The aim of this activity is to develop and experimentally validate a multi-time horizon model predictive control (MPC) to operate EPFL data center (DC) with the least carbon footprint and cost while providing services to the external grid.
This model will highly rely on advance forecasting tool, for the prevision of:
▪ Dispatchable and non-dispatchable loads (el.)
▪ Dispatchable and non-dispatchable DC loads (el.)
▪ Building heating/cooling needs (th.)
▪ RES production (PV) (el.)
▪ Carbon footprint of electrical power
Multi time horizon model predictive control to operate the EPFL DC
- Day-ahead scheduling of controllable resources to minimise CO2eq. and (in case) campus energy needs considering day-ahead forecasts
- Intra-day tracking of the day-ahead scheduling accounting for recent forecasts and used by a distributed MPC to determine the real-time set-points of the controllable resources :
- DC workload
- DESL battery energy storage system
- ORC operation
Integration of the DC into the energy system of the EPFL
The strategic optimal system operation to supply heating, cooling and electricity services while minimizing the overall CO2 emissions will be investigated. This involves:
a) at the design level: the definition of technologies’ sizes and the module sizes and the opportunities for the energy services’ provision, e.g, hot water or specific heating/cooling services
b) at the operational level, the development and experimental demonstration of the strategic optimal system’s operation layer that will schedule each energy units’ operation based on the carbon content