Minimizing electricity generation cost which includes fuel cost, emission cost, operation/maintenance cost and network loss cost of multiple operating units has been a major issue in the power sector. The economic dispatch has the objective of allocating different loads to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. Conventional optimization methods assume generator cost curves to be continuous and monotonically increasing, but modern generators have a variety of nonlinearities in their cost curves making this assumption inaccurate, and the resulting approximate dispatches cause a lot of revenue loss. Computational intelligence optimization like Particle Swarm Optimization performs better for such problems. To know the effectiveness and efficiency in solving economic dispatch, this paper proposes the application of particle swarm optimization. The mathematical model of economic dispatch is developed and then, Particle Swarm Optimization is developed to solve the economic dispatch problem using 3-generator and 6-generator system with multiple fuel option. The test results clearly demonstrated that particle swarm optimization which is capable of achieving global solutions is simple, excellent computationally efficiency and has better and stable dynamic convergence characteristics with a high probability.
Application of Particle Swarm Optimization in Solving Economic Dispatch with Multiple Fuel Options
