Projects
Projects: Projects for Investigator |
||
| Reference Number | NIA2_NGESO027 | |
| Title | Carbon Intensity Modelling | |
| Status | Completed | |
| Energy Categories | Other Cross-Cutting Technologies or Research(Energy Models) 60%; Other Power and Storage Technologies(Electricity transmission and distribution) 40%; |
|
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 20%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 80%; |
|
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given National Grid plc |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 January 2023 | |
| End Date | 31 December 2023 | |
| Duration | ENA months | |
| Total Grant Value | £205,000 | |
| Industrial Sectors | Power | |
| Region | London | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , National Grid plc (100.000%) |
| Industrial Collaborator | Project Contact , National Grid plc (0.000%) |
|
| Web Site | https://smarter.energynetworks.org/projects/NIA2_NGESO027 |
|
| Objectives | "This project consists of three work packages, running sequentially with a mid-term review to reassess the viability of the project and continued alignment with project objectives.Work Package 1 – Creation of the model for gas plant performanceInitial models for the performance of power plant carbon intensity (CCGT/OCGT/Biomass/Coal) will be created based on the values in the literature. These models will be compared against the known values for energy produced by month and input usage by month (subject to data available from BEIS) to test their performance against the current single values used for plant carbon intensity. The model will then be refined by using the physical understanding of power plant operation and an estimation technique such as a Kalman filter to use the available data to refine the estimates of the latent variables in the system. The resulting models will be benchmarked against the previous ones and a measure of their uncertainty provided.It is expected that the data available for biomass/coal will be much less than those for gas plants due to commercial sensitivities around the BEIS data due to the small numbers of energy plants. This means it is expected the models produced will be weaker for biomass/coal than for Open Cycle Gas Turbine (OCGT)/Combined Cycle Gas Turbine (CCGT). The uncertainty around the models for all plants will inform the feasibility of WP2.Work Package 2 – Carbon Intensity OptimiserA proof-of-concept tool will be created to optimise dispatch by considering current constraints (system requirements) and the models created in WP1. The problem of interest will be considered as a stochastic optimal control problem. Reinforcement learning algorithms will be employed to create a tool that will not only consider the current system requirements but also the carbon intensity of those decisions.Work Package 3 – Project Report A report will be produced to provide an overview of the work completed, potential impact and limitations identified. This will include recommendations for follow on projects to further develop and operationalise outcomes of the project, identifying additional data that would allow development of a more accurate model or optimiser.In line with the ENAs ENIP document, the risk rating is scored Low.TRL Steps = 1 (2 TRL steps)Cost = 1 (£205k)Suppliers = 1 (1 supplier)Data Assumptions = 2Total = 5 (Low) " "This project for carbon intensity modelling will:Explore how to accurately model carbon output of carbon plant in different operational modesUse a combination of traditional statistical techniques and cutting-edge machine learning methods to increase accuracy beyond current peak efficiency assumptionBuild upon internal capability with external domain experts.If successful, provide recommendations for future development of carbon intensity tool into Carbon Intensity of Balancing Actions and Virtual Energy Systems (VES)." "Identify key missing data items that will have the most impact on our understanding of the carbon intensity of gas/coal/biomass generation.Improve the carbon intensity modelling of CCGT/OCGT/Coal/Biomass plants in different states by creating models for when they are operating outside of peak efficiency, and by better understanding the relative efficiencies of BMUs where data is available.Develop a PoC tool to optimise the dispatch of the plants on the grid with respect to the carbon intensity of the energy produced (e.g. by generating a carbon merit order, providing recommendations for dispatch which factor in predicted upcoming BM requirements as well as peak / partial load efficiency of available BMUs, or other methods yet to be determined). " | |
| Abstract | "The current methodology for calculating carbon intensity from fossil plants is based upon a simplistic calculation which does not capture variability between different generators of the same type, or within individual generators over time. National Grid ESOs ability to improve the carbon intensity calculation is hampered by lack of available data on the fuel consumption of individual generators, which is considered commercially sensitive by the data owners.Using available data and relevant knowledge from scientific literature, this project will research and develop a refined model that will improve the accuracy of carbon intensity for power generation. This data is important in tracking the progress towards de-carbonising the electricity system, and in future could also be used to optimise the dispatch of power based on carbon intensity." | |
| Publications | (none) |
|
| Final Report | (none) |
|
| Added to Database | 01/11/23 | |
