An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems

Su, Huai; Chi, Lixun; Zio, Enrico; Li, Zhenlin; Fan, Lin; Yang, Zhe; Liu, Zhe; Zhang, Jinjun

doi:10.1016/j.energy.2021.121416

Cited by 21 publications

(4 citation statements)

References 64 publications

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“…For illustrating purposes, the generic IES considered consists of an NPP (for baseload generation), two CCGT plants (for both baseload and peak regulation), a solar PV field, two WFs, a compression station (to overcome losses in the gas pipes) and a P2G station for energy storage (that can be switched off for simulating the CS layout). The system considered is plotted in Figure 2, adapting it from previous works, such as [23,24]. In particular, we assume that the IES mimics the positioning of a number of realistic plants based in central Italy between Lazio and Campania regions (Figure 3): for the NPP we assume the data of the Garigliano BWR nuclear reactor, CCGT are Napoli-levante and Teverola power plants, WF consists in fleets of Vesta V90 (2000 kW) turbines and solar PVs are fields of 1 kW PV panels with 35 • and 180 • of tilt and azimuth angles, respectively, summing up to 200 MW (Table 1), as a compromise of the results presented in [25].…”

Section: The Case Studymentioning

confidence: 99%

A Modeling and Analysis Framework for Integrated Energy Systems Exposed to Climate Change-Induced NaTech Accidental Scenarios

2022

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This paper proposes a novel framework for the analysis of integrated energy systems (IESs) exposed to both stochastic failures and “shock” climate-induced failures, such as those characterizing NaTech accidental scenarios. With such a framework, standard centralized systems (CS), IES with distributed generation (IES-DG) and IES with bidirectional energy conversion (IES+P2G) enabled by power-to-gas (P2G) facilities can be analyzed. The framework embeds the model of each single production plant in an integrated power-flow model and then couples it with a stochastic failures model and a climate-induced failure model, which simulates the occurrence of extreme weather events (e.g., flooding) driven by climate change. To illustrate how to operationalize the analysis in practice, a case study of a realistic IES has been considered that comprises two combined cycle gas turbine plants (CCGT), a nuclear power plant (NPP), two wind farms (WF), a solar photovoltaicS (PV) field and a power-to-gas station (P2G). Results suggest that the IESs are resilient to climate-induced failures.

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Section: The Case Studymentioning

confidence: 99%

A Modeling and Analysis Framework for Integrated Energy Systems Exposed to Climate Change-Induced NaTech Accidental Scenarios

2022

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“…By comparing the local power generation and consumption, the good performances on peakshaving and valley-filling have been highlighted [28]. In this paper percentile values were used to indicate the peak power from the power flow curve.…”

Section: Cluster Characteristicsmentioning

confidence: 99%

Archives of Electrical Engineering

Janík

Schwarz

et al. 2022

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This paper provides a method for simplified description of a regional power grid model aimed to deliver a grid reduction, and improve grid performance observability. The derived power grid model can be used to analyze the regional allocation of the decentralized energy generation and consumption. The expansion of wind and solar generation in the power system affects the residual load. The power balance between electricity consumption and generation was calculated and analyzed based on the temporal and spatial scales. The proposed grid clustering method is a useful approach for performance analysis in systems with a growing share of renewable generation.

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“…The simplified engineering scenario, as well as the self-adaptive deep neural network, was proved to be effective in the flux estimations. Other applications of data-driven techniques and deep learning algorithms in energy transportation systems have been reported in (Zheng et al, 2021) and (Su et al, 2021). In this paper, a more realistic engineering scenario will be proposed to mimic the practical dispatching process so as to suggest on the proper dispatching plans for maintaining or changing natural gas supply to the urban area.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control on Urban Natural Gas Supply Using a Deep-Learning-Assisted Pipeline Dispatch Technique

Zhang

Bai²,

Sun

2022

Front. Energy Res.

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Natural gas has been attracting increasing attentions all around the world as a relatively cleaner energy resource compared with coal and crude oil. Except for the direct consumption as fuel, electricity generation is now another environmentally-friendly utilization of natural gas, which makes it more favorable as the energy supply for urban areas. Pipeline transportation is the main approach connecting the natural gas production field and urban areas thanks to the safety and economic reasons. In this paper, an intelligent pipeline dispatch technique is proposed using deep learning methods to predict the change of energy supply to the urban areas as a consequence of compressor operations. Practical operation data is collected and prepared for the training and validation of deep learning models, and the accelerated predictions can help make controlling plans regarding compressor operations to meet the requirement in urban natural gas supply. The proposed deep neutral network is equipped with self-adaptability, which enables the general adaption on various temporal compressor conditions including failure and maintenance.

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An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems

Cited by 21 publications

References 64 publications

A Modeling and Analysis Framework for Integrated Energy Systems Exposed to Climate Change-Induced NaTech Accidental Scenarios

A Modeling and Analysis Framework for Integrated Energy Systems Exposed to Climate Change-Induced NaTech Accidental Scenarios

Archives of Electrical Engineering

Intelligent Control on Urban Natural Gas Supply Using a Deep-Learning-Assisted Pipeline Dispatch Technique

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