Algorithmic approach to minimize the energy cost of commercial buildings in developing countries
Energy costs from Heating-Ventilation-Air-Conditioners (HVAC) systems form the third largest component of the overall operational costs of commercial buildings. Moreover, in developing countries such as India where grid connectivity in remote areas is inadequate, diesel generators (DGs) are used as a source of backup power. While DGs solve the problem of power outages, the cost of energy from diesel generators is very high, typically 3 to 5 times the grid price. Although using solar power in conjunction with battery storage can offset the high costs of diesel power, the overall energy management system of the building becomes complex. In this paper, we propose an algorithmic approach that reduces the energy cost associated with HVAC systems in the presence of outages and a mix of supply side resources including grid, DG, solar and battery storage. The algorithm schedules supply resources and the HVAC by leveraging weather forecasts and the passive thermal storage capacity of the building. The algorithm is computationally light-weight and can easily be integrated with existing building management systems. Additionally, we validate its performance against an optimization framework that yields optimal solutions. We show that both the algorithm and the optimization framework yield an average savings of 20% relative to business as usual in real world scenarios such as cell towers and office buildings.