复合遗传混合智能算法在配电网检修时间优化中的应用

针对配电网设备检修计划优化模型,提出了一种新的优化算法。该混合智能算法将复合形法作为一个算子嵌入遗传算法内部,充分结合复合形法与遗传算法的优点,弥补复合形法与遗传算法自身存在的缺陷。通过算例计算,验证了该混合智能算法既有复合形法易收敛、局部寻优能力强的特点,又有遗传算法全局搜索能力强的特点。另外,通过算例结果比较,显示了混合智能算法与遗传算法相比在性能上的优越性。该混合智能算法的优化结果应用在配电网设备检修计划中,能很大程度上降低损耗,具有很好的经济应用价值。     

配电网检修计划优化模型

从配电网设备检修计划编制的实际情况出发，建立了考虑多种约束条件的负荷转移路径和设备检修时间联合优化模型。该模型以设备检修时间优化为主问题，以负荷转移路径优化为子问题。通过主问题和子问题的反复优化迭代，最终获得供电企业售电损失最小的检修计划以及停电负荷、开关操作次数和系统网损最小的负荷转移方案。针对该模型的特点，采用免疫禁忌混合算法对主问题进行求解，采用改进的待恢复树切割算法对子问题进行求解。针对基本待恢复切割算法需要对联络开关进行全排列，以穷举的方式获取最优解的不足，通过应用“优先选择备用容量较大的联络开关”启发规则以及在恢复树切割过程中引入网损比较环节，在保证解的质量的同时有效地降低了恢复树的切割次数，提高了计算速度。通过算例计算和分析，验证了所提出的模型和算法的正确性和实用性，适合求解配电网检修优化问题。     

遗传—模拟退火混合算法 在配电网检修优化中的应用

从配电网设备检修计划编制的实际需要出发,在考虑多种约束条件的基础上,建立了以配电网经济性最好为目标的优化模型.针对该模型的特点,采用1种新型混合遗传-模拟退火算法(HGSA)对配电网检修计划进行优化调整.该算法综合了遗传算法和模拟退火算法的优点,使其既具有遗传算法的全局性和并行性,又具有模拟退火算法的局部搜索能力和退火特征.通过遗传算法、模拟退火算法对实际检修计划优化结果的比较,证明了所提出HGSA算法的有效性.     

市场环境下中长期发输电协调检修计划优化

根据电力市场环境中检修计划的特点,建立了协调市场各方利益的发输电一体化检修计划优化的数学模型,该模型综合考虑电力系统安全经济性和电力市场公平性,并考虑机组检修和输电设备检修之间的相互关联关系.针对该优化模型的求解,利用遗传算法(GA)和粒子群优化(PSO)算法相似的优化框架和优化流程,提出一种充分结合GA和PSO算法各自优点的混合智能算法,该算法将群体分成2个子群,分别采用GA和PSO算法进行演化,并充分交换2种算法所获取的优化信息,形成一个紧密耦合的、新型的遗传粒子群优化算法.算例证明,该算法在求解发电、输电设备检修协调优化这样的大规模复杂优化问题时,在全局搜索和局部搜索方面都表现出了良好的均衡性.     

基于遗传拓扑混合算法的配电网多故障抢修策略

从中国配电网的实际情况出发,建立了电力系统在多故障情况下的多目标抢修策略优化模型。针对配电网接线呈辐射状的特点,提出了一种遗传拓扑混合算法作为寻优策略。该算法通过遗传算法寻优,通过拓扑分析算法判断出失电区域、待修复设备以及负荷情况,综合进行适应度评价,反复迭代得到最佳抢修方案。在寻优计算过程中,针对遗传算法在复杂配电网计算中易产生大量不可行解的问题,引入了智能选择的混合交叉因子进行改进。算例结果证明了该混合智能算法的有效性和鲁棒性。     

基于遗传算法的配电网检修计划优化

配电网检修计划优化是一个多目标多约束的优化问题,针对考虑多种约束条件、以经济性最好为目标的配电网检修计划优化模型的特点,对约束条件进行简化处理,采用遗传算法进行优化,获得供电企业售电损失最小的检修计划方案。并通过算例计算和分析,验证了所提出的算法的正确性和实用性,适合求解配电网检修优化问题。     

基于改进的人工鱼群算法的配电网无功优化

研究了基于变异算子与模拟退火算法混合的人工鱼群算法,对算法中的变异算子进行了改进,并提出了1种试探确定变异概率的方法：同时对配电网无功优化的模型进行了改进．将经济性目标和安全性目标相结合。将改进的人工鱼群算法运用到配电网无功优化中．实际系统的仿真计算结果表明,基于改进人工鱼群算法的配电网无功优化方法合理可行。     

基因/禁忌组合算法在配电网网架优化规划中的应用

在比较了基因算法和禁忌算法各自优缺点的基础上,针对配电网网架优化规划中多约束、非线性和整数寻优的特点,提出采用基因／禁忌组合的算法的策略,并应用于配电网网架优化规划。算法计算证明了该组合算法在配电网网架优化规划中应用的可行性和优越性。     

基于蚁群算法的配电网检修计划优化

从配电网检修计划编制的实际情况出发,同时考虑了线路故障等级的检修时间优化和和设备检修时最优负荷转移路径。针对多种约束条件,对经济性最优为目标的检修计划,通过小生境遗传算法和蚁群算法优化的方法得到配电网检修计划时间优化方案。     

基于环网编码和混合智能优化算法的配电网重构

针对配电网重构中基本遗传算法的编码效率低及单一智能算法存在的问题,提出了一种基于环网的编码方案及将克隆遗传和蚊群算法进行融合的混合算法.该方法利用基于环网编码的克隆遗传算法的随机搜索能力强、编码效率高等特点形成蚁群算法的初始信息素分布,依靠蚂蚁算法的并行性、正反馈机制和全局收敛能力进行求解.以含分布式电源的IEEE33节点系统为算例进行验证,实验结果证明了环网编码和混合智能优化算法在配电网重构中的有效性.     

配电网多故障抢修与供电恢复联合优化模型

建立了多故障抢修与供电恢复的联合优化模型,通过故障抢修顺序和停电负荷恢复路径的交互影响和反复迭代,最终得到最优的抢修计划和各个阶段最优的供电恢复策略。应用快速非支配排序遗传算法(NSGA-Ⅱ)对所建立的模型进行求解,通过动态操作系统回支关联矩阵来修正在寻优过程中产生的违背"配电网辐射运行"约束的不可行解。算例计算证明,采用不同的抢修顺序和供电恢复策略将对配电系统的供电可靠性产生不同的影响,所建立的模型是正确有效的。     

A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid tabu search (TS), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid TS–PSO–SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid TS–PSO–SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. TS are used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the TS method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO–SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.     

基于免疫算法的配电网开关优化配置模型

给出了开关优化的数学模型及基于免疫算法的求解方法。分析了开关投资和运行维修费用、停电损失的计算方法，基于等年值法建立开关优化配置模型。该模型是一含约束、不可微、非连续的组合优化模型。基于免疫算法给出了模型的求解算法。通过模型计算确定开关的最优配置数量和位置，并得到系统的可靠性、投资费用和停电损失等。计算实例表明：提出的模型和算法有较强的工程实用性，免疫算法具有较好的全局收敛性和较快的收敛速度。     

基于气候因素的配电网络维修风险管理

配电系统处于电力系统的末端,直接与用户相连,是整个电力系统与用户联系的纽带,是向用户供应电能和分配电能的重要环节。气候因素对配电线路的失效率影响较大,因此,管理配电线路维修期计划应考虑气候因素的影响。基于此,建立了考虑气候因素的配电线路维修风险分析方法,对不同气候条件下的配电线路维修风险水平进行了量化的期望缺供电量比较,为配电线路维修风险管理提供科学的依据。     

基于机会约束2层规划的输电线路检修计划优化

通过对电力市场环境下输电线路检修计划优化问题（TMSOP）的研究,提出基于机会约束 2层规划的TMSOP模型和算法。模型考虑了TMSOP涉及的不确定因素,统筹了检修经济性和检修过程中电网的可靠性,同时综合评估了不安全现象的概率和后果。模型通过基于蒙特卡罗仿真的混合智能优化算法求解,在求解过程中引入禁忌表以提高计算速度。IEEE RTS-79算例仿真结果表明,文中提出的模型和算法不仅优化了线路检修计划和检修资源安排,而且明确了检修期间电网风险最低的运行方式。     

遗传禁忌混合算法及其在电网规划中的应用

电网规划是一个较难解决的NP难问题。文中首先就遗传算法、禁忌搜索算法(TS)及其两者的混合算法在旅行商问题(TSP)中的应用来比较它们之间的优缺点,认为采用了TS变异算子的改进遗传算法将大大提高其优化能力;然后通过该混合算法在典型电网扩展规划算例中的应用来看,认为该混合算法适用于求解复杂的电网规划问题;最后通过对该混合算法在求解实际的城市中压配电网络规划问题时与其他两种单一算法的结果比较来看,其搜索效率相比单一算法得到了很大程度的提高,体现了很好的应用前景。     

Immune algorithm combined with estimation of distribution for traveling salesman problem

This paper describes an artificial immune algorithm (IA) combined with estimation of distribution algorithm (EDA), named IA‐EDA, for the traveling salesman problem (TSP). Two components are incorporated in IA‐EDA to further improve the performance of the conventional IA. First, aiming to strengthen the information exchange during different solutions, two kinds of EDAs involving univariate marginal distribution algorithm and population‐based incremental learning are altered based on the permutation representation of TSP. It is expected that new promising candidate solutions can be sampled from the constructed probabilistic model of EDA. Second, a heuristic refinement local search operator is proposed to repair the infeasible solutions sampled by EDA. Therefore, IA‐EDA can alleviate the deficiencies of the conventional IA and can find better solutions for TSP by well balancing the exploitation and exploration of the search. Experiments are conducted based on a number of benchmark instances with size up to 100 000 cities. Simulation results show that IA‐EDA is effective for improving the performance of the conventional IA and can produce better or competitive solutions than other hybrid algorithms. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Using immune genetic algorithm based hybrid techniques for short-term unit commitment problem

This paper presents a hybrid chaos search (CS), immune algorithm (IA)/genetic algorithm (GA), and fuzzy system (FS) method (CIGAFS) for solving short-term thermal generating unit commitment (UC) problems. The UC problem involves determining the start-up and shut-down schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve, and individual units. First, we combined the IA and GA, then we added the CS and the FS approach. This hybrid system was then used to solve the UC problems. Numerical simulations were carried out using three cases: 10, 20, and 30 thermal unit power systems over a 24 h period. The produced schedule was compared with several other methods, such as dynamic programming (DP), Lagrangian relaxation (LR), standard genetic algorithm (SGA), traditional simulated annealing (TSA), and traditional Tabu search (TTS). A comparison with an immune genetic algorithm (IGA) combined with the CS and FS was carried out. The results show that the CS and FS all make substantial contributions to the IGA. The result demonstrated the accuracy of the proposed CIGAFS approach.     

Research on optimal schedule strategy for active distribution network using particle swarm optimization combined with bacterial foraging algorithm

Comparing with the traditional distribution network, a significant feature of the active distribution network (ADN) is that the performance of distributed generation (DG) units, energy storage units and micro-grid (MG) in the network is controllable for the distribution network operator. Considering the characteristics of the distributed power supply and micro-grid, and giving full play to the advantages of distributed generation technology in the economic, environmental and energy aspects, this paper highlights an environmental protection and energy saving optimal schedule model for ADN. The scheduling model focuses on the minimum network loss, minimum voltage deviation and minimum difference between peak and valley load. In addition, the two stage algorithm is presented to solve the proposed multi-objective scheduling model of ADN. First, a set of Pareto solutions are obtained by using the proposed particle swarm optimization combined with bacterial foraging algorithm (PSO-BFO) to solve multi-objective optimization problems, then the optimal schedule strategy of ADN is gained through evaluating the Pareto solutions with entropy weight decision-making method. To avoid the search falling into local optimal solution, the two-value crossover operator is introduced to exchange the information among subpopulations and update the position of related particles. Meanwhile, the adaptive adjusting inertia constant strategy is used to improve the algorithm convergence speed. Finally, the case study results demonstrate the rationality of the proposed optimal schedule model and the validity of its solution algorithm for ADN.