UC Riverside

A properly sized battery is invaluable for commercial customer electricity bill reduction. This paper proposes a novel battery valuation method to evaluate battery energy storage system sizings for commercial customers. The method considers the battery cycle degradation effect and attempted to find the optimal battery size, based on customers' historic load profiles. Due to the non-convexity of the battery degradation model, a genetic algorithm is applied to optimize the battery size. A simulation study is performed on a group of real world customers. The results show that the proposed method successfully obtained the optimum battery dispatches and sizings. It is shown that battery awareness model has a larger overall net preset value compared to the base model. Suggestions are proposed to commercial customers for choosing batteries.