Residential photovoltaic (PV)-battery energy storage system (BESS) planning often neglects impact of comfort level. We propose a two-stage framework that jointly selects BESS capacity and appliance start-up schedules while accounting for appliance-use comfort, thermal comfort, and BESS degradation. Annual days are clustered by k-means to select a set of typical days. Stage 1 performs a discrete capacity scan (coarse grid plus local refinement). For each candidate, Stage 2 solves a typical day mixed-integer linear programming problem with a linear fractional cost-comfort objective via Dinkelbach's method, maps the resulting binary schedules to all natural days by cluster membership, and then solves an annual rolling linear programming problem for dispatch and degradation-inclusive evaluation. In the baseline case, the selected capacity is 10.75 kWh with an annual total cost of 2623.2 CNY and comfort indices of 0.793 and 0.788; degradation cost is 404.3 CNY. Without the comfort term, the preferred capacity increases to 16.5 kWh and the annual total cost decreases to 2265.9 CNY, while degradation cost rises to 555.1 CNY. Sensitivity analyses show that outcomes vary with comfort settings and time-of-use prices. Overall, the framework quantifies annual cost-comfort-degradation trade-offs and recommends a feasible BESS size and appliance schedules.

As the global impacts of carbon emissions intensify, power markets are shifting from profit-driven models to frameworks that integrate economic performance and carbon mitigation. Current low-carbon approaches commonly exhibit insufficient user response, elevated abatement costs, complex implementation, and limited flexibility. To bridge this gap, we propose a carbon subsidy framework for industrial parks. In this framework, the Industrial Park Operator (IPO) announces real-time shared electricity, carbon, and subsidy prices, while users optimize load scheduling and market participation. The interaction is formulated as a Stackelberg game and solved using a Differential Evolution-Mixed-Integer Quadratic Programming (DE-MIQP) approach. Case studies demonstrate that, compared with a baseline without subsidies, the IPO provides a total subsidy of 1,295.57 CNY, the proposed framework reduces industrial-park CO2 emissions by 40.13 %, and the IPO’s profit decreases by 217.39 CNY, while the users’ aggregate profit increases by 608.3 CNY. Finally, two sensitivity analyses are conducted: one investigates parameter sensitivity, and the other is a ten-user case study that exhibits emission-reduction behavior similar to that of the four-user case. These findings confirm that the carbon subsidy framework effectively incentivizes user participation, reduces emissions, and offers a practical pathway for coordinated low-carbon operation in multi-user industrial parks.

Greenhouse climate control in water-stressed and carbon-intensive regions involves not only energy use and cost, but irrigation-related water demand, CO2 supplementation, and carbon impacts. However, integrated assessment of these coupled energy, water, CO2, and carbon cost factors remains limited. To address this gap, this study develops an optimization and assessment framework under South African time-of-use (TOU) tariffs. The framework captures the coupled dynamics of temperature, relative humidity, CO2 concentration, transmittance, and irrigation-related water demand, and evaluates three operational strategies: Minimum Energy Consumption (MEC), Minimum Energy Cost (MECo), and Minimum Total Cost (MTC). Compared with the MECo strategy, MTC extends the assessment boundary by incorporating groundwater-related penalty cost, CO2 supply cost, and carbon-emission cost into a unified total-cost objective. Under the baseline winter scenario, MTC reduces total cost by 37.01% versus MEC and 39.41% versus MECo, while reducing energy consumption by 13.74% and CO2 supply cost by 89.15% relative to MECo. Supplementary summer simulations show that under cooling-dominated conditions, MTC reduces total cost by 34.69% compared with MECo. Sensitivity analysis indicates that TOU tariffs exert the strongest influence on total cost. Overall, the framework provides a decision-support tool for price-responsive, sustainability-oriented greenhouse operation under coupled energy, water, and carbon considerations.

Greenhouse cultivation plays a vital role in ensuring food security but is often associated with high energy consumption, water usage, and carbon emissions. Integrating renewable energy systems for power supply and utilizing rainwater harvesting for irrigation can help address these challenges. However, balancing these interconnected factors requires advanced control strategies. In this study, we propose a hierarchical model predictive control (MPC) framework to optimize the management of grid-connected photovoltaic-battery systems in greenhouses, accounting for the interactions among energy use, water consumption, carbon emissions, and food production (EWCF nexus). The hierarchical MPC is structured in three layers: the first optimizes greenhouse operations to minimize total costs (MTC); the second manages the scheduling of the hybrid energy system to minimize operational cost (MOC); and the third designs an MPC controller to handle photovoltaic generation and load demand disturbances. Results show that the proposed MTC strategy reduces the total cost by 81.01% compared with the minimizing energy consumption strategy. Moreover, the MOC strategy reduces operational costs by 20.68% compared to the maximizing self-consumption strategy. In addition, the proposed MPC achieves superior performance in tracking the reference trajectory under varying disturbance levels compared to commonly used open loop controllers. This study provides practical guidance for greenhouse management by addressing key resource and environmental challenges, contributing to the sustainable development of controlled-environment agriculture.

Greenhouse cultivation supports stable food production but faces rising electricity costs, water scarcity, and carbon-emission pressures. This study develops a two-stage optimization framework for sustainable greenhouse operation. The framework couples minute-level climate control with hourly multi-source irrigation scheduling through an evapotranspiration-based water-demand mapping. In the first stage, the climate control regulates temperature, relative humidity, CO2 concentration, and light intensity using a total cost minimization (TCM) strategy that considers time-of-use tariffs and CO2 supply cost, compared with an energy consumption minimization (ECM) strategy. In the second stage, the irrigation scheduling allocates harvested rainwater, groundwater, and municipal water via an irrigation cost minimization (ICM) strategy compared with a rule-based approach. To address water demand uncertainty, a model predictive control (MPC) strategy is proposed to enable real-time dynamic adjustment of irrigation decisions. Results show that, compared with the ECM strategy, the proposed TCM strategy reduces the total operating cost by 36.40%. Compared with the rule-based method, ICM reduces the irrigation cost by 16.19%. MPC maintains supply–demand balance across different demand-uncertainty levels and during sudden demand surges, while achieving lower irrigation costs than the rule-based strategy. This study provides a practical pathway to cost-effective and reliable greenhouse operation under coupled electricity and water constraints.

This paper proposes a composite model predictive control strategy for a three-level neutral-point-clamped (NPC) grid-connected inverter. A low common-mode-voltage (CMV) candidate set is first constructed by retaining 19 effective voltage-vector positions and excluding redundant switching states with higher CMV. Based on this candidate set, 24 nearest-three-vector combinations are evaluated, and their dwell times are allocated according to the predicted current-tracking costs. A dual-layer neutral-point (NP) voltage-balancing scheme is then developed. The predictive layer incorporates the synthesized NP current into the cost function, while the modulation layer uses a charge-balance-based zero-sequence voltage injection with sign-boundary correction and dynamic amplitude limitation to compensate for the remaining NP-voltage deviation. Capacitor-current-feedback active damping is embedded in the prediction model to attenuate the LCL-filter resonance. Experiments on a 500 V/20 A NPC inverter prototype achieve a grid-current total harmonic distortion (THD) of 1.85%, a CMV peak of 90 V, and an NP-voltage deviation of 0.5 V. The maximum grid-current spectral component in the predefined LCL-resonance band is attenuated by 21.70 dB. The experimental results under current-reference changes, grid-voltage sag, and passive-parameter mismatch further indicate stable operation within the tested conditions.

Greenhouse operations are energy-intensive and face increasing pressure from high operational costs, carbon emissions, and grid reliability constraints. This study develops a grid-connected photovoltaic-wind-battery hybrid energy system and proposes a two-level hierarchical optimization framework for greenhouse energy management. At the upper level, greenhouse operations are optimized using two alternative strategies: energy demand minimization, which aims to reduce heating, cooling, and ventilation loads, and energy expense minimization, which focuses on minimizing energy costs under time-of-use electricity tariffs. At the lower level, energy system scheduling is addressed through renewable energy utilization maximization and comprehensive cost minimization strategies, the latter accounting for electricity purchases, battery degradation, and carbon emissions. Simulation results demonstrate that the comprehensive cost minimization strategy achieves the best overall balance between economic performance and environmental benefits, reducing total operational costs by 45.30% and carbon emissions by 69.25% compared with the baseline. Sensitivity analysis further reveals that the battery unit cost is the most influential factor affecting the economic performance of the system. The proposed framework provides practical guidance for designing cost-effective and low-carbon greenhouse energy systems, supporting reliable and sustainable energy networks.

Carbon emissions from greenhouse operations pose a challenge to sustainability. Currently, greenhouse research is deficient in reducing carbon emissions. The article devised two operational strategies for comparison. Strategy 1 only ensures that the environment inside the greenhouse meets the growing conditions of the crop. Strategy 2 minimizes carbon emissions based on Strategy 1. The results show that Strategy 2 has 87.2% less carbon emissions. Carbon emissions from greenhouse production are addressed.

The increasing penetration of wind and photovoltaic (PV) generation introduces significant uncertainty and volatility to power systems. To address these challenges, this study proposes a bi-level optimization framework for virtual power plants (VPPs) that integrates electric vehicles (EVs) and demand response (DR) to enhance renewable energy utilization, reduce carbon emissions, and coordinate the economic interests between the VPP operator (OPE) and the aggregator (AGG). The upper level maximizes the OPE’s revenue through dynamic electricity pricing, while the lower level minimizes the AGG’s cost via adaptive load scheduling. Simulation results show that, compared to a baseline case without EV and DR coordination, the proposed framework reduces peak demand by 3.02%, lowers total carbon emissions by 10.13%, and decreases renewable energy curtailment by 37.5% for wind and 42.85% for PV. To further validate robustness, the model was tested under diverse weather conditions over a one-week period, achieving even greater reductions in curtailment: 51.07% for wind and 51.38% for PV. This framework provides a scalable solution for high renewable integration, enabling both economic and environmental benefits.

With the increasing penetration of electric vehicles (EVs) and renewable energy sources, power system scheduling faces multiple challenges in terms of economic efficiency and security. Addressing the limitations of traditional microgrid optimization methods in managing wind, photovoltaic, and EV charging and discharging demands, the paper proposes a three-layer optimization model for EV-integrated wind-solar-storage standalone microgrids. The model offers more refined scheduling and improved system robustness. A built-in solver is used to solve the proposed model, and its advantages in system robustness and economic benefits are validated through typical case studies. The results demonstrate that the model effectively enhances economic performance and stability, achieves peak shaving and valley filling, and adapts to renewable energy output fluctuations, providing a new approach to efficient microgrid scheduling.

This study presents a novel optimization method for the design of a hybrid microgrid system, consisting of wind turbines, photovoltaic systems, battery energy storage systems, and diesel generators. A Continuous Grey Wolf Optimization (CGWO) algorithm is proposed to tackle the challenges of nonlinearity and stochastic disturbances in the system's capacity configuration. The CGWO enhances the traditional Grey Wolf Optimization (GWO) by incorporating an improved convergence factor and a dynamic weighting strategy, significantly increasing convergence speed and solution quality. A case study is conducted to evaluate four power supply schemes for the microgrid. Results indicate that Scheme 3 achieves the lowest total cost and environmental conversion expenses, with reductions of 30.12% and 59.7% compared to Scheme 1, and 16.74% and 39.84% compared to Scheme 2, respectively. In addition, the CGWO reduces diesel generator usage by 23.78% compared to the GWO and 22.04% compared to Particle Swarm Optimization (PSO), while decreasing power shortages by 62.09% and 60.25%, respectively. These findings highlight the CGWO's effectiveness in optimizing microgrid configurations, balancing cost, sustainability, and reliability. The proposed method provides valuable insights for designing cost-efficient and environmentally sustainable energy systems.

This study addresses the challenges of high energy consumption and environmental concerns in traditional greenhouse operations by exploring an integrated greenhouse with grid-tied photovoltaic (PV)-battery systems. A two-layer hierarchical optimization framework is proposed for effective energy management. In the upper layer, greenhouse operations are optimized with the Energy Consumption Minimization (ECM) strategy and the Energy Cost Minimization (ECoM) strategy, ensuring suitable climate conditions while reducing energy use and costs. In the lower layer, the scheduling of the hybrid energy system is optimized with the Self-consumption Maximization (SCM) strategy and the Total Cost Minimization (TCM) strategy, aiming to maximize the self-consumption of PV power and minimize total costs, including energy costs, battery aging costs, and carbon emission costs, while meeting the greenhouse’s electricity demand. A sensitivity analysis is conducted to investigate the impact of electricity prices, feed-in tariffs, battery capacity, and PV array area on hybrid energy system scheduling. Results reveal that the ECoM strategy reduces costs by 6.98% compared to the ECM strategy, and the TCM strategy reduces total costs by 43.50% compared to the SCM strategy. Battery capacity and PV array area are found to have a more significant influence on total costs than electricity prices and feed-in tariffs. This study can offer crucial insights for realizing cost-effective and environmentally friendly greenhouse operations, contributing to the advancement of sustainable agriculture.

In order to reduce the fluctuation of the pantograph-catenary contact force (PCCF) and improve the tracking accuracy of the high-speed train pantograph-catenary system (PCS), an active control strategy of PCCF based on low-pass filtering backstepping was proposed. Firstly, the three degree of freedom PCS model was established and transformed into a cascade form. According to the strict feedback structure of PCS, the controller was designed by backstepping. Then, the first-order derivative control law of virtual control quantity was designed directly by combining the dynamic surface control, which can avoid the “explosion of terms” in backstepping when calculating the first derivative of the virtual control quantity, simplify the controller design structure. A state reconstruction observer was designed to estimate the unmeasured state quantity of PCS. Finally, Lyapunov theory was used to prove the global asymptotic stability of the closed loop system. The simulation results show that proposed control strategy can effectively reduce PCCF fluctuation, improve the current collection quality of PCS, and is suitable for operating environments of different speeds.

To address the problems of insufficient 3D face data, high acquisition costs and low recognition accuracy, this paper proposes a framework of 3D face recognition algorithm based on deep learning reconstruction. Firstly, two-dimensional face features are extracted by High Resolution Network (HRNet), and the reconstructed three-dimensional face is obtained by using Graph Convolutional Network (GCN). Feature matching is completed using the loss function proposed by Arcface to recognise the reconstructed 3D faces. Using 2D face recognition assistance for 3D face recognition, the similarity between the 2D face and the reconstructed 3D face is weighted to improve the problem of face recognition being affected by complex environments. The experimental results show that the recognition accuracy of the 3D face recognition algorithm based on deep learning reconstruction is 95.83%, and the recognition effect is better than that of 2D face recognition alone as well as 3D face recognition alone.

双矢量模型预测控制需要用遍历法预测基本电压矢量组合作用下的系统行为,计算量较大,同时因为缺乏调制单元而存在开关频率不固定和并网谐波含量高的问题.为此,提出一种并网逆变器简化矢量选择预测电流定频控制.通过判断电流预测差值所在扇区,每个周期采用3个电压矢量控制,通过电流无差拍计算每个电压矢量作用时间,结合不连续DPWM1调制得到开关信号,实现计算量减小,输出电流定频控制且谐波含量降低.仿真和实验结果验证了所提方法的可行性和有效性.

In order to realize rapid and accurate detection of foreign objects entering the coal belt during operation, and prevent belt tearing, an improved CenterNet（ICN） algorithm for coal belt foreign objects of coal belt is proposed in this paper. Firstly, the coal belt images are preprocessed to adapt to the CenterNet algorithm,which can improve the effectiveness of detection. Secondly, the network is improved by replacing the standard convolution in the residual module with the deep separable convolution, which can effectively reduce the network computation and redundancy. Thirdly, the group normalization is adopted as the optimization normalization method, which can reduces the requirements for hardware facilities. Finally, the weighted feature map fusion method is used to make full use of the features extracted from each layer to improve the detection accuracy of the network. The experimental results show that the ICN algorithm reduces the false detection rate and missed detection rate, and can effectively improve the detection speed and accuracy of foreign objects with large size difference and uneven distribution.

Multiphase inverter are widely used in ship propulsion, because of it has good fault tolerance, high control freedom and high power output under low voltage. In order to use low voltage level semiconductor, the inverter adopting a multi-level topology. The neutral-point(NP) voltage balance is most important of this inverter. This article presents a novel modulation strategy with carrier-based PWM to keep neutral-point(NP) voltage balance for a six-phase H-bridge neutral-point-clamped (NPC) converter. The converter is used to drive a six-phase open winding PMSM. First, the basic modulation method was determined, then, analyzed the topology. Fine-tuning the duty cycle through proportional regulator in switching period, the deviation between the bus capacitors was reduced. The simulation results show that the proposed modulation strategy can effectively balance the NP voltage, and it has been verified in experiments.

针对煤矿井下运输系统能耗大、生产成本高等问题,提出基于广义回归神经网络(GRNN)的带式输送机模型预测控制(MPC)策略.引入动态自适应权重和莱维飞行策略改进天牛须算法(BAS),并采用改进的天牛须算法对广义回归神经网络进行超参数寻优.建立了带式运输机模型,采用模型预测控制策略对带式输送机的运行进行优化与控制;优化过程采用了基于分时电价的控制策略.实验结果表明:与带式输送机传统的运行方式相比,所提出的控制策略不仅可以减少能源消耗,而且可以有效降低运行成本.

分布式电源DGs(distributed generations)接入配电网中,使得配电网由传统的单电源辐射状网络变成多电源复杂网络,增加了配电网故障定位的难度.针对DG接入配电网定位问题,提出了一种基于改进鸽群算法的故障区段定位方法.首先,建立了适用于含多个分布式电源的开关函数并对电流编码方式重新定义.其次,对基本鸽群算法中的指南针因子和地标算子进行改进,并通过结合模拟退火算法防止其陷入局部最优,提高了算法的容错性.仿真结果表明,该算法适用于含分布式电源配电网的单重和多重故障区段定位,且在相同故障情况下,改进鸽群算法分别比传统鸽群算法和遗传算法在迭代时间上分别降低了17.019％和43.763％,具有一定的实时性.