By: Paula Gonzalez, Hannah Bloomfield, David Brayshaw
The Department’s Energy Meteorology Group recently hosted an online 2-day workshop on the Next Generation Challenges in Energy-Climate Modelling, supported by the EU-H2020 PRIMAVERA project. The event took place on June 22-23, and though it was planned to physically take place in Reading, it evolved into a Zoom meeting due to the COVID-19 pandemic. The workshop was joined by 81 participants from 22 countries in 6 continents.
Climate variability and change have a two-way relationship with the energy system. On the one hand, the need to reduce greenhouse gasses emissions is driving an increase in the use of weather-sensitive renewable energy sources, such as wind and solar power, and the electrification of fossil fuel intensive sectors such as transport. On the other, a changing climate impacts the energy system through changing resource patterns and the need for heating and cooling. As a result, the energy system as a whole is becoming more sensitive to climate and energy researchers are becoming increasingly aware of the risks associated with climate variability and change.
Recent years have therefore seen a trend towards the incorporation of climate risk into energy system modelling. Significant challenges remain, and in many cases climate risk and uncertainty are neglected or handled poorly (e.g., by focussing on ‘Typical Meteorological Years’, or very limited sets of meteorological data rather than extensive sampling of long-term climate variability and change – Bloomfield et al. 2016; Hilbers et al. 2019). Many of the choices made by energy scientists concerning climate are well-founded, being driven by practical limitations (e.g., computational constraints), but in several other cases there is also a poor appreciation of the potential role of climate uncertainty in energy system applications (often focused on system resilience rather than design). Moreover, even when the two communities actively seek to collaborate, they often feel as if they ‘don’t speak the same language’.
The workshop was thus intended to encourage deeper engagement and interaction between energy and climate researchers. It had two main objectives: to encourage an active collaboration between the relevant research communities, and to jointly pinpoint the challenges of incorporating weather and climate risk in energy system modelling while fostering opportunities to address them. Each day of the meeting was designed around a topic and a pre-defined set of research/discussion questions. Day 1 was focused on the use of historical data to investigate climate risks in energy system modelling, whereas Day 2 was centred on the use of future climate data for the assessment of climate change impacts on the energy system. A combination of short ‘thought-provoking’ invited talks, small breakout groups and plenary sessions was used to address the proposed questions.
The outputs from the workshop are being prepared as a manuscript for submission later this summer. However, some of the key outcomes of the discussions are highlighted were:
- Climate data is abundant. The problems that energy modellers face range around data selection, downscaling, bias-correction, sub-sampling. This point was creatively illustrated by the “data truck” in one of the invited talks by Dr Sofia Simões (Figure 1).
- Energy models and data are not always accessible or adequate. Information necessary to run or calibrate energy models (observed generation output, system grid and design, etc.) is not always readily available or of high quality. Additionally, climate scientists are ill-prepared to extract the weather and climate signal from those timeseries which are also impacted by non-meteorological factors (e.g., plant degradation, maintenance, cost decisions, etc.).
- It is important to recognise that weather and climate are just one of the sources of uncertainty affecting the energy system. Energy modellers also face several other unknowns when representing the system, such as policies, market conditions, socio-economic factors, technological changes, etc. More research is needed to understand the extent to which climate uncertainty may affect the outcomes of energy-modelling studies targeting other problems (e.g., technological choices or policy design).
- There is need for a common language. The complexities of the tools of each community and the use of jargon often lead to confusion. Providing training that targets people working on the interface of the communities would be very beneficial.
Figure 1: The ‘climate data truck’ cartoon illustrates an incompatibility between climate data supply and the ability to ingest it into energy system models. Figure courtesy of Dr Sofia Simões and the Clim2Power project (https://clim2power.com/).
The switch to an online event was unexpectedly beneficial for the workshop, which ended up having a much wider reach than anticipated. Firstly, we were able to accommodate more participants than we would have done in a face-to-face workshop. And secondly, the fact that participants did not need to incur in any travel expenses meant that more early career scientists (ECSs) were able to join the event. Given the nature of “energy-climate” as a very new and rapidly evolving research field, the ECS community was one that the workshop purposefully sought to target and support.
The participant feedback was overwhelmingly positive and there was strong interest in organising a similar workshop next year, as well as in exploring the provision of training opportunities such as a Summer School, a YouTube channel, webinars, etc. The members of the organising committee (itself a highly international and multi-disciplinary group of researchers) continue to work together on developing these suggestions, and warmly welcome contributions and advice from interested parties (please see the workshop website for details).
References:
Bloomfield, H.C., D.J. Brayshaw, L.C. Shaffrey, P.J. Coker, and H.E. Thornton, 2016. Quantifying the increasing sensitivity of power systems to climate variability. Environ. Res. Lett., 11(12), p.124025. https://iopscience.iop.org/article/10.1088/1748-9326/11/12/124025
Hilbers, A.P., D.J. Brayshaw, and A. Gandy, 2019. Importance subsampling: improving power system planning under climate-based uncertainty. Appl. Energy, 251, p.113114. https://arxiv.org/abs/1903.10916