Global ETD Search

21	SYSTEM-LEVEL PERFORMANCE AND RELIABILITY OF SOLAR PHOTOVOLTAIC FARMS: LOOKING AHEAD AND BACK Muhammed-Tahir Patel (11798318) 20 December 2021 (has links) <div>In a world of ever-increasing demand for energy while preventing adverse effects of climate</div><div>change, renewable energy has been sought after as a sustainable solution. To this end,</div><div>the last couple of decades have seen an advancement in research and development of solar</div><div>photovoltaic (PV) technology by leaps and bounds. This has led to a steady improvement</div><div>in the cost-effectiveness of solar PV as compared to the traditional sources of energy, e.g.,</div><div>fossil fuels as well as contemporary renewable energy sources such as wind and hydropower.</div><div>To further decrease the levelized cost of energy (LCOE) of solar PV, new materials and</div><div>technologies are being investigated and subsequently deployed as residential, commercial, and</div><div>utility-scale systems. One such innovation is called bifacial PV, which allows collection of</div><div>light from the front as well as rear surfaces of a flat PV panel.</div><div><br></div><div>In this thesis, we present a detailed investigation of bifacial solar PV farms analyzed across</div><div>the globe. We define the problem, explore the challenges, and collaborate with researchers</div><div>from academia and the PV industry to find a novel solution.</div><div><br></div><div>First, we begin by developing a multi-module computational framework to numerically</div><div>model a utility-scale bifacial solar PV farm. This requires integrating optical, electrical,</div><div>thermal, and economic models in order to estimate the energy yield and LCOE of a bifacial</div><div>PV system. The first hurdle is to re-formulate the LCOE so that the economist and the</div><div>technologist can collaborate seamlessly. Thus, we re-parameterize the LCOE expression</div><div>and validate our economic model with economists at the National Renewable Energy Lab</div><div>(NREL).</div><div><br></div><div>Second, we extend the existing optical and electrical models created for stand-alone</div><div>bifacial PV panels to models that can simulate a large-scale bifacial solar PV farm. This</div><div>brings the challenge of mathematically modeling solar farms and light collection on the rows</div><div>of PV panels elevated from the ground by taking into account the mutual shading between</div><div>the rows, reflections from the ground, and elevation-dependent light absorption on the rear</div><div>surface of the PV panels from several neighboring rows. Next, we integrate temperaturedependent</div><div>efficiency models to take into account the effects of location-dependent ambient</div><div>temperature, wind speed, and technology-varying temperature coefficients of the solar PV</div><div>system in consideration.</div><div><br></div><div>Third, we complete the comprehensive modeling of bifacial solar PV farms by including</div><div>two types of single-axis tracking algorithms viz. sun-tracking and power tracking. Using these</div><div>algorithms, we explore the best tracking orientation of solar farms i.e., East-West tracking</div><div>vs. North-South tracking for locations around the world. We further find the best land type</div><div>suitable for installation of these E/W or N/S tracking bifacial solar PV farms.</div><div><br></div><div>Fourth, we reduce the computation time of numerical modeling by utilizing the advantages</div><div>of machine learning algorithms. We train neural networks using data from the alreadybuilt</div><div>models to emulate the numerical modeling of a solar farm. Amazingly, we find the</div><div>computation time reduces by orders of magnitude while accurately estimating the energy</div><div>yield and LCOE of PV farms.</div><div><br></div><div>Fifth, we derive, compare, and experimentally validate the thermodynamic efficiency</div><div>limits of photovoltaic-to-electrochemical energy conversion for the purpose of storing solar</div><div>energy for future needs.</div><div><br></div><div>Finally, we present some new ideas and guidelines for future extensions of this thesis as</div><div>well as new challenges and problems that need further exploration.</div> Applied Physics Solar Energy photovoltaic utility scale solar solar farms reliabililty performance optical thermal electrical levelized cost of energy LCOE machine learning
22	Energy storage and their combination with wind power compared to new nuclear power in Sweden : A review and cost analysis Englund-Karlsson, Simon January 2020 (has links) As intermittent renewable energy sources such as wind and solar power gradually increase around the world, older technologies such as nuclear power is phased out in Sweden and many other countries. It is then important to ensure that the total power need is secured, and that the power grid can remain stable. One way of managing intermittent renewables is by using energy storage. The main goal of this thesis was to compare energy storage methods and their costs. A secondary aim was to investigate how the cost of developing more renewable energy sources, in combination with different energy storage methods, compares to erecting new nuclear power. This thesis was limited to three energy storage technologies, namely pumped hydro storage (PHS), compressed air energy storage (CAES), and four battery storage technologies. They were combined with wind power in the cost analysis. The comparison was done by performing a literature review and economical calculations, which focused especially on levelized cost of storage (LCOS). The results from the economic calculations indicated that PHS and CAES had lower LCOS than battery storage technologies. Similar results could be seen in the literature review as well. When comparing levelized cost of energy (LCOE) nuclear power had the lowest, €0.03-0.12 kWh-1, followed by wind power in combination with PHS and CAES, both around €0.07-0.24 kWh-1. This result was maintained also at sensitivity analysis regarding the discount rate, which both nuclear power and PHS proved rather sensitive to. Keywords: energy storage, nuclear power, wind power, pumped hydro storage, compressed air energy storage, battery energy storage, levelized cost of energy, Sweden Energy storage wind power nuclear power levelized cost of energy levelized cost of storage review cost analysis LCOE LCOS pumped hydro storage compressed air energy storage battery storage PHS CAES Energy Systems Energisystem
23	Developing a Cost Model For Combined Offshore Farms : The Advantages of Co-Located Wind and Wave Energy Blech, Eva January 2023 (has links) Previous research has displayed that multi-source farms provide an opportunity to reduce the cost of energy and improve the energy output quality. This thesis assesses the cost competitiveness of co-located wind-wave farms, specifically floating offshore wind (FLOW) turbines and CorPower’s wave energy converters (WEC). This research was conducted in collaboration with CorPower, a Swedish WEC developer. A cost model is generated, which calculates the levelized cost of energy (LCOE) utilizing a life-cycle cost analysis. The model is developed by combining CorPower’s existing cost model with an agglomeration of FLOW cost models from previous studies. An in depth literature research informs about synergies, which are translated into shared costs within the model. The cost model is applied to a site on the Northern coast of Portugal; the location of a FLOW farm project under development. Including wave energy, improves the annual energy production of the farm by up to 10%. However, the effects on power smoothing are negligible, due to the high seasonal variability of the wave resource and the minimal complementarity of the two energy sources. The LCOE of a 1GW 50% wind - 50% wave farm is 63€/MWh. The high initial investment costs of the wind farm results in the standalone wind LCOE of 73€/MWh. The strong capacity factor of the WECs cause the LCOE to reduce to 55€/MWh, when evaluating a standalone wave farm. In all co-location configurations, savings for FLOW and wave farm developers are exhibited. The highest savings are identified for small wind/wave arrays co-located in large farms. This results in an LCOE reduction of up to 4.5% for both wind and wave farm developers. The largest relative savings are found in the DEVEX costs and the electrical transmission installation costs. The identified cost calculations and savings are inline with previous studies. The savings are in the lower range compared to other studies, due to the conservative estimations of the degree of shared costs. The cost model provides a tool, that can be continuously updated with the most recent findings of cost inputs and wind-wave synergies, i.e. shared cost opportunities. This thesis’ results reflect how co-locating wind and wave farms can improve the cost-competitiveness of both technologies. Nevertheless, more in depth research is required to comprehend the full potential of co-located wind-wave farms. There is a necessity of collaboration between wind and wave industry members to ensure that the synergies and shared cost-opportunities identified, are fully exploited. / Tidigare forskning har visat att parker med flera källor ger möjlighet att minska energikostnaderna och förbättra energiproduktionens kvalitet. I den här avhandlingen utvärderas kostnadskonkurrenskraften hos samlokaliserade vind- och vågkraftsparker, särskilt flytande havsbaserade vindkraftverk (FLOW) och CorPowers vågenergiomvandlare (WEC). Denna forskning genomfördes i samarbete med CorPower, en svensk WEC-utvecklare. En kostnadsmodell genereras, som beräknar den nivellerade energikostnaden (LCOE) med hjälp av en livscykelkostnadsanalys. Modellen är utvecklad genom att kombinera CorPowers befintliga kostnadsmodell med en agglomeration av FLOW-kostnadsmodeller från tidigare studier. En djupgående litteraturstudie ger information om synergier, som översätts till delade kostnader i modellen. Kostnadsmodellen tillämpas på en plats på Portugals norra kust, där ett FLOW-anläggningsprojekt är under utveckling. Genom att inkludera vågenergi förbättras parkens årliga energiproduktion med upp till 10%. Effekterna på effektutjämningen är dock försumbara, på grund av vågresursens stora säsongsvariationer och de två energikällornas minimala komplementaritet. LCOE för en 1GW 50% vind - 50% vågkraftspark är 63€/MWh. De höga initiala investeringskostnaderna för vindkraftsparken resulterar i en LCOE för fristående vindkraft på 73 €/MWh. Den starka kapacitetsfaktorn för WECs gör att LCOE minskar till 55€/MWh, vid utvärdering av en fristående vågkraftspark. I alla samlokaliseringskonfigurationer uppvisas besparingar för FLOW och vågparksutvecklare. De största besparingarna identifieras för små vind-/vågkraftsparker som samlokaliseras i stora parker. Detta resulterar i en minskning av LCOE med upp till 4,5% för både vind- och vågparksutvecklare. De största relativa besparingarna finns i DEVEX-kostnaderna och installationskostnaderna för elektrisk överföring. De identifierade kostnadsberäkningarna och besparingarna är i linje med tidigare studier. Besparingarna ligger i det lägre intervallet jämfört med andra studier, på grund av de konservativa uppskattningarna av graden av delade kostnader. Kostnadsmodellen är ett verktyg som kontinuerligt kan uppdateras med de senaste rönen om kostnadsingångar och synergier mellan vind och våg, dvs. möjligheter till delade kostnader. Resultaten i denna avhandling visar hur samlokalisering av vind- och vågkraftsparker kan förbättra kostnadskonkurrenskraften för båda teknikerna. Det krävs dock mer djupgående forskning för att förstå den fulla potentialen hossamlokaliserade vind- och vågparker. Det finns ett behov av samarbete mellanvind- och vågkraftsindustrin för att säkerställa att de identifierade synergierna ochgemensamma kostnadsmöjligheterna utnyttjas fullt ut. Wave Energy Floating Offshore Wind Co-located Wind-Wave Farm Levelized Cost of Energy Multi-Source Offshore Farm Vågenergi flytande havsbaserad vindkraft samlokaliserad vind- och vågpark nivellerad energikostnad havsbaserad park med flera källor Engineering and Technology Teknik och teknologier
24	A Comparative Study on Two Offshore Wind Farm Siting Approaches in Sweden / En jämförande studie av två tillvägagångssätt för siting av havsbaserade vindkraftsparker i Sverige Nyberg, Anders, Sundström, Oskar January 2023 (has links) This study aims to explore the ability of a multi-criteria decision making with analytical hierarchy process (MCDM-AHP) model to emulate the results of a cost benefit analysis (CBA) model in the context of offshore wind farm siting within the Swedish exclusive economic zone (EEZ). The research question addressed is whether the MCDM-AHP analysis produces similar results to the CBA analysis. In addition to this, the strengths and weaknesses of each model is explored. The MCDM-AHP model employs the spatial criteria in a more basic manner compared to the CBA model, simplifying the evaluation process while still explaining 89.5% of the variation in the CBA model and defining similar areas as suitable. Thus, it can be concluded that the MCDM-AHP model adequately emulates the CBA model within the context of offshore wind farm siting within the Swedish EEZ. However, it is crucial to note that the two models produce outputs on different scales. While the CBA model provides levelized cost of energy (LCOE) values that can be thresholded for investment viability comparisons, the suitability score generated by the MCDM-AHP model remains a relative and arbitrary score within the model. Both models entail uncertainties, limiting their usage beyond making general assumptions or identifying areas of interest. The findings reveal that the CBA model demonstrates greater robustness when confronted with changes in spatial input parameters compared to the MCDM-AHP model. This discrepancy is attributed to the iterative computation process and consideration of flat cost inputs in the CBA model, whereas the MCDM-AHP model represents a linear combination of various spatial parameters. However, the calculated LCOE values in the CBA model are highly sensitive to changes in modeling assumptions regarding external parameters, resulting in significant linear variations. The LCOE values obtained from the CBA model baseline case fall within a range of 52.1 - 98.9 EUR/MWh, which aligns with similar studies, validating the CBA model. Nonetheless, caution should be exercised when considering these results as an accurate representation of the real world due to inherent uncertainties in cost inputs and the LCOE measure. The strengths of the MCDM-AHP model lie in its robustness when the order of relative importance remains stable for key spatial evaluators. It is sensitive to significant changes in water depth and wind speed, which heavily influence its output. The model's simplicity allows for a quick overview of the problem, but it requires assumptions that introduce uncertainties. Validation of the MCDM-AHP model using existing and planned offshore wind farms within the Swedish EEZ was possible but limited by the arbitrary scale and limited validation areas. The comparison between the two models could be enhanced with more comprehensive spatial and economic data for an in-depth CBA model, which could serve as a ground truth for the MCDM-AHP model. Nevertheless, the comparison made in this study considers the CBA model to be closer to the truth, acknowledging the underlying assumptions that should be considered during evaluation. In conclusion, within the context of offshore wind farm siting, the MCDM-AHP model produces outputs that are similar to the CBA model. Multi-Criteria Decision Making Analytical Hierarchy Process Cost Benefit Analysis Offshore Wind Farm Siting Swedish Exclusive Economic Zone Levelized Cost of Energy GIS Sweden Renewable Energy Offshore Wind Energy Spatial Model Comparison Multikriterieanalys Analytisk hierarkiprocess Kostnadsintäktsanalys Siting av havsbaserade vindkraftverk Sveriges exklusiva ekonomiska zon Nivåkostnad för energi GIS Sverige Förnybar energi Havsbaserad vindkraft Jämförelse av spatiala modeller Multi-Criteria Decision Making Analytical Hierarchy Process Cost Benefit Analysis Economics Nationalekonomi
25	Analysis of a novel thermoelectric generator in the built environment Lozano, Adolfo 05 October 2011 (has links) This study centered on a novel thermoelectric generator (TEG) integrated into the built environment. Designed by Watts Thermoelectric LLC, the TEG is essentially a novel assembly of thermoelectric modules whose required temperature differential is supplied by hot and cold streams of water flowing through the TEG. Per its recommended operating conditions, the TEG nominally generates 83 Watts of electrical power. In its default configuration in the built environment, solar-thermal energy serves as the TEG’s hot stream source and geothermal energy serves as its cold stream source. Two systems-level, thermodynamic analyses were performed, which were based on the TEG’s upcoming characterization testing, scheduled to occur later in 2011 in Detroit, Michigan. The first analysis considered the TEG coupled with a solar collector system. A numerical model of the coupled system was constructed in order to estimate the system’s annual energetic performance. It was determined numerically that over the course of a sample year, the solar collector system could deliver 39.73 megawatt-hours (MWh) of thermal energy to the TEG. The TEG converted that thermal energy into a net of 266.5 kilowatt-hours of electricity in that year. The second analysis focused on the TEG itself during operation with the purpose of providing a preliminary thermodynamic characterization of the TEG. Using experimental data, this analysis found the TEG’s operating efficiency to be 1.72%. Next, the annual emissions that would be avoided by implementing the zero-emission TEG were considered. The emission factor of Michigan’s electric grid, RFCM, was calculated to be 0.830 tons of carbon dioxide-equivalent (CO2e) per MWh, and with the TEG’s annual energy output, it was concluded that 0.221 tons CO2e would be avoided each year with the TEG. It is important to note that the TEG can be linearly scaled up by including additional modules. Thus, these benefits can be multiplied through the incorporation of more TEG units. Finally, the levelized cost of electricity (LCOE) of the TEG integrated into the built environment with the solar-thermal hot source and passive ground-based cold source was considered. The LCOE of the system was estimated to be approximately $8,404/MWh, which is substantially greater than current generation technologies. Note that this calculation was based on one particular configuration with a particular and narrow set of assumptions, and is not intended to be a general conclusion about TEG systems overall. It was concluded that while solar-thermal energy systems can sustain the TEG, they are capital-intensive and therefore not economically suitable for the TEG given the assumptions of this analysis. In the end, because of the large costs associated with the solar-thermal system, waste heat recovery is proposed as a potentially more cost-effective provider of the TEG’s hot stream source. / text Thermoelectric generator TEG Novel thermoelectric generator Thermoelectrics Thermoelectric effect Thermoelectric modules Thermoelectric device Patent application Hot stream source Cold stream source EIA EERE Systems-level analysis Energy balance Control volume analysis Thermodynamic analysis Insolation data Insolation incident on a panel Incident insolation Extraterrestrial insolation NREL Ambient temperature data NOAA Renewable energy sources Energy generation technology Electricity generation technology Thermal energy Coupled system Emissions Greenhouse gas GHG Emissions analysis Zero-emission Emissions avoided Emission factor EF Reliability First Corporation RFC Built environment Integrated into the built environment Solar-thermal energy Solar collector system Photovoltaic thermal PVT panel Thermal storage tank Geothermal energy Geothermal heat pump GHP Ground source heat pump GSHP Numerical analysis Numerical model MATLAB Finite-difference Euler explicit Electric grid Annual energetic performance Annual electricity generation Operating efficiency System efficiency Thermodynamic characterization Economic analysis Levelized cost of energy LCOE Waste heat recovery Carbon dioxide equivalent CO2e Capital costs Installed costs Operating and maintenance costs

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