基于安全强化学习的具有相位变化软开点的不平衡配电网络优化调度

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-09-11 DOI:10.1016/j.segan.2024.101521
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引用次数: 0

摘要

分布式能源资源和不均衡的负荷分配会造成配电网三相不平衡,从而损害电力设备的健康并增加运营成本。为改善主动配电网的运行性能,调度软开点的机会正在出现。本文提出了一种改善配电网平衡性能的优化调度策略,即安装一种新型的换相软开点。首先,介绍了一种具有全相变能力的新型变相软开点,以平衡三相功率流。然后,建立了变相软开点调度优化模型,以最小化配电网总成本。此外,该模型被形成为一个受约束的马尔可夫决策过程,并通过基于增强拉格朗日的安全深度强化学习算法和软行为批判方法进行高效求解。最后,通过数值模拟验证了所提方法的有效性、准确性和高效性。
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Optimal dispatch of unbalanced distribution networks with phase-changing soft open points based on safe reinforcement learning

Distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks, which may harm the health of power equipment and increase the operational costs. There is emerging opportunity to dispatch soft open points to improve the operation performance of active distribution network. This paper proposes an optimal dispatch strategy to improve the network balancing performance, where a new type of phase-changing soft open point is installed. First, a new type of phase-changing soft open point with full-phase changing ability is introduced to balance the three-phase power flow. Then, the optimization model is formulated for phase-changing soft open points dispatching to minimize the total cost of distribution network. Furthermore, the model is formed as a constrained Markov decision process and efficiently solved by the augmented Lagrangian-based safe deep reinforcement learning algorithm featuring the soft actor-critic method. Finally, numerical simulations are conducted to validate the effectiveness, accuracy, and efficiency of the proposed method.

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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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