一种新算法在经济负荷分配中的应用

为求解复杂的不连续、非凸、非线性电力系统的经济负荷分配问题,提出了一种单纯形法(NM)和粒子群算法(PSO)相结合的NM-PSO算法.该算法将单纯形算子嵌入到PSO算法中,把适应值最好的一部分粒子用单纯形法来更新,其余粒子用PSO算法寻优,从而提高PSO算法后期的寻优能力.NM-PSO充分利用PSO算法强大的全局搜索能力和NM快速确定性的局部搜索能力,提高了NM-PSO算法的寻优能力和收敛速度,该算法应用于经济负荷分配问题得到的优化结果好于其他方法.     

基于改进果蝇优化算法的分布式电源优化配置

研究分布式电源（distributed generation,DG）接入配电网的优化配置问题,基于模糊隶属度技术建立综合考虑投资效益、电压指标和网损的多目标优化配置模型,有效解决了因各子目标数量级不同而导致的过度优化问题。对一种新颖的仿生智能算法--果蝇优化算法（fruit fly optimization algorithm,FOA）进行改进,效仿细菌在觅食过程中的趋化思想,在算法寻优过程中引入吸引和排斥操作,有效提高了种群多样性,降低了算法陷入局部最优的可能。IEEE 33节点系统的仿真结果表明,与传统果蝇优化算法和粒子群优化算法（particle swarm optimization,PSO）相比,改进果蝇优化算法（improved fruit fly optimization algorithm,IFOA）在寻优速度和求解精度上都具有较大优势,能快速、有效地搜索到最优配置方案,从而验证了改进算法的有效性与合理性。     

基于改进粒子群算法的电动车参与负荷平抑策略

建立了电动车参与负荷平抑的数学模型,在考虑电动车充放电功率及可用容量等约束条件的前提下,应用粒子群优化算法（particle swarm optimization,PSO）对模型进行了求解。针对PSO处理高维问题过早局部收敛的缺陷,提出了基于子向量的改进型PSO算法,在保证算法搜索到空间中的每个区域的同时,将搜索空间分...     

基于大数据和双量子粒子群算法的多变量系统辨识

针对智能算法与历史大数据相结合进行多变量系统辨识过程中不能精确量化每个子系统数学模型的问题,提出了一种有效的数据并行优化计算的解决方案。在辨识过程中,为了解决量子粒子群算法(quantum particle swarm optimization ,QPSO)收敛速度和寻优精度方面的不足,提出了一种改进的 QPSO 算法--双量子粒子群算法(double quantum particle swarm optimization ,D-QPSO)。该算法对粒子种群编码和原有的进化搜索策略同时进行了量子化处理,经过测试函数实验,改进的算法在搜索能力上优于 PSO 和QPSO算法。最后利用现场运行历史数据,通过D-QPSO算法进行参数估计,将设计的解决方案应用于热力发电厂负荷控制系统的传递函数辨识中,得到的模型为控制器的设计与优化奠定了基础。     

基于自适应粒子群算法的直流输电PI控制器参数优化

针对传统粒子群算法(particle swarm optimization,PSO)的不足,提出了一种自适应粒子群优化(adaptive particle swarm optimization,APSO)算法,并应用于直流控制器比例–积分控制器(proportional integral,PI)的参数寻优。文中首先介绍了HVDC控制器的系统模型,然后推导出了贵广直流输电工程中的控制器传递函数,并利用APSO算法进行PI参数寻优。寻优过程采取时间乘绝对误差积分(integral of time multipled by the absolute value of error,ITAE)准则计算目标函数值,取对应ITAE目标函数最小的样本为最优PI参数,得到的PI参数可认为是全局最优解。结果表明了APSO算法在PI控制器参数优化中的全局寻优能力和有效性。     

粒子群优化算法中惯性权重综述

粒子群优化(particle swarm optimization,PSO)算法是基于鸟群觅食行为的一种新型的群体智能算法,而惯性权重是PSO算法中一个极其重要的参数,其值的选取直接关系粒子在寻优过程中的开发能力和探索能力。在介绍PSO算法的基本原理的基础上,分析惯性权重对粒子群优化算法在收敛性方面的影响,综述了现有文献对惯性权重的研究进展,并评述了各种惯性权重取值策略所取得的研究成果和存在的不足之处。     

一种新的混合优化算法求解配电网重构

传统粒子群算法(particle swarm optimization,PSO)容易早熟的问题,限制了其在配电网的应用和发展,故把二进制粒子群遗传算法(genetic binary particle swarm optimization,GBPSO)应用到配电网重构中.该算法利用遗传算法(genetic algorithm,GA)的变异能力改善粒子群算法的早熟情况.同时考虑到配电网的特殊性,采用了十进制编码规则,优化了编码方式.算例证明了该方法用于配电网重构的可行性和优越性.     

基于差分粒子群算法的变电站选址定容规划

针对标准粒子群算法(particle swarm optimization,PSO)易陷入局部最优,差分进化算法(differential evolution,DE)后期收敛速度慢的缺点,提出差分粒子群算法(differential particle swarm optimization,DEPSO)将二者进行混合优化,提高群体的收敛速度和全局寻优能力,并应用于配电网变电站规划。在变电站选址数学模型中结合Voronoi图来确定变电站供电范围和规划容量,继而校验变电站实际负载率,简化计算过程,提高搜索效率。通过某市城区远期规划实例验证得知该算法正确有效,可以满足城区配电网的规划要求。     

PSO-CSO算法在地区电网无功优化的应用

针对电力系统无功优化问题,建立以有功损耗最小为优化目标的数学模型,提出一种基于粒子群优化(particle swarm optimization,PSO)算法和纵横交叉优化(crisscross optimization,CSO)算法的混合智能算法(PSO-CSO)。该方法采用CSO算法横向交叉、纵向交叉的搜索方式,使算法具有很强的全局搜索能力;同时引入PSO算法中以个体最优值和全局最优值为引导的寻优机制,提高了算法的收敛速度。通过对IEEE57节点系统和地区电网模型进行仿真分析,并将优化结果与PSO和CSO等算法的优化结果进行比较,表明PSO-CSO算法在解决电力系统无功优化问题上具有更好的全局搜索能力和收敛能力。     

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。     

基于MATLAB的计及阀点效应的有功功率经济分配

为了在电厂机组间进行负荷经济分配,提高电力系统运行的经济性,针对汽轮机进汽阀突然开启时出现的拔丝现象即阀点效应,提出了采用改进的粒子群算法(HPSO)对火电机组负荷进行经济分配,并将遗传算法的交叉思想引入到PSO算法当中,使其避免局部最优。通过MATLAB仿真,算例结果表明改进的算法寻优质量更好,效率更高,可望应用于更广泛的优化问题。     

电力系统经济负荷分配的混沌粒子群优化算法

提出一种新的混沌粒子群优化(CPSO)算法,将其用于求解复杂的电力系统经济负荷分配(ELD)问题。该算法保持了粒子群优化(PSO)的简单结构,先利用PSO算法的全局收敛能力进行搜索,以获得近似解(即粒子经过的最佳位置),然后利用混沌优化的混沌运动特性在近似解的邻域内进行局部搜索,从而获得精确的全局最优解。多个算例的仿真结果表明,该算法能快速有效求取电力系统ELD问题更精确的最优解。     

Optimization of economic load dispatch of higher order general cost polynomials and its sensitivity using modified particle swarm optimization

This paper presents a modified particle swarm optimization (MPSO) for constrained economic load dispatch (ELD) problem. Real cost functions are more complex than conventional second order cost functions when multi-fuel operations, valve-point effects, accurate curve fitting, etc., are considering in deregulated changing market. The proposed modified particle swarm optimization (PSO) consists of problem dependent variable number of promising values (in velocity vector), unit vector and error–iteration dependent step length. It reliably and accurately tracks a continuously changing solution of the complex cost function and no extra concentration/effort is needed for the complex higher order cost polynomials in ELD. Constraint management is incorporated in the modified PSO. The modified PSO has balance between local and global searching abilities, and an appropriate fitness function helps to converge it quickly. To avoid the method to be frozen, stagnated/idle particles are reset. Sensitivity of the higher order cost polynomials is also analyzed visually to realize the importance of the higher order cost polynomials for the optimization of ELD. Finally, benchmark data sets and methods are used to show the effectiveness of the proposed method.     

Economic load dispatch with nonsmooth cost functions using evolutionary particle swarm optimization

This paper presents an evolutionary particle swarm optimization (EPSO) method for solving the nonconvex economic load dispatch (ELD) problem. In practice, the nonconvex and the discontinuous cost function should be considered when optimizing ELD problem with constraints such as valve point effects, prohibited operating zones, ramp‐rate limits, and transmission loss of the system. In view of these constraints, the ELD problem is difficult to solve by any mathematical method. In EPSO, the evolutionary programming concept (combination, tournament competition, sorting, and selection) is employed in the classical PSO method in order to find the best individual and best group position based on the survival particle. The effectiveness of the EPSO is tested on 3‐, 6‐, 15‐, and 38‐unit systems. The results obtained by EPSO are also compared with classical PSO and other results reported in the literature. It is concluded that the EPSO method can produce lower generation cost compared to the existing methods. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Quantum-Inspired Particle Swarm Optimization for Valve-Point Economic Load Dispatch

Economic load dispatch (ELD) is an important topic in the operation of power plants which can help to build up effective generating management plans. The ELD problem has nonsmooth cost function with equality and inequality constraints which make it difficult to be effectively solved. Different heuristic optimization methods have been proposed to solve this problem in previous study. In this paper, quantum-inspired particle swarm optimization (QPSO) is proposed, which has stronger search ability and quicker convergence speed, not only because of the introduction of quantum computing theory, but also due to two special implementations: self-adaptive probability selection and chaotic sequences mutation. The proposed approach is tested with five standard benchmark functions and three power system cases consisting of 3, 13, and 40 thermal units. Comparisons with similar approaches including the evolutionary programming (EP), genetic algorithm (GA), immune algorithm (IA), and other versions of particle swarm optimization (PSO) are given. The promising results illustrate the efficiency of the proposed method and show that it could be used as a reliable tool for solving ELD problems.      

A novel particle swarm optimization method based on quantum mechanics computation for thermal economic load dispatch problem

This paper presents an efficient strategy to solve the thermal economic load dispatch (ELD) problem by considering several aspects of ELD. ELD performs an important role in the economical operation of power system, which essentially involves nonlinearity according to the characteristics of the generators. The complexity is amplified when the generators' prohibited zones and valve‐point effect are considered, which makes ELD a nonconvex and nonsmooth problem. The strategy employs a mechanism involving a quantum mechanics‐inspired particle swarm optimization (QMPSO). The conventional PSO is modified by integrating quantum mechanical theory which redefines the particles' positions and velocities in a dynamic manner and therefore explores more search space. The QMPSO employs a multipopulation‐based scheme which ensures particle movement and avoids premature convergence at the same time. Moreover, in order to diversify the particles, a dynamic mutation operator is introduced in the proposed method. Such features deliver a fine balance between the local and global searching abilities. Simulations are carried out by considering several cases of thermal units of varying combinations of system configurations such as with and without the valve point, with and without network loss, and for one or several hours of load demand. The results are quite promising and effective compared with several benchmark methods. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Security constrained economic load dispatch using improved particle swarm optimization suitable for utility system

The objective of this paper is to evolve simple and effective methods for the economic load dispatch (ELD) problem with security constraints in thermal units, which are capable of obtaining economic scheduling for utility system. In the proposed improved particle swarm optimization (IPSO) method, a new velocity strategy equation is formulated suitable for a large scale system and the features of constriction factor approach (CFA) are also incorporated into the proposed approach. The CFA generates higher quality solutions than the conventional PSO approach. The proposed approach takes security constraints such as line flow constraints and bus voltage limits into account. In this paper, two different systems IEEE-14 bus and 66-bus Indian utility system have been considered for investigations and the results clearly show that the proposed IPSO method is very competent in solving ELD problem in comparison with other existing methods.     

Combined economic and emission dispatch problems using biogeography-based optimization

In this work, biogeography-based optimization (BBO) is presented for solving different constrained economic load dispatch (ELD) problems combined with economic emission aspects in power system. Nonlinear characteristics of generators like valve point discontinuities, ramp rate limits and prohibited operating zones are considered in the problem. The simulation results show that the proposed BBO algorithm based solutions prove to be the best near-global optimal as compared to the solutions based on Newton–Raphson, Tabu search, genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA), fuzzy logic controlled genetic algorithm (FCGA), particle swarm optimization (PSO) and differential evolution (DE).     

计及阀点效应负荷经济分配的杂交粒子群算法

提出了一种用于求解复杂的非凸、非线性具有阀点效应的火电有功负荷经济分配问题的杂交粒子群算法（HPSO）。HPSO通过粒子追随自己找到的最优解和整个群的最优解来完成优化,并在此基础上将遗传算法的杂交思想引入到PSO算法当中,使其避免局部最优。算例的仿真结果表明：本文的算法有效、可行,可望应用于更广泛的优化问题。