An evolutionary algorithm for bid-based dynamic economic load dispatch in a deregulated electricity market

Evolutionary algorithms for bid-based dynamic economic load dispatch: A large-scale test case

“…In this work, we adapt the results of BBDELD problem from [9], for the Nigerian deregulated electricity market, consisting of 40 generating power stations and 11 customers in 24-hour dispatch periods. In the solution model, we investigate the performance of two optimization solution approaches that have recently provided superior results in the bidding context in [9], and now extend the problem to a largescale test case.…”

“…The DELD has also been the topic of optimizationcentered research [5][6][7][8]. Finally, perhaps the most sophisticated form of the DELD, which is beginning to be addressed, is the bid-based version (BBDELD) [9][10][11], which considers the DELD in the context of a competitive and deregulated electricity market. In this paper we use evolutionary algorithms to address it (and are able to compare with previous work by focusing on part of the solution relating to a single dispatch period and hence relating to previously addressed BBELDs).…”

“…However, the literature of EA applications in this area, particularly as regards ELD problems, reveals that many different algorithms have been explored (particle swarm optimization, evolution strategies, differential evolution, including many variants of these, and similar algorithms; but there have been few or no attempts to design variants of such approaches that are intelligently tailored to the specific features of ELD problems. We developed a smart evolutionary algorithm (SEA), which combines a standard EA, with a "smart mutation" operator, that is tailored to the problem domain [1,5,9]. This operator focuses mutation on genes contributing most to cost and penalty violations in the fitness function.…”

Novel Adaptive Evolutionary Computation Approaches to the Dynamic Economic Load Dispatch Problems with Non-Smooth Fuel Cost Function

“…An investigation is made of three dynamic optimization approaches (D1, D2 and D3) in conjunction with three smart evolutionary algorithms (SEA1, SEA2 and SEA3) from the three variants of smart mutation operators developed in a previous work of [20], to solve the DELD problem with valve-point effects and ramp-rate constraints for a 24-hour dispatch period. Assuming there are N generators and T dispatch periods, the control variables are represented by an array with dimension N x T elements, and output of generator i at time t is given by Pg i , t ; ranging from Pg 11 to Pg NT :…”

“…vii. The genes with the highest cost, including those that violate load balance and/or ramp-rates constraints are subject of the smart mutation, see [20] for details. …”