Dynamic economic load dispatch in microgrid using hybrid moth-flame optimization algorithm

This paper focuses to identify and validate a more appropriate algorithm to solve the proposed problem. The economic load dispatch (ELD) with the emission parameters becomes more complex and diversified on the involvement of renewable energy sources (RES), and this increases the number of constraints incorporation in the distributed system of classical power system. The integrated economic emission dispatch (IEED) and ELD are a measure of minimizing the fuel cost of generators, minimizing the emitted gaseous pollutants of the conventional generators (CGs) due combustion, and fulfilling the load demand while satisfying the given system constraints. This paper develops and proposes the hybrid moth-flame and mayfly optimization algorithm (MFMFOA), the hybridization of moth-flame optimization (MFO), and mayfly algorithm (MFA) to solve the integrated problem of economic load and emission dispatch, while removing the constraints of the existing optimization algorithms. The overall cost saving of 10.03% and 23.95% was obtained in ELD and IEED, respectively, using MFMFOA. The MFMFOA when correlated with reduced gradient method (RGM), ant colony optimization (ACO), cuckoo search algorithm (CSA), interior search algorithm (ISA), bat algorithm (BA), directional bat algorithm (DBA), and MFO for different cases of ELD and IEED have obtained the comparatively minimum operational cost, a closer global optimum point, and better convergence characteristics.