• Planning methodologies for the future distribution system
  • Risk assessment in the future distribution system
  • Next generation asset management
  • Case studies – distribution system planning, risk assessment and asset management

3. Tertiary 2018

Work performed

  • Continued work with preparing the pilots 'Fremtidens digitale nettstasjon' and 'Digital inspeksjon'.
  • Setting up case for the Norwegian case study in the SPREAD planning tool.
  • Finalized the risk assessment of centralized "self-healing" pilot in Sande and started the risk assessment of "smart substation" case in Stavanger.
  • Developed a method and tool for analysing the performance and dependability of advanced communication technologies.

Significant results (highlights and their innovation potential)

  • Method for risk assessment of "self-healing" finalized and documented in a paper. High innovation potential in short term.
  • Method and tool for analysing the performance and dependability of advanced communication technologies documented in a paper. High innovation potential in long term.

Deliverables

  • Memo: Digital inspeksjon (proposal for pilot test)
  • Paper: Dependability Modelling and Analysis of 5G Based Monitoring System in Distribution Grids
  • Paper: A Feasibility Study of a Method for Identification and Modelling of Cybersecurity Risks in the Context of Smart Power Grids
  • Presentation: Risikovurdering av selvhelende nett
  • Five student reports.

2. Tertiary 2018

Significant results (highlights and their innovation potential)

  • Report on Planning methodologies - needs and gaps.
  • Risk model of "self-healing" pilot. High innovation potential –possible input to development of framework and guidelines for DSO risk assessment
  • Finalized memo Automatic inspection of the distribution system for condition monitoring.

Deliverables

  • Report: Planning methodologies needs and gaps
  • Memo: Automatic inspection of the distribution system for condition monitoring

Meetings

  • Partner workshop arranged 4 June – status presented, results discussed, feedback from participants (SliDo), pilot "Fremtidens Digitale Nettstasjon" (FDN) discussed
  • 6 meetings between research partners – mainly between SINTEF Digital and Energy Research on risk assessment (T1.2)
  • 3 meetings between research and industry partners – on development of pilot FDN and general partner meetings

Milestones

  • Several test sites for pilot project "Fremtidens digitale nettstasjon" identified
  • Data collection initiated for test site Lyse smart city grid

PhD Status

  •  PhD position still open, no candidate found

Master theses

  • Tonje Leine Lunden: "Planleggingsmetodikk for fremtidens distribusjonsnett"
  • Torbjørn Slinde: "Visualisering i neste generasjon asset management
  • Nathalie Skyttermoen: "Analyse av framtidig elektrisk transport i Eidsiva Netts område"
  • Liv Ringheim og Marte Brurås: "Elbilers innvirkning på forsyningssikkerhet/ leveringspålitelighet

1. Tertiary 2018

Significant results (highlights and their innovation potential)

  • Mapping of driving forces' impact on the future planning methodology
  • Overview of opportunities and challenges regarding using automatic methods for technical condition assessment for components in the distribution grid.

Deliverables

  • Planning methodologies needs and gaps (report)
  • Automatic inspection of the distribution system for condition monitoring (draft memo)
  • Remaining challenges and future trends for drones within the energy sector (presentation)
  • Report from Fagdag UAS Norway (Blog)

Meetings

  • 7 meetings with industry/ industry partners
  • 6 meetings between research partners (SINTEF Energi, SINTEF Digital and/or NTNU)

PhD Status

  • PhD position advertised three times, candidates are being evaluated
  • Post Doc employed, started 10 April.

Master Thesis 

  • Two master theses proposed for 2018/19

Results 2017

Smart grid development and asset management (WP1)

The primary objective of CINELDI's WP1 is to develop decision support methodologies and tools needed for the optimal planning and asset management of the future robust, flexible and intelligent distribution system.

The expected impact is a more efficient grid through better utilization of existing and new infrastructure, more target-oriented investments, and better control of risks.

Today's practice in the DSOs
In 2017 a questionnaire was sent to the DSOs to assess today's practice regarding grid planning methodologies. The results show that different implementations in Excel, as well as Powel's Netbas are the most common tools used for analyses for grid planning purposes. The DSOs wish to consider new aspects such as prosumers, end user flexibility, distributed storage and variable generation (PV, wind) in their planning, but lack good tools. Uncertainties are only to a limited extent accounted for. Many of the DSOs indicate that the main reason why more analyses are not carried out as part of the grid planning process, is that they do not have sufficient high-quality input data. It was found that part of the data is still stored on paper at 67% of the respondents (9 DSOs in total). They are in the process of digitizing this data.

Planning methodologies needs and gaps
A partner workshop was arranged in June to discuss state of the art of planning methodologies from the DSO perspective. The partners were invited to give input on what they wanted WP1 to focus on. The input from the partners were sorted in three levels; driving forces that influence on the future grid planning; challenges that need to be solved in the future planning methodology; and how to solve the challenges / requirements to the methodology.

In October, the research partners arranged a meeting with Fabrizio Pilo, professor at the University of Cagliari (Italy), and convener of the CIGRÉ WG C6.19 Planning and optimization methods for active distribution systems. The planned research activities in CINELDI WP1 were discussed, and aligned with the research needs from the CIGRÉ working group.

Output from the workshops and other discussions, the results of the questionnaire, together with a literature study on future planning methodology are used as input to describe the needs and gaps related to planning methodology for the future distribution system. This description is still ongoing work.

 

Pole with a woodpecker damage.
Pole with a woodpecker damage. WP1 develops decision support methodologies for optimal planning and asset management for the future distribution grid. (Photo: Hafslund Nett)

Automatic inspection for condition monitoring
Further, work has been carried out regarding "Next generation asset management", to provide methods and tools to support risk-based asset management in the future active distribution system. New sensors, increased sensor coverage, online condition data acquisition and analysis, data management and big data analytics will provide new possibilities, regarding efficient and more precise assessment of technical condition. Use of digital photos requires unstructured data (photo/video) analysis.

So far work has focused on how to establish information of the technical condition for asset management purposes, based on automatic information collection and software for automatic analyses of this information. The objective of the work has been to address the opportunities and challenges with using automatic methods for technical condition assessment for components in the distribution grid.
Condition monitoring for electricity distribution infrastructure can be considered within two coarse areas: monitoring of infrastructure corridors (e.g. for vegetation encroachment), and monitoring of the infrastructure itself (e.g. pylons, lines, and components). The state of the art for automatic monitoring of infrastructure corridors is quite advanced, with several commercial solutions available for accurately detecting and tracking the progress of encroaching vegetation using photogrammetry or LIDAR point cloud data. However, the state of the art within automatic monitoring of infrastructure and components is less mature. Recent Machine Learning approaches (e.g. Deep Learning) have shown fantastic performance within academic studies for identifying and classifying components and faults from unstructured data (e.g. images). The main barriers to industry adoption of these techniques are the time and labour required to collect, annotate, and maintain extensive training data sets as well as their reliance on high-quality imagery captured under good conditions.

Within both areas, input data is typically collected manually using a piloted aircraft. Drone technologies and the regulations associated with their use have now reached a level of maturity where it is more efficient to perform such data collection automatically. Future trends within automatic condition monitoring will therefore include the increased adoption of remotely operated aircraft for data collection, the emergence of commercial solutions for automatic condition assessment of infrastructure and components, as well as the adoption of new and more varied sensor types to extend the range of faults that are detectable and operational conditions.

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