基于退火演化算法和遗传算法的机组优化组合算法

机组组合问题是编制短期发电计划时首先要解决的问题，合理的开停机方案将带来很大的经济效益。现代电力系统对机组优化组合算法的收敛速度和解的质量要求越来越高，作者从改善传统算法这两方面着手，根据退火演化算法和遗传算法各自的特点，提出了一种用于机组优化组合的组合算法。与传统的一些优化算法相比，该组合算法具有搜索速度快，收敛性好，而且解的质量相当高。通过对实际系统的测算，验证了该方法的有效性和优越性。该方法具有良好的并行性，易于在并行计算机上实现。     

基于互补机制的水火电力系统短期节能发电调度

为充分提高水火电力系统联合运行的经济性,将减少非可再生能源的使用量及降低火电成本为主要目标的水火电力系统短期发电调度问题,转化为水力发电量最大、耗水量最小和火力发电燃料总耗量最小且具有时序的3个优化子问题。该优化模型不仅可确定水电的最佳放水策略和火电的最佳出力,还可描述水电和火电的互补作用,充分体现节能和效益的理念。针对水电系统具有强非线性的特点,采用改电磁学算法进行求解,对火电子系统则采用内点法进行求解。算例结果验证了该方法的有效性。     

A genetic algorithm solution to the optimal short-term hydrothermal scheduling

This paper presents an algorithm for solving the hydrothermal scheduling through the application of genetic algorithm (GA). The hydro subproblem is solved using GA and the thermal subproblem is solved using lambda iteration technique. Hydro and thermal subproblems are solved alternatively. GA based optimal power flow (OPF) including line losses and line flow constraints are applied for the best hydrothermal schedule obtained from GA. A 9-bus system with four thermal plants and three hydro plants and a 66-bus system with 12 thermal plants and 11 hydro plants are taken for investigation. This proposed GA reduces the complexity, computation time and also gives near global optimum solution.     

An improved PSO technique for short-term optimal hydrothermal scheduling

This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Hybrid Chemical Reaction Optimization Approach for Combined Economic Emission Short-term Hydrothermal Scheduling

—This article presents the hybridization of a newly developed, novel, and efficient chemical reaction optimization technique and differential evolution for solving a short-term hydrothermal scheduling problem. The main objective of the short-term scheduling is to schedule the hydro and thermal plants generation in such a way that minimizes the generation cost. However, due to strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained hydrothermal scheduling formulation is to estimate the optimal generation schedule of hydro and thermal generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. In this context, this article proposes a hybrid chemical reaction optimization and differential evolution approach for solving the multi-objective short-term combined economic emission scheduling problem. The effectiveness of the proposed hybrid chemical reaction optimization and differential evolution method is validated by carrying out extensive tests on two hydrothermal scheduling problems with incremental fuel-cost functions taking into account the valve-point loading effects. The result shows that the proposed algorithm improves the solution accuracy and reliability compared to other techniques.     

A diploid genetic approach to short-term scheduling ofhydro-thermal system

In this paper a diploid genotype based genetic algorithm (GA) is applied to solve the short-term scheduling of hydrothermal systems. The proposed genetic algorithm uses a pair of binary strings with the same length to represent a solution to the problem. The crossover operator is carried out by means of the separating and recombining technique, which is of the same effect of that of uniform crossover. The dominance mechanism in the algorithm is realized by a simple Boolean algebra calculation. Simulation results show that the proposed algorithm has a strong ability to maintain gene diversity in a limited population due to the diploid chromosomal structure accompanying the dominance mechanism. This ability improves the overall performance and avoids premature convergence. The model can concurrently tackle the requirements of power balance, water balance and water traveling time between cascaded power stations, which are more difficult for other approaches to manage. Several examples are used to verify the validity of the algorithm     

基于免疫遗传算法的超短期发电计划优化

超短期发电计划优化在电力系统调度运行中发挥着越来越重要的作用,但由于其是一个非线性整数约束优化问题,数学模型复杂,很难从理论上找到全局最优解。针对电力系统发电计划优化问题,引入免疫遗传算法,很好地解决了遗传算法局部收敛的问题,实现了群体收敛性和个体多样性间的动态平衡调整,能快速准确求解,及时调整超短期发电计划方案,从而达到安全经济环保调度,优化资源配置的效果。     

Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem

A simple and efficient optimisation procedure based on real coded genetic algorithm is proposed for the solution of short-term hydrothermal scheduling problem with continuous and non-smooth/non-convex cost function. The constraints like load-generation balance, unit generation limits, reservoir flow balance, reservoir physical limitations and reservoir coupling are also considered. The effectiveness of the proposed algorithm is demonstrated on a multichain-cascaded hydrothermal system that uses non-linear hydro generation function, includes water travel times between the linked reservoirs, and considers the valve point loading effect in thermal units. The proposed algorithm is equipped with an effective constraint-handling technique, which eliminates the need for penalty parameters. A simple strategy based on allowing infeasible solutions to remain in the population is used to maintain diversity. The same problem is also solved using binary coded genetic algorithm. The features of both algorithms are same except the crossover and mutation operators. In real coded genetic algorithm, simulated binary crossover and polynomial mutation are used against the single point crossover and bit-flipping mutation in binary coded genetic algorithm. The comparison of the two genetic algorithms reveals that real coded genetic algorithm is more efficient in terms of thermal cost minimisation for a short-term hydrothermal scheduling problem with continuous search space.     

A genetic algorithm solution approach to the hydrothermal coordination problem

In this paper, a genetic algorithm solution to the hydrothermal coordination problem is presented. The generation scheduling of the hydro production system is formulated as a mixed-integer, nonlinear optimization problem and solved with an enhanced genetic algorithm featuring a set of problem-specific genetic operators. The thermal subproblem is solved by means of a priority list method, incorporating the majority of thermal unit constraints. The results of the application of the proposed solution approach to the operation scheduling of the Greek Power System, comprising 13 hydroplants and 28 thermal units, demonstrate the effectiveness of the proposed algorithm.     

Scheduling of hydrothermal power systems

The authors present a method for scheduling hydrothermal power systems based on the Lagrangian relaxation technique. By using Lagrange multipliers to relax system-wide demand and reserve requirements, the problem is decomposed and converted into a two-level optimization problem. Given the sets of Lagrange multipliers, a hydro unit subproblem is solved by a merit order allocation method, and a thermal unit subproblem is solved by using dynamic programming without discretizing generation levels. A subgradient algorithm is used to update the Lagrange multipliers. Numerical results based on Northeast Utilities data show that this algorithm is efficient, and near-optimal solutions are obtained. Compared with previous work where thermal units were scheduled by using the Lagrangian relaxation technique and hydro units by heuristics, the new coordinated hydro and thermal scheduling generates lower total costs and requires less computation time     

考虑配电网电能质量改善的分布式光伏优化调度方法

针对配电网中存在的电压越限、谐波畸变、三相不平衡等问题,从综合改善多电能质量问题的角度出发,研究了一种配电网分布式光伏优化调度模型及其求解方法。首先,分析了分布式光伏并网逆变器进行无功功率、谐波电流及不平衡功率补偿的控制方案;在此基础上,以可调度分布式光伏的三相功率及谐波电流输出为控制变量,基于配电网三相基波及谐波潮流方程,建立了最小化网络损耗、不平衡电压以及谐波电压的最优潮流模型;然后,提出了一种潮流计算与优化计算交替迭代的两阶段优化求解算法,对非凸非线性的最优潮流模型进行变量削减,并将其转换为凸二次约束二次规划模型从而实现高效求解;最后,通过一个162节点三相配电网进行算例分析,结果表明,根据最优潮流模型结果对分布式光伏进行调度后,配电网谐波畸变、三相不平衡以及电压越限问题得到了有效改善,且两阶段优化求解算法的计算效率也满足配电网调度时间要求,验证了所提分布式光伏优化调度方法在改善配电网电能质量方面的有效性。     

考虑配电网电能质量改善的分布式光伏优化调度方法

针对配电网中存在的电压越限、谐波畸变、三相不平衡等问题,从综合改善多电能质量问题的角度出发,研究了一种配电网分布式光伏优化调度模型及其求解方法。首先,分析了分布式光伏并网逆变器进行无功功率、谐波电流及不平衡功率补偿的控制方案;在此基础上,以可调度分布式光伏的三相功率及谐波电流输出为控制变量,基于配电网三相基波及谐波潮流方程,建立了最小化网络损耗、不平衡电压以及谐波电压的最优潮流模型;然后,提出了一种潮流计算与优化计算交替迭代的两阶段优化求解算法,对非凸非线性的最优潮流模型进行变量削减,并将其转换为凸二次约束二次规划模型从而实现高效求解;最后,通过一个162节点三相配电网进行算例分析,结果表明,根据最优潮流模型结果对分布式光伏进行调度后,配电网谐波畸变、三相不平衡以及电压越限问题得到了有效改善,且两阶段优化求解算法的计算效率也满足配电网调度时间要求,验证了所提分布式光伏优化调度方法在改善配电网电能质量方面的有效性。     

基于改进白骨顶鸡算法的局部荫蔽光伏MPPT

针对光伏发电系统在局部荫蔽下传统最大功率点追踪方法极易陷入局部最优而导致功率震荡范围较大等问题,提出一种基于改进白骨顶鸡算法的光伏MPPT方法。该算法在传统白骨顶鸡算法的基础上,将Logistic-Sine-Cosine混沌映射因子引入种群跟随者的链式移动中,从而使链式移动变为混沌移动,让算法具备跳出局部最优解的能力;对每次寻优结束后的当前最优位置进行柯西变异,对比变异前后择优更新替代,增加算法的全局搜索能力。在四种光照模式下,将ICOOT与另外三种算法的MPPT进行仿真分析。结果显示,所提改进算法的追踪速度为0.14 s,1.13 s,0.13 s,1.07 s,系统稳定率为99.43%,99.34%,98.73%,98.80%。综合来看,ICOOT在用于光伏发电局部隐蔽MPPT时能有效解决传统算法易于陷入局部最大功率点而导致寻优速度慢、功率震荡大的问题。     

基于改进微粒群算法的水火电力系统短期发电计划优化

微粒群算法(PSO)来源于对社会模型的模拟,是一种随机全局优化技术。该算法简单,容易实现,且功能强大。中对PSO进行了改进,引入了“分群”和“灾变”思想,并应用于求解水火电力系统的短期有功负荷最优分配问题。通过具体算例验证了改进PSO算法的有效性,而且其收敛速度比遗传算法(GA)快,求解精度比普通的PSO和GA的高。     

综合环境保护及峰谷电价的水火电短期优化调度

为了使电力市场环境中的发电侧能够实现节能环保且高收益的发电目标,对机组出力变化与分时电价波动之间的关系进行了研究,构建了一种新的水火电短期优化调度模型,该模型以实现电力市场条件下最大发电收益为目标,同时综合考虑了峰谷分时电价和环境保护成本对发电侧经济效益的影响,还考虑了梯级水电站群的蓄水量、下泄流量、机组出力等约束条件,由此得出机组的优化调度方案。针对传统优化算法难以处理高维梯级水电站优化调度多约束条件的缺陷,利用微分进化算法对此优化模型进行求解,仿真计算结果证明了该模型的合理性和算法的有效性。     

Optimization-based scheduling of hydrothermal power systems withpumped-storage units

This paper presents an optimization-based method for scheduling hydrothermal systems based on the Lagrangian relaxation technique. After system-wide constraints are relaxed by Lagrange multipliers, the problem is converted into the scheduling of individual units. This paper concentrates on the solution methodology for pumped-storage units. There are, many constraints limiting the operation of a pumped-storage unit, such as pond level dynamics and constraints, and discontinuous generation and pumping regions. The most challenging issue in solving pumped-storage subproblems within the Lagrangian relaxation framework is the integrated consideration of these constraints. The basic idea of the method is to relax the pond level dynamics and constraints by using another set of multipliers. The subproblem is then converted into the optimization of generation or pumping; levels for each operating state at individual hours, and the optimization of operating states across hours. The optimal generation or pumping level for a particular operating state at each hour can be obtained by optimizing a single variable function without discretizing pond levels. Dynamic programming is then used to optimize operating states across hours with only a few number of states and transitions. A subgradient algorithm is used to update the pond level Lagrangian multipliers. This method provides an efficient way to solve a class of subproblems involving continuous dynamics and constraints, discontinuous operating regions, and discrete operating states     

基于多种群遗传算法的含分布式电源的配电网 故障区段定位算法

构建了可动态适应多个分布式电源投切的开关函数,同时针对遗传算法的早熟收敛问题,引入多种群遗传算法,提出基于多种群遗传算法的含分布式电源配电网故障区段定位方法。该算法在故障区段定位时规定以系统电源指向用户的方向为馈线正方向,采用多个种群对解空间协同搜索,避免算法陷入局部最优,以最优个体保持代数作为收敛条件,充分提高收敛效率,适用于复杂的含分布式电源的配电网络。通过算例对配电网的故障定位进行仿真,结果表明算法能准确定位,并具有一定的有效性和容错性。     

电动汽车充放电与风力/火力发电系统的协同优化运行

提出一种通过控制规模化电动汽车的充放电,使其能够与现有的风力/火力发电系统协同运行的优化调度策略。针对传统含电动汽车的电力系统优化模型没有考虑电动汽车用户成本,实用性不高的缺陷,建立了包含电网运行经济性、电动汽车用户成本、CO2排放、最小弃风量的多目标优化模型;提出了将改进的NSGA-II遗传算法和加权尺度法相结合的智能优化算法。应用该算法,求出多目标动态优化模型的帕累托前沿,获得了最符合实际的电力系统综合优化调度方案。对所提出的多目标优化调度方法进行了仿真计算,结果证明,采用所提优化策略可以获得最佳的火电、风电与电动汽车之间的出力方案。该方案符合实际,在合理的电动汽车用户成本范围内可有效地降低电网运行成本、风力发电弃风量和大气碳排放量,应用价值较高。     

基于改进粒子群算法的配电网静态重构

针对配电网静态重构问题,结合配电网的辐射状特点,提出了适应于配电网静态重构的改进二进制粒子群算法,建立以系统网损最小为目标函数的静态重构模型。提出的算法运用破圈法生成和更新粒子群,提高搜索有效解的效率,在迭代过程中采取重新初始化粒子策略避免算法陷入局部最优解,提高粒子群算法得到全局最优解的概率。应用于33节点标准测试系统,验证了算法的可行性。