国家层面成立电磁脉冲委员会并积极推进强电磁脉冲防御计划，南方电网公司高度重视电网设施遭受强电磁脉冲攻击风险。为加强电网安全防御并提升供电可靠性，亟需建立电网设备防御强电磁脉冲试验平台开展相关试验研究。首先研究了强电磁脉冲试验平台功能定位和总平面布置方案。然后依次从场区用电和试验电源、电磁屏蔽室和控制室建设、发输电设备和输电线路配置、接地网设计、消防和给排水等方面开展研究和建设。最后进行了设备现场调试和电磁环境测试。首次在国内建立了面向电网设备的强电磁脉冲平台，完全满足强电磁脉冲各项试验需求，为强电磁脉冲产生和传播特性、对电网主设备和控制保护设备影响规律、防御装置研发等研究提供了平台保障。

At the national level, the Electromagnetic Pulse Committee has been established and the strong electromagnetic pulse defense plan has been actively promoted. China Southern Power Grid attaches great importance to the risk of power grid equipments being attacked by strong electromagnetic pulse. In order to strengthen the security defense of power grid and improve the reliability of power supply, it is urgent to establish strong electromagnetic pulse test platform to carry out relevant experimental research. Firstly, the functional positioning and general layout of the strong electromagnetic pulse test platform have been studied. Then, research and construction have been carried out from field power consumption and test power supply, electromagnetic shielding chamber and control room, substation equipment and transmission line, ground network design, fire protection and water supply. Finally, field debugging and electromagnetic environment testing are carried out. The strong electromagnetic pulse platform for power grid equipment is established for the first time in China, fully meeting the needs of various tests of strong electromagnetic pulse and providing platform support for research on the generation and propagation characteristics of strong electromagnetic pulse, the influence law on power grid main equipment and control & protection equipment, and the research and development of defense devices.

为了实现数据中心的节能和减排，将数据中心融于综合能源系统进行协同优化是实现这一目标的有效途径。首先阐述了数据-模型混合驱动策略的原理，并分别对模型驱动和数据驱动在综合能源系统中的应用情况进行综述。然后，详细介绍了数据中心的负荷预测模型以及数据-模型混合驱动在数据中心综合能源系统中的应用现状，在此基础上，提出了一种基于数据-模型混合驱动的数据中心综合能源系统调度策略框架。最后，对目前研究存在的问题进行了讨论，并对未来的发展方向做出展望，为该领域的研究人员提供参考。

To achieve energy efficiency and emissions reduction in data centers, integrating data centers into integrated energy systems for collaborative optimization is an effective approach to achieve this goal. Firstly, the principles of a hybrid data-model driven strategy are explained, then the applications of model-driven and data-driven approaches in integrated energy systems is reviewed separately. Secondly, the load forecasting model of the data center and the current status of the application of a hybrid data-model driven method in integrated energy systems with data centers are introduced in detail. On this Basis, a framework for scheduling strategies in integrated energy systems with data centers utilizing a data-model hybrid drive is proposed. Finally, the current research issues are discussed, and an outlook on future development directions is provided, providing a reference for researchers in this field.

风电、光伏（photovoltaics，PV）在新型电力系统中的渗透率日益增加，使得配电网电压波动加剧，而储能（energy storage，ES）、电动汽车（electric vehicles，EV）对降低配电网电压波动有重要作用。与此同时，智能电表、智能传感器以及改进的通信网络广泛部署，可获取的数据量越来越大，数据驱动技术兴起。提出了一种基于多智能体深度强化学习（multi-agent deep reinforcement learning，MADRL）的配电网双时间尺度有功-无功功率协调的电压控制策略。慢时间尺度下用双深度Q网络算法（double deep Q-network algorithm，DDQN）求解电容器组（capacitor banks，CBs）、有载调压变压器（on-line tap changer，OLTC）与ES有功-无功功率优化问题。快时间尺度下用具有注意力机制的经验增强多智能体柔性参与者-评论家算法（experience augmentation- multi-agent soft actor critic，EA-MASAC）调节PV、风机（wind turbine，WT）、静止无功补偿装置（static var compensator，SVC）的无功功率与EV的有功功率。最后，在IEEE-33节点系统上验证了所提方法的有效性。

The increasing penetration rate of wind power and photovoltaics （PV） in new power systems exacerbates voltage fluctuations in distribution networks, while energy storage （ES） and electric vehicles （EV） play important roles in reducing voltage fluctuations in distribution networks. At the same time, smart meters, smart sensors and improved communication networks are widely deployed, the amount of data available is increasing, and data-driven technology is emerging.This paper proposes a multi-agent deep reinforcement learning （MADRL） based dual time scale active and reactive power coordinated voltage control strategy for distribution networks.Using the double deep Q-network algorithm （DDQN） to solve the optimization problems of capacitor banks （CBs）, on line tap transformers （OLTC）, and ES active and reactive power at a slow time scale. At a fast time scale, the EA-MASAC algorithm with attention mechanism is used to enhance the reactive power of PV, wind turbines （WT）, and static var compensators （SVCs）, as well as the active power of EVs. Finally, the effectiveness of the proposed method is verified on an IEEE-33 node system.

目的 电力系统面临着虚假数据注入攻击的威胁，而已有的虚假数据注入攻击检测方法存在特征学习能力不足和检测速度较慢的问题，以至于无法对虚假数据注入攻击进行快速精确定位，因此提出一种基于Levy麻雀优化深度极限学习机的电网虚假数据注入攻击定位检测方法。 方法 所提方法将深度极限学习机作为特征提取算法和基础分类器来实现对攻击的快速精确定位；同时采用具有强局部搜索能力、融入Levy飞行策略的麻雀搜索算法对其初始权重与偏置进行优化，以进一步提高方法的定位检测精度。 结果 在IEEE-14和IEEE-57节点系统进行了大量仿真分析，所提方法的检测准确率在94%以上。 结论 与其他检测方法对比，所提方法具有更优的检测精度，可以实现更为快速的虚假数据注入攻击定位检测。

Objectives Power systems are facing threats of false data injection attacks. Existing detection methods for false data injection attacks have the problems of insufficient feature learning ability and slow detection speed, making it difficult to locate false data injection attacks rapidly and accurately. Therefore, this study proposes a method for locating false data injection attacks in power grid based on deep extreme learning machine optimized by Levy flying sparrow search algorithm. Methods The proposed method uses a deep extreme learning machine as the feature extraction algorithm and basic classifier to achieve rapid and accurate attack location. At the same time, a Levy flying sparrow search algorithm with strong local search ability is employed to optimize the initial weight and bias to further improve the location detection accuracy of the method. Results Extensive simulation analyses are conducted on IEEE-14 and IEEE-57 bus power systems. The proposed method achieves a detection accuracy rate of over 94%. Conclusions Compared with other detection methods, the proposed method demonstrates better detection accuracy and enables faster location detection of false data injection attacks.

气候变化对人类社会的影响越来越受关注，随之而来的一系列极端天气引发系统断电的风险也越来越显著。为应对气候变化尤其是极端天气，人类社会需采取减缓和适应2种应对策略，对于发展中国家与小岛国，由于气候变化已经发生，因此气候问题将首先是适应问题。为解决电力系统如何从各个环节完整地适应气候变化问题，建立了一个适应气候变化的电力系统发展体系，提出一种涵盖极端气象因素的电力系统发展路径构建方法。在总结各种极端天气对电力系统影响的基础上，对电网的脆弱性进行评价；研究了适应极端天气的总体策略，并提出电力系统适应极端天气事件的方案，即规划–建设–应急管理–评估（planning-construction-emergency management-assessment，PCEA）抗灾体系。在规划阶段重点进行保底电网规划，构建不停电最小电网主干网；基于方案不同阶段的应用实例，验证了PCEA体系可以促使电力系统更好地适应极端天气。

The impact of climate change on human society has attracted more and more attention, and the risk of power outage caused by a series of extreme weather is becoming more and more significant. In order to deal with climate change, especially extreme weather, human society needs to adopt two coping strategies of mitigation and adaptation. For the developing countries and small island countries, since climate change has already taken place, the climate problem will first be adaptation. In order to solve the problem of how the power system can fully adapt to climate change from all aspects, a power system development system adapted to climate change was established, and a construction method of power system development path covering extreme meteorological factors was proposed. On the basis of summarizing the impacts of various extreme weather on the power system, the vulnerability of power grid was evaluated. The overall strategy of adapting to extreme weather was studied, and the scheme of power system adapting to extreme weather events was proposed, namely planning-construction-emergency management-assessment (PCEA) disaster resistance system. In the planning stage, the study focused on the minimum power grid planning and built the minimum power grid backbone network without power outage. Based on the application examples in different stages of the scheme, it was verified that the PCEA system can make the power system better adapt to the extreme weather.

目的 为应对极端高温天气对电力系统供需平衡造成的冲击，提出一种面向中长期的电力系统韧性提升方法。 方法 首先，模拟持续性极端高温场景下“源-荷”耦合出力特性，并利用时序运行模拟模型对系统韧性表现进行初评估；其次，基于运行模拟结果，在电力供需平衡的基础上以供电成本与负荷削减成本之和最小为目标，建立面向韧性提升的电力资源规划模型，得到韧性提升组合措施；最后，对韧性提升策略场景进行运行模拟，以验证其有效性。 结果 以2025年广东省为案例场景，与未实施电力系统韧性提升策略的场景相比，实施策略后极端高温天气下系统累积缺供电量、最大失负荷规模和因电力短缺而造成的经济损失分别降低了86.71%、60.72%和91.55%，系统最小供电水平提升了11.07%。 结论 通过协同供电资源扩容与负荷资源部署，电力系统韧性提升策略有效地提升了极端高温天气下系统的供电能力，同时降低了因电力短缺而造成的经济损失和社会代价。

Objectives To address the impact of extreme high-temperature weather on the supply and demand balance of power systems, a medium- to long-term method for enhancing power system resilience is proposed. Methods First, the coupled “source-load” output characteristics under sustained extreme high-temperature scenarios are simulated, and an initial evaluation of system resilience is performed using a chronological operation simulation model. Second, based on the operation simulation results, a resilience enhancement-oriented power resource planning model is established, with the objective of minimizing the combined cost of power supply and load shedding under the constraint of supply and demand balance, thereby deriving combined measures for resilience enhancement. Finally, operation simulations of the resilience enhancement strategy scenarios are conducted to verify their effectiveness. Results Taking Guangdong Province in 2025 as a case study, compared with the scenario without the implementation of the resilience enhancement strategies for power systems, the application of such strategies under extreme high-temperature weather decreases the cumulative power shortage, maximum lost load scale, and economic losses caused by power shortage by 86.71%, 60.72%, and 91.55%, respectively. The system’s minimum power supply level increases by 11.07%. Conclusions By coordinating the expansion of power supply resources and deployment of load resources, the resilience enhancement strategies effectively improve the power system’s supply capacity under extreme high-temperature weather, while reducing the economic loss and social cost caused by power shortages.

In today’s world, power distribution systems and information and communication technology (ICT) systems are increasingly interconnected, forming cyber–physical power systems (CPPSs) at the core of smart grids. Ensuring the resilience of these systems is essential for maintaining reliable performance under disasters, failures, or cyber-attacks. Identifying critical nodes within these interdependent networks is key to preserving system robustness. This paper applies complex network (CN) theory—specifically degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC)—to a real-world distribution grid integrated with an ICT layer in northeastern Italy. Simulations are conducted across three scenarios: a directed power network, an undirected power network, and an undirected ICT network. Each centrality metric generates a ranking of nodes which is validated using node removal performance (NRP) analysis. In the directed power network, in-closeness centrality and out-degree centrality are the most effective in identifying critical nodes, with correlations of 84% and 74% with NRP, respectively. DC and BC perform best in the undirected power network, with correlation values of 67% and 53%, respectively. In the ICT network, BC achieves the highest correlation (64%), followed by CC at 55%. These findings demonstrate the potential of centrality-based methods for identifying critical nodes and support strategies for enhancing CPPS resilience and fault recovery by distribution system operators.