基于HSA-PSO的配电网源-储协同优化控制方法

目前通常采用增加无功补偿装置的手段来解决高渗透率分布式电源接入时的配电网电压控制及网损优化问题,而兼顾分布式电源和储能系统调节作用的控制方法较少。考虑储能系统和分布式电源的协同配合,建立以配电网电压偏差最小、有功网损最小及分布式电源消纳量最大的配电网多目标优化控制模型。有效结合和声搜索算法和粒子群优化算法,提出具有自适应能力的和声搜索-粒子群算法（HSA-PSO）对所建立的模型进行求解。通过仿真算例验证了本文优化控制方法的有效性。     

Optimal Planning of Distributed Generators in Distribution Networks Using Modified Firefly Method

Abstract—This article presents a novel algorithm for optimal planning of a dispatchable distributed generator connected to the distribution networks. The proposed algorithm modifies the traditional firefly method to be able to deal with the practically constrained optimization problems by proposing formulas for tuning the algorithm parameters and updating equations. The proposed algorithm rigidly determines the optimal location and size of the distributed generation units in order to minimize the system power loss without violating the system practical constraints. Moreover, the optimal distributed generator location and minimum size for achieving a certain specified power loss are determined using the proposed method and compared to the results of a proposed heuristic technique. The distributed generation units in the proposed algorithms are modeled as voltage controlled nodes with the flexibility to be converted to constant power nodes in the case of reactive power limit violation. The proposed algorithms are implemented in MATLAB and tested on the IEEE 33-bus and the IEEE 37-nodes feeder. The results that are via comparison with published results obtained from other competing methods show the effectiveness, accuracy, and speed of the proposed method.     

Multi objective optimal allocation of fault current limiters in power system

Transmission systems, connection of distributed generation to the grid are increased due to increase in power demands. This fact causes the increase in short circuit level of power networks. The occurrence of fault in such networks leads to large short circuit currents throughout the system, which may exceed the rating of existing circuit breakers and can damage system equipment. There are some approaches to reduce this fault current such as power network reinforcement and utilization of fault current limiter (FCLs) in power systems. Power system reinforcement is too difficult if not impractical. Therefore, the utilization of FCLs can provide an effective way to suppress fault currents. The effectiveness of FCL depends on the number of FCLs and their installation location. In this paper, a novel approach is presented to determine the optimal number and location of FCLs to improve the power network reliability and fault current reduction based on different conflicting objective functions. IEEE 39 BUS system and IEEE 57 BUS system are considered to evaluate the effectiveness and feasibility of the proposed method. The objective functions considered for the optimal allocation are reliability of power system, economic impact and short circuit current reduction. Unlike what has been previously done in literature, in this paper Pareto based optimization algorithms, namely non-dominated sorting algorithm, multiobjective particle swarm optimization and multiobjective evolutionary algorithm based on decomposition, are utilized to deal with this problem. The use of these methods made it possible to obtain the Pareto optimal front in which these objective functions are optimized simultaneously.     

A Multi-objective Optimization for Sizing and Placement of Voltage-controlled Distributed Generation Using Supervised Big Bang–Big Crunch Method

Abstract—This article presents an efficient multi-objective optimization approach based on the supervised big bang–big crunch method for optimal planning of dispatchable distributed generator. The proposed approach aims to enhance the system performance indices by optimal sizing and placement of distributed generators connected to balanced/unbalanced distribution networks. The distributed generation units in the proposed algorithms are modeled as a voltage-controlled node with the flexibility to be converted to a constant power node in the case of reactive power limit violation. The proposed algorithm is implemented in the MATLAB (The MathWorks, Natick, Massachusetts, USA) environment, and the simulation studies are performed on IEEE 69-bus and IEEE 123-node distribution test systems. Validation of the proposed method is done by comparing the results with published results obtained from other competing methods, and the consequent discussions prove the effectiveness of the proposed approach.     

基于改进蚁狮优化算法的可再生能源分布式电源优化配置

分布式电源的合理规划能够降低配电网的功率损耗,针对可再生能源分布式电源规划配置问题,提出了一种基于改进蚁狮优化算法的可再生能源分布式电源优化配置方法。所提方法建立了以最小化实际功率损耗和改善配电网电压分布与电压稳定性为目标的多目标函数;利用改进蚁狮优化算法,通过模仿自然界中蚁狮的狩猎行为,统筹考虑损耗敏感系数和电压敏感系数,推导出不同类型的分布式电源单元的最佳总线位置和容量;以IEEE-33总线径向分布系统进行了仿真实验。实验结果表明,与其他算法相比,所提算法在降低功率损耗和电压分布方面更优,从而验证了所提算法的适应性和有效性。     

Integration of Distributed Generation in Power Networks Considering Constraints on Discrete Size of Distributed Generation Units

Abstract—An optimization algorithm based on a novel discrete particle swarm optimization technique is proposed in this article for optimal sizing and location of distributed generation in a power distribution network. The proposed algorithm considers distributed generation size and location as discrete variables substantially reducing the search space and, consequently, computational requirements of the optimization problem. The proposed algorithm treats the generator sizes as real discrete variables with uneven step sizes that reflect the sizes of commercially available generators, meaning that it can handle a mixed search space of integer (generator location), discrete (generator sizes), and continuous (reactive power output) variables while substantially reducing the search space and, consequently, computational burden of the optimization problem. The validity of the proposed discrete particle swarm optimization algorithm is tested on a standard 69-bus benchmark distribution network with four different test cases. Two optimization scenarios are considered for each test case: a single objective optimization study where network real power loss is minimized and a multi-objective study in which network voltages are also considered. The proposed algorithm is shown to be effective in finding the optimal or near-optimal solution to the problem at a fraction of the computational cost associated with other algorithms.     

基于分布式协同粒子群优化算法的电力系统无功优化

该文提出一种新颖的用于求解无功优化问题的分布式协同粒子群优化算法.考虑到大规模电力系统集中优化难度较大,采用分层控制中的分解-协调思想将大系统分解成若干个独立的子系统,有效地降低求解问题的复杂度,并采用混合策略在各子系统问进行协同进化.此外,子系统的无功优化采用了一种改进的粒子群优化算法,考虑了更多粒子的信息,能有效地提高算法的收敛精度和计算效率.对4个不同大小规模的系统进行的仿真计算结果表明该文提出的方法能够获得高质量的解,并且计算时间短,效率高,适合求解大规模电力系统的无功优化问题.     

基于改进多目标粒子群算法的微电网并网优化调度

微电网能够协调分布式电源,从而充分发挥分布式发电技术在经济、能源和环境中的优势。针对微电网并网时的优化调度问题,建立了考虑发电成本、污染物排放的微电网系统的环保优化模型,并利用改进的多目标粒子群算法,在这两个目标之间进行协调权衡和折中处理,使所有目标函数尽量达到最优。选取微电网案例的日负荷数据进行了优化调度计算,仿真结果表明了所提模型和算法的有效性。     

含分布式发电的配电网无功优化

研究了含风力发电的无功优化模型,介绍了包含分布式发电的配电网无功优化。当大量风力发电注入电力系统时,风力发电机随机输出功率引起配电网潮流大小、方向频繁变化,干扰了系统原有电压调节装置正常运行。为克服电压波动,保证系统电能质量,文中引入静止无功补偿器（SVC）作为配电网补偿设备,并考虑配电网中可控制无功输出的柴油发电机的无功贡献,以有功能耗费用、SVC安装费用、柴油发电机无功生产费用为目标函数,建立了含分布式发电的配电网无功优化模型。针对由蒙特卡罗仿真得到的不同风力发电状态,在实际算例中应用遗传算法确定各状态下SVC最优安装位置、输出以及可控制无功输出的分布式电源的最优输出结果。     

考虑运行风险的主动配电网分布式电源多目标优化配置

考虑配电网运行中的不确定性,文章通过改进蒙特卡洛法生成大量预想事故集,利用潮流计算和拓扑分析得到接入分布式电源后系统运的行风险。提出一种考虑主动配电网运行风险的分布式电源多目标优化配置模型,将主动配电网运行带来的运行风险RL与分布式电源运行成本CDG作为目标函数,采用改进的粒子群算法对多目标优化模型进行求解,获得分布式电源安装位置和安装容量以及运行风险与运行成本之间的权衡关系。仿真算例表明,所提出的考虑主动配电网运行风险的分布式电源多目标优化配置方法,与单一只考虑经济性或者可靠性的优化模型相比更加合理,适用于分布式电源的优化选址和定容,验证了该模型的可行性。     

基于细菌菌落优化算法分布式电源优化配置

分布式电源位置和容量的优化配置可确保其发挥更好的技术经济效用。在分析DG特性的基础上,建立了考虑含分布式电源的有功网损费用最小和综合投资成本以及购电成本最小模型的多目标优化模型,并且提出了一种新颖的智能优化算法细菌菌落算法。细菌菌落优化算法根据单个细菌的生长方式及其群体菌落繁殖的特性演化而来。该算法建立了细菌菌落的生成和死亡的寻优机制,并提供了一种新的寻优算法的结束方式。最后,通过算例验证所提算法具有良好实用性和适应性,并且也验证所提模型的实际意义。     

基于改进蝙蝠算法的配电网分布式电源规划

合理规划接入电网的分布式电源能够提高能源利用效率,提高电力系统运行的经济性、灵活性和可靠性。建立了以分布式电源建设和运行总费用最小、系统网损最小、静态电压稳定指标最大为优化子目标的多目标规划模型。采用了一种新的仿生算法--蝙蝠算法,并针对蝙蝠算法的不足之处进行了改进,有效地解决了该算法易陷入局部最优、后期收敛速度慢等问题。通过14节点配电网测试系统进行了分布式电源选址和定容仿真分析。仿真结果表明,与传统的蝙蝠算法、粒子群算法相比,采用改进的蝙蝠算法能够更好、更快地得到分布式电源接入配电网的最优规划方案,验证了算法的正确性和可行性。     

分布式电源并网优化配置的图解方法

分布式电源(distributed generation,DG)除了具有调节潮流分布等常规作用外,还可被用来治理电压暂降和降低线损等。针对分布式电源引入配电网后最佳安装位置与容量计算的问题,该文基于链式配电网络、恒功率静态负荷模型和分布式电源的功率模型,并考虑DG对降低线损和调节电压的作用,提出一种图解与遗传算法相结合的计算方法。该方法采用图示求解大量方程,避免了传统算法繁琐的过程和过多的假设条件,并且通过基于电压不越界为约束的遗传算法确定分布式电源的最佳容量,有效避免了节点电压接近合格范围的上界。最后通过典型的仿真算例并与传统方法进行比较,充分证明所提方法的正确性和可行性。     

Optimal Allocation of Distributed Generation and Shunt Capacitors for the Reduction of Total Voltage Deviation and Total Line Loss in Radial Distribution Systems Using Binary Collective Animal Behavior Optimization Algorithm

This article presents a novel binary collective animal behavior algorithm to solve the problem of optimal allotment of distributed generation sets and shunt capacitors in radial distribution systems. Simultaneous sizing and placement of distributed generation units and shunt capacitors in distribution systems is a very complex optimization task, because it is a problem of combinatorial analysis with mixed-integer and binary variables and hard restrictions. With the objective of optimal allotment of shunt capacitor banks and distributed generations, a binary collective animal behavior algorithm optimizes the total line loss, or the total voltage deviation separately in a distribution system, by optimally and simultaneously allocating capacitor banks and distributed generations of optimal ratings, considering the topology of a radial distribution network. The binary collective animal behavior algorithm is applied on various balanced IEEE radial distribution networks. The results are compared to those of a conventional binary particle swarm optimization algorithm to establish the optimization superiority of binary collective animal behavior algorithm.     

大规模区域型微网集群的去中心调度

由于未来区域型微网的经营者不同,为了满足不同经营者之间信息隐私的要求,同时为了应对未来大规模微网并入集群带来的计算挑战,本文采用Dantzig-Wolfe分解方法(Dantzig-Wolfe Decomposition, DWD)以去中心的方式分布式求解区域型多能互补微网集群优化调度问题,并与另外3种分布式分解算法进行对比分析。针对总线型的微网集群,本文验证了所提DWD分解算法在冬季场景下的有效性,且其可在较少迭代次数内收敛到最优值。不同于其余三种分布式分解算法,DWD算法迭代次数随着微网数目的增加变化很小,很适合应用于未来大规模微网并入集群的场景中。     

基于差异进化算法的并行容错无功优化

容错是应用分布式并行计算系统时必须解决的一个关键难点.在基于广域网络的新型计算环境下实现基于进化算法的电力系统优化应用时,需要对大量个体进行频繁的迭代评估,现有的各类容错技术难以实现对此类应用的高效容错.文中结合进化算法概率性搜索、个别个体失效不会影响系统整体性能的特点,提出以父代个体取代未按时返回的子代个体的方式实现容错,并结合基于差异进化算法的无功优化问题对所提出的方法进行了仿真分析.IEEE 118节点系统测试表明该方法能以优化性能的降低为代价实现高效容错.在该容错手段支持下,可通过采用更大范围网络计算资源基础上更大的群体规模,取得一致性更好、更接近全局最优的解.     

不确定性环境下数据驱动的电力系统优化调度方法综述

随着可再生能源大规模并网,电力系统运行的不确定性显著增加,考虑不确定性因素的调度方法逐渐得到重视。另一方面,建立准确的不确定性因素模型是求解优化问题的前提与关键,可再生能源出力具有复杂的不确定性,其发电数据为随机调度提供了科学的数据支撑。文中总结了不确定性环境下数据驱动的电力系统调度的理论方法以及应用场景。首先,总结了传统随机优化调度中数据驱动的随机变量建模方法。其次,介绍了鲁棒优化调度中数据驱动的不确定性集合建模方法。然后,针对随机优化中不确定性因素建模不准确以及鲁棒优化结果较为保守的问题,重点阐述了基于数据驱动分布鲁棒的电力系统优化调度理论与方法,梳理了随机变量的概率分布模糊集构建方法和分布鲁棒优化的模型构建及求解算法。最后,对数据驱动的电力系统调度未来的研究工作进行了展望。     

考虑风光的两阶段配电网动态重构方法

由于传统的控制手段已不能抵抗高渗透率分布式电源对电网的冲击,需要对含分布式电源的配电网动态重构问题展开研究。针对传统配网重构中考虑分布式电源不足、过程复杂耗时和实用性较低等问题,提出了基于生物地理学算法的含分布式电源配电网动态重构两阶段优化策略,建立了以全时段网损最小和开关操作总次数最少为目标的多目标优化模型。首先运用整数型环网编码方法以降低变量维数,对配电网进行时段初步划分,运用夹逼策略对优化区间进行“列举”操作,得到初步优化方案。在此基础上,考虑开关操作总次数约束,对其进行时段的二次优化,最终确定动态重构的开关动作时刻及组合。通过算例验证了该动态重构方法能够在保证操作次数较低的同时,达到减少配电网有功损耗、提高节点电压稳定性的目的。     

考虑新能源不确定性边界的主动配电网优化调度

为促进配电网中新能源的充分消纳同时兼顾调度方案经济性,对新能源发电特性进行了分析,并构建了考虑新能源预测误差分布特性的潜在期望风险评估模型。在满足主动配电网中各个机组单位运行约束的前提下,建立了内嵌新能源不确定性边界优化的主动配电网优化调度模型,并提出了一种改进粒子群优化算法与分支定界算法相融合的双层混合优化算法,以有效地求解所建含变限积分的混合整数非线性规划模型。算例结果表明,与现有主动配电网确定性和鲁棒动态经济调度方法相比,所提基于最优新能源不确定边界方法可以合理权衡调度方案运行的经济性和潜在风险,取得更优的总成本。     

计及不确定性因素的分布式电源与电动汽车充电站协调优化

高渗透率分布式电源与电动汽车负荷接入配电系统给电网的安全经济运行带来了显著影响。基于这一背景,文中对传统配电系统规划模型进行改进,使其能有效应对配电系统中分布式电源与电动汽车充电站的协调优化问题。应用离散化的多状态模型表征可再生资源的不确定性,构建以系统网损最小为优化目标的协调配置模型,并使用二阶锥松弛技术对所建优化模型进行等价处理。基于IEEE 33节点配电系统,对所建模型的有效性进行验证,结果表明所提方法可有效计及可再生资源不确定性的影响,制定满足工程实际应用需求的协调优化方案。