Predictive and Prescriptive Analytics for Managing the Impact of Hazards on Power Systems

Natural hazards and extreme weather events have the potential to cause significant disruptions to the electric power grid. The resulting damages are, in some cases, very expensive and time-consuming to repair and they lead to substantial burdens on both utilities and customers. The frequency of such events has also been increasing over the last 30 years and several studies show that both the number and intensity of severe weather events will increase due to global warming and climate change. An important part of managing weather-induced power outages is being properly prepared for them, and this is tied in with broader goals of enhancing power system resilience. Inspired by these challenges, this thesis focuses on developing data-driven frameworks under uncertainty for predictive and prescriptive analytics in order to address the resiliency challenges of power systems. In particular, the primary aims of this dissertation are to: 1. Develop a series of predictive models that can accurately estimate the probability distribution of power outages in advance of a storm. 2. Develop a crew coordination planning model to allocate repair crews to areas affected by hazards in response to the uncertain predicted outages. The first chapter introduces storm outage management and explains the main objectives of this thesis in detail. In the second chapter, I develop a novel two-stage predictive modeling framework to overcome the zero-inflation issue that is seen in most outage related data. The proposed model accurately estimates customer interruptions in terms of probability distributions to better address inherent stochasticity in predictions. In the next chapter, I develop a new adaptive statistical learning approach based on Bayesian model averaging to formulate model uncertainty and develop a model that is able to adapt to changing conditions and data over time. The forth chapter uses Bayesian belief network to model the stochastic interconnection between various meteorological factors and physical damage to different power system assets. Finally, in chapter five, I develop a new multi-stage stochastic program model to allocate and relocate repair crews in impacted areas during an extreme weather event to restore power as quickly as possible with minimum costs. This research was conducted in collaboration with multiple power utility companies, and some of the models and algorithms developed in this thesis are already implemented in those companies and utilized by their employees. Based on actual data from these companies, I provide evidence that significant improvements have been achieved by my models.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168024/1/ekabir_1.pd

SMEs and access to finance : an investigation of different sources of funding

Access to finance is a necessity for the start-up, growth, innovation and survival of any organisation. As a result, access to finance has become an important theme in small business research. Although there is overwhelming research evidence on access to finance, there are still research gaps in the knowledge base of different forms of access to finance, especially in times of uncertainty and economic distress. Specifically, more research is needed on the identification of relevant theories of access to finance, the role of venture capital and crowdfunding, the effect of financial education and self-confidence on access to micro finance and the role of institutions in small firms financial liquidity. The thesis aims to fill these gaps and provide comprehensive reviews and new empirical evidence related to the above issues. Reviewing the academic literature, this research supports the view that venture capital and crowdfunding are both relevant in access to funding for firms as they represent worthy alternatives for different types of firms. Venture capital firms (VCF) have targeted their investment on later-stage, management buy-out and buy-in to limit their risks and increase returns. Although VCF traditionally have huge appetite for high risk and high returns, research show that they concentrate their funding on older innovative firms. In their risk aversion, VCF have become more stringent in their entrepreneurial project selection and monitoring with reduced funding of seed and early stage of projects. Turning to empirical parts of the thesis, a series of interesting and new findings have emerged. First, this thesis supports the view that financial self-confidence of the owner manager contributes to successful access to finance for UK firms whereas financial education is found to have weak explanatory power. However, financial education is found to increase financial self-confidence, and thus can be used as a means of improving access to micro-finance for SMEs. Self-confidence is also found to be affected by past poor performance of the owners’ credit outcomes stressing the importance of building a successful credit history and experience with the financial sector. Finally, at international level this thesis stresses the importance of regulation and institutions in Baltic States and South Caucasus countries in SME access to finance. The analysis points also towards some gender differences which add to the existing debate on differences between males and females