Gerd Kjølle

This paper presents a methodology for reliability assessment of radially operated distribution grids that accounts for multiple measures for managing reliability of supply. This includes self-healing functionality that utilizes the flexibility of the grid topology, and the use of flexibility resources such as energy storage systems and loads with non-firm connection agreements. The methodology is implemented in an open source software tool based on graph-theoretical concepts. It is then demonstrated how this tool can be used to explore the collective impact of different combinations of measures as well as interactions between them. For a case study for a medium-voltage reference grid it was found significant interactions and synergistic effects between the capacity of reserve connection and the extent of self-healing functionality while relatively modest interactions were found for other combinations of measures.

Society depends on a secure electricity supply which is increasingly important as various sectors are being electrified to substitute fossil fuels. The sustainability targets for the future cannot be met unless the necessary transformation of the power system is addressed. The future power system should be renewable, flexible, intelligent, secure, and resilient. This transformation should be made at acceptable costs, without jeopardising the security of electricity supply. The electricity grid is a crucial enabler for the energy transition. Intensified electrification and digitalisation imposes new challenges and requirements for the grid. At the same time, the power system will be more exposed to natural disasters in the future due to climate change and increased weather-related stress. It is important to understand the new risk picture and find the right strategies for dealing with the security of electricity supply in the future power system.

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.

Extreme weather is known to cause failure bunching in electrical transmission systems. However, protection systems can also contribute to the worsening of the system state through various failure modes—spontaneous, missing or unwanted operation. The latter two types of failures only occur when an initial failure has happened, and thus are more likely to happen when the probability of failure of transmission lines is high, such as in an extreme weather scenario. This causes an exacerbation of failure bunching effects, increasing the risk of blackouts, or High Impact Low Probability (HILP) events. This paper describes a method to model transmission line failure rates, considering both protection system reliability and extreme weather exposure. A case study is presented using the IEEE 24 bus Reliability Test System (RTS) test system. The case study, using both an approximate method as well as a time-series approach to calculate reliability indices, demonstrates both a compact generalization of including protection system failures in reliability analysis, as well as the interaction between weather exposure and protection system failures and its impact on power system reliability indices. The results show that the inclusion of protection system failures can have a large impact on the estimated occurrence of higher order contingencies for adjacent lines, especially for lines with correlated weather exposure.