Optimal Siting and Sizing of Used Battery Energy Storage Based on Accelerating Benders Decomposition

In this paper, the financial sense of used batteries providing energy storage (ES) for grid applications is investigated. An investment strategy to determine the optimal site and size of used battery ES is proposed for profit-oriented merchant entities and is formulated as a bilevel model. On the upper level, the decisions on ES investment and bidding and offering in the market are optimized, subject to maximizing the social welfare, which is achieved in the market clearing process on the lower level. Computational tractability is achieved by implementing a solution approach based on Benders decomposition. To speed up the convergence of this algorithm, we propose two acceleration techniques, namely, the valid inequalities and a multi-cut framework. We test the proposed algorithm on the IEEE Reliability Test System. The optimal siting and sizing decisions of the used battery ES are analyzed, followed by the analyses of the impacts of the lifespan, unit cost, and capacity loss of the used batteries on the investment decisions. Finally, we demonstrate that the proposed accelerating Benders decomposition outperforms the classical decomposition methods on computational performance.