Thermal and electrical demand response based on robust optimization

A robust optimization model for combined heat and power demand response is proposed in this paper, which aims to increase the capacity of peak regulation. The model considers three types of electric flexible loads: interruptible loads, shiftable loads, and transferable loads. The predicted mean vote index is introduced to measure the ambiguity of user's temperature perception and combined with the temperature autoregressive moving average to construct a thermal flexible load model. Robust optimization is employed to address un-certainties in wind power output and flexible electric load response, aiming to meet higher-level peak regulation. The case study indicates that the integrated demand response can effectively decouple the rigid correlation between electricity and heat load, exhibiting remarkable robustness and cost-effectiveness. This research has the potential to enhance the benefits of both microgrid and grid systems.

Automated summary

This paper proposes a robust optimization model for combined heat and power demand response to increase peak regulation capacity. The model considers three types of electric flexible loads and introduces the predicted mean vote index to measure the ambiguity of user's temperature perception. By employing robust optimization to address uncertainties, higher-level peak regulation requirements can be effectively met.