Challenges and solutions for N-1 contingency analysis in modern power grids

As more distributed energy resources (DERs) and loads are integrated into the distribution grid, our energy system becomes increasingly complex, making the assurance of supply security more critical. In this context, N-1 contingency analysis plays a vital role. However, due to these dynamic changes in the grid, the underlying processes need to be optimized and accelerated. In this article, we explore how the N-1 criterion is applied in practice and discuss approaches that can contribute to process optimization.

What is N-1 contingency in power grids?

N-1 is a fundamental principle in grid planning and a critical tool for maintaining grid reliability and ensuring supply security. Essentially, N-1 stipulates that a power grid must withstand the failure of a single component (such as a line or transformer) without causing an interruption in supply.

Ensuring N-1 security is essential for minimizing the frequency and duration of power outages, which directly impacts key reliability indices such as SAIDI (System Average Interruption Duration Index), SAIFI (System Average Interruption Frequency Index), and CAIDI (Customer Average Interruption Duration Index).

The N-1 contingency analysis can generally be applied across all grid levels but is not universally practiced. Within distribution grids, being N-1 secure is particularly crucial for medium-voltage grids. A component failure in a medium-voltage grid can lead to widespread grid disruptions, affecting not only individual households but also critical infrastructure that relies on continuous power supply, as well as entire low-voltage grid areas dependent on the affected MV segment. To achieve N-1 security, medium-voltage grids are often designed as meshed or ring networks and typically operate in an open configuration.

In contrast, N-1 contingency analysis is less critical in low-voltage networks, where the impact of a failure is relatively minor compared to MV systems. Therefore, the structure of the LV network often lacks switching options. However, N-1 contingency analysis can still be relevant in certain scenarios, such as during planned maintenance outages of equipment in the low-voltage grid.

Practical implementation of N-1 contingency analysis at distribution level

In practice, ensuring N-1 security is typically achieved through the so-called N+1 redundancy in the grid, for example, when designing switchgear and lines. This often results in certain assets operating at only partial capacity, leaving additional capacity available for contingencies.

Depending on the grid, power can also be rerouted via alternative pathways to maintain supply – the concept of open rings in grid topology – or power flow can be reconfigured using switchgear to compensate for the failure and minimize outages. In distribution grids, the first approach is often applied in high-voltage networks, while the second approach is more common in medium-voltage networks.

N-1 security is evaluated for all assets – whether lines or transformers – by simulating various contingency scenarios. It is checked whether voltages at grid nodes remain within permissible limits and whether remaining assets do not become overloaded. Additionally, it is ensured that there will be no cascading failures or subsequent disturbances.

In transmission grids, N-1 security is continuously monitored during operation. In distribution grids, however, the N-1 contingency analysis is conducted only sporadically. Moreover, this analysis is still characterized by manual interventions, where grid planners simulate individual faults and manually evaluate and determine resupply options.

Challenges of current approaches to N-1 contingency analysis at the distribution level

Given that the number of potential failure points and switching options in medium-voltage grids increases significantly with grid size, in practice, only a small fraction of these failure points are evaluated for N-1 security. Additionally, finding switching options often requires considering neighboring grids, which are frequently not part of the existing grid model and must also be modeled.

While this kind of “fit-and-forget” approach was sufficient in the past, it is increasingly reaching its limits due to the growing complexity and the increasing electrification. Today’s grid must respond not only to traditional loads but also to weather-dependent feed-ins from renewable sources, which can lead to unforeseen peaks or dips in load. Additionally, the large number of decentralized generation systems increases the frequency of reverse power flows, which can result in unfamiliar stresses on the grid.

These growing dynamics in the grids mean that potential vulnerabilities may go undetected if only a portion of the grid is occasionally subjected to N-1 contingency checks.

Instead, N-1 contingency analysis should be conducted regularly across the entire grid area following any significant change in the network, such as new loads, generation sources, switching states, or the integration of voltage control.

Automating the conduction of contingency simulation scenarios

These developments make it necessary to refine the current approach accordingly and achieve a much higher degree of automation in conducting security assessment under N-1 contingency conditions.

Today, grid operators already have access to contingency analysis tools that offer a certain level of automation for running and evaluating various contingency scenarios. For instance, after manually pre-selecting specific failure points, grid planners can conduct automatic checks to see whether any of these points are not N-1 secure.

This partial automation works well for smaller grid areas. In larger grids, though, where the number of potential failure points is larger as well, it can require significant time investment. Therefore, ideally, the identification of all potential locations that could fail under N-1 conditions should also be automated to minimize manual input and time investment.

This is precisely what we offer with the Intelligent Grid Platform (IGP). Our N-1 contingency analysis tool is designed to introduce high levels of automation and help analyze large and complex grids quickly and efficiently. It uses specific parameterizations to closely examine all points in the grid that have assets whose failure could jeopardize the supply security of important grid elements and substations.

Moreover, it goes a step further: For each identified potential failure point, the tool automatically suggests a switching combination that ensures resupply while keeping the load limit factor within tolerated values. This comprehensive automation significantly reduces the manual workload for grid planners and accelerates the entire N-1 contingency analysis process.

Since the IGP has a complete digital twin of the entire grid area, our N-1 contingency analysis tool also considers the neighboring grids by default; there is no need for additional modeling.

The complete automation of N-1 contingency analysis, as IGP enables this, unlocks application areas that would hardly be feasible due to the high manual effort otherwise required. For example, in our Connection Request app, grid planners can conduct contingency analysis for MV loads as early as during the connection request phase to check whether these loads can still be supplied in the event of an asset failure. This is especially important for critical infrastructure such as hospitals or industrial facilities, where reliable supply, even in the event of failures, is essential to ensure uninterrupted operations and prevent potential damage or hazards.

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As the integration of DERs and overall electrification advance and grid complexity increases, continuously monitoring supply reliability becomes more critical than ever before. In this context, fully automating N-1 contingency analysis will become a key factor in meeting the growing demands for supply security.

Moreover, grid redundancies can be applied more strategically, ensuring that assets are neither unnecessarily oversized nor underutilized. This makes the most efficient use of existing capacities and allows for more flexible grid development to optimally support the integration and utilization of renewable energies.