Reliability oriented techno- economic assessment of fast charging stations with photovoltaic and battery systems in paired distribution & urban network

• Published in: Journal of energy storage 2023-11, Vol.72, p.108814, Article 108814
• Main Authors: Kumar, Nikhil, Kumar, Tushar, Nema, Savita, Thakur, Tripta
• Format: Article
• Language: English
• Subjects: Battery electric storage system; Electric vehicle charging station; MOPSO; Optimization; Power distribution system; Reliability indices; Solar PV system
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2023.108814

Installing fast charging electric vehicle stations (FCEVS) is crucial for increasing public acceptance of electric vehicle (EV) adoption. The enormous energy demands of FCEVS, as well as the inclusion of renewable energy resources (RES) into utility grids, may have a significant influence on system reliability and pose challenges owing to uncertainty. Therefore, a new reliability-oriented analysis-based two-stage planning framework for optimal deployment of solar photovoltaic (SPV) powered FCEVS and battery energy storage systems (BESS) in coupled transportation and distribution networks is proposed in this article, while encapsulating the complex interactions between EVs, SPVs, and BESS. A novel composite metric, the average voltage deviation reliability index (AVDRI), has been developed for the deployment of FCEVS infrastructure considering the goals and limitations of both distribution and transportation networks. Models such as the demand for charging electric vehicles, the reliability evaluation system, and the economic analysis model are offered to provide essential inputs for the study of distribution network reliability and profitability. In the first stage, AVDRI, active power loss, captured EV flow, and capital, along with operating and maintenance costs of the FEVCS and SPV system, are all taken into consideration as objectives when optimizing the placement and sizing of SPV assisted FCEVS using Multi Objective Particle Swarm Optimization (MOPSO) and a fuzzy satisfaction-based hybrid technique. Then, the optimal resource sizing, such as the count of charging spaces, the capacity of the SPV system at each optimal FCEVS, and captured EV flow, are estimated. In the second step, a bi-section approach is used to compute the capacity of the BESS and the extra SPV rating necessary to power it at each optimal FCEVS location, taking into account the irradiance profile for all conceivable seasons within a planning year. A 25-node transportation network coupled to an IEEE 33 bus distribution system is used to validate the proposed methodology under a range of seasonal scenarios. •Introducing EVs to transport brings uncertainty. Study needed: EV SOC at nearby chargers, estimate service radius using normal distribution for max coverage.•Creating a stochastic MINLP framework for optimal placement & sizing of FCEVs, PV-based generation in distribution networks. AVDRI's impact assessed via IEEE 33-bus & 25-node transportation network.•Developed: unified method