Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting
| Autor: | Mehmet Kusaf, Diaa Salman |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Environmental effects of industries and plants
Renewable Energy Sustainability and the Environment wind energy performance uncertainty unit commitment economic dispatch recurrent neural network Geography Planning and Development TJ807-830 Management Monitoring Policy and Law TD194-195 Renewable energy sources Environmental sciences GE1-350 |
| Zdroj: | Sustainability, Vol 13, Iss 13609, p 13609 (2021) Sustainability; Volume 13; Issue 24; Pages: 13609 |
| ISSN: | 2071-1050 |
| Popis: | Unit Commitment (UC) is a complicated integrational optimization method used in power systems. There is previous knowledge about the generation that has to be committed among the available ones to satisfy the load demand, reduce the generation cost and run the system smoothly. However, the UC problem has become more monotonous with the integration of renewable energy in the power network. With the growing concern towards utilizing renewable sources for producing power, this task has become important for power engineers today. The uncertainty of forecasting the output power of renewable energy will affect the solution of the UC problem and may cause serious risks to the operation and control of the power system. In power systems, wind power forecasting is an essential issue and has been studied widely so as to attain more precise wind forecasting results. In this study, a recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. The RNN method is compared with the SVM approach in forecasting the wind power performance; the results show that the RNN method provides more accurate and secure results than SVM, with an average error of less than 5%. The suggested approaches are tested by applying them to the standard IEEE-30 bus test system. Moreover, a hybrid of a dynamic programming optimization technique and a genetic algorithm (DP-GA) are compared with different optimization techniques for day ahead, and the proposed technique outperformed the other methods by 93,171$ for 24 h. It is also found that the uncertainty of the RNN affects only 0.0725% of the DP-GA-optimized UC performance. This study may help the decision-makers, particularly in small power-generation firms, in planning the day-ahead performance of the electrical networks. |
| Databáze: | OpenAIRE |
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