Probabilistic Forecasting of Available Load Supply Capacity for Renewable-Energy-Based Power Systems

In order to accurately analyze the load supply capability of power systems with high penetration of renewable energy generation, this paper proposes a probabilistic available load supply capability (ALSC) forecasting method. Firstly, the optimal input features are selected by calculating the maximal information coefficient (MIC) between the input features and the target output. Based on this, a stacking ensemble learning model is applied for the prediction of wind power, photovoltaic power and load power. Secondly, the distributions of the forecasting objects are obtained based on forecasting errors and the error statistics method. Finally, the forecasting distributions of wind power, photovoltaic power and load are set as the parameters of a power system, and then probabilistic ALSC is calculated using Latin hypercube sampling (LHS) and repeated power flow (RPF). In order to simulate a more realistic power system, multiple slack buses are introduced to conduct two types of power imbalance allocations with novel allocation principles during the RPF calculation, which makes the ALSC evaluation results more reasonable and accurate. The results of probabilistic ALSC forecasting can provide a reference for the load power supply capacity of a power system in the future, and they can also provide an early warning for the risk of ALSC threshold overlimit. Case studies carried out on the modified IEEE 39-bus system verify the feasibility and effectiveness of the proposed methods.