Large-scale multi-area economic/emission dispatch based on a new symbiotic organisms search algorithm

The objective of the multi-area economic/emission dispatch problem is to establish an optimum programme (online) for the thermal generating units which operate within different areas of a power system and to determine the powers transferred between the areas so that the operation cost and/or the emissions over the entire system to be minimal, with the satisfaction of several constraints. The characteristics/constraints considered for the analysed systems (the valve-point effects, multi-fuel sources, transmission line losses, prohibited operating zones or the constraints referring to the areas of the system or the interconnecting lines) define the non-linear optimization models with discontinuous and non-smooth cost functions. This paper proposes a new and efficient variety of the Symbiotic Organisms Search (SOS) algorithm, called New SOS (NSOS), to solve the multi-area economic/emission dispatch problem. The modifications brought on the original algorithm refer to the relations which control the updating of the solutions during the iterative process, the elimination of the parasitism phase and the evaluation of the solutions after the updating of each component. The possibility that the modifications introduced in the NSOS algorithm to produce positive effects is investigated, in order to obtain better, more stable and more efficient solutions than the original SOS algorithm or other algorithms used to solve the multi-area economic/emission dispatch problems. The multi-area economic/emission dispatch problem is solved in two stages. The first stage identifies the optimum powers of the generators so that the costs/emissions to be minimum, considering that the units operate in a single area. In the second stage, starting from the results obtained in the first stage, the transferred powers between the areas of the system are determined, so that the balance of the powers to be satisfied for each area of the analysed system. The performance of the proposed NSOS algorithm is tested in nine cases (on five systems with different characteristics comprising 6-unit, 10-unit, 40-unit, 120-unit and 130-unit), the majority of which analysing large-scale systems. The results obtained by using the NSOS algorithm show it performs better than the SOS algorithm or other algorithms reported in literature.