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

Urooj Asgher, Muhammad Babar Rasheed, Ameena Saad Al-Sumaiti, Atiq Ur-Rahman, Ihsan Ali, Amer Alzaidi and Abdullah Alamri
Additional contact information
Urooj Asgher: Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54000, Pakistan
Muhammad Babar Rasheed: Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54000, Pakistan
Ameena Saad Al-Sumaiti: Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi 127788, UAE
Atiq Ur-Rahman: Faculty of Computing and Information Technology, Northern Border University, Rafha 76321, Saudi Arabia
Ihsan Ali: Faculty of Computer Science and IT, University of Malaya, Kuala Lumpur 50603, Malaysia
Amer Alzaidi: Department of Information Systems, University of Jeddah, Jeddah 23890, Saudi Arabia
Abdullah Alamri: Department of Information Technology, University of Jeddah, Jeddah 23890, Saudi Arabia

Energies, 2018, vol. 11, issue 12, 1-26

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.

Keywords: demand side management; demand response; appliances scheduling; real-time pricing; inclining block rate; genetic algorithm; renewable energy sources (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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