Standard IEEE-30 Bus Research Articles

A novel solution for the power transmission congestion of deregulated power system using TCSC and TLBO algorithm

The expansion of the electrical power sector has introduced heightened competition in electric markets, compelling utilities to enhance the efficiency of their power system equipment to provide customers with cost-effective and premium-quality power. Deregulation has, however, led to challenges such as congestion in transmission lines, adversely affecting power quality, voltage profiles, and system security. The congestion can be alleviated either by technical or non-technical methods. To address this issue, the manuscript proposes the implementation of Thyristor Control Series Capacitors (TCSC) as a technical solution for congestion mitigation. TCSC is used as a variable reactance device. The study employs line utilization factors (LUF) and disparity line utilization factors (DLUF) to obtain the optimal locations for TCSC installation. Additionally, the Teaching-Learning-Based Optimization (TLBO) algorithm is employed for the optimization of TCSC reactance. The manuscript also suggests another approach by simultaneously optimizing the location and parameters of multiple TCSCs using the TLBO algorithm without using the sensitivity factors. Comparative analyses with other optimization algorithms like gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) are conducted to determine the superior method for ensuring stable system operation. By application on the standard IEEE-30 bus system, the TLBO algorithm is validated, demonstrating its effectiveness in mitigating congestion by minimizing power loss, and voltage deviation, and enhancing security margins. The findings contribute valuable insights for practitioners seeking robust solutions in managing congestion within deregulated electric markets.

Evaluation of FACTS Contributions Using Branch Flow Model and Newton–Raphson Algorithm

Flexible alternating current transmission systems (FACTSs) have been widely incorporated in electric power systems in order to control system parameters. This paper proposes the modeling of four FACTS devices, using the Branch Flow Model (BF) as an optimization problem to reduce the complexity of the Newton–Raphson (NR) load flow code with FACTS devices. The devices are represented as variable impedances, as a function of a firing angle, and as voltage source converters (VSCs) located on the buses and transmission lines. This proposed model solves the problem associated with the selection of appropriate initial conditions of the parameters of each device that guarantee convergence. The model is validated by evaluating its percentage deviation with respect to the NR method, using the standard test systems, IEEE 5-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems.

Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods

False data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize expected load losses and generator trips through load and power generation adjustments. The selection of strategies of the attacking and defending sides turns out to be a symmetric game process. This article proposes a hybrid game theory method for analyzing the attack–defense confrontation: firstly, a micro-grid-based power market model considering false data injection attacks is established using the Nash equilibrium method; secondly, the attack–defense game function is constructed and solved via the Stackelberg equilibrium algorithm. The Markov game algorithm and distributed learning algorithm are used to update equilibrium function; finally, a dynamic game behavior model of the two players is constructed through simulating the attack–defense probability. The evolutionary game method is used to select the optimal defense strategy for dynamic probability changes. Modified IEEE standard bus systems are illustrated to certify the effectiveness of the proposed model.

Analyzing the effect of power loss allocation to utilities optimally in power systems using constrained load flow

In present day power system operation and control needs modern methodologies for controlling the power flow in power lines. Usually power electronic based converters are used for diverting/controlling the power flow through power lines which is a costlier solution. The alternative solution to this problem is to impose constraints on the system variables. In this paper, shunt capacitors are connected at loadsfor maintaining the desired magnitude at loads. Also, the losses thus obtained are allocated to respective participants (generators/loads) based on their contributions using tracing based methodology. In this, the proportional sharing principal is used to identify the contribution of participants in the power losses. The effect of imposing constraints on loss allocation to participants is analyzed at OPF (OPF) condition. For solving this, a new methodology based on improved ant lion optimization algorithm is developed. Using this method, the OPF problem wasansweredagainst system limitations. The developed methodologies are tested on standard IEEE-14 bus with supportivenumerary.

Multi-Objective Optimization in the Presence of OGIPFC Using NSMMP Algorithm

Background: Customers expect quality, uninterrupted power with cost-effective electricity in the latest trend. However, outages, severe storms, old infrastructure, and cost pressures can lead to ambiguity in power generation and transmission. To improve line power transmission capability, the right flexible AC transmission systems (FACTS) device may save millions of dollars. Methods: In this study, a FACTS controller named Optimal Generalized Interline Power Flow Controller (OGIPFC) was developed. Furthermore, for optimization, the Modified Marine Predator Algorithm (MMPA), which is a modification of the recently developed Marine Predator Algorithm (MPA). The optimum technique was used to evaluate a set of prioritized considered objective minimizations. A variety of factors must be maximized, such as generation cost, emissions, and power loss. Results: The performance of the proposed algorithm was analysed on benchmark test functions, and then single objective optimization problems of standard IEEE-30 bus system were solved and compared with the existing algorithms. The proposed algorithm was restricted to solving the single objective problem only, so it was further implemented with non-dominating sorting to solve the multiobjective optimization problem. The proposed multi-objective version is named as Non-dominating Sorting Modified Marine Predator Algorithm (NSMMPA), and it was validated on benchmark test functions and the IEEE-30 bus system. Conclusion: Finally, the OPF problem was solved with the incorporation of OGIPFC using the proposed methods, which resulted in better solutions and made the system more effective in operation.

Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.

A Novel Electric Vehicle Placement Index for Sustaining Power Quality in a Distribution Network: Proper Allocation of Electric Vehicle Charging Stations

Large-scale deployment of electric vehicles (EVs) has adverse effects on distribution network operations. Inappropriate installation of EV charging stations (EVCS) degrades the distribution network’s power quality, affecting smooth functioning. This article proposes a novel strategy for properly allocating EVCS in a distribution system by analyzing the impact of power quality issues, namely harmonics and loss of life of the distribution transformer. The analysis is carried out in a standard IEEE-33 bus radial distribution system and a real-time distribution system in Kerala, India. The impact of power quality issues is highlighted by perceiving the effect of slow/moderate and fast chargers in the system under study. The results substantiate the potency of the reliability index (RI) strategy. The severity level for planning the charging station (CS) in numerous buses in the distribution network without infrastructural upgrades is determined by employing a modified novel EV placement index (EVPI <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> _m </i> ). The novelty of the index lies in the fact that it incorporates all of the operational parameters with a specific focus on the power quality issues in the distribution network. The severity level of the EVPI <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> _m </i> maintains the power quality without increasing the rating of the components used or any infrastructural upgrades. The sensitivity analysis results prove the robustness of the modified EVPI-based strategy. Moreover, the proposed strategy is validated using real-time experiments on a hardware-in-loop real-time simulator, OPAL-RT OP4510. The results substantiate the efficacy of the strategy and the modified index.

Controlled measurement set randomization–based moving target defense against coordinated cyber–physical attack in smart grids

Smart grids are at risk of a dangerously covert attack called coordinated cyber–physical attack (CCPA), which may cause large-scale line outages or even cascading failures. This is because that the damage of the physical attack can be masked by the coordinated cyberattack. Towards this goal, the attacker usually injects false data into the sensor measurements to manipulate the results of state estimation. In this paper, we propose a moving target defense (MTD) approach that applies controlled randomization to the set of measurements used in state estimation, which invalidates the attacker’s prior acquired knowledge regarding power grids, and thus mitigates the coordinated false data injection (FDI) attack. To ensure that the measurement set is able to observe the system, a formal model with observability constraints is developed to select the measurements. Extensive simulations are performed on the standard IEEE bus systems using the MATPOWER simulator, and results show that the proposed MTD approach can prevent more than 50% of the states from being attacked.

A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems.

The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA's simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA.

Jaya-AEO: An Innovative Hybrid Optimizer for Reactive Power Dispatch Optimization in Power Systems

In the domain of electric power systems, ensuring stable voltage and efficient transmission is crucial. Optimal reactive power dispatch (ORPD) is a vital step toward improving system stability, enhancing economic feasibility, and increasing overall efficiency. Various optimization techniques are employed to minimize real power loss and voltage deviation in the network. This article proposes a hybrid algorithm called Enhanced Jaya and Artificial Ecosystem-Based Optimization (EJAEO) to address the ORPD problem. The algorithm aims to determine the optimal values of variables such as reactive compensation, generators’ voltages, and transformers’ tape ratio. Two single objective functions are tested on standard IEEE 30-Bus and IEEE 57-Bus systems, and the suggested modification of the Artificial Ecosystem-Based Optimization (AEO) improves population effectiveness in achieving optimal solutions. The simulation findings of the EJAEO algorithm demonstrated its superiority over three recent algorithms (AEO), Turbulent Flow of Water-Based Optimization, and Jaya algorithm), as well as certain previously published ORPD methods used for the same problem. In the case of the IEEE 30-Bus system, the proposed EJAEO algorithm achieved the best results in terms of power losses (4.944805) and voltage deviation (0.121196). Similarly, in the IEEE 57-Bus system, our EJAEO algorithm outperformed others, yielding the lowest power losses (23.33052) and voltage deviation (0.579286). The EJAEO algorithm outperforms other algorithms in terms of accuracy, speed of convergence, and robustness. These findings highlight the potential of EJAEO as a promising technique for solving the ORPD problem in power systems.

Probabilistic Optimal Active and Reactive Power Dispatch including Load and Wind Uncertainties considering Correlation

The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system’s reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded.

Multi-objective Optimal Power Flow and Emission Index Based Firefly Algorithm

The economic operation of electric energy generating systems is one of predominant problems in energy systems. In this work one evolutionary optimization method, based on the meta-inference behavior called the Firefly Algorithm (FFA) is applied to solve such as the multipurpose optimum power flow (OPF) and emission index (EI) problems. Our main goal is to improve the objective function necessary to achieve the best balance between production and its energy consumption, which is presented as a non-linear function, taking into account some constraints of equality and inequality. The goal is to reduce the total cost of generations, active losses, and emission index.The FFA approach was examined and tested on a standard IEEE-30 bus system. The validations of obtained results were compared with some well-known and recently published references. The efficiency and credibility of the proposed method has been proven by the obtained results.

Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization

&lt;span lang="EN-US"&gt;Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.&lt;/span&gt;

An Enhanced Adaptive Differential Evolution Approach for Constrained Optimization Problems

Effective constrained optimization algorithms have been proposed for engineering problems recently. It is common to consider constraint violation and optimization algorithm as two separate parts. In this study, a pbest selection mechanism is proposed to integrate the current mutation strategy in constrained optimization problems. Based on the improved pbest selection method, an adaptive differential evolution approach is proposed, which helps the population jump out of the infeasible region. If all the individuals are infeasible, the top 5% of infeasible individuals are selected. In addition, a modified truncated ε-level method is proposed to avoid trapping in infeasible regions. The proposed adaptive differential evolution approach with an improved ε constraint process mechanism (IεJADE) is examined on CEC 2006 and CEC 2010 constrained benchmark function series. Besides, a standard IEEE-30 bus test system is studied on the efficiency of the IεJADE. The numerical analysis verifies the IεJADE algorithm is effective in comparison with other effective algorithms.

Optimal Power Flow with Stochastic Renewable Energy Using Three Mixture Component Distribution Functions

The growing usage of renewable energy sources, such as solar and wind energy, has increased the electrical system’s unpredictability. The stochastic behavior of these sources must be considered to obtain significantly more accurate conclusions in the analysis of power systems. To depict renewable energy systems, the three-component mixture distribution (TCMD) is introduced in this study. The mixture distribution (MD) is created by combining the Weibull and Gamma distributions. The results show that TCMD is better than original distributions in simulating wind speed and solar irradiance by reducing the error between real data and the distribution curve. Additionally, this study examines the optimal power flow (OPF) in electrical networks using the two stochastic models of solar and wind energy. The parameters of the probability distribution function (PDF) are optimized using the Mayfly algorithm (MA), which also solves single- and multi-objective OPF issues. Then, to prove the accuracy of the MA method in solving the OPF problem, single- and multi-objective OPF is applied on a standard IEEE-30 bus system to minimize fuel cost, power loss, thermal unit emissions, and voltage security index (VSI), and results are compared with other metaheuristic methods. The outcomes show that the MA technique is dependable and effective in overseeing this challenging problem. Additionally, the suggested OPF MA-based is studied in the OPF problem while accounting for the uncertainty in the models of the wind and solar systems and taking the emissions, VSI, power loss, and fuel cost into consideration in the objective function. The significance of the work lies in the application of a unique optimization technique to a hybrid electrical system using TCMD stochastic model using actual wind and solar data. The proposed MA method could be valuable to system operators as a decision-making aid when dealing with hybrid power systems.

Optimal Micro-PMUs Placement with Channel Limits using Dynamically Controlled Taguchi Binary Particle Swarm Optimization

The two-directional flow of electrical power has necessitated real-time monitoring of the distribution system (DS), which can otherwise result in power system failure. A micro-phasor measurement unit (micro-PMU) is proficient monitoring equipment that is presently employed to keep track of the changes taking place in DS. However, due to the higher cost of micro-PMU, an optimal number of micro-PMUs with channel limits must be strategically placed in the system. Therefore, an optimization problem for the optimal placement of micro-PMU has been formulated and solved in the present work under different operating scenarios for complete observability with sufficient redundancy. The optimal locations of micro-PMUs with channel limits in DS are obtained using an innovative technique that combines the concepts of Taguchi method (TM) and dynamically controlled binary particle swarm optimization (DBPSO). The proposed micro-PMU placement algorithm has been tested and analyzed on standard IEEE-33 bus and IEEE-69 bus distribution feeders with two different S-shaped and V-shaped sigmoidal functions. The results demonstrate that the proposed technique utilizing V sigmoidal function provides a better solution for optimal micro-PMU placement problem. A comparative investigation of the presented work to the existing micro-PMUs placement techniques for the DS establishes the efficacy of the proposed technique.

Optimal size and location of distributed generations in distribution networks using bald eagle search algorithm

Introduction. In the actual era, the integration of decentralized generation in radial distribution networks is becoming important for the reasons of their environmental and economic benefits. Purpose. This paper investigate the optimal size, location and kind of decentralized generation connected in radial distribution networks using a new optimization algorithm namely bald eagle search. Methods. The authors check the optimal allocation of two kinds of decentralized generation the first is operated at unity power factor and the second is operated at 0.95 power factor, a multi-objective functions are minimized based on reduction of voltage deviation index, active and reactive power losses, while taking into consideration several constraints. Results. Simulation results obtained on Standard IEEE-33 bus and IEEE-69 bus radial distribution networks demonstrate the performance and the efficiency of bald eagle search compared with the algorithms existing in literature and radial distribution networks performances are improved in terms of voltage profile and notably active and reactive power losses reduction, decentralized generation operated at 0.95 power factor are more perfect than those operated at unit power factor.

Early fault detection of distribution network based on the ring law of large-dimensional matrix

When faults in the distribution network occur, they usually last for a period of time, but if cannot be detected and eliminated in time, the faults can easily expand or even cause systemic collapse. Therefore, early fault detection of distribution grid is very important for ensuring the safe operation of the system. The existing fault diagnosis approaches based on the physical mechanism model have great limitations. This paper presents an early fault detection approach based on large-dimensional matrix ring law. We first construct a spatio-temporal data matrix by using the data from the spatial multi-node of the distribution network. The moving data window method and the ring law are used to track the data behavior, and the mean spectral radius is used to characterize the data behavior. According to the test results of the IEEE standard bus system, our approach is robust to system random fluctuations and measuring errors and can identify abnormal conditions at the earliest stage of failure.

False data injection attack detection in dynamic power grid: A recurrent neural network-based method

The smart grid greatly facilitates the transmission of power and information by integrating precise measurement technology and efficient decision support systems. However, deep integration of cyber and physical information entails multiple challenges to grid operation. False data injection attacks can directly interfere with the results of state estimation, which can cause the grid regulator to make wrong decisions and thus poses a huge threat to the stability and security of grid operation. To address this issue, we propose a detection approach against false data injection attacks for dynamic state estimation. The Kalman filter is used to dynamically estimate the state values from IEEE standard bus systems. A long short-term memory (LSTM) network is utilized to extract the sequential observations from states at multiple time steps. In addition, we transform the attack detection problem into supervised learning problem and propose a deep neural network-based detection approach to identify attacks. We evaluate the effectiveness of the proposed detection approach in multiple IEEE standard bus systems. The simulation results demonstrate that the proposed detection approach outperforms benchmarks in improving the detection accuracy of malicious attacks.

Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.