Multi objective large power system planning under sever loading condition using learning DE-APSO-PS strategy

•An efficient planning strategy using DE and APSO in coordination with PS algorithm is proposed.•An interactive process is proposed to balance the exploitation and exploration capability of (DE-APSO) and PS.•Fuel cost, power loss, and voltage deviation considering loading condition are optimized.•Th...

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Bibliographic Details
Published in:Energy conversion and management Vol. 87; pp. 338 - 350
Main Authors: Mahdad, Belkacem, Srairi, K.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.11.2014
Elsevier
Subjects:
Adaptive particle swarm optimization (APSO)
algorithms
Applied sciences
Deviation
Differential evolution
Electric potential
electric power
Energy
energy costs
Exact sciences and technology
Hybrid methods
Interactive
Learning
Optimal power flow (OPF)
Optimization
Pattern search
planning
Power quality
Searching
Security
Shunt dynamic compensator (SVC)
Strategy
system optimization
Pattern search
Adaptive particle swarm optimization (APSO)
Hybrid methods
Optimal power flow (OPF)
Power quality
Differential evolution
Shunt dynamic compensator (SVC)
Loading
Quality
Planning
Optimization
ISSN:0196-8904, 1879-2227
Online Access:Get full text
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Summary:•An efficient planning strategy using DE and APSO in coordination with PS algorithm is proposed.•An interactive process is proposed to balance the exploitation and exploration capability of (DE-APSO) and PS.•Fuel cost, power loss, and voltage deviation considering loading condition are optimized.•The proposed strategy (DE-APSO-PS) is validated on three large practical test systems. 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.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2014.06.090