基于PSO算法与协同进化算法的地区电网无功优化模型

电力系统无功优化属于非线性优化范畴,传统的数学规划算法处理时存在较大的局限性,针对大规模电网全局无功电压优化控制困难,提出了基于协同进化框架的合作协同进化粒子群优化算法(PSO)的电力系统无功优化方法,构建了数学模型。实际大电网计算结果表明,该算法寻优质量高、收敛性好、计算复杂度低,适合求解大规模系统无功优化问题。     

基于DESAPSO混合算法的MMC-HVDC系统控制参数优化

针对传统外环控制器比例积分(PI)参数的选择需要经过长时间的调试且在大扰动下难以实时调节控制可能导致系统持续振荡的问题,提出了一种基于差分进化模拟退火粒子群优化混合算法(DESAPSO)的MMC-HVDC系统控制参数优化方法。基于MMC-HVDC系统的数学模型,在Matlab/Simulink平台上搭建MMC-HVDC系统仿真模型,采用时间绝对误差积分(ITAE)指标构建PI参数优化的目标函数,利用DESAPSO混合算法对PI参数进行优化。通过对比原参数、基于差分进化算法、模拟退火粒子群优化算法与差分进化模拟退火粒子群优化混合算法的优化结果,验证了该方法在MMC-HVDC控制系统参数优化中的有效性与优越性。     

A reliable methodology based on mine blast optimization algorithm for optimal sizing of hybrid PV-wind-FC system for remote area in Egypt

In this paper, a reliable methodology incorporated mine blast algorithm (MBA) is applied to solve the optimal sizing of a hybrid system consisting of photovoltaic modules, wind turbines and fuel cells (PV/WT/FC) to meet a certain load of remote area in Egypt. The main objective of the optimal sizing process is to achieve the minimum annual cost of the system with load coverage. The sizing process is performed optimally based on real measured data for solar radiation, ambient temperature and wind velocity recorded by the solar radiation and meteorological station located at national research institute of astronomy and geophysics, Helwan city, Egypt. Three other meta-heuristic optimization techniques, particle swarm optimization, cuckoo search and artificial bee colony are applied to solve the problem and the results are compared with those obtained by the proposed methodology. A power management strategy that regulates the power flow between each system component is also presented. The obtained results show that; applying the proposed methodology will save about 24.8% in the annual total cost of the proposed system compared with PSO, 8.956% compared with CS and 11.5576% compared with ABC. The proposed algorithm based on MBA is candidate for solving the presented optimization problem of optimal sizing the hybrid PV/WT/FC system.     

基于动态调整学习因子的免疫粒子群算法及其应用

为了使风光水联合发电系统达到经济效益最大化优化调度的目的,针对粒子群算法在进化过程中易早熟、后期收敛速度慢并且精度较低的特点,提出一种动态调整学习因子的免疫粒子群算法.该算法对学习因子进行非对称线性动态调整,增强前期的全局搜索能力,以及后期的局部搜索能力,快速得到全局最优解.该算法在文中联合系统的求解中得到很好的应用,显著提高了搜索精度,表明了模型和算法的有效性.     

Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder-Mead algorithm

In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rate limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterial foraging (BF) algorithm with the Nelder-Mead (NM) method (called BF-NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF-NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system.     

楼宇型分布式能源系统设备容量和运行策略优化研究

设备容量优化和运行策略优化是分布式能源系统设计,运行的关键问题。为实现分布式能源系统的经济效益,能效水平和环境效益最大化,针对楼宇型分布式能源系统建立了相对普适化的物理模型和数学模型,以粒子群优化算法和线性规划相结合,采用两阶段优化方法计算系统的最优容量配置,并给出运行策略。以某写字楼的分布式能源系统为例,得到最优的系统设备容量和全年逐时运行策略,并采用遍历法验证计算结果的准确性。优化的分布式能源系统与传统供能系统相比,费用年值降低7.79%,年总能耗降低24.18%,污染物排放量减少了62.77 %。     

基于PSO-BP神经网络的短期光伏系统发电预测

对光伏发电影响因素进行了分析,建立了粒子群算法优化的前向神经网络光伏系统发电预测模型。该模型利用了粒子群算法来优化神经网络内部连接权值和阈值,兼具粒子群和BP神经模型的优点,具有较好的收敛速度,泛化性能与预测精度。将光伏电站发电历史数据与天气情况作为样本,运用所建立的模型进行了训练与预测。结果表明,经过粒子群优化的BP网络模型预测精度高于典型BP网络,验证了该方法的有效性。     

Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method

In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the economic dispatch (ED) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability of the algorithm. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zones, and nonsmooth cost functions are considered when the proposed method is used in practical generator operation. The feasibility of the QPSO–DM method is demonstrated by three different power systems. It is compared with the QPSO, the differential evolution (DE), the particle swarm optimization (PSO), and the genetic algorithm (GA) in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed QPSO–DM method is able to obtain higher quality solutions stably and efficiently in the ED problem than any other tested optimization algorithm.     

基于混合算法的配电网中电池储能的多目标优化配置研究

基于随机会约束规划理论,计及系统的不确定性因素提出配电网储能电站多目标选址定容模型。首先分析配电网的经济性和可靠性特征,接着基于随机机会约束规划建立储能电站的优化模型。采用二进制粒子群算法和改进粒子群算法的混合算法对模型进行求解。最后利用研究模型,结合IEEE 33标准节点系统建立配电网储能电站优化算例,对离网模式和并网模式2种模式进行仿真,对优化配置结果进行对比和分析。     

Design of a cost-effective on-grid hybrid wind–hydrogen based CHP system using a modified heuristic approach

An economic model and optimization procedure is developed in this paper for grid-connected hybrid wind–hydrogen combined heat and power systems for residential applications in northeastern Iran. The model considers various significant factors: energy production cost, electrical trade with local grid, electrical power generation from the wind/hydrogen energy system, thermal recovery from the fuel cell, and maintenance. Also, various tariffs for purchasing and selling electrical energy from the local grid are considered for the hybrid system operation. The optimization objective is to minimize the system total cost subject to relevant constraints for residential applications. To achieve this aim, an efficient optimization method is proposed based on particle swarm optimization. The proposed algorithm performance is compared with that for the imperialist competition algorithm. The results show that the hybrid system is the most cost-effective for the residential load, and the results of the proposed algorithm are more promising than those for the alternative algorithm.     

一种用于AGC机组调配的混沌多Agent双重粒子群算法

AGC机组调配问题是一个含连续和离散变量的混合非线性优化问题,提出了一种基于混沌多Agent的双重粒子群算法。该算法以混沌和粒子群优化算法以及多Agent技术为基础,利用混沌映射提高初始种群的质量,引入临界算子增强Agent的多样性。在算法迭代中,每一个Agent通过与其随机配置的邻居竞争、合作,并且吸收了粒子群优化算法的进化机制,可以更稳定、快速地收敛到全局最优解。通过算例仿真结果表明,所提出的算法具有质量高的解、稳定性好的收敛特征和快的寻优速度。     

基于粒子群算法的PERT网络优化问题研究

针对工程项目PERT网络计划工期-费用优化问题,尝试引入粒子群算法。在保证不陷入局部最优的前提下,为提高收敛速度,用线性规划方法建立数学模型,并采用了改进的PSO算法。实际算例的仿真结果表明,该方法具有收敛速度快、运算简单、易于实现等优点。     

Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review

Hybrid renewable energy system has been introduced as a green and reliable power system for remote areas. There is a steady increase in usage of hybrid renewable energy units and consequently optimization problem solving for this system is a necessity. In recent years, researchers are interested in using multi-objective optimization methods for this issue. Therefore, in the present study, an overview of applied multi-objective methods by using evolutionary algorithms for hybrid renewable energy systems was proposed to help the present and future research works. The result shows that there are a few studies about optimization of many objects in a hybrid system by these algorithms and the most popular applied methods are genetic algorithm and particle swarm optimization.     

基于改进的PSO算法的电力系统无功优化研究

电力系统无功优化是保证电力系统安全、经济运行的重要措施,粒子群优化算法（PSO）具有模型简单、收敛速度快、参数简洁等优点,但用于求解高维复杂优化问题时易陷入局部最优,针对此缺陷,在PSO算法的基础上提出了自适应随机变异粒子群优化算法（AMPSO）,将该算法用于求解电力系统无功优化问题,并以IEEE30标准节点系统为算例进行验证。结果表明,与PSO算法相比,AMPSO算法有效降低了系统网损,显现出良好的全局收敛特性。     

水文模型参数优选的改进粒子群算法参数分析

借鉴竞争演化和多种群混合进化的思想,对粒子群算法(PSO)进行改进,提出了序列主-从种群混合进化的粒子群算法(SMSE-PSO).鉴于优选水文模型参数算法的有效性与算法控制参数有关,为评价SMSE-PSO算法不同控制参数对优化性能的影响,结合水文模型参数优选的特点提出采用正交试验设计的方法分析.结果显示,正交法较好地识别了关键影响因素并提出可能的最优方案,SMSE-PSO算法能较好地应用于复杂多参数水文模型的参数识别研究中.     

Optimization for ice-storage air-conditioning system using particle swarm algorithm

Ice-storage air-conditioning system, while known for its advantage of shifting power consumption at peak hours during the day to the nighttime, can increase both energy consumption and CO₂ emission. The study adopts particle swarm algorithm to facilitate optimization of ice-storage air-conditioning systems and to develop optimal operating strategies, using minimal life cycle cost as the objective function. Increase in power consumption and CO₂ emission triggered by the use of ice-storage air-conditioning system is also examined and analyzed. Case study is based on a typical air-conditioning system in an office building. Results indicate that, with proper parameters, particle swarm algorithm can be effectively applied to the optimization of ice-storage air-conditioning system. In addition, optimal capacity of the ice-storage tank can be obtained. However, the volume of power consumption and CO₂ emission rises with the increase in ice-storage tank capacity. Consideration of additional costs of power consumption like carbon tax can therefore lead to changes in the optimal system configuration.     

基于改进GA-CPSO融合算法的1 000 MW机组给水系统建模

为解决现有方法对1 000 MW机组给水系统建模复杂、算法收敛速度慢、精度低等问题,提出一种改进遗传算法融合混沌粒子群算法(Genetic Algorithm-Chaotic Particle Swarm Optimization, GA-CPSO)。首先,粒子群算法(Particle Swarm Optimization, PSO)中引入了自适应权重和收缩因子,提升粒子寻优能力;在一维Logistic的基础上提出二维Logistic混沌映射,避免寻优过程中陷入局部最优解;采用轮盘赌选择方法,选取粒子进行下一步的遗传算法优化,提升了全局寻优能力。其次,通过实验仿真数据和现场实际数据验证了改进GA-CPSO算法的精度。将该算法用于1 000 MW机组给水系统,建模精度提高了88.65%,仅需要迭代7次左右即完成收敛。然后,利用数据中加干扰实验进一步挖掘改进GA-CPSO算法的抗干扰能力。实验表明：加入外部大扰动建模误差仅有0.385,算法抗干扰能力强。最后,用皮尔逊相关系数方法验证了机组直流阶段模型间的相关性,相关系数达到了0.9以上,可用一个模型代表。     

基于粒子群蚁群混合算法的配电网故障后重构研究

针对粒子群算法在配电网故障恢复中容易陷于局部干扰和蚁群算法计算速度慢的缺点,提出了适用于配电网故障后重构的基于粒子群蚁群的混合算法,确定了配电网重构时的潮流计算方法和目标函数,根据配电网的网孔对配电网的支路进行分组、编码,并简化网络;最后采用粒子群算法、蚁群算法、混合算法共三种算法对IEEE33节点系统进行了故障后的重构仿真分析,仿真结果验证了混合算法在配电网故障后重构应用中的有效性。     

基于改进粒子群算法的地区电网无功/电压实时优化控制

在建立无功/电压实时优化控制模型的基础上,提出了一种基于改进粒子群算法的地区电网无功/电压实时优化控制实现方法。该方法采用增量启动策略决定控制设备参与优化的程度;通过综合考虑负荷大小、限振系数与动作比例等因素模糊推理出表征动作代价的模糊控制成本,更加合理地限制和分配控制设备的动作次数;将粒子群算法与无功/电压控制特点相结合以改善初始粒子质量、缩小寻优空间并引入交叉算子提高算法的计算速度和效率。以某一实际地区电网为例进行无功/电压实时优化仿真计算,结果表明,所提算法和控制策略是可行和有效的。     

增强输电能力的TCSC选址与定容研究

提出了一种新的TCSC选址方法,采用简易粒子群算法求出系统中较为敏感的支路群,用改进的粒子群算法在较敏感支路群中确定TCSC的最佳安装位置,并求出TCSC的最佳安装个数和安装容量,来达到系统输电能力最大的目的。针对PSO易陷入局部最优的缺陷,把种群熵的概念引入粒子群算法对其进行了改进．IEEE30节点系统的仿真计算结果验证了所提方法的有效性。