Search Results (4,822)

The transition towards smart megacities has deeply integrated Artificial Intelligence (AI) with power–transport networks. While AI empowers complex operations like multi-network coordinated dispatch and emergency rescue, current algorithm-centric perspectives largely ignore its massive physical energy costs. Accordingly, this Perspective examines the dual role of AI, considering it not only as an intelligent decision-support tool but also as a potential source of additional stress on physical infrastructure. First, through a structured synthesis of the representative literature, we deconstruct the functional dependencies between algorithms and physical infrastructures, identifying how AI reshapes the operational paradigms of power, ground transport, and aerial networks under routine and emergency scenarios. We then introduce the concept of the “Computation–Energy Paradox.” Integrating conceptual analysis with a quantitative case study of a typical community, we illustrate a plausible failure mechanism: during extreme disasters, intensified AI invocation for emergency management generates surging computational loads, which paradoxically exacerbate power shortages and reduce the operating margin of already weakened systems. In addition, we analyze core engineering bottlenecks, including spatiotemporal computation–energy mismatches and physical constraints in extreme edge environments. To address these challenges, we outline a prospective roadmap encompassing lightweight emergency AI and computation–power-coordinated offloading mechanisms. Finally, the sustainable development of such systems suggests a paradigm shift: AI must evolve from a purely virtual algorithm into a physical component of an integrated compute–power–transport system. Full article

Large-scale renewable energy bases with high penetration of renewable energy are facing increasing challenges related to renewable energy curtailment. Concentrated solar power plants with thermal energy storage can provide dispatchable power output, while electric heaters offer a promising pathway for converting surplus renewable electricity into useful thermal energy. In this study, a photovoltaic–wind turbine–electric heater–concentrated solar power integrated energy system with a fixed CSP-EH configuration is investigated. The electric heater is introduced as the key electrical-thermal coupling device, which recovers otherwise curtailed photovoltaic and wind power and injects the converted thermal energy into the heat transfer fluid loop of the concentrated solar power plant. A mixed-integer linear programming model is developed to optimize the coordinated scheduling and evaluate different PV/wind capacity mixes under fixed CSP and electric-heater capacities. Results show that, under the fixed capacities of 100 MW concentrated solar power and 150 MW electric heater, the PV/wind capacity mix of 500 MW photovoltaic and 400 MW wind power achieves the best overall performance among the studied cases. Under different typical-day conditions, the electric heater recovers surplus renewable electricity, with recovery rates ranging from 16.06% to 20.21%. The proposed electric heater–concentrated solar power coupling mechanism transforms curtailed renewable electricity into dispatchable thermal energy, thereby reducing renewable energy curtailment, enhancing thermal-side flexibility, and improving the operating revenue of large-scale renewable energy bases under the studied PV/wind capacity-mix scenarios. Full article

High penetration levels of renewable energy and power electronic apparatus create prominent obstacles for novel power grids, which mainly manifested as inadequate system inertia and a deteriorated stability margin. To overcome such drawbacks, this research develops an operational control method to maintain safe and steady grid operation with large-capacity grid-forming energy storage connected to the system. This paper first builds a dynamic voltage model covering grid-forming energy storage, distributed renewable generators, and distribution network frameworks. Then it explores how different control parameter settings of grid-forming storage affect dynamic voltage regulation capabilities under distinct R-L ratio scenarios. Since the correlation between energy storage control variables and voltage regulation features is highly nonlinear and complicated, trajectory sensitivity analysis is adopted to linearize these coupling constraints, which are further embedded into the power system security operation mathematical model. A chaotic particle swarm optimization (PSO) algorithm is used to solve the constructed optimization model. Simulation tests on a modified IEEE 33-bus test system ultimately prove that the proposed method is reliable and practically applicable. Simulation results on the modified IEEE 33-bus test system demonstrate that the proposed strategy restricts grid voltage fluctuation rate to only 2.41%, raises renewable energy accommodation rate up to 98.4%, and achieves a 30.6% reduction in overall system operation cost compared to traditional energy storage configuration schemes, which fully verifies the outstanding effectiveness and practical engineering feasibility of the proposed method. Full article

Underground gas storage (UGS) facilities serve to balance natural gas networks within a given area. The nature of natural gas network balancing is twofold: long-term (seasonal) during periods of significant gas withdrawal (the cold half-year) and short-term (daily) during periods of peak natural gas demand throughout the day. The first type of balancing has been a standard characteristic for many years, covering increased demand during the winter season. In contrast, the importance of daily balancing is growing alongside the ongoing energy transition, where natural gas-based power generation sources flexibly replace renewable energy sources that are dependent on the time of day or weather conditions. The necessity for increased balancing of energy systems makes them more sensitive to crisis situations. This article presents the key role of UGS as a fundamental resilience factor for European energy systems, particularly in the face of energy crises triggered by geopolitical instability. Conflicts are redefining the role of UGS as a pillar of energy security. This study analyzes how strategic gas reserves mitigate the effects of sudden supply disruptions and price shocks caused by geopolitical factors. It describes impact scenarios of two conflicts: Russia’s invasion on Ukraine and the conflict in the Persian Gulf leading to the closure of the Strait of Hormuz. While UGS is essential for the short-term management of natural gas supply flows, its long-term value lies in providing a “strategic buffer” that allows energy systems to adapt to unforeseen geopolitical conflicts. Integrated storage management is indispensable for maintaining the operational integrity of the European transmission and energy system during periods of heightened instability. The paper also identifies necessary directions for the development of UGS systems. Between 2016 and Q1 2026, the share of eastern gas imports declined from over 40% to 5.2%, while LNG increased to 41.7% of total inflows, confirming the strategic importance of underground gas storage in maintaining energy system resilience. Full article

Existing industrial buildings represent a critical but under-addressed field for operational carbon emission reduction, as retrofit decisions are constrained by production continuity, limited investment capacity, and heterogeneous technical options. This study developed a decision support framework integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to evaluate and prioritize operational phase decarbonization retrofit schemes for existing industrial buildings. The framework was applied to the Thyssenkrupp S Plant in eastern China, where ten candidate schemes were identified through an energy audit, on-site investigation, and expert consultation. The results show that heating, ventilation, and air conditioning (HVAC) operational control and temperature set-point optimization ranked first, followed by lighting operational management and automatic control. These management-based measures offer strong near-term applicability because of their low investment, short payback periods, limited implementation disturbance, and immediate emission reduction benefits. Their sustained effectiveness, however, requires standardized procedures, staff education, energy monitoring, and appropriate automation. Rooftop photovoltaics provide the largest annual carbon reduction but have a lower short-term priority because of their high upfront investment. Expert-consistency testing and sensitivity analyses, including criterion weight perturbation, preference scenarios, and Monte Carlo simulation, support the robustness of the leading ranking pattern. The findings support staged retrofit planning that prioritizes durable management measures in the short term, equipment-level efficiency improvements in the medium term, and renewable energy deployment in the long term. Full article

Hydraulic structures disturb natural flow patterns, reducing water conveyance efficiency and increasing hydraulic energy losses, thereby affecting the sustainable management of water structures. Entrance-zone geometry, particularly upstream wing-wall configuration and canal inside slope, plays a critical role in controlling flow behavior, energy dissipation, upstream afflux, and hydraulic performance. However, the coupled effects of these geometric parameters have not been systematically investigated. Therefore, this study employed a validated HEC-RAS model to evaluate the combined influence of canal inside slope and upstream wing-wall configuration on the hydraulic performance of irrigation water structures and to support sustainable hydraulic design. Four wing-wall configurations (box, broken, curved, and splayed) and three canal inside slopes (1:1, 3:2, and 2:1) were analyzed under a fixed contraction ratio of 0.6 and upstream Froude numbers ranging from 0.12 to 0.18 under steady subcritical flow conditions. The model was validated against measurements from a 1:10 laboratory flume, demonstrating excellent agreement, with an average variation of 5.75% and coefficients of determination (R²) ranging from 0.97 to 0.99. Gradual entrance transitions significantly improved hydraulic performance by reducing flow disturbances and enhancing flow uniformity. For a canal inside slope of 1:1, the curved wing-wall configuration reduced relative heading-up and energy loss by 18.02% and 46.83%, respectively, whereas the splayed configuration achieved the best overall performance, with corresponding reductions of 27.63% and 73.11% compared with the conventional box configuration. Furthermore, dimensionless predictive equations were developed for the principal hydraulic performance indicators, achieving R² values of 0.96–0.99 and RMSE values of 0.001–0.01. The proposed framework improves water conveyance efficiency, minimizes hydraulic losses, and provides a validated, cost-effective numerical tool for evaluating alternative design scenarios, reducing reliance on extensive physical experimentation while supporting sustainable irrigation structures and long-term water resources management. Full article

The article explores the evolution of energy transition research through a bibliometric co-occurrence analysis of author keywords extracted from 146 scientific publications. The analysis reveals a shift from the technical aspects of energy systems and traditional decision-support approaches (2014–2018) towards climate change, decarbonization, renewable energy, investments, and energy policy (2018–2021). Recent studies (2022–2024) increasingly emphasize renewable energy, sustainable development, and intelligent decision-support systems, reflecting the growing digitalization of energy systems and the transition towards intelligent energy management. Based on these findings, the study develops a Digital-Twin-Oriented Techno-Economic Decision-Support Framework (DTOTEDSF) for optimizing and managing carbon-reduction strategies under dynamic energy transition conditions. Rather than representing a fully implemented digital twin (DT), the proposed framework constitutes the analytical foundation for its future development. It integrates techno-economic modeling, optimization, scenario analysis, and sensitivity assessment into a unified decision-support methodology. To demonstrate its practical applicability, the framework was applied to four industrial CCS case studies in the cement sector using publicly available technical and economic data. Its analytical core combines technical, economic, and optimization models to evaluate CCS performance under alternative operating conditions. Consequently, the proposed framework provides a methodological basis for the future implementation of fully operational DTs and contributes to the development of intelligent decision-support tools for industrial decarbonization and the sustainable energy transition. Full article

Against the dual strategic backdrop of carbon peaking and carbon neutrality goals and high-quality urbanization development, extant literature exhibits four prominent research gaps: oversimplified evaluation indicator systems, exclusive exploration of the unidirectional carbon impacts exerted by urbanization, a scarcity of long-time-series coupling analyses targeting the Yangtze River Economic Belt (YEB), and functional fragmentation between coupling coordination assessment and predictive simulation tools. Drawing on panel data covering 11 provinces and municipalities within the YEB spanning 2000 to 2021, this study constructs a comprehensive urbanization evaluation framework encompassing four dimensions: population, economy, society, and spatial layout. Meanwhile, an integrated carbon emission assessment system is established from the perspectives of population, economy, energy consumption, and carbon sinks. The entropy-weight method is adopted to assign indicator weights, and a combination of the coupling coordination degree model and system dynamics (SD) model is employed to analyze spatiotemporal evolutionary characteristics and simulate development trends from 2022 to 2032. By organically integrating the coupling coordination model and the SD model, this study establishes an integrated analytical framework that unifies static comprehensive evaluation and driving-mechanism decomposition, thereby compensating for the limitations of time-series forecasting models such as the grey prediction model and ARIMA, which only fit trends from historical data. Empirical results reveal that regional urbanization levels witnessed sustained growth across 2000–2021, with spatial urbanization acting as the core driving pillar. The overall coupling coordination degree maintained a steady upward trajectory, while the east–west regional disparity gradually narrowed. The simulation projections for 2022–2032 demonstrate continuous improvements in coordinated development across the entire basin: the coupling coordination degree ranges from 0.788 to 0.954 for the eastern region, 0.810 to 0.859 for the central region, and 0.752 to 0.865 for the western region. Such spatial differentiation corresponds to distinct practical development pathways: low-carbon stock optimization in the east, low-carbon industrial undertaking in the central zone, and clean energy transition acceleration in the west. All provincial-level administrative regions are projected to achieve an upgrade in their coupling coordination grades by 2032. This study acknowledges several limitations: missing raw data are supplemented via interpolation, only a single baseline scenario is simulated, predictive uncertainty is not quantitatively measured, and subjectivity persists in the weight assignment of coupling subsystems. Ultimately, differentiated low-carbon urbanization governance strategies are proposed for the three sub-regions, offering empirical references for the coordinated realization of dual carbon targets throughout the Yangtze River basin. Full article

This study evaluates the economic feasibility of standalone Battery Energy Storage Systems (BESS) for energy arbitrage in the Portuguese day-ahead electricity market. A Mixed-Integer Linear Programming (MILP) model is developed to optimize the operation of BESS configurations with varying durations (2, 4, 6, and 8 h), using historical price data from 2020 to 2024. The model incorporates realistic operational constraints, and the resulting arbitrage revenues are analyzed under multiple cost scenarios. Additionally, the study performs a Net Present Value (NPV) analysis using average and year-specific price profiles and three different scenarios to assess long-term investment viability. Consistent with current market access conditions in Portugal, the analysis focuses exclusively on day-ahead market arbitrage, and alternative revenue streams (intraday, real-time, ancillary services) are discussed qualitatively due to limited liquidity and restricted participation rules. The results reveal that although BESS can generate positive cash flows in recent high-volatility years, all configurations yield negative NPVs under current cost structures and market conditions. Even with optimistic cost reductions, breakeven is not achieved, indicating that standalone arbitrage remains financially not viable. These findings highlight the importance of cost optimization and the need for complementary revenue streams or policy support to make such investments feasible in Portugal. Full article

The European Union’s climate policy promotes waste incineration as an alternative to landfilling. However, the continuous nature of waste generation, combined with seasonal variability in energy demand, leads to a mismatch between heat production and consumption in waste-to-energy systems. Seasonal Borehole Thermal Energy Storage (BTES) systems are one option for mitigating this mismatch. The aim of this study was to develop a theoretical geological model of a BTES-type storage system located in the Carpathian Foredeep and to simulate its multi-year operation using the FEFLOW numerical modeling software. The model represents an initial assessment of system performance under assumed geological and operational conditions and has not yet been validated against field measurements. Two operating scenarios were analyzed. In the 1st scenario, both heat injection and heat extraction from the storage system occurred through Borehole Heat Exchangers (BHEs) located in the center of the storage system. In the 2nd scenario, heat extraction is initiated through heat exchangers located in the outermost zone of the BTES field. The analysis focused on the temperature of the circulating working fluid in the U-tubes and on variations in heat transfer rate during injection and extraction cycles. The energy performance of the system was evaluated for both configurations. The results showed that the reversed-flow operating strategy provided a higher thermal energy recovery ratio and more favorable long-term thermal performance than the center-to-center flow configuration, indicating the potential feasibility of BTES operation under the assumed Carpathian Foredeep conditions. Full article

Accurate short-term building energy consumption forecasting is crucial for energy-system operational efficiency and demand-side management. Existing deep learning models heavily rely on historical data, making them costly for data-scarce buildings. Furthermore, their cross-building generalization ability is limited, requiring building-specific tuning that hinders large-scale deployment. To address these issues, this study proposes a novel framework based on large language models for short-term building energy consumption forecasting. It adapts large language models through parameter-efficient LoRA fine-tuning and incorporates building domain knowledge by using enhanced feature extraction modules and prompt design. Specifically, a prompt template rich in building physical semantics was designed to leverage the abundant pre-training knowledge of the large language model (LLM). This enables the model to avoid blindly fitting the data, directly aligning with building operating rules, providing a reasonable prediction basis even with limited data, and enhancing cross-building generalization. In addition, a cross-feature attention mechanism is designed to analyze the impact of dynamic meteorological features on energy consumption, thereby improving cross-climate scenario adaptability. Finally, to handle non-typical mutations in actual building operations, depthwise separable convolutional layers decompose residual components to filter out noise while preserving key features of anomalous occupancy patterns, thereby enhancing model robustness. Experiments on five real-world datasets have shown that the proposed framework outperforms state-of-the-art baselines, achieving an average improvement of 6.06% in the MAE and 4.80% in the RMSE. More importantly, it exhibits strong few-shot learning capabilities and extends to zero-shot forecasting. By reducing data dependency and enabling cross-building generalization, the framework developed in this work achieves scalable, low adaptation cost energy consumption forecasting from single buildings to clusters. Full article

With the rapid transition of the global energy structure, large-scale clean energy integration has become a major trend in power system development. Nevertheless, the intermittency and stochastic fluctuation of renewable power generation threaten the secure operation of power systems. With high power density and millisecond-level response capability, supercapacitors act as key technical support for frequency stabilization and grid frequency fluctuation suppression. This paper reviews research advances in the application of supercapacitors to power system frequency regulation. It presents the classification and energy storage mechanisms of supercapacitors, analyzes their technical advantages in frequency regulation, and summarizes key research progress involving control strategies, topologies and capacity optimization schemes. Three typical application scenarios are illustrated: standalone frequency regulation, coordinated thermal-storage frequency regulation, and auxiliary frequency regulation for renewable power plants. Considering future requirements for frequency regulation, potential research directions are put forward to provide references for follow-up related studies. Full article

Urban Building Energy Modeling (UBEM) offers a scalable, physics-based method to simulate energy demand at the district level, enabling data-driven district energy demand planning and optimization. However, translating UBEM into a reliable, openly replicable workflow remains a significant methodological gap. In particular, limited attention has been devoted to the development of transparent and transferable UBEM workflows capable of systematically quantifying the impact of modeling assumptions on district-scale thermal demand accuracy. This paper presents and validates a five-step UBEM pipeline integrating freely available geospatial data from OpenStreetMap (OSM), archetype-based building characterization, multi-scenario EnergyPlus simulation via the Urban Modeling Interface (UMI) within a structured validation framework. To improve interpretability and reproducibility, a dedicated three-scenario simulation protocol was developed to isolate and quantify the influence of geometry simplifications, archetype assumptions, and weather data fidelity on model accuracy. The workflow is demonstrated through application to a real district heating system (DHS) in northern Italy, encompassing UBEM results validated against monitored consumption data at different temporal resolutions. The refined model achieves a district-scale annual magnitude error of 1.30% between real and simulated data. Persistent limitations in domestic hot water representation and peak load estimation are identified as priorities for future development. Full article

Amid the low-carbon transition, university teaching buildings feature high occupancy and energy use, making the synergistic enhancement of their daylighting and thermal environments crucial for “dual carbon” goals. However, traditional “north–south homogenization” designs in cold regions fail to address the spatial heterogeneity of solar radiation and climate resources, intensifying the trade-off between natural daylighting and Heating Energy Use Intensity (E_h) while restricting space performance optimization. Focusing on a typical cold-region teaching building, this study proposes a “parametric modeling–multi-objective optimization–machine learning” integrated framework. Targeting spatial daylight autonomy (sDA), useful daylight illuminance (UDI), and E_h, we compared the homogeneous baseline model with the Pareto-optimal solution set, demarcated key design parameter boundaries, and developed an ensemble-based rapid prediction model. Based on the parametric simulation analysis of this representative case building in a cold region, results indicate that: (1) Compared to the baseline, the overall optimal scheme reduced E_h by 17.43% while increasing UDI and sDA by 12.0% and 10.5%, respectively. (2) The Pareto set strictly converges toward a due-south orientation and a “deep-south, shallow-north” layout (depth ratio: 0.66–0.77); thermal configurations exhibit “enhanced northern insulation and southern heat gain,” confirming heterogeneous design matches cold climates better. (3) The four constructed machine learning models (MLP, LightGBM, XGBoost, and Random Forest) uniformly achieved test recall rates exceeding 99%, enabling highly precise, rapid classification of top-performing design scenarios during early-stage design. This study overcomes climate-matching blindness in traditional design, providing a multi-objective synergistic optimization path balancing low energy and high-quality daylighting with substantial engineering and theoretical value. Full article

Battery energy storage systems can reduce the operating cost of active distribution networks while supporting voltage control through their power electronic converters. This paper develops an application-specific multi-operator Differential Evolution (DE) framework for the coordinated active and reactive power scheduling of distributed battery energy storage systems in radial distribution networks with photovoltaic generation. The optimization model minimizes the daily operating cost associated with conventional energy supply, photovoltaic and storage operation and maintenance, and battery degradation. Candidate schedules encode hourly active and reactive power references for three storage converters, producing a 144 dimensional decision vector for a 24 h horizon. Each candidate is repaired to satisfy active power, state of charge, terminal energy, and converter apparent power limits before being evaluated through an alternating current power flow based on matrix successive approximations. The search framework generates three competing trial schedules per target individual by combining established best-guided, random, and current-to-random DE mutation families with a discrete parameter pool, a common feasibility-repair operator, and greedy selection after AC network evaluation. The method is tested on modified 33-node and 69-node active distribution networks and compared with AJAYA, genetic algorithm, multiverse optimizer, and particle swarm optimization. In the deterministic 33-node case, Differential Evolution obtains the lowest best cost, USD 6846.206, and the largest best cost reduction, 2.1838 percent. The scenario study performs separate deterministic optimizations for pre-generated operating realizations and is therefore interpreted as a scenario-conditioned sensitivity assessment rather than as stochastic or robust optimization of one here-and-now schedule. In this assessment, DE achieves the largest average savings: 2.3487 percent in the 33-node network and 2.9314 percent in the 69-node network. Voltage magnitudes, branch loading, converter ratings, and cyclic state of charge constraints are satisfied in all evaluated cases. The results identify the proposed framework as a competitive day-ahead solver within the evaluated cases, while no claim of global optimality or universal superiority over alternative optimizers is made. Full article