Swarm Intelligent Search and Rescue method for economic emission load dispatch of renewable integrated power system considering uncertainty

In the realm of power systems, Economic Emission Load Dispatch (EELD) problem is one of the most important bi-objective optimisation problem, associated with high complexity and non-linearities. This research proposes a novel metaheuristic optimization approach hybridizing the PSO and SAR and abbreviated as Swarm Intelligent Search and Rescue method (SISAR). It utilizes the features of Particle Swarm Optimization algorithm to strengthen the global searching capability of original SAR algorithm. SISAR overcomes the drawback of SAR of getting trapped into local minima by utilizing velocity-based position update concept of PSO to improve the overall convergence. SISAR approach is initially evaluated on 10-unit, 2000 MW and 6-unit, IEEE30 bus standard test systems. Results are compared with advanced algorithms such as SAR, PSO, GWO, WOA, GA, DE and MFO in order to prove its superiority. Subsequent to the establishment of the proposed algorithm on system without renewable sources, it is further applied to a RE integrated power system comprising of six thermal units, 1 wind and 1 solar unit. Here, uncertainty due to RESs is dealt using a 2-stage uncertainty handling approach to obtain more accurate and feasible EELD solution. Robustness of the uncertainty handling approach is established by investigating the impact of different penetration levels of RE sources on cost and emission while solving EELD. A reduction of 1.67 % in cost and 2.56 % in emission have been achieved by proposed SISAR algorithm as compared to SAR (next competitive method) while performing EELD on RE integrated system with all sources.