Clustering-based Optimal Dynamic Pricing for Residential Electricity Consumers

Electric power shortage in a residential area may occur with an increased probability if appropriate coordination mechanisms are missing. Time-of-Use (ToU) dynamical pricing has been proposed to influence the demand-side consumption to ensure a stable and optimal power system operation. This paper presents a method to find an optimal ToU electricity tariff if a single utility company (UC) provides electricity. The tariff is obtained based on the analysis of historical consumption data. First, the real consumption data is analyzed and clustered to select the possible consumer population to be targeted by ToU tariffs. Second, a simple consumer behavior model is established to predict the consumption profile changes if the ToU tariff is applied. Third, the genetic algorithm-based optimization resulting in the tariff is carried out. The goal is to ensure a win-win situation for the consumers on the demand-side and the UC when the optimal ToU is employed. The effect of dynamic pricing is demonstrated by simulating the case of one consumer category.