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

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 di...

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Bibliographic Details
Published inEnergies (Basel) Vol. 13; no. 7; p. 1657
Main Authors Jeong, Soseul, Lee, Junghun, Yoon, Minhan, Jang, Gilsoo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2020
Subjects
Computer simulation
Cooperation
Disturbance
Energy
Energy charge
Energy sources
Energy storage
ESS
event-driven
Frequency control
Frequency dependence
Frequency response
Frequency stability
Generators
Methods
neural network
Neural networks
Process controls
Renewable energy sources
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13071657