Search Results (10,669)

Occupant-Centric Control (OCC) aims to achieve a balance between personalized comfort and energy efficiency; however, current strategies often optimize either thermal comfort or indoor air quality (IAQ) in isolation. This study presents a model predictive control (MPC) framework that integrates incremental learning of individual thermal preferences with IAQ and energy co-optimization in office buildings. An incremental Naive Bayes classifier updates personalized temperature preference bands. Gray-box models, including an RC-network for temperature and a CO₂ mass-balance model, provide multi-step forecasts calibrated via genetic algorithm cross-validation. These learned preferences, along with a CO₂ limit, are enforced as constraints within the MPC, which minimizes HVAC energy use, supported by a supervisory layer for preventing inefficient operation and allowing manual override. Python–EnergyPlus co-simulations for an open office and a meeting room demonstrate that the framework maintains CO₂ concentrations below 1000 ppm and keeps 95% of temperatures within comfort ranges. Compared with baseline control, HVAC energy use decreased by 66% in winter and 56% in summer for the open office and by 44% in winter and 57% in summer for the meeting room. The proposed framework provides a reproducible approach for HVAC control that simultaneously enhances comfort, indoor environmental quality, and energy performance. Full article

The transition toward carbon neutrality is accelerating the deployment of renewable energy sources (RES), creating new challenges for power balance, stability, and renewable generation curtailment. In the Baltic States, this RES growth coincides with synchronization with the Central European Synchronous Area, which poses additional technical and operational challenges. This paper evaluates the integration of offshore wind farms (OWFs) into the Lithuanian power system for 2027 and 2035, focusing on their impact on system operation, transmission loading, power balance and power system strength. A methodology based on extrapolated historical hourly data is applied to assess Lithuanian power system security under large-scale RES penetration, identifying critical contingencies and lines most prone to overloading. Results indicate that in 2027, network overloads may occur under N–1 contingencies when OWF capacity reaches 1400 MW; higher capacities require curtailment to maintain the generation–load balance. In 2035, planned grid reinforcements eliminate N–1 overloads. However, in both years, system strength remains the limiting factor. With an admissible short-circuit ratio (SCR) of 3, the maximum allowable OWF capacity is 1141 MW in 2027 and 1582 MW in 2035 under N–1, and 562 MW and 1039 MW under N–2 conditions. Full article

A virtual power plant (VPP) faces multiple uncertainties and temporal coupled decisions when participating as an independent entity in electricity and green markets. A multi-level electricity–green coupled market framework is constructed for a VPP participating as an independent market entity. To address uncertainties in renewable energy outputs and market prices, a risk management method based on conditional value at risk entropy weight method information gap decision theory (CVaR-EIGDT) is proposed. To address the temporal coupled challenges in VPP participation across multi-level electricity–green coupled markets, a multi-stage rolling decision-making method coordinating annual, monthly, and daily scales is proposed, achieving deep coupling in the decision-making sequence of multi-level electricity–green coupled markets. Results show that the proposed model enables adaptive decision-making under varying risk preferences, with decisions exhibiting strong practical adaptability while balancing real-time adjustments and long-term planning. The multi-level electricity–green coupled market framework enhances VPP profitability and resilience, while the CVaR-EIGDT method effectively improves decision-making efficiency across multi-level electricity–green coupled markets. Full article

The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed controller unifies model-based predictive optimisation with adaptive reinforcement learning to achieve both short-term operational efficiency and long-term asset preservation. A comprehensive dataset of solar generation, EV charging behaviour, and stochastic load profiles was employed to train and validate the hybrid control framework under realistic operating conditions. Quantitative results indicate that the proposed H-RPEM controller achieves an 18.7% reduction in total operating cost and a 22.5% decrease in carbon emissions, whilst maintaining the battery state-of-health above 0.95 throughout a 24 h operational cycle. When benchmarked against standard predictive control, the hybrid strategy converges 30–40 episodes faster and delivers a 25% improvement in reward stability, demonstrating enhanced robustness and learning efficiency. The results confirm that H-RPEM achieves robust and balanced performance across economic, environmental, and technical domains, establishing it as a scalable and health-conscious control solution for next-generation smart city microgrids. Full article

This study investigates the energy performance of anaerobic digesters in a municipal wastewater treatment plant by integrating empirical data from two tanks located at different distances from the heat source with simulation results. The analysis of measurements enabled the determination of heat transferred to the raw sludge, total heat losses of both systems, and provided input data for an hourly simulation of the thermal balance of the digester envelope. An analytical model was developed, including separate equations for the sludge and biogas phases, considering heat losses caused by mass transfer, conduction, convection, and radiation, as well as solar heat gains. The results show that the temperature difference between sludge and biogas exhibits seasonal variation, with a maximum value of 10.5 K, while the desired operational temperature of sludge fermentation is maintained at 38 °C. The total annual heat balance of the anaerobic digester in 2024 was estimated at 202.8 MWh, with the following structure: aboveground walls 46%, ground-contact partitions 30%, and dome 24%. Model validation using data from one of the digesters indicated a total system energy demand of 1812.0 MWh, distributed as follows: heat transferred to raw sludge 88.6%, heat transfer losses 0.2%, and digester envelope balance 11.2%. Replacing the thermal insulation of the aboveground section could reduce heat losses by 70.7 MWh, decreasing the total energy demand of the system by 3.9%. Comparison with the second digester revealed an energy gap of 166.3 MWh, which may be attributed to higher transmission losses or degradation of the insulation layer. Full article

The dynamics of gas–liquid two-phase flow at fracture junctions are crucial for optimizing fluid transport in the complex fracture networks of coal seams, particularly for coalbed methane (CBM) extraction and gas hazard management. This study presents a comprehensive numerical investigation of transient air–water flow in a two-dimensional, symmetric, cross-shaped fracture junction. Using the Volume of Fluid (VOF) model coupled with the SST k-ω turbulence model, the simulations accurately capture phase interface evolution, accounting for surface tension and a 50° contact angle. The effects of inlet velocity (0.2 to 5.0 m/s) on flow patterns, pressure distribution, and energy dissipation are systematically analyzed. Three distinct phenomenological flow regimes are identified based on interface morphology and force balance: an inertia-dominated high-speed impinging flow (Re > 4000), a moderate-speed transitional flow characterized by a dynamic balance between inertial and viscous forces (∼1000 < Re < ∼4000), and a viscous-gravity dominated low-speed creeping filling flow (Re < ∼1000). Flow partitioning at the junction—defined as the quantitative split of the total inflow between the main (straight-through) flow path and the deflected (lateral) paths—exhibits a strong dependence on the Reynolds number. The main flow ratio increases dramatically from approximately 30% at Re ∼ 500 to over 95% at Re ∼ 12,000, while the deflected flow ratio correspondingly decreases. Furthermore, the pressure loss (head loss, ΔH) across the junction increases non-linearly, following a quadratic scaling relationship with the inlet velocity (ΔH ∝ V₀^1.95), indicating that energy dissipation is predominantly governed by inertial effects. These findings provide fundamental, quantitative insights into two-phase flow behavior at fracture intersections and offer data-driven guidance for optimizing injection strategies in CBM engineering. Full article

This study experimentally evaluates the structural performance of Concrete-Filled Double-Skin (CFDS) hybrid connections that are intended as key components of large-scale floating offshore wind substructures. The innovative aspect of this work lies in the direct experimental comparison of five representative connection details—Headed Stud (HS), Perfobond (PB), L-beam-joint (LJ), L-beam-spacing (LS), and Angle (AN)—with respect to multiple performance indices that are critical under harsh offshore environments. First, full-scale CFDS specimens were fabricated with identical global dimensions while varying only the connection details. The hybrid behavior of the CFDS system arises from the complementary actions of the outer steel tube, which primarily resists tensile forces, and the infilled concrete, which provides dominant compressive resistance and confinement. This composite interaction enhances the stiffness, ductility, and energy absorption capacity of the member under flexural demands, which are essential for floating offshore structures operating under complex marine loading. Second, monotonic bending tests were conducted using a 2000 kN actuator under a cantilever-type configuration, and load–displacement responses were recorded at three locations. Third, the stiffness, ductility, and energy absorption capacity (toughness) were quantified from the measured curves to clarify the deformation and failure characteristics of each connection type. The results show that the PB connection achieved the highest maximum load and exhibited stable ductile behavior with plastic energy dominating the total toughness. The LJ connection provided well-balanced stiffness and deformation capacity with low sensitivity to measurement locations, indicating high reliability for design applications. In contrast, the HS and LS connections experienced localized slip and position-dependent stiffness, while the AN connection showed the lowest load-carrying efficiency. Overall, the findings highlight that connection-level detailing has a decisive influence on the global performance of CFDS hybrid members and provide fundamental data for developing design guidelines for floating offshore structures operating under complex marine loading conditions. Full article

The growing global demand for food and energy requires land-use strategies that support agricultural production and renewable energy generation. Agrivoltaic (APV) systems allow farmland to be used for both agriculture and solar power generation. The aim of this study is to critically synthesize the interactions between the key dimensions of APV implementation—technical, agronomic, legal, and economic—in order to create a multidimensional framework for designing an APV optimization model. The analysis covers APV system topologies, appropriate types of photovoltaic modules, installation geometry, shading conditions, and micro-environmental impacts. The paper categorizes quantitative indicators and critical thresholds that define trade-offs between energy production and crop yields, including a discussion of shade-tolerant crops (such as lettuce, clover, grapevines, and hops) that are most compatible with APV. Quantitative aspects were integrated in detail through a review of mathematical approaches used to predict yields (including exponential-linear, logistic, Gompertz, and GENECROP models). These models are key to quantitatively assessing the impact of photovoltaic modules on the light balance, thus enabling the simultaneous estimation of energy efficiency and yields. Technical solutions that enhance synthesis, such as dynamic tracking systems, which can increase energy production by up to 25–30% while optimizing light availability for crops, are also discussed. Additionally, the study examines regional legal frameworks and the economic factors influencing APV deployment, highlighting key challenges such as land use classification, grid connection limitations, investment costs and the absence of harmonised APV policies in many countries. It has been shown that APV systems can increase water retention, mitigate wind erosion, strengthen crop resilience to extreme weather conditions, and reduce the levelized cost of electricity (LCOE) compared to small rooftop PV systems. A key contribution of the work is the creation of a coherent analytical design framework that integrates technical, agronomic, legal and economic requirements as the most important input parameters for the APV system optimization model. This indicates that wider implementation of APV requires clear regulatory definitions, standardized design criteria, and dedicated support mechanisms. Full article

Europe’s hydrogen strategy has centred almost exclusively on green hydrogen produced through renewable electrolysis as the cornerstone of its decarbonisation agenda. However, recent discoveries of naturally occurring “white hydrogen” in France, Spain, and other parts of Europe raise the prospect of a new, abundant, and low-cost clean energy resource. White hydrogen, generated geologically and extractable directly from subsurface reservoirs, could complement or even disrupt the current power-to-X pathway by offering production costs estimated at €0.75–1 per kilogram, far below today’s €6–8 for green hydrogen. Early geological findings suggest potentially vast reserves, yet the scale, renewability, and environmental impacts remain uncertain. This policy note critically reassesses the European Union’s hydrogen strategy in light of these developments, examining the economic, environmental, and regulatory implications of integrating white hydrogen. It argues for a balanced, adaptive approach: continuing to scale green hydrogen to meet near-term decarbonisation targets while fostering exploration, regulation, and pilot projects for white hydrogen. Such an approach can safeguard Europe’s climate ambitions, mitigate energy security risks, and avoid stranded investments, while positioning the EU to benefit if natural hydrogen proves viable at scale. Full article

The rapid development of renewable energy has highlighted the issue of its accommodation, which has become a critical challenge for power grids with high renewable energy penetration. Accurately assessing a grid’s renewable energy accommodation capability is essential for ensuring power grid operational security, as well as for the rational planning and efficient operation of renewable energy sources and adjustable power resources. This paper adopts a long-term chronological power balance simulation approach, integrating the dynamic balance among multiple types of power sources, loads, and outbound transmission. Dispatch schemes suitable for different types of power sources, including hydropower, thermal power, wind power, solar power, and nuclear power, were designed based on their operational characteristics. Key operational constraints, such as output limits, staged water levels, pumping/generation modes of pumped storage, and nuclear power regulation duration, were considered. A refined analysis model for renewable energy accommodation in regional power grids was constructed, aiming to maximize the total accommodated renewable energy electricity. Using actual data from the Northeast China Power Grid in 2024, the model was validated, showing results largely consistent with actual accommodation conditions. Analysis based on next-year forecast data indicated that the renewable energy utilization rate is expected to decline to 90.6%, with the proportion of curtailment due to insufficient peaking capacity and grid constraints expanding to 8:2. Sensitivity analysis revealed a clear correlation between the renewable energy utilization rate and the scale of newly installed renewable capacity and energy storage. It is recommended to control the expansion of new renewable energy installations while increasing the construction of flexible power sources such as pumped storage and other energy storage technologies. Full article

The accelerating pace of urbanization and the pressing need for sustainability have compelled cities worldwide to integrate renewable energy into their infrastructure. While solar, wind, and hydro sources offer cleaner alternatives to fossil fuels, their inherent variability creates challenges in maintaining balance between supply and demand in urban energy systems. Traditional statistical forecasting methods are often inadequate for capturing the nonlinear, weather-driven dynamics of renewables, highlighting the need for advanced artificial intelligence (AI) approaches that deliver both accuracy and interpretability. This paper proposes a spatio-temporal framework for smart city energy management that combines a Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) for renewable energy generation forecasting, a Gradient Boosting Machine (GBM) for urban demand prediction, and Particle Swarm Optimization (PSO) for cost-efficient energy allocation. The framework was first validated using Spain’s national hourly energy dataset (2015–2018). To rigorously test its generalizability, the methodology was further validated on a separate dataset for the German energy market (2019–2022), proving its robustness across different geographical and meteorological contexts. Results indicate strong predictive performance, with solar generation achieving a 99.03% R² score, wind 96.46%, hydro 93.02%, and demand forecasting 91.56%. PSO further minimized system costs, reduced reliance on fossil-fuel generation by 18.2%, and improved overall grid efficiency by 12%. These findings underscore the potential of AI frameworks to enhance reliability and reduce operational costs. Full article

This paper links the UN Sustainable Development Goal (SDG) of “Affordable and Clean Energy” (nr. 7) to “Partnerships” (nr. 17). These partnerships refer to stakeholders’ participation in renewable energy networks. Given that renewable energy is environmentally superior to fossil fuels and the participatory approaches foster well-being, this paper addresses economic sustainability. Therefore, the costs and benefits of electric power on the grid are compared to the distributed power networks in the EU, the USA, and India. Firstly, the present (dis)incentives for distributed energy networks are identified, concerning power generation, transmission, distribution, and consumption on the grid. Second, the costs of mini-grids and microgrids are assessed based on the existing literature. Thirdly, the benefits of such networks for individual and collective interests of producers and consumers of power are indicated. Although these partnerships are often as yet costly, incorporating those benefits into electricity prices enables price parity with the grid. Policies that pursue those benefits foster the realization of SDGs and improve the balance on the grid. Full article

Seed germination and early growth in rice are critical stages influenced by the hormonal balance between gibberellins (GA) and abscisic acid (ABA), particularly under low-temperature stress. This study investigated the effects of GA₃ and ABA on seed germination, embryonic growth, gene expression, and biochemical activities in rice cultivars with contrasting tolerance to low temperatures. GA₃ markedly improved germination in resistant cultivars Nagdong and CNDH77, whereas susceptible cultivars showed minimal improvement, while ABA strongly inhibited germination, especially under higher concentrations. GA₃ also promoted embryonic growth, with resistant cultivars displaying the longest embryo cells (10.10 µm and 13.49 µm, respectively), whereas ABA suppressed embryonic growth and completely inhibited germination in susceptible cultivars. Upregulation of GA biosynthesis (OsCPS1 and OsKS1) and signaling genes (OsGID1 and OsGID2) in resistant cultivars correlated with enhanced germination and growth, whereas ABA-induced ABI5 expression suppressed germination, particularly in susceptible cultivars. Hormone quantification confirmed increased endogenous GA₃ after GA₃ treatment and reduced ABA levels under ABA treatment. Additionally, GA₃ modulated ABA signaling genes, upregulating OSK3, ABI3, ABI4, and ABI5, while ABA treatment had contrasting effects, particularly between resistant and susceptible cultivars. GA₃ treatment also enhanced the expression of GA biosynthesis and signaling genes (OsCPS1, OsKS1, OsGID1, and OsGID2), whereas ABA treatment upregulated ABA catabolic genes (OsABA8ox2). GA₃ also enhanced amylase activity and sugar-related gene expression, supporting its role in energy mobilization during germination. Conversely, ABA suppressed cell elongation, reducing it to 4.45 µm in CNDH77 under 100 µM ABA. These findings provide valuable insights into the hormonal regulation of rice seed germination and growth under low-temperature stress, offering potential strategies to enhance seed vigor and stress tolerance in rice breeding. Full article

This study presents an integrated intervention strategy for the adaptive reuse of vernacular architecture in a state of ruin, focusing on the fortified village of Moya (Cuenca, Spain). The proposal is framed within a rural revitalization program aimed at educational and cultural tourism uses, with the goal of reactivating abandoned built fabric through the incorporation of new functions that generate social value and contribute to territorial development. The proposed methodology combines archival research, digital documentation, material characterization, and a constructive solution based on the insertion of a reversible, structurally autonomous timber volume within the existing stone masonry. Through material characterization, a differentiated consolidation protocol is developed to stabilize the ruins while maintaining historical legibility. The new architectural volume, built with prefabricated cross-laminated timber (CLT) and insulated with locally sourced expanded cork, is designed to meet contemporary standards of energy efficiency, reversibility, and environmental responsibility, while remaining fully independent from the original structure. The intervention offers a replicable model for sustainable rural regeneration, balancing conservation ethics with functional adaptation. Future lines of research include the dynamic simulation of the energy performance of the inserted dwelling, with the aim of assessing its contribution to climate neutrality and net-zero emissions targets. Full article

State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article