Optimal Fuzzy Energy Trading System in a Fog-Enabled Smart Grid

Abstract

With the recent technological advancements, it has become possible to conceive numerous valuable applications for efficient utilization of energy resources in a smart grid. As distributed energy generation and distributed storage systems become cost-effective, trading energy becomes a lucrative alternative for both prosumers and manufacturers. In this paper, we make use of fuzzy logic to propose a system for optimal energy trading in a fog-enabled smart grid set-up. The existing systems in this realm have inherited issues of network latency, computational expensiveness, information availability, scalability, and performance. Some systems require a specialized transmission line for energy trading and plenty of them based on the dedicated producer-consumer model, putting limits to their practical effectiveness. Our framework makes use of fog-computing infrastructure to address scalability, information availability, and network latency issues. We exploit the fuzzy logic paradigm to handle the issues with crisp values and to improve the computational efficiency of the system. Our model of energy-trading system incorporates various input parameters to decide on the excess energy, including real-time price, time of day, outdoor temperature, buyers’ interest, and storage capacity. Simulation results show that our proposed system possesses promising potential to maximize the profit of energy trading and to minimize electricity usage from the main grid.

RETRACTED: Recent Trends, Challenges, and Future Aspects of P2P Energy Trading Platforms in Electrical-Based Networks Considering Blockchain Technology: A Roadmap Toward Environmental Sustainability

“…To engage in P2P trade, every prosumer should have suitable metering infrastructure while focusing with the standard energy meters, each prosumer must supply with a transactive meter (Shahzad et al, 2021). A transactive energy can determine whether or not to participate in the P2P market based on demand and generation data, as well as market information with (total demand, price, network conditions, and total available generation).…”

Recent Trends and Issues of Energy Management Systems Using Machine Learning

“…Energy forecast models have been proposed for predicting energy consumption patterns and managing power quality issues, thereby enhancing the scalability of EMS. To address scalability, information availability, and network latency issues in EMS, fog-computing infrastructure and fuzzy logic paradigms have been employed, effectively managing the challenges associated with incorporating various input parameters [118]. In [119], security and privacy issues related to the incorporation of ML-based applications into EMS have been discussed.…”

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

“…The cost is not considered [190] Early fire detection system The dataset is too small [192] Energy trading for prosumers in smart grid infrastructure.…”

“…The fuzzy rulebased algorithm comprises research rules that indicate how efficiency, light intensity, output electrical power, temperature, and humidity are related to one another. The fuzzy logic paradigm was utilised in a study [192] to enhance decision-making performance. Instead of providing clear decision-making boundaries, it covers a broad variety of operational conditions.…”