Control of LPV Modeled AC-Microgrid Based on Mixed H2/H∞ Time-Varying Linear State Feedback and Robust Predictive Algorithm

This paper presents a robust model predictive control (RMPC) method with a new mixed H2/H∞ linear time-varying state feedback design. In addition, we propose a linear parameter-varying model for inverters in a microgrid (MG), in which disturbances and uncertainty are considered, where the inverters connect in parallel to renewable energy sources (RES). The proposed RMPC can use the gain-scheduled control law and satisfy both the H2 and H∞ proficiency requirements under various conditions, such as disturbance and load variation. A multistep control method is proposed to reduce the conservativeness caused by the unique feedback control law, enhance the control proficiency, and strengthen the RMPC feasible area. Furthermore, a practical and efficient RMPC is designed to reduce the online computational burden. The presented controller can implement load sharing among distributed generators (DGs) to stabilize the frequency and voltage of an entire smart island. The proposed strategy is implemented and studied in a MG with two DG types and various load types. Specifically, through converters, one type of DGs is used to control frequency and voltage, and the other type is used to control current. These two types of DGs operate in a parallel mode. Simulation results show that the proposed RMPCs are input-to-state practically stable (ISpS). Compared with other controllers in the literature, the proposed strategy can lead to minor total harmonic distortion (THD), lower steady-state error, and faster response to system disturbance and load variation

Predicting the solar maximum with the rising rate

The growth rate of solar activity in the early phase of a solar cycle has been known to be well correlated with the subsequent amplitude (solar maximum). It provides very useful information for a new solar cycle as its variation reflects the temporal evolution of the dynamic process of solar magnetic activities from the initial phase to the peak phase of the cycle. The correlation coefficient between the solar maximum (Rmax) and the rising rate ({\beta}a) at {\Delta}m months after the solar minimum (Rmin) is studied and shown to increase as the cycle progresses with an inflection point (r = 0.83) at about {\Delta}m = 20 months. The prediction error of Rmax based on {\beta}a is found within estimation at the 90% level of confidence and the relative prediction error will be less than 20% when {\Delta}m \geq 20. From the above relationship, the current cycle (24) is preliminarily predicted to peak around October 2013 with a size of Rmax =84 \pm 33 at the 90% level of confidence.Comment: 7 pages, 3 figures, accepted for publication in SCIENCE CHINA Physics,Mechanics & Astronom

A Robust Kalman Filter-Based Approach for <i>SoC</i> Estimation of Lithium-Ion Batteries in Smart Homes

Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC subnetworks to represent the fast and slow transient responses of the terminal voltage. Moreover, the model includes the nonlinear relationship between the open-circuit voltage (OCV) and SoC. The proposed robust CDKF method can accurately estimate the SoC in the presence of the time-varying model uncertainties and measurement noises. Being able to cope with model uncertainties and measurement noises is essential, since they can lead to inaccurate SoC estimations. An experiment test bench is developed, and various experiments are conducted to extract the battery model parameters. The experimental results show that the proposed method can more accurately estimate SoC compared with other Kalman filter-based methods. The proposed method can be used in optimal BMSs to promote battery performance and decrease battery operational costs in smart homes

Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation

This paper proposes centralized and distributed optimization models for V2G applications to provide frequency regulation in power systems and the electricity market. Battery degradation and dynamic EV usages such as EV driving period, driving distance, and multiple charging/discharging locations are modeled. The centralized V2G problem is formulated into the linear programming (LP) model by introducing two sets of slack variables. However, the centralized model encounters limitations such as privacy concerns, high complexity, and central failure issues. To overcome these limitations, the distributed optimal V2G model is developed by decomposing the centralized model into subproblems using the augmented Lagrangian relaxation (ALR) method. The alternating direction method of multipliers (ADMM) is used to solve the distributed V2G model iteratively. The proposed models are evaluated using real data from the Independent Electricity System Operator (IESO) Ontario, Canada. Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards. The simulation also shows that including battery degradation and dynamic EV usage increases the model accuracy. By using the proposed approaches, the high cost and the low efficiency power generation units for frequency regulation can be compensated or partially replaced by EVs, which will reduce the generation cost and greenhouse gas emissions

A Robust Kalman Filter-Based Approach for SoC Estimation of Lithium-Ion Batteries in Smart Homes

Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC subnetworks to represent the fast and slow transient responses of the terminal voltage. Moreover, the model includes the nonlinear relationship between the open-circuit voltage (OCV) and SoC. The proposed robust CDKF method can accurately estimate the SoC in the presence of the time-varying model uncertainties and measurement noises. Being able to cope with model uncertainties and measurement noises is essential, since they can lead to inaccurate SoC estimations. An experiment test bench is developed, and various experiments are conducted to extract the battery model parameters. The experimental results show that the proposed method can more accurately estimate SoC compared with other Kalman filter-based methods. The proposed method can be used in optimal BMSs to promote battery performance and decrease battery operational costs in smart homes

Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation

This paper proposes centralized and distributed optimization models for V2G applications to provide frequency regulation in power systems and the electricity market. Battery degradation and dynamic EV usages such as EV driving period, driving distance, and multiple charging/discharging locations are modeled. The centralized V2G problem is formulated into the linear programming (LP) model by introducing two sets of slack variables. However, the centralized model encounters limitations such as privacy concerns, high complexity, and central failure issues. To overcome these limitations, the distributed optimal V2G model is developed by decomposing the centralized model into subproblems using the augmented Lagrangian relaxation (ALR) method. The alternating direction method of multipliers (ADMM) is used to solve the distributed V2G model iteratively. The proposed models are evaluated using real data from the Independent Electricity System Operator (IESO) Ontario, Canada. Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards. The simulation also shows that including battery degradation and dynamic EV usage increases the model accuracy. By using the proposed approaches, the high cost and the low efficiency power generation units for frequency regulation can be compensated or partially replaced by EVs, which will reduce the generation cost and greenhouse gas emissions

Advances in the chemical constituents, pharmacological activity, and clinical application of Smilacis Glabrae Rhizoma: A review and predictive analysis of quality markers (Q-markers)

Smilacis Glabrae Rhizoma (SGR) is recognized in traditional Chinese medicine for its distinctive therapeutic properties and abundant supply. Its phytochemical profile is diverse, encompassing flavonoids, steroids, saccharides, phenolic glycosides, volatile constituents, organic acids, phenylpropanoids, stilbenoids, among others. Recent pharmacological investigations reveal that SGR possesses a broad spectrum of pharmacological effects with multifaceted clinical applications. This review collates the current knowledge on SGR's chemical composition, pharmacological activities, and its clinical utility. Utilizing network pharmacology and molecular docking approaches, this study provides a preliminary identification of potential quality markers (Q-Markers) within SGR. The findings suggest that compounds such as astilbin, isoengelitin, neoisoastilbin, neoastilbin, astragaloside, diosgenin, resveratrol, stigmasterol, β-sitosterol, and quercetin in SGR are promising candidates for Q-Markers. While flavonoids are the most extensively studied, there is a pressing need to further explore the active monomeric compounds within SGR. The introduction of Q-Markers is instrumental in developing standardized quality metrics. Specifically, astilbin has been noted for its antitumor, antidiabetic, antihypertensive, anti-hyperuricemic, and hepatoprotective potential, warranting further research for therapeutic applications

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship

Evaluation of physical fitness and health of young children aged between 3 and 6 based on cluster and factor analyses

Abstract Background As life improves and sedentary time increases, young children's physical fitness gradually declines. Methods Multi-stage stratified whole cluster sampling was utilized to sample 5584 preschoolers. Young infants' morphology, function, and quality were revealed using cluster and factor analysis. Results The cluster analysis separated 3–6-year-olds into two genders: 1,551 men in group A "high physical fitness" 1,499 men in group B "low physical fitness"; 1,213 women in group A and 1,321 women in group B. Young children's fitness was measured by standing long jump(1.00), weight(1.00), and height(1.00). A cluster analysis of 3–4-year-olds classified them into three groups: 272 “muscular strength,” 75 “average physical fitness,” and 250 “low agility.” Young children's health depends on weight (1.00), height (0.57), and chest circumference (0.54). A cluster analysis of the 4–5-year-olds classified them into two groups: 1070 “balance” and 806 “muscular strength.” Young children’s health depends on weight (1.00), height (0.74), and chest circumference (0.71). A cluster analysis of the 5–6-year-olds divided them into three groups: 1762 “high physical fitness,” 384 “obese,” and 105 “low physical fitness.” Young children’s physical health depends on BMI (1.00), weight (1.00), and chest circumference (1.00). Factor analysis demonstrated that muscle strength, body shape, cardiovascular variables, and physical fitness composite components affected young children's health. Conclusion Women should focus on motor function and strength, while men on flexibility. Male group B “low physical fitness” should focus on strength, motor function, and balance, whereas male group A “high physical fitness” should focus on flexibility. Then, female group A “high physical fitness” should emphasize variety.2) For 3–4-year-olds, group A “muscular strength” should focus on flexibility, and group C “low agility” on motor function. 3) For 4–5-year-olds, group A “balanced” should focus on strength and motor function; 4) For 5–6-year-olds, group B “obese” should emphasize weight loss, and group C “low fitness” should emphasize strength, motor function, and flexibility; 5) Young children’s physical fitness depends on muscle strength, body shape, cardiovascular factors, and physical fitness composite