Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling. (December 2016) - Dubey, Hari Mohan; Pandit, Manjaree; Panigrahi, B.K.
Highlights: A novel ant lion optimization (ALO) algorithm is presented for hydro–thermal–wind scheduling. Practical constraints of hydro–thermal units and wind uncertainty cost are considered. The ALO has simple but unique operations which efficiently control exploration and exploitation. The ALO has no control parameters other than population size and maximum iteration. The performance of ALO is compared with recently published results and found to be better. Abstract: A novel nature inspired (NI) optimization algorithm, known as ant lion optimization (ALO) is used in this paper for solving practical hydrothermal power generation scheduling (HTPGS) problem with wind integration. The ALO algorithm mimics the unique, 6-step hunting activity of ant lions in nature which is modelled by (i) constructing ant lion traps using roulette wheel, (ii) creating random walk of ants, (iii) entrapment of ants in pits, (iv) adaptive shrinking of traps for sliding ant towards ant lion, (v) catching ants and rebuilding the pits, and (vi) applying elitism. The random walk mechanism and roulette wheel operation for building traps provide the ALO with a high exploration capability. The shrinking of trap boundaries and elitism operations increase exploitation efficiency of the ALO, making it a very powerful search technique for complex domains. The wind integrated HTPGS is a non linear, non convex and highly complex optimization problem due to composite operational constraints associated with hydro, thermal and wind units. To demonstrate the applicability of the ALO algorithm for real-world problems, it is tested on four standard test systems. The obtained simulation results are compared with results of other algorithms reported in most recent literature. It is found that the proposed method is proficient in producing encouraging solutions for real-world problems.