Trading Framework for Demand Response Aggregators Using Information-Gap Decision Theory to Address Uncertainty and Risk-Management

In this work a new trading framework for demand response (DR) aggregators is proposed using a non-probabilistic model. In this model, DR is acquired from consumers to sell it to the purchasers by aggregators. Two programs, i.e., time-of-use (TOU) and reward-based DR program, are implemented to obtain DR from consumers. Then, the obtained DR is sold to buyers via two considered agreements, i.e., fixed DR contracts and DR options. The information-gap decision theory is also employed to consider the uncertainties for risk-averse aggregators. Consumer's participation behavior is considered as an uncertain parameter. A robustness function is proposed to examine the immunity of the model against adverse variations of uncertain parameters. The feasibility of the proposed model is studied on the real-world data.