A novel scheduling approach of stochastic cogeneration model in power system environment using improved civilized swarm search algorithm to reduce cost and carbon emission

This study explores a cogeneration system's optimal commitment and generation scheduling while considering power and heat demand uncertainty. The deterministic model corresponding to the Cogeneration Based Unit Commitment Problem (CBUCP) cannot express the uncertainties related to input data and energy demand. In the present work, the stochastic Multi-Objective Cogeneration Based Unit Commitment Problem (MO-CBUCP) aims to obtain the optimal generation schedule at minimum operational cost and emission, considering uncertainties in the heat and power demand. To deal with continuous and binary variables of MO-CBUCP and obtain an optimal solution, an effective combination of nature-inspired techniques such as Civilized Swarm Search Algorithm (CSSA) and Binary Particle Swarm Optimization (BPSO) is employed for the cogeneration-based test system. To improve the exploration ability of CSSA, the predator and seasonal effect has been incorporated, as the movement of particles is affected by weather changes. Improved CSSA effectively balances the exploration and exploitation during the optimization. The results of the proposed technique are compared with other optimization techniques to show efficacy. The comparative analysis shows that the cogeneration plant significantly affects operating cost and emission for the deterministic and stochastic models.