随着新能源渗透率的不断加大,电力系统的电力电量平衡特性、分析方法和应对策略都将发生重大改变.有效应对新能源出力的不确定性,保证电力可靠供应与清洁能源最大限度消纳的难度显著增加.基于新能源出力预测精度与电力调度保供应、保消纳决策目标期望均随时间尺度的缩短而逐渐提高的特点,提出一种按时间尺度逐层分解、递进优化的电力电量平衡分析方法与调度优化策略.通过分层递进与目标解耦,可在新能源高占比场景中实现以电力可靠供应与清洁能源最大限度消纳为目标的高效求解.以某电网的新能源高占比规划场景为例,对该系统中的电力电量平衡分析与调度策略优化问题进行年、月、周、日等各时间尺度上的全过程研究,揭示新能源高占比场景中调度机构开展电力电量平衡工作面临的新问题与新挑战,提出调节资源协同配置、发电机组检修策略调整、送受电策略调整及配套电力市场机制建设等建议措施.

Wind curtailment (WC) and load shedding (LS) are indispensable measures to mitigate the operational risk in high-renewable power systems. Moreover, WC and LS schemes should be pre-scheduled and confirmed by related entities to make them applicable. In this article, we propose a novel chance-constrained economic dispatch (CCED) model which can generate optimal WC and LS schemes accounting for the reserve shortage and transmission congestion problems. In the proposed method, WC and LS power are formulated as random decision variables, with which the infeasibility issues of conventional CCED are fully addressed. To solve the proposed model, we first convert the complicated chance constraints into a set of deterministic inequalities equivalently by employing the conditional Value-at-Risk (CVaR) representation and duality theory. Then, a two-layer iterative algorithm is proposed to solve the equivalent problem efficiently, which is based on the generalized Benders decomposition (GBD) framework. Numerical tests demonstrate the effectiveness and efficiency of the proposed method.

"双碳"目标下,大力发展新能源是保障我国能源安全和应对气候变化的关键举措.截至2022年底,全国风电、光伏总装机容量分别达3. 65亿千瓦、3. 93亿千瓦,其中2022年全国风电、光伏新增装机分别为3763万千瓦、8741万千瓦,创历史新高.

为应对未来电力系统运行的挑战和机遇,该文描绘了电力生态的愿景,梳理了未来电力生态运行的关键特征和需求,提出云大脑-边缘神经元两级融合的调度运行模式.为满足电力生态的运行需求,该文采用主从博弈作为云边两级融合运行的数学模型,并分别采用基于概率图的智能感知、基于风险约束的序贯优化、基于共识约束的自治优化和基于主从博弈的双层优化,对电力生态不确定性、云大脑运行、边缘神经元运行和云大脑-边缘神经元融合运行建模.

随着电力市场建设的不断深入与完善,发电商在现货市场中的报价行为监管问题已经成为电力市场化改革中的关键问题.为此,提出一种基于均值聚类算法的现货市场报价行为分析模型.首先,系统性地介绍了现有的发电商报价行为分析指标,在此基础上提出机组簇报价曲线相似度及机组簇报价持留水平等指标.然后,使用新指标与均值聚类算法对现货市场报价行为进行分析,同时使用报价行为分析结果对均值聚类算法进行改进,克服了传统均值聚类法需要人为指定聚类簇心的缺陷,减少了算法在初值及参数设置上的随机性,提高了算法的效率.最后,以我国某地区电力现货市场的模拟运营数据为例,有效地识别出了其中具有市场力操纵行为的发电商,验证了模型的可行性和适用性.

As massive distributed energy resources(DERs)are integrated into distribution networks(DNs)and the distri-bution automation facilities are widely deployed,the DNs are evolving to active distribution networks(ADNs).This paper in-troduces the architecture and main function modules of an inte-grated distribution management system(IDMS)and its applica-tions in China.This system consists of three subsystems,includ-ing the real-time operation and control system(OCS),outage management system(OMS),and operator training simulator(OTS).The OCS has a hierarchical architecture with three lev-els,including the local controller for DER clusters,the optimiza-tion of DNs incorporated with multi-clusters,and the coordina-tion operation of integrated transmission&distribution(T&D)networks.The OMS is developed based on the geographical in-formation system(GIS)and coordinated with OCS.While in the OTS,both the ADN and its host transmission network(TN)are simulated to make the simulation results more credible.The main functions of the three subsystems and their interaction da-ta flows are described and some typical application scenarios are also presented.

随着非同步电源渗透率增加,电网的惯性逐步降低,扰动后电网的频率变化率变大,频率调控面临着巨大挑战.近年来,快速频率响应(FFR)的概念已被提出,并应用于一次调频之前,为一次调频响应争取时间,使低惯性系统在一次调频响应前不至于到达系统低频减载的频率阈值.文中从典型FFR资源、辅助服务产品设计、市场交易和应用实例等几方面总结了国外FFR市场开展现状.结合中国电网应对未来低惯性系统的调频需求,阐述了FFR技术的研究趋势,并为中国FFR市场建设提出了建议和展望.

在线安全稳定分析数据由本地状态估计数据和上级下发的其他调度机构数据拼接构成,由于两者时间不同步造成区域联络线功率出现偏差.文中提出了一种内外网数据拼接的区域联络线潮流调整方法,在将联络线聚合成联络通道以及将对联络通道灵敏度较大的若干节点聚合的基础上,通过求解聚合节点调整量和联络通道潮流偏差量的线性方程组获得调整量,避免采用优化方法时计算速度慢和部分节点调整量过大的问题;当方程组病态时,通过求解极小范数最小二乘解避免线性方程组无解的问题.该方法可以满足在线安全稳定分析计算速度和准确性要求,且某实际电网的算例验证了该方法的有效性.

Network attacks are one of the main threats to the stable operation of smart grid equipment. As a real-time monitoring system to prevent network attacks, intrusion detection is widely used in smart grid protection. However, the massive data in the network transmission process contains a large number of redundant and irrelevant features, which makes it difficult for the intrusion detection system to process in time and reduce the efficiency. Feature selection is a method to solve this kind of problem. It can improve the speed of intrusion detection by filtering the characteristics of massive data. Therefore, a hybrid feature selection algorithm which combines information gain and genetic search to improve the work efficiency of intrusion detection systems is proposed. The algorithm is mainly divided into three parts. Firstly, the information gain value of all features is calculated by using information gain, according to which all features are ordered, and the ordered features is ranked according to an exponential increase strategy; secondly, the ranked features is used to guide the genetic algorithm search process, and a new fitness function can be used to control the search direction of genetic algorithm; finally, a classification algorithm is used to test the dataset after feature selection. In experiments, by comparing with other feature selection algorithms on 5 sets of high-dimensional UCI datasets, it is concluded that the IGExpGA proposed in this paper significantly improves the detection rate and detection speed. More importantly, in the KDD1998 network data, the algorithm proposed improves the detection rate to 98.8%, which is significantly better than other algorithms.

南方电网交直流互联、 远距离、 大容量、 超高压输电,通过多回交流线路与周边地区及国家互联互通,运行特性复杂.本文结合近年来南方区域电力系统运行实践,对复杂交直流互联电网运行特性进行深入探讨,揭示了当前南方区域电网运行的主要安全风险,包括:单一原因引发共模故障风险突出,一、 二次设备拒动在交直流紧密耦合场景下稳定问题突出;频率、 功角稳定问题严重影响主网安全.在总结梳理稳定控制措施的基础上,提出了构建基于广域信息的电力系统安全稳定防线,以提升未来南方区域电网安全稳定控制水平.

This paper first analyzes the process and characteristics of big data and cloud computing. A cloud-based architecture for cloud-based power grid wide-area monitoring is proposed. This architecture uses parallel computing and big data mining to provide relevant auxiliary decision-making for the grid. The cloud computing architecture of this paper is based on Hadoop and a brief description of the Hadoop architecture is demonstrated. Finally, two scenarios of grid visualization and grid scheduling decision are briefly described.

随着南方区域电力现货市场的建设,技术支持系统的开发工作正在稳步推进.由于南方区域电力现货市场的特殊性,技术支持系统中的出清功能需要进行定制化开发.本文深入分析了南方区域电力现货市场的特点,提出了开发出清功能需要解决的两个关键技术挑战:出清逻辑的设计和计算引擎的设计;最后针对这两个挑战分别给出解决方案,为开发技术支持系统的出清功能提供参考依据.

以先进机器学习方法等为代表的人工智能技术在增强现代电网安全稳定态势感知能力方面展现出巨大的潜力。针对在线暂态电压稳定评估的传统难题,提出基于时序轨迹特征学习的稳定评估方法。通过分析系统能量函数与暂态响应轨迹的相关性,给出学习过程输入数据选取的理论依据。在时序轨迹Shapelet变换基础上,提出以刻画系统稳定/失稳案例关键局部轨迹差异为核心的特征学习方法及稳定评估方案。双机四节点系统和南方电网中的算例测试结果表明,除了实现可靠的稳定监测和评估,还可充分利用文中方法的可解释性从数据层面剖析特定系统的失稳模式和规律。

对南方区域电力现货市场技术支持系统方案进行了研究。基于一体化、模块化、智能化、安全性、开放性、适应性原则,提出了包含中长期计划分解、日前市场、实时市场、辅助服务市场等电力现货市场主要业务的系统架构及功能。针对系统建设的关键问题,本文提出采用云平台架构实现系统的灵活弹性和高可用;建设现货市场运营数据中心实现数据全景建模和数据统一交互;构建完整边界防护、纵深防御体系,保障现货系统对外安全防护;研究统一系统架构、模块化建设方案,实现区域起步、区域两级、区域一级建设路径下系统平滑过渡。

The Yunnan power grid has a large-scale hydropower station, whereby the power generated at the station is transmitted via HVdc lines under an asynchronous operation mode. In the system performance test of the Yunnan asynchronous operation, a 0.05-Hz ultralow-frequency oscillation occurred in the grid. This ultralow-frequency oscillation is a frequency stability problem caused by instability of hydraulic turbine governing system, which is quite common in the islanded system and very rare in the normal synchronous system. The relationship between the speed governing system stability of one unit and that of the system with parallel operation units was proposed to explain that the high proportion of units with an unstable governing system will lead to system frequency instability and the reduction in loads in the traditional sense was proven to further worsen the frequency stability of the Yunnan asynchronous system. By using related measures, the ultralow-frequency oscillation of the Yunnan asynchronous system has been curbed effectively. System performance testing verified the effectiveness of the analysis and the measures.

Yunnan power grid has a large-scale hydropower station whereby the power generated at the station is been transmitted via high-voltage direct current (HVDC) transmission lines under asynchronous operation mode. In the system performance test of Yunnan asynchronous operation, 0.05 Hz ultra-low frequency oscillation occurred in the grid. We analyzed the damping characteristics of different types of units and figured out that the negative damping characteristic of the hydropower units are mainly responsible for the ultra-low frequency oscillation. There is a tendency for ultra-low frequency oscillation in a regional power grid with high ratio of hydropower penetration which transmits electricity using HVDC. The water hammer effect of hydraulic turbine influences the damping characteristic of the hydropower unit while the governor control gain influences the strength of the damping. By setting reasonable governor control gain and optimizing the dead-zone of HVDC frequency limit controller the ultra-low frequency oscillation of the Yunnan asynchronous system can be effectively curbed. Simulation results and system performance test verified the effectiveness of the method.

The negative damping torque of the generator's prime mover system is considered as the major cause of ultra-low-frequency frequency oscillation (ULFFO) and tripping the primary frequency regulation of the units with negative damping can increase the damping of the system and suppress the oscillation.The key to emergency control is online evaluation of damping of prime mover systems.An online analysis and emergency control method of ULFFO based on transient energy flow is proposed.The transient energy flow method useful to the low frequency oscillation analysis is extended to the analysis of ULFFO.The consistency of the energy flow method and the damping torque method in analyzing the prime mover system's damping in ULFFO is proved.An online evaluation method of the damping torque coefficient of the prime mover system is proposed according to the existing wide-area measurement system data, and the procedure of ULFFO emergency control is given.Simulation results are proof of the effectiveness of the proposed method which provides technical means for online control of ULFFO.

Automatic voltage control (AVC) system has great significance in keeping the power system secure, optimized, stable and environment-friendly. This paper reviews the history of AVC system's history, and points out the key technical challenges of implementing AVC system in large-scale complicated power grid, further, the authors introduce a series of autonomous-synergic voltage control techniques to meet the challenges, including the synergic optimization method of the security and economy of huge power grid, the autonomous-synergic voltage control method supporting high penetration of renewable generation and the progressive synergic voltage control method based on automatic adaptive structure. At last, the authors discuss the prospect of AVC system according to real demand and developing trend of Chinese power grid.

This paper proposes secondary device status evaluation comprehensive model based on variable weights matter-element extension aiming at characteristics of the secondary device status evaluation. A secondary device status information classical field is established to get state parameters matrix. At the same time, state parameters' weights are corrected by using variable weights theory to overcome the subjective factors of traditional method, three kinds of weight determination method are compared, the results indicate that variable weighting method enlarges anomaly index to make evaluation result worse, which is in line with the device management requirements. Finally, closeness degree is also introduced to judge the condition of the secondary device comprehensively. A secondary device in an 220 kV substation is evaluated using this method, the result shows that the device is in the state of attention, tending to a greater degree of abnormal state, which is consistent with actual operation status of the device, verifying the correctness of the method.

Phasor measurement units (PMUs) have been widely deployed in modern power grids, which provides a great opportunity for high-precision and real-time power system state estimation. If power networks are completely observable by PMUs, linear state estimation can be achieved with massive synchronous data. In this paper, the analysis of phasor error is presented and a complex number weighted least squares (CWLS) algorithm for state estimation is proposed, which shows high computational efficiency and stronger robustness to phasor errors. Meanwhile, self-adaptive weight and bad data identification techniques are developed to enhance the CWLS’s performance. Test results on various IEEE test systems and the realistic power grid validate the effectiveness and superiority of the proposed CWLS algorithm, outperforming the conventional weighted least squares (WLS) state estimation method as well as other existing industrial-grade approaches.