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This project explores the integration of battery energy storage systems (BESS) in residential settings to optimize energy management with a novel focus on standalone BESS configurations independent of solar photovoltaic (PV) systems. The objective is to analyze electricity price patterns, evaluate different BESS configurations, and develop strategies for efficient charging and discharging. This enables homeowners to store excess electricity during low-demand periods and discharge it during peak demand, reducing grid reliance and saving on electricity costs. The analysis reveals that a standalone BESS can reduce electricity costs by leveraging low-demand periods with electricity prices averaging 0.4 DKK/kWh, compared to peak prices exceeding 0.8 DKK/kWh. For June 2023, optimal charging (6:00–14:00, 18:00–22:00) and discharging (14:00–18:00, 23:00–6:00) periods were identified, leading to potential cost savings of up to 30 %. The findings highlight the potential of BESS to transform energy management and suggestions such as a net metering program are proposed to mitigate these challenges. [ABSTRACT FROM AUTHOR]

Microgrid-equipped electric vehicle charging stations offer economical and sustainable power sources. In addition to supporting eco-friendly mobility, the technology lowers grid dependency and improves energy reliability. The manuscript introduces a hybrid technique for efficient electric vehicle (EV) charging integrating the Dollmaker Optimization algorithm (DOA) and spatial Bayesian neural network (SBNN). This method optimizes the joint operation of photovoltaic (PV), wind turbines (WTs), supercapacitors (SCs), and battery energy storage systems (BESSs) in microgrids to enhance EV charging station efficiency, reliability, and power quality while reducing grid outages. The SBNN predicts EV load demand for improved efficiency and reliability, while DOA manages microgrid (MG) fluctuations to ensure seamless EV charging. The MG system features a four-phase inductor coupled interleaved boost converter (FP-ICIBC) and a fractional-order proportional-integral-derivative (FOPID) controller for optimal power management. An evaluation in MATLAB compares DOA–SBNN with existing approaches, demonstrating its effectiveness in enhancing EV charging performance. The proposed method outperforms all current techniques, including the Multi swarm Optimization (MSO), the Multi-Objective Gray Wolf Optimizer (MOGWO), and the Modified Multi-objective Salp Swarm Optimization algorithm (MMOSSA). The results show that the energy efficiency of the recommended approach is 19.19%, 26.15%, and 32.57% higher than the three current techniques, respectively, and that of total harmonic distortion (THD) is 19.09%, 25.85%, and 31.17% lower than those three techniques, respectively. [ABSTRACT FROM AUTHOR]

The sustainable integration of distributed energy resources (DER) into distribution networks requires accurate forecasting of hosting capacity. The network and DER variables alone do not capture the full range of external influences on DER integration. Traditional models often overlook the dynamic impacts of these exogenous factors, leading to suboptimal predictions. This study introduces a Sensitivity-Enhanced Recurrent Neural Network (SERNN) model, featuring a sensitivity gate within the neural network's memory cell architecture to enhance responsiveness to time-varying variables. The sensitivity gate dynamically adjusts the model's response based on external conditions, allowing for improved capture of input variability and temporal characteristics of the distribution network and DER. Additionally, a feedback mechanism within the model provides inputs from previous cell states into the forget gate, allowing for refined control over input selection and enhancing forecasting precision. Through case studies, the model demonstrates superior accuracy in hosting capacity predictions compared to baseline models like LSTM, ConvLSTM, Bidirectional LSTM, Stacked LSTM, and GRU. Study shows that the SERNN achieves a mean absolute error (MAE) of 0.2030, a root mean square error (RMSE) of 0.3884 and an R-squared value of 0.9854, outperforming the best baseline model by 48 per cent in MAE and 71 per cent in RMSE. Additionally, Feature engineering enhances the model's performance, improving the R-squared value from 0.9145 to 0.9854. The sensitivity gate also impacts the model's performance, lowering MAE to 0.2030 compared to 0.2283 without the sensitivity gate, and increasing the R-squared value from 0.9152 to 0.9854. Incorporating exogenous factors such as the time of day as a sensitivity gate input, further improves responsiveness, making the model more adaptable to real-world conditions. This advanced SERNN model offers a reliable framework for distribution network operators, supporting intelligent planning and proactive DER management. Ultimately, it provides a significant step forward in hosting capacity analysis, enabling more efficient and sustainable DER integration within next-generation distribution networks. [ABSTRACT FROM AUTHOR]

This paper presents a novel power flow problem formulation for hierarchically controlled battery energy storage systems in islanded microgrids. The formulation considers droop-based primary control, and proportional–integral secondary control for frequency and voltage restoration. Several case studies are presented where different operation conditions are selected to highlight the battery formulation reliability and its necessity for detailed power flow analysis. Furthermore, the case studies consider wind-based generation units with power curves, probability-based wind distributions, and load profiles. The results obtained show the proposed formulation features for emulating a hierarchically controlled battery in an islanded system with practical operating conditions; moreover, it is shown that including controllers into the power flow formulation is necessary for obtaining more reliable steady-state solutions for islanded microgrid systems. [ABSTRACT FROM AUTHOR]

In light of recent advancements in energy storage technology, this paper introduces a sophisticated approach to planning the locations and sizes of HV/MV substations, utilizing battery energy storage systems (BESS) to optimize peak load management. Traditional substation planning, reliant on peak load forecasts, often results in substantial investment requirements, elevated operational risks, and diminished efficiency in asset utilization. To counter these issues, the proposed method integrates BESS, aiming to reduce not only the capital and operational expenditures associated with new substations and transmission lines but also the costs associated with implementing BESS for effective peak shaving. The methodology employs a combination of heuristic techniques and an advanced weighted Voronoi diagram to establish the most effective locations and capacities for substations, alongside optimal sites and sizing for BESS installations. This innovative planning tool is designed to enhance grid reliability and cost-efficiency by balancing energy supply with demand fluctuations more effectively. Furthermore, it provides a detailed operational schedule for the BESS, optimizing the charge and discharge cycles according to varying load levels, thereby maximizing the system's operational efficiency. A comprehensive case study illustrates the practical benefits of this approach. When compared to conventional methods, the proposed strategy significantly reduces both the capital investment and operational risks, while simultaneously improving utilization efficiency. The findings demonstrate the potential of integrating BESS in substation planning to facilitate a more resilient and efficient power grid, positioning this method as a forward-thinking solution in modern energy management. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

This research aims to devise a methodology for optimizing the size of a Battery Energy Storage System (BESS) supporting Wind Energy Systems (WES) to enhance power commitment flexibility in the energy market. The methodology involves three essential steps: (i) estimating rated kW, (ii) initializing rated kWh of the BESSs, and (iii) iteratively adjusting the BESS size based on heuristic rules to prevent State of Charge (SoC) limit violations following the load cycle. Three realistic load cycles for the BESSs out of which one load cycle is generated based on maximum error values and the other load cycles are generated based on the mean and $1\sigma $ of the Normal Distribution Curve (NDC) of forecast errors of WES located at Agasthianpalli, Tamil Nadu, India. Two simple yet effective heuristic rules have been proposed to optimize the BESS size, ensuring maximum SoC at the start of each day and maintaining SoC within limits throughout the day. This leads to two scenarios considering a single set of the BESS to serve the load cycle and two sets of the BESS operating alternatively, reaching maximum SoC on the subsequent day by charging from the grid. Cost analysis indicates that scenario 1 is more favorable in terms of both cost and BESS size, surpassing scenario 2 by 8.89% and 9.95%, respectively. This analysis results in shorter payback period for scenario 1. Validation using Genetic Algorithm (GA) is done by comparing the costs of BESSs, emphasizing the suitability of the proposed technique.

This paper presents the preliminary results of studies aiming to use a battery energy storage system (BESS) in the Brazilian transmission system. The main objective of the BESS is to solve congestion problems caused mainly by the large increase in variable renewable generation in certain system areas. The studies were conducted based on actual forecasted system scenarios using a full representation of the electric grid available from the Brazilian system operator data base. In this work, only the steady-state modeling was considered as this may be the first stage in the assessment of a new technology. A qualitative economic comparison of the BESS application with other possible solutions to the congestion problems is also included. [ABSTRACT FROM AUTHOR]

This paper aims to explore the feasibility of establishing self-sufficient electricity generation systems in off-grid remote communities using renewable energy sources. It provides an overview of current trends and developments in Renewable Energy Communities worldwide, with a focus on remote locations. To assess the technical feasibility, simulations were conducted using PVGIS for irradiation data and a load generator for energy consumption data. Different scenarios combining photovoltaic modules with lithium-ion battery systems were simulated using a dedicated optimization model developed in the PYTHON environment. The simulations aimed to size the entire system for three distinct locations: Congo, Australia, and Canada. The optimal number of PV modules determined for each location was 50 for Congo and 55 for Australia, and the battery system sizes were found to be 225 kWh and 150 kWh, respectively, admitting usual practices for the energy not supplied. The results obtained regarding Canada pointed to a system difficult to justify from an economic standpoint due to challenging weather conditions, namely, the existence of several consecutive days without irradiation. [ABSTRACT FROM AUTHOR]

This article proposes a voltage compensation control strategy for the distribution network based on the unified power quality conditioner (UPQC) with battery energy storage systems (BESS) to achieve comprehensive management of the power quality and improve the system dynamic responses. Specifically, aiming at that amplitude and phase jump of the terminal voltage, a capacity configuration compensation scheme is proposed to achieve voltage vector compensation. The series converter is designed to compensate for the amplitude and phase of the load voltage. The parallel converter is designed to realize power compensation and ensure that the grid voltage and current are sinusoidal and in phase. A voltage control approach based on the nonlinear disturbance observer (NDO) is developed for BESS to stabilize the DC bus voltage and improve the anti-interference performance of the current. An NDO is designed to accomplish current disturbance tracking and provide disturbance feedforward compensation. Simulations validate that the proposed approach is effective and feasible. [ABSTRACT FROM AUTHOR]

This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF). The low-magnitude noise signals are specifically crafted to be stealthy, evading easy detection while still effectively causing malfunctions in the estimation processes. Additionally, we introduce a verification case using a different battery model and estimation algorithm to enhance generalization. This case involves high-noise signals with defined thresholds for current and voltage noise levels, which cause significant disruptions to Kalman Filters. These signals serve as a complementary example of adversarial attacks, demonstrating how such noise can destabilize estimation algorithms and lead to critical control errors. This dataset is valuable for researchers and engineers aiming to understand and mitigate the effects of smart cyber-physical attacks on BESS. These attacks can disrupt real-time BESS controllers by injecting false input data related to SoC and SoH, leading to physical control manipulations, increased energy costs, inefficiencies in demand-side management, and incorrect day-ahead scheduling, thereby destabilizing grid operations. The data is reusable in studies focused on enhancing the resilience of SoC and SoH estimation methods, as well as in developing robust defensive strategies against smart DRL-based adversarial attacks.

Microgrid-equipped electric vehicle charging stations offer economical and sustainable power sources. In addition to supporting eco-friendly mobility, the technology lowers grid dependency and improves energy reliability. The manuscript introduces a hybrid technique for efficient electric vehicle (EV) charging integrating the Dollmaker Optimization algorithm (DOA) and spatial Bayesian neural network (SBNN). This method optimizes the joint operation of photovoltaic (PV), wind turbines (WTs), supercapacitors (SCs), and battery energy storage systems (BESSs) in microgrids to enhance EV charging station efficiency, reliability, and power quality while reducing grid outages. The SBNN predicts EV load demand for improved efficiency and reliability, while DOA manages microgrid (MG) fluctuations to ensure seamless EV charging. The MG system features a four-phase inductor coupled interleaved boost converter (FP-ICIBC) and a fractional-order proportional-integral-derivative (FOPID) controller for optimal power management. An evaluation in MATLAB compares DOA–SBNN with existing approaches, demonstrating its effectiveness in enhancing EV charging performance. The proposed method outperforms all current techniques, including the Multi swarm Optimization (MSO), the Multi-Objective Gray Wolf Optimizer (MOGWO), and the Modified Multi-objective Salp Swarm Optimization algorithm (MMOSSA). The results show that the energy efficiency of the recommended approach is 19.19%, 26.15%, and 32.57% higher than the three current techniques, respectively, and that of total harmonic distortion (THD) is 19.09%, 25.85%, and 31.17% lower than those three techniques, respectively.

The current study proposes a strategy for sensing fault detection in the secondary control of an isolated Microgrid based on a high-order Sliding Mode Robust Observers design. The proposed strategy's main objective is to support future diagnostic and fault tolerance systems in handling these extreme situations. The proposal is based on a generation system and a waste management system. Four test scenarios were generated in a typical Microgrid to validate the designed strategy, including two Battery Energy Storage Systems in parallel, linear, and non-linear loads. The scenarios included normal grid operation and three types of sensing faults (abrupt, incipient, and random) directly affecting the secondary control of a hierarchical control strategy. The results showed that the proposed strategy could provide a real-time decision for detection and reduce the occurrence of false alarms in this process. The effectiveness of the fault detection strategy was verified and tested by digital simulation in Matlab/Simulink R2023b. [ABSTRACT FROM AUTHOR]

Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature 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.)

State-of-charge (SoC) consistency and bus voltage regulation are two major control objectives of shipboard DC microgrids. To achieve these objectives, this paper presents a novel distributed model predictive control (DMPC) strategy with multiple cost functions. Firstly, based on the bus voltage derivative and SoC dynamic model, the voltage and SoC control equations in discrete form are established. Subsequently, considering the safe operation of battery energy storage systems (BESSs), a DMPC taking the energy storage current as the control set is designed. Finally, the average voltage compensation is taken to achieve precise control of the average voltage. The proposed method can avoid the complex process of adjusting weight coefficients, thereby simplifying controller design. Furthermore, the robustness and practicality of the proposed DMPC method are verified through MATLAB/Simulink 2021a simulations and hardware-in-the-loop (HiL) experiments. [ABSTRACT FROM AUTHOR]

As the utilization of wind power systems continues to increase, reducing overall system inertia, there is a consequential negative impact on the power system's ability to regulate frequency. Consequently, this study focuses on examining the fast-frequency regulation in high penetration of wind power, especially doubly fed induction generators—the most commonly installed wind turbine type, and an energy storage system installed in the wind farm. This study proposes a coordinated control of wind turbine generators and battery energy storage systems that provides fast-frequency regulation to the system while simultaneously ensuring the safety of the battery. Firstly, the fast-frequency regulation capability of the wind turbine will be studied. Secondly, primary frequency control of the battery energy storage system considering adaptive droop control based on state of charge is proposed to prevent both over-charging and over-discharging of the battery. Finally, this study proposed a coordinated fast-frequency regulation approach for the hybrid wind-storage system, which is evaluated under various wind speed scenarios. This approach involves a detailed analysis of the operational characteristics of the wind turbine generators to ensure optimal performance. The proposed method is validated through simulation using a MATLAB model of the wind-storage system, and comparative results with alternative control methods confirm the effectiveness of the proposed approach in raising the frequency nadir and avoiding the secondary frequency dip. [ABSTRACT FROM AUTHOR]

In this article, an optimal photovoltaic (PV) and battery energy storage system with hybrid approach design for electric vehicle charging stations (EVCS) is proposed. The hybrid approach combines the use of polar transformer networks (PTNs) and the puzzle optimization algorithm (POA); hence it is called as POA–PTN approach. The main objective is to lessen the charging station cost and pollutant emissions. The proposed method is minimizing the pollutant emissions and the annual cost of PV with EVCS, which is done by POA method and predicts the optimal solution is done by PTN approach. The proposed method is executed in the MATLAB platform, and compared with existing methods, including heap‐based optimization, particle swarm optimization, and wild horse optimizer approaches. The proposed technique demonstrates superior performance, achieving the lowest net present cost of €1.45 × 106 and emissions of 2.25 × 108 g year−1 with a shorter computing time of 13.04 s compared to the other methods. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources necessitates effective thermal management of Battery Energy Storage Systems (BESS) to maintain grid stability. This study aims to address this need by examining various thermal management approaches for BESS, specifically within the context of Virtual Power Plants (VPP). It evaluates the effectiveness, safety features, reliability, cost-efficiency, and appropriateness of these systems for VPP applications. Among the various hybrid cooling options, two notably promising combinations are highlighted. First, the integration of heat pipes with phase change materials, which effectively conduct heat away from sources with minimal temperature differences, enabling swift heat transfer. Second, the combination of heat pipes with liquid passive cooling, which utilizes the efficient heat transfer properties of heat pipes and the steady cooling offered by liquid systems. This study offers recommendations for choosing the best thermal management system based on climate conditions and geographic location, thereby enhancing BESS performance and sustainability within VPPs. [ABSTRACT FROM AUTHOR]

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations. [ABSTRACT FROM AUTHOR]

The extensive integration of distributed renewable energy resources (DRES) can lead to several issues in power grids, particularly in distribution grids, due to their inherent intermittency. This paper presents a stochastic simulation-based approach to estimate the maximum permissible penetration level of DRES and to determine the optimal capacity of centralized battery energy storage systems (BESS) in distribution networks while adhering to technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, our proposed approach calculates the maximum allowable penetration level of DRES and the required BESS capacity with different DRES control logics. The maximum allowable penetration level of DRES and the requirements of the BESS capacity are determined by an analysis of various simulation results. This paper’s unique contribution lies in equipping distribution system operators (DSOs) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This aids in mitigating challenges associated with overvoltage and intermittency issues arising from DRES-generated power, thereby enhancing the overall resilience and reliability of the power grid. Case studies that include four voltage control algorithms and three power smoothing methods demonstrate the universality and effectiveness of the proposed approach.

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

This article proposes a voltage compensation control strategy for the distribution network based on the unified power quality conditioner (UPQC) with battery energy storage systems (BESS) to achieve comprehensive management of the power quality and improve the system dynamic responses. Specifically, aiming at that amplitude and phase jump of the terminal voltage, a capacity configuration compensation scheme is proposed to achieve voltage vector compensation. The series converter is designed to compensate for the amplitude and phase of the load voltage. The parallel converter is designed to realize power compensation and ensure that the grid voltage and current are sinusoidal and in phase. A voltage control approach based on the nonlinear disturbance observer (NDO) is developed for BESS to stabilize the DC bus voltage and improve the anti-interference performance of the current. An NDO is designed to accomplish current disturbance tracking and provide disturbance feedforward compensation. Simulations validate that the proposed approach is effective and feasible.

The integration of distributed generation (DG) units into distribution networks (DNs) has brought about several operational challenges, including voltage issues and increased power loss. Energy storage equipped soft open points (E-SOPs) can accurately and flexibly control active and reactive power flows to address these problems. Additionally, the photovoltaic (PV) inverter and the network reconfiguration (NR) play a significant role in voltage control by adjusting the reactive power and the topology of the DN, respectively. However, due to differences in response times, there is a lack of systematic coordination between NR and the inverters of the E-SOP and PV. This paper proposes a multi-time-scale voltage control model that includes day-ahead NR scheduling, inter-day droop control optimization of the PV and E-SOP, and real-time local droop control. Considering the uncertainties of renewable DG outputs and loads, a robust optimization method is used in the day-ahead stage to obtain a reliable network structure. Then, with more accurate intra-day predictions, a stochastic optimization method is used to obtain the optimal state-of-charge interval, aiming to provide a flexible regulation range for battery energy storage to cope with the power fluctuations during the real-time stage. In addition, to address the intra-day voltage control model with bilinear constraints of the droop control function, a particle swarm optimization method is used. The results are verified on a 33-bus DN system through comparative analyses, showing effective performance. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated method to simultaneously determine the optimal placement and sizing of battery energy storage systems (BESSs) in power distribution networks using the hunger games search algorithm (HGSA). The objective of the proposed method focuses on concurrently reducing power losses and improving the voltage deviation index to improve the distribution system performance while keeping all constraints within permissible limits. The HGSA is a nature-inspired algorithm that simulates the behavior of prey and predators in finding food. In this paper, the HGSA is used to search for the optimal solution in a multi-dimensional search space consisting of the candidate BESS locations and sizes. The proposed method is applied to modified IEEE 69-bus and IEEE 85-bus test distribution systems with five different scenarios, and the results indicate that the HGSA can efficiently determine the optimal placement and sizing of BESSs, resulting in significant power loss reduction (i.e., 69.13-98.08% for the first test system and 52.97-95.03% for the second test system) and voltage deviation improvement (i.e., 94.75-99.89% for the first test system and 12.12-93.37% for the second test system) compared to the base case. Furthermore, the performance of the HGSA is compared with other well-known metaheuristic optimization algorithms (i.e., whale optimization algorithm, chaotic neural network algorithm, genetic algorithm, grey wolf optimizer, and water cycle algorithm), and the results show that the HGSA outperforms these optimization algorithms in attaining the best optimal solution with faster convergence and less number of iterations. [ABSTRACT FROM AUTHOR]

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs' strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters. [ABSTRACT FROM AUTHOR]

In recent years, the escalating electricity demand in Taiwan has heightened the prominence and discourse surrounding the issue of power supply. With the enactment of the European climate law, global commitment to achieving net-zero emissions has gained momentum. Concurrently, the Taiwanese government has articulated the Taiwan 2050 net-zero emissions policy. To realize this objective, Taiwan has vigorously promoted renewable energy in recent years, increasing the proportion of renewable energy in its energy mix. However, confronted with the intermittent and unpredictable nature of renewable energy generation, challenges arise concerning the stability and quality of power supply. In response to the impact of integrating renewable energy into the grid, the Taiwan Power Company (Taipower) has introduced the day-ahead ancillary service market. Through this platform, power generation and battery energy storage systems (BESSs) engage in competitive bidding, fostering the integration of diverse distributed energy resources into the system. These resources serve as dispatchable assets for Taipower, contributing to grid stability by providing ancillary services. This study has developed an advanced dynamic regulation reserve controller by employing an industrial PC (IPC) in conjunction with a multifunctional power meter. Through real-time measurement of the power grid frequency by the multifunctional power meter, a sophisticated dynamic frequency reserve control strategy has been devised, taking into account the charging state of energy storage systems. This strategy exhibits high operational quality, effectively regulating the charging and discharging of energy storage systems. In addition to swiftly adjusting the current grid frequency, it encompasses the capability to facilitate the transfer of peak electrical energy. This effectively stabilizes the power grid, alleviating supply pressures during nighttime peaks and enhancing the overall supply stability of the power system. [ABSTRACT FROM AUTHOR]

The rise in the growth of renewables in the distribution network introduces several challenges, such as voltage & frequency fluctuation, while the digitization of the network introduces cyber threats and risks like false data injection (FDI), resulting in a cascading network failure. By determining the optimal sitting, size and cost of the Battery Energy Storage Systems (BESS), these power quality issues & threats can be overcome as BESS can rapidly inject or absorb power as needed. In case of any cyber threat, it can supply energy to critical areas, reducing the cascading effect and increasing the survivability of the distribution network. Therefore, this paper provides a review of the existing methodologies used for the optimal placement, sizing, and cost of BESS in distributed networks involving Distributed Energy Resources and Electric Vehicles. Additionally, it provides insights into cascading effects in power systems due to cyber-attack, especially FDI attacks or component failure. After a thorough literature survey of the existing methods utilized for placement, sitting, costing, and modelling techniques, the paper concludes by providing a framework to enhance the grid resiliency against such power quality issues by improving the quality and reducing the minimum disruption area in case of any FDI attack. The proposed framework differs from the existing methodologies as it provides adaptive ancillary services and dynamic BESS response to address the operational and security challenges, based on this framework EV load penetration is estimated. Further to this EV load will be integrated into the dynamic distribution grid to analyze grid parameters including frequency, voltage, and line losses considered to be the future research work of the authors. However, the proposed framework has some challenges, including data dependency for stochastic modelling, computational challenges, and high quality for reliable optimization results.