IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v271y2023ics036054422300316x.html
   My bibliography  Save this article

Feasibility of low-cost energy management system using embedded optimization for PV and battery storage assisted residential buildings

Author

Listed:
  • Ouedraogo, Kiswendsida Elias
  • Ekim, Pınar Oğuz
  • Demirok, Erhan

Abstract

In this study, an energy management system (EMS) focusing on low-cost hardware and embedded optimization has been built. A benchmark consisting of a residential photovoltaic (PV) and battery connected to the grid but without feed in power has been considered. The proposed EMS accepts input variables as building electrical load data, PV output data, the electricity time of use rates. The master EMS ensures the optimization of the battery charge-discharge profile to reach the lowest possible energy bill. Sensitivity analysis demonstrates that the presence of optimization systems leads to a more stable energy cost even though power demand and PV production vary during the day. In the cases studied, the bill reduction is 32% up to 50% depending on load or solar PV generation variations. By comparison, in the literature where more complex optimization in MATLAB environment were used, a bill reduction of 24%–34% was realized. The system cost is estimated to be around 30$ which is much lower than the typical 100$-600$ price for similar products. The system can be practically integrated in applications such as EMS of schools, residential or public buildings by inserting it through the power distribution panel where all protection devices are located.

Suggested Citation

  • Ouedraogo, Kiswendsida Elias & Ekim, Pınar Oğuz & Demirok, Erhan, 2023. "Feasibility of low-cost energy management system using embedded optimization for PV and battery storage assisted residential buildings," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s036054422300316x
    DOI: 10.1016/j.energy.2023.126922
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422300316X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.126922?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Iwafune, Yumiko & Mori, Yuko & Kawai, Toshiaki & Yagita, Yoshie, 2017. "Energy-saving effect of automatic home energy report utilizing home energy management system data in Japan," Energy, Elsevier, vol. 125(C), pages 382-392.
    2. Elma, Onur & Selamogullari, Ugur Savas, 2015. "A new home energy management algorithm with voltage control in a smart home environment," Energy, Elsevier, vol. 91(C), pages 720-731.
    3. de Souza Dutra, Michael David & Anjos, Miguel F. & Le Digabel, Sébastien, 2019. "A general framework for customized transition to smart homes," Energy, Elsevier, vol. 189(C).
    4. Wu, Xiaohua & Hu, Xiaosong & Yin, Xiaofeng & Zhang, Caiping & Qian, Shide, 2017. "Optimal battery sizing of smart home via convex programming," Energy, Elsevier, vol. 140(P1), pages 444-453.
    5. Lu, Qing & Lü, Shuaikang & Leng, Yajun & Zhang, Zhixin, 2020. "Optimal household energy management based on smart residential energy hub considering uncertain behaviors," Energy, Elsevier, vol. 195(C).
    6. Fletcher, James & Malalasekera, Weeratunge, 2016. "Development of a user-friendly, low-cost home energy monitoring and recording system," Energy, Elsevier, vol. 111(C), pages 32-46.
    7. Javadi, Mohammad Sadegh & Gough, Matthew & Lotfi, Mohamed & Esmaeel Nezhad, Ali & Santos, Sérgio F. & Catalão, João P.S., 2020. "Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries," Energy, Elsevier, vol. 210(C).
    8. Monyei, Chukwuka G. & Adewumi, Aderemi O. & Akinyele, Daniel & Babatunde, Olubayo M. & Obolo, Michael O. & Onunwor, Joshua C., 2018. "A biased load manager home energy management system for low-cost residential building low-income occupants," Energy, Elsevier, vol. 150(C), pages 822-838.
    9. Nizami, M.S.H. & Haque, A.N.M.M. & Nguyen, P.H. & Hossain, M.J., 2019. "On the application of Home Energy Management Systems for power grid support," Energy, Elsevier, vol. 188(C).
    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. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
    2. Toopshekan, Ashkan & Ahmadi, Esmaeil & Abedian, Ali & Vaziri Rad, Mohammad Amin, 2024. "Techno-economic analysis, optimization, and dispatch strategy development for renewable energy systems equipped with Internet of Things technology," Energy, Elsevier, vol. 296(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. Emrani-Rahaghi, Pouria & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2023. "Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids," Applied Energy, Elsevier, vol. 349(C).
    2. Gholami, M. & Sanjari, M.J., 2021. "Multiobjective energy management in battery-integrated home energy systems," Renewable Energy, Elsevier, vol. 177(C), pages 967-975.
    3. Montero-Sousa, Juan Aurelio & Aláiz-Moretón, Héctor & Quintián, Héctor & González-Ayuso, Tomás & Novais, Paulo & Calvo-Rolle, José Luis, 2020. "Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach," Energy, Elsevier, vol. 205(C).
    4. Wang, Jidong & Liu, Jianxin & Li, Chenghao & Zhou, Yue & Wu, Jianzhong, 2020. "Optimal scheduling of gas and electricity consumption in a smart home with a hybrid gas boiler and electric heating system," Energy, Elsevier, vol. 204(C).
    5. Chang, Hsiu-Chuan & Ghaddar, Bissan & Nathwani, Jatin, 2022. "Shared community energy storage allocation and optimization," Applied Energy, Elsevier, vol. 318(C).
    6. Wang, Guotao & Zhou, Yifan & Lin, Zhenjia & Zhu, Shibo & Qiu, Rui & Chen, Yuntian & Yan, Jinyue, 2024. "Robust energy management through aggregation of flexible resources in multi-home micro energy hub," Applied Energy, Elsevier, vol. 357(C).
    7. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    8. Hussain, Sadam & Azim, M. Imran & Lai, Chunyan & Eicker, Ursula, 2023. "New coordination framework for smart home peer-to-peer trading to reduce impact on distribution transformer," Energy, Elsevier, vol. 284(C).
    9. Xiong, Binyu & Wei, Feng & Wang, Yifei & Xia, Kairui & Su, Fuwen & Fang, Yingjia & Gao, Zuchang & Wei, Zhongbao, 2024. "Hybrid robust-stochastic optimal scheduling for multi-objective home energy management with the consideration of uncertainties," Energy, Elsevier, vol. 290(C).
    10. Lu, Qing & Yu, Hao & Zhao, Kangli & Leng, Yajun & Hou, Jianchao & Xie, Pinjie, 2019. "Residential demand response considering distributed PV consumption: A model based on China's PV policy," Energy, Elsevier, vol. 172(C), pages 443-456.
    11. Dong, Weijie & He, Guoqing & Cui, Quansheng & Sun, Wenwen & Hu, Zhenlong & Ahli raad, Erfan, 2022. "Self-scheduling of a novel hybrid GTSOFC unit in day-ahead energy and spinning reserve markets within ancillary services using a novel energy storage," Energy, Elsevier, vol. 239(PE).
    12. Liu, Zhiqiang & Cui, Yanping & Wang, Jiaqiang & Yue, Chang & Agbodjan, Yawovi Souley & Yang, Yu, 2022. "Multi-objective optimization of multi-energy complementary integrated energy systems considering load prediction and renewable energy production uncertainties," Energy, Elsevier, vol. 254(PC).
    13. Mulleriyawage, U.G.K. & Shen, W.X., 2021. "Impact of demand side management on optimal sizing of residential battery energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1250-1266.
    14. 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).
    15. Zhang, Tairan & Sobhani, Behrouz, 2023. "Optimal economic programming of an energy hub in the power system while taking into account the uncertainty of renewable resources, risk-taking and electric vehicles using a developed routing method," Energy, Elsevier, vol. 271(C).
    16. Wakui, Tetsuya & Akai, Kazuki & Yokoyama, Ryohei, 2022. "Shrinking and receding horizon approaches for long-term operational planning of energy storage and supply systems," Energy, Elsevier, vol. 239(PD).
    17. Haider, Haider Tarish & Muhsen, Dhiaa Halboot & Al-Nidawi, Yaarob Mahjoob & Khatib, Tamer & See, Ong Hang, 2022. "A novel approach for multi-objective cost-peak optimization for demand response of a residential area in smart grids," Energy, Elsevier, vol. 254(PB).
    18. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    19. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    20. Monyei, C.G. & Adewumi, A.O., 2017. "Demand Side Management potentials for mitigating energy poverty in South Africa," Energy Policy, Elsevier, vol. 111(C), pages 298-311.

    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:eee:energy:v:271:y:2023:i:c:s036054422300316x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.