Advances in Applied Energy最新文献

{"title":"Advances and challenges in energy and climate alignment of AI infrastructure expansion","authors":"Apoorv Lal ,&nbsp;Fengqi You","doi":"10.1016/j.adapen.2025.100243","DOIUrl":"10.1016/j.adapen.2025.100243","url":null,"abstract":"<div><div>The rapid growth of artificial intelligence (AI) infrastructure deployment presents significant challenges for global energy systems and climate goals. While previous reviews address the sustainability of traditional data centers, Green AI approaches centered on model-level improvements or the application of AI in advancing sustainability across sectors, the energy and climate consequences of deploying AI infrastructure itself remain underexplored in prior literature. This paper reviews existing analyses on AI infrastructure’s energy and climate implications and proposes quantitative scenario-based frameworks, highlighting key research challenges at the intersection of AI-driven energy demand, region-specific clean energy strategies and their economic competitiveness, strategic levers in energy sourcing decisions, and policy dynamics. Additionally, this work identifies future research directions for aligning AI infrastructure growth with clean energy transitions through targeted mitigation opportunities across spatial and temporal horizons. First, the ambitious investment pathways for AI infrastructure development in the US underscore the need for spatially resolved scenario frameworks that reflect regional differences in deployment patterns and clean energy integration, along with the associated cost trajectories, to guide federal and state regulators. Second, the global expansion of AI infrastructure emphasizes the need for comprehensive frameworks that assess country-specific electricity demand shares, renewable transition pathways, and the influence of geopolitical restrictions, offering actionable insights for climate-conscious strategies. Finally, to prevent reinforcing fossil fuel dependency, particularly under disruptive growth scenarios, energy pathways incorporating nuclear power, renewables, energy storage, and varying grid reliance are explored as part of broader clean energy transitions, especially in regions facing energy security challenges.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100243"},"PeriodicalIF":13.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal ultra-short-term probabilistic solar power forecasting with generative AI and Transformer","authors":"Binyu Xiong ,&nbsp;Yuntian Chen ,&nbsp;Xin Zhao ,&nbsp;Zhongyue Su ,&nbsp;Jun Fu ,&nbsp;Dali Chen ,&nbsp;Dongxiao Zhang","doi":"10.1016/j.adapen.2025.100250","DOIUrl":"10.1016/j.adapen.2025.100250","url":null,"abstract":"<div><div>Solar power is one of the most widely adopted forms of renewable energy, with its usage rapidly increasing in recent years. However, solar power generation is highly sensitive to environmental factors, leading to frequent fluctuations. These fluctuations present challenges for large-scale integration into the power grid. Sky images, which provide real-time information about sky conditions, can help improve ultra-short-term solar power forecasting. Nonetheless, due to the inherent uncertainty of cloud movement, solar power generation can be unstable over short time intervals. This instability calls for the inclusion of uncertainty in ultra-short-term forecasting models. Inspired by recent advances in deep learning, we propose a novel framework for ultra-short-term probabilistic solar power generation forecasting. This framework uses both historical solar power generation data and sky images as inputs. First, a video prediction model based on generative artificial intelligence (AI) is applied to generate multiple potential future sky image sequences. Next, a Transformer-based multimodal model combines each predicted sequence with historical solar power generation data to derive a distribution of possible future power outputs. To account for uncertainties in both the video prediction and multimodal models, these distributions are aggregated into a single overall distribution. We conducted experiments on real-world datasets, with prediction times ranging from 15 to 60 min, to compare our method with previous mainstream approaches. The results demonstrate that our method outperforms existing approaches in both deterministic and probabilistic forecasting tasks. In the 15 min ahead forecasting task, compared to the method using only historical solar power generation data, our method reduces the Root Mean Square Error (RMSE) of the deterministic evaluation metric by 20.6% and the Continuous Ranked Probability Score (CRPS) of the probabilistic evaluation metric by 19.4%. When compared to the method using only sky image data, our approach reduces the RMSE by 47.3% and the CRPS by 51.3%. Furthermore, we conduct additional analysis on the performance of various methods under different weather conditions.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100250"},"PeriodicalIF":13.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"District heating network topology optimization and optimal co-planning using dynamic simulations","authors":"Jonathan Vieth,&nbsp;Jan Westphal,&nbsp;Arne Speerforck","doi":"10.1016/j.adapen.2025.100233","DOIUrl":"10.1016/j.adapen.2025.100233","url":null,"abstract":"<div><div>District heating networks play a critical role in the transition of the heating supply of buildings to renewable sources. The transition from coal-fired or gas-fired generation units to heat pumps requires new planning methods for district heating networks, since the efficiency of a heat pump is affected strongly by the supply temperature of the district heating network. Therefore, a co-planning approach including the operation of the district heating network in the planning process is required. This paper presents a novel co-planning approach consisting of two steps. First, an optimal district heating network topology is generated from real geo-referenced data. To determine the optimal topology, a new algorithm designed specifically for district heating networks is presented. Next, a simulation model is automatically generated from the respective topology. An optimization is used for the co-planning approach to select an optimal generation unit, find the optimal supply temperature, and dimension the pipes of the district heating network. In contrast to conventional district heating network planning procedures, the optimization includes a full-year dynamic simulation of the district heating network. The result of the planning process is a full y parameterized district heating network with a matching supply temperature. Furthermore, the use of simulation models allows the results to be reused for sensitivity analyses. This is illustrated by examining the selection of generation units under different <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> price scenarios.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100233"},"PeriodicalIF":13.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential impact of controlled electric car charging and vehicle-to-grid on Germany’s future power system","authors":"Fabio Frank,&nbsp;Till Gnann,&nbsp;Daniel Speth,&nbsp;Bastian Weißenburger,&nbsp;Benjamin Lux","doi":"10.1016/j.adapen.2025.100227","DOIUrl":"10.1016/j.adapen.2025.100227","url":null,"abstract":"<div><div>The increasing diffusion of electric vehicles contributes to a growing electricity demand in the coming years. At the same time, this integrates millions of mobile storage units into the electricity system, which has a rising need for flexibility to balance the intermittent generation from photovoltaic systems and wind turbines. To capture the potential of electric cars as a flexibility resource, we simulate 7,000 vehicle driving profiles in an agent-based model, generating load profiles as well as charging power and state-of-charge boundaries for the German car fleet, which serve as restrictions in energy system optimization. In a scenario-based study for Germany in 2030 and 2045, we compare the installed electric capacities in the optimized system, depending on whether electric vehicle charging is uncontrolled, controlled, or bidirectional. Here we show that a bidirectionally charged car fleet has the potential to replace 32 GW (84 %) of stationary battery storage and 31 GW (64 %) of hydrogen-fired peaking power plants, while enabling an additional solar power expansion of 7 GW (2 %) until 2045. Notably, implementing vehicle-to-grid can limit hydrogen-fired electricity generation to winter months and enable a shift toward combined heat and power plants. On the demand side, it can reduce the expansion of electrolyzers by 19 GW (28 %) and power-to-heat capacities by 25 GW (60 %). Overall, the integrated energy system can substantially benefit from the implementation of smart and especially bidirectional charging as it lowers the need for future capacity expansion in the electricity system but also in coupled hydrogen and heat systems.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100227"},"PeriodicalIF":13.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathways to sustainable fuel design from a probabilistic deep learning perspective","authors":"Rodolfo S.M. Freitas ,&nbsp;Zhihao Xing ,&nbsp;Fernando A. Rochinha ,&nbsp;Roger F. Cracknell ,&nbsp;Daniel Mira ,&nbsp;Nader Karimi ,&nbsp;Xi Jiang","doi":"10.1016/j.adapen.2025.100226","DOIUrl":"10.1016/j.adapen.2025.100226","url":null,"abstract":"<div><div>To achieve net zero CO₂ emissions by 2050–2060, decarbonising the hard-to-abate sectors such as long-distance, heavy-duty transport is a top priority worldwide. These sectors are particularly challenging to decarbonise due to the use of high-energy-density liquid fossil fuels. In this context, designing low-carbon alternative fuels compatible with existing engines and fuel infrastructures is essential. This work presents an advanced fuel design framework to develop sustainable fuels that meet the high energy density requirements of heavy-duty vehicles. The fuel design approach is built upon a probabilistic perspective by considering a conditional generative model to predict the physicochemical properties of pure compounds and fuel blends with confidence bounds required for decision-making tasks. The probabilistic model is then integrated into an inverse design framework to design fuels with specific requirements. Finally, the fuel design framework is employed to develop new diesel fuel compositions according to the desired targets: ignition quality (cetane number) and sooting tendency (yielding sooting index). The AI-assisted fuel design approach can potentially lead to sustainable liquid fuels that are fully compatible with the existing utilisation equipment and can satisfy the requirements of different application sectors.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100226"},"PeriodicalIF":13.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate-resilient energy systems planning via system-informed identification of stressful events","authors":"Francesco De Marco,&nbsp;Jacob Mannhardt,&nbsp;Alfredo Oneto,&nbsp;Giovanni Sansavini","doi":"10.1016/j.adapen.2025.100235","DOIUrl":"10.1016/j.adapen.2025.100235","url":null,"abstract":"<div><div>As the energy mix increasingly relies on weather-dependent renewable sources, energy systems become more vulnerable to climate variability and extremes. However, current planning approaches struggle to incorporate climate uncertainty in the design phase while maintaining computational tractability. We address this challenge by developing a framework that combines system-informed scenario reduction and stochastic optimization to design climate-resilient energy systems. Our method reduces data complexity by identifying representative climate scenarios that capture stress events through system response. Remarkably, five distinct patterns of multi-day energy shortages emerge across Europe, each characterized by different combinations of renewable resource availability and demand profiles. Stochastic optimization then incorporates these representative climate scenarios with their associated probabilities to design energy systems that are resilient across the full spectrum of climate variability. Results show that climate-resilient designs consistently outperform conventional single-climate designs, achieving lower costs (on average 14.8 bn EUR) for equivalent resilience levels. We identify two trade-off regions with different marginal costs of resilience: a low-resilience and a high-resilience region where marginal costs increase fivefold. Despite higher costs, trade-offs between the cost of resilience investments against energy not supplied justify pursuing the high levels of resilience. Combinations of onshore wind and hydrogen storage emerge as effective mitigation against multi-day events of energy shortage. This framework provides energy planners and policymakers with quantifiable insights into resilience investment strategies and technology selection for future climate-aware energy planning.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100235"},"PeriodicalIF":13.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed modularized neural network for advanced building control by deep reinforcement learning","authors":"Zixin Jiang,&nbsp;Xuezheng Wang,&nbsp;Bing Dong","doi":"10.1016/j.adapen.2025.100237","DOIUrl":"10.1016/j.adapen.2025.100237","url":null,"abstract":"<div><div>Physics-informed machine learning (PIML) provides a promising solution for building energy modeling and can be used as a virtual environment to enable reinforcement learning (RL) agents to interact and learn. However, how to integrate physics priors efficiently, evaluate the effectiveness of physics constraints, balance model accuracy and physics consistency, and enable real-world implementation remain open challenges. To address these gaps, this study introduces a Physics-Informed Modularized Neural Network (PI-ModNN), which integrates physics priors through a physics-informed model structure, loss functions, and hard constraints. A new evaluation matrix called “temperature response violation” is developed to quantify the physical consistency of data-driven building dynamic models under varying control inputs and training data sizes. Additionally, a physics prior evaluation framework based on “rule importance” is proposed to quantify the contribution of each individual physical priors, offering guidance on selecting appropriate PIML techniques. The results indicate that incorporating physical priors does not always improve model performance; inappropriate physical priors could decrease model accuracy and consistency. However, hard constraints effectively enforce model consistency. Furthermore, we present a general workflow for developing control-oriented PIML models and integrating them with deep reinforcement learning (DRL). Following this framework, a case study of implementation DRL in an office space for three months demonstrates potential energy savings of 31.4%. Finally, we provide a general guideline for integrating data-driven models with advanced building control through a four-step evaluation framework, paving the way for reliable and scalable implementation of advanced building controls.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100237"},"PeriodicalIF":13.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weather conditions severely impact optimal direct air capture siting","authors":"Henrik Wenzel ,&nbsp;Freia Harzendorf ,&nbsp;Kenneth Okosun ,&nbsp;Thomas Schöb ,&nbsp;Jann Michael Weinand ,&nbsp;Detlef Stolten","doi":"10.1016/j.adapen.2025.100229","DOIUrl":"10.1016/j.adapen.2025.100229","url":null,"abstract":"<div><div>Direct air capture (DAC) is rapidly gaining attention as a key technological approach to mitigating climate change. While techno-economic assessments increasingly incorporate DAC, they often overlook the influence of weather variability on both energy demand and plant productivity. In this study, we analyze how local weather patterns affect the two most promising DAC approaches: the solid sorbent and the liquid solvent processes. We reveal for a German case study, that the integration of DAC with renewable energy sources necessitates temporal and spatial considerations, as fluctuations in energy supply and demand can significantly impact operational feasibility. We demonstrate energy demand fluctuations of DAC exceeding 100 % over the course of a year and estimate future DAC costs in Germany in a range from 197 €/t_CO2 to 1035 €/t_CO2, depending on the region and technology. These results emphasize the need for detailed, site-specific assessments to ensure future cost-optimal DAC deployment.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100229"},"PeriodicalIF":13.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of reinforcement learning in Building-Integrated Photovoltaic (BIPV) optimization","authors":"Jiaqi Li ,&nbsp;Hongbin Xie ,&nbsp;Jingyuan Zhang ,&nbsp;Lianxin Li ,&nbsp;Ge Song ,&nbsp;Hongdi Fu ,&nbsp;Panxi Chen ,&nbsp;Chenyang Liu ,&nbsp;Liyu Zhang ,&nbsp;Zhuoran Shi ,&nbsp;Qing Yu ,&nbsp;Xuan Song ,&nbsp;Haoran Zhang","doi":"10.1016/j.adapen.2025.100239","DOIUrl":"10.1016/j.adapen.2025.100239","url":null,"abstract":"<div><div>Building-Integrated Photovoltaic (BIPV), as an emerging clean energy solution, plays a crucial role in energy saving, emission reduction, and grid load regulation. However, due to the uncertainty of dynamic environments and the complexity of multiple sensitive parameters, traditional scheduling methods fail to achieve optimal results. Considering that reinforcement learning, as an advanced research approach, demonstrates great potential in decision-making for high-dimensional problems and stability in dynamic environments, integrating reinforcement learning with BIPV is a feasible solution to address scheduling challenges in BIPV systems. However, there is still a lack of comprehensive analysis and systematic understanding of reinforcement learning applications in the BIPV field, which, to some extent, limits its further development in the BIPV domain. To this end, this review conducts an in-depth analysis of the effectiveness of reinforcement learning in BIPV applications from the perspective of the system construction life cycle. By considering the algorithm modeling life cycle of reinforcement learning, it comprehensively examines the potential issues in its application to BIPV, highlighting the challenges faced by existing research and future applications. Additionally, this paper integrates cutting-edge reinforcement learning knowledge, summarizes and categorizes its potential applications in BIPV, providing reference guidance for future research directions. Through this systematic review of reinforcement learning applications in the BIPV field, this study aims to offer valuable insights for subsequent research.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100239"},"PeriodicalIF":13.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flexibility potential of electric vehicle charging: A trip chain analysis under bi-criterion stochastic dynamic user equilibrium","authors":"Shuyi Tang,&nbsp;Yunfei Mu,&nbsp;Hongjie Jia,&nbsp;Xiaolong Jin,&nbsp;Xiaodan Yu","doi":"10.1016/j.adapen.2025.100240","DOIUrl":"10.1016/j.adapen.2025.100240","url":null,"abstract":"<div><div>The widespread adoption of electric vehicles (EVs) creates opportunities to use EV charging load as a flexible resource to improve grid operation. In urban areas, EV users typically follow trip chains in their daily travel, offering temporal and spatial flexibility in EV charging. Specifically, charging at slow-charging spots at trip destinations is temporally flexible when the parking duration exceeds the required charging time. In contrast, charging at fast charging stations (FCSs) during trips is spatially flexible, with route and FCS choice influenced by traffic congestion, FCS charging prices, and user perception. In this paper, we propose a bi-criterion stochastic dynamic user equilibrium (SDUE) model with trip chain demand, which captures route and FCS choice of EV users and derives fast and slow charging loads. The model accounts for user response to traffic congestion and FCS charging prices, along with the randomness in user perception of trip utility. A quantitative evaluation is also presented on the spatial flexibility of fast charging driven by price incentives, and the temporal flexibility of slow charging enabled by long parking durations. A case study in Sioux Falls is conducted to evaluate the flexibility potential of EV charging, revealing that reduced randomness in user perception enhances the spatial flexibility potential of fast charging. Additionally, the temporal flexibility potential of slow charging varies across location types, such as home, work, and other locations, depending on arrival times and parking durations. This research provides key insights for optimizing grid management and enhancing EV integration into power systems.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100240"},"PeriodicalIF":13.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}