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 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) [1]. 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.

In today’s European TSOs, asset management has generally shown a higher maturity for substation primary assets [2,3] than for overhead lines (OHL)/cables [4]. Moreover, its application to data and software is primitive despite the close attention paid to treating them as assets [5].

In general, asset management aims to achieve a suitable balance between performance, cost, and risk, as shown in the figure below. 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.

  

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.

Unfortunately, many of the uncertainties that TSO asset managers face cannot be quantified. Even within the classical maintenance management framework, failures in new asset systems such as high voltage direct current (HVDC) are understood with relatively low strength of knowledge. 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.

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.

Examples

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.
TRL 5 for optimisation of all investments in a transmission network.

1. CIGRE WG C1.11 TB 367, “Asset Management Performance Benchmarking,” 2009.

2. CIGRE WG B3.48, TB 858 “Asset Health Indices for Equipment in Existing Substations,” 2021.

3. CIGRE WG B3.38, TB 734 “Management of risk in Substations,” 2018.

4. CIGRE TOR-JWG B2/C1.86 “Approach for Asset Management of Overhead Transmission Lines,” 2021

5. ISO TC251, ISO 55013:2024 “Asset Management: Guidance on the Management of Data Assets,” 2024.

6. CIGRE WG C1.25, TB 597, “Transmission Asset Risk Management: Progress in Application,” TB 422, Cigre, 2014.

7. CIGRE WG C1.38 TB 791 “Valuation as a Comprehensive Approach to Asset Management in View of Emerging Developments,” 2020.

8. CIGRE WG C1.34 TB 787, “ISO Series 55000 Standards: Implementation and Information Guidelines for Utilities,” 2019.

9. CIGRE “CIGRE Green Book: Power System Assets: Investment, Management, Methods and Practices,” 2022.

10. CIGRE WG C1.1, TB 309 “Asset Management of Transmission Systems and Associated CIGRE Activities,” 2006.

11. CIGRE WG C1.16, TB 422, “Transmission Asset Risk Management,” 2010.

12. CIGRE WG C1.25, TB 541, “Asset Management Decision Making Using Different Risk Assessment Methodologies,” 2013.

13. CIGRE WG B3.32, TB 660 “Saving Through Optimised Maintenance in Air Insulated Substations,” 2016.

14. CIGRE WG A2.49, TB 761 “Condition Assessment of Power Transformers,” 2019.

15. CIGRE WG B1.57, TB 815 “Update of Service Experience of HV Underground and Submarine Cable Systems,” 2020.

16. IEC White Paper “Semantic interoperability: Challenges in the digital transformation age,” 2019.

17. 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.

18. ENTSO-E SDC WG AIM Report, Risk Method for Optimal Investment on Assets,” 2023.

19. 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.

20. SINTEF. “Garpur,”

21. https://tsuru.su/en/2020/06/08/cablewalker_first_automatic/

22. 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.

23. CIGRE TOR-JWG B2/C1.86 “Approach for Asset Management of Overhead Transmission Lines.”

24. D. L. Alvarez, et al., “Optimal Decision Making in Electrical Systems Using an Asset Risk Management Framework,” Energies, vol. 14, p. 4987, 2021.

25. CIGRE CIRED JWG C1.C6/37 TB 923, “Optimal Transmission and Distribution Investment Decisions Under Increasing Energy Scenario Uncertainty,” 2024.

26. CIGRE WG C4.47, Working Group Report “Resilience of Interdependent Critical Infrastructure,” ELECTRA, February 2022.

27. ReLife. “ReLife 1.0.0 documentation,”

28. https://www.ree.es/en/press-office/monographs/2019/06/manint-digital-revolution-serving-transmission-grid