Asset management and strategic planning at grid companies (power utilities)

ASSET MANAGEMENT AND STRATEGIC PLANNING

The asset management unit of a power utility bears responsibility for guaranteeing the security of the supply as well as for the long-term development and maintenance of the grid. In this, the focus is, on the one hand, on the development of maintenance and investment strategies that require an assessment of the grid components and a determination of their condition. On the other hand, budgeting and the planning of measures in coordination with the overall business planning comprise the second focal point.

In the development of the schedules of measures and of asset strategies, the decision-makers at the grid companies have available a vast array of diverse data. In the day and age of powerful hardware and ever larger storage capacities, the challenges is not so much posed in the collection of data. Rather, it becomes necessary to master the dependencies between the systems in the prevalent IT system landscapes in such a way that the data collected can be analysed and decision-relevant information can be obtained from it.

In order to be able to live up to these complex tasks, the management of the company-wide distributed data from the different stages of planning and operation certainly is among the challenges critical to success.

Therefore, a consolidated data base constitutes an indispensable planning requirement, independent of the performance of the operative and planning systems. Even though analyses and subsequent systems in the planning process require resilient and reliable foundations, quality-controlled planning data often is available only to a limited extent and/or can be created only with considerable effort. Furthermore, such data often only have a commercial or technical focus and are available for analysis only isolated from one another. At this point, there is the option of a centrally organised information management based on a data warehouse solution that allows for the integration of technical and commercial grid data and allows for access to current and historic data from different perspectives.

For planning tasks, the relevant data must be extracted from the source systems, integrated, consolidated, processed, and made available for different planning perspectives. In practice, these processes are often carried out in the specialised departments, based on Excel. In the layers of a modern data warehouse architecture, these processing steps are implemented automatically in the form of ETL processes (Extract – Transform – Load).

The advantage of such a solution is first and foremost the

• reduction of work effort due to the automation of recurring processing steps,
• homogeneity in data processing,
• avoidance of sources of errors due to automatic conversion and checking mechanisms,
• integration and consistency of the data for cross-unit planning decisions, and
• flexibility in terms of the data utilisation in virtually any subsequent system (simulation, planning, reports).

Information management closes the gap between operational systems and the planning requirements and allows for demand-oriented, quick data access in order to create flexible data views. Due to the increase in quality and transparency, the structuring and automation already in this form have a positive impact on the planning process.

For the presentation and visualisation of the data, a management cockpit can be placed on top. Such a cockpit provides for the joint presentation of indicators and KPIs (Key Performance Indicators) for the analysis of technical and commercial interdependencies and thereby efficiently supports the decision-making process in mid-term planning. The easy switching of perspectives and of aggregation levels further improves the planning process. If even specialised systems such as report, planning, or simulation tools are available, the quality-controlled data management of these systems can take place from the data warehouse.

The consulting service of noventum for the information management at grid companies is placing the focus of the solution concept on the intelligent structuring and integration of technical and commercial business data. The central idea is modelled after the requirement to extract data from the operational systems and process it – at decision-relevant levels of detail – for planning purposes, to manage it, and to make it available in a custom management cockpit. In this, the customer's individuality is taken into consideration in the project approach.

Torsten Kanngiesser