Abstract
Along with economic dispatch, emission dispatch has become a key problem under market conditions. Thus, the combination of the above problems in one problem called economic emission dispatch (EED) problem became inevitable. However, due to the dynamic nature of today’s network loads, it is required to schedule the thermal unit outputs in real-time according to the variation of power demands during a certain time period. Within this context, this paper presents an elitist technique, the second version of the non-dominated sorting genetic algorithm (NSAGII) for solving the dynamic economic emission dispatch (DEED) problem. Several equality and inequality constraints, such as valve point loading effects, ramp rate limits and prohibited operating zones (POZ), are taken into account. Therefore, the DEED problem is considered as a non-convex optimization problem with multiple local minima with higher-order non-linearities and discontinuities. A fuzzy-based membership function value assignment method is suggested to provide the best compromise solution from the Pareto front. The effectiveness of the proposed approach is verified on the standard power system with ten thermal units.
Highlights
In electric power systems, improvement of operation and planning has become more important under the current market conditions and several tools have been developed in this context [1, 2]
The contribution of this study is to show that the NSGA approach used frequently for solving continuous problems can be efficient for non-smooth and non-convex dynamic economic emission dispatch (DEED) problems if a non-domination sorting technique is incorporated in the optimization algorithm
The effectiveness of the proposed optimization algorithm for solving the DEED problem is assessed on the 10-unit system
Summary
Improvement of operation and planning has become more important under the current market conditions and several tools have been developed in this context [1, 2]. DEED is a dynamic optimization problem having the same objectives as SEED over a time period subdivided into smaller time intervals with respect to the constraints imposed on system operation by generator ramp-rate limits. The DEED becomes highly nonlinear and with discontinuous and non-convex cost functions Within this context, this paper presents an elitist multiobjective approach for solving the DEED problem including POZ, valve point loading effects, and ramp rate limit constraints. This paper presents an elitist multiobjective approach for solving the DEED problem including POZ, valve point loading effects, and ramp rate limit constraints This proposed method, called second version of the non-dominated sorting genetic algorithm (NSAGII), incorporates a crowding distance comparison at the end of each iteration in order to facilitate the convergence of the optimization algorithm to the real Pareto optimal front. This approach showed a very competitive performance when compared with the original NSGA algorithm
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