Market assessment for an extreme weather and climate change analytics platform to enhance infrastructure resilience in the global electricity sector
Lead Research Organisation:
University of Oxford
Department Name: Zoology
Abstract
The impact of climate change is evidenced by increases in extreme events such as storms, floods and heatwaves as well as longer-term effects such as drought and biodiversity decline. There is a growing requirement for organisations to build greater resilience into their operations to better manage the impacts of such climate-driven changes. In order to become more resilient they need to understand both the likely severity and frequency of extreme events and the longer-term impacts of climate change. This is where Oxford University's climate projection system is able to help:
We can generate 10,000s to 100,000s of climate model runs through a distributed computing system. From these model runs, we can extract specific events that match defined profiles for storms, floods, droughts etc. By integrating this data via a weather generator, which generates synthetic weather events, into hydrological, hydraulic, loss, economic and related models we will be able to provide organisations with significantly more robust and internally coherent predictions of the impacts of climate change on their operations and businesses than has been available hitherto. This integration of datasets and models will be via an integrated resilience modelling platform. The inherent commercial value of the dataset generated by a NERC-funded project on drought projection, MaRIUS, will be realised through this route.
An initial analysis of the requirements of business sectors for resilience information, looking across the water, insurance, energy and agriculture/food sectors found that the energy sector has the greatest expenditure on climate information and that this is growing as deployment of infrastructure increases in emerging economies in South-East Asia, Africa and South America. This assessment will focus on four key stakeholder groups in the Electricity generation, transmission and distribution market: Investors, Engineering and environmental consultancies, Electricity generation, transmission and distribution companies, and Professional services providers.
The primary users of climate analytics in the investment community are development banks, although usage by investment banks is increasing. Engineering and environmental consultants conducting environmental impact assessments (EIAs) in support of the planning and deployment of infrastructure are also important users. In both these cases, there is a political and regulatory drive to increase the use and robustness of climate information. For example, in the run-up to the COP21 negotiations, the investment community is increasing its emphasis on climate resilience while the EU has mandated that climate change and biodiversity must be incorporated into all EIAs by the end of March 2017.
There is increasing recognition by electricity companies that climate analytics has an important role in informing both their strategic planning and operational decision-making. At the same time, climate services and sustainability are rapid growth service lines in the Big Four accountancy firms.
This assessment will gain a detailed understanding of the climate change information needs of the above stakeholder groups with the aim of identifying first, the scale of the market opportunity in the Electricity generation and transmission sector for Oxford's resilience analytics and, second, the optimal market entry strategy for realising their commercial potential. There is potential to commercialise those analytics in other sectors once market traction has been gained in the Electricity infrastructure sector, although that analysis is outside the scope of this assessment.
We can generate 10,000s to 100,000s of climate model runs through a distributed computing system. From these model runs, we can extract specific events that match defined profiles for storms, floods, droughts etc. By integrating this data via a weather generator, which generates synthetic weather events, into hydrological, hydraulic, loss, economic and related models we will be able to provide organisations with significantly more robust and internally coherent predictions of the impacts of climate change on their operations and businesses than has been available hitherto. This integration of datasets and models will be via an integrated resilience modelling platform. The inherent commercial value of the dataset generated by a NERC-funded project on drought projection, MaRIUS, will be realised through this route.
An initial analysis of the requirements of business sectors for resilience information, looking across the water, insurance, energy and agriculture/food sectors found that the energy sector has the greatest expenditure on climate information and that this is growing as deployment of infrastructure increases in emerging economies in South-East Asia, Africa and South America. This assessment will focus on four key stakeholder groups in the Electricity generation, transmission and distribution market: Investors, Engineering and environmental consultancies, Electricity generation, transmission and distribution companies, and Professional services providers.
The primary users of climate analytics in the investment community are development banks, although usage by investment banks is increasing. Engineering and environmental consultants conducting environmental impact assessments (EIAs) in support of the planning and deployment of infrastructure are also important users. In both these cases, there is a political and regulatory drive to increase the use and robustness of climate information. For example, in the run-up to the COP21 negotiations, the investment community is increasing its emphasis on climate resilience while the EU has mandated that climate change and biodiversity must be incorporated into all EIAs by the end of March 2017.
There is increasing recognition by electricity companies that climate analytics has an important role in informing both their strategic planning and operational decision-making. At the same time, climate services and sustainability are rapid growth service lines in the Big Four accountancy firms.
This assessment will gain a detailed understanding of the climate change information needs of the above stakeholder groups with the aim of identifying first, the scale of the market opportunity in the Electricity generation and transmission sector for Oxford's resilience analytics and, second, the optimal market entry strategy for realising their commercial potential. There is potential to commercialise those analytics in other sectors once market traction has been gained in the Electricity infrastructure sector, although that analysis is outside the scope of this assessment.
Planned Impact
The ability to determine the impact of climate change at a site-specific level will enable better decision-making by businesses in the Electricity sector. There are two primary ways in which this will take place.
First, Oxford's analytics will provide companies with a more robust understanding of both the probability of extreme events occurring and their likely impact. This is important because the value chain from investment to operations is underpinned by this understanding. Investors want comfort that the infrastructure they invest in is sufficiently resilient to climate impacts to provide an acceptable ROI; engineering consultants need to ensure that sufficient resilience can be built in to infrastructure assets at acceptable cost to cope with anticipated climate change impacts; while the infrastructure operators want the same assurance.
In short, they all need accurate assessments of the probability of extreme events. Many organisations still use stochastic perturbation of historic event sets to generate potential future event sets. This approach is generally only valid where the frequency and severity of extreme events is constant over time, whereas both are increasing due to climate change. They are therefore looking for better probabilistic approaches. However, many current climate model ensembles have few members compared to our system, climateprediction.net. An understanding of the probability of low frequency extreme events (between 1 in 100 and 1 in 10,000) is a critical factor in the design of many types of electricity asset (e.g. wind turbines). Oxford's analytics have the potential to address these issues.
The integration of climate datasets with hydrological, hydraulic and impact models should provide a greater level of site-specific detail and accuracy as to climate impacts compared to existing approaches which use standardised datasets (e.g. UK CP09 or CMIP5) from which hydrological and impact data is generated but not in an ensemble-based probabilistic fashion. We have yet to quantify precisely the relative improvement our system can provide to stakeholders as this requires us to conduct a demonstration project with them on some real-world examples, which is the intended subject of a Follow-On Fund project.
Second, our integrated resilience modelling system will provide stakeholders with actionable insights as to the potential consequences for their business that are likely to flow from longer-term stresses on the environment, such as water scarcity. We are therefore able to look at shocks and stresses together, providing for an holistic view of climate change impacts.
The enhanced understanding of risk, and the increased capacity to plan for and mitigate the adverse impacts of climate change that our analytics will provide to both infrastructure operators and investors, could potentially help reduce the cost of capital for such projects.
In addition to these company-specific benefits, there is an important benefit to governments, both national and regional, in ensuring that their energy supply is resilient to climate-derived shocks and stresses. Energy security is a pressing global issue, thanks to the triple pressures of climate change, population growth, and ageing energy infrastructure in both developed and developing economies.
First, Oxford's analytics will provide companies with a more robust understanding of both the probability of extreme events occurring and their likely impact. This is important because the value chain from investment to operations is underpinned by this understanding. Investors want comfort that the infrastructure they invest in is sufficiently resilient to climate impacts to provide an acceptable ROI; engineering consultants need to ensure that sufficient resilience can be built in to infrastructure assets at acceptable cost to cope with anticipated climate change impacts; while the infrastructure operators want the same assurance.
In short, they all need accurate assessments of the probability of extreme events. Many organisations still use stochastic perturbation of historic event sets to generate potential future event sets. This approach is generally only valid where the frequency and severity of extreme events is constant over time, whereas both are increasing due to climate change. They are therefore looking for better probabilistic approaches. However, many current climate model ensembles have few members compared to our system, climateprediction.net. An understanding of the probability of low frequency extreme events (between 1 in 100 and 1 in 10,000) is a critical factor in the design of many types of electricity asset (e.g. wind turbines). Oxford's analytics have the potential to address these issues.
The integration of climate datasets with hydrological, hydraulic and impact models should provide a greater level of site-specific detail and accuracy as to climate impacts compared to existing approaches which use standardised datasets (e.g. UK CP09 or CMIP5) from which hydrological and impact data is generated but not in an ensemble-based probabilistic fashion. We have yet to quantify precisely the relative improvement our system can provide to stakeholders as this requires us to conduct a demonstration project with them on some real-world examples, which is the intended subject of a Follow-On Fund project.
Second, our integrated resilience modelling system will provide stakeholders with actionable insights as to the potential consequences for their business that are likely to flow from longer-term stresses on the environment, such as water scarcity. We are therefore able to look at shocks and stresses together, providing for an holistic view of climate change impacts.
The enhanced understanding of risk, and the increased capacity to plan for and mitigate the adverse impacts of climate change that our analytics will provide to both infrastructure operators and investors, could potentially help reduce the cost of capital for such projects.
In addition to these company-specific benefits, there is an important benefit to governments, both national and regional, in ensuring that their energy supply is resilient to climate-derived shocks and stresses. Energy security is a pressing global issue, thanks to the triple pressures of climate change, population growth, and ageing energy infrastructure in both developed and developing economies.
Organisations
- University of Oxford, United Kingdom (Lead Research Organisation)
- CGI (Collaboration)
- Spirit Consulting Ltd (Collaboration)
- International Institute for Applied Systems Analysis (Collaboration)
- European Space Agency, France (Collaboration)
- Meteorological Office UK (Collaboration)
- Know.space Ltd (Collaboration)
- Trillium Technologies Ltd (Collaboration)
Description | The work enabled the understanding of the market potential for our approach and led to connections with potential users of a product from commercialisation. It also led to a re-focussing of our activities to focus on other capabilities. I have gone on to understand the potential of artificial intelligence / machine learning techniques instead of the earlier modelling techniques, or in combination with them, to address key challenges with resilience. Recent discussions with stakeholders interested in reforestation and water security are enabling an assessment of the potential of AI/ML to advance environmental analytics. This has led onto two projects, one looking at flooding and the other focussed on food security. |
Exploitation Route | This was a commercialisation market assessment. Its outputs have been fed into two other projects that are developing machine learning solutions for weather and climate forecasting. |
Sectors | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Energy,Environment,Financial Services, and Management Consultancy,Government, Democracy and Justice |
Description | The findings of this work have informed further commercialisation assessment work which has modified its direction in light of the findings. Ongoing activity of Oxford Infrastructure Analytics, a new company established by colleagues in Geography is offering commercial services for infrastructure systems analysis. |
First Year Of Impact | 2018 |
Sector | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Energy,Environment,Financial Services, and Management Consultancy,Government, Democracy and Justice |
Impact Types | Economic |
Description | EPSRC IAA funding |
Amount | £35,000 (GBP) |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 07/2016 |
End | 03/2017 |
Description | European Space Agency (not an official scheme; funding from Phi-Lab at ESRIN, Italy |
Amount | € 150,000 (EUR) |
Organisation | European Space Agency |
Sector | Public |
Country | France |
Start | 05/2018 |
End | 08/2018 |
Description | Digital Twin Precursor project on food systems |
Organisation | CGI |
Country | Canada |
Sector | Private |
PI Contribution | Extreme weather modelling from Oxford Physics coupled to Food Systems analytics from Oxford Geography and Machine Learning from Oxford Computer Science - to enable systems integration based on scientific knowledge. |
Collaborator Contribution | ESA Is the project funder; CGI is the space company systems integrator, Trillium Technologies supports on applied AI and IIASA on systems modelling. |
Impact | Collaboration still in progress. Multidisciplinary with climate physics, computer science and systems modelling (geography) together with industry partners. |
Start Year | 2020 |
Description | Digital Twin Precursor project on food systems |
Organisation | European Space Agency |
Department | ESRIN |
Country | Italy |
Sector | Public |
PI Contribution | Extreme weather modelling from Oxford Physics coupled to Food Systems analytics from Oxford Geography and Machine Learning from Oxford Computer Science - to enable systems integration based on scientific knowledge. |
Collaborator Contribution | ESA Is the project funder; CGI is the space company systems integrator, Trillium Technologies supports on applied AI and IIASA on systems modelling. |
Impact | Collaboration still in progress. Multidisciplinary with climate physics, computer science and systems modelling (geography) together with industry partners. |
Start Year | 2020 |
Description | Digital Twin Precursor project on food systems |
Organisation | International Institute for Applied Systems Analysis |
Country | Austria |
Sector | Academic/University |
PI Contribution | Extreme weather modelling from Oxford Physics coupled to Food Systems analytics from Oxford Geography and Machine Learning from Oxford Computer Science - to enable systems integration based on scientific knowledge. |
Collaborator Contribution | ESA Is the project funder; CGI is the space company systems integrator, Trillium Technologies supports on applied AI and IIASA on systems modelling. |
Impact | Collaboration still in progress. Multidisciplinary with climate physics, computer science and systems modelling (geography) together with industry partners. |
Start Year | 2020 |
Description | Digital Twin Precursor project on food systems |
Organisation | Trillium Technologies Ltd |
Country | Singapore |
Sector | Private |
PI Contribution | Extreme weather modelling from Oxford Physics coupled to Food Systems analytics from Oxford Geography and Machine Learning from Oxford Computer Science - to enable systems integration based on scientific knowledge. |
Collaborator Contribution | ESA Is the project funder; CGI is the space company systems integrator, Trillium Technologies supports on applied AI and IIASA on systems modelling. |
Impact | Collaboration still in progress. Multidisciplinary with climate physics, computer science and systems modelling (geography) together with industry partners. |
Start Year | 2020 |
Description | Frontier Development Lab |
Organisation | European Space Agency |
Department | Centre for Earth Observation |
Country | Italy |
Sector | Charity/Non Profit |
PI Contribution | Teams of researchers from across Europe took part in the Frontier Development Lab, hosted in summer 2018 in Oxford. Oxford students were disproportionately represented and generated, as part of their teams, solutions to flooding and temporary settlement identification through applying artificial intelligence techniques to satellite data. |
Collaborator Contribution | European Space Agency provided funding and Nvidia provided technical support. UNICEF also provided in-kind support to the collaboration on temporary settlements. |
Impact | Several papers for conferences from students. Further grant proposals are in progress. |
Start Year | 2018 |
Description | Machine Learning for Climate Change |
Organisation | Know.space Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | I am engaging stakeholders interested in flooding and flood risk to buildings and infrastructure. We are developing machine learning based tools to enable better decisions. |
Collaborator Contribution | Partners are developing machine learning tools and conducting further market analysis |
Impact | Flood modelling expertise coupled to machine learning, market analysis and stakeholder engagement. |
Start Year | 2020 |
Description | Machine Learning for Climate Change |
Organisation | Trillium Technologies Ltd |
Country | Singapore |
Sector | Private |
PI Contribution | I am engaging stakeholders interested in flooding and flood risk to buildings and infrastructure. We are developing machine learning based tools to enable better decisions. |
Collaborator Contribution | Partners are developing machine learning tools and conducting further market analysis |
Impact | Flood modelling expertise coupled to machine learning, market analysis and stakeholder engagement. |
Start Year | 2020 |
Description | Market assessment for climate services |
Organisation | Meteorological Office UK |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | The market assessment for the electricity sector has yielded a contract between the Met Office and Spirit Consulting for some further analysis of market opportunities. |
Collaborator Contribution | Spirit Consulting has led a consultancy project for the Met Office directly resulting from engagement through this project. |
Impact | This is confidential to Spirit Consulting and the Met Office |
Start Year | 2017 |
Description | Market assessment for climate services |
Organisation | Spirit Consulting Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | The market assessment for the electricity sector has yielded a contract between the Met Office and Spirit Consulting for some further analysis of market opportunities. |
Collaborator Contribution | Spirit Consulting has led a consultancy project for the Met Office directly resulting from engagement through this project. |
Impact | This is confidential to Spirit Consulting and the Met Office |
Start Year | 2017 |
Description | Engagement with a range of businesses and representative organisations associated with climate resilience and the electricity sector |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Specific interviews with representative businesses from the electricity sector involved in both operating electricity infrastructure and financing of infrastructure. Furthermore, engaged with specific representative bodies which have been assessing climate resilience interests and requirements for the sector. |
Year(s) Of Engagement Activity | 2016 |
Description | Workshops as part of the Frontier Development Lab |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Brought together teams of physical science/space science researchers with AI researchers to crack problems for ESA that were involved with the environment, including climate related and settlement related. |
Year(s) Of Engagement Activity | 2018 |