Managing uncertainty and flexibility in the modern energy sector [electronic resource] : quantitative modeling of technical risk, economic value, and strategic competition
Keywords: Management Science and Engineering.

The need to mitigate climate change promises an increasingly different, uncertain, and flexible energy landscape. In a climate-constrained world, uncertainty and flexibility complicate the appraisal of new investments in clean energy. They make it more challenging for decision-makers to quantify the technical risk, economic value, or strategic competitiveness of their prospective energy initiatives, for orthodox evaluation techniques like worst-case-scenario and net-present-value become insufficient. Consequently, in order to help investors ride the clean energy wave, one urgent priority is to clarify and quantify uncertainty and flexibility in modern energy systems and industries. This dissertation aims to develop assessment models that achieve this exact goal. The dissertation takes on three decision-centric research endeavors. The first study sheds light on the technical uncertainty related to the leakage of anthropogenic carbon dioxide from geologic storage reservoirs. Specifically, a conceptual methodological framework is developed to help storage-site managers bridge risk assessment and corrective measures through clear and collaborative contingency planning. First, a quantitative risk assessment matrix is presented, highlighting the concept of risk profiles. As the main focus of this study, a contingency planning matrix is then developed based on the risk assessment matrix, and its tier structure is discussed. Lastly, the contingency planning matrix is used to guide the design of a model contingency plan, which covers multiple sections on preparing for leakage risks and responding to leakage incidents. The second study switches from technical uncertainty to economic flexibility, investigating the value of flexible hydrogen-based polygeneration energy systems (PES). PES are multi-input multi-output industrial facilities. This study models a representative PES that uses coal as a primary fuel and produces electricity and fertilizers as end-products. A series of economic propositions allows deriving multiple metrics that quantify the levelized cost of hydrogen, the profitability of PES under both static and flexible operation modes, as well as the real-option values associated with diversification and flexibility. These metrics are subsequently applied to evaluate Hydrogen Energy California (HECA), a PES project under development in California. The results show that the profitability of a static HECA increases in the share of hydrogen converted to fertilizer rather than electricity. However, when configured as a flexible PES, HECA almost breaks even. Ultimately, diversification and flexibility prove valuable for HECA when polygeneration is compared to static monogeneration of electricity, but these two real options have no value when comparing polygeneration to static monogeneration of fertilizers. Finally, aiming to examine uncertainty in strategic competition within an energy industry, the third study proposes a decision analytic modeling of Porter's five forces framework, hereby referred to as DAFF. This work is divided into two parts. The first part addresses the conceptual foundations of DAFF. After explaining how decision analysis tools can enhance the operationalization of the five forces theory, this part provides a detailed description of the various elements in a DAFF model. Subsequently, a series of DAFF models are developed to fulfill the two main objectives of competitive strategy: positioning in the industry, and reshaping the industry. In the second part, DAFF is implemented to help inform a major firm's competitive strategy in the near-future U.S. residential solar PV industry. A DAFF Bayesian Network is designed to evaluate competition in the overall industry. The results reveal moderate competitive powers, with expected earnings before tax (EBT) of 4.05 billion $/year. Also, due to significant yet asymmetrical competitive interdependence, witnessing a single competitive force at its strongest or weakest extreme seems sufficient to vary the industry EBT between 1.86 and 5.77 billion $/year. Analyzing four positioning decisions by the solar firm expands the Bayesian Network into a Decision Diagram with 32 possible positioning tracks. Each track yields a unique EBT value ranging between 0.51 and 3.98 billion $/year. The results show that the highest expected earnings are realized upon: locating in urban areas, managing customers directly without relying on dealers, and offering loan and lease services to solar customers.