Applied Energy最新文献

{"title":"Hybrid multi-agent deep reinforcement learning for multi-type mobile resources dispatching under transportation and power network recovery","authors":"Shaohua Sun ,&nbsp;Gengfeng Li ,&nbsp;Zhaohong Bie ,&nbsp;Dingmao Zhang ,&nbsp;Yuxiong Huang","doi":"10.1016/j.apenergy.2025.126423","DOIUrl":"10.1016/j.apenergy.2025.126423","url":null,"abstract":"<div><div>Rainstorm waterlogging or typhoon can not only cause seriously failure of power network (PN), but also damage the normal traffic of transportation network (TN). Equipment fault of PN affects normal power supply of critical loads, and the interruption of TN severely limits the flexible transfer of mobile resources for recovery of transportation and power network (TPN). Previous work only addresses dispatching of multi-type mobile resources (MMRs) for power network recovery on the assumption of healthy TN, which makes dispatching strategy impractical. To fill this gap, this paper proposes a dispatching model of MMRs for collaborative recovery of TPN, embedding road repair crews (RRCs) dispatching behaviors into road repair constraints. To solve the above model, firstly road island and various topology update strategies are introduced to simplify shortest path searching for MMRs routing. Then, the dispatching model of MMRs is described as a parameterized action Markov decision process, in which MMRs are modeled as different types of intelligent agents considering various discrete-continuous dispatching characteristics. And, a hybrid multi-agent deep reinforcement learning (HMADRL) method characterizing master-slave architecture is developed to improve the solving efficiency and convergence speed of model, where the master module describes the recovery process of TN with dispatching of RRCs, and the slave module is constructed to recovery PN based on the path update strategies. The case analysis based on 15-node PN (18-node TN), 33-node PN (45-node TN) and practical example demonstrates that this approach can elevate the practicality of dispatching strategies and the recovery efficiency of TPN.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126423"},"PeriodicalIF":10.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656638","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 impact of temporal clustering on long-term energy system models","authors":"Matteo Catania ,&nbsp;Giuseppe Muliere ,&nbsp;Fabrizio Fattori ,&nbsp;Paolo Colbertaldo","doi":"10.1016/j.apenergy.2025.126354","DOIUrl":"10.1016/j.apenergy.2025.126354","url":null,"abstract":"<div><div>The field of energy system modelling is experiencing significant development, driven by the urgent need to redesign the national energy systems to achieve carbon neutrality. A growing interest regards long-term energy system models, which enable tracking the pathway and not only the final need for installations. The increase in complexity may easily lead them to face computational limits. Therefore, modelling approaches are required that cluster data to reduce the size of the problem while limiting errors and inaccuracies. This article studies the impact of temporal clustering on the performances of a sector-integrated energy system model, considering the double-layer clustering scheme operating on two distinct temporal scales: intra-year and inter-year. The former is addressed through typical-day clustering (k-means and k-medoids), while the latter introduces multi-year gaps between representative years. This methodology is implemented in the open-source framework <em>oemof</em>, which is customized to the dual clustering approach. The study addresses a sector-integrated energy system, built on the Italian structure, with a multi-vector and multi-sector perspective along the 2020–2050 horizon. The impact is investigated by comparing multiple options with varying number of typical days and multi-year gap, comparing each configuration with a benchmark without clustering. The approach yields coherent representations of the energy system evolution, simultaneously reducing the memory usage down to 4 %. The outcomes show the benefits of balancing the number of representative years with the number of representative days, suggesting that such a trade-off leads to significant computational advantages. Although literature shows that time-series reduction is case-dependent, the double-layer clustering scheme appears promising for application on even more complex models, where a full-hour resolution would be computationally intractable.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126354"},"PeriodicalIF":10.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656639","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":"Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review","authors":"Arash J. Khabbazi ,&nbsp;Elias N. Pergantis ,&nbsp;Levi D. Reyes Premer ,&nbsp;Panagiotis Papageorgiou ,&nbsp;Alex H. Lee ,&nbsp;James E. Braun ,&nbsp;Gregor P. Henze ,&nbsp;Kevin J. Kircher","doi":"10.1016/j.apenergy.2025.126459","DOIUrl":"10.1016/j.apenergy.2025.126459","url":null,"abstract":"<div><div>A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adoption. Field demonstrations could accelerate MPC and RL adoption by providing real-world data that support the business case for deployment. Here we review 24 papers that document field demonstrations of MPC and RL in residential buildings and 80 in commercial buildings. After presenting demographic information – such as experiment scopes, locations, and durations – this paper analyzes experiment protocols and their influence on performance estimates. We find that 71 % of the reviewed field demonstrations use experiment protocols that may lead to unreliable performance estimates. Over the remaining 29 % that we view as reliable, the weighted-average cost savings, weighted by experiment duration, are 16 % in residential buildings and 13 % in commercial buildings. While these savings are potentially attractive, making the business case for MPC and RL also requires characterizing the costs of deployment, operation, and maintenance. Only 13 of the 104 reviewed papers report these costs or discuss related challenges. Based on these observations, we recommend directions for future field research, including: Improving experiment protocols; reporting deployment, operation, and maintenance costs; designing algorithms and instrumentation to reduce these costs; controlling HVAC equipment alongside other distributed energy resources; and pursuing emerging objectives such as peak shaving, arbitraging wholesale energy prices, and providing power grid reliability services.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126459"},"PeriodicalIF":10.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656640","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":"Parallax and cloud shadow correction in satellite-based solar irradiance estimation: A study in tropical environments","authors":"Arindam Roy ,&nbsp;Annette Hammer ,&nbsp;Detlev Heinemann ,&nbsp;Marion Schroedter-Homscheidt ,&nbsp;Ontje Lünsdorf ,&nbsp;Jorge Lezaca","doi":"10.1016/j.apenergy.2025.126457","DOIUrl":"10.1016/j.apenergy.2025.126457","url":null,"abstract":"<div><div>Accurate estimation of Global horizontal solar irradiance (GHI) from geostationary satellite imagery is essential for intraday solar PV power forecasting. Tropical regions show an even more challenging situation: A typically much higher tropopause results in higher cloud tops and correspondingly larger parallax errors in satellite imagery with significantly larger cloud shadow displacements compared to mid-latitudes. This study improves GHI estimates from Meteosat-8 by correcting cloud parallax and shadow displacement using gridded cloud top height (CTH) data. Fractional or sub-pixel displacement of individual cloudy pixels is enabled by bilinear interpolation in contrast to prior methods that allowed only integer shifts or assigned a single CTH value to a grouping of adjacent cloud pixels. Validation against one year of 15-min resolution ground-based measurements at five sites in South and Southeast Asia shows a reduction in relative root mean square error (rel. RMSE) from 23.8 % to 22.1 %. Improvements are more pronounced at higher satellite viewing zenith angles (<span><math><msub><mi>θ</mi><mi>sza</mi></msub></math></span>) and in the presence of high-altitude clouds. The corrected satellite-based GHI exhibits 4–7 percentage points lower rel. RMSE than National Solar Radiation Database (NSRDB) and 2.5 points lower than CAMS solar radiation service for similar <span><math><msub><mi>θ</mi><mi>sza</mi></msub></math></span>. Greatest error reductions occur during partly cloudy conditions for sites within 61° <span><math><msub><mi>θ</mi><mi>sza</mi></msub></math></span>, and under overcast skies for sites close to the edge of Meteosat-8's field of view. Improvements also depend on the co-scattering angle between sun and satellite with respect to the site, and the availability of sufficient upstream cloud information along the path of solar irradiance falling on the site. Ramp detection accuracy improves, particularly at lower detection thresholds, as measured using the Swinging Door Algorithm.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126457"},"PeriodicalIF":10.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656641","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":"Evaluating advanced nuclear fission technologies for future decarbonized power grids","authors":"Emilio Cano Renteria ,&nbsp;Jacob A. Schwartz ,&nbsp;Jesse Jenkins","doi":"10.1016/j.apenergy.2025.126395","DOIUrl":"10.1016/j.apenergy.2025.126395","url":null,"abstract":"<div><div>Advanced nuclear fission, which encompasses various innovative nuclear reactor designs, could contribute to the decarbonization of the United States electricity sector. However, little is known about how cost-competitive these reactors would be compared to other technologies, or about which aspects of their designs offer the most value to a decarbonized power grid. We employ an electricity system optimization model and a case study of a decarbonized U.S. Eastern Interconnection circa 2050 to generate initial indicators of future economic value for advanced reactors and the sensitivity of future value to various design parameters, the availability of competing technologies, and the underlying policy environment. These results can inform long-term cost targets and guide near-term innovation priorities, investments, and reactor design decisions. We find that advanced reactors should cost $5.7–$7.3/W to gain an initial market share (assuming 30 year asset life and 3.5 %–6.5 % real weighted average cost of capital), while those that include thermal storage in their designs can cost up to $6.0/W–$7.7/W (not including cost of storage). Since the marginal value of advanced fission reactors declines as market penetration increases, break-even costs fall <span><math><mo>∼</mo></math></span>32 % at 100 GW of cumulative capacity and <span><math><mo>∼</mo></math></span>51 % at 300 GW. Additionally, policies that provide investment tax credits for nuclear energy create the most favorable environment for advanced nuclear fission. These findings can inform near-term resource allocation decisions by stakeholders, innovators and investors working in the energy technology sector.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126395"},"PeriodicalIF":10.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656176","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 self-optimizing defrost initiation controller for air-source heat pumps: Experimental validation of deep reinforcement learning","authors":"Jonas Klingebiel,&nbsp;Christoph Höges,&nbsp;Janik Horst,&nbsp;Oliver Nießen,&nbsp;Valerius Venzik,&nbsp;Christian Vering,&nbsp;Dirk Müller","doi":"10.1016/j.apenergy.2025.126400","DOIUrl":"10.1016/j.apenergy.2025.126400","url":null,"abstract":"<div><div>Air-source heat pumps (ASHPs) play a key role in sustainable heating, but their efficiency is significantly reduced by frost formation on the evaporator. The timing of defrost initiation is crucial to minimize energy losses, yet conventional demand-based defrosting (DBD) controllers rely on specialized sensors for frost detection and heuristic thresholds for defrost initiation, leading to increased system costs and suboptimal performance. This paper presents an experimental validation of a self-optimizing deep reinforcement learning (RL) controller. With our proposed implementation, RL determines defrost timing using standard temperature measurements and autonomously generates tailored control rules, overcoming the limitations of conventional DBD methods. The study consists of three case studies conducted on a hardware-in-the-loop test bench with a variable-speed ASHP. First, RL’s defrost timing accuracy is evaluated against experimentally pre-determined optima. Across five stationary test conditions, RL achieves near-optimal defrost initiations with maximum efficiency losses of at most 1.9 %. Second, RL is benchmarked against time-based (TBD) and demand-based defrost controllers for three typical days with varying ambient conditions. RL outperforms TBD by up to 7.1 % in <span><math><mi>S</mi><mi>C</mi><mi>O</mi><mi>P</mi></math></span> and 3.6 % in heat output. Compared to DBD, RL improves <span><math><mi>S</mi><mi>C</mi><mi>O</mi><mi>P</mi></math></span> by up to 9.1 % and heat output by 4.9 %. Finally, we assess RL’s ability to adapt its strategy through online learning. We emulate airflow blockage, a common soft-fault condition, caused by obstructions on the evaporator fins (e.g., leaves). RL adjusts its strategy to the changed environment and improves efficiency by 16.6 %. While the results are promising, limitations remain, requiring further research to validate RL in real-world ASHPs.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126400"},"PeriodicalIF":10.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656178","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":"Risk-based data-driven energy management for integrated electrical and hydrogen microgrids with improved hydrogen vehicle charging prediction","authors":"Bifei Tan ,&nbsp;Zipeng Liang ,&nbsp;C.Y. Chung ,&nbsp;Hong Tan ,&nbsp;Hang Wang ,&nbsp;Haosen Yang","doi":"10.1016/j.apenergy.2025.126410","DOIUrl":"10.1016/j.apenergy.2025.126410","url":null,"abstract":"<div><div>The increasing integration of renewable energy sources (RESs) and hydrogen-powered vehicles (HVs) into integrated power and hydrogen microgrids (IPHMs) poses significant operational challenges due to uncertainties in RES generation and dynamic HV fueling demands. Current methods, such as gated recurrent unit (GRU) networks for predicting HV fueling demands, often fail to effectively prioritize and combine the full range of influencing factors. Moreover, standard approaches to RES output uncertainty typically use static, predefined bounds for uncertainty sets, which can introduce subjectivity, reduce adaptability, and lead to suboptimal energy management solutions. This paper addresses these deficiencies by proposing a novel risk-based, data-driven robust energy management framework for IPHMs. The primary goals are to enhance HV fueling prediction accuracy and to optimize IPHM operation under uncertainty. First, this paper develops a multi-head attention-based GRU (MHA-GRU) network, further enhanced with copula functions (MHA-GRU-Copula), to more accurately predict HV fueling demands by embedding a comprehensive suite of features including starting location, destination, hydrogen station selection, transportation system structure, and the correlation between travel time and hydrogen consumption. Second, a risk-based data-driven robust energy management model is formulated to dynamically optimize the bounds of RES uncertainty sets, achieving a better trade-off between robust operation costs and potential risk costs. Case studies on a realistic multiple-IPHM system demonstrate that the MHA-GRU-Copula network achieves significantly improved prediction accuracy, reducing mean absolute error by 18.6 % and mean squared error by 14.4 % compared to standard GRU models. Furthermore, the proposed risk-based optimization approach lowers total operational costs by 7.2 % and risk costs by 24.5 %, outperforming conventional methods with fixed uncertainty bounds. An optimal trade-off was found at an uncertainty set bound of 56 %. The proposed framework ensures more economic and reliable operation of IPHMs by effectively addressing inherent uncertainties in both transportation and energy systems, offering significant applications for the planning and management of advanced, integrated energy infrastructures.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126410"},"PeriodicalIF":10.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657142","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":"Review of advances in scaling non-intrusive load monitoring for real-world applications","authors":"Qingquan Luo,&nbsp;Tao Yu,&nbsp;Minhang Liang,&nbsp;Zhenning Pan,&nbsp;Wenlong Guo,&nbsp;Xiaolei Hu","doi":"10.1016/j.apenergy.2025.126462","DOIUrl":"10.1016/j.apenergy.2025.126462","url":null,"abstract":"<div><div>Sustainable and safe electricity usage is essential to making electrification more environmentally and human-friendly. The first step is to make fine-grained electricity consumption data readily available. Recently, non-intrusive load monitoring (NILM) has gained attention for estimating appliance-level electricity usage only from aggregated measurements, offering a cost-effective solution for large-scale electricity monitoring. However, as NILM scales from the lab to real-world applications, it faces not only methodological difficulties from diverse electricity consumption characteristics under complex disturbances, but also operational difficulties in managing numerous devices and decentralized data across cloud and edge with limited computing resources. Given the critical need to enhance NILM's practicality for widespread adoption, research interest in this field has surged significantly over the past six years. Therefore, we analyze the above difficulties following a brief review of NILM's fundamentals. Then, we highlight the advances across four key aspects of NILM's practicality: robustness, adaptability, collaboration, and deployability. In addition, we discuss the limitations that hinder real-world NILM applications, aiming to inspire further research. Finally, we provide an outlook on the developments in data ecosystem, implementation guidance, application scenarios, and related services</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126462"},"PeriodicalIF":10.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656173","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 non-intrusive load monitoring-enabled framework for load scheduling in the dairy industry","authors":"Apostolos Vavouris,&nbsp;Lina Stankovic,&nbsp;Vladimir Stankovic","doi":"10.1016/j.apenergy.2025.126456","DOIUrl":"10.1016/j.apenergy.2025.126456","url":null,"abstract":"<div><div>The agricultural industry is an important contributor to CO₂ emissions, with dairy accounting for over 15 % of total agricultural output in the United Kingdom (UK). In line with the United Nations’ Sustainable Development Goals for responsible consumption, and affordable and clean energy, decarbonisation of agriculture is being prioritised around the world, with integration of renewables, energy storage systems, and load flexibility widely recognised as viable solutions to achieve net-zero. Analysis and optimisation of energy-consuming agri-processes remains a huge challenge — compared to residential and commercial buildings — due to non-standardised equipment and the emergence of on-site renewables. In contrast to previous studies that are narrow-focused, do not involve end-users, do not consider the diverse and largely varying day-to-day energy-intensive activities, or are not applicable at scale, this article proposes a novel, deep learning-based, data-driven, modular non-intrusive load monitoring (NILM)-enabled approach, where context is set through co-creation with farms and agritech. The proposed approach enables accurate and scalable load disaggregation at very-low frequencies (30-min), through transfer learning, and scheduling of energy-consuming processes, which minimises, simultaneously, total electricity import and carbon footprint, based on renewable production prediction, and granular regional carbon footprint forecasting. Findings from three small to medium/large-scale dairy farms in the UK with renewables and diverse non-standardised dairy equipment demonstrated that through the completely non-intrusive and scalable co-created load scheduling approach based on identified flexible loads, utility is preserved under a very-low frequency (30-min) disaggregation scenario. The proposed system achieves electricity cost and carbon footprint reduction of over 30 % compared to current energy practices on the three farms, and paves the way for completely non-intrusive/no capital investment NILM-enabled systems for the agriculture industry.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126456"},"PeriodicalIF":10.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656175","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":"Exploring harmony search for power system optimization: applications, formulations, and open problems","authors":"Eunsung Oh ,&nbsp;Zong Woo Geem","doi":"10.1016/j.apenergy.2025.126452","DOIUrl":"10.1016/j.apenergy.2025.126452","url":null,"abstract":"<div><div>Modern power systems must optimize large-scale, nonlinear, multi-objective problems created by renewable integration, rapidly growing distributed resources, and strict reliability and efficiency targets. Conventional techniques often falter under these conditions, whereas Harmony Search (HS) has shown strong potential. Unlike earlier HS surveys, this review provides a structured synthesis of power system applications of HS, with attention to how objective functions and constraints are formulated in HS models. It covers both system-level operations (e.g., economic dispatch, optimal power flow, unit commitment, renewable planning) and device-level control (e.g., load frequency regulation and power system stabilization). Comparative results demonstrate HS’s versatility in meeting cost, emission, and reliability goals, and reveal scenarios where careful formulation improves convergence and solution quality relative to other metaheuristics. Practical guidance is distilled on parameter self-adaptation, hybridization with artificial intelligence models, and constraint-handling schemes that mitigate sensitivity and premature convergence. Remaining challenges include inconsistent modeling practices and limited scalability for real-time or very large networks. Recommended remedies encompass standardized HS formulations and automated parameter tuning to improve reproducibility and performance. The review concludes with a future vision of uncertainty-aware, explainable HS frameworks integrated with digital-twin environments, charting a clear agenda for next-generation power-system optimization.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126452"},"PeriodicalIF":10.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656177","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}