Great Britain’s energy landscape is changing at an increasing rate, not only in terms of how energy is generated and distributed but also in how it is used by consumers. This increases the complexity for grid operators in forecasting power system conditions and in developing dispatch control strategies that can utilise more distributed intermittent renewable resources whilst retaining the reliability of supply required. Traditional dispatch tools and processes are becoming increasingly insufficient and cannot take advantage of the advancement of digital technologies such as Artificial Intelligence and Machine Learning. This project builds upon work undertaken in NIA2_NGESO0013 Advance Dispatch Optimisation and will design a blueprint for transformation of control room tools and processes to meet the System Operator needs of the future.

Benefits

Multiple benefits can be realised from the delivery of this project:

Short Term Benefits

• Identification of the detailed requirements for the system components and adaptive input models
• Development of the sequencing required to deliver a control room dispatch optimiser tool capable of assessing the multiple operational scenarios inclusion of distributed intermittent renewable resources present.

Longer Term Benefits

• Optimised forecasting and dispatch by leveraging flexible demand and storage technologies reducing Balancing Mechanism (BM) costs 
• Management of increasingly complex grid operations through utilising adaptive input models and machine learning.
• Improved performance monitoring, evaluation and feedback of the system through machine learning and operator education.
• Improved operator situational awareness through the use of multi-dimensional visualisation tools utilising highly accurate input data, probabilistic trajectories, input scenario information, optimisation results and scenarios.