| Feb. 13, 2024, 4:20 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 63323, "fields": {"project": 11525, "organisation": 11073, "amount": 472215, "start_date": "2020-12-01", "end_date": "2022-06-30", "raw_data": 179163}}]
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| Jan. 30, 2024, 4:24 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 56162, "fields": {"project": 11525, "organisation": 11073, "amount": 472215, "start_date": "2020-12-01", "end_date": "2022-06-30", "raw_data": 155818}}]
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| Jan. 2, 2024, 4:15 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 48972, "fields": {"project": 11525, "organisation": 11073, "amount": 472215, "start_date": "2020-12-01", "end_date": "2022-06-30", "raw_data": 134160}}]
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| Dec. 5, 2023, 4:24 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 41725, "fields": {"project": 11525, "organisation": 11073, "amount": 472215, "start_date": "2020-12-01", "end_date": "2022-06-29", "raw_data": 101826}}]
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| Nov. 27, 2023, 2:14 p.m. |
Added
35
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{"external_links": []}
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| Nov. 21, 2023, 4:38 p.m. |
Created
43
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[{"model": "core.projectfund", "pk": 34431, "fields": {"project": 11525, "organisation": 11073, "amount": 472215, "start_date": "2020-12-01", "end_date": "2022-06-29", "raw_data": 60835}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 100307, "fields": {"project": 11525, "organisation": 14639, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 100306, "fields": {"project": 11525, "organisation": 12585, "role": "PARTICIPANT_ORG"}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
41
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[{"model": "core.projectorganisation", "pk": 100305, "fields": {"project": 11525, "organisation": 14639, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:38 p.m. |
Created
40
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[{"model": "core.projectperson", "pk": 63192, "fields": {"project": 11525, "person": 16658, "role": "PM_PER"}}]
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| Nov. 20, 2023, 2:04 p.m. |
Updated
35
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{"title": ["", "Disruptive platform to accelerate renewable energy with machine learning and grid data analytics"], "description": ["", "\nInvestment in renewable technologies will need to rapidly increase in order to meet the world's future energy supply while reducing the associated greenhouse gas emissions. The Committee on Climate Change estimates that the installed renewable energy (RE) capacity needs to be quadrupled to achieve net zero. However, renewable energy projects are capital intensive and the costs and returns depend on a large number of factors such as location and renewable resource. A major barrier to successful interconnection of renewable energy (solar/wind/storage) projects is the lack of information on the grid network/conditions and unpredictability of capital and operational costs. This means investors must estimate the connection cost of projects which is risky and inaccurate and leads to major investment uncertainty.\n\nTo address this challenge, Enian, in collaboration with academics from the University of Edinburgh, have developed a RE Deal Management and Collaboration Platform which helps streamline project qualification and uses proprietary algorithms to predict RE project costs (LCOE, annual energy output, technical, economic metrics). Although proving useful, to meet expressed industry demand and overcome major investment barriers, it is critical that that the technology is advanced. The proposed project will build on this early achievement to develop the capability to enable power grid data to be digitally captured, calculated and visualised to produce cost prediction models for single interconnection points/integrated networks using machine-enhanced automated processes, thus providing the first data-driven RE analytics platform that enables operational costs of grid-connected solar PV, wind and storage to be rapidly and accurately determined, offering a unique scalable solution for improved and de-risked RE planning and investment. Early feasibility has been investigated, this project advancing the concept to TRL5\\.\n\nImpacts include improved, de-risked, and accelerated decision making leading to increased investments (~30% more RE projects supported); valuable time and cost savings in project due diligence (20 weeks, £500k per year per company); \\>1M tonnes CO2e saved over 5 years due to more RE projects gaining investment. Wider applicability to other power (waste-to-energy, hydro), commercial property/land, waste management/recycling, electrified transport (EV charging networks). The project will deliver significant export led growth for lead applicant Enian, a substantial ROI, increased employment and further opportunity for R&D investment. Project partner the University of Edinburgh will gain crucial commercial knowledge to be applied to future R&D.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:04 p.m. |
Added
35
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{"external_links": [47212]}
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| Nov. 20, 2023, 2:04 p.m. |
Created
35
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[{"model": "core.project", "pk": 11525, "fields": {"owner": null, "is_locked": false, "coped_id": "811d5124-2abc-4a14-b838-c5546022a21e", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 60818, "created": "2023-11-20T13:43:09.393Z", "modified": "2023-11-20T13:43:09.393Z", "external_links": []}}]
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