• Energy Research

The large-scale integration of variable renewable energy sources into the energy system presents techno–economic challenges. Long–term energy system optimization models fail to adequately capture these challenges because of the low temporal resolution of these tools. This limitation has often been addressed either by direct improvements within the long–term models or by coupling them to higher resolution power system models. In this study, a combined approach is proposed to capitalize on the advantages and overcome the disadvantages of both methods. First, the temporal resolution of an energy model was enhanced by approximating the joint probability of the electricity load and the supply of intermittent sources. Second, the projected electricity mix was simulated by a power model at an hourly resolution. This framework was used to analyze mid–century deep decarbonization trajectories for Colombia, subject to future uncertainties of hydroclimatic variability and the development of the bioeconomy. The direct integration method is found to consistently reduce the overestimation of the feasible penetration of VRES. However, its impact is marginal because of its inability to assess the short–term operation of the power system in detail. When combined with the soft–linking method, the reliable operation of the power system is shown to incur an additional overhead of 12–17% investment in flexible generation capacity, 2–5% of the annual energy system cost, and a 15–27% shortfall in achieving the aspired GHG mitigation target. The results obtained by combining both methods are found to be closer to the global optimum solution than using either of these methods individually.

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.

Abstract The importance of spatial resolution for energy modelling has increased in the last years. Incorporating more spatial resolution in energy models presents wide benefits, but it is not straightforward, as it might compromise their computational performance. This paper aims to provide a comprehensive review of spatial resolution in energy models, including benefits, challenges and future research avenues. The paper is divided in four parts: first, it reviews and analyses the applications of geographic information systems (GIS) for energy modelling in the literature. GIS analyses are found to be relevant to analyse how meteorology affects renewable production, to assess infrastructure needs, design and routing, and to analyse resource allocation, among others. Second, it analyses a selection of large scale energy modelling tools, in terms of how they can include spatial data, which resolution they have and to what extent this resolution can be modified. Out of the 34 energy models reviewed, 16 permit to include regional coverage, while 13 of them permit to include a tailor-made spatial resolution, showing that current available modelling tools permit regional analysis in large scale frameworks. The third part presents a collection of practices used in the literature to include spatial resolution in energy models, ranging from aggregated methods where the spatial granularity is non-existent to sophisticated clustering methods. Out of the spatial data clustering methods available in the literature, k-means and max-p have been successfully used in energy related applications showing promising results. K-means permits to cluster large amounts of spatial data at a low computational cost, while max-p ensures contiguity and homogeneity in the resulting clusters. The fourth part aims to apply the findings and lessons learned throughout the paper to the North Sea region. This region combines large amounts of planned deployment of variable renewable energy sources with multiple spatial claims and geographical constraints, and therefore it is ideal as a case study. We propose a complete modelling framework for the region in order to fill two knowledge gaps identified in the literature: the lack of offshore integrated system modelling, and the lack of spatial analysis while defining the offshore regions of the modelling framework.

Owing to the complexity of the sector, industrial activities are often represented with limited technological resolution in integrated energy system models. In this study, we enriched the technological description of industrial activities in the integrated energy system analysis optimisation (IESA-Opt) model, a peer-reviewed energy system optimisation model that can simultaneously provide optimal capacity planning for the hourly operation of all integrated sectors. We used this enriched model to analyse the industrial decarbonisation of the Netherlands for four key activities: high-value chemicals, hydrocarbons, ammonia, and steel production. The analyses performed comprised 1) exploring optimality in a reference scenario; 2) exploring the feasibility and implications of four extreme industrial cases with different technological archetypes, namely a bio-based industry, a hydrogen-based industry, a fully electrified industry, and retrofitting of current assets into carbon capture utilisation and storage; and 3) performing sensitivity analyses on key topics such as imported biomass, hydrogen, and natural gas prices, carbon storage potentials, technological learning, and the demand for olefins. The results of this study show that it is feasible for the energy system to have a fully bio-based, hydrogen-based, fully electrified, and retrofitted industry to achieve full decarbonisation while allowing for an optimal technological mix to yield at least a 10% cheaper transition. We also show that owing to the high predominance of the fuel component in the levelled cost of industrial products, substantial reductions in overnight investment costs of green technologies have a limited effect on their adoption. Finally, we reveal that based on the current (2022) energy prices, the energy transition is cost-effective, and fossil fuels can be fully displaced from industry and the national mix by 2050.

Correction for ‘The global technical and economic potential of bioenergy from salt-affected soils’ by Birka Wicke et al., Energy Environ. Sci., 2011, 4, 2669–2681, DOI: 10.1039/C1EE01029H.

The Loess Plateau, with a large area of marginal land, holds the potential to produce 62–106 Tg per year of switchgrass biomass; however, the economic feasibility of producing bioenergy in the region is unclear. The farm-gate feedstock production (FGFP) cost of switchgrass was calculated in a spatially explicit way by taking the geographic variation in crop yield, soil properties, land quality, and input costs into consideration in order to evaluate the economic performance of bioenergy production. Cost–supply curves were constructed to explore the energy supply potential of switchgrass feedstock. The calculations were conducted using ArcGIS in a 1 km grid and all the evaluations were conducted under different agricultural management practice (AMP) scenarios in parallel. The FGFP costs showed significant spatial variation ranging from 95 to 7373 CNY (Chinese Yuan) per tonne−1 and that the most economically desirable areas are scattered in the south and southeast region. The weighted average FGFP costs are 710, 1125, and 1596 CNY per tonne−1 for small bale (SB), large bale (LB), and chipping (CP) harvest methods, respectively. The projected energy supply potential is 1927 PJ (Petajoules) per year−1, of which 30–93% can be supplied below the market prices of different fossil fuels according to feedstock formats. Compared to current biomass residual pricing, 50–66 Tg (Teragrams) switchgrass feedstock is competitive. The results demonstrated that the Loess Plateau holds the potential to produce bioenergy that is economically feasible. This study provides a methodological framework for spatially explicit evaluation of the economic performance of perennial energy crops. Detailed information obtained from this study can be used to select the optimal locations and AMPs to produce feedstock production at minimum cost.

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.