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This paper presents a vectorized approach for modeling and tuning the state-of-charge (SOC) dynamics in Battery Energy Storage Systems (BESS) equipped with power ramp-rate control (PRRC). The SOC evolution is formulated in discrete time, and parameter estimation is performed using Differential Evolution (DE) integrated with Anatem, a dynamic simulation tool used for electromechanical transient studies. This study compares the traditional generic (WECC-based) SOC model, which employs a fixed-gain integrator, with a proposed variable-gain integrator that adapts to BESS active power through a piecewise-linear function. The methodology is validated using real SCADA data from a 1.1-MW photovoltaic plant coupled with a 0.95-MW/0.49-MWh BESS in Brazil. Measurements were sampled at 1-second intervals, while Anatem simulations employed a 50-ms integration step to ensure numerical accuracy and appropriate representation of converter-level dynamics. A reference day and multiple operational scenarios were used for calibration and cross-validation. Results show that the variable-gain formulation reduces the Root Mean Square Error (RMSE) by 57-91% compared with the standard generic model, providing an improved fidelity during rapid charge and discharge transitions. The method also enhances the alignment between simulated and measured active-power behavior, supporting robust ramp-rate compliance and improved representation of BESS operational limits. The proposed framework is suitable for both short-term dynamic simulations and long-term operational analyses and can be extended to hybrid configurations and other control functions that require parameter tuning from real operational data. The findings highlight the importance of dynamic gain adaptation for accurate SOC modeling in hybrid PV-BESS systems and support the applicability of this method in stability studies, operational planning, and hybrid system design.

The rise of electric aviation presents new challenges for airport infrastructure, particularly regarding the high power demands of electric aircraft charging. This study presents a microgrid design for Norrköping airport in Sweden, featuring a photovoltaic power plant, battery energy storage system, a main grid connection and a 2 MW DC fast charger for electric aircraft. The selection of a photovoltaic system and batteries for energy storage is discussed, motivated by their potential to improve the resilience and sustainability of aviation operations. Simulations were carried out to assess the ability of the microgrid to maintain reliable performance throughout the year, demonstrating that the system can reduce grid dependency and support environmental and operational goals.

The decarbonization of the power generation and transport sector encourage the analysis of connection of distributed energy resources (DER), such as electric vehicles (EVs), to the electrical system, as well as the evaluation of their impact on smart cities. A better understanding of the negative impacts on the power systems will lead to propose mitigation measures and eventually revolutionize the way distributed generation works. This paper aims at modelling and evaluating the impact of EVs on a real distribution network. The energy system chosen operates at 60 Hz, 34.5 kV (medium voltage) and 0.208 kV (low voltage) and it is simulated using PSCAD/EMTDC. To reproduce realistic user consumption profiles, dynamic load profiles based on EV owners behaviour have been simulated. The vehicle-to-grid (V2G) technology is modelled to mitigate the impacts of high penetration of EVs by supporting the network from undervoltage. The results show the importance of active management in modern power systems, especially considering the increase in DER penetration expected for the coming years. This work shows the benefits of implementing V2G technology while highlighting the challenges involved in a real case. This paper aims at modelling and evaluating the impact of EVs on a real distribution network. The V2G technology is modelled to mitigate the impacts of high penetration of EVs by supporting the network from undervoltage. This work shows the benefits of implementing V2G technology while highlighting the challenges involved in a real case.image

Distributed control on a multi-agent format in co-simulation environment has been conceived in a client/server architecture for controlling a series of devices connected to a Direct Current (DC) bus. The implemented system aims for providing the communication infrastructure required for connecting the whole co-simulation environment. Power converters interact via a communication infrastructure orchestrated by a multi-agent system whose algorithm has been built for the proposed scenario. A virtual small village is supplied by a DC power system endowed by some photovoltaic arrays and energy storage by a battery bank. The use of Python, socket Transmission Control Protocol/Internet Protocol (TCP/IP) and Power Simulator (PSIM) with appropriate adaptation is meant to build the system in a lighter computational environment. The interaction among agents helped the co-simulation with a distributed control to maintain the DC bus stable in 180 Vdc and battery voltage oscillating within the state of charge (SoC) range, 99% and 97%, of 144 Vdc fed by a photovoltaic array under the coordination of the multi-agent system.

The present work proposes the development of a simulation platform that integrates the PSCAD software with python language, capable of portraying realistic and dynamic characteristics of the operation of an electrical system with high insertion of DERs. This work considers a scenario with high insertion of alternative sources, which are characterized by intermittent conditions of generation and consumption, therefore causing technical-economic challenges in the operation of the network. In this way, an electrical network will be created with photovoltaic systems, electric vehicles and batterys, in addition to local, coordinated and optimization controls. Local and coordinated controls act on network undervoltage and overvoltage problems. The optimized control, created in Python and based on a Predictive Control Model, determines the optimal operating curve for the batterys, reducing the energy cost of the system, being activated when the network does not present technical problems. Results showed a reduction of more than 16% in the energy cost of the network when enabling the optimized control, keeping the voltage profiles within regulatory limits. Thus, the proposed platform manages to carry out the technical-economic monitoring of the network, becoming a promising tool for the creation of active network management algorithms in dynamic simulations.

The growing integration of renewable energy sources, such as photovoltaic and wind systems, into energy grids has underscored the need for reliable control mechanisms to mitigate the inherent intermittency of these sources. According to the Brazilian grid operator (ONS), there have been cascading disconnections in renewable energy distributed systems (REDs) in recent years, highlighting the need for robust control models. This article addresses this issue by presenting the validation of an active power ramp rate control (PRRC) function for a PV plant coupled with a Battery Energy Storage System (BESS) using WECC generic models. The proposed model underwent rigorous validation over an extended analysis period, demonstrating good accuracy using the Root Mean Squared Error (RMSE) and an R-squared (R²) metrics for the active power injected at the Point of Connection (POI), PV active power, and BESS State of Charge (SOC), providing valuable insights for medium and long-term analyses. The ramp rate control module was implemented within the plant power controller (PPC), leveraging second-generation Renewable Energy Systems (RES) models developed by the Western Electricity Coordination Council (WECC) as a foundational framework. We conducted simulations using the Anatem software, comparing the results with real-world data collected at 100 ms to 1000 ms intervals from a PV plant equipped with a BESS in Brazil. The proposed model underwent rigorous validation over an extended analysis period, with the presented results based on two days of measurements. The positive sequence model used to represent this control demonstrated good accuracy, as confirmed by metrics such as the Root Mean Squared Error (RMSE) and R-squared (R²). Furthermore, the article underscores the critical role of accurately accounting for the power sampling rate when calculating the ramp rate.

Power electronic inverters play a crucial role in modern power systems. They are designed to have fault ride-through capabilities, allowing them to continue operating even during fault conditions. The study focuses on unsymmetrical faults that occur in the presence of inverter-interfaced distributed generators with voltage support capability, which involves the compensation of positive and negative voltage sequences during faults to maintain voltage stability. Different fault scenarios are simulated in MATLAB/Simulink using a detailed model of the distribution system, including the inverter-interfaced distributed generators and their control strategies. The results demonstrate that inverter-interfaced distributed generators with local voltage support can improve the system’s fault ride-through capability by reducing voltage drops and unbalances. Overall, this research contributes to a better understanding of fault behavior in power distribution systems with inverter-interfaced distributed generator voltage support.

The modelling of a three-phase four-leg four-wire grid-forming inverter in a low voltage distribution system 18-bus European Cigre under unbalanced conditions in an autonomous distribution network is presented. The case study has two types of inverters control strategy: (i) grid-forming to supply all the system demand in the interval of the intentional supply interruption and (ii) grid-following to integrate photovoltaic renewable energy resources into power systems. The model suggests a control scheme with two loops: An inner current loop with a proportional-integral controller and an outer voltage loop with a proportional controller, both in the synchronous reference frame (dq0), in which dq-axis are decomposed in positive and negative sequences. Simulation results, carried out using the PSCAD software, showed the effectiveness of the suggested control strategy with smooth synchronization where the grid-forming inverter was able to form a network with an unbalanced degree lower than 2%, sinusoidal voltage and frequency within standard limits 49.5-50.5 Hz.

In this article, a smart inverter model that executes ancillary services with automated decisions is presented, such as powersharing and voltage and frequency stabilization, compensation of unbalance voltage, mitigation of harmonic content, and thebalance of generation and demand. The droop control was utilized for power-sharing between the distributed generations; themitigation of negative-sequence voltage is used for unbalanced voltage compensation, and finally, a feed-forward current isadopted for harmonic compensation. Therefore, the smart inverter was controlled through a supervisory system, identifyingwhich ancillary function to activate at certain times. Results show the successful operation of the system and can be used asvalidation of the proposed control strategies, including the automated decisions in the microgrid