- Oddbjørn Gjerde
- Research Manager
- 997 30 027
- Energy Systems
- SINTEF Energi AS
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.
- 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
Active distribution grid planning
In 2018, the first steps have been taken towards establishing a planning methodology for the future distribution grid. The future distribution system will be penetrated by new technologies such as distributed generation including prosumers and more power intensive loads, resulting in new load and generation patterns. At the same time new sensors and the introduction of IoT and new communication systems are expected to make massive amounts of operational data available. Many of the changes will be customer driven, and new uncertainties will emerge for the distribution system operators, making planning of "future proof" distribution systems more challenging. These changes this will simultaneously offer new planning options. Examples are electrical energy storage, microgrids, demand response schemes and vehicle to grid. Distribution grid planning will rely on measures taken during system operation to a much larger extent than before. The above described transition may be additionally speeded up by the fact that the utilities need to deal with an ageing grid exposed to continuously increasing weather and climate related stresses.
To utilise operational measures in the grid planning, new methods to evaluate active distribution systems as alternatives to grid reinforcements need to be developed. A literature study on planning methodologies and tools for the future distribution system has revealed that considerable work has been done already, and an important reference is the CIGRÉ C6.19 Working Group report [Pilo, F. et. al, “Planning and Optimization Methods for Active Distribution Systems,” CIGRE, working group C6.19, 2014] on planning and optimisation methods for active distribution systems. This report assesses the various requirements to facilitate the transition towards active distribution systems. A general framework for active distribution system planning is outlined, taking into consideration several novelties such as probabilistic load and generation data models based on real data from smart meters, probabilistic grid calculations resulting in stochastic representation of the nodal voltage and branch current variables and the use of active management before exploring traditional grid solutions. A multi-objective approach is suggested as the impact of several new technologies and control architectures required by the active distribution system is hard to characterise exclusively in terms of grid costs. This work will constitute a valuable basis for the further work in CINELDI, dealing with identified gaps related to different planning aspects such as load and generation forecasting, probabilistic load flow analysis, flexibility and microgrids as alternatives or supplements to investments, risk assessment in the future combined power and ICT system, cyber security in power systems, reliability analysis, voltage quality analysis and maintenance and reinvestment analysis. Currently, a case study is being set up in the SPREAD planning tool in a cooperation with a few of the utilities represented in CINELDI and the University of Cagliari. The aim is to analyse alternatives to grid reinforcements in a representative Norwegian grid.
Combined power and ict system reliability
Many of the new active distribution system options are depending on reliable and secure communication as well as robust ICT systems for monitoring and control. Simultaneously new cyber security threats and vulnerabilities are introduced. Work relevant to risk assessment in the future combined power and ICT system has already been carried out, and an approach to identification and modelling of cybersecurity risks in the context of smart power grids is proposed. The aim is that the risk model can be presented to decision makers in a suitable interface, thereby serving as a useful support for planning, design and operation of smart power grids. The approach is tested on a realistic industrial case with a distribution system operator responsible for hosting a pilot installation of the self-healing functionality within a power distribution grid. See "Risk identification and visualisation of vulnerabilities in self-healing grids" in the Selected cases part of the annual report for more information.
The same approach is currently being applied to study a pilot on digital substations, to gain even more experience with it.
Further, to maintain a stable operation in the future distribution system, real time monitoring and control is increasingly needed. In WP1, work has been carried out to develop a method and tool for analysing the performance and dependability of advanced communication technologies, based on Stochastic Activity Network modelling. A novel software tool is developed and employed to analyse the impact of communication failures on the state estimation of a distribution grid. The application of the tool and its capabilities are demonstrated through a case study with promising results.
Remote digital inspection
The future ICT and communication systems will at the one hand be an important provider of asset condition data and on the other hand represent a new asset to be managed optimally in combination with the power system. As an example, in today's regime all distribution system substations are physically inspected once a year. CINELDI WP1 is aiming to make inspections more efficient by extending the inspection interval through utilising the information from existing and new sensors, without jeopardising safety. Currently, work is going on to specify a pilot on digital sensor-based remote inspection of substations in a cooperation between the research partners and some of the industry partners (vendors and utilities).
Altogether five master students finished their specialisation project in cooperation with WP1 in 2018. One student has been working with load patterns for distribution system planning, three students have been working with different questions related to impact on the distribution system from increasing penetration of electric vehicles, and one student with visualisation in next generation asset management. The work is integrated into the work package and will continue as master theses in 2019.
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.
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.