Managing transmission congestion had been a major problem with growing competition in the power networks. Accordingly, competitiveness emerges through the network’s reconfiguration and the proliferation of secondary facilities. Congestion of transmission lines is a critical issue, and their regulation poses a technical challenge as the power system is deregulated. Therefore, the present research illustrates a multi-objective strategy for reaching the optimal capabilities of distributed generators (DG) like wind power plants and geothermal power-producing plants to alleviate congestion throughout the transmission network. Goals such as congestion management during power delivery, power loss reduction, power flow improvement with the enhancement of voltage profile, and investment expenditure minimization are considered to boost the network’s technological and economic reliability. The congestion management is achieved using the locational marginal price (LMP) and calculation of transmission congestion cost (TCC) for the optimal location of DG. After identification of congested lines, DG is optimally sized by particle swarm optimization (PSO) and a newly proposed technique that combines the features of modified IL-SHADE and PSO called hybrid swarm optimization (HSO) which employs linear population size reduction technique which improves its performance greatly by reducing the population size by elimination of least fit individuals at every generation giving far better results than those obtained with PSO. In addition, optimal rescheduling of generations from generators has been done to fulfill the load demand resulting in alleviation of congested lines thereby enhancing the performance of the network under investigation. Furthermore, the performance of the proposed methodology of HSO and PSO has been tested successfully on standard benchmark IEEE-30 & IEEE-57 bus configurations in a MATLAB environment with the application of MATPOWER power system package.

This article presents the selection of location and sizing of multiple distributed generators (DGs) for boosting performance of the radial distribution system in the case of constant power load flow and constant impedance load flow. The consideration of placing and sizing of DGs is to meet the load demand. This article tries to overcome the limitations of existing techniques for determining the appropriate location and size of DGs. The selection of DG location is decided in terms of real power losses, accuracy, and sensitivity. The size of DG is measured in terms of real and reactive power. Both positioning and sizing of DG are analyzed with the genetic algorithm and the heuristic probability distribution method. The results are compared with other existing methods such as ant-lion optimization algorithm, coyote optimizer, modified sine-cosine algorithm, and particle swarm optimization. Further, the power quality improvement of the network is assessed by positioning D-STATCOM, and its location is decided on the basis of the nearby bus having poor voltage profile and high total harmonic distortion (THD). The switching and controlling of D-STATCOM are assessed with fuzzy logic controller (FLC) for improving the performance parameters such as voltage profile and THD at that particular bus. The proposed analytical approach for the system is tested on the IEEE 33 bus system. It is observed that the performance of the system with the genetic algorithm gives a better solution in comparison to heuristic PDF and other existing methods for determining the optimal location and size of DG. The introduction of D-STATCOM into the system with FLC shows better performance in terms of improved voltage profile and THD in comparison to existing techniques.

This paper proposes a method to manage congestion in deregulated environment using particle swarm optimization technique with improved time-varying acceleration coefficients (PSO-ITVAC). The congestion is alleviated by optimally rescheduling the active power outputs of generators selected based on the magnitude of generator sensitivities to the congested line. The cost of rescheduling is minimized using PSO-ITVAC. The proposed algorithm is tested on IEEE 30-bus system and IEEE 118-bus system. A 33-bus Indian network is also taken as a test network for analyzing the effectiveness of the proposed algorithm. The results obtained using proposed algorithm is compared with those obtained by using particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC).

Electricity market economics is one of the vital factors which play a key role in achieving the efficient operation of the power system. Economic operation along with secure & reliable operation is must for ensuring optimum utilization of the resources in order to maximize social welfare. This article presents a method for achieving economic operation of electricity market in case of congestion occurring in the system. The generator sensitivity (GS) method alone can manage the system congestion, however it does not consider the economic benefits which can be achieved by considering the bids submitted by the generators and as a result it leads to situation of market power. To avoid such scenario, bid sensitivity factor (BSF) is proposed to select the generators to participate and regulate their real power generation for alleviating congestion from the system optimally. The BSF takes into account the GS as well as bids of the generators. In this paper, the effectiveness of the proposed method in eliminating market power is realized by changing the bids of the generators. The proposed method is tested on IEEE 30‐bus system and IEEE 118‐bus system. The obtained results show an encouraging pattern for managing the congestion more optimally and economically.