Stochastic control of a micro-grid using battery energy storage in solar-powered buildings

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 (E-s) and transmission line power capacity (P-max) accordingly, and then we investigate how the different E-s and P-max 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 E-s and P-max are large enough, and the lookahead policies outperform the greedy and updated policies in all case studies.

Automated summary

This paper proposes an efficient data-driven building electricity management system integrating BES and photovoltaic panels, and uses various strategies to control the MG system. Results show that the lookahead policy with stochastic forecasts performs best.