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含风光储的主动配电网多目标分层优化控制
Multi-Objective Hierarchical Optimal Control of Active Distribution Network with Wind， Solar， and Energy Storage
【目的】针对分布式电源（distributed generation，DG）并网导致的主动配电网净负荷波动剧烈、弃能量高以及电压偏差大等问题，提出一种融合储能系统（energy storage system，ESS）调度与主动配电网运行优化的分层协同多目标优化方法。【方法】构建基于迭代协调机制的分层优化模型，上层模型以储能系统的经济收益和电网侧的净负荷削峰填谷效果综合最优为目标，引入基于负荷波动的动态权重调节机制，采用改进鲸鱼优化算法（improved whale optimization algorithm，IWOA）优化储能系统的日内充放电策略。下层模型以主动配电网网络损耗最小、节点电压偏差最小和弃风弃光量最小为目标，采用改进多目标粒子群优化（improved multiple objective particle swarm optimization，IMOPSO）算法协同调节无功补偿装置、分布式电源出力与储能功率分布。上下层模型通过交替迭代与信息反馈实现协调优化。【结果】基于改进的 IEEE 33 节点主动配电网系统开展 Matlab 仿真验证，结果表明：所提方法可将储能全天收益提升至427.4元，削峰填谷率达15.1%；系统总网络损耗降至1 796.3 kWh，总弃能量降低至1 725.2 kWh，电压越限平方和下降至0.001，电压偏差得到显著改善。在多类负荷波动场景下，该方法均表现出良好的适应性与显著的综合效益。【结论】所提出的分层协同优化模型能够有效应对分布式电源并网带来的多重挑战，显著提升主动配电网的运行效能和可再生能源消纳能力。该方法具有良好的运行效率和工程可实施性，可为主动配电网在复杂运行环境下的优化调度提供有力支撑。
[Objective] To address severe net load fluctuations， high renewable curtailment， and voltage deviations in active distribution networks caused by penetration of distributed generation （DG）， this paper proposes a hierarchical coordinated multi-objective optimization method that integrates energy storage system （ESS） scheduling with active distribution network operational optimization. [Methods] A hierarchical optimization framework based on an iterative coordination mechanism was established. In the upper layer， a composite objective considering ESS economic benefits and net load peak-shaving performance was formulated. A dynamic weighting adjustment mechanism based on load fluctuation characteristics was introduced， and an improved whale optimization algorithm （IWOA） was employed to determine the day-ahead charging and discharging strategies of the ESS. In the lower layer， a multi-objective model aimed at minimizing network power losses， node voltage deviations， and renewable energy curtailment was constructed. An improved multi-objective particle swarm optimization （IMOPSO） algorithm was adopted to coordinate reactive power compensation devices， distributed generation outputs， and ESS power allocation. Coordination between the two layers was achieved through alternating iterations and information feedback. [Results] Simulation results on an improved IEEE 33-node active distribution network system using MATLAB demonstrate that the proposed method yields a daily energy storage revenue of RMB 427.4 and a peak-shaving and valley-filling rate of 15.1%. It reduces total network loss to 1，796.3 kWh， total renewable energy curtailment to 1，725.2 kWh， and the sum of squared voltage limit violations to 0.001， thereby effectively alleviating voltage deviations. The method exhibits strong adaptability and outstanding comprehensive benefits under diverse load fluctuation conditions. [Conclusions] The proposed hierarchical coordinated optimization framework effectively addresses the operational challenges introduced by high DG penetration， significantly enhancing the operational efficiency and renewable energy accommodation capability in active distribution networks. The method features satisfactory computational efficiency and practical applicability， providing robust technical support for optimal operation under complex conditions.
主动配电网 / 储能系统 / 削峰填谷 / 无功优化 / 分层优化 / 动态权重
active distribution network / energy-storage system / peak shaving and valley filling / reactive power optimization / hierarchical optimization / dynamic weighting mechanism
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为响应“碳达峰、碳中和”的号召,兼顾分布式能源（DG）参与下配电网运行的经济性和环保性,该文构建一种含DG的配电网双层优化模型。该模型以DG投资运行成本及环境效益为上层目标对DG进行优化配置,并以网损和电压偏移度成本为下层目标对配电网进行无功优化。针对模型特点,提出改进麻雀算法进行模型求解,增加Tent混沌映射初始化种群,引入萤火虫扰动机制改进操作算子并优化越界处理机制维持种群多样性。仿真结果表明,该模型能有效提高配电网运行的经济环保性,并验证了改进麻雀算法在求解复杂非线性非凸含离散变量的复杂组合优化问题上的优势。
In response to the call of "carbon peaking, carbon neutralization", considering the economy and environmental protection of distribution network operation with distributed generation（DG）, this paper constructs a bi-level optimization model of distribution network with DG. In this model, DG investment and operation cost and environmental benefits are taken as the upper objectives to optimize the configuration of DG, and network loss and voltage offset cost are taken as the lower objectives to optimize the reactive power of distribution network. According to the characteristics of the model, an improved sparrow search algorithm is proposed to solve the model, adding tent chaotic map to initialize the population, introducing firefly disturbance mechanism to improve the operator, and optimizing the cross-border processing mechanism to maintain the population diversity. The simulation results show that the model can effectively improve the economic and environmental protection of distribution network operation, and verify the advantages of the improved sparrow search algorithm in solving complex high-dimensional nonlinear non-convex combinatorial optimization problems with discrete variables.
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所有作者声明不存在利益冲突。
徐艳春进行了研究设计；李家楼进行对比实验、文献调研与整理并完成实验和数据分析；席磊确定研究对象范围、收集数据；张涛参与了论文写作和修订；王凌云参与撰写论文、绘制图谱；Mi Lu参与修订论文、审核论文。所有作者均阅读并同意了论文终稿内容。
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