Risk-Aware Stochastic Energy Management of Microgrid with Battery Storage and Renewables
This paper deals with optimization-based control of real microgrids with uncertain forecasts of renewable energy production and local consumption. To achieve maximum economic benefits, these uncertainties need to be accounted for in a systematic fashion. Conventionally, this task is approached by employing stochastic model predictive control. While doing so allows to account for uncertainties in the forecasts, the downside is high computational complexity that hinders implementation in real time. In this paper we therefore propose an alternative method that decreases the computational burden by an order of magnitude without inducing significant suboptimality. The approach is based on splitting the stochastic model predictive control problem into two stages, one that employs multiple realizations of the uncertainties combines with a low-fidelity prediction model, and one that uses only the risk-aware realization, combined with a high-fidelity model. The theoretical development is then showcased on a real microgrid to confirm viability of our approach.