A Cost Optimization Method Based on Adam Algorithm for Integrated Demand Response

Abstract

Demand response has gradually evolved into integrated demand response (IDR) as energy integration technology develops in integrated energy systems (IESs). The IES has a large amount of data interaction and an increasing concern for users’ privacy protection. Based on the combined cooling, heating, and power model, our study established an IDR management model considering demand-side energy coupling, focusing on cost optimization. In terms of privacy protection in the IDR management process, an optimization method based on the Adam algorithm was proposed. Only nonsensitive data, such as gradients, were transmitted during the processing of the Adam-based method by relying on a centralized iterative computing architecture similar to federated learning. Thus, privacy protection was achieved. The final simulation results proved that the proposed IDR management model had a cost reduction of more than 9% compared with a traditional power demand response. Further simulations based on this model showed that the efficiency and accuracy of the proposed Adam-based method are better than those of other distributed computing algorithms.