Orateur
Description
We study a robust optimization framework for the optimal operation and sizing of a hybrid virtual power plant (VPP) composed of a thermal generator unit (TGU), a large-scale photovoltaic (PV) plant, and an energy storage system (ESS). The VPP participates as a price-taker in both the day-ahead (DA) and real-time (RT) electricity markets, offering energy and ancillary services (i.e., frequency regulation) across multiple timescales.
The energy management strategy is formulated as a self-scheduling unit commitment (UC) problem over extended horizons, allowing long-term energy sales estimation and enabling sizing of the hybrid plant components (PV and BESS). The TGU is modeled using a network flow formulation, enhanced with problem-specific cutting planes to strengthen it and reduce the overall solution time for corresponding mixed-integer problems.
A data-driven, robust optimization approach addresses uncertainty in solar production and market prices. Polyhedral uncertainty sets are constructed from historical data to capture temporal patterns and variability, ensuring resilient and reliable operation under uncertain future conditions. In the short term, the proposed model determines energy bids in the DA market with hourly granularity and adjusts offers in the RT market with finer temporal resolution while considering the physical constraints of all plant components.
Numerical results based on one year of historical data demonstrate the effectiveness of the proposed approach in maximizing long-term profits and enabling robust sizing and operation of the hybrid power plant under uncertainty.
