IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v108y2017icp132-143.html
   My bibliography  Save this article

Techno-economic and environmental optimization of a household photovoltaic-battery hybrid power system within demand side management

Author

Listed:
  • Yang, Fei
  • Xia, Xiaohua

Abstract

This paper presents a power management system of a household photovoltaic-battery hybrid power system within demand side management under time of use electricity tariff. This system is easy to implement by employing cheap electrical switches, off-the-shelf chargers and inverters. Control system models combining both power dispatching level and home appliance scheduling level are proposed to minimize the residents' energy cost and energy consumption from the grid with the practical constraints strictly satisfied. In addition, the resident comfort inconvenience level is considered in the control system models. The trade-off among operating cost, energy consumption and inconvenience is considered and a multi-objective optimization problem is formulated. The optimal control strategies are derived by solving a mixed-integer nonlinear programming problem. Simulation results show that the energy cost and energy consumption from the grid can be largely reduced with the proposed strategies. These results are important for customers to dispel their major uncertainty in determining whether to newly install or update to such photovoltaic-battery hybrid power systems.

Suggested Citation

  • Yang, Fei & Xia, Xiaohua, 2017. "Techno-economic and environmental optimization of a household photovoltaic-battery hybrid power system within demand side management," Renewable Energy, Elsevier, vol. 108(C), pages 132-143.
    • Handle: RePEc:eee:renene:v:108:y:2017:i:c:p:132-143
    DOI: 10.1016/j.renene.2017.02.054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148117301404
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2017.02.054?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. Nicholls, A. & Sharma, R. & Saha, T.K., 2015. "Financial and environmental analysis of rooftop photovoltaic installations with battery storage in Australia," Applied Energy, Elsevier, vol. 159(C), pages 252-264.
    2. Wu, Zhou & Tazvinga, Henerica & Xia, Xiaohua, 2015. "Demand side management of photovoltaic-battery hybrid system," Applied Energy, Elsevier, vol. 148(C), pages 294-304.
    3. Salpakari, Jyri & Lund, Peter, 2016. "Optimal and rule-based control strategies for energy flexibility in buildings with PV," Applied Energy, Elsevier, vol. 161(C), pages 425-436.
    4. Darcovich, K. & Kenney, B. & MacNeil, D.D. & Armstrong, M.M., 2015. "Control strategies and cycling demands for Li-ion storage batteries in residential micro-cogeneration systems," Applied Energy, Elsevier, vol. 141(C), pages 32-41.
    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. Huang, Pei & Sun, Yongjun, 2019. "A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty," Renewable Energy, Elsevier, vol. 134(C), pages 215-227.
    2. Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
    3. Bidan Zhang & Yang Du & Xiaoyang Chen & Eng Gee Lim & Lin Jiang & Ke Yan, 2022. "Potential Benefits for Residential Building with Photovoltaic Battery System Participation in Peer-to-Peer Energy Trading," Energies, MDPI, vol. 15(11), pages 1-21, May.
    4. Bo Wang & Yanjing Li & Fei Yang & Xiaohua Xia, 2019. "A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    5. Zou, Bin & Peng, Jinqing & Li, Sihui & Li, Yi & Yan, Jinyue & Yang, Hongxing, 2022. "Comparative study of the dynamic programming-based and rule-based operation strategies for grid-connected PV-battery systems of office buildings," Applied Energy, Elsevier, vol. 305(C).
    6. Huang, Zhijia & Lu, Yuehong & Wei, Mengmeng & Liu, Jingjing, 2017. "Performance analysis of optimal designed hybrid energy systems for grid-connected nearly/net zero energy buildings," Energy, Elsevier, vol. 141(C), pages 1795-1809.
    7. Fabio Bignucolo & Massimiliano Coppo & Giorgio Crugnola & Andrea Savio, 2017. "Application of a Simplified Thermal-Electric Model of a Sodium-Nickel Chloride Battery Energy Storage System to a Real Case Residential Prosumer," Energies, MDPI, vol. 10(10), pages 1-29, September.
    8. Yu, Dongmin & liu, Huanan & Bresser, Charis, 2018. "Peak load management based on hybrid power generation and demand response," Energy, Elsevier, vol. 163(C), pages 969-985.
    9. Hongyan Ma, 2021. "The role of clustering algorithm-based big data processing in information economy development," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.

    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. de Oliveira e Silva, Guilherme & Hendrick, Patrick, 2017. "Photovoltaic self-sufficiency of Belgian households using lithium-ion batteries, and its impact on the grid," Applied Energy, Elsevier, vol. 195(C), pages 786-799.
    2. Barelli, L. & Bidini, G. & Bonucci, F. & Castellini, L. & Fratini, A. & Gallorini, F. & Zuccari, A., 2019. "Flywheel hybridization to improve battery life in energy storage systems coupled to RES plants," Energy, Elsevier, vol. 173(C), pages 937-950.
    3. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    4. Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
    5. Antonio Rosato & Antonio Ciervo & Giovanni Ciampi & Michelangelo Scorpio & Sergio Sibilio, 2020. "Integration of Micro-Cogeneration Units and Electric Storages into a Micro-Scale Residential Solar District Heating System Operating with a Seasonal Thermal Storage," Energies, MDPI, vol. 13(20), pages 1-40, October.
    6. Kagiri, Charles & Wanjiru, Evan M. & Zhang, Lijun & Xia, Xiaohua, 2018. "Optimized response to electricity time-of-use tariff of a compressed natural gas fuelling station," Applied Energy, Elsevier, vol. 222(C), pages 244-256.
    7. Sourav Khanna & Victor Becerra & Adib Allahham & Damian Giaouris & Jamie M. Foster & Keiron Roberts & David Hutchinson & Jim Fawcett, 2020. "Demand Response Model Development for Smart Households Using Time of Use Tariffs and Optimal Control—The Isle of Wight Energy Autonomous Community Case Study," Energies, MDPI, vol. 13(3), pages 1-27, January.
    8. Ingrid Hopley & Mehrdad Ghahramani & Asma Aziz, 2024. "Techno-Economic Factors Impacting the Intrinsic Value of Behind-the-Meter Distributed Storage," Sustainability, MDPI, vol. 16(23), pages 1-26, November.
    9. Pothitou, Mary & Hanna, Richard F. & Chalvatzis, Konstantinos J., 2016. "Environmental knowledge, pro-environmental behaviour and energy savings in households: An empirical study," Applied Energy, Elsevier, vol. 184(C), pages 1217-1229.
    10. Tang, Ruoli & An, Qing & Xu, Fan & Zhang, Xiaodi & Li, Xin & Lai, Jingang & Dong, Zhengcheng, 2020. "Optimal operation of hybrid energy system for intelligent ship: An ultrahigh-dimensional model and control method," Energy, Elsevier, vol. 211(C).
    11. Vladimir Z. Gjorgievski & Nikolas G. Chatzigeorgiou & Venizelos Venizelou & Georgios C. Christoforidis & George E. Georghiou & Grigoris K. Papagiannis, 2020. "Evaluation of Load Matching Indicators in Residential PV Systems-the Case of Cyprus," Energies, MDPI, vol. 13(8), pages 1-18, April.
    12. Wanjiru, Evan M. & Sichilalu, Sam M. & Xia, Xiaohua, 2017. "Model predictive control of heat pump water heater-instantaneous shower powered with integrated renewable-grid energy systems," Applied Energy, Elsevier, vol. 204(C), pages 1333-1346.
    13. Finck, Christian & Li, Rongling & Kramer, Rick & Zeiler, Wim, 2018. "Quantifying demand flexibility of power-to-heat and thermal energy storage in the control of building heating systems," Applied Energy, Elsevier, vol. 209(C), pages 409-425.
    14. Joshua M. Pearce & Nelson Sommerfeldt, 2021. "Economics of Grid-Tied Solar Photovoltaic Systems Coupled to Heat Pumps: The Case of Northern Climates of the U.S. and Canada," Energies, MDPI, vol. 14(4), pages 1-17, February.
    15. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    16. Zhang, Lijun & Chennells, Michael & Xia, Xiaohua, 2018. "A power dispatch model for a ferrochrome plant heat recovery cogeneration system," Applied Energy, Elsevier, vol. 227(C), pages 180-189.
    17. Castellini, Marta & Menoncin, Francesco & Moretto, Michele & Vergalli, Sergio, 2021. "Photovoltaic Smart Grids in the prosumers investment decisions: a real option model," Journal of Economic Dynamics and Control, Elsevier, vol. 126(C).
    18. Psimopoulos, Emmanouil & Bee, Elena & Widén, Joakim & Bales, Chris, 2019. "Techno-economic analysis of control algorithms for an exhaust air heat pump system for detached houses coupled to a photovoltaic system," Applied Energy, Elsevier, vol. 249(C), pages 355-367.
    19. Maria Pinamonti & Alessandro Prada & Paolo Baggio, 2020. "Rule-Based Control Strategy to Increase Photovoltaic Self-Consumption of a Modulating Heat Pump Using Water Storages and Building Mass Activation," Energies, MDPI, vol. 13(23), pages 1-21, November.
    20. Katheryn Donado & Loraine Navarro & Christian G. Quintero M. & Mauricio Pardo, 2019. "HYRES: A Multi-Objective Optimization Tool for Proper Configuration of Renewable Hybrid Energy Systems," Energies, MDPI, vol. 13(1), pages 1-20, December.

    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:renene:v:108:y:2017:i:c:p:132-143. 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/renewable-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.