A framework of model predictive control for the safety analysis of an electric power microgrid

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A method is developed to analyze vulnerability of gas pipeline networks.  Supply capacity, gas grid performance and demand change are considered.  Indices are developed to measure gas grid vulnerability and pipeline criticality.  Demand site robustness is analyzed by statistics and topology properties.  The application of the method is studied in a complex natural gas pipeline network.

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“…system dynamic methods, control theory, agent-based modeling, etc. help capturing the dynamic properties of systems [30]- [35].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

A systematic framework of vulnerability analysis of a natural gas pipeline network

Zio

Zhang

et al. 2018

Reliability Engineering & System Safety

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“…In this paper, network structural controllability theory (Leitold et al, 2017) is applied to identify the elements which can control the overall gas pipeline network. This method has been used in real-world complex network systems including gas pipeline networks (Han et al, 2015). The results of the applications show the capability of effectively identifying the driver nodes for the system structural control.…”

Section: Data Preprocessing For Input To the Deep Learning Modelmentioning

confidence: 99%

“…In classical dynamic control theory, Kalman's rank condition can be used as criterion of controllability and to identify the minimum number of driver nodes (Kalman, 1963). However, the classical concept has difficulties when dealing with complex pipeline networks and new concepts of complexity are needed (Zio, 2016;Han et al, 2015): (1) the classical method is usually applied to undirected networks, but natural gas pipeline networks are directed; (2) to apply the Kalman's rank condition, we need to know the weights of all links (i.e. the elements in matrix A), which are usually unknown.…”

Section: Data Preprocessing For Input To the Deep Learning Modelmentioning

confidence: 99%

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Zio

Zhang

et al. 2018

Journal of Natural Gas Science and Engineering

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et al.. A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks. A B S T R A C TThe current framework of management of natural gas pipeline systems, based on off-line simulation, is facing challenges because of the increasing complexity, uncertainty and a number of time-dependent factors. To be effective, it requires comprehensive knowledge of system characteristics, accurate initial and boundary conditions. In an attempt to circumvent these problems, in this work we propose to use the deep learning method in the natural gas transmission system operation and management context. A data-driven prediction method is developed from real-time data of operation pressure and gas consumption. Specifically, the deep learning method is combined with the data window method and structural controllability theory to predict the conditions of gas pipeline network components. The data window method is applied to reconstruct the data structure and build a "memory" for the deep learning method. Structural controllability theory is applied to extract critical parameters, for reducing the problem size. The developed method allows accurate and efficient predictions, especially in abnormal conditions. For demonstration, the method is applied to a complex gas pipeline network. The results show that the developed method can provide accurate real-time predictions useful for reducing potential losses in operation, and perform efficient and effective management of the gas pipeline system. In the case study, the average prediction accuracy is higher than 0.99.

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“…30 Taking a system safety perspective, controllability is the ability to guide the system behavior toward a safe state through the appropriate manipulation of a few input variables. 11,31 From control theory, the system (as described by equation 1) is controllable if and only if its controllability matrix has full rank 32…”

Section: Controllability Indexmentioning

confidence: 99%

“…links and storage devices), which may change due to degradation, failure and other factor. 11 Various approaches for control and energy management of microgrids are reported in the literature. 12 In previous studies, [13][14][15][16] an agent-based modeling approach is proposed to model microgrids and to analyze by simulation the interactions between individual intelligent decision-makers.…”

Section: Introductionmentioning

confidence: 99%

Modeling an electric power microgrid by model predictive control for analyzing its characteristics from reliability, controllability and topological perspectives

Han

Zio

2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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Microgrids can be a key solution for integrating renewable and distributed energy resources. This article analyzes microgrids' characteristics adopting model predictive control. We study the microgrid performance under two operation modes: grid-connected and stand-alone. For each mode, we consider different faulty scenarios, and by dynamic simulations, we investigate the importance of the microgrid components from different perspectives: topological, reliability and controllability. This analysis enables evaluation of the microgrid performance and quantification of the importance of each component with respect to the different perspectives considered. The findings provide information for the design and operation of a microgrid, seeking the right balance of multiple characteristics.

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A framework of model predictive control for the safety analysis of an electric power microgrid

Cited by 3 publications

References 17 publications

A systematic framework of vulnerability analysis of a natural gas pipeline network

A systematic framework of vulnerability analysis of a natural gas pipeline network

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Modeling an electric power microgrid by model predictive control for analyzing its characteristics from reliability, controllability and topological perspectives

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