Energy Reports最新文献

{"title":"Carbon footprint analysis and environmental impact assessment of industrial waste oil regeneration process","authors":"Long Wang ,&nbsp;Chaoyang Wang ,&nbsp;Lincong Luo","doi":"10.1016/j.egyr.2025.108948","DOIUrl":"10.1016/j.egyr.2025.108948","url":null,"abstract":"<div><div>This study focuses on an NMP-enhanced vacuum distillation process (N-Methyl-2-pyrrolidone solvent refining) for industrial waste oil regeneration. Using a systematic life cycle assessment (LCA) framework from environmental sustainability and climate mitigation perspectives, this research conducts a comprehensive qualitative-quantitative evaluation of process carbon footprints and assesses environmental burdens across distinct production stages. The results indicate that the total carbon footprint generated from regenerating 1000 kg of industrial waste oil is 712.49 kg CO₂eq, with the NMP refining stage contributing the most at 363.89 kg CO₂eq (51 %), followed by the transportation and collection stage at 250 kg CO₂eq (35 %). Consumption of electricity, natural gas, and steam in the overall production process accounts for 62 % of the total carbon emissions. In terms of environmental impacts, the most significant factor is fossil fuel consumption at 28.63 Pt, followed by respiratory (inorganic) effects at 19.24 Pt, and climate change at 5.53 Pt. The analysis reveals that direct energy consumption accounts for 62.0 % of total carbon emissions, representing the primary decarbonization opportunity. Consequently, improving energy utilization efficiency and integrating renewable energy sources emerge as paramount strategies for industrial waste oil regeneration enterprises to achieve carbon reduction goals.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108948"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proposing a layered model to develop sustainable affordable housing focusing on operation phase criteria","authors":"Amir Faraji ,&nbsp;Tahereh Meydani Haji Agha ,&nbsp;Elnaz Ghasemi ,&nbsp;Zahra Alirezaei ,&nbsp;Payam Rahnamayiezekavat ,&nbsp;Shima Homayoon Arya","doi":"10.1016/j.egyr.2025.108945","DOIUrl":"10.1016/j.egyr.2025.108945","url":null,"abstract":"<div><div>Today, access to sustainable affordable housing (SAH) for low-income families, especially in developing countries, has become challenging. To achieve SAH, previous research has focused more on strengthening the criteria of the design and construction stages, while the lack of a comprehensive model for the operation stage of the housing project life cycle remains a research gap. This study aims to provide a comprehensive model for achieving SAH through the realization of the operation stage criteria, focusing on energy savings. The research began with library studies, and 78 initial criteria were extracted for all life cycle stages. Using the Delphi method and 33 experts in six stages, 16 criteria related to the operation stage were selected. The criteria were ranked based on the experts' opinions on the importance of their impact, and then the solutions to achieve each criterion and the indicators for evaluating the solutions were obtained. The most important findings of the research are thermal insulation, the use of renewable energy, and climate-friendly design. Effective solutions include installing solar panels, insulating pipes, and using gray water recycling systems. Based on the findings, a proposed model with a three-layer structure, including Criteria, Solutions, and Indicators, is presented, which has the ability to continuously feed back and realize optimization. Using the model can reduce the costs of the operation phase and increase energy efficiency. It is suggested that future studies investigate the practical implementation of this model in real projects.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108945"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 and real-world deployment of a multi-agent system to support the operation of renewable energy communities","authors":"João Tabanêz Patrício,&nbsp;Tiago Lopes,&nbsp;André Rocha,&nbsp;João Martins,&nbsp;Rui Amaral Lopes","doi":"10.1016/j.egyr.2025.108967","DOIUrl":"10.1016/j.egyr.2025.108967","url":null,"abstract":"<div><div>Despite the ongoing measures to support the development of Renewable Energy Communities (RECs), effectively managing energy production, consumption, and sharing, while balancing members’ needs, remains a major challenge. This paper presents a Multi-Agent System-based Community Energy Management System, designed to manage interactions and real-time energy exchanges within a REC to tackle the referred challenge in line with European legislation. The developed system has been deployed in real-world conditions considering a REC located in Portugal, unlike previous studies that rely on simulation. Additionally, the energy flexibility provided by second-life batteries has been controlled by the system to improve self-consumption of local generation and increase benefits of energy sharing in different scenarios. The real-world case study revealed that the developed system demonstrated effective operation, including emergency procedures to prevent deep discharge of batteries. The collected results also show that collective self-consumption with dynamic sharing coefficients is the most interesting REC configuration in terms of global cost savings, reducing energy bills by 25–28 % and 20–24 % when comparing to scenarios where individual self-consumption with fixed and dynamic sharing coefficients are applied, respectively.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108967"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on multi objective dynamic control strategy of electric vehicle air conditioning for energy-consumption comfort air-quality collaborative optimization","authors":"Ning Li ,&nbsp;Kai Xie ,&nbsp;Yuhao Hu ,&nbsp;Ziping Wang ,&nbsp;Chunlin Wang ,&nbsp;Hao Qi","doi":"10.1016/j.egyr.2025.109012","DOIUrl":"10.1016/j.egyr.2025.109012","url":null,"abstract":"<div><div>Aiming at the limitations of traditional single-objective regulation in vehicle air conditioning control, the paper proposed a multi-objective dynamic control scheme for Electric Vehicle Air Conditioning (EVAC) based on energy-saving, comfort, and health collaborative optimization. Firstly, a multi-objective collaborative optimization control scheme for EVAC was based on the Predicted Mean Vote (PMV) of thermal comfort indicators and Indoor Air Quality (IAQ) of air quality indicators. Secondly, the Improved Non-dominated Sorting Genetic Algorithm-II (I-NSGA-II) algorithm was proposed to perform multi-objective optimization on energy consumption, PMV, and IAQ. The Latin Hypercube Sampling (LHS) and Differential Evolution (DE) crossover strategy in the algorithm improved the global search efficiency and solution distribution uniformity during the solving process. During the process of finding the optimal solution using the I-NSGA-II algorithm, the Long Short-Term Memory neural network (DE-QPSO-LSTM) algorithm proposed in the paper, could solve the problems of poor prediction accuracy in adjusting long-term series data and difficulty in determining hyperparameters in prediction models, thus better achieving priority adjustment of air conditioning power allocation. Finally, the simulation and experimental results showed that the scheme can effectively optimize the requirements for comfort, health, and energy efficiency during the use of air conditioning in vehicles.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109012"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bi-level optimization for demand response integration with solar pv and battery energy storage systems: Enhancing distribution network efficiency and sustainability","authors":"Vivek Saxena ,&nbsp;Saurabh Kumar Rajput ,&nbsp;Saibal Manna ,&nbsp;Amaresh Gantayet ,&nbsp;Kusum Lata Agarwal ,&nbsp;Mohammed H. Alsharif ,&nbsp;Mun-Kyeom Kim","doi":"10.1016/j.egyr.2025.12.019","DOIUrl":"10.1016/j.egyr.2025.12.019","url":null,"abstract":"<div><div>The integration of distributed energy resources (DERs) offers significant potential to enhance the efficiency and resilience of distribution networks (DNs). Yet, high penetration of distributed generation (DG), particularly solar photovoltaic (PV), can introduce voltage instability, higher losses, and reverse power flows. Addressing these challenges, this study presents a novel bi-level optimization framework that jointly coordinates DG placement, battery energy storage systems (BESS), and price-based demand response (DR). In the upper level, a Grey Wolf Optimizer (GWO) determines the optimal siting and sizing of DG and BESS; the lower level schedules real-time BESS dispatch with DR participation. Metaheuristic GWO is adopted for its strong exploration–exploitation balance in the nonlinear, mixed-integer search space of AC power-flow constraints. Using the IEEE-33 bus test system, six integration scenarios were evaluated. The best configuration—Case 6 (DG + BESS + DR)—reduced annual energy losses by 46.8 %, improved the average voltage profile to 1.01 p.u., shaved the peak demand by ∼50 %, and achieved a payback period of 4.4 years with the lowest levelized cost of energy (LCOE). These findings demonstrate that coordinated DER planning and real-time control can significantly improve technical and economic performance, providing actionable guidance for utilities seeking smarter and more sustainable power-distribution systems.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108882"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 and exergy analysis of complex gas-steam systems powered by a mixture of biogas and hydrogen","authors":"Artur Harutyunyan,&nbsp;Krzysztof Badyda,&nbsp;Łukasz Szablowski","doi":"10.1016/j.egyr.2025.12.029","DOIUrl":"10.1016/j.egyr.2025.12.029","url":null,"abstract":"<div><div>This study presents an energy and exergy assessment of a Siemens SGT-800 gas turbine integrated within a combined gas–steam cycle with an SST-300 steam turbine, operated on renewable fuel blends of hydrogen with biomethane or biogas. Analyses were performed using Aspen HYSYS combined with external exergy calculations, including physical and chemical exergy. Model results were validated against SIPEP (Siemens Performance Estimation Program), an online tool by Siemens, showing deviations below 5 %, confirming reliability.</div><div>Parametric studies evaluated thermodynamic parameters and exergy values while varying hydrogen content (0–100 %), ambient air temperature (-20–30 <span><math><mi>℃</mi></math></span>), turbine exhaust temperature (TAT, 400–600 <span><math><mi>℃</mi></math></span>), compressor pressure ratio (18–26 bar), and feedwater pressure (30–90 bar). Increasing hydrogen content improved power output and system efficiency, with biomethane-hydrogen blends slightly outperforming biogas-hydrogen blends. The highest exergy destruction, including physical and chemical exergy, occurred in the combustion chamber and decreased with more hydrogen, reaching a minimum at 100 % hydrogen.</div><div>Higher ambient temperatures reduced power by ∼0.20 MW/<span><math><mi>℃</mi></math></span>, while increasing TAT from 400 to 600 <span><math><mi>℃</mi></math></span> raised GT power by 27.6–29.7 MW and efficiency by 2.6–3.0 %. A 1 bar rise in compressor pressure ratio increased GT power by ∼0.55 MW and efficiency by ∼0.39 %, reducing exergy destruction in the combustion chamber but slightly increasing it in the compressor and expander. Feedwater pressure increases improved CCGT power by 0.072 MW/bar and efficiency by 0.05 %/bar. Optimal performance occurred with 40–50 % hydrogen blends, showing hydrogen-enriched fuels can enhance combined cycle performance and support decarbonization.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108892"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 review of deep reinforcement learning algorithms for grid services in grid-interactive efficient buildings","authors":"Aya A. Amer ,&nbsp;Sertac Bayhan ,&nbsp;Haitham Abu-Rub ,&nbsp;Ahmed Massoud ,&nbsp;Mehrdad Ehsani","doi":"10.1016/j.egyr.2025.12.037","DOIUrl":"10.1016/j.egyr.2025.12.037","url":null,"abstract":"<div><div>Integrating deep reinforcement learning (DRL) into building energy management systems offers an innovative method for improving grid stability and efficiency. Grid-interactive efficient buildings (GEBs) that utilize intelligent algorithms can dynamically optimize their energy usage and generation based on real-time grid conditions. This paper provides a comprehensive review of the applications of DRL in GEBs, focusing on its potential to autonomously manage energy consumption, balance renewable energy variability, and support grid services. The paper also examines the simulation tools available for evaluating GEB performance and highlight their integration with DRL frameworks. Despite the promising advancements, DRL applications encounter challenges such as algorithmic complexity, reliance on extensive data, and difficulties in integrating with existing infrastructures. The paper will also present comprehensive analyses that showcase current achievements while also investigate key challenges that must be addressed. Tackling these issues will allow DRL to realize its full potential in building energy management systems, contributing to a more sustainable and efficient energy future.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108900"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 review of topology identification methods and applications for low voltage distribution networks","authors":"Zibin Li ,&nbsp;Mao Tan ,&nbsp;Weikang Jiang ,&nbsp;Xinwei Lv ,&nbsp;Wei Liu ,&nbsp;Jinling Chen","doi":"10.1016/j.egyr.2025.12.051","DOIUrl":"10.1016/j.egyr.2025.12.051","url":null,"abstract":"<div><div>With the widespread integration of distributed energy resources, electric vehicles, and diversified loads, the flexibility and complexity of distribution networks have increased significantly. Real-time and accurate identification of distribution network topology has become a key technology for ensuring safe operation, state estimation, and fault self-healing. This paper systematically reviews the research progress and methodological systems in the field of distribution network topology identification. At the algorithmic level, it summarizes and discusses recent advances, with a particular focus on methods driven either by physical models or data. In addition, the paper examines the challenges of topology identification in various scenarios, such as large-scale distribution networks and those with latent nodes, including issues related to data availability and optimal meter placement. Furthermore, the practical applications of topology identification in fault detection, outage detection, and network reconfiguration are discussed in detail.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108914"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-term discharge-based state of health estimation of lithium-ion batteries","authors":"Nermin M. Salem ,&nbsp;Khairy Sayed ,&nbsp;Mohammed M. Elsayed ,&nbsp;David Kirakosian ,&nbsp;Ahmed A. Mohamed","doi":"10.1016/j.egyr.2025.108931","DOIUrl":"10.1016/j.egyr.2025.108931","url":null,"abstract":"<div><div>Accurate and timely prediction of lithium-ion battery State of Health (SoH) is critical for ensuring the safety, lifespan, and reliability of Battery Management Systems (BMS) in electric vehicles (EVs) and other energy storage applications. Conventional SoH estimation techniques often rely on full discharge cycles, which are both time-consuming and unsuitable for real-time deployment in embedded systems. This study proposes a data-efficient framework that leverages partial discharge profiles, as short as 10 min, for rapid and reliable SoH prediction. Four machine learning (ML) models were comprehensively evaluated using NASA’s battery degradation dataset: a standard Long Short-Term Memory (LSTM), a Bidirectional Long Short-Term Memory network with Multi-Head Self-Attention (BiLSTM-MHSA), a Convolutional Neural Network (CNN), and CatBoost. In this study, we first establish a baseline by training and evaluating models on complete discharge cycles from NASA’s battery degradation dataset. Among the models tested, the LSTM achieved the highest accuracy (R² = 0.9036, RMSE = 0.0302, MAE = 0.0139), followed by BiLSTM-MHSA (R² = 0.8714), CatBoost (R² = 0.8463), and CNN (R² = 0.7269). Building on this baseline, we introduce a data-efficient framework that leverages partial discharge profiles—as short as 10 min—for rapid SoH prediction. Results demonstrate that LSTM maintained superior performance across all durations, achieving R² = 0.9402 (RMSE = 0.0237, MAE = 0.0123) with 30 min of data and R² = 0.9435 (RMSE = 0.0231, MAE = 0.0113) with only 10 min. BiLSTM-MHSA excelled at the shortest duration (R² = 0.9460 at 10 min) while CatBoost emerged as a competitive lightweight alternative (R² = 0.9326 at 20 min). In contrast, CNN underperformed across all durations due to its limited capacity to model long-term dependencies. To enhance interpretability, SHAP (SHapley Additive exPlanations) analysis identified early voltage dynamics and temperature behavior as the most influential predictors of battery degradation. Overall, this work presents a scalable and interpretable framework for SoH prediction from full and partial discharge data, enabling accurate, real-time diagnostics while reducing testing overhead, paving the way for efficient and lightweight BMS deployment.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108931"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermo-chemical conversion of bio-waste for energy carrier production: A spatial analysis for Bavaria, Germany","authors":"Shashank Deepak Prabhu","doi":"10.1016/j.egyr.2025.108986","DOIUrl":"10.1016/j.egyr.2025.108986","url":null,"abstract":"<div><div>The current global geopolitical situation has brought the matter of energy security and energy gap to the forefront. In addition to that, perceived future sustainable development and decarbonization goals demand increased application and use of renewable energy (RE) sources into the energy system. However, considering the spatial availability of RE source’s potential for generating green electricity (GE) is of paramount importance. Limitations in availability of the RE sources such as wind energy and solar potential over various spatial as well as temporal scale, requires identification of alternative sources of energy generation and storage. In this regard, bio-waste (BW) and sewage sludge (SS) processing in urban as well as rural pockets can be seen as a practical source of energy production. In this paper, an attempt has been made to analyze the spatial impact of BW＋SS processing using ‘Thermo-chemical conversion’ (TCC) technologies such as novel ‘Thermo catalytic reforming’ (TCR®) process developed at Fraunhofer UMSICHT. The idea is to understand the sustainable bio-waste treatment potential using TCR® technology and in turn production of energetically and commercially valuable intermediates from it. In accordance with the ‘Sustainable development goals’ (SDG-7, 13), such approach of BW＋SS treatment can provide affordable, reliable, sustainable, and modern energy for all. In addition to that, production of sustainable/green hydrogen (SH/GH) can also be envisioned while saving considerable number of valuable resources such as water. Bavarian region of Germany has been taken as an area of interest for this study. Cities such as Augsburg, Munich, Neu-Ulm and, Nuremburg are identified to be having highest potential for BW＋SS processing. It has also been found that there is a possibility for regional cluster formation to exploit the BW and SS processing potential.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108986"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}