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The emerging electricity market behaves more like an oligopoly than a perfectly competitive market. Each supplier and consumer will have profits of varying extents influenced by differences in the degree of imperfection of knowledge of their rivals. This paper discusses the optimal bidding strategies of the Load Serving Entities (LSEs) using Differential Evolution (DE) algorithm. It is assumed that each LSE bids a linear demand function and chooses the coefficient in the linear demand function to maximize their benefits, subject to expectations of the bidding of the rivals. Bidding behaviors of the rivals are described using a normal Probability Distribution Function (PDF), and the problem of building optimal bidding strategies for LSE is formulated as stochastic optimization problem. The proposed algorithm is tested with three LSEs-three large consumers and three generating companies. The results obtained are compared with those obtained by other optimization techniques. Transmission line flow limits are incorporated and contingency analysis is also carried out for a nine bus system, and the system security and stability are verified. [ABSTRACT FROM AUTHOR]

To meet the growing load demand, power industries throughout the world are undergoing a restructuring process. The Independent Power Producers (IPP) must respond quickly to those load changes with respect to time. This paper describes the solution for Optimal Power Flow (OPF) in a deregulated environment using Differential Evolution (DE) technique. The proposed approach is capable of obtaining minimum solution irrespective of the nature of the objective function. The effectiveness of the approach is compared with the performance of Particle Swarm Optimization (PSO) technique. The algorithm has been demonstrated on IEEE-14, 30 systems. [ABSTRACT FROM AUTHOR]