Residential Aggregator Risk-Constrained Profit Maximization Under Demand Response

This paper proposes a stochastic model for an aggregator operating in the day-ahead and real-time markets in the distribution system. When trading in the real-time market, the aggregator faces multiple risks stemming from uncertain load and real-time market prices. As a result, deciding the retail prices of the customers in an optimal fashion is a challenging task. This paper proposes a stochastic optimization model to determine the optimal day-ahead retail prices that a residential aggregator offers to its customers under uncertainties in real-time market prices. The uncertainties in real-time prices and customers' load are modeled using stochastic programming and the aggregator's profit is quantified via Conditional Value at Risk (CVaR). When the aggregator changes the customers' hourly prices, it provides incentive to the customers to adjust their demand, which results to monetary savings for the customers and the aggregator. A case study is implemented to show the validity of the proposed stochastic model.