IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i12p3494-d190645.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/12/3494/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/12/3494/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2016. "Power demand control scenarios for smart grid applications with finite number of appliances," Applied Energy, Elsevier, vol. 162(C), pages 83-98.
    2. Cortés-Arcos, Tomás & Bernal-Agustín, José L. & Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Contreras, Javier, 2017. "Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology," Energy, Elsevier, vol. 138(C), pages 19-31.
    3. Chao Luo & Yih-Fang Huang & Vijay Gupta, 2018. "Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage," Papers 1801.02128, arXiv.org.
    4. Chen, Wei-Hsin & Wu, Po-Hua & Lin, Yu-Li, 2018. "Performance optimization of thermoelectric generators designed by multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 209(C), pages 211-223.
    5. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    6. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    7. Kadri, Roubila Lilia & Boctor, Fayez F., 2018. "An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: The single mode case," European Journal of Operational Research, Elsevier, vol. 265(2), pages 454-462.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Banhidarah, Abdullah Khamis & Al-Sumaiti, Ameena Saad & Wescoat, James L. & Nguyen, Hoach The, 2020. "Electricity-water usage for sustainable development: An analysis of United Arab Emirates farms," Energy Policy, Elsevier, vol. 147(C).
    2. Rasheed, Muhammad Babar & R-Moreno, María D., 2022. "Minimizing pricing policies based on user load profiles and residential demand responses in smart grids," Applied Energy, Elsevier, vol. 310(C).
    3. 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.
    4. 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.
    5. 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.
    6. Ghoroghi, Ali & Petri, Ioan & Rezgui, Yacine & Alzahrani, Ateyah, 2023. "A deep learning approach to predict and optimise energy in fish processing industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Liu, Yang & Mauter, Meagan S., 2020. "Assessing the demand response capacity of U.S. drinking water treatment plants," Applied Energy, Elsevier, vol. 267(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bilal Naji Alhasnawi & Basil H. Jasim & Pierluigi Siano & Josep M. Guerrero, 2021. "A Novel Real-Time Electricity Scheduling for Home Energy Management System Using the Internet of Energy," Energies, MDPI, vol. 14(11), pages 1-29, May.
    2. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    3. Sajjad Ali & Imran Khan & Sadaqat Jan & Ghulam Hafeez, 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid," Energies, MDPI, vol. 14(8), pages 1-29, April.
    4. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.
    5. Wang, Yongli & Li, Jiapu & Wang, Shuo & Yang, Jiale & Qi, Chengyuan & Guo, Hongzhen & Liu, Ximei & Zhang, Hongqing, 2020. "Operational optimization of wastewater reuse integrated energy system," Energy, Elsevier, vol. 200(C).
    6. Chen, Wei-Hsin & Wang, Chi-Ming & Lee, Da-Sheng & Kwon, Eilhann E. & Ashokkumar, Veeramuthu & Culaba, Alvin B., 2022. "Optimization design by evolutionary computation for minimizing thermal stress of a thermoelectric generator with varied numbers of square pin fins," Applied Energy, Elsevier, vol. 314(C).
    7. Talaat, M. & Hatata, A.Y. & Alsayyari, Abdulaziz S. & Alblawi, Adel, 2020. "A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach," Energy, Elsevier, vol. 190(C).
    8. Cui, Jingshi & Wu, Jiaman & Wu, Chenye & Moura, Scott, 2023. "Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies," Applied Energy, Elsevier, vol. 352(C).
    9. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    10. Alya AlHammadi & Nasser Al-Saif & Ameena Saad Al-Sumaiti & Mousa Marzband & Tareefa Alsumaiti & Ehsan Heydarian-Forushani, 2022. "Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates," Energies, MDPI, vol. 15(18), pages 1-20, September.
    11. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    12. Fernando Lezama & Ricardo Faia & Pedro Faria & Zita Vale, 2020. "Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms," Energies, MDPI, vol. 13(10), pages 1-18, May.
    13. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
    14. Luo, Ding & Wang, Ruochen & Yu, Wei & Zhou, Weiqi, 2019. "Performance evaluation of a novel thermoelectric module with BiSbTeSe-based material," Applied Energy, Elsevier, vol. 238(C), pages 1299-1311.
    15. Meysam Karami Rad & Mahmoud Omid & Ali Rajabipour & Fariba Tajabadi & Lasse Aistrup Rosendahl & Alireza Rezaniakolaei, 2018. "Optimum Thermal Concentration of Solar Thermoelectric Generators (STEG) in Realistic Meteorological Condition," Energies, MDPI, vol. 11(9), pages 1-16, September.
    16. Ray, Manojit & Chakraborty, Basab, 2019. "Impact of evolving technology on collaborative energy access scaling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 13-27.
    17. Okur, Özge & Voulis, Nina & Heijnen, Petra & Lukszo, Zofia, 2019. "Aggregator-mediated demand response: Minimizing imbalances caused by uncertainty of solar generation," Applied Energy, Elsevier, vol. 247(C), pages 426-437.
    18. Xu, Aoqi & Xie, Changjun & Xie, Liping & Zhu, Wenchao & Xiong, Binyu & Gooi, Hoay Beng, 2024. "Performance prediction and optimization of annular thermoelectric generators based on a comprehensive surrogate model," Energy, Elsevier, vol. 290(C).
    19. Abate, Arega Getaneh & Riccardi, Rossana & Ruiz, Carlos, 2022. "Contract design in electricity markets with high penetration of renewables: A two-stage approach," Omega, Elsevier, vol. 111(C).
    20. Yusuf, Aminu & Ballikaya, Sedat, 2022. "Electrical, thermomechanical and cost analyses of a low-cost thermoelectric generator," Energy, Elsevier, vol. 241(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3494-:d:190645. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.