Outage requests from asset owners are ideally scheduled when their effect on constraint limit loss is minimal. Currently, the network planning team model the effect of each outage on constraint limits using PowerFactory models. Although this process can account for the complexities of the network, it can be slow and contributes to a backlog of outage requests from asset owners, which can lead to costly emergency or short-notice outages from unresolved outage requests and results in penalties (Fail to Fly) from Ofgem.
This project aims to explore the relationship between outages and constraint limit losses by examining historical outage and post-fault action data and developing an AI. model that can perform a rapid first pass assessment of outage requests reducing the search-space for time consuming PowerFactory modelling.
Benefits
The primary benefit lies in the expedited assessment of outage requests, as the tool will provide a rapid first pass assessment of outages narrowing the search space for subsequent simulation. Additionally, the assessment of outages over different times could also allow network planning teams to quickly consider other time periods for outages other than those requested by the asset owner, so that outages can still proceed even if the initial timescales are not optimal. This empowers the ESO to make informed decisions promptly, particularly in the context of scheduling outages alongside existing ones.
Moreover, the software's ability to better comprehend the consequences of post-fault actions on constraint limit loss offers a twofold advantage. First, it significantly reduces the need for time-consuming Power Factory modelling, effectively saving valuable time and resources. Second, this improved understanding allows for more precise outage assessments, ultimately mitigating the backlog of outage requests.
Learnings
Outcomes
The FastOut project yielded several notable outcomes despite not progressing to full deployment. The project advanced the understanding of the relationship between outages and constraint limit loss, highlighting the complexities involved in outage scheduling. The engagement with NGESO users and stakeholders facilitated a comprehensive requirements analysis, ensuring that future iterations of the tool can better meet operational needs. Furthermore, the project identified critical data management issues, providing a clear direction for improving data quality and consistency in future projects. These outcomes collectively contribute to the ongoing efforts to enhance network planning and outage management processes.
Lessons Learnt
The FastOut project provided several valuable lessons for future initiatives in outage management and network planning. A key lesson was the importance of ensuring high-quality and consistent data from the outset. The significant time and resources spent on data correction underscored the need for robust data management practices. Additionally, the project highlighted the challenges of modelling complex network interactions and the limitations of existing tools in capturing these dynamics accurately. Future projects should consider more flexible and adaptable modelling approaches. Engaging stakeholders early and throughout the project lifecycle proved essential for aligning project outputs with user needs and expectations. Lastly, the importance of iterative development and testing was reinforced, allowing for continuous refinement based on real-world feedback.
