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Economic Planning of Energy System Equipment

Author

Listed:
  • Biao Li

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Tao Wang

    (Information Engineering School, Hangzhou Dianzi University, Hangzhou 311305, China)

  • Zhen Dong

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Qian Geng

    (State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China)

  • Yi Sun

    (State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China)

Abstract

The asset wall (AW) model is widely used by energy companies to forecast the retirement size of equipment. The AW model is a method of arranging historical data in chronological order and then using extrapolation to predict trends in asset size volumes over time. However, most studies using the AW model treat all equipment as a whole and perform a flat extrapolation mechanically, ignoring the impact of technological improvements and price fluctuations. Furthermore, there are relatively few studies on the assetization of equipment replacement scale. This paper fits a Weibull distribution density function and uses Monte Carlo stochastic simulation to determine the retirement age of each piece of equipment, reducing the ambiguity and randomness generated by the AW approach of treating all equipment as a whole. This modified model is noted in this paper as the Weibull–Monte Carlo stochastic simulation asset model wall (WMCAW). The paper then investigated the assetization of equipment replacement size, comparing the three error indicators Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) in order to select the appropriate optimization model for price forecasting from several combinations of models. Finally, the paper verified the feasibility of the WMCAW model using various types of equipment decommissioned in 1970 and compared the forecasting effects of AW and WMCAW. It is found that the curve of the equipment replacement scale predicted by WMCAW is smoother than that of AW, and the forecasting results are more stable and scientific.

Suggested Citation

  • Biao Li & Tao Wang & Zhen Dong & Qian Geng & Yi Sun, 2022. "Economic Planning of Energy System Equipment," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11464-:d:913774
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    References listed on IDEAS

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    1. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    2. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
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    1. Sala-Garrido, Ramon & Mocholi-Arce, Manuel & Maziotis, Alexandros & Molinos-Senante, María, 2023. "The carbon and production performance of water utilities: Evidence from the English and Welsh water industry," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 292-300.

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