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131	Hydrogen Production and Storage Optimization based on Technical and Financial Conditions : A study of hydrogen strategies focusing on demand and integration of wind power. / Optimering av vätgasproduktion och lagring utifrån tekniska och ekonomiska förutsättningar : En studie av vätgasstrategier med fokus på efterfrågan och integration av vindkraft. Langels, Hanna, Syrjä, Oskar January 2021 (has links) There has recently been an increased interest in hydrogen, both as a solution for seasonal energy storage but also for implementations in various industries and as fuel for vehicles. The transition to a society less dependent on fossil fuels highlights the need for new solutions where hydrogen is predicted to play a key role. This project aims to investigate technical and economic outcomes of different strategies for production and storage of hydrogen based on hydrogen demand and source of electricity. This is done by simulating the operation of different systems over a year, mapping the storage level, the source of electricity, and calculating the levelized cost of hydrogen (LCOH). The study examines two main cases. The first case is a system integrated with offshore wind power for production of hydrogen to fuel the operations in the industrial port Gävle Hamn. The second case examines a system for independent refueling stations where two locations with different electricity prices and traffic flows are analyzed. Factors such as demand, electricity prices, and component costs are investigated through simulating cases as well as a sensitivity analysis. Future potential sources of income are also analyzed and discussed. The results show that using an alkaline electrolyzer (AEL) achieves the lowest LCOH while PEM electrolyzer is more flexible in its operation which enables the system to utilize more electricity from the offshore wind power. When the cost of wind electricity exceeds the average electricity price on the grid, a higher share of wind electricity relative to electricity from the grid being utilized in the production results in a higher LCOH. The optimal design of the storage depends on the demand, where using vessels above ground is the most beneficial option for smaller systems and larger systems benefit financially from using a lined rock cavern (LRC). Hence, the optimal design of a system depends on the demand, electricity source, and ultimately on the purpose of the system. The results show great potential for future implementation of hydrogen systems integrated with wind power. Considering the increased share of wind electricity in the energy system and the expected growth of the hydrogen market, these are results worth acknowledging in future projects. Hydrogen hydrogen energy system hydrogen production hydrogen storage large-scale storage underground storage vessels LRC hydrogen application hydrogen strategies hydrogen transition offshore wind energy electricity grid electricity price volatile electricity prices renewable energy hydrogen refueling station hydrogen port green hydrogen hydrogen trucks hydrogen forklifts fuel cells electrolysis alkaline electrolyzer PEM electrolyzer levelized cost of hydrogen LCOH Vätgas vätgassystem vätgasproduktion vätgaslagring storskalig vätgaslagring bergrum vätgasapplikationer vätgasstrategi vätgasomställning grön omställning havsbaserad vindkraft volatila elpriser elnätet förnybar energi vätgastankstation vätgasmack vätgashamn grön vätgas vätgaslastbilar vätgastruckar bränsleceller elektrolys alkalisk elektrolysör PEM elektrolysör Elektroteknik och elektronik
132	Computational Methods for Renewable Energies: A Multi-Scale Perspective Diego Renan Aguilar Alfaro (19195102) 23 July 2024 (has links) <p dir="ltr">The urgent global shift towards decarbonization necessitates the development of robust frameworks to navigate the complex technological, financial, and regulatory challenges emerging in the clean energy transition. Furthermore, the increased adoption of renewable energy sources (RES) is correlated to the exponential growth in weather data research over the last few years. This circular relationship, where big data drives renewable growth, which feeds back the data pipeline, serves as the primary focus of this study: the development of computational tools across diverse spatial and temporal scales for the optimal design and operation of renewable energy-based systems. Two scales are considered, differentiated by their primary objectives and techniques used. </p><p dir="ltr"> In the first one, the integration of probabilistic forecasts into the operations of RES microgrids (MGs) is studied in detail. It is revealed that longer scheduling horizons can reduce dispatch costs but at the expense of forecast accuracy due to increased prediction accuracy decay (PAD). To address this, a novel method that determines how to split the time horizon into timeblocks to minimize dispatch costs and maximize forecast accuracy is proposed. This forms the basis of an optimal rolling horizon strategy (ORoHS) which schedules distributed energy resources over varying prediction/execution horizons. Results offer Pareto-optimal fronts, showing the trade-offs between cost and accuracy at varying confidence levels. Solar power proved more cost-effective than wind power due to lower variability, despite wind’s higher energy output. The ORoHS strategy outperformed common scheduling methods. In the case study, it achieved a cost of \$4.68 compared to \$9.89 (greedy policy) and \$9.37 (two-hour RoHS). The second study proposes the Caribbean Energy Corridor (CEC) project, a novel, ambitious initiative that aims to achieve total grid connectivity between the Caribbean islands. The analysis makes use of thorough data procedures and optimization methods for the resource assessment and design tasks needed to build such an infrastructure. Renewable energy potentials are quantified under different temporal and spatial coverages to maximize usage. Prioritizing offshore wind development, the CEC’s could significantly surpass anticipated growth in energy demand, with an estimated installed capacity of 34 GW of clean energy upon completion. The corridor is modeled as an HVDC grid with 32 nodes and 31 links. Underwater transmission is optimized with a Submarine-Cable-Dynamic-Programming (SCDP) algorithm that determines the best routes across the bathymetry of the region. It is found that the levelized cost of electricity remains on the low end at \$0.11/kWh, despite high initial capital investments. Projected savings reach \$ 100 billion when compared with ”business-as-usual” scenarios and the current social cost of carbon. Furthermore, this infrastructure has the potential to create around 50,000 jobs in construction, policy, and research within the coming decades, while simultaneously establishing a robust and sustainable energy-water nexus in the region. Finally, the broader implications of these works are explored, highlighting their potential to address global challenges such as energy accessibility, prosperity in conflict zones, and sharing these discoveries with the upcoming generations.</p> Electrical energy storage Photovoltaic power systems Evolutionary computation Modelling and simulation Planning and decision making Satisfiability and optimisation Data engineering and data science Neural networks Reinforcement learning Renewable Energies Optimization Power Systems Microgrids Energy Corridors Machine Learning Artificial Intelligence Robust Optimization Genetic Algorithm Mixed Integer Linear Programming Multi-objective Optimization Economic Dispatch Unit Commitment Wind Energy Solar Energy Caribbean Energy Corridor HVDC Submarine Power Transmission Dynamic Programming Wandering Salesman Problem Big Data Offshore Wind Battery Energy Storage System

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Computational Methods for Renewable Energies: A Multi-Scale Perspective