Optimal scheduling of energy consumptions for air conditioners in a smart community with renewables

Abstract

This paper investigates optimal day-ahead energy consumptions of air conditioners in a smart community with wind-turbine and photovoltaic power generations. The smart community energy management system tends to minimize the purchased power and maximize the sold power from/to the utility grid with different time-of-use tariffs day-ahead. The community consists of inelastic loads and elastic loads. This paper considers the air conditioners as the elastic loads, which can be adjusted according to the time-of-use tariffs, day-ahead outdoor temperatures, wind power and photovoltaic powers. Because the day-ahead wind and photovoltaic power generations as well as outdoor temperatures and inelastic loads are forecasted, they are modeled by appropriate probability density functions. Monte Carlo simulation incorporating with the interior point algorithm was employed to gain the optimal hourly settings of indoor temperatures, which are within the comfort zone. Simulation results using typical days in summer and winter verify the applicability of the proposed method.