Results:

• Active distribution grid planning
• Alternative grid connection agreements for integrating fast-charging stations in the distribution system
• Case study on planning a fast charging station
• Charging profiles for Heavy Duty Electric Vehicles
• Code platform "Flexible Load Analysis"
• Demand flexibility modelling for optimal distribution grid planning
• EV fleet charging profile tool for grid planning
• Grid planning case study in the EU project FlexPlan considering a region in the Norwegian power grid
• Guideline for the compliance of grid planning tool with EU overall strategies and regulatory conditions
• Implementation of framework for distribution grid planning with active measures
• Load Analysis for Evaluating Flexibility Needs in the Planning of an Industrial Distribution Grid
• Mapping of barriers and potential for flexibility
• Practical application of active distribution grid planning to pilot area with new energy solutions

Smart grid development and asset management
CINELDI's Knowledge base

Decision support methodologies and tools are needed to plan the future robust, flexible, and intelligent distribution system. A cost-efficient utilization of existing and new infrastructure is required to reduce investment costs, and at the same time control risks. This research subtopic contributes to socioeconomically sound development of the future active distribution grid.

Initial work identified relevant planning methodologies and the current practice for grid development used by the distribution system operators, describing the needs, gaps and opportunities for further research. A survey of tools used by system operators for distribution grid planning was also performed. Highlights can be found in the blog post [1]. A framework for planning of active distribution grids in Norway was then developed, and illustrated by a case study which demonstrates selected aspects of this framework with appropriate methodology, described in the blog post [2] and a conference paper [3]. Later, the framework was also implemented into a code framework and used to study grid planning of a realistic distribution grid [4].

Flexibility measures imply active utilization of flexibility resources (flexible loads, flexible power production, or energy storage systems) during the operation of the grid. This creates an important tie between long-term grid planning and grid operation, where the options chosen in the grid planning phase in turn may put restrictions on the available options during system operation. An overview of flexibility measures and the challenges that they may contribute to solving in the grid is presented in [5]. This work puts power system flexibility in the framework for active distribution grid planning.

Results from the in-kind project FuChar, aiming to reduce investment- and operational costs due to grid integration of electric transport, also support the research in this subtopic. This is especially related to electric vehicle (EV) charging profiles and the potential future impact on the power grid from EV integration [6]–[9].

Selected publications from CINELDI:

1. Tonje S. Hermansen, "Digitizing grid data for a future-proof SmartGrid", #SINTEFblog, Apr. 2018.
2.  I. B. Sperstad, "Slik bør ein planlegge framtidas aktive distribusjonsnett", #SINTEFblogg, Sept. 2020.
3. I. B. Sperstad, E. Solvang, and O. Gjerde, “Framework and methodology for active distribution grid planning in Norway", in 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Liege, Belgium, Aug. 2020, pp. 1–6. doi: 10.1109/PMAPS47429.2020.9183711.
4. R. Rana, I. B. Sperstad, B. N. Torsæter, and H. Taxt, “Economic assessment of integrating fast-charging stations and energy communities in grid planning”, Sustainable Energy, Grids and Networks, p. 101083, Jun. 2023, doi: 10.1016/j.segan.2023.101083.
5. I. B. Sperstad, “Flexibility measures in active distribution grid planning", SINTEF Energy Research, CINELDI Internal (memo), Feb. 2021.
6. K. Berg, O. A. Hjelkrem, and B. N. Torsæter, “A proposed methodology for modelling the combined load of electric roads and households for long-term grid planning”, in 2020 17th International Conference on the European Energy Market (EEM), Stockholm, Sweden, Sep. 2020, pp. 1–6. doi: 10.1109/EEM49802.2020.9221888.
7. E. Ivarsøy, B. N. Torsater, and M. Korpas, “Stochastic Load Modeling of High-Power Electric Vehicle Charging - A Norwegian Case Study”, in 2020 International Conference on Smart Energy Systems and Technologies (SEST), Istanbul, Turkey, Sep. 2020, pp. 1–6. doi: 10.1109/SEST48500.2020.9203102.
8. V. Lakshmanan, B. N. Torsater, H. Sale, and O. A. Hjelkrem, “Charging Profile Generation Tool for Grid Planning and Flexibility Assessment of EV Fleets”, in 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Bari, Italy, Sep. 2021, pp. 1–6. doi: 10.1109/EEEIC/ICPSEurope51590.2021.9584522.
9. K. K. Fjær, V. Lakshmanan, B. N. Torsæter, and M. Korpås, “Heavy-duty electric vehicle charging profile generation method for grid impact analysis”, in 2021 International Conference on Smart Energy Systems and Technologies (SEST), Vaasa, Finland, Sep. 2021, pp. 1–6. doi: 10.1109/SEST50973.2021.9543135.