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In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.

A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.

Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.

The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

Economic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel options (MFO) is defined as a non-smooth and non-convex optimization problem with equality and inequality constraints, which obliges an efficient heuristic strategy to be addressed. The purpose of this study is to present a new and powerful heuristic optimization technique (HOT) named as squirrel search algorithm (SSA) to solve non-convex ELD problems of large-scale power plants.

The suggested SSA approach is aimed to minimize the total fuel cost consumption of power plant considering their generation values as decision variables while satisfying the problem constraints. It confers a solution to the ELD issue by anchoring with foraging behavior of squirrels based on the dynamic jumping and gliding strategies. Furthermore, a heuristic approach and selection rules are used in SSA to handle the constraints appropriately.

Empirical results authenticate the superior performance of SSA technique by validating on four different large-scale systems. Comparing SSA with other HOTs, numerical results depict its proficiencies with high-qualitative solution and by its excellent computational efficiency to solve the ELD problems with non-smooth fuel cost function addressing the VPL and MFO. Moreover, the non-parametric tests prove the robustness and efficacy of the suggested SSA and demonstrate that it can be used as a competent optimizer for solving the real-world large-scale non-convex ELD problems.

This study has compared various HOTs to determine optimal generation scheduling for large-scale ELD problems. Consequently, its comparative analysis will be beneficial to power engineers for accurate generation planning.

To the best of the authors’ knowledge, this manuscript is the first research work of using SSA approach for solving ELD problems. Consequently, the solution to this problem configures the key contribution of this paper.

The purpose of this paper is to pursue two following main goals: first, theorizing a new concept named as equivalent bus load in order to make a promising simplification over power system analysis. Second, proposing an outstanding fast and simple approach based on introduced concept for voltage estimation after multiple component outages while satisfying required accuracy.

Equivalent load bus theory introduces three transfer matrices that describe power system topology. Mentioned matrices could be calculated simply after system reconfiguration without matrix inversion. Using transfer matrices a large-scale power system can be modeled by a simple two-bus power system from the viewpoint of any desired bus so that load flow calculation leads to same value. The analysis of simplified power system yields to extract a new incremental model based on equivalent bus load theory that will be distinguished as an outstanding fast method for voltage estimation aim.

A deep study for fast voltage estimation aim is dedicated to evaluate proposed method from the accuracy and quickness point of view and the outcomes are compared to a well-known method as Distribution Factors (DF). Results and computational times unveil that presented approach is more accurate and much faster.

A novel and new fast voltage estimation method for assessment of power system component outages is introduced.

The purpose of this paper is to solve commitment problem of generating units in thermal power plants and to find the optimal dispatches of the committed units.

The unit commitment (UC) problem has been solved in two stages. In the first stage, the optimal units are identified using contribution factor. Initially, the generating units to be committed for each interval in the time horizon are obtained without considering the unit operational constraints such as minimum up time, minimum down time and initial state. Then the unit operational constraints are enforced and the optimal UC schedule is obtained. In the second stage, sequential approach with a matrix framework has been proposed to obtain the optimal dispatches of the committed units.

The simple methodologies have been developed for unit selection and to find the optimal dispatches of the committed units. The results of proposed methodology illustrate an improvement in the savings of total cost. The proposed approach is computationally efficient for solving large‐scale systems and successive UC problems.

UC has a major role in electric thermal power plant operation. The problem with one day and one week scheduling horizon has a large potential of use, especially for small‐ and medium‐scale power systems. It reflects reality in a closer way and provides a more complete and realistic knowledge about the system in operation. The techniques developed for UC problem will provide a support to electric power companies for their economic operation and the concepts presented are useful in both graduate teaching and research to understand the UC problem.

The contribution of the paper is the simple methodologies which have been developed for unit selection and economic dispatch.

The purpose of this study is to address the efficiency of power losses representation while still reducing the computational burden of an optimal power flow (OPF) model in transmission expansion planning (TEP) studies.

A modified TEP model is formulated with inclusions of linearized approximation of power losses for a large-scale power system with renewable energy sources. The multi-objectives function determines the effect of transmission line losses on the optimal power generation dispatch in the power system with and without inclusion of renewable energy sources with emphasis on minimizing the investment and operation costs, emission and the power losses.

This study investigates the impact of renewable energy sources on system operating characteristics such as transmission power losses and voltage profile. Sensitivity analysis of the performance for the developed deterministic quadratic programming models was analyzed based on optimal generated power and losses on the system.

In the future, a comparison of the alternating current OPF and direct current (DC) OPF models based on the proposed mathematical formulations can be carried out to determine the efficiency and reduction of computation process of the two models.

This paper proposed an accurate way of computing transmission losses in DC OPF for a TEP context with a view of achieving a minimal computation time.

This paper addresses the following objectives: develop a modified DC OPF with a linearized approximation of power losses in TEP problem with large integration of RES. Investigate the impact of RES on system operating characteristics such as transmission power losses and voltage profile.

This paper aims to focus on the design of a decentralized observation and control method for a class of large-scale systems characterized by nonlinear interconnected functions that are assumed to be uncertain but quadratically bounded.

Sufficient conditions, under which the designed control scheme can achieve the asymptotic stabilization of the augmented system, are developed within the Lyapunov theory in the framework of linear matrix inequalities (LMIs).

The derived LMIs are formulated under the form of an optimization problem whose resolution allows the concurrent computation of the decentralized control and observation gains and the maximization of the nonlinearity coverage tolerated by the system without becoming unstable. The reliable performances of the designed control scheme, compared to a distinguished decentralized guaranteed cost control strategy issued from the literature, are demonstrated by numerical simulations on an extensive application of a three-generator infinite bus power system.

The developed optimization problem subject to LMI constraints is efficiently solved by a one-step procedure to analyze the asymptotic stability and to synthesize all the control and observation parameters. Therefore, such a procedure enables to cope with the conservatism and suboptimal solutions procreated by optimization problems based on iterative algorithms with multi-step procedures usually used in the problem of dynamic output feedback decentralized control of nonlinear interconnected systems.

Efficient calculations of the transient behaviour after disturbances of large-scale power systems are complex because of, among other things, the non-linearity and the stiffness of the overall state equation system (SES). Because of the rising amount of flexible transmission system elements, there is an increasing need for reduced order models with a negligible loss of accuracy. With the Extended Nodal Approach and the application of the singular perturbation method, it is possible to reduce the order of the SES adapted to the respective setting of the desired tasks and accuracy requirements.

Based on a differential-algebraic equation for the electric power system which is formulated with the Extended Nodal Approach, the automatic decomposition into reduced order models is shown in this paper. The paper investigates the effects of different coordinate systems for an automatic order reduction with the singular perturbation method, as well as a comparison of results calculated with the full and reduced order models.

The eigenvalues of the full system are approximated sufficiently by the three subsystems. A simulation example demonstrates the good agreement between the reduced order models and the full model independent of the choice of the coordinate system. The decomposed subsystems in rotating coordinates have benefits as compared to those in static coordinates.

The paper presents a systematic decomposition based only on a differential-algebraic equation system of the electric power system into three subsystems.

The purpose of this paper is to demonstrate the applicability of the Discrete Empirical Interpolation method (DEIM) for simulating the swing dynamics of benchmark power system problems. The authors demonstrate that considerable savings in computational time and resources are obtained using this methodology. Another purpose is to apply a recently developed modified DEIM strategy with a reduced on-line computational burden on this problem.

On-line computational cost of the power system dynamics problem is reduced by using DEIM, which reduces the complexity of the evaluation of the nonlinear function in the reduced model to a cost proportional to the number of reduced modes. The on-line computational cost is reduced by using an approximate snap-shot ensemble to construct the reduced basis.

Considerable savings in computational resources and time are obtained when DEIM is used for simulating swing dynamics. The on-line cost implications of DEIM are also reduced considerably by using approximate snapshots to construct the reduced basis.

Applicability of DEIM (with and without approximate ensemble) to a large-scale power system dynamics problem is demonstrated for the first time.

The purpose of this paper is to present a practical method for computing contingency‐based reliability and quality indices in power systems and to answer questions related to how much the system is reliable, how robust it is in surviving random contingencies, how much it is costing to maintain appropriate system security and reliability levels and, finally, to what extent the desired balance is maintained between generation facilities, transmission capabilities and consumer demand levels in various zones of the electric power system.

The methodology adopted in this paper is based on a combined contingency analysis/reliability evaluation scheme. A three‐component system model is utilized, which can be used effectively for evaluation and sensitivity analysis of reliability and quality in power systems. The model is a reduced (equivalent) system representation that comprises generation, transmission and load components with multi‐state values. The computational scheme presented in the paper integrates both the contingency effect and its probability of occurrence into one routine of analysis while reducing the power system around the region of interest.

The computational scheme presented in the paper can effectively assess both service reliability and system quality. The practical applications presented demonstrated that lower service reliability levels would jeopardize energy supply continuity and increase the likelihood of additional maintenance and restoration costs due to the resulting higher rate of system outages. Poor system quality levels, on the other hand, imply either deficiency or excess in the overall system capabilities as designed by its planners.

The work of this paper contributes to the solution of the reliability and quality assessment problem in practical power systems. As part of the present work, an advanced computerized scheme for fast composite system reliability and quality assessment was developed and then applied to an equivalent system model of the Saudi electricity system. The results obtained are claimed to have far‐reaching implications on various planning and operation aspects of the power system.

For one thing, despite the fact that it is popular to research the minimization of the power losses in power systems, the optimization of single objective seems insufficient to fully improve the performance of power systems. Multi-objective VAR Dispatch (MVARD) generally minimizes two objectives simultaneously: power losses and voltage deviation. The purpose of this paper is to propose Multi-Objective Enhanced PSO (MOEPSO) algorithm that achieves a good performance when applied to solve MVARD problem. Thus, the new algorithm is worthwhile to be known by the public.

Motivated by differential evolution algorithm, cross-over operator is introduced to increase particle diversity and reinforce global searching capacity in conventional PSO. In addition to that, a constraint-handling approach considering Constrain-prior Pareto-Dominance (CPD) is presented to handle the inequality constraints on dependent variables. Constrain-prior Nondominated Sorting (CNS) and crowding distance methods are considered to maintain well-distributed Pareto optimal solutions. The method combining CPD approach, CNS technique, and cross-over operator is called the MOEPSO method.

The IEEE 30 node and IEEE 57 node on power systems have been used to examine and test the presented method. The simulation results show the MOEPSO method can achieve lower power losses, smaller voltage deviation, and better-distributed Pareto optimal solutions comparing with the Multi-Objective PSO approach.

The most original parts include: the presented MOEPSO algorithm, the CPD approach that is used to handle constraints on dependent variables, and the CNS method which is considered to maintain a well-distributed Pareto optimal solutions. The performance of the proposed algorithm successfully reflects the value of this paper.