Optimal Dispatch of Integrated Energy System Based on Flexibility of Thermal Load

doi:10.16183/j.cnki.jsjtu.2021.534

Abstract

Abstract:

The flexibility of thermal loads of buildings is a valuable balancing resource for operation of the heat and electricity integrated energy system (HE-IES). Considering the characteristics of large scale and small single load capacity of the themal load, the non-intrusive data-driven method has become an effective means to quantify the flexibility of building thermal load. However, due to the inaccuracy of the model or the lack of data, this method inevitably produces errors and brings epistemic uncertainty to the optimal dispatch of the HE-IES. An optimal dispatch model of the HE-IES that is compatible with the epistemic uncertainty of demand flexibility in the thermal loads of buildings is proposed. First, a data-driven flexible demand assessment method for building thermal load is described. The measurement errors are modeled as epistemic uncertainty and the multiple error sources are combined by using the D-S evidence theory. Then, the representative scenarios are selected to represent the epistemic uncertainty of the demand flexibility based Latin hypercube sampling(LHS) method, and the scenarios are reduced by the fuzzy clustering method. Finally, the representative scenarios are embedded in the coordinated and optimized dispatch of the HE-IES to realize the comprehensive consideration of the thermal load flexibility and related epistemic uncertainty of the building. The results demonstrate that considering the epistemic uncertainties of the thermal load demand is crucial for reducing the wind power curtailments and improving the operational flexibility of HE-IES.

Key words: demand flexibility, epistemic uncertainty, heat and electricity integrated energy system(HE-IES), optimal dispatch

CLC Number:

TM73

HU Bo, CHENG Xin, SHAO Changzheng, HUANG Wei, SUN Yue, XIE Kaigui. Optimal Dispatch of Integrated Energy System Based on Flexibility of Thermal Load[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 803-813.

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参数	误差来源(误差数量级)
k_p_,_r_,_t	提取过程(10^-3)
	训练过程(10^-2)
C_p_,_r_,_t	提取过程(10^-6)
	训练过程(10^-3)