Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids

Fahad R. Albogamy, Ghulam Hafeez, Imran Khan, Sheraz Khan, Hend I. Alkhammash, Faheem Ali and Gul Rukh
Additional contact information
Fahad R. Albogamy: Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 26571, Saudi Arabia
Ghulam Hafeez: Centre of Renewable Energy, Government Advance Technical Training Centre, Hayatabad, Peshawar 25100, Pakistan
Imran Khan: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Sheraz Khan: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Hend I. Alkhammash: Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Faheem Ali: Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Gul Rukh: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan

Sustainability, 2021, vol. 13, issue 20, 1-29

Abstract: In smart grid, energy management is an indispensable for reducing energy cost of consumers while maximizing user comfort and alleviating the peak to average ratio and carbon emission under real time pricing approach. In contrast, the emergence of bidirectional communication and power transfer technology enables electric vehicles (EVs) charging/discharging scheduling, load shifting/scheduling, and optimal energy sharing, making the power grid smart. With this motivation, efficient energy management model for a microgrid with ant colony optimization algorithm to systematically schedule load and EVs charging/discharging of is introduced. The smart microgrid is equipped with controllable appliances, photovoltaic panels, wind turbines, electrolyzer, hydrogen tank, and energy storage system. Peak load, peak to average ratio, cost, energy cost, and carbon emission operation of appliances are reduced by the charging/discharging of electric vehicles, and energy storage systems are scheduled using real time pricing tariffs. This work also predicts wind speed and solar irradiation to ensure efficient energy optimization. Simulations are carried out to validate our developed ant colony optimization algorithm-based energy management scheme. The obtained results demonstrate that the developed efficient energy management model can reduce energy cost, alleviate peak to average ratio, and carbon emission.

Keywords: energy optimization; day ahead energy prediction; artificial neural network; renewable energy sources; demand response; microgrid; smart grid (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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