改进的混合遗传算法在配电网规划中的应用

利用遗传算法解决NP问题(非确定性多项式问题)的良好能力、模拟退火算法在当前点邻域内搜索最优解的能力和禁忌搜索算法较快的搜索速度,提出了改进的混合遗传算法,即将模拟退火选择算子和禁忌搜索变异算子应用到遗传操作中,提高了种群选择的有效性和遗传算法局部搜索能力,避免了单一遗传算法中收敛速度慢和早熟现象的产生。并将改进的混合遗传算法应用到河南省北部A地区电网规划中,对水平年电力网架进行了优化,结果满足电力系统经济性和可靠性的要求,运行实践证明该算法效果较好。     

基于多策略改进的遗传算法在配电网规划中的应用

针对电力电子化配电网规划复杂的优化问题,提出一种基于多策略改进的多目标遗传算法（简称遗传算法）。将遗传算法与配电网规划进行有效结合,研究了遗传算法在规划方案中的染色体组编码方式;对遗传算法进行具有针对性的多策略改进,涉及种群选择、交叉与变异算子以及自适应遗传算子的改进;通过种群修复提高算法的搜索能力,使染色体的决策变量在满足约束的同时,确保种群多样性启发式地进化为规划问题的最优解。通过Schaffer函数与Griewank函数对基于多策略改进的遗传算法进行性能测试,并对其组成内容、搜索特点与搜索寻优的过程分别进行了分析和讨论。结果表明,基于多策略改进的遗传算法在搜索精度与计算效率方面具有较大优势,对于配电网规划优化具有重要价值。     

禁忌遗传算法在边坡稳定分析中的应用

在遗传算法的选择算子中引入了群体多样性保持策略,以避免遗传算法易出现的早熟收敛现象;同时在交叉、变异算子中采取禁忌搜索技术,构造了适于约束优化问题的两点直线禁忌寻优算子,以寻求新解从而形成一种禁忌遗传算法;利用禁忌遗传算法和遗传算法对两个非均质土坡的最小安全系数进行了搜索,结果证明禁忌遗传算法具有搜索效率高、寻优能力强等特点。     

考虑线路开断限流的输电网双层扩展规划模型及应用

为深入研究考虑线路开断限流的输电网限流的输电网扩散规划情况,将线路开断措施引入到输电网规划中,建立了考虑线路开断限流的输电网双层扩展规划模型,上层规划以输电网全寿命周期总投资成本最小为目标,下层规划在上层规划得出的方案下以开断线路条数最小为目标,上下层交互作用,最终得出满足系统短路电流约束的规划方案,并采用遗传算法和离散粒子群算法结合的混合算法求解所建模型。最后通过Garver6节点算例验证了所提模型和算法的合理性和有效性。     

基于禁忌粒子群的多能源热电联供优化方法研究

为了对混合能源热电联供系统的多目标优化进行求解,采用模糊几何加权将多目标问题转化为单目标问题,充分考虑到混合能源热电联供系统优化算法存在的计算冗余量大、时间长等问题,提出将禁忌粒子群算法应用于混合能源热电联供系统优化求解。通过构造优化过程的禁忌表,阻止搜索过程中出现循环和避免陷入局部最优,进而提高算法的收敛速度和收敛精度。最后以运行费用、环境惩罚费用为子目标函数进行仿真计算,仿真结果表明该算法不仅能够获得最优解,而且大大降低优化求解时间,提高搜索速度。     

动态加速自适应遗传算法的应用

针对标准遗传算法收敛速度慢、易早熟,提出了一种基于进化阶段的动态加速自适应遗传算法,使遗传算法的搜索空间动态缩小并在遗传算法的选择算子中引入模拟退火机制,同时对交叉算于和变异算子做了自适应改进.将该算法应用于优选广东省韩江堂荆流域新安江模型的参数中,效果很好.     

基于差分变异的混合蛙跳算法在配电网重构中的应用

提出了一种基于差分变异的混合蛙跳算法进行配电网重构。采用基于十进制环状编码方法解决了二进制编码产生大量不可行解的问题。给出了十进制环状编码的变异和选择策略,同时结合蛙跳算法的分组更新特点。应用该算法对IEEE33节点和PG&E69节点网络进行配电网重构仿真,结果表明,该方法能有效降低配电网损耗。     

基于相似性矢量距免疫遗传算法的城市电网规划

首次提出基于相似性矢量距选择概率的免疫遗传算法来求解城市高压配电网规划。结合遗传算法(GA)和免疫算法(IA)建立具有动态调整罚因子的目标函数,并引入人工免疫机制对各个抗体之间在编码上的相似性进行处理,以保存种群的多样性同时保证算法能够较快的收敛。通过IEEE-6节点系统的仿真计算,并与其他算法比较,表明该算法计算速度和优化效果都具有明显的优势。     

二倍体多目标遗传算法在水资源优化中的应用

针对遗传算法存在早熟、收敛性和解分布性差的问题,在孟德尔多目标简单遗传算法的基础上提出一种求解多目标的二倍体遗传算法,引入变异算子和聚类方法使求得的Pareto解集具有更好的分布性.通过多目标测试函数和二倍体多目标进化算法在流域水资源调度配置中应用,测试结果较好.     

基于遗传-蚁群算法的库群长期优化调度

为克服蚁群算法前期收敛慢、易陷入局部最优解且参数难以确定的缺点,提出了遗传-蚁群算法进行库群长期优化调度问题的求解。该方法应用遗传算法生成问题的初始解,并将初始解的适应度转化为蚁群算法的信息素初始值,同时引入遗传算法染色体交叉、变异的思想进行蚁群算法参数最优组合的确定,提高了蚁群算法的优化性能和求解精度。对乌江流域4座水电站的计算结果表明,该算法可显著改善优化结果质量,获得良好的调度方案,是求解库群长期优化调度问题的一种有效方法。     

System network planning expansion using mathematical programming, genetic algorithms and tabu search

In this paper, system network planning expansion is formulated for mixed integer programming, a genetic algorithm (GA) and tabu search (TS). Compared with other optimization methods, GAs are suitable for traversing large search spaces, since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will tend to become more common as the search space increases in size. GA’s give an excellent trade off between solution quality and computing time and flexibility for taking into account specific constraints in real situations. TS has emerged as a new, highly efficient, search paradigm for finding quality solutions to combinatorial problems. It is characterized by gathering knowledge during the search and subsequently profiting from this knowledge. The attractiveness of the technique comes from its ability to escape local optimality. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. The DC load flow equations for the network are embedded in the constraints of the mathematical model to avoid sub-optimal solutions that can arise if the enforcement of such constraints is done in an indirect way. The solution of the model gives the best line additions and also provides information regarding the optimal generation at each generation point. This method of solution is demonstrated on the expansion of a 10 bus bar system to 18 bus bars. Finally, a steady-state genetic algorithm is employed rather than generational replacement, also uniform crossover is used.     

Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques

This paper proposes a new hybrid algorithm Meta-heuristic for the problem of network planning systems. The main goal of this paper is, to develop an efficient optimization tool which will minimise the cost functions of the stated optimization problems in network planning systems. The following are the objectives of the research: to investigate the capabilities of genetic algorithm, simulated annealing and tabu search for the defined optimization tasks; to develop a hybrid optimization algorithm which will produce improved iterations compared to those found by GA, SA, and TS algorithms. The performance of the hybrid algorithm is illustrated and six hybrid algorithms are developed, to improve the iterations obtained. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. It is advantageous to use exact DC load flow constraint equations based on the modified form of Kirchhoff's Second Law because the iterative process for line addition is not required. Hence, the computation time is decreased. Finally, the hybrid VI shows to be a very good option for network planning systems given that it obtains much accentuated reductions of iteration, which is very important for network planning.     

Circular genetic operators based RNA genetic algorithm for modeling proton exchange membrane fuel cells

Inspired by the biological RNA, a circular genetic operators based RNA genetic algorithm (cRNA-GA) is proposed to estimate the model parameters of the proton exchange membrane fuel cell (PEMFC). To maintain the population diversity and avoid premature convergence, we design the novel genetic operator of the double-loop crossover operator. To allow the algorithm to jump out of local optima, the adaptive mutation probabilities are presented and the stem-loop mutation operator is adopted with the other mutation operators. The simulated annealing method is also incorporated into the cRNA-GA to improve local search ability. Performance tests conducted on some typical benchmark functions have witnessed the validity of cRNA-GA. The cRNA-GA is also applied to estimate the parameters of the PEMFC model and the satisfactory results have shown its effectiveness.     

输配电协调的网架规划方法

为使输电网规划与配电网规划互相协调、提高电网整体供电可靠性和投资效益,提出了输配电协调的网架规划方法。在传统网架规划模型的基础上,在输电网规划模型中引入配电网转供需求约束和输电网可靠率约束;在配电网规划模型中引入输电网N-2转供需求约束和线路平均负载率约束。并采用自适应搜索离散萤火虫算法求解输配电网网架规划模型,最后以华东某地区输配电网规划作为算例进行方案对比。结果表明,所提方法可行、有效,能提高电网规划的可靠性和投资效益。     

蚁群加速遗传算法在水环境优化问题中的应用

提出了水环境优化问题的一种新方法——蚁群加速遗传算法，给出了实施该算法的详细步骤。并对新方法的收敛性和全局优化性进行了理论和实例分析，在污水处理模型的参数识别问题中，新方法得到了精度较高的全局最优解。新方法具有精度高、速度快和鲁棒性强等特点，是一种既可以较大概率搜索全局最优解，又能进行局部细致搜索的较好的非线性优化方法，可广泛应用于各种水环境优化问题中。     

Power generation expansion planning with adaptive simulated annealing genetic algorithm

In this paper, an adaptive simulated annealing genetic algorithm is proposed to solve generation expansion planning of Turkey's power system. Least‐cost planning is a challenging optimization problem due to its large‐scale, long‐term, nonlinear, and discrete nature of power generation unit size. Genetic algorithms have been successfully applied during the past decade, but they show some limitations in large‐scale problems. In this study, simulated annealing is used instead of mutation operator to improve the genetic algorithm. The improved algorithm is applied to the power generation system with seven types of generating units and a 20‐year planning horizon. The planning horizon is divided into four equal periods. The new algorithm provides approximately 6.6 billion US$ (3.2%) cheaper solution than GA and also shows faster convergence. Copyright © 2006 John Wiley & Sons, Ltd.     

基于PV曲线和改进遗传算法储能选址定容研究

针对集群储能电站选址定容问题,提出基于光伏PV曲线和改进遗传算法的储能电站选址求解方案,构建储能广域布局模型,针对遗传算法的选择算子及变异算子进行改进设计,利用改进遗传算法设计储能广域布局模型的求解流程.以储能电站投运后获得的经济效益最大为运行目标,对储能电站容量优化配置问题进行求解.最后通过对新疆某地区实测数据,验证...     

基于自适应遗传算法的配电网络重构

针对目前配电网络重构基本遗传算法GA易发生早熟、非全局收敛及收敛速度慢等问题,提出了一种基于改进自适应GA配网重构方法,将最优保存策略和两两竞争相结合的方法作为新选择操作数,采用随最优个体相对保留代数自适应变化交叉和变异操作数,将最优个体最少保留代数作为算法终止条件.算例结果表明,该法全局搜索能力强、收敛速度快,为配电网络重构寻优奠定了理论基础.     

含能效电厂的输电网不确定性规划方法

在全球能源互联网背景下,负荷不断增加、能源消耗增多、环境污染严重给输电网规划带来诸多挑战。为此,在输电网规划中引入能效电厂,建立含能效电厂的多阶段输电网不确定性二层规划模型,其上层模型以总投资成本最小为目标函数,下层模型以N、N-1运行条件下的切负荷量最小为目标函数,就可保证上层模型所得最优规划方案的可靠性。结合改进小生境遗传算法和原始—对偶内点法两种算法的优点对所提模型进行求解,进而得到规划的最优结果。以IEEE-RTS 24节点系统为例,验证了所提方法的有效性和实用性。