Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects

Economic exploitation of power systems has always been significant in the electricity industry. However, after restructuring the systems above and separating different sectors of this industry into independent enterprises, economic profitability became twice as important. In this paper, the issue of electricity pricing is examined from a retailer's point of view. The retailer supplies electricity from various sources, including the electricity market, bilateral contracts, and renewable sources, and then tries to sell it to customers at the optimal price. Here, the objective function combines expected profit and the conditional value at risk as a risk measure. Because of demand responsiveness, the retailer can use pricing tools to manage customer demand. Besides customer demand, the electricity market price and power generation of renewable energy sources are stochastic, and the advantage of the chance-constrained programming approach is taken to cover the power balance risk. Eventually, a hybrid chance-constrained and scenario-based method is proposed to model the retail electricity pricing problem based on fixed and real-time pricing policies. Furthermore, the energy storage system is considered a tool to increase the expected profit and control environmental effects; pollution costs are considered for electricity supplied from non-renewable sources. The proposed model maximizes profit and reduces environmental effects by considering pollution costs. To show the effectiveness of the proposed model, a numerical example is presented and solved. Results show that profit is maximized by determining each source's optimal selling price and power. Meanwhile, the energy storage system simultaneously increases this profit.