Analyzing Storage Needs in Energy Systems with Variable Renewable Energy Integration
Lessons from Calliope for WITCH
J.T. de Haan (TU Delft - Technology, Policy and Management)
Alice Di Bella (Politecnico di Milano)
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Abstract
This thesis explores the integration of flexibility measures in the European energy system. The thesis uses an integrated assessment model to simulate the integration of flexibility based on parameters from an energy system model with a high spatial and temporal scope.
The key objectives include evaluating different flexibility technologies' roles in enhancing future energy systems' reliability and resilience.
The foundation of the thesis is identifying and evaluating the most promising storage technologies. Thereafter, the storage technologies are placed into the context of energy modeling, highlighting their strengths, weaknesses, and ability to be modeled.
The research uses the WITCH (World Induced Technical Change Hybrid) model. However, this research is based only on the region of Europe. The WITCH model can run simulations under different climate policy scenarios, including the business-as-usual (BAU) and carbon tax (ctax) pathways. Variables and parameters such as flexibility measures and associated costs are modeled to reflect future energy system configurations based on pre-run cost-optimal configurations from the Calliope framework.
The thesis results show that the main flexibility measures from the literature are storage, grid expansion, demand response, and sector coupling. These measures can enhance the energy system's integration of variable renewable energy sources. Climate policies, i.e., carbon taxes, enable higher levels of VRE and, therefore, flexibility measures, resulting in lower emissions and more efficient energy systems. Fundamentally, the results show a different approach to flexibility than that utilized in long-term models. Using aggregated parameters from energy systems models' pre-run configuration is a novel method of informing other models. This coupling method is effective when the variables of the two models can be harmonized.
The thesis discussion raises areas for future research. The main discussion point is the effectiveness of using pre-run optimization results. The 2030 and 2050-based data provide the energy system's transitional nature. However, extracting insight for a purely transitional model like WITCH proved challenging. Furthermore, the impact of scaling the data from Calliope to match the WITCH data ranges needs further investigation. Lastly, the implications of the elasticity of substitution between the individual flexibility measures, e.g., between storage capacity expansion and transmission grid expansion.