• Energy Research

This paper provides a quantitative technical and economic overview of the status of natural-gas liquefaction (LNG) processes. Data is based on industrial practices in technical reports and optimization results in academic literature, which are harmonized to primary energy input and production cost. The LNG processes reviewed are classified into three categories: onshore large-scale, onshore small-scale and offshore. These categories each have a different optimization focus in academic literature. Besides minimizing energy consumption, the focus is also on: coproduction for large-scale; simplicity and ease of operation for small-scale; and low space requirement, safety and insensitivity to motion for offshore. The review on academic literature also indicated that optimization for lowest energy consumption may not lead to the lowest production cost. The review on technical reports shows that the mixed-refrigerant process dominates the LNG industry, but has competitions from the cascade process in large-scale applications and from the expander-based process in small-scale and offshore applications. This study also found that there is a potential improvement in adopting new optimization algorithms for efficiently solving complex optimization problems. The technical performance overview shows that the primary energy input for large-scale processes (0.031–0.102 GJ/GJ LNG) is lower than for small-scale processes (0.049–0.362 GJ/GJ LNG). However, the primary energy input for identical processes do not necessarily decrease with increasing capacity and the performance of major equipment shows low correlation with scale. The economic performance overview shows specific capital costs varying significantly from 124 to 2255 $/TPA LNG. The variation could be, among others, caused by the different complexities of the facility and different local circumstances. Production cost, excluding feed costs, varies between 0.69 and 4.10 $/GJ LNG, with capital costs being the dominant contributor. The feed cost itself could be 1.51–4.01 $/GJ LNG, depending on the location. Lastly, the quantitative harmonization results on technical and economic performance in this study can function as a baseline for the purpose of comparison.

Combining co-firing biomass and carbon capture and storage (CCS) in power plants offers attractive potential for net removal of carbon dioxide (CO2) from the atmosphere. In this study, the impact of co-firing biomass (wood pellets and straw pellets) on the emission profile of power plants with carbon capture and storage has been assessed for two types of coal-fired power plants: a supercritical pulverised coal power plant (SCPC) and an integrated gasification combined cycle plant (IGCC). Besides, comparative life cycle assessments have been performed to examine the environmental impacts of the combination of co-firing biomass and CCS. Detailed calculations on mass balances of the inputs and outputs of the power plants illustrate the effect of the different content of pollutants in biomass on the capture unit. Life cycle assessment results reveal that 30% co-firing biomass and applying CCS net negative CO2 emissions in the order of 67-85g/kWh are obtained. The impact in all other environmental categories is increased by 20-200%. However, aggregation into endpoint levels shows that the decrease in CO2 emissions more than offsets the increase in the other categories. Sensitivity analyses illustrate that results are most sensitive to parameters that affect the amount of fuel required, such as the efficiency of the power plant and assumptions regarding the supply chains of coal and biomass. Especially, assumptions regarding land use allocation and carbon debt of biomass significantly influence the environmental performance of BioCCS.

AbstractThe oil industry needs to reduce CO2 emissions across the entire lifecycle of fossil fuels to meet environmental regulations and societal requirements and to sustain its business. With this goal in mind, this study aims to evaluate the CO2 mitigation potential of several bio‐oil co‐processing pathways in an oil refinery. Techno‐economic analysis was conducted on different pathways and their greenhouse gas (GHG) mitigation potentials were compared. Thirteen pathways with different bio‐oils, including vegetable oil (VO), fast pyrolysis oil (FPO), hydro‐deoxygenated oil (HDO), catalytic pyrolysis oil (CPO), hydrothermal liquefaction oil (HTLO), and Fischer–Tropsch fuels, were analyzed. However, no single pathway could be presented as the best option. This would depend on the criteria used and the target of the co‐processing route. The results obtained indicated that up to 15% of the fossil‐fuel output in the refinery could be replaced by biofuel without major changes in the core activities of the refinery. The consequent reduction in CO2 emissions varied from 33% to 84% when compared with pure equivalent fossil fuels replaced (i.e., gasoline and diesel). Meanwhile, the production costs varied from 17 to 31€/GJ (i.e., 118–213$/bbleq). Co‐processing with VO resulted in the lowest overall performance among the options that were evaluated while co‐processing HTLO in the hydrotreatment unit and FPO in the fluid catalytic cracking unit showed the highest potential for CO2 avoidance (69% of refinery CO2 emissions) and reduction in CO2 emissions (84% compared to fossil fuel), respectively. The cost of CO2 emissions avoided for all of the assessed routes was in the range of €99–651 per tCO2. © 2020 The Authors. Biofuels, Bioproducts, and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.

AbstractThis study compared the economic and environmental impacts of torrefaction on bioenergy supply chains against conventional pellets for scenarios where biomass is produced in Mozambique, and undergoes pre‐processing before shipment to Rotterdam for conversion to power and Fischer‐Tropsch (FT) fuels. We also compared the impacts of using different land quality (productive and marginal) for feedstock production, feedstocks (eucalyptus and switchgrass), final conversion technologies (XtY and CXtY) and markets (the Netherlands and Mozambique). At current conditions, the torrefied pellets (TOPs) are delivered in Rotterdam at higher cost (7.3–7.5 $/GJ) than pellets (5.1–5.3 $/GJ). In the long term, TOPs costs could decline (4.7–5.8 $/GJ) and converge with pellets. TOPs supply chains also incur 20% lower greenhouse gas (GHG) emissions than pellets. Due to improved logistics and lower conversion investment, fuel production costs from TOPs are lower (12.8–16.9 $/GJFT) than from pellets (12.9–18.7 $/GJFT). Co‐firing scenarios (CXtY) result in lower cost fuel (but a higher environmental penalty) than 100% biomass fired scenarios (XtY). In most cases, switchgrass and the productive region of Nampula provide the lowest fuel production cost compared to eucalyptus and the marginally productive Gaza region. Both FT and ion in Mozambique are more costly than in Rotterdam. For the Netherlands, both FT and power production are competitive against average energy costs in Western Europe. The analysis shows that large‐scale bioenergy production can become competitive against fossil fuels. While the benefits of TOPs are apparent in logistics and conversion, the current higher torrefaction costs contribute to higher biofuel costs. Improvements in torrefaction technology can result in significant performance improvements over the future chain. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd

The techno-economic performance and CO2 equivalent (CO2eq) reduction potential of bio-based aromatic production cases with and without CO2 capture and storage (CCS) have been evaluated and compared to those of fossil-based aromatic production. The bio-cases include tail gas reactive pyrolysis (TGRP), catalytic pyrolysis (CP), hydrothermal liquefaction (HTL), gasification-methanol-aromatics (GMA), and Diels-Alder of furan/furfural combined with catalytic pyrolysis of lignin (FFCA). The crude oil-based naphtha catalytic reforming (NACR) routes have GHG emissions of 43.4 and 43.9 t CO2eq/t aromatics with and without CCS (NACR-CCS), respectively. Except for HTL, all the biomass cases with CCS show negative emissions between −6.1 and −1.1 t CO2eq/t aromatics with avoidance costs ranging from 27.7 to 93.3 $/t CO2eq. Under favorable conditions, GMA with CCS (GMA-CCS) has the lowest emissions (−14.6 t CO2eq/t aromatics), while CP with CCS (CP-CCS) shows the lowest avoidance cost (12.3 $/t CO2eq). All biomass based aromatics production techniques are currently at the laboratory or demonstration stages, except for CP, which has pilot plants. The results indicate that bio-based aromatics production, with their reasonable avoidance costs and low, or potentially negative, greenhouse gas (GHG) emissions, are an attractive option to compensate for the expected aromatic production shortages in the coming decades.

Abstract Detailed analysis of technological learning of energy technologies is scarce. For floating wind, this is missing altogether. In this study, we applied experience curve and bottom-up cost modeling methodologies and assessed the long-term cost reduction potential of fixed-bottom and floating offshore wind in their mature markets. The contributing factors to cost reduction are also quantified and elaborated. Further, to emphasize the role of strongly varying site characteristics of offshore wind farms and their influences, the grid connection cost is separately discussed from the total technology costs (Capital Expenditure and LCoE). Our assessment shows that, excluding grid connection costs, fixed-bottom offshore wind LCoE is 40 €/MWh at 31 GW cumulative capacity (2023–2024) and decline to 28 ± 3 €/MWh by 100 GW. Floating wind LCoE is 123 €/MWh at 1 GW cumulative capacity (2027 – 2030) but decline to 33 ± 6 €/MWh by 100 GW. Moreover, floating wind can achieve cost parity (i.e., 40 €/MWh, excl. grid connection cost) by deploying 21 GW, requiring 44 billion € of learning investment in the form of subsidies to compensate the price gap for the technology in the energy system. Lastly, we analyzed the grid connection costs and their influencing factors, and then determined that an integrated offshore grid would be needed to efficiently connect future offshore wind farms to the onshore grid.

Offshore renewables are expected to play a significant role in achieving the ambitious emission targets set by the North Sea countries. Among other factors, energy technology costs and their cost reduction potential determine their future role in the energy system. While fixed-bottom offshore wind is well-established and competitive in this region, generation costs of other emerging offshore renewable technologies remain high. Hence, it is vital to better understand the future role of offshore renewables in the North Sea energy system and the impact of technological learning on their optimal deployments, which is not well-studied in the current literature. This study implements an improved framework of integrated energy system analysis to overcome the stated knowledge gap. The approach applies detailed spatial constraints and opportunities of energy infrastructure deployment in the North Sea and also technology cost reduction forecasts of offshore renewables. Both of these parameters are often excluded or overlooked in similar analyses, leading to overestimation of benefits and technology deployments in the energy system. Three significant conclusions are derived from this study. First, offshore wind plays a crucial role in the North Sea power sector, where deployment grows to a maximum of 498 GW by 2050 (222 GW of fixed-bottom and 276 GW of floating wind) from 100 GW in 2030, contributing up to 51% of total power generation and declining cumulative system cost of power and hydrogen system by 4.2% (approx. 40 billion EUR in cost savings), when compared with the slow learning and constrained space use case. Second, floating wind deployment is highly influenced by its cost reduction trend and ability to produce hydrogen offshore; emphasizing the importance of investing in floating wind in this decade as the region lacks commercial deployments that would stimulate its cost reduction. Also, the maximum floating wind deployment in the North Sea energy system declined by 70% (162 GW from 276 GW) when offshore hydrogen production was avoided, while fixed-bottom offshore wind deployment remains unchanged. Lastly, the role of other emerging offshore renewables remains limited in all scenarios considered, as they are expensive compared to other technology choices in the system. However, around 8 GW of emerging technologies was observed in Germany and the Netherlands when the deployment potential of fixed-bottom offshore wind became exhausted.

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.