IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i23p16266-d1286953.html
   My bibliography  Save this article

Study of Key Parameters and Uncertainties Based on Integrated Energy Systems Coupled with Renewable Energy Sources

Author

Listed:
  • Xin Liu

    (School of Metallurgy, Northeastern University, Shenyang 110819, China
    State Environmental Protection Key Laboratory of Eco-Industry, Northeastern University, Shenyang 110819, China)

  • Yuzhang Ji

    (School of Metallurgy, Northeastern University, Shenyang 110819, China
    State Environmental Protection Key Laboratory of Eco-Industry, Northeastern University, Shenyang 110819, China)

  • Ziyang Guo

    (School of Metallurgy, Northeastern University, Shenyang 110819, China
    State Environmental Protection Key Laboratory of Eco-Industry, Northeastern University, Shenyang 110819, China)

  • Shufu Yuan

    (School of Metallurgy, Northeastern University, Shenyang 110819, China)

  • Yongxu Chen

    (School of Metallurgy, Northeastern University, Shenyang 110819, China)

  • Weijun Zhang

    (School of Metallurgy, Northeastern University, Shenyang 110819, China
    State Environmental Protection Key Laboratory of Eco-Industry, Northeastern University, Shenyang 110819, China)

Abstract

The extensive research and application of integrated energy systems (IES) coupled with renewable energy sources have played a pivotal role in alleviating the problems of fossil energy shortage and promoting sustainability to a certain extent. However, the uncertainty of photovoltaic (PV) and wind power in IES increases the difficulty of maintaining stable system operation, posing a challenge to long-term sustainability. In addition, the capacity configuration of each device in IES and the operation strategy under different conditions will also significantly impact the operation cost and expected results of the system, influencing its overall sustainability. To address the above problems, this paper establishes an optimization model based on linear programming to optimize the equipment capacity and operation strategy of IES coupled with PV and wind power with the minimum total annual cost as the objective function, thereby promoting economic sustainability. Moreover, an integrated assessment framework, including economic, energy efficiency, and environmental aspects, is constructed to provide a comprehensive assessment of the operation of IES, ensuring a holistic view of sustainability. Finally, taking the IES of an industrial park in Xi’an, China, as the specific case, sensitivity analysis is used to explore the impact of a variety of critical parameters on the equipment capacity and operating strategy. Additionally, the Monte Carlo method is used to explore the impact of source-load uncertainty on the performance of the IES. The results show that the facilitating or constraining relationship between renewable energy access and the cascading utilization of combined heat and power generation (CHP) energy depends on the relative magnitude of the user load thermoelectric ratio to the prime mover thermoelectric ratio. To cope with the negative impact of source-load uncertainty on the stable operation of the IES, the capacities of the electric chiller and absorption chiller should be increased by 4.0% and 5.8%, respectively. It is worth noting that the increase in the penetration rate of renewable energy has not changed the system’s dependence on the grid.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16266-:d:1286953
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/23/16266/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/23/16266/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carpaneto, Enrico & Chicco, Gianfranco & Mancarella, Pierluigi & Russo, Angela, 2011. "Cogeneration planning under uncertainty: Part I: Multiple time frame approach," Applied Energy, Elsevier, vol. 88(4), pages 1059-1067, April.
    2. Wu, Junnian & Wang, Ruiqi & Pu, Guangying & Qi, Hang, 2016. "Integrated assessment of exergy, energy and carbon dioxide emissions in an iron and steel industrial network," Applied Energy, Elsevier, vol. 183(C), pages 430-444.
    3. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
    4. Hawkes, A.D. & Leach, M.A., 2009. "Modelling high level system design and unit commitment for a microgrid," Applied Energy, Elsevier, vol. 86(7-8), pages 1253-1265, July.
    5. 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).
    6. Afzali, Sayyed Faridoddin & Mahalec, Vladimir, 2017. "Optimal design, operation and analytical criteria for determining optimal operating modes of a CCHP with fired HRSG, boiler, electric chiller and absorption chiller," Energy, Elsevier, vol. 139(C), pages 1052-1065.
    7. Sanaye, Sepehr & Khakpaay, Navid, 2014. "Simultaneous use of MRM (maximum rectangle method) and optimization methods in determining nominal capacity of gas engines in CCHP (combined cooling, heating and power) systems," Energy, Elsevier, vol. 72(C), pages 145-158.
    8. Hu, Mengqi & Cho, Heejin, 2014. "A probability constrained multi-objective optimization model for CCHP system operation decision support," Applied Energy, Elsevier, vol. 116(C), pages 230-242.
    9. Cao, Tao & Lee, Hoseong & Hwang, Yunho & Radermacher, Reinhard & Chun, Ho-Hwan, 2016. "Modeling of waste heat powered energy system for container ships," Energy, Elsevier, vol. 106(C), pages 408-421.
    10. Hassan, Muhammed A. & Khalil, Adel & Abubakr, Mohamed, 2021. "Selection methodology of representative meteorological days for assessment of renewable energy systems," Renewable Energy, Elsevier, vol. 177(C), pages 34-51.
    11. Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2017. "Development of an optimization based design framework for microgrid energy systems," Energy, Elsevier, vol. 140(P1), pages 340-351.
    12. Yang, G. & Zhai, X.Q., 2019. "Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition," Energy, Elsevier, vol. 174(C), pages 647-663.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Antonio Piacentino & Roberto Gallea & Pietro Catrini & Fabio Cardona & Domenico Panno, 2016. "On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation," Energies, MDPI, vol. 9(12), pages 1-31, December.
    3. Gao, Lei & Hwang, Yunho & Cao, Tao, 2019. "An overview of optimization technologies applied in combined cooling, heating and power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    4. 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).
    5. Ersoz, Ibrahim & Colak, Uner, 2016. "Combined cooling, heat and power planning under uncertainty," Energy, Elsevier, vol. 109(C), pages 1016-1025.
    6. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    7. 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).
    8. Pan, Guangsheng & Gu, Wei & Wu, Zhi & Lu, Yuping & Lu, Shuai, 2019. "Optimal design and operation of multi-energy system with load aggregator considering nodal energy prices," Applied Energy, Elsevier, vol. 239(C), pages 280-295.
    9. Onishi, Viviani C. & Antunes, Carlos H. & Fraga, Eric S. & Cabezas, Heriberto, 2019. "Stochastic optimization of trigeneration systems for decision-making under long-term uncertainty in energy demands and prices," Energy, Elsevier, vol. 175(C), pages 781-797.
    10. Cui, Qiong & Ma, Peipei & Huang, Lei & Shu, Jie & Luv, Jie & Lu, Lin, 2020. "Effect of device models on the multiobjective optimal operation of CCHP microgrids considering shiftable loads," Applied Energy, Elsevier, vol. 275(C).
    11. 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).
    12. Wang, Jiangjiang & Yang, Ying & Mao, Tianzhi & Sui, Jun & Jin, Hongguang, 2015. "Life cycle assessment (LCA) optimization of solar-assisted hybrid CCHP system," Applied Energy, Elsevier, vol. 146(C), pages 38-52.
    13. Xiao Gong & Fan Li & Bo Sun & Dong Liu, 2020. "Collaborative Optimization of Multi-Energy Complementary Combined Cooling, Heating, and Power Systems Considering Schedulable Loads," Energies, MDPI, vol. 13(4), pages 1-17, February.
    14. Burek, Jasmina & Nutter, Darin W., 2019. "A life cycle assessment-based multi-objective optimization of the purchased, solar, and wind energy for the grocery, perishables, and general merchandise multi-facility distribution center network," Applied Energy, Elsevier, vol. 235(C), pages 1427-1446.
    15. Zhou, Yuan & Wang, Jiangjiang & Dong, Fuxiang & Qin, Yanbo & Ma, Zherui & Ma, Yanpeng & Li, Jianqiang, 2021. "Novel flexibility evaluation of hybrid combined cooling, heating and power system with an improved operation strategy," Applied Energy, Elsevier, vol. 300(C).
    16. Bingke Yan & Bo Wang & Lin Zhu & Hesen Liu & Yilu Liu & Xingpei Ji & Dichen Liu, 2015. "A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet," Energies, MDPI, vol. 8(11), pages 1-24, November.
    17. Helistö, Niina & Kiviluoma, Juha & Morales-España, Germán & O’Dwyer, Ciara, 2021. "Impact of operational details and temporal representations on investment planning in energy systems dominated by wind and solar," Applied Energy, Elsevier, vol. 290(C).
    18. Schachter, Jonathan A. & Mancarella, Pierluigi & Moriarty, John & Shaw, Rita, 2016. "Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation," Energy Policy, Elsevier, vol. 97(C), pages 439-449.
    19. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    20. Duan, Fude & Bu, Xiongzhu, 2024. "Stochastic optimization of a hybrid photovoltaic/fuel cell/parking lot system incorporating cloud model," Renewable Energy, Elsevier, vol. 237(PC).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16266-:d:1286953. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.