Risk-based cooperative scheduling of demand response and electric vehicle aggregators

Abstract

This paper proposes a new cooperative scheduling framework for Demand Response Aggregators (DRAs) and Electric Vehicle Aggregators (EVAs) in a day-ahead market. The proposed model implements the Information-Gap Decision Theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees to obtain the predetermined profit by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenariobased and bi-level IGDT-based methods, respectively. The DRA provides DR from two demand-side management programs including Time-Of-Use (TOU) and reward-based DR. Then, the obtained DR is offered in day-ahead markets. Furthermore, the EVAs not only meet the EV owners' demand economically, but also participate in the day-ahead market while are willing to set DR contracts with the DRA. The objective function is to maximize the total profit of DR and EV aggregators by pursuing two different strategies to deal with price uncertainty, i.e., risk-seeking strategy and risk-averse strategy. The proposed plan is formulated in a risk-based approach and its validity is evaluated with respect to a case study with realistic data of electricity markets.