IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3268-d568182.html
   My bibliography  Save this article

Energy–Water–CO 2 Synergetic Optimization Based on a Mixed-Integer Linear Resource Planning Model Concerning the Demand Side Management in Beijing’s Power Structure Transformation

Author

Listed:
  • Yuan Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Qinliang Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of Renewable Electric Power and Low Carbon Development, North China Electric Power University, Beijing 102206, China
    Research Center for Beijing Energy Development, North China Electric Power University, Beijing 102206, China)

  • Jian Han

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Mingxin Guo

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Studies on the energy–water–CO 2 synergetic relationship is an effective way to help achieve the peak CO 2 emission target and carbon neutral goal in global countries. One of the most valid way is to adjust through the electric power structure transformation. In this study, a mixed-integer linear resource planning model is proposed to investigate the energy–water–CO 2 synergetic optimization relationship, concerning the uncertainties in the fuel price and power demand prediction process. Coupled with multiple CO 2 emissions and water policy scenarios, Beijing, the capital city of China, is chosen as a case study. Results indicate that the demand-side management (DSM) level and the stricter environmental constraints can effectively push Beijing’s power supply system in a much cleaner direction. The energy–water–CO 2 relationship will reach a better balance under stricter environmental constraints and higher DSM level. However, the achievement of the energy–water–CO 2 synergetic optimization will be at an expense of high system cost. Decision makers should adjust their strategies flexibly based on the practical planning situations.

Suggested Citation

  • Yuan Liu & Qinliang Tan & Jian Han & Mingxin Guo, 2021. "Energy–Water–CO 2 Synergetic Optimization Based on a Mixed-Integer Linear Resource Planning Model Concerning the Demand Side Management in Beijing’s Power Structure Transformation," Energies, MDPI, vol. 14(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3268-:d:568182
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3268/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/11/3268/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Myers, Stewart C & Turnbull, Stuart M, 1977. "Capital Budgeting and the Capital Asset Pricing Model: Good News and Bad News," Journal of Finance, American Finance Association, vol. 32(2), pages 321-333, May.
    2. Dahlbeck, Mirko, 2021. "A mixed-integer linear programming approach for the T-row and the multi-bay facility layout problem," European Journal of Operational Research, Elsevier, vol. 295(2), pages 443-462.
    3. Zhao, Xiaoli & Yao, Jin & Sun, Chuyu & Pan, Wengeng, 2019. "Impacts of carbon tax and tradable permits on wind power investment in China," Renewable Energy, Elsevier, vol. 135(C), pages 1386-1399.
    4. Chinese, Damiana & Santin, Maurizio & Saro, Onorio, 2017. "Water-energy and GHG nexus assessment of alternative heat recovery options in industry: A case study on electric steelmaking in Europe," Energy, Elsevier, vol. 141(C), pages 2670-2687.
    5. Zhu, Lei & Fan, Ying, 2010. "Optimization of China's generating portfolio and policy implications based on portfolio theory," Energy, Elsevier, vol. 35(3), pages 1391-1402.
    6. Chai, Li & Liao, Xiawei & Yang, Liu & Yan, Xianglin, 2018. "Assessing life cycle water use and pollution of coal-fired power generation in China using input-output analysis," Applied Energy, Elsevier, vol. 231(C), pages 951-958.
    7. Liu, Yuan & He, Li & Shen, Jing, 2017. "Optimization-based provincial hybrid renewable and non-renewable energy planning – A case study of Shanxi, China," Energy, Elsevier, vol. 128(C), pages 839-856.
    8. Leonard, J. Mark & Decker, Christopher S., 2012. "Determinants of voluntary electricity demand management program participation," Utilities Policy, Elsevier, vol. 20(1), pages 17-21.
    9. Saeed Esfandi & Simin Baloochzadeh & Mohammad Asayesh & Mehdi Ali Ehyaei & Abolfazl Ahmadi & Amir Arsalan Rabanian & Biplab Das & Vitor A. F. Costa & Afshin Davarpanah, 2020. "Energy, Exergy, Economic, and Exergoenvironmental Analyses of a Novel Hybrid System to Produce Electricity, Cooling, and Syngas," Energies, MDPI, vol. 13(23), pages 1-27, December.
    10. Yuan Liu & Qinliang Tan & Jian Han & Mingxin Guo, 2021. "Energy-Water-Carbon Nexus Optimization for the Path of Achieving Carbon Emission Peak in China Considering Multiple Uncertainties: A Case Study in Inner Mongolia," Energies, MDPI, vol. 14(4), pages 1-21, February.
    11. Elshkaki, Ayman, 2019. "Material-energy-water-carbon nexus in China’s electricity generation system up to 2050," Energy, Elsevier, vol. 189(C).
    12. Bin Ye & Minhua Zhou & Dan Yan & Yin Li, 2020. "Multi-Objective Decision-Making for Hybrid Renewable Energy Systems for Cities: A Case Study of Xiongan New District in China," Energies, MDPI, vol. 13(23), pages 1-25, November.
    13. Kuriqi, Alban & Pinheiro, António N. & Sordo-Ward, Alvaro & Bejarano, María D. & Garrote, Luis, 2021. "Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    14. Angineh Zohrabian & Kelly T. Sanders, 2020. "The Energy Trade-Offs of Transitioning to a Locally Sourced Water Supply Portfolio in the City of Los Angeles," Energies, MDPI, vol. 13(21), pages 1-19, October.
    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. Ju, Liwei & Tan, Qinliang & Lin, Hongyu & Mei, Shufang & Li, Nan & Lu, Yan & Wang, Yao, 2020. "A two-stage optimal coordinated scheduling strategy for micro energy grid integrating intermittent renewable energy sources considering multi-energy flexible conversion," Energy, Elsevier, vol. 196(C).
    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. Inzunza, Andrés & Muñoz, Francisco D. & Moreno, Rodrigo, 2021. "Measuring the effects of environmental policies on electricity markets risk," Energy Economics, Elsevier, vol. 102(C).
    2. Paulino Martinez-Fernandez & Fernando deLlano-Paz & Anxo Calvo-Silvosa & Isabel Soares, 2019. "Assessing Renewable Energy Sources for Electricity (RES-E) Potential Using a CAPM-Analogous Multi-Stage Model," Energies, MDPI, vol. 12(19), pages 1-20, September.
    3. Zhao, Xiaoli & Yao, Jin & Sun, Chuyu & Pan, Wengeng, 2019. "Impacts of carbon tax and tradable permits on wind power investment in China," Renewable Energy, Elsevier, vol. 135(C), pages 1386-1399.
    4. Hao Li & Yuhuan Zhao & Jiang Lin, 2020. "A review of the energy–carbon–water nexus: Concepts, research focuses, mechanisms, and methodologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(1), January.
    5. Zhu, Lei & Fan, Ying, 2011. "A real options–based CCS investment evaluation model: Case study of China’s power generation sector," Applied Energy, Elsevier, vol. 88(12), pages 4320-4333.
    6. Hung-Ta Wen & Jau-Huai Lu & Mai-Xuan Phuc, 2021. "Applying Artificial Intelligence to Predict the Composition of Syngas Using Rice Husks: A Comparison of Artificial Neural Networks and Gradient Boosting Regression," Energies, MDPI, vol. 14(10), pages 1-18, May.
    7. Migo-Sumagang, Maria Victoria & Tan, Raymond R. & Aviso, Kathleen B., 2023. "A multi-period model for optimizing negative emission technology portfolios with economic and carbon value discount rates," Energy, Elsevier, vol. 275(C).
    8. Agnieszka Operacz, 2021. "Possibility of Hydropower Development: A Simple-to-Use Index," Energies, MDPI, vol. 14(10), pages 1-19, May.
    9. M. Jenabi & S. M. T. Fatemi Ghomi & S. A. Torabi & Moeen Sammak Jalali, 2022. "An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1304-1336, December.
    10. Berna Tektas Sivrikaya & Ferhan Cebi & Hasan Hüseyin Turan & Nihat Kasap & Dursun Delen, 2017. "A fuzzy long-term investment planning model for a GenCo in a hybrid electricity market considering climate change impacts," Information Systems Frontiers, Springer, vol. 19(5), pages 975-991, October.
    11. H. Qi & C. K. Woo & K. H. Cao & J. Zarnikau & R. Li, 2024. "Price responsiveness of solar and wind capacity demands," Post-Print hal-04597188, HAL.
    12. Yuan, Peng & Pu, Yuran & Liu, Chang, 2021. "Improving electricity supply reliability in China: Cost and incentive regulation," Energy, Elsevier, vol. 237(C).
    13. Yuqi Su & Yi Liang & Li Chai & Zixuan Han & Sai Ma & Jiaxuan Lyu & Zhiping Li & Liu Yang, 2019. "Water Degradation by China’s Fossil Fuels Production: A Life Cycle Assessment Based on an Input–Output Model," Sustainability, MDPI, vol. 11(15), pages 1-12, July.
    14. Reza Nadimi & Masahito Takahashi & Koji Tokimatsu & Mika Goto, 2024. "The Reliability and Profitability of Virtual Power Plant with Short-Term Power Market Trading and Non-Spinning Reserve Diesel Generator," Energies, MDPI, vol. 17(9), pages 1-19, April.
    15. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    16. Dylan Sheneth Edirisinghe & Ho-Seong Yang & Min-Sung Kim & Byung-Ha Kim & Sudath Prasanna Gunawardane & Young-Ho Lee, 2021. "Computational Flow Analysis on a Real Scale Run-of-River Archimedes Screw Turbine with a High Incline Angle," Energies, MDPI, vol. 14(11), pages 1-18, June.
    17. Cui, Qi & He, Ling & Han, Guoyi & Chen, Hao & Cao, Juanjuan, 2020. "Review on climate and water resource implications of reducing renewable power curtailment in China: A nexus perspective," Applied Energy, Elsevier, vol. 267(C).
    18. De Jonghe, C. & Hobbs, B. F. & Belmans, R., 2011. "Integrating short-term demand response into long-term investment planning," Cambridge Working Papers in Economics 1132, Faculty of Economics, University of Cambridge.
    19. Huo, Zhihong & Xu, Chang, 2022. "Distributed cooperative automatic generation control and multi-event triggered mechanisms co-design for networked wind-integrated power systems," Renewable Energy, Elsevier, vol. 193(C), pages 41-56.
    20. Shen, Jian-jian & Cheng, Chun-tian & Jia, Ze-bin & Zhang, Yang & Lv, Quan & Cai, Hua-xiang & Wang, Bang-can & Xie, Meng-fei, 2022. "Impacts, challenges and suggestions of the electricity market for hydro-dominated power systems in China," Renewable Energy, Elsevier, vol. 187(C), pages 743-759.

    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:14:y:2021:i:11:p:3268-:d:568182. 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.