文献类型：期刊文献

英文题名：Interpretable Deep Reinforcement Learning for Optimizing Heterogeneous Energy Storage Systems

作者：Xiong, Luolin[1]; Tang, Yang[1]; Liu, Chensheng[1]; Mao, Shuai[2]; Meng, Ke[3]; Dong, Zhaoyang[4]; Qian, Feng[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, The Engineering Research Center of Process System Engineering, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Electrical Engineering, Nantong University, Nantong, 226019, China; [3] School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, 2052, Australia; [4] School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230378365)

语种：英文

外文关键词：Cost functions - Decision making - Deep learning - Energy utilization - Hydrogen storage - Power markets

摘要：Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. ESS operators can reap benefits from energy arbitrage by optimizing operations of storage equipment. To further enhance ESS flexibility within the energy market and improve renewable energy utilization, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed, which leverages the unique characteristics of battery energy storage (BES) and hydrogen energy storage (HES). For scheduling tasks of the heterogeneous PV-ESS, cost description plays a crucial role in guiding operator’s strategies to maximize benefits. We develop a comprehensive cost function that takes into account degradation, capital, and operation/maintenance costs to reflect real-world scenarios. Moreover, while numerous methods excel in optimizing ESS energy arbitrage, they often rely on black-box models with opaque decision-making processes, limiting practical applicability. To overcome this limitation and enable transparent scheduling strategies, a prototype-based policy network with inherent interpretability is introduced. This network employs human-designed prototypes to guide decision-making by comparing similarities between prototypical situations and encountered situations, which allows for naturally explained scheduling strategies. Comparative results across four distinct cases underscore the effectiveness and practicality of our proposed pre-hoc interpretable optimization method when contrasted with black-box models. Copyright ? 2023, The Authors. All rights reserved.