Risk Assessment of Supply and Demand Imbalance in Power Systems with High Proportion of Renewable Energy Under Extreme Operating Scenarios

Within a substantial segment of renewable energy systems, the production of wind energy and solar panels heavily relies on natural resources and weather conditions. The production of fresh energy could persist at a minimal level, leading to a scarcity of power and pushing the system into severe operational states, potentially triggering grave impacts on both production and functioning. Current studies typically employ novel energy production levels or weather benchmarks to assess extreme situation risks, making it challenging to delineate the risk variance in these scenarios from a supply-demand balance viewpoint. For this purpose, we suggest a method to evaluate risks in extreme operational situations. Initially, utilizing the ‘source-load’ random mismatch approach, this technique uncovers the distribution patterns of power supply and demand equilibrium in large-scale renewable energy systems, elucidating the variance in the intensity of diverse extreme situations. Next, the ALARP (As Low As Reasonably Practicable) standard is employed to categorize the risk associated with extreme operational situations, while the CVaR (Conditional Value at Risk) index characterizes the anticipated loss when the risk surpasses a specified limit. The likelihood of losing tail risk in areas of high risk is measured to establish a precise foundation for making risk-related decisions. Ultimately, a sample analysis is conducted, focusing on a substantial segment of the renewable energy power system. The findings indicate that the suggested technique is capable of precisely assessing the risk of imbalances in supply and demand due to severe operational situations. In contrast to a risk classification-based evaluation approach, this method more accurately mirrors the distribution traits of extreme situations in high-risk regions, offering practical assistance for adaptable system resource distribution and operational decision-making.