PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.

The VIRTUS project aims to create a Virtual Power Plant (VPP) prototype coordinating the Distributed Energy Resources (DERs) of the power system and providing services to the system operators and the various players of the electricity markets, with a particular focus on the industrial sector agents. The VPP will be able to manage a significant number of DERs and simulate realistic plants, components, and market data to study different operating conditions and the future impact of the policy changes of the Balancing Markets (BM). This paper describes the project’s aim, the general structure of the proposed framework, and its optimization and simulation modules. Then, we assess the scalability of the optimization module, designed to provide the maximum possible flexibility to the system operators, exploiting the simulation module of the VPP.

An energy model for non-residential buildings based on state space equations has been developed and experimentally validated. This model can predict with precision the Heating Ventilation and Air Conditioning (HVAC) system energy consumption considering several variables including temperature setpoints, external temperature, humidity, wind speed, solar irradiance, and occupancy. This last variable is estimated through deep transfer learning (TL). The proposed simplified energy model can be easily exploited by Model Predictive Control (MPC) strategies for HVAC optimal control.

PhotoVoltaic (PV) plants can bring a positive impact on the sustainability of electric grids. Its variability and uncertainty, however, leads to challenging technical problems related to the efficient and secure operations of power systems. In this paper, a novel hybrid methodology for the day-ahead PV power forecasting is proposed. The methodology is able to use a Clear Sky Model (CSM) or an Artificial Neural Network (ANN), according to the day-ahead weather forecasting. Selection among these two methods is performed by a threshold on the Cloud Cover Index (CCI) determined by linear regression. The method has been tested and validated on a real PV plant.