A multiobjective planning framework for EV charging stations assisted by solar photovoltaic and battery energy storage system in coupled power and transportation network. (9th November 2021)

• Record Type: Journal Article
• Authors: Kumar, Nikhil; Kumar, Tushar; Nema, Savita; Thakur, Tripta
• Is Part Of: International journal of energy research. Volume 46:Number 4(2022)
• Page Start: 4462
• Page End: 4493
• Publication Date: 2021-11-09
• Subjects: battery energy storage system -- distribution network -- electric vehicle charging station -- optimization -- solar PV; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.7442
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: The global shift away from internal combustion (IC) engines and toward electric vehicles (EVs) is well underway. The sustainability of this transition requires a coordinated approach for planning of charging stations integrated with solar photovoltaic (SPV) and battery energy storage system (BESS) with due consideration to the power distribution and transportation network. A two‐stage multiobjective planning framework is proposed to find effective service radius, optimal sites, and sizing of fast charging electric vehicle stations (FCEVS), photovoltaic (PV) plants, and battery energy storage systems (BESS). The dynamics of EV charging demand are estimated using EV flow modeling in the M1/M2/N queuing model for the transportation network, which incorporates EV population with multiple battery capacities and waiting time constraints. The multiobjective grey wolf optimization (MOGWO) algorithm is used in the first stage to obtain a nondominated solution set for EVCS and PV siting and sizing. The best solution from the nondominated solution set is then obtained using fuzzy satisfaction‐based selection. In the second stage, the bisection point method is used iteratively to obtain adequate BESS capacity as well as additional PV capacity required to support EV load during peak traffic hours. The effectiveness of the proposed framework is investigated using a 25‐node transportation network coupled to an IEEE 123‐bus distribution test system for several scenarios integrating seasonal irradiance and traffic patterns. The findings demonstrate a cost‐effective placement of FCEVS with reduction in active power losses, voltage variation, and increased capture of EV flow. Abstract : A two‐stage multi‐objective planning framework is designed to determine optimal placements and sizing of FCEVS, Photovoltaic (PV) facilities, and Battery Energy Storage Systems incorporating seasonal variations for the generation and weekly patterns for traffic flow. A mathematical model for the optimal planning of solar‐powered EVCS is developed, with multiple objectives for (i) reduced power loss, (ii) improved voltage profile, (iii) minimization of total cost associated with EVCS, and (iv) increased EV served flow as the objective function. MOGWO and fuzzy techniques are further used for optimizing the above planning problem.