目的 随着可再生能源电网占比逐年增加，电网的波动性和不确定性显著提升，给配电网的安全运行带来挑战。针对高比例新能源电网分布式配网重构问题，提出一种在线滚动优化框架方法。 方法 通过分布式共识协议获取网络拓扑和节点运行信息，在N-1和N-2线路故障状态下自动重构，实现配电网无额外外部触发信号情况下自动恢复正常运行，保证电网经济运行。同时采用滚动优化方法处理高比例可再生能源所导致的电网波动问题，并利用生成对抗网络(generative adversarial network，GAN)技术生成新数据，结合历史数据实现电网运行数据高精度预测。 结果 所提方法在正常状态、单点故障和两点故障3种情况下，均能实现电网的自动经济运行优化和自愈修复。 结论 与鲁棒规划和随机规划等方法相比，所提出方法可提升电网预测精度。

Objectives As the proportion of renewable energy in power grids increases year by year, the volatility and uncertainty of the grid are significantly heightened, posing challenges to the safe operation of distribution networks. To address the issue of distributed network reconfiguration in high-proportion renewable energy grids, this paper proposed an online rolling optimization framework. Methods The framework utilized a distributed consensus protocol to obtain network topology and node operation information. It can enable automatic reconfiguration in the event of N-1 and N-2 line failures, allowing the distribution network to automatically restore normal operation without the need for additional external triggering signals, thus ensuring economic operation of the grid. Additionally, a rolling optimization method was employed to handle grid fluctuations caused by the high proportion of renewable energy, and generative adversarial network (GAN) technology was used to generate new data, which combined with historical data. It can help to achieve high-precision forecasting of grid operation data. Results The proposed method can achieve automatic economic optimization and self-healing in normal, single-point failure, and two-point failure scenarios. Conclusions This method provides an effective solution for ensuring the safe operation of distributed networks in high-proportion renewable energy grids.

新能源以分布式电源的形式接入配电网，给系统带来了不可控性、随机性和波动性问题。借助现代电力电子、信息通信及自动控制等技术，灵活可控的主动配电网成为发展趋势，其中主动配电网规划是近年来研究的热点之一。综合国内外在这一领域的研究成果，对主动配电网规划相关研究内容及其方法进行总结、分析及展望。对主动配电网基本结构进行了描述，介绍了配电网组成元素的特点；根据其控制变量的不同，对主动配电网规划模型进行了归类，总结了模型中的优化目标；针对常用模型求解算法及其优缺点进行分析、总结；通过对关键性问题的讨论，分析了未来主动配电网的发展趋势。

The new energy is connected to the distribution network in the form of distributed generation, which brings uncontrollability, randomness and volatility to the system. With the help of modern power electronics, information and communication and automatic control technologies, the active distribution network with flexibility and controllability has become a development trend. The active distribution network planning is one of the major research fields in recent years. Based on the research results in this field at home and abroad, the research contents and methods of active distribution network planning were summarized, analyzed and prospected. The basic structure of the active distribution network was described, and the characteristics of the components of the distribution network were introduced. According to the different control variables, the planning models of active distribution network were classified, the optimization objectives in the model were summarized. The commonly used model solving algorithms and their advantages and disadvantages were analyzed and summarized. Through the discussion of key issues, the development trend of active distribution network in the future was analyzed.

随着碳达峰、碳中和目标的提出，电动汽车以其绿色、低碳、节能环保优势逐渐普及。电动汽车兼具负荷与储能双重特性，其充放电行为具有时间和空间的随机性和波动性，精准的电动汽车充电负荷时空分布预测是研究电动汽车入网影响、电网规划运行、与电网互动的基础。首先，分析影响电动汽车充电负荷时空分布的主要因素；然后，对充电负荷建模、时空分布预测方法进行系统阐述；随后，考虑电动汽车可以作为移动储能装置参与电网互动，评估其放电潜力并综述电动汽车入网(vehicle to grid, V2G)技术研究现状；最后，总结现有研究方法面临的挑战并进行展望。

With national targets for carbon peaking and carbon neutrality, electric vehicles (EVs) are gaining popularity owing to their advantages of being green, low-carbon, energy-saving, and environmentally friendly. EVs have both load and energy storage characteristics, and their charge-discharge behavior is random and fluctuates in time and space. Accurate prediction of the spatiotemporal distribution of EV charging and discharging loads is the basis for studying the influence of EV entering the grid, power grid planning and operation, and interaction with the power grid. The main factors influencing the prediction of the spatiotemporal distribution of the EV charging load are analyzed. The modeling of the charging load and prediction method for the spatial and temporal distributions are systematically described. Considering that electric vehicles can be used as mobile energy-storage devices to participate in grid interactions, the discharge potential is evaluated, and the research scenario of V2G technology is reviewed. Finally, the challenges faced by existing research methods are summarized and discussed.

高比例分布式电源、电动汽车接入配电台区下,将面临着承载能力不足、负载率不均衡以及电压越限等问题,台区柔性互联是有效的解决方法之一。提出了一种考虑经济性和供电能力的台区柔性互联主从博弈规划模型,以经济性决策和供电能力决策为主、从体博弈者,共同参与规划决策过程。首先,建立了直流母线分段链式柔性互联结构下的台区潮流模型;然后建立了主从博弈规划模型,以互联方案和互联设备装机容量、年综合成本最小为主体博弈者的策略和支付,以台区所供负荷视在功率、N-0安全性下供电能力最大为从体博弈者的策略和支付,并计及台区运行潮流约束,再基于粒子群算法进行求解;最后通过台区接入高比例光伏发电的IEEE 33节点配电网进行算例分析,结果表明采用所提规划模型能均衡提升系统的运行经济性和供电能力。

A large proportion of distributed generation and electric vehicles are connected to distribution station areas. This results in several problems, such as insufficient hosting capacity, unbalanced load rates, and out-of-limit voltage. The flexible interconnection of the station areas is an effective solution to these problems. This paper proposes a master-slave game planning model for the flexible interconnection of distribution station areas, considering economic and power supply capacities. The proposed model considers economic decision-making as the main player and power supply capacity decision-making as the subordinate player, jointly participating in the planning decision-making process. First, a power flow model of the substation area is established using a DC bus-segmented chain flexible interconnection structure. Subsequently, a planning model based on a master-slave game is established. The interconnection schemes and installed capacity of the interconnected devices are used as the main player strategy, the minimum annual comprehensive cost is the payment. The load apparent power of the substation areas is used as the subordinate player strategy, and the maximum power-supply capacity under N-0 security constraints is the payment. The power-flow constraints of the substation areas operation are considered in the model. The particle swarm optimization algorithm is used to solve this problem. Finally, an IEEE-33 node distribution network connected to a high proportion of photovoltaic power generation in the substation area is analyzed. The results show that the proposed model can balance the operational economy and power supply capacity of the system.

在配电网低碳化背景下,提出了考虑碳排放和柔性负荷的有源配电网混合整数二阶锥规划模型,目标是在满足网络运行约束和CO₂排放上限的前提下,给出总成本最小的投资策略。考虑新能源、负荷和能源价格的不确定性,提出了基于k均值的场景聚类方法。模型的决策变量为更换过载线路、投建新能源和储能装置,以及投建稳压器和电容器组等电压控制设备,并考虑了多项式形式的电压相关型的柔性负荷、网络重构以及碳排放额约束。针对规划模型的非凸非线性特征,采用虚拟需求法将网络重构建模为混合整数线性规划形式,并提出了一种基于泰勒展开的改进二阶锥松弛方法,以解决柔性负荷模型给传统二阶锥松弛带来的难题。通过69节点系统对该模型进行测试,结果表明,所提模型不仅总规划成本较低,而且有助于减少碳排放。

In the context of low-carbon distribution network, this paper proposes a mixed-integer second-order cone programming model for active distribution networks considering carbon emissions and flexible loads, with the investment strategy with minimum total cost. Considering the uncertainty of renewable energy, load and energy price, a scenario clustering method based on K-means is proposed. The decision variables of the model are the replacement of overloaded lines, the construction of new energy and energy storage devices, and the construction of voltage control equipment such as voltage regulators and capacitor banks. The polynomial voltage-dependent flexible loads, network reconfiguration and carbon emission constraints are considered. Aiming at the non-convex nonlinear characteristics of the planning model, the virtual demand method is used to model the network reconstruction as a mixed integer linear programming form, and an improved second-order cone relaxation method based on Taylor expansion is proposed to solve the problem of traditional second-order cone relaxation caused by flexible load model. The model is tested with a 69-node system, and the results show that the proposed model not only has a lower overall planning cost, but also helps reduce carbon emissions.