Calculating power distribution system reliability indexes from Smart Meter data

Kuhi, Kristjan; Korbe, Kati; Koppel, Ott; Palu, Ivo

doi:10.1109/energycon.2016.7513905

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…C10. SG failures fault status detection [41], [46], [61], [62], [126], [127], [142], [176], fault type classification [197], power distribution reliability [149], [195] As it can be seen, there is large variability in the aspects covered by the research. Themes that are covered by more articles are consumption prediction (69 papers), load profile clustering (19), forecast renewable power sources (19), false data injection attacks (14), consumption clustering (12), power quality disturbances classification (11), and power data compression (11).…”

Section: Sms Resultsmentioning

confidence: 99%

“…Security Intrusion detection ( [8], [15], [52], [74], [76], [77], [143], [148], [209]),false data injection attacks ( [13], [16], [17], [31], [100], [119], [151], [163], [168], [184], [185], [230], [251], [257]), energy theft ( [54], [99], [169], [191], [254], [258]), distinguishing cyber-attacks from physical faults ( [18]) C10. SG failures fault status detection [41], [46], [61], [62], [126], [127], [142], [176], fault type classification [197], power distribution reliability [149], [195] As it can be seen, there is large variability in the aspects covered by the research. Themes that are covered by more articles are consumption prediction (69 papers), load profile clustering (19), forecast renewable power sources (19), false data injection attacks (14), consumption clustering (...…”

Section: B Rq2mentioning

confidence: 99%

See 1 more Smart Citation

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Section: B Rq2mentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…Taking into account this saving will improve rate of return of the projects and enhance customer contributions into DR programs. Smart meters are devices that allow information such as energy consumption measurements of appliances, load profiles, time-of-use tariffs, interruption events, voltage levels, phase loss, and asymmetry to be communicated to end-consumers of electricity [41]. With this newfound knowledge, consumers can now respond to power signals and make smarter energy consumption decisions, thus becoming active participants in the power market [15].…”

Section: Demand Response Programsmentioning

confidence: 99%

“…Thus, DR aggregators provide the power system with a means to captivate available energy capacities that as singular parts may not have been realized or deemed valuable enough to enter into the market [15]. This is exceptionally useful for operators who need to secure extra system capacity due to rising levels of renewable energy penetration on the Smart meters are devices that allow information such as energy consumption measurements of appliances, load profiles, time-of-use tariffs, interruption events, voltage levels, phase loss, and asymmetry to be communicated to end-consumers of electricity [41]. With this newfound knowledge, consumers can now respond to power signals and make smarter energy consumption decisions, thus becoming active participants in the power market [15].…”

Section: Demand Response Aggregatormentioning

confidence: 99%

Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities, and Threats

et al. 2018

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The world is progressing towards a more advanced society where end-consumers have access to local renewable-based generation and advanced forms of information and technology. Hence, it is in a current state of transition between the traditional approach to power generation and distribution, where end-consumers of electricity have typically been inactive in their involvement with energy markets and a new approach that integrates their active participation. This new approach includes the use of distributed energy resources (DER) such as renewable-based generation and demand response (DR), which are being rapidly adopted by end-consumers where incentives are strong. This paper presents the role of the DR aggregator to effectively integrate DER technology as a new source of energy capacity into electricity networks using information communication technology and industry knowledge., Based on DR aggregators, this framework will efficiently facilitate renewable energy integration and customer engagement into the electricity market. To this aim, advantages and disadvantages of DR aggregators are discussed in this paper from political, economic, social, and technological (PEST) points of view. Based on this analysis, a strengths, weaknesses, opportunities, and threats (SWOT) analysis for a typical DR aggregator is presented.

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“…This has limited consumer awareness to the fact that electricity price changes with time, and hence has prevented them from being able to make informed energy decisions, however, this is changing with the roll-out of smart meters [21]. voltage levels, phase loss and asymmetry to be communicated to end-consumers of electricity [22]. With this new found knowledge consumers can now respond to power signals and make smarter energy consumption decisions, thus becoming active participants in the power market [8].…”

Section: Demand Response Programsmentioning

confidence: 99%

Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities and Threats

Ponds¹,

Arefi²,

Sayigh³

et al. 2018

Preprint

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The world is progressing towards a more advanced society where end-consumers have access to local renewable-based generation and advanced forms of information and technology. Hence, it is in a current state of transition between the traditional approach to power generation and distribution, where end-consumers of electricity have typically been inactive in their involvement with energy markets and a new approach that integrates their active participation. This new approach includes the use of distributed energy resources (DER) such as renewable-based generations and demand response (DR), which are being rapidly adopted by end-consumers, where incentives are strong. This paper presents the role of DR aggregator to effectively integrate DER technologies as a new source of energy capacity, into the electricity networks using information communication technology and industry knowledge. This framework based on DR aggregators will facilitate renewable energy integration and customer engagement in electricity market efficiently. To this aim, advantages and disadvantages of DR aggregators are discussed in this paper from political, economic, social and technological (PEST) point of views. Based on this analysis, a strengths, weaknesses, opportunities, and threats (SWOT) analysis for a typical DR aggregator is presented.

show abstract

Calculating power distribution system reliability indexes from Smart Meter data

Cited by 8 publications

References 9 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities, and Threats

Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities and Threats

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