Real-Time Pricing by On-Line Estimation of Consumer Characteristics

Abstract

We discuss a real-time pricing method for an electric power system based on model predictive control (MPC) by the real-time optimization and estimation of a consumer's parameter using a particle lter. Consumer characteristics are modeled by a sigmoidal demand function that represents saturation effects and the price elasticity of demand, and the price elasticity is estimated on line by a particle lter. Then, the electricity price is determined by MPC to suppress the frequency deviation in the entire power system and the deviation of power consumption in heat pump water heaters. We conducted numerical simulations to compare the performance of our proposed method with that of the direct control scheme used in the previous research. We demonstrate that a supply-demand balance is achieved by price presentation in spite of a large uctuation in wind power and the uncertainty in a consumer's parameter.