遗传算法在电力系统无功优化中的应用

在利用遗传算法求解电力系统的无功优化问题，在优化编码和变异概率取值两个方面进行了研究，进一步推动了遗传算法在实际系统优化问题中的应用。在电力系统无功优化这个具有多局部极小值的寻优方面，把遗传算法所求得的无功优化结果和传统的基于梯度寻优方向的非线性规划法所得的优化结果进行比较，指出了遗传算法在处理非连续的和非平滑的函数寻优方面优于传统的寻优方法，具备全局寻优的能力。     

Optimization-a new tool in a simulation program system [for powernetworks]

In this paper an optimization tool in the simulation program system NETOMAC is presented. The motivation is to provide users with a simple and efficient way of flexibly solving various optimization problems arising in power systems. The most attractive features of the optimization within the simulation program system are that: (1) an objective function and all constraints related can be implicitly formulated with the block-oriented simulation language; and (2) the performance of various optimizations does not need any modification to the source program. Three improved optimization algorithms have been implemented. These algorithms have been developed in a real problem-oriented way. They are efficient and robust in practical applications. Capabilities of the optimization tool implemented are demonstrated in this paper by solving four optimization problems arising in power systems     

Data-enabled extremum seeking: A cooperative concurrent learning-based approach

This paper introduces a new class of feedback-based data-driven extremum seeking algorithms for the solution of model-free optimization problems in smooth continuous-time dynamical systems. The novelty of the algorithms lies on the incorporation of memory to store recorded data that enables the use of information-rich datasets during the optimization process, and allows to dispense with the time-varying dither excitation signal needed by standard extremum seeking algorithms that rely on a persistence of excitation (PE) condition. The model-free optimization dynamics are developed for single-agent systems, as well as for multi-agent systems with communication graphs that allow agents to share their state information while preserving the privacy of their individual data. In both cases, sufficient richness conditions on the recorded data, as well as suitable optimization dynamics modeled by ordinary differential equations are characterized in order to guarantee convergence to a neighborhood of the solution of the extremum seeking problems. The performance of the algorithms is illustrated via different numerical examples in the context of source-seeking problems in multivehicle systems.     

Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem

This paper proposes an efficient optimization approach, namely quasi-oppositional teaching learning based optimization (QOTLBO) for solving non-linear multi-objective economic emission dispatch (EED) problem of electric power generation with valve point loading. In this article, a non-dominated sorting QOTLBO is employed to approximate the set of Pareto solution through the evolutionary optimization process. The proposed approach is carried out to obtain EED solution for 6-unit, 10-unit and 40-unit systems. For showing the superiority of this optimization technique, numerical results of the four test systems are compared with several other EED based recent optimization methods. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques.     

基于改进PSO算法在含风电场的电力系统无功优化控制

含风电场的电力系统无功优化是一种具有多状态、多约束条件的非线性规划问题.针对其存在易陷入局部最优解的缺点,提出了改进的PSO算法.该算法改变了初始化方法和粒子更新方法,并在算法后期引入变异因子.在放射状配电网络系统的仿真计算中,改进PSO算法与遗传算法相比较,结果表明,改进PSO算法可在较短时间内取得更好的优化效果.     

新能源电力系统不确定优化调度方法研究现状及展望

风电场和光伏电站出力的不确定性给电力系统优化调度带来很大技术挑战。文中主要介绍了考虑新能源不确定性的电力系统优化调度方法的研究现状及后续研究方向展望。首先,重点论述了各种不确定优化调度（UOD）方法,包括随机优化方法、鲁棒优化方法、随机鲁棒优化结合方法和基于人工智能技术的方法。其中,随机优化方法包括场景法、机会约束规划法和近似动态规划法;鲁棒优化方法包括传统鲁棒优化法和分布鲁棒优化法;随机鲁棒优化结合方法包括采样鲁棒优化法和分布鲁棒机会约束规划法。然后,介绍了每一种方法的优化模型形式、模型的转化和求解原理及其优缺点。最后,对UOD的后续重点研究方向进行展望,包括兼顾多个目标的UOD问题及多目标不确定优化方法、输配系统UOD问题及分布式不确定优化方法、考虑稳定性约束的UOD问题及含常微分方程约束的不确定优化方法、考虑管道传输动态的综合能源系统UOD问题及含偏微分方程约束的不确定优化方法。     

遗传算法在电力系统无功优化中的应用

遗传算法根据自然界适者生存的原则进行搜索和优化。将遗传算法应用于电力系统无功优化，不仅能避免一般优化算法的局部最优问题，并能解决无功优化中变量的离散问题，避免维数灾难，提供最优及次优方案，使无功优化更切实际。遗传算法的引入，为电力系统无功优化提供了一种新的计算方法，使无功优化方法更加完善和实用。     

Optimal coordination of directional overcurrent relays ininterconnected power systems

The authors present a new methodology based upon the principles of optimization theory, to treat the problem of optimal coordination of directional overcurrent relays in interconnected power systems. With the application of the proposed technique, this coordination problem is stated as a parameter optimization problem, which in general, is of a large dimension, especially when many different system configurations and perturbations are to be considered. Several optimization procedures, including direct methods and decomposition techniques, for solving this large scale coordination problem are described, and results of optimally coordinating directional overcurrent relays in power systems with up to 30 buses are presented     

多能互补系统全寿命周期优化配置方法

针对含风力发电、光伏发电和储能系统的多能互补系统的优化配置问题,提出一种考虑系统与配电网交互和需求侧响应成本的多能互补系统全寿命周期优化配置方法,构建双层优化模型：上层以总等年值成本最小为目标进行全局优化,确定多能互补系统的最优配置方案与储能出力范围;下层以系统的日运行成本和可再生能源未利用率最小为目标,建立多能互补系统的多目标日前优化调度模型。分别考虑独立型和并网型多能互补系统,以某地实际风光数据为例,验证所提优化配置策略的正确性与有效性,并量化分析多能互补系统运营方式对优化配置策略的影响。     

Switched-capacitor neural networks for differential optimization

The primary goal of this paper is to solve some types of optimization problems whose objective functions and constraints are smooth and continuously differentiable and which are not suited for digital signal processing. the optimization problems are mapped into systems of first- and second-order non-linear ordinary differential equations and/or systems of difference equations. These systems of equations are simulated by appropriate switched-capacitor (SC) circuits employing some neural network (neurobiological) principles. New switched-capacitor architectures for on-line solving of non-linear optimization problems are proposed and their properties are investigated. Various circuit structures are investigated to find which are best suited for SC CMOS implementation. the structures developed exhibit a high degree of modularity, and a relatively small number of basic building blocks (computing cells) are required to implement effective and powerful optimization algorithms. Basic mathematical operations, e.g. multiplication, addition and non-linear scaling transformation, are accomplished employing advanced SC techniques. the validity and performance of the circuit structures developed are illustrated by intensive computer simulations employing TUTSIM and NAP programmes.     

多能互补系统全寿命周期优化配置方法

针对含风力发电、光伏发电和储能系统的多能互补系统的优化配置问题,提出一种考虑系统与配电网交互和需求侧响应成本的多能互补系统全寿命周期优化配置方法,构建双层优化模型：上层以总等年值成本最小为目标进行全局优化,确定多能互补系统的最优配置方案与储能出力范围;下层以系统的日运行成本和可再生能源未利用率最小为目标,建立多能互补系统的多目标日前优化调度模型。分别考虑独立型和并网型多能互补系统,以某地实际风光数据为例,验证所提优化配置策略的正确性与有效性,并量化分析多能互补系统运营方式对优化配置策略的影响。     

Hybrid Elephant Herding and Particle Swarm Optimizations for Optimal DG Integration in Distribution Networks

Abstract

In this article, the amalgamation of two well-established meta-heuristic optimization methods is presented to solve the multi-objective distributed generation (DG) allocation problem of distribution systems. To overcome some of the shortcomings of newly developed elephant herding optimization (EHO), an improvement is suggested and then, a prominent feature of particle swarm optimization is introduced to the modified version of EHO. The suggested modifications are validated by solving a single objective DG integration problem where various performance parameters of the proposed hybrid method are compared with their individual standard variants. After validation, the proposed technique is exploited to solve a multi-objective DG allocation problem of distribution systems, aiming to minimize power loss and node voltage deviation while simultaneously maximizing the voltage stability index of three benchmark distribution systems namely, 33-bus, 69-bus and 118-bus. The obtained simulation results are further compared with that of the same available in the existing literature. This comparison reveals that the proposed hybrid approach is promising to solve the multi-objective DG integration problem of distribution systems as compared to many existing methods.     

Allocation of capacitor banks in distribution systems through a modified monkey search optimization technique

This work presents a methodology for the allocation of fixed capacitor banks in electrical power distribution systems by applying a bio-inspired optimization technique. The goal is to optimize the distribution network operation over a planning horizon by minimizing the system losses with minimum cost of investment in capacitors. For this aim to be achieved, this work proposes improvements to the Monkey Search optimization technique to achieve a better representation of the capacitor allocation problem and to increase the computational efficiency. Distribution systems that are widespread in the literature are used to evaluate the proposed methodology.     

PMU最优配置问题的混合优化算法

为使得电力系统在完全可观测的条件下,PMU安装数目最少,提出了一种混合优化算法以解决相量测量单元PMU的最优配置问题.混合优化算法以粒子群优化算法为主体,引入交叉、变异操作,并结合模拟退火机制控制粒子的更新.在处理解的约束问题时,采用了一种基于概率的启发式修补策略,避免修复后的解特征单一.将混合算法与其他算法在多个IEEE标准系统上进行了比较分析,结果表明在较大规模系统上,混合优化算法收敛率比标准粒子群算法提高数倍,计算量比模拟退火算法减少了数十倍,表明了较好的可行性和较高的效率.     

应用图形处理器实现无功优化并行计算

以求解无功优化问题的内嵌离散惩罚非线性原对偶内点法为基础,利用高性能图形处理器实现了线性修正方程的并行求解。将计算密集部分在图形处理器上实现,其余部分在CPU上执行,并且采用单精度和双精度两种模式进行对照。该算法充分利用了图形处理器强大的并行处理能力和极高的存储器带宽,可获得显著的加速效果。在IEEE 118节点系统和实际538、1133和2212节点系统的计算表明,采用单精度浮点运算的无功优化计算速度最快,加速效果最好,在2212节点系统上的加速比达到近30倍。     

Fuzzy Genetic Algorithm Approach for the Optimal Placement of Flexible AC Transmission Systems Devices in a Power System

Abstract—This article presents a novel approach for optimal flexible AC transmission systems devices planning in an interconnected power system under different loading conditions. The static VAR compensator and thyristor-controlled series capacitor are two types of flexible AC transmission systems devices considered for optimal power system operation. In the proposed approach, a fuzzy membership function is used to determine weak nodes in the power system for the placement of static VAR compensators as a flexible AC transmission systems device. The thyristor-controlled series capacitor is the other type of flexible AC transmission systems devices for which its positions are determined by the reactive power flow in lines. The genetic algorithm is used for the optimal setting of the power system variables, including flexible AC transmission systems devices. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes or point of VAR compensation is determined by eigenvalue analysis, and the amount of flexible AC transmission systems devices is determined by evolutionary techniques, such as the genetic algorithm, differential evolution, and particle swarm optimization. The superiority of the proposed fuzzy-based optimization approach is established by the results and comparative analysis with other methods.     

微能源网多能源耦合枢纽的模型搭建与优化

多能源耦合枢纽作为微能源网的核心部件,其系统模型的准确性将影响微能源网的运行效率。文中针对一种包含太阳能和风能发电系统、储能系统、有机朗肯循环系统、传统冷热电三联供系统的多能源耦合枢纽,综合考虑负荷需求、价格因素和环境因素,以系统运行成本、一次能源消耗量和碳排放量最小化为目标,建立了微能源网中多能源互补的多目标优化模型;采用粒子群算法对发电机和储能的运行策略进行求解,得到最优目标。最后,与微电网进行对比分析,验证了多能耦合系统在提高能源利用率和减少碳排放方面的优势。     

配电网络电容器优化投切的制约进化策略

针对传统进化规划方法寻优效率低的问题,提出了配电网络电容器优化投切的制约进化策略.利用配电网络物理规则求得投切点无功补偿容量调节范围,用于约束解的产生和进化,缩小寻优空间.通过在解空间中心点附近合理配置初始点,避免初始解不可行.采用小种群多代进化方式,避免求解过程产生大量不可行解.最后选择适合电容器优化投切问题的随机动态步进方式,在寻优的不同阶段,采用不同的步长,使优化效果和效率得到明显改善.使用制约进化策略对不同的配电系统进行分析,结果表明:制约进化策略在保证优化精度的同时,具有更高的优化效率,可以适应在线应用.     

新型电网故障诊断专家系统的设计

针对单独运用专家系统和优化算法在电力系统故障诊断中存在的一些缺陷,设计了将专家系统推理过程融合到基于进化算法的优化算法中,形成一种新的推理优化算法,该算法根据可能的故障假设,结合约束条件,通过专家系统判断信息的合理性,从而得到最合理的故障情况。在实际应用中,该算法诊断结果可靠,具有一定的工程应用价值。     

Joint Optimization for Power Loss Reduction in Distribution Systems

In distribution systems, network reconfiguration and capacitor control, generally, are used to reduce real power losses and to improve voltage profiles. Since both capacitor control and network reconfiguration belong to the complicated combinatorial optimization problems, it is hard to combine them efficiently for better optimization results. In this paper, a joint optimization algorithm of combining network reconfiguration and capacitor control is proposed for loss reduction in distribution systems. To achieve high performance and high efficiency of the proposed algorithm, an improved adaptive genetic algorithm (IAGA) is developed to optimize capacitor switching, and a simplified branch exchange algorithm is developed to find the optimal network structure for each genetic instance at each iteration of capacitor optimization algorithm. The solution algorithm has been implemented into a software package and tested on a 119-bus distribution system with very promising results.