Projects
Projects: Projects for Investigator |
||
| Reference Number | NIA_SPEN_0037 | |
| Title | Electric Vehicle Uptake Modelling (EV-Up) | |
| Status | Completed | |
| Energy Categories | Energy Efficiency(Transport) 80%; Other Cross-Cutting Technologies or Research(Environmental, social and economic impacts) 20%; |
|
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 30%; ENGINEERING AND TECHNOLOGY (Architecture and the Built Environment) 20%; |
|
| UKERC Cross Cutting Characterisation | Sociological economical and environmental impact of energy (Consumer attitudes and behaviour) 100% | |
| Principal Investigator |
Project Contact No email address given SP Energy Networks |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 February 2019 | |
| End Date | 01 August 2021 | |
| Duration | ENA months | |
| Total Grant Value | £625,000 | |
| Industrial Sectors | Power | |
| Region | Scotland | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , SP Energy Networks (100.000%) |
| Other Investigator | Project Contact , SP Energy Networks (100.000%) |
|
| Industrial Collaborator | Project Contact , SP Energy Networks (0.000%) |
|
| Web Site | https://smarter.energynetworks.org/projects/NIA_SPEN_0037 |
|
| Objectives | Working with Field Dynamics, this project will look to better understand customers ability to transition to electrified transport by combining each households ability to park off street with key demographic information; such as age profile and economic activity. It will also help inform understanding of customers ability to transition to electrified heat by combining each households adjacent spatial characteristics and current heating type with the aforementioned key demographic information. Combining this information will enable SPEN to have greater understanding on the probability of specific areas to transition to EVs and decarbonised heat, greatly improving future demand profiling for domestic customers and understanding of what network reinforcement solutions could be adopted.The project will investigate the following areas; Probability of an owner of an EV being able to park and charge at homeProbability of a household to be able install a Heat PumpThe demographic of the Low Carbon Technology owner, including income and behavioursThe make and model of the vehicles (and corresponding battery size)The type of heat pump likely to be installed (Air source, Ground source etc.)Field Dynamics propose to supplement existing network forecasting tools with output from a model deployed on Field Dynamics Accelerated Insight Platform. This will provide scenarios using a combination of detailed parking probability data, demographics and vehicle data, heating types and adjacent spatial availability, which will be updated and improved in collaboration with SPEN as the innovation project progresses.The solution will enhance forecasting accuracy; help with resilience planning, which in turn will more accurately inform LV infrastructure upgrades and replacement programmes. There will be Two distinct project phases firstly covering the SPM licence area with the second phase focused on the SPD area.1.a. Setting up and integrating the model output into SPENs environment 1.b. Additional sprints to improve the model by refining or adding business rules based on additional site surveys and other validation exercises for SPM licence area1.c. cost benefit analysis update2. Additional sprints and subsequent validation work for SPD licence areaSPEN has a number of network forecasting tools and initial information about where EVsand Heat Pumps are currently registered, but there is a requirement for a more granular view in order to accurately forecast and look ahead. NIA project Network Constraints Early Warning System (NCEWS) has developed a solution to create a basic LV connectivity model, with the ability to backfill missing data; ensuring the LV network is topologically sound so that data can be aggregated from household level up to flex a range of scenarios.Field Dynamics will work collaboratively to define and setup the initial model with a roadmap for further iteration, all with the goal of improving accuracy. Model output will then be absorbed as a new data set into existing systems to enhance existing understanding. EV-Up will contribute to the development of data sets to improve our understanding of customers ability to transition to Electric Vehicles (EVs) based on off-street parking opportunity and customer demographics. This will enable improved understanding on the likely network areas which will see increased domestic demand and better inform future investment programmes. In addition the dataset will complement existing work being carried outin other innovation projects such as NCEWS and Charge.SPEN are adding forecasting of Heat Pump uptake as SPEN require examination on whether this will improve the usefulness of EV uptake forecasting. It is anticipated that where EV uptake coincides with Heat Pump uptake is where the greatest networks problems are likely to arise. Therefore, it is pivotal SPEN are able to model/forecast effects of both EVs and Heat pumps simultaneously. | |
| Abstract | EV-Up will allow Network Licencees to better understand the impact of Electric Vehicles (EVs), and the ability of customers to transition to using EVs. | |
| Publications | (none) |
|
| Final Report | (none) |
|
| Added to Database | 14/12/22 | |
