Covariance matrix adapted evolution strategy-based decentralised congestion management for multilateral transactions

Abstract

This study proposes AC load flow-based decentralised model for congestion management in the forward markets using resource allocation technique. In this model, each transaction maximises its profit under the limits of transmission line capacities allocated by independent system operator. The voltage and reactive power impact of the system are also incorporated in the model. A covariance matrix adapted evolution strategy (CMAES) algorithm is used to solve decentralised congestion management problem for multilateral transactions. The proposed approach is tested on IEEE 30 bus, IEEE 118 bus and practical Indian utility 62 bus systems for three, six and two multilateral transactions, respectively, with smooth and non-smooth cost functions. The results obtained for decentralised model using CMAES algorithm is compared with particle swarm optimisation (PSO) algorithm and sequential quadratic programming (SQP) technique. The statistical performances of various algorithms such as best, worst, mean and standard deviations of social welfare for 20 independent trials are given. To prove the validity of the proposed improved decentralised model, the results obtained are also compared with centralised model. Simulation results clearly demonstrate the better performance of CMAES algorithm over PSO and SQP.