Continuous Approximate Dynamic Programming Algorithm to Promote Multiple Battery Energy Storage Lifespan Benefit in Real-Time Scheduling

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.