Optimization under uncertainty in industrial energy systems
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School of Engineering |
Master's thesis
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Date
2024-09-30
Department
Major/Subject
Power and Heat Engineering
Mcode
Degree programme
Nordic Master Programme in Innovative and Sustainable Energy Engineering
Language
en
Pages
77
Series
Abstract
The growing global demand for improved energy efficiency in the process industries, which are significant consumers of both fuels and raw materials, necessitates advanced optimization strategies. This thesis presents a robust optimization framework aimed at enhancing the efficiency and sustainability of industrial energy systems under uncertainty. The framework employs Mixed-Integer Linear Programming to model and optimize energy and mass transfer within industrial clusters, considering the volatility of energy prices. The methodology integrates a set-induced robust optimization reformulation to ensure solutions remain feasible and optimal under uncertainty. This approach contrasts with traditional sensitivity analysis and stochastic programming by focusing on the worst-case scenario within predefined uncertainty bounds, thus providing a reliable decision-making tool for planners and operators in the energy sector. Key components of the study include the development of base case models for existing technologies such as natural gas boilers, cogeneration units, and absorption chillers, as well as models for decarbonization technologies like biomass-fired boilers, amine CO2 capture, and high-temperature electrolysis. The framework’s application demonstrates significant potential for optimizing energy consumption and reducing emissions in industrial clusters. The results underscore the importance of robust optimization in managing uncertainties, facilitating informed decisions that enhance the sustainability and efficiency of resource use. This research contributes to the broader field of industrial energy system optimization, offering a robust and practical solution to the challenges posed by fluctuating variables and the integration of renewable energy sources.Description
Supervisor
Lahdelma, RistoThesis advisor
Maréchal, FrançcoisKeywords
decarbonization, energy efficiency, industrial energy systems, mixed-integer linear programming, robust optimization, uncertainty management