Optimal Economic–Environmental Operation for AC-MTDC Grids by Improved Crow Search Algorithm

Abstract

This article develops a nonlinear, multimodal, and multiobjective formulation of the combined economic environmental operation (CEEO) problem in hybrid AC-multiterminal (AC-MT) high-voltage direct current grids. In these grids, the technologies of voltage source converters support more active and reactive power control in AC grids. The aim of the CEEO issue is to minimize the overall cost of fuel and the pollutant emissions of generators. Also, transmission loss minimization is another target. An improved crow search algorithm (ICSA) is proposed for obtaining the solution of the formulated problem. The proposed ICSA combines the merits of CSA by randomly switching into local search around the best crows’ position. Pareto dominance is activated to improve the crow's memory and the external repository for multiobjective models. The ICSA is tested on modified IEEE 30-bus, the Egyptian West Delta Power Network, and the large-scale 118-bus system to solve the CEEO problem in AC-MTDC grids. The simulation results illustrate the proposed ICSA capability for finding diversified Pareto solutions with several possible operating points. Furthermore, the effectiveness of the proposed ICSA is demonstrated in terms of its solution robustness compared with previous techniques.