Multi-objective Reactive Power Optimization Research Articles

Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm

In this paper, an optimization model with the minimum active power loss and minimum voltage deviation of node and maximum static voltage stability margin as the optimization objective is proposed for the reactive power optimization problems. By defining the index value of reactive power compensation, the optimal reactive power compensation node was selected. The particle swarm optimization algorithm was improved, and the selection pool of global optimal and the global optimal of probability (p-gbest) were introduced. A set of Pareto optimal solution sets is obtained by this algorithm. And by calculating the fuzzy membership value of the pareto optimal solution sets, individuals with the smallest fuzzy membership value were selected as the final optimization results. The above improved algorithm is used to optimize the reactive power of IEEE14 standard node system. Through the comparison and analysis of the results, it has been proven that the optimization effect of this algorithm was very good.

A multi-objective reactive power optimization strategy for mitigating voltage fluctuation in power network caused by geomagnetic storm

A multi-objective reactive power optimization strategy for mitigating voltage fluctuation in power network caused by geomagnetic storm

Multi-Objective Reactive Power Optimization of Distribution Network with Distributed Generation

Distributed generation (DG) is considered to be a very promising alternative of power generation because of its tremendous environmental, social, and economic benefits. But the randomness and intermittent of DGs brings new problems to the system. This paper analyzes the reactive power optimization problem of distribution network with correlative DGs based on scenario analysis method. A new scenario division rule according to the joint distribution function of wind-PV power outputs is proposed in the paper. Then a multi-objective reactive power optimization model whose objects include the minimum active power losses, the minimum voltage deviation and the maximum static voltage stability margin is established. Non-dominated sorting genetic algorithm-II is used to solve the model. At the last of the paper, the model and the algorithm proposed are verified with an improved IEEE 33-bus system. The results show that the model will be a reference to the reactive power optimization problem in distribution system.

Piecewise Normalized Normal Constraint Method Applied to Minimization of Voltage Deviation and Active Power Loss in an AC–DC Hybrid Power System

In this paper, we establish a mathematical model of the multi-objective reactive power optimization (MORPO) problem in an ac–dc hybrid power system. To get an evenly distributed set of Pareto solutions of this MORPO problem throughout a whole day, we propose a piecewise normalized normal constraint (PNNC) method. Based on this method, the Pareto frontier is divided into four arcs so that the fluctuation of angle between the tangent line at each Pareto point and its sub-Utopia line is reduced. By doing so, the original MORPO problem is transformed into four sets of single objective optimization problems, which can be solved efficiently by a nonlinear primal-dual interior-point method. Performance of the PNNC method is tested on a real ac–dc interconnection system. The results demonstrate that it has the capability to generate a more evenly distributed set of Pareto solutions when compared with the normalized normal constraint (NNC) and weighted sum (WS) methods. In addition, a fuzzy-based membership value assignment method is employed to derive 96 optimal compromise solutions corresponding to 96 time periods in a whole day; the numerical outcomes show that a saving in active power loss and an improvement in the voltage profile can be gained simultaneously.

Opposition-Based Improved PSO for Optimal Reactive Power Dispatch and Voltage Control

An opposition-based improved particle swarm optimization algorithm (OIPSO) is presented for solving multiobjective reactive power optimization problem. OIPSO uses the opposition learning to improve search efficiency, adopts inertia weight factors to balance global and local exploration, and takes crossover and mutation and neighborhood model strategy to enhance population diversity. Then, a new multiobjective model is built, which includes system network loss, voltage dissatisfaction, and switching operation. Based on the market cost prices, objective functions are converted to least-cost model. In modeling process, switching operation cost is described according to the life cycle cost of transformer, and voltage dissatisfaction penalty is developed considering different voltage quality requirements of customers. The experiment is done on the new mathematical model. Through the simulation of IEEE 30-, 118-bus power systems, the results prove that OIPSO is more efficient to solve reactive power optimization problems and the model is more accurate to reflect the real power system operation.

Research on Algorithm of Distributed Reactive Power Optimization Based on Cloud Computing and Improved NSGA-II

A distributed reactive power optimization algorithm is put forward based on cloud computing and improved NSGA-II (fast non-dominated sorting genetic algorithm) in this paper. It is designed to solve problem of multi-objective reactive power optimization with huge amounts of data in power grid, whose difficulties lie in local optimum and slow processing speed. First, NSGA-II's crossover and mutation operator are improved based on Cloud Model, so as to satisfy the adaptive characteristics. In this way, we improved global optimization ability and convergence speed when dealing with large-scale reactive power optimization. Second, we introduced cloud computing, parallelized the proposed algorithm based on MapReduce programming framework. In this way, we achieved distributed improved NSGA-II algorithm, effectively improved the calculation speed of handling massive high-dimensional reactive power optimization. Through theoretical study demonstrated the superiority of the algorithm to solve the Multi-Objective reactive power optimization.

Simulation on Multi-Objective Wind Power Integration Using Genetic Algorithm with Adaptive Weight

Aiming at problems which were brought by large-scale wind power integration, and the problem of multi-objective reactive power optimization considering the coexistence of discrete variables and continuous variables, a method of simulation based on genetic algorithm with adaptive weight is brought out. A solving thinking presents that capacitor switching and transformer tap adjusting and other discrete equipments are first, and the action sequence of generator and dynamic reactive power compensation (DRPC) devices and other continuous equipments setting follows, which is presented that optimization problem is decomposed into continuous variable optimization and discrete variable optimization, then they are solved respectively and cross iteration until convergence. In view of the optimization complexity and the coexistence of discrete variables and continuous variables, genetic algorithm with adaptive weight is presented for finding global optimal solution. Case studies show that the proposed thinking and algorithm for solving multi-objective reactive power optimization are reasonable.

A Review of Regional Reactive Power Optimization Techniques

With increasingly complex of the power grid structure and increasingly user requirements of power quality, Power grid voltage reactive power optimization is still the difficult points in power system operation control. This paper introduces the general optimization methods of multi-objective reactive power optimization, intelligent algorithm, the development of the hybrid method, and their respective advantages, disadvantages and improvement; It also analyzes and summarizes the simplification of search space for reactive power optimization and the key issues and research development trend of the real-time reactive voltage control system.

Reactive Power Optimization Using Multi-Objective Particle Swarm Optimization Algorithm Based on Pareto

In order to avoid the defect that particle swarm optimization algorithm is easy to trap into local optimal solution, an improved multi-objective particle swarm algorithm based on the Pareto optimal set is proposed to deal with reactive power optimization of power system. Taking the minimum active network loss and voltage offset as objective, index functions of multi-objective reactive power optimization are established. The algorithm uses a group fitness variance judging mechanism to update each particle’s inertia weight so as to enhance their global searching ability, and adopts the elite archiving technology to get a set of Pareto optimal solutions so as to improve the diversity of the solution. Simulation of IEEE 30 bus system demonstrates that the proposed method has fast convergence speed and high optimization accuracy.

Multi-objective reactive power optimization strategy for distribution system with penetration of distributed generation

The study investigates multi-objective reactive power optimization (MORPO) of distribution system penetrated with distributed generation (DG). Integrating the reactive power of DG as one type of decision variables, a multi-objective model for RPO has been established to decrease the system active power loss, reduce voltage deviation and minimize the total capacity of reactive power compensation (RPC) devices (or minimize investments on RPC devices). Instead of converting the multiple objectives into a single one, a dynamically adaptive multi-objective particle swarm optimization (DAMOPSO) algorithm with introduction of special adaptive techniques has been proposed and validated and then applied to the MORPO problem with continuous and discrete variables. In order to the proposed MORPO model and the application of DAMOPSO, and to obtain a deep insight into MORPO with different objectives, a series of simulations on IEEE 33-bus system along with analysis and discussion are carried out. The results verified the feasibility and effectiveness of the proposed strategy.

Multi-Objective Reactive Power Optimization Based on Refined Chaos Particle Swarm Optimization Algorithm

In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved adaptive chaotic particle swarm optimization algorithm is proposed to solve the problem. Through the using of cubic chaotic mapping, the particle population is initialized to enhance the diversity of its value; In the optimization process, poor fitness particles are updated with chaos disturbance, and their inertia weight are adjusted dynamically with particles fitness value so as to avoid local convergence. Simulation of IEEE 30 bus system shows that the proposed algorithm for reactive power optimization can avoid premature convergence effectively, and converge to optimal solution rapidly.

A New Variation Particle Swarm Optimization for Multi-objective Reactive Power Optimization

A New Variation Particle Swarm Optimization for Multi-objective Reactive Power Optimization

Reactive Power Optimization in Distribution Network with Wind Farm

The traditional methods to adjust voltage in distribution network reactive power optimization is discretization，and it is difficult to realize the continuous voltage adjustment. A reactive power optimization model and algorithm in distribution network with wind farm is proposed. The network loss，deviation of voltage and stability of voltage are taken into account in the multi-objective reactive power optimization model. The quantum particle swarm optimization（QPSO）algorithm has been used to solve the reactive power optimization problem. The algorithm described particle state by wave function, not only increase the diversity of population，but also avoid premature convergence. The comparison of the simulation result between QPSO and PSO on the modified IEEE 33-bus system demonstrated the effectiveness and advantage of quantum particle swarm optimization.

Fuzzy based Stochastic Algorithms for Multi-Objective Reactive Power Optimization including FACTS Devices

Fuzzy based stochastic algorithms for solving Multi-objective Reactive Power Optimization (MORPO) problem including FACTS devices is presented in this paper. The Multi-Objective Reactive Power Optimization problem is formulated as a nonlinear constrained multi-objective optimization problem where the active power, voltage deviation and investment cost has to be minimized simultaneously. Fuzzy logic strategy incorporated with Evolutionary Programming (EP), Tabu search (TS) algorithms and Particle Swarm Optimization (PSO) has been proposed to handle the problem as a true multi-objective problem. The proposed algorithm has been used to solve the MORP problem with and without FACTS devices namely STATCOM, TCSC and UPFC.IEEE 30-bus system is used as a test system. The simulation results are promising and show the effectiveness and robustness of the proposed approach.

Solution for Multi-Objective Reactive Power Optimization Using Fuzzy Guided Tabu Search

A fuzzy guided tabu search (FGTS) algorithm for solving multi-objective reactive power optimization problem is presented. The problem is formulated as a non-linear constrained multi-objective problem with three competing objectives, viz., minimization of losses, cost of VAR sources, and improvement of voltage profile. The proposed algorithm is based on the combination of fuzzy logic strategy incorporated in tabu search (TS). The standard IEEE 30-bus system and practical Indian 76-bus system are used as the test systems. The results are encouraging and indicate the viability of the proposed technique. The proposed method is capable of generating diverse and well-distributed pareto-optimal solutions.

Application of Optimization Algorithm on Simulating the Fisher Fishing in Multi-objective Optimal Reactive Power

Optimization algorithm on simulating the fisher fishing (SFOA) is a novel optimization algorithm, it was presented based on simulating the behavior and habit of fisher's fishing. By the algorithm, and algorithm for solving the power system SFOA multi-objective reactive power optimization(ORP) problem. The model of ORP included line loss and average voltage offset as optimal target, and the method that can transform target function to constraint based on user reference region was proposed. ORP was optimization problem with multi-variable, and optimization speed of SFOA would be exposed to restrict. So the method that randomly initialized in the their cube was adopted, which could simply moving search and shrinking search in the cube and improve searching speed. The simulation results for IEEE-30 nodes and IEEE-57 nodes system showed that SFOA had global search performance and steady convergence rates, which could improve the economics and safety of power system effectively.

Multi-Objective Reactive Power Optimization Based On The Fuzzy Adaptive Particle Swarm Algorithm

To research the problem on the multi-objective reactive power optimization, to utilize the theory of multiobjective fuzzy optimization to change the multi-objective optimization into the single-objective optimization, and to adopt the fuzzy adaptive particle swarm algorithm to carry out solutions. Comprehensively considering the security and economical efficiency of the system, as well as the condition of the operation constraints, to propose a comprehensive and practical multi-objective reactive power optimization model. To consider the multi-objective reactive power optimization model of the voltage stability index can optimize the economic benefit and safety benefit of the system. Applying the theory of multi-objective fuzzy optimization combined with the adaptive particle swarm optimization algorithm to the problem of the multi-objective reactive power optimization could solve the problem of the different dimensional multi-objective optimization in a better way. After adopting the fuzzy adaptive particle swarm algorithm, the superiorities, such as achieving the global optimal solution, reducing the computational complexity, and improving the computational efficiency,are displayed.

Reactive power optimization with artificial bee colony algorithm

Reactive power optimization (RPO) is an important issue for providing the secure and economic run of the power systems. The importance of reactive power planning on economic profit and secure running has been increasing, because of the increasing fuel costs and investment funds. It is also quite important for an electric operator to provide voltage in a specified range for the customers. As such, RPO provides voltage control in power systems. Furthermore, it is used for decreasing active power loss and making better power coefficents. In this study, multi-objective RPO was used considering voltage deviations of buses, active power losses and reactive power generator costs. In this study, a new metaheuristic optimization method, which is an artificial bee colony (ABC), was used for the optimization. However, ABC was applied on ten bus system and the results were compared with the improving strength pareto evolutionary algorithm. It was observed that the system runs more effectively and economically with the results found with ABC. Key words: Artificial bee colony, reactive power optimization, active power loss.