Open AccessShort-term optimal hydro-thermal scheduling using clustered adaptive teaching learning based optimization
Author(s) -
Surender Reddy Salkuti
Publication year - 2019
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v9i5.pp3359-3365
Subject(s) - computer science , power balance , schedule , mathematical optimization , thermal power station , scheduling (production processes) , span (engineering) , convergence (economics) , electric power system , rate of convergence , power (physics) , mathematics , engineering , physics , civil engineering , quantum mechanics , waste management , economics , economic growth , operating system , computer network , channel (broadcasting)
In this paper, Clustered Adaptive Teaching Learning Based Optimization (CATLBO) algorithm is proposed for determining the optimal hourly schedule of power generation in a hydro-thermal power system. In the proposed approach, a multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, net head and power generation is considered. Constraints such as power balance, water balance, reservoir volume limits and operation limits of hydro and thermal plants are considered. The feasibility and effectiveness of the proposed algorithm is demonstrated through a test system, and the results are compared with existing conventional and evolutionary algorithms. Simulation results reveals that the proposed CATLBO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques.