An Incentive Pricing Approach for Integrated Demand Response in Multi-energy System Based on Consumer Classification

Abstract

The rapid growth of energy demand has put tremendous pressure on the power system. In order to ease the pressure of the power system and balance supply and demand in the peak periods, we devise an integrated demand response (IDR) program by taking different types of consumers into consideration. Different from most of the existing works that model the responsive demand without classification, we classify consumers into different clusters using the method of k-means. Based on the classification result, the responsive demand models are obtained through historical data. The proposed IDR program is modeled as a nonlinear programming problem. By establishing the Karush-Kuhn-Tucker conditions, the closed-form optimal energy scheduling strategy is given. Moreover, we design an incentive pricing mechanism from which utility company can make optimal decisions. Simulations validate that the proposed IDR can solve the problem of the imbalance between supply and demand in the peak periods and the total costs of utility company can be reduced by implementing different incentive prices for different clusters of consumers.