Application of Robust Optimization Approach to Determine Optimal Retail Electricity Price in Presence of Intermittent and Conventional Distributed Generation Considering Demand Response

Abstract

In this paper, a robust optimization approach is proposed to determine selling electricity price for a retailer who procures its obligated energy through two different resources: (1) wholesale market and (2) self-generation facilities. Regarding the self-generation facilities, two different kinds of distributed resources, including gas turbine units (GT) and roof-top photovoltaic sites (RPV) with considering energy storage systems (ESS), are addressed as the deterministic and intermittent power resources. Considering the wholesale market, the retailer can procure some parts of its obligated energy through bilateral contracts and day-ahead market. To overcome the uncertainties associated with power output forecasting of solar sites, a new statistical approach is used considering the dependency of power output to the weather issues, such as irradiation, temperature and wind speed. The problem is formulated by using a robust mixed-integer quadratic program considering a confidence bound for the wholesale electricity price uncertainty. To determine the optimal selling price, a successive algorithm is developed through two iterative optimizations, including inner and outer iterative procedures. Regarding the outer optimization, the confidence bound of wholesale electricity price is portioned into subintervals to evaluate the impacts of each robust subregion of wholesale price on the offered retail selling price. Through the inner optimization, the consumers’ response to the offered price is evaluated using a complete demand function model. Finally, a case study containing the bilateral contracts, wholesale market, RPV units, GT units, ESS, flexible demands and the retailer providing demand response is considered to demonstrate the proficiency of the proposed approach.