Boosting Kernel Search Optimizer with Slime Mould Foraging Behavior for Combined Economic Emission Dispatch Problems

Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions.Boosting kernel search optimizer(BKSO)is introduced in this research to solve the combined economic emission dispatch(CEED)problem.Inspired by the foraging behavior in the slime mould algorithm(SMA),the kernel matrix of the kernel search optimizer(KSO)is intensified.The proposed BKSO is superior to the standard KSO in terms of exploitation ability,robustness,and convergence rate.The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms.BKSO performs better in statistical results and convergence curves.At the same time,BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases,and the Pareto solution obtained is also better than other MAs.Based on the experimental results,BKSO has better performance than other comparable MAs and can provide more economical,robust,and cleaner solutions to CEED problems.

A Multi-Agent Particle Swarm Optimization for Power System Economic Load Dispatch

A new versatile optimization, the particle swarm optimization based on multi-agent system (MAPSO) is presented. The economic load dispatch (ELD) problem of power system can be solved by the algorithm. By competing and cooperating with the randomly selected neighbors, and adjusting its global searching ability and local exploring ability, this algorithm achieves the goal of high convergence precision and speed. To verify the effectiveness of the proposed algorithm, this algorithm is tested by three different ELD cases, including 3, 13 and 40 units IEEE cases, and the experiment results are compared with those tested by other intelligent algorithms in the same cases. The compared results show that feasible solutions can be reached effectively, local optima can be avoided and faster solution can be applied with the proposed algorithm, the algorithm for ELD problem is versatile and efficient.

Economic Load Dispatch Based on Efficient Population Utilization Strategy for Particle Swarm Optimization

In this paper, the efficient population utilization strategy for particle swarm optimization (EPUSPSO) is proposed to solve the economic load dispatch (ELD) problem of power system. This algorithm improves the accuracy and the speed of its convergence by changing the number of particles effectively, and improving the velocity equation and position equation. In order to verify the effectiveness of the algorithm, this algorithm is tested in three different ELD cases of power system include IEEE 3-unit case, 13-unit case, and 40-unit case, and the obtained results are compared with those obtained from other algorithms using the same system parameters. The compared results show that the algorithm can find the optimal solution effectively and accurately, and avoid falling into the local optimal problem;meanwhile, faster speed can be ensured in the case.

Economic Dispatch with Convex and Non-Convex Fuel Cost Functions Including Line Losses Using Pattern Search

This article presents an application of generalized pattern search （PS） algorithm to solve economic load dispatch （ELD） problems with convex and non-convex fuel cost objective functions. Main objective of ELI） is to determine the most economic generating dispatch required to satisfy the predicted load demands including line losses. Relaxing various equality and inequality constraints are considered. The unit operation minhnum/maximum constraints, effects of valve-point and line losses are considered for the practical applications. Several case studies were tested and verified, which indicate an improvement in total fuel cost savings. The robustness of the proposed PS method have been assessed and investigated through intensive comparisons with reported results in recent researches. The results are very encouraging and suggesting that PS may be very useful tool in solving power system ELD problems.

水电站机组负荷降维优化分配中的时间尺度效应

时间尺度是影响水电站负荷优化分配结果的重要因素。为探究时间尺度对水电站负荷分配的影响,研究论述了水电站调度中的时空尺度效应内涵及出现的原因,提出了包含发电耗水量、跨越振动区次数、机组启停次数、求解耗时等的尺度效应量化指标,并以ZM水电站为研究对象,基于水电站机组负荷优化分配模型,逐次递进分析了典型日考虑负荷过程随机性与负荷水平的水电站机组负荷优化分配尺度效应。结果表明:1)时间尺度越小更易捕捉负荷变化与水库运行过程,电站负荷优化分配的耗水量、机组启停与跨越振动区次数越大。时间尺度越大更易忽视或弱化机组启停以及跨越振动区等行为,产生潜在的安全运行风险。2)考虑负荷过程的随机性时,水电站负荷优化分配同样呈现尺度效应现象,但其影响程度与时间尺度大小影响相当。3)负荷水平越小或越大,不同时间尺度下电站机组运行方式变化较小,时间尺度效应越不显著;负荷水平居中时,时间尺度效应相对显著。研究结果可为水电站负荷优化分配的时间尺度选择以及模型降维求解算法提供科学依据。

Reserve Constrained Dynamic Economic Dispatch with Valve-point Effect:A Two-stage Mixed Integer Linear Programming Approach

This paper proposes a deterministic two-stage mixed integer linear programming(TSMILP)approach to solve the reserve constrained dynamic economic dispatch(DED)problem considering valve-point effect(VPE).In stage one,the nonsmooth cost function and the transmission loss are piecewise linearized and consequently the DED problem is formulated as a mixed integer linear programming(MILP)problem,which can be solved by commercial solvers.In stage two,based on the solution obtained in stage one,a range compression technique is proposed to make a further exploitation in the subspace of the whole solution domain.Due to the linear approximation of the transmission loss,the solution obtained in stage two dose not strictly satisfies the power balance constraint.Hence,a forward procedure is employed to eliminate the error.The simulation results on four test systems show that TSMILP makes satisfactory performances,in comparison with the existing methods.

A hybrid BFA-PSO algorithm for economic dispatch with valve-point effects

This paper presents a novel and efficient method for solving the economic dispatch （ED） problems with valve-point effects, by integrating the biased velocity of particle swarm optimization （PSO） to the chemotaxis, swarming and reproduction steps of bacterial foraging algorithm （BFA）. To include valve point effects sinusoidal terms are added to the fuel cost function. This makes the ED problems highly non-linear. In order to solve such problems the best cell （or particle） biased velocity （vector） is added to the random velocity of the BFA to reduce randomness in movement （evolution） and to increase swarming. This results in the hybrid bacterial foraging algorithm （HBFA）. To demonstrate the effectiveness of the proposed HBFA method, numerical studies have been performed for three different sample systems. Comparison of the results obtained by the HBFA with the BFA and other evolutionary algorithms clearly show that the proposed method outperforms other methods in terms of convergence rate and solution quality in solving the ED problems with valve-point effects.

快速群搜索优化算法及其在电力系统经济调度中的应用

经济调度(economic dispatch,ED)是电力系统优化的一项重要工作。为得到一个更加精确且实用的ED模型,在模型中考虑了机组阀点效应和多燃料。该ED模型具有非凸、高维、非线性和不可微的特性,求解较为困难。为更好地求解该ED模型,提出一种快速群搜索优化算法(fast groupsearch optimizer,FGSO),该算法能以更少的计算资源得到更高的收敛精度。对3个测试系统的ED模型进行求解,结果表明,FGSO比其他算法收敛速度更快,计算时间更短,能得到更小的发电费用值,且在高维的、具有多局部最优点的复杂优化问题中具有更加突出的优势。因此,FGSO用于求解该复杂ED模型是可行的、快速的和有效的。

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。

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。

基于PSO-BBO混合优化算法的动态经济调度问题

动态经济调度(Dynamic Economic Dispatch,DED)问题是电力系统运行与控制领域比较经典的多变量、非线性、强约束优化问题。为解决该问题,提出了将粒子群优化算法(Particle Swarm Optimization,PSO)和基本生物地理学优化算法(Biogeography-Based Optimization,BBO)相结合的改进生物地理学优化算法,并将该改进方法应用于一天24时段10机39节点标准算例。在考虑网损与不考虑网损两种情况下分别进行仿真分析,并将仿真结果与PSO和基本BBO算法以及参考文献中提出的六种智能算法进行对比,验证了该改进算法的有效性及在寻优能力上的提高。

基于可信性理论的含风电场电力系统动态经济调度

由于隶属度函数不具有自对偶性,传统模糊论进行置信水平下的调度决策时遇到困难。该文结合可信性理论和模糊机会约束规划,考虑风电预测误差的模糊性,研究了模糊置信水平下的动态经济调度问题。在模型建立上,推导了模糊风电预测误差的可信性分布函数模型,利用机会约束给出模糊置信水平下的调度可行域,由于采用可信性分布作为测度函数,保证了可行域能够覆盖置信水平下的模糊事件集合,确保不确定调度的优化解落在可信解空间。在解法上,将模糊机会约束条件转化为其清晰等价类,避免模糊模拟导致的模型求解效率降低,由于在目标函数中考虑了阀点效应,采用混沌粒子群算法求解。基于该文方法,给出10机含风电场系统的动态优化调度方案并进行了有关分析。

电力系统经济负荷分配的改进粒子群算法

为解决电力系统中的经济负荷分配问题,将改进粒子群算法用于其中。该算法是以基本粒子群算法为基础,利用优化惯性权重策略以及改进最优最差粒子策略,使改进粒子群算法具有高效率全局搜索能力。对三个算例进行仿真测试,证实该算法可有效地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基本粒子群算法及其它优化算法所求得的解。

电力系统经济负荷分配的混合粒子群优化算法

为解决电力系统中的经济负荷分配问题,提出一种将约束优化与粒子群优化算法相结合的混合算法,同时引入直接搜索方法。使得混合后的粒子群优化算法不但具有高效的全局搜索能力,而且具有较强的局部搜索能力,避免陷入局部最优,提高求解精度。对两个实例进行测试,与其他智能算法的结果比较,证明提出的算法可以有效找到可行解,避免陷入局部最优,实现问题的快速求解。

求解电力系统多目标环境经济调度的帕累托最优MFO算法

本文提出了一种利用MFO算法解决电力系统环境经济调度的新方法,该算法利用飞蛾扑火原理对设定目标进行螺旋式搜索,并在目标位置进行重复检索。MFO算法对于大规模非线性规划问题具有较强的适应性和有效性。在求解环境经济调度问题中,结合实际发电系统运行过程中应满足的功率平衡约束和容量约束等,以总燃料成本和污染排放最低为目标建立多目标规划数学模型。运用帕累托最优前沿求取帕累托非劣性最优解,得到帕累托最优配置方案,在可行域中搜索出全局最优解。在MATLAB仿真平台对含40台发电机组系统进行仿真计算,结果表明本文提出算法在求解电力系统环境经济调度中具有较高的收敛性和较强的适应性。

大规模电力系统经济调度的改进竞争群优化算法

提出了一种求解大规模电力系统经济调度(Economic Dispatch,ED)问题的改进竞争群优化算法(Improved Competitive Swarm Optimizer,ICSO)。CSO算法本质上受粒子群优化PSO算法所启发,具有原理简单,容易实现,搜索能力强等特点。在分析CSO算法存在的不足的基础上,提出了相应的劣质个体改进更新策略和控制参数自适应调节策略,以更好地平衡局部搜索和全局搜索。通过4个算例从经济性、收敛性、鲁棒性等多维角度对比分析了ICSO算法的可行性和有效性。算例仿真结果表明,ICSO具有较好的优化性能,可作为大规模ED问题的可靠求解方法。

改进的差分进化算法在电力经济调度中的应用

以优化发电系统中的发电总费用为目标,结合实际运行中机组的约束条件和阀点效应,建立了电力经济调度(ED)模型,并提出了求解该模型的改进的差分进化算法(ADE)。针对标准差分进化算法存在的种群多样性和收敛性能之间的矛盾,在度量种群多样性的基础上,引入了基于排序的可行解选取递减策略改进变异策略current-to-best。此外,提出一种新颖的等式约束修复机制,确保求解的可行性。最后,利用13个机组的测试系统进行仿真试验,结果证明了ADE算法求解ED模型的有效性。

水火联合电力系统长期优化调度模型与算法

本文提出了水火联合电力系统长期优化调度的数学模型,较全面地考虑了负荷预侧的不确定性、火电机组发电出力的随机性以及水电厂径流来水的随机性,推导出水电厂径流来水相互独立、相邻时段相关和上、下游水电厂同时段径流相关三种情况下优化调度模型.通过分解协调原理将整个优化问題分成火电子问題和水电子问题,火电子问题用等微增率准则求解,水电子问題按河系分解成一系列小问题,用随机动态规划方法求解,最后将两个子问题适当协调起来.

考虑动态出力系数的某水库优化调度模型及应用

针对综合出力系数是水电站高效运行的重要动态参数且现有研究中多采用定值、过于理想化的问题,建立考虑厂内经济运行和动态出力系数的某水库优化调度模型,并应用遗传算法求解。对某水库的优化调度结果表明,采用动态出力系数的优化调度模型效益提升相较于定出力系数更优,发电效益最大可提升5.15%,达到了高效发电的目标,可为水库日常调度提供参考。