Assessment of Urban Energy Flexibility Needs: Dynamic Spatio-Temporal Visualization Approach Integrating Rooftop PV Projections

By Paula Golunska

Abstract
The increasing deployment of rooftop PV (photovoltaic) systems in urban areas offers new prospects for distributed energy provision and contributes greatly to urban decarbonization efforts. With anticipated rise in electricity consumption, ensuring flexibility in energy systems becomes crucial for maintaining a reliable power supply in the evolving energy landscape. Understanding the spatial and temporal dynamics of energy flows is needed for proper assessment of flexibility requirements. However, existing research highlights gaps in this understanding, emphasizing the need for enhanced spatio-temporal resolution and advanced visualization techniques. This thesis explores urban energy flexibility opportunities through a dynamic spatio-temporal visualization approach, considering various rooftop PV deployment scenarios. The proposed methodology evaluates energy flexibility requirements in the case study of Amsterdam, the Netherlands, through GIS-based spatio-temporal urban energy modeling approach. By employing open-source data, the study models the residential building electricity consumption and rooftop PV potential under the current and increased deployment scenarios. The findings demonstrate that dynamic visualization effectively identifies detailed energy flexibility needs across space and time, with potential applications including identifying areas suitable for energy storage, prioritizing load shifting, and anticipating great load variability. These insights provide valuable input to inform effective policy formulation and resource allocation strategies. Limitations in methodology highlight the need for refinement, including the incorporation of advanced computational techniques for more accurate data generation and estimation. Future recommendations emphasize stakeholder engagement to improve data accuracy, exploration of alternative methodologies for improved PV potential estimation, utilization of PV detection algorithms, and the development of integrated visualization solutions for enhanced usability.