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Optimal battery sizing of smart home via convex programming

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  • Wu, Xiaohua
  • Hu, Xiaosong
  • Yin, Xiaofeng
  • Zhang, Caiping
  • Qian, Shide

Abstract

This paper develops a convex programming (CP) framework for optimal sizing and energy management of smart home with battery energy storage system (BESS) and photovoltaic (PV) power generation, for the goal of maximizing home economy, while satisfying home power demand. We analyse the historical electric energy data of three different homes located in California and Texas, and indicate the necessity and importance of a BESS. Based on the structures and system models of these smart homes, the CP problem is formulated to rapidly and efficiently solve the optimal design/control issue. Based on different time horizons, maximal powers to grid, prices of BESS, the optimal parameters of BESS and its potential to electric energy cost savings are systematically compared for the three homes. A deviation analysis between the results obtained by CP and DP (dynamic programming) is also presented.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:140:y:2017:i:p1:p:444-453
    DOI: 10.1016/j.energy.2017.08.097
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    1. Mohseni, Amin & Mortazavi, Seyed Saeidollah & Ghasemi, Ahmad & Nahavandi, Ali & Talaei abdi, Masoud, 2017. "The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid," Energy, Elsevier, vol. 139(C), pages 315-328.
    2. Fattahi, Salar & Ashraphijuo, Morteza & Lavaei, Javad & Atamtürk, Alper, 2017. "Conic relaxations of the unit commitment problem," Energy, Elsevier, vol. 134(C), pages 1079-1095.
    3. Munkhammar, Joakim & Widén, Joakim & Rydén, Jesper, 2015. "On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production," Applied Energy, Elsevier, vol. 142(C), pages 135-143.
    4. Batista, N.C. & Melício, R. & Matias, J.C.O. & Catalão, J.P.S., 2013. "Photovoltaic and wind energy systems monitoring and building/home energy management using ZigBee devices within a smart grid," Energy, Elsevier, vol. 49(C), pages 306-315.
    5. Elma, Onur & Taşcıkaraoğlu, Akın & Tahir İnce, A. & Selamoğulları, Uğur S., 2017. "Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts," Energy, Elsevier, vol. 134(C), pages 206-220.
    6. Robert L. Fares & Michael E. Webber, 2017. "The impacts of storing solar energy in the home to reduce reliance on the utility," Nature Energy, Nature, vol. 2(2), pages 1-10, February.
    7. Shirazi, Elham & Jadid, Shahram, 2017. "Cost reduction and peak shaving through domestic load shifting and DERs," Energy, Elsevier, vol. 124(C), pages 146-159.
    8. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    9. Entchev, E. & Yang, L. & Ghorab, M. & Lee, E.J., 2013. "Simulation of hybrid renewable microgeneration systems in load sharing applications," Energy, Elsevier, vol. 50(C), pages 252-261.
    10. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    11. Amirioun, Mohammad Hassan & Kazemi, Ahad, 2014. "A new model based on optimal scheduling of combined energy exchange modes for aggregation of electric vehicles in a residential complex," Energy, Elsevier, vol. 69(C), pages 186-198.
    12. Hu, Xiaosong & Zou, Yuan & Yang, Yalian, 2016. "Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization," Energy, Elsevier, vol. 111(C), pages 971-980.
    13. Prinsloo, Gerro & Mammoli, Andrea & Dobson, Robert, 2017. "Customer domain supply and load coordination: A case for smart villages and transactive control in rural off-grid microgrids," Energy, Elsevier, vol. 135(C), pages 430-441.
    14. Shen, Jianjian & Cheng, Chuntian & Wu, Xinyu & Cheng, Xiong & Li, Weidong & Lu, Jianyu, 2014. "Optimization of peak loads among multiple provincial power grids under a central dispatching authority," Energy, Elsevier, vol. 74(C), pages 494-505.
    15. Wang, Ge & Zhang, Qi & Li, Hailong & McLellan, Benjamin C. & Chen, Siyuan & Li, Yan & Tian, Yulu, 2017. "Study on the promotion impact of demand response on distributed PV penetration by using non-cooperative game theoretical analysis," Applied Energy, Elsevier, vol. 185(P2), pages 1869-1878.
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