Design of Isolated Microgrid System Considering Controllable EV Charging Demand

sustainability

Article Design of Isolated Microgrid System Considering Controllable EV Charging Demand

Sang Heon Chae 1 , Gi Hoon Kim 2, Yeong-Jun Choi 2 and Eel-Hwan Kim 2,* 1 Electric Energy Research Center, Jeju National University, Jeju-si 63243, Korea; [email protected] 2 Department of Electrical Engineering, Jeju National University, Jeju-si 63243, Korea; [email protected] (G.H.K.); [email protected] (Y.-J.C.) * Correspondence: [email protected]; Tel.: +82-64-754-3678

Received: 22 October 2020; Accepted: 19 November 2020; Published: 22 November 2020

Abstract: Microgrid construction is promoted globally to solve the problems of energy inequality in island regions and the use of fossil fuels. In the application of a microgrid system, it is important to calculate the capacities of renewable energy sources and storage systems (ESSs) to ensure economic feasibility. In some microgrids that have recently had environmental challenges, there are island regions where the policy is to consider both the installation of the microgrid system and the supplement of electric vehicles (EV). However, an EV load pattern that does not match the solar radiation pattern may increase the required ESS capacity. Therefore, in this study, we designed and analyzed a method for reducing the microgrid system cost using a controllable EV charging load without the requirements of vehicle-to-grid technology and real-time pricing. The power system operations at similar capacities of photovoltaic and ESS were shown by applying EV charging control steps in 10% increments to analyze the eﬀect of EV charging demand control on the microgrid. As a result of the proposed simulation, the amount of renewable power generation increased by 2.8 GWh over 20 years only by moving the charging load under the same conditions. This is an eﬀect that can reduce CO2 by about 2.1 kTon.

Keywords: controllable load; electric vehicles; energy storage system; microgrid; renewable energy

1. Introduction The installation of microgrids has been promoted worldwide to concurrently solve the issues of energy inequality and environmental challenges [1–3]. Because microgrids use limited power, the capacity design of distributed sources is vital to ensure economic and electrical feasibility. In particular, for islands that are sensitive to environmental issues, such as nature heritages, both microgrids and electric vehicles (EVs) can be considered simultaneously in order to achieve zero fossil fuel use [4]. However, most islands do not have factory loads; therefore, the total load is higher in the evening than the daytime, and EV charging loads also tend to be higher in the evening depending on the consumer’s lifestyle. These load patterns signiﬁcantly reduce the peak contribution of photovoltaic (PV) power generation and may require a larger energy storage system (ESS) for microgrids. There are various studies in which EV charging load and microgrids are connected. Fan Wu et al. proposed a method to minimize operating costs through optimal scheduling of the microgrid-type electric vehicle rapid charger [5]. Ronak Deepak Mistry et al. presented the impact of aggregated EV charging stations as ﬂexible load [6]. Mostafa F. Shaaban et al. proposed a joint planning method of EV charging and microgrids [7]. Yan Li et al. proposed the cascade charging strategy for scheduling EV charging [8]. S. G. Liasi et al. suggested the peak shaving of the microgrid using vehicle to gird (V2G) [9,10]. E. Mortaz et al. proposed the energy management system (EMS) of EV and microgrids in connection with the power market [11]. M. Zhang et al. suggested the optimal operation

Sustainability 2020, 12, 9746; doi:10.3390/su12229746 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 9746 2 of 14 method of microgrids in connection with V2G [12]. C. Chen et al. proposed an economical operation plan for plug-in hybrid EVs [13]. B. Aluisio et al. suggested the optimal operation method of the microgrid considering V2G, and T. Rawat confirmed the effect [14,15]. B. Wang et al. proposed a microgrid scheduling algorithm based on user cost linking V2G [16]. Several studies combining the EV charging load and microgrid design have been conducted, and most major studies have focused on V2G technology. Although V2G is an excellent method to resolve these issues, it has the following limitations. First, V2G technology can only be implemented using a fast charger. However, most fast chargers are developed using a Vienna rectifier to reduce the cost of production that can only operate unidirectionally (grid to vehicle) [17]. Second, because an electricity power trading system must be installed for a small number of users on the microgrid, the cost of installation and operation and management (O&M) may increase. Third, a real-time price system considering weather conditions and load forecasting should be established, which may complicate the operation of the power system. These constraints can be overcome by using time-varying pricing or critical peak pricing to adjust the EV charging pattern. Therefore, this study analyzed the design results of a microgrid system depending on controlled and uncontrolled EV charging loads. On the microgrid, which is a small power grid, the impact of the EV charging load is so great that it must be considered at the design stage. Therefore, this paper simulated the operation of a microgrid for 20 years, taking into account the gradual increase of EVs on the island every year. In addition, the cost benefit of the EV charging load pattern and the solar power generation pattern becoming similar was presented. From the results of this study, the effectiveness of the EV charging demand control was represented under the same capacities of ESS and PV by applying charging control steps in 10% increments. To achieve this, renewable energy sharing, reduction of CO2 emissions, the levelized cost of energy (LCOE), curtailed renewable energy, and an adequate budget for EV charging control were shown to verify the proposed application [18–24]. Only PV, diesel generators and ESS were considered in the proposed method to show the results because a solar radiation pattern is typically easy to predict and the lack of energy can be supported by the diesel generator. The actual load and weather data of San Cristobal Island in the Galapagos were measured to apply this analysis for a year. Brute force is known as the exhaustive search method used to find the optimal point and make comparisons to the microgrid cost model using the MATLAB program [25].

2. Basic Data Analysis of the Applied Power System

2.1. General and EV Information The design of the microgrid system was conducted using actual information and measured data from San Cristobal Island in the Galapagos. The main industry of this island is tourism. Table1 lists the population for San Cristobal Island, the number of power contract households, and the number of cars and motorcycles. In the calculations, there are several assumptions that are described as follows. First, electric cars and motorcycles have 25 and 3 kWh batteries, respectively. Second, considering the battery capacity of vehicles and low daily mileage on the island, the electric cars are charged once every three days, and the electric motorcycles are charged once every two days. Third, every year, 5% of conventional vehicles will gradually change to EVs. Figure1 shows the daily EV charging energy requirements by project year. In the ﬁnal project year, the daily power demand of EVs reaches approximately 4379 kWh.

Table 1. General and electric vehicle (EV) information of an example island.

Items Quantity Population 7088 Consumers 3292 No. of cars 435 No. of motorcycles 503 Sustainability 2020, 12, x FOR PEER REVIEW 3 of 15 Sustainability 2020, 12, 9746 3 of 14 Sustainability 2020, 12, x FOR PEER REVIEW 3 of 15

Figure 1. Daily EV charging consumption by project year. FigureFigure 1.1. DailyDaily EVEV chargingcharging consumptionconsumption byby projectproject year.year. Fourth, the power system operator or local government can control the EV charging load using theirFourth,Fourth, own methods, thethe powerpower such systemsystem as the operator operatorEV purchase oror locallocal support governmentgovernment policy or cancan the control controlcharging thethe electricity EVEV chargingcharging billing loadload systems. usingusing theirtheirFigure ownown 2 methods,showsmethods, the suchsuch daily asas EV thethe charging EVEV purchasepurchase patterns. supportsupport As policy policyshown oror in the Figure charging 2, typical electricity EV billingcharging systems. loads, FigureFiguredenoted2 2shows byshows “No the the control”, daily daily EV EVwhich charging charging is from patterns. patterns. the advanced As As shown shown research in in Figure Figure on EV2, 2, typicalcharging typical EV EVloads charging charging of the loads, Nordic loads, denoteddenotedregion [26], byby “No “Novary control”, control”,according whichwhich to the isis owner’s fromfrom thethe life advancedadvanced pattern, researchasresearch the typical onon EVEV daily chargingcharging load. loadsAsloads the ofof control thethe NordicNordic stage regionregionincreases, [[26],26], the vary EV according according charging to toload the the owner’spattern owner’s becomelife life pattern, pattern,s similar as asthe theto typical the typical solar daily daily power load. load. Asgeneration the As control the controlpattern. stage stageincreases,Although increases, the EVEV the charging charging EV charging load load patterns load pattern pattern are become different, becomess similar similarthere areto tothe integral the solar solar values power power for generation the eleven pattern.pattern. lines in AlthoughAlthoughFigure 2, thewhichthe EVEV implies charging that load the patterns total required are are different, diﬀ erent,energy there there is theare are same.integral integral These values values charging for for the the elevencurves eleven linescan lines bein inFigurecalculated Figure 2,2 ,which whichas: implies implies that that the the total total required energy isis thethe same.same. TheseThese chargingcharging curvescurves cancan be be calculatedcalculated as:as: 𝑃 (𝑖, 𝑦) = 𝐴(𝑦){𝑁 (𝑖) + 0.01𝑠𝑁 (𝑖) −𝑁 (𝑖)} (1) PV(i, y) = A(y) NE(i) + 0.01s(NP(i) NE(i)) (1) 𝑃(𝑖, 𝑦) = 𝐴(𝑦){𝑁(𝑖) + 0.01𝑠𝑁(𝑖) −−𝑁(𝑖)} (1)

FigureFigure 2. 2.Daily Daily chargingcharging patterns patterns of of EV EV following following control control steps. steps. Figure 2. Daily charging patterns of EV following control steps. 2.2.2.2. ConventionalConventional PowerPower LoadLoad 2.2. Conventional Power Load FigureFigure3 3shows shows the the typical typical load load data data measured measured at at the the same same island island power power system system with with general general information.information.Figure 3 showsThe The peak the typicalload was load approximately data measured 2.9 at MWMWthe same inin May,May, island andand power thethe minimumsystemminimum with loadload general waswas approximatelyinformation.approximately The 0.80.8 MWpeakMW ininload December,December, was approximately as as shown shown in in Figure 2.9Figure MW3 a,b.3a,b. in Figure May,Figure 3andc 3c shows showsthe minimum the the histogram histogram load and wasand distributionapproximatelydistribution of of the the0.8 load loadMW data datain December, with with a a mean mean as ofofshown 1.81.8 MW.MW. in Figure From From Figure Figure3a,b. 3Figure d,3d, the the usual3c us showsual maximum maximum the histogram load load occurs occurs and atdistributionat approximately approximately of the 18:00, load18:00, whichdata which with means means a mean that that humanof 1.8 human MW. lifestyles From lifestyles generally Figure generally 3d, determine the us determineual the maximum island the load loadisland pattern, occurs load andatpattern, approximately the peak and contributionthe peak 18:00, contribution which of PV ismeans inevitably of PVthat is low. humaninev Furthermore,itably lifestyles low. Furthermore, thegenerally pattern determine between the pattern the the conventional betweenisland load the loadpattern,conventional and theand EV theload charging peak and thecontribution demand EV charging tends of PVdemand to coincide,is inev tendsitably which to coincide,low. further Furthermore, which deteriorates further the the patterndeteriorates peak contributionbetween the peak the ofconventionalcontribution PV generation. ofload PV and generation. the EV charging demand tends to coincide, which further deteriorates the peak contribution of PV generation. Sustainability 2020, 12, 9746 4 of 14 Sustainability 2020, 12, x FOR PEER REVIEW 4 of 15 Sustainability 2020, 12, x FOR PEER REVIEW 4 of 15

Figure 3. Load data of an example island: (a) Boxplot, (b) Time series plot, (c) Histogram and FigureFigure 3. LoadLoad data data of of an an example example island: island: (a (a) )Boxplot, Boxplot, ( (bb)) Time Time series series plot, plot, ( (cc)) Histogram and and distribution, and (d) Average daily time series plot. distribution,distribution, and ( d)) Average Average daily time series plot. 2.3. Solar Radiation and Temperature 2.3. Solar Radiation and Temperature Figure4 shows the solar radiation data measured in the same region of the conventional load. Figure 4 shows the solar radiation data measured in the same region of the conventional load. In December and April, the monthly average solar radiation reached the highest and lowest levels In December and April, the monthly average solar radiation reached the highest and lowest levels of 249of 249 and and 177 177 W/m W/2m, 2respectively,, respectively, as as shown shown in in Figure Figure 4a4a,b.,b. Figure Figure 44cc showsshows thethe histogram histogram and and 249 and 177 W/m2, respectively, as shown in Figure 4a,b. Figure 4c shows the histogram and distribution of daytime data only. Figure4d shows the average daily pattern of solar radiation. In this distribution of daytime data only. Figure 4d shows the average daily pattern of solar radiation. In study, the various EV charging patterns changed based on this irradiation pattern. These data were this study, the various EV charging patterns changed based on this irradiation pattern. These data used as input to the solar power output. Figure5 shows the temperature data synchronized with solar were used as input to the solar power output. Figure 5 shows the temperature data synchronized radiation data. As shown in Figure5a,b, the highest and lowest average monthly temperature was 26.9 with solar radiation data. As shown in Figure 5a,b, the highest and lowest average monthly and 20.9 C in January and June, respectively. Figure5c shows the histogram and distribution of the temperature◦ was 26.9 and 20.9 °C in January and June, respectively. Figure 5c shows the histogram temperature data with an annual mean of 23.8 C. The temperature was generally higher during the and distribution of the temperature data with◦ an annual mean of 23.8 °C. The temperature was daytime than at night, as shown in Figure5d. However, the peak load was recorded between 18:00 generally higher during the daytime than at night, as shown in Figure 5d. However, the peak load and 19:00, according to Figure3d, because islands generally have fewer facilities that use electricity was recorded between 18:00 and 19:00, according to Figure 3d, because islands generally have fewer during the day, such as oﬃces and factories. facilities that use electricity during the day, such as offices and factories.

Figure 4. Solar radiationradiation datadata ofof an an example example island: island: (a )(a Boxplot,) Boxplot, (b )(b Time) Time series series plot, plot, (c) Histogram(c) Histogram and Figure 4. Solar radiation data of an example island: (a) Boxplot, (b) Time series plot, (c) Histogram anddistribution, distribution, and and (d) Average (d) Average daily daily time time series series plot. plot. and distribution, and (d) Average daily time series plot. Sustainability 2020, 12, 9746 5 of 14 Sustainability 2020, 12, x FOR PEER REVIEW 5 of 15

Figure 5. Temperature data of an example island: ( a) Boxplot, ( b) Time series plot, ( c) Histogram and distribution, and (d) Average dailydaily timetime seriesseries plot.plot. 3. System Modelling 3. System Modelling 3.1. Photovoltaic 3.1. Photovoltaic The output of the PV model is calculated using the measured solar radiation and temperature data.The These output data of measured the PV model for one is year calculated are extrapolated using the overmeasured the duration solar radiation of the project. and temperature The output powerdata. These of the data PV canmeasured be expressed for one as year [27 ]:are extrapolated over the duration of the project. The output power of the PV can be expressed as [27]: n oCPG(i, y) 𝐶𝐺(𝑖,𝑦) PP(i, y) = 1 + Kt(Ta(i, y) + 0.0256G(i, y)) Tre f (2) 𝑃(𝑖, 𝑦) = 1 +𝐾 (𝑇(𝑖,) 𝑦 + 0.0256𝐺(𝑖,) 𝑦) −𝑇− G (2) 𝐺re f

3 2 2 where K𝐾t isis −3.73.7 ×10 10 1/ 1/°C,C, G 𝐺is 1000 is 1000 W/m W/m, and, andT is𝑇 25 isC. 25 The °C. output The output power power of the PVof the system PV − × − ◦ re f re f ◦ issystem assumed is assumed to include to include a power a conversionpower conversion system (PCS).system (PCS).

3.2. ESS The ESS controls the energy balance between demanddemand and supply. It consists of a PCS and a battery, which is typically a lithium-ion battery. AlthoughAlthough the PCS capacity of the PV is the same as that of itsits inverter,inverter, C𝐶Iis is di differentfferent from fromC B𝐶because because the the requirement requirement of CofI should𝐶 should be determinedbe determined by theby sutheffi sufficientcient or insu or insufficientfficient power power between between the renewable the renewable energy energy output output and demand and demand load. Inload. this In study, this thestudy,CI absolutethe 𝐶 absolute maximum maximum value of PvalueE considering of 𝑃 considering the unit capacity the unit is determined. capacity is Thedetermined. output power The ofoutput the ESS power and of the the capacity ESS and of the the capacity inverter of can the be inverter calculated can as: be calculated as: n o 𝑃(𝑖,) 𝑦 = {𝑃(𝑖)(1 + 𝑔 y 1)+𝑃(𝑖,𝑦)} −{𝑃 (𝑖,) 𝑦 +𝑃(𝑖,) 𝑦 } (3) PE(i, y) = PL(i)(1 + g − ) + PV(i, y) PP(i, y) + PD(i, y) (3) − 𝐶 = [𝑚𝑎𝑥(|𝑃 |)/𝐶 ] 𝐶 (4) CI = [max( PE)/CIU] CIU (4) | | RU In Equation (3), the sign of 𝑃 is determined by the power supply and demand. The [] is the In Equation (3), the sign of PE is determined by the power supply and demand. The []RU is the round up operator in Figure 4. If 𝑃 has positive and negative signs, the ESS will be discharged and round up operator in Figure4. If PE has positive and negative signs, the ESS will be discharged and charged respectively. This energy flow is used to calculate the state of charge (SOC), which can be charged respectively. This energy flow is used to calculate the state of charge (SOC), which can be expressed by the following equation [27]: expressed by the following equation [27]:

𝑃(𝑖,𝑦) ⎧ 𝑆(𝑖−1,𝑦) + P (i,y) 𝑖𝑓 𝑃 (𝑖,) 𝑦 ≤0 ⎪  S(i 1, y){(+ E ) i} f P (i, y) 0  1−𝐷(1 D(𝑦(y 1)) −C 1) e𝐶𝑒 E 𝑆(S𝑖,(i,) 𝑦 y=) =  − − B − B E ≤ (5)(5) ⎨  { 𝑃P(E𝑖,(i,y)) 𝑦 e𝑒E } 𝑆(𝑖−1,𝑦S(i 1,) +y) + i f P 𝑖𝑓E(i, 𝑃y()𝑖,> ) 𝑦0>0 ⎪ − (1 DB(y 1))CB ⎩ {(1−𝐷{ − (𝑦− − 1))}𝐶}

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3.3. Energy Management System 3.3. Energy Management System EMS is essential for the stable operation of a microgrid. Its main purpose is to prevent over- chargingEMS and is essential -discharging for the of stable the battery. operation Figure of a microgrid. 6 shows simplified Its main purpose EMS logic is to that prevent can determine over-charging the operationand -discharging of the diesel of the battery.generator Figure and6 renewableshows simpliﬁed curtailment. EMS logic In this that logic, can determine the ESS thesupplies operation and absorbsof the diesel power generator based on and the renewable net load when curtailment. the SOC In is thiswithin logic, a stable the ESS range. supplies If the and SOC absorbs is above power the upperbased limit, on the the net renewable load when energy the SOC sources is within are curtaile a stabled. Under range. the If thelower SOC limit is aboveof the theSOC, upper the diesel limit, generatorthe renewable supports energy the sources demand are load. curtailed. The final Under PV the output lower power, limit of including the SOC, thecurtailment, diesel generator diesel operation,supports the and demand the capacity load. Theof the ﬁnal dies PVel outputgenerator power, can includingbe expressed curtailment, as [28]: diesel operation, and the capacity of the diesel generator can be expressed as [28]: 𝑃(𝑖,) 𝑦 =𝑏(𝑖, 𝑦)𝑃(𝑖,) 𝑦

PD(i, y) =0 b D (𝑖𝑓i, y) 𝑆(P𝑖,L() 𝑦i,>𝑆y) 𝑏(𝑖, 𝑦) = (1 𝑖𝑓 𝑆(𝑖,) 𝑦 ≤𝑆 0 i f S(i, y) > SL (6) bD(i, y) = 1 i f S(i, y) SL (6) 𝑖𝑓 𝑏 (𝑖,) 𝑦 = 1, 𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜𝑏 ≤(𝑛, 𝑦) >𝑑 iP+d i f bD(i, y) = 1, subject to bD(n, y) > d n = i

𝐶 = [𝑚𝑎𝑥(𝑃)/𝐶]𝐶 (7) CD = [max(PD)/CDU]RUCDU (7)

P𝑃(i𝑖,, y)) 𝑦 =(1−𝐷= (1 DP(𝑦(y −1 1))𝑃))PP((𝑖,𝑦i, y)) 𝑖𝑓i f S 𝑆((𝑖,i, y) 𝑦 )>𝑆> SH (8)(8) C − − ( 00 𝑖𝑓i f S 𝑆((𝑖,i, y) 𝑦 ) >𝑆> SH PP𝑃(i(,𝑖,y) ) 𝑦 == (9)(9) (1(1 − 𝐷DP(𝑦(y −1 1))𝑃))PP((𝑖,i, y) 𝑦) 𝑖𝑓i f S 𝑆((𝑖,i, y) 𝑦) ≤𝑆SH − − ≤

FigureFigure 6. 6. SimplifiedSimpliﬁed energy energy management management system system (EMS). (EMS). 4. Problem Formulation 4. Problem Formulation The main objective function, whose smallest value is the optimal point, focuses on improving The main objective function, whose smallest value is the optimal point, focuses on improving the economics of the microgrid, and its determining variables are the capacities of the facilities. the economics of the microgrid, and its determining variables are the capacities of the facilities. The The installation cost was taken into account; the project duration and the lifetime of each installation installation cost was taken into account; the project duration and the lifetime of each installation did did not match. Therefore, we consider the replacement cost and convert the remaining life into cost. not match. Therefore, we consider the replacement cost and convert the remaining life into cost. The The operating cost was estimated as the fuel cost for the diesel generator. The operating cost was operating cost was estimated as the fuel cost for the diesel generator. The operating cost was calculated annually for all facilities installed in the microgrid. The total cost function ZT for estimating calculated annually for all facilities installed in the microgrid. The total cost function 𝑍 for the capacity of the microgrid and the functions related to the operation cost ZO, installation cost ZI, estimating the capacity of the microgrid and the functions related to the operation cost 𝑍 , and maintenance ZM for calculating ZT are expressed as follows: installation cost 𝑍, and maintenance 𝑍 for calculating 𝑍 are expressed as follows:

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ZT = ZI + ZO + ZM (10)   (Lx p)IxCx i f p > Lx Z =  h − (11) r  p Lx  ( − ]MOD) IxCx i f p Lx Lx ≤ p X Rx y 1 ZI = (1 + [ ] )IxCx Zr p (12) { Lx × Lx + 1 } − ( + r) y = 1 (1 + r) RD 1

Xp 8760X Z = F aCD + bPD(i, y) (13) O { } y = 1 i = 1

p X 1 ZM = MxCx (14) ( + r)y y = 1 1

In Equations (11) and (12), []MOD is the remainder operator to calculate the value of the remaining life of the facilities and []RD is the round down operator for calculating the replacement cost of equipment, respectively. From the power ﬂow result and cost functions of Equations (2)–(14), LCOE, renewable energy sharing rate, renewable energy curtailment rate, and CO2 emissions are calculated by Equations (15)–(18). These results are used to verify the eﬀect of EV charging demand control.

Z LCOE = T (15) Pp P8760 PL(i, y) + PV(i, y) y = 1 i = 1{ } Pp P8760 ( ) y = 1 i = 1 PP i, y R = 100 (16) S Pp P8760 PL(i, y) + PV(i, y) × y = 1 i = 1{ } Pp P8760 ( ) y = 1 i = 1 PC i, y R = 100 (17) C Pp P8760 PP(i, y) + P (i, y) × y = 1 i = 1{ C } Xp 8760X CO2 = 0.699 PD(i, y) (18) y = 1 i = 1 Table2 lists the calculation parameters for the microgrid. These values were determined similar to the actual information. Particularly, the initial SOC has a lower limit because it minimizes the error in the energy inside the original battery. The cost of replacing the equipment is 80% of the initial installation cost because it excludes the civil engineering costs. The project period is set to 20 years according to the life of PV panels. Figure7 shows the process of estimating the capacity of a microgrid. Because an exhaustive search technique was used, all results were calculated in per unit capacity until the maximum capacity boundaries were reached. The EV charging demand control scenarios were calculated from 0% to 100% in 10% control units. Sustainability 2020, 12, 9746 8 of 14

Sustainability 2020, 12, x FOR PEER REVIEW 8 of 15 Table 2. Calculation parameters.

ItemsTable 2. Value Calculation Items parameters. Value

Itemsp 20Value LPItems Value20 𝑝 0.0220 𝐿 20 g LD 30 𝑔 (Linearly)0.02 (Linearly) 𝐿 30 CIU𝐶 100100 r 𝑟 0.050.05 CBU𝐶 500500 RI 𝑅 0.80.8 CPU𝐶 100100 RB 𝑅 0.80.8 CDU𝐶 500500 RP 𝑅 0.80.8 CUB𝐶 35,00035,000 RD 𝑅 0.80.8 C I UP𝐶 15,00015,000 I 𝐼 127.1127.1 e 0.95 I 396.6 E𝑒 0.95 B 𝐼 396.6 D 0.015 I 1525.4 B𝐷 0.015 P 𝐼 1525.4 D 0.01 I 882 P𝐷 0.01 D 𝐼 882 S 0.2 F 1.3 L𝑆 0.2 𝐹 1.3 S 0.9 a 0.0235 U𝑆 0.9 𝑎 0.0235 S 0.2 b 0.2417 0𝑆 0.2 𝑏 0.2417 d 1 MI 4.5 𝑑 1 𝑀 4.5 LI 10 MB 8.2 𝐿 10 𝑀 8.2 LB 10 MP 38.6 𝐿 10 𝑀 38.6 MD 26.3 𝑀 26.3

Start

Data and parameters input

Initialize variables

Power system operation by equations (2)-(9)

Result calculation by equations (10)-(18) and save

No CP=CUP CP=CP+CPU

Yes No CB=CUP CB=CB+CBU

Yes

No s=s+10 s=100 Equation (1) Yes

End

Figure 7. Calculation process. Figure 7. Calculation process. Sustainability 2020, 12, x FOR PEER REVIEW 9 of 15

5. Simulation Results and Discussion Sustainability 2020, 12, x FOR PEER REVIEW 9 of 15 SustainabilityFigure2020 8 shows, 12, 9746 the results of the capacity calculation when the EV charging demand control9 was of 14 not5. Simulation performed. Results The PV and and Discussion battery capacities that minimize the overall cost were determined to be 6200 kW and 8500 kWh, respectively. Furthermore, the minimum cost at the optimal point was 5. Simulation Results and Discussion calculatedFigure as 8 showsUSD 72.06 the results million. of Figurethe capacity 9 shows calcul theation results when of the the powerEV charging system demand operation control over was the 20-yearnot performed.Figure period8 shows of The the PV the project. and results battery It can of the becapacities capacityconfirmed that calculation that minimize the output when the ofoverall the the EV diesel cost charging weregenerator determined demand increased control to beas wasthe6200 PV notkW output performed. and degraded8500 ThekWh, PVand respectively. and the batterypower capacitiesFurthermorload increased. thate, the minimize It canminimum be theseen overallcost that atthe costthe utilization wereoptimal determined ratepoint of wasESS to bealsocalculated 6200 gradually kW as and USD decreases 8500 72.06 kWh, million.as the respectively. load Figure increases. 9 Furthermore,shows the results the minimumof the power cost system at the optimaloperation point over was the calculated20-year period as USD of the 72.06 project. million. It can Figure be confirmed9 shows the that results the output of the of power the diesel system generator operation increased over the as 20-yearthe PV output period degraded of the project. and the It can power be conﬁrmed load increased. that the It outputcan be ofseen the that diesel thegenerator utilization increased rate of ESS as thealso PV gradually output degradeddecreases andas the the load power increases. load increased. It can be seen that the utilization rate of ESS also gradually decreases as the load increases.

Figure 8. Optimization result. Figure 8. Optimization result.

Figure 8. Optimization result.

Figure 9. Annual power system operation result. Figure 9. Annual power system operation result. Table3 lists the total cost, renewable energy sharing rate, renewable energy curtailment rate, Table 3 lists the total cost, renewable energy sharing rate, renewable energy curtailment rate, and CO2 emissions when the same capacity is applied as the result value for each scenario. As the EV Figure 9. Annual power system operation result. chargeand CO demand2 emissions control when ratio the increased, same capacity the total is applied cost gradually as the result decreased, value thereby for each increasing scenario. renewableAs the EV energycharge sharingdemand and control reducing ratio curtailment. increased, The the renewable total cost sharing gradually and curtailmentdecreased, inthereby Figure 10increasing increase Table 3 lists the total cost, renewable energy sharing rate, renewable energy curtailment rate, andrenewable decrease energy almost sharing linearly; and however, reducing the curtailmen total cost rapidlyt. The decreasesrenewable when sharing the EVand charging curtailment load in is and CO2 emissions when the same capacity is applied as the result value for each scenario. As the EV controlledFigure 10 increase by 70%, asand shown decrease in Figure almost 11 .linearly; This phenomenon however, the occurs total when cost therapidly required decreases diesel generatorwhen the charge demand control ratio increased, the total cost gradually decreased, thereby increasing capacityEV charging decreases load is and controlled proves thatby 70%, the chargingas shown load in Figure control 11. reduces This phenomenon the peak load occurs in the when evening the renewable energy sharing and reducing curtailment. The renewable sharing and curtailment in withoutrequired PV diesel output generator as represented capacity in decreases Figure 12 .and The pr increaseoves that in thethe peakcharging contribution load control of PV reduces generation the Figure 10 increase and decrease almost linearly; however, the total cost rapidly decreases when the reduced the peak load in the evening, thus avoiding overdesign of diesel power generation. Due to EV charging load is controlled by 70%, as shown in Figure 11. This phenomenon occurs when the these results, the proposed design initially predicted a linear operating cost reduction, but it was required diesel generator capacity decreases and proves that the charging load control reduces the Sustainability 2020, 12, x FOR PEER REVIEW 10 of 15 peak load in the evening without PV output as represented in Figure 12. The increase in the peak contribution of PV generation reduced the peak load in the evening, thus avoiding overdesign of diesel power generation. Due to these results, the proposed design initially predicted a linear operating cost reduction, but it was confirmed that the installation and management cost of the diesel generator can be reduced through the shift of the peak load. Figure 13 shows the difference between Sustainability 2020, 12, 9746 10 of 14 the total cost of not meeting the charging demand control and the total cost of each scenario, which is the advantage of the EV charging demand control. These advantages can be used by central or local governmentsconfirmed that to the facilitate installation the spread and management of EVs, or they cost can of thebe used diesel for generator the costs can required be reduced to control through the chargingthe shift ofloads, the peak such load.as monitoring, Figure 13 showsand the the proc diffeedserence of betweenEV owners the from total costtariff of adjustments. not meeting The the microgrid,charging demand which controlwas the and test themodel total of cost this of study, each scenario,could reduce which the is total the advantage cost by approximately of the EV charging USD 1.1demand million, control. which These is 1.52% advantages of the total can becost used when by central100% of or the local charge governments load control to facilitate is achieved. the spread This wouldof EVs, give or they all the can 938 be usedEV owners for the a costs USD required1172 benefi to controlt. Renewable the charging energy loads,sharing such increased as monitoring, by 1.7% comparedand the proceeds to no control, of EV owners which fromwas 2.8 tari GWh.ff adjustments. In addition The to microgrid, the above-mentioned which was theadvantages, test model the of annualthis study, capacity could reducefactor theof totalPV rose cost byfrom approximately 19.07% to USD19.39%. 1.1 million,Furthermore, which isthe 1.52% annual of the renewable total cost whencurtailment 100% ofrate the was charge reduced load from control 12.51% is achieved. to 11.01%. This This would can givebe expected all the 938 to EVimprove owners the a USDefficiency 1172 bybenefit. about Renewable 1.5% compared energy to sharingwhen not increased controlled. by 1.7% compared to no control, which was 2.8 GWh. In addition to the above-mentioned advantages, the annual capacity factor of PV rose from 19.07% to 19.39%. Furthermore, theTable annual 3. Calculation renewable results curtailment depend on rate control was reducedsteps. from 12.51% to 11.01%. This can be expected to improve the efficiency by about 1.5% compared to when not controlled. Control~~ Total Diesel~~~ CO2~~~ Renewable~~ Renewable~~~ LCOE~~ ~ Costs~~ Capacity~~ Emission~~ ~ Curtailment~~ Table 3. Calculation results depend on control steps. ~ Steps~~~ ~ ~ ~ Sharing~~~ ~ [$/kWh] [%]Control [M$] [kW] Diesel [kTon]CO Renewable[%] Renewable[%] Total Costs 2 LCOE Steps Capacity Emission Sharing Curtailment No control 71.06 [M$] 4500 168.39 41.07 12.51 [$/kWh] 0.1786 10 [%] 71.01 4500 [kW] 168.14[kTon] 41.16[%] [%] 12.33 0.1784 No20 control70.96 71.064500 4500 167.94 168.39 41.22 41.07 12.51 12.19 0.1786 0.1783 30 1070.90 71.014500 4500 167.72 168.14 41.29 41.16 12.33 12.04 0.1784 0.1782 20 70.96 4500 167.94 41.22 12.19 0.1783 40 3072.86 70.904500 4500 167.52 167.72 41.36 41.29 12.04 11.90 0.1782 0.1781 50 4072.79 72.864500 4500 167.35 167.52 41.42 41.36 11.90 11.78 0.1781 0.1779 60 5072.74 72.794500 4500 167.07 167.35 41.51 41.42 11.78 11.58 0.1779 0.1777 70 6070.11 72.744000 4500 166.89 167.07 41.56 41.51 11.58 11.45 0.1777 0.1759 80 7070.05 70.114000 4000 166.67 166.89 41.64 41.56 11.45 11.29 0.1759 0.1758 80 70.05 4000 166.67 41.64 11.29 0.1758 90 9070.01 70.014000 4000 166.51 166.51 41.69 41.69 11.20 11.20 0.1757 0.1757 100100 69.96 69.964000 4000 166.28 166.28 41.77 41.77 11.03 11.03 0.1756 0.1756

Sustainability 2020, 12, x FOR PEER REVIEWFigure 10. Renewable sharing and curtailment. 11 of 15

Figure 11. TotalTotal cost and renewable sharing rate.

Figure 12. Day and night peak load.

Figure 13. Benefits of EV charging control.

6. Conclusions In this study, a microgrid system was designed considering controllable EV charging demand. The effect was analyzed and verified when the EV charging load was controlled by the charging fee or policy without V2G technology and real-time pricing. As a result of capacity calculations and computer analysis, the EV charge demand control could help reduce peak loads in the evening. By reducing the number of diesel generators required, it was proved that the effect is maximized by reducing the installation, operation, and maintenance costs of diesel generators. Additionally, both the renewable energy sharing and CO2 emissions were improved when the EV charging demand control was performed at 100%, which was approximately 2.1 kTon and 2.8 GWh. If microgrids and EVs are considered concurrently in an isolated area, this work will contribute to better operation by calculating the EV charging demand control and its essential costs.

Author Contributions: S.H.C. performed Software, visualization and writing-original draft. G.H.K. performed formal analysis, data curation and writing—original draft. Y.-J.C. provided the methodology, validation and Sustainability 2020, 12, x FOR PEER REVIEW 11 of 15 Sustainability 2020, 12, x FOR PEER REVIEW 11 of 15

Sustainability 2020, 12, 9746 Figure 11. Total cost and renewable sharing rate. 11 of 14 Figure 11. Total cost and renewable sharing rate.

Figure 12. Day and night peak load. FigureFigure 12.12. DayDay andand nightnight peakpeak load.load.

Figure 13. BenefitsBenefits of EV charging control. Figure 13. Benefits of EV charging control. 6. Conclusions 6. Conclusions 6. Conclusions In this study, a microgrid system was designed considering controllable EV charging demand. In this study, a microgrid system was designed considering controllable EV charging demand. In this study, a microgrid system was designed considering controllable EV charging demand. TheThe eeffectffect was analyzed analyzed and and verified verified when when the the EV EV charging charging load load was was controlled controlled by bythe thecharging charging fee The effect was analyzed and verified when the EV charging load was controlled by the charging fee feeor policy or policy without without V2G V2G technology technology and andreal-time real-time pricin pricing.g. As a As result a result of capacity of capacity calculations calculations and or policy without V2G technology and real-time pricing. As a result of capacity calculations and andcomputer computer analysis, analysis, the EV the charge EV charge demand demand control control could could help help reduce reduce peak peak loads loads in the in theevening. evening. By computer analysis, the EV charge demand control could help reduce peak loads in the evening. By Byreducing reducing the the number number of ofdiesel diesel generators generators required required,, it itwas was proved proved that that the the effect effect is is maximized by reducing the number of diesel generators required, it was proved that the effect is maximized by reducing thethe installation,installation, operation, operation, and and maintenance maintenance costs costs of of diesel diesel generators. generators. Additionally, Additionally, both both the renewablereducing the energy installation, sharing andoperatio CO2n,emissions and maintenance were improved costs of when diesel the generators. EV charging Additionally, demand control both the renewable energy sharing and CO2 emissions were improved when the EV charging demand the renewable energy sharing and CO2 emissions were improved when the EV charging demand wascontrol performed was performed at 100%, at which 100%, was which approximately was approximately 2.1 kTon 2.1 and kTon 2.8 GWh.and 2.8 If GWh. microgrids If microgrids and EVs and are control was performed at 100%, which was approximately 2.1 kTon and 2.8 GWh. If microgrids and consideredEVs are considered concurrently concurrently in an isolated in an area, isolated this workarea, willthis contributework will contribute to better operation to better by operation calculating by EVs are considered concurrently in an isolated area, this work will contribute to better operation by thecalculating EV charging the EV demand charging control demand and control its essential and its costs. essential costs. calculating the EV charging demand control and its essential costs. Author Contributions: S.H.C. performed Software, visualization and writing-original draft. G.H.K. performed formalAuthor analysis,Contributions: data curation S.H.C. performed and writing—original Software, visualization draft. Y.-J.C. and provided writing-original the methodology, draft. G.H.K. validation performed and Author Contributions: S.H.C. performed Software, visualization and writing-original draft. G.H.K. performed writing—reviewformal analysis, &data editing. curation E.-H.K. and provided writing—original the conceptualization, draft. Y.-J.C. supervision, provided the and methodology, writing—review validation and editing. and Allformal authors analysis, have readdata andcuration agreed and to wr theiting—original published version draft. of Y.-J.C. the manuscript. provided the methodology, validation and Funding: This research was funded by KETEP and KEPCO. Acknowledgments: The Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20194030202310). This research was supported by Korea Electric Power Corporation. (Grant number: R18XA03). Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2020, 12, 9746 12 of 14

Nomenclature

CO2 Carbon dioxide ESS Energy storage system EMS Energy management system EV Electric vehicle LCOE Levelized cost of energy O&M Operation and management PV Photovoltaic PCS Power conversion system SOC State of charge V2G Vehicle to grid $ United states dollar Indices i Index of hourly time segments (1, 2, 3, ) ··· x Index of facilities (PV, battery, PCS, diesel generator) y Index of project year (1, 2, 3, ) ··· s Index of EV charging control steps (0–100%) Variables A() Requirement of overall EV charging energy (kWh) bD() Binary operator for diesel operation CB() Capacity of battery (kWh) CI() Capacity of PCS (kW) CP() Capacity of PV (kW) CD() Capacity of diesel generator (kW) NE() Normalized EV charging demand without control (p.u.) NE() Normalized PV output pattern (p.u.) PV() Hourly EV charging demand (kW) PL() Measured hourly conventional power demand (kW) PE() Hourly output power of ESS (kW) PD() Hourly output power of diesel generation (kW) PP() Hourly output power of PV (kW) Pc() Hourly curtailed output power of PV (kW) S() SOC of ESS (p.u.) ZT Total cost of microgrid (M$) ZI Installation cost (M$) Zr Remaining lifetime cost (M$) ZO Operational cost (M$) ZM Management cost (M$) Parameters a Cost of diesel generation intercept factor (L/kW) b Cost of diesel generation slope factor (L/kWh) CIU Unit capacity of inverter (kW) CBU Unit capacity of battery (kW) CPU Unit capacity of PV (M$) CDU Unit capacity of diesel generator (M$) CUB Upper boundary capacity of battery (kWh) CUP Upper boundary capacity of PV (kW) DB Annual capacity degradation of battery (%/year) DP Annual output degradation of PV (%/year) d Minimum operation time of diesel generator (h) eE Total eﬃciency of the ESS (%) F Diesel fuel price ($/L) 2 Gre f Reference solar radiation (W/m ) G() Measured solar radiation data (W/m2) g Load growth rate (p.u.) Sustainability 2020, 12, 9746 13 of 14

II Installation cost of inverter ($/kW) IB Installation cost of battery ($/kWh) IP Installation cost of PV ($/kW) ID Installation cost of diesel generator ($/kW) Kt Coeﬃcient of PV output (1/◦C) LI Lifetime of inverter (years) LB Lifetime of battery (years) LP Lifetime of PV (years) LD Lifetime of diesel generator (years) MI Maintenance cost of inverter ($/kW) MB Maintenance cost of battery ($/kWh) MP Maintenance cost of PV ($/kW) MD Maintenance cost of diesel generator ($/kW) p Project duration (years) r Discount rate (p.u.) RI Ratio of replacement cost to new inverter (p.u.) RB Ratio of replacement cost to new battery (p.u.) RP Ratio of replacement cost to new PV (p.u.) RD Ratio of replacement cost to new diesel generator (p.u.) RS Ratio of renewable sharing (%) RC Ratio of renewable curtailment (%) SL Lower limit of SOC (p.u.) SU Upper limit of SOC (p.u.) S0 Initial SOC (p.u.) Ta() Measured temperature data (◦C) Tre f Reference temperature (◦C)

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