IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i8p2797-d791414.html
   My bibliography  Save this article

A Stochastic Multi-Objective Model for China’s Provincial Generation-Mix Planning: Considering Variable Renewable and Transmission Capacity

Author

Listed:
  • Shuangshuang Zhou

    (Center for Energy Environmental Management and Decision-Making, China University of Geosciences, Wuhan 430074, China
    School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

  • Juan Yang

    (Center for Energy Environmental Management and Decision-Making, China University of Geosciences, Wuhan 430074, China
    School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

  • Shiwei Yu

    (Center for Energy Environmental Management and Decision-Making, China University of Geosciences, Wuhan 430074, China
    School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

Abstract

The uncertain output of variable renewables adds significant challenges to the generation of affordable, reliable, and sustainable power sources in a country or region. Therefore, we propose a new stochastic nonlinear multi-objective model to optimize the power generation structure in 31 provinces of China. Considering variable renewable integration, we use Monte Carlo simulation to describe the randomness and uncertainty of renewable power output. The learning curve in the exponential expression is used to describe the nonlinear relationship between generation cost and installed capacity. The optimized results show that China can substitute fossil power with clean power. Renewable power will account for more than 42% of total power in the optimal power generation structure in 2040. In particular, the annual average growth rate of non-hydro renewable generation is expected to be 12.06%, with solar photovoltaic (PV) power growing the most by 17.95%. The share of renewable power exceeds that of thermal power in 14 provinces, and PV power represents the highest proportion at 30.21%. Reducing transmission capacity can promote the development of advantageous power in each region, such as wind power in the Northwest region and PV power in the South region, with the share increasing by 36.33% and 132.59%, respectively.

Suggested Citation

  • Shuangshuang Zhou & Juan Yang & Shiwei Yu, 2022. "A Stochastic Multi-Objective Model for China’s Provincial Generation-Mix Planning: Considering Variable Renewable and Transmission Capacity," Energies, MDPI, vol. 15(8), pages 1-26, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2797-:d:791414
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/8/2797/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/8/2797/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daş, Gülesin Sena & Gzara, Fatma & Stützle, Thomas, 2020. "A review on airport gate assignment problems: Single versus multi objective approaches," Omega, Elsevier, vol. 92(C).
    2. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    3. Hui, Jingxuan & Cai, Wenjia & Wang, Can & Ye, Minhua, 2017. "Analyzing the penetration barriers of clean generation technologies in China’s power sector using a multi-region optimization model," Applied Energy, Elsevier, vol. 185(P2), pages 1809-1820.
    4. Makhloufi, Saida & Khennas, Smail & Bouchaib, Sami & Arab, Amar Hadj, 2022. "Multi-objective cuckoo search algorithm for optimized pathways for 75 % renewable electricity mix by 2050 in Algeria," Renewable Energy, Elsevier, vol. 185(C), pages 1410-1424.
    5. Handayani, Kamia & Krozer, Yoram & Filatova, Tatiana, 2019. "From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning," Energy Policy, Elsevier, vol. 127(C), pages 134-146.
    6. Chen, Siyuan & Liu, Pei & Li, Zheng, 2019. "Multi-regional power generation expansion planning with air pollutants emission constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 382-394.
    7. Yi, Bo-Wen & Xu, Jin-Hua & Fan, Ying, 2016. "Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model," Applied Energy, Elsevier, vol. 184(C), pages 641-658.
    8. Atabaki, Mohammad Saeid & Aryanpur, Vahid, 2018. "Multi-objective optimization for sustainable development of the power sector: An economic, environmental, and social analysis of Iran," Energy, Elsevier, vol. 161(C), pages 493-507.
    9. Yu, Shiwei & Zheng, Shuhong & Gao, Shiwei & Yang, Juan, 2017. "A multi-objective decision model for investment in energy savings and emission reductions in coal mining," European Journal of Operational Research, Elsevier, vol. 260(1), pages 335-347.
    10. Wang, Can & Ye, Minhua & Cai, Wenjia & Chen, Jining, 2014. "The value of a clear, long-term climate policy agenda: A case study of China’s power sector using a multi-region optimization model," Applied Energy, Elsevier, vol. 125(C), pages 276-288.
    11. Guo, Zheng & Ma, Linwei & Liu, Pei & Jones, Ian & Li, Zheng, 2016. "A multi-regional modelling and optimization approach to China's power generation and transmission planning," Energy, Elsevier, vol. 116(P2), pages 1348-1359.
    12. Vithayasrichareon, Peerapat & MacGill, Iain F., 2012. "A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 41(C), pages 374-392.
    13. Luz, Thiago & Moura, Pedro & de Almeida, Aníbal, 2018. "Multi-objective power generation expansion planning with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2637-2643.
    14. Gong, J.W. & Li, Y.P. & Lv, J. & Huang, G.H. & Suo, C. & Gao, P.P., 2022. "Development of an integrated bi-level model for China’s multi-regional energy system planning under uncertainty," Applied Energy, Elsevier, vol. 308(C).
    15. Hobbs, Benjamin F., 1995. "Optimization methods for electric utility resource planning," European Journal of Operational Research, Elsevier, vol. 83(1), pages 1-20, May.
    16. Pratama, Yoga Wienda & Purwanto, Widodo Wahyu & Tezuka, Tetsuo & McLellan, Benjamin Craig & Hartono, Djoni & Hidayatno, Akhmad & Daud, Yunus, 2017. "Multi-objective optimization of a multiregional electricity system in an archipelagic state: The role of renewable energy in energy system sustainability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 423-439.
    17. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    18. von Hirschhausen, Christian & Andres, Michael, 2000. "Long-term electricity demand in China -- From quantitative to qualitative growth?," Energy Policy, Elsevier, vol. 28(4), pages 231-241, April.
    19. Yu, Shiwei & Zhou, Shuangshuang & Zheng, Shuhong & Li, Zhenxi & Liu, Lancui, 2019. "Developing an optimal renewable electricity generation mix for China using a fuzzy multi-objective approach," Renewable Energy, Elsevier, vol. 139(C), pages 1086-1098.
    20. Tobias Junne & Karl-Kiên Cao & Kim Kira Miskiw & Heidi Hottenroth & Tobias Naegler, 2021. "Considering Life Cycle Greenhouse Gas Emissions in Power System Expansion Planning for Europe and North Africa Using Multi-Objective Optimization," Energies, MDPI, vol. 14(5), pages 1-26, February.
    21. Taimur Al Shidhani & Anastasia Ioannou & Gioia Falcone, 2020. "Multi-Objective Optimisation for Power System Planning Integrating Sustainability Indicators," Energies, MDPI, vol. 13(9), pages 1-32, May.
    22. Ekata Kaushik & Vivek Prakash & Om Prakash Mahela & Baseem Khan & Adel El-Shahat & Almoataz Y. Abdelaziz, 2022. "Comprehensive Overview of Power System Flexibility during the Scenario of High Penetration of Renewable Energy in Utility Grid," Energies, MDPI, vol. 15(2), pages 1-29, January.
    23. He, Jiaxin & Liu, Ying & Lin, Boqiang, 2018. "Should China support the development of biomass power generation?," Energy, Elsevier, vol. 163(C), pages 416-425.
    24. Thangavelu, Sundar Raj & Khambadkone, Ashwin M. & Karimi, Iftekhar A., 2015. "Long-term optimal energy mix planning towards high energy security and low GHG emission," Applied Energy, Elsevier, vol. 154(C), pages 959-969.
    25. Reimers, Andrew & Cole, Wesley & Frew, Bethany, 2019. "The impact of planning reserve margins in long-term planning models of the electricity sector," Energy Policy, Elsevier, vol. 125(C), pages 1-8.
    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. Bai, Bo & Wang, Yihan & Fang, Cong & Xiong, Siqin & Ma, Xiaoming, 2021. "Efficient deployment of solar photovoltaic stations in China: An economic and environmental perspective," Energy, Elsevier, vol. 221(C).
    2. Yu, Shiwei & Zhou, Shuangshuang & Zheng, Shuhong & Li, Zhenxi & Liu, Lancui, 2019. "Developing an optimal renewable electricity generation mix for China using a fuzzy multi-objective approach," Renewable Energy, Elsevier, vol. 139(C), pages 1086-1098.
    3. Hailin Mu & Zhewen Pei & Hongye Wang & Nan Li & Ye Duan, 2022. "Optimal Strategy for Low-Carbon Development of Power Industry in Northeast China Considering the ‘Dual Carbon’ Goal," Energies, MDPI, vol. 15(17), pages 1-22, September.
    4. Liu, Hailiang & Brown, Tom & Andresen, Gorm Bruun & Schlachtberger, David P. & Greiner, Martin, 2019. "The role of hydro power, storage and transmission in the decarbonization of the Chinese power system," Applied Energy, Elsevier, vol. 239(C), pages 1308-1321.
    5. Xu, Jin-Hua & Yi, Bo-Wen & Fan, Ying, 2020. "Economic viability and regulation effects of infrastructure investments for inter-regional electricity transmission and trade in China," Energy Economics, Elsevier, vol. 91(C).
    6. Pratama, Yoga Wienda & Purwanto, Widodo Wahyu & Tezuka, Tetsuo & McLellan, Benjamin Craig & Hartono, Djoni & Hidayatno, Akhmad & Daud, Yunus, 2017. "Multi-objective optimization of a multiregional electricity system in an archipelagic state: The role of renewable energy in energy system sustainability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 423-439.
    7. Wang, Hongye & Su, Bin & Mu, Hailin & Li, Nan & Jiang, Bo & Kong, Xue, 2019. "Optimization of electricity generation and interprovincial trading strategies in Southern China," Energy, Elsevier, vol. 174(C), pages 696-707.
    8. Yi, Bo-Wen & Xu, Jin-Hua & Fan, Ying, 2016. "Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model," Applied Energy, Elsevier, vol. 184(C), pages 641-658.
    9. Kim, Dowon & Ryu, Heelang & Lee, Jiwoong & Kim, Kyoung-Kuk, 2022. "Balancing risk: Generation expansion planning under climate mitigation scenarios," European Journal of Operational Research, Elsevier, vol. 297(2), pages 665-679.
    10. Chen, Siyuan & Liu, Pei & Li, Zheng, 2019. "Multi-regional power generation expansion planning with air pollutants emission constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 382-394.
    11. Zhang, Yaru & Ma, Tieju & Guo, Fei, 2018. "A multi-regional energy transport and structure model for China’s electricity system," Energy, Elsevier, vol. 161(C), pages 907-919.
    12. Jie, Dingfei & Xu, Xiangyang & Guo, Fei, 2021. "The future of coal supply in China based on non-fossil energy development and carbon price strategies," Energy, Elsevier, vol. 220(C).
    13. Horasan, Muhammed Bilal & Kilic, Huseyin Selcuk, 2022. "A multi-objective decision-making model for renewable energy planning: The case of Turkey," Renewable Energy, Elsevier, vol. 193(C), pages 484-504.
    14. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    16. Li, Yuan & Zhou, You & Yi, Bo-Wen & Wang, Ya, 2021. "Impacts of the coal resource tax on the electric power industry in China: A multi-regional comprehensive analysis," Resources Policy, Elsevier, vol. 70(C).
    17. Assadi, Mohammad Reza & Ataebi, Melikasadat & Ataebi, Elmira sadat & Hasani, Aliakbar, 2022. "Prioritization of renewable energy resources based on sustainable management approach using simultaneous evaluation of criteria and alternatives: A case study on Iran's electricity industry," Renewable Energy, Elsevier, vol. 181(C), pages 820-832.
    18. Wang, B. & Liu, L. & Huang, G.H. & Li, W. & Xie, Y.L., 2018. "Effects of carbon and environmental tax on power mix planning - A case study of Hebei Province, China," Energy, Elsevier, vol. 143(C), pages 645-657.
    19. Chen, Huadong & Wang, Can & Cai, Wenjia & Wang, Jianhui, 2018. "Simulating the impact of investment preference on low-carbon transition in power sector," Applied Energy, Elsevier, vol. 217(C), pages 440-455.
    20. Diankai Wang & Inna Gryshova & Anush Balian & Mykola Kyzym & Tetiana Salashenko & Viktoriia Khaustova & Olexandr Davidyuk, 2022. "Assessment of Power System Sustainability and Compromises between the Development Goals," Sustainability, MDPI, vol. 14(4), pages 1-23, February.

    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:jeners:v:15:y:2022:i:8:p:2797-:d:791414. 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.