Optimal spot pricing in electricity market with inelastic load using constrained bat 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. Spot pricing or Locational Marginal Pricing (LMP) or Nodal pricing is a popular method in restructured power markets to address these issues. This paper presents a DC optimal power flow (DCOPF) based spot pricing approach in single auction model with fuel cost minimization as objective function. This is solved with a heuristic technique called Bat algorithm and the results are compared with Linear Programming (LP) and Genetic algorithm (GA) approaches in a constrained pool based restructured electricity market. The developed models have been tested on IEEE 14 bus system, New England 39 bus system and 75 bus Indian practical power system. Different cases such as without loss, concentrated loss and distributed loss are considered for this problem. Two types of generator bids i.e., fixed bids and linear bids are considered for generators. Load is assumed to be inelastic. Generator profit, ISO profit and Social surplus during congestion have been computed in all the cases. In most of the cases studied, Bat algorithm is proven to be better than LP and GA algorithms for fuel cost minimization and social welfare (Social surplus) improvement.