Fuzzy controlled parallel PSO to solving large practical economic dispatch (original) (raw)
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Economic Power Dispatch with Discontinuous Fuel Cost Functions using Improved Parallel PSO
Journal of Electrical Engineering and Technology, 2010
This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, MTS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.
Application of Improved Particle Swarm Optimization in Economic Dispatch of Power System
2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017
This paper introduces an improved particle swarm optimization to solve economic dispatch problems involving numerous constraints. Depending on the type of generating units, there are optimization constraints and practical operating constraints of generators such as prohibited operating zones and ramp rate limits. The algorithm is a hybrid technique made up of particle swarm optimization and bat algorithm. Particle swarm optimization as the main algorithm integrates bat algorithm in order to boost its velocity and adjust the improved solution. The new technique is firstly tested on five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units. The simulation results show that it performs better than both particle swarm and bat technique.
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch
IEEE Transactions on Evolutionary Computation, 2008
Economic dispatch is a highly constrained optimization problem encompassing interaction among decision variables. Environmental concerns that arise due to the operation of fossil fuel fired electric generators, transforms the classical problem into multiobjective environmental/economic dispatch (EED). In this paper, a fuzzy clustering-based particle swarm (FCPSO) algorithm has been proposed to solve the highly constrained EED problem involving conflicting objectives. FCPSO uses an external repository to preserve nondominated particles found along the search process. The proposed fuzzy clustering technique, manages the size of the repository within limits without destroying the characteristics of the Pareto front. Niching mechanism has been incorporated to direct the particles towards lesser explored regions of the Pareto front. To avoid entrapment into local optima and enhance the exploratory capability of the particles, a self-adaptive mutation operator has been proposed. In addition, the algorithm incorporates a fuzzy-based feedback mechanism and iteratively uses the information to determine the compromise solution. The algorithm's performance has been examined over the standard IEEE 30 bus six-generator test system, whereby it generated a uniformly distributed Pareto front whose optimality has been authenticated by benchmarking against the epsiv -constraint method. Results also revealed that the proposed approach obtained high-quality solutions and was able to provide a satisfactory compromise solution in almost all the trials, thereby validating the efficacy and applicability of the proposed approach over the real-world multiobjective optimization problems.
Power Economic Dispatch Using Particle Swarm Optimization
Current market environment, ever growing difference between depleting energy resources and increasing power demand and increased expectations of customers from utility companies has made it necessary to adopt some good operational policies by electric utility companies. So the focus of utility companies has shifted towards increased customer focus, enhanced performance and to provide reliable supply at low cost. The electric power system must be operated in a way to schedule generations economically of generation facilities. In last two decades many evolutionary techniques has been developed to solve the optimization problems. Particle swarm optimization has acquired much recognition due to less memory requirement and its inherent simplicity. Particle swarm optimization technique proved to be having strong potential for solving complex and high dimensional optimization problem. PSO is free from local minimum solution convergence which is often encountered while solving nonlinear and non-convex optimization problem through conventional techniques. This paper presents a summarized view of application of PSO for solving power economic dispatch problem.
An Extension of Particle Swarm Optimization (E-PSO) Algorithm for Solving Economic Dispatch Problem
2013 1st International Conference on Artificial Intelligence, Modelling and Simulation, 2013
Producing the energy power that meets the load demands at a minimum cost while satisfying the constraints is known as economic dispatch. Economic dispatch becomes one of the most complex problems in the planning and operation of a power system that aims to determine the optimal generation scheduling at minimum cost. For that reason many optimization researches on finding an optimal solution regarding the total cost of generation have been carried out. This paper presents the implementation of the extension of the PSO (E-PSO) system in solving the continuous nonlinear function of the cost curves of the generator. In this paper, a 6unit generation system has been applied to show the effectiveness of the E-PSO compared to the standard PSO. The results show that E-PSO is capable in solving the economic dispatch problem in term of minimizing the total cost of generation while considering the generator limits and transmission losses.
Multi-area Economic Dispatch Using Improved Particle Swarm Optimization
Energy Procedia, 2015
This paper presents improved PSO (IPSO) to solve Multi Area Economic Dispatch (MAED) problem. The objective of MAED problem is to determine the optimal value of power generation and interchange of power through tie-lines interconnecting areas in such a way that total fuel cost of thermal generating units of all areas is minimized while satisfying operational constraints. The control equation of the proposed PSO is modified by suggesting improved cognitive component of the particle's velocity by suggesting preceding experience. The operating parameters of the control equation are also modified to maintain a better balance between cognitive and social behavior of the swarm. The effectiveness of the proposed method has been tested on four areas, 40 generators test system. The application results show that IPSO is very promising to solve large-dimensional MAED problem.
This paper describe about the optimization of economic loading dispatch (ELD) problem. Economic loading dispatch is one of the important optimization tasks which provide economic condition for a power system. The ELD problems have non-smooth objective function with equality and inequality constraints. This paper presents particle swarm optimization (PSO) method for solving the economic dispatch(ED) problem in power system. The particle swarm optimization is an efficient and reliable evolutionary computational technique, which is used to solve economic load dispatch with line power flows. This paper describes, a new PSO framework used to deal with the equality and inequality constraints in ELD problem. The proposed PSO can always provide satisfying results within a realistic computation time. The PSO is applied with non-smooth cost function. The six thermal units, 26 buses and 46 transmission lines system is used in this paper. The proposed PSO method results are compared with the genetic algorithm (GA) and conventional method to show the effectiveness of PSO method to solve the ELD problems in power system.
Modified Particle Swarm Optimization for Solution of Reactive Power Dispatch
Research Journal of Applied Sciences, Engineering and Technology, 2018
Reactive Power Dispatch (RPD) is a complex, non−continuous and it is famous and essential problem in the power system. The calculation of this problem is really part of optimal load flow calculations. In this study, two types of Particle Swarm Optimization (PSO) algorithm are utilize as an optimization tools to solve RPD problem in order to minimize real Power Loss (P Loss) in the power system and keep voltage at all buses within acceptable limit. First type of PSO algorithm is Conventional PSO and the second type is utilize to improve the searching quality, also to decrease the time calculation and to enhance the convergence characteristic in the first type, it is called Modified PSO (MPSO). These types of PSO algorithm are tested on IEEE Node− 14, Node−30, Node−57 and Node−118 power systems to test their efficiency and ability in solving RPD problem in small and large power systems. The simulation results in four power systems show that the MPSO algorithm has a better performance in decreasing losses, decreasing time calculation and enhancement of voltage profile when compared to the Conventional PSO and other algorithms that reported in the literature.
One of the important optimization problems regarding power system issues is to determine and provide an economic condition for generation units based on the generation and transmission constraints, which is called Economic Dispatch (ED). The nonlinearity of the present problems makes conventional mathematic methods unable to propose a fast and robust solution, especially when the power system contains high number of generation units. In the present paper, an evolutionary modified Particle Swarm Optimization (PSO) is used to find fast and efficient solutions for different power systems with different generation unit numbers. The proposed algorithm is capable of solving the constraint ED problem, determining the exact output power of all the generation units. In such a way, proposed algorithm minimizes the total cost function of the generation units. To model the fuel costs of generation units, a piecewise quadratic function is used and B coefficient method is used to represent the transmission losses. The acceleration coefficients are adjusted intelligently and a novel algorithm is proposed for allocating the initial power values to the generation units. The feasibility of the proposed PSO based algorithm is demonstrated for four power system test cases consisting of 3, 6, 15, and 40 generation units. The obtained results are compared to existing results based on previous PSO implementing and Genetic Algorithm (GA). The results reveal that the proposed algorithm is capable of reaching a higher quality solution including mathematical simplicity, fast convergence, and robustness to cope with the non-linearities of economic load dispatch problem.