| Around the world, renewable energy-generating systems (RES) have expanded dramatically in capacity and in energy generated. A variety of means have driven this expansion, including mandates to meet RES-based generation targets. Much of the literature has focused on improving technical aspects of performance, reducing integration barriers, or estimating benefits from increased RES generation. Little work has considered how RES technologies could satisfy mandated utility-scale generation targets. This work proposes a quantitative risk assessment (QRA) model to estimate the probability of meeting a national RES generation target. The research has as its context the United Kingdom's (UK) steps to meet its mandate under the European Union's 2009 Renewable Energy Directive (EU 2009).;This dissertation introduces the concept, the target, the approach, and the data sources. This work integrates these inputs using a set of 72 total energy and eight transportation scenarios into the QRA model. The dissertation describes the model's assumptions and sensitivity analysis. Before applying the QRA model, the work documents a preliminary risk assessment of meeting the target, then proceeds to apply the QRA model to an expected value analysis, and then to a discrete event simulation. The expected value analysis increases the quantified nature of the risk assessment, while the simulation further improves the QRA by incorporating uncertainty inherent in the data. A case study demonstrates the utility of the resulting QRA model for a range of policy questions.;The QRA results suggest the UK has probabilities of 45.8% and 50% of meeting total energy and transportation targets by 2020, respectively, but those results drop to 45.5% and 38.4%, respectively, when simulation accounts for some of the uncertainty in the data. The case study indicates the potential for substantially lower probabilities of meeting the EU 2009 target if the UK removed wind RES subsidies.;The dissertation begins with a detailed literature review of the primary fields of RES engineering, risk analysis, and Systems Engineering to indicate both the state of the field, as well as the extent to which these fields have overlapped in the context of increasing renewable energy generation.;The dissertation concludes with a detailed set of recommendations for further research to improve the model for energy enterprise forecasting in an era of heightened support for further RES deployment. |
