Search Results (163)

Traditional electricity market bidding typically focuses on unilateral structures, where independent energy storage units and flexible loads act merely as price takers. This reduces bidding motivation and weakens the balancing capability of regional power systems, thereby limiting the large-scale utilization of renewable energy. To address these challenges and support sustainable power system operation, this paper proposes a double-sided auction market strategy for heterogeneous multi-resource (HMR) participation based on multi-agent reinforcement learning (MARL). The framework explicitly considers the heterogeneous bidding and quantity reporting behaviors of renewable generation, flexible demand, and energy storage. An improved incentive mechanism is introduced to enhance real-time system power balance, thereby enabling higher renewable energy integration and reducing curtailment. To efficiently solve the market-clearing problem, an improved Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm is employed, along with a temporal-difference (TD) error-based prioritized experience replay mechanism to strengthen exploration. Case studies validate the effectiveness of the proposed approach in guiding heterogeneous resources toward cooperative bidding behaviors, improving market efficiency, and reinforcing the sustainable and resilient operation of future power systems. Full article

This study investigates the reliability and potential genetic utility of data recorded by automatic milking systems by comparing them with official milk recording data. Analyses focused on phenotypic distributions, correlations, systematic differences, and heritability estimates for milk production and quality traits including milk yield, fat and protein percentage, somatic cell count, and electrical conductivity. Automatic milking system data and official milk recording data shared similar distributions. Correlations between the two systems were high for milk yield (r = 0.93), but moderate for fat (r = 0.52) and protein percentage (r = 0.48), and somatic cell count (r = 0.62), suggesting that while the former provides consistent data for quantity traits, quality-related ones may be less reliable. Systematic deviations between automatic milking systems and official milk recordings emerged across different lactation stages. Heritability estimates based on automatic milking system data were generally higher than the official control for production traits, supporting their use in genetic evaluations. Electrical conductivity displayed a similar heritability to somatic cell count, but its measure is insufficiently detailed and its use as an indirect indicator of udder health is not recommended. Automatic milking system data demonstrates potential for integration into genetic selection programs, although further refinement of sensor accuracy is recommended. Full article

To address the issues of benefit coordination and energy supply risk management in energy trading between integrated energy parks and the main grid utility, this paper proposes a bi-level game-based optimal scheduling model for the electricity–heat–hydrogen integrated energy system considering energy supply risks. A bi-level game framework of the integrated energy park (IEP), aggregator, and utility is firstly built, where the aggregator acts as an intermediary coordination entity. The upper-level and lower-level game models, the trading strategies between the aggregator and the utility, as well as the trading strategies between the aggregator and the IEP, are, respectively, optimized after achieving the equilibrium. Furthermore, a conditional value-at-risk (CVaR)-based energy supply risk quantification model is introduced to characterize the operational risks caused by differences in traded energy quantities and then is incorporated into the proposed game-based optimal scheduling model. Finally, a bi-level game-based optimal scheduling model of the IEP, aggregator, and utility considering energy supply risk is proposed. Case studies demonstrate that the proposed model can effectively reduce the operating cost of the utility, reasonably allocate the benefit of the aggregator and the IEP, and can effectively balance energy supply risk and social welfare maximization of the electricity–heat–hydrogen integrated energy system. Full article

To alleviate the peak-shaving pressure caused by large-scale renewable energy integration, this paper proposes a bilateral trading strategy for wind–thermal energy storage (TES) systems. Based on the classification of TES electricity-receiving and heat-receiving pathways, the distinct electrical and thermal flexibilities of TES are quantified, and a Stackelberg game is formulated in which TES enterprises bid quantities, whereas wind farms bid prices. By doing so, the complex coupling between TES and thermal power units is clearly decoupled, significantly enhancing the market participation of both TES enterprises and wind farms. Finally, simulations using operational data from a real wind farm with 1665 MWh of curtailed wind demonstrate that the proposed method accommodates 61.26% of the curtailed energy and raises the net-load valley by 131.6 MW, confirming its effectiveness and practical feasibility of the proposed strategy. Full article

The integration of LED lighting enables precise radiation control in plant factory cultivation systems. While LEDs offer energy efficiency and spectral tuning, achieving a uniform photosynthetic photon flux density (PPFD) remains a critical technical challenge. This study evaluated the impact of three spatial LED configurations on irradiance uniformity using commercial horticultural LEDs and a light recipe of 75% red and 25% blue. Optical simulations in TracePro^® 2017 were conducted to analyze radiant flux, optical efficiency, and uniformity, along with LED quantity, system cost, and electrical consumption under two environmental scenarios: open (without reflective walls) and closed (with reflective walls). Results show that distribution 3, which featured reduced central LED density, achieved 4–8% higher homogeneity in the open scenario, and 2.7–6.5% in the closed scenario, compared to symmetric layouts (distribution 1 and 2). Reflective walls increased average PPFD by up to 20% and optical efficiency by around 9%, with a minimal effect on uniformity. Lowering the lamp-to-canopy distance from 35 cm to 30 cm resulted in a 10% increase in PPFD. Despite a reduction in total photon flux, distribution 3 exhibited superior irradiance homogeneity. One-way ANOVA confirmed significant effects of environment, height, and LED model (p < 0.05), but not of spatial alone. This simulation-based methodology offers a robust framework for optimizing energy-efficient lighting systems. Future work will explore the integrating of non-visible wavelengths and experimental validations to extend practical applicability. Full article

The article presents an overview of knowledge in the field of energy microgrids as smart structures enabling energy self-sufficiency, with particular emphasis on decarbonisation. Based on a review of the literature and technical solutions, the characteristics have been classified and, emphasising the potential for integrating different technologies within microgrid structures, the role that microgrids and their users can play in the functioning of the energy system has been defined. Energy microgrids can be the pillar on which smart energy structures and smart grids, including energy systems using multiple energy carriers, will be based. Microgrids can guarantee energy self-sufficiency within their area of operation and support the entire energy system in this respect. Sensors that respond to both electrical and non-electrical quantities must play a special role in such structures, as they form the technical basis for the functioning of the smart energy sector. Full article

Accurate quantity takeoff (QTO) is essential for cost estimation and project planning in the construction industry. However, current practices are often fragmented and rely on manual or semi-automated processes, leading to inefficiencies and errors. This study introduces a cloud-based framework that integrates automated QTO with a rule-based Quantity Precision Check (QPC) to ensure that quantities are derived only from validated and consistent BIM data. The framework is designed to be scalable and compatible with open data standards, supporting collaboration across teams and disciplines. A case study demonstrates the implementation of the system using structural and architectural models, where automated validation detected parameter inconsistencies and significantly improved the accuracy and reliability of takeoff results. To evaluate the system’s effectiveness, the study proposes five quantitative validation metrics, Inconsistency Detection Rate (IDR), Parameter Consistency Rate (PCR), Quantity Accuracy Improvement (QAI), Change Impact Tracking (CIT), and Automated Reporting Efficiency (ARE). These indicators are newly introduced in this study to address the absence of standardized metrics for automated QTO with pre-takeoff, rule-based validation. However, the current validation was limited to a single project and discipline-specific rule set, suggesting that broader testing across mechanical, electrical, and infrastructure models is needed to fully confirm scalability and generalizability. The proposed approach provides both researchers and practitioners with a replicable, transparent methodology for advancing digital construction practices and improving the quality and efficiency of BIM-based estimation processes. Full article

Recent innovations in thermoplastic extrusion 3D printing have promoted the development of functional materials, such as conductive composites, which lead the way to the creation of sensors embedded directly into printed structures. To this aim, this paper presents a feasibility study on the use of a commercial conductive PLA filament for the realization of a 3D-printed temperature sensor integrated into a thermoplastic structure. To this end, a series of experiments were conducted on 3D-printed samples to analyse the correlation between electrical resistance and temperatures. The results obtained show a clear and reproducible relationship between the two quantities, from which a useful function was derived to estimate the temperature from the resistance measurement. This study confirms the potential of conductive PLA as a low-cost and customisable solution for thermal monitoring and represents a step forward towards the integration of functional sensors through additive manufacturing. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Effective diagnosis of distribution network faults is crucial to ensuring the reliability of power systems. However, the bidirectional power flow caused by the integration of new energy limits the effectiveness of traditional detection methods. Although data-driven approaches are not restricted by power flow direction, their performance is heavily dependent on the quantity and quality of training samples. In addition, factors such as measurement noise, variable fault impedance, and volatile photovoltaic output complicate fault information. To address this, we present a new fault diagnosis model named the dynamic, adaptive, and coupled dual-field-encoding graph neural network (DACDFE-GNN), which introduces a dynamic aggregation module to assign different weights to reduce noise interference and fully integrates information from observable nodes. On this basis, the coupled dual-field-encoding module is proposed, which encodes topological information and physical–electrical domain information as part of the initial features, thereby capturing fault features and learning the law of feature propagation. The experimental results for the IEEE 34- and IEEE 123-node feeder systems indicate that the proposed model surpasses recent fault diagnosis methods in detection performance, particularly regarding its low training sample rate. Full article

This work presents a novel and fully virtual flow for extracting the SPICE model of a power MOSFET, starting exclusively from TCAD simulations. Unlike traditional approaches that rely on experimental silicon data, our methodology enables designers to optimize the device performance and extract accurate electrical parameters before any physical prototyping is required. By leveraging advanced TCAD tools, we generate a realistic device structure and obtain all the key electrical characteristics, which are then used for precise SPICE model extraction and macromodel integration. The extracted model is dynamically validated using a gate-charge test performed identically in both the TCAD and SPICE environments, demonstrating excellent agreement with less than a 2% error in the charge quantities, Q_gs and Q_gd. This approach proves that initial silicon prototyping can be confidently bypassed, and it is highly innovative because it enables designers to achieve highly faithful device simulations before hardware fabrication. This significantly reduces the need for costly and time-consuming prototyping and design re-spins, accelerating the development process while enhancing the accuracy in terms of the transient and dynamic characteristics of MOSFETs designed for specific applications; in our case, for an e-fuse to be integrated into a more complex system. Full article

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms. Full article

The life-cycle carbon emissions (LCCE) assessment of dairy barns is crucial for identifying low-carbon transition pathways and promoting the sustainable development of the dairy industry. We applied a life cycle assessment approach integrated with building information modeling and EnergyPlus to establish a full life cycle inventory of the material quantities and energy consumption for dairy barns. The LCCE was quantified from the production to end-of-life stages using the carbon equivalent of dairy barns (CEDB) as the functional unit, expressed in kg CO₂e head⁻¹ year⁻¹. A carbon emission assessment model was developed based on the “building–process–energy” framework. The LCCE of the open barn and the lower profile cross-ventilated (LPCV) barn were 152 kg CO₂e head⁻¹ year⁻¹ and 229 kg CO₂e head⁻¹ year⁻¹, respectively. Operational carbon emissions (OCE) accounted for the largest share of LCCE, contributing 57% and 74%, respectively. For embodied carbon emissions (ECE), the production of building materials dominated, representing 91% and 87% of the ECE, respectively. Regarding carbon mitigation strategies, the use of extruded polystyrene boards reduced carbon emissions by 45.67% compared with stone wool boards and by 36% compared with polyurethane boards. Employing a manure pit emptying system reduced carbon emissions by 76% and 74% compared to manure scraping systems. Additionally, the adoption of clean electricity resulted in a 33% reduction in OCE, leading to an overall LCCE reduction of 22% for the open barn and 26% for the LPCV barn. This study introduces the CEDB to evaluate low-carbon design strategies for dairy barns, integrating building layout, ventilation systems, and energy sources in a unified assessment approach, providing valuable insights for the low-carbon transition of agricultural buildings. Full article

Virtual power plants (VPPs) enable the efficient participation of distributed renewable energy resources in electricity markets by aggregating them. However, the profitability of VPPs is challenged by market volatility and regulatory constraints, such as price caps and imbalance penalties. This study examines the joint impact of varying price cap levels and imbalance penalty structures on the bidding strategies and revenues of VPPs. A stochastic optimization model was developed, where a three-stage scenario tree was utilized to capture the uncertainty in electricity prices and renewable generation output. Simulations were performed under various market conditions using real-world price and generation data from the Korean electricity market. The analysis reveals that higher price cap coefficients lead to greater revenue and more segmented bidding strategies, especially under asymmetric penalty structures. Segment-wise analysis of bid price–quantity pairs shows that over-bidding is preferred under upward-only penalty schemes, while under-bidding is preferred under downward-only ones. Notably, revenue improvement tapers off beyond a price cap coefficient of 0.8, which indicates that there exists an optimal threshold for regulatory design. The findings of this study suggest the need for coordination between price caps and imbalance penalties to maintain market efficiency while supporting renewable energy integration. The proposed framework also offers practical insights for market operators and policymakers seeking to balance profitability, adaptability, and stability in VPP-integrated electricity markets. Full article

With the increasing penetration of renewables, the importance of electrical energy storage (EES) for power supply stabilization is growing. The intermittency of renewable energy sources remains the main issue limiting their rapid integration; however, the development of high-capacity batteries capable of storing large quantities of energy offers a way to address this challenge. This article presents and describes dedicated algorithms for controlling the EES system to enable the provision of individual system services. Five services are planned for implementation: RES power stabilization; voltage regulation using active and reactive power; reactive power compensation; power stabilization of unstable loads; and power reduction on demand. The aim of this paper is to develop new, dedicated energy storage control algorithms for delivering these specific services. Additionally, the voltage regulation algorithm includes two operating modes: short-term regulation (voltage fluctuation stabilization) and long-term regulation (triggered by an operator signal). Full article