A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem

Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder–Mead (NM) simplex search called FAPSO-NM. In the resulting hybrid algorithm, the NM algorithm is used as a local search algorithm around the global solution found by FAPSO at each iteration. Therefore, the proposed approach improves the performance of the FAPSO algorithm significantly. The algorithm is tested on two typical systems consisting of 13 and 40 thermal units whose incremental fuel cost functions take into account the valve-point loading effects.     

基于双层优化模型的风-光-储互补发电系统优化配置

提出一种基于双层优化模型的风-光-储互补发电系统优化配置方法,外层优化以互补发电系统年净收益最大化为目标、通过量子粒子群算法优化风-光-储容量配置;内层优化以互补发电系统日出力波动率和日出力峰谷差最小化为目标、通过8760 h生产模拟优化储能充放电功率。实际算例仿真计算结果表明,所提算法能有效解决风-光-储互补发电系统在规划阶段的优化配置问题。     

Solution to economic dispatch problem with valve-point loading effect by using catfish PSO algorithm

This paper proposes application of a catfish particle swarm optimization (PSO) algorithm to economic dispatch (ED) problems. The ED problems considered in this paper include valve-point loading effect, power balance constraints, and generator limits. The conventional PSO and catfish PSO algorithms are applied to three different test systems and the solutions obtained are compared with each other and with those reported in literature. The comparison of solutions shows that catfish PSO outperforms the conventional PSO and other methods in terms of solution quality though there is a slight increase in computational time.     

基于QPSO-LSTM网络的火电机组一次调频能力建模

为了提升火电机组一次调频能力,提出一种基于长短期记忆网络(Long Short Term Memory Network, LSTM)与量子粒子群算法(Quantum behaved Particle Swarm Optimization, QPSO)的一次调频能力计算方法。将负荷指令、机组实发功率、主蒸汽压力、汽轮机总阀位开度和发电机转速作为特征变量,基于某600 MW燃煤火电机组调峰运行工况下的实际数据,构建一次调频能力计算模型。利用QPSO算法优化模型隐含层节点数、训练次数和学习率,解决了因网络结构及模型参数的不确定性产生的精度问题,并将该模型与传统的神经网络模型进行了对比。结果表明：本文所提出的方法具有更高的模型精度,从而能够为机组一次调频能力的限制因素分析和调频性能的优化提升提供模型基础。     

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。     

基于区间建模的新能源电网无功优化策略

  目的  新能源发电具有间歇性和随机性,其功率为不确定性数据,会造成电网电压和频率的变化,对电力系统安全运行构成威胁。为保证大规模新能源并网后电网电压的安全,考虑新能源发电波动不确定性,提出一种基于区间建模的新能源电网无功优化策略。  方法  该策略采用区间数描述无功优化模型中的不确定参数,进而建立区间无功优化模型,采用基于优化场景的区间潮流算法求解区间潮流方程,获取状态变量区间,确定控制变量的可行性,在此基础上采用改进的粒子群优化算法求解区间无功优化模型,在粒子群算法中加入局部搜索环节和离散变量交叉处理操作以提高算法寻优能力。为了验证所提方法的有效性和优越性,分别采用IEEE 14节点和IEEE 30节点算例进行仿真计算,与自适应遗传算法和普通粒子群算法进行对比分析。  结果  仿真结果表明:与自适应遗传算法和普通粒子群算法相比,采用改进粒子群的区间无功优化策略具有更快的收敛速度,更强的寻优能力,并且可有效处理模型中离散变量。  结论  所提策略可有效解决区间无功优化问题,能保障大规模新能源并网后电网电压的运行安全。     

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.     

Artificial bee colony optimization for economic dispatch with valve point effect

In recent years, various heuristic optimization methods have been proposed to solve economic dispatch (ED) problem in power systems. This paper presents the well-known power system ED problem solution considering valve-point effect by a new optimization algorithm called artificial bee colony (ABC). The proposed approach has been applied to various test systems with incremental fuel cost function, taking into account the valve-point effects. The results show that the proposed approach is efficient and robust when compared with other optimization algorithms reported in literature.     

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。     

基于粒子群破环的主动配电网故障重构协同优化

针对主动配电网新能源接入时对故障自愈稳定性和节能优化运行的多重要求,文章提出一种粒子群"破环"的优化算法,以实现故障自愈过程中新能源接入承载力与线损协同的优化方案.首先给出配电网自愈重构的约束条件和优化目标函数,采用FIoyd算法搜寻配电网的最小环结构,并采用一种改进的"破环"粒子群优化算法对故障自愈重构进行优化.最后...     

基于ADQPSO-KELM风电功率短期预测模型的研究

为了改善传统风电功率预测方法中误差较大且稳定性较差的问题,引入量子粒子群(QPSO)优化算法、自适应早熟判定准则及混合扰动算子,构建了自适应扰动量子粒子群(ADQPSO)优化算法,通过ADQPSO算法对核极限学习机(KELM)模型进行优化,建立了自适应扰动量子粒子群优化的核极限学习机(ADQPSOKELM)风电功率短期预测模型,并利用内蒙古高尔真风电场采集的风电功率时间序列数据为试验样本进行48h预测分析。结果表明,ADQPSO-KELM风电功率短期预测模型与其他基于KELM优化的风电预测模型及传统风电预测模型相比,其预测的误差更小、准确度更高,且预测稳定性显著增强。     

An improved PSO for dynamic load dispatch of generators with valve-point effects

Dynamic load economic dispatch problem (DLED) is important in power systems operation, which is a complicated nonlinear constrained optimization problem. It has nonsmooth and nonconvex characteristics when generator valve-point effects are taken into account. This paper proposes an improved particle swarm optimization (IPSO) to solve DLED with valve-point effects. In the proposed IPSO method, feasibility-based rules and heuristic strategies with priority list based on probability are devised to handle constraints effectively. In contrast to the penalty function method, the constraint-handling method does not require penalty factors or any extra parameters and can guide the population to the feasible region quickly. Especially, equality constraints of DLED can be satisfied precisely. Furthermore, the effects of two crucial parameters on the performance of the IPSO for DLED are also studied. The feasibility and the effectiveness of the proposed method are demonstrated applying it to some examples and the test results are compared with those of other methods reported in the literature. It is shown that the proposed method is capable of yielding higher-quality solutions.     

A new honey bee mating optimization algorithm for non-smooth economic dispatch

The non-storage characteristics of electricity and the increasing fuel costs worldwide call for the need to operate the systems more economically. Economic dispatch (ED) is one of the most important optimization problems in power systems. ED has the objective of dividing the power demand among the online generators economically while satisfying various constraints. The importance of economic dispatch is to get maximum usable power using minimum resources. To solve the static ED problem, honey bee mating algorithm (HBMO) can be used. The basic disadvantage of the original HBMO algorithm is the fact that it may miss the optimum and provide a near optimum solution in a limited runtime period. In order to avoid this shortcoming, we propose a new method that improves the mating process of HBMO and also, combines the improved HBMO with a Chaotic Local Search (CLS) called Chaotic Improved Honey Bee Mating Optimization (CIHBMO). The proposed algorithm is used to solve ED problems taking into account the nonlinear generator characteristics such as prohibited operation zones, multi-fuel and valve-point loading effects. The CIHBMO algorithm is tested on three test systems and compared with other methods in the literature. Results have shown that the proposed method is efficient and fast for ED problems with non-smooth and non-continuous fuel cost functions. Moreover, the optimal power dispatch obtained by the algorithm is superior to previous reported results.     

基于改进粒子群算法的风光水互补发电系统短期调峰优化调度

风光水互补发电系统优化调度需要考虑风光电源的间歇性及波动性,同时还要处理梯级水库复杂的水力联系及不同电源之间的电力联系,因而建立风光水互补发电系统短期调峰优化调度模型,并采用粒子群算法进行求解,针对粒子群算法的早熟及后期收敛速度慢等问题,从惯性因子和种群拓扑结构两方面对粒子群算法进行改进,并对福建省电力调控中心管辖的12座常规水电站、木兰溪1座抽水蓄能电站、31座风电场、5座光伏电站组成的风光水多种电源互补系统进行数值分析。结果表明,所建模型能较好地实现对电网负荷的削峰填谷,所提算法显著提高了求解效率和求解质量,是一种解决风光水互补发电系统短期联合优化调峰调度实用性很强的有效算法。     

基于SA-PSO的风电消纳经济性动态规划分析

  [目的]  电网运行规划研究在电力系统中具有十分重要的地位,对电源布局具有决策意义,其本质上是一个多约束组合优化问题,重点研究了风电运行成本的计算模型。  [方法]  该模型对电源布局规划中需要重点考虑的约束问题进行了分析,采用粒子群算法用于解决此类目标优化问题,并结合模拟退火理论对传统粒子群算法进行了优化处理。  [结果]  通过仿真计算,与遗传算法及粒子群算法进行了对比分析,证实了这种算法的优越性,可以找到满足各种约束条件的最优电网出力方案。最后,进一步分析了在不同风速下对风电并网运行成本影响。  [结论]  该模型应用于电网规划分析是可行且有效的。     

基于HOMER和SA-PSO算法的风光氢储系统的优化匹配

优化匹配是风光氢储系统在一定经济约束下安全可靠运行的关键和基础。以某通信基站为供电对象,以系统最小成本支出和最大负荷支撑为优化目标进行配置。首先利用HOMER模拟得到总净现成本最低的配置方案;然后将HOMER优化结果输出来初始化粒子群,以负荷缺电率为目标函数,采用基于轮盘赌输策略的模拟退火粒子群算法进行求解,得到兼具经济性和可靠性的最优配置组合,同时验证了风光氢储系统的优越性;最后依据风光资源不同将河北省分为四类典型地区,利用所提算法求取各个地区的系统最优化配置组合。结果表明,与其他地区相比,第一类地区能以最低成本的容量配置满足系统可靠性的要求。     

Artificial immune system for fixed head hydrothermal power system

This paper presents artificial immune system for optimal scheduling of thermal plants in coordination with fixed head hydro units. Numerical results of two test systems have been presented to demonstrate the performance of the proposed algorithm. The results obtained from the proposed algorithm are compared with those obtained from differential evolution, particle swarm optimization and evolutionary programming technique. From numerical results, it is found that the proposed artificial immune system based approach is able to provide better solution than differential evolution, particle swarm optimization and evolutionary programming in terms of minimum cost and computation time.     

Economic dispatch using particle swarm optimization: A review

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Meta-heuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of PSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper.     

考虑风电消纳能力单目标及多目标模糊机组组合模型及应用

从机组经济调度优化的角度入手,研究了风电消纳的合理应对方式。为使机组组合安排能在消纳更多风电的同时兼顾电力系统运行的安全、可靠性准则,并满足一定的经济性,分别以弃风电量、机组运行费用和机组运行风险度三方面为优化目标建立了机组组合的优化模型及这三者共同的多目标优化模型。利用粒子群算法及模糊多目标优化方法对模型进行求解,并将上述模型和算法应用于某10机算例的计算中。分析结果表明,该建模思路能为风电的有效接纳提供有益的指导。     

气电联合系统的协同规划方法及应用

鉴于传统规划方法分析单一能源系统存在的不足,提出了一种气电联合系统的协同规划方法,其考虑了天然气系统和电力系统的安全运行约束,以总投资成本、运行成本及碳排放成本最小为目标,建立了气电联合系统的协同规划模型,并采用改进的粒子群算法进行求解,最后对IEEE39节点系统和12节点天然气系统构建的算例进行分析。结果表明,与独立规划相比,气电联合系统协同规划所得规划方案总成本更低,碳排放量也更小。