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Energies 2018, 11(12), 3494; https://doi.org/10.3390/en11123494

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

1
Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54000, Pakistan
2
Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi 127788, UAE
3
Faculty of Computing and Information Technology, Northern Border University, Rafha 76321, Saudi Arabia
4
Faculty of Computer Science and IT, University of Malaya, Kuala Lumpur 50603, Malaysia
5
Department of Information Systems, University of Jeddah, Jeddah 23890, Saudi Arabia
6
Department of Information Technology, University of Jeddah, Jeddah 23890, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Received: 31 October 2018 / Revised: 27 November 2018 / Accepted: 6 December 2018 / Published: 14 December 2018
(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)
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Abstract

Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort. View Full-Text
Keywords: demand side management; demand response; appliances scheduling; real-time pricing; inclining block rate; genetic algorithm; renewable energy sources demand side management; demand response; appliances scheduling; real-time pricing; inclining block rate; genetic algorithm; renewable energy sources
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Asgher, U.; Babar Rasheed, M.; Al-Sumaiti, A.S.; Ur-Rahman, A.; Ali, I.; Alzaidi, A.; Alamri, A. Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources. Energies 2018, 11, 3494.

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