Distribution grids in industrial areas are under pressure to increase the utilization of the existing grid and to build more grid due to electrification of existing industries and new power-intensive industries. Distribution system operators (DSOs) are currently considering if and how they can use power system flexibility, such as demand response, to simultaneously avoid overloading their grid assets and avoid investing in new grid. The DSO may choose to operate an asset, such as  a transformer, beyond its rated load. However,  this accelerates the aging of the transformer, hence decreasing its remaining life. In this work, a methodology is developed which relates the cost of transformer overload to the value of power system flexibility, to determine the DSO’s willingness to pay for a flexibility service. The methodology finds the spatial dependency of a flexibility services’ value by optimizing load shedding in the grid to minimize transformer loss of life (LoL). The methodology is demonstrated in a case study using real grid and load data measured in an industrial area in Norway that includes both medium voltage (MV) and low voltage (LV) transformers.

Traditional power grid planning is based on passive measures such as grid reinforcement and using present value calculations to compare grid development plans. However, this approach does not accurately represent the real options value of using active measures such as demand-side flexibility for post-poning grid reinforcement through a ”wait-and-see” approach to manage the long-term uncertainty in load growth. However, the value of using active measures may come at the price of reducing the security margins in grid operation. This leads to an increased risk of operational challenges during operation, and this risk must be weighed against the value of using active measures. This paper presents a methodology for quantifying both the value and risk (or price) of real options related to grid development strategies using active measures, providing grid planners with more comprehensive information about the advantages and disadvantages. The methodology is demonstrated using an illustrative and simple case for a medium voltage reference distribution system, where flexibility from local energy communities is considered as an example of an active measure. The case study illustrates how some risk-taking is required to realize the value from using active measures.

New power-intensive industries and electrification of existing industrial processes put pressure on electric distribution grids. To evaluate whether flexibility measures such as procuring congestion management services can be considered to defer grid reinforcements, distribution system operators (DSOs) first need to characterize the need for flexibility in their grids. While characterization and modelling of flexibility resources have been intensively studied over the last years, characterizing the need for flexibility from the DSO’s perspective has received much less attention. This paper proposes a methodology for quantitatively characterizing the need for flexibility in distribution grids based on time series load demand data. The methodology and its application to active distribution grid planning is demonstrated for a real, industrial grid area in Norway with a varied set of industries and plans for electrification. The benefits were demonstrated by comparing probabilistic metrics for flexibility needs with the traditional methods currently used for grid planning by DSOs, which provide much more limited information.

This reference data set describes a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system. Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts: 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points. 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future. 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system. This work is funded by CINELDI - Centre for intelligent electricity distribution, an 8 year Research Centre under the FME-scheme (Centre for Environment-friendly Energy Research, 257626/E20). The authors gratefully acknowledge the financial support from the Research Council of Norway and the CINELDI partners. The work is also funded by ChiNoZen - a Chinese-Norwegian collaboration project (Grant No. 2019YFE0104900).