Learning, future cost and role of offshore renewable energy technologies in the North Sea energy system

The pace of cost decline of offshore renewable energy technologies significantly impacts their role in the North Sea energy transition. However, a good understanding of their remains a critical knowledge gap in the literature. Therefore, this thesis aims to quantify the future role of offshore renewables in the North Sea energy transition and assess the impact of cost development on their optimal deployments. The following findings were observed in this thesis, 1) Fixed-bottom offshore wind is well established in the North Sea region and is already competitive with onshore renewables 2) Floating wind is emerging and their current costs are high, but it can reach about 40 EUR/MWh by early 2040 and would require 44 billion EUR of learning investment.3) Grid connection costs will become a major factor as wind farm moves further away. Policy actions and innovation is needed in this space to avoid increasing integration costs. 4) Offshore wind (fixed-bottom and floating) can play a significant role in the North Sea energy system, comprising 498 GW of deployments in 2050 (222 GW of fixed-bottom and 276 GW of floating wind) and contributing up to a maximum of 51% of total power generation in the North Sea power system. 5) The role of the investigated low-TRL offshore renewables, including the tidal stream, wave technology, and bioethanol, was limited in all scenarios considered, as they remain expensive compared to other mature technologies in the system

Development of a combined-cycle cogeneration power plant model with focus on heat-electricity decoupling methods

The energy system is rapidly changing in all aspects, and also renewable energy is integrated on a large scale to the grid in recent years. Renewable energy provides advantages in decarbonising energy system, energy security and also improving energy access. It is certain that renewable energy brings benefits, but also brings operational challenges to the energy system. Variability in the renewable energy resource asks for larger flexibility to compensate the losses and balance the system as a whole. In future, some of the conventional generators which have characteristics like highly efficient, low specific emissions are highly desirable in providing flexibility at large scale. In this study, a combined cycle cogeneration power plant is chosen, and flexibility improvements using heat-electricity decoupling methods are analyzed. A techno-economic analysis of a combined cycle cogeneration power plant is conducted using DYESOPT (DYnamic System OPTimizer) tool, to compare power plant performance in different operational conditions. The öresundverket power plant located in Malmö, Sweden is considered as a reference power plant to develop a simulation model. Annual performance of the power plant is analyzed at three different modes of operation namely full condensation mode, design alpha value with constant DH demand and a varying DH demand modeled using statistical data. Technical, economic and environmental performance indicators are used to analyze the performance. One of the drawbacks is that power plant does not have provision to take advantage of energy prices in both electricity and district heating markets, i.e. excess electricity is produced at decreased heat generation in the cogeneration power plant. A hot water accumulator of 500 MWh capacity is considered as a heat-electricity decoupling strategy to overcome the drawback and also to improve flexibility. Two weeks of power plant operation with storage system improved economics by 12,800 USD. It is found that combined operation of the power plant with thermal storage has provided better operational flexibility for power plant operation in electricity and district heating market

Improving the analytical framework for quantifying technological progress in energy technologies

This article reviews experience curve applications in energy technology studies to illustrate best practices in analyzing technological learning. Findings are then applied to evaluate future performance projections of three emerging offshore energy technologies, namely, offshore wind, wave & tidal, and biofuel production from seaweed. Key insights from the review are: First, the experience curve approach provides a strong analytical construct to describe and project technology cost developments. However, disaggregating the influences of individual learning mechanisms on observed cost developments demands extensive data requirements, e.g., R&D expenditures, component level cost information, which are often not publicly available/readily accessible. Second, in an experience curve analysis, the LR estimate of the technology is highly sensitive towards the changes in model specifications and data assumptions.. Future studies should evaluate the impact of these variations and inform the uncertainties associated with using the observed learning rates. Third, the review of the literature relevant to offshore energy technology developments revealed that experience curve studies have commonly applied single-factor experience curve model to derive technology cost projections. This has led to an overview of the role of distinct learning mechanisms (e.g., learning-by-doing, scale effects), and factors (site-specific parameters) influencing their developments. To overcome these limitations, we propose a coherent framework based on the findings of this review. The framework disaggregates the technological development process into multiple stages and maps the expected data availability, characteristics, and methodological options to quantify the learning effects. The evaluation of the framework using three offshore energy technologies signals that the data limitations that restrict the process of disaggregating the learning process and identifying cost drivers can be overcome by utilizing detailed bottom-up engineering cost modeling and technology diffusion curves; with experience curve models

Technological progress observed for fixed-bottom offshore wind in the EU and UK

Offshore wind is a rapidly maturing low-carbon energy technology, for which the technology cost has increased before starting to decline. In literature, the cost development trends of offshore wind and factors responsible were poorly studied. Understanding the factors contributing to the cost developments and their individual impacts are vital for long-term energy policy actions and investment decisions. Therefore, this study combined three different but highly complementary quantitative methodologies to analyze the technological progress observed for fixed-bottom offshore wind in the EU and UK. The technology diffusion curve was first applied to identify the individual development phases of offshore wind technology. Then, the cost developments observed across the identified phases were quantified using experience curve and bottom-up cost modeling methodologies. In the formative phase of the development process, the offshore wind farm's specific capital expenditure had increased from 2 M€/MW in 2000 to 5 M€/MW in 2010, thereby resulting in negative LR. The increase in specific capital expenditure increased the Levelized Cost of Energy (LCoE) from ~110 €/MWh to above 150 €/MWh. After that, during the upscaling and growth phase, the specific capital expenditure declined from 5.4 M€/MW in 2011 to 3.3 M€/MW in 2020. LR of 8–11 % was observed for specific capital expenditure in this phase. In the same phase, the LCoE declined more rapidly than the specific capital expenditure, i.e., from roughly 150 €/MWh in 2011 to 69 €/MWh in 2020, a 54 % decline. This rapid decline observed in recent years was due to the favorable financing conditions, increased capacity factor, and decreased technology costs, including investment and operational costs. Based on the technological progress assessed for offshore wind and its contributing factors in this study, we also estimated the near-term offshore wind LCoE, 55 €/MWh in 2021–2023 and 48 €/MWh in 2024–2026, which aligns well with recent auction outcomes

Technological progress observed for fixed-bottom offshore wind in the EU and UK

Offshore wind is a rapidly maturing low-carbon energy technology, for which the technology cost has increased before starting to decline. In literature, the cost development trends of offshore wind and factors responsible were poorly studied. Understanding the factors contributing to the cost developments and their individual impacts are vital for long-term energy policy actions and investment decisions. Therefore, this study combined three different but highly complementary quantitative methodologies to analyze the technological progress observed for fixed-bottom offshore wind in the EU and UK. The technology diffusion curve was first applied to identify the individual development phases of offshore wind technology. Then, the cost developments observed across the identified phases were quantified using experience curve and bottom-up cost modeling methodologies. In the formative phase of the development process, the offshore wind farm's specific capital expenditure had increased from 2 M€/MW in 2000 to 5 M€/MW in 2010, thereby resulting in negative LR. The increase in specific capital expenditure increased the Levelized Cost of Energy (LCoE) from ~110 €/MWh to above 150 €/MWh. After that, during the upscaling and growth phase, the specific capital expenditure declined from 5.4 M€/MW in 2011 to 3.3 M€/MW in 2020. LR of 8–11 % was observed for specific capital expenditure in this phase. In the same phase, the LCoE declined more rapidly than the specific capital expenditure, i.e., from roughly 150 €/MWh in 2011 to 69 €/MWh in 2020, a 54 % decline. This rapid decline observed in recent years was due to the favorable financing conditions, increased capacity factor, and decreased technology costs, including investment and operational costs. Based on the technological progress assessed for offshore wind and its contributing factors in this study, we also estimated the near-term offshore wind LCoE, 55 €/MWh in 2021–2023 and 48 €/MWh in 2024–2026, which aligns well with recent auction outcomes

Development of a combined-cycle cogeneration power plant model with focus on heat-electricity decoupling methods

The energy system is rapidly changing in all aspects, and also renewable energy is integrated on a large scale to the grid in recent years. Renewable energy provides advantages in decarbonising energy system, energy security and also improving energy access. It is certain that renewable energy brings benefits, but also brings operational challenges to the energy system. Variability in the renewable energy resource asks for larger flexibility to compensate the losses and balance the system as a whole. In future, some of the conventional generators which have characteristics like highly efficient, low specific emissions are highly desirable in providing flexibility at large scale. In this study, a combined cycle cogeneration power plant is chosen, and flexibility improvements using heat-electricity decoupling methods are analyzed. A techno-economic analysis of a combined cycle cogeneration power plant is conducted using DYESOPT (DYnamic System OPTimizer) tool, to compare power plant performance in different operational conditions. The öresundverket power plant located in Malmö, Sweden is considered as a reference power plant to develop a simulation model. Annual performance of the power plant is analyzed at three different modes of operation namely full condensation mode, design alpha value with constant DH demand and a varying DH demand modeled using statistical data. Technical, economic and environmental performance indicators are used to analyze the performance. One of the drawbacks is that power plant does not have provision to take advantage of energy prices in both electricity and district heating markets, i.e. excess electricity is produced at decreased heat generation in the cogeneration power plant. A hot water accumulator of 500 MWh capacity is considered as a heat-electricity decoupling strategy to overcome the drawback and also to improve flexibility. Two weeks of power plant operation with storage system improved economics by 12,800 USD. It is found that combined operation of the power plant with thermal storage has provided better operational flexibility for power plant operation in electricity and district heating market

Improving the analytical framework for quantifying technological progress in energy technologies

This article reviews experience curve applications in energy technology studies to illustrate best practices in analyzing technological learning. Findings are then applied to evaluate future performance projections of three emerging offshore energy technologies, namely, offshore wind, wave & tidal, and biofuel production from seaweed. Key insights from the review are: First, the experience curve approach provides a strong analytical construct to describe and project technology cost developments. However, disaggregating the influences of individual learning mechanisms on observed cost developments demands extensive data requirements, e.g., R&D expenditures, component level cost information, which are often not publicly available/readily accessible. Second, in an experience curve analysis, the LR estimate of the technology is highly sensitive towards the changes in model specifications and data assumptions. Future studies should evaluate the impact of these variations and inform the uncertainties associated with using the observed learning rates. Third, the review of the literature relevant to offshore energy technology developments revealed that experience curve studies have commonly applied single-factor experience curve model to derive technology cost projections. This has led to an overview of the role of distinct learning mechanisms (e.g., learning-by-doing, scale effects), and factors (site-specific parameters) influencing their developments. To overcome these limitations, we propose a coherent framework based on the findings of this review. The framework disaggregates the technological development process into multiple stages and maps the expected data availability, characteristics, and methodological options to quantify the learning effects. The evaluation of the framework using three offshore energy technologies signals that the data limitations that restrict the process of disaggregating the learning process and identifying cost drivers can be overcome by utilizing detailed bottom-up engineering cost modeling and technology diffusion curves; with experience curve models

Techno-economic details of fixed-bottom offshore wind projects deployed in the European markets

Version (with all files) - Updated version (research article is accepted). Publishing Date: July 10, 2022 This dataset describes the techno-economic information of fixed-bottom offshore wind projects deployed in the North Sea region (DK, NL, BE, DE, and the UK).  Contents:  1) Offshore wind farm project prices and technical characteristics (farm size, turbine rated power, water depth, etc.,) 2) Offshore wind farm capacity factor and cumulative energy generation 3) Monopile weight  4) Offshore wind farm installation duration  5) UK offshore wind farms' transmission system cos

Improving the analytical framework for quantifying technological progress in energy technologies

This article reviews experience curve applications in energy technology studies to illustrate best practices in analyzing technological learning. Findings are then applied to evaluate future performance projections of three emerging offshore energy technologies, namely, offshore wind, wave & tidal, and biofuel production from seaweed. Key insights from the review are: First, the experience curve approach provides a strong analytical construct to describe and project technology cost developments. However, disaggregating the influences of individual learning mechanisms on observed cost developments demands extensive data requirements, e.g., R&D expenditures, component level cost information, which are often not publicly available/readily accessible. Second, in an experience curve analysis, the LR estimate of the technology is highly sensitive towards the changes in model specifications and data assumptions. Future studies should evaluate the impact of these variations and inform the uncertainties associated with using the observed learning rates. Third, the review of the literature relevant to offshore energy technology developments revealed that experience curve studies have commonly applied single-factor experience curve model to derive technology cost projections. This has led to an overview of the role of distinct learning mechanisms (e.g., learning-by-doing, scale effects), and factors (site-specific parameters) influencing their developments. To overcome these limitations, we propose a coherent framework based on the findings of this review. The framework disaggregates the technological development process into multiple stages and maps the expected data availability, characteristics, and methodological options to quantify the learning effects. The evaluation of the framework using three offshore energy technologies signals that the data limitations that restrict the process of disaggregating the learning process and identifying cost drivers can be overcome by utilizing detailed bottom-up engineering cost modeling and technology diffusion curves; with experience curve models