Multi-objective evolutionary algorithm for SSSC-based controller design

In this paper, an evolutionary multi-objective optimization approach is employed to design a static synchronous series compensator (SSSC)-based controller. The design objective is to improve the transient performance of a power system subjected to a severe disturbance by damping the multi-modal oscillations namely; local mode, inter-area mode and inter-plant mode. A genetic algorithm (GA)-based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented and compared with a PI controller under various disturbances namely; three-phase fault, line outage, loss of load and unbalanced faults to show the effectiveness and robustness of the proposed approach.     

Multi-objective harmony search algorithm for optimal power flow problem

This paper proposes a multi-objective harmony search (MOHS) algorithm for optimal power flow (OPF) problem. OPF problem is formulated as a non-linear constrained multi-objective optimization problem where different objectives and various constraints have been considered into the formulation. Fast elitist non-dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Finally, a fuzzy based mechanism has been used to select a compromise solution from the Pareto set. The proposed MOHS algorithm has been tested on IEEE 30 bus system with different objectives. Simulation results are also compared with fast non-dominated sorting genetic algorithm (NSGA-II) method. It is clear from the comparison that the proposed method is able to generate true and well distributed Pareto optimal solutions for OPF problem.     

Frequency domain design of fractional order PID controller for AVR system using chaotic multi-objective optimization

A fractional order (FO) PID or FOPID controller is designed for an Automatic Voltage Regulator (AVR) system with the consideration of contradictory performance objectives. An improved evolutionary Non-dominated Sorting Genetic Algorithm (NSGA-II), augmented with a chaotic Henon map is used for the multi-objective optimization based design procedure. The Henon map as the random number generator outperforms the original NSGA-II algorithm and its Logistic map assisted version for obtaining a better design trade-off with an FOPID controller. The Pareto fronts showing the trade-offs between the different design objectives have also been shown for both the FOPID controller and the conventional PID controller to enunciate the relative merits and demerits of each. The design is done in frequency domain and hence stability and robustness of the design is automatically guaranteed unlike the other time domain optimization based controller design methods.     

基于改进非劣分层遗传算法及基点和熵多属性决策的互联电网交易方法

利用改进非劣分层遗传算法（non—dominated sorting genetic algorithmⅡ,NSGA．Ⅱ）对互联电网多目标交易优化模型进行求解,得到多样化的帕雷托前沿,为决策提供丰富的信息,进一步与基于基点和熵的多属性决策方法结合,筛选出最优解。算例分析结果表明,该方法得出的解比用基于沙普利值的合作博弈...     

Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization

In this paper, a new method for optimal locating multi-type FACTS devices in order to optimize multi-objective voltage stability problem is presented. The proposed methodology is based on a new variant of particle swarm optimization (PSO) specialized in multi-objective optimization problem known as non-dominated sorting particle swarm optimization (NSPSO). The crowding distance technique is used to maintain the Pareto front size at the chosen limit, without destroying its characteristics. To aid the decision maker choosing the best compromise solution from the Pareto front, the fuzzy-based mechanism is employed for this task. NSPSO is used to find the optimal location and setting of two types of FACTS namely: Thyristor controlled series compensator (TCSC) and static var compensator (SVC) that maximize static voltage stability margin (SVSM), reduce real power losses (RPL), and load voltage deviation (LVD). The optimization is carried out on two and three objective functions for various FACTS combinations considering. For ensure the robustness of the proposed method and gives a practical sense of our study, N − 1 contingency analysis and the stress of power system is considered in the optimization process. The thermal limits of lines and voltage limits of load buses are considered as the security constraints. The proposed method is validated on IEEE 30-bus and realistic Algerian 114-bus power system. The simulation results are compared with those obtained by particle swarm optimization (PSO) and non-dominated sorting genetic algorithms (NSGA-II). The comparisons show the effectiveness of the proposed NSPSO to solve the multi-objective optimization problem and capture Pareto optimal solutions with satisfactory diversity characteristics.     

Environmental/economic power dispatch problem using multi-objective differential evolution algorithm

This paper presents a multi-objective differential evolution (MODE) algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost, emission and system loss. The proposed MODE approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach have been carried out on the IEEE 30- and 118-bus test system. The results demonstrate the capability of the proposed MODE approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective EED problem. The comparison with reported results of other MOEAs reveals the superiority of the proposed MODE approach and confirms its potential for solving other power systems multi-objective optimization problems.     

考虑降损和平抑峰谷的配电网储能电池Pareto优化模型

为发挥储能电池在配电网运行中降损及平抑峰谷的作用,建立了一种考虑降损和平抑峰谷的配电网储能电池Pareto多目标优化模型。该模型以配电系统中有功损耗最小和1天中各时段负荷方差最小为目标函数,以储能电池的充放电功率为控制变量,以罚函数的形式处理电池容量约束和静态安全约束。依据日负荷曲线获取储能电池最佳充放电时段,结合前推回代潮流计算方法和带精英策略的快速非支配排序遗传算法（non-dominated sorting genetic algorithm II,NSGA-II）对多目标模型进行求解。基于最大满意度,在Pareto解集中分别分析了网损最小、削峰填谷效果最优和网损与削峰填谷折中最优3种优化方案,以获取不同的储能电池运行优化方案。最后,以IEEE33配电网系统为例,验证了所提方法的实用性和有效性,并分析了不同决策策略下运行方案的优劣,为配电网经济运行提供决策参考。     

考虑换流站损耗特性的交直流系统多目标无功优化控制

交直流互联系统中各个直流输电通道在输送大量电能的同时,也会产生很大的电能损耗。通过协调优化多回超/特高压直流输电系统的电压无功控制措施,能够有效地降低整个交直流系统运行的损耗电量。提出一种考虑换流站详细损耗特性的交直流系统多目标无功优化控制方法,以包括换流站内部各主要设备损耗的整个交直流输电系统总网损和所有关键节点电压偏差平方和的最小化为目标,建立交直流互联系统的多目标无功优化控制模型。提出一种自适应加权和算法,结合GAMS/CONOPT解法器求解得到多目标优化问题的均匀分布的帕累托最优解集。并根据各个最优解的模糊隶属度和熵权信息从帕累托前沿曲线中确定出折中最优解,作为多目标无功优化控制方案。通过对某个6056节点交直流互联系统的计算表明,所提出的算法求得的帕累托最优解集分布均匀,且所获得的多目标无功优化控制方案能够有效降低换流站运行损耗,提高关键节点的电压质量。     

含大规模电动汽车接入的主动配电网多目标优化调度方法

针对大规模电动汽车接入配电网无序充电带来的负荷峰值增加等问题,提出一种含大规模电动汽车接入的主动配电网多目标优化调度方法。首先基于蒙特卡洛抽样方法分析了大规模电动汽车的充电负荷需求;然后,以含大规模电动汽车接入的主动配电网运行成本最小化和负荷曲线方差最小化为优化目标,综合考虑电动汽车的充电需求和配电网的运行约束,构建含规模化电动汽车接入的主动配电网多目标优化调度模型,采用带精英策略的非支配排序遗传算法(NSGA-II)对多目标优化模型进行求解,针对多目标优化得到的帕累托(Pareto)最优解集规模大,蕴含信息丰富,导致运行人员难以决策的问题,提出一种基于模糊聚类的方法对多目标Pareto最优解集进行筛选。通过改进的IEEE 34节点算例的多场景对比分析,结果表明:所提出的模型和方法可在保证系统经济运行的同时,有效利用电动汽车的优化充电降低系统负荷峰谷差。     

水风储微电网电源容量的优化配置

由于配电系统线路功率和节点电压的约束,在微电网规划中需要弃风弃水来减少设备容量。文中提出一种高效能的友好并网型水风储微电网优化配置方法。通过量化风电资源和水电资源的最大可发电量,结合所提的微电网运行控制策略及运行约束,建立以微电网投资及运营成本、年弃风弃水电量和电源年发电量为目标函数的多目标优化模型;通过基于精英策略的非支配性排序遗传算法(NSGA-II)求出模型的最优解集,并采用逼近理想解排序方法(TOPSIS)选择满意的配置方案。通过韶关地区某实际配电系统进行多场景仿真验证,结果表明该模型提高了可再生能源利用率,保证了配电系统的电能质量,验证了该配置方法的实用性和经济性。     

基于适应度空间距离评估选取的多目标粒子群算法在电网无功优化中的应用

提出了一种基于适应度空间距离评估选取最优解的多目标粒子群算法。该方法避免了目前多目标优化求解方法中权重选择的难题，保证了寻优方向的多向性，可以获得多目标优化问题的Pareto解集。将该算法应用于网损最小、静态电压稳定裕度最大为目标的多目标无功优化问题，算例表明在有效性和最优性等方面均有良好表现。     

基于多目标遗传算法和多属性决策的水轮机PID控制器参数整定

提出了一种基于多目标遗传算法和多属性决策的PID参数设计方法,综合考虑系统超调量、稳定时间和ITAE指标,采用多目标遗传算法求出Pareto最优解.由这些Pareto最优解构成决策矩阵,使用客观赋权的信息熵法对最优解的属性进行权值计算,然后采用逼近理想解的排序方法进行多属性决策研究,对Pareto最优解给出排序.计算了一个水轮机控制的数值算例,结果表明所设计的PID性能优异,适合工程实际应用.     

火电厂厂级负荷分配的多目标优化和决策研究

火电厂的负荷优化分配系统通常是以机组煤耗特性为基础的,其经济分配对应于满足稳态工况下全厂发电成本最低的要求。对于自动发电控制方式下的厂级负荷运行分配还要满足调整时间的要求,以尽可能快的速度满足目标负荷的调整。考虑机组运行的经济性和快速性,将基于进化算法的多目标优化技术与多属性决策方法联合运用,针对火电厂厂级负荷优化分配的问题进行研究。对于多目标优化问题,采用改进的非支配解排序的多目标遗传算法,求出Pareto最优解,由Pareto最优解构成决策矩阵,使用客观赋权的信息熵方法对最优解的属性进行权值计算,然后用逼近理想解的排序方法进行多属性决策研究,对Pareto最优解给出排序。文中给出了10台机组负荷分配的优化设计算例。     

多区域并行协同的多目标分布式帕累托最优潮流算法

在开放电力市场的环境下,各区域电网合作与利益博弈共存,区域电网之间的信息保密问题显得愈发重要。现有的帕累托最优潮流求解方法均属于集中式算法,在优化时需要获知全网的信息,无法满足高私密性以及高可靠性的要求。在该背景下,寻求一种去中心化的分布式优化方法以保障系统内各区域电网的信息安全显得尤为重要。基于此,文中提出了一种多区域并行协同的多目标分布式帕累托最优潮流求解算法。该算法以法线边界交叉法为基础,将整个系统的多目标潮流优化问题分解为与多个子区域对应的子优化问题。每个子区域采用独立的优化器完成子问题的优化,区域之间仅交换联络线上的边界变量以及虚拟目标因子进行全局调整,不断逼近原问题的帕累托最优解集。IEEE 118节点算例仿真结果表明:所提算法可在有效实现多目标帕累托最优潮流分布式并行求解的同时,还可提高求解精度和减小计算内存,从而适用于在开放电力市场背景下各区域电网合作与利益博弈共存的运营模式。     

多目标电能质量监测器的优化配置

针对电能质量监测器的优化配置问题,建立了以监测程度和监测器个数为指标的多目标优化配置模型。采用带精英策略的快速非支配排序遗传算法(non-domtnated soring genetic algorithm,NSGA-Ⅱ),获得此多目标优化问题的Pareto最优解集。该方法能保证种群的多样性,避免传统加权求解时权值的选择和解的偏好性。最后,对Pareto最优解集的各个目标函数进行归一化处理,将最大值对应的方案作为合适的最优解。通过对2个算例进行仿真,得到了合理的电能质量监测器的配置方案,验证了该方法的可行性和有效性。     

Evolutionary computation based multi-objective pole shape optimization of switched reluctance machine

This paper presents the application of elitist Non-dominated Sorting Genetic Algorithm version II (NSGA-II) to determine optimum pole shape design for performance enhancement of Switched Reluctance Machine (SRM). In SRM, torque output and torque ripple are sensitive to stator and rotor pole arcs and their selection is a vital part of SRM design process. The problem of determining optimal pole arc is formulated as a multi-objective optimization problem with the objective of maximizing average torque, minimizing torque ripple and copper loss. In order to account for the geometry as well as for the nonlinearity of material utilized, the Finite Element Method (FEM) is used to determine the performance of the machine. The proposed optimization technique is applied to determine optimal pole shape of an 8/6, four-phase, 5 HP, 1500 rpm SRM. The results show the effectiveness of the proposed approach and confirm the application of NSGA-II as a promising tool for solving SRM design problems. The results obtained by NSGA-II are compared and validated with classical multi-objective approach based on weighted sum method using Differential Evolution (DE) algorithm.     

基于无重访NSGA-II算法的配电网多目标重构

将基于空间二叉分割理论的无重访机制与基于多目标Pareto最优化思想的第二代非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm-II, NSGA-II)相结合,构建了无重访NSGA-II算法,并应用于求解同时考虑网损降低和供电可靠性提高的多目标配电网络重构问题。所构建的无重访NSGA-II算法实现了严格意义上的不重复搜索,避免了重复方案的潮流及可靠性计算,节约了计算资源。IEEE16、IEEE33测试系统的计算结果表明能够在较少的迭代次数下得到每个目标方向上的最优解以及包含若干非支配解的Pareto最优前沿解集。根据网损与可靠性目标之间的关联关系及相应重构方案的拓扑结构分析表明在解空间的全局范围内网损与可靠性目标具有较明显的一致关联性,不论对于网损还是可靠性的优化,网络拓扑都应该接近广度优先树而规避深度优先树。     

多目标配电网故障定位的Pareto进化算法

提出一种用于配电网故障定位的多目标优化模型,采用带精英策略的快速非支配排序遗传算法(NSGA-II)进行求解。传统多目标优化问题通过加权方式转换为单目标问题,对权值比较敏感,且每次只能得到一种权值下的最优解。NSGA-II则避免了传统加权求解时权值的选择和解的偏好性。该算法采用快速非支配排序机制,计算复杂性低;同时考虑个体拥挤距离,从而保证种群的多样性;最后,提出适用于故障定位的最优解集处理方法,便于从多目标最优解集中筛选出唯一符合故障情况的解。算例测试分别模拟单点、多点故障,以及信息完备和部分信息畸变的情况,测试结果表明,所提方法均能准确地定位故障区段。     

面向配电网风电消纳的冰蓄冷空调系统多目标优化策略研究

未来配电系统可再生能源渗透率日益提高。针对配网中风电消纳问题,考虑在系统中引入具有负荷转移特性的冰蓄冷空调进行需求响应控制,将夜间产生的风能转化为冷能并以冰形式存储,在日间用电峰段融冰释冷削减空调的用电峰值。具体来说,考虑系统内供电公司和风电生产商的综合运行成本以及空调用户的用电成本,建立基于能源效率和经济效益的多目标优化模型。以供电公司、风电生产商和空调用户的利益最大化为目标,采用非支配排序遗传算法-II(NSGA-II)对Pareto非支配解集进行求解,生成的Pareto解集,通过模糊隶属度法过滤选取最优解。最终根据最优解内参数变量的数值,对空调制冷机功率和蓄冰设备的融冰量进行调控。提出的算例验证了所提方法的有效性和可行性。     

促进清洁能源消纳的多网联合优化与决策模型

针对我国西南地区,弃电量大、清洁能源消纳受阻的问题,考虑清洁能源外送消纳的方式,本文以互联电网各自运行成本最小为目标,建立了多网联合多目标优化模型。基于小生境多目标粒子群算法对模型进行求解,应用基于熵权法的逼近理想解排序法进行最佳均衡解决策。以云南送广东、广西的“一个送端+两个受端”系统为例进行实例分析,结果表明：小生境多目标粒子群算法求解得到了均匀分布的近似Pareto前沿;基于熵权法的逼近理想解排序法决策得到的最佳均衡解显示,送端电网清洁能源消纳率达到100%,外送通道日利用小时数最大,表明多网联合优化计算能够大大提高清洁能源的消纳能力和外送通道的利用程度。