A two-stage fuzzy multiobjective optimization for phase-sensitive day-ahead dispatch of battery energy storage system

Abstract

Operation and management of unbalanced distribution network (UDN) is challenging and complex for optimum utilization of active resources such as distributed generation and battery energy storage systems (BESSs). In order to exploit the inherent unbalancing of UDNs as an advantage in exploiting the most benefits from BESS, a phase-sensitive day-ahead dispatch strategy for BESS is proposed. The objectives incorporated in the proposed multiobjective, multiperiod optimization include a) maximizing peak shaving, b) improving voltage profile, c) loss minimization, d) reducing tap changer operations of voltage regulators, and e) minimizing discharge-recharge cycles. A two-stage fuzzy multiobjective optimization platform is developed to 1) represent uncertainty in load demand, 2) incorporate a human-friendly linguistic interface, and 3) provide flexibility to operator for changing priorities as a function of time. A novel application of max-average and max-product compositions in fuzzy systems is employed for multiperiod optimization. The effectiveness of the proposed approach is tested on an IEEE 37-node standard test feeder with comparative results of phase-balanced and phase-sensitive BESS dispatch. It is further validated on a feeder in Northwest USA with an existing BESS. Single-objective and multiobjective optimization results with multiple Pareto solutions demonstrate the stability and robustness of the proposed approach.