricity and heat from the electrical grid and naturalgas network, respectively (see Table 1), a reduction of 8.42%in cost was obtained with a reduction of 37.54% in CO2 emis-

sion. Otherwise, the results considering the optimal capacityof PV/T and PV systems (36m2 of PV and 30m2 of PV/T)are shown in Table 4. Compared with the result shown inTable 1, a great improvement is reached. A decrease of10.61% in cost with a reduction of 30.02% in electricity con-sumption and 60.07% in gas consumption is achieved. Areduction of 39.66% in CO2 emissions is also obtained.

5. Energy Hub with a Battery-Based ESS

The renewable generation is sometimes greater than cus-tomer’s demand and part of electricity generated from a

Table 3: Hot water tank dimension.

Name Signal Value Unit

Tank volume Vd 0.326 m3

Panel surface area Ac 3.26 m2

Tank height Hd 0.744 m

Tank bottom diameter Dd 0.744 m

Tank surface area Ad 5.22 m2

35

30

25

20

Tem

pera

ture

15

10

5

0

3530

4540

5550

60

PV in Jan

252015

Time (hour)0 10 20 30

PV/T in JanPV/T in JunPV in Jun

Figure 9: Panel temperature comparison between the PV and PV/Tsystems.

0.0

0.2

0.4

2 4 6 8 10 12 14 16 18 20 22 24Time (hour)

Effic

iency

JanFebMarApr

MayJunJulAug

SepOctNovDec

Figure 10: Daily efficiency for different months.

0.0Apr Aug

MonthDec

0.3

0.6G

ener

atio

n (k

Wh)

0.9

1.2

PVPV/T

Figure 11: Average daily electricity generation for 1m2 of PV andPV/T panel for every month.

Table 4: Cost, electricity and gas consumption, and CO2 emissionsfor the optimal sizing (20 kW of HP, 36m2 of PV, and 30m2 ofPV/T).

Cost(euro/year)

Electricityconsumption(kWh/year)

Gasconsumption(kWh/year)

CO2emission(kg/year)

10470 43800 4419 13100

8 International Journal of Photoenergy

PV plant must be injected into the electrical grid orwasted. In order to improve the EH system performance,a BESS is integrated. The BESS provides a solution incase the renewable energy cannot be injected due to theregulation code (zero-injection schemes) or due to techni-cal restrictions at that time. It is even able to providevoltage and frequency support and contribute to demandresponse procedures such as peak shaving. So, theadvanced EH system structure is shown in Figure 12 withthe BESS integrated.

In this work, the battery is optimally charged and dis-charged to compensate the generation fluctuation of the PVand PV/T. The BESS dynamic model used is

E × SOCt = E × SOC0 + ts 〠t

γ=1ηcPb,γ −

Pb,γ

ηd, 1 ≤ t ≤N

29

E is the BESS capacity, SOC0 is the initial SOC of theBESS, SOCt is the SOC at the given time t, Pb,γ and Pb,γ

are the continuous variables at time step γ, and ts∑tγ=1ηcPb,γ

Pe

Pg

Heat pump

Gas boiler

Photovoltaic

PV/T

Le

Lh

Battery

Energyhub

Gas routeElectricity routeHeating route

Figure 12: Ultimate energy hub structure for a building.

Table 5: Advanced EH system performance for several BESSsizes.

Batterycapacity(kWh)

Cost(euro/year)

Electricityconsumption(kWh/year)

CO2 emission(kWh/year)

1 10211 41690 12520

2 10205 41650 12510

3 10200 41620 12500

4 10195 41590 12500

5 10190 41560 12490

6 10185 41530 12480

7 10179 41500 12470

10 10165 41410 12450

12 10214 41340 12430

15 10230 41280 12410

0.14

0.16

Elec

tric

ity p

rice (

euro

/kW

h) 0.18

Spring

3 6 9 12 15Time (hour)

18 21 24

SummerAutumnWinter

Figure 13: Daily electricity price for different seasons.

9International Journal of Photoenergy

and ts∑tγ=1 Pb,γ/ηd are the BESS energy during the charging

and discharging period, respectively.BESS constraints are applied to the model:

0 2 ≤ SOCt ≤ 0 8, t = 1,⋯,N , 30

Pb,t × Pb,t = 0, t = 1,⋯,N 31

N is the number of sampling intervals; in this case, itis 8760.

The capacity limits are given in equations (30) and (31),which does not allow the BESS to charge and discharge atthe same time. This constraint also permits the idle stateof BESS.

The BESS involved here is an ideal model, which has alinear charge state and a SOC around 0.2 to 0.8. The optimi-zation objective of BESS is the same as the previous system,minimum cost and minimum CO2 emission. Due to theeconomic goal of the whole system, the BESS will becharged during the low electricity price period, and whenthe electricity price is high, it will discharge energy to satisfythe demand.

Now the matrix of this advanced EH system is shownas follows; S is the storage coupling matrix and E is thestorage energy.

L = C1P + C2R − SE 32

Table 5 shows the results of the simulation for differ-ent sizes of battery. The lowest cost is obtained for a bat-tery of 10 kWh of capacity. Comparing with the results ofTable 4, 2.91% cost and 5.46% CO2 emission will be saved.Comparing with the results of Tables 1, 13% cost, 42.7%CO2 emission, and 33.8% will be saved. Therefore, thesimulation results confirm that adopting the proposedadvanced EH system and the related optimization schedul-ing process not only significantly promotes efficiency andcost savings but also provides relief from the global pres-sure of greenhouse effect.

Four representative days for the four seasons of the yearhave been selected. Daily electricity prices for these days arepresented in Figure 13. In Figure 14, the energy absorbedfrom PV, PV/T, and electrical grid for these representativedays is shown.

14

12

10

8

6

4

2

0

Spring

Time

Supp

lied

from

grid

14

12

10

8

6

4

2

0

Summer

Time

Supp

lied

from

grid

14

12

10

8

6

4

2

0

Autumn

Time

Supp

lied

from

grid

14

12

10

8

6

4

2

0

Winter

3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00

3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00Time

Supp

lied

from

grid

PVT generationPV generation

Supplied from gridLoad

Figure 14: Energy structure in the proposed energy hub for different days of the four seasons.

10 International Journal of Photoenergy

Sometimes the supply is not totally equal to the demanddue to the presence of BESS. When electricity price is high,the EH system avoids the supply of electricity from the elec-trical grid. In Figure 14, it is easy to find out that the EH sys-tem generates more electricity than the demand in autumnand winter which is related to the fact that electricity priceis higher in these two seasons; indeed, the EH system choosesdischarging via BESS to meet the customers’ demand.

During the high demand hours, the EH system showedan outstanding performance for reduction of the absorptionof energy from the electrical grid (Figure 15). Calculatingaccording to that high demand hours from 8:00 a.m. to16:00 p.m., the EH system can reduce 65.3%, 61.6%, 57.9%,and 33.9% absorption from the electrical grid. Meanwhile,during this period, 6.21 euro, 7.95 euro, 6.37 euro, and 4.14euro are saved per day, respectively, for this building. Besides,

due to the function of BESS, the electrical grid will supplymore electricity when the price is relatively low.

6. Conclusions

This paper proposes an advanced multicarrier energy hubintegrating not only generation components such as PVand PV/T systems but also a battery-based energy storagesystem to improve the performance. The optimal capacityof each component of the multicarrier energy hub in orderto minimize the energy cost and the CO2 emissions is basedon the use of an optimization scheduling process solved onthe YALMIP platform in the MATLAB environment. Thesimulation results confirm that large-scale utilization ofthe proposed approach will lead to significant cost andCO2 savings.

150.18

0.16

Euro

0.14

12

9

6

3

04 8 12 16 20 24

Spring

Time (hour)

kWh

Power loadMain grid inputElectricity price

(a) Spring

150.18

0.16

Euro

0.14

12

9

6

3

00 8 16 24

Summer

Time (hour)

kWh

Power loadMain grid inputElectricity price

(b) Summer

150.18

0.16

Euro

0.14

12

9

6

3

00 8 16 24

Autumn

Time (hour)

kWh

Power loadMain grid inputElectricity price

(c) Autumn

150.18

0.16

Euro

0.14

0.13

12

9

6

3

08 16 24

Winter

Time (hour)

kWh

0

Power loadMain grid inputElectricity price

0.17

0.15

(d) Winter

Figure 15: Comparison between electrical grid supply without the EH system and with the advanced EH system for different representativedays for the four seasons.

11International Journal of Photoenergy

Data Availability

The data used to support the findings of this study areincluded within the article.

Conflicts of Interest

The authors declare that there is no conflict of interestregarding the publication of this paper.

Acknowledgments

The authors gratefully acknowledge the support of NationalNatural Science Foundation of China (Grant 51876070)and (Grant 51576074).

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