Identifying weather robust high-performing energy system configurations to aid decision-making

Abstract

The ever-rising greenhouse gas emissions move the European Union to transition towards a future decarbonized energy system. To achieve this, the future energy system will mainly comprise variable renewable energy generation sources. Energy production from these sources is susceptible to weather fluctuations. Uncertainty of future weather scenarios translates into energy system models that help decision-makers to design the future renewable energy system. Energy system models often use a single weather year to simulate weather behaviour. Thus, many energy system models fail to take weather fluctuations between various years into account. Identifying energy system designs that are robust to weather fluctuations, i.e. systems that are able to suffice demand regardless of the weather circumstances, is therefore key.

The research described in this thesis has aimed to develop and test a method to give insight to decision-makers into the composition of robust and efficient energy systems, given weather uncertainty. To achieve this, the SPORES methodology has been used and extended to identify energy system configurations that are both robust and efficient. For this, a decision option space for decision-makers has been created that has been diversified based on renewable energy generation and storage technologies. To test the developed method, the North Sea region has been used as a case, as this is the region thought by policy to have great potential to house renewable generation sources in Europe.

The method developed in this research systematically covers the decision option space over three weather scenarios (worst, typical and best). From these decision spaces, configurations that meet demand with installed capacities that exist across the whole weather options space have been selected as robust. Clustering was used to identify types of energy system configurations, having commonalities in the installed generation capacities. Energy efficiency has been identified as key for measuring energy system performance. This research, therefore, takes curtailment and energy system yield into account to quantify efficiency. Using a Pareto analysis, both robust and efficiency-wise high-performing energy system configurations were identified as most promising for decision-makers.

The no-regret decisions, visualized by the SPORE-core, are minimum capacities required across the whole decision space. Results showed that robust energy systems are typically comprised of balanced configurations, meaning that solar PV and wind power both have the largest capacity of energy generation sources. The balanced configurations also contain high transmission capacities and typically no storage capacities indicating energy is distributed rather than stored. The robust and efficient configurations need additional capacity investments on top of the no-regret decisions. Especially solar PV needs a large increase in capacity when robustness and efficiency are required. Combined heat and power from biofuels and electrolysis capacity are also key to robust and efficient configurations. Additional results showed that the majority of robust and efficient configurations utilised more offshore than onshore wind capacity.

The findings of this research are based on a case of the North Sea energy system with a high level of aggregation and are thus of limited use for precise designs of the North Sea energy system. The method created in this study can be adapted to contain more detail and offers space for researchers to include their own performance indicators. However, this research already used significant computational efforts, so adding more resolution and detail will mean the computational process can be restricting. Future research should focus on using the developed method to select promising and robust energy system configurations with higher levels of detail and conduct further weather scenario analyses on the selected configuration.