IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p1112-d741036.html
   My bibliography  Save this article

An Investment Decision-Making Approach for Power Grid Projects: A Multi-Objective Optimization Model

Author

Listed:
  • Lei Gao

    (School of Economics and Management, Institute of Disaster Prevention, Sanhe 065201, China)

  • Zhen-Yu Zhao

    (Beijing Key Laboratory of New Energy and Low-Carbon Development, School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Cui Li

    (School of Economics and Management, Institute of Disaster Prevention, Sanhe 065201, China)

Abstract

With the reform of the power system in China, investments in power grid projects across the whole power system are increasing. However, there are various objectives to achieve in the investment decision processes of power grid projects, so the rational investments of a grid project can be seen as a multi-objective optimization problem. Meanwhile, these issues have rarely been studied at home and abroad, and this paper will fill this gap. As a result, this study critically analyzed the application of a multi-objective optimization model to power grid investment. Firstly, the objective factors of grid investments were explored, which were quantified through quantitative methods. Secondly, based on the characteristics of power grid investment, a multi-objective optimization model was established, and the assumptions and constraints of the model were presented. Finally, NSGA-II was used for solving the multi-objective optimization model. The results show that: (1) Multi-objective optimization models are suitable for the study of and deriving solutions for power grid investment by establishing suitable objective functions, assumptions and constraints, (2) According to the conventional steps of NSGA-II, suitable steps can be established to search for an optimal solution to the objective set of a power grid investment and (3) Due to the different concerns of different project scenarios, Pareto frontier solutions can be selected as the practical references of power grid projects. Therefore, the solution set makes the implementation scheme more flexible.

Suggested Citation

  • Lei Gao & Zhen-Yu Zhao & Cui Li, 2022. "An Investment Decision-Making Approach for Power Grid Projects: A Multi-Objective Optimization Model," Energies, MDPI, vol. 15(3), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1112-:d:741036
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/1112/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/1112/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schachter, J.A. & Mancarella, P., 2016. "A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 261-271.
    2. He, Y.X. & Jiao, J. & Chen, R.J. & Shu, H., 2018. "The optimization of Chinese power grid investment based on transmission and distribution tariff policy: A system dynamics approach," Energy Policy, Elsevier, vol. 113(C), pages 112-122.
    3. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    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. Wu, Jiansong & Zhang, Linlin & Bai, Yiping & Reniers, Genserik, 2022. "A safety investment optimization model for power grid enterprises based on System Dynamics and Bayesian network theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Darya Pyatkina & Tamara Shcherbina & Vadim Samusenkov & Irina Razinkina & Mariusz Sroka, 2021. "Modeling and Management of Power Supply Enterprises’ Cash Flows," Energies, MDPI, vol. 14(4), pages 1-17, February.
    3. Liu, Dunnan & Xiao, Bowen, 2018. "Exploring the development of electric vehicles under policy incentives: A scenario-based system dynamics model," Energy Policy, Elsevier, vol. 120(C), pages 8-23.
    4. Ye, Rui-Ke & Gao, Zhuang-Fei & Fang, Kai & Liu, Kang-Li & Chen, Jia-Wei, 2021. "Moving from subsidy stimulation to endogenous development: A system dynamics analysis of China's NEVs in the post-subsidy era," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    5. Wang, Yongli & Zhou, Minhan & Zhang, Fuli & Zhang, Yuli & Ma, Yuze & Dong, Huanran & Zhang, Danyang & Liu, Lin, 2021. "Chinese grid investment based on transmission and distribution tariff policy: An optimal coordination between capacity and demand," Energy, Elsevier, vol. 219(C).
    6. Hu, Yu & Chi, Yuanying & Zhao, Hao & Zhou, Wenbing, 2022. "The development of renewable energy industry under renewable portfolio standards: From the perspective of provincial resource differences," Energy Policy, Elsevier, vol. 170(C).
    7. Izzet Alp Gul & Gülgün Kayakutlu & M. Özgür Kayalica, 2020. "Risk Analysis in Renewable Energy System (RES) Investment for a Developing Country: A Case Study in Pakistan," Arthaniti: Journal of Economic Theory and Practice, , vol. 19(2), pages 204-223, December.
    8. Yilmaz, S. & Rinaldi, A. & Patel, M.K., 2020. "DSM interactions: What is the impact of appliance energy efficiency measures on the demand response (peak load management)?," Energy Policy, Elsevier, vol. 139(C).
    9. Dmitry Borisoglebsky & Liz Varga, 2019. "A Resilience Toolbox and Research Design for Black Sky Hazards to Power Grids," Complexity, Hindawi, vol. 2019, pages 1-15, June.
    10. Sen Guo & Wenyue Zhang & Xiao Gao, 2020. "Business Risk Evaluation of Electricity Retail Company in China Using a Hybrid MCDM Method," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    11. Xu, Xiaomin & Niu, Dongxiao & Xiao, Bowen & Guo, Xiaodan & Zhang, Lihui & Wang, Keke, 2020. "Policy analysis for grid parity of wind power generation in China," Energy Policy, Elsevier, vol. 138(C).
    12. Wu, Zhongqun & Zheng, Ruijin, 2022. "Research on the impact of financial transmission rights on transmission expansion: A system dynamics model," Energy, Elsevier, vol. 239(PA).
    13. Schreiner, Lena & Madlener, Reinhard, 2021. "A pathway to green growth? Macroeconomic impacts of power grid infrastructure investments in Germany," Energy Policy, Elsevier, vol. 156(C).
    14. Esmaieli, M. & Ahmadian, M., 2018. "The effect of research and development incentive on wind power investment, a system dynamics approach," Renewable Energy, Elsevier, vol. 126(C), pages 765-773.
    15. Wang, Yongli & Zhang, Fuli & Zhang, Yuanyuan & Wang, Xiaohai & Fan, Lisha & Song, Fuhao & Ma, Yuze & Wang, Shuo, 2019. "Chinese power-grid financial capacity based on transmission and distribution tariff policy: A system dynamics approach," Utilities Policy, Elsevier, vol. 60(C), pages 1-1.
    16. Birong Huang & Zilong Wang & Yuan Gu, 2023. "ESG Investment Scale Allocation of China’s Power Grid Company Using System Dynamics Simulation Modeling," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
    17. Jingqi Sun & Nuermaimaiti Ruze & Jianjun Zhang & Haoran Zhao & Boyang Shen, 2019. "Evaluating the Investment Efficiency of China’s Provincial Power Grid Enterprises under New Electricity Market Reform: Empirical Evidence Based on Three-Stage DEA Model," Energies, MDPI, vol. 12(18), pages 1-17, September.
    18. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    19. Yang, Lin & Pang, Shujiang & Wang, Xiaoyan & Du, Yi & Huang, Jieyu & Melching, Charles S., 2021. "Optimal allocation of best management practices based on receiving water capacity constraints," Agricultural Water Management, Elsevier, vol. 258(C).
    20. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).

    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:3:p:1112-:d:741036. 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.