International Journal of Energy and Environmental Engineering
The International Journal of Energy and Environmental Engineering (IJEEE) is a multi-disciplinary, double-blind peer-reviewed open access journal, covering all areas of energy and environment related fields that apply to the science and engineering communities. IJEEE aims to promote rapid communication and dialogue among researchers, scientists, and engineers working in the areas of energy and environmental engineering. The journal provides a focus for activities concerning the development, assessment and management of energy and environmental engineering related programs. The journal aims at becoming an important factor in raising the standards of discussion, analyses, and evaluations relating to energy and environment programs. This journal operates on an open access model and charges an Article Processing Charge (APC) to cover the costs of publication and ensure free and permanent access to published articles. We are committed to supporting researchers worldwide and offer APC waivers for authors from low- and middle-income countries, as well as in cases of financial hardship. Waiver requests are considered on a case-by-case basis and do not influence editorial decisions. For more information on APCs and waiver eligibility, please visit our APC & Waivers Policy page. Journal DOI: 10.57647/ijeee Journal Abbreviation: Int. J. Energy Environ. Eng. Journal Title Acronym: IJEEE Journal Owner: Islamic Azad University Publisher: Islamic Azad University, Co-publishing, OICC Press, UK Director-in-Charge: Farivar Fazelpour, PhD
Thermodynamic Simulation of An Innovative Combined System Including CO2 Power Cycle and Organic Rankine Cycle in Order to Reduce CO2 Emission
In this research, by simulating the newly invented Allam cycle and creating a combined cycle based on Allam, the CO2 produced in the Allam cycle is used as the working fluid and in addition to reducing the greenhouse effect, the energy wasted in the Allam cycle will be used as the energy supplier for two medium temperature and low temperature cycles. Reducing energy waste by creating a new combined cycle will lead to an increase in the LHV efficiency of the power plant and consequently reduce global warming and environmental hazards.Using Thermoflow software, the Allam cycle, organic Rankine cycle, and Rankine cycle were simulated, and then the aforementioned cycles were combined to reduce energy loss and the results were examined. The simulated combined cycle, in an ideal case, will increase LHV by 0.5% compared to the Allam cycle, and by considering the energy loss by the components in the Allam cycle, and then creating a combined cycle, it will increase efficiency by 0.98%. The creation of a combined cycle led to a reduction in energy loss in the system. The CO2 produced in the power plant was contained in the system and also by examining the environmental conditions of the power plant and increasing the pressure to 1.127 bar, relative humidity of 10 %, and temperature of zero degrees, the LHV efficiency of the power plant increased. Increasing the efficiency of the power plant, reducing energy loss, and also absorbing CO2, all will lead to a reduction in global warming and increasing environmental protection.
Copyright (c) 2026 Abdulla Ammer Abdulkader, Alireza Saraei, Hossein Ahmadi Danesh Ashtiani, Mohsen Jabbari (Author) https://creativecommons.org/licenses/by/4.0
Cyber Security Resilience Score Index: A Comprehensive Framework for Assessing and Improving Organizational Cybersecurity Posture
A framework designed to assess and monitor organizational cybersecurity readiness continuously. Addressing the limitations of static, compliance-based methodologies, the CRSI synthesizes the People, Process, and Technology (PPT) dimensions into a unified, network-agnostic metric. Unlike traditional binary assessments, this framework employs a dynamic maturity model applicable across diverse environments, including Information Technology (IT), Operational Technology (OT), and Critical Infrastructure Networks (CIN). The methodology integrates threat-informed weighting algorithms with qualitative control evaluations to generate a holistic resilience index. The framework’s efficacy is demonstrated through a simulated case study of a hybrid utility environment ("EnergyTech"), validated by a sensitivity analysis that confirms the model's stability against subjective weighting variations. Ultimately, the CRSI empowers organizations to diagnose capability gaps with precision, benchmark performance against industry standards, and optimize strategic investments in security infrastructure.
Copyright (c) 2026 Riyadh Jasim Mohammad, Ali Broumandnia, Razieh Farazkish, Mona Moradi (Author) https://creativecommons.org/licenses/by/4.0
A Novel Market-Based Strategic CoordinationFramework for Aggregators and V2G Owners UsingBi-Level MPEC
The accelerating electrification of societies is imposing stress on power grids, particularly during peak demand. While renewable integration offers a promising pathway to address challenges, its intermittency necessitates advanced storage strategies. Vehicle-to-Grid (V2G) technology, leveraging electric vehicles (EVs) as distributed storage, provides a scalable cost-effective solution to balance supply and demand. This paper introduces a novel Stackelberg bi-level optimization framework establishing a local market between the aggregator and EV owners, enabling fair dynamic price-setting for energy exchange. Recognizing emerging trends where aggregators increasingly own distributed energy resources (DERs), the model accounts for their influence on market prices strategically. The aggregator’s commitments to the grid are embedded as prioritized constraints, ensuring reliability and hierarchical coordination with operators. Crucially, battery degradation costs for both EVs and stationary battery energy storage systems (BESS) are modeled, balancing short-term revenues and long-term health. The model incorporates key battery and charger technical limitations—state of charge (SoC), charging rate (C-rate), and depth of discharge (DOD)—along with a minimum SoC requirement to meet the EV owner’s personal mobility demand, all within a continuous framework avoiding explicit binary variables. Implemented in GAMS and validated under diverse scenarios, the model demonstrates potential for scalable V2G integration and market-driven grid flexibility.
Mathematical programming with equilibrium constraints (MPEC)
Copyright (c) 2026 Saeed Zafaranchi Zadeh Moghadam, Mohammad Agha Shafiyi, Gholam Reza Latif Shabgahi, Seyed Saied Taheri (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Energy Consumption in Office Buildings: A Sustainable Design and Novel Materials Approach
Optimizing energy performance and achieving sustainable design in buildings are critical global challenges. This study addresses these challenges by examining the Molla Sadra Administrative-Educational Building as a case study for data‑driven sustainable design. Using a mixed‑methods approach, the research integrates theoretical analysis, energy simulation, life cycle assessment (LCA), and machine learning (ML) applied to twelve months of operational data. Advanced models, including Random Forest and MLP, achieved an accuracy of 92% in predicting energy consumption and identified three primary influencing factors: occupancy (38%), solar radiation (24%), and ventilation (19%). Based on these insights, dynamic temperature adjustments between 22 °C and 25 °C were recommended, leading to an estimated 18% reduction in energy use. The discussion highlights the synergy between sustainable architectural design and intelligent data utilization in enhancing building performance. Overall, the Molla Sadra project is positioned as a benchmark for sustainable, data‑driven buildings that achieve substantial reductions in both energy consumption and carbon emissions.
Copyright (c) 2026 Hedieh Dabiri, Rasul Pahlavanpour (Author) https://creativecommons.org/licenses/by/4.0
Integrating Digital Innovation and Green Practices for Advancing Corporate Sustainability in Modern Business Environments
Due to the growing emphasis on sustainability and digitalization in today's business context, there are newly grounds to consider the need to address developing Digital Innovation (DI) alongside green practices to improve firm sustainability. The current research seeks to investigate from DI, which consists of include Artificial Intelligence and Internet of Things, as well as green practices which consists of renewable energy solutions and waste reductions, the constructs of corporate sustainability and business performance. To aid in understanding this relationship, a survey of 200 individuals across the diverse industries was collected then analyzed using descriptive statistics, as well as using correlation and regression approaches. Results indicated the mean of DI (2.9800), Green Practices (2.9793), and Sustainability Outcomes (3.0742) all reflected low levels of implementation. Correlation and regression analyses indicated weak development relationships reflecting of statistically insignificant relationships (p > 0.05), indicating that leadership appear to be potentially more influential that other observable factors towards sustainability performance. The research provides insight on how little emerged from the interaction data between digital and green purposes, and puts forth additional implications for future research on how cultural and regulative strategies uniquely influence the change-based nature of sustainable corporate performance.
Copyright (c) 2026 Yanli Yu (Author) https://creativecommons.org/licenses/by/4.0
Quantification of Microplastics and Total Petroleum Hydrocarbons in Seawater and Sediments of the Southwestern Caspian Sea
Coastal environments are increasingly threatened by emerging contaminants; however, integrated assessments of multiple pollutant classes remain scarce. This study provides a simultaneous investigation of microplastics (MPs) and total petroleum hydrocarbons (TPHs) in seawater and sediments along the southwestern coast of the Caspian Sea. Samples were collected from fourteen coastal stations during the dry season. MPs were characterized by type, size, shape, color, and polymer composition using microscopy and Raman spectroscopy, while TPHs were quantified using GC-MS. The results revealed MP abundances ranging from 0.05 to 2.35 articles L⁻¹ in water and from 2.01 to 24.13 particles kg⁻¹ in sediment. Fibers and fragments in the 100–500 μm size class were dominant, with polyethylene (PE) and polyethylene terephthalate (PET) identified as the most prevalent polymers. TPH concentrations varied from not detected to 20.152 μg L⁻¹ in water and from 8.37 to 1835.29 μg kg⁻¹ in sediment. Spatial analysis showed no overlap between the contamination hotspots of MPs and TPHs, indicating distinct sources and transport pathways. This concurrent assessment underscores a complex contamination profile in the region and highlights potential ecological risks from both physical and chemical stressors. The distinct spatial patterns provide critical information for designing targeted pollution control measures.
Copyright (c) 2026 Roghayeh Shahbazi, Ebrahim Fataei, Ali Mehdinia, Maryam Hazrati Niari, Hossein Saadati (Author) https://creativecommons.org/licenses/by/4.0
Synergistic Analysis of Adsorption-Desorption Characteristics Based on Pressure-Desorption Capacity: A Case Study of Xiaohuigou Coal Mine in China
To thoroughly reveal the intrinsic relationships among coal gas adsorption-desorption characteristics, coal structure, and oxidation activity under deep high-temperature and high-pressure conditions, and optimize gas extraction schemes for high-gas mines, we performed a study. Coal samples from the 2201 mining face of No.2 coal seam in Xiaohuigou Coal Mine, China, were selected as the research object. A series of tests were systematically conducted under experimental temperature: 30-120℃, pressure: 0.5-2.5MPa, including proximate analysis, ultimate analysis, adsorption-desorption experiments, thermogravimetric analysis (TG), and Brunauer-Emmett-Teller (BET) pore structure test. The regulatory mechanism of coal gas adsorption and desorption under the coupling effect of temperature and pressure was clarified. The response mechanism between the pore structure of coal samples and their pyrolysis characteristics was determined. An optimization method for gas extraction parameters based on "synergistic regulation of pressure and desorption capacity" was established. This technology was successfully applied to the gas control scenario of the 2201 working face. After applying this technology, the average gas concentration at the upper corner of the working face decreased to 0.40%, with the maximum concentration controlled below 0.67%. The width of the goaf oxidation zone was reduced from the original 40-50 m to 25-30 m. The pure gas extraction flow rate per borehole reached 3.77 m3/min, equivalent to the extraction effect of 580 Φ113 mm reverse drilling holes. The cumulative pure gas extraction volume reached 7.01×105 m³, which effectively guarantees the safe production during the high-intensity mining of the working face.
Copyright (c) 2025 Yongming Zou, aolin Cao, Jie Kang, Yuntao Liang, Congna Hao, Linlin Ding, Fuchao Tian (Author) https://creativecommons.org/licenses/by/4.0
Enhanced Photovoltaic Performance of UMGSilicon via Boron/Phosphorus Co-Doping and Data-Driven Process Optimization
The transition toward cost-effective photovoltaic technologies requires alternatives to high-purity polysilicon, where upgraded metallurgical-grade (UMG) silicon represents a promising candidate. Yet, the intrinsic limitations of UMG silicon namely elevated impurity concentrations, high defect densities, and shortened minority carrier lifetimes remain key barriers to its widespread adoption. In this study, we address these challenges by exploring boron (B) and phosphorus (P) co-doping in UMG silicon nanostructures as a route to performance enhancement. A multi-tiered methodology was established, integrating controlled doping experiments, advanced electrical characterization, and compact-device modeling informed by Shockley Read Hall recombination kinetics. To systematically capture process property performance relationships, a design of experiments (DOE) framework was implemented, wherein dopant concentrations, carrier lifetimes, and interfacial resistances were varied. The extracted photovoltaic parameters open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), and conversion efficiency (η) were quantitatively benchmarked against undoped references. Simulation results indicated that optimized B/P co-doping reduces recombination-active defect states and enhances carrier transport, yielding Voc improvements exceeding 60 mV and FF gains up to 8% under typical operating conditions. Monte Carlo–based statistical analysis further confirmed the robustness of the identified optima, with η improvements of 15–20% compared to baseline UMG silicon. These findings substantiate the role of synergistic B/P co-doping as a technically viable and economically attractive strategy to elevate UMG silicon toward high-efficiency photovoltaics. The presented optimization framework provides not only a mechanistic understanding of dopant–defect interactions but also practical guidelines for scaling laboratory protocols to industrial solar cell manufacturing.
Upgraded Metallurgical-Grade Silicon (UMG-Si)
Design of Experiments (DOE) in Solar Cells
Copyright (c) 2025 Mohsen Jabbari, Ehsan Fallah Tafti, Mohammadreza Hedayati, Alireza Saraei (Author) https://creativecommons.org/licenses/by/4.0
Energy Consumption and Sustainable Development of the Tourism Industry: A Study from a Global Perspective
Energy consumption will cause environmental pollution to intensify, affect the development of modern tourism, and a large number of tourism behaviors will also exacerbate carbon emissions, so new energy technologies such as wind power and photovoltaic can promote the sustainable development of tourism and balance the relationship between social development and entertainment consumption. This paper examines the relationship between green energy and the tourism industry, focusing on wind and photovoltaic power to optimize tourism energy structures and broaden renewable applications. Results indicate that photovoltaic and wind generation fluctuates but remains stable, while rising tourism energy demand is offset by renewables, reducing consumption without harming operations or economic benefits. Tourism energy use peaks between 13:00–18:00, when photovoltaic supply is stable and maximal; demand grows from 8:00–13:00; and from 18:00–24:00 photovoltaic output declines while wind power provides storage. Savings increase with photovoltaic shares up to 50%. Energy stability reaches 70–75%, and satisfaction ranges from 65.32–85.36%. Therefor the sustainable development of new energy can enhance the sustainability of tourism, promote its development in the direction of energy conservation and environmental protection, and improve the integration level of electricity and tourism.
Copyright (c) 2025 Jingying Li (Author) https://creativecommons.org/licenses/by/4.0
Knowledge Graph–Enhanced NB-IoT Architecture for Intelligent Educational Data Management and System Innovation
This study proposes a Knowledge Graph–Enhanced NB-IoT Architecture to improve intelligent educational data management and support system innovation in learning environments. The purpose is to address limitations of traditional innovation and entrepreneurship education, which often lacks real-time contextual information and personalized guidance. The proposed approach integrates NB-IoT for distributed, real-time data acquisition with a domain-specific knowledge graph for semantic organization, correlation, and enrichment of learning resources. A multi-layer framework was designed to support data collection, knowledge representation, and intelligent recommendation processes. Experimental evaluation demonstrates that the architecture enhances the relevance of learning activities, improves resource organization, and delivers more accurate and personalized educational interventions. The results indicate that combining NB-IoT infrastructure with knowledge graph intelligence can significantly strengthen adaptive learning environments and better support the development of innovative competencies.
Copyright (c) 2025 Yang Xianxian, Li Jiangtao (Author) https://creativecommons.org/licenses/by/4.0
Integrating Solar Energy and Pumped Hydro Storage for Sustainable Power Supply: A Case Study of the West Bengal Power Sector
This paper undertakes a real-world performance evaluation of the Purulia Pumped Storage Plant (PPSP) situated in West Bengal and its integration with solar photovoltaic (PV) power plant to assess the technocommercial feasibility of the hybrid system towards mitigation of intermittency nature of solar energy which is a necessity for India’s energy transition and ambitious renewable energy goals, using high-resolution real operational SCADA data instead of simulation-based approaches. Pumped Hydroelectric Plants (PHPs) are primarily utilised during peak demand periods and in instances of major power plant outages. India’s very first pumped storage plant was commissioned in 1985. Presently, out of the 4.75 GW of pumped storage plants installed across the country, 3.3 GW are operational in pumping mode. However, these plants have fallen short of their projected energy generation figures due to insufficient availability of off-peak power for pumping operations, until the advent of various renewable energy sources, notably solar energy, in India. Against this backdrop, this analysis delves into the economic viability of the Purulia Pumped Storage Plant considering real solar generation profiles distributed across the west Bengal state and PPSP operational data of six representative days viz., 6th Feb, 26th April, 8th June, 24th Aug, 4th Oct and 30th Dec across different seasons of the year 2023 to assess critical performance indicators such as Daily Efficiency (DE), Cumulative Pumped Ratio (CPR) and Levelized Cost of Storage (LCOS) for both actual grid power operational scenario and hypothetical solar power generation driven pumping scenario matching with the actual PPSP operation. The study findings reveal that the hypothetical solar-PPSP hybrid system doesn’t just slightly improve economics, it fundamentally enhances the value proposition by drastically reducing reliance on the grid electricity, achieving up to a 100% displacement on four days except 6????ℎ Feb and 30????ℎ Dec. This substitution of high-cost grid power of up to 6.01 INR/kWh with low-cost solar energy with LCOE of 2.55 INR/kWh directly caused a dramatic surge in the CPR by 15% to 136% and halved the LCOS range to 0.31-1.05 INR/kWh, fundamentally transforming the plant’s economic viability. CPR on the six representative days have increased from 159.50, 98.36, 102.20, 40.23, 137.37 and 152.18 in actual operational scenario to 182.92, 178.98, 225.64, 94.82, 294.13 and 230.83 respectively in the hypothetical scenario. On the other hand, LCOS has decreased from the range of 0.38 - 2.32 INR/kWh in case of actual operational scenario to the range of 0.31 - 1.05 INR in the hypothetical scenario. This work may therefore contribute as a data-driven evidence base on the technoeconomic advantages of solar-powered pumping for PHP with varying seasonal demand and solar generation patterns, marking a significant departure from model-driven studies and would be useful for policymakers in reinforcing the role of hybridization of the existing and the upcoming PHPs in achieving India’s ambitious renewable energy goal.
Copyright (c) 2025 Sarwar Hossain, Ratan Mandal (Author) https://creativecommons.org/licenses/by/4.0
Edge-Based AI Models for Detecting Nonlinear Energy Consumption Patterns in Smart Grid Environment Networks
This paper presents an innovative Edge AI framework to detect nonlinear energy usage patterns in real-time with the application to Smart Grid infrastructures. Designed for use on Edge devices where there is limited processing power but required to be highly analytic error-free, the proposed framework is lightweight. The authors developed GBOCLE - Energy, an efficient Anomaly Detector Model based on advanced Gradient Boosting methods specifically for low latency and real-time energy consumption data analysis. This model uses compressed forms of Light Gradient Boosting and One-Class SVM Algorithms to discover temporal, contextual, and relational anomalies across multiple Nodes on the Smart Grid network. Additionally, three techniques, Simplified Terrestrial Analysis, Adaptive Isolation-based Scoring, and Lightweight Graph-Based Neighbour Mechanisms, were used to further enable the detection of nonlinear relationships and interactions among the different components within the grid. The model uses Compact Nonlinear Indicators (e.g., Consumption Dev Index, Device Influence Vector and Reduced-Order Chaotic Metrics) as Analytical Features and Hidden Indicators for Anomalous Energy Usage. The experimental findings indicate that the suggested ensemble model has excellent detection accuracy (0.984), precision (0.974), sensitivity (0.975), specificity (0.965), F1-score (0.975), and AUC (0.984) while minimizing the computational and memory requirements for edge deployment. These findings support the conclusion that optimized edge-based detection is an efficient and feasible solution for detecting energy consumption anomalies and therefore offers the potential to enable predictive management and enhance operational performance in the Smart Grid environment.
Smart Grid Systems, Energy Consumption Analysis
Copyright (c) 2025 Wei Hu (Author) https://creativecommons.org/licenses/by/4.0
Multimodal AI-Driven Edge Framework for Energy-Efficient Human Activity Monitoring in Smart Grids
This publication presents a Multimodal AI-Driven Edge Framework designed to provide energy-efficient monitoring of human activity in Smart Grids. Current approaches for monitoring energy networks utilize Centralized Cloud Infrastructures that place heavy demands on computers and create very high latencies in communication as well as limiting the ability to create real-time recommendations to optimize energy consumption. The proposed framework will utilize a combination of compressed and full deep learning pipelines combined with quantized inference models, and tracking methods based on TinyML technology; allowing for the utilization of multimodal data (occupancy, movement, and usage) directly on edge devices (NVIDIA Jetson Nano, Raspberry Pi 5, ARM-based Processors) therefore minimizing memory footprint, computational load, and power consumption while generating very accurate analysis across all )modality types tested. Test results demonstrate 42%-70% decreased latency, 30%-55% decreased memory footprint and that they operate in an energy-efficient manner making them ideal for continued field deployment. Our Multimodal AI-Driven Edge Framework enables the collection of activity data to develop predictive energy management systems, implement adaptive demand responses and support real-time energy optimization for Smart Grids without dependence on cloud connectivity. We have demonstrated lightweight, multimodal AI technology that will augment the energy efficiency, scalability, and intelligence of future Smart Grids.
Copyright (c) 2025 Xiaodong Xue, Yunhao Tang (Author) https://creativecommons.org/licenses/by/4.0
Feature Selection and Seasonal Variability in Forecasting Global Horizontal Irradiance: Insights from Riyadh Meteorology
This study investigates the seasonal variability of meteorological feature importance in forecasting Global Horizontal Irradiance (GHI) using machine learning (ML) and deep learning models. High-resolution solar and meteorological data from NREL’s NSRDB (24.25°N, 45.34°E, 740 m) were seasonally partitioned into winter, spring, summer, and autumn. Feature selection was conducted using Pearson correlation (threshold 0.25), followed by dimensionality reduction through Principal Component Analysis (PCA). Six ML models XGBoost, LightGBM, Random Forest, SVR, MLP, and LSTM were trained on the processed datasets, and SHAP analysis was used to interpret feature contributions. The results revealed that clear-sky irradiance parameters (GHI, DNI, DHI) consistently dominated GHI prediction (correlation >0.95; SHAP >10⁻¹), while features like temperature and relative humidity varied across seasons. Wind direction, though weakly correlated, showed increased influence in winter. PCA enhanced model stability in spring and winter but slightly reduced accuracy during periods of high irradiance variability. Overall, XGBoost and Random Forest models provided the most accurate and reliable forecasts across seasons.
Copyright (c) 2025 Debasish Sarker, Mohammad K Hossain, Md Arifuzzaman (Author) https://creativecommons.org/licenses/by/4.0
Energy production and future perspectives of active photovoltaic materials
The rapid growth of the photovoltaic device market is imperative for meeting global sustainability goals, such as reducing chlorofluorocarbon emissions to improve air quality, fulfilling the increasing public energy demands, and ultimately lowering the cost of electricity production. The development and application of advanced energy materials are gaining significant attention within the scientific and industrial communities. In this context, recent research has focused on exploring various photophysical mechanisms that can be integrated into photovoltaic devices to achieve conversion efficiencies theoretically surpassing the Shockley-Queisser limit. This limit, which represents the maximum theoretical efficiency for single-junction solar cells, has long been considered a fundamental constraint. However, innovative strategies such as multi-junction architectures, down-conversion and up-conversion layers, hot carrier extraction, and plasmonic enhancements are being investigated to overcome these limitations. It is noteworthy that approximately 55% of incident photon energy is lost, predominantly due to sub-bandgap losses, where photons have insufficient energy to excite electrons across the bandgap, and thermalization losses, where excess photon energy above the bandgap is dissipated as heat. Addressing these intrinsic loss mechanisms is crucial for the development of next-generation photovoltaic technologies capable of delivering higher efficiencies and supporting a sustainable energy future.
Copyright (c) 2025 Nisar Ali, Otman Abida, Mohamed Essalhi, H. Alrobei, Bakhtiar Ulhaq (Author) https://creativecommons.org/licenses/by/4.0
Transactive Control-Based Optimal Scheduling with Uncertain PV Generation and Demand Response
Electric vehicles (EVs) are transforming transportation, requiring planned charging networks to handle rising demand, manage power systems, reduce congestion, and integrate renewables. This work develops a stochastic optimization framework for EV charging stations with transactive control to cut carbon emissions and promote EV adoption. Consequently, deploying EVCS will become a crucial strategy for addressing the integration of renewable energy. An innovative approach to supplying electric power stored in EV fleets involves using transportation networks as supplementary infrastructure .The article explores how transportation networks affect EVCS and DR scheduling under uncertain PV output, highlighting the benefits of DR integration for improved efficiency and grid reliability. A mixed-integer linear programming (MILP) model is proposed to enhance the solution quality of the DC power flow method, thereby reducing computational complexity. A detailed evaluation of economic and environmental factors is included, showing a 1.288% overall system cost reduction with EV fleets and DR in scheduling costs, and a 2.431% decrease in EV fleet and solar PV power costs. This research aims to offer a sustainable, cost-effective, and scalable solution for EV charging infrastructure to support future electric mobility, with the dispatching problem verified on a modified IEEE 30-bus system.
Copyright (c) 2026 Arpita Bharti, Rajeev Kumar Chauhan, Pranda Prasanta Gupta (Author) https://creativecommons.org/licenses/by/4.0
Green Energy Materials: Pathway to the Green Future
The increases in power costs and environmental pollution are major contributors to global warming worldwide. Modern power generating systems therefore require green technologies that focus on improving energy harvesting-based power generation. Green energy materials are essential for converting other types of energy into electrical energy. Among the major energy storage technologies, heat energy storage has maximum efficiency and can be converted into electrical energy by thermoelectric devices. Thermoelectric materials play a dual role: they convert thermal energy into electricity via the Seebeck effect and provide heating or cooling when electrical energy is applied through the Peltier effect. This review highlights the role of thermoelectric devices in converting heat to electricity and in the reverse process for effective thermal management. The review also encompasses the utilization of renewable energy sources, such as photovoltaics, for hydrogen generation to mitigate the intermittency of solar energy.
Copyright (c) 2026 Nisar Ali, Musarrat Jabeen, Otman Abida, Mohamed Essalhi, Bakhtiar Ulhaq, H. Alrobei (Author) https://creativecommons.org/licenses/by/4.0
Integrated Optimization of Energy Storage Systems in a Multi-Energy Hub with Waste Heat Recovery from Modular Multilevel Converter
This paper presents a novel reliability-constrained optimization framework for the design and sizing of a Multi-Energy Hub (MEH) that integrates Combined Heat and Power (CHP), Electrical Energy Storage Systems (ESS), Thermal Storage Systems (TSS), renewable energy sources, and uniquely waste heat recovery from a Modular Multilevel Converter (MMC). The proposed energy hub model simultaneously meets the electricity and thermal demands of an industrial consumer under real tariff conditions and climate data from Phoenix, Arizona. The key innovation lies in modeling the thermal losses of the MMC as a valuable source of heat recovery, which reduces boiler fuel consumption and operational expenditures (OPEX). The optimization framework employs a Genetic Algorithm (GA) to minimize the total cost, encompassing capital expenditure (CAPEX) and OPEX, while enforcing constraints on energy balance, storage limitations, system capacity, and permissible energy shortage. Two scenarios one with MMC heat recovery and the other without are evaluated for both summer and winter conditions. The results show that incorporating MMC heat recovery reduces total cost by up to 2.3%, mainly due to reduced gas consumption in the boiler. Furthermore, a reliability-based constraint ensures that at least 95% of the energy demand is met, minimizing Energy Not Supplied (ENS) and enhancing system resilience. The proposed method provides a scalable and flexible design tool for next-generation industrial energy systems, particularly in hot climates with highly variable energy demand. Integrating waste heat from power electronics into hybrid energy systems introduces a novel dimension in thermal-electric synergy.
Copyright (c) 2026 Sayed Payam Safavizadeh, Javad Olamaei, Sayed Mostafa Abedi (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Optimization of Management Decision Support Systems Using Data Mining Techniques
As patterns of energy usage become more complicated, smart, data-driven strategies are needed for effective forecasting and management. In order to optimise decision support systems in the energy industry, this study suggests a hybrid data mining framework that combines Extreme Gradient Boosting (XGBoost) with K-Means clustering. The model is intended to increase the precision and interpretability of energy usage forecasts while detecting discrete consumption behaviour clusters by utilising the publicly accessible UCI Individual Household Electric Power Consumption dataset. The suggested XGBoost + K-Means model performs noticeably better than conventional models like Linear Regression, Decision Tree, and Random Forest, according to a comparative analysis. It achieves a high R2 score of 0.91, a mean absolute error (MAE) of 39.7 Wh, and a root mean square error (RMSE) of 49.6 Wh. Furthermore, evaluation criteria including F1-score, precision, and recall attest to the model's resilience and appropriateness for real-time applications. These results demonstrate how hybrid machine learning techniques can convert energy data into useful insights, which will ultimately help develop more intelligent and sustainable energy management plans.
Copyright (c) 2025 Shizhou Feng, Jing Du (Author) https://creativecommons.org/licenses/by/4.0
Predictive Thermal Analysis of 500kV GIS Circuit Breakers via Coupled Multi-Field Simulation
The temperature of the contacts is a crucial signal for successfully identifying thermal failures in GIS (Gas Insulated Switchgear) disconnects switches, which may cause dangerous electrical mishaps. This highlights the critical need of doing temperature field computations for GIS. In any electrical system, gas insulated switchgear (GIS) plays an essential role. The insulating gas's efficacy and the device's lifespan would be diminished if the conductor on the GIS busbar were to overheat. Subsequently, we optimize the busbar's output by adjusting its center distance, conductor thickness, and rotation angle. Gas insulated switchgear and gas insulated transmission lines are essential components of any reliable electrical system. For long-distance, high-power transmission, GILs are progressively displacing traditional overhead lines because to their lower overall cost and improved efficiency in space usage and power transfer. Studying GILs' power loss with temperature profiles is important for their dependable functioning and their design optimization ease. Variations in conductor thickness accounted for over 70% of the variation in maximum temperature as well as power loss, according to the research. Combining the aforementioned methods (A1, B5, C5) allows for a decrease in GIS thermal load and energy usage. Also, following structural adjustment, the study demonstrates that SF6 gas maintains its exceptional insulating ability. This is proven by calculating the gas breakdown margin. Finally, this work adds to our knowledge of how threephase GIS busbars conduct heat, which is useful for optimizing and designing these systems. The skin effect of current is a major contributor to the temperature of gas insulated bus bars, which are the result of linked multiphysics fields including fluid, thermal, and eddy current. This study builds a 3D model of a GIS bus bar and uses additional fine grids to account for the skin impact of current in order to get an accurate loss value. The three-phase GIS bus bar's temperature field distribution is predicted using the finite element approach in conjunction with fluid analysis. The study shows that multi-physics field coupling simulations are useful for enhancing the performance and reliability of power system components, particularly GILs, by looking at the temperature field of 500kV GIS circuit breakers. Doing so improves the efficiency and dependability of the power system's components. The primary finding of this study is that three-phase GIS busbars should be designed with optimal heat transfer and other factors in mind. A 500 kV GIS disconnect switch is modeled using a multi-physics simulation in this article. To begin, the distribution of losses is determined by electromagnetic modeling calculations. The distributions of both the temperature and the flow field are then obtained by coupling the computed losses to the fluid field's temperature as heat sources. In anomalous contact situations, this is used as a basis for additional temperature field investigation for varied contact resistance values. According to the findings, the GIS disconnect switch's top contacts are where the majority of the temperature spikes occur. When there is insufficient contact, the maximum temperature is 45. 23 percent greater than when everything is running well. For precise analysis of the temperature field of a 500kV GIS circuit breaker, multi-physics field coupling simulation is essential.
Copyright (c) 2025 Runkun Guo (Author) https://creativecommons.org/licenses/by/4.0
SARSA: A Novel model for Market Trading Mechanisms and Operational Strategies for Multi-Type Energy Storage Systems in Power Markets
To maximize performance and profitability, power markets may use a variety of trading methods and operational techniques for multi-type energy storage systems. In accordance with the capacity and structure of the market, these systems may take part in energy markets, supplementary services markets, along with local or P2P marketplaces. With the use of pricing signals and demand estimates, operational strategies aim to maximize income through intelligently charging and releasing the various storage kinds. Regional distribution networks that rely on distribution network operators can now quantitatively determine their energy storage supply and demand with the help of this study's suggested approach for determining Hybrid Golden Flower Pollination (HGFP) Method energy storage action deviations. This method will be crucial for their future participation in market trading. Second, taking into account the economic advantages for all parties involved, the study created a pricing mechanism that incorporates a valley compensation mechanism to encourage autonomous and active user participation using the SARSA Deep Learning technique. The trading mechanism relies on combinatorial auctions and accommodates various types of market participants. Taking into consideration the variations in HGFP energy storage action when different regional networks are involved, numerical simulations were run to confirm the trading mechanism's practicality and rationale.
Copyright (c) 2025 Bin Zhao, Lining Wang, Bo Wei (Author) https://creativecommons.org/licenses/by/4.0
Research on Cooperative Optimal Configuration of Distributed Energy Storage System and Renewable Energy
The intermittency, volatility, and intrinsic unpredictability of renewable energy sources provide significant problems to the dependable operation of power grids, especially as these sources are becoming widely available. An essential resource for improving the power grid's flexibility, absorbing a large amount of additional energy, and meeting the dynamic demand-supply balance is energy storage. Energy storage is still somewhat expensive, and finding the best way to set it up is the main challenge right now. Distributed generation, a source of grid energy, is becoming more significant in the distribution network as part of the overall trend toward green energy growth. Distributed generating on a massive scale introduces uncertainty and unpredictability, which threatens distribution network dependability and stability. In order to mitigate the negative effects of dispersed generation and maximize the positive effects on distribution network operations, this article investigates the optimal design of energy storage systems. It provides a reasonable analysis of the possibility of collaboration to increase stability and profitability by building a mathematical model of a distributed generating and energy storage system. According to the operational data, distributed generation exhibits seasonal traits, and the scenario reduction approach groups together a collection of common daily scenarios for spring, summer, fall, and winter and clustered using K-means clustering technique. We solve the optimum building planning problem using an objective function and limitations based on the IEEE 33-bus distribution network using Deep Neural Networks (DNNs). Throughout the year, there are four distinct configurations for energy storage. But in order to limit the impact of dispersed generations, it is generally closed. Finally, it suggests some directions for distribution network expansion in the future and summarizes the paper's operations.
Copyright (c) 2024 Wang Xianchun, Gao Lijuan, Yang Haiyue, Gao Bing, Zhang Kang (Author) https://creativecommons.org/licenses/by/4.0
Research on Carbon Finance Risk Prediction of the Optimal Integration Index of New Energy Resources
As a research hotspot in the field of energy, the development degree of new energy is related to the development of carbon finance market. In order to quickly promote the realization of the dual carbon goals and improve the sustainability of new energy development, this paper proposes a carbon finance analysis method, combined with ant colony algorithm to analyze new energy data such as wind, photovoltaic and hydropower, and judge the carbon finance market. Then, risk prediction is carried out based on the energy value created by wind and photovoltaic power, as well as the depth of energy development. Finally, the utilization effect of new energy sources such as wind power and photovoltaic power is output, the stability of energy output, and the development depth of wind power and photovoltaic energy. The results show that there is a fluctuating relationship between the energy output of wind and photovoltaic power generation and their risk level, and the increase of electricity will inhibit the financial risk, and the energy output value is greater than the threshold of 15%. The risk reduction rate was 36.28% and 34.50%, and the integration of new energy and carbon finance was -0.2896, indicating that new energy has an impact on carbon finance, and the score of carbon emission was relatively low, which was 0.0178. The index integrates energy to reduce the risk rate of carbon finance, and the reduction rate is about 25%. Therefore, the research on carbon finance risk prediction can provide support for the development of wind power and photovoltaic power generation, expand the application scope of new energy, and realize the application depth of wind energy and photovoltaic power.
Copyright (c) 2025 Jiantao Jiang (Author) https://creativecommons.org/licenses/by/4.0
Metaheuristic-Driven Deep Learning Framework for Soil Liquefaction Assessment
Soil liquefaction in saturated sandy deposits remains a major concern in seismically active regions. This study presents a metaheuristic-driven deep learning framework for liquefaction assessment using a dataset of 512 boreholes collected across Mazandaran Province, Northern Iran. The dataset consists of 316 non-liquefied and 196 liquefied cases derived from field observations and verified geotechnical parameters. The proposed framework integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) architectures, whose hyperparameters are optimized using the Grey Wolf Optimizer (GWO) and the Whale Optimization Algorithm (WOA). Model performance was evaluated using stratified repeated train–test experiments (10 iterations) to ensure reproducibility. The ensemble model achieved an average accuracy of 94.5%, a correlation coefficient of 0.93, and AUC values above 0.95, outperforming all individual models and conventional liquefaction evaluation methods. SHAP-based analysis identified SPT blow count and peak ground acceleration as the most influential variables. The proposed framework demonstrates strong robustness and practical applicability for regional seismic risk assessment and can be extended to other geotechnical prediction tasks.
Copyright (c) 2024 Shima Aghakasiri, Ghodratollah Mohammadi, Mohammad Emami Kourandeh, Amir Taban (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of Automatic Algorithm Learning Techniques for Wind Speed Prediction and Modeling: The Case of the City of Amdjarass
Wind speed forecasting is essential for electrical energy management and planning. Wind turbine energy production varies according to fluctuations in wind speed. Unpredictable variations in wind patterns create vulnerabilities for wind power installations. To mitigate this unpredictability, an effective approach is to anticipate wind speed at specific heights, which is crucial for the operation of wind farms. Although previous work has examined different learning algorithms, researchers are still striving to find a stable model that minimizes prediction error. The objective of this study is to predict wind speed using machine learning algorithms. The results of the study highlight the remarkable predictive performance of eleven models, which are based on regression and classification methods with cross-validation. On the test data, the evaluation reveals impressive results, with coefficients of determination ranging from 0.986023 to 0.794080. The comparative study of the different algorithms shows that the proposed LightGBM (LGB) model outperforms most similar models in the literature, achieving statistically significant accuracy values. This result made it possible to evaluate the power of a wind turbine over the 12 months of the year. It shows that March generates the most energy, while August produces the least.
Copyright (c) 2024 Osée Mounkang, Claude Vidal Aloyem Kaze, Nzoko Tayo Dieudonné, Germaine Djuidje Kenmoe, René Tchinda (Author) https://creativecommons.org/licenses/by/4.0
Experimental Optimization of Mahua Biodiesel Performance Using Butylated Hydroxytoluene (BHT) and Ascorbic Acid Additives
The present study focuses on the experimental optimization of Mahua (Madhuca indica) biodiesel performance using Butylated Hydroxytoluene (BHT) and Ascorbic Acid as antioxidant additives to enhance fuel stability and engine efficiency. Biodiesel produced from Mahua oil via base catalyzed transesterification was blended with diesel in proportions of biodiesel blends. A single cylinder, four-stroke diesel engine was used to test the effects of different BHT and ascorbic acid concentrations (500–2000 ppm) on the blend's oxidative stability, combustion, and emission properties. The experiments were designed using the Response Surface Methodology (RSM) to identify the optimum additive concentration for improved performance. Results revealed that BHT improved oxidation stability and reduced nitrogen oxide (NOx) emissions, while Ascorbic Acid demonstrated superior control over carbon monoxide (CO) and unburned hydrocarbon (HC) emissions. The optimized blend, B20 with 1500 ppm BHT, achieved the highest Brake Thermal Efficiency (BTE) and lowest Specific Fuel Consumption (SFC) among all tested samples. Statistical analysis confirmed strong model accuracy with a desirability index above 0.9. Overall, the study demonstrates that the combined application of antioxidants significantly enhances the storage stability, combustion efficiency, and emission performance of Mahua biodiesel, positioning it as a sustainable and oxidation-resistant fuel for compression ignition engines.
Copyright (c) 2024 A. Vinothkumar, Pandian Balu, N. Anbazhaghan, C. Thamotharan (Author) https://creativecommons.org/licenses/by/4.0
Application of Geological Route Selection and Teaching Project with Low Energy and Reducing Energy Consumption
This study aims to explore the effectiveness of implementing low-energy technologies. Through experiments, it was found that the traditional geological route selection method consumes 156 kilowatt hours per kilometer of electricity, while the average electricity consumption of the new low energy consumption technology is 90 kilowatt hours per kilometer. Low energy consumption technology can reduce energy consumption by about 37%. In a six-month 200 student teaching project, students' understanding of energy conservation and emission reduction has greatly improved, and their understanding rate of energy-saving technology has increased from 40% before teaching to 87% after teaching. These findings are of great significance for promoting education in energy efficiency and sustainable development.
Copyright (c) 2024 Ning Ma, Xiaoyan Zhao, Gang Luo (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Greenhouse Gas Emissions from a Combined Cycle Power Plant using RETScreen
Population growth and increasing energy demand have intensified fossil fuel consumption, resulting in higher greenhouse gas (GHG) emissions and environmental impacts. This study evaluates the environmental performance of the Saravan combined cycle power plant in Guilan Province, Iran, under two proposed seasonal fuel mix scenarios using RETScreen Clean Energy Management Software. Scenario 1 represents normal winter conditions, with natural gas used for ten months (March–December) and gasoil for two months (January–February), achieving total annual GHG emissions of 4,814,379 tCO₂ and a projected 30-year reduction of 71,882,453 tCO₂. Scenario 2 reflects severe winter conditions, with natural gas for eight months (April–November) and gasoil for four months (December–March), resulting in 5,018,646 tCO₂ of annual emissions and a projected 30-year reduction of 32,780,122 tCO₂. The analysis demonstrates that strategic seasonal adjustments in fuel usage—prioritizing natural gas when available and supplementing with gasoil during peak cold months—can substantially reduce GHG emissions while maintaining reliable electricity production. Furthermore, risk assessment indicates that the optimized seasonal fuel mixes provide economic benefits, with estimated savings from carbon shadow pricing of $119,804,088 for Scenario 1 and $54,633,537 for Scenario 2 over 30 years. Implementing such optimized seasonal fuel strategies enhances both environmental performance and operational flexibility, offering an effective approach for sustainable electricity generation and climate change mitigation in fossil fuel-based combined cycle power plants.
Copyright (c) 2024 Aida Mehdizadeh Mojdehi, Hasan Karimzadegan, Mohammad Dehdar Dargahi, Leila Ooshaksaraei (Author) https://creativecommons.org/licenses/by/4.0
Financial Modeling of Social and Environmental Impacts of Wind Farm in Urban Zones: A Case Study of Zawia-Libya
This study presents both a physical and monetary evaluation of the key social and environmental impacts linked to the development of a 20 MW wind farm in Zawia, Libya. The analysis considers a range of externalities, including carbon footprint, water use, noise emissions, visual impacts, shadow and shadow flicker, land occupation, and potential electromagnetic interference. The project is planned on a 2 km² site and will incorporate 10 Gamesa G-114 wind turbines. Using the System Advisor Model (SAM) for energy yield estimation, the annual electricity generation reaches approximately 104,240.6 MWh. The project's initial capital cost is estimated at $39.36 million. The conventional levelized cost of energy (LCoE), calculated without factoring in social or environmental externalities, stands at $86.84/MWh. When externalities are monetized, they contribute an additional $7.92/MWh, representing nearly one-third of the operation and maintenance expenses ($25/MWh). If the evaluation internalizes only the carbon footprint, the LCoE decreases to $49.43/MWh. Conversely, when all social and environmental impacts are fully incorporated, the adjusted LCoE rises to $57.35/MWh. These findings highlight the critical importance of integrating external costs into wind energy assessments, providing a fairer comparison with conventional energy sources and strengthening the case for renewables in competitive energy markets.
Copyright (c) 2024 Abdulbari A. Amhimmid, Wedad A. El-Osta, Yasser F. Nassar, Hala J. El Khozondar, Mansour A. Salem (Author) https://creativecommons.org/licenses/by/4.0
A Systematic Review of Data Analytics Approaches in Smart Electricity Markets
The rapid digitalization, decentralization, and integration of renewable energy in electricity markets have heightened the demand for advanced data analytics to manage their complexity and ensure sustainability. This systematic review explores the transformative role of data analytics, machine learning and artificial intelligence in enhancing efficiency across key areas of smart electricity markets. These include forecasting (price, demand, load, carbon emissions, and supply), trading, security, bidding, and the impact of renewable energy on pricing. Following PRISMA guidelines to conduct this systematic literature review (SLR), a rigorous selection process narrowed 504 initial articles to 23 high-impact studies from 2014 to 2024, offering a focused analysis of emerging techniques such as hybrid models, neural networks, blockchain, and explainable AI. The review identifies advancements in predictive accuracy, adaptive decision-making, and decentralized trading mechanisms, emphasizing their ability to address market volatility and enhance operational stability. Furthermore, the findings highlight gaps in existing approaches and propose future research directions, including the integration of real-time analytics, reinforcement learning, and decentralized frameworks. By providing a structured overview, this study serves as a resource for researchers and practitioners, underscoring the critical role of data-driven solutions in the evolving landscape of smart electricity markets.
Copyright (c) 2024 Muhammad Muwahid Asim, Fareed Ud Din, Asif Karim, Niusha Shafiabady (Author) https://creativecommons.org/licenses/by/4.0