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Photovoltaic power plant design considering multiple uncertainties and risk

Author

Listed:
  • Yasemin Merzifonluoglu

    (Middle East Technical University)

  • Eray Uzgoren

    (Middle East Technical University)

Abstract

The objective of this study was to develop stochastic optimization tools for determining the best strategy of photovoltaic installations in a campus environment with consideration of uncertainties in load, power generation and system performance. In addition to a risk neutral approach, we used Conditional Value-at-Risk to estimate the risk in our problem. The resulting Mixed Integer Programming models were formulated using a scenario-based approach. To minimize the mismatch between supply and demand, hourly solar resource and electricity demand levels were characterized via refined models. A sample-average approximation (SAA) method was proposed to provide high-quality solutions efficiently. The SAA problems were solved using exact and heuristic methods. A complete numerical study was conducted to examine the performance of the proposed solution methods, identify optimal selection strategies and consider the sensitivity of the solution to varying levels of risk.

Suggested Citation

  • Yasemin Merzifonluoglu & Eray Uzgoren, 2018. "Photovoltaic power plant design considering multiple uncertainties and risk," Annals of Operations Research, Springer, vol. 262(1), pages 153-184, March.
    • Handle: RePEc:spr:annopr:v:262:y:2018:i:1:d:10.1007_s10479-017-2557-5
    DOI: 10.1007/s10479-017-2557-5
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    References listed on IDEAS

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    1. Ekren, Orhan & Ekren, Banu Y., 2010. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing," Applied Energy, Elsevier, vol. 87(2), pages 592-598, February.
    2. Senjyu, Tomonobu & Hayashi, Daisuke & Yona, Atsushi & Urasaki, Naomitsu & Funabashi, Toshihisa, 2007. "Optimal configuration of power generating systems in isolated island with renewable energy," Renewable Energy, Elsevier, vol. 32(11), pages 1917-1933.
    3. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, September.
    4. Ludwig Kuznia & Bo Zeng & Grisselle Centeno & Zhixin Miao, 2013. "Stochastic optimization for power system configuration with renewable energy in remote areas," Annals of Operations Research, Springer, vol. 210(1), pages 411-432, November.
    5. Yasemin Merzifonluoglu, 2015. "Impact of risk aversion and backup supplier on sourcing decisions of a firm," International Journal of Production Research, Taylor & Francis Journals, vol. 53(22), pages 6937-6961, November.
    6. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
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    Cited by:

    1. Oliveira, Fernando S. & Ruiz Mora, Carlos, 2023. "Risk management in solar-based power plants with storage: a comparative study," DES - Working Papers. Statistics and Econometrics. WS 38369, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    3. Eduardo Manuel Godinho Rodrigues & Radu Godina & Mousa Marzband & Edris Pouresmaeil, 2018. "Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity," Energies, MDPI, vol. 11(11), pages 1-21, October.

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