Projects
Projects: Projects for Investigator |
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| Reference Number | EP/T021713/1 | |
| Title | Resilient Operation of Sustainable Energy Systems (ROSES) | |
| Status | Started | |
| Energy Categories | Renewable Energy Sources 10%; Other Cross-Cutting Technologies or Research(Energy system analysis) 10%; Other Power and Storage Technologies(Electric power conversion) 25%; Other Power and Storage Technologies(Electricity transmission and distribution) 30%; Other Power and Storage Technologies(Energy storage) 25%; |
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| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 10%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 90%; |
|
| UKERC Cross Cutting Characterisation | Not Cross-cutting 70%; Systems Analysis related to energy R&D (Other Systems Analysis) 20%; Other (Energy technology information dissemination) 10%; |
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| Principal Investigator |
Dr B Pal No email address given Department of Electrical and Electronic Engineering Imperial College London |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 July 2020 | |
| End Date | 30 June 2024 | |
| Duration | 48 months | |
| Total Grant Value | £775,785 | |
| Industrial Sectors | Energy | |
| Region | London | |
| Programme | Energy : Energy | |
| Investigators | Principal Investigator | Dr B Pal , Department of Electrical and Electronic Engineering, Imperial College London (99.998%) |
| Other Investigator | Dr X Zhao , School of Engineering, University of Warwick (0.001%) Dr A Singh , Sch of Electronics and Computer Sc, University of Southampton (0.001%) |
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| Industrial Collaborator | Project Contact , UK Power Networks (0.000%) Project Contact , FTI Consulting, USA (0.000%) Project Contact , GEIRI Europe (0.000%) Project Contact , Beijing GZ Control Technology (0.000%) Project Contact , Elin VERD. S.A (0.000%) Project Contact , Manitoba Hydro International Ltd (0.000%) Project Contact , TGOOD Global Ltd (0.000%) |
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| Web Site | ||
| Objectives | ||
| Abstract | Upgrade of UK energy system is at the core of Smart systems and flexibility plan laid out in the Government's Industrial Strategy. Beijing, Shanghai, Guangzhou and Shenzhen are aiming to build up world-class distribution networks as they are among the target areas for export promotion. The massive uptake of renewable generations in both the UK and China offers a huge challenge requiring expensive balancing mechanism. It is only through fundamental research focused on addressing these challenges that truly transformative changes to our energy future beyond 2050 can happen. The purpose of this proposal is to carryout underpinning research with an objective to develop tools that will make our electricity supply system resilient as well as sustainable. It will be both data and model driven activities of a strong consortium of technical experts from both sides. The data driving machine learning tool will deliver operational health index of various components in the system. It will employ dynamic state estimation to develop new network automation procedure to ascertain adequate margin of stability of operation of the network from adverse interactions between the non-synchronous generation and synchronous generation of the system. It will explore novel control and protection technology to safe guard the integrity of the operation of the system with randomly fluctuating output from renewables. The technical competence of the team covers range of expertise in power plant and network modelling, big data, machine learning, system dynamics, estimation, control, and power electronics in the context of interconnected power network operation and protection. Tasks proposed in the program of work will explore several methods of data pre-processing, feature extraction and dimensionality reduction. Faster and accurate identification of the fault location in the cable through impedance transfer function enabled eigen-value approach is revolutionary and so is the ML approach to sensor data optimization in fault location.This is a consortium involving academic and industrial partners from a range of disciplines and different research environments and cultures. The PIs propose a jointly led project management team comprising of all the investigators. All the work packages involve researchers from both sides requiring regular exchange of researchers to carry on with the technical tasks. The RAs and investigators will spend two weeks in every visit to China with partner's organizations. Each work package has joint WP leaders who will coordinate within his/her group of researcher and reports to the PI. Both the PIs have led multinational consortia of even larger sizes and between them. A project advisory board (PAB) will be set up inviting the members from industry partners and technical experts from GEIRI, UKPN, Elin VERD, MHI, FTI Consulting. The PAB will help facilitate explore opportunity for engaging with industry and other user of the research outcome.ThePIs from both sides will network with other approved projects through a high-level board comprising of all PIs and representatives from RCUK and NSFC. There will be further networking through sponsoring session and technical paper in big conferences such as Power Tech, ISGT, CIGRE, CIRED, Power and Energy Society general meeting (PESGM), and participating in low carbon network innovation (LCNI).The availability of meaningful data is at times challenging. Our strategy to manage such challenge will be to work on simulation data from model available in public domain, promised by industry supporter and introduce noise, contamination and missed data based on trend and practice in big data analytic domain in the context of power engineering drawing upon the experience and insight of the industry partners. | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 15/10/21 | |
