电力系统经济负荷分配的量子粒子群算法

本文首次将量子粒子群算法用于电力系统经济负荷分配中。该算法是以粒子群中粒子的收敛特性为基础,依据量子物理理论提出的,改变了传统粒子群算法的搜索策略,可使粒子在整个可行解空间中搜索寻求全局最优解。同时该算法的进化方程中不需要速度向量,而且进化方程的形式更简单,参数较少且容易控制。对两个算例进行仿真测试,证实该算法可有效解决经济负荷分配问题;性能对比显示,该算法求得的解优于已有的改进粒子群算法及其它优化算法所求得的解。本文为量子粒子群算法用于经济负荷分配的实用化研究奠定了必要的理论基础。     

基于自调节粒子群算法的电力系统经济负荷分配

带阀点效应的经济负荷分配问题具有不连续、不可导、非凸、非线性的目标函数,同时还受到电力平衡和运行约束的制约,很难应用经典数学算法求解。针对忽略网损的经济负荷分配问题,文章提出了一种自调节粒子群算法,通过可行化调整机制保证解的可行性,同时采用自适应变异算子提高解的多样性,防止算法早熟收敛,提高算法的寻优速度。为验证算法的有效性,文中对多个经济负荷分配问题进行了测试,与其它智能算法的比较结果证明该算法可以有效找到可行解,避免陷入局部最优,能实现问题的快速求解。     

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。     

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。     

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.     

改进粒子群算法及其在电力系统经济负荷分配中的应用

提出了一种用于求解一般形式的非连续、非凸、非线性约束优化问题的改进粒子群算法，用于求解复杂的非凸、非线性电力系统经济负荷分配问题。基于随机分析理论，证明了该算法依概率收敛至全局最优，且收敛性与粒子群的初始分布无关，并提出一个收敛的充分条件。多个算例的仿真结果表明：文中提出的算法有效、可行，可望应用于更广泛的优化问题     

基于粒子群算法的电力系统经济负荷分配研究

电力系统经济负荷分配,是指在满足电力系统或发电机组运行约束条件的基础上,在各台机组间合理地分配负荷以达到最小化发电成本的目的,是经济调度中非常重要的问题。粒子群算法是一种源于对鸟群捕食的行为研究的进化计算技术,具有全局优化能力强、收敛性好和编程实现简单等优点。将粒子群算法应用于电力系统经济负荷分配问题的研究中,通过对实际算例进行仿真测试,证实该算法可有效解决经济负荷分配问题,性能对比显示,该算法求得的解优于传统优化算法所求得的解。     

广义蚁群与粒子群结合算法在电力系统经济负荷分配中的应用

提出了一种结合广义蚁群算法和粒子群算法的优化算法,并将其用于求解复杂的非凸、非线性的电力系统经济负荷分配问题.该结合算法同时具有广义蚁群算法的大规模寻优特性和粒子群算法的较强局部搜索能力,在确保全局收敛性的基础上,能够快速搜索到高质量的优化解.多个算例的仿真结果表明了该结合算法的有效性和可行性.     

电力系统经济负荷分配的混沌优化方法

提出了一种应用混沌优化理论求解电力系统经济负荷分配的新方法。该方法以发电费用信息为目标函数,将优化变量各机组有功功率转变成混沌变量,利用混沌运动的遍历性、随机性和规律性直接对目标函数寻优。该方法可考虑网损及传统拉格朗日乘数法不能计及的汽轮机的阀点效应,从而能更有效地保证解的精度,同时将其与遗传算法进行了比较。仿真结果表明,所提出的方法搜索速度快,求解精度高,易于掌握,是解决电力系统经济负荷分配问题     

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.     

求解经济调度问题的改进粒子群算法

提出了一种求解电力系统经济调度问题的改进粒子群算法。该算法考虑了机组的爬坡速率、工作死区等多种约束条件,并计及了网损。该算法以粒子群算法为基础,提出了新的修补策略对违反各种约束条件的粒子进行积极的修正,并与罚函数技术相结合,使粒子尽可能地在可行解区域或尽量接近可行解的区域内寻优。由于大大减少了粒子在非可行解区域内寻优的概率,因而有效地提高了算法的精度和速度。仿真算例的结果表明,该算法具有速度快、精度高和收敛性好的特点。     

协进化粒子群算法在含有风电的电力系统动态经济调度中的应用

为了解决电力系统中含有风电的动态经济调度问题,文章在考虑了实际运行中的机组爬坡率、运行约束和旋转备用约束等多种约束条件后,利用风速求出了风电24小时的功率曲线,将风电的投资和维护成本折算成风电的发电成本,提出了一个含有常规机组阀点效应的发电成本、风电发电成本和系统备用成本的目标函数和所有约束条件的罚函数,应用提出的协进化粒子群优化算法求解该问题,该算法通过两个种群的自主进化和交互信息,得到了全局最优解和最佳罚因子。最后通过实例的仿真结果验证了该算法具有良好的搜索性能和收敛特性,获得的解得质量明显好于其它算法。     

Stochastic economic emission load dispatch through a modified particle swarm optimization algorithm

Conventional economic load dispatch problem uses deterministic models, which are however not able to reflect some real situations in practical applications since certain inaccurate and uncertain factors are normally involved in system operations. Stochastic models are more suited to be used for investigating some of the power dispatch problems. In this paper, both deterministic and stochastic models are first formulated, and then an improved particle swarm optimization (PSO) method is developed to deal with the economic load dispatch while simultaneously considering the environmental impact. Comparative studies are carried out to examine the effectiveness of the proposed approach. First, a comparison is made between the proposed PSO approach and other approaches including weighted aggregation and evolutionary optimization. Then, based on the proposed PSO, the impacts of different problem formulations including stochastic and deterministic models on power dispatch results are investigated and analyzed.     

基于改进粒子群算法的电力系统经济负荷分配

提出了一种应用随机优化理论求解电力系统经济负荷分配的新方法,该方法以电力市场全天购电费用最小为目标函数,将高斯算子和交叉算子引入基本粒子群算法中。针对基本粒子群算法（PSO）的局限性,通过引入新的算子,克服了PSO算法前期精度低、后期收敛速度慢、易于陷入局部最优等缺点,在速度和精度上满足了计算要求。算例结果表明,所提出的方法能有效解决电力市场电力系统经济负荷分配问题。     

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.     

应用粒子群优化算法求解市场环境下的电力系统动态经济调度问题

在竞争性的电力市场环境下,为了获得最大化的社会利润,提出了基于竞价机制的动态经济调度模型,该模型综合考虑了发电机组的爬坡约束、输电线路的容量约束和污染气体排放量的约束。针对该模型,提出了一种新的求解方法:粒子群优化算法(PSO)。算例的结果表明,PSO算法能够有效地得到一个高性能的优化调度结果     

Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm

The environmental issues that arise from the pollutant emissions produced by fossil-fueled electric power plants have become a matter of concern more recently. The conventional economic power dispatch cannot meet the environmental protection requirements, since it only considers minimizing the total fuel cost. The multi-objective generation dispatch in electric power systems treats economic and emission impact as competing objectives, which requires some reasonable tradeoff among objectives to reach an optimal solution. In this paper, a fuzzified multi-objective particle swarm optimization (FMOPSO) algorithm is proposed and implemented to dispatch the electric power considering both economic and environmental issues. The effectiveness of the proposed approach is demonstrated by comparing its performance with other approaches including weighted aggregation (WA) and evolutionary multi-objective optimization algorithms. All the simulations are conducted based on a typical test power system.