An Environmentally Conscious Robust Optimization Approach for Planning Power Generating Systems

Carbon dioxide is a main greenhouse gas that is responsible for global warming and climate change. The reduction in greenhouse gas emission is required to comply with the Kyoto Protocol. Looking at CO2 emissions distribution in Canada, the electricity and heat generation sub-sectors are among the largest sources of CO2 emissions. In this study, the focus is to reduce CO2 emissions from electricity generation through capacity expansion planning for utility companies. In order to reduce emissions, different mitigation options are considered including structural changes and non structural changes. A drawback of existing capacity planning models is that they do not consider uncertainties in parameters such as demand and fuel prices. Stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in past literature different scenarios were developed by either assigning arbitrary values or by assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and can be inputted to the scenario set. The first part of this thesis focuses on long term forecasting of electricity demand using autoregressive, simple linear, and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario electricity demand as a case study, the annual energy, peak load, and base load demand were forecasted, up to year 2025. In order to generate different scenarios, different ranges in economic, demographic and climatic variables were used. The second part of this thesis proposes a robust optimization capacity expansion planning model that yields a less sensitive solution due to the variation in the above parameters. By adjusting the penalty parameters, the model can accommodate the decision maker’s risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for the year 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested to close most of the coal power plants and to build new natural gas combined cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and, as expected, the model was found to be less sensitive than the deterministic model

OR models in urban service facility location : a critical review of applications and future developments

[EN] Facility location models are well established in various application areas with more than a century of history in academia. Since the 1970s the trend has been shifting from manufacturing to service industries. Due to their nature, service industries are frequently located in or near urban areas that results in additional assumptions, objectives and constraints other than those in more traditional manufacturing location models. This survey focuses on the location of service facilities in urban areas. We studied 110 research papers across different journals and disciplines. We have analyzed these papers on two levels. On the first, we take an Operations Research perspective to investigate the papers in terms of types of decisions, location space, main assumptions, input parameters, objective functions and constraints. On the second level, we compare and contrast the papers in each of these applications categories: (a) Waste management systems (WMS), (b) Large-scale disaster (LSD), (c) Small-scale emergency (SSE), (d) General service and infrastructure (GSI), (e) Non-emergency healthcare systems (NEH) and (f) Transportation systems and their infrastructure (TSI). Each of these categories is critically analyzed in terms of application, assumptions, decision variables, input parameters, constraints, objective functions and solution techniques. Gaps, research opportunities and trends are identified within each category. Finally, some general lessons learned based on the practicality of the models is synthesized to suggest avenues of future research.Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards (No. DPI2015-65895-R) financed by FEDER funds.Farahani, RZ.; Fallah, S.; Ruiz García, R.; Hosseini, S.; Asgari, N. (2019). OR Models in Urban Service Facility Location: A Critical Review of Applications and Future Developments. European Journal of Operational Research. 276(1):1-27. https://doi.org/10.1016/j.ejor.2018.07.036S127276

Nuclear Power Plant Siting in the Columbia River Basin: Current Trends, Effects of Climate Change, and Associated Uncertainties

This PhD dissertation describes and evaluates a geographical analysis of candidate areas for siting nuclear plants utilizing a wet cooling tower in the Columbia River Basin (CRB). It focuses on the analysis of water availability for cooling and how it may be limited by climate change effects on river streamflow. The CRB, which includes portions of OR, WA, ID and MT, is projected to require more sources of energy in the future. Oregon, Washington, and Idaho are projected to have a total energy shortfall of 58,676 MWe by 2050. Given the limitations on alternative low-carbon energy sources, nuclear power is a potential source of renewable low-carbon energy in the CRB. This study applied siting criteria required by the Nuclear Regulatory Commission (NRC) and a GIS-based multicriteria decision analysis (MCDA) approach to identify candidate areas of the CRB appropriate for constructing nuclear reactors. Only 4.6% and 3.1% of the CRB were found to be suitable for siting small and large reactors, respectively. The two main candidate areas are Middle Columbia River, and Snake River plain. One of these regions already contains a nuclear power plant (Columbia Generating Station, WA), and the other site is currently under consideration for a nuclear plant (Payette County, ID). Water availability for cooling was the most important factor restricting nuclear power plants, but earthquake hazards and landslide hazards were also significant limiting factors. The restricted area available means that future nuclear plants could meet only a portion of the projected future energy shortfall in the Pacific Northwest. This study examined the possible effects of climate change on minimum streamflow requirements for siting nuclear power plants in the CRB, by analyzing projected future daily discharge data from several CMIP3 and CMIP5 climate models, downscaled using three different techniques under high (A1B/RCP8.5) and medium (B1/RCP4.5) emission scenarios. Projected future streamflow eliminated small clusters of potential sites in several parts of the CRB, while the two main candidate areas appeared to be relatively resilient to it, because of high initial streamflow. Finally, the study discussed the uncertainty associated with the siting process for nuclear power plants, with the potential future effects of climate change on water availability necessary for cooling, and with overall public perceptions of nuclear power. While siting criteria and projected changes in streamflow may significantly reduce the number of potential sites, public opposition to nuclear power could entirely prevent construction of reactors within areas that are physically and economically suitable for siting

Supply Chain Design

Diese Arbeit wurde mit dem Ernst-Zander-Preis 2005 der Ruhr-Universität Bochum ausgezeichnet. Das Supply Chain Design als strategisches Supply Chain Management beinhaltet als wesentlichen Aspekt die Konfiguration der Supply Chain. Dabei sind die über eine Preisminimierung hinausgehenden Auswahlkriterien der Zulieferer sowie die aus dem langfristigen Planungshorizont der Fragestellung resultierende Unsicherheit geeignet zu berücksichtigen. In dieser Arbeit werden die vielfältigen betriebswirtschaftlichen Aspekte des Supply Chain Design umfassend diskutiert und ein Kriterienkatalog zur Auswahl von Zulieferern entwickelt. Die Unsicherheit wird mit Hilfe des neu entwickelten Konzeptes der Zielrobustheit abgebildet. Die Planungsentscheidungen im Rahmen des Supply Chain Design können durch das vorgestellte Vorgehen unterstützt und in ihren Konsequenzen quantifiziert werden

Supply Chain Design

Diese Arbeit wurde mit dem Ernst-Zander-Preis 2005 der Ruhr-Universität Bochum ausgezeichnet. Das Supply Chain Design als strategisches Supply Chain Management beinhaltet als wesentlichen Aspekt die Konfiguration der Supply Chain. Dabei sind die über eine Preisminimierung hinausgehenden Auswahlkriterien der Zulieferer sowie die aus dem langfristigen Planungshorizont der Fragestellung resultierende Unsicherheit geeignet zu berücksichtigen. In dieser Arbeit werden die vielfältigen betriebswirtschaftlichen Aspekte des Supply Chain Design umfassend diskutiert und ein Kriterienkatalog zur Auswahl von Zulieferern entwickelt. Die Unsicherheit wird mit Hilfe des neu entwickelten Konzeptes der Zielrobustheit abgebildet. Die Planungsentscheidungen im Rahmen des Supply Chain Design können durch das vorgestellte Vorgehen unterstützt und in ihren Konsequenzen quantifiziert werden