抄録
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.
