IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6163-d644393.html
   My bibliography  Save this article

Energy Optimisation Models for Self-Sufficiency of a Typical Turkish Residential Electricity Customer of the Future

Author

Listed:
  • Doğukan Aycı

    (Department of Electrical Engineering, Istanbul Technical University, Istanbul 34467, Turkey)

  • Ferhat Öğüt

    (Department of Physics Engineering, Istanbul Technical University, Istanbul 34467, Turkey)

  • Ulaş Özen

    (Department of Naval Architecture and Ocean Engineering, Istanbul Technical University, Istanbul 34467, Turkey)

  • Bora Batuhan İşgör

    (Department of Electronics Engineering, Istanbul Technical University, Istanbul 34467, Turkey)

  • Sinan Küfeoğlu

    (Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
    Scientific and Technological Research Council of Turkey, Ankara 06100, Turkey)

Abstract

This paper utilises a two-stage demand response-enabled energy management algorithm for a typical Turkish self-sufficient living space. The proposed energy management model provides an additional gain in line with the goal of self-sufficiency by scheduling flexible loads and energy storage systems at home according to a static time of use tariff. The impact of load scheduling and battery optimisation were evaluated in the scope of self-sufficiency, economic gain and return on investment performances. According to the results, the proposed two-stage structure provided a net saving increase of 9.5% in the one-battery scenario, and it rises to 14% in the design with three batteries. On the other hand, when we inspect the energy management scenarios with the return on investment (ROI) calculations, we see that the single battery system has a higher ROI than the two or three battery systems due to the increased battery cost. Moreover, the ROI value, 13.9% without optimisation, increased to 15.3% in the proposed Home Energy Management System (HEMS) model. As can be seen from this calculation, intelligent management of batteries and flexible loads provided a 10% increase in ROI value.

Suggested Citation

  • Doğukan Aycı & Ferhat Öğüt & Ulaş Özen & Bora Batuhan İşgör & Sinan Küfeoğlu, 2021. "Energy Optimisation Models for Self-Sufficiency of a Typical Turkish Residential Electricity Customer of the Future," Energies, MDPI, vol. 14(19), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6163-:d:644393
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6163/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6163/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sheraz Aslam & Zafar Iqbal & Nadeem Javaid & Zahoor Ali Khan & Khursheed Aurangzeb & Syed Irtaza Haider, 2017. "Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes," Energies, MDPI, vol. 10(12), pages 1-25, December.
    2. Hepeng Li & Peng Zeng & Chuanzhi Zang & Haibin Yu & Shuhui Li, 2017. "An Integrative DR Study for Optimal Home Energy Management Based on Approximate Dynamic Programming," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    3. Maytham S. Ahmed & Azah Mohamed & Raad Z. Homod & Hussain Shareef, 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy," Energies, MDPI, vol. 9(9), pages 1-20, September.
    4. Mingfu Li & Guan-Yi Li & Hou-Ren Chen & Cheng-Wei Jiang, 2018. "QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles," Energies, MDPI, vol. 11(9), pages 1-16, September.
    5. Methee Srikranjanapert & Siripha Junlakarn & Naebboon Hoonchareon, 2021. "How an Integration of Home Energy Management and Battery System Affects the Economic Benefits of Residential PV System Owners in Thailand," Sustainability, MDPI, vol. 13(5), pages 1-20, March.
    6. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    7. Modawy Adam Ali Abdalla & Wang Min & Omer Abbaker Ahmed Mohammed, 2020. "Two-Stage Energy Management Strategy of EV and PV Integrated Smart Home to Minimize Electricity Cost and Flatten Power Load Profile," Energies, MDPI, vol. 13(23), pages 1-18, December.
    8. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, Elsevier, vol. 45(C), pages 671-679.
    9. Hafiz Majid Hussain & Nadeem Javaid & Sohail Iqbal & Qadeer Ul Hasan & Khursheed Aurangzeb & Musaed Alhussein, 2018. "An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid," Energies, MDPI, vol. 11(1), pages 1-28, January.
    10. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
    11. Omaji Samuel & Sakeena Javaid & Nadeem Javaid & Syed Hassan Ahmed & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(11), pages 1-27, November.
    Full references (including those not matched with items on IDEAS)

    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. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    2. Omaji Samuel & Sakeena Javaid & Nadeem Javaid & Syed Hassan Ahmed & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(11), pages 1-27, November.
    3. Makhadmeh, Sharif Naser & Khader, Ahamad Tajudin & Al-Betar, Mohammed Azmi & Naim, Syibrah & Abasi, Ammar Kamal & Alyasseri, Zaid Abdi Alkareem, 2019. "Optimization methods for power scheduling problems in smart home: Survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    4. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    5. Herie Park, 2020. "Human Comfort-Based-Home Energy Management for Demand Response Participation," Energies, MDPI, vol. 13(10), pages 1-15, May.
    6. Nadeem Javaid & Adnan Ahmed & Sohail Iqbal & Mahmood Ashraf, 2018. "Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles," Energies, MDPI, vol. 11(6), pages 1-28, June.
    7. Steffen Limmer & Nils Einecke, 2022. "An Efficient Approach for Peak-Load-Aware Scheduling of Energy-Intensive Tasks in the Context of a Public IEEE Challenge," Energies, MDPI, vol. 15(10), pages 1-23, May.
    8. 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.
    9. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
    10. Godiana Hagile Philipo & Josephine Nakato Kakande & Stefan Krauter, 2022. "Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping," Energies, MDPI, vol. 15(14), pages 1-18, July.
    11. Kim, Hakpyeong & Choi, Heeju & Kang, Hyuna & An, Jongbaek & Yeom, Seungkeun & Hong, Taehoon, 2021. "A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    12. Sana Iqbal & Mohammad Sarfraz & Mohammad Ayyub & Mohd Tariq & Ripon K. Chakrabortty & Michael J. Ryan & Basem Alamri, 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment," Sustainability, MDPI, vol. 13(13), pages 1-23, June.
    13. Zaid A. Al Muala & Mohammad A. Bany Issa & Daniel Sansó-Rubert Pascual & Pastora M. Bello Bugallo, 2023. "Realistic Home Energy Management System Considering the Life Cycle of Photovoltaic and Energy Storage Systems," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    14. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    15. Fang, Debin & Wang, Pengyu, 2023. "Optimal real-time pricing and electricity package by retail electric providers based on social learning," Energy Economics, Elsevier, vol. 117(C).
    16. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    17. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    18. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    19. Francesco Lo Franco & Mattia Ricco & Riccardo Mandrioli & Gabriele Grandi, 2020. "Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization," Energies, MDPI, vol. 13(19), pages 1-25, September.
    20. Giovanni Pau & Mario Collotta & Antonio Ruano & Jiahu Qin, 2017. "Smart Home Energy Management," Energies, MDPI, vol. 10(3), pages 1-5, March.

    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:14:y:2021:i:19:p:6163-:d:644393. 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.