Applied Energy最新文献

Optimization of plasma-thermal system for non-oxidative coupling of methane to ethylene and hydrogen

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-28 DOI: 10.1016/j.apenergy.2025.125411

Rui Liu , Dongxing Li , Qian Chen , Zhiwei Gong , Li Wang , Hongchen Guo , Yanhui Yi

{"title":"Optimization of plasma-thermal system for non-oxidative coupling of methane to ethylene and hydrogen","authors":"Rui Liu , Dongxing Li , Qian Chen , Zhiwei Gong , Li Wang , Hongchen Guo , Yanhui Yi","doi":"10.1016/j.apenergy.2025.125411","DOIUrl":"10.1016/j.apenergy.2025.125411","url":null,"abstract":"<div><div>This study explores an integrated hybrid plasma-thermal system to achieve a non-oxidative coupling of methane (2CH<sub>4</sub> ⇌ C<sub>2</sub>H<sub>4</sub> + 2H<sub>2</sub>) by employing an atmospheric pressure non-thermal plasma and a thermal cracking reactor. The size and configuration of the plasma reactor (Stage 1) and the thermal cracking reactor (Stage 2) have been optimized, and the residence time in both reactors is identified to be crucial for non-oxidative coupling of methane. Under the optimized reactors, a 32 % methane conversion has been achieved with remarkable selectivity of 68 % for ethylene and 63 % for hydrogen, at residence time of around 500 s and 10 s for Stage 1 and Stage 2, respectively. Therefore, a 21.8 % yield of ethylene has been achieved for non-oxidative conversion of methane to ethylene at temperature below 900 °C. Furthermore, the study explores the complex relationship between ethylene selectivity in the plasma-assisted non-oxidative coupling of methane and residence time in thermal cracking reactor, revealing a first increase and then decrease trend with an optimized residence time at ca. 10 s.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125411"},"PeriodicalIF":10.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum Reinforcement Learning for real-time optimization in Electric Vehicle charging systems

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-28 DOI: 10.1016/j.apenergy.2025.125279

Hairun Xu , Ao Zhang , Qingle Wang , Yang Hu , Fang Fang , Long Cheng

{"title":"Quantum Reinforcement Learning for real-time optimization in Electric Vehicle charging systems","authors":"Hairun Xu , Ao Zhang , Qingle Wang , Yang Hu , Fang Fang , Long Cheng","doi":"10.1016/j.apenergy.2025.125279","DOIUrl":"10.1016/j.apenergy.2025.125279","url":null,"abstract":"<div><div>The rapid growth of electric vehicles (EVs) presents new challenges for EV charging scheduling, particularly due to the unpredictable nature of charging demand and the dynamic availability of resources. Currently, Deep Reinforcement Learning (DRL) has become a critical technology for improving scheduling efficiency. At the same time, advancements in quantum computing have led to Quantum Neural Networks (QNNs), which use the superposition states of quantum bits for more efficient information encoding. Building on these advancements, this study explores Quantum Reinforcement Learning (QRL) for EV charging systems. We propose a method called QRL-based Electric Vehicle Charging Scheduling (Q-EVCS) to optimize charging resource allocation based on real-time user demand. This approach aims to reduce average charging service times, increase the service success rate, and lower operational costs. We provide the detailed design and implementation of our approach, and our experimental results demonstrate that Q-EVCS maintains performance levels comparable to the DRL-based method while significantly reducing the number of model parameters.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125279"},"PeriodicalIF":10.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid methodology for assessing hydropower plants under flexible operations: Leveraging experimental data and machine learning techniques

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-28 DOI: 10.1016/j.apenergy.2025.125402

Ali Amini , Samuel Rey-Mermet , Steve Crettenand , Cécile Münch-Alligné

{"title":"A hybrid methodology for assessing hydropower plants under flexible operations: Leveraging experimental data and machine learning techniques","authors":"Ali Amini , Samuel Rey-Mermet , Steve Crettenand , Cécile Münch-Alligné","doi":"10.1016/j.apenergy.2025.125402","DOIUrl":"10.1016/j.apenergy.2025.125402","url":null,"abstract":"<div><div>In recent years, there's been a demand for more flexible hydropower to offset power fluctuations from intermittent renewables, leading to increased wear in turbines due to extended off-design operations and transients. The present paper proposes novel methods to analyze and assess experimental data of a hydropower plant for a better monitoring over time. Measurements are performed in a run-off-river plant with Francis turbines in Switzerland. During two experimental campaigns, high-frequency acquisitions up to 51.2 kHz are realized with more than 30 sensors while the SCADA data are collected at 10 Hz. In the physics-based analysis, statistical metrics and time-frequency decompositions are deployed to assess the operating conditions. To aggregate all information, the Vibrational Content Index is introduced, which unifies all sensors data by normalizing their frequency spectra with the Best Efficiency Point (BEP). This provides a single-value indicator of overall vibration, which is more sensitive than traditional metrics and easier to track over time. In the data-driven approach, the operating points are segmented into three clusters with distinct physical interpretations by applying dimensionality reduction algorithms with only two coded dimensions. This method correctly detects an abrupt change in the machine behavior for a slight power shift from <span><math><mn>1.23</mn><msub><mi>P</mi><mi>BEP</mi></msub></math></span> to <span><math><mn>1.30</mn><msub><mi>P</mi><mi>BEP</mi></msub></math></span> – due to full-load vortex self-excitation. Finally, the concept of virtual sensors is developed by corroborating the coded high-frequency experimental data with low-frequency SCADA using machine learning. The trained model uses SCADA data as input to estimate the sensors high-frequency response in real-time. This promising approach enables improved continuous monitoring without the need for permanent installations.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125402"},"PeriodicalIF":10.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recent progress in building energy retrofit analysis under changing future climate: A review

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-28 DOI: 10.1016/j.apenergy.2025.125441

Pengyuan Shen , Yu Li , Xiaoni Gao , Yiqian Zheng , Peiying Huang , Ang Lu , Wei Gu , Shuxing Chen

{"title":"Recent progress in building energy retrofit analysis under changing future climate: A review","authors":"Pengyuan Shen , Yu Li , Xiaoni Gao , Yiqian Zheng , Peiying Huang , Ang Lu , Wei Gu , Shuxing Chen","doi":"10.1016/j.apenergy.2025.125441","DOIUrl":"10.1016/j.apenergy.2025.125441","url":null,"abstract":"<div><div>Climate change presents challenges to building sector energy consumption and system performance. This review assesses how future climate change impacts building retrofit analysis and implementation by synthesizing recent research and providing comprehensive insights for adaptation and mitigation strategies. The effectiveness of retrofit measures shows regional variation, while passive strategies become less effective in some areas as climate conditions change in the future. This work also reviewed methodological advances that are crucial for the future climate adaptive retrofit analysis, including better coupling between climate models and building simulations, developing more reliable downscaling techniques for global climate models, easy-to-use simulation tools that has balanced performance on computational cost and accuracy, and more intuitive optimization and decision-making support methods. Challenges remain in translating global climate projections to building scale applications, especially in urban environments where microclimate effects cannot be ignored. Implementation barriers fall into technical, economic and policy domains, which require both immediate performance requirements and long-term climate resilience to be considered in an integrated framework. Future research priorities include developing integrated modeling approaches which considers global and local climate effects, developing retrofit optimization methods that can handle climate uncertainty, and developing robust policy frameworks to support future climate adaptive implementation of building retrofit.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125441"},"PeriodicalIF":10.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of non-uniform reactions in PEMFC based on the multi-physics quantity fusion graph auto-encoder network

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-27 DOI: 10.1016/j.apenergy.2025.125365

Pulin Zhang , Diankai Qiu , Linfa Peng

{"title":"Prediction of non-uniform reactions in PEMFC based on the multi-physics quantity fusion graph auto-encoder network","authors":"Pulin Zhang , Diankai Qiu , Linfa Peng","doi":"10.1016/j.apenergy.2025.125365","DOIUrl":"10.1016/j.apenergy.2025.125365","url":null,"abstract":"<div><div>To meet the demands of high power output, proton exchange membrane fuel cells (PEMFCs) with large area have become a significant focus of research. However, non-uniform reactions in fuel cells are unavoidable in practice, leading to performance degradation and reduced stack lifespan. Understanding the distribution of physical quantity changes within the fuel cell and predicting its future internal states are crucial for control and maintenance of fuel cells. This paper proposes a Multi-Physics quantity fusion Graph Auto-Encoder network (MP-GAE), which is a transient prediction model for the performance and multi-physical field distribution in fuel cell by focusing on three aspects: reaction time, spatial location, and the coupling relationships of multiple physical fields. Based on graph attention mechanisms and temporal networks, a Partitioned Temporal Graph Attention Network (PT-GAT) is established to extract spatiotemporal regularities. Based on the Auto-Encoder structure and the interrelationships among the five physical quantities, these prediction models are integrated into MP-GAE to enhance the model's prediction performance. Experimental results show that MP-GAE can accurately predict changes in physical fields and performs well under complex conditions such as variations in load current density, gas pressure, inlet relative humidity, stoichiometric ratio and temperature. The proposed model effectively predicts the non-uniform variation processes of five physical quantities within the reaction area of fuel cells, providing information and assistance for the control and management of large-area fuel cells.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125365"},"PeriodicalIF":10.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterizing effective building clusters: An extensive study on diverse cluster composition, distributed energy resource utilization, and energy performance

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-27 DOI: 10.1016/j.apenergy.2025.125376

Gregory Kaminski, Philip Odonkor

{"title":"Characterizing effective building clusters: An extensive study on diverse cluster composition, distributed energy resource utilization, and energy performance","authors":"Gregory Kaminski, Philip Odonkor","doi":"10.1016/j.apenergy.2025.125376","DOIUrl":"10.1016/j.apenergy.2025.125376","url":null,"abstract":"<div><div>Distributed energy resources (DERs) hold immense potential for urban sustainability and resilience. However, effectively integrating DERs across diverse building types remains a challenge. The concept of “building clusters,” groups of buildings with shared energy generation and storage resources, has emerged as a promising way to better utilize these DERs. Using validated synthetic building load data and multi-dimensional performance metrics, this study investigates the impact of building cluster composition on energy performance, advocating for a shift from conventional homogeneous cluster design. We analyze a vast dataset of over 15,000 unique five-building clusters, encompassing 16 distinct building types, each equipped with solar PV and storage. Using a multi-metric ranking system, we evaluate and rank cluster energy performance based on grid independence, energy cost reduction, and DER utilization efficiency. Our findings reveal that building type diversity significantly enhances cluster performance, outweighing traditionally emphasized factors such as total building area. High-performing clusters exhibit complementary load profiles across daily and seasonal timescales, enabling more efficient utilization of shared DER resources. Notably, strategic combinations of buildings with contrasting operational patterns (e.g., schools and office buildings) demonstrate superior year-round performance and grid independence. This study offers vital insights for urban planners and energy designers, highlighting the importance of prioritizing strategic diversity in building clusters over traditional homogeneity to maximize the potential of DERs. Its insights can provide an empirical foundation for zoning policies and incentives that promote mixed-use communities, which naturally support high-performing DER-integrated clusters. While focused on New York, the findings provide a foundation for broader applications in urban energy systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125376"},"PeriodicalIF":10.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adaptive spatio-temporal graph recurrent network for short-term electric vehicle charging demand prediction

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-27 DOI: 10.1016/j.apenergy.2025.125320

Shengyou Wang , Yuan Li , Chunfu Shao , Pinxi Wang , Aixi Wang , Chengxiang Zhuge

{"title":"An adaptive spatio-temporal graph recurrent network for short-term electric vehicle charging demand prediction","authors":"Shengyou Wang , Yuan Li , Chunfu Shao , Pinxi Wang , Aixi Wang , Chengxiang Zhuge","doi":"10.1016/j.apenergy.2025.125320","DOIUrl":"10.1016/j.apenergy.2025.125320","url":null,"abstract":"<div><div>Predicting Electric vehicle (EV) charging demand can facilitate the efficient operation and management of the smart power grid and intelligent transportation systems. We propose an adaptive spatial-temporal graph recurrent network (ASTGRN) to predict the EV charging demand in short term at the charging station level. Specifically, we design an adaptive graph learning layer that learns the spatial correlations in a data-driven manner. Additionally, an embedding project layer is integrated to enhance the graph learning layer. Subsequently, a graph recurrent layer consisting graph convolutional kernel and gated recurrent unit is employed to extract spatial-temporal features from the observations. We evaluate the proposed ASTGRN model using a real-world EV GPS trajectory dataset containing charging information of over 76,000 EVs in Beijing. The experiment results suggest that ASTGRN achieves state-of-the-art performance compared to those advanced spatial-temporal prediction models (e.g., Temporal Graph Convolutional Network and GraphWave Net). The effectiveness of the proposed model in charging demand prediction indicates that the spatial correlation between different charging stations may not be related to geographical distance in the charging demand prediction task, and the use of prior knowledge of geographical location may undermine model performance.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125320"},"PeriodicalIF":10.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced strain assistance for SOC estimation of lithium-ion batteries using FBG sensors

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-27 DOI: 10.1016/j.apenergy.2025.125385

Wenjuan Sheng , Junkai Wang , G.D. Peng

{"title":"Enhanced strain assistance for SOC estimation of lithium-ion batteries using FBG sensors","authors":"Wenjuan Sheng , Junkai Wang , G.D. Peng","doi":"10.1016/j.apenergy.2025.125385","DOIUrl":"10.1016/j.apenergy.2025.125385","url":null,"abstract":"<div><div>The accurate estimation of the state of charge (SOC) is essential to guarantee the safe and reliable operation of battery systems. Recently, more and more studies and applications have adopted optic fiber sensors to aid SOC estimation. However, it faces challenges such as limited performance and high costs. To address these challenges, this work proposed using a novel multi-position strain to enhance strain assistance for SOC estimation. Three fiber Bragg grating (FBG) sensors are arranged near the negative electrode, near the positive electrode, and in the middle of the battery, respectively. Strains at multiple positions are utilized as input features for the SOC estimation model, either individually, in dual combination, or triple combination. The impact of the number and placement of FBG sensors on SOC estimation is assessed. Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) were employed to evaluate the effectiveness of multi-position strain. Furthermore, an FBG demodulation system based on a tunable Fabry-Perot (F<img>P) filter was built to obtain strain information from wavelength signals. Compared to the commercially demodulation systems, the proposed demodulation system achieves a cost reduction of over 90 %. Experimental results verify that, compared to a traditional single strain, the dual strains significantly improve SOC estimation accuracy. In static tests, the root mean squared error (RMSE) and mean absolute error (MAE) are reduced by up to 73.66 % and 71.72 %, respectively. In dynamic tests, RMSE and MAE reductions reach up to 72.49 % and 74.01 %, respectively.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125385"},"PeriodicalIF":10.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing electricity price forecasting accuracy: A novel filtering strategy for improved out-of-sample predictions

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-26 DOI: 10.1016/j.apenergy.2025.125357

Andrea Cerasa, Alessandro Zani

{"title":"Enhancing electricity price forecasting accuracy: A novel filtering strategy for improved out-of-sample predictions","authors":"Andrea Cerasa, Alessandro Zani","doi":"10.1016/j.apenergy.2025.125357","DOIUrl":"10.1016/j.apenergy.2025.125357","url":null,"abstract":"<div><div>Reliable electricity price forecasts are key for energy sector strategy. The presence of market volatility and price spikes may negatively affect the accuracy of predictions if not properly addressed. In this study, we introduced a novel filtering strategy designed to enhance the accuracy of electricity price forecasting by effectively identifying and replacing extreme price spikes. Our approach is grounded in the application of robust statistical techniques within a rolling window framework, allowing for the systematic cleansing of input data used for forecasting models. We validated the efficiency and accuracy of our method using state-of-the-art statistical and deep learning models within an open-access dataset framework encompassing six different energy markets. The comparison of accuracy metrics and the outcome of statistical tests consistently demonstrated improvements in forecast accuracy when using our filtered data, with gains of up to 4% for certain models with respect to the predictions obtained with unfiltered inputs. Finally, the proposed filtering strategy exhibits reasonable and affordable computational requirements, making it suitable for practical applications in a real-world market setting.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125357"},"PeriodicalIF":10.1,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PyPSA-Earth sector-coupled: A global open-source multi-energy system model showcased for hydrogen applications in countries of the Global South

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-01-26 DOI: 10.1016/j.apenergy.2025.125316

Hazem Abdel-Khalek , Leon Schumm , Eddy Jalbout , Maximilian Parzen , Caspar Schauß , Davide Fioriti

{"title":"PyPSA-Earth sector-coupled: A global open-source multi-energy system model showcased for hydrogen applications in countries of the Global South","authors":"Hazem Abdel-Khalek , Leon Schumm , Eddy Jalbout , Maximilian Parzen , Caspar Schauß , Davide Fioriti","doi":"10.1016/j.apenergy.2025.125316","DOIUrl":"10.1016/j.apenergy.2025.125316","url":null,"abstract":"<div><div>This study presents sector-coupled PyPSA-Earth: a novel global open-source energy system optimization model that incorporates major demand sectors and energy carriers in high spatial and temporal resolution, to enable energy transition studies worldwide. The model includes a workflow that automatically downloads and processes the necessary demand, supply and transmission data to co-optimize investment and operation of energy systems of countries or regions of Earth. The workflow provides the user with tools to forecast future demand scenarios and allows for custom user-defined data in several aspects. Sector-coupled PyPSA-Earth introduces novelty by offering users a comprehensive methodology to generate readily available sector-coupled data and model of any region worldwide, starting from raw and open data sources. The model provides flexibility in terms of spatial and temporal detail, allowing the user to tailor it to their specific needs. The capabilities of the model are demonstrated through two showcases for Egypt and Brazil. The Egypt case quantifies the relevant role of PV, exceeding 35 GW, and electrolysis in Suez and Damietta regions, for meeting 16% of the EU hydrogen demand. Complementarily, the Brazil case confirms the model’s ability in handling hydrogen planning infrastructure, including repurposing of existing gas networks which results in 146 M€ lower costs than building new pipelines. The results prove the suitability of sector-coupled PyPSA-Earth to meet the needs of policymakers, developers, and scholars in advancing the energy transition. The authors invite the interested individuals and institutions to collaborate in the future developments of the model within PyPSA meets Earth initiative.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"383 ","pages":"Article 125316"},"PeriodicalIF":10.1,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143221615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0