Operation of Distributed Battery Considering Demand Response Using Deep Reinforcement Learning in Grid Edge Control

Wenying Li, Ming Tang, Xinzhen Zhang, Danhui Gao and Jian Wang
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Wenying Li: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China
Ming Tang: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China
Xinzhen Zhang: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China
Danhui Gao: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China
Jian Wang: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China

Energies, 2021, vol. 14, issue 22, 1-18

Abstract: Battery energy storage systems (BESSs) are able to facilitate economical operation of the grid through demand response (DR), and are regarded as the most significant DR resource. Among them, distributed BESS integrating home photovoltaics (PV) have developed rapidly, and account for nearly 40% of newly installed capacity. However, the use scenarios and use efficiency of distributed BESS are far from sufficient to be able to utilize the potential loads and overcome uncertainties caused by disorderly operation. In this paper, the low-voltage transformer-powered area (LVTPA) is firstly defined, and then a DR grid edge controller was implemented based on deep reinforcement learning to maximize the total DR benefits and promote three-phase balance in the LVTPA. The proposed DR problem is formulated as a Markov decision process (MDP). In addition, the deep deterministic policy gradient (DDPG) algorithm is applied to train the controller in order to learn the optimal DR strategy. Additionally, a life cycle cost model of the BESS is established and implemented in the DR scheme to measure the income. The numerical results, compared to deep Q learning and model-based methods, demonstrate the effectiveness and validity of the proposed method.

Keywords: grid edge control; demand response (DR); deep reinforcement learning (DRL); multi-agent algorithm; distributed battery energy storage system (BESS); three-phase unbalance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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