Adaptive simulated annealing schedule to the unit commitment problem

This paper presents an approach for solving the unit commitment problem based on a simulated annealing algorithm with an adaptive schedule. The control parameter, temperature, is adapted to the cost levels on which the algorithm operates during the annealing process. This shortens the time taken to find a good solution meeting all constraints and improves the convergence of the algorithm. The operators specific to this problem, mutation and transposition, are used as the transition operators. The method incorporates time-dependent start-up costs, demand and reserve constraints, minimum up and down time constraints and unit power generation limits. There are different definitions of the objective function for the feasible and infeasible solutions. Test results showed an improvement in effectiveness compared to results obtained from simulated annealing with a static schedule, genetic algorithm and other techniques.     

Unit commitment by genetic algorithm with specialized search operators

An approach for solving the unit commitment problem based on genetic algorithm with new search operators is presented. These operators, specific to the problem, are mutation with a probability of bit change depending on load demand, production and start-up costs of the generating units and transposition. The method incorporates time-dependent start-up costs, demand and reserve constraints, minimum up and down time constraints and units power generation limits. Repair algorithms or penalty factors in the objective function are applied to the infeasible solutions. Numerical results showed an improvement in the solution cost compared to the results obtained from genetic algorithm with standard operators and other techniques.     

Unit commitment solution methodology using genetic algorithm

Solution methodology of unit commitment (UC) using genetic algorithms (GA) is presented. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as minimization of the total objective function while satisfying the associated constraints. Problem specific operators are proposed for the satisfaction of time dependent constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented     

Fuzzy unit commitment solution—A novel twofold simulated annealing approach

The authors propose a twofold simulated annealing (twofold-SA) method for the optimization of fuzzy unit commitment formulation in this paper. In the proposed method, simulated annealing (SA) and fuzzy logic are combined to obtain SA acceptance probabilities from fuzzy membership degrees. Fuzzy load is calculated from error statistics and an initial solution is generated by a priority list method. The initial solution is decomposed into hourly-schedules and each hourly-schedule is modified by decomposed-SA using a bit flipping operator. Fuzzy membership degrees are the selection attributes of the decomposed-SA. A new solution consists of these hourly-schedules of entire scheduling period after repair, as unit-wise constraints may not be fulfilled at the time of an individual hourly-schedule modification. This helps to detect and modify promising schedules of appropriate hours. In coupling-SA, this new solution is accepted for the next iteration if its cost is less than that of current solution. However, a higher cost new solution is accepted with the temperature dependent total cost membership function. Computation time of the proposed method is also improved by the imprecise tolerance of the fuzzy model. Besides, excess units with the system dependent probability distribution help to handle constraints efficiently and imprecise economic load dispatch (ELD) calculations are modified to save the execution time. The proposed method is tested using standard reported data sets. Numerical results show an improvement in solution cost and time compared to the results obtained from other existing methods.     

Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach

This paper presents a new approach to solve the hydro-thermal unit commitment problem using Simulated Annealing embedded Evolutionary Programming approach. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A utility power system with 11 generating units in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consist of 25, 44 and 65 units. Numerical results are shown comparing the cost solutions and computation time obtained by conventional methods.     

模拟退火算法及其在短期负荷预测中应用

将模拟退火算法应用于短期负荷预测领域,其特点是模型简单、运算效率高,并具有较好的全局最优性能,从而很好地克服了传统BP算法容易陷入局部极小点的缺陷。该模型考虑温度和预测日类型,可进行工作日和非工作日的预测。实例表明,该模型实用有效、预测精度高。     

基于模拟退火算法的燃料电池建模

提出了一种基于模拟退火算法的优化建模方法,描述了模拟退火算法在燃料电池建模研究中的应用。结果表明,这种方法建立的数学模型和实验数据之间能够达到很高的拟合精度,平均平方误差为0.000 3%,相关系数达99.9%,接近最优结果。     

基于模拟退火算法的模糊控制器优化设计

提出了一种解析规则模糊控制器的改进结构，引入了能够动态调整模糊控制规则的修正函数，并给出了模糊控制器输出控制量在稳态附近的局部连续化算法；同时，通过模拟退火算法，实现了模糊控制器控制参数的组合优化设计。结果表明，使用该方法设计的模糊控制器具有响应快、超调小、稳态精度高等优点，改善了模糊控制系统的性能。     

基于分层分枝定界算法的机组组合

针对常规机组组合算法计算量大、计算精度不高的问题,提出了一种分层分枝定界算法.该算法采用分层求解策略,对传统机组组合模型进行线性化处理,进而基于线性规划算法求解松弛整数变量的线性化机组组合模型,通过取整策略形成初始分枝,作为分枝定界算法的上层;采用经典广度优先搜索算法的节点搜索策略,以及考虑负荷备用和机组启停时间约束并结合最大、最小边界的分枝策略,不断进行分枝、定界和剪枝获得下层问题的最优解.不同测试算例分析表明,所提出的分层分枝定界算法快速、稳定,能够考虑机组爬坡约束,可以在保证合理计算时间内有效提高求解精度.     

基于Spark的并行模拟退火算法求解TSP

模拟退火算法是求解无约束优化问题的有效方法,但求解旅行商问题时存在精度较差、容易陷入局部最优且收敛速度慢等缺点。为了改进上述问题,本文提出了一种基于Spark平台的并行模拟退火算法。修改模拟退火算法的降温函数,构造旅行商问题的解空间,采用大邻域搜索技术和2-opt算子增强局部搜索能力,引入OX交叉思想增强全局搜索能力,提出交叉协同试验并行策略与Spark平台并行实现。选取若干TSPLIB数据集进行仿真实验,对求解质量和运行时间两个方面进行测试,与其它Spark框架的并行算法进行对比实验。仿真结果表明,该算法求解精度有较大的提高,求解速度上对比其他算法提升3-10倍,能够有效求解旅行商问题。     

基于模拟退火的并行进化规划多用户检测算法

模拟退火和多种群并行进化规划是2种较好的改进进化算法性能的方法。将这2种思想有机地结合起来。提出了一种基于模拟退火的并行进化规划多用户检测算法。在该算法中,进化在多个不同的子群中并行进行,利用模拟退火算法的爬山性能,避免单种群进化过程中出现的过早收敛现象,提高整个算法的收敛速度。仿真结果表明,这种新的多用户检测算法抗多址干扰和抗远近效应能力都优于单种群的模拟退火进化规划多用户检测算法,并且在多址干扰和远近效应存在的条件下,其收敛速度明显优于基于单种群的模拟退火进化规划检测器。     

基于Spark的并行k均值聚类模拟退火算法求解MMTSP

多起点闭回路多旅行商问题是旅行商问题的扩展。针对这个问题文中提出了一种基于Spark框架的并行k均值聚类模拟退火算法。该算法首先采用k均值聚类算法将所有城市分类,然后对应每个类建立一个旅行商问题,并通过一种改进的模拟退火算法对旅行商问题求解,MMTSP的解由这些类的最短路径之和计算得出。所提算法采用先聚类再执行模拟退火算法的求解策略可以极大的缩减模拟退火的搜索空间,并且由于Spark框架可以将聚类算法分好的若干类并行求解,从而更快的得到MMTSP问题的最优解。选取TSPLIB数据库中若干测试实例进行仿真实验,对求解精度和运行时间两个方面进行测试,与其他几种相关算法进行对比实验。实验结果表明,与目前FCMPGA、IPGA、IWO等算法相比,求解精度提高了5%~40%,求解效率上对比其他算法提升1~5倍,尤其在K值较大时表现更优。     

实数编码遗传模拟退火算法SHEPWM控制技术

针对多电平逆变器SHEPWM(selected harmonic elimination pulse width modulation,SHEPWM)消谐模型的求解问题,提出一种遗传算法和模拟退火算法相融合的新型算法。该算法根据个体适应度值进行自适应交叉和变异操作,采用模拟退火算法进行个体更新,以增加种群的多样性,增强全局寻优能力,避免陷入局部最优,并以实数编码保存个体来提高计算精度。以二极管箝位三电平逆变器SHEPWM为例,给出了全调制度下的开关角度轨迹及较高调制度下的另外两组解,绘制了谐波失真含量(total harmonic distortion,THD)随调制度变化的曲线,并给出了详细的仿真结果。最后通过建立的二极管箝位三电平逆变器实验平台进行了实验验证,仿真和实验结果证明了该算法的正确性和可行性。     

基于遗传粒子群混合算法的机组组合优化

节能发电调度的目标是实现能耗量最小,合理安排机组发电计划则更为至关重要。在参考文献的基础上,提出了一种用于机组组合优化的遗传粒子群混合优化算法。先用遗传算法求解机组组合,再用粒子群优化算法求解负荷经济分配。按照节能调度思路对遗传算法进行了改进,提高了优化性能。给出了10机算例系统优化结果,验证了该混合算法的可行性和有效性。     

基于遗传粒子群混合算法的机组组合优化

节能发电调度的目标是实现能耗量最小,合理安排机组发电计划则更为至关重要.在参考文献的基础上,提出了一种用于机组组合优化的遗传粒子群混合优化算法.先用遗传算法求解机组组合,再用粒子群优化算法求解负荷经济分配.按照节能调度思路对遗传算法进行了改进,提高了优化性能.给出了10机算例系统优化结果,验证了该混合算法的可行性和有效性.     

基于改进遗传模拟退火算法的PMU优化配置

分析了遗传算法和模拟退火算法的优缺点,将具有较好全局寻优性能的遗传算法和具有较强局部搜索能力的模拟退火算法结合,形成的遗传模拟退火MGASA算法用于解决以电力系统状态完全可观测和PMU配置数目最小为目标的PMU优化配置问题.在寻优过程中,先将每一代群体进行遗传操作,再对产生的新群体中各个体进行模拟退火操作,同时在选择、交叉、变异和复制操作过程中实施最优保留策略,复制策略采用Metropolis判别准则.通过采用IEEE14和IEEE39节点系统对该算法进行验证表明,MGASA算法在解决PMU优化配置问题上具有较高的寻优性能和搜索效率.     

基于模拟退火的粒子群算法在水电站水库优化调度中的应用

介绍了一种基于模拟退火的粒子群算法,并用其求解以水电站年发电量最大建立的优化调度的数学模型.考虑到基本的粒子群算法(PSO)后期粒子趋向同一化,使其进化速度变慢,精度较差,本文将模拟退火的思想应用到具有杂交和变异的粒子群算法当中,通过模拟退火的降温过程来提高算法后期的进化速度和精度.最后,以普定水电站的优化调度为例进行了计算,结果表明,该算法的性能较基本粒子群算法有了较大改善,且明显优于常规调度方法和动态规划.     

改进的拉格朗日松弛法求解机组组合问题

提出了一种求解机组组合问题的改进拉格朗日松弛算法.与现有此类方法相比,此算法进行了以下三个方面改进:基于启发式排序法得到较优的拉格朗日乘子初值;改进了动态规划求解单机问题时的累计费用以获得更优的机组启停决策;采用自适应性次梯度法结合集结投影次梯度法的联合优化策略加快算法收敛速度.10机到100机的6个算例表明,自适应性次梯度法和集结投影次梯度法的联合优化策略更易跳出其单一策略时的振荡现象,从而加快收敛.     

基于模拟退火遗传算法的渠系配水优化编组模型研究

科学的渠系配水决策可减少渠系输水过程中的渗水损失,提高渠系水利用率.本文在分析渠系配水优化编组模型特点的基础上,针对基于固定惩罚函数遗传算法的模型求解方法中存在的量纲难以统一、搜索精度不高和过早收敛到局部最优解的问题,引入动态罚函数来处理约束条件,提出了基于模拟退火遗传算法的模型求解方法.通过灌区实际配水编组决策应用实例验证表明,本文方法收敛速度快,计算精度高,确定的下级渠道配水时间搭配合理,上级配水流量均匀,具有较高的工程应用价值.     

基于自适应模拟退火遗传算法的多目标最优潮流

采用自适应遗传算法来确定基本遗传算法的交叉率和变异率,保证遗传算法的收敛性。同时引入模拟退火法思想,通过拉伸目标函数的适应度使优秀个体在产生后代时具有明显的优势,从而加速寻优的过程,形成一种新的算法:自适应模拟退火遗传算法。应用该算法进行电力系统多目标最优潮流计算,IEEE30试验系统计算结果表明了该算法的灵活性和有效性。