Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning

Chenyu Guo, Xin Wang, Yihui Zheng and Feng Zhang

Energy, 2022, vol. 238, issue PC

Abstract: Microgrid (MG) is an effective way to integrate renewable energy into power system at the consumer side. In the MG, the energy management system (EMS) is necessary to be deployed to realize efficient utilization and stable operation. To help the EMS make optimal schedule decisions, we proposed a real-time dynamic optimal energy management (OEM) based on deep reinforcement learning (DRL) algorithm. Traditionally, the OEM problem is solved by mathematical programming (MP) or heuristic algorithms, which may lead to low computation accuracy or efficiency. While for the proposed DRL algorithm, the MG-OEM is formulated as a Markov decision process (MDP) considering environment uncertainties, and then solved by the PPO algorithm. The PPO is a novel policy-based DRL algorithm with continuous state and action spaces, which includes two phases: offline training and online operation. In the training process, the PPO can learn from historical data to capture the uncertainty characteristic of renewable energy generation and load consumption. Finally, the case study demonstrates the effectiveness and the computation efficiency of the proposed method.

Keywords: Microgrid; Optimal energy management; Uncertainties; Deep reinforcement learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221021216

DOI: 10.1016/j.energy.2021.121873

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