Dispatch Optimization Scheme for High Renewable Energy Penetration Using an Artificial Intelligence Model

Mahmood Alharbi () and Ibrahim Altarjami
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Mahmood Alharbi: Department of Electrical Engineering, Taibah University, Madinah 42353, Saudi Arabia
Ibrahim Altarjami: Department of Electrical Engineering, Taibah University, Madinah 42353, Saudi Arabia

Energies, 2024, vol. 17, issue 12, 1-17

Abstract: The scientific community widely recognizes that the broad use of renewable energy sources in clean energy systems will become a substantial and common trend in the next decades. The most urgent matter that has to be addressed is how to enhance the amount of renewable energy integration into the system while ensuring system stability in the presence of sudden fluctuations in generation and system faults. This study introduces a methodology that may be applied to any power system to optimize the level of renewable energy sources (RESs) integration. The methodology relies on using a trilayered neural network (TNN), which is a model utilized in the field of artificial intelligence. In order to apply and analyze the outcomes of the proposed optimization technique, the Kundur power system is employed as a case study. The objective of this methodology is to enhance the operation dispatches of a power system to attain a higher level of renewable energy output, specifically photovoltaic (PV) generation, while maintaining the stability of the system. This would enhance the stakeholders’ or utility providers’ capacity to make well-informed judgments on operation dispatch processes. The findings of this study suggest that it is generally recommended to raise the dispatchable power values for the generators in the loading region and lower the dispatchable power values for the generators in the generating area.

Keywords: renewable energy penetration; artificial intelligence; trilayered neural network; optimal power dispatch (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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