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Hybrid Gravitational Search Algorithm-Particle Swarm Optimization with Time Varying Acceleration Coefficients for large scale CHPED problem

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  • Beigvand, Soheil Derafshi
  • Abdi, Hamdi
  • La Scala, Massimo

Abstract

This paper proposes a novel optimization algorithm, namely hybrid Time Varying Acceleration Coefficients-Gravitational Search Algorithm-Particle Swarm Optimization (hybrid TVAC-GSA-PSO), to solve the large-scale, highly nonlinear, non-convex, non-smooth, non-differential, non-continuous, and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The suggested algorithm is based on the Newtonian laws of gravitation and motion as well as swarm behaviors. The proposed technique combines the best performances of three optimization methods (i.e. GSA, TVAC-GSA, and TVAC-PSO) in terms of their specific strategies in particle movements through a self-adoptive learning strategy. The effectiveness and robustness of the suggested algorithm are tested on a set of five benchmark functions and two large-scale CHPED problems (considering valve-point loading effect and prohibited zones of conventional power units, transmission losses, as well as special characteristic of CHP units). The obtained results by the suggested algorithm in terms of quality solution, computational performance, and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better fuel cost with a less expensive solution.

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  • Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "Hybrid Gravitational Search Algorithm-Particle Swarm Optimization with Time Varying Acceleration Coefficients for large scale CHPED problem," Energy, Elsevier, vol. 126(C), pages 841-853.
    • Handle: RePEc:eee:energy:v:126:y:2017:i:c:p:841-853
    DOI: 10.1016/j.energy.2017.03.054
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    References listed on IDEAS

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    1. Derafshi Beigvand, Soheil & Abdi, Hamdi & La Scala, Massimo, 2016. "Optimal operation of multicarrier energy systems using Time Varying Acceleration Coefficient Gravitational Search Algorithm," Energy, Elsevier, vol. 114(C), pages 253-265.
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    Cited by:

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    3. Paramjeet Kaur & Krishna Teerth Chaturvedi & Mohan Lal Kolhe, 2023. "Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique," Energies, MDPI, vol. 16(3), pages 1-17, January.
    4. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & Gharehpetian, G.B., 2018. "A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2128-2143.
    5. Ali Sulaiman Alsagri & Abdulrahman A. Alrobaian, 2022. "Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview," Energies, MDPI, vol. 15(16), pages 1-34, August.
    6. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "Economic dispatch of multiple energy carriers," Energy, Elsevier, vol. 138(C), pages 861-872.
    7. Hamdi Abdi, 2023. "A Survey of Combined Heat and Power-Based Unit Commitment Problem: Optimization Algorithms, Case Studies, Challenges, and Future Directions," Mathematics, MDPI, vol. 11(19), pages 1-36, October.
    8. Urazel, Burak & Keskin, Kemal, 2023. "A new solution approach for non-convex combined heat and power economic dispatch problem considering power loss," Energy, Elsevier, vol. 278(PB).
    9. Nazari-Heris, Morteza & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Siano, Pierluigi, 2020. "Optimal generation scheduling of large-scale multi-zone combined heat and power systems," Energy, Elsevier, vol. 210(C).

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