A novelty approach to solve an economic dispatch problem for a renewable integrated micro-grid using optimization techniques
DOI:
https://doi.org/10.20998/2074-272X.2023.4.12Keywords:
economic dispatch, combined heat and power source, solar power, thermal generators, wind power, optimization techniquesAbstract
Introduction. The renewable integrated microgrid has considered several distributed energy sources namely photovoltaic power plant, thermal generators, wind power plant and combined heat and power source. Economic dispatch problem is a complex operation due to large dimension of power systems. The objective function becomes non linear due to the inclusion of many constraints. Hourly demand of a commercial area is taken into consideration for performing economic dispatch and five combinations are considered to find the best optimal solution to meet the demand. The novelty of the proposed work consists of a Sparrow Search Algorithm is used to solve economic load dispatch problem to get the better convergence and accuracy in power generation with minimum cost. Purpose. Economic dispatch is performed for the renewable integrated microgrid, in order to determine the optimal output of all the distributed energy sources present in the microgrid to meet the load demand at minimum possible cost. Methods. Sparrow Search Algorithm is compared with other algorithms like Particle Swarm Optimization, Genetic Algorithm and has been proved to be more efficient than Particle Swarm Optimization, Genetic Algorithm and Conventional Lagrange method. Results. The five combinations are generation without solar power supply system and Combined Heat and Power source, generation without solar and wind power supply systems, generation including all the distributed energy sources, generation without wind power supply system and Combined Heat and Power source, generation without thermal generators. Practical value. The proposed optimization algorithm has been very supportive to determine the optimal power generation with minimal fuel to meet the large demand in commercial area.
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