Utilization of Integer Programming for Scheduling Maintenance at Nuclear Power Plants

This thesis develops a thought that naturally explores three specific motifs for solving the complexities of scheduling maintenance at Nuclear Power Plants (NPP). The first chapter of this paper will develop the initial thought around creating a schedule for a given work week, including all the various constraints inherent to this problem. Such constraints include but are not limited to personnel availability, allowable component out-of-service time, and the Plant Risk Assessment. The objective function being to minimize the total cost of worker’s compensation for that given week. The second chapter addresses the question of whether this simple schedule can be implemented with a long time horizon as the goal. This section delves into the concept of utilizing maintenance task frequencies and extended preventive maintenance frequencies to once again minimize the objective function of cost due to compensation. The third chapter focuses on the ability of the program to respond to adaptive circumstances. One major obstacle in running any large commercial facility is unplanned downtime of required systems or components. Simulating failures of certain components that shorten the overall allowable out-of-service time, the program will be required to still minimize the objective function while navigating these changing timelines

Proceedings of the 2nd Annual Conference on NASA/University Advanced Space Design Program

Topics discussed include: lunar transportation system, Mars rover, lunar fiberglass production, geosynchronous space stations, regenerative system for growing plants, lunar mining devices, lunar oxygen transporation system, mobile remote manipulator system, Mars exploration, launch/landing facility for a lunar base, and multi-megawatt nuclear power system

Electric propulsion for near-Earth space missions

A set of missions was postulated that was considered to be representative of those likely to be desirable/feasible over the next three decades. The characteristics of these missions, and their payloads, that most impact the choice/design of the requisite propulsion system were determined. A system-level model of the near-Earth transportation process was constructed, which incorporated these mission/system characteristics, as well as the fundamental parameters describing the technology/performance of an ion bombardment based electric propulsion system. The model was used for sensitivity studies to determine the interactions between the technology descriptors and program costs, and to establish the most cost-effective directions for technology advancement. The most important factor was seen to be the costs associated with the duration of the mission, and this in turn makes the development of advanced electric propulsion systems having moderate to high efficiencies ( 50 percent) at intermediate ranges of specific impulse (approximately 1000 seconds) very desirable

CO2 Highways for Europe: Modeling a Carbon Capture, Transport and Storage Infrastructure for Europe

We present a mixed integer, multi-period, cost-minimizing carbon capture, transport and storage (CCTS) network model for Europe. The model incorporates endogenous decisions about carbon capture, pipeline and storage investments; capture, flow and injection quantities based on given costs, certificate prices, storage capacities and point source emissions.The results indicate that CCTS can theoretically contribute to the decarbonization of Europe's energy and industry sectors. This requires a CO2 certificate price rising to 55 EUR in 2050, and sufficient CO2 storage capacity available for both on and offshore sites. However, CCTS deployment is highest in CO2-intensive industries where emissions cannot be avoided byfuel switching or alternative production processes. In all scenarios, the importance of the industrial sector as a first mover to induce the deployment of CCTS is highlighted. By contrast, a decrease of available storage capacity or a more moderate increase in CO2 prices will significantly reduce the role of CCTS as a CO2 mitigation technology, especially in the energy sector. Continued public resistance to onshore CO2 storage can only be overcome by constructing expensive offshore storage. Under this restriction, to reach the same levels of CCTS penetration will require doubling of CO2 certificate prices.carbon capture and storage, pipeline, infrastructure, optimization

A multiple objective optimization approach to the decommissioning and dismantling of a nuclear power plant.

The complexity, relevance and critical nature of the decommissioning of nuclear power plants (NPP) are of great significance in today\u27s society. Following the catastrophe in Fukushima a shift in the general public\u27s perception of NPP took place throughout the world and in Europe in particular. In this dissertation interdisciplinary methods will be discussed to identify solutions which take into account the technological complexity and organizational issues involved in the dismantling and decommissioning process of NPP. Operations research, lean management, simultaneous engineering, cost analysis, multiple-objective optimization, project management, software tools are powerful concepts and methodologies when undertaking the dismantling and decommissioning process of NPP. Besides the presentation of a wide range of terminological and methodological definitions and technical terms based on the Literature Review, in the dissertation a framework for model development of a Multiple objective optimization problem (MOOP) will discussed focusing on empirical data from a virtual NPP. The theoretical foundation of the framework is at the intersection of two successful approaches used to describe and accomplish similar complex challenges, and the integration of state-of-the-art process approaches such as lean management. The procedural conception of the model is mainly leant on the OMEGA model (International Atomic Energy Agency (IAEA) (2008)). Mathematically the model is derived from Jones et. al. (1998). Finally the application of the model using different software tools (AIMMS, MATLAB, R and SPSS) will be presented. In conclusion the work will be put into a position to venture a critical outlook and discussion for the future of the decommissioning and dismantling processes of NPP. The main goal of this dissertation is to define the requirements for the optimization of three objectives: Minimizing the total project cost, reducing the safety hazard (risk) and managing project duration. Also a description of how the programming language R and the AIMMS program interfaces with the OMEGA application and how R will be used to solve the MOOP will be given. The software Microsoft Project will be leveraged in order to model this objective

Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

Strategic Real Option and Flexibility Analysis for Nuclear Power Plants Considering Uncertainty in Electricity Demand and Public Acceptance

Nuclear power is an important energy source especially in consideration of CO2 emissions and global warming. Deploying nuclear power plants, however, may be challenging when uncertainty in long-term electricity demand and more importantly public acceptance are considered. This is true especially for emerging economies (e.g., India, China) concerned with reducing their carbon footprint in the context of growing economic development, while accommodating a growing population and significantly changing demographics, as well as recent events that may affect the public's perception of nuclear technology. In the aftermath of the Fukushima Daiichi disaster, public acceptance has come to play a central role in continued operations and deployment of new nuclear power systems worldwide. In countries seeing important long-term demographic changes, it may be difficult to determine the future capacity needed, when and where to deploy it over time, and in the most economic manner. Existing studies on capacity deployment typically do not consider such uncertainty drivers in long-term capacity deployment analyses (e.g., + 40 years). To address these issues, this paper introduces a novel approach to nuclear power systems design and capacity deployment under uncertainty that exploits the idea of strategic flexibility and managerial decision rules. The approach enables dealing more pro-actively with uncertainty and helps identify the most economic deployment paths for new nuclear capacity deployment over multiple sites. One novelty of the study lies in the explicit recognition of public acceptance as an important uncertainty driver affecting economic performance, along with long-term electricity demand. Another novelty is in how the concept of flexibility is exploited to deal with uncertainty and improve expected lifecycle performance (e.g. cost). New design and deployment strategies are developed and analyzed through a multistage stochastic programming framework where decision rules are represented as non-anticipative constraints. This approach provides a new way to devise and analyze adaptation strategies in view of long-term uncertainty fluctuations that is more intuitive and readily usable by system operators than typical solutions obtained from standard real options analysis techniques, which are typically used to analyze flexibility in large-scale, irreversible investment projects. The study considers three flexibility strategies subject to uncertainty in electricity demand and public acceptance: 1) phasing (or staging) capacity deployment over time and space, 2) on-site capacity expansion, and 3) life extension. Numerical analysis shows that flexible designs perform better than rigid optimal design deployment strategies, and the most flexible design combining the above strategies outperforms both more rigid and less flexible design alternatives. It is also demonstrated that a flexible design benefits from the strategies of phasing and capacity expansion most significantly across all three strategies studied. The results provide useful insights for policy and decision-making in countries that are considering new nuclear facility deployment, in light of ongoing challenges surrounding new nuclear builds worldwide