Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index. (December 2016) - Dubey, Hari Mohan; Pandit, Manjaree; Panigrahi, B.K.
Abstract: A solution to the combined hydro-thermal-wind scheduling problem of multi reservoir cascaded hydro plants is presented employing a novel ant lion optimization (ALO) algorithm. Five objectives, cost, various emissions and power loss, are simultaneously optimized. The optimal schedules of thermal, hydro and wind power (WP) units are determined for continuously varying load subject to a large number of practical operational constraints. The effect of reserve and penalty coefficients and WP uncertainty is also investigated for the multi-objective (MO) problem. The newly proposed ALO algorithm has unique features like random walk, roulette wheel, and boundary shrinking. These operations provide a judicious balance between exploration and exploitation, and create a powerful optimization technique for complex real-world problems. Finding the best compromise solution (BCS) is a tedious task when multiple objectives are involved. A composite ranking index (CRI) is proposed as a performance metrics for MO problems. The CRI helps the decision maker in ranking the large number of Pareto-optimal solutions. The developed model is tested on three standard systems, having a mix of hydro, thermal and wind generators. The performance is found to be superior to published results and comparable with established algorithms like artificial bee colony (ABC) and differential evolution (DE). Graphical abstract: Highlights: A composite index is proposed as merit criterion for optimizing multi-objective problems. Hydro-thermal-wind scheduling problem is solved using a novel ant lion optimization (ALO). Cost, various emissions and power loss are simultaneously optimized with complex constraints. Importance of wind power reserve/penalty coefficients on wind power scheduling is investigated. Applicability of ALO algorithm compared with other algorithms for complex real-world problems.