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Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target

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  • Li, Jinghua
  • Luo, Yichen
  • Wei, Shanyang

Abstract

Currently, system dynamics (SDs) is thought to be an effective method to forecast the evolution process of electricity consumption, which is of great significance for the long-term planning of the power system. However, for the traditional SDs forecast model, the interactions between system variables are not considered, resulting in that it may be invalid in the carbon-neutral situation where the inter-relationship between system variables is complex. Moreover, the electricity consumption of various industries is only simply superimposed in the traditional SDs model. This also leads to that the changes of future electricity consumption cannot be understood and grasped as a whole. Thus, a long-term load forecasting method involving carbon-neutral situation is proposed in the current work. First, a new system of influencing factors for electricity consumption containing carbon-neutral factors is constructed. Then, based on the information feedback mechanism, a new SDs forecast model is established, which can more accurately reflect the interaction of system variables. In addition, an electricity consumption identity is also developed to reveal the quantitative relationship between factors, electricity consumption, and carbon emissions at a holistic level. Finally, the evolution process of electricity consumption under various carbon-neutral scenarios is forecasted based on the new established forecast model. Correspondingly, the forecast results are expected to guide power system planning in the future carbon-neutral situation.

Suggested Citation

  • Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).
  • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028218
    DOI: 10.1016/j.energy.2021.122572
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    as
    1. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    2. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    3. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    4. Kazemzadeh, Mohammad-Rasool & Amjadian, Ali & Amraee, Turaj, 2020. "A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting," Energy, Elsevier, vol. 204(C).
    5. Du, Kerui & Cheng, Yuanyuan & Yao, Xin, 2021. "Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities," Energy Economics, Elsevier, vol. 98(C).
    6. Ghasemi, A. & Shayeghi, H. & Moradzadeh, M. & Nooshyar, M., 2016. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management," Applied Energy, Elsevier, vol. 177(C), pages 40-59.
    7. Dehghan, Hamed & Amin-Naseri, Mohammad Reza & Nahavandi, Nasim, 2021. "A system dynamics model to analyze future electricity supply and demand in Iran under alternative pricing policies," Utilities Policy, Elsevier, vol. 69(C).
    8. Qu, Hui & Chen, Wei & Niu, Mengyi & Li, Xindan, 2016. "Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models," Energy Economics, Elsevier, vol. 54(C), pages 68-76.
    9. Wang, Jianjun & Li, Li, 2016. "Sustainable energy development scenario forecasting and energy saving policy analysis of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 718-724.
    10. Zhao, Ning & You, Fengqi, 2020. "Can renewable generation, energy storage and energy efficient technologies enable carbon neutral energy transition?," Applied Energy, Elsevier, vol. 279(C).
    11. Song, Malin & Wang, Shuhong & Yu, Huayin & Yang, Li & Wu, Jie, 2011. "To reduce energy consumption and to maintain rapid economic growth: Analysis of the condition in China based on expended IPAT model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 5129-5134.
    12. Xie, Wanli & Wu, Wen-Ze & Liu, Chong & Zhao, Jingjie, 2020. "Forecasting annual electricity consumption in China by employing a conformable fractional grey model in opposite direction," Energy, Elsevier, vol. 202(C).
    13. Gutiérrez, R. & Gutiérrez-Sánchez, R. & Nafidi, A., 2006. "Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors," Applied Energy, Elsevier, vol. 83(10), pages 1139-1151, October.
    14. Carvallo, Juan Pablo & Larsen, Peter H. & Sanstad, Alan H. & Goldman, Charles A., 2018. "Long term load forecasting accuracy in electric utility integrated resource planning," Energy Policy, Elsevier, vol. 119(C), pages 410-422.
    15. Meng, Ming & Wang, Lixue & Shang, Wei, 2018. "Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models," Energy, Elsevier, vol. 165(PA), pages 143-152.
    16. He, Yongxiu & Jiao, Jie & Chen, Qian & Ge, Sifan & Chang, Yan & Xu, Yang, 2017. "Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin," Energy, Elsevier, vol. 133(C), pages 9-22.
    17. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    18. Günay, M. Erdem, 2016. "Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey," Energy Policy, Elsevier, vol. 90(C), pages 92-101.
    19. Mohammed, Nooriya A., 2018. "Modelling of unsuppressed electrical demand forecasting in Iraq for long term," Energy, Elsevier, vol. 162(C), pages 354-363.
    20. Xu, Bin & Lin, Boqiang, 2018. "Assessing the development of China's new energy industry," Energy Economics, Elsevier, vol. 70(C), pages 116-131.
    21. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
    22. De Felice, Matteo & Alessandri, Andrea & Catalano, Franco, 2015. "Seasonal climate forecasts for medium-term electricity demand forecasting," Applied Energy, Elsevier, vol. 137(C), pages 435-444.
    23. Tang, Lei & Wang, Xifan & Wang, Xiuli & Shao, Chengcheng & Liu, Shiyu & Tian, Shijun, 2019. "Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory," Energy, Elsevier, vol. 167(C), pages 1144-1154.
    24. Bilgili, Faik & Koçak, Emrah & Bulut, Ümit & Kuloğlu, Ayhan, 2017. "The impact of urbanization on energy intensity: Panel data evidence considering cross-sectional dependence and heterogeneity," Energy, Elsevier, vol. 133(C), pages 242-256.
    Full references (including those not matched with items on IDEAS)

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