Asset Management AIM
Overview
(Physical) asset management is generally a holistic, integrated decision-making process that involves:
- shaping the appropriate balance between risk, cost, and performance over the life cycle of the assets within the asset portfolio
- managing teams and providing guidance to ensure that the correct asset portfolio is addressed at the suitable time with the appropriate resources
In today's European TSOs, asset management has generally shown a higher maturity or substation primary assets [1,2] than for overhead lines (OHL)/cables [3]. Moreover, its application to data and software is primitive despite the close attention paid to treating them as assets [4].
Benefits
In general, asset management aims to achieve a suitable balance between performance, cost, and risk, as shown in Figure 1. A TSO should align each category's metrics with its business strategies to ensure that the optimum decision is well defined. Recent practices have shown the following trends:
- Performance: ranging from personal safety and grid reliability to overall support for energy transition and benefits to societal values (e.g. environment) [6]
- Cost: from capital and operational financial costs to enterprise resources such as people or manpower and the supply chain [7]
- Risk: extending from asset failure to non-technical uncertainties [3]:
- with a link to enterprise risk management (e.g. cybersecurity, climate impacts)
- embracing the positive effects of uncertainties as opportunities
The strategic alignment reveals the objectives of asset management. To maximise the chance of reaching such objectives, each TSO should further coordinate its practice from the strategy to operation level through tailored solutions and processes, as shown in Figure 2. Nevertheless, such solutions might have some common features in the following aspects, as summarised in [8] and [9]:
- Aligning objectives with timeframes: Daily excellence is achieved technically, whereby long-term resource planning will gradually benefit. Long-term objectives should at least consider national and other long-term grid development plans, such as the ENTSO-E Ten-Year Network Development Plan (TYNDP).
- Measuring non-technical performance/risk and non-financial resources with support from strategic alignment within the organisation:
- monetising strategic objectives for trade-offs in decision-making
- identifying environmental or regulatory risks as strategic risks
- Introducing knowledge management and data governance:
- enhancing the organisation's capability to manage new performance, cost, and risk domains
- increasing the strength of knowledge so that more quantitative risk analysis can be performed on technical or economic uncertainties on assets, the grid, and company
- ensuring an effective and efficient link between technical asset information and asset management strategies
Challenges
In a transmission system operator (TSO), asset management was initially applied to optimise maintenance activities for safety or grid reliability, e.g. indicated by the System Average Interruption Duration Index (SAIDI) or System Average Interruption Frequency Index (SAIFI) [5]. In this sense, many challenges are being addressed to link asset information with grid information, e.g. from system operation and development.
Many TSOs have recently targeted a broader area of optimisation, namely the overall investment in grid expansion and societal benefits. This scope presents additional challenges in effectively and efficiently linking the technical knowledge of assets to business strategies.
Current Enablers
Quality framework of asset management
Given that asset management is primarily a way of organising experts, a quality framework is necessary to coordinate a wide range of activities. The most commonly cited joint effort is the ISO 5500X series of standards. At the heart of the ISO 55000 asset management standard is the concept that assets exist to create value for the organisation and its stakeholders. Value-based decision-making (VDM) can help organisations to focus decisions to derive the most value from their assets. Further valuable works include:
- IEC 63223/63224 adapts the ISO 5500X standards to the electricity grid sector, with its technical terminology and organisational concepts closer to TSO's practice.
- Cigre SC C1 has published many technical brochures [1,6,7,8,10,11,12] summarising practical experiences of grid operators to organise asset management.
Maintenance strategies
The maintenance strategy concerns maintaining and replacing assets within an 'asset fleet' of similar asset types and identifying asset failure as a risk. The core of asset management has been inspired by maintenance strategies to consider performance, cost, and risk in subsequent development stages [9]:
- Corrective: Accept the risk of asset failures on cost and performance.
- Time-based: Raise the cost of preventive replacements to a certain but high level regardless of assets' failure probability. Ignore opportunities for reducing costs.
- Condition-based: Define failure probability as asset performance. Minimise the total failure frequency in an asset fleet under given costs.
- Reliability-centred (early stage): Define network criticality as asset performance that combines failure probability models of different asset types and their redundancy in the grid. Maximise the grid's available capacity under given costs.
- Risk-based (further development from reliablity-centred maintenance): Monetise asset performance and allow its trade-off with costs.
Risk-informed decision-making framework
ISO 31000 has provided general risk management guidelines, in which risks are commonly precepted as a combination of quantitatively assessed probabilities and consequences. This structure supports the optimal risk trade-off with performance/cost in asset management.
According to modern risk management methodology, TSO asset managers should adopt three strategies.
- Quantitative risk assessment: Aims to compare risk mitigation solutions based on socio-economic criteria. It requires a probabilistic model representing uncertainty and monetary evaluations of the consequences, implying the highest knowledge strength.
- Cautionary principle: Applies in cases of significant uncertainty and focuses on improving knowledge and implementing temporary risk solutions that are certain to reduce risk while avoiding generating new constraints for risk stakeholders.
- Discursive strategy: Considered when stakeholders are misaligned regarding risks, objectives, constraints, and conflicts. It involves finding a compromise that is satisfactory for the parties, including updating objectives.
IEC 63224-2 drafted by IEC TC123 is currently standardising the aforementioned strategies in the regime of TSO asset management. Within its draft, knowledge management is stated as the key to turning from the cautionary principle to quantitative risk assessments, thus paving the way towards optimal decision-making.
Data collection and decision support technologies
As asset managers, transmission grids have been digitalising the process from maintenance activities to investment decision-making regarding the following aspects:
- Advanced diagnostic technology: Supports the reliability assessment of assets. Individual technologies standardised by IEC, reported by Cigre (e.g. in [13,14,15]) and available on the market.
- Data interoperability: As the asset manager, the TSO should ensure that its optimisation can use data sources developed and run by different entities throughout the asset lifecycle without heavy human involvement. This issue has been generally recognised by the IEC [16].
- Grid contingency analysis: Combines asset reliability information with grid operation and grid planning, currently applied in maintenance management [17].
- Portfolio planning: The branch of enterprise resource planning for asset management, identified internally in ENTSO-E [18].
- Advanced techniques and computing tools: Applied in the optimisation of asset management [19, 20].
Some aspects are further described in the section below.
Applications
DSO
| Location: Portugal | Year: 2019 |
|---|---|
| Description: The development of smart grids, initiated with the smart meter deployment, brought several challenges related to the amount of data and events being generated. The GridControl platform was designed to manage and analyse all data being generated by LV smart meters, supporting E-REDES to efficiently manage the LV network and be able to effectively integrate new market trends, such as flexibility services. It displays several functionalities such as fault location through smart pings; detection of MV outages and fallen conductors; detection of overvoltage and undervoltage events at LV level. | |
| Design: It is a server-based application with connection to the smart meter infrastructure. As challenges in the implementation, we highlight the need to overcome communication failures, since the effectiveness of the solution is dependent from the information received via PLC from the smart metering infrastructure. | |
| Result: Since its inception it has helped E-REDES to effectively manage the LV network making use of the variety and volume of data provided by smart meters. It has successfully detected many broken MV conductors, located thousands of LV faults and prevented the dispatch of field teams due to customers' in-house faults. | |
| Technology Readiness Level (TRL): TRL 9 | |
| References: | |
| Location: Portugal | Year: 2024 |
|---|---|
| Description: The EOVM2 (Earth Observation for Vegetation Management 2) project uses satellite imagery to support vegetation management beneath high and medium voltage power lines. Through periodic analysis of low- and medium-resolution images, it is possible to identify vegetation density and prioritize field interventions, promoting sustainable vegetation management.  | |
| Design: The project explored three different use cases:
Country-specific factors influencing the application of the technology: E-REDES is present throughout the mainland territory, managing an overhead electricity network with a total length of 185,000 km, of which ~15% are installed in forested areas. For E-REDES, as the DSO, asset management and vegetation management become key components in preventing and anticipating failures and incidents that could negatively impact the network and its various stakeholders. Difficulties/challenges with implementation: The presence of clouds and shadows during certain times of the year can compromise the quality of the analysis in some regions, requiring reprocessing or waiting periods for new image acquisition. | |
| Result: | |
| Technology Readiness Level (TRL): TRL 9 | |
| References: Lisboa, A., Borges, R., Figueiredo, A., Craveiro, F., Messias, R., & Pinto, D. (2025). E-REDES implement advanced analytics applied to satellite imagery to optimise vegetation management (Paper No. 654). CIRED 2025. | |
| Location: Portugal | Year: 2022 |
|---|---|
| Description: The "Analytics4Vegetation" solution was developed by E-REDES to monitor and predict vegetation growth near overhead power lines using AI. The aim is to prevent incidents, ensure regulatory compliance, and optimize maintenance. It processes geospatial, climatic, and biological data to identify risk zones and dispatch field teams proactively. In 2025, a new version 2.0 introduced an upgraded growth model, trained on a new dataset of 108,409 trees observed through LiDAR. This enabled more accurate growth-by-species forecasts and improved risk-based planning. | |
| Design: The project was composed of four different phases (1) Design and functional specification of the solution; (2) Application of ML and Big Data analytics techniques to develop models for vegetation growth and risk index determination; (3) Integration with internal systems through dedicated web services to interact with the system and allow the automatic creation of work orders based on the outcomes of the system; (4) National roll-out and monitoring of the solution. Country-specific factors influencing the application of the technology: E-REDES manages ~85,000 km of HV and MV power lines, of which ~80% are overhead lines, of which ~40% crosses forest areas. E-REDES is responsible for carrying out maintenance and ensure the conservation of power lines, based on regular inspections, to ensure compliance with safety distances. Difficulties/Challenges with implementation:
| |
| Result: The main gains and advantages achieved were the following:
| |
| Technology Readiness Level (TRL): TRL 9 | |
| References: | |
TSO
| Location: Greece | Year: 2023-2025 |
|---|---|
| Description: The Asset Performance Management System (APMS) project 2023-2025 is implemented by IPTO, a Greek TSO. The main objective is to achieve optimal management of the TSO's assets through the control and evaluation of their condition, assessment of risks related to asset criticality and the point of failure, as well as timely actions to increase the transmission system's efficiency and reliability by formulating novel maintenance strategies. | |
| Design: In combination with the online monitoring systems, the APMS will support the TSO's migration from a purely traditional time-based to a condition-based maintenance (CBM) strategy or a balanced combination. | |
| Result: In operation | |
| Technology Readiness Level (TRL): TRL 7 | |
| References: | |
| Location: France | Year: 2022 |
|---|---|
| Description: The ReLife Algorithmic Library is an open-source Python algorithmic library developed by RTE and released in 2022 [1]. Its purpose is to provide tools for optimising asset management strategies. | |
| Design: ReLife offers several key functions used by RTE, including:
| |
| Result: Open for adoption | |
| Technology Readiness Level (TRL): TRL 9 | |
| References: | |
| Location: Montenegro | Year: 2021 |
|---|---|
| Description: Smart Grid Manager (SGM) is an application for integrating secondary systems implemented at the management centre level (national dispatching centre). Its purpose is to enable users to optimise maintenance by collecting process data from a number of different systems, which are processed, archived, and displayed in the application's user interface. Another use is the analysis of events in secondary systems and the generation of reports. | |
| Design: All data required for its work are collected from secondary systems in substations via the communication network. According to this concept, every system that wants to be integrated into the application must be able to communicate to collect data. The system was implemented to enable the automatic collection of data from several different systems or sources into a common database in which all data are aggregated, validated, and archived. The SGM collects data from three main sources:
| |
| Result: In operation. | |
| Technology Readiness Level (TRL): TRL 9 | |
| References: | |
| Location: Spain | Year: 2017-2020 |
|---|---|
| Description: The MANINT programme 2017-2020 run by the Spanish TSO Red Eléctrica de España aimed at developing optimised maintenance techniques for high-voltage (HV) assets to increase grid availability and manage assets performance[1]. | |
| Design: A holistic approach to grid asset maintenance has been implemented, whereby for each asset, a condition and a probability of failure is calculated and that index is combined with a grid criticality index to determine risk at asset level, combining both the asset and the grid perspective. Final risk is an aggregation of different risk dimensions (availability of the grid, safety, environmental impact, impact to third parties...). Risk level is used to adapt maintenance strategies automatically and to identify replacement needs, creating mid-long term projections and potencial future scenarios. | |
| Result: In operation. | |
| Technology Readiness Level (TRL): TRL 9 | |
| References: | |
| Location: Slovenia | Year: 2018 |
|---|---|
| Description: The Diagnostics and Analytics Centre (DAC) is a meeting point for big data, advanced analytics, technical expertise, and engineering practices that data engineers and scientists are transforming into the modern management of high-voltage assets. DAC is still evolving, and new areas of work are being added. | |
| Design: The DAC connects the physical world with the information world and aims to discover additional technical or cost-efficient opportunities in electric power transmission. An integral part of the DAC is a dedicated team of experts with first-hand knowledge of operating, maintaining, and developing high-voltage infrastructure, which enables focusing on reliable solutions with long-term value. This knowledge enables developing a range of comprehensive analyses to support decision-making in the various ELES departments. | |
| Result: The centre implements a multi-domain approach to the management of high-voltage infrastructure, including - but not limited to - in-house software development to support vegetation management, rights of way management, business key performance indicator (KPI) reporting, implementation of predictive maintenance, process automation and optimisation, cross-platform integration, digitisation of financial and other documents, asset monitoring and condition assessment, data quality assurance, technical study governance and support, R&D, and the implementation of digital solutions for training and field operations, such as VR, AR, and digital twins technologies. | |
| Technology Readiness Level (TRL):
TRL is not assigned given the reference to the DAC and not to a specific
project.
| |
| References: | |
R&D Needs
Several R&D activities listed below can contribute to further improving the technology:
- Advanced diagnostic technology:
- Sensor technology in the condition monitoring equipment and services market has been enriching asset reliability data for three decades (e.g. drones in [21]) and needs to be further developed.
- Advanced information processing such as artificial intelligence (AI) image recognition accelerates the process from raw data from sensors to reliability assessment [22].
- Robots that integrate sensoring and processing, as stated above.
- Data interoperability with TSO use cases needs to be more standarised.
- Digital twins benefiting from IT advances (Internet of Things (IoT), cloud, and edge computing) applied on TSO assets [23].
- Probabilistic grid reliability analysis for pan-European grid and new load profiles, with potential extensions as follows:
- Practically integrating reliability assessments on assets from TSOs in probabilistic grid reliability models (such as [24, 25]).
- Accelerating the international acceptance of managing reliability in grid nodes for investment planning rather than in asset fleet for maintenance optimum.
- Assessing deep uncertainties that cannot be handled with classic techniques in reliability engineering such as statistics or FMECA, e.g. for the resilience of power networks exposed to periodic or random climate events (hurricanes, flooding) [20, 26].
The technology is in line with milestones “Standardisation of asset management approaches” and “Circular economy and environmentally friendly components included in planning and asset management” under Mission 1 and milestone “Transition to probabilistic risk management approach” under Mission 4 of the ENTSO-E RDI Roadmap 2024-2034.
Technology Readiness Level (TRL)
The technology readiness level (TRL) of asset management (processes, tools, and procedures) strongly varies depending on its actual use and implementation. Therefore, the TRL is based on the current use of asset management within the TSOs.
TRL 9 for maintenance strategy for a fleet of assets of the same type.
TRL 7 for risk-based maintenance for the whole TSO.
References
CIGRE WG B3.48, TB 858 “Asset Health Indices for Equipment in Existing Substations,” 2021.
IGRE WG B3.38, TB 734 “Management of risk in Substations,” 2018.
CIGRE TOR-JWG B2/C1.86 “Approach for Asset Management of Overhead Transmission Lines,” 2021
ISO TC251, ISO 55013:2024 “Asset Management: Guidance on the Management of Data Assets,” 2024.
CIGRE WG C1.11 TB 367, “Asset Management Performance Benchmarking,” 2009.
CIGRE WG C1.25, TB 597, “Transmission Asset Risk Management: Progress in Application,” TB 422, Cigre, 2014.
CIGRE WG C1.38 TB 791 “Valuation as a Comprehensive Approach to Asset Management in View of Emerging Developments,” 2020.
CIGRE WG C1.34 TB 787, “ISO Series 55000 Standards: Implementation and Information Guidelines for Utilities,” 2019.
CIGRE “CIGRE Green Book: Power System Assets: Investment, Management, Methods and Practices,” 2022.
CIGRE WG C1.1, TB 309 “Asset Management of Transmission Systems and Associated CIGRE Activities,” 2006.
CIGRE WG C1.16, TB 422, “Transmission Asset Risk Management,” 2010.
CIGRE WG C1.25, TB 541, “Asset Management Decision Making Using Different Risk Assessment Methodologies,” 2013.
CIGRE WG B3.32, TB 660 “Saving Through Optimised Maintenance in Air Insulated Substations,” 2016.
CIGRE WG A2.49, TB 761 “Condition Assessment of Power Transformers,” 2019.
CIGRE WG B1.57, TB 815 “Update of Service Experience of HV Underground and Submarine Cable Systems,” 2020.
IEC White Paper “Semantic interoperability: Challenges in the digital transformation age,” 2019.
P. S Woo and K. H. Balho, “Contingency Analysis to Evaluate the Robustness in Large-Scale Smart Grids: Based on Information Security Objectives and Frequency Stability,” Energies, vol. 13, p. 6267, 2020.
ENTSO-E SDC WG AIM Report, Risk Method for Optimal Investment on Assets,” 2023.
N. L. Deghani et al., “Optimal Lifecycle Resilience Enhancement of Aging Power Distribution Systems: A MINLP-Based Preventive Maintenance Planning,” IEEE Access, Vol. 8, pp. 22324-22334, 2020.
SINTEF, “Garpur,” SINTEF [Online]
https://tsuru.su/en/2020/06/08/cablewalker_first_automatic
N. Jung et al., “Development an AI Algorithm and Drone Operation System for Diagnosis of Transmission Facilities in KEPCO,” D2-119, Cigre Session, Paris, 2020.
CIGRE TOR-JWG B2/C1.86 “Approach for Asset Management of Overhead Transmission Lines.”
D. L. Alvarez, et al. “Optimal Decision Making in Electrical Systems Using an Asset Risk Management Framework,” Energies, vol. 14, p. 4987, 2021.
CIGRE CIRED JWG C1.C6/37 TB 923, “Optimal Transmission and Distribution Investment Decisions Under Increasing Energy Scenario Uncertainty,” 2024.
CIGRE WG C4.47, Working Group Report “Resilience of Interdependent Critical Infrastructure,” ELECTRA, February 2022.