Assessment of Load Reduction Potential Based on Probabilistic Prediction of Demand Response Baseline Load

The uncertainty of baseline load forecasting critically influences both the assessment of load reduction potential and demand response (DR) settlement. Therefore, this paper focuses on assessing load reduction potential based on probabilistic predictions of the baseline load. First, the uncertainty of the baseline load prediction is analyzed through calculating the conditional probability density function (PDF) and interval estimation of baseline load prediction errors from the convolutional neural network (CNN) model. Then, the probabilistic model of load reduction potential is proposed based on the results from the probabilistic prediction of baseline load and the terms about the interruptible load in DR contracts. Finally, the Monte Carlo simulation method is used to assess the load reduction potential, and probability distributions of the load reduction states, the lower and upper limits of the load reduction potential, are analyzed. Case studies demonstrate that the proposed method effectively characterizes the uncertainty of prediction results, with the prediction interval normalized average width (PINAW) decreased by 10.97%, thereby enabling the effective assessment of load reduction potential from the probabilistic perspective, helping decision makers take better choices.