Economic Load Dispatch Problem with Ant Lion Optimization Using Practical Constraints

Year 2019, Volume: 32 Issue: 2, 524 - 542, 01.06.2019

Y.venkata Krishna Reddy , M.damodar Reddy

Abstract

This paper presents Ant
Lion Optimization (ALO) algorithmffffor solvingfffEconomic Load Dispatch
(ELD) problemfffwith practical constraints. ALO is a newly developed
optimization algorithm, which draws inspiration from mimics, the huntingfffmechanism of antlions in
nature. The antlions have a unique hunting mechanism and exhibit high
capability of reaching global optima, exploring the search space to find the
optimalfffsolution within a low computational time. For
practical ELD problem needs to take care about the characteristics of
generators, and their operational constraints, such as ramp rate limits,
prohibited operating zones, generation operating limits, transmission loss,
valve-point loading and non-linear emission functions. In order to validate the potency of the proposed method, four case
studies are investigated on different 6-unit systems and correlated with
recently published ELD solution methods. The results of the present work shows
that the proposed ALO is dominant than other
methods to finding out optimal results. Stastical analysisfffof the results among 30
trails has beenfffcarried out to validate the ALO as a highly potent
method. This algorithmfffis considered to be a promising best alternative
algorithm for solving the ELD problemff in power systems.

Keywords

Economic Load Dispatch, Ant Lion Optimization, Ramp rate limits, Prohibited operating zones, Transmission loss, Valve-point loading, Non-linear emission

References

• [2]. Hardiansyah, Junaidi, “Solving Economic Load Dispatch problem using particle swarm Optimization Technique”, I.J. Intelligent Systems and Applications, 2012, 12, 12-18.
• [6]. K. K. MandaI, N. Chakraborty, “Differential Evolution based environmentally constrained Economic Dispatch”, 2008 IEEE.
• [7]. D. Nelson Jayakumar, P. Venkatesh, “Glowworm swarm optimization algorithm with topsis for solving multiple objective environmental economic dispatch problem”, Applied Soft Computing 23 (2014) 375–386.
• [8]. Chatterjee, S.P. Ghoshal “Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm”, Electrical Power and Energy Systems 39 (2012) 9–20.
• [9]. M. A. Abido, “Multiobjective Evolutionary Algorithms for Electric power dispatch algorithm”, ieee transactions on evolutionary computation, vol. 10, no. 3, june 2006.
• [10]. B.Y. Qu, J.J. Liang, “Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm”, Information Sciences (2016).
• [11]. Mostafa Modiri-Delshad, Nasrudin Abd Rahim “Multi-objective Backtracking Search Algorithm for Economic Emission Dispatch Problem”, Applied Soft Computing 2015.
• [12]. Abd Allah “Hybrid ant optimization system for multiobjective economic emission load dispatch problem under fuzziness”, Swarm and Evolutionary Computation 18 (2014) 11–21.
• [13]. Celal Yas¸ “A new hybrid approach for nonconvex economic dispatch problem with valve-point effect”, Energy 36 (2011) 5838-45.
• [14]. Tahir Nadeem Malik, Azzam ul Asar “A new hybrid approach for the solution of nonconvex ED problem with valve-point effects, Electric Power Systems Research 80 (2010) 1128–1136.
• [15]. Serhat Duman, “A novel modified hybrid PSOGSA based on fuzzy logic for non-convex ED problem with valve-point effect”, Electrical Power and Energy Systems 64 (2015) 121–135.
• [16]. Meisam Moradi, Ali “Non-Convex Constrained economic dispatch with valve point loading effect using a GWO algorithm”, 6th conference on Thermal Power Plants, janaury 2016, Iran.
• [17]. Mostafa Modiri-Delshad, “Backtracking search algorithm for solving economic diapatch problems with valve-point effects and multiple fuel optins”, Energy 116 (2016) 637e649.
• [18]. Zwe-Lee Gaing “Particle Swarm Optimization to Solving the economic dispatch considering the generator constrints”, ieee transactions on power systems, vol. 18, no. 3, august 2003.
• [19]. Naser Ghorbani, Ebrahim Babaei “Exchange market algorithm for economic load dispatch”, Electrical Power and Energy Systems 75 (2016) 19–27.
• [20]. Wael Taha Elsayed, Yasser G. Hegazy “Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem”, ieee transactions on industrial informatics, vol. 13, no. 3, june 2017.
• [21]. Seyedali Mirjalili “The Ant Lion Optimizer”, Advances in Engineering Software 83 (2015) 80–98.