Economic dispatch and operating cost optimization for thermal power in 500 KV system using genetic algorithm (GA)

3rd Annual Applied Science and Engineering Conference

工业技术;自然科学

Azkiya, N.M.^1 ; Abdullah, A.G.^1 ; Hasbullah, H.^1

Department of Electrical Engineering Education, Universitas Pendidikan Indonesia, Bandung, Indonesia^1

关键词: Economic Dispatch;    Electricity distribution;    Electricity production;    Electricity system;    Heuristic approach;    Research stations;    Thermal generation;    Thermal generators;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/434/1/012013/pdf
DOI  :  10.1088/1757-899X/434/1/012013

来源: IOP

【 摘 要 】

Electricity system is divided into three main parts, namely power plants, electricity distribution or transmission, and distribution of electricity. Power generation is the biggest cost component in a power system. The analysis to minimize generational costs is called Economic Dispatch. The meaning of Economic Dispatch is the division of loading on existing generating units in the system optimally and economically at a certain load price. With the implementation of Economic Dispatch it will get a minimum cost of generation of electricity production. Then the need for optimization that is by using a heuristic approach with Genetic Algorithm (GA). With this approach will be obtained an economical operating costs. Genetic algorithms are able to find the optimal power generation value. The case studies in this research are available on thermal generation in the Java-Bali 500 kV system. The research stations are Suralaya, Muara Tawar, Tanjung Jati, Gresik, Paiton and Muara Karang plants. Software used is Matlab R2017a. In this research we use the data of heatrate equation function and equation of operating cost at thermal generator then made arrangement to output (power) from generator to reduce operating cost of generation. Genetic Algorithm is very simple because it involves only crossover and string mutations so this research uses the algorithm to find the solution of the problem. With genetic algorithm, the result of operating cost where the biggest cost is 3.172.653.871 Rp / h and the smallest cost is 2.336.431.759 Rp / h.