PhD Position Studying Power System Resilience to Climate Extremes through Geospatially Explicit Energy Modelling

Introduction:

In the evolving landscape of global energy systems, the integration of sophisticated modelling techniques with geospatial analysis is crucial for addressing the multifaceted challenges posed by climate extremes leading to so-called energy droughts with low production of solar, wind and hydropower. Examples of significant challenges in renewable energy systems are hydrological droughts and Dunkelflautes—periods of low solar and wind energy availability. This PhD research proposes to enhance the field of energy systems modelling through the development of an advanced framework that models extreme climate events, and incorporates explicit geospatial elements for long-term, country-level planning. Utilizing Model Supply Regions, the research will account for land-use conflicts, topography, and restricted areas, essential for plotting geospatial roadmaps for the strategic expansion of solar, wind, and grid infrastructures across multiple nations. The work will also focus on the impact of climate extremes on hydropower generation. The universal tools developed in this project will be subsequently applied to the African context.
By marrying traditional energy modelling with geospatial analytics, the PhD aims to navigate the complexities of sustainable energy infrastructure development with the goal to develop an improved energy system model that can generate energy scenarios for African countries and the continent while explicitly accounting for the effects of geospatial resource dynamics and climate extremes on energy systems. The outcomes are expected to provide optimized strategies and robust policy frameworks that enhance energy system resilience, guiding global efforts towards reliable and sustainable energy provision in the face of increasing climate variability and competing resource demands.

Study region/s:
Subregions and individual countries of interest in the African continent.

Research Objectives:
1. Develop a Comprehensive Energy System Model: Construct a model that addresses extreme climate events, integrating scenarios like                Dunkelflautes and droughts, and utilizing geospatial analysis to assess and enhance the resilience of diverse energy systems, including wind, solar, and hydro.
2. Assess Vulnerabilities and Optimize Configurations: Identify and analyze vulnerabilities of current energy systems to extreme climate events, optimize system configurations, and explore diversified energy portfolios for enhanced resilience.
3. Policy and Strategy Formulation: Develop strategies and policy recommendations based on model outcomes to mitigate the impacts of climate extremes on energy supply.

Research Questions:
1. How does integrating implicit factors like temporal climate variability and geographical variations enhance the accuracy and utility of these models?
2. What modelling techniques can effectively evaluate and represent the impacts of extreme climate events on diverse energy systems (solar, wind, hydro)?
3. Which strategic interventions are most effective in ensuring energy system resilience during various extreme climate scenarios in context of African continent?

Novelty of the proposed PhD topic:
1. Integration of Energy Drought Representations: Addressing prolonged periods of low renewable energy production (energy drought) in energy system resilience studies is relatively new and not widely explored.
2. Temporal and Geospatial Analysis for Energy Planning: Using Model Supply Regions to incorporate temporal (hydrological, meteorological and climatic extremes) and geospatial elements like land use conflicts and topography in long-term, country-level energy planning is innovative and rarely integrated to a high level of depth.
3. Policy and Strategy Formulation Based on Advanced Modelling: Directly linking advanced, multidimensional modelling outcomes to specific, actionable policy frameworks remains underdeveloped in current literature.

PhD supervision:
Vrije Universiteit Brussel (VUB)
Promotor – Prof. Dr. Sebastian Sterl, Department of Water and Climate, VUB
Co-promotor – Prof. Dr. Wim Thiery, Department of Water and Climate, VUB

Vlaamse Instelling voor Technologisch Onderzoek (VITO)
Co-promotor – Dr. Ruchi Gupta, International Energy and Climate, SESAM, VITO-EnergyVille
Alternate Contact – In Dr. Ruchi Gupta's maternity leave absence, Dr. Pieter Valkering, SESAM, VITO-EnergyVille.

Registration and Budget:
Fulltime 4 year position
Registration: The PhD student will be registered at VUB, Brussels.
Funding mode: Grant student at university.
Work Location: The PhD student will split their time between VUB and the VITO EnergyVille campus, according to a schedule agreed upon by both institutions.

Key references:
1. Sterl, S., Hussain, B., Miketa, A. et al. An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. Sci Data 9, 664 (2022). https://doi.org/10.1038/s41597-022-01786-5
2. Sterl S, Devillers A, Chawanda CJ et al. A spatiotemporal atlas of hydropower in Africa for energy modelling purposes [version 3; peer review: 2 approved, 1 approved with reservations]. Open Res Europe 2022, 1:29 (https://doi.org/10.12688/openreseurope.13392.3)
3. Sterl, S., Vanderkelen, I., Chawanda, C.J. et al. Smart renewable electricity portfolios in West Africa. Nat Sustain 3, 710–719 (2020). https://doi.org/10.1038/s41893-020-0539-0
4. Sterl, S., Fadly, D., Liersch, S. et al. Linking solar and wind power in eastern Africa with operation of the Grand Ethiopian Renaissance Dam. Nat Energy 6, 407–418 (2021). https://doi.org/10.1038/s41560-021-00799-5
5. Mertens, T. Long-term energy-system optimization models On generation adequacy, capacity credits and the representation of cross-border trade, 2021. Long-term energy-system optimization models (kuleuven.be)
6. Poncelet, K. Long-term energy-system optimization models Capturing the challenges of integrating intermittent renewable energy sources and assessing the suitability for descriptive scenario analyses, 2018. Long-term energy-system optimization models (kuleuven.be)