配电网无功补偿的动态优化算法

配电网无功补偿动态优化的目的是在考虑负荷变化和电容器操作次数约束的条件下,求得各节点电容器的最优动作时间和投入容量。该文将动态优化问题描述为变约束优化问题,并提出一种求解算法。算法先将动态优化问题分解为一系列单节点电容器动态优化子问题,然后通过迭代求解一系列子问题的方式得到整个动态优化问题的最优解。由于给出的单节点电容器动态优化子问题的求解算法可以在变约束条件下搜索到子问题的最优解,而且在迭代求解过程中电容器的投入容量和动作时问都可以得到修正,因此使整个动态优化问题能得到更好的优化结果。算例表明,提出的算法是可行和有效的。     

Optimal siting and sizing of UPFC using evolutionary algorithms

This paper addresses an application of evolutionary algorithms to optimal siting and sizing of UPFC which are formulated as single and multiobjective optimization problems. The decision variables such as optimal location, both line and distance of UPFC from the sending end, control parameters of UPFC and system reactive power reserves are considered in the optimization process. Minimization of total costs including installation cost of UPFC and enhancement of the loadability limit are considered as objectives. To reduce the complexity in modeling and the number of variables and constraints, transformer model of UPFC is used for simulation purposes. CMAES and NSGA-II algorithms are used for optimal siting and sizing of UPFC on IEEE 14 and 30 bus test systems. NSGA-II algorithm is tested on IEEE 118 bus system to prove the versatility of the algorithm when applied to large systems. To validate the results of transformer model of UPFC for optimal siting and sizing, results using other models are considered. In single objective optimization problem, CMAES algorithm with transformer model yields better results when compared to other UPFC models. The statistical results conducted on 20 independent trials of CMAES algorithm authenticate the results obtained. For validating the results of NSGA-II with transformer model for optimal siting and sizing of UPFC, the reference Pareto front generated using multiple run CMAES algorithm by minimizing weighted objective is considered. In multiobjective optimization problem, the similarity between the generated Pareto front and the reference Pareto front validates the results obtained.     

基于改进遗传退火算法的输配电网协调规划方法

针对目前对输配电网协调性考虑不足的问题,考虑机组间负荷优化分配因素,引入耗量成本,构建了包含输配电网协调性指标的电网评价指标体系。结合电网经济性指标和可靠性指标,通过优化算法选出综合性能最优的输配电网规划方案。在解决遗传算法"早熟"、易陷于局部最优等问题的基础上,提出了一种优化算法并将其应用于考虑输配网协调性的电网规划问题中。仿真计算结果表明,所提出的方法是可行、高效的。     

Hydroelectric generation scheduling with an effective differentialdynamic programming algorithm

An effective multiplier method-based differential dynamic programming (DDP) algorithm for solving the hydroelectric generation scheduling problem (HSP) is presented. The algorithm is developed for solving a class of constrained dynamic optimization problems. It relaxes all constraints but the system dynamics by the multiplier method and adopts the DDP solution technique to solve the resultant unconstrained dynamic optimization problem. The authors formulate the HSP of the Taiwan power system and apply the algorithm to it. Results demonstrate the efficiency and optimality of the algorithm for this application. Computational results indicate that the growth of the algorithm's run time with respect to the problem size is moderate. CPU times of the testing cases are well within the Taiwan Power Company's desirable performance; less than 30 minutes on a VAX/780 mini-computer for a one-week scheduling     

Considering Different Network Topologies in Optimal Overcurrent Relay Coordination Using a Hybrid GA

The directional overcurrent relays (DOCRs) coordination problem is usually studied based on a fixed network topology in an interconnected power system, and is formulated as an optimization problem. In practice, the system may be operated in different topologies due to outage of the transmission lines, transformers, and generating units. There are some situations for which the changes in the network topology of a system could cause the protective system to operate without selectivity. The aim of this paper is to study DOCRs coordination considering the effects of the different network topologies in the optimization problem. Corresponding to each network topology, a large number of coordination constraints should be taken into account in the problem formulation. In this situation, in addition to nonlinearity and nonconvexity, the optimization problem experiences many coordination constraints. The genetic algorithm (GA) is selected as a powerful tool in solving this complex and nonconvex optimization problem. In this paper, in order to improve the convergence of the GA, a new hybrid method is introduced. The results show a robust and optimal solution can be efficiently obtained by implementing the proposed hybrid GA method.     

Fault Location Observability Using Phasor Measurement Units in a Power Network Through Deterministic and Stochastic Algorithms

This article studies deterministic and stochastic algorithms for placing minimum number of phasor measurement units (PMUs) in a power system in order to locate any fault in the power system. The optimization problem is initially formulated in a mixed integer linear programing framework with binary-valued variables as well as in a binary integer linear programing model. Then, the optimization problem is formulated as an equivalent non-linear programing model, minimizing a quadratic objective function subject to equality non-linear constraints defined over a bounded and closed set. The problem is solved by using a Sequential Quadratic Programming algorithm. The non-linear program is illustrated with a 7-bus test system. Also, stochastic algorithms such as binary-coded genetic algorithm and particle swarm optimization have been implemented in solving the optimal PMU placement under fault condition. The accuracy of suggested algorithms is independent from the fault type and its resistance. The optimization models are applied to the IEEE systems. The numerical results indicate that the proposed algorithms locate minimizers at the optimal objective function value in complete agreement with those obtained by branch-and-bound algorithms.     

离散粒子群优化算法在输电网络扩展规划中的应用

输电网络扩展规划是一个非常复杂的大规模组合优化问题。文中研究了离散粒子群优化(DPSO)算法在单阶段输电网络扩展规划中的应用,提出一种基于黑板系统的多智能体协调模型。该模型中,DPSO算法利用黑板系统进行粒子间信息共享,有效地提高了算法的全局收敛能力。通过构造节点扩展矩阵和速度矩阵,建立了输电网络扩展规划问题的求解模型和DPSO求解算法。该算法已成功用于IEEE Garver-6等两个系统,计算结果表明了该方法的可行性和有效性。     

改进差分进化算法优化多值属性系统诊断策略

测试序列优化问题是故障诊断过程中的关键性问题;针对多值属性系统的测试序列优化问题,采用自适应差分进化算法,结合多值属性系统的特点,分析变异算子在算法中的作用,并设计了个体的编码策略以及两种不同的诊断方式,提出一种将高斯,柯西变异算子与多差分策略进行融合的差分进化算法;通过实验对比分析,结果表明该算法不仅可以很好的应用于多值属性系统,而且在处理二值属性系统的测试序列优化问题时,与已有算法相比,该算法得到的测试点数目更少,期望测试代价更低,可用于多值属性系统求解诊断策略问题。     

电力系统无功优化多目标处理与算法改进

电力系统无功优化属于典型的多目标非线性复杂优化问题,求解非常困难。近年来,众多智能优化算法应用于该问题,其中粒子群优化(Particle Swarm Optimization,PSO)算法最具代表性;但PSO算法性能仍有待提高,如可能陷入局部极值。提出一种多策略融合粒子群优化(Particle Swarm Optimization with Multi-Strategy Integration,MSI-PSO)算法,对速度更新公式引入选择操作,分阶段加速因子调整和惯性权重动态调整,以平衡粒子局部搜索与全局探索能力;同时,随机选取部分性能差的粒子,将其速度更新公式中的个体认知部分修改为社会认知部分,以提高算法搜索精度和收敛速度。建立以系统网络损耗最小和系统电压稳定裕度最大为目标的无功优化仿真模型,分别考虑加权法、隶属度函数法和Pareto法实施多目标处理。针对IEEE30节点测试系统进行仿真实验,结果表明,和其他几种改进PSO算法以及基于pareto最优解集PSO算法进行对比,所提MSI-PSO算法具有更好的性能,能够有效求解电力系统多目标无功优化问题。     

基于改进多目标海樽群算法的电力系统优化调度

文中提出了一种新的多目标海樽群优化算法,将其与等式约束修正技术和可行解占优约束处理技术相结合,用于求解高度约束的电力系统环境经济优化调度问题。该算法采用高斯采样策略和变异操作增强其寻优性能;通过一种改进的基于动态拥挤距离的非支配排序方法获得分布均匀的帕累托最优前沿;应用模糊集理论为决策者提供最佳折中解。在IEEE 30节点6机组标准测试系统上进行算例仿真,并与其它优化算法进行了对比。结果表明,所提算法在求解电力系统环境经济调度问题时具有更好的优化效果。     

结合区块链的园区综合能源系统可信协调优化方法

分布式能源的日益普及与随之而来更复杂的能源系统结构,极大地增加了传统集中式求解能源协同优化问题的难度与成本。分布式优化方法与复杂的能源结构有良好的适配性,但缺少天然可信的协调中心,存在主体隐私保护困难、主体间交互数据真伪难辨等问题,因此提出了结合区块链的园区综合能源系统可信协调优化方法。首先,引入协调者角色组成协调委员会,并利用无偏随机排序算法选取协调者。之后,通过分解协调算法将协调优化问题解耦为上下两层的求解最优值问题,并利用区块链共识算法保障多协调者间可信。然后,设计了双区块链园区综合能源系统数据存储模型,保证数据安全和交易链效率。最后,仿真实验表明结合区块链的园区综合能源系统可信协调优化方法可公平选取协调者,有效实现园区综合能源协同优化、降低节点作恶影响、保护数据隐私,具有较好的鲁棒性和容错性。     

基于鲸鱼优化算法的无功优化调度

针对目前电力系统中的无功优化问题尚缺乏一种能兼顾求解的高效性与全局搜索最优性的方法,本文将一种新的启发式算法--鲸鱼优化算法(WOA)运用到电网无功优化调度中,以系统有功功率损耗最低为目标函数,通过引入惩罚函数建立无功优化模型,对IEEE-14节点系统与IEEE-30节点系统进行仿真,并利用单因素方差分析法(One-way ANOVA)将所得结果与之前的粒子群优化算法(PSO)及引入加速度系数的时变粒子群优化(PSO-TVAC)进行比较,研究表明WOA算法在迭代次数、搜索能力及收敛问题上的潜力,并证明了在解决电力系统无功优化问题上的鲁棒性和有效性,同时也为解决非线性约束问题提供了新途径。     

Comprehensive coordination of combined directional overcurrent and distance relays considering miscoordination reduction

This paper presents a novel approach to solve the miscoordination problem of combined directional overcurrent and distance relays in transmission and subtransmission systems. In order to reduce relays miscoordinations, a general objective function is presented to find optimum directional overcurrent relays time setting multipliers, characteristics, and pickup currents by optimization algorithms. In previous researches, different approaches have been presented but they cannot find a reliable solution to avoid from having negative discrimination times between the backup and main relay (miscoordination), which means operation of the backup relay before the main relay. Using proposed approach, not only the number of miscoordinations can be greatly decreased but also the positive discrimination times can be minimized. The proposed method is tested on 9-bus and 39-bus test system. Genetic algorithm and human behavior-based optimization are used as optimization tools to find optimum settings. The results indicate that the proposed approach is capable of solving the miscoordination problem, in addition to minimization of discrimination and relay operation times compared with previous approaches.     

REI等值技术在多区域无功优化计算中的应用

提出将REI等值技术应用于求解多区域电力系统的无功优化问题,对系统各个分区的外部网络进行REI等值化简,应用非线性原对偶内点法独立进行各个分区的无功优化计算。并对REI节点归并方案、相角传递、等值网络初值计算、内层迭代计算、外层协调计算与REI等值网络修正等关键问题提出了具体的解决办法。以IEEE 39节点和某538节点实际系统作为算例,通过与集中优化方法进行比较,验证了所提方法的有效性,并分析了三种REI节点归并方案对分解协调优化计算的影响。     

Optimal allocation,sizing of PHEV parking lots in distribution system

The emerging of plug-in-hybrid electric vehicles (PHEV) results in the increase in the utilization of vehicles batteries for grid support. This paper presents a multi-objective algorithm to optimally determine the number of parking lots to be allocated in a distribution system. In addition, the algorithm optimally selects the locations and sizes of these parking lots. The proposed algorithms determine also the corresponding energy scheduling of the system resources. The objective of the proposed algorithm is to minimize the overall energy cost of the system. The problem is formulated as an optimization problem which is solved using artificial bee colony (ABC) and firefly algorithm taking into consideration the power system and PHEV operational constraints. The proposed algorithms are applied to a 33-bus radial distribution network. The test results indicate an improvement in the operational conditions of the system.     

基于改进的SOS算法的反时限过电流保护协调优化

通过优化设置继电保护定值,可以优化电力系统中继电保护装置的协调性,减小继电器动作时间,提高电力系统的可靠性。文中针对配电网中保护的协调配合问题,将反时限过电流保护的整定计算转化为一种含有多个约束条件的非线性规划问题,提出了一种基于改进的SOS算法的反时限过电流保护协调优化方案。在IEEE 3节点系统和IEEE 8节点系统中的仿真结果表明,改进的SOS算法具有更好的收敛速度和精度,能够克服解陷入局部最优,基于改进的SOS算法的保护优化协调方案能够提高保护的速动性。     

基于原-对偶内点法和混合整数规划法的机组组合问题研究

电力系统机组组合能带来显著的经济效益,随着电力市场的不断发展,在电力系统优化运行中变得尤为重要。然而电力系统机组组组合问题是一个大规模、非凸、非线性混合整数优化问题,至今仍然没有找到一种理想的优化算法。因此针对其特点,采用了原-对偶内点法和混合整数规划法相结合的算法,首先利用混合整数规划法形成伴随规划来处理离散变量,再通过内点法求解伴随规划子问题和负荷经济分配,充分发挥了两种算法在求解机组组合问题上的优势。文中对标准10机组24时段系统算例进行了仿真测试并与之前的各种优化算法进行了对比,结果表明了本文算法模型解决机组组合问题的有效性和优越性。     

An -dominance-based multiobjective genetic algorithm for economic emission load dispatch optimization problem

In this paper, a novel multiobjective genetic algorithm approach for economic emission load dispatch (EELD) optimization problem is presented. The EELD problem is formulated as a non-linear constrained multiobjective optimization problem with both equality and inequality constraints. A new optimization algorithm which is based on concept of co-evolution and repair algorithm for handling non-linear constraints is presented. The algorithm maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of -dominance. The use of -dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto-set approximation by choosing an appropriate value.The proposed approach is carried out on the standard IEEE 30-bus 6-genrator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions of the multiobjective EELD problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem.     

现代电力系统恢复控制研究综述

综述了现代电力系统恢复控制的任务、主要问题以及解决方法。从宏观角度将恢复控制划分为恢复计划和恢复培训、有功平衡与频率控制、无功平衡与电压控制和继电保护以及安全自动装置的配合4个主要问题。恢复计划的制定是一个复杂的优化问题,一般可离线预计划,并采用人工智能技术和最优化方法相结合,予以解决。对于有功平衡与频率控制问题,要考虑电源恢复和负荷恢复2个方面。电源恢复是一个动态规划问题,要求在满足各种约束条件下,使系统的发电量最大;而负荷恢复目前是研究热点,其要求和电源恢复同步进行,满足频率约束、网络约束并考虑社会经济效益。主要方法有遗传算法、蚁群算法以及一些近似算法。对于无功平衡与电压控制问题,综述了持续工频过电压、操作过电压和谐波过电压的产生原因、控制手段以及目前的预测和控制方法,主要包括基于灵敏度分析、概率统计以及神经网络的方法。最后,综述了系统恢复不同阶段中继电保护以及安全自动装置的配合的主要问题和处理方法。     

基于斐波那契树辨识算法的区域电网调速系统参数辨识

当前电网中单通道输送功率占比较高,电网出现大功率扰动的概率增大,频率稳定问题重新引起人们的关注。针对目前电力系统频率特性仿真准确度不足的问题,引入具有全局和局部交替寻优特点的斐波那契树优化算法,用于区域电网调速系统参数整体辨识。通过限制树的结构深度、调整全局搜索策略等调整,提高全局寻优能力和收敛速度,使算法适用于参数辨识工作,形成斐波那契树辨识算法。基于实测频率响应曲线,运用该算法对区域电网原动机及调速系统参数进行整体辨识,并进行了多算法对比分析。仿真结果表明,斐波那契树辨识算法应用于此领域具有较好的适用性与优越性。