Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm

Electrical distribution network reconfiguration is a complex combinatorial optimization process aimed at finding a radial operating structure that minimizes the system power loss or/and maximizes the system reliability while satisfying operating constraints. In this paper, a distribution network reconfiguration method is presented for both the indices of power loss reduction and reliability improvement. The enhanced genetic optimization algorithm is used to handle the reconfiguration problem so as to determine the switch operation schemes. Based on the information of a single loop caused by closing a normally open switch, we improve the algorithm on crossover and mutation operations of original Genetic Algorithms. The effectiveness of the proposed method is demonstrated on 33-bus, 69-bus, and 136-bus radial distribution systems.     

Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization

This paper presents a newly developed teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal reactive power dispatch (ORPD) problem by minimizing real power loss, voltage deviation and voltage stability index. To accelerate the convergence speed and to improve solution quality quasi-opposition based learning (QOBL) concept is incorporated in original TLBO algorithm. The proposed TLBO and quasi-oppositional TLBO (QOTLBO) approaches are implemented on standard IEEE 30-bus and IEEE 118-bus test systems. Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem.     

Numerical experiments with an optimal power flow algorithm based onparametric techniques

This paper presents the results of numerical experiments with a new optimal power flow (OPF) algorithm based on a parametric technique. The approach consists of relaxing the original OPF problem by incorporating parametric terms to the objective function, the equality and inequality constraints. Such relaxation assures that any arbitrary initial solution, feasible or unfeasible, be the optimal solution of the OPF problem. As the scalar parameter changes, a family of OPF problems is created, whose necessary conditions are solved by Newton's method. An efficient strategy is proposed for updating the parameter and the optimal set of active inequality constraints of each intermediate problem. Two applications of the methodology are reported: the economic dispatch problem and the minimum transmission loss problem. These problems were solved for an 810-bus and a 2256-bus equivalent network of the South/Southeast interconnected Brazilian power system. The results show that the parametric approach is robust and efficient when applied to large-scale OPF problems     

Loss minimization by the predictor–corrector modified barrier approach

This paper presents a new approach, predictor–corrector modified barrier approach (PCMBA), to minimize the active losses in power system planning studies. In the PCMBA, the inequality constraints are transformed into equalities by introducing positive auxiliary variables, which are perturbed by the barrier parameter, and treated by the modified barrier method. The first-order necessary conditions of the Lagrangian function are solved by predictor–corrector Newton's method. The perturbation of the auxiliary variables results in an expansion of the feasible set of the original problem, reaching the limits of the inequality constraints. The feasibility of the proposed approach is demonstrated using various IEEE test systems and a realistic power system of 2256-bus corresponding to the Brazilian South-Southeastern interconnected system. The results show that the utilization of the predictor–corrector method with the pure modified barrier approach accelerates the convergence of the problem in terms of the number of iterations and computational time.     

A hybrid genetic algorithm-interior point method for optimal reactive power flow

By integrating a genetic algorithm (GA) with a nonlinear interior point method (IPM), a novel hybrid method for the optimal reactive power flow (ORPF) problem is proposed in this paper. The proposed method can be mainly divided into two parts. The first part is to solve the ORPF with the IPM by relaxing the discrete variables. The second part is to decompose the original ORPF into two sub-problems: continuous optimization and discrete optimization. The GA is used to solve the discrete optimization with the continuous variables being fixed, whereas the IPM solves the continuous optimization with the discrete variables being constant. The optimal solution can be obtained by solving the two sub-problems alternately. A dynamic adjustment strategy is also proposed to make the GA and the IPM to complement each other and to enhance the efficiency of the hybrid proposed method. Numerical simulations on the IEEE 30-bus, IEEE 118-bus and Chongqing 161-bus test systems illustrate that the proposed hybrid method is efficient for the ORPF problem.     

基于人工鱼群算法的最优潮流计算

提出了基于人工鱼群优化算法(AFSA)的最优潮流(OPF)计算方法;算法结合动态调整罚函数的方式,将最优潮流问题转化为一个无约束求极值问题,有效提高了算法的全局收敛能力和计算精度.应用此算法对标准IEEE30节点的电力系统进行最优潮流计算,并与粒子群算法和遗传算法进行了比较,仿真结果表明,该算法能够更好地获得全局最优解,具有实用意义.     

考虑最少线路切换数量的电压稳定性增强优化方法

为提升电力系统的电压稳定性,提出一种基于输电线路切换的电压稳定性增强控制模型,该模型以最少的切换输电线路数量为目标来满足电力系统负荷裕度的提升需求。为灵活构建线路切换数学模型,提出基于支路追加法将线路切换融入原问题模型中。为降低模型求解难度,提出一种阶段式求解策略：基于负荷裕度对线路参数的灵敏度快速预筛选出可以提高系统负荷裕度的候选切换集,降低0-1整数变量的搜索范围;基于外逼近和松弛策略将原问题分解为2个子问题进行迭代求解,进一步提高模型的求解速度和精度。IEEE 118节点和IEEE 662节点系统仿真结果验证了所提模型与方法的有效性。     

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.     

Artificial bee colony algorithm for solving multi-objective optimal power flow problem

This paper presents a new and efficient method for solving optimal power flow (OPF) problem in electric power systems. In the proposed approach, artificial bee colony (ABC) algorithm is employed as the main optimizer for optimal adjustments of the power system control variables of the OPF problem. The control variables involve both continuous and discrete variables. Different objective functions such as convex and non-convex fuel costs, total active power loss, voltage profile improvement, voltage stability enhancement and total emission cost are chosen for this highly constrained nonlinear non-convex optimization problem. The validity and effectiveness of the proposed method is tested with the IEEE 9-bus system, IEEE 30-bus system and IEEE 57-bus system, and the test results are compared with the results found by other heuristic methods reported in the literature recently. The simulation results obtained show that the proposed ABC algorithm provides accurate solutions for any type of the objective functions.     

N-1故障状态下电力系统静态电压稳定极限的快速计算

为了快速计算电力系统支路故障状态下的静态电压稳定临界点,提出了一种基于泰勒级数的计算方法。以支路导纳系数为参数,通过求解原系统的静态电压稳定临界点对故障支路导纳系数的1至n阶导数,用泰勒级数法逼近电压崩溃点,从而快速求解出N-1故障情况下电压稳定临界点的精确解。采用该方法对IEEE 30及118母线系统进行验证,结果表明该方法能快速、精确地求得故障状态下的静态电压稳定临界点。     

基于混合整数二阶锥规划的动态无功优化方法

动态无功优化对提升电力系统的安全经济运行水平有着重要意义。然而,它是一个多时段紧密耦合的非凸非线性混合整数规划问题。为高效、高精度地求解该问题,文中提出了基于混合整数二阶锥规划(MISOCP)的动态无功优化方法。该方法通过将非凸的交流潮流方程近似转化为凸的二阶锥约束及采用一般不等式约束等效替代绝对值约束,在高精度地反映交流潮流物理规律的同时,将原非凸的混合整数规划问题转化为凸的MISOCP问题,从而大大降低了求解的复杂度。通过求解MISOCP模型,能够高效地得到动态无功优化的高精度解。基于IEEE-30节点系统和IEEE-118节点系统的算例分析验证了所提出方法的有效性和鲁棒性。     

基于矢量化运算模式的电力系统潮流计算

通过把电力系统潮流方程的求解转化为一个新的非线性规划模型的求解,解决了病态系统潮流计算发散的问题,为给定条件下的潮流计算是否有解提供了一个新的判断途径。利用牛顿法获得具有对称不定系数矩阵的修正方程后,采用AMD算法对系数矩阵进行排序,并采用LDLT算法进行求解,提高了修正方程的求解速度。整个潮流计算模型以矢量化形式表达,简化了程序复杂度,提高了代码的通用性和易维护性。对节点数从118到703共3个测试系统进行了仿真计算,结果验证了文中所提方法的正确性。     

基于改进粒子群优化算法的最优潮流计算

提出应用粒子群优化算法（PSO）求解最优潮流问题（OPF），并结合动态调整罚函数法将最优潮流问题转化成一个无约束求极值问题，有效提高了PSO算法的全局收敛能力和计算精度。应用此算法对标准IEEE30节点系统进行潮流计算，并与线性规划算法和遗传算法进行了比较，结果表明，该算法能够更好地获得全局最优解，具有实用意义。     

A decoupled time-domain Simulation method via Invariant subspace partition for power system analysis

A decoupled method is proposed to deal with time-domain simulation for power system dynamic analysis. Traditionally, there are two main categories of numerical integration methods: explicit methods and implicit methods. The implicit methods are numerically stable but require more computational time to solve the nonlinear equations, while explicit methods are relatively efficient but may cause a numerical stability problem. This paper proposes a new hybrid method to take advantage of both explicit and implicit methods based on the invariant subspace partition. The original power system equations are decoupled into two parts that correspond to the stiff and nonstiff subspaces. For the stiff invariant subspace, the implicit method is applied to achieve numerical stability, and the explicit method is employed to handle nonstiff invariant subspace for the computational efficiency. As a result, the new hybrid method is both numerically stable and efficient. The approach is demonstrated through New England 39-bus and IEEE 118-bus systems.     

Planning of multi-type FACTS devices in restructured power systems with wind generation

Many electrical power systems are changing from a vertically integrated entity to a deregulated, open-market environment. This paper proposes an approach to optimally allocate multi-type flexible AC transmission system (FACTS) devices in restructured power systems with wind generation. The objective of the approach is to maximize the present value of long-term profit. Many factors like load variation, wind generation variation, generator capacity limit, line flow limit, voltage regulation, dispatchable load limits, generation rescheduling cost, load shedding cost, and multilateral power contracts are considered in problem formulation. The proposed method accurately evaluates the annual costs and benefits obtainable by FACTS devices in formulating the large-scale optimization problem under both normal condition and possible contingencies. The overall problem is solved using both Particle Swarm Optimization (PSO) for attaining optimal FACTS devices allocation as main problem and optimal power flow as sub optimization problem. The efficacy of the proposed approach is demonstrated for modified IEEE 14-bus test system and IEEE 118-bus test system.     

Quasi-oppositional differential evolution for optimal reactive power dispatch

This paper presents quasi-oppositional differential evolution to solve reactive power dispatch problem of a power system. Differential evolution (DE) is a population-based stochastic parallel search evolutionary algorithm. Quasi-oppositional differential evolution has been used here to improve the effectiveness and quality of the solution. The proposed quasi-oppositional differential evolution (QODE) employs quasi-oppositional based learning (QOBL) for population initialization and also for generation jumping. Reactive power dispatch is an optimization problem that reduces grid congestion with more than one objective. The proposed method is used to find the settings of control variables such as generator terminal voltages, transformer tap settings and reactive power output of shunt VAR compensators in order to achieve minimum active power loss, improved voltage profile and enhanced voltage stability. In this study, QODE has been tested on IEEE 30-bus, 57-bus and 118-bus test systems. Test results of the proposed QODE approach have been compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed QODE based approach is able to provide better solution.     

Improving large-disturbance stability through optimal bifurcation control and time domain simulations

This paper presents an approach for improving, through practical assumptions, the stability of power systems subjected to large disturbances. The proposed method is composed of two steps, solved iteratively. The first step solves an optimal bifurcation control problem that guarantees the small-signal stability of the equilibrium point. The proposed optimal bifurcation control addresses saddle-node and Hopf bifurcations. The second step is an N − 1 contingency analysis computed through time domain simulations. The second step guarantees the large-disturbance stability of the equilibrium point. The WSCC 9-bus and New England 39-bus systems are used to illustrate and test the proposed technique.     

基于光滑化函数的ATC新模型及其有效算法

基于光滑化函数理论，建立了光滑化的可用传输容量(ATC)新模型。该模型将所有不等式约束的信息浓缩于一个等式中，从而避免了处理众多不等式约束而引起的非光滑性及由此产生的困难。采用牛顿法对光滑化的ATC模型进行求解，对IEEE9节点、30节点及118节点标准测试系统进行了试算，并与传统的延拓潮流方法进行了比较。计算结果表明，所提出的模型和方法能快速可靠地识别出起作用的不等式约束，编程简单，是一种有效的ATC计算方法。     

Optimal allocation of phasor measurement unit for full observability of the connected power network

This paper presents binary particle swarm optimization (BPSO) technique for the optimal allocation of phasor measurement units (PMUs) for the entire observability of connected power network. Phasor measurement units are considered as one of the most important measuring devices in the prospect of connected power network. PMUs function may be incorporated to the wide-area connected power networks for monitoring and controlling purposes. The optimal PMU placement (OPP) problem provides reference to the assurance of the minimal number of PMUs and their analogous locations for observability of the entire connected power networks. Binary particle swarm optimization (BPSO) algorithm is developed for the solution of OPP problem. The efficacy and robustness of the proposed method has been tested on the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, IEEE 118-bus and Northern Regional Power Grid (NRPG) 246-bus test system. The results obtained by proposed approach are compared with other standard methods and it is observed that this BPSO based placement of phasor measurement units is found to be the best among all other techniques discussed.     

基于多智能体系统的多区域互联分布式潮流算法

针对多区域间潮流计算存在数据难以共享及数据隐私问题,提出一种基于多智能体系统(MAS)的多区域互联分布式潮流算法。首先,介绍基于MAS的多区域分布式潮流计算框架,并给出一种考虑数据保护的各级多智能体协调互动方式。然后基于主网、区域管理(包括外层管理智能体和内层管理智能体)两级多智能体结构,利用改进迭代投影搜索法构建分布式潮流内外层双重迭代算法,以实现多区域间通信少量非重要信息,各区域内指数快速收敛的目标。最后,分别以6节点、30节点、118节点系统为测试系统,并与集中式算法进行比较分析,验证所提算法的有效性和可拓展性。