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Uncertainty analysis of energy and economic performances of hybrid solar photovoltaic and combined cooling, heating, and power (CCHP + PV) systems using a Monte-Carlo method

Author

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  • Ahn, Hyeunguk
  • Rim, Donghyun
  • Pavlak, Gregory S.
  • Freihaut, James D.

Abstract

This study examines the impacts of uncertainties in energy demands and solar resources on the energy and economic performances of hybrid solar photovoltaic and combined cooling, heating and power (CCHP + PV) systems with variations in PV penetration levels. This study investigates two models: a deterministic and stochastic model. The deterministic model uses hourly demands of the U.S. Department of Energy (DOE) reference large office building in San Francisco, CA and solar irradiance profiles in the Typical Meteorological Year (TMY) data as the independent variables. The stochastic model accounts for uncertainties in these independent variables using a Monte-Carlo method. The results show that regardless of PV penetration levels, the uncertainties in building energy demands and solar irradiance marginally influence the energy performance of CCHP + PV systems; however, they can notably increase annual operating costs up to $75,000 per year (13%). The annual cost increase is mainly attributed to a significant increase in demand charges (up to $79,000 per year). The demand charges tend to increase with higher uncertainties in the peak demand. The results suggest that in cases of the demand charge being responsible for a large portion in electricity bills (i.e., demand tariffs), a deterministic model tends to underestimate operating costs of CCHP + PV systems or other analogous distributed energy systems compared to a stochastic model. The errors with the deterministic model can become more extreme when demand charges outweigh energy charges.

Suggested Citation

  • Ahn, Hyeunguk & Rim, Donghyun & Pavlak, Gregory S. & Freihaut, James D., 2019. "Uncertainty analysis of energy and economic performances of hybrid solar photovoltaic and combined cooling, heating, and power (CCHP + PV) systems using a Monte-Carlo method," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919314400
    DOI: 10.1016/j.apenergy.2019.113753
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    Cited by:

    1. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad & Hajizadeh, Amin & Mohammadi-ivatloo, Behnam, 2022. "A comprehensive review on optimization challenges of smart energy hubs under uncertainty factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Pina, Eduardo A. & Lozano, Miguel A. & Serra, Luis M., 2021. "Assessing the influence of legal constraints on the integration of renewable energy technologies in polygeneration systems for buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Ana Cabrera-Tobar & Alessandro Massi Pavan & Giovanni Petrone & Giovanni Spagnuolo, 2022. "A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids," Energies, MDPI, vol. 15(23), pages 1-38, December.
    4. Chopra, K. & Tyagi, V.V. & Popli, Sakshi & Pandey, A.K., 2023. "Technical & financial feasibility assessment of heat pipe evacuated tube collector for water heating using Monte Carlo technique for buildings," Energy, Elsevier, vol. 267(C).
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    6. Li, Ruonan & Mahalec, Vladimir, 2022. "Integrated design and operation of energy systems for residential buildings, commercial buildings, and light industries," Applied Energy, Elsevier, vol. 305(C).
    7. Xin Liu & Yuzhang Ji & Ziyang Guo & Shufu Yuan & Yongxu Chen & Weijun Zhang, 2023. "Study of Key Parameters and Uncertainties Based on Integrated Energy Systems Coupled with Renewable Energy Sources," Sustainability, MDPI, vol. 15(23), pages 1-29, November.
    8. Niu, Jide & Li, Xiaoyuan & Tian, Zhe & Yang, Hongxing, 2023. "A framework for quantifying the value of information to mitigate risk in the optimal design of distributed energy systems under uncertainty," Applied Energy, Elsevier, vol. 350(C).
    9. Farhan Lafta Rashid & Muhammad Asmail Eleiwi & Hayder I. Mohammed & Arman Ameen & Shabbir Ahmad, 2023. "A Review of Using Solar Energy for Cooling Systems: Applications, Challenges, and Effects," Energies, MDPI, vol. 16(24), pages 1-34, December.
    10. Ahn, Hyeunguk, 2024. "A framework for developing data-driven correction factors for solar PV systems," Energy, Elsevier, vol. 290(C).
    11. Guo, Jiacheng & Zhang, Peiwen & Wu, Di & Liu, Zhijian & Liu, Xuan & Zhang, Shicong & Yang, Xinyan & Ge, Hua, 2022. "Multi-objective optimization design and multi-attribute decision-making method of a distributed energy system based on nearly zero-energy community load forecasting," Energy, Elsevier, vol. 239(PC).
    12. Wang, Meng & Yu, Hang & Lin, Xiaoyu & Jing, Rui & He, Fangjun & Li, Chaoen, 2020. "Comparing stochastic programming with posteriori approach for multi-objective optimization of distributed energy systems under uncertainty," Energy, Elsevier, vol. 210(C).
    13. Yuan, Yu & Bai, Zhang & Liu, Qibin & Hu, Wenxin & Zheng, Bo, 2021. "Potential of applying the thermochemical recuperation in combined cooling, heating and power generation: Route of enhancing the operation flexibility," Applied Energy, Elsevier, vol. 301(C).
    14. Fayza S. Mahmoud & Ashraf M. Abdelhamid & Ameena Al Sumaiti & Abou-Hashema M. El-Sayed & Ahmed A. Zaki Diab, 2022. "Sizing and Design of a PV-Wind-Fuel Cell Storage System Integrated into a Grid Considering the Uncertainty of Load Demand Using the Marine Predators Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-26, October.
    15. Ahn, Hyeunguk & Miller, William & Sheaffer, Paul & Tutterow, Vestal & Rapp, Vi, 2021. "Opportunities for installed combined heat and power (CHP) to increase grid flexibility in the U.S," Energy Policy, Elsevier, vol. 157(C).
    16. Swaminathan, Siddharth & Pavlak, Gregory S. & Freihaut, James, 2020. "Sizing and dispatch of an islanded microgrid with energy flexible buildings," Applied Energy, Elsevier, vol. 276(C).
    17. Àlex Alonso-Travesset & Diederik Coppitters & Helena Martín & Jordi de la Hoz, 2023. "Economic and Regulatory Uncertainty in Renewable Energy System Design: A Review," Energies, MDPI, vol. 16(2), pages 1-30, January.
    18. Zou, Bin & Peng, Jinqing & Yin, Rongxin & Li, Houpei & Li, Sihui & Yan, Jinyue & Yang, Hongxing, 2022. "Capacity configuration of distributed photovoltaic and battery system for office buildings considering uncertainties," Applied Energy, Elsevier, vol. 319(C).
    19. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    20. Serrano-Arévalo, Tania Itzel & López-Flores, Francisco Javier & Raya-Tapia, Alma Yunuen & Ramírez-Márquez, César & Ponce-Ortega, José María, 2023. "Optimal expansion for a clean power sector transition in Mexico based on predicted electricity demand using deep learning scheme," Applied Energy, Elsevier, vol. 348(C).
    21. Afzali, Sayyed Faridoddin & Cotton, James S. & Mahalec, Vladimir, 2020. "Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions," Applied Energy, Elsevier, vol. 259(C).
    22. Zhang, Dong & Zhang, Rui & Zhang, Bin & Zheng, Yu & An, Zhoujian, 2023. "Environment dominated evaluation modeling and collocation optimization of a distributed energy system based on solar and biomass energy," Renewable Energy, Elsevier, vol. 202(C), pages 1226-1240.
    23. Elminshawy, Nabil A.S. & El-Damhogi, D.G. & Ibrahim, I.A. & Elminshawy, Ahmed & Osama, Amr, 2022. "Assessment of floating photovoltaic productivity with fins-assisted passive cooling," Applied Energy, Elsevier, vol. 325(C).
    24. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).
    25. Meng, B. & Loonen, R.C.G.M. & Hensen, J.L.M., 2022. "Performance variability and implications for yield prediction of rooftop PV systems – Analysis of 246 identical systems," Applied Energy, Elsevier, vol. 322(C).

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