The Electricity National Control Centre’s (ENCC) current approach of manual instructions is limiting the number of units that can be instructed in real-time operation of the GB electricity system. With an increasing number of units to dispatch across multiple services, this approach is not sustainable for future economic and reliable operation of the system. The project will deliver a proof-of-concept decision-support tool to the ENCC, aiming to release operators of manual tasks and enabling them to focus on validating results and ensuring timely decisions are made. While control room decisions within-day span operational planning, scheduling and dispatch, the project will first focus on course-correction for dispatch decision-support problems. The tool will be developed and tested using advanced optimisation techniques and data-driven approaches, including development and implementation of a mathematical model, demonstrating the model performance on selected real-world data examples.
Benefits
The project aims to discover, develop, and test a world first course-correction methodology to balance an electricity system. The tool delivered will be a key enabler towards future automation of dispatch actions, and for operating a zero-carbon electricity system. The decision-support models developed will provide economic and transparent decisions, reducing skip rates and informing control room engineers with explainable reasons behind an instruction. The proof-of-concept tool will also provide means for development of further ENCC decision-support methodologies.
Learnings
Outcomes
The project aims to develop world’s first course correction technology for electricity control room applications. So far, the project has developed a framework and implemented an initial demonstrator that can be utilised for testing. The current research focuses on the further development and enhancement of predictive models, as well as the testing and validation on a large set of exemplars.
Lessons Learnt
The work within the project has resulted in the development of several scripts used to process and generate input data for the testing and validation of our predictive models. The project will further enhance and refine the scripts, creating a robust mechanism for automated testing and validation. These exemplars generated as part of this project will be particularly valuable for research and innovation projects focused on ancillary services of reserve, response and inertia, as well as the second phase of the project, which focuses on long-term dispatch and scheduling.
One of the deliverables from this project is energy requirement for a given time horizon, which is closely linked to the bulk dispatch optimizer of Open Balancing Platform within the Balancing Programme project. The energy requirement serves as an input to the bulk-dispatch optimization, which is currently manually drawn. This project will close the loop by providing the means to automate determination of energy requirement.