内嵌数据驱动的三相不平衡有源配电网两阶段动态最优潮流调度方法

doi:10.19595/j.cnki.1000-6753.tces.250556

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摘要

随着大规模分布式光伏和电动汽车的接入,有源配电网面临的电压越限、网损增加及三相不平衡等多重运行挑战日益凸显。传统数学规划方法因计算复杂度高难以支持在线决策,而深度强化学习算法在多时间尺度主动管理设备调节方面存在局限性。为此,提出一种内嵌数据驱动的三相不平衡有源配电网“日前-日内”两阶段动态最优潮流调度方法,通过结合物理机理建模与数据驱动学习的优势,实现对多类型调节资源的精细化协调控制。首先,综合考虑主动管理与需求响应约束,以调压降损为目标,建立了三相不平衡有源配电网动态最优潮流模型。其次,在日前调度的小时级长时间尺度下,通过采用lift-and-project松弛方式的混合整数二阶锥规划,确定慢速离散调节设备的档位;在日内调度的分钟级短时间尺度下,将动态最优潮流问题转换为马尔可夫决策过程,提出基于专家知识和层次化奖励重塑机制的改进近端策略优化算法,对快速连续调节设备进行在线调控。最后,以IEEE 33节点与123节点三相辐射状配电网为算例进行仿真分析,结果验证了所提方法的有效性和优越性。

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关键词 ：有源配电网, 调压降损, 三相不平衡, 两阶段动态最优潮流, 混合整数二阶锥规划, 改进近端策略优化

Abstract：

In the context of energy transition and the construction of new type power systems, distribution networks are gradually evolving into a critical link for supporting the consumption of renewable energy. The large-scale integration of distributed photovoltaic systems and electric vehicles has driven a structural transformation of distribution networks from a unidirectional radial topology to a bidirectional interactive one, leading to technical challenges such as node voltage violations, increased network losses, and three-phase voltage unbalance. Traditional mathematical programming methods face challenges due to their high computational complexity, which makes online decision-making difficult to implement. Meanwhile, deep reinforcement learning algorithms exhibit limitations in managing the regulation of multi-timescale active management devices. There is an urgent need to research operational adjustment strategies tailored to the characteristics of active distribution networks to ensure their safe, stable, and efficient operation.
To address these issues, a “day-ahead and intra-day” two-stage dynamic optimal power flow dispatch method for three-phase unbalanced active distribution networks is proposed, incorporating data-driven approaches. This method leverages the advantages of both physical mechanism modeling and data-driven learning to achieve refined coordinated control of multiple types of regulation resources. Firstly, considering the constraints of active management and demand response, a dynamic optimal power flow model for three-phase unbalanced active distribution networks is established, with the objectives of voltage regulation and loss reduction. Then, at the hour-level long timescale of day-ahead dispatch, the tap positions of slow discrete regulating devices are determined using mixed-integer second-order cone programming with lift-and-project relaxation. At the minute-level short timescale of intra-day dispatch, the dynamic optimal power flow problem is formulated as a Markov decision process, and an improved proximal policy optimization algorithm based on expert knowledge and a hierarchical reward shaping mechanism is proposed for online dispatch of fast continuous regulating devices.
Simulation analyses are conducted using the IEEE 33-bus and 123-bus three-phase radial distribution networks as case studies, drawing the following conclusions: (1) During the day-ahead optimization stage, considering the inter-phase and temporal coupling relationships, the tap positions of slow discrete regulating devices such as capacitor banks and on-load tap-changers are determined by using mixed-integer second-order cone programming with lift-and-project relaxation. Both the fitting error and the relaxation deviation degree of the proposed method are less than 10^-3, indicating an improvement in computational accuracy compared to traditional methods. (2) During the intra-day optimization stage, the dynamic optimal power flow problem is transformed into a Markov decision process, and online control of fast continuous regulating devices such as distributed photovoltaic systems, electric vehicles, energy storage systems, and static var compensators is carried out based on the improved proximal policy optimization algorithm. By comprehensively considering the constraints of active management and demand response, a collaborative optimization of voltage regulation and loss reduction in active distribution networks is achieved, while ensuring that the three-phase voltage unbalance degree meets the national standard limit of 2%. (3) The proposed improved proximal policy optimization algorithm, on one hand, enhances the enforcement capability of the three-phase voltage unbalance degree constraint and effectively guides the agent to reduce active power curtailment of distributed photovoltaic systems by introducing expert knowledge. On the other hand, it adopts a hierarchical reward shaping mechanism that dynamically couples the immediate reward with long-term performance indicators to adapt to the multi-timescale dispatch requirements of the day-ahead and intra-day stages. The algorithm can balance both solution efficiency and accuracy, exhibiting superior computational performance.

Key words： Active distribution network voltage regulation and loss reduction three-phase unbalance two-stage dynamic optimal power flow mixed-integer second-order cone programming improved proximal policy optimization

收稿日期: 2025-04-08

PACS:

TM732

基金资助:

国家自然科学基金（U24B2083,52407098）和国家资助博士后研究人员计划（GZC20240463）资助项目

通讯作者: 张智男,1994年生,博士,讲师,研究方向为新能源电力系统规划和运行。E-mail：ZhangzhiEE@ncepu.edu.cn

作者简介: 作者简介陈艳波男,1982年生,教授,博士生导师,研究方向为新能源电力系统优化与分析。E-mail：chenyanbo@ncepu.edu.cn

引用本文:

陈艳波, 田昊欣, 强涂奔, 张智. 内嵌数据驱动的三相不平衡有源配电网两阶段动态最优潮流调度方法[J]. 电工技术学报, 0, (): 20250556-20250556. Chen Yanbo, Tian Haoxin, Qiang Tuben, Zhang Zhi. A Two-Stage Dynamic Optimal Power Flow Dispatch Method for Three-Phase Unbalanced Active Distribution Networks Incorporating Data-Driven Approaches. Transactions of China Electrotechnical Society, 0, (): 20250556-20250556.

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