Interconnected Power System Research Articles

Voltage stability index: a review based on analytical method, formulation and comparison in renewable dominated power system

In an interconnected complex power system network voltage stability evaluation is indispensable to guarantee secure power system operation. To further increase the system performance and to make the system safer assessment, the voltage stability improvement is obligatory. In the various literature different voltage security assessment method using the voltage stability index has been presented. Different lists proposed in literature can be utilized to realize the weak buses and weak transmission lines to enacting the countermeasures against issues of voltage insecurity. Additionally, the arrangement and measuring of inexhaustible assets, on the web and disconnected observing of force framework and the measure of load to be shed at whatever point essential. This paper shows a survey on the different voltage stability index from various perspectives. The audit results on various record gives a far and wide logical to perceive the impending works in this field and to choose the best index for variety of applications such as voltage security assessment, renewable energy integration, distributed generation (DG) placement and sizing, online monitoring of the power system, and shedding of load.

Stabilization analysis for nonlinear interconnected system with memory based coupled sampled data control via quantum-inspired genetic algorithm

In the realm of modern interconnected systems, achieving stability represents a pivotal challenge due to the complex dynamics among various components. This paper presents a new approach to stability analysis, leveraging linear matrix inequalities and Lyapunov-Krasovskii functionals. The framework of memory-based coupling sampled data control is enhanced by a quantum genetic algorithm. By incorporating linear matrix inequality approach, this study establishes a robust mathematical foundation for characterizing stability conditions and synthesizing control gains. This innovative integration provides a powerful toolset to optimize control parameters while mitigating the impact of disturbances. The simulation findings are explicitly based on experimental values of two-area interconnected power systems with doubly fed induction generator based wind farm, which guarantees the asymptotic stability of the proposed controller.

Multi-Area Load Dispatch with Emission and Fuel Cost Constraints Using Deep Recurrent Lstm Under Stochastic Pv and Wind Power Integration

An interconnected power system must employ multi-area economic load dispatch (MAELD) to keep generation dispatch efficient, satisfy load demands, and stay within technological limits. The goal of MAELD's operational constraints is to reduce fuel costs and emissions from multi-area power production facilities. Consequently, optimization solutions that successfully tackle MAELD problems are highly sought after. By merging an alternative model with the Deep Recurrent Neural Network (DRNN) one, this paper suggests a fresh approach to addressing MAELD problems. This paper provides a detailed account of the various optimization methodologies used to address the issue of load dispatch when non-conventional energy sources are present. Four domains utilizing 3-, 13-, and 40-unit systems were used to test the suggested DRNN and LSTM approach. Comparing this method to various optimization algorithms as firefly, Salp Swarm, Squirrel search, Particle Swarm, and Gross Hopper, results produced from the MATLAB/Simulink environment show that it delivers superior trade-off solutions without breaching limitations.

Designing PID controller using SSA for interconnected thermal power systems

Designing PID controller using SSA for interconnected thermal power systems

The role of load and distributed energy resources modeling in voltage recovery studies

Load modeling in power systems is crucial to stability studies. The analysis of certain dynamic phenomena requires proper load modeling. Fault Induced Delayed Voltage Recovery (FIDVR) relates directly to Residential Air Conditioner (RAC) stalling. Therefore, the modeling of induction motor (IM) loads is essential to this analysis. Delayed voltage recovery in the system can result in delayed power recovery in inverters connected to the transmission system, which connects photovoltaic and wind power plants to the Brazilian Interconnected Power System (BIPS). In the last few years, there has been a considerable growth of Distributed Energy Resources (DER) in Brazil. Modern and legacy ride-through settings coexist in power systems around the world. Inverters with legacy settings are more likely to disconnect in systemic events. This creates a context that has been assessed in this paper. The modeling of induction motors in the Acre-Rondonia system leads to a deterioration of this system’s dynamic performance, which can lead to collapse. DER’s voltage support can contribute to post-fault system voltage recovery, mitigating the delay caused by RAC stalling and avoiding collapse. Thus, more robust ride-through settings are required to avoid inverter disconnection. Moreover, dynamic voltage support and reactive current priority requirements are demands from inverters connected to the system. The results obtained show that this consideration enable more consistent results than the ones obtained by the classic ZIP load model.

Load frequency control of multi-area power system incorporated renewable energy considering electrical vehicle effect using modified cascaded controller tuned by BESSO algorithm

In power systems, load frequency control (LFC) matters significantly to achieve stability. Dealing with the fluctuations in the frequency of a multi-area power system becomes more challenging by incorporating additional energy resources. In this research, a multi-area power system is built by integrating thermal power systems with photovoltaic (PV) cells, wind turbines, and electric vehicles (EV). The addition of an electric vehicle “to a thermal power system which is integrated with a renewable energy source (RES)” increases the system productivity but also increases the system complexity, making it more problematic for LFC. Looking at the stability criteria for LFC, frequencies in two areas (Area-1 & Area-2) and tie-line power are considered for measurements. For the tuning of the proposed cascaded (1+PI)-PID controller, a new approach Bald Eagle Sparrow Search Optimization (BESSO) algorithm is implemented which is strongly inspired by nature. BESSO is a combination of bald eagle and sparrow searching techniques and performs comparatively better for fast convergence due to their strong food-seeking natural behavior to find the best solution for controller gains. Controller effects on multi-area systems are compared with the addition of PV, wind, and EV and resulting measurements meet the stability criteria with high accuracy even with the complexity of the system and also undertake a stability analysis to prove the performance by minimizing undershoot, overshoot, steady-state error and settling time for system frequencies and tie-line power. Simulation results are examined at different load-changing conditions. In contrast with similar combinations of PID controller with proposed cascaded (1+PI)-PID controller, it is claimed that the effect of the proposed controller is much finer and more reliable, even with electric vehicles to avoid system blackout caused by frequency fluctuations in interconnected power system.

Fuzzy analytic hierarchy process based generation management for interconnected power system

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

Hybrid neuro-fuzzy-based 3DOF-PDN controller for AGC of multi-area interconnected power system incorporated hydrogen aqua electrolyze fuel cell units and unified power flow controller

Hybrid neuro-fuzzy-based 3DOF-PDN controller for AGC of multi-area interconnected power system incorporated hydrogen aqua electrolyze fuel cell units and unified power flow controller

Investigation of rank order centroid method for optimal generation control.

Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher-learner based optimization algorithm (TLBOA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller's performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.

A novel predictive optimal control strategy for renewable penetrated interconnected power system

AbstractThe stable and efficient regulation of voltage and frequency is a critical issue associated with the functioning of modern power system network having renewable integration. This manuscript focuses on the concurrent regulation of voltage and frequency in a power network comprising multiple generating sources. Herein, along with conventional thermal and diesel plants, renewable generations from different sources have been considered such as wind and solar photovoltaic (PV) resources. Further, in a fresh attempt, the authors have modeled wind and solar PV generation using two different stochastic modeling methods concerning combined voltage and frequency loops in the regulated power system model. Furthermore, the novel Leader Harris Hawks Optimized Model Predictive Controller (MPC‐LHHO) has been applied to concurrently minimize the voltage and frequency deviations in the given power network. To address the intermittent nature of load and renewable generations, various auxiliary frequency controllers, such as the unified power flow controller (UPFC) and electric vehicle (EV) integration with the grid, have been implemented. The robustness of the proposed control method has been successfully validated through diverse scenarios of random loadings.

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).

Event-based [formula omitted] load frequency control for fuzzy Markov-jump IPS with wind power under hybrid attacks

This paper addresses the H∞ load frequency control problem of interconnected power systems that incorporate high-permeability wind power and face a mixed attack of deception and denial of service. The interconnected power system is modeled as a Takagi–Sugeno (T–S) fuzzy Markov jump system. Given the involvement of wind power, optimizing the utilization of already in-demand communication resources becomes imperative. Thus, a new event-triggering mechanism is adopted to reduce data transmission traffic. Simultaneously, in response to the general network’s vulnerability to network attacks in power systems, a closed-loop system with Markov jump, modeled using the T–S fuzzy approach, is studied. A novel multi-agent Event-Triggering Strategy (ETS) composed of membership functions is proposed to address the mismatch between the membership functions of fuzzy systems and fuzzy controllers, and ensure the consistency of multi-agent systems. Subsequently, employing Lyapunov–Krasovskii theory and stochastic analysis techniques, conditions sufficient to ensure the asymptotic stability of interconnected power systems are derived from linear matrix inequalities. Finally, simulation verification is provided to ensure the accuracy and feasibility of the obtained theory.

Proximity Index Value Based Systematized Approximation of Higher-Order Interconnected Power System Model

Proximity Index Value Based Systematized Approximation of Higher-Order Interconnected Power System Model

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart Grid involves application of Information and Communication Technology (ICT) for monitoring, protection, operation, and control of interconnected power systems under various scenarios. Smart Grid Control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats, and attacks under central and distributed control architectures. Cyber Security of smart grid control, especially, Load Frequency Control (LFC) is an important issue that is addressed in this article. The state-of-the-art in cyber security and attacks on smart grid control and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic Vulnerability Assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for Vulnerability Assessment of the standard 39-bus New England test system and the 96 bus reliability test system to illustrate the concept of cyber security and Vulnerability Assessment of smart grid LFC.

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

LFC performance advancement of two-area RES penetrated multi-source power system utilizing CES and a new CFOTID controller

This study addresses the challenges of load fluctuations and the assimilation of renewable energy sources (RES) causing undesirable frequency deviations. To avoid widespread deviations in frequency, an intelligent, competent and robust load frequency control (LFC) scheme and energy storage system are obligatory for disturbance rejection in RES penetrated realistic interconnected multi-source power system (IMSPS). Hence, a new cascade fractional order tilt integral derivative (CFOTID) controller is suggested with capacitive energy storage (CES) for a pragmatic RES based thermal-hydro-gas IMSPS having governor dead-band and generation rate constraint nonlinearities. The salp swarm algorithm (SSA) is utilized successfully for fine-tuning of the controller parameters. Scrutiny of the outcome results reveals the sovereignty of the CFOTID controller over several existing well proven controllers and SSA-based FOTID. It is observed that power generation due to CES further advance the system results in terms of minimized overshoot, undershoot, and settling time. Lastly, a sensitivity analysis is conducted to confirm the robustness of the controller.

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

Distributed Multi-Area Intraday Economic Dispatch Using Modified Critical Region Projection Algorithm

Convergence acceleration is always a critical issue in distributed multi-area scheduling. The critical region projection (CRP) algorithm based on multi-parametric quadratic programming (MPQP) shows better convergence performance for multi-area static economic dispatch. However, its imperfect decomposition framework cannot be directly applied to dynamic economic dispatch. Therefore, this paper modifies the CRP algorithm to achieve fast distributed multi-area intra-day economic dispatch (MAIDED). First, we introduce slack variables to each area and add the corresponding penalty term into their objective functions to present a primal decomposition framework with penalty relaxation. It is more realistic than the decomposition framework of traditional CRP (TCRP). Then, an iterative algorithm of double spatial scale search (DSSS) is developed to improve the convergence rate of distributed solving based on the similar optimal value function in adjacent critical regions (CRs). Moreover, we design an initial value selection method based on data fitting to further reduce the number of iterations. Finally, three interconnected power systems of different sizes are used for numerical testing to demonstrate that the proposed modified CRP (MCRP) algorithm can meet the practical application and has higher convergence efficiency. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problem of distributed MAIDED for power systems but it also applies to other multi-agent networks with a coordinator. The convergence speed of the existing distributed optimization algorithm is slow, which increases the risk of communication failure and attack. Meanwhile, more iterations will result in increased communication and computing costs. This paper modified the TCRP algorithm for high-efficient distributed MAIDED. The penalty relaxation is used to ensure that the decomposition framework is consistent with the actual system operation. The DSSS algorithm and the initial value selection method based on data fitting are designed to effectively reduce the number of iterations. In this paper, the DSSS is that the coordinator enlarges the CRs uploaded from each area to perform a rough optimization for approaching the global optimal solution fast, and then in the next iteration, conducts a precise optimization like the TCRP algorithm to determine the precise optimal solution. The numerical testing of the interconnected power systems of different sizes indicates that the MCRP algorithm has fewer iterations and calculation times than the TCRP algorithm. Future work will extend the MCRP algorithm to solve the distributed multi-area unit commitment problem.