An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch

Economic dispatch is carried out at the energy control center to find out the optimal output of thermal generating units such that power balance criterion is met, unit operating limits are satisfied and the fuel cost is minimized. With growing environmental awareness and strict government regulations throughout the world, it has become essential to optimize not only the total fuel cost but also the harmful emissions, both, under static as well as dynamic conditions. The static environment economic dispatch finds the optimal output of generating units for a fixed load demand at a given time, while the dynamic environmental economic dispatch schedules the output of online generators with changing power demands over a certain time period (normally one day) so as to minimize these two conflicting objectives, simultaneously. In this paper, the price penalty factor approach is employed for simultaneous minimization of cost and emission. The generator ramp rate constraints, non-convex and discontinuous nature of cost function and the large number of generators in practical power plants, make this problem very difficult to solve. Here, a fuzzy ranking approach is employed to identify the solution which offers the best compromise between cost and emission objectives.This paper proposes an improved bacterial foraging algorithm (IBFA) in which a parameter automation strategy and crossover operation is used in micro BFA to improve computational efficiency. The performance of IBFA is compared with classical BFA and with previously published literature on four standard test systems and is found to be better.

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► An improved bacterial foraging algorithm is proposed for solving static/dynamic environmental economic dispatch problem.
► A parameter automation strategy and crossover operation is used in micro BFA to improve computational efficiency.
► A price penalty factor is used to find the Pareto-optimal solutions.
► Best solution is identified using a fuzzy ranking method.