分布式电压无功全局优化控制系统的研制与应用

分析了目前广东电网电压无功控制存在的不足，介绍了分布式电压无功全局优化控制系统的结构原理、技术难点。系统由主站系统、变电站电压无功实时控制子系统（VQC）和通信子系统构成。各子站系统的电压和无功控制范围通过全网离散无功优化计算获得，可实现对电网电压无功全局优化和分布式实时控制.     

分布式电压无功全局优化控制系统的研制与应用

分析了目前广东电网电压无功控制存在的不足,介绍了分布式电压无功全局优化控制系统的结构原理、技术难点。该系统由主站系统、变电站电压无功实时控制子系统(VQC)和通信子系统构成。通过全网离散无功优化计算获得各子站系统的电压和无功控制范围,可实现对电网电压无功全局优化和分布式实时控制。     

分布式电压无功全局优化控制系统的研制

分析了目前广州电网电压无功控制存在的不足，介绍了自主开发的分布式电压无功全局优化控制系统的结构原理、研制过程、技术难点及应用情况。该系统已在广州鹿鸣电网挂网运行，运行良好。系统由主站系统、变电站电压无功实时控制子系统（VQC)和通信子系统构成。各子站系统的电压和无功控制范围通过全网离散无功优化计算获得，可实现对电网电压无功全局优化和分布式实时控制。     

分布式电压无功全局优化控制系统的研制与应用

分析了目前广州电网电压无功控制存在的不足 ,介绍了自主开发的分布式电压无功全局优化控制系统的结构原理、研制过程、技术难点及应用情况。该系统已在广州鹿鸣电网挂网运行 ,运行良好。系统由主站系统、变电站电压无功实时控制子系统 (VQC)和通信子系统构成。各子站系统的电压和无功控制范围通过全网离散无功优化计算获得 ,可实现对电网电压无功全局优化和分布式实时控制     

分布式电压无功全局优化控制系统的研制与应用

针对目前广州电网电压无功控制的不足,介绍了自主开发的分布式电压无功全局优化控制系统的结构、原理、研制过程、技术难点及应用情况.系统由主站系统、变电站电压无功实时控制子系统和通信子系统构成,各子站系统的电压和无功控制范围通过全网离散无功优化计算获得,可实现对电网电压无功全局优化和分布式实时控制.该系统已在广州鹿鸣电网挂网运行,运行良好.     

分布式电压无功全局优化控制系统

由主站系统、变电站电压无功实时控制子系统(VQC)和通信子系统构成优化控制系统。其各子站系统的电压和无功控制范围通过全网离散无功优化计算获得,即可实现对电网电压无功全局优化和分布式实时控制。     

分布式电压无功全局优化控制系统的研制与应用

分析了目前广州电网电压无功控制存在的不足,介绍了由广东省广电集团有限公司广州供电分公司自主开发的分布式电压无功全局优化控制系统的结构原理、研制过程、技术难点及应用情况。该系统已在广州鹿鸣电网挂网运行,运行良好。系统由主站系统、变电站电压无功实时控制子系统(VOC)和通信子系统构成。各子站系统的电压和无功控制范围通过全网离散无功优化计算获得,可实现对电网电压无功全局优化和分布式实时控制。     

分布式电压无功全局优化控制系统的研制与应用

介绍了分布式电压无功全局优化控制系统的结构原理、技术难点。电压和无功控制通过全网离散无功优化计算获得,实现对电网电压无功全局优化和分布式实时控制。     

基于多Agent的配电网电压无功优化控制及其应用

分析了目前配电网电压无功控制技术的现状，提出了基于多Agent系统实现电压无功优化控制的原理、结构及实现框架，并对全局电压无功优化控制的数学模型和目标函数进行了分析与改进。进一步提出配电网综合调度自动化系统的站级Agent子系统结构和实现框架，并对规则库的构造及实际运行状况进行了深入的探讨。     

基于多Agent技术的分布式电压无功优化控制系统

全网电压无功控制是一个复杂的、分布式的优化控制问题,为了更好地解决这个问题,文章提出了基于多Agent技术的分布式电压无功优化控制系统,该系统符合电压无功控制装置分散配置的特点,Agent之间相互协调,根据辅助问题原理进行全网并行无功优化计算,数据通信量少,收敛速度快.Agent根据优化计算结果自动调整电压无功模糊判据的控制范围,进行电压无功实时控制,自学习能力强.该系统功能强大,具有较高的适应性、灵活性、智能性和可扩展性.     

电力系统无功优化调度研究综述

较为全面地综述了国内外学术界对无功优化调度问题的研究状况.通过对静态和动态无功优化调度数学模型及各种优化算法的分析,总结了该项研究的五个关键问题,即寻优质量、离散变量处理、求解效率、动态优化调度及协同优化计算和控制等.提出采用多智能体(Agent)建模及其协同优化方法进行全局实时动态无功优化调度的计算和控制.最后指出了有待深入研究的问题,例如无功优化调度的控制次序和负荷模型等.     

Optimal reactive power dispatch using ant colony optimization algorithm

This paper proposed a procedure to solve the optimal reactive power dispatch (ORPD) problem using ant colony optimization (ACO) algorithm. The objective of the ORPD problem is to minimize the transmission power losses under control and dependent variable constraints. Proposed sensitivity parameters of reactive power at generation and switchable sources are derived based on a modified model of fast decoupled power flow. The proposed ACO-based algorithm is applied to the IEEE standard 14-bus, 30-bus systems, and a real power system at West Delta Network as a part of the Unified Egyptian Network. The obtained simulation results are compared with those of conventional linear programming, genetic algorithm, and particle swarm optimization technique. Simulation results show the capability of the proposed ACO-based algorithm for solving the ORPD problem, especially with increasing the system size.     

Reactive power dispatch considering voltage stability with seeker optimization algorithm

Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In the last decades, computation intelligence-based techniques such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA) based method is proposed for ORPD considering static voltage stability and voltage deviation. The SOA is based on the concept of simulating the act of human searching where search direction is based on the empirical gradient by evaluating the response to the position changes and step length is based on uncertainty reasoning by using a simple Fuzzy rule. The algorithm's performance is studied with comparisons of two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms on the IEEE 57 and 118-bus power systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the ORPD problem.     

Optimal VAR control for real power loss minimization using differential evolution algorithm

Differential evolution algorithm (DEA) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been proved to be a promising evolutionary algorithm for solving the ORPD problem and many engineering problems. However, the success of DEA in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies (mutation strategies) and their associated control parameter values. This paper presents a differential evolution technique with various trial vector generation strategies based on optimal reactive power dispatch for real power loss minimization in power system. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts compensator to be switched, for real power loss minimization in the transmission systems. The DE method has been examined and tested on the IEEE 14-bus, 30-bus and the equivalent Algerian electric 114-bus power system. The obtained results are compared with two other methods, namely, interior point method (IPM), Particle Swarm Optimization (PSO) and other methods in the literature. The comparison study demonstrates the potential of the proposed approach and shows its effectiveness and robustness to solve the ORPD problem.     

电力系统分布式模型预测控制方法综述与展望

模型预测控制作为一类约束优化控制策略,在电力系统控制领域得到了广泛的应用,传统的集中式模型预测控制难以满足复杂互联电力系统的多目标优化、可靠性、实时优化等要求。分布式模型预测控制能够协同多个局部控制器实现全局优化或纳什优化。文中全面综述分布式模型预测控制在电力系统中的研究现状和发展方向。首先,按照控制结构对模型预测控制方法进行了分类和对比分析,接着重点阐述了非协作式和协作式2类分布式模型预测控制方法。然后,围绕分布式模型预测控制在不同应用场景分别进行综述和分析。最后,指出了分布式模型预测控制在电力系统控制应用的研究方向。     

Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm

This paper presents a novel hybrid algorithm combining Firefly Algorithm (FA) and Nelder Mead (NM) simplex method for solving power system Optimal Reactive Power Dispatch (ORPD) problems. The ORPD is a very important aspect of power system operation and is a highly nonlinear, non-convex optimization problem, consisting of both continuous and discrete control variables. Like many other general purpose optimization methods, the original FA often traps into local optima and in order to overcome the shortcoming, in this paper, an efficient local search method called NM simplex subroutine is introduced in the internal architecture of the original FA algorithm. The proposed Hybrid Firefly Algorithm (HFA) avoids premature convergence of original FA by exploration with FA and exploitation with NM simplex. The proposed method is applied to determine optimal settings of generator voltages, tap positions of tap changing transformers and VAR output of shunt capacitors to optimize two different objective functions; such as minimization of real power loss and voltage deviations. The program is developed in Matlab and the proposed hybrid algorithm is examined on two standard IEEE test systems for solving the ORPD problems. For validation purpose, the results obtained with the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version of FA and other existing methods. It is revealed that the proposed hybrid method is able to provide better solutions.     

Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms

Management of reactive power resources is essential for secure and stable operation of power systems in the standpoint of voltage stability. In power systems, the purpose of optimal reactive power dispatch (ORPD) problem is to identify optimal values of control variables to minimize the objective function considering the constraints. The most popular objective functions in ORPD problem are the total transmission line loss and total voltage deviation (TVD). This paper proposes a hybrid approach based on imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) to find the solution of optimal reactive power dispatch (ORPD) of power systems. The proposed hybrid method is implemented on standard IEEE 57-bus and IEEE 118-bus test systems. The obtained results show that the proposed hybrid approach is more effective and has higher capability in finding better solutions in comparison to ICA and PSO methods.     

Optimal Reactive Power Dispatch Considering Costs of Adjusting the Control Devices

The traditional model of optimal reactive power dispatch (ORPD) for power systems is based on the principle of income maximization, which aims at minimizing active power loss of the whole networks. However, such a model may bring on excessive operations of device-control devices in real-time application. To realize reactive power dispatch, power utilities should increase equipment investment and added manpower for operation and maintenance. On the other hand, the operations would augment the fault probability of power systems. Therefore, the costs of adjusting the control devices (CACDs) are investigated, and a novel mathematical model of ORPD is presented in this paper, whose objective function is to minimize the energy loss at the current time interval and the CACD. A simulation test is presented to demonstrate that the proposed model reflects the principle of profit maximization and describes the ORPD problem with time-varying loads appropriately since it can decrease active power loss and avoid excessive controls simultaneously.