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This paper aims to survey the need for full capacity utilisation of transmission lines in power systems network operations. It proposes a review of the N-1 security criterion that does not ensure reliable dispatch of optimum power flow during outage contingency. The survey aims to enlarge the network capacity utilisation to rely on the entire transmission lines network operation.

The paper suggests transmission line switching (TLS) approach as a viable corrective mechanism for power dispatch. The TLS process is incorporated into a constraint programming language extension optimisation solver that selects the switchable line candidates as integer variables in the mixed integer programming problem.

The paper provides a practical awareness of reserve capacity in the lines that provide network security in outage contingency. At optimum power flow dispatch, the TLS is extended to optimal transmission line switching (OTLS) that indicates optimal capacity utilisation (OCU) of the available reserve capacity (ARC) in the network lines.

Computational efficiency influenced the extension of the OTLS to optimal transmission switching of power flow (OTSPF). The application of OTSPF helps reduce the use of flexible AC transmission systems (FACTS) and construction of new transmission lines..

The paper surveys TLS efforts in network capacity utilisation. The suggested ARC fulfils the need for an index with which the dispatchable lines may be identified for the optimal capacity utilisation of transmission lines network.

The purpose of this study is to obtain the optimal frequency for low-frequency transmission lines while minimizing losses and maintaining the voltage stability of low-frequency systems. This study also emphasizes a reduction in calculations based on mathematical approaches.

Telegrapher’s method has been used to reduce large calculations in low-frequency high-voltage alternating current (LF-HVac) lines. The static compensator (STATCOM) has been used to maintain voltage stability. For optimal frequency selection, a modified Jaya algorithm (MJAYA) for optimal load flow analysis was implemented.

The MJAYA algorithm performed better than other conventional algorithms and determined the optimum frequency selection while minimizing losses. Voltage stability was also achieved with the proposed optimal load flow (OLF), and statistical analysis showed that the proposed OLF reduces the frequency deviation and standard error of the LF-HVac lines.

The optimal frequency for LF-HVac lines has been achieved, Telegrapher’s method has been used in OLF, and STATCOM has been used in LF-HVac transmission lines.

This paper aims to propose a novel methodology for optimal voltage source converter (VSC) station installation in hybrid alternating current (AC)/direct current (DC) transmission networks.

In this analysis, a unified power flow model has been developed for the optimal power flow (OPF) problem for VSC-based high voltage direct current (VSC-HVDC) transmission network and solved using a particle swarm optimization (PSO) algorithm. The impact of the HVDC converter under abnormal conditions considering N-1 line outage contingency is analyzed against the congestion relief of the overall transmission network. The average loadability index is used as a severity indicator and minimized along with overall transmission line losses by replacing each AC line with an HVDC line independently.

The developed unified OPF (UOPF) model converged successfully with (PSO) algorithm. The OPF problem has satisfied the defined operational constraints of the power system, and comparative results are obtained for objective function with different HVDC test configurations represented in the paper. In addition, the impact of VSC converter location is determined on objective function value.

A novel methodology has been developed for the optimal installation of the converter station for the point-to-point configuration of HVDC transmission. The developed unified OPF model and methodology for selecting the AC bus for converter installation has effectively reduced congestion in transmission lines under single line outage contingency.

To realize the operation optimizing of today’s distribution power system (DPS), like economic dispatch, contingency analysis, and reliability and security assessment etc., it is beneficial and indispensable that a faster linear load flow method is adopted with a reasonable accuracy. Considering the high R/X branch ratios and unbalanced features of DPS, the purpose of this paper is to propose a faster and non-iterative linear load flow solution for DPS.

Based on complex function theory, the derivations of the injection current linear approximation have been proposed for the balanced and the single-, double- and three-phase unbalanced loads of DPS on complex plane. Then, a simple and direct linear load flow has been developed with loop-analysis theory and node-branch incidence matrix.

The methodology is appropriate for balanced and single-, double- and three-phase hybrid distribution system with different load models. It provides a fast and robust load flow method with a satisfactory accuracy to handle the problems of DPS whenever the load flow solutions are required.

The distributed generators (DGs) with unity or fixed power factors can be easily included. But the power and voltage nodes cannot be dealt with directly and need to be further studied.

By combining the current linear approximation with the loop theory-based method, a new linear load flow method for DPS has been proposed. The method is valid and acute enough for balanced and unbalanced systems and has no convergent problems.

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.

The purpose of this study is to provide a non-iterative linear method to solve the power flow equations of alternating current (AC) power grid. Traditional iterative power flow calculation is limited in speed and reliability, and it is unsuitable for the real-time and online applications of the modern distribution power system (DPS). Thus, it would be of great significance if a fast and flexible linear power flow (LPF) solution could be introduced particularly necessary for the robust and fast control of DPS, especially when the system consists of star and delta connections ZIP load (a constant impedance, Z, load, a constant current, I, load and a constant power, P, load) and the high penetration of distributed solar and wind power generators.

Based on the features of DPS and considering the approximate balance of three-phase DPS, several approximations corresponding to the three-phase power flow equations have been discussed and analyzed. Then, based on those approximations, two three-phase LPF models have been developed under the polar coordinates. One model has been formulated with the voltage magnitudes [referred to the voltage magniudes based linear power flow method (VMLPF)], and another model has been formulated with the logarithmic transform of voltage magnitudes [referred to the logarithmic transform of voltage based linear power flow method LGLPF)].

The institute of electrical and electronic engineers (IEEE) 13-bus, 37-bus, 123-bus and an improved 615-bus unbalanced DPSs are used to test the performances of the methods considering star and delta connections ZIP load and PV buses (voltage-controlled buses). The test results validate the effectiveness and accuracy of the proposed two models. Especially when considering the PV buses and delta connection ZIP load, the proposed two models perform much well. Moreover, the results show that VMLPF performs a bit better than LGLPF.

Except for the transformer with Yg–Yg connection winding can be dealt with directly, the transformers with other connections are not discussed in this proposed paper and need to be further studied.

These proposed two models can deal with ZIP load with star and delta connections as well as multi slack buses and PV buses. The single-phase, two-phase and three-phase hybrid networks can be directly included too. The proposed two models are capable of offering enough accuracy level, and they are therefore suitable for online applications that require a large number of repeated power flow calculations.

The purpose of this work is that of providing the guidelines of an efficient implementation of power flow computations using the MATLAB computation environment.

The goal of obtaining high efficiency from MATLAB programs often proves elusive unless special care is taken in exploiting the vectorising capability of MATLAB programming. In the present paper the implementation of Newton‐Raphson power flow in MATLAB is examined with particular emphasis on the way of obtaining a vectorisable code capable of achieving effective numerical performance by exploiting its formulation in terms of complex variables.

Tests on actual networks with up to 1,300 buses are presented. They show that the complex power flow is as efficient as the best implementations of the Newton Raphson power flow using real variables, as long as the operations involved are reordered with the aim of exploiting the vectorisation capabilities of the MATLAB environment.

It is shown that improved numerical efficiency in the MATLAB can be obtained through its formulation in terms of complex variables. The complex Newton‐Raphson load flow, not very common in practical uses, is shown to have many desirable qualities from the point of view of MATLAB programming and is presented in detail.

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.

The purpose of this paper is to introduce a neural network‐based market agent, which develops optimal bidding strategies for a power generating company (Genco) in a day‐ahead electricity market.

The problem of finding optimal bidding strategy for a Genco is formulated as a two‐level optimization problem. At the top level, the Genco aims at maximizing its total daily profit, and at the bottom level, the independent system operator obtains the power dispatch quantity for each market participant with the objective of maximizing the social welfare. The neural network is trained using a particle swarm optimization (PSO) algorithm with the objective of maximizing daily profit for the Genco.

The effectiveness of the proposed approach is established through several case studies on the benchmark IEEE 30‐bus test system for the day‐ahead market, with an hourly clearing mechanism and dynamically changing demand profile. Both block bidding and linear supply function bidding are considered for the Gencos and the variation of optimal bidding strategy with the change in demand is investigated. The performance is also evaluated in the context of the Brazilian electricity market with real market data and compared with the other methods reported in the literature.

Strategic bidding is a peculiar phenomenon observed in an oligopolistic electricity market and has several implications on policy making and mechanism design. In this work, the transmission line constraints and demand side bidding are taken into account for a more realistic simulation.

To the best of the authors' knowledge, this paper has introduced, for the first time, a neural network‐based market agent to develop optimal bidding strategies of a Genco in an electricity market. Simulation results obtained from the IEEE 30‐bus test system and the Brazilian electricity market demonstrate the superiority of the proposed approach, as compared to the conventional PSO‐based method and the genetic fuzzy rule‐based system approach, respectively.

Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs.

The problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO.

Taking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible.

Developing a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.