Multi-objective generation scheduling of integrated energy system using hybrid optimization technique

This paper formulates multi-objective (MO) generation scheduling problem of an integrated energy system (IES). The integrated energy system comprises combined heat and power (CHP), hydroelectric, thermal and heat units. The interdependency of heat and power in CHP units, water transport delay among various multi-chain hydroelectric units and valve point loading effect of thermal units makes the generation scheduling problem a complex, constrained, discontinuous, non-differentiable and multimodal optimization problem. The main motive of this research work is to concurrently reduce the overall generation cost and pollutant emissions emitted by various generating units. The generation scheduling problem is treated as a MO problem owing to the contradictory nature of these objectives. Thus, a very proficient hybrid optimization approach, i.e., quantum-based cuckoo search algorithm (QCSA) with mutation operators, is proposed for searching for the optimal generation schedule of the MO-integrated energy system generation scheduling (IESGS) problem. The proposed technique significantly improves the solutions obtained by QCSA by utilizing three mutation operators, i.e., Cauchy, Gaussian, and opposition-based mutation. The proposed strategy has been implemented to MO-hydroelectric-thermal (HT) generation scheduling and MO-IESGS problems to demonstrate the efficacy of the proposed method. The cardinal priority method is utilized for finding the most suitable non-dominated solution for both problems. The obtained results are comparatively better than the published results. The proposed approach’s robustness compared to the QCSA has been further verified using the t-test. Based on comparisons and statistical analysis, the proposed technique is a promising approach for handling complex, multi-dimensional and non-convex generation scheduling optimization problems.