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.

Distribution grid companies and distribution system operators (DSOs) still mostly follow a traditional framework for grid planning. Such frameworks have so far served DSOs well in the economic assessment and cost-benefit analysis of passive measures, such as grid reinforcement. However, the development towards active distribution grids requires DSOs to also be able to assess an extended set of active measures. To this aim, this paper extends and implements a general planning framework for active distribution grids that builds upon the well-proven traditional framework. The methodology integrated in the framework includes: 1) decoupled models for i) operation with active measures and ii) optimal grid investment, and 2) methods for economic assessment considering active measures from both i) a DSO cost-benefit analysis perspective and ii) a willingness-to-pay perspective. In this paper, operational models are integrated for two examples of active measures, namely the use of fast-charging stations (FCS) and local energy communities (LEC). The methodology is demonstrated in a long-term grid planning case study for a realistic Norwegian medium voltage distribution system. For this case, grid planning with FCS as an active measure reduces the present value of grid investment costs by 70% compared with a passive grid planning strategy. The results also demonstrate how the methodology can be used in negotiating the price of active measures between the DSO and distribution system actors such as LEC and FCS operators.

The electric power system is a critical infrastructure in which power transformers play a key role in linking together generation and end-use of electricity. The consequences of transformer breakdown can be significant, and aging transformers have a higher probability of failure. For decisions in asset management and power system development, it will therefore be useful to capture how deteriorating component condition affects failure probabilities and the overall reliability of the power system. Since such decisions have planning horizons of multiple years, the analysis should also capture similar time horizons. To this end, this paper proposes an analytical approach to power system reliability analysis (PSRA) accounting for time dependencies in the technical condition of components. An analytical PSRA methodology integrating a transformer condition model is extended to analysis horizons of multiple years. This analytical methodology is compared with a Monte Carlo simulation (MCS) approach to PSRA by applying both to a realistic case study. The comparison validates the analytical approach by showing that the inaccuracies its approximations introduce are negligible, at least for the considered case. This means that the proposed methodology can be a computationally viable alternative to MCS methods, especially when it is too time consuming to assess the impact of different scenarios with sufficient statistical precision using MCS. However, drawbacks with the analytical approach for further extensions of the methodology are also discussed.

There is an increasing need for flexibility in power systems worldwide, giving rise to European policy documents outlining how Distribution System Operators (DSOs) should procure flexibility and include flexibility in planning and operation of their electricity distribution grids. This implies a remarkable change from today’s situation where DSOs rely on investments in grid assets that they have full control of, to a new regime where DSOs should rely on flexibility provided by third parties. The objective of this work has been to gain a better understanding of the feasibility of and barriers to wide-spread utilization of flexibility in planning and operation of electricity distribution grids. Building upon a previous literature review and a taxonomy for classifying and characterizing power system flexibility, we propose frameworks for i) classifying flexibility resources and flexibility enablers in grid operation and planning, and ii) classifying and understanding barriers to utilizing them in terms of a flexibility value chain. These theoretical frameworks are tested against empirical data collected in semi-structured in-depth interviews with a representative selection of Norwegian DSOs. Mapping the findings to the frameworks gives a systematic overview of the flexibility situation in Norway and presents both country-specific and general insights about barriers to utilization of flexibility.

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.

Optimization tools for long-term grid planning considering flexibility resources require aggregated flexibility models that are not too computationally demanding or complex. Still, they should capture the operational benefits of flexibility sufficiently accurately for planning purposes. This paper investigates the sufficiency of an aggregated flexibility model for planning tools by comparing it against a detailed flexibility model. Two different constraint formulations, namely based on recovery period and temporal proximity were tested to account for post activation dynamics of flexibility resources. The results show that the recovery period based formulation results in excessive demand reduction. The proximity constraint formulation on the other hand results in realistic activation of flexibility resources which represents an improvement over the base formulation without constraints for post activation dynamics.

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).

Deteriorated power system components have a higher probability of failure than new components. Still, reliability of supply analyses traditionally model all components of the same type with the same probability of failure, and thus neglects the effect of deteriorated components. This paper presents a methodology to integrate a condition dependent component probability of failure model into a power system reliability analysis. The component state is described by a semi-Markov process, and the paper shows how this, under reasonable assumptions, can be approximated by a Markov process. The Markov assumption simplifies the analysis and allows the model to include preventive retirement and be calibrated to statistical data. A case study using statistical data for Norwegian power transformers shows that, in the Norwegian power system, the proportion of failures that are due to poor condition is small, partly due to the common strategy of preventive retirement. However, if the condition of the transformers were worse, the impact of poor condition can be considerable. The methodology further enables identification of the transformers that contribute most to the risk to reliability of supply. The paper thus highlights the importance of accounting for component condition in strategic decisions such as long-term renewal planning.

This article describes a reference data set for 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.