Solar energy replacing conventional non-renewable energy has been widely implemented around the world. Currently, one of the most challenging problems is how to improve the efficiency of producing solar energy. Before installing solar panels, assessing where solar panels should be placed can significantly benefit panel performance. This study aims to conduct a site selection analysis for solar panel installation using Geographical Information Systems (GIS). The University of Waterloo main campus and the City of Waterloo were selected as study areas for micro-scale and macro-scale, respectively. The focus of the micro-scale analysis is on building rooftop installations, while the macro-scale analysis considers ground-mounted installation at the city-level. Knowledge about solar gains incident on different land cover types (e.g., urban and farmland) is useful for assessing potential solar energy installation sites in a local area. In this study, Light Detection and Ranging (LIDAR) data were applied to automatically derive accumulated solar radiation energy under clear-sky and overcast conditions at the micro-scale level from which ideal sites for solar panel placement on building rooftops were determined. Macro-scale solar radiation maps were based on Digital Elevation Model (DEM) data using ArcGIS software. Optimal ground-mounted solar panel installation sites were determined using a multi-criteria analysis approach that considered various environmental and socioeconomic factors. A questionnaire survey was distributed to select solar power companies in Southern Ontario to assess current solar panel installation practices, which were then used to better inform and modify the GIS multi-criteria approach. Finally, a feasibility assessment was performed with ground truth information to verify selected sites.

This research analyses the California Solar Initiative (CSI) Program data to identify and describe peer effects and price elasticity to adoption affecting the patterns of residential PV adoption. Descriptive statistics and adoption trends are analyzed to explore the impacts of peer effects and third-party owned system on the diffusion of residential solar PV in California. As the residential solar PV technology is still in an early stage of market formation, understanding the patterns of adoption in relatively more mature market can have broad implications for wider diffusion of the technology at the national level. In the first part of the thesis, I build an econometric model to estimate the influence of system cost and peer effects on the rate of diffusion at the zip code-level. The results reveal significant and positive installed base effects in the rate of future adoption. These results provide support to the hypothesis that peer effects help accelerate the adoption of new technologies. The cost-to-customer reduction is negative and significant at the state level. The impact of installed base in inducing new adoption is larger in zip codes with higher overall adoptions. The second part of the thesis presents trends in installation and choice of system capacity of major adoption clusters in California and analyzes the spread of third-party owned systems. Evidence from major adoption clusters in California has shown that growth in leasing adoption exhibits exponential characteristics while growth of customer owned system shows strongly linear feature. This suggests that third-party owned systems play a role in expanding the solar PV market to a significantly large population, especially given that this business would significantly reduces information cost associated with PV adoption. These results offer direct policy and marketing insights that would be useful in speeding up the diffusion of residential PV. / text

Solar energy replacing conventional non-renewable energy has been widely implemented around the world. Currently, one of the most challenging problems is how to improve the efficiency of producing solar energy. Before installing solar panels, assessing where solar panels should be placed can significantly benefit panel performance. This study aims to conduct a site selection analysis for solar panel installation using Geographical Information Systems (GIS). The University of Waterloo main campus and the City of Waterloo were selected as study areas for micro-scale and macro-scale, respectively. The focus of the micro-scale analysis is on building rooftop installations, while the macro-scale analysis considers ground-mounted installation at the city-level. Knowledge about solar gains incident on different land cover types (e.g., urban and farmland) is useful for assessing potential solar energy installation sites in a local area. In this study, Light Detection and Ranging (LIDAR) data were applied to automatically derive accumulated solar radiation energy under clear-sky and overcast conditions at the micro-scale level from which ideal sites for solar panel placement on building rooftops were determined. Macro-scale solar radiation maps were based on Digital Elevation Model (DEM) data using ArcGIS software. Optimal ground-mounted solar panel installation sites were determined using a multi-criteria analysis approach that considered various environmental and socioeconomic factors. A questionnaire survey was distributed to select solar power companies in Southern Ontario to assess current solar panel installation practices, which were then used to better inform and modify the GIS multi-criteria approach. Finally, a feasibility assessment was performed with ground truth information to verify selected sites.

I combine quantitative data on minute-resolved electricity-consumption profiles and survey data with qualitative interviews of PV adopters to create a holistic understanding of how PV adoption influences behavioral change of electricity use. In particular, I examine the information and heuristics consumers use to make energy-related choices and evaluate how consumption behavior affects the total amount and timing of electricity use. Consumption behavior post adoption can significantly alter the environmental benefits of solar PV. Post-adoption changes such as decreases in energy consumption or load shifting from times of high peak demand to times of lower peak demand increase the amount of solar PV generation that is exported to the grid. Higher outflows may reduce the need for less efficient peaking generation units during peak demand, particularly in the summer when solar PV is at its highest generation capacity and electricity demand is greatest. I find that PV adoption does trigger increases in awareness of electricity use. However, while adopters report small or insignificant decreases in household consumption post-adoption, examination of actual records shows both significant increases and decreases in consumption post-PV adoption at the household level. I explain this seeming discrepancy by noting that these households were already energy-conscious prior to PV adoption and had newer, more energy efficient homes, which could offset effects of increased awareness. Supporting this, a majority of respondents considered PV adoption as one action within a larger electricity conservation campaign initiated prior to system adoption. Because they had already implemented several energy efficiency measures, respondents could not easily identify additional ways to reduce electricity use. Most respondents have a method of monitoring consumption, but their attentiveness to monitoring declines after installation-- which could explain the awareness gap as well as the consumption increase. In addition, exogenous factors such as the purchase of an electric vehicle and changes in household size may explain increases in consumption. While I find changes in total consumption after adoption of solar PV at the individual household level, the aggregate mean consumption for all households is just 1.0% but the change in means is insignificant. / text

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Rural renewable electrification programs are increasingly being used as a means of providing the rural poor in developing countries with access to electricity. Those programs have varied significantly in design and implementation, as well as in their degrees of success. The explanations for the lack of success and the non-sustainability of the earliest programs are widely accepted as these tended to be technical demonstration projects that relied totally on funding from donor organisations or governments. These projects ignored or overlooked the vital question of how ongoing operational, maintenance and replacement costs would be met. Many genuine programs, however, also met with limited success and much effort has been invested in attempting to understand the reasons for this lack of success. To increase the rates of success of these programs, best practice guidelines were developed. Despite these efforts, many programs have continued to meet with limited success. These points required better explanations of the reasons for program success and failure, which requires a greater understanding of these programs. This thesis looks more closely at solar PV electrification programs being undertaken in the Asia-Pacific region to understand why some programs continue to be more successful than others. It aims to understand the decisions behind the planning and implementation of the programs and the reasons that are being implemented in the way that they are, the selection of program objectives, the actual benefits of the programs the and causes of any factors that contribute to their apparent success or lack of success. This understanding is obtained by undertaking in-depth comprehensive field surveys to obtain the views of all program stakeholders. The outcomes of these field surveys are then used to develop a comprehensive set of success criteria and a set of indicators that can be used to measure the success of rural renewable electrification programs. A roadmap that could be followed by the program planners and implementers to ensure program success is also provided.

Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters. This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities. Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making. The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions.

A blackout is defined as the loss of electric power for a given period in a particular area. With increasing dependence on reliable electric power, the social and economic ramifications of blackouts are dire, negatively impacting the productivity, safety, and security of communities. To reduce blackout occurrence, power system planners incorporate redundancy and advanced controls to the grid to make it more adaptable to disturbances. However, adding redundant transmission lines is not only expensive, it is suboptimal in some contexts. While it is unattainable to have no blackout, it is possible and necessary to implement measures that minimize the likelihood and scale of these outages. This work proposes a solution that uses a microgrid with advanced energy storage and solar PV to mitigate blackouts in Kigali, the capital of Rwanda. A description and steady state analysis of major weaknesses in the Rwandan electric grid is presented. A microgrid application capable of islanding from the system is simulated in the steady state and shown to strengthen the system and decrease the likelihood of blackouts in Kigali. The composition of the microgrid is then designed, simulated, and optimized for technical and financial feasibility using the HOMER model. A microgrid that uses energy storage and solar PV is shown to not only be feasible, but also competitive with current costs of electricity in Rwanda. For comparison, different combinations that include diesel generation are also simulated. / Master of Science

This thesis applies structural reliability measures for the performance based design of residential PV system structures. These measures are intended to support designers in delivering systems with quantified and consistent reliability. Existing codified practices prescribe global factors (allowable stress design) and partial factors (load and resistance factor design) intended to provide an acceptable level of reliability as defined by historical practice. When applied to residential PV systems this prescriptive approach has two flaws, (1) calibration efforts needed to ensure consistency across structural system types have not kept up with the commercially available system types and (2) the actual expected reliability is not quantified and available to support decisions. The proposed reliability measures include probability of failure conditioned to wind speed in a fragility curve and the reliability index β, both of which are commonly used in performance based design. The approach is demonstrated through the application of the reliability measures to code compliant designs. Diverse system types are utilized to demonstrate how the existing code prescribed approach may lead to non-uniform structural performance. For each of the system types on which the reliability measures are demonstrated, a code compliant design is developed for three roof slopes, wind tunnel testing is conducted to provide an experimental measure of wind pressure coefficients, system specific fragility curves are generated to quantify the probability of failure conditioned to a set of wind speeds, and then, a site specific wind model is applied to produce a probability of failure and reliability index β. Through the performance based approach proposed in this thesis, two key outputs show non-uniform and unanticipated structural performance of PV systems designed according to the prescriptive code method. The two key outputs which illustrate this finding are fragility curves which illustrate the probability of failure over a range of wind speeds and reliability index, β values which couple the structural and wind distributions for a single measure of reliability.

Studying organizational capabilities from the perspective of national institutions has been widely adopted in high tech industries in developed countries, but scarcely in developing countries. This research applies this framework into China to study the solar PV industry, but at the regional level. The solar PV industry in China has strongly developed in the global market in recent years, having been the biggest solar PV producer since 2007. However, there are contrast regional differences in the solar PV industry development between Jiangsu and Shandong. The solar PV producers in Jiangsu have stronger organizational capabilities to perform well compared to those in Shandong. Thus this research adopts the framework to explore the influence of regional institutions (the role of regional government, financial systems, inter-firm relations and education and labor systems) in building organizational capabilities (R&D capabilities in technology, capabilities in finance, managerial coordination and human resources) in solar PV industry in Jiangsu and Shandong regions. Comparative case studies are adopted in my research and the research has investigated six solar PV companies and several associated organizations. It is concluded that different regional institutions have different influences (to promote or hinder) in building organizational capabilities in the solar PV industry. Compared to Shandong, the regional institutions in Jiangsu can help solar PV companies to build organizational capabilities. The research findings indicate the importance of regional institutions in China.