Risk aware decomposition of online scheduling for large flexible consumers considering the age of information

The stability and security of the power system is significantly influenced by large electricity consumers, especially those with flexible demand, whose electricity purchase scheduling has been extensively studied. However, the high penetration of renewable energy and various widespread distributed storage systems have increased uncertainty of the power system, making it difficult to plan effective day-ahead electricity purchase for major electrical consumers. Online scheduling is therefore required so that the surplus can be sold to and the insufficient can be purchased from the real-time market at the lowest electricity cost. To this end, we formulate a multi-stage online scheduling model for large power consumers with flexible demand. To improve the efficiency of online scheduling, we decompose the problem into multiple single-stage sub-problems. Aimed at each single-stage scheduling, an online algorithm is designed to ensure the performance of optimal scheduling strategies maintains at a level comparable to the offline fixed strategies obtained from an oracle who has complete knowledge of the problem’s settings. We prove that our proposed online energy schedule algorithm is able to achieve O(T)regret bound for the single-stage online scheduling problem. To further highlight the significance of data and improve the multi-stage performance of our online scheduling algorithm, we also involve the notion of age of information combined with regret bound to help determine the optimal interval length