A Stochastic DecisionMaking Model for an Electricity Retailer with Intermittent Renewable Energy and Shortterm Demand Response This paper presents a shortterm decisionmaking model for an electricity retailer with selfproduction of renewable energy. In the proposed model, a new trading mechanism for shortterm demand response (DR) between retail customers equipped with smart grid technology and an electricity retailer is presented. Through the proposed trading mechanism, retail customers submit shortterm DR offer curves to the retailer in order to increase or decrease their energy consumption for a given time period at favorable prices. On the other hand, the retailer decides its involvement in the shortterm DR market for every time period to avoid unfavorable prices in the realtime market and, thus, maximize its profit. The stochastic nature of dayahead and realtime prices, renewable energy production, electricity demand, and DR participation of retail customers is taken into account in the formulation of this work. The resulting model is a mixedinteger linear programming (MILP) which can be solved by existing commercial solvers. Case studies using realworld data are performed to demonstrate the effectiveness of the proposed model.

Incentivizing Strategy for Demand Response Aggregator Considering Market Entry Criterion: A Game Theoretical Approach This paper presents a novel incentivizing strategy for a demand response (DR) aggregator to participate in an intraday market organized by the grid operator (GO). By taking the market entry criterion into consideration, the proposed incentivizing strategy is able to help the aggregator determine the optimal incentive for each customer based on their identified characteristics or preferences. The hierarchical decision making procedure between the aggregator and customers is casted into a Stackelberg game. In accordance, an optimal incentivizing strategy can be ascertained for the aggregator, which further helps the aggregator elicit optimal load reductions from enrolled customers meanwhile maximizing its potential profit. Simulation results show that the proposed incentivizing strategy is effective in helping the aggregator determine optimal incentive for each customer in face of different external factors.