地区电网无功电压分布式二级控制系统在金华电网中的应用

调度模式分为调度与监控两个层次后,原有的无功电压自动控制系统已不适应.文章提出了一种适合该种模式的二级控制系统,并使用分布式技术,将传统的集中控制系统扩展为分布式二级控制.电网无功电压分布式二级控制系统通过调度自动化SCADA系统采集全网各节点遥测、遥信等实时数据进行在线分析和计算,在确保电网与设备安全运行的前提下,以各监控点电压合格、关口功率因数等为约束条件,从全网角度进行电压无功优化控制,实现全网无功分层分区最佳的动态平衡,以有效地提高全网各节点的电压合格率,减少网损.     

电压无功控制在地调主站端实现的方式

提出了在地区电网调度自动化系统SCADA功能普遍推广实用化的基础上,可以参照3级控制模式,在地调主站实现电压无功控制的模式。主要思路是通过自动选择一级电压控制、二级电压控制和三级电压控制,在局部、区域或者全局进行电压无功优化,达到在保证系统电压安全的基础上,为用户提供质量合格的电压;然后在保证用户电压合格的基础上,实现电网的经济运行。     

配电网无功优化控制研究

设计了无功优化控制系统的软件体系结构，建立了动态无功优化数学模型。提出的高压配电网无功优化控制基于现有地调自动化系统，在母线负荷预测的基础上，利用遗传算法求解整个电网的无功优化问题，得到的优化结果为各个变电站VQC的合理限值。该方法将全局优化与VQC分散控制的优点结合起来，克服了各变电站无功、电压就地最优控制的弊端，节电效益显著。在某地区电网的应用中验证了该系统和方法的有效性，经过优化计算，在满足电压约束和控制设备动作次数限制的条件下，降低了电能损耗，有功损耗比优化前下降约3％。     

浅谈电网调度与自动化

随着电力系统技术的发展,电力企业自动化水平不断提高。电网调度自动化系统使电力生产、输送、分配和消费一体化的监视、控制系统,为提高供电企业的生产率和降低变电所成本,迫切需要全面应用电力调度自动化系统。本文就电力调度自动化系统的发展和应用做出了一些有益的探索。     

基于多Agent优化协调的电力系统二级电压控制

在多Agent的二级电压控制系统基本原理的基础上,将多Agent系统的相关技术引入无功电压快速协调控制,探讨了在电力系统紧急情况下,基于多Agent的二级电压控制系统的协调和协作方法以及优化控制策略.通过引入虚拟控制Agent分组的概念,将多Agent系统中常用的合同网协议应用于二级电压控制的多Agent协调,改善了紧急状态下系统的电压调控能力.以装有多个静态无功补偿器的新英格兰系统为例进行数字仿真研究,验证了控制策略的有效性和灵活性.     

电网自动电压控制系统在地县级调度的应用和实现

电网自动电压控制(AVC)系统可实现电网无功电压优化运行闭环控制的系统,保证电网安全、经济、优质运行,是电网安全稳定预防性控制措施的重要技术手段.地县级调度AVC子系统是省调AVC系统的重要组成部分,可实现地区电网内的自动电压控制,并与省调主站之间实现分级协调控制.     

电网调度自动化系统的设备结构性能分析

随着通信技术和网络技术的快速发展,用来支持调度中心与用户之间数据交换的自动化系统的应用愈加普遍。毋庸置疑,电网调度自动化系统在实现电网调度的自动化功能和安全性运营方面发挥着极其重要的作用。笔者在对电网调度自动化系统进行概述的基础上,从设备结构性能方面进行深入分析,以促进电网调度自动化系统的不断优化发展。     

基于多Agent的微电网电压无功控制系统

将多Agent技术引入微电网的电压无功控制中,并给出了基于多Agent的三级电压无功控制系统结构,重点分析了微网级(二级)电压无功控制。受电气距离思想的启发,提出了以操作优劣距离最短来确定最优操作动作的控制策略。以IEEE—39系统为基础的仿真说明了多Agent电压无功控制对微电网系统的电压无功能起到良好的调节控制作用。     

基于改进的遗传算法实现电网电压无功实时控制

在利用遗传算法求解电力系统电压与无功的优化控制中，文章在遗传算法的基础上，针对计算过慢的缺点，提出了多种改进算法，提高解算速度，以实现实时的电压无功控制，通过了IEEE14节点测试系统的验证，并实际应用于贵阳地区电网，为调度运行部门提供实时的科学调控依据。     

基于SCADA系统的变电站电压无功综合控制

根据电压无功综合控制九区图，通过考虑不同负荷时段对无功功率的要求和负荷突变情况下的控制要求，运用模糊控制技术，设计了变电站电压无功模糊控制系统，并基于SCADA系统设计了后台电压无功综合控制软件模块。     

多目标差分进化算法的电力系统无功优化

 在传统电力系统无功优化( Reactive Power Optimization,RPO) 模型中引入电压水平 指标,建立了以网损最小,电压水平最好为目标的多目标差分进化算法( Differential Evolution Algorithm) 的模型。针对基本差分进化算法易陷入局部最优解、收敛速度慢的缺点,提出一种 具有自适应参数策略的改进差分进化算法并首次用于多目标电力系统无功优化问题。通过在 算法进化过程中调整变异因子F 和交叉因子CR,在初期增加种群的多样性、扩大全局搜索区 域; 从而可以避免算法陷入局部最优解; 同时在后期也加快了收敛速度。将该算法用于电力系 统无功优化并仿真计算了IEEE-14 节点标准测试系统,结果验证模型和算法的有效性。     

遗传算法在电力系统无功优化中的应用及研究

该文阐述了将遗传算法应用于电力系统无功优化模块中,目的是可以有效地降低电力系统网络有功损耗．提高系统的电压合格率．从而降低电力网络运行费用,提高供电质量。     

基于随机自适应粒子群算法的电力系统无功优化

无功优化是保证电力系统安全经济运行的有效手段,是提高电力系统电压质量的重要措施之一。本文首先介绍无功优化的一般数学模型,然后重点分析粒子群优化算法的组成结构与工作原理,进而提出一种改进的粒子群优化算法。该算法采用随机自适应策略,能够对当前所产生的局部最优值进行变异,再重回粒子群算法中搜寻全局最优值,从而可以有效改善传统粒子群算法求解电力系统无功优化问题时存在的收敛精度不高、容易陷入局部最优等不足,一定程度上提高了粒子群算法的寻优能力。最后,通过在IEEE 30节点上进行仿真实验比较,结果表明该算法是可行和有效的,达到了提高供电质量、降低线损的目的。     

Comprehensive learning particle swarm optimization for reactive power dispatch

Reactive power dispatch (RPD) is an optimization problem that reduces grid congestion by minimizing the active power losses for a fixed economic power dispatch. RPD reduces power system losses by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks and provides better system voltage control, resulting in an improved voltage profile, system security, power transfer capability and over all system operation. In this paper, RPD problem is solved using particle swarm optimization (PSO). To overcome the drawback of premature convergence in PSO, a learning strategy is introduced in PSO, and this approach called, comprehensive learning particle swarm optimization (CLPSO) is also applied to this problem and a comparison of results is made between these two. Three different test cases have been studied such as minimization of real power losses, improvement of voltage profile and enhancement of voltage stability through a standard IEEE 30-bus and 118-bus test systems and their results have been reported. The study results show that the approaches developed are feasible and efficient.     

Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach

Optimizing reactive power flow in electrical network is an important aspect of system study as the reactive power supports network voltage which needs to be maintained within desirable limits for system reliability. A network consisting of only conventional thermal generators has been extensively studied for optimal active and reactive power dispatch. However, increasing penetration of renewable sources into the grid necessitates power flow studies incorporating these sources. This paper presents a formulation and solution procedure for stochastic optimal reactive power dispatch (ORPD) problem with uncertainties in load demand, wind and solar power. Appropriate probability density functions (PDFs) are considered to model the stochastic load demand and the power generated from the renewable energy sources. Numerous scenarios are created running Monte-Carlo simulation and scenario reduction technique is implemented to deal with reduced number of scenarios. Real power loss and steady state voltage deviation of load buses in the network are set as the objectives of optimization. Success history based adaptive differential evolution (SHADE) is adopted as the basic search algorithm. SHADE has been successfully integrated with a constraint handling technique, called epsilon constraint (EC) handling, to handle constraints in ORPD problem. The effectiveness of a proper constraint handling technique is substantiated with case studies for deterministic ORPD on base configurations of IEEE 30-bus and 57-bus systems using SHADE-EC algorithm. The single-objective and multi-objective stochastic ORPD cases are also solved using the SHADE-EC algorithm. The results are discussed, compared and critically analyzed in this study.     

Differential evolution approach for optimal reactive power dispatch

Differential evolution based optimal reactive power dispatch for real power loss minimization in power system is presented in this paper. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts to be switched, for real power loss minimization in the transmission system. The problem is formulated as a mixed integer nonlinear optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling. The formulation also checks for the feasibility of the optimal control variable setting from a voltage security point of view by using a voltage collapse proximity indicator. The algorithm is tested on standard IEEE 14, IEEE 30, and IEEE 118-Bus test systems. To show the effectiveness of proposed method the results are compared with Particle Swarm Optimization and a conventional optimization technique – Sequential Quadratic Programming.     

Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique

In this paper, a newly surfaced nature-inspired optimization technique called moth-flame optimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature.     

Using the gray wolf optimizer for solving optimal reactive power dispatch problem

This paper presents the use of a new meta-heuristic technique namely gray wolf optimizer (GWO) which is inspired from gray wolves’ leadership and hunting behaviors to solve optimal reactive power dispatch (ORPD) problem. ORPD problem is a well-known nonlinear optimization problem in power system. GWO is utilized to find the best combination of control variables such as generator voltages, tap changing transformers’ ratios as well as the amount of reactive compensation devices so that the loss and voltage deviation minimizations can be achieved. In this paper, two case studies of IEEE 30-bus system and IEEE 118-bus system are used to show the effectiveness of GWO technique compared to other techniques available in literature. The results of this research show that GWO is able to achieve less power loss and voltage deviation than those determined by other techniques.     

Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers

This study presents a particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) for the solution of optimal reactive power dispatch (ORPD) problem. The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss and the total voltage deviations are to be minimized separately. In order to evaluate the performance of the proposed algorithm, it has been implemented on IEEE 30-, 57- and 118-bus test power systems and the optimal results obtained are compared with those of the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving the ORPD problem of power system.