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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Urooj Asgher & Muhammad Babar Rasheed & Ameena Saad Al-Sumaiti & Atiq Ur-Rahman & Ihsan Ali & Amer Alzaidi & Abdullah Alamri, 2018. "Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources," Energies, MDPI, vol. 11(12), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3494-:d:190645
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    References listed on IDEAS

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    Cited by:

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    3. Thamer Alquthami & Ahmad H. Milyani & Muhammad Awais & Muhammad B. Rasheed, 2021. "An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    4. Chitchai Srithapon & Prasanta Ghosh & Apirat Siritaratiwat & Rongrit Chatthaworn, 2020. "Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost," Energies, MDPI, vol. 13(2), pages 1-20, January.
    5. Mostafa Rezaeimozafar & Mohsen Eskandari & Mohammad Hadi Amini & Mohammad Hasan Moradi & Pierluigi Siano, 2020. "A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids," Energies, MDPI, vol. 13(7), pages 1-25, April.
    6. Wongchai Anupong & Muhsin Jaber Jweeg & Sameer Alani & Ibrahim H. Al-Kharsan & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq," Energies, MDPI, vol. 16(2), pages 1-14, January.
    7. Ameena Saad Al-Sumaiti & Abdollah Kavousi-Fard & Magdy Salama & Motahareh Pourbehzadi & Srikanth Reddy & Muhammad Babar Rasheed, 2020. "Economic Assessment of Distributed Generation Technologies: A Feasibility Study and Comparison with the Literature," Energies, MDPI, vol. 13(11), pages 1-28, June.
    8. Ivan Lorencin & Nikola Anđelić & Vedran Mrzljak & Zlatan Car, 2019. "Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation," Energies, MDPI, vol. 12(22), pages 1-26, November.
    9. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    10. Mágui Lage & Rui Castro, 2022. "A Practical Review of the Public Policies Used to Promote the Implementation of PV Technology in Smart Grids: The Case of Portugal," Energies, MDPI, vol. 15(10), pages 1-20, May.
    11. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Fawaz Alassery & Habib Hamam & Omar Cheikhrouhou, 2021. "A Novel Machine Learning-Based Price Forecasting for Energy Management Systems," Sustainability, MDPI, vol. 13(22), pages 1-26, November.
    12. Muhammad Arshad Shehzad Hassan & Ussama Assad & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Dynamic Price-Based Demand Response through Linear Regression for Microgrids with Renewable Energy Resources," Energies, MDPI, vol. 15(4), pages 1-17, February.
    13. Liu, Yang & Mauter, Meagan S., 2020. "Assessing the demand response capacity of U.S. drinking water treatment plants," Applied Energy, Elsevier, vol. 267(C).

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