.png){kind=icon}
返回
Interpretable Deep Reinforcement Learning for Optimizing Heterogeneous Energy Storage Systems
.png){kind=icon}
OA
AI
DOI：10.1109/TCSI.2023.3340026.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Heterogeneous energy storage systems
deep reinforcement learning
pre-hoc interpretability
期刊
IF:
5.2
论文数:
9.8K
被引数:
2.2W
机构
引用论文
.png){kind=icon}
Multistage Dynamic Planning of Integrated Hydrogen-Electrical Microgrids Under Multiscale Uncertainties多尺度不确定性下的氢电一体化微电网多级动态规划
PREAI
分享收藏Deep-Reinforcement-Learning-Based Capacity Scheduling for PV-Battery Storage System基于深度强化学习的光伏电池储能系统容量调度
PREAI
分享收藏Deep Reinforcement Learning-Based Explainable Pricing Policy for Virtual Storage Rental Service
PREAI
分享收藏A Two-Level Energy Management Strategy for Multi-Microgrid Systems With Interval Prediction and Reinforcement Learning基于区间预测和强化学习的多微网系统两级能量管理策略
PREAI
分享收藏
查看更多内容