Mathematical Programming Applied in Energy System Optimization through Advanced Data-Driven and Co-Simulation Frameworks

Identifier:  TDX:4494

Handle: https://hdl.handle.net/20.500.11797/TDX4494

Authors:  Elomari, Youssef

Abstract:
optimizados. En resumen, proporciona herramientas para diseñar y operar comunidades energéticas y sistemas de calefacción distrital, apoyando las decisiones hacia los objetivos de sostenibilidad de la UE. This PhD thesis investigates the pivotal roles of Energy Communities (ECs) and District Heating Systems (DHS) in significantly reducing greenhouse gas emissions and enhancing sustainable energy practices within residential areas. As the global energy landscape shifts towards low-carbon sources in response to growing environmental concerns and escalating energy demands, this research addresses urgent needs for innovative solutions. The research commences with an extensive literature review utilizing bibliometric techniques and science mapping, enabled by a sophisticated data analysis tool developed in the R programming language. This foundational phase discerns emerging trends and obstacles in the integration of renewable energy, providing a basis for subsequent inquiries. Following this, the thesis introduces a multi-objective optimization model designed to minimize life cycle costs and environmental impacts while maximizing the adoption of green energy. This model is enhanced by a multi-criteria decision-making framework employing the Weighted Sum Model (WSM), which facilitates stakeholders in overcoming traditional barriers by selecting optimal solutions for energy communities. Moreover, the study integrates machine learning with life cycle assessment (LCA) and life cycle cost (LCC) analyses to advance the design of renewable energy systems.