Demand response via optimal pre-cooling combined with temperature reset strategy for air conditioning system: A case study of office building

Abstract

Demand response (DR) through air conditioning (AC) systems in buildings is a promising way to balance the power grid. However, the majority of existing DR strategies are rule-based. The other ones are model-based but generally have adopted oversimplified AC system models. Consequently, the effect of model-based optimal control cannot be evaluated properly. To address these limitations, a co-simulation framework is developed to minimize power consumption and electricity costs by adopting both pre-cooling and temperature reset strategies. The co-simulation framework combines an EnergyPlus model that is calibrated by measured data and the genetic algorithm through Functional Mock-up Unit socket. Both time-of-use electricity prices and the constraints of thermal comfort are considered in the optimization problem. A real office building with a variable refrigerant flow (VRF) system is used to test the co-simulation framework. Compared with rule-based strategies, the power consumption of the VRF system following the optimal control strategy during the DR period is reduced by 80.12%, 74.70% and 55.60%, and the daily electricity cost is reduced by 14.98%, 12.11% and 8.35%, respectively. The quantitative analysis also reveals that 37.96% of the cold energy stored by the envelope in the pre-cooling period is released into the air during the DR period.