Energy Storage System Event-Driven Frequency Control Using Neural Networks to Comply with Frequency Grid Code

Soseul Jeong, Junghun Lee, Minhan Yoon and Gilsoo Jang
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Soseul Jeong: School of Electrical Engineering, Korea University, Seoul 02841, Korea
Junghun Lee: School of Electrical Engineering, Korea University, Seoul 02841, Korea
Minhan Yoon: Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Korea
Gilsoo Jang: School of Electrical Engineering, Korea University, Seoul 02841, Korea

Energies, 2020, vol. 13, issue 7, 1-17

Abstract: As the penetration of renewable energy sources (RESs) increases, the rate of conventional generators and the power system inertia are reduced accordingly, resulting in frequency-stability concerns. As one of the solutions, the battery-type energy storage system (ESS), which can rapidly charge and discharge energy, is utilized for frequency regulation. Typically, it is based on response-driven frequency control (RDFC), which adjusts its output according to the measured frequency. In contrast, event-driven frequency control (EDFC) involves a determined frequency support scheme corresponding to a particular event. EDFC has the advantage that control action is promptly performed compared to RDFC. This study proposes an ESS EDFC strategy that involves estimating the required operating point of the ESS according to a specific disturbance through neural-network training. When a disturbance occurs, the neural networks can estimate the proper magnitude and duration of the ESS output to comply with the frequency grid code. A simulation to validate the proposed control method was performed for an IEEE 39 bus system. The simulation results indicate that a neural-network estimation offers sufficient accuracy for practical use, and frequency response can be adjusted as intended by the system operator.

Keywords: ESS; frequency control; neural network; event-driven (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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