Production analytics for renewable energy portfolio management: A comparative study of active and passive strategies in digital twin

Abstract

Developed societies are facing a number of problems due to technological advances, including the increased requirement for power, resulting in small electrical grids utilizing local power sources, also known as a microgrid (MG). The MG is linked to the central electrical network or runs alone in the form of a power island based on the renewable energy sources and dispatchable units. On the other hand, the smart grid concept is built based on the employment of sensors and cloud computing systems to address the problems. It is possible to decrease energy consumption problems by using a smart MG; however, proper implementation is necessary to avoid generating too much information. As a result, the study uses fog computing for energy production and consumption analysis and management by reducing the amount of information passing to the cloud. The proposed model would reduce service response times in an active strategy. To this end, an enhanced particle swarm optimization with Levy Walk is used for solving the optimization problem based on the real-time data receiving from the digital twin of the system. As a result, to evaluate the efficiency and accuracy of the fog in the case, efficiency evaluations based on active and passive strategies are carried out in order to determine whether parameters including the number of residents, the optimization algorithm, moving appliances, and the power resource affect response times and source demands.