This paper presents an efficient data-driven building electricity management system that integrates a battery energy storage (BES) and photovoltaic panels to support decision-making capabilities. In this micro-grid (MG) system, solar panels and power grid supply the electricity to the building and the BES acts as a buffer to alleviate the uncertain effects of solar energy generation and the demands of the building. In this study, we formulate the problem as a Markov decision process and model the uncertainties in the MG system, using martingale model of forecast evolution method. To control the system, lookahead policies with deterministic/stochastic forecasts are implemented. In addition, wait-and-see, greedy and updated greedy policies are used to benchmark the performance of lookahead policies. Furthermore, by varying the charging/discharging rate, we obtain the different battery size (Es) and transmission line power capacity (Pmax) accordingly, and then we investigate how the different Es and Pmax affect the performance of control policies. The numerical experiments demonstrate that the lookahead policy with stochastic forecasts performs better than the lookahead policy with deterministic forecasts when the Es and Pmax are large enough, and the lookahead policies outperform the greedy and updated policies in all case studies.
- Lookahead policies
ASJC Scopus subject areas
- Decision Sciences(all)
- Management Science and Operations Research