Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system

Li, Jiawen; Yu, Tao; Zhang, Xiaoshun; Li, Fusheng; Lin, Dan; Zhu, Hanxin

doi:10.1016/j.apenergy.2020.116386

Cited by 95 publications

(28 citation statements)

References 34 publications

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“…However, as the PEMFC system is a complex system with multiple inputs and outputs, nonlinear, approximately east, with random disturbances, time-varying and high order (Yang et al, 2019b;Li and Yu, 2021a), it is difficult to achieve satisfactory control results with traditional PID control (Li et al, 2021). In order to obtain accurate and fast response results, various advanced control strategies have been applied in the research of PEMFC output control strategies.…”

Section: Introductionmentioning

confidence: 99%

Control Method for PEMFC Using Improved Deep Deterministic Policy Gradient Algorithm

2021

Front. Energy Res.

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A data-driven PEMFC output voltage control method is proposed. Moreover, an Improved deep deterministic policy gradient algorithm is proposed for this method. The algorithm introduces three techniques: Clipped multiple Q-learning, policy delay update, and policy smoothing to improve the robustness of the control policy. In this algorithm, the hydrogen controller is treated as an agent, which is pre-trained to fully interact with the environment and obtain the optimal control policy. The effectiveness of the proposed algorithm is demonstrated experimentally.

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Section: Introductionmentioning

confidence: 99%

Control Method for PEMFC Using Improved Deep Deterministic Policy Gradient Algorithm

2021

Front. Energy Res.

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“…At the same time, distributed energy resources (DERs) are being deployed in the electric power distribution systems at an unprecedented pace (Yang et al, 2016;Yang et al, 2017;Yang et al, 2018;Yang et al, 2019a). To fully exploit the benefits of the DERs, the distribution network must be actively managed (Yang et al, 2019b;Xi et al, 2020;Yang et al, 2020;Li et al, 2021). The low-voltage distribution network is the last link to connect users in the whole power system.…”

Section: Introductionmentioning

confidence: 99%

Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

Guan

et al. 2021

Front. Energy Res.

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For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.

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“…A microgrid allows for the effective connection and control of distributed power generation [1]. The strong stochastic and intermittent natures of renewable energy generation necessitate power control at the microgrid level so that load changes can be tracked quickly [2, 3].…”

Section: Introductionmentioning

confidence: 99%

Data‐driven cooperative load frequency control method for microgrids using effective exploration‐distributed multi‐agent deep reinforcement learning

Shuzi

2021

IET Renewable Power Gen

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To reduce the total power generation cost and improve the frequency stability of an island microgrid integrating renewable energy generation sources, a data‐driven cooperative load frequency control (DC‐LFC) method is proposed for solving the coordination control problem occurring between the controller and power distributor of the system. A novel algorithm, termed the effective exploration‐distributed multiagent twin‐delayed deep deterministic policy gradient (EED‐MATD3) algorithm, is further proposed, the design of which is structured based on the concepts of imitation learning, ensemble learning, and curriculum learning. The EED‐MATD3 method employs various exploration strategies, and the controller and power distributor are treated as two agents. Through centralized training and decentralized execution, a robust cooperative control strategy is realized. The performance of the proposed algorithm is verified in an LFC model of Zhuhai Tandang Island, an island microgrid in the China Southern Power Grid.

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Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system

Cited by 95 publications

References 34 publications

Control Method for PEMFC Using Improved Deep Deterministic Policy Gradient Algorithm

Control Method for PEMFC Using Improved Deep Deterministic Policy Gradient Algorithm

Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

Data‐driven cooperative load frequency control method for microgrids using effective exploration‐distributed multi‐agent deep reinforcement learning

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