Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA ² B approach

Abstract

Demand response acts as an effectual tool to better balance electricity demand and supply in the smart grid. The response to prices and incentives is described as "changes on electricity usage from conventional consumption patterns to end-use customers." In this paper, a hybrid approach is proposed for Electric Vehicle Based Grid connected to the distribution generation (DG). The proposed system is joined performance of student psychology optimization algorithm (SPOA) and AdaBoost algorithm, thus it known as SPOA 2 B approach. The major aspire of this study is "to minimize the peak power cutoff, voltage regulation, spin reserve for making the optimization mode ideally convex, and accurate second-order conic relaxations." Additionally, the SPOA 2 B method is a proficient deal by standard business solvers with allocation models, and then acquires least annualized costs. In this study, the electric vehicle charging station (EVCS) is linked to DG, and then it gives charging service to electric vehicles (EVs). When the EV appears at the charging station, it informs the EVCS operator of their own energy requirement with anticipated departure time. Every EVCS can capture the information of all EVs using an SPOA 2 B technique. By then, the proposed system is performed on MATLAB/ Simulink site and the efficiency is compared with other processes. The number of iteration, like, 100, 250, 500,

Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks

“…The planning of charging station location and battery-swapping stations have been presented as multi-objective problems to mitigate the total cost, enhance the satisfaction of user, and reduce the EV's consumed energy [32]. A hybrid approach combining the student psychology optimizer and the AdaBoost algorithm has been introduced to allocate the EV charging station linked to distribution generation such that the peak power and voltage regulation are mitigated [33]. Al Wahedi et al [34] implemented a techno-economic analysis via the HOMER software for renewable-based charging stations to evaluate its optimal configuration in different cities in Qatar.…”

Optimization Model of Electric Vehicles Charging and Discharging Strategy Considering the Safe Operation of Distribution Network

“…The aim was to minimize the net present value of investments considering feeder routing, substation alterations, and construction while maximizing the utilization of proposed charging stations. In the literature [20], a hybrid approach was proposed for an electric vehicle-based grid connected to the distribution generation (DG). The major aspiration of this study was to minimize the peak power cutoff, voltage regulation, and spin reserve for making the optimization mode ideally convex and accurate second-order conic relaxations.…”

Optimal Location and Sizing of Hybrid Photovoltaic Public Charging Stations in Reconfigurable Feeders Using Levy Flight Honey Badger Algorithm

“…Many researchers have attempted to improve the performance of distribution feeders using optimal network reconfiguration (ONR) [4][5][6], optimal allocation of distribution generation (OADG) [7][8][9][10][11][12] and optimal allocation of charging stations (OACS) [13][14][15][16][17]. Other studies have focused on simultaneous approaches using (i) ONR and OADG [18][19][20] ONR and OACS [21][22][23], and (iii) OADG and OACS [24][25][26] respectively. In [4], simultaneous OADG and OACS problem is solved by focusing loss reduction and voltage stability enhancement.…”

“…They used stochastic optimisation to size the charging infrastructure under uncertainty. Student psychology optimization algorithm (SPOA) and AdaBoost algorithm, (SPOA 2 B) optimises DGs and EVCSs allocation [24]. The unique allocation method is the main contribution of this study.…”