Abstract
Photovoltaic solar energy is one of the fastest growing renewable energy resources which is being adopted by households and large organizations alike. Encouraging citizens to switch to this renewable energy source requires an understanding of the factors that influence this phenomenon. Technology hereby plays an important role to comprehend the
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complex process and present it in a way that is straightforward for both policymakers and citizens to recognize and realize. Moreover, integration of this energy source into the existing power grid, implies the need for strategies aimed at identifying new routes that minimize visual and environmental impact, cost and social concerns related to new infrastructures. Addressing all these aspects requires an interdisciplinary approach. In this context, the impact of using Geographic Information Systems (GIS) for evaluating the present status, solar potential, policy implications and future tools is investigated in this thesis. Chapter 2 presents a method to evaluate the present status of small and medium scale rooftop solar photovoltaic (PV) installations in the Netherlands. Artificial Intelligence (AI) specifically, Deep Learning (DL) algorithms and geo-spatial techniques are applied on very high-resolution aerial imagery to detect PV installations. Solar potential is an important factor useful to assess the maximum possible deployable rooftop solar capacity. It can also helpl in urban planning for building energy generating buildings in the future. A GIS based method to estimate solar potential is presented in Chapter 3. The solar radiation model used for solar potential calculation has atmospheric parameters that need calibration and validation to produce results closer to reality. Chapter 4 shows how this has been conducted for the Netherlands using the ArcGIS toolbox. In Chapter 5, the visualization component of GIS has been explored. PV system data gathered through crowd sourcing over multiple years has been processed, mapped, and analysed using various visualization techniques to evaluate their performance and monitor them. Understanding and mapping the effectiveness of current policies in driving the diffusion of PV adoption, is helpful in visualizing the driving factors behind adoption and for adjusting the future policies accordingly. In chapter 6, GIS was incorporated to evaluate the economic and geographic effectiveness of one such policy, i.e., the Postal Code Rose policy, which was in effect till April 2021 to determine the realistic potential. Chapter 7 shows the capability of GIS in modelling and mapping of scenario studies in the context of self-consumption and greenhouse gas (GHG) reduction potential. Scenarios relating to PV rooftop utilization, battery storage systems, energy demand and electric vehicle demand were evaluated for a city in the Netherlands. To summarize, different spatial-data science techniques inherent to GIS have been applied in photovoltaic solar energy. In particular, GIS-based analysis for practical implementation in support of the energy transition have been explored. In addition, the bottlenecks with regards to this multi-disciplinary approach, the gap between technology and policy is addressed in this thesis. To conclude, the prospective of using spatial science is imperative to be able to answer questions related to the energy transition leading to a sustainable future.
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