A Demand Response Transaction Method for Integrated Energy Systems with a Trigonometric Membership Function-Based Uncertainty Model of Costumers’ Responsive Behaviors

Abstract

As an important regulation tool for power systems, demand response can greatly improve system flexibility and economy. However, when an integrated energy system with a large number of flexible loads is aggregated for a demand response transaction, the uncertainty in the amount of the load response should be considered. Therefore, a demand response transaction model for an integrated energy system that considers the uncertainty of customer demand responses is proposed in this paper. We first analyze the uncertainty of incentive-based demand responses. Next, we investigate the relationship between the incentive level and the fluctuation of customer response volume. The flexible loads are classified into curtailable loads, translatable loads, and replaceable loads. Fuzzy variables are then used to represent the response volume of users, and a trigonometric membership function is used to represent the degree of uncertainty in the response volume of different flexible loads. Finally, the objective functions and chance constraints containing fuzzy variables are converted into explicit equivalence classes for solving. In the case study, the impact of the uncertainty of the user response volume on the revenue of each transaction entity and the impact of the fuzzy chance constraint confidence level on the response revenue are investigated. The results show that the revenue of each transaction entity decreases to a certain extent under the consideration of the uncertainty of the user response volume; the social welfare of the whole transaction increases as the confidence level of the chance constraint changes from high to low.

Study of the Multilevel Fuzzy Comprehensive Evaluation of Rock Burst Risk

“…The roof strata and mining depth are two continuous quantitative influencing factors. To ascertain the membership degree of these factors, the present paper adopts the triangular membership distribution function [45], as presented in Equations ( 10) and (11). The triangular membership function is a widely used fuzzy set membership function that is easy to implement.…”