Distribution Power System Resiliency Improvement Using Distributed Generation and Automated Switching

Diahovchenko, Illia; Kandaperumal, Gowtham; Srivastava, Anurag K.

doi:10.1109/ess.2019.8764185

Cited by 22 publications

(12 citation statements)

References 10 publications

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“…Operational practice guidelines and decision‐making tools help the operator to apply the best plan to minimise the impact of an event, but mainly for known and analysed events. Effective proactive strategies with quantified measures in terms of resiliency metric can lead to substantial improvement in resilient power distribution grids [ 116 , 117 , 118 , 119 , 120 ]. Training for extreme events will be important.…”

Section: Solutions For Minimising Impact Of Covid‐19mentioning

confidence: 99%

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Noorazar

Srivastava

Pannala

et al. 2021

The Journal of Engineering

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Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID‐19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi‐fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML‐driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID‐19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID‐19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID‐19 pandemic.

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Section: Solutions For Minimising Impact Of Covid‐19mentioning

confidence: 99%

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Noorazar

Srivastava

Pannala

et al. 2021

The Journal of Engineering

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show abstract

“…In [24], some metrics for evaluating resiliency of an industrial control system based on the time of occurrence or elimination of the accident and the amount of loss of normal and critical loads is presented. The structures of resources, branches, and switches with some factors have been used in a resilience vector with the aim of utilising distribution energy and automated switches to enhance the resilience in the distribution networks [25]. Similarly, other methods and criteria including the vector of effective components of the distribution system in the resiliency vector employ factors such as betweenness centrality or critical fraction [1].…”

Section: Nomenclaturementioning

confidence: 99%

“…It should be noted that OSI a and CFI a can be directly calculated using (14) and (25), but because equations such as bgt a and tclc a are needed in other calculations (such as BDI a and AOCLI a ), these equations were calculated first.…”

Section: Aocli = Total Capacity Of Generation Units Total Capacity Ofmentioning

confidence: 99%

Metrics for assessing the resiliency in generation systems

Ghorbani¹,

Sheisi²,

Aghaei³

2020

IET Generation, Transmission & Distribution

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“…The IEEE 33-bus radial distribution system (see Fig. 1) was chosen for this study [13]. The voltage amplitude at the head bus of the system was adjusted in accordance with the Ukrainian rated value for rural distribution networks, which is 10.5 kV.…”

Section: Optimal Allocation Of a Single Grid-connected Dg Unitmentioning

confidence: 99%

“…Their advantages are modular size, low emission, low operation, and maintenance cost [15]. Given this MTs are selected to supply the rest 25% of consumers, which are the critical loads (CLs), where continuous and uninterrupted power delivery are crucial to maintain a high power system reliability and resiliency [13].…”

Section: Selection Of a Distributed Generation Sourcementioning

confidence: 99%