1. Introduction
Nowadays, climate change and global warming on account of greenhouse gas (GHG) emissions such as CO
2, are negatively impacting the world environment. Moreover, CO
2 emissions in the transportation sector are also the cause of particulate matter (PM) 2.5 pollution which is generated by internal combustion vehicles. Many countries are looking into changing their policies to increase electric vehicle (EV) usage and there has been a consequent increase of EVs to over 5 million cars worldwide in 2018 [
1]. However, a large increase in EVs can significantly impact the power network, especially the low voltage (LV) network, such as the urban village power system with a higher potential of energy consumption [
2]. EV charging can affect the distribution network in many ways, for example, power quality issue and reliability problems [
3], power fluctuation due to the uncertainty of charging [
4], voltage drop due to charging demand exceeding prior design specifications [
5] and network overloading because of dump charging [
6,
7]. Moreover, its impacts on grid asset deterioration have been reported in references [
8,
9,
10]. The research in reference [
8] presented that the impact of EVs charging demand in the city with a high annual average temperature environment can significantly reduce the loss of life of the distribution transformer. In reference [
9], the simulation result illustrated that the dump of peak load from uncontrolled EV charging can cause the power network bottleneck problem and overloading. In reference [
10], it was shown that uncoordinated EV charging causes the distribution transformers and the grid components operate under high risk of failures. All of these create real-life challenges for EV congestion management in village networks for the distribution system operator (DSO) or the independent system operator (ISO).
Regarding the negative impacts of EV charging, many studies have been published on how to deal with the EV charging impacts. To reduce the EV impacts on grid assets, optimal charging has been a popular method in recent years. Research work in reference [
11] proposed a smart schedule for EV in the residential low-voltage system to flatten the load profile of the substation transformer that can reduce the transformer aging. Reference [
12] proposed the distributed scheduling algorithm to coordinate the EV charging in a residential area in Pattaya, Thailand. The goal of this work is to control the network load profile and prevent the transformer overloading. The optimization-based coordination strategies on the voltage stability and phase imbalance in the semi-urban low voltage grid was presented in reference [
13] where one of the study optimization strategies was to minimize the variance of the aggregated load at the transformer. Reference [
14], proposed optimal EV charging with considering the distribution network constraint set includes transformer and line limitations to prolong the transformer lifetime and to protect the line damage. However, the above references do not include the transformer aging due to temperature variation and the characteristic based on the winding thermal mechanism model was not illustrated in these studies as well. Optimization of EV charging to decelerate the distribution transformer loss of life using the exponential thermal models have been reported by some researchers. Reference [
15] presents the technical and economical optimization of EV charging in a parking garage with photovoltaic (PV) and battery energy storage system (BESS) considering the impact on transformer aging. Reference [
16] discusses optimized EV charging in the parking lot where the critical power limit of the distribution transformer is modeled with the ambient temperature and the aging acceleration factors. In reference [
17], the optimal strategies of the home energy management system with EV charging for minimizing the distribution transformer aging were shown. However, both studies did not consider the benefit of the EV owners’ energy arbitrage when the electricity tariff is taken into account, such as the time of use (TOU) rate.
In the research work [
18], the authors demonstrated the impact of the transformer aging in community distribution networks under different EV charging strategies such as dump charging, TOU charging and the proposed optimal charging considering minimization of energy costs and the transformer loss of life. In reference [
19], researchers proposed the optimal centralized model for a residential grid to minimize the transformer aging considering the energy arbitrage benefits for EV owners. In research work [
20], optimal EV charging considering the cost of the distribution transformer aging, network energy losses and charging cost in the residential area were proposed. However, they did not perform the optimal EV charging using power flow analysis to consider the network operating constraints such as the conductor current rating and bus-voltage boundary limitation. In addition, all reviewed literature did not consider the loss of EV owner’s energy arbitrage benefit when they change their charging profile to the optimal charging pattern that only supports the slowing down of the transformer aging.
In order to fill the gap in previous research works, this paper proposes a methodology to optimize EV charging allocation in an urban village network considering the EV owner’s benefit from EV battery price arbitrage and the DSO network operation cost including the transformer loss of life cost. The focus of this optimization work is to fulfill the objectives, namely, minimization of the loss cost of energy arbitrage, peak demand cost, network power loss cost, and the transformer aging cost. The model of transformer thermal characteristics is considered in the transformer loss of life cost. Also, the constraint function of a transformer operating limit is added to avoid the gassing of transformer insulation. Furthermore, the actual residential load of the urban village and the ambient temperature in Udon Thani, Thailand, are used to validate the outcomes of the proposed methodology. Numerical simulations with power flow analysis are done using MATLAB (version R2013b, Natick, MA, USA). In addition, the optimization problem is solved by using the genetic algorithm (GA) which is one of the metaheuristic optimization methods. The major contributions of this study are summarized as follows:
The loss of the EV owner’s benefit from battery energy arbitrage is formulated and taken into account in the objective function, which has not been studied, in previous works.
To optimize EVs charging allocation in the urban village environment, the network operating limitation is considered in the optimization constraints and evaluated based on the power flow analysis.
The network peak demand, power loss and transformer loss of life are formulated to be the economic term which represents the network operating costs for including in the objective function.
Finally, the transformer aging is calculated using the winding thermal characteristic model where the real baseload and the actual local ambient temperature, of an urban village, are utilized to demonstrate the effectiveness of the proposed optimization.
The remainder of the paper is organized as follows.
Section 2, describes the mathematical problem formulation.
Section 3 explains the proposed method that includes optimization functions and the framework. Simulation results are discussed in
Section 4, followed by the conclusions in
Section 5.
4. Simulation and Results
4.1. A Studied Urban Village Case
To test the proposed optimization approach, we have selected the distribution radial network in Udon Thani, Thailand, which represents an urban village network. A typical single-phase distribution transformer with a rating of 50 kVA connects the main grid with the 22 kV voltage system. This transformer serves 12 households with a low voltage level of 230 V and delivers power using the 50 THW conductor line, which spans 40 m for the main feeder and 10 m for the service drop line. The network topology for this study is shown in
Figure 2. In order to evaluate the transformer aging, the village baseload and ambient temperature of a day in summer (hottest) and winter (coldest) in 2018 were used for simulations. The village baseload profile with the time step of 15 min was obtained from the automatic meter recording (AMR) of the Provincial Electricity Authority (PEA) [
28] with a constant power factor of 0.9 (lagging). In addition, the hourly ambient temperature data were obtained from the Thai Meteorological Department (TMD) [
29], Thailand. Both village demand power and the environment temperature profile used for this study are presented in
Figure 3.
4.2. EV Model
A report of the top 10 EV sales by model in the U.S. [
30], is used to select 3 EV models for this study, namely the Chevy Volt [
31], Nissan Leaf [
32] and Tesla Model 3 [
33]. Because the area of this study is focused on the urban village, therefore, we have assumed one EV per house in this urban village study. The specifications of the three selected EV types are listed in
Table 1. In this work, we expect all participating EVs in a village must have completed their charging when they park at home. Since the interval time (∆
t) is set to be 15 min, the total number of time slots in a day is 96. The EV charger equipment at home is AC level type 2, which operates with a unity power factor. The charging and discharging rates follow the electricity tariffs based on the TOU rate. The electricity tariff and the monthly demand charge fee for a residential network in this study are shown in
Table 2. The energy rates are converted from THB to USD using the exchange rate of 1 USD equals 32 THB.
The uncertainties associated with EVs travel such as departure time, arrival time, and daily traveling distance are applied in this study through random generation of data based on the statistical probability of the traveling pattern from the National Household Travel Survey (NHTS) data [
35]. The normal distribution is used to create the random set of the departure time and the arrival time. The departure times from home are randomly generated with mean μ = 7.0 and standard deviation σ = 1.5 while the arrival times at home are randomly generated with the mean μ = 18.0 and the standard deviation σ = 3.0. Based on reference [
36], the random values for the daily driven distance is generated using a lognormal distribution with mean μ = 3.2 and standard deviation σ = 0.88. The probability distribution function (PDF) of the departure time, the arrival time and the daily driven distance are generated using the MATLAB probability density function shown in
Figure 4 and
Figure 5, respectively.
The village baseload at each house in summer and winter cases, EV model and its initial
of each EV battery are shown in
Table 3. The baseload power of each house is obtained from the monthly electricity bill while the daily load profile of an individual house is generated using the similar load pattern of the transformer. The peak load of each house is multiplied with a normalized curve of the transformer daily load profile to represent the daily load curve of each house.
4.3. Simulation and Scenarios
Based on the village network topology, EV models and the studied data as mentioned before, the power flow analysis was computed using the Newton Raphson method. Next, MATLAB is used for simulation to demonstrate the ability of the proposed optimization. To evaluate the transformer loss of life, the transformer thermal parameters used in this study are listed in
Table 4. For simulations, existing EV datasets for both summer and winter seasons are used. Finally, the numerical simulations were done for 3 scenarios as explained below:
Case I: Dump charging (uncontrolled charging), each EV starts charging immediately when it arrives at home with a rated charging power without V2G operation.
Case II: TOU charging (EV owner’s perspective), every EV will be delayed for charging for low electricity TOU rates. In addition, the discharging mode will be used at the high TOU rate period.
Case III: Optimal charging, all EVs are aggregated to charge and discharge with the proposed method.
4.4. Impact on Transformer Aging
Bar graphs in
Figure 6 illustrate the EVs charging scheduling in 24 h for each scenario. The transformer power was calculated using Equation (4) which is shown its load profiles in
Figure 7. In addition, the transformer aging acceleration factor (
) profile versus the EV’s charging load is presented in
Figure 8, and the correlation of the transformer loss of life (
) and the winding hottest-spot temperature (
) is shown in
Figure 9.
In EV charging with the dump charging method, results show that when the EVs are connected to the village network with uncontrolled charging, the transformer peak load is increased to 95.56 kVA (1.91 p.u.) due to dumping of the EVs charging load during a day in summer and 90.87 kVA (1.82 p.u.) in winter. Thus, the winding hottest-spot temperature exceeds 110 °C, as a result, is more than 1 for both the summer and winter days. Under this condition, the transformer experiences accelerated aging and its lifetime is reduced. In Case 2, when EVs are charged following TOU charging, this reflects the basic preference of EV owners who want to make profit by the energy arbitrage. Without any optimization, the EV that has the capability to discharge its stored energy is operated in V2G mode during the high rate period (e.g., 9 a.m. to 10 p.m.). Consequently, the large amount of EV discharging (V2G) can cause reverse power flow between 7 p.m.–10 p.m., which in turn, may create overvoltage related issues in the village network. Moreover, all EVs status will change to dump charge when the tariff rate is low, which occurs at 10 p.m. This leads to the transformer peak load at 112.78 kVA (2.26 p.u.) during a summer day and 104.62 kVA (2.09 p.u.) during a winter day. This condition rapidly increases the winding hottest-spot temperature over 250 °C during a summer day and over 190 °C during a winter day. Consequently, it leads to the extreme raising of the accelerated aging factor, which ultimately reduces the transformer lifetime. As a result, the insulation is gassed, which can lead to an explosion.
In the case of optimal charging, results show that the proposed EV charging method using optimal scheduling of both charging and discharging can minimize the transformer peak load to 68.81 kVA (1.38 p.u.) in summer and 65.22 kVA (1.30 p.u.) in winter. In addition, the transformer winding hottest-spot temperature is lower than 140 °C during the day of summer and lower than 110 °C during the day of winter. Accordingly,
declines indicating that the proposed optimization can prolong the transformer aging. Results of transformer loading and its winding hottest spot temperature were evaluated and the summary of the transformer aging including the equivalent of the aging acceleration factor and the percent of daily transformer loss of life for each scenario in summer and winter season are presented in
Table 5.
The TOU charging method results in the highest of the equivalent of the aging acceleration factor and the percent transformer loss of life, followed by the dump charging method. Compared to TOU and dump charging, the proposed optimal charging method can reduce the aging acceleration factor with the lowest percent transformer loss of life.
4.5. Cost Evaluation
This subsection presents the simulation results estimating the penalty cost of the EV owners’ arbitrage benefit loss, peak demand cost, power loss cost and the transformer loss of life cost under all three scenarios. All these costs are evaluated based on per day and the daily energy arbitrage (
) values that are obtained from the TOU charging method case. In addition, the transformer capital cost (
) is set to be 166.1
$/kVA [
19]. A summary of the economic cost evaluation is presented in
Table 6. Also, the efficiency improvement of the proposed optimization method based on the cost evaluation compared with the dump charging method and TOU charging method is demonstrated in
Figure 10.
In the case of dump charging, the penalty cost and the power loss cost are the highest when compared to the other charging methods because the energy arbitrage opportunity by V2G operation is not considered. The TOU charging case shows none of the penalty costs, which means EV owners can profit from the energy arbitrage using the V2G mode, but the peak demand cost, the transformer loss of life cost and the total cost are the highest. For the EV optimal charging case, results show that it induces a penalty cost; however, this value is lower than the dump charging case. This is because, in the proposed optimization method, we have included objective and constraint functions to minimize peak load, power loss and slow down the transformer aging. Hence, the transformer loss of life by the optimized case is the lowest among the EV charging cases. It can be observed that in both summer and winter the costs of peak demand, power loss and the transformer loss of life of the proposed optimized EV charging are the lowest compared to other charging methods. Also, the overall cost of the optimal charging method in both season cases is the lowest. However, due to the addition of penalty cost being presented, the EV owners’ arbitrage benefit loss. Thus, the DSO or ISO could manage this cost with the suitable electricity tariff program to motivate the EV owner to participate in this optimal charging strategy.
5. Conclusions
This paper presents the optimization of the EV charging method in an urban village network considering energy arbitrage and network operating cost. The focus of this optimization work is to minimize the loss of the EV owner’s benefit, peak demand cost, power loss cost, and the transformer aging cost. The loss of the EV owner’s benefit from battery energy arbitrage is introduced as the penalty cost function. The proposed optimization process includes distribution transformer aging affected by EV charging load. Also, the transformer loss of life model is formulated based on the winding hottest-spot temperature rising mechanism. Besides, the network operation limitations such as bus voltage, branch current and the transformer loading are set as constraints and are obtained from the power flow analysis result. To validate the outcomes of the proposed optimal EV charging method, a typical distribution radial network in Udon Thani, Thailand with real village baseload and the local ambient temperature was selected for simulations.
The numerical simulations were undertaken using MATLAB. Results show that the negative impacts due to the EVs dump charging and the TOU charging method on distribution network that consists of the network overloading, increased power loss, and the transformer aging, can be mitigated by the proposed optimization charging strategy. Also, the proposed optimization of the EVs charging method presents the lowest of the peak demand cost, power loss cost and the transformer loss of life cost when compared with other charging methods. In addition, the result shows that the loss of the EV owner’s energy arbitrage benefit can be reduced and the overall network operation cost is minimized. The simulation results prove that the proposed optimization method based on the genetic algorithm works efficiently for a variety of EV models, different initial EV battery energies, arrival times and departure times. Moreover, the robustness of the algorithm is presented when it performs with the different seasons of the study village case. Where the proposed optimization EVs charging shows high performance in both season cases. In summary, the application of the proposed optimal EV charging strategy can benefit both the EV owner and the distribution system operator (DSO) or independent system operator (ISO).