Electric vehicle charging station with multilevel charging infrastructure and hybrid solar-battery-diesel generation incorporating comfort of drivers

Journal of Energy Storage 26 (2019) 100924

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Electric vehicle charging station with multilevel charging infrastructure and hybrid solar-battery-diesel generation incorporating comfort of drivers Hasan Mehrjerdia, Reza Hemmatib, a b

T

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Electrical Engineering Department, Qatar University, Doha, Qatar Department of Electrical Engineering, Kermanshah University of Technology, Kermanshah, Iran

A R T I C LE I N FO

A B S T R A C T

Keywords: Electric vehicle charging station Hybrid generation Multilevel charging infrastructure Network reinforcement Parameter uncertainty

Electric vehicle charging station is connected to the distribution network and it is equipped with battery energy storage system, diesel generator, and solar panels. The three-level charging facility including fast, medium, and slow speed chargers is incorporated and optimally designed. The proposed model optimizes the rated power of charging facilities, power and capacity of battery energy storage system, hourly operation of diesel generator, and hourly operation of battery energy storage system. The capacity of charging station (i.e., number of parking slots) and level of network reinforcement are modeled and optimized. The uncertainties of the parameters are also involved. The results demonstrate that the proposed model successfully utilizes all available options to design electric vehicle charging station.

1. Introduction Fossil energy comes with various disadvantages and public awareness is recently improved about the drawbacks of the fossil fuels. Fossil fuels release the environmental pollutions and contribute to global warming. Fossil fuels are non-renewable and non-sustainable resources and limit amount of them are available. Electric vehicles are therefore attracting more attention as a good replacement for the cars powered by fossil fuels [1] and they use one-board energy storage systems [2]. However, many factors (e.g., technical level and supposed risks) have impacts on public acceptance of electric vehicles [3]. In [3], it is shown that about 18.1% of the people prefer to replace their conventional cars with an electric vehicle. One of the main problems associated with the electric vehicles is to design and deployment of proper charging stations for the electric vehicles. Unavailability of proper charging stations decreases the adoption of electric vehicles [4]. It has been proven that designing an electric vehicle charging station (EVCS) with enough parking spot is necessary to improve public acceptance of the electric vehicle [5]. Electric vehicles can be charged at homes by residential charging facility or they can use public charging stations [6]. In order to proper operation of electric vehicles, it is required to design EVCS with enough parking slots, appropriate charging infrastructures, and suitable locations [7]. EVCS are often connected to the electrical distribution networks or

⁎

smart-grids and microgirds [8]. Therefore, the impacts of these charging stations on the electrical grid must be investigated. Electric vehicle charging stations may make impact on voltage [9], electricity price [10], and network power flow [11] in the electrical grids. As a result, it is necessary to consider such electrical items for proper sizing and deployment of electric vehicle charging stations in the electrical grids. The optimal operation and utilization of EVCS can make positive impacts on the electrical grids. The charging strategy of the electric vehicles can be planned to help load management and demand response in the grid [12]. Since EVCS charges energy to the vehicles at different hours, they are proper resources for load regulation in the networks [13]. The plug-in electric vehicles can be used for peak shaving in the network, where the energy of the vehicles is discharged and sent to the grid during on-peak hours [14]. The charging stations can also improve the reliability of the network and support load demand under electricity outage [15]. 1.1. Motivations and innovations of the paper The motivation of the paper is to present a comprehensive model for EVCS including various energy resources and technologies in order to minimizing the investment cost, operational cost, peak load cutting, and drivers’ comfort at the same time. EVCS is optimally designed while the sizing, siting, and operation of the energy resources and technologies are optimized. Such comprehensive model opens the door for

Corresponding author at: Department of Electrical Engineering, Kermanshah University of Technology, P.O.Box: 67156-85420, Kermanshah, Iran. E-mail addresses: [email protected] (H. Mehrjerdi), [email protected] (R. Hemmati).

https://doi.org/10.1016/j.est.2019.100924 Received 13 July 2019; Received in revised form 11 August 2019; Accepted 4 September 2019 2352-152X/ © 2019 Elsevier Ltd. All rights reserved.

Journal of Energy Storage 26 (2019) 100924

H. Mehrjerdi and R. Hemmati

Prbi Prdg r P fcf r Pscf r Pmcf Pr s i, ts, td Pcb i, ts, td Pdb i, ts, td Pdg s, i, vn, ts, td P fcf PLs, i, j, ts, s, i, vn, ts, td Pmcf

Nomenclature Symbol definition Cb Ccf Cdg Ce Cevcs Cnr Csb Csp dr Di.j Ebi, ts, td Erbi Evf Ev0 Eevs, i, vn, ts, td Erev Epts, td EVs Fc FiC i, j L ri, j lt N, Ncs Nevs, i, ts, td i, j Pmax Pds, i, ts, td

Annualized investment cost on battery energy storage system ($/year) Annualized investment cost on three-level charging facility ($/year) Annualized investment cost on diesel generator ($/year) Annualized cost of consumed energy by charging station ($/year) Capacity of charging station (number of vehicles) Annualized investment cost on network reinforcement ($/year) Annualized investment cost on charging station capacity ($/year) Annualized investment cost on solar system ($/year) Discount rate (%) Distance of line (km) Energy of battery (p.u.) Rated capacity of energy storage system (p.u.) Full battery capacity of electric vehicle (kWh) Initial energy of electric vehicle (kWh) Energy of electric vehicle (p.u.) Rated capacity of electric vehicle (kWh) Electricity price ($/kWh) The required space for each vehicle (square meter) Fuel cost ($/kW) Final annualized cost ($/year) Indexes of buses Level of reinforcement for line (%) Asset life time (year) Set of buses, set of buses with charging station Number of vehicles in charging station Maximum power of line (p.u.) Load level (p.u.)

Rated power of energy storage system (p.u.) Rated power of diesel generator (p.u.) Rated power of fast speed charger (p.u.) Rated power of slow speed charger (p.u.) Rated power of medium speed charger (p.u.) Probability of scenarios Power of battery charging (p.u.) Power of battery discharging (p.u.) Power of diesel generator (p.u.) Power of fast speed charger (p.u.) Power of line (p.u.) Power of medium speed charger (p.u.)

td

s, i, vn, ts, td Pscf Power of slow speed charger (p.u.) Psps, i, ts, td Power of solar system (p.u.) s, S Index of scenarios, set of scenarios Duration of time interval (minutes) Ttd Tds Coefficient to convert daily cost to seasonal cost td, TD Index of time intervals, set of time intervals in one day ts, TS Index of seasons, set of seasons uevs, i, vn, ts, td Binary variable showing entered vehicle Vθs, i, ts, td Voltage angle (Rad) Vmmin Minimum voltage of network (p.u.) Voltage magnitude (p.u.) Vms, i, ts, td Vmmax Maximum voltage of network (p.u.) vn, VN Index of vehicles, set of vehicles Verb Investment cost on capacity of battery ($/kWh) Vprb Investment cost on power of battery ($/kW) Vcf Investment cost on charging facility ($/kW) Vsb Investment cost on charging station ($/m2) i, j Investment cost on line reinforcement ($/km) Vnr Vrdg Investment cost on diesel generator ($/kW) Vsp Investment cost on solar system ($/kW) Yi, j Admittance of line (p.u.) ηbi Efficiency of energy storage system (%)

Fig. 1. EVCS on bus 10 connected to the 30-bus distribution network. 2

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H. Mehrjerdi and R. Hemmati

storage system is given by (9) [21].

further developments towards more efficient and reliable EVCS. The main aspects and innovations of the proposed model are as follows; ■ The capacity of charging station is optimally designed. ■ The charging station is equipped with multilevel charging facility and rated powers of the chargers are optimally designed. ■ Wellbeing (comfort) of the car drivers is considered in the planning. ■ The charging station is powered by solar panels, BESS, and diesel generator. The sizing and hourly operation pattern are optimized for these energy resources. ■ The charging station is connected to the electrical distribution network. The distribution grid reinforcement is modeled and included. ■ The planning minimizes the investment and operational costs of all available technologies. ■ The initial energy of the entered electric vehicles, the solar energy, and the load demand are modeled by probability distribution functions and dealt by stochastic programming.

∀ i ∈ Ncs, ts ∈ TS, td ∈ TD

TD i, ts, td ∑td = 1 Pcb TD i, ts, td ∑td = 1 Pdb

(8)

∀ i ∈ Ncs, ts ∈ TS (9)

i, ts, td i, ts, td Ebi, ts, td = Ebi, ts, td − 1 + Pcb × T td − Pdb

× T td ∀ i ∈ Ncs, ts ∈ TS, td ∈ TD Ebi, ts, td ≤ Erbi ∀ i ∈ Ncs, ts ∈ TS, td ∈ TD

(10) (11)

In this paper, the rated power, rated capacity, and charging-discharging regime of storage unit are the design variables and optimized. i, ts, td i, ts, td , Pdb , Prbi , Erbi . The design variables are: Pcb 2.3. Diesel generator Diesel generator is added to the charging station as a supplementary generation system to deal with high pricing time sections. The operation of diesel generator is formulated by (12). The generated power by i, ts, td diesel generator (i.e., Pdg ) is the design variable and optimized by the programming. Diesel generator is a suitable deal for peak shaving in the network and it can produce power during high-peak time sections. i, ts, td Pdg ≤ Prdg ∀ i ∈ Ncs, ts ∈ TS, td ∈ TD

(12)

2.4. Multilevel charging facilities and parking spots Three different charging infrastructures are considered for charging station to charge the electric vehicles. The charging infrastructures are slow, medium, and fast speed charging facilities. Considering different charging infrastructures reduces the investment cost on charging facilities [23]. As described by (13), the electric vehicles are classified based on their initial energy. If the initial energy of electric vehicle is more than 2/3 of full battery capacity, the vehicle is charged by slow speed charging facility. If the initial energy is between 1/3 and 2/3 of full battery capacity, it is charged by medium speed facility. Moreover, the vehicle is charged by fast speed charger if its initial energy is less than 1/3 of full battery capacity. The energy of electric vehicle at each time section is calculated by (14). The rated battery capacity of electric vehicle is defined by (15) and the rated powers for charging facilities are defined through (16) to (18).

PL s, i, j, ts, td = Y i, j × (Vθs, i, ts, td − Vθs, j, ts, td ) ∀ s ∈ S, i, j ∈ N , ts ∈ TS, td ∈ TD (1) (2) (3)

Power balance on the buses without charging station is satisfied by (4) and equilibrium of power on the buses equipped with EVCS is given by (5). In (5), the impacts of multilevel charging facility, BESS diesels generator, and solar system are modeled [19]. N

⎛ ⎞ P s, i, j, ts, td = 0 ∀ s ∈ S, i ∈ Nncs, ts ∈ TS, td ∈ TD ⎜∑ L ⎟ = j 1 ⎝ ⎠

(7)

Prbi

The stored energy in the battery pack at each time section is calculated by (10). The rated capacity (nominal capacity) of the BESS is defined by (11) and (8) [22].

EVCSs are often connected to electrical grids and especially electrical distribution networks. In this paper, IEEE standard 30-bus distribution network [16] is considered and connected to charging station as shown in Fig. 1. The location of charging station is assumed on bus 10 and it is equipped with battery energy storage system (BESS), diesel generator, and solar system. The charging facilities appear as a load on the distribution network, the diesel generator and solar system inject power to the grid. BESS injects power to the grid during discharging time sections and consumes power form the grid during charging time periods [17,18]. The power flow of the network including EVCS is given through (1) to (5). The power through each line of the network is expressed by (1) and limit of each lines is given by (2). Voltage limit on all buses is addressed as (3) [19].

Pds, i, ts, td +

i, ts, td Pcb ≤ Prbi ∀ i ∈ Ncs, ts ∈ TS, td ∈ TD

ηb =

2.1. Power flow formulation for grid connected charging station

Vmmin ≤ Vms, i, ts, td ≤ Vmmax ∀ s ∈ S, i ∈ N , ts ∈ TS, td ∈ TD

(6)

i, ts, td Pdb ≤

2. Modeling and operation of charging station

i, j PL s, i, j, ts, td ≤ Pmax ∀ s ∈ S, i, j ∈ N , ts ∈ TS, td ∈ TD

i, ts, td i, ts, td ≥ 0 ⇒ Pdb =0 ⎧if Pcb ∀ i ∈ Ncs, ts ∈ TS, td ∈ TD i, ts, td i, ts, td ⎨ f Pdb P ≥ ⇒ = 0 0 cb ⎩

(4)

s, i, vn, ts, td =0 ⎧ ⎧ Pscf ⎪ ⎪ 1 f s, i, vn, ts, td 0 =0 ⇒ Pmcf ⎪if 0 ≤ Ev ≤ 3 Ev ⎨ ⎪ s, i, vn, ts, td ⎪ =0 P ⎪ ⎩ fcf ⎪ s, i, vn, ts, td ⎪ ≥0 ⎧ Pscf ⎪ ⎪ s, i, vn, ts, td 2 f 1 f 0 ≥0 if 3 Ev ≤ Ev ≤ 3 Ev ⇒ Pmcf ⎨ ⎨ s, i, vn, ts, td ⎪ P fcf ⎪ =0 ⎩ ⎪ s, i, vn, ts, td ⎪ =0 ⎧ Pscf ⎪ ⎪ if 2 E f ≤ E 0 ≤ E f ⇒ ⎪ P s, i, vn, ts, td = 0 v v ⎪ 3 v ⎨ mcf s, i, vn, ts, td ⎪ ⎪ P fcf ≥0 ⎩ ⎩ ∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD, vn ∈ VN

N VN s, i, vn, ts, td s, i, vn, ts, td s, i, vn, ts, td )+ Pds, i, ts, td + ⎜⎛∑ j = 1 PL s, i, j, ts, td⎟⎞ + ∑vn = 1 (Pscf + Pmcf + P fcf ⎝ ⎠ i, ts, td i, ts, td i, ts, td Pcb − Pdb − Pdg − Psps, i, ts, td = 0

∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD (5) 2.2. BESS BESS in installed on the EVCS to increase operation flexibility and possible reduction in the planning cost. The BESS is modeled through (6) to (11) [20]. In (6), it is confirmed that the battery can only operate on charging or discharging state at each time section. The rated power (nominal power) of the BESS is defined by (7) and (8). The efficiency of 3

(13)

Journal of Energy Storage 26 (2019) 100924

H. Mehrjerdi and R. Hemmati s, i, vn, ts, td s, i, vn, ts, td s, i, vn, ts, td Eevs, i, vn, ts, td = (Pscf + Pmcf + P fcf ) Ttd + Eevs, i, vn, ts, td − 1

∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD, vn ∈ VN

Ce =

(15)

r s, i, vn, ts, td P fcf ≤ P fcf

(16)

r s, i, vn, ts, td Pscf ≤ Pscf ∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD, vn ∈ VN

(17)

r s, i, vn, ts, td Pmcf ≤ Pmcf ∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD, vn ∈ VN

(18)

⎧ ⎪

S

∑ ∑ ts = 1

Eevs, i, vn, ts, td ≤ Erev ∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD, vn ∈ VN ∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD, vn ∈ VN

TS

(14)

⎨ s=1 ⎪ ⎩

⎛⎡ TD ⎜⎢ ∑ ⎢ ⎜ ⎢ td = 1 ⎜⎢ ⎝⎣

VN

∑ vn = 1

s, i, vn, ts, td ⎫ +⎞ ⎞ ⎤ ⎛ Pscf ⎪ ⎜ P s, i, vn, ts, td+⎟ × E ts, td⎥ × Pr s⎟ × Tds ∀ p ⎥ mcf ⎟ ⎬ ⎜ ⎟ ⎥ ⎜ P s, i, vn, ts, td ⎟ ⎪ ⎟ ⎥ ⎝ fcf ⎠ ⎦ ⎠ ⎭

(25)

i ∈ Ncs

The annualized investment cost on BESS is given by (26). The last term is multiplied to calculate the annualized cost.

dr × (1 + dr )lt ⎞ Cb = (Erb × Verb + Prb × Vprb) × ⎛ lt ⎝ (1 + dr ) − 1 ⎠ ⎜

The rated powers for charging facilities are the design variables and optimized by the programming.

⎟

The annualized investment cost on three-level charging facility is addressed by (27) and the annualized investment cost on the charging station capacity is given by (28).

3. Parking capacity

dr × (1 + dr )lt ⎞ r r r ) × Cevcs × Vcf × ⎛ Ccf = (P fcf + Pmcf + Pscf lt ⎝ (1 + dr ) − 1 ⎠

(27)

dr × (1 + dr )lt ⎞ ⎟ Csb = Cevcs × EVs × Vsb × ⎜⎛ lt ⎝ (1 + dr ) − 1 ⎠

(28)

⎜

There is a meter to count number of electric vehicles that are entered to the charging station. In (19) and (20), if the electric vehicle is entered to the charging station at time section 'td', then the related binary variable is set to one, otherwise it is set to zero.

uevs, i, vn, ts, td = 0 If vehicle entered to the station at this td ∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD, vn ∈ VN uevs, i, vn, ts, td = 1 If vehicle is not entered to the station at this td ∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD, vn ∈ VN

=

VN ∑vn = 1 uevs, i, vn, ts, td

+

(19)

(20)

dr × (1 + dr )lt ⎞ i, j Cnr = L ri, j × Vnr × Di . j × ⎛ lt ⎝ (1 + dr ) − 1 ⎠ ⎜

Nevs, i, ts, td ≤ Cevcs

∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD

⎜

TS

+

TD

∑ ⎡⎢ ∑

ts = 1

(29)

⎣ td = 1

⎟

⎤ i, ts, td (Pdg × T td × Fc ) × Tds⎥ ⎦

(30)

(21)

dr × (1 + dr )lt ⎞ Csp = Psp × Vsp × ⎛ lt ⎝ (1 + dr ) − 1 ⎠ ⎜

(22)

⎟

(31)

Regarding all the mentioned costs, the final planning cost is given as (32) which is summation of all mentioned costs. It is obvious that the final cost is per year. The proposed planning minimizes this annualized cost as objective function.

In this paper, the parking capacity (number of charging slots) is considered as a design variable and optimized by the planning. As shown by (22), the parking capacity is equal to maximum number of electric vehicles in the charging station. As a result, all the entered cars to the charging station at each time section can immediately plug their vehicles in and start charging their battery without staying in boring queue. This point would increase investment cost of charging station, but it also increases wellbeing of the drivers and results in public acceptance of electric vehicles.

FC = Ce + Cb + Ccf + Csb + Cnr + Cdg + Csp

(32)

6. Case study As presented in Fig. 1, IEEE 30 bus distribution grid is taken into account as case study and bus 10 is equipped with EVCS. The base power of the network is 10 MVA and the voltage is 12.66 kV [16]. The charging station is supported by diesel generator, BESS, multilevel charging infrastructure, and solar panels. Nominal power of solar system is 30 kW and it is modeled by Gaussian probability distribution function with mean 30 and standard deviation 20% [24]. Fig. 2 shows solar energy profile at different seasons [24]. The daily time period is modeled by 96 time sections each interval equal to 15 min. The entered vehicles to the charging station at each time section are achieved from the historical data as depicted in Fig. 3 [13]. The initial energy of electric vehicles is randomly generated based on normal distribution with Mean 50% of fully charged vehicle, Standard Deviation 15%, and Positive Skewness 15% to deal with realistic. The Positive Skewness means that the entered vehicles probably have the initial energy less than 50% of their full battery capacity. As shown in Fig. 3, 20 vehicles are arrived to the charging station at time section 70. The initial energy for one scenario of performance at time section 70 is depicted in Fig. 4. Full capacity of each electric vehicle is 100 kWh [23] and it is shown that the initial energies is less

4. Network reinforcement When the distribution network is equipped with EVCS, it needs reinforcement to increase capacity of lines to deal with high demand of energy in the charging station. In this paper, network reinforcement is combined with BESS, diesel generator, and flexible charring strategy in order to optimize the reinforcement cost. In (24) the maximum capacity of each line is defined as a base capacity multiplied by reinforcement factor. The reinforcement factor is a design variable and achieved by the planning [19]. i, j PL s, i, j, ts, td ≤ Pmax × L ri, j ∀ s ∈ S, i, j ∈ N , ts ∈ TS, td ∈ TD

⎟

dr × (1 + dr )lt ⎞ Cdg = Prdg × Vrdg × ⎛ lt ⎝ (1 + dr ) − 1 ⎠

Nevs, i, ts, td − 1

∀ s ∈ S, i ∈ Ncs, ts ∈ TS, td ∈ TD

⎟

The annualized investment cost on network reinforcement is calculated as (29). The annualized investment cost on diesel generator and the solar system are also presented in (30) and (31), respectively.

Number of the vehicles inside the charging station at each time section is calculated by (21) and the parking capacity (number of charging slots) is denoted by (22).

Nevs, i, ts, td

(26)

(24)

5. Planning costs and objective function The planning cost comprise several terms that are expressed through (25) to (31). The cost of consumed energy by the charging station is calculated by (25). This term calculated the expected value of cost including all scenarios of performance. 4

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H. Mehrjerdi and R. Hemmati

than 100 kWh [25]. The loading profile under different seasons of the year is given in Fig. 5 [23]. The seasonal profile is used to deal with realistic condition. The energy price is modeled by time-of-use pricing scheme as shown in Fig. 6 [26]. The economic data of problem including investment and operational costs are listed in Table 1 and the other noneconomic data of the planning are presented in Table 2 [27]. 6.1. Assumption in the model and data The assumptions in the model and data are as follows; the efficiencies of battery storage system, diesel generator, and electric vehicles are assumed to be 100%. The environmental pollution of the system and diesel generator is not considered. The daily time period is modeled by 96 time intervals each one 15 min. The power flow of the network is simulated by linear model. The full capacity of electric vehicle is considered 100 kWh. Nowadays, most of the electric vehicles have capacity less than 100 kWh. However, some advanced electric vehicles such as Tesla Model X 100D (2018) and Tesla Model S 100D (2018) have 100 kWh lithium ion on-board battery. As a result, the 100 kWh battery is practical and realistic.

Fig. 2. Solar energy profile at different seasons.

7. Numerical results The numerical results of the introduced planning are given after solution in GAMS software. The problem is solved by a personal computer with processor core i3, CPU@ 3.3 GHz, and RAM 4GB. Table 3 lists the planning costs for each item and also the final planning cost. All costs are annualized. The most cost of the network is devoted to the consumed energy by the charging station. The reinforcement also covers a big portion of cost; other costs are not very significant. By the way, the plan uses all available options (i.e., battery energy storage, solar, network reinforcement, and diesel generator) to present the optimal solution for charging station and charging facilities. Table 4 demonstrates the technical outputs of the plan. The outputs indicate that three different charging facilities (fast, medium, and slow speed) are optimized and installed together. The rated power of fast charger is about 46% greater than the medium one and the medium charger is about 124% greater than the slow charger. Such flexibility in the available chargers for the electric vehicles reduces the charging time and planning cost at the same time. The desired capacity for charging station is optimized for 22 vehicles. It means that the charging

Fig. 3. Number of entered vehicles to the charging station at each time section.

Fig. 4. Initial energy for one scenario of performance at time section 70.

Fig. 5. Load profile at different seasons. 5

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H. Mehrjerdi and R. Hemmati

Table 4 Technical outputs of the plan. Parameter

Optimal level

Fast speed charger of charging station (kW) Medium speed charger of charging station (kW) Slow speed charger of charging station (kW) Capacity of charging station Rated power of battery energy storage (kW) Rated capacity of battery energy storage (kW) Rated power of diesel generator (kW)

388 265 118 22 223 111.5 10

Table 5 Percentage of reinforcement on lines of network.

Fig. 6. Energy time of use pricing.

Level

Investment cost of power in battery energy storage ($/kW) Investment cost of capacity in battery energy storage ($/kWh) Investment cost of charging facilities ($/kW) Investment cost of constructing charging station ($/square meter) Required space for each vehicle (square meter) Investment cost for network reinforcement ($/km) Length of each line (km) Investment cost of diesel generator ($/kW) Operational cost of diesel generator ($/kWh) Investment cost of solar system ($/kW)

300 300 200 200 12 25,000 2 200 0.2 1000

Level

Full capacity of electric vehicle (kWh) Maximum time to get fully charge (hour) Maximum permitted power of diesel generator (kW) Initial energy of battery storage system (kWh) Efficiency of battery storage system (%) Discount rate (%) Life time of battery storage system (year) Life time of diesel generator (year) Life time of charging facilities (year) Life time of charging station building (year) Life time of new lines (year)

100 2 10 0 100 10 10 10 10 15 10

Level ($/year)

Annualized cost of consumed energy by charging station Annualized cost of battery energy storage Annualized cost of charging facility Annualized cost of constructing charging station Annualized cost of network reinforcement Annualized cost of diesel generator Investment cost of solar system Annualized planning cost

2,285,284.984 17,648.051 552,683.361 6941.815 429,177.915 690.491 3944.213 3,296,370.831

Capacity of line is expanded by 527% No reinforcement

The charging station is also equipped with a diesel generator to operate under peak loading or emergency condition. The hourly operation of the diesel generator is optimized by the plan as listed in Table 6. As demonstrated by the outputs, the diesel generator only operates under on-peak hours when the electricity price is high. Such optimal operation can help the network to reduce the planning cost and cutting the peak of loading profile under on-peak conditions. The introduced plan not only optimized the rated power and capacity of BESS, but also optimizes charring-discharging pattern of the storage system at all time-intervals over the day. Fig. 7 shows the charging-discharging regime of BESS at daily time intervals and Fig. 8 represents its energy. The outputs indicate that BESS stores energy during off-peak hours and discharges energy under on-peak hours. Such operation reduces the planning cost and helps the network to deal with on-peak loading conditions. Such peak cutting strategy (arbitraging energy from off-peak time periods to on-peak intervals) reduces congestion in the network lines and the network will need less reinforcement resulting in less cost. As a result, coordination of BESS, diesel generator, and network reinforcement provides a flexible planning with minimum cost.

Table 3 Planning cost for each item and the final planning cost. Cost

Lines between bus 1 to bus 10 Rest of lines

7.1. Peak cutting strategy

Table 2 Noneconomic data of the planning. Parameter

reinforcement on line capacity

to 10 kW. The results denote that BESS needs a big converter to transfer power from the battery to the network at a very short time period (each time period is 15 min). As a result, it is not required to install a big battery and the capacity of battery is less than rated power of converter. The large energy demanded by charging station needs to be supplied by electrical grid. Thus, one extra option of the planning is to reinforce the network lines. The percentage of reinforcement is considered as a design variable and optimized by the plan as shown in Table 5. The outputs declare that the network needs significant reinforcement in the lines connected between bus 1 and bus 10 (where the charging station is located). The other lines of the network do not need expansion.

Table 1 Economic data of problem. Parameter

Line number

7.2. Operation of three level charging facility The proposed programming installs three charging facility to charge Table 6 Output power of diesel generator.

station needs 22 parking slots (parking spots) to charge the vehicles. The rated power and capacity of BESS are also optimized as 223 kW and 111.5 kWh, respectively. The rated power of diesel generator is also set 6

Time section

Power (kW)

1 to 68 69 to 70 71 to 96

0 10 0

Journal of Energy Storage 26 (2019) 100924

H. Mehrjerdi and R. Hemmati

Fig. 9. Transferred power from bus 9 to 10 and from bus 10 to 11.

Fig. 7. Charging-discharging regime of BESS at daily time intervals.

Table 8 Annualized costs of proposed model. Cost

Level ($/year)

Annualized cost of model including all proposed infrastructures Annualized cost of model excluding battery storage system Annualized cost of model excluding diesel generator Annualized cost of model excluding line reinforcement Annualized cost of model excluding all proposed infrastructures Annualized profit of the proposed model

3,296,370.831 3,307,678.286 3,307,690.478 4,641,724.115 4,872,134.197 1,575,763.366

the battery of vehicles. Because the vehicle with initial energy close to the full capacity (e.g., vehicle 4 with initial energy equal to 83 kWh) does not need to be charged with a very high power charger and charging such vehicle by a very big charger reduces the life time of its battery. As a result, the proposed planning charges its battery with slow speed charger to increase battery life time. As it was described, the electric vehicle charring station is located in bus 10. Fig. 9 shows the power from bus 9 to10 and from bus 10 to 11. The difference of these two powers is consumed by the charging station. It is clear that charging station appears as a big load on the network during on-peak periods such as time intervals 60 to 75. The loading profile of the network is on the peak condition at these time-periods and the extra load demand of charging station leads to severe loading condition. The proposed plan by this paper utilizes several options to deal with such critical situation. As it was described, coordinated utilization of BESS, solar panels, diesel generation, and network reinforcement supports the network to handle such loading profile.

Fig. 8. Stored energy in BESS at daily time intervals.

the electric vehicles at minimum time with best quality of service (i.e., without staying in the queue, without extra charge and pressure on the battery of vehicle). Table 7 studies the status of energy in the electric vehicles that arrive to the charging station at time interval 5. Four vehicles are entered to the charging station at this time period. The full capacity of all vehicles is assumed to be 100 kWh. The electric vehicles with initial energy less than 1/3 of full capacity (33 kWh) are charged by fast charger such as vehicles 1 and 3. The vehicles with initial energy between 1/3 and 2/3 of full capacity (between 33 and 66 kWh) are charged by medium charging facility such as vehicle 2. The vehicles with initial energy more than 2/3 of full capacity (greater than 66 kWh) are charged by slow charging facility such as vehicle 4. Eventually all the vehicles are fully charged equal to 100 kWh. Such procedure provides several advantages. For example, the extra stress is not applied on

7.3. Life-cycle cost-benefit analysis Life-cycle cost-benefit analysis of the proposed model is addressed in Tables 8 and 9. Table 8 lists the annualized costs of proposed model and shows the impacts of the proposed infrastructures on the model. It is clear that the proposed infrastructures reduce the cost and the model without the proposed infrastructures has significant cost equal to

Table 7 Status of energy in the electric vehicles that arrive to the charging station at time interval 5. Vehicle number

Initial Energy

Charging time and power

Final energy (kWh)

1 2 3 4

21 54 24 83

Charging Charging Charging Charging

100 100 100 100

by by by by

fast charger at time Section 8 by 316 kW medium charger at time Section 6 by 184 kW fast charger at time section 10 by 304 kW slow charger at time Section 6 by 68 kW

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Journal of Energy Storage 26 (2019) 100924

H. Mehrjerdi and R. Hemmati

Table 9 Total costs of model on its life time. Cost

Level (106 $/lifetime)

Total cost of model excluding the proposed infrastructures Total cost of model including the proposed infrastructures Total profit of the proposed model

48.72 32.96 15.75

References

4,872,134.197 ($/year). The profit of the proposed model is 1,575,763.366 ($/year). Table 9 lists the costs of the model on the life time. It is demonstrated that the proposed model provides 15.75 million dollars profit per the model lifetime.

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8. Summary of the numerical results The numerical results demonstrate that the plan installs various infrastructures to reduce the planning cost. The annualized planning cost including all the proposed infrastructures is 3,296,370.831 ($/year). The proposed infrastructures make significant impacts on the cost. For instance, the model cost excluding battery storage system, diesel generator, line reinforcement is increased to 3,307,678.286, 3,307,690.478, and 4,641,724.115 ($/year), respectively. The annualized profit of the proposed model is 1,575,763.366 ($/year) and its total profit per lifetime is 15.75 million dollars. The proposed model installs and operates three charging facilities including fast, medium, and slow speed with rated powers equal to 388, 265, and 118 kW, respectively. The capacity of charging station is optimized on 22 slots, the BESS is optimized on 223 kW power and 111.5 kWh capacity. As well, one 10 kW diesel generator is equipped on the system. The capacity of the lines between buses 1 to bus 10 are also reinforced by 527%.

9. Conclusions This paper designs the EVCS equipped with various energy resources. The capacity of charging station, charging facilities, battery storage system, and network reinforcement are optimally designed to minimize the cost. The optimal operation pattern of diesel generator and battery storage system are also determined by the model. All the above mentioned items are carried out under parametric uncertainties in the model. The results confirm that the proposed model successfully designs the available energy resources and the objectives such as minimizing cost and peak-load cutting are met, where the total investment cost is reduced by 15.75 million dollars per lifetime. As well, the hourly operation of diesel generator and battery are optimized to supply the load demand under on-peak loading that successfully shaves the peak load demand. The rated power of fast speed charger is about 46% greater than the medium speed and the medium speed charger is about 124% greater than the slow speed charger. Such big fast power charger is required to keep the capacity of charging station as much as possible low and reduce the planning cost; where, the capacity of charging station is optimized on 22 vehicles. The outputs denote that the electric vehicles with initial energy less than 33 kWh are charged by fast charger, the vehicles with initial energy between 33 and 66 kWh are charged by medium facility, and the vehicles with initial energy more than 66 kWh are charged by slow charging. Further to this work, it is suggested to consider following items; considering environmental pollutions in the model, incorporating the hydrogen and Fuel-cell vehicles in the model, and evaluating the impacts of charging station on the upstream electric network, considering nonlinear power flow model for distribution grid.

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D. Raimondo, Optimising lithium-ion cell design for plug-in hybrid and battery electric vehicles, J. Energy Storage 22 (2019) 228–238. [26] R. Hemmati, Optimal design and operation of energy storage systems and generators in the network installed with wind turbines considering practical characteristics of storage units as design variable, J. Clean. Prod. 185 (2018) 680–693.

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