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The purpose of this paper is to optimally locate and size the FACTS device, namely, interline power flow controller in order to minimize the total cost and relieve congestion in a power system. This security analysis helps independent system operator (ISO) to have a better planning and market clearing criteria during any operating state of the system.

A multi-objective optimization problem has been developed including real power performance index (RPPI) and expected security cost (ESC). A security constrained optimal power flow has been developed as expected security cost optimal power flow problem which gives the probabilities of operating the system in all possible pre-contingency and post-contingency states subjected to various equality and inequality constraints. Maximizing social welfare is the objective function considered for normal state, while minimizing compensations for generations rescheduling and maximizing social welfare are the objectives in case of contingency states. The proposed work is viewed as a two level problem wherein the upper-level problem is to optimally locate IPFC using RPPI and the lower-level problem is to minimize the ESC subjected to various system constraints. Both upper-level and lower-level problem are solved using particle swarm optimization and The performance of the proposed algorithm is tested under severe line outages and has been validated using IEEE 30 bus system.

The proposed methodology shows that IPFC controls the power flows in the network without generation rescheduling or topological changes and thus improves the performance of the system. It is found that the benefit achieved in the ESC due to the installation of IPFC is greater than the annual investment cost of the device. ISO cannot achieve minimum total system cost by merely rescheduling generators. Instead of rescheduling, FACTS devices can be used for compensation by achieving minimum cost. IPFC can be used to compensate the congested lines and transfer cheaper power from generators to consumers.

Operational reliability, financial profitability and efficient utilization of the existing transmission system infrastructure has been achieved using single FACTS device. Instead of using multiple FATCS devices, if a single FACTS device like IPFC which itself can compensate several transmission lines is used, then in addition to the facility for independently controlled reactive (series) compensation of each individual line, it provides a capability to directly transfer real power between the compensated lines. Hence an attempt has been made in this paper to incorporate IPFC for relieving congestion in a deregulated environment. However, no previous researches have considered incorporating compensation of multi-transmission line using single IPFC in minimizing ESC. Thus, in this paper, the authors indicate how much the ESC is reduced by installing IPFC.

The electric power system is a complex system, whose operating condition may not remain at a constant value. The various contingencies like outage of lines, transformers, generators and sudden increase of load demand or failure of equipments are more common. This causes overloads and system parameters to exceed the limits thus resulting in an insecure system. The purpose of this paper is to enhance the power system security by alleviating overloads on the transmission lines.

Fuzzy logic system (FLS) with particle swarm optimization based optimal power flow approach is used for overload alleviation on the transmission lines. FLS is modeled to find the changes in inertia weight by which new weights are determined and their values are applied to particle swarm optimization (PSO) algorithm for velocity and position updation.

The proposed method is tested and examined on the standard IEEE-30 bus system under base case and increased load conditions at different contingency. This method gives better results in terms of optimum fuel cost and fast convergence under base case and could alleviate the line overloads at different contingency with optimum generation cost, when compared to adaptive particle swarm optimization (APSO) and PSO.

FLS is modeled in MATLAB environment. The effectiveness of the proposed method is tested and examined on the standard IEEE-30 bus system and their results are compared with APSO and PSO under MATPOWER environment. The results show that the proposed algorithm is capable of improving the transmission security with optimum generation cost.

Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems.

This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions.

Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively.

This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

An alternative formulation of a linear method for transmission line overload alleviation is described based on the relationship between the line currents and voltage parameters defined in rectangular coordinates; the fast‐decoupled loadflow method is used for base calculations. Tests are carried out on the Saskatchewan Power Corporation 6‐bus network and the IEEE 14‐bus system.

The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations.

Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm.

An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC.

In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques.

Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems.

The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem.

The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems.

As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

Static VAR compensators (SVC) have been recognized to be one of the most important flexible AC transmission systems devices used for mitigating the low-frequency electrochemical oscillations occurring in the system and for reactive power compensation, thereby improving the overall dynamic stability and efficiency of the system. The purpose of this paper is to optimize and dynamically tune the control parameters of the classical proportional integral and derivative (PID) controller of the SVC for a two-machine system by designing a new robust optimization technique.

The angular speed deviation between the two machines is used as an auxiliary signal to SVC for generation of the required damping output. To justify the efficacy of the system undertaken, a light load fault at time t = 1 s is projected to the system. The simulation is carried out in MATLAB/Simulink architecture.

The proposed technique helps in the enhancement of system efficiency, reliability and controllability and by effectively responding to the non-linearities taking place in a power grid network. The results obtained are indicative of the fact that the proposed modified brain storming optimization (MBSO) technique reduces system disturbances very quickly, increases the system response in terms of better rise time, settling time and peak overshoot and improves the efficiency of the system.

A detailed comparison of the MBSO technique is compared with the conventional brain storming optimization (BSO) and PID technique. Total harmonic distortion through fast Fourier transform is also compiled to prove that the values of the proposed MBSO method found out to be confined well within the prescribed IEEE-514 boundaries.

This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

In the vast majority of published papers, the optimal allocation of photovoltaic distributed generation (PVDG) units and reconfiguration problems are proposed along with the number of PVDG used in the simulation. However, optimisation without selecting the number of PVDG units installed in the distribution grid is insufficient to achieve a better operational performance of power systems. Moreover, multi-objective installation of PVDG units and reconfiguration aims to simultaneously relieve congestion problems, improve voltage profile and minimise the active and reactive power losses. Therefore, this paper aims to propose a new modified camel algorithm (NMCA) to solve multi-objective problems considering radial distribution system to achieve secure and stable operation of electric power system with good performance.

In this paper, the decision variables include the location and size of PVDG units with specific rang to determine the number of PVDG units needed to install and open network lines determined using NMCA based on the L_∞ technique. This also satisfies the operating and radial constraints. Furthermore, a benchmark comparison with different well known optimisation algorithms has been made to confirm the solutions. Finally, an analysis of the findings was conducted, and the feasibility of solutions was fully verified and discussed.

Two test systems – the institute of electrical and electronics engineers (IEEE) 33-bus and IEEE 69-bus, were used to examine the accuracy and effectiveness of the proposed algorithm. The findings obtained amply proved the efficiency and superiority of the NMCA algorithm over the other different optimisation algorithms.

The proposed approach is applied to solve the installation PVDG unit’s problem and reconfiguration problem in the radial distribution system, satisfying the operating and radial constraints. Also, it minimises active and reactive power losses and improves voltage profile.

In a companion paper, a theoretical framework of an algorithm was described which determines the optimal power flows of a network via the Dommel‐Tinney approach with or without the incorporation of security constraints. This can be handled via sensitivity analysis and least‐squares minimization techniques. The aim of this paper is to report on the computational experiments of the method using the 5‐bus, 14‐ and 30‐bus networks.