Contributions of information systems research to decision support for wind market players

The mitigation of climate change through the transition toward sustainable and efficient energy systems based on renewable energy technologies is one of the greatest challenges of the 21st century pursued by an ever-growing number of individuals, organizations, and societies in large. The extensive financial support of many nations for renewable energies has led to a rapid global spread of these technologies in the last two decades. Nowadays, as renewable energy technologies are maturing, governments tend to implement more market-based support mechanisms in order to scale back financial support, which poses new challenges for all market players. Consequently, in a consolidating market environment, only those players can establish themselves in the market, who have the right information at the right time in order to make the best possible decisions on newly emerging issues. In this context, this thesis demonstrates the high potential of information systems (IS) research on decision support systems (DSS) in making solution-oriented and impactful contributions to affected renewable energy stakeholders by improving the decision-making process through aggregated information. Six consecutive thematic topics are presented and discussed based on several research articles, each addressing a specific challenge of different renewable energy stakeholders by means of quantitative design science research (DSR) on DSS. The thematic spectrum ranges from micro-level challenges of individual renewable energy operators to macro-level challenges of policy-makers. A strong focus is placed on renewable energy finance and policy topics in the field of the wind energy sector. Findings indicate that the role of appropriate and customized DSS is becoming increasingly important for all market players, due to the constantly growing diversity of information and amount of data available in the rapidly digitalizing renewable energy sector. They further point to the strength and necessity of IS research with regard to its integrative function between other research areas and how this property could be used in order to respond to the need for more practical support for decision-makers concerned with environmental and sustainability issues.Die Abschwächung des Klimawandels durch den Übergang zu nachhaltigen Energiesystemen auf Grundlage Erneuerbarer Energien (EE) ist eine der größten Herausforderungen des 21. Jahrhunderts. Die umfangreiche Förderung vieler Nationen für EE hat in den vergangenen zwei Jahrzehnten weltweit zu einer großen Verbreitung dieser Technologien geführt. Da EE seither immer wettbewerbsfähiger werden, neigen politische Entscheidungsträger vieler Nationen heutzutage dazu, zunehmend marktbasierte Fördermechanismen einzuführen, um die finanzielle Förderung dauerhaft zu reduzieren, wodurch neue Herausforderungen für Marktteilnehmer entstehen. In einem sich konsolidierenden Marktumfeld können sich nur diejenigen Akteure langfristig am Markt etablieren, die über die richtigen Informationen zur richtigen Zeit am richtigen Ort verfügen. In diesem Zusammenhang zeigt die vorliegende kumulative Dissertation das hohe Potenzial der IS Forschung im Bereich von Entscheidungsunterstützungssystemen (EUS) lösungsorientierte und wirkungsvolle Beiträge gegenüber EE-Marktteilnehmern zu leisten, indem sie deren Entscheidungsprozesse durch aggregierte Informationen verbessert. Sechs aufeinander folgende thematische Abschnitte werden auf Grundlage von Forschungsartikeln vorgestellt und diskutiert und befassen sich jeweils mit der Lösung einer spezifischen Herausforderung eines oder mehrerer Marktteilnehmer mittels quantitativer DSR Methoden. Das thematische Spektrum reicht von mikroskaligen Herausforderungen einzelner EE-Betreiber bis hin zu makroskaligen Herausforderungen politischer Entscheidungsträger. Ein besonderer Schwerpunkt liegt auf dem Windenergiemarkt. Die Ergebnisse deuten darauf hin, dass die Rolle von EUS für alle Marktteilnehmer aufgrund der ständig wachsenden Diversität an Informationen und Datenmengen im sich schnell digitalisierenden EE-Sektor immer wichtiger wird. Sie weisen ferner auf die Stärke und Notwendigkeit der IS Forschung im Hinblick auf ihre integrative Funktion zwischen anderen Forschungsbereichen hin und zeigen auf, wie diese Eigenschaft eingesetzt werden kann, um dem Bedarf an praxisorientierter Unterstützung für Entscheidungsträger zu begegnen

Regional rotor blade waste quantification in Germany until 2040

Worldwide, wind turbine stocks are ageing and questions of reuse and recycling particularly of rotor blades become urgent. Especially, rising rotor blade wastes face lacking good recycling options and exact quantification is difficult due to information gaps on the rotor blade size, mass and exact material composition. In a combined approach, the expected rotor blade waste is quantified and localized on a national level for Germany until 2040. Fibre-reinforced plastics (FRP) from rotor blades are in focus and differentiated into two material classes: glass-fibre reinforced plastics (GFRP) and glass- and carbon-fibre reinforced plastics (GFRP/CFRP). The quantification approach is based on a national power plant stock database (Marktstammdatenregister) and regression models, combined with a power class-based estimation for missing datasets. As a result, between 325,726 and 429,525 t of waste from the GFRP material class and between 76,927 t and 211,721 t of waste from the GFRP/CFRP material class arise from obsolete rotor blades in Germany until 2040. This corresponds to a share of between 11% and 32% of wind turbines with GFRP/CFRP rotor blade material in Germany. For GFRP, waste peaks in 2021, 2035 and 2037 are expected with around 40,000 t of waste per year. For GFRP/CFRP, waste peaks in 2036 and 2037 will induce more than 20,000 t/a. Mostly affected federal states are Lower Saxony, Brandenburg, North Rhine-Westphalia and Schleswig-Holstein. The methods are applicable and transferable to other countries, particularly with ageing wind turbines fleets

Reducing carbon footprint of deep-sea oil and gas field exploitation by optimization for Floating Production Storage and Offloading

Deep-sea oil and gas fields are acting as a vital role by providing substantial oil and gas resource, and Floating Production Storage and Offloading is an indispensable tool for the development of offshore oil and gas fields effectively. Here, Life Cycle Assessment is applied to evaluate environmental loads in the whole life cycle of the deep-sea oil and gas production. This paper explores the carbon footprint of Floating Production Storage and Offloading as the time axis. It is found that Floating Production Storage and Offloading is a conceptual product at the design stage and does not generate carbon emission, while the operational stage releases considerable emission by the fuel combustion process, accounting for 88.2% of the entire life cycle. To decrease this part of carbon emission, distributed energy system is considered as a promising choice because it integrates different energy resources and provides an economic and environmental energy allocation scheme to meet the energy demand. For the operation stage, this paper establishes a Multi-objective Mathematical Programming model to determine the selection and capacity of facilities with minimum annual total cost and carbon emissions by considering the energy balance and technical constraints. The model is validated by an example and solved by the weight method. According to designer's demand, distributed energy system can optimize economic objectives in a maximum range of 14.6%, and a maximum emission reduction of 4.53% can be expected compared with the traditional scheme. Sensitivity analysis shows that cost is more sensitive to natural gas price

Disassembly of Large Composite-Rich Installations

Considering the demanufacturing of large infrastructures (as wind blades and aircrafts) rich in composite materials, the most impacting step in terms of costs is disassembly. Different routes could be followed for dismantling and transportation and several factors influence the final result (as the technology used, the logistic and the administrative issues). For this reason, it is fundamental to understand which solution has to be followed to reduce the impact of decommissioning on the overall recycling and reusing cost. This work, after the formalization of the different possible disassembly scenarios, proposes a Decision Support System (DSS) for disassembly of large composite-rich installations, that has been designed and implemented for the identification of the most promising disassembly strategy, according to the process costs minimization. The mathematical models constituting the core of this tool are detailed and the DSS is applied to disassembly of onshore wind blades, underling the importance of similar systems to optimize demanufacturing costs

What are the key multidimensional success criteria required for reducing LCOE through digital transformation in offshore wind farms?

Formålet med denne studien er å undersøke de flerdimensjonale suksesskriteriene som er avgjørende for å redusere energikostnaden også kjent som Levlized cost of Energy (LCOE) gjennom digital transformasjon innenfor offshore vind prosjekter. For å besvare problemstilling vil studien sette søkelys på fire underspørsmål som omhandler: (1) For å sikre operational excellence og tilpasning til FNs bærekraftsmål gjennom digital transformasjon: Hvilke suksessfaktorer må være på plass? (2) Er data tilgjengelig for bruk til den digital transformasjon? (3) Hvordan kan man muliggjør optimal Grid Integration av vindparken? (4) Kan man utnytte digitale verktøy for å redusere LCOE i en havvindpark? Studien fremhever den uunnværlige rollen av teknologi i form av digitale verktøy og data, som spiller som katalysatorer for å styrke operasjonell effektivitet og maksimere verdiskaping i offshore vindenergisektoren. Studien er gjennomført som kvalitativ Case-studier analyse i form av ti individuelle dybdeintervjuer med deltakere fra ulike selskaper i verdikjeden til offshore vind industri. Studien undersøker den betydelige påvirkningen FNs bærekraftsmål har på utviklingen av offshore vindprosjekter, samt den vitale rollen operational excellence har for å lykkes. Den vurderer om offshore vind industrien er klar for Industri 5.0, dens evne til å redusere LCOE, og dens innflytelse på sektorens fremtid. Funnene understreker betydningen av tilgjengelig data, optimalisert effektivitet, og bruk av sanntidsdata for å forbedre sikkerhet, bærekraft og effektiv energiproduksjon i vindparker. Videre dykker studien ned i implementeringen av digital transformasjon, og viser til hvordan digitale verktøy og automatisering, sammen med menneskelig inngripen, driver informert beslutningstaking. Funnene legger vekt på nødvendigheten av datasamarbeid, kunnskapsdeling, og kompetent personell for å fremme industriell vekst, samtidig som det opprettholdes en balanse mellom kompleksitet og kompetanse, og utforsker avansert digital tvilling-teknologi og hvordan det kan påvirke i redusering av LCOE. Studien tilbyr verdifull innsikt for interessenter og hjelper til med å håndtere utfordringer og muligheter i digital transformasjon av offshore vindparker. Den fremhever offshore vinindustriens avgjørende rolle i utviklingen av renere, effektive energisystemer, og støtter en bærekraftig og fremgangsrik fremtid.This purpose of this study is to thoroughly examine the multidimensional success criteria crucial in reducing the levelized cost of energy (LCOE) through digital transformation within the context of offshore wind farm projects. To help answer the research question, this study will focus on four preliminary research questions: (1) To ensure Operational Excellence and Alignment with UN SDGs through Digital Transformation: What success factors need to be in place? (2) Is Data available to be used to enable Digital Transformation? (3) How do you enable optimal Grid Integration of the wind park? (4) Can you leverage digital tools to reduce LCOE in an offshore wind farm? The research spotlights the indispensable role of technology in form of digital tools and data, as catalysts for bolstering operational efficiency and maximizing value creation in the offshore wind energy sector. The study has been carried out as a qualitative case study analysis in the form of ten individual in-depth interviews with participants from various companies in the value chain of the offshore wind industry. The study investigates the substantial impact of United Nations (UN) sustainability goals on offshore wind project development and the vital role of operational excellence. It evaluates the industry's preparedness for Industry 5.0, its capacity to reduce LCOE, and its influence on the sector's future. The research and findings underscore the significance of accessible data, optimized efficiency, and real-time data utilization to enhance safety, sustainability, and energy production in wind farms. Additionally, the research delves into Industry 5.0's implementation, demonstrating how digital tools and automation, combined with human input, drive informed decision-making. The findings emphasize the necessity for data collaboration, knowledge sharing, and skilled personnel to foster industry growth while maintaining a balance between complexity and competence and explores advanced digital twin technology and how it can influence in reducing LCOE. The study offers valuable insights for stakeholders and aids in addressing challenges and opportunities in offshore wind farm digital transformation. It accentuates the offshore wind industry's pivotal role in advancing cleaner, efficient energy systems, promoting a sustainable and prosperous future

Energy Production Analysis and Optimization of Mini-Grid in Remote Areas: The Case Study of Habaswein, Kenya

Rural electrification in remote areas of developing countries has several challenges which hinder energy access to the population. For instance, the extension of the national grid to provide electricity in these areas is largely not viable. The Kenyan Government has put a target to achieve universal energy access by the year 2020. To realize this objective, the focus of the program is being shifted to establishing off-grid power stations in rural areas. Among rural areas to be electrified is Habaswein, which is a settlement in Kenya’s northeastern region without connection to the national power grid, and where Kenya Power installed a stand-alone hybrid mini-grid. Based on field observations, power generation data analysis, evaluation of the potential energy resources and simulations, this research intends to evaluate the performance of the Habaswein mini-grid and optimize the existing hybrid generation system to enhance its reliability and reduce the operation costs. The result will be a suggestion of how Kenyan rural areas could be sustainably electrified by using renewable energy based off-grid power stations. It will contribute to bridge the current research gap in this area, and it will be a vital tool to researchers, implementers and the policy makers in energy sector

Effective planning of-end-of-life scenarios for offshore windfarm

Many offshore wind turbines (OWTs) are approaching the end of their estimated operational life soon. It is challenging to develop a general decommissioning procedure for all OW farms. Therefore, this research aims to comprehend the available end-of-life (EoL) scenario for OWTs to decide on their application procedures and propose an innovative systematic framework for considering the EoL scenario. The first part of the research critically reviewed the various end-of-life strategies for offshore wind farms, available technological options and the influencing factors that can inform such decisions. The study proposed a multi-attribute framework for supporting optimum choices in terms of main constraints, such as the possibility of end-of-life strategies based on unique characteristics and influencing factors. In the selection of techno-economic, the primary procedure parameters influencing the three major end-life strategies, i.e. life extension, repowering, and decommissioning, are discussed, and the benefits and issues related to the influencing variables are also identified. In the next part, an initial comparative assessment between two of these scenarios, repowering and decommissioning, through a purpose-developed techno-economic analysis model calculates relevant key performance indicators. With numerous OW farms approaching the end of service life, the discussion on planning the most appropriate EoL scenario has become popular. Planning and scheduling those main activities of EoL scenarios depends on forecasting leading environmental indicators such as significant wave height. This research proposes a novel probabilistic methodology based on multivariate and univariate time series forecasting of machine learning (ML) models, including LSTM, BiLSTM, and GRU. In the end, the role of optimum selection of end-of-life scenarios is investigated to achieve the highest profitability of offshore wind farms. Various end-of-life scenarios have been evaluated through a TOPSIS technique as a multi-criteria decision-making procedure to determine an appropriate way according to environmental, financial, safety Criteria, Schedule impact, and Legislation and guidelines. Keywords: Offshore Wind Turbine; Decommissioning; End-of-life scenarios; Decision making; Levelized Cost of Energy; Machine learning, ForecastingMany offshore wind turbines (OWTs) are approaching the end of their estimated operational life soon. It is challenging to develop a general decommissioning procedure for all OW farms. Therefore, this research aims to comprehend the available end-of-life (EoL) scenario for OWTs to decide on their application procedures and propose an innovative systematic framework for considering the EoL scenario. The first part of the research critically reviewed the various end-of-life strategies for offshore wind farms, available technological options and the influencing factors that can inform such decisions. The study proposed a multi-attribute framework for supporting optimum choices in terms of main constraints, such as the possibility of end-of-life strategies based on unique characteristics and influencing factors. In the selection of techno-economic, the primary procedure parameters influencing the three major end-life strategies, i.e. life extension, repowering, and decommissioning, are discussed, and the benefits and issues related to the influencing variables are also identified. In the next part, an initial comparative assessment between two of these scenarios, repowering and decommissioning, through a purpose-developed techno-economic analysis model calculates relevant key performance indicators. With numerous OW farms approaching the end of service life, the discussion on planning the most appropriate EoL scenario has become popular. Planning and scheduling those main activities of EoL scenarios depends on forecasting leading environmental indicators such as significant wave height. This research proposes a novel probabilistic methodology based on multivariate and univariate time series forecasting of machine learning (ML) models, including LSTM, BiLSTM, and GRU. In the end, the role of optimum selection of end-of-life scenarios is investigated to achieve the highest profitability of offshore wind farms. Various end-of-life scenarios have been evaluated through a TOPSIS technique as a multi-criteria decision-making procedure to determine an appropriate way according to environmental, financial, safety Criteria, Schedule impact, and Legislation and guidelines. Keywords: Offshore Wind Turbine; Decommissioning; End-of-life scenarios; Decision making; Levelized Cost of Energy; Machine learning, Forecastin

Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations

Offshore wind energy installations in coastal areas have grown massively over the last decade. This development comes with a large number of technological, environmental, economic, and scientific challenges, which need to be addressed to make the use of offshore wind energy sustainable. One important component in these optimization activities is suitable information from observations and numerical models. The purpose of this study is to analyze the gaps that exist in the present monitoring systems and their respective integration with models. This paper is the second part of two manuscripts and uses results from the first part about the requirements for different application fields. The present solutions to provide measurements for the required information products are described for several European countries with growing offshore wind operations. The gaps are then identified and discussed in different contexts, like technology evolution, trans-European monitoring and modeling initiatives, legal aspects, and cooperation between industry and science. The monitoring gaps are further quantified in terms of missing observed quantities, spatial coverage, accuracy, and continuity. Strategies to fill the gaps are discussed, and respective recommendations are provided. The study shows that there are significant information deficiencies that need to be addressed to ensure the economical and environmentally friendly growth of the offshore wind farm sector. It was also found that many of these gaps are related to insufficient information about connectivities, e.g., concerning the interactions of wind farms from different countries or the coupling between physical and biological processes.publishedVersio