Cooperative optimal operation of hybrid energy integrated system considering multi-objective dragonfly algorithm

To meet the recent energy demand, hybrid energy systems (HESs) play an important role in providing more stable power as well as backup power to the grid. It is always preferable for the power system network to be operated in a secure, dependable, stable, and cost-effective manner. The optimal power flow (OPF) problem is used to determine the ideal power system network control parameter settings. The majority of the OPF problem, fuel cost reduction is treated as an objective function, but the environmental impact of the generating is ignored. Both fuel expense and emission are treated as objective functions of the work in this case. This research describes a solution for multi-objective optimal power flow (MOOPF) with a HES integrated conventional power system. HES in this work is made up of a wind park and a compressed air energy storage system (CAES). In this case, locational marginal pricing (LMP) is employed to determine the best location of HES in the power system. To achieve the goals of this work, the multi-objective dragonfly algorithm (MODA) is used. The IEEE 30 bus system is used to evaluate the MODA. The MODA method findings are validated against other well-known optimization algorithms such as the multi-objective ant colony system (MOACS) algorithm, multi-objective enhanced self-adaptive differential evolution (MOESDE) algorithm, modified multi-objective evolutionary algorithm-based decomposition (MOEA/D), multi-objective particle swarm algorithm (MOPOSO), and non-dominated sorting genetic algorithm (NSGA-II). image