Applied Energy最新文献

{"title":"Optimization of sail-hybrid electric power system for ships considering correlated environmental uncertainties","authors":"Jianyun Zhu ,&nbsp;Li Chen ,&nbsp;Rui Miao","doi":"10.1016/j.apenergy.2025.125862","DOIUrl":"10.1016/j.apenergy.2025.125862","url":null,"abstract":"<div><div>The sail-hybrid electric power system (sail-HEPS) has gained significant attention in the maritime industry as an eco-friendly solution to reduce greenhouse gas (GHG) emissions. The key to successful implementation of sail-HEPS lies in the integrated optimal sizing to effectively leverage the advantages of multiple energy sources. Considering sail-HEPS is constantly influenced by multiple uncertain and correlated factors in the environment, existing deterministic optimization methods based on single scenario are inadequate to ensure optimal performance of the system throughout its lifecycle. To address this issue, this study proposes a probabilistic optimization method that integrates multiple energy sources and considers correlated uncertainties. A vine copula method is employed to model the interdependencies among wave direction, significant wave height, wave period, wind direction, and wind speed. The design space exploration and multiple criteria decision making are performed with multi-objective particle swarm optimization (MOPSO) algorithm and the technique for order preference by similarity to an ideal solution (TOPSIS). A case study of a 20-m yacht in the South China Sea validates the proposed method, demonstrating its superiority over deterministic optimization and quasi-probabilistic optimization, which disregards the correlation among environmental variables. Furthermore, it is observed that there is no significant difference in the performance of the Pareto designs obtained from deterministic optimization and quasi-probabilistic optimization when correlated uncertainties are introduced, highlighting the importance of considering the correlation of the uncertainties.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125862"},"PeriodicalIF":10.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828080","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":"Decision-making and cost models of generation company agents for supporting future electricity market mechanism design based on agent-based simulation","authors":"Zhanhua Pan,&nbsp;Zhaoxia Jing","doi":"10.1016/j.apenergy.2025.125881","DOIUrl":"10.1016/j.apenergy.2025.125881","url":null,"abstract":"<div><div>The large-scale surge in renewable energy installations has transformed the capacity mix of power systems and the roles of generation companies (GENCOs). For example, some thermal generators are now operated at low output levels to ensure the generation capacity and ramping capability of the power system. As a result, the nonlinear characteristics of GENCOs’ marginal generation costs have gradually become prominent, rendering some previously linear assumption-based models obsolete. It is essential to reexamine the decision-making and cost models of GENCOs to support the equilibrium solution and mechanism design of the electricity market during this transition. This paper analyzes the impact of different cost model assumptions on GENCOs, thereby examining the relationship between GENCOs’ bidding models and cost models. We propose standardized expressions for GENCOs’ linear bidding models and piecewise step bidding models in multi-agent simulations of the electricity market. The applicability of different bidding models is analyzed. To address the issue of overly compressed decision space for GENCOs in previous studies, we propose a Multi-worker decision model based on (deep) reinforcement learning. This allows the decision space of GENCOs’ piecewise step bidding to fully cover the bidding space in actual market rules. Finally, various electricity market experiments based on multi-agent simulations are conducted. On the one hand, our proposed GENCOs decision model more effectively reproduces GENCOs’ behavior in actual electricity markets. On the other hand, using real mechanism design as an example, previous GENCOs models may lead to incorrect conclusions in simulations. The decision model proposed in this paper, employing piecewise step bidding and polynomial cost functions, makes the simulation results more consistent with actual rules, thereby effectively supporting future-proof market design.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825380","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":"Effects of novel nonlinear flow channels inspired by classical mathematical function on the output performance and low-grade heat recovery efficiency of thermally regenerative ammonia-based flow battery","authors":"Jiebo Yang ,&nbsp;Qinghua Yu ,&nbsp;Yu Lei ,&nbsp;Sheng Chen ,&nbsp;Yang Yu ,&nbsp;Fuwu Yan","doi":"10.1016/j.apenergy.2025.125917","DOIUrl":"10.1016/j.apenergy.2025.125917","url":null,"abstract":"<div><div>In addressing the challenges of enhancing the output performance and low-grade waste heat recovery efficiency of thermally regenerative ammonia-based flow battery (TRAFB), this study introduces four novel nonlinear flow channels: the Hyperbolic tangent function flow channel (HTF-FC), Elliptic function flow channel (EF-FC) Quadratic function flow channel (QF-FC), and Exponential function flow channel (ExF-FC). These flow channel designs are inspired by classical mathematical function curves, enabling more targeted mass transfer enhancement based on species distribution. Multiple quantitative metrics are employed to evaluate the effects of these nonlinear structures on cross-scale mass transfer, reactant distribution, power output, and thermoelectric conversion efficiency under both Forward Flow mode (FF mode) and Reverse Flow mode (RF mode). The findings reveal that optimizing mass transfer at the electrode interface of the channel end is more critical for enhancing performance in TRAFB. The overall performance of the nonlinear flow channels in FF mode is superior to that in RF mode, yet both outperform the conventional straight channel (S-FC), with the ExF-FC showing the best performance and the HTF-FC the least. The ExF-FC exhibits the highest overall mass transfer efficiency and uniformity of active species in the electrode region, and its nonlinear contraction zone at the channel tail induces a significant acceleration effect, increasing the Cu²⁺ flux by ∼39.13 times in the reactant-starved region. When the inlet flow rate is 1 mL/min, the HTF-FC, QF-FC, EF-FC, and ExF-FC can enhance the peak power density by up to ∼1.30 %, ∼11.52 %, ∼54.67 %, and ∼ 80.65 %, respectively, compared to the S-FC, and when the inlet flow rate is increased to 3.8 mL/min, these enhancements reach ∼1.62 %, ∼42.60 %, ∼101.38 %, and ∼ 142.84 %, respectively. Moreover, the nonlinear channels significantly improve the energy storage capacity and waste heat recovery performance of TRAFB, particularly at high current densities. When the current density is 350 A/m², at an inlet flow rate of 1 mL/min, the ExF-FC can enhance the electrical capacity and thermoelectric conversion efficiency by ∼2.81 times and ∼ 5.41 times, respectively, compared to the S-FC, and at an inlet flow rate of 3.8 mL/min, these increases are ∼1.11 times and ∼ 2.98 times, respectively.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125917"},"PeriodicalIF":10.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825399","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":"Development of a physics-informed coarse-mesh method and applications to the thermohydraulic analysis of rod bundles with mixing vane spacers","authors":"Guanqun Ding ,&nbsp;Yao Xiao ,&nbsp;Hanyang Gu","doi":"10.1016/j.apenergy.2025.125847","DOIUrl":"10.1016/j.apenergy.2025.125847","url":null,"abstract":"<div><div>The efficient and accurate analysis of fluid flow and heat transfer in large-scale complex tube bundle structures is of great significance for the optimal design of energy systems. To address this challenge, this paper proposes a physics-informed coarse-mesh method (PICM). In a representative application involving thermohydraulic analysis of reactor fuel rod bundles with mixing-vane spacers, this method reduces the computational time by approximately three orders of magnitude while maintaining accuracy. In detail, the PICM method avoids the explicit modeling of detailed structures such as spacers and employs coarse meshes to capture the main geometric features of tube bundles. The wall source terms are corrected by empirical correlations based on lumped parameters. The spacer-induced pressure loss and wall heat transfer enhancement are implicitly simulated by additional models. The virtual momentum source terms are adopted to reproduce the flow-sweeping effects. Experimental validation confirms that the PICM method exhibits excellent geometric adaptability, enabling efficient and accurate simulations for various tube bundle structures. This research offers an efficient numerical analysis method for large-scale complex tube bundles in energy systems and provides a new direction for the refinement development of reactor subchannel analysis.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825011","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":"Strategic bidding with price-quantity pairs based on deep reinforcement learning considering competitors' behaviors","authors":"Fei Hu ,&nbsp;Yong Zhao ,&nbsp;Yaowen Yu ,&nbsp;Changshun Zhang ,&nbsp;Yicheng Lian ,&nbsp;Cheng Huang ,&nbsp;Yuanzheng Li","doi":"10.1016/j.apenergy.2025.125874","DOIUrl":"10.1016/j.apenergy.2025.125874","url":null,"abstract":"<div><div>In a smart electricity market, self-interested market participants may leverage a large amount of market data to bid strategically to maximize their profits. However, the existing studies in strategic bidding often ignore competitors' bidding behaviors and only consider strategic actions on prices without quantities. To bridge the gap, this paper develops a novel deep reinforcement learning-based framework to model and solve the strategic bidding problem of a producer. To capture competitors' historical bidding behaviors in the market environment, their demand-bid mappings are established based on a data-driven method combining K-medoids clustering and a deep neural network. To make full use of the bidding action space and increase the profit of the strategic producer, a bilevel optimization model considering bids in price-quantity pairs is formulated. To efficiently solve the problem with competitors' bidding behaviors, a twin delayed deep deterministic policy gradient-based algorithm is developed. Case studies on the IEEE 57-bus system show that the proposed framework obtains a 27.37 % higher expected value and a 47.60 % lower standard deviation of the profit compared to the existing approach, demonstrating its profitability and robustness under market dynamics. Another case on the IEEE 118-bus test system achieves a 33.34 % increase in the expected profit, further validating the advantages in profitability. These cases together demonstrate the effectiveness and scalability of our approach in systems of different sizes, as well as its potential application to strategic bidding in smart electricity markets.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825012","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":"Energy-efficient greenhouse climate control using Gaussian process-based stochastic model predictive control","authors":"Jinsung Kim ,&nbsp;Fengqi You","doi":"10.1016/j.apenergy.2025.125841","DOIUrl":"10.1016/j.apenergy.2025.125841","url":null,"abstract":"<div><div>This paper proposes a Gaussian process-based stochastic model predictive control (GP-SMPC) framework for energy-efficient greenhouse climate control. In greenhouse systems, uncertainties arise from variations in crop growth rates and fluctuations in outdoor weather conditions, leading to suboptimal energy usage and increased operational costs. By incorporating a Gaussian process regression (GPR) model, the framework probabilistically captures uncertainties arising from crop growth variations and fluctuating outdoor weather conditions, enhancing robustness and efficiency. An online learning algorithm further improves the generalizability of the GPR model by capturing real-time observations, preventing overfitting problems. Numerical experiments using real-world greenhouse data demonstrate the significant energy-saving potential of the proposed framework. Compared to nonlinear MPC, the GP-SMPC framework achieves tracking error reductions of up to 67 % during the winter and 48 % in spring. Moreover, it reduces energy and CO₂ costs by up to 51.4 % during the winter season and 40 % during the spring season, minimizing resource wastage and operational inefficiencies. By optimizing resource usage while maintaining optimal growing conditions, the GP-SMPC framework provides a robust and sustainable solution for greenhouse climate control. This enhances the economic viability of high-tech food production systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125841"},"PeriodicalIF":10.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829076","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":"Mixed strategy Nash equilibrium analysis in real-time pricing and demand response for future smart retail market","authors":"Ze Hu ,&nbsp;Ziqing Zhu ,&nbsp;Xiang Wei ,&nbsp;Ka Wing Chan ,&nbsp;Siqi Bu","doi":"10.1016/j.apenergy.2025.125815","DOIUrl":"10.1016/j.apenergy.2025.125815","url":null,"abstract":"<div><div>Real-time pricing and demand response (RTP-DR) is a key problem for profit-maximizing and policy-making in the deregulated retail electricity market (REM). However, previous studies overlooked the non-convexity and multi-equilibria caused by the network constraints and the temporally-related non-linear power consumption characteristics of end-users (EUs) in a privacy-protected environment. This paper employs mixed strategy Nash equilibrium (MSNE) to analyze the multiple equilibria in the non-convex game of the RTP-DR problem, providing a comprehensive view of the potential transaction results. A novel multi-agent Q-learning algorithm is developed to estimate subgame perfect equilibrium (SPE) in the proposed game. As a multi-agent reinforcement learning (MARL) algorithm, it enables players in the game to be rational “agents” that learn from “trial and error” to make optimal decisions across time periods. Moreover, the proposed algorithm has a bi-level structure and adopts probability distributions to denote Q-values, representing the belief in environmental response. Through validation on a Northern Illinois utility dataset, our proposed approach demonstrates notable advantages over benchmark algorithms. Specifically, it provides more profitable pricing decisions for monopoly retailers in REM, leading to strategic outcomes for EUs. The numerical results also find that multiple optimal pricing decisions over a day exist simultaneously by providing almost identical profits to the retailer, while leading to different energy consumption patterns and also significant differences in total energy usage on the demand side.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125815"},"PeriodicalIF":10.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829077","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":"Feasibility assessment of e-methanol value chains: Temporal and regional renewable energy, costs, and climate impacts","authors":"Jani Sillman ,&nbsp;Antti Ylä-Kujala ,&nbsp;Jaakko Hyypiä ,&nbsp;Timo Kärri ,&nbsp;Mari Tuomaala ,&nbsp;Risto Soukka","doi":"10.1016/j.apenergy.2025.125887","DOIUrl":"10.1016/j.apenergy.2025.125887","url":null,"abstract":"<div><div>A power-to-X economy can provide low-carbon alternatives to a fossil-based economy, thereby mitigating climate change. <em>E</em>-methanol is a potential alternative but is currently not economically feasible, mainly due to the cost of hydrogen production. Other factors impacting feasibility include the source of carbon dioxide, storage, investment time, capital cost and regulation. Furthermore, multiple industrial operators need to establish a power-to-X value chain, all seeking profitable business opportunities. A cross-disciplinary study was conducted to analyse the influence of these different factors on economic and environmental feasibility. Dynamic modelling was used to optimize e-methanol production based on variable renewable energy generation. Life cycle assessment and costing were used to compare the economic and environmental sustainability of the studied value chains. Over 30-years, the discounted net cash flow of a value chain can become profitable with sufficiently low electricity prices (less than 37€/MWh) and considerable investment subsidies for hydrogen producer. Similar profitability can be achieved with the electricity price given and without subsidies when the weighted average cost of capital is low (5 %). Therefore, hydrogen producers may face challenges in generating profit; highlighting the need for profit-sharing in the value chain and/or subsidies. As capital expenditure for certain technologies is predicted to decline, gradually increasing the production capacity with timed investments is preferable. However, trade-offs may arise in climate change mitigation if investments in cleaner alternatives are delayed.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125887"},"PeriodicalIF":10.1,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823543","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":"First-principles calculations insight into non-noble-metal bifunctional electrocatalysts for zinc–air batteries","authors":"W.W. Zhang ,&nbsp;Y. Wang ,&nbsp;Y.C. Li ,&nbsp;L.L. Sun ,&nbsp;X.Y. Zhang","doi":"10.1016/j.apenergy.2025.125925","DOIUrl":"10.1016/j.apenergy.2025.125925","url":null,"abstract":"<div><div>Zinc–air batteries (ZABs) have triggered a research boom in energy storage technologies due to their low cost, high safety and environmental friendliness. The slower kinetics of the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) at the air-electrode of ZABs have led to an increasing demand for improving the performance of air-electrode electrocatalysts. First-principles calculations explain the properties and behaviors of electrocatalyst materials and catalytic mechanisms at the atomic scale and provide rational design strategies for cathodic electrocatalyst materials, which makes it has become a powerful technique for developing efficient new electrocatalysts. We present an overview of first-principles calculations methods and emphasize their important role in the contemporary study of air-electrode electrocatalyst materials for ZABs. Firstly, the electronic structure of the air-electrode electrocatalyst, the interface effect of the air-electrode | electrolyte with the diffusion of oxygen and water molecules, and the catalytic reaction mechanism are systematically summarized, and some representative examples are presented. Emphasis is placed on several aspects such as the d-band center of the transition metal, the dynamic behavior of the diffusion of oxygen and water molecules, and the Gibbs free energy of the ORR/OER process. The way in which theoretical calculations support experiments is also explored. Finally, the challenges and prospects for development of first-principles calculations applied to ZABs are discussed from a personal perspective.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125925"},"PeriodicalIF":10.1,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823554","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":"Tracking decarbonisation: Scalable and interpretable data-driven methods for district energy systems","authors":"Massimiliano Manfren ,&nbsp;Karla M. Gonzalez-Carreon","doi":"10.1016/j.apenergy.2025.125883","DOIUrl":"10.1016/j.apenergy.2025.125883","url":null,"abstract":"<div><div>The urgent push for decarbonisation demands innovative, transparent methods to analyse and track decarbonisation strategies. This study addresses the problem of modelling energy consumption patterns at both building and district scales, ensuring transparency and scalability. By integrating well-established measurement and verification (M&amp;V) techniques with interpretable data-driven modelling strategies, the research proposes a modelling workflow to track energy performance on a dynamic basis. The methods makes use of readily available metering data for electricity, district heating, and natural gas across a district, collected within a digital platform. A multi-resolution modelling approach is employed, with data at monthly, daily, and hourly intervals, that pinpoints anomalies and is meant to support a continuous refinement of operational strategies and efficiency measures. The Highfield Campus at the University of Southampton serves as the case study, illustrating how scalable, interpretable data-driven models can identify performance deviations and inform both short-term facilities management and long-term decarbonisation strategies. Findings reveal that simple and interpretable regression models can identify substantial variations in energy consumption pattern over longer time frames (ranging from months to years), whereas high-resolution analyses enhance the comprehension of dynamic operational patterns (days to hours). Both objectives can be achieved while maintaining a level of continuity in the modelling process, progressing from basic to detailed models while retaining interpretability. Further research will refine these models through additional physics-based constraints and explore deeper integrations with digital energy management platforms, offering replicable insights for broader district and urban-scale applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125883"},"PeriodicalIF":10.1,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823556","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}