COMMANDER

The end consumer, Distribution Network Operators (DNOs) and Distributed Energy Resources (DERs) are becoming active participants in providing increasing levels of flexibility to the Great Britain (GB) electricity system but there is uncertainty in relation to the roles and responsibilities of the ESO and DSOs in this new smart energy world. This project aims to address those gaps and has the potential to deliver whole systems benefits by creating new flexibility market opportunities for potential service providers. 

Deliverables of the project will include reports on national and international trends, a techno-economic feasibility assessment of the developed ESO/DSO coordination schemes at operational timescales, an impact assessment of the ESO/DSO coordination schemes and a roadmap for the physical deployment of the preferred ESO/DSO coordination scheme. 

Benefits

The energy system is rapidly changing with end consumers, DNO’s and DERs becoming active participants in providing flexibility across the GB electricity system. This project has the potential to deliver whole systems benefits by creating new flexibility market opportunities for potential service providers. 

Enabling efficient access to DERs through streamlined ESO/DSO coordination will deliver:  

• Opportunities for customers to realise value from services and new technology
• More sustainable energy markets and networks
• Reduced costs to consumers through more optimised use of services  
• Enhanced security of supply
• Transition to net zero at the lowest overall cost for customers  

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Outcomes

The initial work package developed models that improved upon the current single-value carbon intensity of a power plant, and produced models for gas, coal, and biomass. These models were compared against the single values currently used for carbon intensity. 
 
The models demonstrated reasonable initial performance given the limited available data and were refined to optimise performance and reduce the risk of overfitting. They were built using features derived from the half-hourly operation of power plants, with various iterations and combinations of these features used as inputs—including interactions and nonlinear terms. 
 
Due to the low volume of data and the emphasis on explainability, a linear regression approach was used. Only a small number of features were included in the final models to further mitigate the risk of overfitting.

The project suggested several activities as follow-on activities to further the impact of the project: 

• NESO needs more access to more granular data on power plant inputs.
• With additional data, additional modelling techniques should be explored to improve the performance of the machine learning.
• Explore more nonlinear relationships between the variables and correlations between the variables
• The best performing model for CCGT plants is a Bayesian regression model using time spent on and time spent in ramp up as features.
• If data is available, look at the biomass plants separately to separate the behaviour of Drax from the other plants.
• Linear regression, using only the time the plant was on, is the best-performing model for biomass plants.
• The most predictive model for coal is that without an intercept that has the variables time spent on and % of time constant.
• Replace the current values in the grid carbon intensity calculation with the modelled ones for the different gas generators.
• Do not change the value used for coal in the grid carbon intensity calculation.
• Explore a project conducting full lifecycle analysis of carbon emissions for all the main fuel types.
• Calculate improved carbon intensity calculations for the non-wood pellet biomass plants 

Lessons Learnt

Lessons learnt from this project indicate that the data, which was collected monthly and for groups of power plants, restricts the modelling techniques available for assessing the performance of these plants. This issue could be alleviated by collecting data at more frequent intervals, such as daily or weekly, or by focusing on individual plants. Future projects should extend this work to get predictions of carbon intensity per BM Unit per half hour with more granular data. 

With improved modelling, detailed analysis of the impact of dispatch and flexibility services should be undertaken.