Ricerc@Sapienza

Buildings with mixed residential and commercial units show relevant power peak that are further increased by shifting from gas-driven systems to electric source. The proposed solution is to organize a microgrid for such type of buildings, aggregating different users with a common electric distribution system with a single connection to the grid, a local common generation and a common heating/cooling system (electric-driven). This approach upgrades a group of independent small users with rigid loads and chaotic behavior, to a large user with a flexible and controlled profile.

Most of the power demand at Container Terminals (CT) is related to Ship to Shore (STS) cranes. These cranes work simultaneously together for loading and unloading container. This issue causes the peak demand increase significantly. Considering the STS group crane's activity to move containers (from ship to shore and vice versa), finding the best delay time between STS cranes can play an important role to reduce the total power demand. The peak shaving strategy which has been used in this paper is Demand Side Management (DSM).

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthesis procedure, of an Energy Management System (EMS).

Microgrids (MGs) are the most affordable solution for the development of smart grid infrastructures. They are conceived to intelligently integrate the generation from Distributed Energy Resources (DERs), to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well.