To achieve more precise monitoring of state fluctuations in the power network close to renewable energy sources, it is necessary to utilize phasor measurements and shorten the time interval between state estimations. For large-scale power systems, however, estimating all of their states with shorter time intervals means a drastic increase in computational burden. As a tradeoff between accuracy and computational efficiency, a multi-time interval forecasting-aided state estimation approach is proposed in this paper, where states with various degrees of fluctuations are estimated asynchronously with different time intervals. Based on the newest state estimate, forecasting-aided state estimators are employed to predict states at time moments prior to the next round of measurement update and state estimation. Extensive numerical tests have demonstrated the effectiveness of the proposed approach.

This paper proposes a novel power system load frequency control method that employs observer-based integral sliding mode control. Firstly, the tieline power is taken as an external disturbance. Therefore, a reduced-order dynamic function is derived. Secondly, a novel sliding surface is designed to improve the disturbance rejection performance. Thirdly, a memory-based feedback control law is designed to improve the dynamic performance of the load frequency control. The stability of the proposed LFC method is proved through the Lyapunov-Krasovskii function (LKF). The linear matrix inequality (LMI) is employed to solve LKF to obtain the gain values of the feedback control, thus guaranteeing the LFC system’s dynamic performance. The proposed method shows superior transient performance and stronger robustness against disturbance arising from load variation. Simulation results are presented to verify the effectiveness of the proposed method.

To achieve high reliability, the urban distribution networks are mesh-constructed and radial-operated, in which the outage load can be restored to adjacent feeders via tie-lines after faults. Conventionally, iterative optimization-simulation methods and heuristics are adopted for distribution network planning, which cannot guarantee global optimality. Besides, existing reliability-constrained planning model cannot explicitly assess the reliability indices for mesh distribution networks, so the resulted plan scheme may be overly invested. In this paper, we propose a novel multistage expansion planning model for mesh distribution networks, in which reliability assessment is explicitly implemented as constraints. The different investment/reliability preferences for buses are also customized. Specifically, post-fault load restoration between feeders through tie-lines is modeled as a case of post-fault network reconfiguration. The planning model is then cast as an instance of mixed-integer linear programming and can be effectively solved by off-the-shelf solvers. We use a 54-node system to test the performance of proposed model. Simulation results show the effectiveness and flexibility of this methodology.

The topic of multi-area power system state estimation is covered in this chapter. We present the problem model and review relevant literature in this field, including some recent results on hierarchical multi-area state estimation. Our discussion focuses on the optimality and computation and communication costs of hierarchical state estimators.

Most distribution network planning models rely on predefined candidate electric line routes (branches), i.e. the planning model is to select a subset of the candidate branches to form a network with minimal investment cost and guarantee the power supply to consumers with specified reliability. However, the manually presented candidate branch set may be thoughtless while the exhaustively generated a large set of candidate branches may make the planning model intractable. In this study, the authors propose a distribution network planning model based on feeder corridors instead of the candidate branches, since feeder corridors are commonly given in municipal planning. Based on the graph model formulated by feeder corridors, the proposed planning model can generate the network with minimal investment cost while meeting the specific reliability requirement. Furthermore, since post-fault load restoration strategies between feeders are fully incorporated, the proposed model is applicable for mesh networks. The planning model is formulated as a mixed-integer linear programming (MILP) problem and can be effectively solved by off-the-shelf software. Simulation results show the effectiveness and flexibility of the proposed method.

To guarantee the power supply reliability of some critical customers, back-up power sources may be needed when sustained faults occur in distribution networks. In this paper, back-up power sources including back-up distributed generators (DG) and uninterruptible power supply (UPS) are optimally placed and sized with explicit reliability constraints for not only distribution system but also specific customers. This model is then cast as an instance of mixed integer linear programming and can be effectively solved by on-the-shelf software. Case studies on a 53-node distribution network show the scalability and efficiency of the proposed model.

Analytical methods for evaluating the reliability of simple and radial distribution networks have been well established. Since these analytical methods cannot consider post-fault load transfer between feeders, the reliability indices are significantly underestimated for mesh-constructed distribution networks. To accommodate various application scenarios, Monte-Carlo simulations are widely used for complex distribution networks and heavy computation burden is involved. In this paper, we propose a novel linear programming model which includes precisely assessing reliability and considers post-fault network reconfiguration strategies involving operational constraints. Moreover, this model also can formulate the influences of demand variations, uncertainty of distributed generations and protection failures on the reliability indices. Numerical simulations show that the proposed model yields the same results as the simulation-based algorithm. Specifically, the system average interruption duration indices are reduced when considering post-fault network reconfiguration strategies in all tested systems. Moreover, the proposed model is suitable for inclusion in reliability-constrained operational and planning optimization models for power distribution systems.

The combined heat and power system introduces a higher efficiency in energy conversion and consumption. By exploiting the flexibility of district heating systems (DHSs), the joint operation of heat and power systems can improve the overall system flexibility, reduce renewable energy curtailment, and decrease system operating costs. In a typical DHS, the heat exchange station (HES) is a key component which can help adjust the heat distribution among heat loads. In this paper, a joint hourly commitment of generation units and HESs is proposed. The DHS model is presented in which thermal storage and inertia of pipelines and heat loads are characterized. In addition, an approximation is applied to the HES model, making the overall joint commitment problem tractable. Numerical simulations are carried out, which demonstrate that the proposed joint commitment solution can introduce additional benefits in reducing wind power curtailment and system operation cost.

随着电网运行方式时变性和复杂性日益增强,传统基于人工离线规则的调度决策机制难以为继,成为国内外复杂电网运行迫切需要解决的问题.基于人工智能、立足现有调度机制,研究面向调度决策的智能机器调度员(automatic operator,AO)关键技术及应用.在总结10余年研究的基础上,提出了电网调度运行"邻域知识"模型,构建了AO体系架构,提出了知识自动发现、管理和在线应用的技术路线,研发了复杂电网AO系统,并在多个省级电网调控中心在线投运.AO将"专家智能"离线制定粗放运行规则的模式,变革为"人工智能"在线发现精细运行规则的模式,推动调度决策从"自动化"到"智能化"的跨越.最后,指出了尚待研究的问题.

大量分布式资源并网运行的主动配电网,若采用传统集中式的调控决策体系,面临控制敏捷性、系统可靠性、海量通信和信息隐私等问题。文中设计了"集群自律-群间协调-输配协同"的主动配电网能量管理与运行调控的体系结构,并开发了相应的系统。然后,重点介绍了这种集群控制、多级协调的调控体系特点和关键技术:①主动配电网网络分析技术;②分布式集群调控技术;③考虑不确定性的配电网有功和无功协调优化技术;④输配电网分布式协调调度技术。最后,简要介绍了该系统在高比例分布式可再生能源配电网的应用效果,并对后续的研究方向进行了展望。

Line routing is a critical issue in distribution network planning and conventional raster solutions for line routing have certain deficiencies. This study presents a new methodology for a distribution network planning problem based on a hexagonal raster in a geographic information system. Differing from the conventional square raster, the hexagonal raster-based model involves less binary variables, but with the same arm length, and guarantees that the planning routes are arm-connected. An iterative refining method is proposed to reduce the computational burden with the guaranteed resolution, in which a mixed-integer linear programming problem is solved in each stage. The proposed model can simultaneously optimise electric line routes and pole positions, in contrast to conventional models where the poles are not considered. Numerical case studies illustrate the effectiveness of the proposed approach for distribution network planning.

Requirements of economic and reliability criteria are often stressed simultaneously in operating and planning models for distribution systems. However, conventional reliability evaluation algorithms are hard to be integrated to these optimization models analytically. Recently, several optimization model-based reliability assessment methods have been proposed, which can be embedded into such models. Nevertheless, the detailed placement and actions of circuit breakers and switches are ignored or oversimplified in these methods, leading to an inconsistency with the real world situation. Thus, we propose a new optimization model-based reliability assessment method that fully considers detailed placement and actions of circuit breakers and switches in distribution networks. Based on fictitious fault flows, strategies for tripping circuit breakers to cut fault current, operating switches to isolate the fault and processing post-fault network reconfiguration are linearly modeled as constraints. Therefore, this method can be easily integrated into operating and planning models of distribution networks. Case studies on 54-node, 85-node, 137-node and 417-node distribution networks show the scalability and efficiency of the proposed model.

Conventionally, the urban distribution network planning task is conducted manually with the aid of planning software. It is difficult and time-consuming to artificially determine the candidate line set for a new urban area from scratch. Moreover, in most existing planning models, customers within a block are regarded as a single load point, which is inaccurate and may lose plasticity for the planned network. In this paper, we propose the candidate line set generating strategy via exploiting the street layout. For each block, an optimization solution is presented to determine the location and number of local distribution transformers which are influenced by the block shape and its load demand. Finally, we develop a mixed integer linear programming (MILP) model for urban distribution network planning which can be solved analytically. Based on the spatial load forecast, the proposed model can simultaneously generate the optimal locations and capacities of substations and feeders for the distribution network. Numerical tests based on the geographical information system (GIS) of a planned industrial park at a city in China, show that the proposed model is effective and practical.

Line-commutated-converter-based high-voltage direct current (LCC-HVDC) is an important and attractive method of transmitting large-scale integrated wind power. However, the contingencies induced by LCC-HVDCs and nearby areas usually lead to an overvoltage problem in hybrid AC/DC sending-side systems, which may result in severe cascading trip problems. Therefore, a robust voltage control strategy for hybrid AC/DC sendingside systems is proposed in this paper to prevent cascading trip failures and accommodate more wind power. The proposed strategies address the voltage control complexity of LCC-HVDCs and converter stations, appropriately formulate the different locally fastresponse switching strategies of filter banks and different control modes of the LCC-HVDCs in the automatic voltage control system. To compute the formulated problem efficiently and quickly for online application, a method of transforming the robust optimization problem and the Benders decomposition method are introduced. Based on case studies, the computational efficiency and accuracy of the proposed method are determined using operational data for the TianShan District in the Northwest China Power Grid.

In this letter, an improved real-time, short-term voltage stability monitoring method is introduced. The impact of voltage magnitude oscillation on the calculation of the Lyapunov exponent is analyzed, and a phase rectification method to eliminate the negative influence of oscillation is proposed. The simulation work was conducted on the provincial power grid at Guangdong in China. Based on our simulation results, the proposed method is expected to improve the effectiveness of short-term voltage stability monitoring.

Network reconfiguration and demand response can both reduce the loss and improve the security of the distribution networks (DNs). In this paper, we describe a day-ahead DN reconfiguration schedule model considering demand response, in which network reconfiguration is conducted by distribution system operator (DSO) while the demand response strategies are realized by customer aggregators. To preserve information privacy and reduce computational burden, a two-level decomposition & coordination algorithm is proposed to solve this model, which is based on a log-barrier cost function. In the upper level, the DSO optimizes the network reconfiguration schedule, calculates the barrier cost of every load bus and then sends it to the corresponding customer aggregator. In the lower level, every customer aggregator exploits available controllable demands like heat pump (HP), electric vehicle (EV) to minimize the nodal daily cost based on the barrier cost calculated in DSO. Numerical tests verify the promising convergence of this decomposition algorithm and show a reduction of the total cost in DN.