Reliable Operation Of Power System Research Articles

Offshore Electrical-Oil Production Coupling System Reliability Analysis

The role played by offshore oil resources in energy supply is becoming more and more important, and the normal operation of oil production system on offshore oil platforms requires the reliable operation of the electric power system as a prerequisite; the failure of the components in the oil production system may lead to a large-scale production shutdown, and cause the related electrical components to shut down, making the power system ineffective in a certain range. In this paper, a coupled system model of the electric power system and production system of an offshore oil platform based on massively parallel computing is proposed. By analysing the connection between the oil production system and the electric power system, the coupled system model is established and the load data are calculated. Meanwhile, the reliability model of the coupling components is established based on the fault data, and the electric-related reliability index and the production-related reliability index of the coupling system are proposed. The broad-first search (BFS) algorithm was used to determine whether the coupling nodes were powered, and the depth-first search (DFS) algorithm was used to determine the connectivity of the coupling system. In addition, a minimum load-shedding model based on the integrated fault degree is proposed to reduce the load shedding of the coupled system.

Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting

Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to effectively combine the clustering process and prediction process. More specifically, the clustering process adopts the maximum deviation similarity criterion clustering algorithm (MDSC) as the clustering framework. A bee-foraging learning particle swarm optimization is further applied to realize the adaptive optimization of its hyperparameters. The prediction process consists of three parts: (i) a 9-dimensional load feature vector is proposed as the classification feature of SVM to obtain the load similarity cluster of the predicted days; (ii) the same kind of data are used as the training data of long short-term memory network; (iii) the trained network is used to predict the power load curve of the predicted day. Finally, experimental results are presented to show that the proposed scheme achieves an advantage in the prediction accuracy, where the mean absolute percentage error between predicted value and real value is only 8.05% for the first day.

A fractional-order electrochemical lithium-ion batteries model considering electrolyte polarization and aging mechanism for state of health estimation

Accurate state of health (SOH) estimation of lithium-ion batteries is critical for the safe and reliable operation of battery power system. To achieve better SOH estimation with low computational complexity, a fractional-order model considering electrolyte polarization and aging mechanism (FOMeA) is proposed. The proposed model simplifies solid phase lithium-ion distribution with fractional-order Padé approximation and electrolyte phase lithium-ion distribution with a two-state system. The solid electrolyte interphase (SEI) layer formation and lithium plating side reactions, which lead to battery aging, are modeled to establish the relationship between the cycle number and the aging parameters in the proposed simplified electrochemical model. Finally, FOMeA is compared with the pseudo-2D (P2D) aging model. The result shows that FOMeA can achieve accurate voltage prediction and SOH estimation in whole battery cycle life, and greatly save computational time.

Steady state Contingency Analysis of IEEE-39 Bus system Using PSS/E

Nowadays the stable, secure and reliable power system operation is the main objective of anyelectric power utility. The Power system is vulnerable to any contingency i.e. loss of Transmission line,Transformer, Generating unit etc. The power system control engineer should have detail analysis of at leastthe most probable and sever contingencies of the system and accordingly devise the remedial action plan.Thus, Power system contingency analysis and evaluation play vital role in ensuring power system stabilityand reliability. Keeping in view the importance of contingency analysis in power system operation andcontrol, this research focuses on steady state Contingency analysis of IEEE-39 bus system using newtonRaphson method. PSS/E is used for the analysis, different contingencies are evaluated and ranked basedupon the Power flow (Overloading), Voltage and phase angle deviation. Once the ranking is performed, thePV and QV analysis is performed for most severe contingencies. Remedial Actions is proposed for themost severe contingencies and this research could be applied to any real time network.

Health assessment of converter transformer pressboard insulation based on FDS and digital image processing

The stable operation of the converter transformer is an essential task for power system operation reliability and security. Nowadays, Frequency domain spectroscopy (FDS) technology is prominently utilized for assessing oil-impregnated pressboard insulation. The present study examines the effect of the pressboard insulation material as a function of frequency and elevated temperature. The experimental analysis on oil-impregnated pressboard insulation is carried out at temperatures from 30 °C to 130 °C with an incremental rise of 20 °C intervals with frequency variation from 1 Hz to 10 MHz. The frequency-dependent permittivity, conductivity and loss tangent angle studies also confirm the deterioration of oil-impregnated pressboard insulation. The surface morphological changes inside the pressboard insulation are recorded with the help of scanning electron microscopy (SEM). The synergistic effect generated on pressboard insulation is examined by fiber width changeand image processing approach by randomly selecting the average of three local areas of SEM image. A canny operator is selected to extract the exact boundaries of images and more change is recorded in the edge detection count after 90 °C.The porosity and pore size distribution can be increased with elevated temperatures. A single-phase, 315 MVA valve side star winding with 60 discs of single-phase converter transformers model is developed in MATLAB Simulink. An impulse of 100 kV, 1.2/50μsec is applied across the star winding to identify the pressboard insulation degradation derived from FDS data with the help of mathematical morphology and wavelet transform technique. The energy of the wavelet coefficient on the neutral current capture during the impulse test adds a significant contribution to analyzing pressboard insulation degradation. The results presented are in good agreement with the published work.

LSTM based deep neural network model of power system stabilizer for power oscillation damping in multimachine system

AbstractPower system oscillation is an unavoidable threat to the stability of an interconnected modern power system. The reliable operation of a modern power system is widely related to the dampening of electromechanical low‐frequency oscillations (ELFOs). These ELFOs must be dampened appropriately to maintain the stability and reliability of the system. However, it is relatively difficult to resolve the problem of ELFOs completely with traditional power system stabilizers (PSSs). Consequently, research should be directed towards the development of efficient damping controllers, or PSSs, for power oscillation damping. Motivated from the aforementioned fact, this article presents the design of a proportional integral derivative power system stabilizer (PID‐PSS) via a long short‐term memory neural network (LSTM) based deep neural approach for damping power system oscillations in interconnected power systems. LSTM is used to train the parameters of PID‐PSS. To evaluate the performance of the proposed LSTM based PID‐PSS, diverse test cases under different operating conditions are examined. Further, the performance of the proposed LSTM based PID‐PSS is compared with traditional PSSs through time‐domain simulations. The test cases reveal the desired efficiency achieved by the proposed LSTM based PID‐PSS under diverse loading conditions.

Optimal frequency support method for urban microgrids of building prosumers

High penetration of renewable energy sources (RES) is raising a number of technical challenges for the sustainable and reliable operation of electric power systems. To this end, the need for ancillary services provided by the building sector to the electric power system is becoming particularly significant; with frequency support being one among them. Urban microgrids and especially those of them hosting building prosumers will have a key role in frequency support as they comprise flexible components that can be exploited to this end. In this paper, detailed models for the accurate simulation of frequency support mode of operation are developed. Frequency support in microgrids of buildings is enhanced by properly utilizing the flexibility of building prosumers. Frequency is also controlled by optimally regulating building local generation units and their adjustable electrical loads in a flexibility-based way and taking into consideration gas emission limitation. Emphasis is placed on the enhancement of the computational efficiency of the proposed support method by suitably exploiting smart power dispatch techniques. The developed method is validated through the simulation of a realistic complex urban microgrid.

Wind Power Forecasting Based on WaveNet and Multitask Learning

Accurately predicting the power output of wind turbines is crucial for ensuring the reliable and efficient operation of large-scale power systems. To address the inherent limitations of physical models, statistical models, and machine learning algorithms, we propose a novel framework for wind turbine power prediction. This framework combines a special type of convolutional neural network, WaveNet, with a multigate mixture-of-experts (MMoE) architecture. The integration aims to overcome the inherent limitations by effectively capturing and utilizing complex patterns and trends in the time series data. First, the maximum information coefficient (MIC) method is applied to handle data features, and the wavelet transform technique is employed to remove noise from the data. Subsequently, WaveNet utilizes its scalable convolutional network to extract representations of wind power data and effectively capture long-range temporal information. These representations are then fed into the MMoE architecture, which treats multistep time series prediction as a set of independent yet interrelated tasks, allowing for information sharing among different tasks to prevent error accumulation and improve prediction accuracy. We conducted predictions for various forecasting horizons and compared the performance of the proposed model against several benchmark models. The experimental results confirm the strong predictive capability of the WaveNet–MMoE framework.

Sensitivity analysis index to determine the optimal location of multi-objective UPFC for improvement of power quality parameters

Today, advanced technologies are used for the reliable operation of power systems. In order to access a reliable and practical function in the systems, it is necessary to use precise control systems with higher efficiency in the systems to minimize the problems of the distribution system. Devices that are made on the basis of power electronics have wide applications in power systems today. These devices, which are called FACTS devices, provide the possibility of improving energy transmission with the least investment cost, as well as quick control of power system problems. FACTS devices are placed in series, parallel or series-parallel transmission lines and control the operation of distribution systems in permanent states as well as the dynamic behavior of the system in transient states. FACTS devices are an effective method to solve the problems and limitations of lines. Transmission and replacement networks are used to create new lines in the network. From the point of view of power distribution control, FACTS controllers can be placed anywhere in the transmission line, but the elements are most effectively used when they are placed at the most critical point. Unified Power Flow Controller (UPFC) is one of the most important FACTS devices that can improve parameters. The quality of power used in the power system depends on the effect of this controller in improving the loss reduction, reducing the total harmonic distortion (THD), and removing the clogged lines to their proper location in the power system. This device is capable of all different effective parameters at a time. These parameters usually include voltage, impedance, and phase. This paper uses a new sensitivity analysis index to determine the optimal size and location of the UPFC to improve the power quality parameters of the distribution system. The proposed method is evaluated on the IEEE 14-bus network in the Simulink environment of MATLAB software. Finally, according to the simulation results and the results obtained from the base network, it is determined that using the proposed sensitivity analysis index in UPFC location, losses Network, total harmonic distortions (THD) and eclipses Network lines are much improved. The results indicate that the proposed method has provided a good answer to determine the optimal size and location of UPFC to improve power quality parameters. Using the results of the sensitivity analysis method, the best place to install UPFC in the power grid, mode number 6 between buses 2 and 11 is obtained. and The amount of network power losses after sensitivity analysis, compared to the base network Active losses of about 55% and reactive losses of about 11% deceased, Due to the circular structure of the network, the current passing through the lines is different depending on the amount of load in the bases near that line, and with the presence of UPFC, the network is optimized and the current passing through the lines is also reduced. 4) Percentage of total harmonic distortion (THD) mains voltage before the presence of UPFC is about 27.12%, which after installation in the 6th place with a maximum size has been reduced to about 11.11%.

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, optimal reactive power dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. Various control variables, such as adjusting generator bus voltages, transformer tap locations, and switchable shunt capacitor sizes, are utilized to achieve this objective. Additionally, the integration of wind power can greatly impact power quality and mitigate power loss. In this study, the Grey Wolf Optimization (GWO) approach was applied to the ORPD issue for the first time to discover the best placement of newly installed wind power in the power system while taking into account tap changer settings, shunt capacitor sizes, and generated power levels. The main objective was to determine optimal wind placement to minimize power loss and voltage deviation, while maintaining control variables within specified limits. On the basis of IEEE 30-bus and IEEE 118-bus systems, the performance of the proposed method was investigated. The results demonstrated the superiority of GWO in multiple scenarios. In IEEE-30, GWO outperformed the PSO, GA, ABC, OGSA, HBMO, and HFA methods, reducing total loss by 10.36%, 18.03%, 9.19%, 7.13%, 5.23%, and 7.73%, respectively, and voltage deviation by 68.00%, 1.59%, 36.34%, 41.97%, 46.29%, and 71.08%, respectively. In wind integration scenarios, GWO achieved the simultaneous reduction of power loss and voltage deviation. In IEEE-118, GWO outperformed the ABC, PSO, GSA, and CFA methods, reducing power loss by approximately 19.91%, 16.83%, 14.09%, and 4.36%, respectively, and voltage deviation by 8.50%, 14.15%, 16.19%, and 7.17%, respectively. These promising results highlighted the potential of the GWO algorithm to facilitate the integration of renewable energy sources, and its role in promoting sustainable energy solutions. In addition, this study conducted an analysis to investigate site-specific wind placement by using the Weibull distribution function and commercial wind turbines.

A Comprehensive Analysis of the Penetration of Detailed Type 4 Wind Turbine Generators in the Two-Area Benchmark System

The shift towards RES introduces challenges related to power system stability due to the characteristics of inverter-based resources (IBRs) and the intermittent nature of renewable resources. This paper addresses these challenges by conducting comprehensive time and frequency simulations on the IEEE two-area benchmark power system with detailed type 4 wind turbine generators (WTGs), including turbines, generators, converters, filters, and controllers. The simulations analyse small-signal and transient stability, considering variations in active and reactive power, short-circuit events, and wind variations. Metrics such as rate of change of frequency (RoCoF), frequency nadir, percentage of frequency variation, and probability density function (PDF) are used to evaluate the system performance. The findings emphasise the importance of including detailed models of RES in stability analyses and demonstrate the impact of RES penetration on power system dynamics. This study contributes to a deeper understanding of RES integration challenges and provides insights for ensuring the reliable and secure operation of power systems in the presence of high levels of RES penetration.

Study on the Dynamic Characteristics of Tensional Force for Ice Accumulated Overhead Lines Considering Instantaneous Wind Speed

Icing is one of the key factors affecting the security and reliability of power system operation. Ice accumulation on the overhead line (OHL) conductors is often accompanied by strong winds, whose magnitude and direction are varied constantly, which imposes challenges to the on-line monitoring of OHL’s ice load. This paper analyzed the dynamic characteristics of the tensional force for ice accumulated OHLs through both the on-site experiment and the finite element method (FEM). The correlation between the instantaneous wind speed, the thickness of the accumulated ice and the dynamic tensional force, the aerodynamic resistance coefficient of the conductor, is investigated. It is found that the axial dynamic tension at the end of the iced conductor is approximately proportional to the square of the instantaneous wind speed. The higher the wind speed, the larger the difference between the static tension and the maximum dynamic tension calculated using the 10-min average wind speed. Under the conditions of the example in this article (crescent-shaped icing), when the average wind speed is 20 m/s, the ratio of the difference to static tension is 11.19%. These conclusions are verified with the computational fluid dynamic (CFD) simulation, the same correlation is identified in comparison with the experiments. Due to the fact that the magnitudes of tensional force and vibration are determined by the instantaneous wind speed, significant error exists within traditional calculation methods where average wind speed is used instead.

Two-Stage Short-Term Power Load Forecasting Based on SSA–VMD and Feature Selection

Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-influencing factors, this paper proposes a two-stage short-term load forecasting method, SSA–VMD-LSTM-MLR-FE (SVLM–FE) based on sparrow search algorithm (SSA), to optimize variational mode decomposition (VMD) and feature engineering (FE). Firstly, an evaluation criterion on the loss of VMD decomposition is proposed, and SSA is used to find the optimal combination of parameters for VMD under this criterion. Secondly, the first stage of forecasting is carried out, and the different components obtained from SSA–VMD are predicted separately, with the high-frequency components input to a long short-term memory network (LSTM) for forecasting and the low-frequency components input to a multiple linear regression model (MLR) for forecasting. Finally, the forecasting values of the components obtained in the first stage are input to the second stage for error correction; factors with a high degree of influence on the load are selected using the Pearson correlation coefficient (PCC) and maximal information coefficient (MIC), and the load value at the moment that has a great influence on the load value at the time to be predicted is selected using autocorrelation function (ACF). The forecasting values of the components are fused with the selected feature values to construct a vector, which is fed into the fully connected layer for forecasting. In this paper, the performance of SVLM–FE is evaluated experimentally on two datasets from two places in China. In Place 1, the RMSE, MAE, and MAPE are 128.169 MW, 102.525 MW, and 1.562%, respectively; in Place 2, the RMSE, MAE, and MAPE are 111.636 MW, 92.291 MW, and 1.426%, respectively. The experimental results show that SVLM–FE has high accuracy and stability.

AN INNOVATIVE APPROACH TO THE DEVELOPMENT OF NEW SCHEMES OF HORIZONTAL DIRECT-FLOW HYDRAULIC TURBINES

The impact of the rapid development of renewable energy sources and the reduction of electricity production by traditional generating capacities on the change in the development trend in world energy is analyzed. This led to a sharp change in the priorities of the national energy policy of Ukraine with an increase in the capacities of renewable energy sources, which in 2035 should be 25 billion kWh of generating capacity against 2,8 billion kWh in 2018. It is shown that such trends require the growth of balancing highly maneuverable capacities in hydropower to regulate load schedules and ensure stability and reliable operation of combined power systems. Under such trends, Ukraine is transitioning to a new innovative model of the operation of the electric power industry in the conditions of the gradual creation of a full-fledged energy market and the planned integration of the energy system of Ukraine with the European energy system (ENNSO-E). It is claimed that the problem of covering peak loads becomes acute in the modern conditions of power system operation. This makes it necessary to pay more attention to the operation of maneuverable hydropower equipment in order to optimize the distribution of loads to consumers. It is proposed to solve these problems comprehensively, both by developing new blade systems and water passages of traditional type hydroturbines, and by improving the working process due to innovative constructive solutions for the layout of hydropower equipment for new hydropower facilities. Potetenko O. V. (Professor of the Department of Hydraulic Machines named after G. F. Proskura, NTU "KhPI") founded a scientific direction in which for 10 years the tasks of introducing high-speed bladed hydraulic turbines at high heads in the development of new design schemes in hydropower were developed and researched. Dozens of patents for inventions and utility models are the results of this. Further development of innovative schemes of horizontal capsule hydraulic units, which is related to the features of the working process, is aimed to achieve high average operating efficiency while simultaneously expanding operation and reliability areas of the proposed design schemes.

Forecasting the Distortion in Solar Radiation during Midday Hours by Analyzing Solar Radiation during Early Morning Hours

Knowing the fluctuation of solar radiation is essential for reliable and safe operation of power systems with a high share of solar PV power plants. This paper introduces a novel method for forecasting the distortion in global solar radiation during the midday time (during the peak of solar radiation at which the generation of the PV plants is maximum), by analyzing the solar radiation during the very early hours. This paper shows that there is a strong relation between the condition of the solar radiation during the very early minutes of the day (when the generation of the PV plants is low) and the condition of the solar radiation during the midday hours (when the output of the PV plants is enormous). This relation can be utilized to help power systems operators in determining the safe share of PV plants that can be fed to power systems. We analyzed real data of one complete year for two sites in Saudi Arabia to approve this approach. The difference between each two readings was calculated for the hours 7 a.m. and 8 a.m. Then, the negative, zero, and amplitude of the difference was used to formulate a distortion index (DI) that predicts the distortion/fluctuation in the global radiation. It was concluded that the DI could classify the days into three categories: clean, distorted, and medium level according to the value of the DI. The accuracy of this approach was 85.2% and the error was 14.8%.

Reliability Assessment of Power Systems in High-Load Areas with High Proportion of Gas-Fired Units Considering Natural Gas Loss

The “dual-carbon” policy underscores the crucial importance of a secure and stable natural gas supply to ensure the reliable operation of power systems. In high-load areas with a high proportion of gas-fired units and no alternative energy supply, urgent attention needs to be paid to the impact of natural gas loss on power system reliability. Given this background, a method to evaluate power system reliability that considers natural gas supply fluctuations is proposed. In this method, a reliability model of the natural gas supply chain based on the minimal cut set theory is constructed and the influence of policy regulations and economic market factors on system components is quantified. Then, a reliability-evaluation model for a power system that considers gas loss is constructed, and a non-sequential Monte Carlo simulation is used to solve it. Afterward, a reliability-evaluation method considering the power system reserve capacity is proposed. Finally, case studies on a natural gas system with a 14-node power system of a certain area are performed to verify the effectiveness of the proposed method, and the simulation results demonstrate that the reliability of the energy supply directly affects the reliability of the power system.

The Effect of Power Flow Entropy on Available Load Supply Capacity under Stochastic Scenarios with Different Control Coefficients of UPFC

With the sharp increase in fluctuant sources in power systems, the deterministic power flow (DPF) calculation has been unable to meet the demands of practical applications; thus, the probabilistic method becomes indispensable for the reliable and stable operation of power systems. This paper adopts the probabilistic power flow (PPF) method, which is a Monte Carlo simulation (MCS) based on the Latin hypercube sampling (LHS) method, to analyze the uncertainties of power systems. Specifically, the available load supply capability (ALSC) based on the branch loading rate is used to analyze the safety margin of the whole system, while the improved power flow entropy is introduced to quantify the equilibrium of power flow distribution. The repeated power flow (RPF) calculation is combined with the PPF method, and, hence, the probabilistic repeated power flow (PRPF) method is proposed to calculate the power flow entropy at the initial state and the probabilistic ALSC. To flexibly control the power flow, the unified power flow controller (UPFC) is added to the AC power system. The different control coefficients of UPFC are set to reveal the relationship between power flow entropy and available load supply capability under the stochastic scenarios. Finally, the modified IEEE14 test system is used to study the adjustment abilities of UPFC. With consideration of uncertainties in the test case, the positive effect of UPFC on the power flow entropy and the probabilistic ALSC under stochastic scenarios is deeply studied.

Discharge Characteristics and Numerical Simulation of the Oil–Gas Surface under DC Voltage

Low insulation strength at the oil–gas surface due to oil leakage and partial discharge of oil-immersed power equipment is a major threat to the safe and reliable operation of power systems. This paper investigates the initiation and development of the oil–gas surface discharge. The oil–gas surface discharge test platform was established, and discharge tests were carried out at different gap distances (1–2.5 mm). By coupling the electric field and flow field, the multi-layer dielectric discharge streamer model was built, and the characteristics of charge and electric field distribution at different gap distances were studied. The test results show that the liquid surface between the electrodes rises during the discharge process. Furthermore, the surface discharge voltage exceeds the air gap discharge voltage. With the simulation analysis, the oil–gas surface discharge is a typical streamer development process. Under 50 kV applied voltage and 2.5 mm gap distance, the average development speed of the streamer is 12.5 km/s. The larger the gap distance is, the greater the average streamer development speed is. The recording and numerical simulation of the discharge process are of great significance for exploring the mechanism of oil–gas surface discharge, optimizing the discharge process, and diagnosing partial discharges.

Voltage constraint-oriented management of low carbon technologies in a large-scale distribution network

With the rising concerns about climate change and technological developments, the deployment of low carbon technologies (LCT) is gaining importance for reliable and sustainable power system operation. To decarbonize the heat and transportation sectors, LCTs such as heat pumps (HP) and electric vehicles (EV) are integrated into the power system from the low voltage (LV) distribution network, where predominantly end-users are connected. However, the increasing penetration of EVs and HPs, together with distributed photovoltaic (PV) systems on the demand side, can cause technical challenges in LV distribution networks, such as under/over voltages at the far end of the feeders. Therefore, the hosting capacity of a grid for LCTs is limited due to network constraints such as line ampacity, bus voltage, and frequency. In this paper, voltage-constrained management approaches for LCTs are proposed to improve the grid hosting capacity in a real large-scale distribution network. Moreover, HPs, PVs, battery energy storage systems (BESS), and EVs as well as their vehicle-to-grid (V2G) feature are evaluated as LCTs. The uncertain behavior of these technologies is taken into consideration for more realistic analyses. In addition, the charging and discharging interactions of BESSs are controlled by following the power consumption profiles from the economic point of view. Furthermore, the hourly carbon data is used to control BESSs from an emission-sensitive aspect. To demonstrate the effectiveness of the proposed algorithms, a series of tests is conducted on a real distribution network model. The results show that the hosting capacity of the considered LV network can be increased from 35% to 50% using the proposed algorithms. The promising results obtained in this study pave the way for future active LCT management studies to improve hosting capacity in urban networks, and pilot demonstrations in the field.

Adaptation of Battery Energy Storage System on Under-Frequency Load Shedding Scheme Design

The reliable operation of power systems is crucial for ensuring uninterrupted power supply to consumers. However, any deficiency in power generation can lead to frequency deviation, disrupting the entire power system. To address this challenge, an active power source with a fast response, such as a Battery Energy Storage System (BESS), can prove to be a highly effective countermeasure. The BESS has gained immense popularity for its diverse applications, including load leveling, frequency and voltage support during loss of generation, improving transient and dynamic stability, and enhancing power quality. This has made the BESS an invaluable contribution to power system restructuring. One of the most important applications of the BESS is in Load Frequency Control, where a proportional-integral (PI) controller is employed to modify the power output of the BESS, resulting in further optimization of the system. In this work, a two-area hydro-thermal interconnected system is considered, and simulations are performed in MATLAB to analyze the impact of the BESS with and without a PI Controller. The results demonstrate a significant reduction in the load shedding amount, and the under-frequency load shedding (UFLS) scheme is made even more effective, ensuring the reliable and uninterrupted operation of the power system.