Energy Reports最新文献

{"title":"Dynamic carbon emission model of distribution network considering mix distributed generation","authors":"Jun Su ,&nbsp;Zhengyu Chen ,&nbsp;Chaolong Tang ,&nbsp;Zhiquan Liu","doi":"10.1016/j.egyr.2025.06.017","DOIUrl":"10.1016/j.egyr.2025.06.017","url":null,"abstract":"<div><div>Conventionally, Carbon emissions in power grids mainly refer to thermal power generation. However, the carbon emission factors are not constant anymore with the integration of renewable generation; they could considerably vary depending on the efficiency of generator units under dispatching schedules and the penetration of renewable generation. Therefore, it is essential to implement the accurate apportionment of carbon responsibility for the regional power grid. The ever-increasing deployment of distributed renewable energy generation makes the apportionment of carbon responsibility more complex in the distribution system, as it contains power flow contributed by dynamic thermal power generation, photovoltaic generation and power distribution loss. This paper proposes a carbon emission apportionment model that considers the dynamic carbon emission factors and the whole life cycle of Photovoltaic (PV) to realize the accurate calculation of carbon emissions in the distribution system. Through the case study, the carbon emission apportionment of the model meets the carbon flow balance, which can provide an accurate and effective method for the definition of system carbon responsibility and guide the load side and power side to participate in carbon emission reduction.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 320-331"},"PeriodicalIF":4.7,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313163","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 Hybrid GRU-MHA model for accurate battery RUL forecasting with feature selection","authors":"Abeer Aljohani ,&nbsp;Saad Aljohani","doi":"10.1016/j.egyr.2025.05.059","DOIUrl":"10.1016/j.egyr.2025.05.059","url":null,"abstract":"<div><div>The State of Health (SoH) and Remaining Useful Lifetime (RUL) forecasting of batteries is an important part of the health prognostics of electronic devices. This research presents a combination of Deep Learning (DL) and Machine Learning (ML) models to forecast the RUL battery’s linear and nonlinear behavior. The proposed method is separated into two phases. The initial phase separates the features into correlated and uncorrelated features, and the second phase determines the architecture of the proposed methods based on the two sets of correlated and uncorrelated features. The proposed method combines Gated Recurrent Units (GRU) and Multi Head Attention (MHA) with dense layers as the memory units. Finally, ridge regression is used for final forecasting. NMC-LCO 18650 and NASA lithium batteries datasets are used to evaluate the proposed method. Experimental results indicate 0.002 MAE, 0.044 MSE, and 99.99 R2 score for RUL forecasting of NMC-LCO 18650. The evaluation results on NASAB0005 indicated 0.005 MAE, 0.0706 MSE, and 99.64 R2 score for capacity estimation. The proposed method is compared with similar research and ML models such as Support Vector Regression (SVR) and DL models like Transformers and outperformed them. This research indicates superior performance in predicting declining trends of battery capacity using GRU and MHA with feature selection.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 294-309"},"PeriodicalIF":4.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313161","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":"Empowering off-river pumped hydro energy storage: An economic and environmental analysis of renewable energy production - storage systems","authors":"Diyono Diyono ,&nbsp;Hans Cappon ,&nbsp;Katarzyna Kujawa-Roeleveld ,&nbsp;Karel J. Keesman","doi":"10.1016/j.egyr.2025.06.026","DOIUrl":"10.1016/j.egyr.2025.06.026","url":null,"abstract":"<div><div>Off-river pumped hydro energy storage (PHES) is a developing technology that requires ongoing evidence to support its growth. Economic and environmental analyses demonstrating off-river PHES as a promising energy storage system (ESS) are explained in this study. A case study was conducted using Java-Bali data, Indonesia. This study compares the economic and environmental potential of off-river PHES with lithium-ion battery (LiB) and compressed air energy storage (CAES) systems. Additionally, this study examines the integration of these technologies with offshore wind, solar, and geothermal energy mixes using hourly weather and supply-demand data. Results indicate that off-river PHES is superior economically and environmentally. For Java and Bali, off-river PHES requires the lowest investment for annual electricity generation, with total project costs ranging from $0.04 billion/year to $157 billion/year and annual operation and maintenance (O&amp;M) cost or OPEX from $0.02 billion/year to $76 billion/year. Despite high annual OPEX, off-river PHES emits the least CO₂ at 3 Mt/year, reducing emissions by approximately 97 % compared to coal-based power plants. Next, social and technical considerations provide a more comprehensive feasibility comparison between the involved ESS. Ultimately, this paper provides scientific evidence supporting off-river PHES for renewable energy-based hourly electricity generation.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 310-319"},"PeriodicalIF":4.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313162","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":"Advanced geospatial analytics and multi-objective optimization framework for national rooftop solar assessment: Cuba case study","authors":"Manuel Soto Calvo ,&nbsp;Han Soo Lee","doi":"10.1016/j.egyr.2025.06.012","DOIUrl":"10.1016/j.egyr.2025.06.012","url":null,"abstract":"<div><div>This study presents a comprehensive assessment of Cuba's rooftop solar potential through an innovative methodological framework that integrates advanced geospatial analysis, big data analytics, and multi-objective optimization. Our approach specifically addresses the critical methodological gap between microscale precision and macroscale applicability, enabling granular analysis scalable to large regions without compromising computational efficiency. The framework employs the electrical storm optimization algorithm to process over 2.8 million rooftops, identifying 1424,789 suitable locations for photovoltaic (PV) installations across Cuba. The results demonstrate a potential capacity of 1375 MWp under current technological capabilities, which could meet 39.6 % of Cuba's projected 2050 midday peak electricity demand and generate 2175 GWh yearly (11.1 % of the country's projected annual energy demand). Through scenario-based logistic modelling incorporating technological, economic, and social variables, we simulate six distinct scenarios from \"Economic Stagnation\" to \"Disruptive Innovation\" for the period 2024–2050, quantifying installed capacity, energy production, self-consumption patterns, and economic viability metrics. Regional analysis reveals southeastern provinces as optimal for PV deployment because of superior solar irradiance (6.2 kWh/m²/day), whereas urban areas such as Havana show the highest feasibility because of dense rooftop availability. This research provides evidence-based strategies for policymakers by identifying critical success factors, such as technological advancements (elasticity coefficient of 3.1 for installed power), economic incentives (ROI averaging of 21.42 %), and regionalized deployment approaches prioritizing high-irradiance areas.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 254-267"},"PeriodicalIF":4.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306152","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 harvesting from living plant: A review on past research and way forward","authors":"Sk Rakibur Rahman ,&nbsp;Ngu Eng Eng ,&nbsp;Muhammad Aqeel Ashraf ,&nbsp;Wai-Leong Pang ,&nbsp;Kar Ban Tan ,&nbsp;Ajay Kumar Singh ,&nbsp;Kah-Yoong Chan","doi":"10.1016/j.egyr.2025.06.021","DOIUrl":"10.1016/j.egyr.2025.06.021","url":null,"abstract":"<div><div>In the face of the global warming crisis, reliance on non-renewable energy sources poses significant ecological threats, prompting the international community to explore renewable alternatives. Numerous alternative energy sources are currently being studied, including solar, wind, hydropower, geothermal, plant-based, and biomass. This review highlights recent advancements in extracting energy from living plants by looking at 70 peer-reviewed research studies published from 2010 to 2025, including 15 that focused on Plant-Based Cells (PBCs), 24 on Plant Microbial Fuel Cells (PMFCs), and 21 that examined the physical traits of plants that influence how efficiently energy can be harvested. PMFC harnesses electron generation through the interaction of plant roots and soil bacteria during organic matter decomposition, while PBC captures electrical energy directly from the plant’s metabolic processes via embedded electrodes. PMFCs typically generate lower voltage and power, whereas PBCs achieve significantly higher outputs, up to 3.5 V and 58.8 mW. The analysis examines key factors influencing energy efficiency, including plant species, age, soil composition, electrode materials, environmental conditions, and circuit design, and addresses scalability and integration challenges. The findings underscore the necessity for further research on optimal sizing, aging effects, and practical applications. Overall, this review aims to provide a solid foundation for advancing green energy technologies and their deployment in low-energy devices, such as IoT sensors, in smart farming and remote areas.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 268-281"},"PeriodicalIF":4.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313165","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":"Repurposing depleted hydrocarbon reservoirs for geothermal energy: A case study of the Vilkyčiai Cambrian sandstone in Lithuania","authors":"Abdul Rashid Memon,&nbsp;Pijus Makauskas,&nbsp;Ieva Kaminskaitė-Baranauskienė,&nbsp;Mayur Pal","doi":"10.1016/j.egyr.2025.06.027","DOIUrl":"10.1016/j.egyr.2025.06.027","url":null,"abstract":"<div><div>Lithuania, located on the eastern edge of the Baltic sedimentary basin, hosts a geothermal anomaly in its southwestern region. This anomaly comprises two main geothermal complexes: the Cambrian and Devonian aquifers. The Cambrian formation, consisting of tight sandstones, extends beyond 2000 m in depth and reaches reservoir temperatures of up to 96°C. The Devonian formation, composed of unconsolidated sands from the Pärnu–Kemeri formation, lies at depths exceeding 1000 m with reservoir temperatures up to 46°C. Several depleted Cambrian oil fields in Lithuania operate at 99 % water-cut, making them promising candidates for geothermal heat utilization. This study explores the repurposing of depleting oil and gas reservoirs for geothermal energy development with potential application of horizontal wells for geothermal production in the Vilkyčiai field. From the simulation results it is confirmed that a single horizontal well is capable of producing 447 m³ /day of water, supporting a power output of 1148 KW over a 25-year period. Furthermore, findings suggest that increasing horizontal well length up to 4500 m and application of fracturing can significantly enhance water production by 23 %, thereby boosting power generation. A full-field simulation over 30 years confirmed that no thermal breakthrough occurred between wells, validating a 2000 m well spacing as the optimal scenario for field development. These results highlight the advantages of horizontal well technology in geothermal applications, demonstrating increased water production, improved energy recovery, lower drilling costs, and greater operational efficiency—mirroring advancements in hydrocarbon extraction. In addition, the requirement for home heating at a considerable distance from the old oilfield to residential neighborhoods presents a significant logistical challenge. This study also underscores the potential for substantial heat loss through both surface and subsurface pathways during long-distance hot water transportation, emphasizing the necessity for effective insulation to minimize thermal losses. Thus, this study shows that old oil and gas reservoirs can be reused to produce geothermal energy. Moreover, it also supports Lithuania’s goals for using more renewable energy. In addition to that it also highlights how horizontal wells can help develop geothermal resources.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 243-253"},"PeriodicalIF":4.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306151","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":"Developing an urban geographic archetype dataset to support energy retrofit strategy on residential buildings in Düsseldorf, Germany","authors":"Qinghua Yu ,&nbsp;Gerald Mills ,&nbsp;Gunnar Ketzler ,&nbsp;Michael Leuchner","doi":"10.1016/j.egyr.2025.06.004","DOIUrl":"10.1016/j.egyr.2025.06.004","url":null,"abstract":"<div><div>Cities play an important role in climate change mitigation, with building energy use often accounting for the largest share of urban CO₂ emissions. This study introduces an urban geographic archetype dataset integrated with the Urban Building Energy Model (UBEM) tool, CitySim, to assess the long-term impact of refurbishment strategies in residential areas. While the case study focuses on Düsseldorf, Germany, the methodology uses different building archetypes provided by the European TABULA project and climate data under RCP8.5 scenario for the years 2020, 2050, and 2100 to offer broader insights applicable to other European cities. Unlike many existing UBEM applications that neglect boundary conditions, our framework accounts for buildings surrounding the study area, improving simulation accuracy. Our results identify high-energy-demand building types, especially historic and older multi-family residential buildings built before 1957, which occupy over 60 % of the housing stock, accounted for 70 % of the total heating energy demand. Tailored renovation strategies in these buildings can significantly reduce annual CO₂ emissions by 42–46 % and decrease heating energy demand by approximately 70 % by 2050. Such strategy also can bring significant cost savings, with annual energy costs decreasing from 16.6 to 18.2 €/m² to 9.6–9.9 €/m². This work contributes to urban sustainability planning by prioritizing retrofit strategies based on building archetypes, climate projections, and spatial context, thus aligning with EU decarbonization targets.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 282-293"},"PeriodicalIF":4.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313166","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":"Emerging innovations in solar photovoltaic (PV) technologies: The perovskite solar cells and more","authors":"Panneerselvam Priya,&nbsp;Albert Alexander Stonier","doi":"10.1016/j.egyr.2025.06.003","DOIUrl":"10.1016/j.egyr.2025.06.003","url":null,"abstract":"<div><div>Solar energy is a renewable and sustainable power source that reduces dependence on fossil fuels, helping to mitigate climate change. It lowers greenhouse gas emissions and provides an eco-friendly solution for meeting global energy demands. This review comprehensively overviews conventional and emerging light-absorbing materials in solar photovoltaic (PV) systems. It begins by detailing traditional materials such as silicon (monocrystalline and amorphous), cadmium telluride (CdTe), cadmium sulfide (CdS), and thin-film technologies, highlighting their roles in current solar energy production. The review then transitions to explore the innovations in solar PV technologies, focusing on perovskite solar cells (PSCs), poised to revolutionize the field due to their high efficiency and low manufacturing costs. Other advanced materials discussed include organic photovoltaics (OPVs), quantum dot solar cells (QDSCs), dye-sensitized solar cells (DSSCs), and tandem solar cells (TSCs), with in-depth analysis of their chemical compositions, structural designs, functional mechanisms, fabrication processes, and the advantages and limitations of each technology. Environmental impacts, degradation rates, and future research directions are also critically examined. The review further explores the evolution of solar panel configurations, comparing conventional monofacial panels with innovative bifacial panels and solar window technologies. By synthesizing current and emerging trends, this review offers valuable insights into the future trajectory of solar PV systems, emphasizing the potential for improved efficiency, cost-effectiveness, and sustainability in solar energy technologies.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 216-242"},"PeriodicalIF":4.7,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280346","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":"Wind farm parameter optimization identification method based on multi-agent SAC","authors":"Qiu Quan Deng,&nbsp;Cui Yun Luo,&nbsp;Yin Wu,&nbsp;Guang Ming Li,&nbsp;Xie Jin Ling,&nbsp;Zhen Cheng Liang","doi":"10.1016/j.egyr.2025.06.002","DOIUrl":"10.1016/j.egyr.2025.06.002","url":null,"abstract":"<div><div>As more wind farms are integrated into power grid, the safe and stable operation of power system becomes increasingly challenged, making accurate wind farm modeling particularly important. Based on multi-agent soft actor critic (SAC) deep reinforcement learning (DRL), this method identifies wind farm parameters under multiple fault conditions. The method compares reactive power output curves between the detailed model and multi-agent SAC identified model, ultimately obtaining high-accuracy parameters. Finally, the effectiveness and superiority of the proposed method are verified by comparing with the identification results of Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO).</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 205-215"},"PeriodicalIF":4.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280230","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 novel multi-objective approach to user-centric energy management systems","authors":"Bahman Ahmadi ,&nbsp;Gerwin Hoogsteen ,&nbsp;Paweł Zawadzki ,&nbsp;Marco E.T. Gerards ,&nbsp;Weronika Radziszewska ,&nbsp;Sebastian Bykuć ,&nbsp;Johann L. Hurink","doi":"10.1016/j.egyr.2025.05.084","DOIUrl":"10.1016/j.egyr.2025.05.084","url":null,"abstract":"<div><div>The growing complexity of micro-grid management and the demand for resilient, sustainable energy systems require solutions that go beyond traditional management strategies. This paper introduces a Multi-Objective Energy Management System (MOEMS) designed for micro-grids and energy communities, emphasizing resilience and sustainability. Unlike conventional Energy Management Systems (EMS), which mainly focus on cost and efficiency, MOEMS takes a user-centered approach. It incorporates a democratic decision-making process that involves all stakeholders, enabling personalized energy management tailored to user preferences and environmental considerations. MOEMS is used to address grid challenges like power congestion and voltage issues while balancing diverse stakeholder goals. The optimization problem is formulated as a mixed-integer non-linear program and adopted to be solved using a free and open-source solver. The proposed framework leverages the results of a multi-objective optimization model, allowing users to define their preferences. By specifying an acceptable solution space, the central controller in the micro-grid can optimize operations while ensuring that the selected solutions align with user expectations. The system is validated through simulations and a real-world micro-grid case study, demonstrating its adaptability to different setups. To evaluate its effectiveness, MOEMS is compared with traditional EMS approaches, including profile steering and methods that prioritize economic and environmental factors. A real-time implementation in the Kezo micro-grid further demonstrates its capability to dynamically manage energy flows, meet user energy demands, and adapt to real-time fluctuations in supply and demand. Significantly, MOEMS achieved up to 22% higher annual electricity cost savings and a 37% reduction in CO2 emissions compared to traditional EMS methods.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 185-204"},"PeriodicalIF":4.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280229","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}