Energy nexus最新文献

{"title":"A Multi-Hazard Resilience Framework for DER-Integrated Distribution Systems with Hierarchical Coordination and Sustainable Energy Markets","authors":"Devareddy Harsha,&nbsp;Pabitra Kumar Biswas,&nbsp;Sudhakar Babu Thanikanti,&nbsp;T. Yuvaraj,&nbsp;Mohammad Khishe","doi":"10.1016/j.nexus.2026.100806","DOIUrl":"10.1016/j.nexus.2026.100806","url":null,"abstract":"<div><div>The increasing frequency of compound extreme weather events, such as cyclones and floods, significantly threatens the resilience of active distribution systems by causing cascading infrastructure failures, prolonged outages, and communication disruptions. This study presents a multi-stage cyber–physical restoration framework for enhancing the resilience of DER-integrated radial distribution systems (RDSs) under compound multi-hazard conditions. The framework integrates probabilistic wind–flood fragility modeling, hierarchical decentralized restoration, microgrid islanding, inter-zone network reconfiguration, battery energy storage systems (BESS), electric vehicles (EVs), blockchain-enabled peer-to-peer (P2P) energy trading, repair crew scheduling with accessibility constraints, and AC power flow validation. A multi-objective optimization model is formulated to simultaneously minimize Energy Not Served (ENS), operational cost, Conditional Value-at-Risk (CVaR), voltage deviation, blockchain latency, and restoration unfairness while maximizing DER trading profit. The optimization problem is solved using a Modified Mountain Gazelle Optimization Algorithm (MMGOA) with a Dynamic Adaptive Weight Updating Mechanism (DAWUM). Topology-aware blockchain zonal sharding provides secure market coordination, transaction validation, and settlement between resilience zones. The proposed hierarchical coordination framework provides a flexible way to prioritize the objectives and restore the large-scale distribution systems in a computationally efficient manner. The proposed framework is validated on a modified IEEE 118-bus RDS under three representative compound hazard scenarios and compared with MGOA, PSO, GWO, and DE. The results show that MMGOA achieves the lowest average ENS (0.48 MWh), highest cumulative DER trading profit (38.42 k$), best overall multi-objective function (0.372), highest Hypervolume (0.891), and lowest GD (0.041) and IGD (0.048). Furthermore, the blockchain framework maintains an average transaction latency of 214 ms with 96.3% confirmation reliability while demonstrating stable scalability from IEEE 33-bus to IEEE 118-bus systems. These results confirm that the proposed framework effectively improves restoration resilience, economic performance, cyber–physical coordination, and computational scalability, providing a practical solution for resilient DER-integrated active distribution systems under compound multi-hazard conditions.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100806"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling of higher heating value using Physico-chemical properties of biomass: Statistical and machine learning approaches with validation on Bangladeshi biomass","authors":"Kiron Akter,&nbsp;Dilshad Zahan Ethen,&nbsp;Md. Rostom Ali,&nbsp;Tumpa R. Sarker,&nbsp;Moutusee Amir Meem,&nbsp;Bristy Sarker","doi":"10.1016/j.nexus.2026.100737","DOIUrl":"10.1016/j.nexus.2026.100737","url":null,"abstract":"<div><div>The global shift toward renewable energy underscores the importance of biomass as a sustainable and carbon-neutral energy source. Bangladesh relies heavily on biomass for rural energy, and agricultural residues have a massive energy potential. Modelling of the Higher Heating Value (HHV) of biomass is an important and time-demanding issue to accurately predict energy content based on basic compositional information of biomass. This study aimed to develop precise empirical and machine learning (ML)-based models to predict the higher heating value of agricultural residues and herbaceous biomass using physicochemical properties. A dataset comprising 150 biomass samples, including proximate, ultimate, and combined compositional analyses with experimentally measured HHVs, was compiled from the literature and used for model development. Traditional multiple linear regression methods and advanced ML algorithms were applied, and their performances were assessed using R², root mean square error, mean absolute error, and mean absolute percentage error. The developed regression models demonstrated strong predictive capability, with the best-fitting regression models achieving R² of 92.7%, MAPE below 4%, and RMSE within 1.2. Among the ML approaches, non-linear ML models, particularly Random Forest, providing the highest predictive accuracy across proximate (R² = 89.7%), ultimate (R² = 91.3%), and combined (R² = 92.0%) datasets. Independent experimental validation was performed using seven biomass samples collected in Bangladesh, including rice husk, rice straw, wheat straw, maize residue, sugarcane bagasse, banana peel, and coconut husk. Comparison with published HHV prediction correlations demonstrated that the developed models provided improved predictive accuracy and lower prediction errors. The findings demonstrate that machine learning models, particularly ensemble methods such as Random Forest, provide a reliable, effective, and scalable alternative to conventional HHV determination methods, such as calorimetric measurements and can support biomass resource assessment, feedstock screening, bioenergy system design, and renewable energy planning. The developed models were trained using a globally sourced biomass database and validated using Bangladeshi biomass samples, demonstrating their potential applicability across diverse biomass resources and geographical regions.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100737"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148853969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy shocks and food price volatility: A multi-period CGE analysis, 1997–2023","authors":"Tetsuji Tanaka","doi":"10.1016/j.nexus.2026.100805","DOIUrl":"10.1016/j.nexus.2026.100805","url":null,"abstract":"<div><div>This study examines structural changes in the transmission of crude oil and agricultural supply shocks to food and headline consumer price indices by comparing four benchmark years: 1997, 2007, 2014, and 2023. We develop a stochastic multi-regional, multi-sectoral Computable General Equilibrium (CGE) model calibrated to four benchmark databases from the Global Trade Analysis Project (GTAP). The model introduces Monte Carlo simulations of productivity shocks to crude oil, oilseeds, and soybeans, with shock magnitudes calibrated from historical production volatility using EIA and FAOSTAT data. To identify the structural mechanisms behind changing price responses across the four benchmark years, we also combine the CGE simulations with a Leontief inverse-based cumulative input analysis that measures embodied petroleum dependency in food supply chains, including direct fuel use and indirect effects through transport and chemical inputs. Results reveal a dramatic structural shift: food CPI responsiveness to oil shocks surged between 1997 and 2007 by a factor of 3.53 in the USA and 4.74 in Brazil, driven by global deepening of cumulative petroleum dependency. Variance decomposition confirms oil shocks became the dominant contributor by 2007 relative to agricultural productivity shocks. Although 2023 data suggest emerging resilience in China and Brazil through reduced energy intensity, energy-food linkages remain significantly stronger than in the late 1990s, especially in resource-importing regions. Food price stability is thus increasingly contingent on the energy architecture of agricultural supply chains.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100805"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Turning trash into power: bioenergy, economics, and carbon offsets from hydrothermal technologies","authors":"Chih-Chun Kung,&nbsp;Clovis Awah Che,&nbsp;Philippe M. Heynderickx","doi":"10.1016/j.nexus.2026.100807","DOIUrl":"10.1016/j.nexus.2026.100807","url":null,"abstract":"<div><div>Thermal decomposition of biomass is a carbon-negative renewable energy pathway to improve energy sustainability and mitigate climate change. Nevertheless, conventional thermal technologies, such as pyrolysis and co-firing, require drying feedstocks, which will significantly increase production costs. This study provides a pilot analysis of the conditions for advanced hydrothermal technology to be economically feasible for large-scale development and to what extent it substitutes for existing technologies. The energy conversion rate, processing modes, break-even condition, and uncertain market conditions of municipal solid waste, such as food waste, wood chips, and paper waste, are examined. Results indicate that (1) hydrothermal technology is relatively more cost-effective than pyrolysis and possibly reduces investment and operating risks of producers, (2) the use of hydrochar results in a significant trade-off between energy output and agricultural sustainability, (3) for food waste, 76.49% to 80.55% of profits comes from energy sales, whereas for wood chips and paper waste, 39.65–89.06% of profit comes from environmental benefits, (4) depending on hydrothermal modes, a 32.4%-54.7% decrease in energy output or energy price leads to loss-making conditions, and (5) profitability is relatively insensitive to changes in emission prices.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100807"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning for optimal power dispatch and reliability analysis of hybrid DG-PV-wind-battery and grid system in farming applications","authors":"chima tansi uwaezuoke,&nbsp;Nnamdi I. Nwulu","doi":"10.1016/j.nexus.2026.100808","DOIUrl":"10.1016/j.nexus.2026.100808","url":null,"abstract":"&lt;div&gt;&lt;div&gt;This paper proposes a reinforcement learning-based framework for optimal electrical power dispatch and reliability analysis in hybrid renewable energy systems designed for agricultural applications. The system integrates photovoltaic (PV) panels, wind turbines, battery energy storage, grid connectivity, and a diesel generator (DG) as a dispatchable backup source. Proximal Policy Optimisation (PPO) is employed to develop adaptive control strategies for these complex hybrid configurations. Five system configurations of increasing renewable integration complexity are systematically evaluated, ranging from a DG-Grid baseline (Case I) to a fully off-grid DG-PV-Wind-Battery arrangement (Case V), using a mathematical optimisation framework formulated by Gbadamosi and Nwulu (2020) as a comparative benchmark, implemented here via a MILP solver in AIMMS.&lt;/div&gt;&lt;div&gt;The power dispatch strategy adopts a weighted multi-objective formulation. A sensitivity analysis across six weighting scenarios identified optimal weights of 0.5 for system reliability, 0.2 for operational cost, and 0.3 for renewable energy penetration, corresponding to the configuration that simultaneously achieved the lowest LOLP (0.010) and the highest renewable generation (77.17 MWh/year) among all scenarios tested. Compared to the AIMMS MILP baseline, the PPO-based approach in the most comprehensive configuration (Case V) reduced operational cost from $3,186/year to $2,738 ± $566/year (a 14.1% reduction), increased renewable utilisation from 80.3 MWh/year to 86.5 ± 19.30 MWh/year (a 7.7% improvement), and reduced the Loss-of-Load Probability (LOLP) from 0.027 to 0.010 ± 0.003 (a 63.0% improvement). Across all five configurations, LOLP improvements ranged from 41.9% to 63.0% and LOLE improvements from 40.6% to 50.8%, with the Cost of Energy Not Served (CENS) reduced in four of the five cases. These absolute improvements, together with mean ± standard deviation metrics derived from 200 Monte Carlo simulation runs, provide a fully interpretable and statistically grounded measure of practical benefit.&lt;/div&gt;&lt;div&gt;Statistical robustness was ensured through 200 Monte Carlo simulation runs in which load demand and renewable resource availability were independently varied. Mean performance metrics and associated uncertainty bounds were computed across all simulation outcomes to confirm that reported improvements are not attributable to random variance.&lt;/div&gt;&lt;div&gt;The PPO agent was implemented using a three-layer feedforward neural network comprising two hidden layers of 128 neurons each with ReLU activation functions, and an output layer with a tanh policy head. Training was conducted using the Adam optimiser with a learning rate of 3 × 10⁻⁴, a discount factor (γ) of 0.99, a clipping ratio (ε) of 0.2, and a minibatch size of 64 over 2,000 time steps. Convergence was assessed through average episodic reward trajectories and policy stability metrics.&lt;/div&gt;&lt;div&gt;These findings demonstrate that r","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"23 ","pages":"Article 100808"},"PeriodicalIF":8.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Least-cost electrification pathways for Senegal by 2030: A nationwide analysis using open-source spatial electrification tool (OnSSET)” [Energy Nexus 21 (2026) 100621]","authors":"Adama Sarr ,&nbsp;Aldo Bischi ,&nbsp;Umberto Desideri ,&nbsp;Cheikh Mouhamed Fadel Kebe","doi":"10.1016/j.nexus.2026.100689","DOIUrl":"10.1016/j.nexus.2026.100689","url":null,"abstract":"","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"22 ","pages":"Article 100689"},"PeriodicalIF":9.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the thermodynamic performance of cascade phase change thermal storage system under unsteady conditions","authors":"Xiaoxia Li ,&nbsp;Yifan Gao ,&nbsp;Guobin Yuan ,&nbsp;Xiao Guo ,&nbsp;Jinping Li","doi":"10.1016/j.nexus.2026.100732","DOIUrl":"10.1016/j.nexus.2026.100732","url":null,"abstract":"<div><div>Cascaded phase change thermal storage (CPCTS) technology has broad application prospects in thermal energy regulation and management as a flexible and efficient means of thermal energy storage. However, most practical thermal storage engineering applications are under complex, unsteady conditions. Therefore, the effect of different arrangements of thermal storage units of low and medium-temperature phase change materials on the thermodynamic performance of the thermal storage system under unsteady conditions is addressed in this paper. This study focuses on the CPCTS device applied in the solar drying system and analyzes the thermodynamic performance of the cascade configuration under unsteady typical days. The results show that the two-dimensional model of the CPCTS is highly accurate when applied to the study of storage and exothermic processes. Under stable conditions, the effects of inlet temperature, flow rate, and melting point on the energy, exergy, and entransy performance of single-stage phase change thermal storage systems were analyzed. It is found that the matching of the phase change material's melting point and the inlet temperature is the core factor determining the heat storage performance. Furthermore, the thermodynamic characteristics of four cascaded configurations are compared under unsteady typical day conditions. Research shows that the CPCTS system has significant performance advantages under complex weather conditions. CPCTS provides up to 39.50% higher thermal storage capacity than a single-stage system, more stable energy quality (exergy efficiency) than a single-stage system under unsteady typical daily operating conditions, and up to 33.82% higher heat transfer capacity (entransy efficiency). The results verify the potential of CPCTS systems in improving heat source adaptability and comprehensive thermodynamic performance. The research results provide a theoretical basis for multi-scale modeling and engineering optimization of CPCTS systems.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"22 ","pages":"Article 100732"},"PeriodicalIF":9.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust greenhouse evapotranspiration modeling for sustainable multi-sectoral assessment","authors":"Anwar Hegazy ,&nbsp;Ajit Govind ,&nbsp;Mohammed Farid","doi":"10.1016/j.nexus.2026.100730","DOIUrl":"10.1016/j.nexus.2026.100730","url":null,"abstract":"<div><div>Traditional greenhouse crop evapotranspiration (ET) models rely heavily on post-installation operational data, limiting their utility for pre-construction planning and design. This study developed a greenhouse-coupled ET model that integrates crop biophysical characteristics directly into a thermal model to overcome this barrier. By utilizing only local environmental data, the model quantifies cooling, heating, dehumidification, and irrigation requirements for selected crops prior to construction. Validation against experimental data demonstrated high reliability, with a normalized Root-Mean-Square Error (nRMSE) below 9% and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> exceeding 0.93. Simulations in the arid conditions of Minya, Egypt, revealed that crops significantly impact the microclimate, reducing peak internal temperatures by 11 °C in summer and 7 °C in winter compared to an unplanted greenhouse. Additionally, the model quantified crop-induced humidity increases, allowing for the development of targeted climate control strategies. This integrated modeling approach optimizes resource management and energy efficiency, offering a robust tool for sustainable greenhouse design and multisectoral applications in precision agriculture.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"22 ","pages":"Article 100730"},"PeriodicalIF":9.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Hybrid Renewable Microgrid Optimization for Cost Reduction, Emission Mitigation, and Net-Metering Grid-Connected Stability in University Student Residences for Developing Countries” [Energy Nexus 21 (2026) 100649]","authors":"Md. Feroz Ali ,&nbsp;Md. Mahadi Hasan Rony ,&nbsp;Diganto Biswas ,&nbsp;Md. Esmay Azam ,&nbsp;Sk․ A. Shezan ,&nbsp;Innocent Kamwa","doi":"10.1016/j.nexus.2026.100711","DOIUrl":"10.1016/j.nexus.2026.100711","url":null,"abstract":"","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"22 ","pages":"Article 100711"},"PeriodicalIF":9.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Modeling power and workforce demand in French field agriculture highlights variability, fleet redundancy, and magnitude comparable to renewable electricity generation” [Energy Nexus 21 (2026) 100654]","authors":"Cyprien Drommi,&nbsp;Petros Chatzimpiros","doi":"10.1016/j.nexus.2026.100687","DOIUrl":"10.1016/j.nexus.2026.100687","url":null,"abstract":"","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"22 ","pages":"Article 100687"},"PeriodicalIF":9.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}