Real-Time Scheduling Optimization of Integrated Energy Systems in Smart Grids based on Approximate Dynamic Programming

Abstract

With the large-scale integration of renewable energy (RE) sources and rapid advancements in smart grid (SG) technologies, the efficient integration of diverse energy resources to achieve supply-demand balance and maximize costeffectiveness has emerged as a research hotspot in the energy sector. This paper addresses the real-time scheduling challenge in integrated energy systems (IES) within the context of SG, emphasizing pivotal factors such as electric and thermal load scheduling, energy storage control, dynamic electricity pricing, carbon emission mechanisms, and demand response (DR). To this end, we propose a comprehensive scheduling model tailored for IES, aiming to minimize the total cost over the dispatch cycle. Furthermore, an optimal scheduling algorithm based on approximate dynamic programming (ADP) was designed to solve this model. Numerical experiments reveal that, while ensuring user comfort, the proposed real-time scheduling scheme, by comprehensively considering the interactions among various system inputs, significantly enhances system flexibility and economic performance. It effectively tackles the uncertainty of RE, thereby improving energy utilization efficiency.