Applied Energy最新文献

{"title":"Collaborative optimization framework for capacity planning of a prosumer-based peer-to-peer electricity trading community","authors":"Richard A. Mahuze,&nbsp;Ali Amadeh,&nbsp;Bo Yuan,&nbsp;K. Max Zhang","doi":"10.1016/j.apenergy.2025.125289","DOIUrl":"10.1016/j.apenergy.2025.125289","url":null,"abstract":"<div><div>Peer-to-peer (P2P) electricity trading offers significant potential benefits for community microgrids, including reduced costs, enhanced demand flexibility, and increased self-sufficiency. However, optimizing the capacities (i.e., on-site renewable generation and battery storage) in a P2P trading environment is complex. This complexity is due to the dual need to achieve broad system objectives while catering to individual households' unique energy needs and preferences. To tackle this, our study proposes an innovative bi-level optimization framework that harmonizes these diverse needs through a collaborative approach that accounts for demand flexibility and network topology. At the heart of our method is a “collaborative optimization” process. This entails a synergistic planning strategy where system-wide objectives and individual household preferences are jointly considered to determine the optimal infrastructure setup. The upper level of the framework employs Borg Multiobjective Evolutionary Algorithm (MOEA) to reconcile various system-wide objectives, such as maximizing renewable energy use, minimizing costs, and ensuring grid stability. The lower-level optimization uses Mixed Integer Nonlinear Programming (MINLP) with a dynamic pricing mechanism based on Supply-Demand Ratio (SDR). Our results show that collaborative optimization significantly outperforms individual household optimization strategies (where households plan capacity independently before adopting P2P trading). In our case study, we observed a 24.6 % reduction in bi-monthly peak load averages with collaborative optimization, compared to a modest 10.2 % reduction under individual planning scenarios. This not only signifies enhanced efficiency but also translates into significant economic benefits. For instance, the investment payback period for solar photovoltaic (PV) panels and battery storage is drastically reduced—from an average of 5.1 years to just 1.8 years. Our findings highlight the significant economic and grid-stabilizing advantages gained through collaborative planning of P2P trading.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125289"},"PeriodicalIF":10.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143332386","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}

{"title":"Planning a robust echelon utilization network for used electric vehicle batteries based on two decision-making criteria","authors":"Qi Wang ,&nbsp;Yankui Liu","doi":"10.1016/j.apenergy.2025.125453","DOIUrl":"10.1016/j.apenergy.2025.125453","url":null,"abstract":"<div><div>The echelon utilization of electric vehicle batteries offers opportunities to mitigate pollution from used batteries and decrease costs in energy storage and low-speed electric vehicles. Based on two decision-making criteria, this study addresses the echelon utilization network planning problem about used batteries. Our problem plans the locations and battery transportation to meet diverse quality requirements in the secondary market. First, we develop a risk-neutral adaptive distributionally robust optimization (ADRO) model under uncertainty in secondary market demand and quantity of high-quality batteries. The proposed model is reformulated as a mixed-integer second-order conic programming (SOCP) model and solved by accelerated Benders decomposition (BD). Second, we propose a risk-averse ADRO model based on the mean-conditional value-at-risk (CVaR). Subsequently, we devise a tailored BD algorithm to solve a pair of subproblems in each iteration. The results of our numerical experiments demonstrate the following: (i) The application of echelon utilization can reduce the operational costs of the battery remanufacturing network by 3.7%. (ii) Our ADRO model exhibits better out-of-sample performance compared with the sample average approximation (SAA) model, which achieves the balance between economy and robustness for echelon utilization network planning. (iii) The accelerated BD algorithm significantly outperforms the classical BD method. (iv) The sensitivity analysis on key model parameters reveals trade-offs among optimality and recovery quantity, ambiguity set size, and risk preference to the informed energy managers. These results suggest that ADRO echelon utilization network planning models can be applied to energy management problems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125453"},"PeriodicalIF":10.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143332054","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}

{"title":"A hierarchical multi-objective co-optimization framework for sizing and energy management of coupled hydrogen-electricity energy storage systems at ports","authors":"Pingxu Ge ,&nbsp;Daogui Tang ,&nbsp;Yuji Yuan ,&nbsp;Josep M. Guerrero ,&nbsp;Enrico Zio","doi":"10.1016/j.apenergy.2025.125451","DOIUrl":"10.1016/j.apenergy.2025.125451","url":null,"abstract":"<div><div>Hydrogen-electricity integrated multi-energy systems are promising approaches to reduce carbon emissions in ports. However, the stochastic nature of renewable energy and the imbalance between the renewable generation and load demand in ports necessitate the design of an appropriate coupled hydrogen-electricity energy storage systems (CHEESS). This paper proposes a multi-objective optimization model for CHEESS configuration in random imbalanced port integrated multi-energy systems (PIMES), aiming to minimize its life-cycle cost and carbon emissions through co-optimization of sizing and energy management. A hierarchical two-stage framework is proposed to solve the multi-objective model. The proposed optimization framework is applied to a real PIMES at the Ningbo-Zhoushan Port. The results show that the proposed method can save 10.54 % of the monetary cost and 19.67 % of carbon emissions over the entire life-cycle of the system. The study demonstrates that the proposed framework has the potential to generate significant economic and environmental benefits and provides a feasible solution for port authorities seeking to implement CHEESS, aiming to promote sustainability in port operations.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125451"},"PeriodicalIF":10.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143332051","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}

{"title":"Assessing the effects of photovoltaic and solar thermal ratios on performance, cost, and emissions in combined solar configurations","authors":"Arash Kazemian ,&nbsp;Hongxing Yang ,&nbsp;Changying Xiang","doi":"10.1016/j.apenergy.2025.125438","DOIUrl":"10.1016/j.apenergy.2025.125438","url":null,"abstract":"<div><div>This study introduces a Photovoltaic Thermal with Solar Thermal Enhancer (PVT-STE) system, designed to outperform traditional Photovoltaic Thermal (PVT) systems. By integrating a solar thermal enhancer, the PVT-STE system improves both thermal and electrical efficiencies through a unique sequential heat transfer mechanism. The system architecture has the heat transfer fluid first passing through the PVT module, then into an enhanced solar thermal (ST) module, achieving higher fluid temperatures suitable for a range of applications, from residential to industrial. The system configurations range from fully solar thermal to entirely photovoltaic-thermal, including mixed setups, allowing for detailed performance evaluation across various scenarios. Using advanced ANSYS software, the system's performance was rigorously simulated under Shanghai's seasonal variations, focusing on metrics like electrical and thermal outputs, system cost, surface temperature distribution, Levelized Cost of Energy (LCOE), payback periods, and carbon emission reduction potentials. A key finding shows a trade-off between thermal and electrical efficiencies: more photovoltaic elements typically decrease thermal output. Economic evaluations show that systems with a strong solar thermal component achieve the lowest LCOE, at $0.016/kWh, and offer the shortest payback periods and substantial carbon emission reductions. These systems can reach payback periods as short as 1.35 years and generate significant annual savings. Conversely, systems with more photovoltaic components, while having longer payback periods and lesser environmental benefits, can produce considerable electricity. This adaptability emphasizes the PVT-STE system's capability to provide customized energy solutions, optimizing based on specific needs for electrical or thermal output in various operational contexts.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125438"},"PeriodicalIF":10.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143331878","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}

{"title":"Predict-then-optimise based day-ahead scheduling towards demand response and hybrid renewable generation for wastewater treatment","authors":"Chuandang Zhao ,&nbsp;Jiancheng Tu ,&nbsp;Xiaoxuan Zhang ,&nbsp;Jiuping Xu ,&nbsp;Poul Alberg Østergaard","doi":"10.1016/j.apenergy.2025.125434","DOIUrl":"10.1016/j.apenergy.2025.125434","url":null,"abstract":"<div><div>Promoting a 100% renewable energy system requires intelligent scheduling strategies, yet the challenge remains on the prediction and optimisation of variable renewable energy supply and demand. This study proposes a Predict-then-optimise paradigm to explore day-ahead scheduling strategies for high renewable energy systems and demonstrates its application in a grid-connected biogas–solar–wind-storage system with load shifting for wastewater treatment plants. The scheduling strategy aims to maximise energy prosumption and minimise operation costs. Demand response is enabled by the wastewater pre-treatment reservoir, battery storage, and biogas storage, all mathematically modelled in this study. The Temporal Convolutional Network-based Transformer model is applied to forecast uncertain variable renewable energy generation and wastewater flow for the upcoming day. Then budget uncertainty sets are constructed based on forecast errors for robust optimisation. A case from Sichuan, China is analysed to explore the practicality and effectiveness of the proposed framework. The results indicate that the robustness of the model increases the day-head scheduling operational cost and decreases the self-sufficiency ratio. Wastewater pre-treatment reservoir scheduling can effectively shift the demand load, promoting cost reduction and system prosumption; besides, pre-treatment reservoir, battery storage and biogas storage have substitution and combination effects on demand response, can reduce daily operating costs by 20%–50%. The influence of a defined allowable sale ratio, seasons, and weather conditions are also discussed. Overall, the proposed predict-then-optimise framework is an effective solution for the upcoming day’s decision-making.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125434"},"PeriodicalIF":10.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143331871","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}

{"title":"Techno-economic and environmental optimization of agrivoltaics: A case study of Cornell University","authors":"Tikumporn Kumdokrub ,&nbsp;Fengqi You","doi":"10.1016/j.apenergy.2025.125436","DOIUrl":"10.1016/j.apenergy.2025.125436","url":null,"abstract":"<div><div>Agrivoltaic (AgV) co-locates crops and solar panels to mitigate land-use conflicts from rising production demands. This study advances AgV research by optimizing system efficiency through specific objective ratios for cost and environmental impact. Four models are developed to assess optimal AgV site design and benefits: two mixed-integer nonlinear programs (MINLP) that independently target economic gains and operational emission reductions, and two fractional programming (FP) models that evaluate economic and environmental benefits per water consumed, with varying minimum cropland constraints. Applied to agricultural and solar sites at Cornell University, Ithaca, New York, across seven crop types, optimal results from the MINLP models indicate that land should prioritize solar panels due to their superior economic and environmental benefits, with cabbage being the only exception due to its high crop value. In the FP models, prioritizing solar installation minimizes irrigation requirements, but economic benefits increase as more land is allocated to crops; a 90 % cropland allocation yields the highest revenues, ranging from 10.78 % to 186.77 % (US$5.86–34.88/m³) and achieving a land equivalent ratio of 4.40. The FP environmental model suggests limiting cropland to below 60 % for optimal emission reductions, reducing emissions to 54.01–112.18 metric tons of CO₂eq/m³, which is lower than emissions from conventional separate crop and solar systems. The FP models balance economic and environmental benefits per irrigation unit, demonstrating AgV's water-use efficiency, and linking the complex relationship between inputs and outputs. Ultimately, site design choices should align with decision-makers' goals, whether prioritizing economic, environmental, or balanced system efficiencies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125436"},"PeriodicalIF":10.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143331877","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}

{"title":"The riddle of the sands: C02 emissions reduction and California's renewables portfolio","authors":"G. Cornelis van Kooten","doi":"10.1016/j.apenergy.2025.125475","DOIUrl":"10.1016/j.apenergy.2025.125475","url":null,"abstract":"<div><div>Development of nuclear energy in northern Alberta has been proposed as a means of reducing the environmental costs of oilsands extraction; rather than open-pit mining, steam from nuclear power plants would be used for in situ extraction of petroleum. Such development could be facilitated by the export of electricity to California, thereby facilitating achievement of the State's legislative target that 60 % of electricity come from renewable sources by 2030 and 100 % by 2045. Using a policy-oriented, grid allocation model and projections of future power requirements in California, this study determines whether there is indeed potential for Alberta to export carbon-free electricity to California to the benefit of both jurisdictions. We find that doing so could reduce California's CO₂ emissions in the electricity sector by some 70 to 85 %. However, if California decided to rely more on in-house generation of nuclear power, the market available to Alberta would be constrained by the extent to which the State exploits nuclear capacity. It is also constrained by the extent to which the load profile can be altered and the ability to exploit wind and solar regimes that differ from those currently used to generate power.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125475"},"PeriodicalIF":10.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143331879","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}

{"title":"Privacy-protected P2P electricity and carbon emission trading markets based on distributionally robust proximal atomic coordination algorithm","authors":"Chengwei Lou ,&nbsp;Zekai Jin ,&nbsp;Yue Zhou ,&nbsp;Wei Tang ,&nbsp;Lu Zhang ,&nbsp;Jin Yang","doi":"10.1016/j.apenergy.2025.125409","DOIUrl":"10.1016/j.apenergy.2025.125409","url":null,"abstract":"<div><div>As global power systems modernize towards intelligent infrastructures, peer-to-peer (P2P) energy trading is increasingly adopted worldwide as an innovative electricity market mechanism. This paper explores the decision-making behaviors of diverse agents, market mechanisms, and privacy protections in fully decentralized P2P electricity and carbon emission trading (CET), accounting for uncertainties from renewable energy sources. A novel P2P energy trading mechanism is proposed based on asymmetric Nash bargaining theory. The P2P electricity and carbon market models are decomposed into a cooperative alliance operation problem and an asymmetric cost distribution problem. Additionally, a contribution factor calculation method is introduced, considering both P2P electricity trading and CET marginal effect contribution. To manage renewable energy output uncertainties, a distributionally robust model using Kullback–Leibler (KL) divergence is reformulated as a chance-constrained problem. A proximal atomic coordination (PAC) algorithm is implemented to enhance privacy protection within a fully decentralized framework. Case studies demonstrate that P2P energy trading can reduce total costs by 10.29% and carbon quotas by 11.86% for cooperative alliances. Furthermore, the PAC algorithm decreases total computational time by 12.65% compared to the ADMM algorithm, highlighting its effectiveness in improving computational efficiency and safeguarding user privacy.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125409"},"PeriodicalIF":10.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143332052","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}

{"title":"Near real-time machine learning framework in distribution networks with low-carbon technologies using smart meter data","authors":"Emrah Dokur ,&nbsp;Nuh Erdogan ,&nbsp;Ibrahim Sengor ,&nbsp;Ugur Yuzgec ,&nbsp;Barry P. Hayes","doi":"10.1016/j.apenergy.2025.125433","DOIUrl":"10.1016/j.apenergy.2025.125433","url":null,"abstract":"<div><div>The widespread adoption of low-carbon technologies, such as photovoltaics, electric vehicles, heat pumps, and energy storage units introduces challenges to distribution network congestion and power quality, particularly raising concerns about voltage stability. Enhanced voltage visibility in low-voltage networks is increasingly vital for active grid management, making efficient voltage forecasting tools essential. This study introduces a novel data-driven approach for forecasting node voltages in low-voltage networks with high penetration of low-carbon technologies. Using time series of power measurements from smart meter data, the study integrates an Extreme Learning Machine with the Single Candidate Optimizer to enhance computational efficiency and forecasting accuracy. The model is validated using smart meter datasets from two different low-voltage networks with low-carbon technologies and is compared with several established machine learning models. The results demonstrate that the optimization algorithm significantly improves the tuning of model parameters, achieving up to a 17-fold reduction in computation time compared to the fastest metaheuristic methods implemented. The proposed model demonstrated superior accuracy, with an average voltage deviation of 0.56%. Although the computation time per node achieved is not yet suitable for real time applications, the study shows that the optimization method significantly improves the performance of the forecasting tool.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125433"},"PeriodicalIF":10.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143331860","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}

{"title":"Harnessing direct seawater electrolysis for a sustainable offshore Hydrogen future: A critical review and perspective","authors":"Faiza Meharban ,&nbsp;Xiangmin Tang ,&nbsp;Shuang Yang ,&nbsp;Xiaotong Wu ,&nbsp;Chao Lin ,&nbsp;Lei Tan ,&nbsp;Weibo Hu ,&nbsp;Dequan Zhou ,&nbsp;Jianming Li ,&nbsp;Xiaopeng Li","doi":"10.1016/j.apenergy.2025.125468","DOIUrl":"10.1016/j.apenergy.2025.125468","url":null,"abstract":"<div><div>The global drive for green hydrogen is pushing the boundaries of electrolyzer technology, aiming to operate efficiently in dynamic environments using impure water sources. Offshore hydrogen production presents a compelling opportunity by leveraging vast marine resources and reducing land conflicts. This paper explores the potential of offshore hydrogen production coupled with offshore renewable energy resources (wind and wave energy) utilizing direct seawater electrolysis (DSWE), highlighting their ability to reduce infrastructure complexity, lower energy consumption, and adapt to space constraints inherent to offshore environments. Current offshore projects, such as offshore wind and wave power generation, underscore the increasing relevance of offshore hydrogen production. Projects like Sealhyfe (France) and PosHYdon (Netherlands) are pioneering the integration of offshore wind with offshore green hydrogen production, demonstrating the feasibility of large-scale offshore operations. Despite existing challenges, such as unavailability of infrastructure for hydrogen transport to onshore consumer, electrolyzer compatibility with direct seawater and need for robust desalination techniques. Advancements in hybrid electrolyzer designs and novel electrocatalyst resistant to corrosion, can pave the way for efficient and cost-effective offshore hydrogen production. This paper discusses the state-of-the-art developments in DSWE technology and the strategic role it can play in sustainable offshore hydrogen generation, emphasizing the need for continued research and collaboration to overcome technical hurdles and accelerate commercialization.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125468"},"PeriodicalIF":10.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143331861","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}