Multi-Stage and Multi-Objective Feed-in Damping-Based Battery Aging-Aware Energy Management Strategy for Renewable Energy Integration

Abstract

Battery control strategies are crucial in optimizing energy storage systems' performance and economic feasibility. A comprehensive analysis of battery control strategies should include an integrated perspective considering optimal battery capacities, life cycle cost (LCC), self-consumption rates (SCR), and battery degradation. This study proposed a multi-stage and multi-objective feed-in damping-based energy management strategy that minimizes LCC using a two-layer solution and considers long-term battery degradation. In the first stage, the optimal battery capacities are determined by the particle swarm optimization (PSO) algorithm without considering battery aging. In the second stage, the performance of the proposed multi-objective feed-in damping-based energy management strategy (MOFD) sensitive to battery aging is evaluated. Also, the performance of the developed strategy is assessed against traditional control architectures in terms of various technical, economic and environmental decision criteria. The results show that optimal capacity allocation increases renewable potential by up to 37.4% compared to basic sizing approaches. The proposed method that considers battery degradation has lower LCC and reduces curtailment energy by up to 18.4%. Conversely, battery degradation can cause up to 2% reductions in throughput. It has shown that it will provide a more accurate and realistic evaluation procedure for battery control strategies' long-term sustainability and cost-effectiveness.