IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i23p6387-d455524.html
   My bibliography  Save this article

Two-Stage Energy Management Strategy of EV and PV Integrated Smart Home to Minimize Electricity Cost and Flatten Power Load Profile

Author

Listed:
  • Modawy Adam Ali Abdalla

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
    Department of Electrical and Electronic Engineering, College of Engineering Science, Nyala University, Nyala 63311, Sudan)

  • Wang Min

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Omer Abbaker Ahmed Mohammed

    (Department of Electrical and Electronic Engineering, College of Engineering Science, Nyala University, Nyala 63311, Sudan
    School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract

The efficient use of the incorporation of photovoltaic generation (PV) and an electric vehicle (EV) with the home energy management system (HEMS) can play a significant role in improving grid stability in the residential area and bringing economic benefit to the homeowner. Therefore, this paper presents an energy management strategy in a smart home that integrates an electric vehicle with/without PV generation. The proposed strategy seeks to reduce the household electricity costs and flatten the load curve based on time-of-use pricing, time-varying household power demand, PV generation profile, and EV parameters (arrival and departure times, minimum and maximum limit of the state-of-charge, and initial state-of-charge). The proposed control strategy is divided into two stages: Stage A, which operates in three operating modes according to the unavailability of PV power generation, and Stage B, which operates in five operating modes according to the availability of PV generation. In this study, the proposed strategy enables controlling the amount of energy absorbed by the EV from the grid and/or PV and the amount of energy injected from the EV to the load to ensure that the household electricity costs are minimized, and the household power load profile is flattened. The findings show that both household electricity costs reduction and flattening of the power load profile are achieved. Moreover, the corresponding simulation results exhibit that the proposed strategy for the smart home with EV and PV provides better results than the smart home with EV and without PV in terms of electricity costs reduction and power load profile flattening.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6387-:d:455524
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/23/6387/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/23/6387/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhu, Rui & Duan, Bin & Zhang, Chenghui & Gong, Sizhao, 2019. "Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Dominic A. Savio & Vimala A. Juliet & Bharatiraja Chokkalingam & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Frede Blaabjerg, 2019. "Photovoltaic Integrated Hybrid Microgrid Structured Electric Vehicle Charging Station and Its Energy Management Approach," Energies, MDPI, vol. 12(1), pages 1-28, January.
    3. Chowdhury Akram Hossain & Nusrat Chowdhury & Michela Longo & Wahiba Yaïci, 2019. "System and Cost Analysis of Stand-Alone Solar Home System Applied to a Developing Country," Sustainability, MDPI, vol. 11(5), pages 1-13, March.
    4. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2019. "Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid," Energy, Elsevier, vol. 167(C), pages 312-324.
    5. Kapustin, Nikita O. & Grushevenko, Dmitry A., 2020. "Long-term electric vehicles outlook and their potential impact on electric grid," Energy Policy, Elsevier, vol. 137(C).
    6. Thomas Kemmler & Bernd Thomas, 2020. "Design of Heat-Pump Systems for Single- and Multi-Family Houses using a Heuristic Scheduling for the Optimization of PV Self-Consumption," Energies, MDPI, vol. 13(5), pages 1-18, March.
    7. 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.
    8. Sinsel, Simon R. & Riemke, Rhea L. & Hoffmann, Volker H., 2020. "Challenges and solution technologies for the integration of variable renewable energy sources—a review," Renewable Energy, Elsevier, vol. 145(C), pages 2271-2285.
    9. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2013. "A review of photovoltaic systems size optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 454-465.
    10. Wang, Qian & Jiang, Bin & Li, Bo & Yan, Yuying, 2016. "A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 106-128.
    11. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2017. "A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid," Energy, Elsevier, vol. 118(C), pages 197-208.
    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. Modawy Adam Ali Abdalla & Wang Min & Gehad Abdullah Amran & Amerah Alabrah & Omer Abbaker Ahmed Mohammed & Hussain AlSalman & Bassiouny Saleh, 2023. "Optimizing Energy Usage and Smoothing Load Profile via a Home Energy Management Strategy with Vehicle-to-Home and Energy Storage System," Sustainability, MDPI, vol. 15(20), pages 1-28, October.
    2. Daud Mustafa Minhas & Josef Meiers & Georg Frey, 2022. "Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets," Energies, MDPI, vol. 15(5), pages 1-29, February.
    3. Kenji Araki & Yasuyuki Ota & Anju Maeda & Minoru Kumano & Kensuke Nishioka, 2023. "Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors," Energies, MDPI, vol. 16(8), pages 1-25, April.
    4. Xuehan Zhang & Yongju Son & Sungyun Choi, 2022. "Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources," Energies, MDPI, vol. 15(6), pages 1-18, March.
    5. 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.

    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. Yu, Hang & Shang, Yitong & Niu, Songyan & Cheng, Chong & Shao, Ziyun & Jian, Linni, 2022. "Towards energy-efficient and cost-effective DC nanaogrid: A novel pseudo hierarchical architecture incorporating V2G technology for both autonomous coordination and regulated power dispatching," Applied Energy, Elsevier, vol. 313(C).
    2. Sebastian Pater, 2023. "Increasing Energy Self-Consumption in Residential Photovoltaic Systems with Heat Pumps in Poland," Energies, MDPI, vol. 16(10), pages 1-14, May.
    3. Serra, Daniele & Mardero, Daniele & Di Stefano, Luca & Grillo, Samuele, 2021. "Post-metering value-added services for low voltage electricity users: Lessons learned from the Italian experience of CHAIN 2," Applied Energy, Elsevier, vol. 304(C).
    4. Modawy Adam Ali Abdalla & Wang Min & Gehad Abdullah Amran & Amerah Alabrah & Omer Abbaker Ahmed Mohammed & Hussain AlSalman & Bassiouny Saleh, 2023. "Optimizing Energy Usage and Smoothing Load Profile via a Home Energy Management Strategy with Vehicle-to-Home and Energy Storage System," Sustainability, MDPI, vol. 15(20), pages 1-28, October.
    5. Wu, Wei & Lin, Boqiang, 2021. "Benefits of electric vehicles integrating into power grid," Energy, Elsevier, vol. 224(C).
    6. Zapata, Sebastian & Castaneda, Monica & Aristizabal, Andres J. & Dyner, Isaac, 2022. "Renewables for supporting supply adequacy in Colombia," Energy, Elsevier, vol. 239(PC).
    7. Mohammed, Abubakar Gambo & Elfeky, Karem Elsayed & Wang, Qiuwang, 2022. "Recent advancement and enhanced battery performance using phase change materials based hybrid battery thermal management for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    8. Rittichai Liemthong & Chitchai Srithapon & Prasanta K. Ghosh & Rongrit Chatthaworn, 2022. "Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs," Energies, MDPI, vol. 15(2), pages 1-22, January.
    9. Wang, Mingtao & Zhang, Juan & Liu, Huanwei, 2022. "Thermodynamic analysis and optimization of two low-grade energy driven transcritical CO2 combined cooling, heating and power systems," Energy, Elsevier, vol. 249(C).
    10. 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.
    11. Bruno Cárdenas & Lawrie Swinfen-Styles & James Rouse & Seamus D. Garvey, 2021. "Short-, Medium-, and Long-Duration Energy Storage in a 100% Renewable Electricity Grid: A UK Case Study," Energies, MDPI, vol. 14(24), pages 1-28, December.
    12. Julien Walzberg & Annika Eberle, 2023. "Modeling Systems’ Disruption and Social Acceptance—A Proof-of-Concept Leveraging Reinforcement Learning," Sustainability, MDPI, vol. 15(13), pages 1-13, June.
    13. Shah Rukh Abbas & Syed Ali Abbas Kazmi & Muhammad Naqvi & Adeel Javed & Salman Raza Naqvi & Kafait Ullah & Tauseef-ur-Rehman Khan & Dong Ryeol Shin, 2020. "Impact Analysis of Large-Scale Wind Farms Integration in Weak Transmission Grid from Technical Perspectives," Energies, MDPI, vol. 13(20), pages 1-32, October.
    14. Mohammed W. Baidas & Rola W. Hasaneya & Rashad M. Kamel & Sultan Sh. Alanzi, 2021. "Solar-Powered Cellular Base Stations in Kuwait: A Case Study," Energies, MDPI, vol. 14(22), pages 1-26, November.
    15. Abadie, Luis Mª & Chamorro, José M., 2023. "Investment in wind-based hydrogen production under economic and physical uncertainties," Applied Energy, Elsevier, vol. 337(C).
    16. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    17. Yinhe Bu & Xingping Zhang, 2021. "On the Way to Integrate Increasing Shares of Variable Renewables in China: Experience from Flexibility Modification and Deep Peak Regulation Ancillary Service Market Based on MILP-UC Programming," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    18. Luo, Lizi & He, Pinquan & Gu, Wei & Sheng, Wanxing & Liu, Keyan & Bai, Muke, 2022. "Temporal-spatial scheduling of electric vehicles in AC/DC distribution networks," Energy, Elsevier, vol. 255(C).
    19. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    20. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(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:13:y:2020:i:23:p:6387-:d:455524. 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.