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In order to meet the requirements of power grid frequency assessment and ensure the real-time balance of power generation and utilization, it is necessary to predict the demand for automatic generation control (AGC) regulation capacity of the power grid. To overcome this problem and the shortcomings of the forecasting method of China’s frequency regulation, the paper propose a new method based on autoregressive moving average (ARMA) model. This forecasting method is mainly through the chip to process the historical data of the areal control error (ACE) signal, establishing an \(ARMA\left( {p,q} \right)\) model and calculating its residuals. According to the akaike information criterion (AIC) criterion, the autocorrelation function (ACF) and partial autocorrelation function (PACF) methods are used to determine the optimal value of p and q. The numerical simulation results based on the historical data show that the proposed method can effectively predict the frequency regulation capacity.

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Forecasting Short-Term Capacity Demand in Frequency Regulation Auxiliary Service Market Based on Time Series Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Jiaxi Huang,
  • Yanlong Yang,
  • Zhian He,
  • Chuangsheng Chen,
  • Xiaocong Wang,
  • Yanhong Zhang,
  • Binghua Fang

摘要

In order to meet the requirements of power grid frequency assessment and ensure the real-time balance of power generation and utilization, it is necessary to predict the demand for automatic generation control (AGC) regulation capacity of the power grid. To overcome this problem and the shortcomings of the forecasting method of China’s frequency regulation, the paper propose a new method based on autoregressive moving average (ARMA) model. This forecasting method is mainly through the chip to process the historical data of the areal control error (ACE) signal, establishing an \(ARMA\left( {p,q} \right)\) model and calculating its residuals. According to the akaike information criterion (AIC) criterion, the autocorrelation function (ACF) and partial autocorrelation function (PACF) methods are used to determine the optimal value of p and q. The numerical simulation results based on the historical data show that the proposed method can effectively predict the frequency regulation capacity.