The behind-the-meter technologies integrating "all-in-one" photovoltaic plants, storage systems, and other technological solutions can transform consumers into active prosumages to both reduce their energy costs and provide flexibility to the grid. To exploit those flexibility services, it is necessary to manage the end-users in an aggregated form. End-user aggregation is currently becoming a suitable solution to manage energy flows to obtain environmental, economic, and social benefits. In this scope, the paper presents an algorithm to opportunely manage the energy flows inside this aggregation operating in a Power Cloud framework. The algorithm schedules the energy flows that the end-user storage systems must exchange inside the aggregation to maximize the use of renewable sources, provide grid flexibility services, and simultaneously provide balancing services. The algorithm is organized into three different steps: the day-ahead step, the real-time step, and the balancing one. Some simulation results are illustrated to demonstrate the effectiveness of the proposed algorithm.
Power Cloud Framework for Prosumer Aggregation to Unlock End-User Flexibility
Giovanni Brusco;Daniele Menniti;Anna Pinnarelli
;Nicola Sorrentino;Pasquale Vizza
2023-01-01
Abstract
The behind-the-meter technologies integrating "all-in-one" photovoltaic plants, storage systems, and other technological solutions can transform consumers into active prosumages to both reduce their energy costs and provide flexibility to the grid. To exploit those flexibility services, it is necessary to manage the end-users in an aggregated form. End-user aggregation is currently becoming a suitable solution to manage energy flows to obtain environmental, economic, and social benefits. In this scope, the paper presents an algorithm to opportunely manage the energy flows inside this aggregation operating in a Power Cloud framework. The algorithm schedules the energy flows that the end-user storage systems must exchange inside the aggregation to maximize the use of renewable sources, provide grid flexibility services, and simultaneously provide balancing services. The algorithm is organized into three different steps: the day-ahead step, the real-time step, and the balancing one. Some simulation results are illustrated to demonstrate the effectiveness of the proposed algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.