Optimal location and sizing of electric vehicles charging stations and renewable sources in a coupled transportation-power distribution network

• Published in: Renewable & sustainable energy reviews 2024-10, Vol.203, p.114767, Article 114767
• Main Authors: Nareshkumar, Kutikuppala, Das, Debapriya
• Format: Article
• Language: English
• Subjects: Electric vehicle charging station; Fuzzy max–min; Hong’s 2m＋1 point estimate method; Network reconfiguration; Renewable distributed generation
• ISSN: 1364-0321
• DOI: 10.1016/j.rser.2024.114767

The rapid growth of electric vehicles (EVs) and renewable distributed generators (DGs), which support net-zero emissions, poses technical challenges to the planning of transportation (TN) and distribution (DN) networks. This work proposes a two-stage strategy for the optimal planning of EV charging stations (EVCS) and DGs in a coupled TN-DN. In the first stage, a fuzzy max–min framework embedded particle swarm optimization and an EV waiting time constraint-encapsulated M/M/S queuing theory are used to obtain the optimal locations and sizes of EVCS. The objectives considered in this stage are maximizing EVCS operator profit and minimizing the costs of EV users and DN operator (DNO). In the second stage, a distribution system integrated with EVCS is used to find suitable sites and capacities for renewable DGs. A sensitivity analysis is carried out to identify the ideal locations of renewable DGs, and an exhaustive search-based analytical method is used to determine their sizes. Moreover, in this stage, a cost–benefit analysis is performed to assess the economic viability of planning renewable DGs. To address the uncertainties associated with EVs and renewable sources, Hong’s 2m+1 point estimation method is used. The efficacy of the suggested approach is tested on a coupled 28-node TN and a 69-node grid-connected DN. The findings reveal that the EVCS and DN operators will achieve an average incremental profit of 21.80% and 42.03%, respectively, while meeting all the system constraints. Further, the DNO gains an additional revenue of 20.17% by adopting distribution network reconfiguration. [Display omitted] •Stochastic planning of EV charging stations and DGs is done for a coupled network.•Uncertainties of both EVs and renewable DGs are considered.•A fuzzy max–min framework is used to solve the multi-objective planning problem.•Economic feasibility of DG placement is evaluated using a cost–benefit analysis.•Network reconfiguration is done to enhance distribution network operator profit.