an annual framework for clustering-based pricing for an electricity retailer

in the competitive environment, it is necessary for a retailer to increase his/her profit as much as possible. there are few researches focused on the subjects related to the retailer and the retail market. in addition, those researches have mostly focused on the participation of the retailer in the wholesale market. in order to determine the optimal selling price, the knowledge of how and when consumers use electricity is essential to the retailer. this type of information can be found in load profiles of customers. in this paper, an annual framework for optimal price offering by a retailer is proposed which is based on clustering technique. for this purpose, load profiles of customers are used as their consumption patterns. also, a profit function is defined as the objective of optimization problem based on the load profile considering conditional value at risk (cvar) for risk modeling. also, a new acceptance function is proposed to overcome drawbacks of the traditional ones. the objective function is a mixed-integer nonlinear problem which is solved by gams software.