Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem

Solution of optimal power flow (OPF) problem aims to optimize a selected objective function such as fuel cost, active power loss, total voltage deviation (TVD) etc. via optimal adjustment of the power system control variables while at the same time satisfying various equality and inequality constraints. In the present work, a particle swarm optimization with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power systems. The proposed approach is examined and tested on modified IEEE 30-bus and IEEE 118-bus test power system with different objectives that reflect minimization of fuel cost or active power loss or TVD. The simulation results demonstrate the effectiveness of the proposed approach compared with other evolutionary optimization techniques surfaced in recent state-of-the-art literature. Statistical analysis, presented in this paper, indicates the robustness of the proposed ALC-PSO algorithm.     

Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms.     

Optimal reactive power dispatch for real power loss minimization and voltage stability enhancement using Artificial Bee Colony Algorithm

Reactive Power Dispatch (RPD) plays important role in power system reliability and security. This paper proposes the Optimal Reactive Power Dispatch (ORPD) for real power loss minimization, voltage deviation minimization and voltage stability enhancement using Artificial Bee Colony (ABC) Algorithm. ORPD is a mixed integer nonlinear optimization problem which includes both continuous and discrete control variables. The ABC algorithm is used to find the setting of control variables such as generator voltage magnitude, tap position of tap changing transformer and reactive power output of the compensation devices. The proposed algorithm is tested on IEEE 30 and 57 bus systems, Simulation results show that the proposed approach converges to better solutions and much faster than the earlier reported approaches in the literature. The optimization strategy is general and can be used to solve other power system optimization problems.     

Optimal Placement and Sizing of Distributed Generation Using Metaheuristic Algorithm

Power loss and voltage uncertainty are the major issues prevalently faced in the design of distribution systems. But such issues can be resolved through effective usage of networking reconfiguration that has a combination of Distributed Generation (DG) units from distribution networks. In this point of view, optimal placement and sizing of DGs are effective ways to boost the performance of power systems. The optimum allocation of DGs resolves various problems namely, power loss, voltage profile improvement, enhanced reliability, system stability, and performance. Several research works have been conducted to address the distribution system problems in terms of power loss, energy loss, voltage profile, and voltage stability depending upon optimal DG distribution. With this motivation, the current study designs a Chaotic Artificial Flora Optimization based on Optimal Placement and Sizing of DGs (CAFO-OPSDG) to enhance the voltage profiles and mitigate the power loss. Besides, the CAFO algorithm is derived from the incorporation of chaos theory concept into conventional artificial flora optimization AFO algorithm with an aim to enhance the global optimization abilities. The fitness function of CAFO-OPSDG algorithm involves voltage regulation, power loss minimization, and penalty cost. To consider the actual power system scenario, the penalty factor acts as an important element not only to minimize the total power loss but to increase the voltage profiles as well. The experimental validation of the CAFO-OPSDG algorithm was conducted against IEEE 33 Bus system and IEEE 69 Bus system. The outcomes were examined under various test scenarios. The results of the experiment established that the presented CAFO-OPSDG model is effective in terms of reducing the power loss and voltage deviation and boost-up the voltage profile for the specified system.     

Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers

This study presents a particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) for the solution of optimal reactive power dispatch (ORPD) problem. The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss and the total voltage deviations are to be minimized separately. In order to evaluate the performance of the proposed algorithm, it has been implemented on IEEE 30-, 57- and 118-bus test power systems and the optimal results obtained are compared with those of the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving the ORPD problem of power system.     

基于序列二次规划法的分布式电源优化配置研究

针对分布式电源配置对配电网的影响,提出一种带二阶项配网潮流约束的方法解决分布式电源优化配置问题,以实现分布式电源价值的最大化。从降损角度出发建立优化配置的数学模型,并用序列二次规划求解优化问题。在充分发挥序列二次规划法收敛性好的基础上,提高计算精度,并适用于各种复杂的配电网络。以IEEE33节点系统为例,验证所提方法在分布式电源优化配置问题求解中具有很强的全局搜索能力,可以有效、准确地实现分布式电源的最优配置,计算过程简单可靠,具有实用价值。     

Chaotic krill herd algorithm for optimal reactive power dispatch considering FACTS devices

Conventionally, optimal reactive power dispatch (ORPD) is described as the minimization of active power transmission losses and/or total voltage deviation by controlling a number of control variables while satisfying certain equality and inequality constraints. This article presents a newly developed meta-heuristic approach, chaotic krill herd algorithm (CKHA), for the solution of the ORPD problem of power system incorporating flexible AC transmission systems (FACTS) devices. The proposed CKHA is implemented and its performance is tested, successfully, on standard IEEE 30-bus test power system. The considered power system models are equipped with two types of FACTS controllers (namely, thyristor controlled series capacitor and thyristor controlled phase shifter). Simulation results indicate that the proposed approach yields superior solution over other popular methods surfaced in the recent state-of-the-art literature including chaos embedded few newly developed optimization techniques. The obtained results indicate the effectiveness for the solution of ORPD problem of power system considering FACTS devices. Finally, simulation is extended to some large-scale power system models like IEEE 57-bus and IEEE 118-bus test power systems for the same objectives to emphasis on the scalability of the proposed CKHA technique. The scalability, the robustness and the superiority of the proposed CKHA are established in this paper.     

Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique

In this paper, a newly surfaced nature-inspired optimization technique called moth-flame optimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature.     

多目标差分进化算法的电力系统无功优化

 在传统电力系统无功优化( Reactive Power Optimization,RPO) 模型中引入电压水平 指标,建立了以网损最小,电压水平最好为目标的多目标差分进化算法( Differential Evolution Algorithm) 的模型。针对基本差分进化算法易陷入局部最优解、收敛速度慢的缺点,提出一种 具有自适应参数策略的改进差分进化算法并首次用于多目标电力系统无功优化问题。通过在 算法进化过程中调整变异因子F 和交叉因子CR,在初期增加种群的多样性、扩大全局搜索区 域; 从而可以避免算法陷入局部最优解; 同时在后期也加快了收敛速度。将该算法用于电力系 统无功优化并仿真计算了IEEE-14 节点标准测试系统,结果验证模型和算法的有效性。     

Statistical analysis based reactive power optimization using improved differential evolutionary algorithm

This study presents a novel improved differential evolutionary (IDE) algorithm for optimizing reactive power management (RPM) problems. The effectiveness of IDE algorithm is tested on different unimodal and multimodal benchmark functions. The objective function of the RPM is considered as the minimization of active power losses. Initially, the power flow analysis approach is employed to detect the optimal position of flexible AC transmission system (FACTS) devices. The proposed method is used to determine the optimal value of control variables such as generator's reactive power generation, transformer tap settings, and reactive power sources. Furthermore, the efficacy of the IDE approach is compared with other promising optimization methods such as variants of differential evolution algorithm, moth flame optimization (MFO), brainstorm-based optimization algorithm (BSOA), and particle swarm optimization (PSO) on various IEEE standard test bus (i.e., IEEE-30, -57, -118, and -300) systems with active and reactive loading incorporating FACTS devices. A Static VAR compensator (SVC) for shunt compensation and a thyristor-controlled series compensator (TCSC) for series compensation were used as FACTS devices. The proposed IDE method significantly reduces the active power loss, that is, 55.65% in IEEE 30, 39.68% in IEEE 57, 16.32% in IEEE 118, and 8.56% in IEEE 300 bus system at nominal loading. Finally, the statistical analysis such as Wilcoxon signed-rank test (WSRT) and ANOVA test were thoroughly analysed to demonstrate the firmness and accuracy of the proposed technique.     

Using the gray wolf optimizer for solving optimal reactive power dispatch problem

This paper presents the use of a new meta-heuristic technique namely gray wolf optimizer (GWO) which is inspired from gray wolves’ leadership and hunting behaviors to solve optimal reactive power dispatch (ORPD) problem. ORPD problem is a well-known nonlinear optimization problem in power system. GWO is utilized to find the best combination of control variables such as generator voltages, tap changing transformers’ ratios as well as the amount of reactive compensation devices so that the loss and voltage deviation minimizations can be achieved. In this paper, two case studies of IEEE 30-bus system and IEEE 118-bus system are used to show the effectiveness of GWO technique compared to other techniques available in literature. The results of this research show that GWO is able to achieve less power loss and voltage deviation than those determined by other techniques.     

Adaptive differential evolution algorithm for efficient reactive power management

This paper introduces a proposed procedure to solve the optimal reactive power management (ORPM) problem based on a multi-objective function using a modified differential evolution algorithm (MDEA). The proposed MDEA is investigated in order to enhance the voltage profile as well as to reduce the active power losses by solving the ORPM problem. The ORPM objective function aims to minimize transmission power losses and voltage deviation considering the system constraints. The MDEA aims to enhance the convergence characteristic of the differential evolution algorithm through updating the self-adaptive scaling factor, which can exchange information dynamically every generation. The scaling factor dynamically adopts the global and local searches to efficiently eliminate trapping in local optima. In addition, a strategy is developed to update the penalty factor for alleviating the effects of various system constraints. Numerical applications of different case studies are carried out on three standard IEEE systems, i.e., 14-bus, 30-bus and 57-bus test systems. Also, the proposed procedure is applied on Western Delta Network, which is a real part of the Egyptian main grid system. The flexibility of synchronous machines to provide controllable reactive power is proven with less dependency on the discrete reactive power controllers, such as installing the switchable devices and variations of tap changers. The obtained results show the effectiveness of the proposed enhanced optimization algorithm as an advanced optimization technique that was successively implemented with good performance characteristics.     

Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis

This paper presents an evolving ant direction particle swarm optimization algorithm for solving the optimal power flow problem with non-smooth and non-convex generator cost characteristics. In this method, ant colony search is used to find a suitable velocity updating operator for particle swarm optimization and the ant colony parameters are evolved using genetic algorithm approach. To update the velocities for particle swarm optimization, five velocity updating operators are used in this method. The power flow problem is solved by the Newton–Raphson method. The feasibility of the proposed method was tested on IEEE 30-bus, IEEE 39-bus and IEEE-57 bus systems with three different objective functions. Several cases were investigated to test and validate the effectiveness of the proposed method in finding the optimal solution. Simulation results prove that the proposed method provides better results compared to classical particle swarm optimization and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

A multi-objective approach to optimal placement and sizing of multiple active power filters using a music-inspired algorithm

This paper proposes a new multi-objective framework for optimal placement and sizing of the active power filters (APFs) with satisfactory and acceptable standard levels. total harmonic distortion (THD) of voltage, harmonic transmission line loss (HTLL), motor load loss function (MLLF), and total APFs currents are the four objectives considered in the optimization, while harmonic distortions within standard level, and maximum allowable APF size, are modeled as constraints. The proposed model is one of non-convex optimization problem having a non-linear, mixed-integer nature. Since, a new modified harmony search algorithm (MHSA) is used and followed by a min–max technique in order to obtain the final optimal solution. The harmony search algorithm is a recently developed optimization algorithm, which imitates the music improvisation process. In this process, the Harmonists improvise their instrument pitches searching for the perfect state of harmony. The newly developed method has been applied on the IEEE 18-bus test system and IEEE 30-bus test system by different scenarios and cases to demonstrate the feasibility and effectiveness of the proposed method. The detailed results of the case studies are presented and thoroughly analyzed. The obtained results illustrate the sufficiency and profitableness of the newly developed method in the placement and sizing of the multiple active power filters, when compared with other methods.     

A multiobjective approach for optimal placement and sizing of distributed generators and capacitors in distribution network

Both active and reactive power play important roles in power system transmission and distribution networks. While active power does the useful work, reactive power supports the voltage that necessitates control from system reliability aspect as deviation of voltage from nominal range may lead to inadvertent operation and premature failure of system components. Reactive power flow must also be controlled in the system to maximize the amount of real power that can be transferred across the power transmitting media. This paper proposes an approach to simultaneously minimize the real power loss and the net reactive power flow in the system when reinforced with distributed generators (DGs) and shunt capacitors (SCs). With the suggested method, the system performance, reliability and loading capacity can be increased by reduction of losses. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is adopted to select optimal sizes and locations of DGs and SCs in large scale distribution networks with objectives being minimizing system real and reactive power losses. MOEA/D is the process of decomposition of a multiobjective optimization problem into a number of scalar optimization subproblems and optimizing those concurrently. Case studies with standard IEEE 33-bus, 69-bus, 119-bus distribution networks and a practical 83-bus distribution network are performed. Output results of MOEA/D method are compared with similar past studies and notable improvement is observed.     

A hybrid approach for optimal location and capacity of UPFC to improve the dynamic stability of the power system

In this document, the Firefly Algorithm (FA) and Cuckoo Search (CS) algorithm based on optimal location and the capacity of UPFC to improve the dynamic stability of the power system are proposed. The novelty of the proposed method is exemplified in the improved searching ability, random reduction and reduced complexity. In this regard, the generator fault affects the system dynamic stability constraints such as voltage, power loss, real and reactive power. Here, the FA technique optimizes the maximum power loss line as the suitable location of the UPFC. The affected location parameters and dynamic stability constraints are restored into secure limits using the optimum capacity of the UPFC, which in turn, has been optimized with reduced cost by using the CS algorithm. The attained capacity of the UPFC has been located in the affected location and the power flow of the system analyzed. The proposed method is implemented in the MATLAB/Simulink platform and tested under IEEE 30 and IEEE 14 standard bench mark system. The proposed method performance is evaluated by comparison with those of different techniques such as ABC-GSA, GSA-Bat, Bat-FA and CS algorithms. The comparison results invariably prove the effectiveness of the proposed method and confirm its potential to solve the related problems.     

Group search optimizer based optimal location and capacity of distributed generations

This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system—the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.     

Multi-objective GA with fuzzy decision making for security enhancement in power system

Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.     

考虑经济性和电压稳定性的含电子电力变压器的多目标最优潮流计算

为探究含电子电力变压器的电力系统最优潮流问题,在分析电子电力变压器简化模型、最优潮流的控制变量以及约束条件的基础上,建立了综合考虑经济因素和电压稳定性的含电子电力变压器的多目标最优潮流模型。模型中将减少发电成本和提高负荷裕度指标作为目标函数,考虑了电子电力变压器灵活的有功无功调节能力、有载调压变压器的电压调节能力、可调度负荷及可调无功电源的有功无功调节能力,提出使用基于遗传算法和内点算法的混合算法对最优潮流模型进行求解,算法的主要思想是以遗传算法为框架,对离散变量进行优化,在遗传算法的每一次迭代过程中,采用内点算法对每个体进行连续变量的优化和适应度评估。基于IEEE-14节点算例,分别进行了基于混合算法和基于内点法的最优潮流计算,计算结果验证了文章所提模型的合理性和算法的有效性。     

Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm

In this paper, Message Passing Interface (MPI) based parallel computation and particle swarm optimization (PSO) algorithm are combined to form the parallel particle swarm optimization (PPSO) method for solving the dynamic optimal reactive power dispatch (DORPD) problem in power systems. In the proposed algorithm, the DORPD problem is divided into smaller ones, which can be carried out concurrently by multi-processors. This method is evaluated on a group of IEEE power systems test cases with time-varying loads in which the control of the generator terminal voltages, tap position of transformers and reactive power sources are involved to minimize the transmission power loss and the costs of adjusting the control devices. The simulation results demonstrate the accuracy of the PPSO algorithm and its capability of greatly reducing the runtimes of the DORPD programs.