Optimal power flow by enhanced genetic algorithm

This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus voltage magnitude limits, and generator reactive capabilities, are included as penalties in the GA fitness function (FF). Advanced and problem-specific operators are introduced in order to enhance the algorithm's efficiency and accuracy. Numerical results on two test systems are presented and compared with results of other approaches     

Optimal power flow using differential evolution algorithm

This paper presents an evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution (DE) algorithm for optimal settings of OPF control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objective functions that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. In addition, non-smooth piecewise quadratic cost function has been considered. The simulation results of the proposed approach are compared to those reported in the literature. The results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the OPF problem for the systems considered.     

Optimal power flow using differential evolution algorithm

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution algorithm for optimal settings of OPF problem control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results are compared with the results reported in the literature. The results show the effectiveness and robustness of the proposed approach.     

Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm

This paper focuses primarily on implementation of optimal power flow (OPF) problem considering wind power. The stochastic nature of wind speed is modeled using two parameter Weibull probability density function. The economic aspect is examined in view of the system wide social cost, which includes additional costs like expected penalty cost and expected reserves cost to account for wind power generation imbalance. The optimization problem is solved using Gbest guided artificial bee colony optimization algorithm (GABC) and tested on IEEE 30 bus system. The simulation results obtained using proposed method are compared with other methods available in the literature for a case of conventional OPF as well as OPF incorporating stochastic wind. Subsequently an extensive simulation study is conducted to investigate the effect of wind power and different cost components on optimal dispatch and emission. Numerical simulations indicate that the operation cost of system and emission depends upon the transmission system bottlenecks and the intermittency of wind power generation.     

Optimal reactive power dispatch using an adaptive genetic algorithm

This paper presents an adaptive genetic algorithm (AGA) for optimal reactive power dispatch and voltage control of power systems. In the adaptive genetic algorithm, the probabilities of crossover and mutation, p_c and p_m, are varied depending on the fitness values of the solutions and the normalized fitness distances between the solutions in the evolution process to prevent premature convergence and refine the convergence performance of genetic algorithms. The AGA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system.     

用遗传算法求解广义系统最优同时镇定问题

研究了用改进的遗传算法（简称GA）求解同时镇定一族线性定常广义系统的最优输出反馈控制律问题。在满足稳定性的条件,将最优同时镇定转化为一个受约束的非线性最小问题。引入了自适应机制和惩罚函数变换,对传统的GA进行改造。并用于受约束非线性问题的全局优化。计算结果和数值仿真说明GA是求解同时镇定问题的一种有效的数值方法。     

Optimal power dispatch in multinode electricity market using genetic algorithm

The application of the genetic algorithm to solve the optimal power dispatch problem for a multi-node auction market is proposed in this paper. The optimal power dispatch problem is a non-linear optimisation problem with several constraints. The objective of the proposed genetic algorithm is to maximise the total participants’ benefit at all nodes in the system. The proposed algorithm is simple to implement and can easily incorporate additional constraints. The algorithm was tested on a 17-node, 26-line system. The results have shown that the proposed algorithm yields good results that are consistent with typical market behaviour.     

Optimal reactive power dispatch using self-adaptive real coded genetic algorithm

In this paper, self-adaptive real coded genetic algorithm (SARGA) is used as one of the techniques to solve optimal reactive power dispatch (ORPD) problem. The self-adaptation in real coded genetic algorithm (RGA) is introduced by applying the simulated binary crossover (SBX) operator. The binary tournament selection and polynomial mutation are also introduced in real coded genetic algorithm. The problem formulation involves continuous (generator voltages), discrete (transformer tap ratios) and binary (var sources) decision variables. The stochastic based SARGA approach can handle all types of decision variables and produce near optimal solutions. The IEEE 14- and 30-bus systems were used as test systems to demonstrate the applicability and efficiency of the proposed method. The performance of the proposed method is compared with evolutionary programming (EP) and previous approaches reported in the literature. The results show that SARGA solves the ORPD problem efficiently.     

Optimal corrective actions for power systems using multi-objective genetic algorithms

In this paper, optimal corrective control actions are presented to restore the secure operation of power system for different operating conditions. Genetic algorithm (GA) is one of the modern optimization techniques, which has been successfully applied in various areas in power systems. Most of the corrective control actions involve simultaneous optimization of several objective functions, which are competing and conflicting each other. The multi-objective genetic algorithm (MOGA) is used to optimize the corrective control actions. Three different procedures based on GA and MOGA are proposed to alleviate the violations of the overloaded lines and minimize the transmission line losses for different operation conditions. The first procedure is based on corrective switching of the transmission lines and generation re-dispatch. The second procedure is carried out to determine the optimal siting and sizing of distributed generation (DG). While, the third procedure is concerned into solving the generation-load imbalance problem using load shedding. Numerical simulations are carried out on two test systems in order to examine the validity of the proposed procedures.     

Optimal power flow for distribution networks using gravitational search algorithm

This paper presents a gravitational search algorithm (GSA)-based approach to solve the optimal power flow (OPF) problem in a distribution network with distributed generation (DG) units. The OPF problem is formulated as a nonlinear optimization problem with equality and inequality constraints, where optimal control settings in case of fuel cost minimization of DG units, power loss minimization in the distribution network, and finally simultaneous minimization of the fuel cost and power loss are obtained. The proposed approach is tested on an 11-node test system and on a modified IEEE 34-node test system. Simulation results obtained from the proposed GSA approach are compared with that obtained using a genetic algorithm approach. The results show the effectiveness and robustness of the proposed GSA approach.     

Fault section estimation in power systems using a genetic algorithm

In this paper, a new method of fault section estimation in power systems using genetic algorithms (GAs) is presented. The main contributions of this paper include the following three aspects: (a) the fault section estimation problem is formulated as a 0–1 integer programming problem; (b) an efficient method to identify the faulty subnetworks is developed by using information from circuit breakers, and thus the fault diagnosis can be fulfilled in a very short time for large-scale power systems and can be implemented online; (c) a new method based upon a genetic algorithm is used to solve the fault section estimation problem, and the simulation results show that the GA based method can find multiple optimal solutions directly and efficiently in a single run, which is very suitable for complex fault diagnosis problems, especially for situations where protective relays and/or circuit breakers malfunction, because different combinations of fault sections and protective relay and/or circuit breaker malfunctions may give the same alarms.     

无功优化遗传算法中的潮流算法改进研究

分析了潮流计算的数学模型 ,介绍了快速解耦法求解潮流 ,提出了一些改进措施 ,借以避免每次潮流计算都要反复形成第一因子表的缺点 ,采取收敛精度可变的手段 ,可使无功优化计算时间下降 65 %左右     

Optimal power quality monitor placement using genetic algorithm and Mallow’s Cp

This study presents a method to determine the optimal number and placement of power quality monitors (PQMs) in power systems by using genetic algorithm (GA) and Mallow’s Cp which is a statistical criterion for selecting among many alternative subset regressions. This procedure helps to avoid the dependency of set voltage sag threshold values of PQMs in the conventional monitor reach area based (MRA) method. In the proposed GACp method, the fitness function for problem modeling aims to minimize allocated monitors and minimize the difference between the Mallow’s Cp and the number of variables used for the multivariable regression model during estimation of unmonitored buses. After obtaining the optimal placements of PQMs by using the GACp method, the observability and redundancy of the monitors are tested to further reduce the redundant PQMs. The IEEE 30 bus test system is simulated using the DIGSILENT power factory software to validate the proposed method. The simulated results show that the GACp method requires only two PQMs to observe all voltage sags that may appear at each bus in the test system without redundancy.     

A new sequential AC–DC power flow algorithm for multi‐terminal HVDC systems

This paper presents a new algorithm based on the sequential method for power flow calculation in integrated multi‐terminal, high‐voltage, direct current (HVDC) systems. Unlike similar studies in the literature, a real equivalent circuit model is considered for under‐load tap changer (ULTC) transformers of the DC converters, for the first time. So, new DC equations are obtained. Thus, exact and accurate results can be obtained for practical applications by the proposed algorithm. Adjustment effects of the DC converters' ULTCs tap values are included in the Jacobian matrix instead of the bus admittance matrix in the sequential AC power flow algorithm as well as other ULTCs in AC system. To this aim, new equations for the calculation of power and Jacobian matrix elements are obtained for the AC system. The proposed approach is tested on the modified IEEE 17‐bus AC–DC test system. Numerical results show that the proposed approach is accurate and reliable in convergence. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于改进布罗伊登法的交直流潮流计算

在分析求解非线性方程组的布罗伊登法和一种改进的布罗伊登法的基础上,针对交直流混联系统,运用改进的布罗伊登法,提出了一种潮流计算的统一迭代法,设计了算法的具体实现步骤,并以一个IEEE9节点修改系统进行仿真计算,结果表明本文采用的改进布罗伊登法交直流潮流计算方法有效可行.     

Optimal power flow with UPFC using security constrained self-adaptive differential evolutionary algorithm for restructured power system

The restructuring of the power industry begins with the 21st century and this restructuring of the power system requires a careful analysis because of the nature of the real-time operations. For the restructuring power system (RPS), the self-adaptive differential evolutionary (SADE) algorithm is proposed for enhancing and controlling the power flow using Unified Power Flow Controller (UPFC) under practical security constraints (SCs). The new formulas for the tuning parameters of Differential Evolutionary technique are designed in such a manner that they become automatically adaptive throughout the whole iteration. The UPFC is modeled considering losses of the both converters, transmission loss in UPFC and losses of the coupling transformers. The unique mathematical modeling of the cost function is developed considering practical SCs. The proposed algorithm and other evolutionary algorithms are applied on the IEEE standard and ill-conditioned test systems. With and without UPFC, the power flow and line losses are observed for the three sets of user-defined active and reactive power. The use of UPFC not only enhances the power flow but also reduces the total line losses. Comparing characteristics, convergence rate, success rate for all cases, the best performances are observed for the proposed Security Constraint SADE (SCSADE) algorithm.     

Optimal clustering of power networks using genetic algorithms

This paper presents a new method for optimal network decomposition based on genetic algorithms (GAs). GAs present a powerful, globally oriented optimization method which exploits the mechanism of natural genetics, working on populations of candidate solutions in an effort to reach optima or near optima. Test results on IEEE standard networks are given and compared with those using simulated annealing. The genetic algorithm approach is found to produce significantly better solutions.     

Unit commitment solution methodology using genetic algorithm

Solution methodology of unit commitment (UC) using genetic algorithms (GA) is presented. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as minimization of the total objective function while satisfying the associated constraints. Problem specific operators are proposed for the satisfaction of time dependent constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented     

考虑暂态稳定约束的电力系统最优潮流算法

将模糊集理论应用于暂态稳定约束最优潮流问题,结合电力系统实际特性,对传统暂态稳定约束最优潮流模型进行改进,将功角约束、电压约束及目标函数模糊化处理,采用最大最小算子建立了以求解满意度最大化的暂态稳定约束最优潮流模糊新模型。构建了适于大规模非线性优化问题的协同进化粒子群算法,用于TSCOPF模糊优化问题的求解。为提高算法求解效率,结合模型特点采用提前终止暂态稳定仿真的加速策略。并利用Matlab并行工具箱对算法进行主从并行化改造,显著提高了算法运行效率。最后利用新英格兰10机系统仿真测试,证明了方法有效可行。