Consumer Psychology Based Optimal Portfolio Design for Demand Response Aggregators

• Main Authors: Yunwei Shen, Yang Li, Qiwei Zhang, Fangxing Li, Zhe Wang
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: Journal of Modern Power Systems and Clean Energy
• Subjects: Demand response (DR); aggregator; contract; consumer psychology; multi-objective problem; Pareto optimization
• Online Access: https://ieeexplore.ieee.org/document/9153212/

Demand response (DR) has received much attention for its ability to balance the changing power supply and demand with flexibility. DR aggregators play an important role in aggregating flexible loads that are too small to participate in electricity markets. In this work, a DR operation framework is presented to enable local management of customers to participate in electricity market. A novel optimization model is proposed for the DR aggregator with multiple objectives. On one hand, it attempts to obtain the optimal design of different DR contracts as well as the portfolio management so that the DR aggregator can maximize its profit. On the other hand, the customers' welfare should be maximized to incentivize users to enroll in DR programs which ensure the effective and flexible load control. The consumer psychology is introduced to model the consumers' behavior during contract signing. Several simulation studies are performed to demonstrate the feasibility of the proposed model. The results illustrate that the proposed model can ensure the profit of the DR aggregator whereas the customers' welfare is considered.