Continuous Approximate Dynamic Programming Algorithm to Promote Multiple Battery Energy Storage Lifespan Benefit in Real-Time Scheduling
Xizhen Xue, Xiaomeng Ai, Jiakun Fang, Yazhou Jiang, Shichang Cui, Jinsong Wang, T.H. Ortmeyer, Jinyu Wen
Abstract
This paper aims to promote the lifespan benefit of multiple battery energy storage (BES) in real-time scheduling. An effective real-time scheduling model is formulated with the proposed concept of multiple BES (MBES) comprehensive lifespan benefit, which makes a tradeoff between MBES short-term operation and long-term profits. Then, a novel piece-wise linear function (PLF) based continuous ADP (PLFC-ADP) algorithm is proposed to optimize the scheduling model under uncertainties. A new decomposed value function approximation method employing both BES state of charge and BES cumulative life loss is proposed to achieve high optimality and wide applicability. Combined with the difference-based decomposed slope update method to train the PLF slopes with empirical knowledge, the proposed PLFC-ADP algorithm can handle the increasing computation complexity of MBES scheduling and obtain the approximate optimality of stochastic real-time scheduling. Numerical analysis demonstrates the validity of the proposed scheduling model, and superior computation tractability and solution optimality of the proposed PLFC-ADP algorithm.