A novel stochastic search method for the solution of economic dispatch problems with non-convex fuel cost functions

This paper develops a Novel Stochastic Search (NSS) method for the solution of economic dispatch problems with non-convex fuel cost functions. The NSS solution procedure consists of three steps, namely Direct Search (DS), Goal Neighborhood Approximation (GNA) and Marginal Cost Dispatch (MCD). The DS step identifies a set of feasible solutions in accordance with prescribed equality and inequality constraints. The GNA step processes those feasible solutions to identify an appropriate direction for searching the global optimal solution. Finally, in the MCD step, the marginal cost of each generating unit is regulated in order to establish the global optimal solution. The proposed NSS scheme is applied to solve three examples systems of increasing complexity. The results are compared to those obtained using the conventional Simulated Annealing (SA), Genetic Algorithm (GA), and Evolutionary Programming (EP) methods. The results demonstrate that the NSS method provides a fast, robust and highly effective scheme for the solution of economic dispatch.

► We develops a Novel Stochastic Search (NSS) method for the solution of economic dispatch problems.
► NSS employs a stochastic technique to enhance the search performance which leads to a higher probability of obtaining the global optimum.
► The NSS method successfully accommodates the non-linear characteristics of the cost functions and achieves a reasonable execution time.
► NSS can achieve a better performance than other evolutionary algorithms.