A data-driven model for the operation and management of prosumer markets in electric smart grids

The digital transformation of electric power systems requires forecasts and planning for optimal management, as well as real-time data streaming for the ongoing optimization of the system during operation. Recent research efforts have developed models for power system capacity planning, real-time monitoring and control, fault analysis, and energy efficiency assessment. However, those models are usually not integrated and do not combine operational data with management information and real-time decision-making. This paper conceives a datadriven model that integrates optimization and machine learning techniques for optimal operation and management of prosumer markets in electric smart grids. While classical optimization is used during day-ahead mode for operation planning, Gaussian Processes are used to predict demand forecasts for day-ahead and pre-dispatch modes while assimilating real-time measurements. The proposed approach is applied in a case study comprising a community manager coordinating a smart grid with prosumers operating thermal and renewable generators. Results highlight that the data-driven model helps achieve near-optimal operation of the smart grid in normal conditions while guaranteeing its reliability under disruptive events.