AbstractâIn this study, flexible vehicle-to-grid (V2G) coordi- nation schemes are proposed for office buildings equipped with electric vehicle (EV) charging ...
Connected Electric Vehicles for Flexible Vehicle-to-grid (V2G) Services Seungwook Yoon, Kanggu Park, and Euiseok Hwang School of Mechatronics, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea Email: {ysw1207, kgpark09, euiseokh}@gist.ac.kr Abstract—In this study, flexible vehicle-to-grid (V2G) coordination schemes are proposed for office buildings equipped with electric vehicle (EV) charging stations. EVs can be connected to electric grid during the rest hours and have a potential to provide V2G or vehicle-to-building (V2B) services such as electric load distributions and demand responses. Especially for smart buildings, the charging stations can be operated efficiently by integrating distributed energy resources (DER) such as Photovoltaic (PV) and battery energy storage systems (BESS). In this scenario, the charging coordination problem can be simplified as the integer linear programming (ILP), by assuming constant-rate charging with known schedules of visiting EVs at the station. Numerical evaluations are conducted with the building’s expected daily electricity load, PV generation, and electricity price datasets. Public service buildings are investigated for flexible V2G supports, based on the actual vehicles’ in and out patterns of the local office. The proposed coordination scheme shows potential gain of 14.3% of energy cost reduction compared to the first-come first-serve approach, under medium sized smart building scenario integrated with the PV and BESS.
I. I NTRODUCTION Electric vehicles (EV) share is growing and expected to reach 20 million by 2020 [1], with raised concerns on environment and energy, as well as their fuel efficiency, comfort, etc. In addition, EV has a potential to support the electric grid by using its battery as a distributed energy resources (DER), called vehicle-to-grid (V2G) services [2]. Since the vehicles are not used most of the time, i.e., around 90% of a day [3], [4], EVs can be charged or discharged locally during their rest hours to provide benefits on the charging costs or incentives on V2G services. For example, the energy price or grid support incentives varies widely within a day, and energy flow of a number of EVs parked at a building can be jointly coordinated to minimize overall charging costs or to maximize the grid support incentives [5]. This study focuses on the coordination of the EV charging and discharging specifically for smart buildings. V2G aimed for the building, also called vehicle-to-building (V2B), have shown the potential benefits delivering the specific requests like demand side management (DSM) and outage management (OM) [6]. Alternatively in this study, a flexible V2G coordination scheme is proposed for every day benefits for the charging stations in the building. As the EV penetration increases, most of the buildings will have V2G available EVs parked for a while, and their charging stations can provide charging services with optional V2G supports. The charging c 978-1-5090-5124-3/17/$31.00 2017 IEEE
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stations have a potential to distribute the total electric load of the buildings such that the overall electricity cost is minimized or the grid support incentive is maximized, where the benefits can be shared by the station and the EV owners depending on the depth of their participation. In addition, the charging station can be operated efficiently by taking advantage of the electric load characteristics of the building, and by integrating the DERs such as Photovoltaic (PV) and battery energy storage systems (BESS). The flexible V2G coordination problem for such smart building can be simplified by the integer linear programming (ILP) with constant rate charging constraints. Note that the charging station level cost optimization based on the ILP was proposed by the author in [7], while the smart buildings with DERs can also be coordinated by the ILP even with additional controls of other resources. Since the building’s electricity load are correlated, in general, to the number of parked vehicles, the flexible V2G’s potential is increasing as the EV share grows. This paper is organized as follows: Section II discusses the proposed flexible V2G coordination scheme for EV charging station at the smart building. Section III presents numerical evaluations and results. Finally, Section IV summarizes the paper. II. C HARGING S CHEME FOR F LEXIBLE V2G Optimal EV charging strategy varies depending on the available information at the charging station or control center. If no a prior information about EVs’ connectivity is available, individual EV needs to be served in the first-come firstserve (FCFS) manner. However, FCFS is inefficient since the vehicles are parked most of the time, while the electricity price widely varies [8]. If schedules of associated EVs’ are known to the charging stations a head of time, charging can be a priori coordinated such that the cost of aggregated EVs’ charging is minimized under the constraints [7] or to optimize other potential measures with V2G services. Especially for smart buildings, the overall energy cost can be controlled by the V2G coordination by accounting the information about energy usage patterns of the building, renewable generations, as well as EV mobility patterns, as follows. For a given EV i, its j-th connection from the arrival time ta,ij to departure time td,ij need to be charged from EV battery normalized SoC ψa,ij to ψd,ij , with a constant rate of γc . The ψd,ij should be set to high enough to cover the next trip. The connected period for charging is td,ij − ta,ij = Mij , while
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actual charging period is Lij = (ψd,ij − ψa,ij )/γc . Then the EV charging cost for the ij connection is Bij = γc bTij 1Lij ∗ xij (1)
where ∗ denotes the convolution operator and the binary vector xij ∈ {0, 1}Mij −Lij indicates the initial charging index kij , where xkij ,ij = 1 and xk�=kij ,ij = 0. The bij is the sequence of the unit electricity [$/kWh] for the time index of ij from the daily profile {bk }, k ∈ {1, 2, ..., K}, and 1Lij denote a length Lij vector of one. For the smart building, the electricity load and PV generation profiles are denoted by Φ = {φk } and Q = {Qk } respectively, and BESS battery SoC is ζ ib = ζkib for the ith b unit with the time index k ∈ {1, 2, ..., K}. Note that for fair comparison with an optional V2G scenario, the BESS charging and discharging is limited one cycle per day at a constant rate γc , and initial and ending state is fixed as ib ζ1ib = ζK = ζ0 . For BESS battery, two binary vectors zcib , d zib ∈ {0, 1}K indicating initial time index for charging and discharging are introduced for joint coordination with the period of Lbess = (0.8 − ζ0 )/γc . The energy cost for the ith b BESS can be expressed as (2) Sib = γc bT 1Lbess ∗ (zcib − zdib ) A. FCFS Charging for Smart Building
For FCFS charging scheme, the EV charging cost is given ˜ij } by the fixed schedule {˜ as {B xij }, and the only control for the energy cost is from the BESS as below, ˜ij + Sib + bT (Φ − Q) (3) B minimize{zci ,zdi } b
i,j
b
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ib
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i,j
where extra constraints denote one-way charging only and charging before discharging, respectively. III. N UMERICAL E VALUATIONS The proposed EV charging coordination scheme is numerically evaluated based on the smart building scenario. For flexible V2G supports, daily variations of the number of EVs, electricity load, PV generations are emulated based on the dataset from local office or campus buildings [9], and the time-varying electricity price is employed [8]. Fig. 1 shows the number of parked vehicle counts for every 15 minutes at the local administration building in Gwangju city, providing public services as well as administrative tasks. There are commute vehicles staying around 10 hours at the building, and visiting vehicles, staying shortly. For emulating the energy consumption in the building, Fig. 2 shows a typical electricity load and PV generation at the administrative building at GIST. As illustrated, the electricity load and the number of EVs, particularly for visiting EVs, show strong correlation, which can be accounted for energy cost savings by employing V2G.
ib
ib
,zd i }
subject to (4) and (1Lcij ∗ xij )K + (1Ldij ∗ yij )K ≤ 1K cumsum (1Lcij ∗ xij )K − (1Ldij ∗ yij )K ≥ 0K CS (1Lcij ∗ xij )K − (1Ldij ∗ yij )K ≤ (Emax /γc )1K
BS 1Lbess ∗ (zcib − zdib ) ≤ (Emax /γc )1K , (4)
CS ˜ ij )K ≤ (Emax (1Lij ∗ x /γc )1K ,
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subject to
b
minimize{xij ,yij ,zci
(5)
i,j
K ˜ ij where 1Lij ∗ x is a zero padded length-K vector for CS BS ˜ ij and Emax 1Lij ∗ x and Emax is the maximum power for charging station and BESS, respectively. This is an integer linear programming (ILP) and can be solved by the standard numerical solvers.
where Lcij = (0.8 − ψa,ij )/γc and Ldij = (0.8 − ψd,ij )/γc . Now, the best charging profiles can be obtained from
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Time [h] Fig. 1. The number of parked vehicles at the public service parking lot.
B. Flexible V2G Coordination
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For flexible V2G services, EV can sell or discharge the battery saved electricity when the price is high by extra charging at low cost than the needed for driving. From the previous strategy, the charging can be extended to maximum safe SoC, i.e., 80%, and discharged to ψd,ij later some time at yij , where the charging cost can be v2g Bij (6) = γc bTij 1Lcij ∗ xij − 1Ldij ∗ yij
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Fig. 2. Daily variation of energy load and PV generation of the campus building in July 1, 2016.
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Fig. 3. Daily variation of energy price from [8], and spline interpolated one.
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Fig. 4. Numerical simulation results of (a) the charging coordination of FCFS and FV2G for minimizing the energy cost of the building and (b) the accumulated cost over a day.
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Fig. 3 illustrates a daily profile of electricity wholesale price [8], interpolated for every 15 min, ranging {bk } from 0.0109 to 0.133 [$/kWh]. For V2G, the efficiency of discharging from EV is set to 95% based on [10]. Two schemes, FCFS and flexible V2G, denoted as FV2G, are compared under the scenario of 60 EVs are for commute purpose so that charging can be coordinated, while the rest 40 ones are for visiting, therefore they need to be charged in FCFS manner in both schemes. The BESS is considered and operated by the same objective to minimize the energy cost. Numerical simulation results are illustrated in Fig. 4 (a) the charging coordination of FCFS and FV2G for minimizing the energy cost of the building and (b) the accumulated cost over a day. As illustrated, the proposed scheme shows potential gain of ∼ 15.7 USD/Day or 14.3% reduction compared to the FCFS approach, based on the 250 kW capacity charging station for the public service building with max load of 300 kW, PV generation capacity of 40 kW, two batteries of capacity 100 kWh, and the charging rate γc = 7 kW. Depending on the DERs and V2G, the electricity load of the building varies as illustrated in Fig. 5, where the original building power profile is modulated inversely proportional to the energy price, illustrated in Fig. 3.
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Fig. 5. Aggregated load variations of the building depending on the DERs and V2G.
IV. S UMMARY AND D ISCUSSION
R EFERENCES
In this study, smart charging coordination schemes are proposed for flexible vehicle-to-grid (V2G) supports at the buildings with electric vehicle (EV) charging stations. The proposed V2G scheme shows potential energy cost savings by distributing the overall buildings electricity load with V2G coordination. Due to strong correlation between EVs and electricity load, numerical evaluation of the proposed scheme illustrates potential gain of 14.3% of energy cost reduction compared to the first-come first-serve approach, under medium sized smart building scenario integrated with the PV and BESS. Note that uncertainties in EVs’ in and out patterns and other variations need to be accounted in actual implementations, and the proposed scheme can quickly recoordinate the schedules by the event-driven fast ILP.
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ACKNOWLEDGMENT This work was supported by the GIST Research Institute (GRI) in 2016.
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