Scenario-Based Stochastic MPC for Energy Hubs with EV Fleets Under Persistent Grid Outages

TL;DR

This paper develops a scenario-based stochastic MPC for energy microgrids with EV fleets, improving resilience and emissions reduction during grid outages by incorporating probabilistic outage scenarios.

Contribution

It introduces a novel SMPC that accounts for grid outages and EV charging needs using probabilistic models, enhancing microgrid performance and resilience.

Findings

SMPC achieves within 1% of perfect-forecast performance.

Naive MPC offers no economic or sustainability benefits over rule-based control.

Buffering against EV consumption uncertainty reduces over 90% of state-of-charge violations.

Abstract

Emissions reduction and resilience to outages motivate the adoption of renewable microgrids. Surprisingly, research integrating both probabilistic grid outages and electric vehicle (EV) charging requirements remains limited. This paper addresses this gap by developing a scenario-based stochastic model predictive controller (SMPC) for a microgrid energy hub comprising solar generation, battery storage, diesel backup, and an EV fleet connected to a weak grid. Grid outage and campus load scenarios are generated from a continuous-time Markov chain and a Gaussian Process, respectively. Using 2023 operational data from the Ashesi University Energy Hub in Ghana, we demonstrate that the SMPC achieves performance within 1\% of a perfect-forecast benchmark. In contrast, a naive MPC that assumes continuous grid availability offers no economic or sustainability advantage over rule-based control, with both incurring significantly higher costs and emissions than the SMPC. These results highlight that outage anticipation is essential for economic viability. Finally, we show that incorporating a deterministic buffer against EV consumption uncertainty eliminates over 90\% of state-of-charge violations with negligible impact on total operating costs