Intelligent particle swarm optimization augmented with chaotic searching technique to integrate distant energy resources

Summary This paper proposes a long‐term framework for generation expansion and transmission expansion planning taking into account the renewable energy integration. To solve the problem, a hybrid technique is used. The mechanism of this technique is based on decomposing the original problem into mas...

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Bibliographic Details
Published in:International transactions on electrical energy systems 2017-12, Vol.27 (12), p.n/a
Main Authors: Javadi, Mohammad Sadegh, Esmaeel Nezhad, Ali
Format: Article
Language:English
Subjects:
Algorithms
Case studies
chaotic searching technique
Computer simulation
Energy sources
Energy transmission
Genetic algorithms
Heuristic methods
intelligent particle swarm optimization
Long-term planning
Particle swarm optimization
power system planning
Renewable energy
renewable energy integration
Searching
Solvers
System effectiveness
transmission expansion planning
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Summary:Summary This paper proposes a long‐term framework for generation expansion and transmission expansion planning taking into account the renewable energy integration. To solve the problem, a hybrid technique is used. The mechanism of this technique is based on decomposing the original problem into master and slave subproblems where the master subproblem is solved using a heuristic optimization algorithm and slave subproblems are solved using general algebraic modeling system, which is a well‐known software with powerful mathematical solvers. The proposed heuristic algorithm is a combination of the intelligent particle swarm optimization and chaotic searching technique. Finally, the proposed model is simulated using 3 case studies including 6‐bus Garver test system, IEEE 24‐bus, and modified IEEE 118‐bus test systems to validate the effectiveness of the long‐term planning framework while the simulation results are compared to those obtained from classic genetic algorithm (GA‐Classic) and classic particle swarm optimization (PSO‐Classic) to verify the efficiency of the technique used in this paper.
ISSN:2050-7038
2050-7038
DOI:10.1002/etep.2447