Outage planning is currently based on a worst-case scenario for each outage. There is limited accounting for the potential impact of increasingly changing system conditions (generation, weather, etc.) or of changes to one outage as a result of other outages. This has historically been done using “rules of thumb”.
With the rapid pace of change, the current planning methods are starting to show their limitations. In particular, a lot of work is devoted to reacting and re-planning.
This project will provide added value by providing a solution to the imperative need for better integration of risk estimation into the planning optimization so that the amount of work remains manageable for the NAP process.
Objectives
The ultimate objective of this project is to develop a tool that
- facilitates the most efficient economic decision-making from the year-ahead plan to three-weeks ahead, and
- identifies and tracks risks from year-ahead to day-ahead.
Learnings
Outcomes
The project has started discussions with ESO IT regarding a design for future implementation which would link into production systems. It is anticipated that further work would be required to adopt the processes proposed to work with ESO systems.
Over the remaining months of the project work will take place to define the development that would be needed to implement the models. A review of data requirements leading to the identification of the internal system interfaces required will be carried out. Following this, a detailed business case and implementation plan would be produced.
Lessons Learnt
There would be significant time saving for future projects by having an openly available power system model with tools available to generate and apply scenarios. An open-source model would also enable academics and industry to evaluate future operational proposals on a common model. This could also provide an environment for testing project results.
The project has used Optimal Power Flow models to generate models for a range of potential scenarios, this is used to ensure the scenarios are credible by managing overloads using the sort of actions the Power System Engineer would take. This sort of automated scenario generation would benefit from further study. Including, for example, the application of voltage control circuits, running arrangements and automated switching schemes.
Challenges were encountered in validating the model performance due to the simplified development models using historic published data. This has made it difficult to validate against future scenarios or perform parallel testing to operational processes. To mitigate this risk and enable the project to progress historic outages have been used for validations. In the practical implementation this would be tackled by linking the proposed outage optimisation models to the operational power system model. The open-source model proposed above could help mitigate this in future projects.
Throughout this project, software was released towards the end of each work package for user testing of all updates applied in that work package. It would have been beneficial to develop a more Agile workflow, releasing smaller and more regular updates to generate a continuous feedback loop.
