Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NGSO0002 | |
| Title | GB Non-renewable Embedded Generation Forecasting Study | |
| Status | Completed | |
| Energy Categories | Other Power and Storage Technologies(Electricity transmission and distribution) 100%; | |
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 25%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 75%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given National Grid Electricity Transmission |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 June 2017 | |
| End Date | 01 January 2018 | |
| Duration | 7 months | |
| Total Grant Value | £91,500 | |
| Industrial Sectors | Power | |
| Region | London | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , National Grid Electricity Transmission (100.000%) |
| Web Site | http://www.smarternetworks.org/project/NIA_NGSO0002 |
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| Objectives | To design the embedded generation models, this project will Investigate how large is their total contribution, and if there characteristic generation profiles for these generators. Determine if there a characteristic profiles for the net effect of all the embedded generation (excluding wind and solar as these are weather dependent). Identify any there correlations between the net effect of the embedded generation and any other generator type. Identify any variables that have a significant impact on the generation profiles for each of the generators. Identify incremental project benefit by defining forecasting error with and without the developed non-renewable embedded generation models. Detailed models of embedded generation (other than wind and solar) resulting from data analysis. These models will be validated for implementation within National Grid’s Energy Forecasting System. Incremental project benefit demonstrated from the developed embedded generation models. | |
| Abstract | The amount of embedded generation connecting to the distribution network has increased significantly in recent years. Primarily this embedded generation takes the form of wind and solar PV, though biomass, landfill gas, diesel generation have also increased. In part this is due to commercial mechanisms that make the connection of these technologies viable at lower voltages. The impact is that the electricity demand at Grid Supply Point and therefore national level is reduced. There is no obligation for such distributed technologies connected at lower voltages to install metering and National Grid has no direct visibility of this growth in generation. There is ongoing research into forecasting solar PV and developing models to minimise forecasting errors; NIA_NGET0170 and NIA_NGET0177 refer. However, further development is required to address how the increase in other embedded technologies correlate with wind and solar PV and their impact on demand forecasting. This unknown quantity of non-renewable embedded generation directly relates to how much generation for reserve and response National Grid instructs in order to operate the transmission system. This balancing system cost is socialised across the industry and therefore passed on to consumers. Reducing errors in demand forecasting will bring a clear benefit in lowering these costs. For this project, The Smith Institute for Industrial Mathematics and System Engineering will utilise embedded generation data available from ElectraLink. Embedded generation models will be developed to directly improve forecasting accuracy by accounting for types of embedded generation that are not currently included in National Grid’s Energy Forecasting System (EFS) and validate these using mathematical and statistical techniques. Further learning outcomes will provide insight into fuel type constraints faced by non-renewable embedded generation that impact their operational behaviour with respect to energy forecasting.Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 15/08/18 | |
