Abstract
Energy advancement and innovation have generated several challenges for large modernized cities, such as the increase in energy demand, causing the appearance of the small power grid with a local source of supply, called the Microgrid. A Microgrid operates either connected to the national centralized power grid or singly, as a power island mode. Microgrids address these challenges using sensing technologies and Fog-Cloudcomputing infrastructures for building smart electrical grids. A smart Microgrid can be used to minimize the power demand problem, but this solution needs to be implemented correctly so as not to increase the amount of data being generated. Thus, this paper proposes the use of Fog computing to help control power demand and manage power production by eliminating the high volume of data being passed to the Cloud and decreasing the requests’ response time. The GridLab-d simulator was used to create a Microgrid, where it is possible to exchange information between consumers and generators. Thus, to understand the potential of the Fog in this scenario, a performance evaluation is performed to verify how factors such as residence number, optimization algorithms, appliance shifting, and energy sources may influence the response time and resource usage.
Highlights
The growing increase in energy demand complemented by the decrease in the means of energy production point to the need for a new energy infrastructure
Izadbakhsh et al (2015) [12] implemented the scheduling of energy sources within a microgridcomposed of micro-turbines, photovoltaics, fuel cells, and wind turbines through a framework that dealt with the reduction of operating costs and emissions
The concern of this paper was with the peaks of energy and the conditions of adjustment between sources of renewable and non-renewable energy in a Microgrid environment
Summary
The growing increase in energy demand complemented by the decrease in the means of energy production point to the need for a new energy infrastructure. Fog computing can obtain data on the amount of energy produced and consumed in real time to perform processing tasks and reduce the need for Cloud operators and still process information with high bandwidth and low latency, and with the help of the Cloud, it is possible to make decisions that reduce the energy bill and the amount of pollutants [4]. Authors such as Shahryari [6] and Zahoor [4] have already proposed the use of the Fog to assist the energy management of homes and industries.
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