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Optimal Investment Strategies for Solar Energy Based Systems

Author

Listed:
  • Yuchen Song

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, No. 2006, XiYuan Avenue, Chengdu 611731, China)

  • Weihao Hu

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, No. 2006, XiYuan Avenue, Chengdu 611731, China)

  • Xiao Xu

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, No. 2006, XiYuan Avenue, Chengdu 611731, China)

  • Qi Huang

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, No. 2006, XiYuan Avenue, Chengdu 611731, China)

  • Gang Chen

    (State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China)

  • Xiaoyan Han

    (State Grid Sichuan Electric Power Company, Chengdu 610041, China)

  • Zhe Chen

    (Department of Energy Technology, Aalborg University, Pontoppidanstraede 101, DK-9220 Aalborg, Denmark)

Abstract

Solar energy, as an inexhaustible renewable energy, can be used to produce heat and electricity. It is of great importance to examine the strategy for investment on solar energy technology. In response to varying electricity price in the electricity market, the battery energy storage system (BESS) can be used to get price arbitrage. This paper proposes an optimal configuration model for a photovoltaic (PV) system, solar heating system, and BESS in order to obtain maximum profit for investors. The investment potential of these systems is compared and analyzed based on return on investment (ROI) index which is defined to evaluate economic profitability. A bi-level programming is adopted to optimize the operation strategy of batteries (inner layer), the size of PV system and solar heating system, and the size of batteries (outer layer) including their maximum discharge/charge power and capacity. Sequential quadratic programming (SQP) method and particle swarm optimization (PSO) are used as optimization methods. In the case study, five investment strategies are investigated in order to decide how to invest in PV modules, batteries, and solar thermal collectors. The results show that the BESS may be a preferable choice for the investors if the investment cost of BESS goes down a lot in the future. Investing in solar energy for both heat and power may be not reasonable because the ROI of this strategy is always higher than either investing in heat or in power. The optimal strategy may be changed with the fluctuation of heat and electricity prices.

Suggested Citation

  • Yuchen Song & Weihao Hu & Xiao Xu & Qi Huang & Gang Chen & Xiaoyan Han & Zhe Chen, 2019. "Optimal Investment Strategies for Solar Energy Based Systems," Energies, MDPI, vol. 12(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2826-:d:250655
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    References listed on IDEAS

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    1. Seddegh, Saeid & Wang, Xiaolin & Henderson, Alan D. & Xing, Ziwen, 2015. "Solar domestic hot water systems using latent heat energy storage medium: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 517-533.
    2. Raisul Islam, M. & Sumathy, K. & Ullah Khan, Samee, 2013. "Solar water heating systems and their market trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 17(C), pages 1-25.
    3. Lai, Chun Sing & McCulloch, Malcolm D., 2017. "Levelized cost of electricity for solar photovoltaic and electrical energy storage," Applied Energy, Elsevier, vol. 190(C), pages 191-203.
    4. Li, Hailong & Sun, Qie & Zhang, Qi & Wallin, Fredrik, 2015. "A review of the pricing mechanisms for district heating systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 56-65.
    5. Wang, Ni & Li, Jian & Hu, Weihao & Zhang, Baohua & Huang, Qi & Chen, Zhe, 2019. "Optimal reactive power dispatch of a full-scale converter based wind farm considering loss minimization," Renewable Energy, Elsevier, vol. 139(C), pages 292-301.
    6. Gökmen, Nuri & Hu, Weihao & Hou, Peng & Chen, Zhe & Sera, Dezso & Spataru, Sergiu, 2016. "Investigation of wind speed cooling effect on PV panels in windy locations," Renewable Energy, Elsevier, vol. 90(C), pages 283-290.
    7. Winterscheid, Carlo & Dalenbäck, Jan-Olof & Holler, Stefan, 2017. "Integration of solar thermal systems in existing district heating systems," Energy, Elsevier, vol. 137(C), pages 579-585.
    8. Daud, Abdel-Karim & Ismail, Mahmoud S., 2012. "Design of isolated hybrid systems minimizing costs and pollutant emissions," Renewable Energy, Elsevier, vol. 44(C), pages 215-224.
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    Cited by:

    1. Alexandra G. Papadopoulou & George Vasileiou & Alexandros Flamos, 2020. "A Comparison of Dispatchable RES Technoeconomics: Is There a Niche for Concentrated Solar Power?," Energies, MDPI, vol. 13(18), pages 1-22, September.
    2. Elisa Marrasso & Carlo Roselli & Francesco Tariello, 2020. "Comparison of Two Solar PV-Driven Air Conditioning Systems with Different Tracking Modes," Energies, MDPI, vol. 13(14), pages 1-24, July.

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