Multi-objective electric vehicle charge scheduling for photovoltaic and battery energy storage based electric vehicle charging stations in distribution network

Recently, with the increasing demand of the electric vehicle (EV) in transportation, the power grid faces critical challenges in meeting the extra power demand. Companies are focusing on expanding EV charging infrastructure to meet customer requirements. Ensuring power supply security, reliability, and economics for EV charging stations remains a challenge, despite efforts to align photovoltaic (PV) and battery energy storage system (BESS) based designs with distribution system requirements. A criteria weight ranking mechanism has been designed to accept charging requests for EVs depending on the criteria weights specified by the EV owner. This paper uses a multi-objective remora optimization algorithm (MOROA) to determine the optimal location of two electric vehicle charging stations (EVCS) in the distribution system, and capacity of PV & BESS units in two EVCS for optimizing three conflicting objective functions, such as (1) minimizing total power loss; (2) minimizing annual substation power cost, and annual capital, operation & maintenance cost of the PV and BESS, and (3) minimizing emission from upstream grid. Moreover, the EVs are also scheduled optimally at each charging station. The effectiveness of these methodologies has been demonstrated through four case studies using IEEE 33 bus radial distribution system (RDS). Furthermore, the smart EV charge scheduling reduces the overall load burden on the grid network and the benefit of EVCS operators and EV owners. • Proposed MOROA for optimal EVCS allocation in distribution systems with PV and BESS. • A framework for CSO, EVCS, PV and BESS interaction in RDS for sustainable transport. • A ranking algorithm to approve EV charging requests based on owners' criteria. • Optimally allocated power between two PV & BESS units for EV scheduling in RDS. • Formulated an objective to minimize power loss, costs, and environmental emissions.