Optimal Unit Commitment for Minimizing Total Operating Cost using Ant Lion Optimizer

This paper presents a new technique namely Ant Lion Optimizer (ALO) to determine optimal unit commitment. The proposed technique is simulated on IEEE 39-bus test system which consists of 10-unit generators with consideration of system constraints in unit commitment such as power balance, system reserve requirement, generation limit of generators, and minimum up and down time constraints. ALO is inspired based on hunting behavior of ant lion. There are five main steps, which include random walk of ants, trapping of ants in antlions' trip, building trap, sliding of ants towards antlion, catching prey and rebuilding the pit. The proposed ALO algorithm is able to identify the global optimum solution since the intensity of ants' movement is adaptively decreased as the number of iterations increase. In addition, the exploration of search space is guaranteed within the limitation of set-up boundaries. This behavior will enhance the optimization towards the optimal and global solution. The performance of the proposed algorithm is compared with the performance of Dynamic Programming (DP) technique in terms of generation scheduling, total operating cost (TOC) and computation time. From the results obtained, ALO provides better generation scheduling with lower TOC, as compared to DP technique. The cost saving per year performed by ALO technique as compared to DP is $236,520. Based on the results, ALO provides better solution as compared to DP in terms of providing better generation scheduling, and significant reduction of TOC and with lower computation time.