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Search Results (1,617)

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Keywords = load coordination

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21 pages, 1756 KiB  
Article
Structure/Aerodynamic Nonlinear Dynamic Simulation Analysis of Long, Flexible Blade of Wind Turbine
by Xiangqian Zhu, Siming Yang, Zhiqiang Yang, Chang Cai, Lei Zhang, Qing’an Li and Jin-Hwan Choi
Energies 2025, 18(16), 4362; https://doi.org/10.3390/en18164362 - 15 Aug 2025
Abstract
To meet the requirements of geometric nonlinear modeling and bending–torsion coupling analysis of long, flexible offshore blades, this paper develops a high-precision engineering simplified model based on the Absolute Nodal Coordinate Formulation (ANCF). The model considers nonlinear variations in linear density, stiffness, and [...] Read more.
To meet the requirements of geometric nonlinear modeling and bending–torsion coupling analysis of long, flexible offshore blades, this paper develops a high-precision engineering simplified model based on the Absolute Nodal Coordinate Formulation (ANCF). The model considers nonlinear variations in linear density, stiffness, and aerodynamic center along the blade span and enables efficient computation of 3D nonlinear deformation using 1D beam elements. Material and structural function equations are established based on actual 2D airfoil sections, and the chord vector is obtained from leading and trailing edge coordinates to calculate the angle of attack and aerodynamic loads. Torsional stiffness data defined at the shear center is corrected to the mass center using the axis shift theorem, ensuring a unified principal axis model. The proposed model is employed to simulate the dynamic behavior of wind turbine blades under both shutdown and operating conditions, and the results are compared to those obtained from the commercial software Bladed. Under shutdown conditions, the blade tip deformation error in the y-direction remains within 5% when subjected only to gravity, and within 8% when wind loads are applied perpendicular to the rotor plane. Under operating conditions, although simplified aerodynamic calculations, structural nonlinearity, and material property deviations introduce greater discrepancies, the x-direction deformation error remains within 15% across different wind speeds. These results confirm that the model maintains reasonable accuracy in capturing blade deformation characteristics and can provide useful support for early-stage dynamic analysis. Full article
38 pages, 1283 KiB  
Article
Aggregation and Coordination Method for Flexible Resources Based on GNMTL-LSTM-Zonotope
by Bo Peng, Baolin Cui, Cunming Zhang, Yuanfu Li, Weishuai Gong, Xiaolong Tao and Ruiqi Wang
Energies 2025, 18(16), 4358; https://doi.org/10.3390/en18164358 - 15 Aug 2025
Abstract
Demand-side flexible resources in building energy systems hold significant potential for enhancing grid reliability and operational efficiency. However, their effective coordination remains challenging due to the complexity of modeling and aggregating heterogeneous loads. To address this, this paper proposes a feasible region aggregation [...] Read more.
Demand-side flexible resources in building energy systems hold significant potential for enhancing grid reliability and operational efficiency. However, their effective coordination remains challenging due to the complexity of modeling and aggregating heterogeneous loads. To address this, this paper proposes a feasible region aggregation and coordination method for load aggregators based on a GNMTL-LSTM-Zonotope framework. A Gradient Normalized Multi-Task Learning Long Short-Term Memory (GNMTL-LSTM) model is developed to forecast the power trajectories of diverse flexible resources, including air-conditioning systems, energy storage units, and diesel generators. Using these predictions and associated uncertainty bounds, dynamic feasible regions for individual resources are constructed with Zonotope structures. To enable scalable aggregation, a Minkowski sum-based method is applied to merge the feasible regions of multiple resources efficiently. Additionally, a directionally weighted Zonotope refinement strategy is introduced, leveraging time-varying flexibility revenues from energy and reserve markets to enhance approximation accuracy during high-value periods. Case studies based on real-world office building data from Shandong Province validate the effectiveness, modeling precision, and economic responsiveness of the proposed method. The results demonstrate that the framework enables fine-grained coordination of flexible loads and enhances their adaptability to market signals. This study is the first to integrate GNMTL-LSTM forecasting with market-oriented Zonotope modeling for heterogeneous demand-side resources, enabling simultaneous improvements in dynamic accuracy, computational scalability, and economic responsiveness. Full article
26 pages, 22649 KiB  
Article
Street Vitality Evaluation of the Mengzi East Street Historical District Based on Space Syntax and POI Big Data
by Zhihong Wu, Min Mao, Jian Yang, Chen Peng and Huafen Zha
Buildings 2025, 15(16), 2896; https://doi.org/10.3390/buildings15162896 - 15 Aug 2025
Abstract
The decline and revitalization of vitality in historic districts of small- and medium-sized cities undergoing rapid urbanization is a frontier issue in global heritage conservation and urban regeneration. Using the East Street Historic District in Mengzi, Yunnan, as a case study, this study [...] Read more.
The decline and revitalization of vitality in historic districts of small- and medium-sized cities undergoing rapid urbanization is a frontier issue in global heritage conservation and urban regeneration. Using the East Street Historic District in Mengzi, Yunnan, as a case study, this study proposes a “space–function–time” coupling framework. Topological accessibility is quantified through space syntax metrics—Integration Value (2021) and Integration Value (2025), as well as Choice Value (2021) and Choice Value (2025)—while functional aggregation is represented by POI kernel density analysis. A “Deviation Degree–Change in Deviation Degree” model is developed to track the dynamic evolution before and after the implementation of the conservation plan (2021–2025). The findings indicate that (1) the linear correlation between Integration Value and POI density decreases from a moderate level (r = 0.42) in 2021 to a weak correlation (r = 0.32) in 2025, revealing that the spatial–functional coordination mechanism in small- and medium-sized city historic districts is considerably more fragile than in large cities; (2) Identifying streets with abnormal deviations: The primary street, Renmin Middle Road, exhibits a deviation degree as high as 4.160 due to excessive commercial aggregation, resulting in a “high accessibility–high load” imbalance. The secondary street, Dashu Street, although demonstrating a relatively high Integration Value (0.663), shows a “high accessibility–low vitality” condition due to insufficient functional facilities; (3) the Deviation Degree–Change in Deviation Degree model accurately identifies High Deviation Streets, Medium Deviation Streets, and Low Deviation Streets, and provides quantitative thresholds for planning feedback. This study introduces the Deviation Degree–Change in Deviation Degree model for the first time into the evaluation of historic district renewal in small- and medium-sized cities, establishing a closed-loop “diagnosis–intervention–reassessment” tool. The proposed framework offers both a methodological and operational paradigm for precision-oriented urban regeneration in historic districts. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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29 pages, 6335 KiB  
Article
Advancing Power Supply Resilience: Optimized Transmission Line Retrofitting Through Deep Q-Learning Algorithm
by Lin Liu, Tianjian Wang, Xiuchao Zhu and Chenming Liu
Energies 2025, 18(16), 4335; https://doi.org/10.3390/en18164335 - 14 Aug 2025
Abstract
This study explores practical approaches to improving the reliability of power supply systems through the expansion and optimization of substation power lines. As electricity demand steadily increases, ensuring a stable and efficient power delivery network has become essential to support industrial growth and [...] Read more.
This study explores practical approaches to improving the reliability of power supply systems through the expansion and optimization of substation power lines. As electricity demand steadily increases, ensuring a stable and efficient power delivery network has become essential to support industrial growth and socio-economic development. This study focuses on challenges such as vulnerability to single-line faults, limited transmission capacity, and complex coordination in system operation. To address these issues, the proposed strategy includes building redundant transmission lines, improving network configuration, and applying modern transmission technologies to enhance operational flexibility. Notably, a Deep Q-Learning algorithm is introduced during the planning and optimization process. Its ability to accelerate convergence and streamline decision making significantly reduces computation time while maintaining solution accuracy, thereby increasing overall efficiency in evaluating large-scale network configurations. Simulation results and case studies confirm that such improvements lead to shorter outage durations, enhanced fault tolerance, and better adaptability to future load demands. The findings highlight strong practical value for industrial applications, offering a scalable and cost-conscious solution for strengthening the reliability of modern power systems. Full article
(This article belongs to the Special Issue Flow Control and Optimization in Power Systems)
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14 pages, 372 KiB  
Communication
Multi-Level Coordination-Level Evaluation Study of Source-Grid-Load-Storage Based on AHP-Entropy Weighting
by Benhong Wang, Ligui Wu, Peng Zhang, Fangqing Zhang and Jiang Guo
Energies 2025, 18(16), 4321; https://doi.org/10.3390/en18164321 - 14 Aug 2025
Viewed by 36
Abstract
With the development of the power system, the ability to present a comprehensive and reasonable evaluation of its coordination level has become important for the collaborative optimization of source-grid-load-storage. By identifying uncertain risk factors fully, the present work develops a multi-level coordination-level evaluation [...] Read more.
With the development of the power system, the ability to present a comprehensive and reasonable evaluation of its coordination level has become important for the collaborative optimization of source-grid-load-storage. By identifying uncertain risk factors fully, the present work develops a multi-level coordination-level evaluation of source-grid-load-storage based on AHP-entropy weighting. Building on previous studies, the present work reflects interactive characteristics of the collaborative optimization of source-grid-load-storage. Meanwhile, to determine the indicator weighting more reasonably, AHP-entropy weighting is adopted; this method combines the advantages of subjective AHP weighting and objective entropy weighting. Firstly, the multi-level coordination-level evaluation of source-grid-load-storage is introduced and includes both direct factors and indirect factors. Next, based on AHP-entropy weighting, the indicator weighting of the multi-level coordination-level evaluation is determined. Lastly, a case study is conducted that involves evaluating the coordination levels of the power systems of three regions. Additionally, the effectiveness of the multi-level coordination-level evaluation of source-grid-load-storage is validated. Full article
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21 pages, 1697 KiB  
Article
Coordinative Scheduling Method for Source–Load–Storage Integrated Systems Considering the Utilization of Energy-Intensive Industry Loads for Regulation
by Zhongzheng Li, Gaohang Zhang, Mengke Liao and Erbiao Zhou
Sustainability 2025, 17(16), 7321; https://doi.org/10.3390/su17167321 - 13 Aug 2025
Viewed by 183
Abstract
With the increasing penetration of renewable energy in power systems, it is vital to adopt methods to enhance the acceptance capacity of renewable energy. Energy-intensive loads have excellent potential for regulating the utilization of renewable energy. Existing studies have often overlooked the regulatory [...] Read more.
With the increasing penetration of renewable energy in power systems, it is vital to adopt methods to enhance the acceptance capacity of renewable energy. Energy-intensive loads have excellent potential for regulating the utilization of renewable energy. Existing studies have often overlooked the regulatory potential of energy-intensive industrial loads. The coordinated optimization of source, load, and storage can improve the matching degree between power supply and load demand and achieve on-site consumption of renewable energy. This paper proposes a coordinated optimization method for source–load–storage integrated systems, utilizing for regulation energy-intensive industrial loads such as electrolytic aluminum load and polysilicon load. The operational characteristics and regulatory ability of electrolytic aluminum load and polysilicon load were analyzed in the production process. Operation models of energy-intensive loads are proposed. A coordinated operation model of a source–load–storage integrated system is established. The operation schemes of thermal units, energy storage, and energy-intensive loads are jointly optimized to guarantee power supply capacity and renewable energy consumption. In addition, power purchase from the bulk power system and the time-of-use electricity price are considered to ensure a reliable power supply for energy-intensive loads. The case results showed that on the premise of ensuring that the production meets the requirements, the flexibility and economy of system operation were effectively improved. Reasonably rated power and capacity for the energy storage system can improve the regulation ability and reduce the operating costs of regional systems. Full article
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19 pages, 2982 KiB  
Article
Immersion and Invariance Adaptive Control for Unmanned Helicopter Under Maneuvering Flight
by Xu Zhou, Yousong Xu, Siliang Du and Qijun Zhao
Drones 2025, 9(8), 565; https://doi.org/10.3390/drones9080565 - 12 Aug 2025
Viewed by 221
Abstract
An asymptotic stability velocity tracking controller is designed to enable the autonomous maneuvering flight of unmanned helicopters. Firstly, taking the UH-60A without pilots as the research object, a high-efficient rotor aerodynamic modeling is developed, which incorporates a free-wake vortex method with the flap [...] Read more.
An asymptotic stability velocity tracking controller is designed to enable the autonomous maneuvering flight of unmanned helicopters. Firstly, taking the UH-60A without pilots as the research object, a high-efficient rotor aerodynamic modeling is developed, which incorporates a free-wake vortex method with the flap response of blades. The consummate flight dynamic model is complemented by wind tunnel-validated fuselage/tail rotor load regressions. Secondly, a linear state–space equation is derived via the small perturbation linearization method based on the flight dynamic model within the body coordinate system. A decoupled model is formulated based on the linear state–space equation by employing the implicit model approach. Subsequently, a system of ordinary differential equations is constructed, which is related to the deviation between actual velocity and its expected value, along with higher-order derivatives of this discrepancy. The I&I (immersion and invariance) theory is then employed to facilitate the design of a non-cascade control loop. Finally, the response of desired velocity in longitudinal channel is simulated with step signal to compare the control effect with a PID (proportional–integral–derivative) controller. By adjusting the coefficients, the response progress of the PID controller is similar to the effect of adaptive controller with I&I theory. However, there is no obvious overshoot in the process with I&I adaptive controller, and the average response amplitude accounts for 16.69% of the random white noise, which is 7.38% of the oscillation level under the PID controller. The parameter tuning complexity when employing I&I theory is significantly lower than that of the PID controller, which is evaluated by mathematical derivations and simulations. Meanwhile, the sidestep and pirouette maneuvers are simulated and analyzed to examine the controller in accordance with the performance criteria outlined in the ADS-33E-REF standards. The simulation results demonstrate that the speed expectation-oriented asymptotic stability control can achieve a fast response. Both sidestep and pirouette maneuvers can satisfy the desired performance requirements stipulated by ADS-33E-REF. Full article
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28 pages, 1465 KiB  
Article
A Three-Layer Coordinated Planning Model for Source–Grid–Load–Storage Considering Electricity–Carbon Coupling and Flexibility Supply–Demand Balance
by Zequn Wang, Haobin Chen, Haoyang Tang, Lin Zheng, Jianfeng Zheng, Zhilu Liu and Zhijian Hu
Sustainability 2025, 17(16), 7290; https://doi.org/10.3390/su17167290 - 12 Aug 2025
Viewed by 308
Abstract
With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon [...] Read more.
With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon coupling and flexibility supply–demand balance. The model incorporates a dynamic pricing mechanism that links carbon pricing and time-of-use electricity tariffs, and integrates multi-source flexible resources—such as wind, photovoltaic (PV), conventional generators, energy storage systems (ESS), and controllable loads—to quantify the system’s flexibility capacity. A hierarchical structure encompassing “decision–planning–operation” is designed to achieve coordinated optimization of resource allocation, cost minimization, and operational efficiency. To improve the model’s computational efficiency and convergence performance, an improved adaptive particle swarm optimization (IAPSO) algorithm is developed which integrates dynamic inertia weight adjustment, adaptive acceleration factors, and Gaussian mutation. Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed model outperforms conventional approaches in terms of operational economy, carbon emission reduction, system flexibility, and renewable energy accommodation. The approach provides effective support for the coordinated deployment of diverse resources in next-generation power systems. Full article
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22 pages, 3713 KiB  
Article
Co-Adaptive Inertia–Damping Control of Grid-Forming Energy Storage Inverters for Suppressing Active Power Overshoot and Frequency Deviation
by Huiping Zheng, Boyu Ma, Xueting Cheng, Yang Cui and Liming Bo
Energies 2025, 18(16), 4255; https://doi.org/10.3390/en18164255 - 11 Aug 2025
Viewed by 230
Abstract
With the large-scale integration of renewable energy through power electronic inverters,
modern power systems are gradually transitioning to low-inertia systems. Grid-forming
inverters are prone to power overshoot and frequency deviation when facing external
disturbances, threatening system stability. Existing methods face two main challenges [...] Read more.
With the large-scale integration of renewable energy through power electronic inverters,
modern power systems are gradually transitioning to low-inertia systems. Grid-forming
inverters are prone to power overshoot and frequency deviation when facing external
disturbances, threatening system stability. Existing methods face two main challenges in
dealing with complex disturbances: neural-network-based approaches have high computational
burdens and long response times, while traditional linear algorithms lack sufficient
precision in adjustment, leading to inadequate system response accuracy and stability. This
paper proposes an innovative coordinated adaptive control strategy for virtual inertia and
damping. The strategy utilizes a Radial Basis Function neural network for the adaptive
regulation of virtual inertia, while the damping coefficient is adjusted using a linear algorithm.
This approach provides refined inertia regulation while maintaining computational
efficiency, optimizing the rate of change in frequency and frequency deviation. Simulation
results demonstrate that the proposed control strategy significantly outperforms traditional
methods in improving system performance. In the active power reference variation
scenario, frequency overshoot is reduced by 65.4%, active power overshoot decreases by
66.7%, and the system recovery time is shortened. In the load variation scenario, frequency
overshoot is reduced by approximately 3.6%, and the maximum frequency deviation is
reduced by approximately 26.9%. In the composite disturbance scenario, the frequency
peak is reduced by approximately 0.1 Hz, the maximum frequency deviation decreases by
35%, and the power response improves by 23.3%. These results indicate that the proposed
method offers significant advantages in enhancing system dynamic response, frequency
stability, and power overshoot suppression, demonstrating its substantial potential for
practical applications. Full article
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22 pages, 5073 KiB  
Article
Stochastic Bidding for Hydro–Wind–Solar Systems in Cross-Provincial Forward–Spot Markets: A Dimensionality-Reduced and Transmission-Aware Framework
by Yan Zhang, Xue Hu, Xiangzhen Wang, Xiaoqian Zhou, Yuyang Liu, Bohan Zhang and Yapeng Li
Energies 2025, 18(16), 4222; https://doi.org/10.3390/en18164222 - 8 Aug 2025
Viewed by 272
Abstract
Integrated hydro–wind–solar power generators (IPGs) in China face multi-timescale bidding challenges across provincial forward–spot markets, which are further compounded by hydropower nonconvexity and transmission constraints. This study proposes a stochastic optimization model addressing uncertainties from wind–solar generation and spot prices through scenario-based programming, [...] Read more.
Integrated hydro–wind–solar power generators (IPGs) in China face multi-timescale bidding challenges across provincial forward–spot markets, which are further compounded by hydropower nonconvexity and transmission constraints. This study proposes a stochastic optimization model addressing uncertainties from wind–solar generation and spot prices through scenario-based programming, integrating three innovations: average-day dimensionality reduction to harmonize monthly–hourly decisions, local generation function approximation to linearize hydropower operations, and transmission-aware coordination for cross-provincial allocation. Validation on a southwestern China cascade hydropower base transmitting power to eastern load centers shows that the model establishes hydropower-mediated complementarity with daily “solar–daytime, wind–nighttime” and seasonal “wind–winter, solar–summer” patterns. Furthermore, by optimizing cross-provincial power allocation, strategic spot market participation yields 46.4% revenue from 30% generation volume. Additionally, two transmission capacity thresholds are found to guide grid planning: 43.75% capacity marks the economic optimization inflection point, while 75% represents technical saturation. This framework ensures robustness and computational tractability while enabling IPGs to optimize profits and stability in multi-market environments. Full article
(This article belongs to the Special Issue Optimal Schedule of Hydropower and New Energy Power Systems)
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16 pages, 2373 KiB  
Article
Simulation and Control of Water Pollution Load in the Xiaoxingkai Lake Basin Based on a System Dynamics Model
by Yaping Wu, Dan Chen, Fujia Li, Mingming Feng, Ping Wang, Lingang Hao and Chunnuan Deng
Sustainability 2025, 17(15), 7167; https://doi.org/10.3390/su17157167 - 7 Aug 2025
Viewed by 299
Abstract
With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment [...] Read more.
With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment model based on the system dynamics methodology, incorporating subsystems for population, agriculture, and water pollution. The model focuses on four key indicators of pollution severity, namely, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (NH3-N), and simulates the changes in pollutant loads entering the river under five different scenarios from 2020 to 2030. The results show that agricultural non-point sources are the primary contributors to TN (79.5%) and TP (73.7%), while COD primarily originates from domestic sources (64.2%). NH3-N is mainly influenced by urban domestic activities (44.7%) and agricultural cultivation (41.2%). Under the status quo development scenario, pollutant loads continue to rise, with more pronounced increases under the economic development scenario, thus posing significant sustainability risks. The pollution control enhancement scenario is most effective in controlling pollutants, but it does not promote socio-economic development and has high implementation costs, failing to achieve coordinated socio-economic and environmental development in the region. The dual-reinforcement scenario and moderate-reinforcement scenario achieve a balance between pollution control and economic development, with the moderate-reinforcement scenario being more suitable for long-term regional development. The findings can provide a scientific basis for water resource management and planning in the Xiaoxingkai Lake basin. Full article
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18 pages, 1152 KiB  
Article
Coordinated Truck Loading and Routing Problem: A Forestry Logistics Case Study
by Cristian Oliva, Manuel Cepeda and Sebastián Muñoz-Herrera
Mathematics 2025, 13(15), 2537; https://doi.org/10.3390/math13152537 - 7 Aug 2025
Viewed by 241
Abstract
This study addresses a real-world logistics problem in forestry operations: the distribution of plants from cultivation centers to planting sites under strict delivery time windows and limited depot resources. We introduce the Coordinated Truck Loading and Routing Problem (CTLRP), an extension of the [...] Read more.
This study addresses a real-world logistics problem in forestry operations: the distribution of plants from cultivation centers to planting sites under strict delivery time windows and limited depot resources. We introduce the Coordinated Truck Loading and Routing Problem (CTLRP), an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) that integrates routing decisions with truck loading schedules at a single depot with constrained capacity. To solve this NP-hard problem, we develop a metaheuristic algorithm based on Ant Colony Optimization (ACO), enhanced with a global memory system and a novel stochastic return rule that allows trucks to return to the depot when additional deliveries are suboptimal. Parameter calibration experiments are conducted to determine optimal values for the return probability and ant population size. The algorithm is tested on a real forestry dispatch scenario over six working days. The results show that an Ant Colony System (ACS–CTLRP) algorithm reduces total distance traveled by 23%, travel time by 22%, and the number of trucks used by 13 units, while increasing fleet utilization from 54% to 83%. These findings demonstrate that the proposed method significantly outperforms current company planning and offers a transferable framework for depot-constrained routing problems in time-sensitive distribution environments. Full article
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14 pages, 1536 KiB  
Article
Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
by Peiyu Chen, Wenqing Cui, Jingan Shang, Bin Xu, Chao Li and Danyang Lun
Appl. Sci. 2025, 15(15), 8656; https://doi.org/10.3390/app15158656 - 5 Aug 2025
Viewed by 160
Abstract
In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy [...] Read more.
In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy for multiple battery energy storage stations (BESSs), improving the performance of peak shaving. Firstly, the strategy involves constructing an optimization model incorporating load forecasting, capacity constraints, and security indices to design a coordination mechanism tracking the target load band with the equivalent power. Secondly, it establishes a quantitative evaluation system using metrics such as peak–valley difference and load standard deviation. Comparison based on typical daily cases shows that, compared with the constant power strategy, the coordinated variable-power control strategy has a more obvious and comprehensive improvement in overall peak-shaving effects. Furthermore, it employs a “dynamic dispatch of multiple BESS” mode, effectively mitigating the risks and flexibility issues associated with single BESSs. This strategy provides a reliable new approach for large-scale energy storage to participate in high-precision peaking. Full article
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20 pages, 1895 KiB  
Article
Distributed Low-Carbon Demand Response in Distribution Networks Incorporating Day-Ahead and Intraday Flexibilities
by Bin Hu, Xianen Zong, Hongbin Wu and Yue Yang
Processes 2025, 13(8), 2460; https://doi.org/10.3390/pr13082460 - 4 Aug 2025
Viewed by 257
Abstract
In this paper, we present a distributed low-carbon demand response method in distribution networks incorporating day-ahead and intraday flexibilities on the demand side. This two-stage demand dispatch scheme, including day-ahead schedule and intraday adjustment, is proposed to facilitate the coordination between power demand [...] Read more.
In this paper, we present a distributed low-carbon demand response method in distribution networks incorporating day-ahead and intraday flexibilities on the demand side. This two-stage demand dispatch scheme, including day-ahead schedule and intraday adjustment, is proposed to facilitate the coordination between power demand and local photovoltaic (PV) generation. We employ the alternating direction method of multipliers (ADMM) to solve the dispatch problem in a distributed manner. Demand response in a 141-bus test system serves as our case study, demonstrating the effectiveness of our approach in shifting power loads to periods of high PV generation. Our results indicate remarkable reductions in the total carbon emission by utilizing more distributed PV generation. Full article
(This article belongs to the Special Issue Modeling, Operation and Control in Renewable Energy Systems)
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35 pages, 9464 KiB  
Article
Numerical Investigation of Progressive Collapse Resistance in Fully Bonded Prestressed Precast Concrete Spatial Frame Systems with and Without Precast Slabs
by Manrong Song, Zhe Wang, Xiaolong Chen, Bingkang Liu, Shenjiang Huang and Jiaxuan He
Buildings 2025, 15(15), 2743; https://doi.org/10.3390/buildings15152743 - 4 Aug 2025
Viewed by 372
Abstract
Preventing progressive collapse induced by accidental events poses a critical challenge in the design and construction of resilient structures. While substantial progress has been made in planar structures, the progressive collapse mechanisms of precast concrete spatial structures—particularly regarding the effects of precast slabs—remain [...] Read more.
Preventing progressive collapse induced by accidental events poses a critical challenge in the design and construction of resilient structures. While substantial progress has been made in planar structures, the progressive collapse mechanisms of precast concrete spatial structures—particularly regarding the effects of precast slabs—remain inadequately explored. This study develops a refined finite element modeling approach to investigate progressive collapse mechanisms in fully bonded prestressed precast concrete (FB-PPC) spatial frames, both with and without precast slabs. The modeling approach was validated against available test data from related sub-assemblies, and applied to assess the collapse performance. A series of pushdown analyses were conducted on the spatial frames under various column removal scenarios. The load–displacement curves, slab contribution, and failure modes under different conditions were compared and analyzed. A simplified energy-based dynamic assessment was additionally employed to offer a rapid estimation of the dynamic collapse capacity. The results show that when interior or side columns fail, the progressive collapse process can be divided into the beam action stage and the catenary action (CA) stage. During the beam action stage, the compressive membrane action (CMA) of the slabs and the compressive arch action (CAA) of the beams work in coordination. Additionally, the tensile membrane action (TMA) of the slabs strengthens the CA in the beams. When the corner columns fail, the collapse stages comprise the beam action stage followed by the collapse stage. Due to insufficient lateral restraints around the failed column, the development of CA is limited. The membrane action of the slabs cannot be fully mobilized. The contribution of the slabs is significant, as it can substantially enhance the vertical resistance and restrain the lateral displacement of the columns. The energy-based dynamic assessment further reveals that FB-PPC spatial frames exhibit high ductility and residual strength following sudden column removal, with dynamic load–displacement curves showing sustained plateaus or gentle slopes across all scenarios. The inclusion of precast slabs consistently enhances both the peak load capacity and the residual resistance in dynamic collapse curves. Full article
(This article belongs to the Special Issue Research on the Seismic Performance of Reinforced Concrete Structures)
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