A A Wind Forecasting Model Using Regression and Genetic Algorithm to Solve Economic Dispatch for Evaluating a Hybrid Power System

  • Haidar Rahman Ridwan Budi Prasetyo
  • Ridwan Budi Prasetyo National Laboratory for Energy Conversion Technology and Agency for the Assessment and Application of Technology
DOI: https://doi.org/10.46923/ijets.v2i2.69
Keywords: Economic Dispatch, Genetic Algorithm, Kernels Regression Standalone Power Plant.

Abstract

In this research, the problem to find an evaluator to determine a location to build the standalone power system can be seen as problem which can be solved with Kernels Regression where, it will receive 2 inputs such as time and wind speed in order to predict the future wind speed. Afterward the obtained predicted wind speed will be converted into potential electrical energy with maximum and minimum energy and we will be using the Genetic Algorithm (GA) to solve the Economic Dispatch (EDC) to see the operational cost when dispatch into the grid. The data was taken from Baron Techno-Park and PLTH Pantai Baru, and will only be using data from the month of September - December since it is the rainy season. Therefore, since significant parameters such as energy per currency will show that operational cost of Baron Techno-Park have the least operational cost then PLTH Pantai Baru, hence the creation of renewable power plants in Baron Techno-Park are suitable and will have a good operational cost justification.

Keywords: Economic Dispatch, Genetic Algorithm, Kernels Regression Standalone Power Plant.

 

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Submitted
2020-06-19
Accepted
2020-12-01
How to Cite
Rahman, H., & Budi Prasetyo, R. (2020). A A Wind Forecasting Model Using Regression and Genetic Algorithm to Solve Economic Dispatch for Evaluating a Hybrid Power System. International Journal of Engineering Technology and Natural Sciences, 2(2), 43 - 50. https://doi.org/10.46923/ijets.v2i2.69
Issue
Vol. 2 No. 2 (2020): International Journal of Engineering, Technology and Natural Sciences
Section
Articles

Copyright (c) 2021 Haidar Rahman

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