Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources

Abstract

The problem of optimally activating the flexible energy sources (short-and long-term storage capacities) of electricity microgrid is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been used in order to extract knowledge from past consumption and production time series as well as any available forecasts. The approach is empirically illustrated in the case of off-grid microgrids located in Belgium and Russia.

Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region

“…Prior work on the community of MGs mainly focuses on energy cooperation. In this way, each MG coordinates its local resources [20][21][22][23][24] or the distribution power network [25,26], as well as other MGs [27][28][29][30][31].…”

“…Recently, for managing local resources of an MG, there were stochastic optimization models were proposed based on the deep reinforcement learning [21][22][23][24]. Such machine learning models have demonstrated effectiveness and certain advantages such as a reduction in the computational complexity of a multi-objective problem that, solving non-convex optimization problems.…”

“…Such machine learning models have demonstrated effectiveness and certain advantages such as a reduction in the computational complexity of a multi-objective problem that, solving non-convex optimization problems. In [21,22] have introduced a deep Q-network (DQN) architectures for addressing the problem of operating an isolated MGs in a stochastic DER environment, which included PV systems, batteries, hydrogen storages, diesel generators. These approaches were empirically illustrated in the case of isolated AC/DC MGs located in Belgium and Eastern Siberia (Russia).…”

Deep learning and reinforcement learning approach on microgrid

“…The decision-making in sequential problem is resolved using DRL to initiate optimal usage of flexible energy sources, which helps to extract time series date for future prediction with specified architecture. 39 Energy management is an important task in power system existing microgrids. Paper 40 contributes to RL-based energy management in a storage system.…”

“…The learning mechanism of DRL implemented with predictive dataset of predefined states. The decision‐making in sequential problem is resolved using DRL to initiate optimal usage of flexible energy sources, which helps to extract time series date for future prediction with specified architecture 39 …”