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[1,2];Tang, Yang[1,2];Liu, Chensheng[1,2];Mao, Shuai[3];Meng, Ke[4];Dong, Zhaoyang[5];Qian, Feng[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China;[3]Nantong Univ, Dept Elect Engn, Nantong 226019, Peoples R China;[4]Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia;[5]Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

年份：2024

卷号：71

期号：2

起止页码：910

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS

收录：;EI(收录号：20235215272106);WOS:【SCI-EXPANDED(收录号:WOS:001126135300001)】；

基金：No Statement Available

语种：英文

外文关键词：Heterogeneous energy storage systems; deep reinforcement learning; pre-hoc interpretability

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, a practical cost function 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 explainable 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 demonstrate the effectiveness and practicality of our proposed pre-hoc interpretable optimization method when contrasted with black-box models.