323 research outputs found

    Integration of solar energy and optimized economic dispatch using genetic algorithm: A case-study of Abu Dhabi

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    © 2017 IEEE. The United Arab Emirates is focusing on cultivating Renewable Energy (RE) to meet its growing power demand. This also brings power planning to the forefront in regards to keen interests in renewable constrained economic dispatch. This paper takes note of UAE's vision in incorporating a better energy mix of Renewable Energy (RE), nuclear, hybrid system along with the existing power plants mostly utilizing natural gas; with further attention for a sound economic dispatch scenario. The paper describes economic dispatch and delves into the usage of Genetic Algorithm to optimize the proposed system of thermal plants and solar systems. The paper explains the problem formulation, describes the system used, and illustrates the results achieved. The aim of the research is in line with the objective function to minimize the total costs of production and to serve the purpose of integrating renewable energy into the traditional power production in UAE. The generation mix scenarios are assessed using genetic algorithm using MATLAB simulation for the optimization problem

    A Modified ABC Algorithm for Solving Non-Convex Dynamic Economic Dispatch Problems

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    In this paper, a modified artificial bee colony (MABC) algorithm is presented to solve non-convex dynamic economic dispatch (DED) problems considering valve-point effects, the ramp rate limits and transmission losses. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The feasibility of the proposed method is validated on 5 and 10 units test system for a 24 h time interval. The results are compared with the results reported in the literature. It is shown that the optimum results can be obtained more economically and quickly using the proposed method in comparison with the earlier methods

    Pembangkitan Ekonomis pada Unit Pembangkit Listrik Tenaga Diesel Telaga Gorontalo Menggunakan Algoritma Genetika

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    The increasing daily need towards electrical energy demands for generation companies to conduct operational cost-saving strategy including the generation fuel. One of the strategies that can be done is through economical generation optimization. The genetics algorithm of the heuristics method is known for its ability to overcome the problems characterized as non-linear, non convex, integer/ discrete, not continuous, and a system with a lot of variables. The evaluation technique employing the evolution theory has been applied to the case of IEEE 26 buses power system and diesel power generation in a unit in Telaga, Gorontalo. The result shows that the proposed method is believed to be able to minimize the generation cost better than the previous method. The method is tested by applying for its real system in Telaga, Gorontalo and it is found that the total cost at Rp 20.201.000,00 per hour with total load at 5.000 kW
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