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Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

2023-11-09

Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

基于充电特征与改进原子搜索算法优化反向传播神经网络的健康状态估计方法

Jinling Lu¹, Dingyue Huang¹, Hui Ren¹

1.Department of Electrical and Electronic Engineering, North China Electric Power University (Baoding),Baoding 071003, P. R. China

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Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

Abstract

A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed. The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems (IESs) in the operation scheduling problem of integrated energy production units (IEPUs). First,to solve the problem of inaccurate prediction of renewable energy output, an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction. Subsequently, to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs, a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established. The system considers the further utilization of energy using hydrogen energy coupling equipment (such as hydrogen storage devices and fuel cells) and the comprehensive demand response of load-side schedulable resources. The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions, improve the source-load interaction of the IES, realize the efficient use of hydrogen energy, and improve system robustness.

Keywords

Hydrogen energy coupling; Data-driven; Robust kernel density estimation; Robust optimization; Integrated demand response

Fig. 1 Composition of comprehensive energy system containing hydrogen energy

Fig. 2 Robust stochastic optimization model solution flowchart

Fig. 3 Fitting results of probability density distribution of KDE and VKDE

Fig. 4 Fitting results of probability density distribution of RKDE and IRKDE

Fig. 5 Power optimization scheduling results

Fig. 6 Thermal power optimization scheduling results

Fig. 7 Gas power optimization scheduling results

Fig. 8 Hydrogen power optimal scheduling results

Fig. 9 Difference in optimal output values between robust stochastic model and robust model when σ = 0.9

Fig. 10 Difference in optimal output values between robust stochastic model and robust model when σ = 1.1

本文引文信息

Lu JL, Huang DY, Ren H (2023) Data-driven source–load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling, Global Energy Interconnection, 6(4): 375-388

卢锦玲，黄鼎越，任惠 (2023) 基于数据驱动的氢能耦合综合能源生产单元源荷鲁棒优化调度. 全球能源互联网（英文）, 6(4): 375-388

Biographies

Jinling Lu

received bachelor’s degree at North China Electric Power University in 1993, Baoding; received master’s degree at North China Electric Power University in 1996, Baoding; received PhD at North China Electric Power University in 2009, Baoding. Now, she is working as an associate professor in North China Electric Power University, Baoding.

Dingyue Huang

received the bachelor’s degree from Northeast Electric Power University, Jilin, 2021. She is currently pursuing master’s degree in North China Electric Power University, Baoding. Her main research fields are Power system operation, analysis and control.

Tiezhou Wu

received bachelor’s degree at North China Electric Power University in 1994, Baoding; received master’s degree at North China Electric Power University in 1997, Baoding; received PhD at North China Electric Power University in 2009, Baoding. Now, she is working as a professor in North China Electric Power University, Baoding.

编辑：王彦博

审核：王伟

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