Cash flow at risk of offshore wind plants

Offshore wind power plants might be seen as high risk investments. Their risk depends on technical and financial elements. When some corporations decide to invest in a plant, they decide to take all above-mentioned risks. The question “Given a specific investor, a specific plant, etc., how big are the investment risks?” has not a clear answer. In fact, the impact of the previous risk factors on cash flows is not completely quantified, mainly because all the risks are related, but the dependency structure is difficult to be modelled. Hence, it is important to have a measure of the impact of the risks into the cash flows. Due to the lack of knowledge in this quantification, we have decided to investigate it more in the detail. The paper aims to measure the variability of cash flows and how effective are the strategies for locking electricity prices, ship freight rates, or both in the reduction of this variability. We adopt the Monte Carlo approach for simulating all the possible cash flows and for measuring all the uncertainties. The output shows that seasonal and uncertain cash flows. The strategies, for reducing the probability of negative cash flows, work only with locked electricity prices

On using simulation to model the installation process logistics for an offshore wind farm

The development of offshore wind farms (OWFs) in Europe is progressing to sites which are characteristically further from shore, in deeper waters, and of larger scale than previous sites. A consequence of moving further offshore is that installation operations are subject to harsher weather conditions, resulting in increased uncertainty in relation to the cost and duration of any operations. Assessing the comparative risks associated with different installation scenarios and identifying the best course of action is therefore a crucial problem for decision makers. Motivated by collaboration with industry partners, we present a detailed definition of the OWF installation process logistics problem, where aspects of fleet sizing, composition, and vessel scheduling are present. This article illustrates the use of simulation models to improve the understanding of the risks associated with logistical installation decisions. The developed tool employs a realistic model of the installation operations and enables the effect of any logistical decision to be investigated. A case study of an offshore wind farm installation project is presented in order to explore the impact of key logistical decisions on the cost and duration of the installation, and demonstrates that savings of up to 50% can be achieved through vessel optimization

Computational methods and parallel strategies in dynamic decision making

Cada uno de estos objetivos han sido tratados en un capítulo independiente de esta tesis. En el segundo capítulo, un modelo de programación estocástica es presentado para un problema práctico de planificación de producción de un producto perecedero en un horizonte de tiempo finito. Una política estática es estudiada para el modelo. Tal política ha demostrado ser óptima asumiendo una estrategia de incertidumbre estática, que es considerada para instancias con un tiempo de espera largo. El tercer capítulo trata el uso de computación paralela para los algoritmos desarrollados en el capítulo previo. Dos implementaciones fueron desarrolladas para plataformas heterogéneas: una versión multi-GPU usando CUDA y una versión multinúcleo usando Pthreads y MPI. Para la primera implementación la simulación de Monte Carlo (la tarea más costosa) es paralelizada. La versión multinúcleo mostró una buena escalabilidad, una vez tratada la carga no balanceada entre los procesadores. El cuarto capítulo trata la efectividad de heurísticas para un problemas de tamaño de lote de productos perecederos similar. La clásica heurística de Silver es extendida para productos perecederos y se presentan variantes del procedimiento: una analítica y una basada en simulación. Los resultados de la heurística son comparados con las soluciones óptimas dadas por un modelo SDP generado para el problema, mostrando que los costes de las heurísticas son se presentan, de media, un 5% sobre el coste óptimo para la estrategia basada en simulación y un 6% para la aproximación analítica. En el quinto capítulo, se presenta un modelo MILP para seleccionar la flota de embarcaciones óptima para el mantenimiento de un parque eólico marino. El modelo se presenta como un problema de dos niveles, seleccionando la flota optima en el primer nivel y optimizando la programación de las operaciones, usando dicha flota, en el segundo. Dado que el modelo es determinístico, como otros en la literatura que aspiran a resolver problemas con un horizonte temporal largo usando periodos cortos, el sexto capítulo trata la cuestión de cómo la anticipación de los eventos estocásticos como los fallos en las turbinas o las condiciones meteorológicas afectan la decisión de la flota de embarcaciones óptima. Este capítulo presenta una heurística que ilustra este efecto.Esta tesis analiza aplicaciones de toma de decisiones dinámica para un conjunto de problemas. Pueden diferenciarse dos líneas principales. La primera trata problemas de gestión de la cadena de suministro para productos perecederos, mientras que la segunda estudia el diseño de flotas de embarcaciones para realizar labores de mantenimiento en parques eólicos marinos. Los modelos de inventario para productos perecederos estudiados en esta tesis consideran un único producto, única localización de suministro y una planificación de producción sobre un horizonte de tiempo finito. El problema de toma de decisiones para programar las operaciones de mantenimiento en parques eólicos marinos es tratado como un problema de cadena de suministro: la instalación requiere programar operaciones de mantenimiento y atender los fallos en turbinas durante el horizonte planificado. Una flota de embarcaciones tiene que ser seleccionada para realizar estas operaciones. Para este conjunto de problemas, las decisiones no son solo dinámicas, sino que además se realizan bajo incertidumbre. Los principales objetivos de esta tesis son los siguientes: (1) estudiar que políticas de pedido son las más apropiadas para los problemas de tamaño de lote? ¿En qué casos una política de pedido da una solución óptima?; (2) analizar el efecto del uso de computación paralela para mejorar el rendimiento de los algoritmos derivados para diseñar políticas para problemas de tamaño de lote de productos perecederos; (3) explorar como de efectivas pueden ser las heurísticas para problemas de toma de decisiones dinámica sobre tamaño de lote de productos perecederos; (4) elaborar un modelo MILP para seleccionar una flota de embarcaciones para realizar las operaciones de mantenimiento en parques eólicos marinos; y (5), diseñar una heurística para programar las operaciones de mantenimiento en parques eólicos marinos considerando fallos en turbinas e incertidumbre meteorológica

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

An accounting-based profitability analysis of deploying offshore wind at Sørlige Nordsjø II: A new North Sea adventure or a renewable energy fallacy?

Offshore wind has received a great amount of attention the last decade, with industry initiatives underpinning development across nations. European offshore wind auction strike prices have decreased drastically, indicating falling costs in the industry. Additionally, cost figures in the literature are often based on public domain sources rather than actual costs from financial statements. The former is prone to distortion, yielding uncertainty regarding its reliability. Thus, this thesis reviews historic project costs in the North Sea using audited accounts from 38 UK offshore wind farms’ special purpose vehicles (SPV). A profitability analysis of deploying 1400 MW capacity at Sørlige Nordsjø II (SNII) in 2030 has been conducted, assuming a radial connection to the Norwegian mainline grid NO2. Doing so, a discounted cash flow model (DCF) combined with Monte Carlo Simulation (MCS) has been applied. Additionally, a Levelized Cost of Energy (LCOE) for SNII has been computed and compared against literature estimates. This paper shows that offshore wind development for the first phase of Sørlige Nordsjø II is unprofitable. With certain optimistic assumptions, our good case scenario barely obtained a positive net present value (NPV). LCOE was to some degree in line with other literature estimates. Higher costs in audited accounts compared to reported figures in addition to complicated site characteristics contributed to the negative results. Consequently, significant technological cost developments, more efficient supply chain operations as well as a substantial growth in electricity price are needed to overcome profitability obstacles. As of this, developing SNII is unattractive for investors under current assumptions. Subsidies are likely needed, albeit at a cost for the government. On the other hand, this paper provides concluding recommendations to seek other project solutions, namely hybrid cables to trade partners. Not being profitable with a radial connection to Norway, potential higher electricity prices across borders might increase the likelihood of profitability.nhhma