Resilience index system and comprehensive assessment method for distribution network considering multi-energy coordination

Abstract In the integrated energy system (IES), multi-energy coordination can provide power supply for distribution networks under extreme disturbances, yielding a positive impact on the resilience. This paper proposes a resilience assessment index system and its corresponding comprehensive assessment method for distribution networks considering multi-energy coordination. Firstly, a set of multi-dimensional resilience assessment indexes is selected based on the analysis of the resilience influence factors, in which multi-energy indexes are introduced to quantify the influence of multi-energy coordination. Secondly, a resilience assessment index system is established as the network hierarchical structure based on the analytic network process (ANP). The assessment index system can reflect the correlations among indexes, making it applicable for the resilience assessment. Then, an optimal weighting model is built to combine the subjective and objective factors, yielding more scientific comprehensive weights of indexes. Finally, the assessment results of distribution network resilience are obtained based on the fuzzy comprehensive assessment method, making the most of each index information. Case study shows the effectiveness of the proposed assessment index system and method in resilience assessment for distribution networks considering multi-energy coordination.

[1]  P. Hines,et al.  Large blackouts in North America: Historical trends and policy implications , 2009 .

[2]  Pierluigi Mancarella,et al.  Power System Resilience to Extreme Weather: Fragility Modeling, Probabilistic Impact Assessment, and Adaptation Measures , 2017, IEEE Transactions on Power Systems.

[3]  Ali Reza Seifi,et al.  An Integrated Steady-State Operation Assessment of Electrical, Natural Gas, and District Heating Networks , 2016, IEEE Transactions on Power Systems.

[4]  Jamal Moshtagh,et al.  Radial distribution systems reconfiguration considering power losses cost and damage cost due to power supply interruption of consumers , 2013 .

[5]  Ross Baldick,et al.  Research on Resilience of Power Systems Under Natural Disasters—A Review , 2016, IEEE Transactions on Power Systems.

[6]  Henrik Lund,et al.  Integrated energy systems and local energy markets , 2006 .

[7]  Qingxue Shang,et al.  Seismic resilience assessment of emergency departments based on the state tree method , 2020 .

[8]  Silvio Simani,et al.  Planning of a Resilient Underground Distribution Network Using Georeferenced Data , 2019, Energies.

[9]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[10]  Furong Li,et al.  Battling the Extreme: A Study on the Power System Resilience , 2017, Proceedings of the IEEE.

[11]  Pierluigi Mancarella,et al.  The Grid: Stronger, Bigger, Smarter?: Presenting a Conceptual Framework of Power System Resilience , 2015, IEEE Power and Energy Magazine.

[12]  Thomas L. Saaty,et al.  The Modern Science of Multicriteria Decision Making and Its Practical Applications: The AHP/ANP Approach , 2013, Oper. Res..

[13]  Chen-Ching Liu,et al.  Distribution Systems: Reliable But Not Resilient? [In My View] , 2015 .

[14]  Min Ouyang,et al.  A three-stage resilience analysis framework for urban infrastructure systems , 2012 .

[15]  Desheng Dash Wu,et al.  Simulation of fuzzy multiattribute models for grey relationships , 2006, Eur. J. Oper. Res..

[16]  Wen Zhang,et al.  Enhancing Distribution System Resilience to Extreme Weather Using Multi-Energy Coordination , 2019, 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[17]  Shan Feng,et al.  Decision support for fuzzy comprehensive evaluation of urban development , 1999, Fuzzy Sets Syst..

[18]  Tao Wang,et al.  Probability‐based seismic reliability assessment method for substation systems , 2018, Earthquake Engineering & Structural Dynamics.

[19]  Qingxue Shang,et al.  Probability-based seismic resilience assessment method for substation systems , 2020, Structure and Infrastructure Engineering.

[20]  Jian Ma,et al.  A subjective and objective integrated approach to determine attribute weights , 1999, Eur. J. Oper. Res..

[21]  Anurag K. Srivastava,et al.  Resilience of the electric distribution systems: concepts, classification, assessment, challenges, and research needs , 2020, IET Smart Grid.

[22]  Esteban Inga,et al.  A Decision-Making Tool for Electric Distribution Network Planning Based on Heuristics and Georeferenced Data , 2019, Energies.

[23]  T. Saaty.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[24]  Prabodh Bajpai,et al.  A Novel Metric to Quantify and Enable Resilient Distribution System Using Graph Theory and Choquet Integral , 2018, IEEE Transactions on Smart Grid.

[25]  Dan Wang,et al.  Review of key problems related to integrated energy distribution systems , 2018, CSEE Journal of Power and Energy Systems.

[26]  José Luis Rueda Torres,et al.  Cyber-Physical System Modeling for Assessment and Enhancement of Power Grid Cyber Security, Resilience, and Reliability , 2020 .

[27]  Hao Zhou,et al.  Evaluation of Black-Start Schemes Employing Entropy Weight-Based Decision-Making Theory , 2010 .

[28]  K. C. Kapur,et al.  Methodology for Assessing the Resilience of Networked Infrastructure , 2009, IEEE Systems Journal.

[29]  Xiao-Bang Xu,et al.  Determination of maximum span length of utility lines based on consideration of eliminating magnetically induced subsequent fault , 2004 .