Appino, Riccardo R.Riccardo R.AppinoWang, HanHanWangGonzález Ordiano, Jorge ÁngelJorge ÁngelGonzález OrdianoFaulwasser, TimmTimmFaulwasserMikut, RalfRalfMikutHagenmeyer, VeitVeitHagenmeyerMancarella, PierluigiPierluigiMancarella2024-02-142024-02-142021-06Electric Power Systems Research 195: 106738 (2021-06)https://hdl.handle.net/11420/45677Virtual Power Plants (VPPs) comprising renewables and hydrogen production through power-to-gas technologies can help to increase renewable penetration and to improve operational flexibility and economic performance. However, the uncertainty inherent to forecasts of renewable generation and energy prices renders cost effective operation difficult. The present paper approaches the issue by means of receding-horizon stochastic optimization (i.e. by stochastic Model Predictive Control (MPC)). Differently from previous works, we do not tackle computational tractability with a sampling-based approach, but by mapping quantile forecasts of virtual energy profiles to the mode of operation that has the highest probability of being optimal. This way, we reduce the computational load and the forecasting burden. Furthermore, simulation studies show that the proposed algorithm can attain a significant percentage of the revenue of optimal control with perfect forecasts.en0378-77962021ElsevierPower to gasProbabilistic forecastReal-time marketsSector integrationStochastic model predictive controlElectrical Engineering, Electronic EngineeringJournal Article10.1016/j.epsr.2020.106738Journal Article