Abstract
The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method.
Highlights
Electric vehicles (EVs) have gained increasing attention in recent years because of the enormous amount of carbon dioxide gas released from conventional vehicles
The total costs of transportation energy loss (TEL) and BU using the proposed objective function are reduced by 15%, in which TEL cost is reduced by 8% due to the fewer number of rapid charging station (RCS) than that using the conventional objective function
This study suggests a novel approach for optimal RCS placement and sizing by considering Google Maps API, battery state of charge (SOC), traffic density, and harmonic load flow
Summary
Electric vehicles (EVs) have gained increasing attention in recent years because of the enormous amount of carbon dioxide gas released from conventional vehicles. A novel approach that considers transportation loss (which introduces Google Maps JavaScript API to calculate the real time and distance from the current location of an EV to a CS in consideration of SOC), buildup (BU) cost (which incorporates the costs of the distribution transformer, underground cable, and charger in addition to other costs), and grid power loss cost (which comprises harmonic power loss) is proposed in this study to determine the optimal placement and sizing of RCSs. A new optimization technique, called binary lightning search algorithm (BLSA) [23], is utilized as an optimization tool to solve the RCS planning problem. ; NEV ; ð5Þ i1⁄41 where RCSP is the RCS placement decision vector, SOCtis is the SOC after time Ti_min, and NEV is the number of EVs. If ðSOCtis À SOCminÞ is negative, EVi cannot reach the RCS. The normalized total BU cost (BUnorm) can be derived from Eq (11) as
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