Multi objective large power system planning under sever loading condition using learning DE-APSO-PS strategy Belkacem Mahdad ⇑ , K. Srairi University of Biskra, Department of Electrical Engineering Biskra, Algeria article info Article history: Received 8 March 2014 Accepted 29 June 2014 Keywords: Power quality Differential evolution Optimal power flow (OPF) Pattern search Adaptive particle swarm optimization (APSO) Hybrid methods Shunt dynamic compensator (SVC) abstract This paper introduces an efficient planning strategy using new hybrid interactive differential evolution (DE), adaptive particle swarm optimization (APSO), and pattern search (PS) for solving the security optimal power flow (SOPF) considering multi distributed static VAR compensator (SVC). Three objective functions such as fuel cost, power loss and voltage deviation are considered and optimized considering sever loading conditions. The main idea of the proposed strategy is that variable controls are optimized based on superposition mechanism, the best solutions evaluated by DE and APSO at specified stages are communicated to PS to exploit new regions around this solution, alternatively the new solution achieved by PS is also communicated to DE and APSO, this interactive mechanism search between global and local search is to balance the exploitation and exploration capability which allows individuals from different methods to react more by learning and changing experiences. The robustness of the proposed strategy is tested and validated on large practical power system test (IEEE 118-Bus, IEEE 300-Bus, and 40 units). Comparison results with the standard global optimization methods such as DE, APSO PS and to other recent techniques showed the superiority and perspective of the proposed hybrid technique for solving practical power system problems. Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction The best way to dispatch the active and reactive powers among the generating units is called optimal power flow strategy. The main objective of optimal power flow strategy is to determine the optimal operating state of a power system by optimizing a par- ticular objective function while satisfying specified physical and security constraints. The OPF becomes even more complex when many conflicting objectives (fuel cost, gaz emission, real power loss, voltage deviation, and voltage stability) and practical genera- tors constraints (multi fuel, valve point effect and prohibited zones) are considered [1]. A number of mathematical programming-based techniques such as the Newton method [2], linear programming [3], non- linear programming [3], and interior point method [4] have been employed to solve many problems related to power system operation and control such as economic dispatch, reactive power planning and the OPF problem. Experience confirmed that this optimization category based conventional methods is fast but fail to achieve the global optimum solution when solving highly nonlinear and multi modal problems. The drawbacks associated with using these conventional optimization methods for solving complex problems related to practical power system operation and control have contributed to the development of alternative techniques. During the last two decades; the interest in applying new metaheuristic optimization methods in power system field has grown rapidly. In the literature many standard optimization methods and hybrid variants based metaheuristic algorithms have been proposed and applied with success for solving many complex problems related to power system planning, operation and control like: quantum genetic algorithm (QGA) [5], artificial immune system (AIS) [6], adaptive particle swarm optimization (APSO) [7], improved PSO (IPSO) [8], improved chaotic particle swarm optimization (ICPSO) [9], gravitational search algorithm [10]. These methods and many other techniques have a better searching ability in finding near global optimal solution compared to mathe- matical methods and to the standard evolutionary algorithms. Based on experience and many research presented by authors con- firmed that each global optimization method has its advantages and drawbacks. Hybrid methods considered as and efficient solu- tion to combine different methods. Recently many hybrid methods have been proposed and applied with success for solving many complex problems related to power system planning, operation and control. http://dx.doi.org/10.1016/j.enconman.2014.06.090 0196-8904/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail address: bemahdad@mselab.org (B. Mahdad). Energy Conversion and Management 87 (2014) 338–350 Contents lists available at ScienceDirect Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman