Decision Analysis of Load Aggregator Considering Dynamic Behavior of Residential Users

Fully tapping into the demand response potential of load aggregators is of great significance for energy conservation and emission reduction. To study the impact of resident behavior on aggregator decision-making, this paper proposes a load aggregator decision-making analysis method that considers the dynamic behavior of resident users. Firstly, considering the multiple influencing factors of resident participation in demand response, a Markov based bounded rationality decision behavior model is generated to predict user participation level. Secondly, the information gap decision theory is adopted to address the uncertainty of user participation level, and the aggregators are divided into risk investment type and risk avoidance type. Finally, by quantitatively evaluating the opportunity profits and risk losses, the aggregators with different risk preferences can select appropriate load reduction strategies based on this algorithm to ensure the maximization of expected returns. The case study results show that when the information of resident users is incomplete, the proposed method can effectively deal with the uncertainty of resident user participation in demand response, and the load aggregators can more reasonably make decisions to ensure expected returns.