Research on Demand Response with Passive Building Thermal Storage based on Co-Simulation of Beetle Antennae Search (BAS) Algorithm and EnergyPlus

In order to fully investigate the economic benefits of demand response with passive building thermal storage, this paper proposes an optimization method to control of indoor temperature to reduce the air-conditioning operating cost based on Beetle Antennae Search (BAS) algorithm. A co-simulation between BAS algorithm and EnergyPlus is developed based on Python. A case study of an office building in Tianjin is conducted to demonstrate the effectiveness of the BAS algorithm and the flexibility of the co-simulation. Based on the electricity price policy in Tianjin, the indoor temperature setpoint of an office building is optimized. The results show that over 42% of cooling energy in the optimal scene is consumed during the valley-price time, and the operating cost of the air conditioning system is reduced by about 14%. So that, building thermal mass could provide flexibility to public power grid. More importantly, the BAS algorithm has highly search efficiency that can be used to guide building participation in demand response.