Search Results (1,080)

Electric vehicles are an integral part of transportation electrification and are increasingly embedded within smart-grid-integrated energy systems that support accessibility, efficiency, and reduced environmental impact. As electric vehicle adoption grows, new challenges emerge in intelligent vehicle control, energy management, load management, and EV integration into the smart grid. In response, this paper presents a comprehensive survey of electric vehicle systems covering market evolution, enabling technologies, operational performance, and the energy systems that underpin scalable electric mobility. The survey illustrates the need for real-time monitoring, control, and optimization while exploring advanced computational approaches in quantum computing and machine learning that can address these challenges. Finally, this work identifies open research challenges and future directions related to energy optimization, smart-grid integration, and intelligent load management to provide a unified perspective on electric vehicles as a key component of both intelligent vehicle systems and sustainable smart transportation. Full article

The integration of large-scale electric vehicle (EV) fleets into vehicle-to-grid (V2G) systems offers significant potential for enhancing the operation of renewable-based smart grids. However, stochastic uncertainties in photovoltaic (PV) generation, vehicle availability, and load demand—coupled with battery degradation and life-cycle assessment (LCA) carbon emissions—pose major challenges to optimal scheduling. This paper proposes a scenario-based multi-objective MILP framework for a 500-EV fleet aggregator. The model incorporates Monte Carlo simulations for multi-source uncertainty quantification (±25% PV forecast errors, ±40% availability), LCA penalties (45 kgCO₂eq/kWh), and ancillary service revenues (25 USD/MW-h). Long-term state-of-health (SOH) projections, including a 1-year fade to 96.5%, are also integrated. Comparative analysis of V2X scenarios shows that the V2G Hybrid strategy reduces daily costs by 34.6% (from ~11,000 USD in the uncontrolled case to 7741 USD when reserve revenues are included), achieves over 50% peak shaving, and maintains voltage stability within 0.994–1.008 pu. The stochastic Pareto frontier identifies knee-point solutions that lower normalized expected costs to 134.61 while achieving 1–2% lower expected emissions compared to deterministic baselines. These results demonstrate a comprehensive framework, uncertainty-aware framework that balances economic viability, grid resilience, and environmental sustainability, offering actionable insights for fleet aggregators and policymakers working toward net-zero energy systems. Full article

This study explores a photovoltaic/thermal (PV/T)-based electrolysis system designed for dual production of hydrogen fuel and domestic hot water (DHW), providing a sustainable energy solution amid rising global emissions. A dynamic rule-based control mechanism with hysteresis thresholds on hydrogen-storage state of charge (SoC) is implemented to balance electrolyzer operation with intermittent solar availability, maintaining PV/T power outputs while preventing storage overfilling and minimizing start–stop cycling. The system is assessed across 27 geographically diverse cities spanning a wide range of solar irradiation and energy price structures. Annual hydrogen yields range from 20 kg/yr in high-latitude locations (Helsinki, Stockholm) to 33.5 kg/yr in high-irradiation regions (Riyadh, Abu Dhabi), while the levelized cost of hydrogen (LCOH) spans from 6.47 USD/kg (Riyadh) to 22.86 USD/kg (Helsinki). Economically, the system achieves its strongest performance in solar-rich, high-energy-cost environments: Rome records the highest net annual cash flow (858.9 USD/yr) and shortest payback period (2.47 years), followed by Davos, Madrid, Brasília, and Canberra. In contrast, locations with subsidized energy tariffs—such as Algiers, Kyiv, and Tehran—yield low or negative net cash flows, rendering the system economically unviable without policy support. Environmental analysis reveals annual CO₂ avoidance ranging from 0.33 ton/yr (Stockholm) to 2.97 ton/yr (Riyadh), with a global mean of 1.095 ton/yr and a combined total of approximately 29.6 tons/yr across all examined sites. A machine learning model is developed to generalize performance predictions across unseen locations, achieving leave-one-out (LOO) R² values of 0.953 (net cash flow), 0.935 (LCOH), and 0.947 (LCO-DHW), with mean absolute errors below ±1 USD/kg and ±0.03 USD/kWh. The findings confirm that, under fixed capital cost assumptions, local electricity price and solar irradiation are the dominant drivers of economic viability, while grid carbon intensity and solar resource jointly govern environmental performance, with markets offering irradiation above 1500 kWh/m²·yr and electricity prices exceeding 0.2 USD/kWh representing the most promising deployment targets. Full article

As modern power systems undergo a paradigm shift toward decentralization, driven by substantial investments in Distributed Energy Resources (DERs), Virtual Power Plants (VPPs) have emerged as the primary mechanism for their effective technical and commercial integration. This paper provides a seminal and comprehensive literature review, dissecting the VPP ecosystem through operational, infrastructural, and coordination strategy perspectives. By categorizing VPPs into distinct technical and commercial frameworks, this study critically evaluates their role in optimizing smart grid components, including demand response, multifaceted market structures, cooperative game-theoretic behaviors, and multi-carrier energy systems. The analysis transcends basic infrastructure, focusing on the resolution of fundamental challenges: mitigating carbon emissions and energy costs, characterizing generation uncertainty and asynchrony, and maintaining the dynamic equilibrium between supply and demand. Furthermore, the review explores advanced strategies for incentivizing prosumer engagement, enhancing market pricing transparency, and ensuring transaction integrity within rigorous operational constraints. A significant methodological evolution is identified, highlighting the transition toward advanced mathematical frameworks and data-driven optimization techniques designed to enhance system resilience and operational stability under multifaceted uncertainties. The synthesis reveals that VPP-led sector coupling integrating electricity, thermal, and hydrogen vectors provides a robust pathway for minimizing grid imbalances and diminishing the overall carbon footprint. By evaluating the subject through a multidimensional lens (technical, economic, environmental, and regulatory) this study serves as a critical reference and strategic roadmap for researchers, planners, and policymakers aiming to navigate the complexities of future smart grids and build a sustainable energy ecosystem. Full article

Addressing the prevalent issues of scattered data sources, reliance on multi-software collaboration, and low integration efficiency between Building Information Modeling (BIM) and Life Cycle Assessment (LCA) in current building life cycle carbon emission accounting, this study aims to construct a BIM-driven, data-traceable automated method for building life cycle carbon accounting. This paper proposes a life cycle carbon accounting framework based on Revit secondary development. By defining unified data mapping rules and constructing a scalable localized carbon emission factor database, this framework achieves a seamless workflow from BIM model information extraction and intelligent factor matching to phased accounting and report generation. Taking an office building in Nanning as an empirical case study, the results indicate that the operational stage and the building material production stage are the primary emission sources, accounting for 78.82% and 24.13% of the total emissions, respectively; the transportation stage accounts for 1.68%; the construction stage accounts for 0.40%; and the demolition and recycling stage exhibits negative emissions of –3.53% due to material recovery benefits. The accounting results of the developed plugin exhibit a relative error of 6.67% compared to traditional methods, and the robustness of the accounting framework is verified through uncertainty analysis. Sensitivity analysis further reveals that the grid emission factor, key material factors, and building design service life are the core variables affecting carbon emissions. The contribution of this study lies in proposing an operable and scalable BIM-LCA integrated solution. Its practical value resides in providing a real-time data feedback tool for low-carbon optimization during the building design stage, as well as offering a highly transparent methodological reference for carbon accounting in engineering practice, thereby supporting data-driven decision-making in the pursuit of sustainable urban development. Full article

The transition toward carbon-neutral cities and sustainable infrastructure requires massive capital mobilization, yet traditional static valuation models like discounted cash flow (DCF) systematically undervalue green projects due to high initial capital expenditures and long-term uncertainty. To address this critical gap in sustainable finance, this study proposes a novel Artificial Intelligence–Blockchain–Multiple Real Options (AI-MRO) integrated framework. This model aligns infrastructure profitability with Environmental, Social, and Governance (ESG) criteria and United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (Sustainable Cities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). The core approach integrates AI-based probabilistic forecasting for carbon footprint optimization and cash flow prediction, MRO-based operational flexibility assessment, and blockchain-based smart contracts (Security Token Offerings, STOs) to ensure transparent green finance governance and social inclusion. Through empirical validation at Singapore’s Punggol Digital District (PDD)—a flagship smart city project featuring a district-level smart grid reducing 1700 tonnes of CO₂ and generating 3000 MWh of solar energy annually—this model successfully captured investment resilience (Extended Net Present Value, ENPV > 0) even in crisis scenarios where conventional DCF models failed. The results demonstrate that integrating digital twins and AI-driven ESG metrics structurally reduces the risk premium and amplifies the strategic value of sustainable investments. This study represents a substantial methodological contribution toward data-driven, automated, and transparent governance, offering a scalable financial framework for global net-zero infrastructure development. Full article

The increasing integration of distributed photovoltaic (PV) systems in urban environments requires planning frameworks that simultaneously address economic viability, environmental sustainability, and power system performance. This study develops a simulation-based techno-economic and environmental assessment framework for evaluating hybrid rooftop photovoltaic (PV) and building-integrated photovoltaic (BIPV) deployment under harsh climatic conditions. Detailed system modelling using PVsyst and ETAP is conducted to analyse energy production, economic performance, environmental impact, and grid interaction characteristics, including voltage deviation and harmonic distortion. To support deployment planning and operational decision-making, the simulation outputs are incorporated into a multi-objective optimisation framework that evaluates trade-offs among levelized cost of energy (LCOE), net present value (NPV), carbon emission reduction, and power quality indicators. Three deployment configurations including rooftop PV only, BIPV only, and a hybrid PV–BIPV system are assessed using structured trade-off analysis and Pareto optimality principles. Results indicate that the hybrid configuration provides the most balanced performance across technical, economic, and environmental objectives. The system achieves an average performance ratio of 77.36% and generates approximately 2075 MWh of annual energy while maintaining grid voltages within acceptable limits and harmonic distortion well below IEEE 519 thresholds. Economic analysis shows strong financial feasibility with an LCOE of approximately 0.05 USD/kWh, a payback period of 8.1 years, a net present value of about 2.88 million USD, and a return on investment exceeding 145%. Loss analysis further identifies temperature effects and dust accumulation as the dominant performance constraints under harsh environmental conditions. Moreover, Pareto-based evaluation confirms the hybrid PV–BIPV configuration as the preferred deployment strategy among the evaluated alternatives. The proposed framework demonstrates how integrated simulation and multi-objective optimization can serve as a practical decision-support tool for planners and policymakers seeking to optimise distributed renewable energy deployment under climatic and operational uncertainties. Full article

Promoting equitable and sustainable urban mobility requires accessibility assessment approaches that extend beyond purely geometric proximity measures toward service-sensitive and behavior-informed evaluation. This study develops an open-source GIS workflow that integrates General Transit Feed Specification (GTFS) datasets, demographic grid data, and spatial proximity modelling to construct three complementary accessibility-related indicators. Transit operational data are processed to derive service-strength indicators representing temporal service intensity at the stop level. Spatial proximity is evaluated through distance-based measurements between population grid centroids and the nearest public transport stops, subsequently transformed into a normalized proximity score reflecting perceived spatial effort. Demographic attributes, specifically age and gender structure, are also translated into a behavior potential index representing relative travel sensitivity across the urban grid. Then, rather than aggregating these components into a single composite accessibility indicator, the study analyses the spatial distribution of service strength (Sg), behavior potential (Bg), and proximity score (Pg) independently. Its empirical application to the metropolitan area of Valencia, Spain, reveals notable spatial disparities across these dimensions and highlights zones where demographic demand potential diverges from operational service provision. By relying exclusively on standardized open datasets and non-proprietary GIS tools, the proposed framework enhances methodological transparency, reproducibility, and transferability. The workflow provides a diagnostic foundation for future integrated accessibility modelling while preserving interpretative clarity at the indicator level. Full article

Heating, ventilation, and air conditioning (HVAC) systems struggle to maintain optimal thermal comfort because perception is subjective and varies significantly across individuals. Traditional uniform cooling strategies often overlook demographic diversity, leading to inequitable comfort outcomes and inefficient building operations. To address this limitation, this study analyzed a web-based survey of 366 university occupants using a partial proportional odds model with multiple imputation and inverse-frequency weighting. Interaction terms, specifically Age–Activity, Gender–Clothing, and Age–Clothing, were included to assess combined effects that reflect demographic disparities in adaptive capacity. The results show that clothing insulation, activity, age, gender, race/ethnicity, and space type significantly influence thermal responses. Notably, male occupants were more than three times as likely to report feeling too warm (odds ratio [OR] = 3.24), whereas older adults exhibited significantly lower odds of reporting feeling too warm (OR = 0.42). Substantial variation was observed across racial and ethnic groups (ORs ranging from 2.4 to 6.5). These findings highlight the limitations of traditional population-average comfort approaches and provide valuable scientific insights for demand-response-ready HVAC strategies that adjust temperature setpoints dynamically without sacrificing comfort. By offering accurate, real-time estimates across diverse thermal ranges, these occupant-centric models reduce HVAC energy use and associated emissions at the building scale while supporting ancillary services for flexible load shifting and smarter coordination within low-carbon electric grids. Ultimately, incorporating demographic and contextual diversity into building controls reduces unnecessary cooling waste while promoting thermal equity, establishing a human-centric foundation for sustainable built environments. Full article

Wind energy is a cornerstone of the global transition to renewable and sustainable energy systems. However, the same meteorological processes that generate this clean energy can also produce extreme wind events that threaten the structural integrity and operational reliability of wind turbines and power grids. Therefore, accurately predicting extreme wind speeds is a critical link between promoting clean energy and ensuring infrastructure resilience. Traditional models often struggle to capture the multimodal characteristics of extreme wind speeds under complex meteorological conditions due to fixed distribution assumptions or unstable training of mixture models, leading to estimation biases that undermine planning reliability and may result in catastrophic turbine failures or overly conservative designs. To address these issues—particularly weight imbalance and overfitting–this study proposes an enhanced regularized extreme value mixture model (ERDC-EVMM). This method integrates elastic network regularization and Kullback–Leibler divergence constraints within a Mixture of Experts framework, and employs K-means initialization and momentum-based training to enhance convergence stability. Validated using daily extreme wind speed sequences from coastal and inland wind farms, the model outperforms standard GEV and mixture models in terms of goodness-of-fit, percentile accuracy, and return period estimates, while achieving a convergence speed that is more than 30% faster (82 iterations). By balancing accuracy and training stability, the ERDC-EVMM model provides a reliable statistical tool for extreme wind speed forecasting, supporting the safe expansion of wind energy infrastructure and the design of climate-resilient communities. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article

In future active distribution networks with high penetrations of renewable energy, flexible loads are expected to play an increasingly important role as reserve resources to support the sustainable and reliable operation of power grids. Accurate evaluation of flexible load reserve capacity is therefore essential for reliable reserve scheduling. Existing research mainly focuses on the operational characteristics and physical constraints of flexible loads, while insufficiently accounting for user response willingness and the uncertainty of user decision-making behavior, which may lead to biased reserve capacity assessments and impair the sustainability of reserve supply in actual grid operation. To address this issue, this paper proposes a results-oriented reserve capacity evaluation method for flexible loads that explicitly incorporates user response willingness. Specifically, a fuzzy logic system is developed to quantitatively characterize the response willingness of electric vehicle (EV) and air-conditioning (AC) users under multiple influencing factors. Then, a probabilistic modeling approach for user decision-making behavior is established using the theory of planned behavior, enabling explicit representation of behavioral uncertainty. Furthermore, a comprehensive reserve capacity evaluation framework for flexible loads is constructed by integrating user willingness states, sustainable response duration, and operational power constraints. Finally, the case studies demonstrate that the proposed method can effectively improve the objectivity of flexible load reserve capacity assessments while maintaining high user participation willingness, thus supporting the long-term sustainable application of flexible loads as grid reserve resources. Full article

With the rapid development of offshore wind power, the fractional frequency offshore wind power system based on the modular multilevel matrix converter (M3C) faces severe frequency stability challenges due to the reduced inertia under high wind power penetration. This paper focuses on its frequency control and proposes a set of coordinated strategies. Modified frequency regulation schemes for wind turbines (WTs) under different operating states avoid secondary frequency drop (SFD) and accelerate rotor speed recovery. A coordinated power allocation strategy combining energy storage (ES) and automatic generation control (AGC) suppresses wind-induced power fluctuations, with a reducing pitch angle variation method to extend WTs’ life. Meanwhile, an adaptive virtual inertia control strategy for M3C enhances sustained inertia support. A coordinated frequency control scheme between wind farm, M3C, and ES is further constructed to achieve faster and better frequency stabilization under wind and load variations. Simulation results under a 10.5 MW load disturbance show that, compared with the uncontrolled scheme, the proposed scheme raises the frequency nadir from 49.01 Hz to 49.67 Hz, limits the maximum rate of change of frequency (ROCOF) to 0.583 Hz/s with a 49.8% reduction, fully eliminates SFD, and provides theoretical support for the stable grid integration of fractional frequency offshore wind power. Full article

This work presents the design, development, and proof-of-concept validation of a smart greenhouse for romaine lettuce (Lactuca sativa var. longifolia) that integrates Internet of Things (IoT) sensing/actuation with an image-based crop state assessment pipeline. The proposed pipeline combines a lightweight AI image classifier with fractal texture descriptors (box-counting fractal dimension) to support the non-destructive monitoring of leaf condition and growth stage. The system also implements resilience-oriented resource strategies, including rainwater harvesting, graywater reuse, and a hybrid power supply (photovoltaic + grid backup). Water and energy indicators are reported as estimated values derived from the prototype operating profile and literature-based baseline values (i.e., contextual comparisons rather than a contemporaneous controlled trial). Using an expanded dataset (n = 1500 images) and an independent held-out test subset (n = 350), the image classifier achieved 97.1% accuracy, with detailed precision/recall/F1 metrics reported in the Results. Overall, the proposed architecture and evaluation workflow provide an accessible and reproducible pathway toward sustainable, low-cost smart greenhouses in resource-constrained settings. Full article

Sustainable renovation of existing residential building stocks is essential to reduce greenhouse gas emissions, improve energy performance, and support long-term climate-neutral housing strategies. However, decisions based only on operational indicators may overlook important product-stage embodied impacts, especially in highly integrated renovation solutions. This study evaluates how alternative renovation pathways for a public residential building portfolio in the Comunitat Valenciana (Spain) perform from a stock-level sustainability perspective, comparing five INFINITE industrialised retrofit kits (Kit 1–Kit 5) with five paired conventional renovation scenarios (S1–S5). A bottom-up building stock modelling workflow is applied, combining building-energy simulation to quantify operational performance and emissions (B6) with a screening life-cycle assessment of product-stage embodied carbon reported as GWP (A1–A3). To relate upfront and in-use impacts, the study computes carbon payback, cumulative emissions avoided, and a horizon-based partial life-cycle climate indicator, PLC(H), assessed for 2030, 2035, and 2050. The results show a clear sustainability trade-off: renovation packages that sharply reduce operational emissions often require higher upfront embodied carbon, shifting net climate benefits towards longer time horizons. Low-embodied options provide earlier benefits, with Kit 1 reducing PLC(H) by 15.5% by 2030, whereas deeper decarbonisation packages achieve stronger long-term outcomes, with S5 reducing PLC(H) by 70.7% by 2050. A bounded electricity-decarbonisation sensitivity further shows that these long-horizon rankings are affected by lower grid-emission factors, particularly for highly electrified pathways, although the strongest 2050 pathways remain robust across the tested cases. Overall, the findings show that sustainable stock-level renovation planning should jointly consider operational and embodied carbon, carbon payback, and milestone-based cumulative impacts in order to support balanced portfolio sequencing between broadly deployable fast-payback measures and selective deep retrofits. Full article