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Stochastic optimisation of district integrated energy systems based on a hybrid probability forecasting model

Author

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  • Yan, Yi
  • Wang, Xuerui
  • Li, Ke
  • Li, Chengdong
  • Tian, Chongyi
  • Shao, Zhuliang
  • Li, Ji

Abstract

Day-ahead scheduling optimisation plays a significant role in solving stable operation problems of district integrated energy systems (DIESs) by carrying out scheduling optimisation of key equipment of the system based on forecasting data. However, the stable operation of DIESs is affected by multiple uncertainties from source-load. Day-ahead scheduling optimisation method is designed in this study for DIESs based on “prediction-optimisation” to reduce the impact of source-load uncertainty on the stable operation of system. Considering multidimensional source-load data, hybrid probability forecasting model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-Long Short-Term Memory (LSTM)-Gaussian Process Quantile Regression (GPQR) is introduced to quantify the uncertainty using prediction interval. K-means method is used to generate typical scenarios in optimisation step and these scenarios are fed into stochastic optimisation to obtain day-ahead scheduling optimisation results. The experimental results in case study show that hybrid probability prediction model exhibits good prediction performance, while the day-ahead scheduling optimisation based on stochastic optimisation for system's key equipment has the smallest error in typical summer and winter days compared with the real situation, which is 0.018 and 0.0176, respectively. The “prediction-optimisation” approach proposed in this paper shows significant advantages for improving the performance of day-ahead scheduling optimisation of DIESs.

Suggested Citation

  • Yan, Yi & Wang, Xuerui & Li, Ke & Li, Chengdong & Tian, Chongyi & Shao, Zhuliang & Li, Ji, 2024. "Stochastic optimisation of district integrated energy systems based on a hybrid probability forecasting model," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224022606
    DOI: 10.1016/j.energy.2024.132486
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