Biomass is widely seen as essential for decarbonization, providing renewable energy, carbon-based fuels, and negative emissions, but rising land-use and biodiversity concerns are increasing pressure to restrict its use in Europe. Using a sector-coupled energy system model of Europe (PyPSA-Eur-Sec), we quantify the cost consequences of solid biomass scarcity under net-zero emissions targets across alternative technology futures. Restricting biomass availability from present-day levels to a severely constrained level increases total European system costs by 14–28%, with biomass used primarily for liquid fuels and industrial heat rather than electricity. The magnitude of this cost increase depends strongly on the maturity of substitute technologies: scarcity is most costly when hydrogen and electrofuel production remain expensive or inefficient, more so than when carbon storage or renewable electricity deployment is constrained. These results indicate that policies restricting biomass use should be paired with support for alternative carbon-neutral fuel pathways.

Abstract Renewable energy resources are widely available, yet they are unevenly distributed globally. In a renewable future, countries lacking high-quality renewable resources may choose to import energy from other countries. To assess the resource-dependent and techno-economic basis for global renewable energy trade and identify potential importers and exporters, this study introduces two new metrics: Renewable Export Cost Index (Cost Index) and Renewable Export Volume Index (Volume Index). These metrics are computed based on regional resource potential, domestic energy demand and varying financial costs across countries, without the need for any energy system modeling. By applying these two metrics to 165 countries/regions, we identify countries with significant potential for exporting renewable energy (e.g., the US, China) and those that lack the domestic resources to satisfy demand (e.g., South Korea, Japan). The Cost Index and Volume Index are validated through a separate analysis, employing a comprehensive energy system model for each country/region.

Previous research has highlighted concerns about week-long energy droughts in renewables-based energy systems. Reservoir hydropower could offer a viable solution to mitigate such energy shortfalls. However, current energy systems models often oversimplify hydropower by assuming it can operate continuously at maximum output. This study investigates the ability of reservoir hydropower to sustain a high output and thereby mitigate energy droughts. In contrast to most energy system models, the hydropower model used in this study includes cascading, head dependency, turbine efficiency curves and environmental constraints. We estimate that Swedish hydropower can sustain between 77% and 96% of its installed capacity for one week, with the higher end of this range achievable during spring. This range in sustained output is equivalent to about 3 GW, or about 20% of average demand in Sweden, which underscores the importance of understanding the operational limitations of hydropower. Our findings indicate that river bottlenecks, primarily due to regulations on maximum flows, are the main factor limiting hydropower's ability to sustain higher outputs. With the upcoming renewal of environmental permits for hydropower plants in Sweden, these findings provide valuable insights for policymakers. The importance of analysing hydropower's ability to sustain high outputs is not unique to Sweden; the method proposed in this study can serve as a critical tool for similar assessments in other hydro-rich countries. Moreover, the sustained output capabilities demonstrated in this study challenge the prevalent simplified representations of hydropower in energy models, highlighting the need for more sophisticated modelling approaches.

The One Sun One World One Grid (OSOWOG) initiative advocates the development of a global Super grid for sharing renewable energy, especially solar energy. This study evaluates the economic benefits of such a Super grid, which connects six large regions spanning from Australia to the US, utilizing a detailed energy system optimization model and considering heterogeneous discount rates among countries. Integrating the six regions into a Super grid reduces the electricity system cost by 3.8% compared to isolating them. In contrast, grid expansion within each region reduces the electricity system cost by 12% on average. The economic benefits of the OSOWOG initiative's global Super grid expansion seem to be rather limited. Moreover, the allowance for a Super grid consistently results in decreased investments in solar power, indicating that it is not an effective strategy for enhancing the deployment of solar power, even when transmission grids covering 18 time zones are available.

Biomass is a versatile renewable energy source that can be used in all parts of the energy system, but it is a limited resource and usage needs prioritisation. Here we use a sector-coupled European energy system model to explore the range of cost-effective near-optimal solutions for achieving stringent emissions targets. We show that provision of biogenic carbon rather than energy is the main value of biomass, with the energy system cost increasing by 20% if biomass is excluded. It is not crucial in which sector biomass is used if it is combined with carbon capture to enable negative emissions and e-fuel production. A shortage of renewable electricity or hydrogen primarily increases the value of biomass for fuel production, which appears as the marginal abatement option and is most sensitive to uncertainties. Biomass usage is significantly affected if the biomass is associated with upstream emissions.

Biomass associated with low upstream emissions offers cost-effective renewable carbon for negative emissions and production of chemicals, aviation and shipping fuels, reducing the need for more costly options like direct air capture. Policy support for sustainable biomass use alongside emerging technologies reduces energy system costs and the risk of missing emissions targets.

Capacity expansion models used for policy support have increasingly represented both the variability and uncertainty of weather-dependent generation (wind and solar). However, although also uncertain, as demonstrated by the performance of the French nuclear power fleet in 2022, uncertainty arising from nuclear power outages has been largely neglected in the literature. This paper presents the first capacity expansion model that considers uncertainty in nuclear power availability caused by unplanned outages. We propose a mathematical model that combines a scenario-based stochastic optimization approach (to deal with weather-related uncertainties) with a data-driven adjustable robust optimization approach (to deal with nuclear failure-related uncertainties). The robust model represents the bulky behavior of nuclear power plants, with large (1 GW) units that are either on or off, while at the same time letting the model decide on the optimal amount of nuclear capacity. We tested the model in a case for Northern Europe (seven nodes) with a time resolution of 1250 time steps. Our findings show that nuclear power outages do, in fact, impose a vulnerability on the energy system if not considered in the planning phase. Our proposed model performs well and finds solutions that prevent Loss-of-Load (at a price of robustness of 0.6 even in more extreme weather conditions. Robust solutions are characterized by a higher capacity of gas plants, but, perhaps surprisingly, nuclear power capacity is barely affected.

This paper investigates the influence of geographical allocation of wind power generation in Northern Europe, assuming large scale integration of wind power.The work applies a linear cost optimization model of the heat and power sector with a 1-hour time resolution.The model minimizes the sum of running costs to meet the heat and power demand and the wind power and transmission investment costs.Wind data are taken from modelled wind speed data from the Swedish Meteorological and Hydrological Institute.The Nordic countries and Germany were divided into regions and the 200 sites with the highest yearly output were chosen to represent the region.The model gives the most favourable distribution of wind power between the regions.In addition, the paper provides an assessment of the effect of geographical distribution of wind power with respect to influence on the aggregated wind power production (only considering the wind power generation itself).The modelling results show that the largest investments in wind power are made in the windy region of Southern Norway.However, depending on the cost of transmission allocating wind power near large load centers in Germany may also be favourable.As for the assessment of distribution of wind power, the wind data gives that if the 400 best sites in Europe were used, this would result in a capacity factor of 38.5% and a lowest output of 2.5 % of rated power (applying 2009 wind data).

Liberalized power markets are characterized by a missing market problem: a limited availability of long-term contracts leaves risk-averse investors exposed to uninsured risk. We explore how this problem affects a power system’s capacity mix and overall emissions. For this purpose, we develop a new equilibrium generation expansion model that endogenously captures investors’ risk exposure in incomplete markets. Our approach addresses the problem of multiple equilibria and, partly, the computational burden inherent to such models. We solve our model for an abstract system with gas, wind, solar, and battery storage under demand and gas price uncertainty. The results first show that, when risk markets are missing, investment risk can cause higher emissions and less clean energy investment than what would be implied by a model that omits investment risk. The impact of risk on investment depends only partly on technologies’ capital intensities and largely on how technologies interact at the systems level. We also compare system outcomes with missing long-term markets to the socially optimal case, where risk-averse investors and consumers trade risk via complete long-term markets. In the absence of long-term markets, we observe higher emissions, less investment in renewables and storage, and more investment in gas. These results suggest that long-term market mechanisms for electricity generation and storage may advance climate goals while addressing inefficiencies in current markets.

To model a future power system with high shares of variable renewables, it is essential to capture the flexibility of dispatchable technologies such as hydropower. However, the representation of hydropower is often oversimplified in energy system investment models, such that the flexibility of hydropower is significantly exaggerated. This suggests the need for improved representations of hydropower that capture physical river dynamics but are computationally efficient to maintain the tractability of large models. Here, we develop a series of hydropower optimization models for a single river with various levels of techno-physical detail to evaluate options for hydropower representations in energy system investment models. All models operate hourly over a full year with perfect foresight. We explore trade-offs between accuracy and computational time involved in including features such as the river network, head-dependent power production, and discharge -dependent turbine efficiencies. We find that the level of detail significantly affects the optimal production and confirm that a simplistic hydropower representation similar to those often used in investment models significantly overestimates the flexibility of hydropower. The most detailed nonconvex model includes a full river network, head-dependency, and turbine efficiencies and is solved in just one hour on a modern desktop computer. Furthermore, we linearize this detailed model, thereby reducing computation time to one minute while featuring production dynamics substantially more similar to the full nonconvex model than a naive linear network model. These contributions pave the way for improving hydropower representations in investment models to avoid overestimating the flexibility that hydropower may provide.

Decarbonisation of the Nordic power sector entails substantial variable renewable energy (VRE) adoption. While Nordic hydropower reservoirs can mitigate VRE output’s intermittency, strategic hydro producers may leverage increased flexibility requirements to exert market power. Using a Nash-Cournot model, we find that even the current Nordic power system could yield modest gains from strategic reservoir operations regardless of a prohibition on “spilling” water to increase prices. Instead, strategic hydro producers could shift generation from peak to offpeak seasons. Such temporal arbitrage becomes more attractive under a climate package with a €100/t CO2 price and doubled VRE capacity. Since the package increases generation variability, lowers average prices, and makes fossil-fuelled plants unprofitable, strategic hydro producers face lower opportunity costs in shifting output from peak to off-peak seasons and encounter muted responses from price-taking fossil-fuelled plants. Hence, a climate package that curtails CO2 emissions may also bolster strategic hydro producers’ leverage.

The market revenue for variable renewable electricity (VRE) assets has been under intense scrutiny during the last few years. The observation that wind and solar power depress market prices at times when they produce the most has been termed the 'cannibalization effect'. This can have a substantial impact on the revenue of these technologies, the magnitude of which has already been established within the economic literature on current and future markets. Yet, the effect is neglected in the capital budgeting literature assessing green investments in the electricity sector (e.g. including methods such as portfolio- and real-options theory). In this paper, we present an analytical framework that explicitly models the correlation between VRE production and electricity price, as well as the impact on revenues of the surrounding capacity mix and cost to emit CO$_2$. In particular, we derive closed-form expressions for the short-term and long-term expected revenue, the variance of the revenue and the timing of investments. The effect of including these system characteristics is illustrated with numerical examples, using a wind investment in the Polish electricity system as a test case. We find the cannibalization effect to have major influence on the revenues, making the projected profit of a project decrease from 33% to between 13% and -40% (i.e. a loss), depending on the assumption for the rate of future VRE capacity expansion. Using a real options framework, the investment threshold increases by between 13% and 67%, due to the inclusion of cannibalization. Our results likewise indicate that subjective beliefs and uncertainty about the future electricity capacity mix, e.g.\ VRE capacity growth, significantly affect the assessment of the revenue and investment timing.

A critical parameter in modeling studies of future decarbonized energy systems is the potential future capacity for onshore wind power. Wind power potential in energy system models is subject to assumptions regarding: (i) constraints on land availability for wind deployment; (ii) how densely wind turbines may be placed over larger areas, and (iii) allocation of capacity with respect to wind speed. By analyzing comprehensive databases of wind turbine locations and other GIS data in eleven countries and seventeen states in Australia, Canada, and the US; all with high penetration levels of wind power, we find that: i) large wind turbines are installed on most land types, even protected areas and land areas with high population density; ii) it is not uncommon with a deployment density up to 0.5 MW/km2 on municipality or county level, with rare outlier municipalities reaching up to 1.5 MW/km2 installed capacity; and iii) wind power has historically been allocated to relatively windy sites with average wind speed above 6 m/s. In many cases, allocation methods used in energy system models do not consistently reflect actual installations. For instance, we find no evidence of concentration of installations at the windiest sites, as is frequently assumed in energy system models. We conclude that assumptions made in models regarding wind power potentials are poorly reflective of historical installation patterns, and we provide new data to enable assumptions that have a more robust empirical foundation.

Long distance transmission within continents has been shown to be one of the most effective variation management strategies to reduce the cost of renewable energy systems. In this paper, we test whether the system cost further decreases when transmission is extended to intercontinental connections. We analyze a Eurasian interconnection between China, Mid-Asia and Europe, using a capacity expansion model with hourly time resolution. Our modelling results suggestthat a supergrid option decreases total system cost by a maximum of 5%, compared to continental grid integration. The maximum cost reductionis achieved when (i) the generation is constrained to be made up almost entirely by renewables, (ii) the land available for VRE farms is relatively limited and the demand is relatively high and (iii) the cost for solar PV and storage is high. This is explained by that a super grid allows for harnessing of remote wind-, solar- and hydro resources demand centers. As for low-cost storage, it represents a competing variation management option, and may substitute part of the role of the supergrid, which is to manage variations through long-distance trade. We conclude that the benefits of a supergrid from a techno-economic perspective are in most cases negligible, or modest at best.

Abatement options for the hard-to-electrify parts of the transport sector are needed to achieve ambitious emissions targets. Biofuels based on biomass, electrofuels based on renewable hydrogen and a carbon source, as well as fossil fuels compensated by carbon dioxide removal (CDR) are the main options. Currently, biofuels are the only renewable fuels available at scale and are stimulated by blending mandates. Here, we estimate the system cost of enforcing such mandates in addition to an overall emissions cap for all energy sectors. We model overnight scenarios for 2040 and 2060 with the sector-coupled European energy system model PyPSA-Eur-Sec, with a high temporal resolution. The following cost drivers are identified: (i) high biomass costs due to scarcity, (ii) opportunity costs for competing usages of biomass for industry heat and combined heat and power (CHP) with carbon capture, and (iii) lower scalability and generally higher cost for biofuels compared to electrofuels and fossil fuels combined with CDR. With a-80% emissions reduction target in 2040, variable renewables, partial electrification of heat, industry and transport, and biomass use for CHP and industrial heat are important for achieving the target at minimal cost, while an abatement of remaining liquid fossil fuel use increases system cost. In this case, a 50% biofuel mandate increases total energy system costs by 123-191 billion euro, corresponding to 35%-62% of the liquid fuel cost without a mandate. With a negative-105% emissions target in 2060, fuel abatement options are necessary, and electrofuels or the use of CDR to offset fossil fuel emissions are both more competitive than biofuels. In this case, a 50% biofuel mandate increases total costs by 21-33 billion euro, or 11%-15% of the liquid fuel cost without a mandate. Biomass is preferred in CHP and industry heat, combined with carbon capture to serve negative emissions or electrofuel production, thereby utilising biogenic carbon several times. Sensitivity analyses reveal significant uncertainties but consistently support that higher biofuel mandates lead to higher costs.

Capacity Expansion Models (CEMs) are optimization models used for long-term energy planning on national to continental scale. They are typically computationally demanding, thus in need of simplification, where one such simplification is to reduce the temporal representation. This paper investigates how using representative periods to reduce the temporal representation in CEMs distorts results compared to a benchmark model of a full chronological year. The test model is a generic CEM applied to Europe. We test the performance of reduced models at penetration levels of wind and solar of 90%. Three measures for accuracy are used: (i) system cost, (ii) total capacity mix and (iii) regional capacity. We find that: (i) the system cost is well represented (~ 5% deviation from benchmark) with as few as ten representative days, (ii) the capacity mix is in general fairly well (~ 20% deviation) represented with 50 or more representative days, and (iii) the regional capacity mix displays large deviations (> 50%) from benchmark for as many as 250 representative days. We conclude that modelers should be aware of the error margins when presenting results on these three aspects.

Renewable energy resources are widely available, yet they are unevenly distributed globally. In a renewable future, countries lacking high-quality renewable resources may choose to import energy from other countries. To assess the resource-dependent and techno-economic basis for global renewable energy trade and identify potential importers and exporters, this study introduces two new metrics: Renewable Levelized Cost of Energy available for Export (RLCOE_Ex) and Potential Energy Export Volume (PEEV). These metrics are computed based on regional resource potential, domestic energy demand and varying financial costs across countries, without the need for any energy system modeling. By applying these two metrics to 165 countries/regions, we identify countries with significant potential for exporting renewable energy (e.g., the US, China) and those that lack the domestic resources to satisfy demand (e.g., South Korea, Japan). The RLCOE_Ex and PEEV metrics are validated through a separate analysis, employing a comprehensive energy system model for each country/region.

A critical parameter in modeling studies of future decarbonized energy systems is the socio-technical potential of onshore wind power. Here, we review the assumptions made in several energy system models. The wind potential in these studies is subjected to assumptions regarding: (i) the constraints on land availability for wind deployment; (ii) the maximum percentage of land area that may be exploited; and (iii) the allocation of capacity with respect to the wind speed. By analyzing comprehensive databases of wind turbine locations and GIS data in three countries and eleven US states with high penetration levels of wind power, we find that: i) wind power is installed on most land types, even protected areas and land areas with high population density; ii) the share of land used for wind deployment is up to 20% in some municipalities and counties; and iii) wind power has historically been allocated to relatively windy sites with an average wind speed >6 m/s. In many cases, the allocation methods used in energy system models do not reflect consistently the actual installations. For instance, we find no evidence of concentration of installations to the windiest sites, as is frequently assumed in energy system models. We conclude that assumptions made in models regarding wind power potentials are poorly reflective of the historical installation patterns, and we provide new data to enable assumptions that have a more robust empirical foundation.

Renewable energy resources are widely available, yet they are unevenly distributed globally. In a renewable future, countries lacking high-quality renewable resources may choose to import energy from other countries. To assess the resource-dependent and techno-economic basis for global renewable energy trade and identify potential importers and exporters, this study introduces two new metrics: Renewable Levelized Cost of Energy available for Export (RLCOE_Ex) and Potential Energy Export Volume (PEEV). These metrics are computed based on regional resource potential, domestic energy demand and varying financial costs across countries, without the need for any energy system modeling. By applying these two metrics to 165 countries/regions, we identify countries with significant potential for exporting renewable energy (e.g., the US, China) and those that lack the domestic resources to satisfy demand (e.g., South Korea, Japan). The RLCOE_Ex and PEEV metrics are validated through a separate analysis, employing a comprehensive energy system model for each country/region.

Electricity transmission expansion has suffered many delays in Europe in recent decades, despite its importance for integrating renewable electricity into the energy system. A hydrogen network which reuses the existing fossil gas network would not only help supply demand for low-emission fuels, but could also help to balance variations in wind and solar energy across the continent and thus avoid power grid expansion. We pursue this idea by varying the allowed expansion of electricity and hydrogen grids in net-zero CO2 scenarios for a sector-coupled European energy system with high shares of renewables and self-sufficient supply. We cover the electricity, buildings, transport, agriculture, and industry sectors across 181 regions and model every third hour of a year. With this high spatio-temporal resolution, we can capture bottlenecks in transmission and the variability of demand and renewable supply. Our results show a consistent benefit of a pan-continental hydrogen backbone that connects high-yield regions with demand centers, synthetic fuel production and geological storage sites. Developing a hydrogen network reduces system costs by up to 6%, with highest benefits when electricity grid reinforcements cannot be realised. Between 58% and 66% of this backbone could be built from repurposed natural gas pipelines. However, we find that hydrogen networks can only partially substitute for power grid expansion, and that both can achieve strongest cost savings of 12% together.