Search

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications. [ABSTRACT FROM AUTHOR]

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS. [ABSTRACT FROM AUTHOR]

System strength is both supplied and demanded in a power system during normal operations and in the presence of disturbances. This is characterised by stable voltage and frequency, supporting renewable generation such as wind and solar. Because the retirement of synchronous generators reduces system strength supply, and the connection of new inverter-based resource (IBR) generators increases demand, there is an urgent need for new sources of system strength. This paper provides an overview of the challenges brought about by grid modernisation. It highlights tangible solutions provided by synchronous condensers (SCs) to bolster grid strength, stability, and reliability while accommodating the rising influx of renewable energy sources (RESs). Furthermore, this paper examines the role of SCs in improving weak grids, voltage control, power quality, short-circuit levels, and inertia management. It introduces the role of innovative hybrid synchronous condenser (HSC) systems to boost grid reliability and resilience. It also elaborates on the optimisation strategies for SC sizing, placement, and control and outlines economic aspects of their deployment. The review also highlights future directions and challenges in SC technology, emphasising the need for ongoing research and development to enhance system design and operation. [ABSTRACT FROM AUTHOR]

The power system faces a growing need for increased transmission capacity and reliability with the rising integration of renewable energy resources. To tackle this challenge, Battery Energy Storage Systems (BESSs) prove effective in enhancing grid capacity and relieving transmission congestion. This paper focuses on the PJM market, conducting a thorough revenue analysis to identify and characterize highly profitable nodes for BESS market participants. A comparison between stationary and transportable BESSs reveals that the transportable BESSs can generate higher potential revenue in energy and regulation markets. Based on these findings, we propose an optimal placement algorithm to support market participants in selecting strategic sites for BESS installation, validated with real PJM market data. This paper provides valuable insights for navigating market-based power system participants and promoting the effective integration of BESSs. [ABSTRACT FROM AUTHOR]

GAPF (Generalized Active Power Filter) is a non-tracking filter. Through outputting fundamental positive sequence voltages synchronized with the system it can achieve reactive power compensation, harmonic suppression, negative sequence filtering and voltage stabling effectively without the need of load current detection. In this paper a GAPF combined with Battery Energy Storage System (BESS) is proposed which can enlarge operation range, improve performance, extend functionality and realize four-quadrant compensation to the system. The topology and operating principle of GAPF/BESS are discussed in detail. Two control methods are presented. The correctness of theoretical analysis and the feasibility of control methods are verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

Advances in energy storage technology have allowed the application of load shifting in the utility grid for a more efficient power system operation. However, the economy of the Battery Energy Storage System (BESS) application is not linear to the cost reduction obtained from excluding the costly generators. This paper presents the economic analysis of cost-based load shifting implementation and an approach to determine the generation units to be deactivated and replaced by BESS on three large-scale power grids in Indonesia: Sumbagut, Sumbagteng, and Sumbagsel systems. The economic feasibility is evaluated using the Net Present Value (NPV), considering the savings and the BESS investment cost. The result shows that the proposed approach is more robust than the conventional methods, such as removing the most expensive units, units with the highest cost reduction, units with the best economic return contribution, or the maximum number of units that can be replaced. [ABSTRACT FROM AUTHOR]

The sustainable development of an area is highly reliant on a reliable electrical energy supply. Microgrids are important in integrating distributed energy resources (DERs) using power electronic converters. However, microgrid control becomes challenging with the increasing number of distributed generators and loads. With the conventional droop control method, power contributions from DER converters cannot be accurately shared due to a mismatch of line impedances. In this paper, an augmented hierarchical control mechanism is proposed to solve the issues mentioned above. This hierarchical control mechanism consists of primary and secondary controllers. The primary stage utilized the droop controller to improve optimal power flow, mainly for the resistive network. The secondary stage is based on an improved methodology to compensate for the voltage and frequency variations during small and large signal disturbances. Moreover, the modelling and analysis for PMSG-based wind energy conversion systems are also presented. The response of the primary controller for active and reactive power sharing is investigated. The analysis emphasizes the demonstration of optimal power-sharing under normal and abnormal conditions for the considered load. Finally, the suggested robust controller's performance is evaluated in a MATLAB environment, and simulation results show the proposed scheme's superiority under different operating conditions. The frequency is stable at 50 Hz after a 50 KW load is added. [ABSTRACT FROM AUTHOR]

Due to increasing load demand and the energy crisis, microgrids (MGs) have attracted more attention. The idea and technology of microgrids (MGs) have undergone significant advancements largely aimed at enabling the automation of distribution systems and enhance the integration of renewable energy sources (RESs). In this paper, the MG is a combined form of various distributed generations (DGs), battery energy storage system (BESS), and plug-in hybrid electric vehicles (PHEVs). A novel approach is introduced to minimize the operational expenses of the grid-connected low-voltage microgrid by leveraging a probabilistic expert optimization technique. This methodology takes into account various factors such as power supply costs, power dissipation, PHEV charging requirements, and the optimal capacity of BESS. Three charging techniques are proposed in this paper to control the PHEVs' charging demand at homes and public charging stations. This study employs a comprehensive model to simulate a whole day, accounting for the stochastic charging requirements of PHEVs, load profiles, RESs outputs, and variations in energy price. To address the inherent nonlinearity of the optimization problem, the energy management within the MG is tackled using the slime mould algorithm, a population-based stochastic optimizer. The primary goal is to minimize operational costs in the microgrid by optimizing the efficient utilization of available resources. [ABSTRACT FROM AUTHOR]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources (RESs) and the growing demand for sustainable power solutions have necessitated the widespread deployment of energy storage systems. Among these systems, battery energy storage systems (BESSs) have emerged as a promising technology due to their flexibility, scalability, and cost-effectiveness. This paper aims to provide a comprehensive review of the diffusion and deployment of BESSs across various applications, analyzing their impact on grid stability, renewable energy integration, and the overall energy transition. The paper examines the key drivers and challenges associated with BESS adoption, as well as market trends influencing their proliferation. Through an analysis of empirical data, this study aims to shed light on the current state of BESS diffusion. Finally, this research contributes to the knowledge base surrounding battery storage technology and provides insights into its role in achieving a sustainable and reliable energy future. [ABSTRACT FROM AUTHOR]

Global society is significantly speeding up the adoption of renewable energy sources and their integration into the current existing grid in order to counteract growing environmental problems, particularly the increased carbon dioxide emission of the last century. Renewable energy sources have a tremendous potential to reduce carbon dioxide emissions because they practically never produce any carbon dioxide or other pollutants. On the other hand, these energy sources are usually influenced by geographical location, weather, and other factors that are of stochastic nature. The battery energy storage system can be applied to store the energy produced by RESs and then utilized regularly and within limits as necessary to lessen the impact of the intermittent nature of renewable energy sources. The main purpose of the review paper is to present the current state of the art of battery energy storage systems and identify their advantages and disadvantages. At the same time, this helps researchers and engineers in the field to find out the most appropriate configuration for a particular application. This study offers a thorough analysis of the battery energy storage system with regard to battery chemistries, power electronics, and management approaches. This paper also offers a detailed analysis of battery energy storage system applications and investigates the shortcomings of the current best battery energy storage system architectures to pinpoint areas that require further study. [ABSTRACT FROM AUTHOR]

This study describes a laboratory model of a battery energy storage system (BESS) designed for testing algorithms aimed at reducing peak power consumption in railway traction substations. The system comprises a DC/DC converter and battery energy storage. This article details a laboratory model of a bidirectional buck-boost DC/DC converter, which is used to transfer energy between the battery energy storage and a DC line. It presents an analysis of DC/DC converter systems along with simulation studies. Furthermore, the results of laboratory tests on the DC/DC converter model are also provided. The control algorithm of the system in the traction substation is focused on reducing peak power, offering benefits such as lower charges for the railway operator due to the possibility of reducing contracted power requirements. From the perspective of the power grid, the reduction in power fluctuations and, consequently, voltage sags, is advantageous. This paper includes a description of a hardware simulator for verifying the system's control algorithms. The verification of the control algorithms was performed through experimental tests conducted on a laboratory model (a hardware simulator) of the system for dynamic load reduction in traction substations, on a power scale of 1:1000 (5.5 kW). The experimental tests on the laboratory model (hardware simulator) demonstrated the effectiveness of the algorithm in reducing the peak power drawn from the power source. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources to achieve carbon neutrality is increasing. However, the uncertainty and volatility of renewable resources are causing problems in power systems. Flexible and low-carbon resources such as Energy Storage Systems (ESSs) are essential for solving the problems of power systems and achieving greenhouse gas reduction goals. However, ESSs are not being installed because of Korea's fuel-based electricity market. To address this issue, this paper presents a method for determining the optimal investment timing of Battery Energy Storage Systems (BESSs) using the Least Squares Monte Carlo (LSMC) method. A case study is conducted considering the System Marginal Price (SMP) and Capacity Payment (CP), which are electricity rates in Korea. Revenue is calculated through the arbitrage of a 10 MW/40 MWh lithium-ion BESS, and linear programming optimization is performed for ESS scheduling to maximize revenue. The ESS revenue with uncertainty is modeled as a stochastic process using Geometric Brownian Motion (GBM), and the optimal time to invest in an ESS is determined using an LSMC simulation considering investment costs. The proposed method can be used as a decision-making tool for ESS investors to provide information on facility investments in arbitrage situations. [ABSTRACT FROM AUTHOR]

The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed. [ABSTRACT FROM AUTHOR]

The battery energy storage system (BESS) is considered as an effective way to solve the lack of power and frequency fluctuation caused by the uncertainty and the imbalance of renewable energy. Based on these, this paper proposes a mixed control strategy for the BESS. First, this paper divides the demand for frequency modulation, peak regulation, and state of charge (SOC) of the battery into different zones. Then the Kuramoto model modulates the frequency, and the self‐recovery strategy is used to optimize the SOC. Meanwhile, the proposed mixed control strategy is used to integrate frequency control, demand control, and SOC optimization. Finally, the simulation results verify the rationality of the strategy. [ABSTRACT FROM AUTHOR]

The need for a more flexible usage of power is increasing due to the electrification of new sectors in society combined with larger amounts of integrated intermittent electricity production in the power system. Among other cities, Uppsala in Sweden is undergoing an accelerated transition of its vehicle fleet from fossil combustion engines to electrical vehicles. To meet the requirements of the transforming mobility infrastructure, Uppsala municipality has, in collaboration with Uppsala University, built a full-scale commercial electrical vehicle parking garage equipped with a battery storage and photovoltaic system. This paper presents the current hardware topology of the parking garage, a neural network for day-ahead predictions of the parking garage's load profile, and a simulation model in MATLAB using rule-based peak shaving control. The created neural network was trained on data from 2021 and its performance was evaluated using data from 2022. The performance of the rule-based peak shaving control was evaluated using the predicted load demand and photovoltaic data collected for the parking garage. The aim of this paper is to test a prediction model and peak shaving strategy that could be implemented in practice on-site at the parking garage. The created neural network has a linear regression index of 0.61, which proved to yield a satisfying result when used in the rule-based peak shaving control with the parking garage's 60 kW/137 kWh battery system. The peak shaving model was able to reduce the highest load demand peak of 117 kW by 38.6% using the forecast of a neural network. [ABSTRACT FROM AUTHOR]

This paper presents modeling and nonlinear control of a two-stage 1-MWh battery energy storage system (BESS) connected to a distribution grid. The BESS is based on a cascaded H-bridge (CHB) multilevel converter offering the distribution of the batteries among multiple submodules which provides safer operation and more flexibility in the voltage design of the batteries. The main function of the BESS is to exchange active and reactive power by controlling the current of the CHB using voltage oriented control strategy. Moreover, this paper proposes a nonlinear state feedback exact linearization controller to control the bidirectional buck–boost converter in each submodule to charge/discharge the batteries and stabilize the dc-link capacitor voltage throughout the operation of the BESS. The proposed controller performance is compared with the conventional PI controller and validated through simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a battery management algorithm to optimize the lifetimes of retired lithium batteries with heterogeneous states of health in a battery energy storage system under dynamic power demand. A battery energy storage system allows for the use of retired lithium batteries for applications such as backup power in homes, data centers, etc. In a battery energy storage system, a battery pack consists of several retired batteries connected in parallel or in series to fulfill the required power demand. Owing to the retired batteries' different capacity levels, i.e., states of health, a scheduling strategy is required to turn battery cells inside the battery pack on and off such that the secondary lifetimes of the retired batteries are extended. To establish the optimal scheduling policy, it is necessary to determine the correct states of each battery cell, including the state of charge and the state of health. To that end, the proposed algorithm first estimates the state of charge and state of health for all cells based on data measured using an extended Kalman filter. Then, a deep reinforcement learning scheduling algorithm is implemented to connect/disconnect the battery cells to/from the battery pack based on their states. Via simulation, we show that the proposed algorithm estimates the state of charge and state of health of each battery cell with low error and extends the lifetime of battery packs by 20.6%, compared to methods proposed in previous works. [ABSTRACT FROM AUTHOR]

The highly variable power generated from a battery energy storage system (BESS)–photovoltaic distributed generation (PVDG) causes harmonic distortions in distribution systems (DSs) due to the intermittent nature of solar energy and high voltage rises or falls in the BESS. Harmonic distortions are major concerns in the DS, especially when the sizes and locations of these resources are sub-optimal. As a result, many studies are being performed on the optimal allocation of BESS/PVDG systems in distribution network systems. In this regard, this paper seeks to review the existing planning models, optimization methods and renewable energy resources that uncertainty models have employed in solving BESS/PVDGs allocation problems in terms of obtaining optimal solutions/allocations and curtailing the harmonic contents of the DSs. However, studies on optimal allocation planning of BESS/PVDGs have achieved minimum cost but were not able to meet the standard harmonic level of the DSs. The results identified GA, PSO and AIS as some of the most used methodologies while LP, MILP and different configurations of NLP were used in the model formulations of BESS/PVDGs problems. The results also revealed DC-link voltage and switching and grid voltage harmonics as the notable causes and sources of harmonic distortions in BESS/PVDG systems. The current allocation models presented in the recent literature for the planning of BESS/PVDGs do not include the variables necessary for curtailing the harmonic contents in their planning formulations. This paper, therefore, recommends an improved and all-encompassing planning model with an efficient intelligent search algorithm capable of obtaining a global optimum solution and curtailing harmonic distortions from the BESS/PVDG-connected DSs. [ABSTRACT FROM AUTHOR]

Anticipating and relieving congestions is an ongoing challenge for transmission system operators. Distributed grid‐scale battery energy storage systems enable operators to shift power flows and remedy congestion through virtual power lines and grid boosters. This paper includes battery energy storage systems in a combined preventive and curative congestion management optimization. First, it analyzes the impact of the two operational strategies in a case study of the German transmission grid. Furthermore, it outlines curative ad‐hoc measures to overcome uncertainties during operational planning and real‐time operation. The simulation results indicate that battery energy storage systems further increase the use of curative measures and reduce congestion management costs. [ABSTRACT FROM AUTHOR]

The rapid increase in the integration of renewable energy sources into the electrical grid is posing new challenges for the dynamic responses due to the global inertia reduction. In this regard, the impact on frequency stability of such reduction and the potential support from wind power have been investigated lately. However, it is well-known that the variability of wind power and its reduced inertia may not be enough to handle the power unbalance. Energy storage systems (e.g., batteries) may provide the required additional flexibility to ensure proper response. In this paper, an analysis of how the different control loops for frequency support on wind power and batteries interact and perform is presented. To gain insights from the different impacts, a sensitivity analysis comparison for frequency regulation through (i) inertia and droop control of a variable speed wind turbine and (ii) battery is performed. The analysis was carried out through simulations using the well-known 4 generator 2-area model adapted to include a wind farm. From a battery perspective, its ramping capabilities were varied to provide frequency regulation. The paper shows how, depending on the control parameters and battery size, the frequency response may even become unstable due to interactions of the various pieces of technology. Thus, it shows that coordinated actions, control optimization and grid status among different actors on the grid (as battery and wind) is required for stable operation. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today's modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed energy resources (DERs) may cause security and economic risks in the distributed network. In this paper, the optimal allocation of battery energy storage systems (BESS) is proposed to mitigate the risks in the radial distribution network by considering future uncertainties, such as the uncertainties of load and renewable energy. The interacting levels of the proposed hierarchical planning framework are (1) determination of the BESS location based on the calculated adjusted voltage violation risk; (2) obtaining the capacity of the BESS by solving an optimization problem assisted by supervised learning. In the previous works, the steady-state is evaluated by the DistFlow equations in the distribution system. In our paper, we have utilized a data-driven method to calculate the power flow and the voltage, thus leading to higher accuracy. Through case studies, the effectiveness of the proposed method is verified. Furthermore, the data-driven assisted optimization model reduces the computational burden to a large extent because massive state variables, the power flow constraints and voltage constraints are substituted. [ABSTRACT FROM AUTHOR]

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the proportion of renewable energy generation systems increases, traditional power generation facilities begin to face challenges, such as reduced output power and having the power turned off. The challenges are causing changes in the structure of the power system. Renewable energy sources, mainly wind and solar energy cannot provide stable inertia and frequency regulation capability. Ultimately, the power system's emergency response capability to face an N-1 is reduced, which leads to a reduction in system stability. Therefore, the application technology of the battery energy storage system is used to support the impact of changes in the new power system structure. This paper designed control technologies based on the WECC second-generation generic model, namely, dynamic regulation, steady regulation, and virtual inertia regulation. The models and control strategies are verified on Taiwan's 2025 power system target conditions, which consider the expected capacities for battery energy storage systems, and renewable energy sources with different load and N-1 fault levels. According to the simulation results, the capabilities of the RoCoF limitation, frequency nadir, frequency recovery, and system oscillation regulation are evaluated in the proposed strategies. Finally, the analysis results can help power operators make informed decisions when selecting and deploying battery energy storage systems. [ABSTRACT FROM AUTHOR]

Ancillary frequency control schemes (e.g., droop control) are used in wind farms to improve frequency regulation in grids with substantial renewable energy penetration; however, droop controllers can have negative impacts on the damping of wind turbine torsional mode, thereby reducing the lifespan of the turbine gearbox. This paper presents a battery energy storage system (BESS) damper to improve the damping of torsional vibrations when using doubly fed induction generators (DFIGs) for frequency regulation in a microgrid. We formulated a linearized model comprising diesel generators, a wind turbine with five-mass drivetrain, and BESS. We also designed a feedforward compensator to deal with phase lag between the BESS damper signal and DFIG torque. The proposed BESS damper was shown to improve the torsional mode damping by moving the eigenvalues for torsional mode leftward to desirable locations on the complex plane. Dynamic simulations performed using MATLAB/SIMULINK demonstrated the efficacy of the proposed BESS torsional mode damping scheme in terms of torsional mode 1 damping performance and frequency response. [ABSTRACT FROM AUTHOR]

The recent increase in the use of carbonless energy systems have resulted in the need for reliable energy storage due to the intermittent nature of renewables. Among the existing energy storage technologies, compressed-air energy storage (CAES) has significant potential to meet techno-economic requirements in different storage domains due to its long lifespan, reasonable cost, and near-zero self-decay. When viewed as a battery system, the key performance metrics of CAES, like energy density (ED), round trip efficiency (RTE), and the depth of discharge (DoD), have poor values when compared with other battery technologies in similar domains. This prevents CAES from transitioning to a state-of-the-art form of energy storage. This paper reviews the transition of CAES concepts from carbonized to carbonless types of CAES, along with different single-objective optimization strategies and their effects on the overall system's performance. It was discovered that competing performance metrics attributes cause single-objective optimization to have trade-offs that worsen at least one other preferred metric. The topology limitations of the generic CAES design were noted to prevent its use in different domains. To ensure that the optimal convergence of subsystem parameters is retained during charging and discharging periods, a suitable topology and subunit combinations for different domains are necessary. Possible options for solving these problems are identified so that the effects of the trade-offs imposed by optimization are either suppressed or eliminated. [ABSTRACT FROM AUTHOR]

This paper presents a parametric procedure to size a hybrid system consisting of renewable generation (wind turbines and photovoltaic panels) and Battery Energy Storage Systems (BESS). To cope with the increasing installation of grid-scale BESS, an innovative, fast and flexible procedure for evaluating an efficient size for this asset has been developed. The tool exploits a high-fidelity empirical model to assess stand-alone BESS or hybrid power plants under different service stacking configurations. The economic performance has been evaluated considering the revenue stacking that occurs when participating in up to four distinct energy markets and the degradation of the BESS performances due to both cycle- and calendar-aging. The parametric nature of the tool enables the investigation of a wide range of system parameters, including novel BESS control logic, market prices, and energy production. The presented outcomes detail the techno-economic performances of a hybrid system over a 20-year scenario, proposing a sensitivity analysis of both technical and economic parameters. The case study results highlight the necessity of steering BESS investment towards the coupling of RES and accurate planning of the service stacking. Indeed, the implementation of a storage system in an energy district improves the internal rate of return of the project by up to 10% in the best-case scenario. Moreover, accurate service stacking has shown a boost in revenues by up to 44% with the same degradation. [ABSTRACT FROM AUTHOR]

In this paper, a bidirectional converter with multi-mode control strategies is proposed for a battery energy storage system (BESS). This proposed converter, which is composed of a half-bridge-type dual-active-bridge (HBDAB) converter and an H-bridge inverter, is able to operate the BESS with different power conditions and achieve the DC–AC function for lower input DC voltage applications. For the HBDAB converter, the variable-frequency control (VFC) and phase-shift control (PSC) are both adopted to achieve zero-voltage switching over a wider power range and the battery module balance control capability for BESS, respectively. In addition, the interleaved configuration is used to reduce the current ripple and increase the overall current rating. For the H-bridge inverter, the unipolar control mode (UCM) and totem-pole control mode (TPCM) are adopted to manage the real and reactive current control under different AC grid conditions. The UCM offers a reduction in current ripple for real and reactive power control. The TPCM is able to eliminate switching losses and achieve higher conversion efficiency for pure real power control. Considering applications for battery energy storage systems, the principle of operation and voltage gain analysis are described. Finally, computer simulations and hardware experimental results from a prototype system are presented to verify the performance of the proposed converter with the different control strategies. [ABSTRACT FROM AUTHOR]

Summary: This paper aims at specifying the optimal allocation of vanadium redox flow battery (VRB) energy storage systems (ESS) for active distribution networks (ADNs). Correspondingly, the appropriate operation strategy and the rated capacity and rated power of VRB ESS allocation are obtained. The dynamic characteristics of the external working performance of VRB are analyzed in this paper to accurately describe the operation condition of VRB. Then, A variety of operation strategies considering multiple scenarios are proposed to constrain the working state of VRB ESS. In addition, the comprehensive operation benefits are described quantitatively, to determine the optimal allocation of ESS by comparing the operation benefits. Finally, the proposed optimal allocation method was validated and tested by experimental field data of ADNs, and the optimal allocations under different operation strategies are compared, selecting the appropriate operation strategy can improve the benefits by 95%, also the consumption and the operation benefits will increase by 230% and 190% when double the capacity that can be learned from influence analysis. [ABSTRACT FROM AUTHOR]

With DC microgrids becoming popular, the concept of virtual inertia control (VIC) is gaining attention to minimize the DC bus voltage fluctuations caused by intermittent sources and loads. VIC scheme controls the power electronic converter (PEC) to achieve a smooth voltage profile. However, the intrinsic output impedance of the converter acts as an output disturbance and distorts the inertial response. An additional feed-forward controller has been used in previous works to mitigate this. However, the design proposed in this paper eliminates the additional controller while achieving a smooth voltage profile. This paper proposes a zero placement technique-based design of virtual inertia controller for a DC microgrid. Small-signal models for the system with the proposed controller designed using zero placement were developed to analyze the stability and the effect of parameter variation for optimal performance. Next, the application of the proposed design methodology is extended to a system employing two battery energy storage system units for analyzing parallel operation. Finally, the effectiveness of the controller designed using the zero placement technique was verified under different loading conditions through MATLAB/Simulink simulations and experiments carried out on the developed laboratory prototype. [ABSTRACT FROM AUTHOR]

A wind turbine, battery energy storage system, and photo-voltaic system are the three main sources included in this work as hybrid renewable energy systems (HRESs). To alleviate power quality (PQ) issues, i.e., swell, sag, harmonics, interruption, and fluctuation, a unified power flow controller (UPFC) is utilized in this paper. The major goal of this research is to compensate the load demand, mitigate the PQ issues, and reduction of cost as well as power flow along with IEEE-14 bus system. In HRESs, the PQ issues are mitigated by utilizing a novel hybrid optimized technique called GWO-CS approach-based UPFC. By utilizing the hybrid optimized technique, the PID controller is employed to drive the pulse width modulation of this UPFC. Numerous PQ issues like fluctuations, interruptions, estimation of harmonics, sags, and swells are evaluated in this paper by validating the enactment of the presented methodology, and also this technique is simulated in MATLAB/Simulink to analyze the outcomes. Moreover, to validate the efficacy of the proposed method, the presented technique is compared with the conventional approaches, i.e., GWO, PSO, and hybrid PSO-based GWO. [ABSTRACT FROM AUTHOR]

The rapid growth in renewable energy-based distributed generation has raised serious concerns about the grid's stability. Due to the intrinsic rotor inertia and damping feature and the voltage (reactive power) control ability, traditional bulk power plants, which are dominated by synchronous generators (SG), can readily sustain system instability. However, converter-based renewable energy sources possess unique properties, such as stochastic real and reactive power output response, low output impedance, and little or no inertia and damping properties, leading to frequency and voltage disturbance in the grid. To overcome these issues, the concept of virtual synchronous generators (VSG) is introduced, which aims to replicate some of the characteristics of the traditional synchronous generators using a converter control technique to supply more inertia virtually. This paper reviews the fundamentals, different topologies, and a detailed VSG structure. Moreover, a VSG-based frequency control scheme is emphasized, and the paper focuses on the different topologies of VSGs in the microgrid frequency regulation task. Then, the characteristics of the control systems and applications of the virtual synchronous generators are described. Finally, the relevant critical issues and technical research challenges are presented, and future trends related to this subject are highlighted. [ABSTRACT FROM AUTHOR]

Battery participation in the service of power system frequency regulation is universally recognized as a viable means for counteracting the dramatic impact of the increasing utilization of renewable energy sources. One of the most complex aspects, in both the planning and operation stage, is the adequate characterization of the dynamic variation of the state of charge of the battery in view of lifetime preservation as well as the adequate participation in the regulation task. Since the power system frequency, which is the input of the battery regulation service, is inherently of a stochastic nature, it is easy to argue that the most proper methodology for addressing this complex issue is that of the theory of stochastic processes. In the first part of the paper, a preliminary characterization of the power system frequency is presented by showing that with an optimal degree of approximation it can be regarded as an Ornstein–Uhlenbeck process. Some considerations for guaranteeing desirable performances of the control strategy are performed by assuming that the battery-regulating power depending on the frequency can be described by means of a Wiener process. In the second part of the paper, more realistically, the regulating power due to power system changes is described as an Ornstein–Uhlenbeck or an exponential shot noise process driven by a homogeneous Poisson process depending on the frequency response features requested of the battery. Because of that, the battery state of charge is modeled as the output of a dynamic filter having this exponential shot noise process as input and its characterization constitutes the central role for the correct characterization of the battery life. Numerical simulations are carried out for demonstrating the goodness and the applicability of the proposed probabilistic approach. [ABSTRACT FROM AUTHOR]

Summary Mitigating inter‐area low‐frequency oscillations is a significant concern in multi‐machine power systems due to their adverse effects on system stability. These oscillations are intricately linked with power oscillations. So appropriate power modulation through the Battery Energy Storage System (BESS) can be an effective strategy for preserving system stability. In this paper, the maximal of all minimal residue indices under variations in power system operating conditions is utilized as the index for identifying the location for installation of the BESS, the damping control loop, and the feedback signal. A fixed‐structure H∞$$ {H}_{\infty } $$ scheme‐based wide‐area damping controller (WADC) is proposed for the BESS, providing sufficient damping of inter‐area oscillation modes. A modified IEEE 39‐bus system is simulated using a real‐time digital simulator as a test system in this work. The simulation results confirmed that the proposed WADC could effectively damp various inter‐area oscillation modes. Furthermore, it offers robust damping performance over contingencies associated with the system's various operating scenarios as well as the uncertainty associated with fixed and variable communication delays in the feedback signal of WADC and the integration of solar photovoltaic systems. Moreover, a comparative analysis of the proposed WADC is carried out with a BESS‐based wide‐area power system stabilizer, which is found to be more effective in mitigating system inter‐area oscillations. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization methodology for sizing and operating battery energy storage systems (BESS) in distribution networks. A BESS optimal operation for both frequency regulation and energy arbitrage, constrained by battery state-of-charge (SoC) requirements, is considered in the proposed optimization algorithm. We use utility historical data as input in a case study on a real-world distribution circuit. The results indicate that a BESS generally brings high profits by participating in the frequency regulation market and should be sized to its highest allowable power capacity rather than its energy capacity. [ABSTRACT FROM AUTHOR]

The occurrence of unforeseen incidents is one of the major causes of extensive blackouts and financial damages to electrical networks. The utilization of distributed energy resources (DERs) as a local energy supplier can potentially reduce the destructive effects of unforeseen events along with their benefits for the normal operation of the network. In this paper, a new framework is proposed for the optimal siting and sizing of solar photovoltaic distributed generations (PVDGs) and battery energy storage systems (BESSs) in the distribution network to increase resiliency against the earthquake event considering the advantages of these resources in both normal and event conditions. Furthermore, an optimal energy pattern for BESSs has been provided that, in addition to being used for the normal condition, also prepares them for transferring to the emergency state. The objective function is formulated as a mixed‐integer linear programming (MILP) problem aiming at minimizing both the normal and emergency costs. Eventually, the numerical results of implementing the proposed model on the IEEE‐33 bus system are presented to illustrate its effectiveness and accuracy. These results show by using PVDG installation, the resilience can be improved by 14% while total installation and operating costs are reduced by 1.7%. It is possible, however, to decrease the load curtailment up to 55.46% by involving BESSs along with PVDGs, despite an 8.57% increase in costs. [ABSTRACT FROM AUTHOR]

This paper presents the issue of the Sub-synchronous resonance (SSR) phenomenon in a series compensated DFIG-based wind power plant and its alleviation using a Battery Energy Storage-based Damping Controller (BESSDCL). A supplementary damping signal is developed considering the angular speed deviation and is incorporated into the BESS control system. Wide-area Measurement System data is used to determine the angular speed deviation. A linearized system model is developed to perform eigenvalue analysis, and to detect and examine unstable SSR modes. The variation of wind speed and three-phase fault are also taken into consideration to validate the robustness of the controller. To further verify the efficacy of the proposed damping controller, time-domain simulations are performed using MATLAB/Simulink. The application of the proposed BESSDCL stabilizes all the unstable system modes effectively at wind speeds of 7 m/s, 9 m/s, and 11 m/s, and at 40%, 50%, and 60% series compensation levels, as well three-phase fault conditions. [ABSTRACT FROM AUTHOR]

In the XFLEX HYDRO Vogelgrun demonstrator, a run-of-river hydropower plant, the hybridization of one turbine-generator unit with a battery energy storage system is being investigated. This paper describes the integration methodology of the hybrid control algorithm without replacing the existing speed governor of the unit. Furthermore, the comparison of the performances of a non-hybrid and hybrid unit is discussed, and first experiences gained during the operation and monitoring of the hybrid operating mode are presented. [ABSTRACT FROM AUTHOR]

This paper aims to perform a literature review and statistical analysis based on data extracted from 38 articles published between 2018 and 2023 that address hybrid renewable energy systems. The main objective of this review has been to create a bibliographic database that organizes the content of the articles in different categories, such as system architecture, energy storage systems, auxiliary generation components used, and software employed, in addition to showing the algorithms and economic and reliability criteria for the optimization of these systems. In total, 38 articles have been analyzed, compared, and classified to provide an overview of the current status of simulation and optimization projects for hybrid renewable energy systems, highlighting clearly and appropriately the relevant trends and conclusions. A list of review articles has also been provided, which cover the aspects required for understanding HRESs. [ABSTRACT FROM AUTHOR]

Energy distribution technique is an essential obligation of an intelligent household system to assure optimal and economical operation. This paper considers a small-scale household system detached from the power grids consisting of some electrical components in day-to-day life. Optimal power distribution generated from a photovoltaic system is vital for ensuring economic and uninterrupted power flow. This paper presents an optimal energy distribution technique for a small-scale smart household system to ensure uninterrupted and economical operation. A photovoltaic (PV) system is considered as the primary generation system, and a battery energy storage system (BESS) is viewed as a backup power supply source. The actual load and PV generation data are used to validate the proposed technique collected from the test household system. Two different load profiles and photovoltaic power generation scenarios, namely summer and winter scenarios, are considered for case studies in this research. An actual test household system is designed in MATLAB/Simulink software for analyzing the proposed technique. The result reveals the effectiveness of the proposed technique, which can distribute the generated power and utilize the BESS unit to ensure the optimal operation. An economic analysis is conducted for the household system to determine the economic feasibility. The capital investment of the system can be returned within around 5.67 years, and the net profit of the system is 2.53 times more than the total capital investment of the system. The proposed technique can ensure economical operation, reducing the overall operating cost and ensuring an environment-friendly power system. The developed strategy can be implemented in a small-scale detached interconnected smart household system for practical operation to distribute the generated energy optimally and economically. [ABSTRACT FROM AUTHOR]