Cost optimal operation of thermal energy storage system with real-time prices

In this paper we propose a method to optimize operation of a thermal energy storage (TES) system for heating, ventilation and air conditioning (HVAC) in terms of electricity cost. We pose this optimization problem as a mixed integer linear programming (MILP) problem where future thermal demand and electricity prices are predicted. The proposed method uses a branch and bound algorithm to solve the problem, using linear relaxation of the integer variables that represent future on-off states of the equipment. We conduct simulations based on real building data, which show that significant cost reduction can be obtained.