Short-term uncertainty in the dispatch of energy resources for VPP: A novel rolling horizon model based on stochastic programming

•Markets participation and real-time operation of multi-resources VPPs.•Batteries, electric vehicles and water heaters as regulating resources.•Two-stage stochastic MILP model for the optimal bidding on the markets.•Rolling Horizon Stochastic Programming to incorporate short-term uncertainty.•Techno-economic comparison with Deterministic and Omniscient methods. The intermittent nature of distributed energy resources introduces new degrees of uncertainty in the operation of energy systems; hence, short-term decisions can no longer be considered fully deterministic. In this article, an energy management system (EMS) was proposed to optimize the market participation and the real-time operation of a virtual power plant (VPP) composed of photovoltaic generators, non-flexible loads, and storage systems (e-vehicle, stationary battery, and thermal storage). The market bidding process was optimized through a two-stage stochastic formulation, which considered the day-ahead forecast uncertainty to minimize the energy cost and make available reserve margins in the ancillary service market. The real-time management of regulating resources was obtained through an innovative rolling horizon stochastic programming model, taking into account the effects of short-term uncertainties. Numerical simulations were carried out to demonstrate the effectiveness of the proposed EMS. The architecture proved to be effective in managing several distributed resources, enabling the provision of ancillary services to the power system. In particular, the model developed allowed an increase in the VPP’s profits of up to 11% and a reduction in the energy imbalance of 25.1% compared to a deterministic optimization.