Energy transition is essential for combating climate change and achieving sustainability, with artificial intelligence (AI) playing a key role in advancing this transition and developing a modern energy system. This paper uses data from 261 prefecture-level cities across China to explore the core aspects of energy transition from three perspectives: quantity, quality, and structure. By linking AI with energy transition, this study investigates the impacts and mechanisms through which AI influences the energy transition within an integrated framework. Additionally, considering the characteristics of AI's pervasiveness, integration, and synergy, the paper examines the spatial spillover effects of AI on energy transition, offering a novel perspective for policy discussions on AI and green energy. The findings show that AI can reduce energy consumption, enhance energy efficiency, and optimize energy structure, thereby promoting the energy transition across three dimensions: quantity, quality, and structure. After conducting robustness and endogeneity tests, the conclusions remain robust. Mechanism analysis reveals that AI improves human-machine alignment by leveraging the complementary strengths of both machines and workers, fostering coordination and ultimately supporting energy transition. Furthermore, AI can generate positive externalities, such as economies of scale, technological spillovers, and knowledge sharing, by facilitating economic agglomeration, further advancing energy transition. The moderating effect analysis indicates that AI is more effective in promoting energy transition in regions with strong digital infrastructure, high technological absorption capacity, and labor-intensive economies. The spatial spillover effects demonstrate that energy transition exhibits significant geographic clustering. As globalization and information technology evolve, inter-regional interactions are increasing, and AI has the potential to overcome geographic barriers, generating spillover effects on energy transition across regions. However, the siphon effect, which concentrates technological advancements in certain areas, is stronger than the trickle-down effect, which benefits surrounding regions. As a result, AI may foster local technological growth hubs, advancing energy transition in those areas while indirectly depleting human resources and other factors in neighboring regions, thus hindering energy transition in less developed areas. This study enhances the understanding of the opportunities presented by AI, providing valuable insights for promoting energy transition and ecological civilization construction.
How to cite: Ji, R., Zhang, S., Hao, Y., Li, Y., Liu, X., and Hasi, E.: Blessing or Peril? The Impact of Artificial Intelligence on China's Energy Transition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-382, https://doi.org/10.5194/egusphere-egu25-382, 2025.
There are tens of thousands of anemometer towers currently being built for wind resource assessment. In this study, we show these towers provide a precious opportunity to improve wind resource modeling, which is the basis for the development of wind energy industry. In atmospheric models, aerodynamic roughness length (z0) is a critical parameter for the simulation of wind speed in the near-surface layer (0-200 m). However, current gridded z0 datasets in atmospheric models are usually estimated from land cover types and may have large uncertainties. Although some efforts have been made to produce accurate gridded z0 datasets using machine-learning methods, their accuracy and applicability remain unknown. In this pilot study, we enriched z0 ground truth from wind profile data of 101 anemometer towers in China and assessed the uncertainty of existing gridded z0 datasets and their effects on wind speed simulations.
Specifically, we show that although the latest gridded z0 dataset obtained with a machine-learning model performs better than z0 reanalysis datasets (i.e., ERA5 and CFSv2), all of these datasets contain considerable uncertainty and fail to capture the evident variability of z0 observed within each land cover type. Furthermore, the errors in gridded z0 datasets do map to systematic biases in the simulated near-surface wind speed. For example, we find that z0 in ERA5 is overestimated in wind-rich regions of China, causing an underestimation of near-surface wind speed, which is contrast to its widespread overestimation on wind speed in urbanized areas of China. Our results suggest that there is an urgent need for better gridded z0 datasets, and the tens of thousands of anemometer towers currently being built for wind resource assessment may already provide a solution to this problem.
How to cite: wang, J., Yang, K., Yuan, L., Liu, J., Peng, Z., Ren, Z., and Zhou, X.: Deducing Aerodynamic Roughness Length from Abundant Anemometer Tower Data to Inform Wind Resource Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2352, https://doi.org/10.5194/egusphere-egu25-2352, 2025.
Climate change will impact wind and, therefore, wind power generation with largely unknown effect and magnitude. Climate models can provide insights and should be used for long-term power planning. In this work, we use Gaussian processes to predict power output given wind speeds from a global climate model. We validate the aggregated predictions from past climate model data with actual power generation, which supports using CMIP6 climate model data for multi-decadal wind power predictions and highlights the importance of being location-aware. We find that wind power projections of the two in-between climate scenarios, SSP2-4.5 and SSP3-7.0, closely align with actual wind power generation between 2015 and 2023. Our location-aware future predictions up to 2050 reveal only minor yearly wind power generation changes. Our analysis also reveals larger uncertainty associated with Germany's coastal areas in the North than Germany's South, motivating wind power expansion in regions where future wind is likely more reliable. Overall, our results indicate that wind energy will likely remain a reliable energy source. The methodology we present is adaptable to any other country or region with known wind farm locations and known historical aggregate power generation.
How to cite: Effenberger, N. and Ludwig, N.: Turbine location-aware multi-decadal wind power predictions using CMIP6, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5783, https://doi.org/10.5194/egusphere-egu25-5783, 2025.
The low-cost wind and solar energy may reduce the investments in hydropower and thus increase the share of fossil energy and finally increase the carbon emissions, leading to an "Energy Transition Paradox" [1]. To solve this problem, this study proposes to use subsidies to reconcile the conflicts involved in the capacity planning of hydropower and VRE, which has seldom been addressed in the shift to a low-carbon electricity system. The electricity system in Hubei Province, China is selected as a case study, where we examine the effects of different scenarios of fixed-subsidies, in addition to the market-clearing price, on renewable power generation. First, we estimate the long-term electricity prices based on the cost of marginal units. Next, we design several representative subsidy scenarios and determine the net present values and investments for increasing both hydropower and VRE capacity under these scenarios. Finally, the optimal or most effective subsidy scenario is identified by evaluating the carbon emissions and power generations. Results indicate that, 50% of the subsidy originally allocated to variable renewables should be re-allocated to hydropower to reduce the total carbon emissions. This means that a higher proportion of subsidies should be allocated to the hydropower rather than all subsidies are used to support the VRE alone. This study not only provides an effective economic policy to resolve the energy transition paradox but also shows the potential of enhancing the synergy between different renewable energies.
How to cite: Li, X., Liu, P., Feng, M., and Zhang, X.: A solution to energy transition paradox: optimal subsidy policy for minimizing the carbon emissions from future hybrid electricity system with hydropower and variable renewables., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10488, https://doi.org/10.5194/egusphere-egu25-10488, 2025.
The energy transition is a main pillar of climate mitigation strategies in countries around the world, including Switzerland. According to the International Energy Agency, renewable energy capacity has doubled worldwide since 2015, now accounting for 30% of total power generation. This capacity is projected to triple by 2030 compared to 2023 levels. Such expansion requires a comprehensive long-term planning that strives for resilience and accounts for risks, including those posed by a changing and variable climate. However, the current planning of energy systems often lacks integration of future climate information. Many of these planning processes consider minimal climate data, typically covering only a few historical representative weather years. To future-proof the energy transition, an interdisciplinary approach is essential to bridge this gap.
With this goal in mind, we design climate-driven projections for hydropower, solar energy, wind power and energy demand in Switzerland, based on existing research and energy systems modelers' needs. Different datasets are derived, spanning 2020 to 2050 and covering various representative concentration pathways (RCPs). . We leverage high-resolution regional climate model simulations from the EURO-CORDEX archive that include transient aerosols and bias-correct the relevant variables using CERRA and CERRA-Land reanalysis data. The modeling pipeline harnesses open-source tools, such as GSEE and windpowerlib, along with technical specifications provided by energy systems modelers, to convert the processed climate data into the desired energy quantities.
This framework is collaborative and flexible, allowing for the co-design of scenarios and the incorporation of expert knowledge to assess climate change impacts on energy systems and produce accurate input time series for energy systems modeling in Switzerland.
How to cite: Haddad, Y. Y., Sieber, P., Gudmundsson, L., and Seneviratne, S. I.: Connect, adapt, overcome: Informing energy systems modeling with future climate projections in Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11545, https://doi.org/10.5194/egusphere-egu25-11545, 2025.
As the climate crisis intensifies, switching to renewable energy remains a critical piece of the solution to ensure rapid decarbonization. However, renewable energy generation is highly reliant on the ambient environmental conditions, making it difficult to estimate the long-term generation—a task that is likely to get more difficult under climate change. Accounting for the impact of climate change is particularly difficult, as there remains uncertainty related to the magnitude of climate change within the mid- and long-term in addition to the relatively unknown impacts of climate change on generation of renewable energy technologies. In this work, we aim to fill this gap by leveraging machine learning to investigate the impact of climate change on state-level renewable energy generation across the US. Using data from the Energy Information Administration (EIA), we project the solar, wind, and hydropower generation across multiple US states under two key climate change scenarios. Our goal is to answer two key questions: (1) How will climate change impact renewable energy generation; and (2) Do these impacts differ across states? To answer these questions, we leveraged several machine learning techniques, as well as an ensemble of models, to first model the observed relationship between renewable energy generation and the surrounding weather and climate. Then, we used those same models to project the changes to the system, given the most recent IPCC climate change scenarios. Here, we will present the results from the projection analysis across multiple US states, including the states of California, New York, Florida, and Georgia, which contain some of the largest electric utilities in the country. The results indicate significant changes across different states and seasons, which could impact grid management and planning. Ultimately, the results will provide critical insights into the sustainability of renewable energy technologies over the long-term, given the reality of climate change.
How to cite: Obringer, R., Adul, J., and Kumar, R.: Harnessing Machine Learning to Investigate Climate Change Impacts on Renewable Energy Systems in the United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12776, https://doi.org/10.5194/egusphere-egu25-12776, 2025.
Reliable wind speed data is crucial for applications such as estimating local (future) wind power. Global Climate Models (GCMs) and Regional Climate Models (RCMs) provide forecasts over multi-decadal periods. However, their outputs vary substantially, and higher-resolution models come with increased computational demands. In this study, we analyze how the spatial resolution of different GCMs and RCMs affects the reliability of simulated wind speeds and wind power, using ERA5 data as a reference. We present a systematic procedure for model evaluation for wind resource assessment as a downstream task. Our results show that higher-resolution GCMs and RCMs do not necessarily preserve wind speeds more accurately. Instead, the choice of model, both for GCMs and RCMs, is more important than the resolution or GCM boundary conditions. The IPSL model preserves the wind speed distribution particularly well in Europe, producing the most accurate wind power forecasts relative to ERA5 data.
How to cite: Schmidt, L., Morelli, S., Effenberger, N., and Ludwig, N.: Climate data selection for multi-decadal wind power forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16795, https://doi.org/10.5194/egusphere-egu25-16795, 2025.
Climate change scenarios and ensembles are growing in number and applications. However, the wide range of modelling outputs, whilst attempting to giving voice to a more inclusive and equitable research community, can delay the implementation of policies for the decarbonisation. This may occur due to the diffusion of scenarios with inherent opacity in the modelling assumptions, with the risk of exposing climate economic modelling and integrated assessment models to erosion of accountability and lack of credibility. In the context of energy system electrification, although renewable energies are strongly emerging as a pillar for the low-carbon transition, the questions which remain open about timeliness of interventions, magnitude of investments, local diffusion of low-carbon technologies, and their diffusion rates across the sectors are delaying the strengths of governmental interventions. Among the key grey areas of the modelling, there are choices or background assumptions that scenario and ensembles render opaque or invisible. Examples include dataset choices, where bias may inadvertently crop in as data result from collection efforts in different locations, by bodies with different profiles and interests. Opacity could be amplified by the trend of providing highly detailed representations of bio-physical and socio-economic processes. In fact, whilst the use of geo-referential data adds an enormous value to the modelling exercise, especially when addressing the local availability of renewables, this has the drawback of increasing the challenges to the full inspection of models and interpretation of outputs.
Here, we propose an auditing approach to analyse scenario and scenario ensemble. The framework promotes an inspection of scenarios and of scenario ensemble, allowing to define their taxonomy and classification in a format useful to support decision-making in the promotion of policies and investments in renewable energy for climate change mitigation. Considering the design of scenario and ensembles, we provide a review of audit tools that can be used to assess their credibility and policy-relevance. We will consider not only renewable energy diffusion, but its interplay with key decarbonisation technologies, such as carbon capture and storage and nuclear energy. We will provide a framework for classifying and inspect the credibility of the scenarios. We will use the scenarios submitted to the Sixth Assessment Report of the IPCC in addition to those developed by other public and private institutions, as a basis to assess the data input accessibility and availability as well as to stress-test the usefulness of the methodology. The discussion will highlight the potential for a combined use of quantitative and qualitative tools for auditing scenarios and ensembles. Additionally, we identify key areas for future methodological research, including new benchmarks and machine learning tools for analysis. Building a wide auditing infrastructure for climate change modelling is a key step towards achieving greater transparency and accountability. Achieving such a level of accountability, will be key for acccelerating the decarbonization pace and the diffusion of renewable energy.
How to cite: Giarola, S. and Nappo, F.: Auditing methods of renewable energy diffusion in climate scenario ensembles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18493, https://doi.org/10.5194/egusphere-egu25-18493, 2025.
Although the popularity and viability of solar power have grown in the pursuit of a low-carbon and climate-resilient society, the impact of changes in climate attributes on the future potential of solar power output remains uncertain. While the warming level of the global mean generally correlates with varying greenhouse gas concentrations across different emission scenarios, changes in global mean temperature do not necessarily translate linearly to regional and local scales. Furthermore, the temperature dependence of solar cells is not uniformly linear across a wide range of temperature variations. Although it is well established that the efficiency of solar panels tends to decrease as temperatures rise, this relationship may exhibit nonlinear characteristics. In this regard, this study presents a comparative assessment of future changes in solar power potential estimated using various empirical formulas under low and high emission scenarios. The state-of-the-art climate simulations based on multiple global climate models (GCMs) participating in the Coupled Model Intercomparison Project (CMIP) are used to provide climate variables during the historical period (1976-2005) and future period (2071-2100). While the sensitivity of the empirical formula to future change patterns in solar power potential offers insights into the robustness of the results, the discrepancies between low and high emission scenarios provide significant scientific evidence that underscores the advantages of mitigation efforts and the practicality of implementing large-scale solar power initiatives in a changing climate.
Acknowledgments
This research was supported by the General Research Fund (GRF16308722) from the Research Grants Council (RGC) of Hong Kong. In addition, Youngmi Lee and Hyejin Lee were supported by a grant (Project number: 20022818) of Cultivation Program on Advanced Technology Center (ATC+) funded by Ministry of Trade, Industry and Energy (MOTIE, Korea).
How to cite: Im, E., Ha, S., Zhou, Z., Lee, Y., and Lee, H.: Assessing the impact of mitigation on future solar power potential based on CMIP multi-model ensembles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3119, https://doi.org/10.5194/egusphere-egu25-3119, 2025.
Amid climate change, credible, reliable climate information specifically tailored to the energy sector is essential for the advancement of the future low-carbon economy. Additionally, regional climate information available at a global scale is crucial for making informed decisions on several aspects of renewable energy planning. From the identification of new locations for the installation of renewable power plants to their future operation, assessing the state of future climate is of ever-growing importance. With the global rise in renewable energy adoption, energy security is increasingly tied to shifts in atmospheric and climatic conditions. We present a tool developed in the frame of the Destination Earth initiative, designed to deliver tailored climate information in streaming mode, synchronously with the production of high-resolution climate model outputs, and enable the provision of timely climate information.
The current version of our Python-based tool, "Energy Indicators", provides essential metrics for the wind energy sector at an unprecedented 5 km horizontal resolution. These include hourly to decadal wind speed statistics, capacity factors for various turbine types, and demand metrics such as heating and cooling degree days.
This development marks a major step forward in operationalising climate projections, enabling the timely delivery of actionable insights through the Climate Adaptation Digital Twin as part of Destination Earth.
How to cite: Ghosh, S., Roura-Adserias, F., Grayson, K., Lacima-Nadolnik, A., Soret, A., and Doblas-Reyes, F. J.: Climate Information in Action: Advancing Renewable Energy Services Through Streaming Climate Simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6239, https://doi.org/10.5194/egusphere-egu25-6239, 2025.
An economic feasibility analysis considering the effects of subsidies was performed for an offshore wind-power project under construction in Korea. The assessment of this project utilized traditional economic indicators such as Net Present Value (NPV) and Internal Rate of Return (IRR), and the result showed a positive NPV and an IRR of 6.7% that exceeded the Weighted Average Cost of Capital (WACC) of 4.8%, suggesting that the offshore wind-power project exhibited economically feasible. However, this feasibility was contingent upon governmental institutional supports, i.e., subsidies and policies, – specifically, Renewable Energy Certificates (RECs), REC multiplier, and long-term fixed price contracts that might generate stable cash flows. As a result of sensitive analysis, it indicated that the project at the viewpoint of stock holders would struggle to maintain economic feasibility if the current levels of subsidies were reduced by more than 10%. A comparison of the Levelized Cost of Electricity (LCOE) was calculated excluding the effects of subsidies to further evaluate the competitiveness of this project relative to power generation facilities utilizing other energy sources. The results showed that the LCOE of this project was 129.4 USD/MWh, nearly twice that of offshore wind-power project in the United States (64.6 USD/MWh). This disparity was attributed to relatively low wind resource availability, stemming from its geographical location, as well as the higher associated costs of labor and financing. The results suggested that the energy transition should be carried out gradually with an appropriate mix of traditional and renewable energy sources to mitigate the societal and economic burdens.
How to cite: Lee, D., Park, C., Ahn, T., and Ki, S.: An economic feasibility study including subsidies for an offshore wind-power project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8268, https://doi.org/10.5194/egusphere-egu25-8268, 2025.
How to cite: Strnad, F. and Ludwig, N.: Assessing the impact of future dunkelflaute events on the European electricity grid using generative deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12022, https://doi.org/10.5194/egusphere-egu25-12022, 2025.
Weather and climate model data are increasingly used as the basis for assesing climate risk in energy system operations and planning. The reliability of such studies is therefore heavily dependent on the quality of the input meteorological data and, in particular, the accurate representation of extreme events.
In this study, we analysed the representation of Dunkelflaute events over Europe (periods of calm and cloudy weather typically associated with increased power supply stress) using estimated national wind- and solar- capacity factors from two tailored climate products (C3S-Energy and one of its predecessors, ECEM). A particular focus was on the ability of the derived energy-variables from climate models to represent their respective reanalysis equivalents (C3S-Energy uses ERA5 as the reference, while ECEM relies on ERA-Interim).
Preliminary results suggest that there are potentially significant differences between the representation of Dunkelflaute events across the two datasets. In particular, while the overall seasonal evolution of Dunkelflaute occurrence appears to be well represented (compared to their respective reanalyses), there are noticeable differences in winter-time Dunkelflaute frequency across many areas of Europe with the climate models typically simulating fewer Dunkelflautes in the northern part of the region and more frequent events in the south (potentially up to a ~few 10’s of percent depending on country and area).
While the availability of datasets such as ECEM and C3S-Energy offers unprecedented opportunities for energy system experts to explore climate risk, these preliminary results nevertheless suggest that some care is required in their use.
How to cite: Poovadiyil, S., Brayshaw, D., Kirk-Davidoff, D., and Fischer, L.: Accuracy of Climate Model Derived Energy-Datasets During Renewable Energy Lulls., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16661, https://doi.org/10.5194/egusphere-egu25-16661, 2025.
Impact of climate change on photovoltaic energy production in Italy using convection permitting models
Milan Mathew1, Giorgia Fosser1, Andrea Cafforio2, Anna Malagò2 and the CORDEX-FPS-CONV community*
1Department of Science, Technology and Society, University School for Advanced Studies (IUSS), Pavia, Italy
2 A2A S.p.A., Milano, Italy
*A full list of authors appears at the end of the abstract
Italy aims to achieve carbon neutrality by 2050 and to reach 55% of renewables in electricity generation. Currently, renewables account for 33% in electricity generation, with solar energy production steadily increasing since 2010 and presently contributing 30% to total renewable energy production. As investments rise on these renewable energy sources, it is crucial to understand how they will be affected in the future by climate change. Clouds and atmospheric aerosols play an important role in the amount of incident solar radiation on the earth’s surface and thus photovoltaic (PV) energy generation. Previous studies have found that km-scale Convection-Permitting Models (CPMs), which can explicitly resolve deep convection, represent more realistically extreme precipitation, winds and snow especially over regions with complex orography, compared to coarser resolution models. However, little is known in the CPMs ability to represent and project solar energy production.
Here, we use an ensemble of CPMs from CORDEX-FPS on Convective Phenomena over Europe and the Mediterranean (FPS Convection) to understand how PV energy production potential over Italy will be impacted in a warming climate. First, we assess the CPMs capability in representing PV production, estimated as a function of surface downwelling shortwave radiation, temperature and wind speed, against actual recorded production in four PV power plants in Italy. We found that most models are capable of representing the PV production and its variability. Further, we assess how PV production in Italy will be affected by climate change towards the mid-century (2041-2050) and end of the century (2090-2099) under the IPCC’s RCP8.5 scenario. The ensemble median indicates negligible change in PV production towards the mid-century. However, at the end of the century, the ensemble median indicates a slight increase in PV production of 2-3% over most parts of Italy, despite the substantial increase in temperatures (~50C). These results suggest that photovoltaic energy production over Italy is unlikely to be significantly threatened by future climate change and highlights the continued potential of PV energy as a key contributor to achieving the country’s renewable energy targets.
CORDEX-FPS-CONV community: Marianna Adinolfi3, Cécile Caillaud4, Samuel Somot4, Luna Lehmann5, Andreas Dobler6, Erika Coppola7, Hendrik Feldmann8, Hylke de Vries9, Rita Margarida Cardoso10, Pedro M. M. Soares10, Klaus Goergen11
3 CMCC Foundation - Euro- Mediterranean Center on Climate Change, Caserta, Italy. 4Université de Toulouse, Météo-France, CNRS, Toulouse, France. 5Institute for Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland. 6Norwegian Meteorological Institute, Oslo, Norway. 7The Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy. 8Institute for Meteorology and Climate Research (IMK-TRO). 9Royal Netherlands Meteorological Institute KNMI, De Bilt, Netherlands. 10IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal. 11Institute of Bio and Geosciences (IBG-3, Agrosphere), Research Centre Juelich, Juelich, Germany
How to cite: Mathew, M., Fosser, G., Malagò, A., and Cafforio, A.: Impact of climate change on photovoltaic energy production in Italy using convection permitting models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19644, https://doi.org/10.5194/egusphere-egu25-19644, 2025.
As a clean and renewable form of energy, photovoltaic (PV) power generation converts solar energy into electrical energy, reducing the consumption of fossil fuels and significantly lowering greenhouse gas emissions. China, with its vast territory and wide distribution of solar resources, naturally possesses an advantage in developing the PV industry. The technical potential of land centralized PV power in China is about 41.88×109 kW, and its spatial pattern is basically consistent with the spatial pattern of solar energy resource endowment. The “Three North” regions (Northeast, Northwest, and North China) account for 90.95% of the country’s total, while the central and southeastern regions (Central China, East China, and South China) account for only 9.05%. For specific provinces, Xinjiang has the largest potential of centralized PV power, higher than 20×109 kW. The technical potential of distributed PV power in China is about 3.73×109 kW, with the “Three North” regions accounting for 51.34% of the national total, and the central and southeastern regions accounting for 48.66%. In terms of specific provinces, Shandong has the largest technical potential of distributed PV power, close to 400×106 kW. According to the National Energy Administration, in 2023, China’s newly added grid-connected PV power capacity was 216.3×106 kW, including 120.014×106 kW for centralized PV power stations and 96.286×106 kW for distributed PV power, among which the installed capacity of residential distributed PV reached 43.483×106 kW. By the end of 2023, the accumulated grid-connected capacity reached a total of 608.92×106 kW, with centralized at 354.48×106 kW and distributed at 254.44×106 kW. According to data from the National Bureau of Statistics, in 2023, PV power generation for industrial enterprises above a designated size (with a main business income of more than 2×107 yuan) totaled 294×109 kWh, making a year-on-year increase of 17.2%. Overall, the PV power generation in 2023 was 583.3×109 kWh, up by 36.4% compared to the previous year. Currently, China has established a complete PV industry chain that ranges from silicon material preparation to module production. China is also actively exploring the integrated development of PV with other industries, forming a diversified development model of “PV +”, which greatly promotes the diverse application and sustainable development of PV technology. China is actively engaged in the construction and planning of numerous large-scale wind and PV power bases. Forecasts indicate that by 2030, the nation’s cumulative installed PV capacity could range from 840×106 kW to 1260×106 kW, with a further anticipated expansion to 2996×106 kW to 3845×106 kW by 2060. Concurrently, the total electricity generation from PV power is projected to be between 1.47×1012 kWh and 2.28×1012 kWh by 2030, potentially surging to a range of 3.11×1012 kWh to 6.00×1012 kWh by the year 2060. Capitalizing on the surging global demand for clean energy, China’s PV sector is positioning itself as a cornerstone in the pivot towards a sustainable energy future.
How to cite: Jia, L.: China's Photovoltaic Power Generation Facilitates Carbon Emission Reduction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8928, https://doi.org/10.5194/egusphere-egu25-8928, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 4
EGU25-7155 | ECS | Posters virtual | VPS16
A global investigation of atmospheric circulation regimes driving wind power generation and its extremes at country and continent scalesMon, 28 Apr, 14:00–15:45 (CEST) | vP4.10
Large-scale wind power installations are expanding across the world as part of electricity decarbonization efforts. Extreme wind energy events including wind droughts can pose major challenges for decarbonizing electricity grids that increasingly depend on renewable, including wind, power generation. In the context of conversions of available potential to horizontal kinetic energy predominantly over oceanic regions that are often remote from wind farms as well as load centers, we simulate country and continental scale wind power generation across the world and examine factors driving wind droughts. We use ERA-5 reanalysis wind speed and a wind turbine power curve to estimate daily wind generation at existing sites across the world. Site-level generation is aggregated to estimate daily generation patterns at country and continental scales. We estimate wind drought patterns in absolute terms and with respect to anomalies in relation to daily climatology and examine associations between wind droughts and characteristics of the large-scale atmospheric circulation.
Long-range advection of horizontal kinetic energy can also play an important role in maintaining wind power, and we systematically explore and distinguish the roles of local and remote factors in driving wind power variability at three types of scales: site-level, country-scale, continental-scale. This study offers a systematic approach to comprehending interactions between the large-scale kinetic energy budget and wind power variability across scales. We investigate the following questions: What background conditions over open oceanic regions facilitate long-range advection of wind energy, and how critical is advection for wind power variability? What specific circulation regimes are more instrumental in driving overall variability? The results offer insights for understanding controls from the mechanical energy budget on decarbonizing energy systems, and factors driving their variability across timescales.
How to cite: Sahu, S., Gangopadhyay, A., and Seshadri, A. K.: A global investigation of atmospheric circulation regimes driving wind power generation and its extremes at country and continent scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7155, https://doi.org/10.5194/egusphere-egu25-7155, 2025.
