Economic Emission Generation Scheduling of Interconnected Energy System Using Hybrid Optimization Technique

This work formulates an economic emission generation scheduling problem of an interconnected energy system (IES). The IES comprises hydro, thermal, combined heat and power and heat units. The EE scheduling problem of the IES system is a multi-objective, non-convex, discontinuous and highly constrained optimization problem, which requires an efficient optimization technique. Thus, a hybrid optimization technique is proposed, which integrates a quantum-based cuckoo search algorithm (QCSA) with the grey wolf optimizer (GWO) for balancing the algorithm's exploration and exploitation. The QCSA explores the search space due to its excellent exploration capability and GWO is utilized due to its well-known superior exploitation and local optima avoidance ability. Further, the chaotic tent map based on chaos theory has been pertained to the QCSA-GWO algorithm to obtain the self-adaptive parameter settings in QCSA-GWO, to avoid premature convergence and to improve the convergence speed. The proposed chaotic QCSA-GWO technique has been applied to three test systems, including the EE scheduling problem of a hydro-thermal system and small and medium-sized scheduling problems of IES, to demonstrate its applicability and efficacy. The proposed technique outperformed the published findings from recent contemporary techniques like modified DE, improved QPSO, PPO, PSO-PPS, PPO-PPS, QADEVT, HMOCA, ASMMODE, PSO-SWT, MCPSO-SWT, QCSA, GWO and QCSA-GWO.