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

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  • 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
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    References listed on IDEAS

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    1. Wu, Zhou & Tazvinga, Henerica & Xia, Xiaohua, 2015. "Demand side management of photovoltaic-battery hybrid system," Applied Energy, Elsevier, vol. 148(C), pages 294-304.
    2. 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.
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    Cited by:

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    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.

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