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Context: The Balance Error Scoring System (BESS) is a commonly used clinical tool to evaluate postural control that is traditionally performed through visual assessment and subjective evaluation of balance errors. The purpose of this study was to evaluate an automated computer-based scoring system using an instrumented pressure mat compared to the traditional human-based manual assessment. Design: A descriptive cross-sectional study design was used to evaluate the performance of the automated versus human BESS scoring methodology in healthy individuals. Methods: Fifty-one healthy active participants performed BESS trials following standard BESS procedures on an instrumented pressure mat (MobileMat, Tekscan Inc). Trained evaluators manually scored balance errors from frontal and sagittal plane video recordings for comparison to errors scored using center of force measurements and an automated scoring software (SportsAT, version 2.0.2, Tekscan Inc). A linear mixed model was used to determine measurement discrepancies across the 2 methods. Bland–Altman analyses were conducted to determine limit of agreement for the automated and manual scoring methods. Results: Significant differences between the automated and manual errors scored were observed across all conditions (P <.05), excluding bilateral firm stance. The greatest discrepancy between scoring methods was during the tandem foam stance, while the smallest discrepancy was during the tandem firm stance. Conclusion: The 2 methods of BESS scoring are different with wide limits of agreement. The benefits and risks of each approach to error scoring should be considered when selecting the most appropriate metric for clinical use or research studies. [ABSTRACT FROM AUTHOR]

ABSTRACT Recently, battery energy storage systems (BESS) have gained importance due to the growing introduction of intermittent renewable energy power plants. Although BESS already has multiple applications, the current standard approach presents several drawbacks aggravated by the second‐life batteries use. In order to deal with this issue, the literature proposes the integration of dc/dc converters in the batteries, an approach known as smart battery. However, due to the different capacities of the batteries, the conventional control of smart battery packs does not guarantee a constant dc‐link voltage, because during operation, some dc/dc converters can reach their physical voltage limit. Therefore, this paper proposes an algorithm called offset compensation network (OCN) that guarantees constant dc‐link voltage even when there are saturated converters. Simulations in software PLECS and hardware‐in‐the‐loop experiments validate the proposal. The results prove the improvement of the technology as the possibility to operate with fixed dc‐link voltage, enabling the use of second‐life batteries, OCN algorithm reduces the SOC balancing time in 6.25% and avoids dc‐link voltage variations during the balancing process. Finally, this technology is fully compatible with current power conversion systems, allowing an easy retrofit of standard BESS. [ABSTRACT FROM AUTHOR]

In recent years, the global energy sector has seen significant transformation, particularly in Europe, with a notable increase in intermittent renewable energy integration. Italy and the European Union (EU) have been among the leaders in this transition, with renewables playing a substantial role in electricity generation as of the mid-2020s. The adoption of Battery Energy Storage Systems (BESS) has become crucial for enhancing grid efficiency, sustainability, and reliability by addressing intermittent renewable sources. This paper investigates the feasibility and economic viability of batteries in wholesale electricity markets as per EU regulation, focusing on the dynamics of very different markets, namely the Day-Ahead Market (DAM) based on system marginal price and the Cross-Border Intra-day Market (XBID) based on continuous trading. A novel model is proposed to enhance BESS operations, leveraging price arbitrage strategies based on zonal price predictions, levelized cost of storage (LCOS), and uncertain bid acceptance in continuous trading. Machine learning and deep learning techniques are applied for price forecasting and bid acceptance prediction, respectively. This study finds that data-driven techniques outperform reference models in price forecasting and bid acceptance prediction (+7–14% accuracy). Regarding market dynamics, this study reveals higher competitiveness in the continuous market compared to the DAM, particularly with increased risk factors in bids leading to higher profits. This research provides insights into compatibility between continuous markets and BESS, showing substantial improvements in economic profitability and the correlation between risk and profits in the bidding strategy (EUR +9 M yearly revenues are obtained with strategic behavior that reduces awarded energy by 60%). [ABSTRACT FROM AUTHOR]

This study investigates the IEEE 69-bus distribution network with three wind turbines (WTs) connected at the same buses of three battery energy storage systems (BESSs), with three 20- or 30-outlet electric vehicle charging stations (EVCSs) for charging electric vehicles (EVs). The honey badger algorithm (HBA) is adopted to minimize daily energy loss. The HBA determines the best size and position for three WT-BESS buses and three EVCS buses. The HBA calculates BESS size and operation mode to minimize daily energy loss. The demand of EVCSs varies throughout the day depending on the random choice of the number and state of charge of EVs entering the station. This results in the active and reactive energy losses and utility input energy decreasing by 63.5%, 60.6% and 59.6%, respectively, and the minimum voltage increasing from 0.9256 to 0.9839 pu. The network voltage profile and stability are also improved. [ABSTRACT FROM AUTHOR]

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal. [ABSTRACT FROM AUTHOR]

This study investigates the application of a PID controller with a fractional derivative component (PIDDμ), optimized using the stochastic fractional search (SFS) technique, for load frequency control (LFC) in power systems. Additionally, solar PV and battery energy storage systems (BESS) and flywheel energy storage systems (FESS) are integrated as backup sources to enhance the LFC performance. To assess the effectiveness of the proposed approach, three error metrics—Integral Time Absolute Error (ITAE), Integral Time Multiplied Square Error (ITSE), and Integral of Absolute Error (IAE)—are evaluated. The results are compared with recent optimization-based LFC techniques across various scenarios. It is found that the SFS-PIDDμ achieves IAE=0.006786, ITSE=1.07e-05, and ITAE=0.01853, which are significantly lower than the error values produced by other LFC techniques. Furthermore, integrating solar PV, along with BESS and FESS, further reduces these error values. The graphical LFC responses of the SFS-PIDDμ outperform other optimization-based LFC techniques. The robustness of the approach is also validated against random load changes of varying magnitudes and for broader parametric variations.

ObjectiveTo provide insights into the learning curve of unilateral biportal endoscopic (UBE) spine surgery by synthesizing available evidence on critical points and associated clinical outcomes.MethodsA comprehensive literature search was conducted across multiple databases, yielding a pool of relevant studies. Inclusion criteria encompassed studies reporting on UBE learning curves and quantitative data related to clinical outcomes (operative time, hospital stay, and complications).ResultsA total of five studies were included in the analysis, providing six datasets to elucidate the UBE learning curve. Three of the five studies analyzed learning curves using the Cumulative Sum method and identified cutoff points. One study plotted learning curves and determined cutoff points based on surgical time analysis, while the remaining one study (providing two datasets) plotted learning curves using the phased analysis method. The mean value of the cutoff point in terms of the number of cases required to reach proficiency in time to surgery was calculated at 37.5 cases, with a range spanning from 14 to 58 cases. Notably, there was a statistically significant difference in time to surgery between the late group and the early group, with the late group demonstrating a significantly reduced time to surgery (P

As the world continues to enact progressive climate change targets, renewable energy solutions are needed to achieve these goals. One such solution is large‐scale lithium‐ion battery (LIB) energy storage systems which are at the forefront in ensuring that solar‐ and wind‐generated power is delivered when the grids need it most. However, the perceived hazards of LIBs due to recent events in the United States and Australia pose a risk to their future success. When a battery energy storage system (BESS) has a multilayered approach to safety, the thermal runaway, fire, and explosion hazards can be mitigated. Successful implementation of this approach requires cooperation, collaboration, and education across all stakeholder groups to break down these preconceived notions. Much can be learned from the recent BESS fire and explosion events to inform safer design and operation. These events and their contributing factors share many commonalities with historic losses in the hydrocarbon industry. Fire and process safety engineers who have traditionally worked in the hydrocarbon industry can be of immense value to the BESS industry. [ABSTRACT FROM AUTHOR]

Partial state of charge (PSOC) is an important use case for lead–acid batteries. Charging times in lead–acid cells and batteries can be variable, and when used in PSOC operation, the manufacturer's recommended charge times for single-cycle use are not necessarily applicable. Knowing how long charging will take and what the variability in time required is allows for better planning of operations and algorithm creation for battery energy storage system (BESS) manufacturers. This paper details and demonstrates a procedure for identifying the charging time of cells when different charge throughputs occur prior to reaching full charge. The results showed that the charging time in PSOC operations was highly variable when a charge-factor-controlled full-charge procedure was used. Also noted were that higher voltages for the same state of charge were reached as the number of cycles following reaching full charge increased. None of the regimes tested in this paper caused any significant capacity degradation, which demonstrates that PSOC operations can be performed even on cells not specifically designed for them, provided the correct regime is chosen. [ABSTRACT FROM AUTHOR]

In this research, the performance of vanadium redox flow batteries (VRFBs) in grid-connected energy storage systems centering on frequency and power sharing using voltage source inverters was evaluated. VRFBs are increasingly promising due to their scalability and long lifespan. We explore the impact of voltage source inverters on the frequency of the power supply when there is a change in load and power sharing between the grid and VRFB through MATLAB simulations. The test system demonstrates a storage system at an 11-kV substation and considers a load profile commencing from residential and commercial consumers. The outcome of this study reveals that VRFBs provide the required power to maintain load demand. Due to fast response time, VRFBs also regulate the frequency efficiently during the change in load demand and maintaining current total harmonic distortions (THDs). This research confirms the design and operation of VRFB-based energy storage systems, contributing to resilient grid infrastructure and reliable power distribution systems. [ABSTRACT FROM AUTHOR]

Frequency response performance in power systems is becoming vulnerable due to the transition toward the higher penetration of renewable energy such as achieving carbon neutrality. In particular, reducing power system inertia energy as the asynchronous generation increases could result in violating the frequency stability constraint when a disturbance occurs in the power systems. In order to control the rapidly fluctuating frequency of the power system with low inertia, it is necessary to introduce fast frequency response resources such as a Battery Energy Storage System (BESS). This paper proposes a method to calculate the required capacity of BESS for compensating the frequency control performance of the power system using inertia energy. For calculating the required capacity of BESS, the inertia energy in the critical power system, where frequency control performance marginally satisfies frequency stability constraint, should be calculated. Also, the inertia energy in the evaluated power system having deficit inertia energy should be calculated. By comparing power systems that respond with different dynamics when the same disturbance occurs, the proposed calculation corresponds to the ratio of inertia energy deficiency based on critical power system inertia energy within the power imbalance. Through various case studies employing Korean power systems, the effectiveness of the inertia energy-based calculation method for the required BESS is verified by the fact that the BESS integrated power system marginally satisfies the frequency stability constraint. In these study cases, it is found that the instant response of BESS is very effective for compensating the frequency control performance of the low inertia power system. By applying the proposed method, it is also found that about 840 MW of BESS can achieve carbon neutrality in the Korean power system. [ABSTRACT FROM AUTHOR]

Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing, which affects the stabilization of the PS (power system). This paper suggests integrated optimal dispatching of thermal power generators and BESS (battery energy storage system) taking wind energy emission grading punishment and deep peak clipping into consideration. Firstly, in order to minimize wind abandonment, a hierarchical wind abandonment penalty strategy based on fuzzy control is designed and introduced, and the optimal grid-connected power of wind energy is determined as a result of minimizing the peak cutting cost of the system. Secondly, considering BESS and thermal power, the management approach of BESS-assisted virtual peak clipping of thermal power generators is aimed at reducing the degree of deep peak clipping of thermal power generators and optimizing the output of thermal power generators and the charging and discharging power of BESS. Finally, Give an example of how this strategy has been effective in reducing abandonment rates by 0.66% and 7.46% individually for different wind penetration programs, and the daily average can reduce the peak clipping power output of thermal power generators by 42.97 and 72.31MWh and enhances the effect and economy of system peak clipping. [ABSTRACT FROM AUTHOR]

This paper introduces a novel modelling framework for conducting dynamic analysis of DC microgrids (MG), considering components such as battery energy storage systems (BESS), combined heat and power (CHP) cogeneration, photovoltaic (PV) plants and DC/DC converters. The MG dynamics are accurately captured using well-founded modelling practices of the power system transient analysis field. Firstly, the power device models are derived based on fundamental operating principles using a lumped-type parameter modelling approach. Secondly, the DC microgrid is formulated using nodal power injections, which is combined with Newton–Raphson and implicit trapezoidal methods to efficiently solve fair-sized networks of arbitrary topology. The dynamic outcomes of a 13-bus MG were compared with those of an electromagnetic transient (EMT) simulation conducted in Simscape Electrical for load and solar irradiance changes and for a DC/DC converter outage. For these events, errors smaller than 2.5% were yielded by the new approach, only requiring 3.1% of the computing time employed by the EMT simulation. The method’s applicability was also showcased by assessing the dynamic performance of a larger DC microgrid containing 118 buses, twelve distributed generators and five BESS, which was subjected to typical contingencies such as load increases and distribution line disconnections.

The increasing integration of renewable energy sources (RESs) into the power system reduces system inertia, which presents substantial challenges for maintaining frequency stability. Existing studies have mainly focused on using the load frequency control method (LFC) to stabilize area frequency and tie-line power by optimizing the parameters of supplementary control for synchronous generators (SGs). However, these works primarily used simplified first-order transfer functions for wind turbines (WT), high-voltage direct current (HVDC), and battery energy storage systems (BESS). They neglected dynamic models and, importantly, the impact of control parameters in supplementary frequency control strategies on the stability of the studied system. Therefore, this study proposes a novel load frequency control strategy that coordinates the operation of WT, HVDC, and BESS in conjunction with traditional SGs to participate in frequency regulation. The proposed strategy develops a comprehensive state-space model that incorporates accurate models of WT, HVDC, BESS, and SGs, along with their supplementary control strategies. Then, the developed state-space model was utilized to investigate the impact of control parameters in the supplementary frequency control strategies of BESS, HVDC, and WT on system stability. As a result, this work yields a specific range of coefficients for supplementary strategies to improve stability in the studied system. Lastly, a two-area interconnected benchmark system is adopted to validate the effectiveness of the proposed strategy. Based on the suggested parameters, the results demonstrate that the proposed strategy ensures frequency stability with a high penetration of RESs into the power grid. Furthermore, the results indicate that incorporating diverse sources, such as HVDC, WT, and BESS, to support SGs in frequency regulation reduces the impacts of low system inertia. This approach leads to an improved frequency response not only in areas where disturbances occur but also in other regions, provided that tie-line power interchanges are maintained in balance.

The paper presents an adaptation of the microinverter platform from Texas Instruments to incorporate a battery energy storage system (BESS) alongside the development of the BESS system itself. Initially designed for unidirectional power flow between PV panels and an electric grid, the platform required modifications to accommodate bidirectional energy transfer for BESS integration. These modifications encompass software adjustments and hardware enhancements, which are all detailed within the paper. The electrical configuration includes selecting and deploying components such as DCDC power converters, microcontrollers, measured signals, and actuating signals to facilitate battery connection to the platform's DC bus. Furthermore, a supervisory control and data acquisition (SCADA) system is devised for supervisory control and monitoring, with its implementation outlined. Control software tailored for the chosen microcontroller of the DCDC converters is described in terms of structure and functionality. A hardware-in-the-loop (HIL) methodology is employed to validate the proposed modifications and microgrid configuration. Utilizing the real-time simulator OPAL-RT, the paper presents experimental results and their analysis within the considered microgrid environment. [ABSTRACT FROM AUTHOR]

The emission reductions mandated by International Maritime Regulations present an opportunity to implement full electric and hybrid vessels using large-scale battery energy storage systems (BESSs). lithium-ionion batteries (LIB), due to their high power and specific energy, which allows for scalability and adaptability to large transportation systems, are currently the most widely used electrochemical storage system. Hence, BESSs are the focus of this review proposing a comprehensive discussion on the commercial LIB chemistries that are currently available for marine applications and their potential role in ship services. This work outlines key elements that are necessary for designing a BESS for ships, including an overview of the regulatory framework for large-scale onboard LIB installations. The basic technical information about system integration has been summarized from various research projects, white papers, and test cases mentioned in available studies. The aim is to provide state-of-the-art information about the installation of BESSs on ships, in accordance with the latest applicable rules for ships. The goal of this study is to facilitate and promote the widespread use of batteries in the marine industry. [ABSTRACT FROM AUTHOR]

The Battery Energy Storage System (BESS) is one of the possible solutions to overcoming the non-programmability associated with these energy sources. The capabilities of BESSs to store a consistent amount of energy and to behave as a load by releasing it ensures an essential source of flexibility to the power system. Nevertheless, BESSs have some drawbacks that pose limitations to their utilization. Indeed, effectively managing the stored and released energy is crucial, considering the degradation of performance associated with these systems over time. The substantial capital expenditure (CAPEX) required to install these systems represents a current constraint, impeding their broader adoption. This work evaluates a techno-economic analysis of a 2MW/2MWh BESS providing multiple services, namely participating in capacity and balance markets. The analysis is based on a BESS model implemented in SIMULINK, adopting online data gathered from a Lithium Iron Phosphate (LFP) battery facility. The model evaluates the auxiliary power consumption, state-of-charge (SoC), state of health (SoH), and the round-trip efficiency (RTE) of the overall system. The analysis is based on three price profiles: 2019 (Business-As-Usual), 2020 (COVID-19), and 2022 (Gas Crisis). Furthermore, this work conducts a case study to analyze the behavior of the BESS. It entails a sensitivity analysis, specifically evaluating the influence of CAPEX and upward bid price on the economic viability of the project. The results show a strong relation between the CAPEX variation and the Internal Rate of Return (IRR) of the project. [ABSTRACT FROM AUTHOR]

Voltage source converter (VSC)-based multi-terminal direct current (MTDC) transmission technology has been a research focus, and the low-voltage ride-through (LVRT) and recovery in receiving-end systems is one of the major problems to consider. A coordinated control strategy for a VSC-MTDC system is proposed to improve the frequency and voltage dynamics in the receiving-end system during the LVRT and recovery processes. A battery energy storage system (BESS) plays a significant role in providing frequency and voltage support with its flexible power control capability. During the LVRT process, the BESS can provide reactive current injection and active current absorption to improve system stability in the AC side, and during the recovery process, an adaptive current limitation method is proposed for the BESS converter to dynamically adjust the active and reactive power outputs according to the frequency and voltage deviation severity. Meanwhile, the coordination of the sending-end systems and DC chopper can reduce the power output to avoid DC overvoltage during LVRT, and it can also provide frequency support to the receiving-end system with the DC voltage transmitting frequency information during the recovery process. A simulation was carried out on the MATLAB/Simulink platform, and a three-terminal VSC-MTDC system was used to validate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Installation of hybrid systems with storage is a way to maximize the amount of energy generated through exploring the complementarity of different sources. Understanding hybrid power plant (HPP) operation is crucial for optimizing new systems and reconfiguring existing plants, to their enhance efficiency. Alongside technical aspects, economic feasibility is also a fundamental feature. This study simulated an off-grid HPP to consider the energy consumption of Casa Nova in Bahia, Brazil. The methodology consisted of the selection of energy sources, choosing a reference location, acquisition of generation and operational data, modeling and simulating the system in different scenarios, and a financial analysis. HOMER Pro software Version 3.16.2 was used to optimize the plant configuration, and the outputs were evaluated using the perspectives of levelized cost of energy (LCOE), simple payback, and power load fulfillment. As a result, scenario 3 was the most competitive, emphasizing that the use of different energy sources increased the system generation capacity. However, the addition of battery energy system storage (BESS) resulted in a high LCOE when compared to individual sources, which demonstrated that the cost of battery integration is not yet nationally competitive. Moreover, the results highlighted the importance of research investments, energy governance, and regulation in promoting hybrid system adoption. [ABSTRACT FROM AUTHOR]

In view of the stochastic nature of wind and solar energy, maintaining quality power in grid-connected hybrid energy system is crucial for efficient energy utilization, especially in the presence of non-linear loads. This paper deals with the control and performance assessment of grid-integrated hybrid energy system that comprises of doubly fed induction generator (DFIG) and photovoltaic (PV) system including battery energy storage (BES) as a stand-by supply to meet utility and load demand. In order to maintain power balance and address various power quality issues encountered with hybrid renewable integration along the source and the grid/load end, a dual-layer variable-step size adaptive control is implemented for the grid-interfaced converter of the proposed system. The variable step-size based dual-layer least mean square (LMS) adaptive control functions by extracting weights of various control signals (stator and load current) where the step-size parameter adjusts according to the conditions that limit the maladjustment error and increase the rate of convergence. The DC-link voltage control realized by bidirectional BES control reduces the steady-state error and fluctuations in the voltage. An artificial bee colony (ABC) optimization is adopted for optimal gain tuning of the DC-link voltage controller as a result of which the battery performance and grid-side converter performance is enhanced with the increased power exchange to the grid. The system model is developed in MATLAB/Simulink and controller performance is evaluated for diverse operating conditions including steady-state, unbalanced non-linear load conditions, wind-speed and solar irradiation variation and specified grid power modes. [ABSTRACT FROM AUTHOR]

Today, many countries are focused on smart grids due to their positive effects on all sectors of a power system, including those of operators, utilities, and consumers. Furthermore, the usage of renewable energy sources for power production is quickly expanding due to the depletion of fossil fuels and the emissions caused by their use. Additionally, intermittent power generation from renewable energy sources, such as wind and solar, necessitates the use of energy storage devices with which to ensure a continuous power supply to meet demand. This can be accomplished by employing an appropriate storage device with a sufficient storage capacity, thus enabling a grid-connected solar PV and wind system to have enhanced performance and to reduce adverse effects on the power quality of the grid. In this study, a probabilistic planning model that takes the intermittent natures of solar irradiances, wind speeds, and system demands into account is introduced. A novel criterion is also adopted to map the three-dimensional spaces of intermittency with the proposed model for optimizing BESS charging/discharging decisions. This planning model is intended to minimize the economic costs of investment and operation of a battery energy storage system (BESS) for a planning period. Moreover, the substation and feeder upgrade costs, as well as the overall system loss costs, are included in the proposed model. Particle swarm optimization (PSO) is utilized to find the optimal sizing, location, and operation of energy storage systems. The proposed methodology was validated using a 69-bus distribution system. [ABSTRACT FROM AUTHOR]

In this research, the performance of vanadium redox flow batteries (VRFBs) in grid-connected energy storage systems centering on frequency and power sharing using voltage source inverters was evaluated. VRFBs are increasingly promising due to their scalability and long lifespan. We explore the impact of voltage source inverters on the frequency of the power supply when there is a change in load and power sharing between the grid and VRFB through MATLAB simulations. The test system demonstrates a storage system at an 11-kV substation and considers a load profile commencing from residential and commercial consumers. The outcome of this study reveals that VRFBs provide the required power to maintain load demand. Due to fast response time, VRFBs also regulate the frequency efficiently during the change in load demand and maintaining current total harmonic distortions (THDs). This research confirms the design and operation of VRFB-based energy storage systems, contributing to resilient grid infrastructure and reliable power distribution systems.

The previous stochastic hosting capacity prediction method using the Monte Carlo method for high photovoltaic (PV) penetration with a battery energy storage system (BESS) required a large number of computations to achieve the expected accuracy. The problem of high computational load must be addressed so that the electrical distribution planner can practically use the PV hosting capacity prediction in actual situations. Therefore, this study developed a Markov-chain-based PV hosting capacity prediction method for high PV penetration using BESS. The proposed method is described in detail, followed by case and validation studies. The obtained hosting capacity was 123.58 kW, which increased to 3676.4 kW after the utility-scale BESS implementation. The results demonstrate that the proposed Markov-chain-based PV hosting capacity prediction method outperforms the Monte Carlo method, which is the most popular stochastic hosting capacity method, in terms of accuracy and computational cost.

The strategic positioning and appropriate sizing of Distributed Generation (DG) and Battery Energy Storage Systems (BESS) within a DC delivery network are crucial factors that influence its economic feasibility and dependable performance. To tackle this vital aspect, we have formulated a multi-objective optimization model aimed at determining the most advantageous locations and capacities for DG and BESS. Our approach establishes a framework for multi-objective optimization that systematically addresses all objectives, minimizing bias towards any specific set of objectives and enhancing overall efficiency. Furthermore, from a variety of collective objectives we introduce an improved Multi-Objective Particle Swarm Optimizer (IMOPSO) tailored to produce and choose Pareto best solutions. To identify the best placement and sizing options for DG and BESS among the Pareto optimal solutions, we apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) strategy, integrating an information entropy method. We have conducted simulations using the 33-bus DC distribution grid of IEEE to validate the practical benefits of our suggested methodology.

Low-voltage ride-through (LVRT) capability is crucial for wind power plants that are grid-connected. A grid code requires wind farms to remain on-grid and inject a specific reactive current when a grid fault occurs. The requirements are satisfied by a cooperative control scheme of a battery energy storage system (BESS) and grid-side converter (GSC) for improving the LVRT capacity of wind turbine generators using permanent magnet synchronous generators. The proposed approach uses the BESS to absorb a power difference that appears in the back-to-back converters during the LVRT operation. Hence, the DC-link voltage is regulated by the BESS. In addition, the GSC control allows reactive current to be injected into the grid according to the grid code requirements. Finally, the wind turbine generators, which use the proposed approach, are built in MATLAB/Simulink. The theoretical analysis of the proposed method is validated by simulation.

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal.

The previous stochastic hosting capacity prediction method using the Monte Carlo method for high photovoltaic (PV) penetration with a battery energy storage system (BESS) required a large number of computations to achieve the expected accuracy. The problem of high computational load must be addressed so that the electrical distribution planner can practically use the PV hosting capacity prediction in actual situations. Therefore, this study developed a Markov-chain-based PV hosting capacity prediction method for high PV penetration using BESS. The proposed method is described in detail, followed by case and validation studies. The obtained hosting capacity was 123.58 kW, which increased to 3676.4 kW after the utility-scale BESS implementation. The results demonstrate that the proposed Markov-chain-based PV hosting capacity prediction method outperforms the Monte Carlo method, which is the most popular stochastic hosting capacity method, in terms of accuracy and computational cost. [ABSTRACT FROM AUTHOR]

The present work has taken a challenge to design a model predictive controller (MPC) for automatic load frequency control (ALFC) of two-area, wind-integrated thermal power system equipped with battery energy storage system (BESS) and demand response (DR) for frequency regulation task. Primarily, the incremental BESS model employs a new state of charge (SOC)-based strategy to regulate the power from battery for saving battery life. Then, DR, along with the SOC-based BESS, is employed in ALFC for frequency regulation. A modified state space model of MPC incorporating all BESS variables is developed and employed in ALFC of the studied power system. The performance of the designed MPC is examined for inertia issues arising from wind in the conventional two-area power system. Furthermore, the capability of BESS for frequency regulation and effect on the life of BESS with the proposed control strategy are measured through MPC-based ALFC and results are compared with system performance when integral controller in ALFC and inertia controller from wind are present. In addition to DR and BESS in ALFC, double-fed induction generator-based proportional derivative (PD) inertia controller also contributes in the power system for frequency support from wind energy section to avoid inertia issues. So, all the controllers of the test power system such as integral controller in ALFC and PD controller in wind are tuned concurrently for smooth frequency control. However, performance of MPC is tested for smooth frequency regulation by tuning PD controller gain of wind only while keeping MPC gain parameters as available in the literature. Particle swarm optimization is used to tune the integral controller gain of ALFC for the studied power system to compare the results with MPC-based ALFC. A transfer function model of wind-integrated two-area thermal power system is taken into consideration in the present study to verify the effectiveness of the battery variable concerning MPC design for ALFC collaborating with DR for smooth frequency control and provide safe battery life. Finally, results confirm the effectiveness of the designed MPC-based ALFC, collaborating with DR- and SOC-based incremental BESS through various case studies. [ABSTRACT FROM AUTHOR]

In the last few years, the energy industry has seen an exponential increase in the quantity of lithium‐ion (LI) utility‐scale battery energy storage systems (BESS). Standards, codes, and test methods have been developed that address battery safety and are constantly improving as the industry gains more knowledge about BESS. These standards address the minimum requirements for shipping, installation, commissioning, and operation of the battery. In addition to minimum standards, there are recommended practices that enhance the safety of utility‐scale energy storage installations. This paper reviews the recommended practices that, through knowledge and experience with BESS, are being adopted by electric utilities. The focus is on fire, explosion, and toxic emission hazards of thermal runaway events of the battery and their mitigation. The paper also addresses utility considerations of minimum requirements dictated by codes, standards, and expectations of authorities having jurisdiction (AHJs) and insurance companies. This paper is intended to increase the technical acumen of environmental health and safety personnel and to provide practical information to utilities and developers installing LI BESS. [ABSTRACT FROM AUTHOR]

This article presents a robust analysis based on the data obtained from a genuine microgrid in operation, simulated by utilizing a diesel generator (DG) in lieu of the Battery Energy Storage System (BESS) to meet the same load during periods of elevated energy costs. The study reveals that the BESS significantly outperforms the DG and the conventional electrical grid in various financial and environmental aspects. Environmentally, BESS accounts for zero CO2 emissions, compared to the 67.32 tons of CO2 emitted annually by the DG. Financially, the total cost of BESS over 20 years (USD 1,553,791.31) is lower than that of DG (USD 1,564,965.18) and the electrical grid (USD 2,726,181.09). Furthermore, BESS displays a lower Required Average Discharge Price—RADP (USD 0.38/kWh) and Required Average Price Spread—RAPS (USD 0.18/kWh) compared to DG (RADP: USD 0.39/kWh; RAPS: USD 0.22/kWh) and the electrical grid (RADP: USD 0.71/kWh; RAPS: USD 0.38/kWh). During periods of high-energy tariffs, BESS provides significant environmental benefits, but it also offers a more economically advantageous option to meet the load. It offers an energy-efficient and economically feasible solution for the operation of microgrids. [ABSTRACT FROM AUTHOR]

Decentralized generation has gained importance in the energy industry, since self-consumption with renewable resources presents attractive costs and allows load management actions. In this sense, photovoltaic generation systems are a promising technology. This work presents a proposal for a peak shaving system using solar photovoltaic (PV) energy and a battery storage system, known as battery energy storage systems (BESS), to be installed by an industrial customer to reduce energy consumption during peak hours. For the study, a hybrid approach is presented, starting from deterministic variables, such as the demand curve of the industry under study, and the generation of stochastic variables, such as the energy production of the photovoltaic system. For the analysis of the proposed peak shaving system, the design and sizing of the photovoltaic systems are developed in a base case of self-generation and an optimized system for the system to cover the energy demand generated during peak hours. The technical–economic study carried out in the research allowed us to determine the optimal power of the photovoltaic system with the storage system. The proposed system allows the integration of a peak shaving strategy from a certain power limit, in order to cover the peak demand over this power limit, allowing the system to be profitable under the current regulations and standards in Ecuador. [ABSTRACT FROM AUTHOR]

This research addresses strategic recommendations regarding the applications of battery energy storage systems (BESS) in the context of the deregulated electricity market. The main emphasis is on regulatory dimensions, incentive mechanisms, and the provision of marketable storage services. The study's findings demonstrate that battery energy storage systems (BESS) have distinct characteristics that challenge their conventional classification as a load or generator within power systems. The study additionally emphasizes the insights, lessons learned, and good practices gained from early adopter countries in implementing energy storage systems (ESS). These insights include the importance of establishing a precise definition of ESS, promoting collaborative engagement with relevant stakeholders, and developing a series of incentive strategies. The results show that nations that pioneered BESS's application in their electricity matrices have effectively promoted storage services in deregulated markets, employing storage assets for various purposes such as peak reduction, frequency regulation, renewable energy support, and energy arbitrage applications. These applications underline the potential of BESS to increase grid stability and minimize exposure to risk and volatility in the revenues of storage agents in deregulated markets. [ABSTRACT FROM AUTHOR]

Synchrophasor-driven smart grid applications aiming to orchestrate a diverse set of Distributed Energy Resources (DERs) require extensive infrastructure including substantial instrumentation hardware, communication network extensions and controller installations for coordinated operation. This can make the overall installation expensive. Additionally, due to the computational complexity and data-intensive nature of the PDC functionality, most of the existing PDC implementations are on a purely software level, making them unsuitable for the real-time applications. To address this, the current paper proposes an alternate architecture for the real-time synchrophasor-based control of DER applications (e.g., microgrids) incorporating a centralized synchronization hardware designed to replace aggregation Phasor Data Concentrators (PDCs) and supplementary control algorithms into a singular reconfigurable hardware. This particular hardware is termed a Synchrophasor Synchronization Gateway and Controller (SSGC). The robustness of the proposed architecture is tested by using real-time (RT) Controller Hardware-In-the-Loop (CHIL) simulation-based experiments by manipulating the communication network that connects the SSGC with multiple Phasor Measurement Unit (PMU) streams broadcasting data through the IEEE C37.118.2 protocol in real time. These PMU streams were generated by using a real-time microgrid model running on a Typhoon HIL 604 simulator. To manipulate the communication interface between the proposed SSGC hardware and the PMU streams, a configurable Wide Area Network (WAN) emulator and communication network impairment appliance deployed in the Candela Technologies CT910 external hardware was utilized. The real-time control system was expanded by incorporating a low-pass filter to eliminate the potential overswitching of a Battery Energy Storage System (BESS). The proposed architecture demonstrated a reliable performance under ideal to moderately tampered communication networks. However, under a significantly corrupted network, the performance of this architecture is acutely affected. [ABSTRACT FROM AUTHOR]

Participation in the electricity market requires making commitments without knowing the real generation or electricity prices. This is problematic for renewable generators due to their fluctuating output. Battery energy storage systems (BESSs) integrated with renewable sources in a hybrid farm (HF) can alleviate imbalances and increase power system flexibility. However, the impact of battery degradation on long-term profitability must be taken into account when choosing the correct market participation strategy. This study evaluates the state-of-the-art on energy management systems (EMS) for HFs participating in day-ahead and intraday markets, incorporating both BESSs' calendar and cycling degradation. Results suggest that efforts to attain additional profits in intraday markets can be detrimental, especially when the degradation effect is considered in the analysis. A new market participation strategy is proposed that aims to address the limitations of market overlapping and forecasting errors. The results demonstrate that the proposed method can enhance long-term benefits while also reducing battery degradation. [ABSTRACT FROM AUTHOR]

Partial state of charge (PSOC) is an important use case for lead–acid batteries. Charging times in lead–acid cells and batteries can be variable, and when used in PSOC operation, the manufacturer’s recommended charge times for single-cycle use are not necessarily applicable. Knowing how long charging will take and what the variability in time required is allows for better planning of operations and algorithm creation for battery energy storage system (BESS) manufacturers. This paper details and demonstrates a procedure for identifying the charging time of cells when different charge throughputs occur prior to reaching full charge. The results showed that the charging time in PSOC operations was highly variable when a charge-factor-controlled full-charge procedure was used. Also noted were that higher voltages for the same state of charge were reached as the number of cycles following reaching full charge increased. None of the regimes tested in this paper caused any significant capacity degradation, which demonstrates that PSOC operations can be performed even on cells not specifically designed for them, provided the correct regime is chosen.

Frequency response performance in power systems is becoming vulnerable due to the transition toward the higher penetration of renewable energy such as achieving carbon neutrality. In particular, reducing power system inertia energy as the asynchronous generation increases could result in violating the frequency stability constraint when a disturbance occurs in the power systems. In order to control the rapidly fluctuating frequency of the power system with low inertia, it is necessary to introduce fast frequency response resources such as a Battery Energy Storage System (BESS). This paper proposes a method to calculate the required capacity of BESS for compensating the frequency control performance of the power system using inertia energy. For calculating the required capacity of BESS, the inertia energy in the critical power system, where frequency control performance marginally satisfies frequency stability constraint, should be calculated. Also, the inertia energy in the evaluated power system having deficit inertia energy should be calculated. By comparing power systems that respond with different dynamics when the same disturbance occurs, the proposed calculation corresponds to the ratio of inertia energy deficiency based on critical power system inertia energy within the power imbalance. Through various case studies employing Korean power systems, the effectiveness of the inertia energy-based calculation method for the required BESS is verified by the fact that the BESS integrated power system marginally satisfies the frequency stability constraint. In these study cases, it is found that the instant response of BESS is very effective for compensating the frequency control performance of the low inertia power system. By applying the proposed method, it is also found that about 840 MW of BESS can achieve carbon neutrality in the Korean power system.

In this paper, a high gain nonisolated three-port bidirectional DC–DC converter is proposed to interface solar photovoltaic and battery energy storage system to DC bus with a reduced number of components. Four modes of operation based on the power flow and load demand are identified. The operating principle of the proposed converter and its operational waveform for all four modes of operation is described in this paper. The steady-state analysis of the proposed converter is performed to determine the voltage gain of the converter in all four modes of operation. The steady-state analysis is done for both continuous conduction mode and discontinuous conduction mode; and the boundary condition. The reduced number of components to achieve high-voltage gain ensures the lowering of the cost and weight of the converter. The analytical results are validated utilizing the simulation results from PSCAD and hardware results. Also, the results indicate that the ripple in the current and voltage is reduced significantly. [ABSTRACT FROM AUTHOR]

This article reviews the most popular energy storage technologies and hybrid energy storage systems. With the dynamic development of the sector of renewable energy sources, it has become necessary to design and implement solutions that enable the maximum use of the energy obtained; for this purpose, an energy storage device is suggested. The most popular methods of electric energy storage are described, with an indication of the features of each technology, along with the presentation of the advantages and disadvantages of a given storage reservoir. Next, hybrid energy storage systems are presented along with their suggested applications and advantages resulting from the hybridization of technologically diverse energy storage systems. [ABSTRACT FROM AUTHOR]

A growing interest has been given to examining the heterogeneity of children's health to provide for their particular needs. This study examined subgroups of elementary school children's social, emotional, and behavioral functioning (SEB) using teacher ratings of children with the Behavioral and Emotional Screening System. A Latent Profile Analysis (LPA) was conducted with 5,150 students aged from kindergarten to fifth grade. Subscale scores in the areas of externalizing risk, internalizing risk, and adaptive skills risk were used to identify profiles. Students' grade level, sex, race, and status of receiving special education services were added to investigate the impact of covariates on the classification of latent profiles. Four profiles of risk were identified: Well Development (60.5%), Normal Development (25.8%), Externalizing and Adaptive Skills Risk (9.1%), and Elevated Risk (4.6%). Significant differences of covariates including sex, race, and status of receiving special education services were identified. Specifically, males and African American students were more likely to be classified into the "Elevated Risk" profile than other profiles. Students who did not receive special education services were more in the "Well Development" profile. The results may have important implications for the stakeholders to allocate intervention and treatment resources effectively and accurately. [ABSTRACT FROM AUTHOR]

This paper presents case studies for replacing diesel generators (DGs) that are used as the main and emergency power sources for alternating current (AC) auxiliary services in substations (SS) within the transmission network (voltage greater than or equal to 230 kV). The objective of this research is to present a solution that is more reliable, environmentally friendly, and financially viable than DGs. To achieve this, the proposed solutions incorporate Battery Energy Storage Systems (BESSs) with or without the integration of Photovoltaic (PV) Systems. These solutions were simulated using the HOMER PRO Version 3.14.5 software for the Messias SS /AL, and the results were analyzed and compared to the DG in terms of reliability, financial viability, and environmental impact. Based on the conducted analyses, the BESS solution with the PV system was found to be the most suitable for the main source. However, in the case of the emergency source, if one of the main sources is a DG/BESS, maintaining the emergency DG is the preferable option. If both main sources are independent, the BESS solution with the PV system is a suitable solution. [ABSTRACT FROM AUTHOR]

Minimally invasive surgery has many advantages, including early recovery and cosmetic preservation. However, the higher radiation exposure to physicians and patients has drawbacks. Preoperative tissue dyeing techniques are feasible options for reducing radiation exposure and procedure time, but their efficacy has not yet been evaluated. Therefore, this study aimed to evaluate surgical outcomes and reduce radiation exposure during unilateral biportal endoscopy surgery. This was a prospective, case-controlled analysis in a tertiary hospital. Patients receiving experimental tissue dye and controls in the nondye group were compared from May 2020 to September 2021. The ipsilateral posterolateral approach (IPA) and the far lateral approach (FLA) were analyzed separately among all single-level spinal procedures without instrumentation. Operative details (operation time, improvement of back and leg pain, and length of hospital stay) and radiation exposure (dose and duration) were compared. A total of 88 cases were included, consisting of 64 interlaminar approaches (experimental: 33, control: 31) and 24 FLAs (experimental: 13 and control: 11). In the IPA approach, the patient and physician radiation exposure doses and duration decreased significantly. Conversely, for the FLA, only the duration of the physician exposure decreased significantly. Preoperative tissue dyeing techniques using IPA can reduce radiation exposure for physicians and patients. However, a decrease in the duration of radiation was observed only in physicians using the FLA. The dyeing technique is effective in IPA, but the efficacy of FLA is doubtful. [ABSTRACT FROM AUTHOR]