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Drawing on case study evidence, this article explores the motivations a small liberal arts university internationalize and the challenges in facilitating internationalization. It employs qualitative methodology with qualitative data were collected from organizational documents, interviews, and field observations. The findings suggest that though a strong missionary is the most significant motivation for expansion, other internal and external factors impact the international decision-making process.

Due to high computation complexity, traditional design methods for LLC converter usually consider limited kinds of performance, and the process for searching the optimal parameter scheme is discrete, which might cause the missing of real optimal solution. In order to solve these problems, a multi‐objectives optimization design method for the LLC converter is proposed in this paper. According to the numerical calculation method, a closed parameter space for resonant parameters is established under the constraints of operation mode, switching frequency, zero voltage switching (ZVS), and voltage stress of resonant capacitor. Besides, the root‐mean‐square (RMS) value of resonant current and secondary current, as well as the core loss of the transformer, are selected as the optimization objectives. Non‐dominated sorting genetic algorithm (NSGA‐II) is utilized to deal with these multi‐objective formulations. In order to reduce the computation complexity, a data‐driven method, called adaptive polynomial approximation (APA), is selected to obtain the explicit expressions of the parameter space and the optimization objectives. Then, by substituting this simplified surrogate model into the NSGA‐II algorithm, the optimal parameter scheme is obtained. The comprehensive comparison analysis is performed, and a 400 W/48 V experimental prototype was built to verify the theoretical analysis, which shows that the proposed method features the highest efficiency of 95%. [ABSTRACT FROM AUTHOR]

Constructing a renewable energy-based power system has become an important development path for the power industry's low-carbon transformation. However, as the proportion of renewable energy generation (REG) increases, the power grid gradually changes to uncertainty. Technologies to address this issue have been introduced. However, the majority of existing reviews focus on specific uncertainty modeling approaches and applications, lacking the consideration of temporal and spatial interdependence. Therefore, this paper provides a comprehensive review of the uncertainty modeling of temporal and spatial interdependence. It includes the discrete and continuous stochastic process-based methods to address temporal interdependence, the correlation coefficient and copula functions in modeling spatial interdependence, and the Itô process and random fields theory to describe temporal and spatial interdependence. Finally, their applications in power system stability, control, and economic scheduling are summarized. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is a crucial way to achieve the goal of green and sustainable development. To accomplish this goal, electric vehicles (EVs) are considered system‐schedulable energy storage devices, suppressing the negative impact of the randomness and fluctuation of renewable energy on the system's operation. In this paper, a coordination control strategy aimed at minimising the carbon emissions of a distribution network between EVs, energy storage devices, and static var compensators (SVCs) is proposed. A model‐free deep reinforcement learning (DRL)‐based approach is developed to learn the optimal control strategy with the constraint of avoiding system overload caused by random EV access. The twin‐delayed deep deterministic policy gradient (TD3) framework is applied to design the learning method. After the model learning is completed, the neural network can quickly generate a real‐time low‐carbon scheduling strategy according to the system operating situation. Finally, simulation on the IEEE 33‐bus system verifies the effectiveness and robustness of this method. On the premise of meeting the charging demand of electric vehicles, this method can optimise the system operation by controlling the charge‐discharge process of EVs, effectively absorbing the renewable energy in the system and reducing the carbon emissions of the system operation. [ABSTRACT FROM AUTHOR]

Future power systems will face more extreme operating condition scenarios, and system emergency dispatch will face more severe challenges. The use of distributed control is a well-designed way to handle this. It enables multi-energy complementation by means of autonomous communication, which greatly improves the flexibility of the grid. First, in the context of global energy conservation and emission reduction, this paper adopts the energy usage method of "renewable energy is the main source of energy, supplemented by thermal power and energy storage" to reduce the system abandoned wind (light) rate while supplementing the energy storage capacity. Second, a consensus algorithm is added to the system while considering the coordination between thermal units and energy storage. An "interface" for autonomous communication between thermal units and energy storage is created using the incremental cost of each agent. To address the recurring issue of power imbalance during emergency dispatch of the system, the consensus algorithm is enhanced so that the communication interval varies with the unit rate. This is based on the climbing characteristics of each thermal power unit. Finally, the effectiveness of the proposed method is verified in an IEEE-30 bus system. [ABSTRACT FROM AUTHOR]

The loss of small signal stability under parameter variation (e.g., fluctuation of loads and wind powers) can be ascribed to local bifurcations, i.e., saddle-node, Hopf, singularity-induced, and limit-induced bifurcations. Classic bifurcation calculation methods like the direct method and continuation method can only provide a bifurcation point or two-parameter bifurcation curve. Based on Galerkin method and the implicit function theorem, this paper proposes accurate polynomial approximations of bifurcation hypersurfaces in the multi-dimensional parameter space of interest. The proposed method can ensure high accuracy in this parameter space, and thus is preferable to the existing Taylor expansion-based local approximation method, given the intrinsic large-variation characteristic of wind powers. Acquired bifurcation hypersurfaces are immediately used to construct the small signal stability region of the continuous system, and then compute the stability margin for the operating point subjected to uncertainty of wind generation. Besides, the validity scope of the proposed method is analyzed, and the handling of limit-triggered equation switching as well as its computational difficulty is discussed. Computational results on the two-parameter 11-bus two-area and six-parameter IEEE 145-bus test systems validate the high accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Reflecting the uncertainty of renewable energy generations and loads, power system AGC signals are essentially random processes. For the sake of the optimal operation and control of the AGC participant, such as energy storage systems (ESSs) in a performance/mileage-based regulation market, taking the uncertain nature, especially the temporal correlation, of the AGC signals into consideration can be beneficial; hence, random process models of the AGC signals are needed. However, a continuous random process model of the AGC signal that jointly considers the probability distribution and the temporal correlation is still lacking. To fill this gap, this paper first presents a systematic methodology for modeling the continuous random processes of AGC signals based on stochastic differential equations (SDEs). It is shown that AGC signals may have a saturated stationary probability density function and a biexponential temporal correlation, which are very different from the renewable generations. To capture these special characteristics, SDEs are then carefully constructed, which are easy to use in optimization and control. Using the PJM traditional and dynamic regulation (RegD and RegA) signals and the signal received from a battery ESS (BESS) plant in Jiangsu, China for example, simulation shows that the SDE is able to simultaneously capture the probability distribution and temporal correlation accurately. [ABSTRACT FROM AUTHOR]

To enable power supply in rural areas and to exploit clean energy, fully renewable power systems consisting of cascaded run‐of‐the‐river hydropower and volatile energies such as photovoltaic (pv) and wind are built around the world. In islanded operation, the primary and secondary frequency controls, that are hydro governors and automatic generation control (AGC), are responsible for the frequency stability. However, due to limited water storage capacity of run‐of‐the‐river hydropower and river dynamics constraints, without coordination between the cascaded plants, the traditional AGC cannot fully exploit the adjustability of hydropower. When imbalances between the volatile energy and load occur, load shedding can be inevitable. To address this issue, this paper proposes a coordinated tertiary control approach by jointly considering power system dynamics and the river dynamics that couples the cascaded hydropower plants. To unify the multi‐timescale dynamics of the power system and river dynamics to establish a controller that coordinates the cascaded plants, the relation between AGC parameters and turbine discharge is approximated by a polynomial surrogate model. The cascaded plants are coordinated by optimising AGC participation factors in a receding‐horizon manner. Simulation of a real‐life system shows the proposed method significantly reduces load loss under pv volatility. [ABSTRACT FROM AUTHOR]

Wake effects impose significant aerodynamic interactions among wind turbines. To improve the wind farm operating performance, practical wind farm online control considering wake effects becomes very important. To achieve online optimal wind farm control while responding to grid demands, this paper proposes a novel optimal wind farm supervisory control (SC) model and its explicit solutions. From the controller modelling perspective, the two major wind farm operating modes, the maximum power point tracking mode and the set‐point tracking mode, are first analysed and unified in one optimisation model while considering wake effects. In this way, wind farm power production and rotor kinetic energy reserve can be simultaneously considered to conveniently modify the operation mode in response to different grid demands. Aside from controller modelling, the collocation method is first introduced to address the online application problem of such wake‐effect aware optimal WF control. Although a few optimisation algorithms have been proposed to find the optimum offline, online optimal control is still challenging because of the computational complexity brought by wake model non‐linearity and non‐convexity. The proposed collocation method explicitly approximates the optimal solutions to the proposed supervisory control model, through which only a direct algebraic operation is required for online optimal control instead of repeated optimisations. Case studies are carried out on different wind farms under various wind conditions, showing that the wind farm power production potential and releasable power reserve are improved compared to traditional greedy control in both modes. The accuracy of the collocation method is verified. A detailed analysis of the wind farm production capacity under different wind speeds and directions is also provided. [ABSTRACT FROM AUTHOR]

To exploit the massive solar energy available in the region, photovoltaic plants have been built in the mountain areas in Southwest China, coexisting with many small cascaded run-of-the-river hydropower plants, interconnected by short-distance transmission lines. Network constraints inside these systems are often negligible. However, due to the volatile nature of solar power and the weak connection to the external power grid, without proper coordination, solar power curtailment and water spillage are inevitable. To address this issue, this paper proposes an ultra-short-term stochastic generation control method for cascaded hydropower to mitigate solar power volatility. Two technical challenges are specifically addressed: to tackle the time-varying stochastic volatility of solar power, the Itô process model is introduced; to characterize the spatial-temporal hydraulic coupling of the cascaded hydropower plants and river operation constraints, a state-space dynamic river model derived from shallow water equations is included. Followingly, a stochastic control (SC) approach for hydropower generation based on stochastic programming is proposed, where hydropower generation commands are parameterized as affine functions of measured solar power, enabling quick response to solar power volatility. Simulation of a real-life cascaded hydro-solar system and a modified system verifies the proposed control method. [ABSTRACT FROM AUTHOR]

The imbalance of generation and load caused by the increasing integration of volatile generations poses challenges on frequency regulation. AGC is required to respond to the generation fluctuations without violating operational and security constraints. Explicit model predictive control (EMPC) provides an approach to reaching such requirements, which calculates the control laws of MPC in an explicit form, allowing for offline validation of the controller and enabling fast online computation. However, the partition number of EMPC's piecewise affine control laws grows exponentially with the number of constraints and prediction/control horizons, which hinders its application in large systems. In this paper, we propose an alternative explicit control approach for AGC by approximating the control laws of EMPC using Legendre polynomial series expansions, thus entirely eliminating the partition issue. The Galerkin method is applied to the KKT conditions of EMPC's multiparametric quadratic programming (mp-QP) problem to compute the approximation. Case studies in an illustrative system and IEEE 118-Bus System verify the performance and efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Parametric problems in the transient analysis refer to the investigations of the relationship between parameters and dynamic behavior. To solve parametric problems, we propose a Galerkin method-based polynomial approximation. The proposed method uses a series of polynomials combined with coefficients to approximately represent state variables with parameters, where the Galerkin method is introduced to determine the coefficients. Thereby, the relationship between parameters and dynamic behavior is established through the polynomial expressions. Because models in power system transient analysis are a set of complicated differential-algebraic equations, practical issues related to the non-polynomial model terms, parameterization of initial operation point are discussed in this paper. The proposed method is tested on a three-machine system and the IEEE 145-bus test system. The case studies show that compared to trajectory sensitivities, the proposed method can maintain high accuracy in cases of large parameter variations and strong non-linearity. We also illustrate that the validity of the proposed method for parametric problems like trajectory approximation, finding the critical parameter values and uncertainty quantification. [ABSTRACT FROM AUTHOR]

Power system static voltage stability region boundary (SVSRB) is a complicated hypersurface in parameter space. Due to inequality constraints such as generator reactive power limits, different critical points on the SVSRB may be related to different groups of active constraints and can be associated with different types of loadability limit, which makes it difficult to approximate the whole SVSRB at once or assemble many local approximations of the SVSRB. To overcome the difficulty, this paper proposes an approach for globally approximating the whole SVSRB considering generator reactive power limits at one go. First, a parametric optimization model incorporating all inequality constraints is formulated to describe the SVSRB, where the optimal solutions are the critical points on the SVSRB. The optimality condition, i.e., parametric Karush–Kuhn–Tucker conditions (KKT conditions), is also derived. Then, by applying the Galerkin method to the parametric KKT conditions in an interior-point-method-like manner, the whole SVSRB is approximated and represented using a single polynomial expression. Case studies in a 10-bus system and IEEE 300-bus system verify the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

A novel method for globally approximating the static voltage stability region boundaries (SVSRBs) of power systems is proposed by applying parametric polynomial approximation to the criterion equation which defines the SVSRB. Known as the Galerkin method, the implicit function portraying the SVSRB is described as a linear combination of basis polynomial functions, and the coefficients of the basis functions are obtained by projecting the SVSRB criterion onto each basis function. The approximation guarantees high precision globally in the whole domain of the parameter of interest rather than only in the neighborhood of a point, and the error can be controlled by the degree of polynomial basis functions. In the meantime, analytical expression of the left or right eigenvector of the system's Jacobian matrix corresponding to the zero eigenvalue is obtained in the form of polynomial, which provides valuable information for online voltage stability control or monitoring. Case studies in a 10-bus test system and IEEE 118-bus test system verifies the validity, accuracy, and flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

• CNTs and N-doped carbon decoration can enhance electronic conductivity of Si-based anode. • CoSi 2 can improve the structural stability of Si-based anode. • The introduced Co can catalyze the graphitization of carbon shell. • The designed Si-based anode delivers enhanced electrochemical properties. [Display omitted] Silicon is evaluated as a competitive lithium-ion batteries anode material by virtue of its ultrahigh specific capacity and relatively low discharge potential. However, the large volume change during lithiation/delithiation processes, and the low intrinsic electric conductivity have seriously impeded its widespread practical application. Herein, silicon nanoparticles (SiNPs) coated with CNTs-intertwined N-doped porous carbon (NPC) are prepared via a facile solution-phase method, followed by thermal annealing treatment. In this hierarchical structure, the bimetallic ZIF-derived porous carbon can shorten the migration channels of Li+ and electrons. Moreover, the formed Co nanoparticles can not only catalyze the graphitization of carbon, but also propel in-situ growth of CNTs to constitute a 3D interconnected conductive network, thereby improving the electrochemical performance. Benefiting from the unique structure, the as-prepared Si@CoSi 2 /Co-NPC@CNTs electrode exhibits superior electrochemical performance of 1191 mAh g−1 at 0.5 A g−1 after 200 cycles. Meanwhile, the electrode also shows excellent rate capability of 930 mAh g−1 even at a high current density of 6 A g−1. This work provides a new strategy to design exceptionally high electrical conductivity and structure stability of Si-based anode materials. [ABSTRACT FROM AUTHOR]

To provide automatic generation control (AGC) service, wind farms (WFs) are required to control their operation dynamically to track the time-varying power reference. Wake effects impose significant aerodynamic interactions among turbines, which remarkably influence the WF dynamic power production. The nonlinear and high-dimensional nature of dynamic wake model, however, brings extremely high computation complexity and obscure the design of WF controllers. This paper overcomes the control difficulty brought by the dynamic wake model by proposing a novel control-oriented reduced order WF model and a deep-learning-aided model predictive control (MPC) method. Leveraging recent advances in computational fluid dynamics (CFD) to provide high-fidelity data that simulates WF dynamic wake flows, two novel deep neural network (DNN) architectures are specially designed to learn a dynamic WF reduced-order model (ROM) that can capture the dominant flow dynamics. Then, a novel MPC framework is constructed that explicitly incorporates the obtained WF ROM to coordinate different turbines while considering dynamic wake interactions. The proposed WF ROM and the control method are evaluated in a widely-accepted high-dimensional dynamic WF simulator whose accuracy has been validated by realistic measurement data. A 9-turbine WF case and a larger 25-turbine WF case are studied. By reducing WF model states by many orders of magnitude, the computational burden of the control method is reduced greatly. Besides, through the proposed method, the range of AGC signals that can be tracked by the WF in the dynamic operation is extended compared with the existing greedy controller. • A novel control-oriented dynamic wind farm model is developed based on deep learning. • The model reduction considers both the modeling accuracy and control compatibility. • The dynamic wake interactions and wake losses are alleviated by the proposed MPC. • The trackable AGC signal range in the wind farm dynamic operation is increased. • The computation burden of the optimal control is practical for online applications. [ABSTRACT FROM AUTHOR]

A novel triple-component TiO 2 /Ag 3 PO 4 /graphene oxide (TiO 2 /Ag 3 PO 4 /GO) photocatalyst with dual channels for photo-generated charges separation has been synthesized to improve the photocatalytic activity and stability of Ag 3 PO 4 under visible light. The synthesis involved in-situ growth of Ag 3 PO 4 nanoparticles on GO sheets to form Ag 3 PO 4 /GO, and then deposited TiO 2 nanocrystals on the surface of Ag 3 PO 4 by hydrolysis of Ti(SO 4 ) 2 at low-temperature hydrothermal condition. The TiO 2 /Ag 3 PO 4 /GO exhibited superior photocatalytic activity and stability to bare Ag 3 PO 4 , TiO 2 /Ag 3 PO 4 and Ag 3 PO 4 /GO in degradation of Rhodamine B and phenol solutions under visible light. It is suggested that the photo-generated electrons in the conduction band of Ag 3 PO 4 can be quickly transferred to GO, while the holes in the valence band of Ag 3 PO 4 can be transferred to the valence band of TiO 2 . The dual transfer channels at the interfaces of TiO 2 /Ag 3 PO 4 /GO result in effective charges separation, leading to enhanced photocatalytic activity and stability. Furthermore, the content of noble metal Ag significantly reduces from 77 wt% in bare Ag 3 PO 4 to 55 wt% in the nanocomposite. The concept of establishing dual channels for charges separation in a triple-component heterostructure provides a promising way to develop photocatalysts with high efficiency. [ABSTRACT FROM AUTHOR]