System dynamic models for hundreds of inverter-based generators are necessary to represent the whole behavior and to perform stability analysis at the transmission and distribution level in the electric power system. Some aggregated models, like DER_A and PVD1, have been suggested in the literature. However, these models include a considerable number of parameters, and so far, there is a lack of a formalized online method for parameterizing them. Several parameterization strategies for this particular model require the use of smooth functions. The parameters of these aggregated models may experience fluctuations and changes in the foreseeable future as the power system becomes more reliant on inverter-based generation. Consequently, an online parameter estimation will be necessary. This research proposes a way for parameter identification via online moving horizon estimation (MHE) for the grid voltage support function of the DER_A model by using a smooth mathematical representation. MATLAB/Simulink simulation shows that the proposed approach using MHE with a smooth representation of the DER_A model closely estimates the grid voltage support function parameters in the presence or absence of measurement noise. This study establishes the groundwork for the potential use in a detailed system including several inverter-based generators, with a primary focus on grid voltage support.

This paper presents a project-based learning initiative, the Resilient Infrastructure and Sustainability Education - Undergraduate Program (RISE-UP), aimed at addressing the infrastructure challenges in Puerto Rico caused by natural disasters. The program, funded by the National Science Foundation, involves interdisciplinary collaboration between the Civil Engineering Department, School of Architecture, and Psychology Department at the University of Puerto Rico. The focus of this paper is on the design of modular classrooms as a solution to the educational facilities' shortage caused by recent hurricanes and earthquakes. The design solution incorporates features such as portability, natural lighting, ventilation, and self-sufficiency in power and water supply during emergencies. The project follows a three-phase approach, including conceptualization, problem analysis, and detailed design. The students utilized design tools such as virtual reality to enhance their understanding and visualization of the project. The paper discusses the positive impact of the project-based learning experience on student development of interdisciplinary critical thinking skills and collaboration. This initiative demonstrates the effectiveness of integrating design education, research skills, and real-life case studies to prepare future professionals for designing resilient and sustainable infrastructure. The authors acknowledge the support of the NSF and express gratitude to the RISE-UP teaching team and student participants for their contributions to the design projects.

This paper presents a parametric comparative analysis between the virtual synchronous generator (VSG) method and the droop control method to emulate inertia in the voltage-source inverter (VSI). Droop controllers are commonly used to regulate sharing power in microgrids and distribute power generation proportionally among VSI’s depending on their rated power. Additionally, VSG has been used to regulate the Rate-of-Change-of-Frequency (RoCoF) of the microgrid using virtual inertia. Although both methods can be used to regulate the frequency variation, the influence of each method on the closed-loop eigenvalues is not the same. In this work, the transient response of the frequency is analyzed for each method to determine their advantages and disadvantages regarding frequency regulation in microgrid applications. The results were verified by conducting experimental trials using VSI’s. These experiments demonstrated that VSG is more suitable for regulating RoCoF and frequency nadir than droop controllers since it provides inertial support and improves frequency response.

In a Microgrid, the integration of many tasks makes possible an adequate energy management system. Jobs involving real-time support and information procession for having an autonomous and scalable management system, and others such as massive storage capabilities and security considerations to guarantee reliability and validity, are a few. This paper considers them to propose a real-time energy management system based on the economic dispatch problem under a cloud-based architecture, ensuring the appropriately supervised learning functionality in a Microgrid cluster. Namely, it was necessary to revise and run Microgrid implementations, integrate real-time simulation platforms, connect to a virtual server from a Microgrid control, and set the energy management system using cloud computing and machine learning. Based on the results, this article presents a scalable and autonomous cloud-computing architecture for a real-time energy management system using machine learning techniques that allows power generation and energy consumption prediction.

This paper presents a design for a PR controller structure and explains in detail how to calculate their coefficients, for a Voltage Source Converter (VSC) operating in islanding mode. The design is developed in the discrete domain using a state-space model that includes a decoupling of the capacitor voltage technique and a cascade control structure. Moreover, the optimal values of the Proportional-Resonant controller coefficients are found based on the gain and phase margins. The validation of this design considers a 1 kVA VSC with PR controllers, simulation in MATLAB/Simulink, and experimental validation. This also presents a stability study of the proposed design.

This paper presents a technique we call redundant iterative semantic paths (RISP) that can be applied to a class of existing distributed iterative numerical solvers to achieve agent or communication link fault tolerance. We transformed an existing distributed iterative power flow solver for radial power distribution systems using RISP and tested the result for fault tolerance. RISP achieves fault-tolerance by expanding the normal scope of local knowledge for each agent to include that of its neighboring peers (i.e. input clustering) and adapting the usual messaging scheme to allow the redundant computation of voltages and current for all buses. The solver requires the decoupling of the input sensing function from the compute function. This decoupling enables making knowledge otherwise local to an agent available to its neighbors, even if the agent itself, or its communication link, is not available. The level of clustering can be controlled incrementally, according to the desired level of fault-tolerance. Results for our solution, as applied to the 13-node IEEE test feeder at various redundancy levels, show significant improvement in agent and communication fault-tolerance, as compared to the non-RISP solution. Further work is needed to understand how the increased complexity of RISP can be managed in real systems to minimize any resulting adverse effects on the availability of infrastructure resources.

This paper presents a voltage control design algorithm for a three-phase voltage source converter (VSC) connected to a linear load using a passive LCL filter. The controller is based on an optimal regulator, combined with the integral of the voltage error, which is called LQI that achieves null tracking of the error. The main objective of this paper is to present an algorithm to design a voltage controller through frequency analysis of the singular values of the system, the weight of the states involved in the system, the movement of the closed poles and their respective step response to evaluate performance and robustness against load changes. The simulation results show a satisfactory operation of the voltage controller with fast recovery after a resistive load change.

This paper presents a formal methodology for the analysis and design for discrete-time proportional-resonant classic (PR) controllers applied to a single-phase DC/AC converter using Sisotool/Matlab. This tool allows observing the response of the system before being simulated or experimentally implemented. Also, it allows to integrate and visualize the classical control theory requirements (overshoot, settling time, etc.) with the design of Proportional Resonant (PR) controllers. Simulations results demonstrate the effectiveness of the methodology presented for the design of PR controllers.

This paper presents an evaluation of the new JEDEC standard JEP173. The JEP173 establishes a characterization procedure to reliably assess the dynamic ON-resistance of GaN lateral power transistors. DC and pulsed measurement setups were developed to evaluate the proposed methods for hard and soft switching conditions. Several devices were tested under a wide range of test conditions. We found that the proposed procedures in JEP173 allow for accurate acquisition of the ON-resistance under several test conditions. However our experiment found the standard, in its current version, does not account for the stress voltage and temperature effects on the dynamic RDSON, leading to under-characterization of parts.

In this work, an optimal power sharing controller for a three-phase Inverter-based Generator (IG) in a synchronous d-q reference frame is presented. The optimization of this controller is computed using a Linear-Quadratic (LQ) tracking index that measures the tracking error. This approach has many advantages regarding to stability and robustness over classical Proportional-Integral (PI) or Proportional-Resonant (PR) controllers that use droop functions for power sharing. In addition, a comprehensive model that represents a grid-connected IG sharing power to the main grid is developed using the superposition principle. This model integrates the Voltage-Current (V-I) and power sharing dynamics in a single state space expression. To the best of our knowledge, although there have been approaches in V-I and power sharing control that improve microgrid stability and transient response, there are no formal methods that integrate both controllers as a single entity. The results of this method were compared against a known Proportional-Resonant controller that use droop functions for power sharing. Results show that the optimal power sharing controller improves transient response, improves power decoupling, and also reduces the quadratic cost associated with microgrid states and inputs.

This article presents a novel methodology for the determination of soiling appearance on photovoltaic panels by means of data analysis of their energy production and operating conditions. The proposed methodology is based on the generation of a daily-based grey box model for each supervised panel, fitted through the sequential quadratic programming optimization approach, and the evolution analysis of the fitted coefficients to determine the appearance of soiling through the calculation of its monotonic drift and slope. The presented approach has been developed in the framework of a CORFO R&D project and validated under real operating conditions in a utility-scale photovoltaic power plant of one axis mount, located in Chile.

Due to the climate change and the decrease in fossil fuel reserves, the industrial and tertiary sectors have been focused on the implementation of advanced energy management systems in order to improve their energy efficiency and reduce their overall emissions. One way to achieve that goal, which is also the focus of this work, is by optimizing the energy use in their operation processes. This paper presents a hybrid optimization method, combined by neuro-fuzzy inference systems and the quadratic programming optimization method, to calculate the short-term demand forecasting of a multi-carrier energy system and to optimize its energy flow. The objective of the optimization is to fulfill the system's energy demands and minimize a set of established optimization criteria. Moreover, the algorithm considers the system's dynamics and inertias in order to guarantee that the obtained results present a feasible and stable operation strategy for the energetic plant. The method has been applied and validated under real conditions in a car manufacturing plant of Spain, in the framework of a FP7 European research project using online production and consumption data.

This document analyses the large-signal stability for an inverter-based generator such as photovoltaic and wind power sources. The objective of this study is to determine the stability region taking into account the electrical and control signal of the generator. The generator uses the concept of the electrostatic machine for the model of the generator. Finally, the applied procedure to find the Lyapunov's function is the Popov method, which not only permits to generate a valid function but also to determine the stability region of the system.

Model predictive control algorithms have recently gained more importance in the field of power electronics and motor drives. One of the important categories of model predictive control methods is improved deadbeat control in which the reverse system model is used to calculate the appropriate inputs for the next iteration of controlling process. In this paper, a new improved deadbeat algorithm is proposed to control the stator currents of a five-phase BLDC machine. Extended Kaiman filter is used in the structure of proposed controlling method, and system model equations are used to calculate the appropriate voltages for the next modulation period. Proposed controlling method is evaluated by simulations in MATLAB environment.

This paper presents an energy optimization methodology applied on industrial plants with multiple energy carriers. The methodology combines an adaptive neuro-fuzzy inference system to calculate the short-term load forecasting of a plant, and the sequential quadratic programming algorithm to optimize its energy flow. Furthermore, the mathematical models of the plant's equipment are considered into the optimization process, in order to calculate the dynamic system response and the equipment's inertias. The final algorithm optimizes the operation of the plant in order to satisfy the energy demand, minimizing several optimization criteria. The methodology has been tested and evaluated in an automotive factory plant using real production and consumption data.

In this paper, a distributed multiagent based algorithm is proposed to achieve SoC balance for DES in the DC microgrid by means of voltage scheduling. Reference voltage given is adjusted instead of droop gain. Dynamic average consensus algorithm is explored in each agent to get the required information for scheduling voltage autonomously. State-space analysis on a single energy storage unit and simulation verification shows that the proposed method has two advantages. Firstly, modifying the reference voltage given has less impact on system stability compared to gain scheduling. Secondly, by adopting multiagent methodology, the proposed distributed control has less communication dependence and more reliable during communication topology changes.

This paper presents consensus-based distributed control strategies for voltage regulation and power flow control of dc microgrid (MG) clusters. In the proposed strategy, primary level of control is used to regulate the common bus voltage inside each MG locally. An SOC-based adaptive droop method is introduced for this level which determines droop coefficient automatically, thus equalizing SOC of batteries inside each MG. In the secondary level, a distributed consensus based voltage control strategy is proposed to eliminate the average voltage deviation while guaranteeing proper regulation of power flow among the MGs. Using the consensus protocol, the global information can be accurately shared in a distributed way. This allows the power flow control to be achieved at the same time as it can be accomplished only at the cost of having the voltage differences inside the system. Similarly, a consensus-based cooperative algorithm is employed at this stage to define appropriate reference for power flow between MGs according to their local SOCs. The effectiveness of proposed control scheme is verified through detailed hardware-in-the-loop (HIL) simulations.