An Improved Catastrophic Genetic Algorithm and Its Application in Reactive Power Optimization

This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed. Then, a new probability algorithm of crossover depending on the number of generations, and a new probability algorithm of mutation depending on the fitness value are designed to solving the main conflict of the convergent speed with the global astringency. In these ways, the ICGA can prevent premature convergence and instability of genetic-catastrophic algorithms (GCA). Finally, the ICGA is applied for power system reactive power optimization and evaluated on the IEEE 14-bus power system, and the application results show that the proposed method is suitable for reactive power optimization in power system.

A Genetic Algorithm for Reactive Power Optimizationof Power System

Tis paper presents a genetic algorithm for reactive power optimization of power system in a more effective and rapid manner, and verifies the results with an IEEE 30-bus test system.

Power System Reactive Power Optimization Based on Fuzzy Formulation and Interior Point Filter Algorithm

Considering the soft constraint characteristics of voltage constraints, the Interior-Point Filter Algorithm is applied to solve the formulation of fuzzy model for the power system reactive power optimization with a large number of equality and inequality constraints. Based on the primal-dual interior-point algorithm, the algorithm maintains an updating “filter” at each iteration in order to decide whether to admit correction of iteration point which can avoid effectively oscillation due to the conflict between the decrease of objective function and the satisfaction of constraints and ensure the global convergence. Moreover, the “filter” improves computational efficiency because it filters the unnecessary iteration points. The calculation results of a practical power system indicate that the algorithm can effectively deal with the large number of inequality constraints of the fuzzy model of reactive power optimization and satisfy the requirement of online calculation which realizes to decrease the network loss and maintain specified margins of voltage.

Optimal Reactive Power Compensation of Distribution Network to Prevent Reactive Power Reverse

The capacitive reactive power reversal in the urban distribution grid is increasingly prominent at the period of light load in the last years.In severe cases,it will endanger the security and stability of power grid.This paper presents an optimal reactive power compensation method of distribution network to prevent reactive power reverse.Firstly,an integrated reactive power planning(RPP)model with power factor constraints is established.Capacitors and reactors are considered to be installed in the distribution system at the same time.The objective function is the cost minimization of compensation and real power loss with transformers and lines during the planning period.Nodal power factor limits and reactor capacity constraints are new constraints.Then,power factor sensitivity with respect to reactive power is derived.An improved genetic algorithm by power factor sensitivity is used to solve the model.The optimal locations and sizes of reactors and capacitors can avoid reactive power reversal and power factor exceeding the limit.Finally,the effectiveness of the model and algorithm is proven by a typical high-voltage distribution network.

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.

Enhancement of Available Transfer Capability with Facts Device in the Competitive Power Market

In order to facilitate the electricity market operation and trade in the restructured environment, ample transmission capability should be provided to satisfy the demand of increasing power transactions. The conflict of this requirement and the restrictions on the transmission expansion in the restructured electrical market has motivated the development of methodologies to enhance the Available Transfer Capability (ATC) of the existing transmission grids. The insertion of FACTS devices in electrical systems seems to be a promising strategy to enhance ATC. In this paper, the viability and technical merits of boosting ATC using Thyristor Controlled Series Compensator (TCSC) is being analyzed. The work has been carried out on IEEE 30 bus and IEEE 118 bus systems. Bilateral and multilateral transactions are considered. Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) are employed to obtain the optimal settings of TCSC.

含新能源和电动汽车充电站并网的主动配电网无功优化模型

针对新能源和电动汽车充电站规模化并网引起的无功优化问题,首先建立以主动配电网运行成本和电压偏差最小,系统稳定性最优的多目标无功优化模型。其次,基于PV曲线上、下半支解的接近程度,提出一种新的静态电压稳定性指标,以改善系统电压稳定性。最后,采用具有交叉反馈机制的混合优化算法对模型进行求解,并提出一种基于Tent混沌映射和自适应变异算子的改进飞蛾扑火算法提高求解效率。仿真结果表明,所提模型在降低系统运行成本,减少电压波动的同时,还能提高系统稳定性,且该混合优化算法能够快速、准确地求解混合整数的非凸、非线性优化模型。

基于多目标沙猫群算法的含风光储配电网无功优化

针对现有智能优化算法在求解配电网无功优化时存在的收敛速度慢、易陷入局部最优解等问题,提出一种基于多目标沙猫群算法(MOSCSO)的含风光储配电网无功优化方法.MOSCSO融合了多目标算法中外部储存集的更新和选择机制,具有较好的全局寻优能力,而沙猫群算法(SCSO)特有的搜索和攻击的种群更新方式保证了其具有较快收敛速度和较好寻优能力.建立储能设施(ESS)作为控制变量的IEEE 33节点系统数学模型,应用MOSCSO进行仿真验证.结果表明,本文所提方法在平衡风光发电系统的同时能够降低网损和提高电网稳定性,通过与传统算法比较,验证了MOSCSO在无功优化模型上的有效性和稳定性.

基于双向安德森加速分解协调算法的输配协同无功优化

新型电力系统中新能源渗透率不断增加,输电网和配电网协同运行环境复杂多变,对输配协同优化算法的收敛性提出了更高的挑战。鉴于此,应用广义主从分裂法将输配协同无功优化问题分解为输电网和多个配电网的并行优化问题;为改善广义主从分裂法的计算效率,提出了基于功率、电压等多元信息交互及历史信息修正的双向安德森加速策略;在此基础上,提出了融合原对偶内点法、启发式算法和辅助函数的输配协同无功优化方法,实现了输电网和配电网多种类灵活性资源的协调配合和优化控制。算例仿真结果表明,所提方法能有效改善主从分裂法边界的收敛性能,在大规模分布式能源接入的输配协同无功优化场景中具有更好的应用价值。

考虑碳交易和无功补偿的分布式电源优化配置

“双碳”背景下,在规划分布式电源时引入碳交易和无功补偿机制是实现电力系统低碳化、稳定化的重要手段。为此,提出一种考虑碳交易和无功补偿的分布式电源优化配置方法。首先,引入碳交易机制,建立碳交易成本计算模型。其次,建立考虑碳交易和无功补偿的分布式电源双层优化配置模型,上层以年综合成本为优化目标,决策变量为光伏和燃气轮机接入配电网中的位置及容量;下层以系统网损、电压偏移量、节点电压稳定裕度为优化目标,决策变量为无功补偿电容器在配电网中的位置和容量。最后,通过改进的自适应遗传算法对优化配置模型进行求解。结合IEEE33节点构造的算例进行仿真分析,结果表明,该模型可在保证系统经济性和环保性的基础上有效降低网损,并提高系统电压的稳定性。

孤岛电网大功率电机启动案例分析与优化策略

大功率电机连接到孤岛电网,一般会采用软起动、变频器等,以提高电机的起动性能和供电的稳定性,同时设置无功补偿装置,以提高供电系统的功率因数和降低线路损耗。但如果软启动或变频器等选型不当,或者无功补偿配置不合理,可能会出现电压波动、功率振荡、供电质量下降,甚至引发安全等问题。本文以某供水泵站的供电系统为研究对象,首先介绍了泵站的供电系统,接着对泵站试车过程中出现的电气故障进行分析,然后对孤岛电网中大功率电机起动方案的优化及无功补偿装置的设置进行探讨,接着根据项目实际情况,提出将固态软起动器与实时无功补偿系统进行有机结合,采用合适容量的实时无功补偿系统,可降低大电机的起动电流和电网压降,较好地解决孤岛电站配电系统中大功率电动机启动期间电能质量差的问题,同时也对现有泵站供电系统提出几个备选方案供参考,最后给出几点建议。

基于蛇优化算法的电力系统无功优化

无功优化在电力系统中具有重要的意义,可以提高系统的功率因数,减少有功损耗并改善电压稳定性。蛇优化算法具有全局搜索能力和快速收敛的特点,故将该算法作为一种新的优化方法运用到无功优化中,建立以机端电压、变压器变比和无功补偿量为系统控制变量,以网络损耗最小为目标的无功优化模型。通过采用57节点系统,将蛇优化算法与粒子群算法、改进粒子群算法分别进行比较验证。实验结果表明,蛇优化算法能够有效地优化电力系统的无功功率,提高系统的性能和稳定性。

复合电压薄弱性指标及以其为导向的无功优化策略

提出了一种新的电力系统薄弱环节确定指标,并提出以系统薄弱环节分析为导向的多目标函数无功优化方法。首先,融合薄弱性指标电压和功率两方面的特点和优势,定义了新的薄弱环节复合裕度判定指标,综合描述负荷正常工作点与电压崩溃点的距离;以该指标为判定标准识别出系统的电压薄弱环节点集,由此点集构成无功优化的待补偿节点集。然后,建立多目标无功优化模型,采用改进自适应遗传算法,在算法选择环节采用确定式选择原则替换传统轮盘赌法,进而完成多目标无功优化模型求解。最后,以IEEE 30节点系统和新英格兰IEEE 39节点系统为仿真试验案例,通过优化后多项系统运行指标与传统方法计算结果的对比,验证了以新的薄弱环节判定指标为无功优化策略的有效性和优越性,同时也证明了改进后的自适应遗传算法在多目标无功优化计算中的高效性。

基于电气自动化的无功补偿装置在电源系统中的协调控制研究

电源系统中,无功功率的产生会导致电压波动、线损增加、电能质量下降,为有效提升电源系统的稳定性,减少人工劳动强度,增加电力运行的安全性,文章对基于电气自动化的无功补偿装置在电源系统中的协调控制进行研究,在深入了解无功补偿原理的基础上提出一种固定补偿和动态补偿相结合的智能化无功补偿方式,实现电源系统的协调和控制。该种方式有效解决了有功功率波动和无功补偿的问题,在降低有功网损的同时降低了电源运行成本,实现了经济效益和技术效益的双重提升。

基于改进遗传算法的10 kV线路无功补偿装置优化配置策略研究

在10 kV线路中,无功补偿设备配置不合理容易导致无功流通路径过长,从而增加系统网损,降低供电电压质量。为此提出一种基于改进遗传算法的无功补偿设备最优配置方法。该方法将年线损率最小作为目标函数,将线路电压合格作为约束条件,采用实编码遗传算法求解无功补偿设备的最优安装位置和容量。依照北京某地区配电网构架和实际运行数据搭建仿真模型。仿真结果验证该方法能有效降低系统的投资成本和运行损耗,提高线路供电质量。

基于改进布谷鸟算法的电网静态选址定容策略

针对传统的算法在求解多目标函数上存在局限性的问题,利用改进布谷鸟搜索算法实现综合电压约束条件的多目标最优的新型无功优化策略。该策略首先确定对网损降低最大且补偿成本最小的节点,然后构建以成本投入最小,补偿后节约的有功网损最大,综合系统电压约束条件为目标构建新型目标函数。利用改进的布谷鸟算法,通过引入混沌映射初始化种群,并对算法的自适应性做了改进来平衡全局搜索能力和局部搜索能力,实现该目标函数的最优解求解,即最优无功补偿方案。在IEEE 34节点系统进行算例仿真,验证了所提方法的有效性和可行性,仿真结果表明提出的改进布谷鸟算法在搜索精度和搜索速度以及收敛性方面均优于其他算法,同时得到的电网总网损和成本投入都是最优,因此提出的基于改进型布谷鸟算法的无功优化策略具有重要意义。

基于鲸鱼算法的配电网无功补偿经济性优化配置方法

无功优化需对配电网的无功补偿选址与容量设定进行合理配置。提出了基于鲸鱼算法的配电网无功补偿经济性优化配置方法,根据功率损失系数确定无功补偿的优选节点,并利用鲸鱼优化算法解出符合经济性与配电网运行规则的无功补偿设备最优接入节点位置与容量。以IEEE 33节点配电网系统为模型进行的仿真模拟表明,该方法能缩短计算时间、提高系统无功补偿效率。

考虑时序场景的分布式电源规划及无功优化策略

针对分布式电源发电和负荷需求的时序性和不确定性问题,通过风光概率出力模型生成初始场景并对场景进行缩减,建立考虑时序性的典型日风光电源—负荷场景。以综合配电网经济性和环境效益的综合性能指标最小为目标优化模型,采用改进遗传算法对分布式电源接入位置与容量进行规划。根据无功二次精确矩选择无功补偿设备的最佳补偿位置以降低网损,并以提高电压水平为目标确定无功补偿容量。对含分布式电源的IEEE33节点配电网系统进行仿真分析,结果表明优化后的策略能提升配电网的电压质量,降低网络损耗和提高经济效益,验证了该方法的有效性与合理性。

基于改进遗传算法的配电网多目标无功优化设计

以某地区的配电网为研究对象,对该区域配电网的供电方式及负荷性质进行了分析,针对引起该区域电压不合格及无功不足问题的原因,建立了考虑成本效益回报和线损率的多目标无功优化模型。将模拟退火算法(SA)中的Boltzmann策略,加入到传统遗传算法(GA)中,构成改进遗传算法(SA-GA),以提升遗传算法的全局搜索能力。通过实际配电网仿真算例,验证了本模型及算法对解决该区域无功及电压问题的有效性。

基于鲸鱼群算法的交直流混联系统无功电压控制方法研究

高效安全地实现交直流混联系统降损与电压控制受到广泛关注,以网络损耗或电压偏移量为单一目标的传统的无功优化模型不能精确反映电网的安全性和经济性。该文针对交直流混联系统,建立了兼顾网络损耗和电压偏差的多目标无功电压控制优化模型,提出一种基于传统的鲸鱼算法(whale optimization algorithm,WOA)与粒子群(particle swarm optimization,PSO)相结合的鲸鱼群(WOA-PSO)模型求解算法。通过引入基于鲸鱼算法的包围策略,加速粒子群的迭代搜索过程,从而提高算法的全局优化能力;通过算例验证,并与基本粒子群算法、鲸鱼算法对比分析,说明所提方法具有良好的收敛性和寻优能力。