The application of equilibrium optimizer for solving modern economic load dispatch problem considering renewable energies and multiple-fuel thermal units (original) (raw)
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Decision-making for operational optimization Economic Load Dispatch (ELD) is one of the most important tasks in thermal power plants, which provides an economic condition for power generation systems. The aim of this paper is to analyze the application of evolutionary computational methods to determine the best situation of generation of the different units in a plant so that the total cost of fuel to be minimal and at the same time, ensuring that demand and total losses any time be equal to the total power generated. Various traditional methods have been developed for solving the Economic Load Dispatch, among them: lambda iteration, the gradient method, the Newton's method, and so others. They allow determining the ideal combination of output power of all generating units in order to meet the required demand without violation of the generators restrictions. This article presents an analysis of different mathematical methods to solve the problem of optimization in ELD. The results show a case study applied in a thermal power plant with 10 generating units considering the loss of power and its restrictions, using MATLAB tools by developed techniques with particle swarm algorithm.
A Case Study of Economic Load Dispatch for a Thermal Power Plant using Particle Swarm Optimization
2013
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
IEEE Access
This paper presents a hybrid metaheuristic optimization algorithm developed to solve the Economic Dispatch Problem (EDP) encountered in different combinations of power plants. The algorithm is developed by assimilating the prominent features of Particle Swarm Optimization (PSO) and Bat Algorithm (BA) and improves cost reduction and convergence with lesser computational time. The developed algorithm is employed for the solution of EDP consisting of only Renewable Energy Sources (RESs) implemented at various locations in Pakistan. The all RES based EDP consists of scenarios composed of sub-scenarios having no constraints, with time-varying loads and multi-area economic dispatch (MAED). The algorithm is also tested for three different combinations of power plants, comprising of RES integrated with thermal power plants (TPPs), the small-scaled thermal power system with constraints, and a large-scaled power system with Valve Point Loading (VPL) effect. The comparative analysis of the results for the developed metaheuristic algorithm with various existing techniques shows a reasonable reduction in the cost, improved computational time, and fast convergence.
This paper deals with the optimization of economic load dispatch (ELD) problem; this is to find the optimal combination of generators in order to minimize the operating costs of the system. This is done by using the particle swarm optimization (PSO) algorithm. PSO is applied to search for the optimal schedule of all the generator units that can supply the required demand at minimum fuel cost while satisfying all system constraints. The PSO algorithm has been implemented using MATLAB optimization toolbox and was applied to solve the ELD problem of the Nigeria thermal power stations. The results were compared with published results obtained via micro-GA, conventional-GA and differential evolution (DE) techniques.
Particle Swarm Optimization for Solving the Economic Load Dispatch Including Wind Energy
Economic load dispatch among thermal units is one of the most important problems in power systems operation. Usually so called equal marginal cost criterion is adopted to this calculation. Recently global trend of utilizing more and more renewable energy such as wind power makes this problem more important than ever. With the continuing search for alternatives to conventional energy sources, it is necessary to include wind energy generators (WEG) in the ELD problem. This paper presents a solution of economic load dispatch incorporation wind energy using a particle swarm optimization algorithm (PSO). The effect of wind energy generators system inclusion on ELD problem is investigated, with the source wind susceptible to short duration variations, which is the uncertainty of wind speed around a short-duration-stable mean value. A six unit test system is resolved using PSO to illustrate the variation in the optimal cost, losses, and system-λ with the variation of short-duration-stable ...
Minimization of Environmental Emission and cost of generation by using economic load dispatch
Research Square (Research Square), 2022
most of the electrical power is generated by thermal power plants. When a thermal plant is operated it can induce carbon dioxide and other toxic gases which can pollute the environment and reduce the life of living nature. Also, the cost of electrical power generation increases if the demand increases. This work proposed the economic load dispatch, which can help to generate the electrical power as per the load demand and save coal as well as reduce the emission of toxic gases. This work considers the latest variant of particle swarm optimization techniques, which can help to optimize the economic load dispatch. The help case study shows the effectiveness of ELD and PSO techniques.
International Journal of Electrical and Computer Engineering (IJECE), 2021
In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The suggested IPSO is the modified version of Particle swarm optimization (PSO) by changing velocity and position updates. The five best solutions are employed to replace the so-far best position of each particle in velocity update mechanism and the five best solutions are used to replace previous position of each particle in position update. In addition, constriction factor is also used in the suggested IPSO. PSO, constriction factor-based PSO (CFPSO) and bat optimization algorithm (BOA) are also run for comparisons. Two systems are used to run the four methods. The first system is comprised of nine TUs with multiple fuels and one wind farm. The second system is comprised of eight TUs with multiple fuels and two WFs. From the comparisons of results, IPSO is much more powerful than three others and it can find optimal power generation with the lowest total power generation cost.
Implementing Particle Swarm Optimization to Solve Economic Load Dispatch Problem
2009 International Conference of Soft Computing and Pattern Recognition, 2009
Economic Load Dispatch (ELD) is one of an important optimization tasks which provides an economic condition for a power systems. In this paper, Particle Swarm Optimization (PSO) as an effective and reliable evolutionary based approach has been proposed to solve the constraint economic load dispatch problem. The proposed method is able to determine, the output power generation for all of the power generation units, so that the total constraint cost function is minimized. In this paper, a piecewise quadratic function is used to show the fuel cost equation of each generation units, and the B-coefficient matrix is used to represent transmission losses. The feasibility of the proposed method to show the performance of this method to solve and manage a constraint problems is demonstrated in 4 power system test cases, consisting 3,6,15, and 40 generation units with neglected losses in two of the last cases. The obtained PSO results are compared with Genetic Algorithm (GA) and Quadratic Programming (QP) base approaches. These results prove that the proposed method is capable of getting higher quality solution including mathematical simplicity, fast convergence, and robustness to solve hard optimization problems.