Optimal Control Policy for Energy Management of a Commercial Bank

Abstract

There has been substantial research on Building Energy Management Systems. Most of the work has focused more on the management scheme and less on the specific structure or the nature of activities within each building. However, recently some attention is being paid to these specifics, and this paper is one of such efforts, where we consider the structure and nature of activities in the building for developing an energy management system custom designed for a bank branch where customers may arrive randomly based on a known probability distribution. Specifically, this paper presents a model for generating an optimal control policy to manage the electrical energy of a commercial bank building. A Markov Decision Process (MDP) model is proposed. The MDP model is solved for the calculation of an optimal control policy using stochastic dynamic programming. The advantage of the proposed model is that it can incorporate uncertainty involved in the problem. Another advantage is that the output control policy is optimal with respect to a discounted cost/reward function. A disadvantage of the proposed scheme is computational complexity. To overcome this disadvantage, a decomposition-based approach is proposed. A unique feature of the proposed MDP-based model is that it was developed for a specific type of building, i.e., a bank. The need for a Building Management System (BMS) that is specific for a particular type of building arises due to the fact that each building has its own working parameters and environment. Our focus is to give a customized BMS framework for a bank building. Practical implementation of the developed model is discussed and a case study is included for demonstration purposes. Results obtained from the case study indicate that considerable savings in the electrical energy expenditure can be achieved without compromising comfort. This is possible due to optimization of the control policy using the statistical information relevant to the problem.