Incorporation Of Renewable Energy Sources Research Articles

COST EFFICIENCY ENHANCEMENT IN BIOMASS PELLET MANUFACTURING THROUGH RAW MATERIALS OPTIMIZING

This project explores innovative approaches to enhance efficiency and reduce costs in biomass pellet production. By integrating smart technologies, process optimization, and sustainable practices, the aim is to make biomass pellet production more affordable and environmentally friendly. By implementing smart solutions, the goal is to address challenges related to variability in biomass feedstock, energy inefficiencies, and operational costs. This research not only seeks to improve the economic viability of biomass pellet production but also aims to contribute to the broader goal of fostering renewable energy solutions for a more sustainable future. In addition, the project explores the incorporation of renewable energy sources, such as solar or biomass-based energy, into the production process to further reduce environmental impact and operational costs. The optimization of supply chain logistics is also a focal point, aiming to minimize transportation-related emissions and ensure a more sustainable overall production lifecycle. Through a holistic approach, this research endeavors to provide a roadmap for the integration of smart and sustainable practices, fostering a greener and economically feasible future for biomass pellet production.” Key Points : Feed Hopper, Motor, Roller, Cycle Chain

A novel scheme of load frequency control for a multi-microgrids power system utilizing electric vehicles and supercapacitors

Incorporation of renewable energy sources (RESs) into power systems has recently experienced a rapid surge, primarily driven by environmental concerns and the desire for more sustainable power generation. However, the replacement of traditional generating units with RESs lowers the system inertia significantly and causes intermittent generation. These issues make frequency stability a greater concern, particularly in microgrids (MGs), and produce mechanical stress on the conventional generation units. Moreover, in interconnected MGs, the complexity is further magnified owing to interaction between microgrids through the tie power which may increase the disturbance propagation among microgrids. This paper suggests a novel control scheme based on a centralized fractional order proportional integral controller cascaded with a virtual inertia emulator (C.FOPI+VI). The virtual inertia emulation is provided using a supercapacitor (SC) along with electric vehicles (EVs) controlled through a virtual inertia controller. However, the EV is suffering from uncertainty, hence the supercapacitor can mitigate this uncertainty. The control system is applied on three interconnected microgrids, each has a thermal plant, a supercapacitor, and an electric vehicle aggregator. The performance of the system incorporating the proposed control technique is compared with five different inertia emulation topologies known as virtual inertia (VI), virtual synchronous generator (VSG), distributed fractional order proportional integral controller cascaded with virtual inertia (D.FOPI+VI), distributed fractional order proportional integral controller cascaded with virtual synchronous generator (D.FOPI+VSG), and without synthetic inertia. To optimize the parameters of different controllers, the Transit Search (TS) optimization algorithm is employed considering the frequency deviation, the restoration time, and the tie power. The proposed C.FOPI+VI has proven its superiority in providing fast frequency restoration, less stress on the conventional generation units, and minimizing the spreading of the disturbance within the system. Furthermore, the proposed controller enhances the Gain Margin (G.M) to 21.1 dB compared to operation without inertia support (0.322 dB), VI (3.38 dB), VSG (5.23 dB), DFOPI+VI (8.25), and DFOPI+VSG (8.32).

The Existing Dynamics of Electric Grid Systems in the Province of Takhar, Afghanistan

The research paper investigates about the development of electric networks which states the amount of electricity that the people of Takhar currently use in Afghanistan, and expansion of Takhar’s electrical networks. The electric networks have become a significant importance research area to the socio-economic development of the country. It is in a state of flux and development among difficulties in Takhar province, currently. The region still has a long way to go before attaining widespread and dependable electrification, even with the notable progress made in recent years toward expanding access to power and strengthening infrastructure. Takhar's electrical grid is made up of both conventional and renewable energy sources, while it imports a significant amount of power from nearby nations. However, it has been determined that a crucial tactic for improving the province's energy security and sustainability is the incorporation of renewable energy sources, particularly hydroelectric and solar power. The research explores the present situation, difficulties, and potential futures of the province's electric networks. The main problem for using a mixed-methods methodology that combines qualitative interviews with important stakeholders including local utility providers, government officials (Ministry of Energy and Water, Da Afghanistan Brishna Shirkat (DABS)), and community members with quantitative data analysis. In order to overcome current obstacles and meet the region's electrical demands, this abstract emphasizes the dynamic character of the electric network growth in Takhar province and emphasizes the vital need for ongoing investment, innovation, and cooperation. Finally, the researcher found out the amount of renewable energy sources in Takhar, the amoun of energy that Takhar can produce, the amount of electricity that the people of Takhar currently use, and the amount of electricity that is needed by 2032 so that the people of Takhar Be self-sufficient in electricity.

Grid Integration for Electric Vehicles: A Realistic Strategy for Environmentally Friendly Mobility and Renewable Power

The promotion of electric vehicles (EVs) as sustainable energy sources for transportation is advocated due to global considerations such as energy consumption and environmental challenges. The recent incorporation of renewable energy sources into virtual power plants has greatly enhanced the influence of electric vehicles in the transportation industry. Vehicle grid integration offers a practical and economical method to improve energy sustainability, addressing the requirements of consumers on the user side. The effective utilisation of electric vehicles in stationary applications is highlighted by technological breakthroughs in the energy sector. The continuous advancement in science and industry is confirming the growing efficiency of electric vehicles (EVs) as virtual power plants. Nonetheless, a thorough inquiry is imperative to elucidate the principles, integration, and advancement of virtual power plants in conjunction with electric automobiles, specifically targeting academics and researchers in this field. The examination specifically emphasises the energy generation and storage components used in electric vehicles. In addition, it explores several vehicle–grid integration (VGI) configurations, such as single-stage, two-stage, and hybrid-multi-stage systems. This study also considers the various types of grid connections and the factors related to them. This detailed investigation seeks to offer insights into the various facets of incorporating electric vehicles into virtual power plants. It takes into account technology improvements, energy sustainability, and the practical ramifications for users.

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.

Neural network-assisted integration of renewable sources in microgrids: A case study

This study examines the incorporation of renewable energy sources into microgrids using neural network-assisted optimization methods. The objective is to tackle the difficulties related to the fluctuation and uncertainty of renewable energy production. An examination of the collected data over various time periods indicates encouraging patterns in the production of renewable energy. The solar energy use shows a steady rise from 120 kWh to 140 kWh, representing a 16.67% increase. Similarly, wind energy usage also demonstrates an upward trend, increasing from 80 kWh to 95 kWh, marking an 18.75% expansion. The biomass energy production has seen a substantial increase from 50 kWh to 65 kWh, representing a significant 30% rise. The examination of microgrid load consumption demonstrates the increasing energy needs in residential, commercial, and industrial sectors. The household load consumption has increased from 150 kWh to 165 kWh, representing a 10% spike. Additionally, the commercial load and industrial load have also seen a surge of 15%. The predictions made by the neural network demonstrate a high level of accuracy, closely matching the actual output of renewable energy. The accuracy rates for solar, wind, and biomass projections are 98.4%, 95.5%, and 97.3% correspondingly. The assessment of improved energy distribution emphasizes the effective usage of renewable sources, guaranteeing grid stability and optimal resource utilization. The results highlight the capacity of neural network-assisted methods to precisely predict renewable energy outputs and efficiently incorporate them into microgrids, hence promoting sustainable and resilient energy solutions. This report provides valuable insights on improving microgrid operations, decreasing reliance on traditional energy sources, and accelerating the shift towards sustainable energy systems.

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

This study presents a novel Fuzzy Logic-Based Energy Management model that aims to optimize the incorporation of renewable energy sources into smart grids. The research used simulated data to evaluate the model’s performance in important metrics, revealing significant improvements in the consumption of renewable energy, stability of the grid, dependability of energy storage, and overall efficiency of the system. The Fuzzy Logic Controller adjusts energy distribution in response to current inputs, leading to a significant 20% improvement in the usage of renewable energy. The capacity to adapt is crucial in dealing with the inherent fluctuation of solar, wind, and biomass sources. The approach greatly improves grid stability, as shown by a 15% decrease in grid frequency variations, highlighting its efficacy in assuring a more regulated and steady electricity supply. Furthermore, the dependability of energy storage systems exhibits a notable 25% enhancement in the state of charge, suggesting optimal cycles of charging and discharging. This increased dependability enhances the stability of the energy supply during times of high demand and variations in the power system. The Fuzzy Logic-Based Energy Management model exhibits a significant 22% improvement in total system efficiency when compared to conventional management systems. This indicator encompasses the model’s combined effect on the usage of renewable energy, the stability of the power grid, and the optimization of energy storage. Comparative analyses conducted against traditional control strategies, such as proportional-integralderivative controllers, consistently demonstrate the superiority of the fuzzy logic approach. This approach results in a 10% decrease in grid frequency deviations, a 15% enhancement in energy storage state of charge, and a 12% boost in overall system efficiency. The resilience of the Fuzzy Logic Controller is highlighted by sensitivity analysis, since it demonstrates consistent performance even when parameters vary significantly. The model’s practical usefulness and adherence to ethical principles are further confirmed by validation using real-world data from operational smart grid installations. This study provides valuable insights, establishing the Fuzzy Logic-Based Energy Management model as an innovative method for addressing the difficulties associated with integrating renewable energy into smart grids. This model promotes a more sustainable and efficient energy environment for the future.

The Role of Performance in Smart Meter’s Acceptance: A Survey in Joinville, Brazil

The incorporation of renewable energy sources necessitates the upgrade of the electrical grid to a smart grid, which involves the implementation of smart meters. Although smart meters provide benefits to users, many smart meter implementation projects have failed to be accepted by users. This article assesses the factors influencing the acceptance of household smart meters in Joinville, a city in the south of Brazil. Based on the Unified and Extended Theory of Acceptance and Use of Technology (UTAUT2), a structural equation model was estimated using data from a sample of 136 respondents in the city of Joinville. The results indicate that Performance Expectancy, Hedonic Motivation, and Social Influence constructs have a more substantial effect on the Intention to Use smart meters. The results provide evidence for planning the upgrade of the electrical grid by implementing smart meters in southern Brazil.

AI-Powered Object Detection to The Seamless Integration of Renewable Energy Into Electric Vehicles

This article explores the symbiotic relationship between cutting-edge technologies, focusing on the evolution from AI-driven object detection systems to the seamless incorporation of renewable energy sources into electric vehicles (EVs). Initially, advancements in artificial intelligence, particularly in the realm of object detection, have revolutionized real-time identification processes. The integration of TensorFlow models within edge computing architectures has significantly enhanced accuracy and efficiency, serving as a cornerstone across various industries. Concurrently, research efforts have been directed towards the integration of renewable energy sources into EV systems. This multifaceted approach aims to minimize carbon footprints and augment the sustainability quotient of transportation. Understanding the pivotal role of meticulous electrical design, harnessing mechanisms, and structural optimizations in EVs, this article emphasizes their interconnectedness with the broader scope of renewable energy integration. Through the amalgamation of AI-powered object detection systems and renewable energy synergies within electric vehicles, this article encapsulates the technological trajectory towards a more efficient, sustainable, and interconnected future in transportation.

Management of public and fiscal policies for the energy transition and sustainable development in Mexico

One of the biggest problems facing the population worldwide is global warming, mainly derived from the anthropogenic footprint. The excessive increase in actions that impact the environment such as public transportation, the use of fossil fuels, industry, etc., have unbalanced ecosystems. The measures and actions to mitigate the environmental impact have not been enough, this has disturbed the United Nations Organization (UN), which has taken a series of international measures such as the proposal of 17 objectives that help to safeguard different large social. Among the goals for 2030 is what is relevant to "clean energy". To align with these strategies, Mexico must take concrete and efficient actions; these actions must be established through the strict elaboration of public policies, which manage to integrate different scientific and technological areas, with timely participation of the different actors involved. The proposal of this document is the integration of public policies together with fiscal and energy policies that contribute to common objectives, through different actions such as energy efficiencies and increasing the participation of renewable energies in the country's energy matrix. Mexico has currently stopped some strategies planned in previous government periods, which have been stopping an energy transition that manages to establish an optimal use of infrastructure and develop a plan that allows the diversification of the energy matrix with the incorporation of renewable energy sources to reduce the use of fossil fuels, which have a share of more than 70% causing various damages to the environment and health. The execution of the proposed strategy required the integration and commitment of the different actors involved, due to the complex nature of the public policy formulation process, the authors propose working with a quality regulatory framework and monitoring the different governmental changes in the country.

Allocation of synchronized phasor measurement units for power grid observability using advanced binary accelerated particle swarm optimization approach

Large-scale power grid observability is still a challenge because of deteriorating infrastructure and the incorporation of renewable energy sources. A smart grid that makes use of cutting-edge technology, such as a phasor measurement unit (PMU), is an excellent option for monitoring and bringing networks up to speed with the latest information. Latterly, the considerable investment required for the deployment locations has slowed down the adoption of PMU. Therefore, because PMUs are expensive, it is necessary to deploy them in the best possible places on large-scale power grids. The most significant share of optimal PMU placement problems (OPPP) is defined as 0–1 knapsack problems. Considering this, the development of an effective optimization technique that can handle difficulties has emerged as an appealing topic in recent years. In this paper, a meta-heuristic algorithm based on the binary particle swarm algorithm (BPSO), a binary accelerated particle swarm optimization (BAPSO), is offered for solving OPPP. Since earlier research has shown that BPSO is likely to stick to local optima, the majority of them evaluated their suggested technique using small-scale test systems. The technique that has been suggested searches for the optimal solution by employing two topologies—one global and one local—that are analogous to BPSO. This work determines the optimal PMU position for a large network in a reasonable amount of time by fine-tuning the acceleration factor. Additionally, in order to employ fewer PMUs, an integration strategy was put into place for the radial buses. The OPPP solutions are provided by the suggested method within a reasonable period with prior solutions published in reliable publications, according to computational findings.

A modified differential evolution algorithm for frequency management of interconnected hybrid renewable system

In recent decades, the insufficiency in power production has led to the incorporation of renewable energy sources into microgrid systems. However, ambiguity in the generation of renewable sources and load variation impacts on the system frequency influencing the stable operation of microgrid system. To augment the stable operation of the system an intelligent controller is required for continuous electric power. The implementation of a modified differential evolution (MDE)-based cascaded PIDFN controller using ITAE is proposed in this work. In order to demonstrate the robustness and efficacy of the proposed MDE optimization technique is compared with DE, TLBO, IWO and PSO.MDE-based cascaded PIDFN controller is implemented for governing the frequency in two-area interconnected microgrid system. The system is substantiated over load perturbations, system uncertainties, communication delay and real time data of solar irradiance and wind speed and action of UPFC in MATLAB<sup>®</sup>/SIMULINK environment.

Optimized cascaded controller for frequency stabilization of marine microgrid system

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller.

An Enhanced Hybrid Rhino Herd–PSO Optimizer for Optimal Technical and Economic Operation of Power Systems Considering Environmental Concerns

The distributed generation based on renewable energy is gaining importance due to increased concerns about energy costs, energy security, and environmental issues. To effectively demonstrate the combination of different generators under various limitations, such as those imposed by energy systems using traditional fossil fuel generators and the incorporation of renewable energy sources, this article highlights the great potential for economic and efficient operation with environmental benefits. The reasons why the use of optimal power flow is regarded as the most significant operational challenge in power systems and the use of single- and multi-objective frameworks to analyze various objective functions are investigated. The objective functions are considered to reflect the power system’s technical, economic, and environmental requirements, including generation cost minimization, environmental pollution emission reduction, and voltage profile. Additionally, the article addresses the complexity of irregular mix structures such as solar and wind power and justifies the usage of swarm intelligence and an evolutionary approach to get the best solution. Furthermore, the article utilizes and validates the Rhino Herd Optimizer (RH), which is a modern type of swarm-based meta-heuristic search system, on various electrical power systems to evaluate the outcomes of an RH optimizer and show the capabilities.

Thermoeconomic and environmental impacts of vortex generator-equipped multi-drain heat recovery systems under various renewable sources

In response to escalating energy demands, intensifying climate change concerns, and growing environmental degradation, the incorporation of renewable energy sources has gained traction across diverse sectors and regions. Concomitantly, scientists and engineers have recognized the potential of heat recovery systems in mitigating energy consumption, fostering further investigations into their practical applications. This study introduces an innovative design, integrating vortex generators into a concentric tube heat exchanger for heat recovery from a multi-drain water system that serves 48 accommodations. The design's sustainability is assessed by evaluating its economic, and environmental implications when paired with various renewable energy sources. Specifically, the aim is to quantify both cost and environmental savings engendered by implementing this design in a multi-drain application for a building with 48 accommodations.A numerical investigation elucidates the effects of flow rate variations on heat transfer, overall heat transfer, and thermal enhancement factors. Four types of renewable energy input - solar, wind, biomass, and hydropower - along with one storage system (pumped storage) are analyzed. The study reveals that the design implementation results in an elevation of cold water temperature between 3.5 and 7.5 °C. Furthermore, daily environmental savings for solar, wind, biomass, hydropower, and pumped storage are estimated to be 0.783 €, 0.339 €, 0.141 €, 0.027 €, and 1.356 €, respectively. Conversely, daily economic savings are calculated to be 3.62 €, 2.49 €, 5.05 €, 3.62 €, and 6.70 € for each corresponding energy source. This research underscores the viability of the proposed design in fostering sustainability through both environmental preservation and economic efficiency.

Renewable-Based Microgrids: Design, Control and Optimization

To achieve carbon neutrality by 2050, additional measures must be taken, including the extensive incorporation of renewable energy sources (RESs) [...]

Integration of EVs through RES with Controlled Interfacing

Abstract Electric cars have a lot of promise in future energy markets as a manageable load. A popular vehicle-to-grid control interface, which enables the aggregation of the charging mechanism for energy management in the distribution grid, is one of the most significant road blocks to realize this opportunity. Understanding the ecology of electric transportation and integrating it in local communities to alleviate the energy shortage at peak hours is very complicated. In this research paper, recent standardization initiatives aimed at overcoming obstacles such as the integration of electric cars into smart grids are discussed. A charge control scheme focused on vehicle-to-grid connectivity is implemented. It is observed that the rise of environmentally sustainable energy sources, such as photovoltaic (PV) and wind energy, is straining the power network and their infrequent power generation is causing problems in power system operation, regulation and planning. The introduction of electric vehicles (EVs) into the electricity grid has been proposed to overcome grid load variations. Finally, the article discusses the incorporation of renewable energy sources and latest potential solutions involving electric vehicles.

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

Electric vehicles (EVs) represent a promising green technology for mitigating environmental impacts. However, their widespread adoption has significant implications for management, monitoring, and control of power systems. The integration of renewable energy sources (RESs), commonly referred to as green energy sources or alternative energy sources, into the network infrastructure is a sustainable and effective approach to addressing these matters. This paper provides a comprehensive review of the integration of RESs and EVs into power systems. The bibliographic analysis revealed that IEEE Access had the highest impact among journals. In order to enhance the classification of the reviewed literature, we have provided an analytical summary of the contributions made by each paper. The categorization facilitated the recognition of the primary objectives explored in the reviewed works, including the classification of EVs and RESs, the incorporation of RESs and EVs into power systems with an emphasis on emissions, the establishment of EV charging stations and parking facilities, EV batteries and battery energy storage systems, strategies for managing the integration of RESs with EVs, EV aggregators, and the financial implications. In order to provide researchers with a valuable synopsis of the implementation particulars, the papers were bifurcated into two primary classifications, namely mathematical algorithms and heuristic algorithms. The mixed integer linear programming algorithm and particle swarm optimization algorithm were commonly utilized formulations in optimization. MATLAB/Simulink was the primary platform used for executing a considerable portion of these algorithms, with CPLEX being the dominant optimization tool. Finally, this study offers avenues for further discourse and investigation regarding areas of research that remain unexplored.

Frequency Stability of a Wind-based Energy System by Virtual Inertia Controller of an Inverter Connected to Grid

The dwindling inertia of the power system due to the incorporation of renewable energy sources is a vital concern. This is the genesis of the frequency volatility in a system. A non-linear controller formulated on fuzzy logic has been developed for mimicking the operation of a synchronous generator. This non-linear controller is proficient in utilizing the energy present in the rotating blades of the wind turbine and nourishing the requisite quantity of inertia in the system ensuring the alleviation of the frequency variation issues. The system’s improved behavior is evaluated with this innovative controller in comparison with a traditional PD/PID controller for various transients such as sudden addition, deletion of large loads, and faults.

Load Frequency Control and Automatic Voltage Regulation in Four-Area Interconnected Power Systems Using a Gradient-Based Optimizer

Existing interconnected power systems (IPSs) are being overloaded by the expansion of the industrial and residential sectors together with the incorporation of renewable energy sources, which cause serious fluctuations in frequency, voltage, and tie-line power. The automatic voltage regulation (AVR) and load frequency control (LFC) loops provide high quality power to all consumers with nominal frequency, voltage, and tie-line power deviation, ensuring the stability and security of IPS in these conditions. In this paper, a proportional integral derivative (PID) controller is investigated for the effective control of a four-area IPS. Each IPS area has five generating units including gas, thermal reheat, hydro, and two renewable energy sources, namely wind and solar photovoltaic plants. The PID controller was tuned by a meta-heuristic optimization algorithm known as a gradient-based optimizer (GBO). The integral of time multiplied by squared value of error (ITSE) was utilized as an error criterion for the evaluation of the fitness function. The voltage, frequency, and tie-line power responses of GBO-PID were evaluated and compared with integral–proportional derivative (GBO-I-PD), tilt integral derivative (GBO-TID), and integral–proportional (GBO-I-P) controllers with 5% step load perturbation (SLP) provided in each of the four areas. Comprehensive comparisons between GBO-PID and other control methodologies revealed that the proposed GBO-PID controller provides superior voltage, frequency, and tie-line power responses in each area. The reliability and efficacy of GBO-PID methodology were further validated with variations in the turbine time constant and speed regulation over a range of  ± 25%. It is evident from the outcomes of the sensitivity analysis that the proposed GBO-PID control methodology is very reliable and can successfully stabilize the deviations in terminal voltage, load frequency, and tie-line power with a shorter settling time in a four-area IPS.