1. Executive Summary
Transgrid has enhanced its suite of asset management decision making frameworks to support timely, effective, and efficient asset management investment decision making and to manage changing risk. In conjunction with enhancing its decision making frameworks, Transgrid automated the risk cost calculation for tens of thousands of network assets including transmission lines, substation equipment and relays. The output of the enhanced model calculates future asset risk and reliability for a given investment program. This has enabled Transgrid to balance cost, risk and performance when managing its assets, generating savings for our customers and efficiency improvements for Transgrid.
2. Description of Project
2.1. Need for Project
Regulated electricity network businesses, such as Transgrid, periodically apply to the Australian Energy
Regulator (AER) to assess their revenue requirements. In the previous investment cycle, Transgrid
received feedback from both the AER and consumer groups that our risk modelling did not fully
demonstrate the need and value to consumers of the investment, and hence did not fully satisfy them on
prudency and efficiency. Delivering value to our customers is a critical pillar of our business and so we
undertook to enhance the key risk frameworks.
2.2. Project Details
To address key stakeholder feedback, and to ensure that we efficiently analyse and evaluate network asset risks in a systematic and consistent manner, Transgrid made considerable enhancements to three integrated decision making frameworks:
– Network Risk Assessment Methodology
– Network Asset Health Framework
– Network Asset Criticality Framework
These documents create the framework whereby each Transgrid network asset investment decision is data driven, benefit based and aligned with safety and performance expectations. The investment portfolio is then fine-tuned for deliverability, optimal timing and efficiency.
Figure 1 shows how these frameworks are integrated with Transgrid’s Risk Management Framework, corporate and asset management objectives, which in turn drive our asset management plans. Figure 2 illustrates how these frameworks work together to produce a quantified risk cost, based on asset health, probability of failure, likelihood of consequence and consequence of failure.
Figure 1 – Asset Management Decision Frameworks
In conjunction with updating the decision frameworks, extensive changes to the data and calculation engines for determining asset health and risk cost quantification were undertaken. Transgrid’s Risk Assessment Methodology Framework is presented in Figure 2 below, with the components which have undergone significant system improvements (C1-C3) within in the past 12 months highlighted.
Figure 2 – Risk Assessment Methodology
The following sections briefly describe each of these decision making frameworks and the overall
approach.
2.2.1. Network Risk Assessment Methodology
The Network Risk Assessment Methodology provides the guiding principles for the management of risk within the Asset Management System. The overriding principle is to provide benefit to the consumer and Transgrid’s stakeholders in accordance with Transgrid’s strategic pillars and aligned to Transgrid’s asset management objectives and the Enterprise Risk Management Framework.
2.2.2. Network Asset Health Framework
The Network Asset Health Framework outlines the methodologies and processes applied to calculate the current and future effective age of individual network assets, and the effective age and probability of failure for each network asset class.
2.2.3. Network Asset Criticality Framework
The Network Asset Criticality Framework outlines the manner in which consequences for network asset failures are consistently assessed and quantified across the business. Asset criticality considers the severity of the consequences of the asset failure occurring and the likelihood the consequence will eventuate. The analysis leverages data from past events, relevant research and technical insights to determine an economic value of the impact.
A key enhancement to address stakeholder concerns was to determine asset criticality at the asset level instead of applying a generic criticality to all our assets. This then enabled Transgrid to better understand its risks, and to better identify and prioritise its investments.
2.3. Overview of key challenges
The key challenges addressed by the project included the following: Consolidation (C1) of asset information to provide a holistic view of all assets from the time of
commissioning through to disposal in a single ‘flat file’. Transgrid’s existing asset data systems consists of a number of siloed corporate systems, each managed by disparate business units. Further complicating this challenge was transforming and standardising asset data across three different asset classes. The challenge was to not only consolidate and standardised asset information into a single, easy to use source, but to automate this task. An additional and related challenge was estimating loss of supply costs and electricity market impacts, and incorporating these into the risk cost calculations. This included developing a relational database to store
the input data and associated spreadsheets to perform the asset criticality calculations, followed by undertaking power system simulations. More than 3,000 power system simulations were required to cater for multiple demand scenarios, various levels of system redundancy and system interconnectivity.
A further challenge was to accurately depict, calculate and automate 25 Asset Health Index Models (C2) across transmission lines, substations and digital infrastructure assets in line with requirements as documented in Network Asset Health Framework. The enhanced approach means that the overall health of an individual structure/span is a function of the health of each component and a health score is calculated for each component. The overall challenge (C3) was to accurately calculate and quantify every asset’s risk, based on the founding principles set out in the three decision making frameworks. Effectively bringing together various inputs, models and other facets to arrive at quantifying asset risk $. In excess of 100 variables for a risk $ quantification were identified for each asset to perform the calculations.
2.4. Program and project management
The key milestones for the project were:
– Starting with the identification of need for the project during 2018/ 2019, based on feedback from the AER and other consumer groups.
– During 2020, Transgrid subject matter experts surveyed the industry for best practice, and developed enhancements to our decision making frameworks.
– During 2021, Transgrid consolidated its asset information, modelled asset health and calculated risk for all assets in alignment with its decision making frameworks. In addition, Transgrid refined its capital portfolio optimisation processes.
– In 2021, we engaged a technical expert to undertake a technical assurance of our project scoping and estimation processes, and our repex program inputs. The outcomes of this independent review were that these elements are in line with good industry practice.
– Finalised the development of the three key decision frameworks and the asset risk quantification modelling for input into the investment framework – November 2021
– Finalised our capital portfolio – December 2021.
2.5. Benefit of the project to the community and organisation
A fundamental requirement of asset management is to provide a balance between the cost of providing an asset performance to an agreed level of risk. Our decision frameworks provide a repeatable process which address various best practice asset management principles:
– Calculating the residual risk to deliver an agreed asset performance is aligned to the Transgrid risk management framework.
– Activities are focused on deriving long term value for the organisation.
– Managing assets in alignment with organisational purpose and strategy.
For end consumers we were able to propose a forward 5-year program of investment with only minimal increases that can be demonstrated to retain risk and reliability performance at the same levels, despite an increase in overall asset age. The system enhancements described earlier also provides significant secondary benefits to Transgrid. It provides an efficient, replicate-able and maintainable tool to update risk costs across the entire asset base. Traditionally it has taken hundreds of man hours, and as a consequence, such updates were rarely undertaken to calculate Transgrid’s asset risks. Now asset data consolidation (C1) can be updated in 18 hours, asset health indexes (C2) re-calculated within a few minutes and refreshing asset risk costs (C3) in a few hours.
Specific Contribution
Significant effort and collaboration was used extensively throughout the project, as follows:
Developing the necessary criticality enhancements to cater for future loss of supply costs and electricity market impacts took 2 years, from the review and development phase, identifying areas for improvement, through to implementing the enhancements and producing the relevant outputs. The majority of time and effort was expended on conducting power system simulations. In developing the enhanced network asset criticality methodology, the Asset Analytics and Insights team worked closely with Transgrid’s Network Operations to obtain the required knowledge and skills to use the Power World power system simulation software.
Developing (C1) the “flat file”, the associated calculations and SQL scripting took 6 months to complete. This challenge required intimate knowledge of the various systems, the databases, the structure of the data contained within, the logical linkages, the data itself and scripting ability.
Enhancing (C2) the asset health indexes took 10 weeks to develop, with the Asset Insights and Analytics team working closely with the asset strategists. Whilst developing the asset risk costs (C3) took 3 months to develop including subject matter expert review and assurance.
To accurately calculate and quantify each asset’s risk cost (C3), based on the all the principles set out in the Network Asset Risk Assessment Methodology, Network Asset Criticality Framework and Network Asset Health Framework required 3 months effort. Effectively bringing together all the inputs, models and other various facets to arrive at asset risk $. It was a large and challenging task to join the various pieces of the puzzle together, which was only achieved by a collaborative effort from across the organisation.
General Comments
We measure our total network risk in the form of a ‘risk index’, which is a multi-dimensional measure for safety, environmental, bushfire and reliability risk. The risk index is the sum of the residual risk of each individual asset, which is then baselined, so that we can monitor relative changes in network risk over time.
Figure 3 displays Transgrid’s forecast accumulated asset risk for each year 2019 to 2029, inclusive, after undertaking capital portfolio optimisation. In addition, Figure 4 provides our forward view of predicted reliability for the optimised portfolio.
Figure 3 – Annual Network Risk Index
Figure 4 – Expected Annual Network Reliability 2023-28
The figure below displays Transgrid’s transmission lines near Canberra. Each dot represents a transmission pole or tower line structure. The smaller green dots indicate those structures whose asset health is best, whereas the larger red dots indicate those structures whose asset health is poorer.
Figure 5 – Transmission Line Structure Asset Health