Collaborative power tracking method of diversified thermal loads for optimal demand response: A MILP-Based decomposition algorithm

Abstract

Thermal load is an important type of demand response (DR) resources to maintain the power balance of regional electricity-heating energy systems. However, the diversity of power-consuming patterns and time series coupling of production processes arise challenges for precisely tracking the scheduled command of industrial and domestic thermal loads. This paper proposes a feasible solution to achieving collaborative scheduling of diversified thermal loads meanwhile optimizing the tracking performance of scheduled power, via a mixed integer linear programming (MILP) based decomposition algorithm. The power-temporal granularity is firstly introduced and modeled to characterize the available regulated capacity of diversified thermal loads in each power regulation event. Then, the time series coupling model among multiple thermal devices is established to describe the coupling relationship in production processes. On this basis, the diversified thermal loads are co-regulated in the considered system to accomplish the decomposition objectives and without taking up extra generation and storage regulation resources. Case studies based on real data of a regional thermal loads are conducted, which demonstrates the feasibility and effectiveness of proposed method in improving comprehensive benefits and reducing power deviation.