Congestion Management In Electricity Market Research Articles

Bid responsive generation rescheduling for congestion management in electricity market

AbstractElectricity 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.

Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser

Considering system uncertainties prevailing in the restructured power system, congestion management (CM) is a vital issue in power system operation and control. The objective of CM is to remove congestion in the lines while satisfying all the constraints with minimum congestion cost. This study proposes a generation rescheduling-based approach for CM in electricity market using a novel ant lion optimiser (ALO) algorithm. ALO is a recently developed the nature-inspired algorithm based on the hunting mechanism of antlions. The effectiveness of the proposed approach is tested on modified IEEE 30-bus, modified IEEE 57-bus and IEEE 118-bus test systems. The security constraints like load bus voltage and line loading impact are incorporated in this study. To prove the validity of the proposed technique, the obtained results are compared to the results offered by various recent optimisation algorithms. The results show that the proposed ALO algorithm outperforms the other comparative algorithms. The proposed approach uses less number of fitness function evaluations, not traps into local minima and offers promising convergence characteristic. The proposed approach will ease the system operator to remove the contingency rapidly for secured and reliable operation of the power system under deregulated environment.