Estimation of Locational Marginal Price in a Restructured Electricity Market with Different Loss Cases using Seed Genetic Algorithm

Abstract

In restructured electricity markets, an effective transmission pricing is required to address transmission issues and to generate correct economic signals. These prices depend on generator bids, load levels and transmission network constraints. A congestion charge is incurred when the system is constrained due to physical limitations. Locational marginal pricing (LMP) is a popular method in restructured power markets to address these issues. Seed genetic algorithms performs powerful global searches and is a well-proven optimization algorithm. This paper combines a seed Genetic Algorithm approach with DC optimal power flow (DCOPF) to estimate LMP at all buses while minimizing the net system generation costs or fuel cost in a constrained pool-based restructured electricity market. Various cases like LMP without loss, concentrated loss and distributed loss have been attempted. Both fixed bids and linear bids are considered for generators. Load is assumed to be inelastic. The developed models have been tested on IEEE 14 bus, New England 39 bus and 75 bus Indian Power systems. Comparison is made between linear programming-based DCOPF using Power World Simulator and the developed GA approach for all cases of fuel cost. In all the cases studied, GA approach is found to estimate better LMP and minimum fuel cost. ISO profits during congestion have also been evaluated in all cases. In this paper the proposed distributed loss model is stated to be the feasible operation compared with concentrated loss model.