Advances in Applied Energy最新文献

{"title":"A data-aided robust approach for bottleneck identification in power transmission grids for achieving transportation electrification ambition: a case study in New York state","authors":"Qianzhi Zhang ,&nbsp;Yuechen Sopia Liu ,&nbsp;H.Oliver Gao ,&nbsp;Fengqi You","doi":"10.1016/j.adapen.2024.100173","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100173","url":null,"abstract":"<div><p>As the enthusiasm for electric vehicles passes the range anxiety and other tests, large-scale transportation electrification becomes a prominent topic in research and policy discussions. In consequence, the public attention has shifted upstream and holistically towards the integration of large-scale transportation electrification to power systems. This paper proposes a method to identify bottlenecks in power transmission systems to accommodate large-scale and stochastic electric vehicles charging demands. First, a distributionally robust chance-constrained direct current optimal power flow model is developed to quantify the impacts of stochastic electric vehicles charging demands. Subsequently, an agent-based model with the trip-chain method is applied to obtain the spatiotemporal distributions of electric vehicles charging demands for both light-duty electric vehicles and medium and heavy-duty electric vehicles. The first two moments of those distributions are extracted to build an ambiguity set of electric vehicles charging demands. Finally, a 121-bus synthetic transmission network for New York State power systems is used to validate the future transportation electrification in New York State from 2025 to 2035. Results show that the large-scale transportation electrification in New York State will account for approximately 13.33 % to 16.79 % of the total load demand by 2035. The transmission capacity is the major bottleneck in supporting New York State to achieve its transportation electrification. To resolve the bottlenecks, we explore some possible solutions, such as transmission capacity expansion and distributed energy resources investment. Wind power shows an advantage over solar energy in terms of total operational costs due to better peak alignment between wind power and electric vehicles charging demand.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000118/pdfft?md5=f52488c0b3d8b48dd2976c65034b9e55&pid=1-s2.0-S2666792424000118-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140639244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT","authors":"Yuhao Nie ,&nbsp;Eric Zelikman ,&nbsp;Andea Scott ,&nbsp;Quentin Paletta ,&nbsp;Adam Brandt","doi":"10.1016/j.adapen.2024.100172","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100172","url":null,"abstract":"<div><p>The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud dynamics, hinders the transition to reliable renewable energy systems. Information on future sky conditions, especially cloud coverage, holds the promise for improving PV output forecasting. Leveraging recent advances in generative artificial intelligence (AI), we introduce <em>SkyGPT</em>, a physics-constrained stochastic video prediction model, which predicts plausible future images of the sky using historical sky images. We show that <em>SkyGPT</em> can accurately capture cloud dynamics, producing highly realistic and diverse future sky images. We further demonstrate its efficacy in 15-minute-ahead probabilistic PV output forecasting using real-world power generation data from a 30-kW rooftop PV system. By coupling <em>SkyGPT</em> with a U-Net-based PV power prediction model, we observe superior prediction reliability and sharpness compared with several benchmark methods. The propose approach achieves a continuous ranked probability score (CRPS) of 2.81 kW, outperforming a classic convolutional neural network (CNN) baseline by 13% and the smart persistence model by 23%. The findings of this research could aid efficient and resilient management of solar electricity generation, particularly as we transition to renewable-heavy grids. The study also provides valuable insights into stochastic cloud modeling for a broad research community, encompassing fields such as solar energy meteorology and atmospheric sciences.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100172"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000106/pdfft?md5=9fe829b2f1a0245854798ffc7c7f513a&pid=1-s2.0-S2666792424000106-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demand flexibility and cost-saving potentials via smart building energy management: Opportunities in residential space heating across the US","authors":"Shiyu Yang ,&nbsp;H. Oliver Gao ,&nbsp;Fengqi You","doi":"10.1016/j.adapen.2024.100171","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100171","url":null,"abstract":"<div><p>Leveraging demand-side flexibility resources (e.g., buildings) is a crucial and cost-effective strategy for addressing the operational and infrastructure-related challenges in power grids pursuing deep decarbonization with high renewable energy penetration. However, the demand flexibility opportunities and financial benefits for residential space heating, which are sizeable demand-side flexibility resources, through emerging building energy management solutions (i.e., smart control and phased change material (PCM) thermal storage) across the US are not fully understood. In this paper, we systematically assess the demand flexibility and cost-saving/revenue potentials in residential space heating through detailed building-level simulations for five consecutive years at a 5-min temporal resolution in 20 metro areas across the high-heating-demand regions of the US. The results show a high degree of synergy between PCM thermal storage and smart control, which enables substantial demand flexibility potential, reaching 98.5% of peak load shifting, and electricity cost-saving/revenue potential, reaching 338.3% of electricity cost reductions, for residential space heating in the US. By achieving such performance, adopting smart control and PCM thermal storage is financially viable in 50% of the tested metro areas. The results reveal that the demand flexibility and cost-saving/revenue potentials of residential space heating in the US are further enhanced by higher volatilities in electricity prices. Active PCM thermal storage has lower energy efficiency but much higher energy flexibility than passive PCM thermal storage. Based on the findings, recommendations for integrating PCM thermal storage and smart control systems within residential space heating are provided.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100171"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266679242400009X/pdfft?md5=105ccca94a62a76764cbdc21aaff3ff0&pid=1-s2.0-S266679242400009X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140069500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven energy management of virtual power plants: A review","authors":"Guangchun Ruan ,&nbsp;Dawei Qiu ,&nbsp;S. Sivaranjani ,&nbsp;Ahmed S.A. Awad ,&nbsp;Goran Strbac","doi":"10.1016/j.adapen.2024.100170","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100170","url":null,"abstract":"<div><p>A virtual power plant (VPP) refers to an active aggregator of heterogeneous distributed energy resources (DERs), which creates a promising pathway to expand renewable energy and demand-side electrification for deep decarbonization. The VPP market is projected to have a significant growth potential, with the global investment surging from $6.47 billion in 2022 to $16.90 billion by 2030. Up to now, VPPs still face technical challenges in dealing with the inherent uncertainty of DERs, and data emerge as a promising and essential resource to handle this issue. This paper makes the first effort to review the development of VPP technologies from a data-centric perspective, and then analyze the major role of data within every decision phase of VPPs. We examine the VPP energy management through a data lifecycle lens, and extensively survey the progress in data creation, data communication, data-driven decision support, data sharing and privacy, as well as technical solutions stemming from reinforcement learning, peer-to-peer sharing, blockchain, and market participation. In addition, we offer a unique overview of open data and recent real-world projects around the world to showcase the latest VPP practices. We finally discuss the major challenges and future opportunities in detail, with a focus on topics such as public benchmarks, internet of things, 5G, explainable artificial intelligence, and federated learning. We highlight the need for technical advances in data management and support systems for the growing scale of future VPP systems, and suggest VPPs delivering more ancillary grid services in the future.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100170"},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000088/pdfft?md5=f52f61ef82375f66628906042ebd8a79&pid=1-s2.0-S2666792424000088-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140067424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control","authors":"Wei Liang ,&nbsp;Han Li ,&nbsp;Sicheng Zhan ,&nbsp;Adrian Chong ,&nbsp;Tianzhen Hong","doi":"10.1016/j.adapen.2024.100167","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100167","url":null,"abstract":"<div><p>Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000052/pdfft?md5=8be83178cd724fc0a8c0ed963da3bef9&pid=1-s2.0-S2666792424000052-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139998858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current challenges in the utilization of hydrogen energy-a focused review on the issue of hydrogen-induced damage and embrittlement","authors":"Binhan Sun ,&nbsp;Huan Zhao ,&nbsp;Xizhen Dong ,&nbsp;Chaoyi Teng ,&nbsp;Aochen Zhang ,&nbsp;Shuai Kong ,&nbsp;Jingjing Zhou ,&nbsp;Xian-Cheng Zhang ,&nbsp;Shan-Tung Tu","doi":"10.1016/j.adapen.2024.100168","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100168","url":null,"abstract":"<div><p>The development of reliable and longevous infrastructures and structural components is the key for the implementation of a hydrogen economy that is currently enjoying unprecedented political and research momentum due to the globally strong demand for clean energy. This is, however, strongly impeded by the risk and concerns of hydrogen embrittlement (or hydrogen-induced degradation in mechanical properties) that generally exists in almost all metallic materials. Structural components and materials operated in the hydrogen production-transport-storage-usage chain can be subjected to a very wide range of temperature, environmental and loading scenarios, which will essentially trigger different hydrogen embrittlement responses and even different embrittling mechanisms. It is thus important to have a systematic assessment and discussion of hydrogen embrittlement behavior of different materials at different testing conditions, which is the focus of the presented review. Here we cover the typical materials (mainly metallic materials) that have been used or planned to be used in the fields of hydrogen energy. We first briefly summarize the current understanding of fundamental hydrogen embrittlement mechanisms in metallic materials and the research progress in recent years. Then we analyze and discuss the hydrogen -induced damage phenomenon in typical materials used in the field of high-pressure hydrogen transport and storage. In addition to room-temperature hydrogen embrittlement behavior, the hydrogen embrittlement phenomenon of some alloys at elevated and cryogenic temperatures is also reviewed, with the aim to provide some guidelines of material selection and design in developing fields such as hydrogen gas turbines and long-flight-duration hydrogen powered aircraft. Finally, the current challenges in the study of hydrogen embrittlement are identified and discussed to guide future research efforts.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000064/pdfft?md5=0e3b45de17bdeb4463f22c807f1691b0&pid=1-s2.0-S2666792424000064-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140160136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing building related energy burden, air pollution, and carbon emissions of a low-income community in Southern California","authors":"Robert Flores ,&nbsp;Sammy Houssainy ,&nbsp;Weixi Wang ,&nbsp;Khanh Nguyen Cu ,&nbsp;Xiao Nie ,&nbsp;Noah Woolfolk ,&nbsp;Ben Polly ,&nbsp;Ramin Faramarzi ,&nbsp;Jim Maclay ,&nbsp;Jaeho Lee ,&nbsp;Jack Brouwer","doi":"10.1016/j.adapen.2024.100169","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100169","url":null,"abstract":"<div><p>This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10–20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17–22 %. Appliance electrification also reduces carbon emissions 5–10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100169"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000076/pdfft?md5=f169256a041a844a2a1b895d58c9ed6a&pid=1-s2.0-S2666792424000076-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139993031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the feasibility of CO2 removal strategies in achieving climate-neutral power systems: Insights from biomass, CO2 capture, and direct air capture in Europe","authors":"Rebeka Béres ,&nbsp;Martin Junginger ,&nbsp;Machteld van den Broek","doi":"10.1016/j.adapen.2024.100166","DOIUrl":"10.1016/j.adapen.2024.100166","url":null,"abstract":"<div><p>To achieve the European Union's goal of climate neutrality by 2050, negative emissions may be required to compensate for emissions exceeding allocated carbon budgets. Therefore, carbon removal technologies such as bioenergy with carbon capture (BECCS) and direct air capture (DAC) may need to play a pivotal role in the power system. To design carbon removal strategies, more insights are needed into the impact of sustainable biomass availability and the feasibility of carbon capture and storage (CCS), including the expensive and energy-intensive DAC on achieving net-zero and net-negative targets. Therefore, in this study the European power system in 2050 is modelled at an hourly resolution in the cost-minimization PLEXOS modelling platform. Three climate-neutral scenarios with targets of 0, -1, and -3.9 Mt CO₂/year (which agree with varying levels of climate justice) are assessed for different biomass levels, and CCS availability. Findings under baseline assumptions reveal that in a climate-neutral power system with biomass and CCS options, it is cost-effective to complement variable renewable energy with a mix of combined cycle natural gas turbines (CCNGT) for flexibility and BECCS as base load to compensate for the CO₂ emissions from natural gas and additional carbon removal in the net-negative scenarios. The role of these technologies becomes more prominent, with -3.9 GtCO₂/year target. Limited biomass availability necessitates additional 0.4–4 GtCO₂/year DAC, 10–50 GW CCNGT with CCS, and 10–50 GW nuclear. Excluding biomass doubles system costs and increases reliance on nuclear energy up to 300 TWh/year. The absence of CCS increases costs by 78%, emphasizing significant investments in bioenergy, nuclear power, hydrogen storage, and biogas. Sensitivity analysis and limitations of the study are fully discussed.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000040/pdfft?md5=72bf980ea63b5e718607059bb7fb346f&pid=1-s2.0-S2666792424000040-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139966492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network","authors":"Han Guo,&nbsp;Bin Huang,&nbsp;Jianhui Wang","doi":"10.1016/j.adapen.2024.100165","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100165","url":null,"abstract":"<div><p>The burgeoning proliferation of integrated energy systems has fostered an unprecedented degree of coupling among various energy streams, thereby elevating the necessity for unified multi-energy forecasting (MEF). Prior approaches predominantly relied on independent predictions for heterogeneous load demands, overlooking the synergy embedded within the dataset. The two principal challenges in MEF are extracting the intricate coupling correlations among diverse loads and accurately capturing the inherent uncertainties associated with each type of load. This study proposes an attentive quantile regression temporal convolutional network (QTCN) as a probabilistic framework for MEF, featuring an end-to-end predictor for the probabilistic intervals of electrical, thermal, and cooling loads. This study leverages an attention layer to extract correlations between diverse loads. Subsequently, a QTCN is implemented to retain the temporal characteristics of load data and gauge the uncertainties and temporal correlations of each load type. The multi-task learning framework is deployed to facilitate simultaneous regression of various quantiles, thereby expediting the training progression of the forecasting model. The proposed model is validated using realistic load data and meteorological data from the Arizona State University metabolic system and National Oceanic and Atmospheric Administration respectively, and the results indicate superior performance and greater economic benefits compared to the baselines in existing literature.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000039/pdfft?md5=b54e77f93d7199836be85cd37c0a9d2f&pid=1-s2.0-S2666792424000039-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geographically constrained resource potential of integrating floating photovoltaics in global existing offshore wind farms","authors":"Yubin Jin ,&nbsp;Zhenzhong Zeng ,&nbsp;Yuntian Chen ,&nbsp;Rongrong Xu ,&nbsp;Alan D. Ziegler ,&nbsp;Wenchuang Chen ,&nbsp;Bin Ye ,&nbsp;Dongxiao Zhang","doi":"10.1016/j.adapen.2024.100163","DOIUrl":"10.1016/j.adapen.2024.100163","url":null,"abstract":"<div><p>Marine renewable energy is gaining prominence as a crucial component of the energy supply in coastal cities due to proximity and minimal land requirements. The synergistic potential of integrating floating photovoltaics with offshore wind turbines presents an encouraging avenue for boosting power production, amplifying spatial energy generation density, and mitigating seasonal output fluctuations. While the global promise of offshore wind-photovoltaic hybrid systems is evident, a definitive understanding of their potential remains elusive. Here, we evaluate the resource potential of the hybrid systems under geographical constraints, offering insights into sustainable and efficient offshore energy solutions. We compile a database with 11,198 offshore wind turbine locations from Sentinel-1 imagery and technical parameters from commercial project details. Our analysis reveals an underutilization of spatial resources within existing offshore wind farms, yielding a modest 26 kWh per square meter. Furthermore, employing realistic climate-driven system simulations, we find an impressive potential photovoltaic generation of 1372 ± 18 TWh annually, over seven times higher than the current offshore wind capacity. Notably, floating photovoltaics demonstrated remarkable efficiency, matching wind turbine output with a mere 17 % of the wind farm area and achieving an average 76 % increase in power generation at equivalent investment costs. Additionally, the hybrid wind and photovoltaic systems exhibit monthly-scale complementarity, reflected by a Pearson correlation coefficient of -0.78, providing a consistent and reliable power supply. These findings support the notion that hybrid offshore renewable energy could revolutionize the renewable energy industry, optimize energy structures, and contribute to a sustainable future for coastal cities.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"13 ","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000015/pdfft?md5=76e91364e8313daf52e2fc98c7dba1dd&pid=1-s2.0-S2666792424000015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}