Evaluating the network stability using probabilistic modelling of the future e-mobility ecosystem in urban areas
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Environmental concerns about greenhouse gas emission from transportation sector along with advancements in battery technologies has led to massive private and public investment into electrification of the sector (buses, vehicles, scooters, bikes). In this paradigm shift, managing e-mobility ecosystem and overcoming range anxiety are known as the key factors to unlock widespread adoption of the technologies. In this project, we intend to develop a stochastic optimal strategy for charging infrastructure design within distribution networks in urban areas to support future e-mobility ecosystem in both vehicle-to-grid and grid-to-vehicle operation. Several sources of uncertainties, including local traffic pattern, drivers' behaviour and renewable energy resources, will be considered in the proposed planning tool through stochastic modelling. A physical model of the transportation system (including roads, popular stopping points, parking lots, on-street parking) will be mapped over local low- and medium-voltage power network to account for network capacity availability and constraints. The outcome of the tool will be the optimal location of the future e-parking lots and on-street charging points, the size and types of the chargers at each location as well as the future network expansion to fulfil the e-mobility ecosystem requirements.
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Potential Impact:
ReNU's enhanced doctoral training programme delivered by three uniquely co-located major UK universities, Northumbria (UNN), Durham (DU) and Newcastle (NU), addresses clear skills needs in small-to-medium scale renewable energy (RE) and sustainable distributed energy (DE). It was co-designed by a range of companies and is supported by a balanced portfolio of 27 industrial partners (e.g. Airbus, Siemens and Shell) of which 12 are small or medium size enterprises (SMEs) (e.g. Enocell, Equiwatt and Power Roll). A further 9 partners include Government, not-for-profit and key network organisations. Together these provide a powerful, direct and integrated pathway to a range of impacts that span whole energy systems.
Industrial partners will interact with ReNU in three main ways: (1) through the Strategic Advisory Board; (2) by providing external input to individual doctoral candidate's projects; and (3) by setting Industrial Challenge Mini-Projects. These interactions will directly benefit companies by enabling them to focus ReNU's training programme on particular needs, allowing transfer of best practice in training and state-of-the-art techniques, solution approaches to R&D challenges and generation of intellectual property. Access to ReNU for new industrial partners that may wish to benefit from ReNU is enabled by the involvement of key networks and organisations such as the North East Automotive Alliance, the Engineering Employer Federation, and Knowledge Transfer Network (Energy).
In addition to industrial partners, ReNU includes Government organisations and not for-profit-organisations. These partners provide pathways to create impact via policy and public engagement. Similarly, significant academic impact will be achieved through collaborations with project partners in Singapore, Canada and China. This impact will result in research excellence disseminated through prestigious academic journals and international conferences to the benefit of the global community working on advanced energy materials.
Northumbria University | LEAD_ORG |
Otaski Energy Solutions Ltd | STUDENT_PP_ORG |
Abbas Mehrabidavoodabadi | SUPER_PER |
Mousa Marzband | SUPER_PER |
Subjects by relevance
- Renewable energy sources
- Small and medium-sized enterprises
- Traffic
- Networks (societal phenomena)
- Emissions
- Cooperation (general)
- Private transport
- Enterprises
- Electrification
Extracted key phrases
- Future network expansion
- Key network organisation
- Network stability
- Future e
- Network capacity availability
- Voltage power network
- Distribution network
- Mobility ecosystem requirement
- Probabilistic modelling
- Stochastic modelling
- New industrial partner
- Medium scale renewable energy
- Project partner
- Urban area
- Greenhouse gas emission