严寒地区冬季的极端冰灾天气易给电力设施带来严重危害。同时,随着电-热联合系统的广泛应用,电力设施的故障也会影响到用户供热需求的满足。为保障人们对供电与供热的需求,加强电-热联合系统抵御冰灾天气的能力,文章提出了一种考虑输配协同的电-热联合系统韧性提升方法。首先建立冰灾天气对输电线路影响的综合载荷模型,并利用蒙特卡洛状态抽样法建立输电网的故障场景集。其次,分析输配协同下含光热电站的电-热联合系统组成并构建其运行模型。然后,构建了考虑韧性提升的电-热联合系统的负荷削减模型。最后,利用算例系统验证了所提模型的有效性。结果表明该策略可以兼顾冰灾天气下对系统电、热负荷的需求,对电-热联合系统起到一个较好的韧性提升作用。

Extreme ice-disasters during severe winter conditions in cold areas pose significant risks to basic power facilities, often leading to disruptions in heating services, particularly with the widespread adoption of electric-heat combined systems. To ensure uninterrupted power supply and heating services, alongside enhancing the resilience of electric-heat combined system against ice disaster weather, this study proposes a method that incorporates the coordination of transmission and distribution networks. Initially, a comprehensive load model for ice disaster conditions on transmission lines was established, and a fault scenario within the transmission network was generated using the Monte Carlo state sampling method. Subsequently, the composition and modeling of the electric-heat combined system pertaining to a concentrating solar power station were analyzed. Furthermore, a load-reduction model was constructed concerning the electric-heat combined system considering resilience improvement. Finally, the effectiveness of the proposed model was verified. The results demonstrated that the proposed strategy addresses both electrical and heat load requirements during ice disasters, significantly enhancing the resilience of electric-heat combined systems.

分布式电源逐渐高比例接入已成为城乡配电网发展的趋势，源、荷、自然环境等各种不确定性因素带来的各种扰动不可忽略，因此配电网需要具备一定的抵御不同扰动并能快速恢复的能力，这种能力被称为配电网韧性。同时，配电网源网荷储各侧灵活性资源可为配电网供需平衡和韧性提升提供一定支撑。如何准确刻画各类扰动事件的影响、有效挖掘源网荷储各侧灵活性资源的协同作用，是研究配电网韧性的关键问题。文章旨在对新型配电网规划、安全稳定运行以及韧性研究给出一定的建设性的思路。首先，介绍了配电网韧性的基本概念，分析了配电网所面临的各类扰动及扰动下配电网韧性特征；随后，归纳了极端灾害类扰动和波动类扰动建模方法和韧性评估方法的研究进展；接着，介绍了配电网在扰动前、中、后三阶段下韧性提升的关键技术；最后，对未来配电网韧性待研究的关键问题进行了展望。

The development trend in urban and rural power distribution networks is to gradually access a high proportion of distributed power sources. However, the various types of disturbances caused by uncertain factors, including power sources, loads, and the natural environment, cannot be ignored. Therefore, the distribution network must have the ability to resist various disturbances and recover quickly, which is called the resilience of the distribution network. Meanwhile, all types of flexible resources from the source, network, load, and storage of distribution networks can provide support for the balance between supply and demand, and improvement of toughness of the distribution network. Accurately describing the impact of various disturbance events and effectively determining the synergy of flexible resources on the source, network, load, and storage sides are key factors in studying the resilience of distribution networks. This study aims to provide insights and constructive ideas for the planning, safe and stable operation, and resilience research of new distribution networks. This study first introduces the basic concept of distribution network resilience, and analyzes various types of disturbances faced by the distribution network and the characteristics of distribution network resilience under disturbances. The study then summarizes the research progress in extreme disaster disturbances, wave disturbance modeling methods, and resilience evaluation methods. Next, the key technologies for improving the resilience of distribution networks in the pre-disturbance, mid-disturbance, and post-disturbance stages are also introduced. Finally, the key problems in the future research on distribution network resilience are discussed. 

地震等极端灾害可使主动配电网(active distribution network,ADN)和交通网同时遭受严重损害,进而引发交通道路受损拥堵和大面积停电事故,给电网恢复供电造成困难。对此,从震后交通网和ADN特征着手,提出一种融合有限源荷资源、网络重构和抢修调度的ADN动态协同恢复策略。首先,基于震后交通网与ADN难以割裂和互为映射的联接关系,构建交通网道路与配电网线路的联合灾损模型,并建立计及通行能力和交通流量综合影响的交通网抢修通行时间模型以及结合负荷时需和重要性的ADN恢复韧性指标;其次,构建考虑震后交通网路况的ADN分层动态协同恢复优化模型,外层以恢复韧性指标最大和总抢修时间最小为目标,内层以最小化停电损失和加权开关动作次数为目标;然后,采用改进灰狼优化算法求解所提模型,协调优化多类型发电、应急需求响应负荷、网络重构和抢修队等资源,提升震后ADN韧性;最后,通过地震灾损场景下的算例分析,验证所述策略的可行性和有效性。

Extreme disasters such as earthquakes can cause severe damage to active distribution networks (ADN) and transportation networks. These disasters can lead to road damage, congestion, and large-scale power outage, which makes it difficult to restore power supply to the power grid. In this study, an ADN dynamic collaborative recovery strategy is proposed based on the characteristics of the traffic network and ADN after an earthquake. The proposed strategy integrates finite source and load resources, network reconstruction, and rush repair scheduling. First, a joint disaster damage model of roads and lines of the transportation network was constructed based on the relationship between the transportation network and ADN that is difficult to separate and map to each other after the earthquake. A model of the traffic rush repair travel time of the transportation network was established considering the comprehensive influence of the traffic capacity and traffic flow, and the ADN resilience index combining the time demand and load importance. Second, an ADN hierarchical dynamic cooperative restoration optimization model reflecting the post-earthquake traffic network conditions was established. The outer layer was targeted at determining the maximum resilience index and minimum total emergency repair time, whereas the inner layer was targeted at minimizing economic losses and weighted switch operation times. Third, the improved grey wolf optimization algorithm was used to solve the proposed model, and resources such as multi-type power generation, emergency demand response load, network reconstruction and emergency repair team were coordinated and optimized to improve the resilience of ADNs after earthquakes. Finally, the feasibility and effectiveness of the proposed strategy are verified using an example analysis of the earthquake damage scenario.

近年来,电网的节能降损工作受到广泛关注。随着灵活调节资源大规模接入配电网,充分挖掘灵活调节资源的降损潜力显得至关重要。提出一种面向降损场景的移动储能与网络重构两阶段协同优化策略:第一阶段,采用场景分析法刻画源荷不确定性,以综合网损最小为目标建立网络重构模型,并求解最优重构方案。由于配电网节点较多,为缩小搜索范围提升求解效率,提出一种网损灵敏度分析方法,并结合重构方案为移动储能预先筛选充放电节点集合。在第二阶段,以配电网网损及移动储能通行成本最小为目标函数,综合考虑移动储能的连接/通行状态约束、充放电功率/容量约束和电力网的功率平衡、潮流安全约束,构建交通网-电力网融合的移动储能充放电调度模型,并调用CPLEX求解器求解移动储能的通行及充放电功率调度方案。最后结合IEEE 33节点配电系统进行仿真分析,仿真结果表明有功网损降低552.17 kWh,降损幅度达31.9%,验证了所提策略的有效性。

In recent years, the energy savings and loss reduction of power grids have been widely studied. As flexible regulated resources gain large-scale access to distribution networks, exerting the loss reduction potential of flexibly regulated resources by proposing a mobile energy storage device (MESD) and a network reconfiguration collaborative optimization strategy for loss reduction scenarios becomes necessary. The collaborative optimization strategy is divided into two stages. In the first stage, the source-load uncertainty is characterized by the scenario analysis method, and the network reconfiguration model is established with the minimum network loss as the objective function to obtain the network reconfiguration scheme. Owing to the large number of distribution network nodes, a network loss sensitivity analysis method is proposed to narrow the search range in order to enhance the efficiency of the solution, and the above reconfiguration scheme is combined with it to pre-screen the charging/discharging node set for MESD. In the second stage, the objective is to minimize the network loss of the distribution network and the traffic cost of mobile energy storage by considering the connection/operation state constraint, charging/discharging power or capacity constraint of the MESD, and the power balance and power flow safety constraint of the power network. A charging/discharging dispatching model of traffic network-power network convergence applicable to the MESD is constructed, and the CPLEX solver is invoked to solve the traffic planning and charging/discharging power dispatching plan of the MESD. Finally, a simulation analysis is performed using the IEEE 33-bus distribution system. The simulation results reveal that the active network loss of the system is reduced by 552.17 kWh, and the loss reduction range is reduced by 31.9%, verifying the effectiveness of the proposed strategy.