As energy systems fundamentally change in a way not seen before, radical systems-level thinking is critical to adapt. The current balancing process is highly manual, placing increasing pressures on engineers. To overcome this, the ESO needs to design and develop optimisation tools that improve advice to the control room engineer. This project will deliver a consistent modelling approach to progressively handle new components and components that are currently resolved manually. The project aims to develop an Underpinning Balancing Model (UBM) and map existing manual processes to analytical equations aligned with the UBM. This will help develop a strategic approach to address control room challenges and needs, holistically formulating the balancing problem and informing future system design within a coherent mathematical framework.
Benefits
This project will deliver a mathematical framework to enable improved understanding and clear development of robust requirements needed to define the set of interlocking optimisation problems. The MSBO will also deliver clarity on the market inputs that future balancing optimiser tools will require, providing the ESO with a well-founded model and understanding that can be used to transparently engage with market players to facilitate future market changes that may be required. This project will also help identify areas to automate or improve processes that are currently resolved manually, increasing balancing efficiency whilst handling the increased complexity of a net zero decentralised grid,
Learnings
Outcomes
1. Developed an underpinning balancing model that provides a mathematically rigorous description of balancing.
2. Formulated the overarching optimisation problem that ideally NESO would solve to optimally balance the grid.
3. Identified simplifications that have been made to the grand optimisation problem to reach a practically solvable problem that NESO currently tackles.
4. Showcased the impact that these simplifications have on the resulting system, the benefits and drawbacks of relaxing these simplifying assumptions, and suggestions on how to relax them.
5. Showcased how the underpinning balancing model can be expanded to include new actions, services and entities in the future that do not currently exist.
6. Showcased how an optimiser design philosophy can be defined by consciously making decisions about the formulation of their optimisation problem.
7. Visualised the current process of balancing mechanism in GB’s control room.
Lessons Learnt
All balancing actions that are taken in the control room were articulated, categorised by roles and mapped to relevant tools and applications. While it is valuable to have such articulation of balancing actions, it is still very complicated to comprehend and use for future design purposes. After several brainstorming sessions it was agreed to develop a dynamic visualisation tool (Knowledge graph) to improve the ease of use of the articulated actions.
For articulating the process in the control room, because of the complexity, a bottom-up approach was the best to take.
Through the course of the project, it was learned that creating a visual representation of the processes of the balancing programme was extremely valuable in helping to understand it.