The EU is embracing prosumerism and grid reinforcement due to energy transition, emerging renewable generation sources, and active consumer base. Keeping up with agenda 2030 commitments, distribution system operators (DSO) model adopters are also growing for better energy management. A DSO is an entity that owns and operates distribution network within an area. This includes energy transmission from local generation source to the consumption point through the distribution network. This could ensures secure, affordable, and uninterrupted supply of clean energy to households. This converges with sustainability development goals (SDG) 7, 9, and bring a step closer towards achieving SDG 11 and 13.

An outlook on the distribution system operator’s landscape in Europe

In the wake of decentralization, a whole range of renewable energy units are rapidly growing to deliver European energy demands. They act complimentarily to the traditional and larger non-renewable energy infrastructure that has been supporting the EU economy so far. The European energy trends forecast that there will be demand flexibility, thus requiring modern framework and agile business model.

The EU resolved to reduce 20% greenhouse gas emissions (GHG) by 2020 and 40% by 2030 compared to 1990 levels. Similarly, they set-up a renewable energy source (RES) target of 27% of total energy consumption by 20301. By 2022, a tweet informed that annual GHG emissions are down by 4%. Additionally, present emission rate is ~939 million ton of CO2 equivalent. Conversely, the EU has already met 15% of its RES expectations by 2013. But the progress declined due to delayed policy implementation and interrupted investments.

The climate and energy framework 2030 is based on three aspects namely, GHG, RES, and energy efficiency for identical pricing. However, the EU distribution network requires ~350 billion euros investment before 2030 for infrastructure support and smart grid adoption2. Simultaneously managing this fund in terms of delivery time and scaling-up given the dynamic energy landscape could be very challenging. We think that recent advances in energy storage and demand flexibility are driving for DSO model adoption and network modernization.

A distribution system operator’s challenge in delivering energy transition

Presently, the distribution system operators are encountering three main challenges in delivering the energy transition. Lack of investment planning and execution is troubling better anticipation of future needs and optimization of present resources. Perhaps legislative regulations and policies can bring an ease of investment and lucrative returns of interest (ROIs) for the investors.

Furthermore, smart metering is another emerging trend towards grid modernization. It is expected to enhance grid stability, increased resilience, enhanced data management and cybersecurity. However, this requires a multi-dimensional approach by a distribution system operator to catch investments. We need to mitigate technical obsolesce by continuous monitoring and real-time data analysis to enable distributed energy resources (DER) management and demand flexibility. This can improve asset management by recognizing associated risk, reliability and health.

Distribution system operator approach for asset management

As of 2020, EU is the home of about 2300 DSOs that are unevenly distributed across countries3. The existing approach includes integration of asset management principles into the DSO model. These emphasizes proactive asset management by effect monitoring, maintenance, and investment planning for optimizing asset performance and availability. At the core of all developments is the need for development of advanced and real-time monitoring and management of assets in the distribution network. This requires comprehensive asset management plans that outline the strategies and actions required to manage ageing assets. These plans typically include regular inspections, condition assessments, maintenance schedules, and investment strategies to optimize the lifecycle of the assets.

DSOs implement condition monitoring techniques to assess the health and performance of ageing assets. This includes conducting regular inspections, using sensors for real-time monitoring, and performing diagnostic tests such as oil analysis, thermography, and partial discharge monitoring. Furthermore, implement proactive maintenance programs to address the specific needs of ageing assets. They identify critical assets, assess the potential consequences of failure, and allocate resources accordingly. By focusing on assets with the highest risk levels, DSOs ensure that limited resources are utilized effectively to mitigate the most significant risks to the system’s reliability.

It’s important to note that the specific strategies employed by DSOs can vary based on factors such as regulatory frameworks, asset types, system characteristics, and available resources. DSOs continuously review and update their asset management practices to ensure the reliable and efficient operation of their distribution systems despite ageing infrastructure challenges.

Asset fleet behavior with data management

Power transformers are critical assets of the distribution system that represent major capital investments. A report suggests that 40-55% of assets within EU could be >40 years by 2030 and require additional efforts for managing to avoid abrupt grid failure. The DSOs strive to maximize asset availability without compromising their reliability, performance and/or lifespan. However, the underlying goal is often to reduce operational (OPEX) and capital (CAPEX) expenses.

For assets that have reached the end of their useful life or are no longer cost-effective to maintain, DSOs plan for timely replacements or modernization projects. This may involve replacing ageing equipment with new, more efficient technologies or upgrading infrastructure to meet changing operational requirements and regulatory standards. They rely on data analysis and predictive modeling to support decision-making processes related to asset management. They utilize historical performance data, condition monitoring data, and advanced analytics techniques to forecast asset deterioration, optimize maintenance schedules, and allocate resources effectively.

DSOs actively participate in industry networks and collaborations to share best practices, lessons learned, and research findings related to asset management. Collaborative initiatives facilitate the exchange of knowledge, experiences, and innovative solutions for managing ageing assets effectively. They could develop long-term investment plans that consider the expected lifespan of assets and the financial resources required for their maintenance, refurbishment, or replacement. By aligning investment planning with asset lifecycle management, DSOs ensure the availability of funds and resources for managing ageing infrastructure proactively.

AI-driven asset management decisions

Effective analysis of ageing asset requires enhanced data management tools and techniques. Distribution fleets could consists of thousands of strategic and non-strategic assets. Manual collection, managing, and analyzing data from such a large number of assets or from a fleet for conditional tracking and/or reliability ranking is a challenge. Besides automation of the condition monitoring using online monitors and Internet of Things (IoT) gateways, industry also facilitates the use of health indexing for this purpose. To help with these challenge, Seetalabs has developed Ronin AI-a robust and interactive platform to rank your assets according to their health index. It helps you strike an optimum balance between asset performance and risk against OPEX and CAPEX.

This incorporates not only the present condition of your asset, but also past repair and/or renewal strategies including filtration and degassing. This simplifies comparative scenario analysis of various maintenance tasks on the overall performance and health of transformers (either stand alone or in a fleet). For a non-expert on condition monitoring and diagnostics, Ronin AI helps to make decisions. It helps in sorting machines by urgency and logic. It works best with CSV files containing at least the dissolved gases in oil of transformers. The accuracy and precision moves multifold if all data is available. Ronin AI uses the best recognition models to curate the most potential health index scenario and predicts the score.

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  1. European Energy Industry Investments (source:
  2. Challenges facing distribution system operator in a decarbonized power system. (Source:
  3. Outlook on European DSO landscape 2020. (Source: