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Optimal operation of multicarrier energy systems using Time Varying Acceleration Coefficient Gravitational Search Algorithm

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

Abstract

This paper describes a novel modified optimization algorithm based on a new heuristic method, namely Time Varying Acceleration Coefficient Gravitational Search Algorithm (TVAC-GSA), to solve both single- and multi-objective Optimal Power Flow (OPF) problems in hybrid systems especially focusing on electricity-gas network. The suggested method is based on the Newtonian laws of gravitation and motion. Sum of the complexity of both electrical and gas-based networks in terms of the valve-point loading effect of generator units, energy hub structure, energy flow equations, and different related equality and inequality constraints make the optimization problem highly nonlinear, non-convex, non-smooth, non-differential, and high-dimensional. The effectiveness of the proposed algorithm to solve such a complex problem is verified on a new introduced hybrid system based on a modified version of IEEE 14-bus network. Comparison of results obtained by the presented method with those obtained by GSA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) shows the better accuracy and fast convergence of the new method in finding an operating point with lower objective function value.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:253-265
    DOI: 10.1016/j.energy.2016.07.155
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    References listed on IDEAS

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    1. Lin, Shin-Yeu & Chen, Jyun-Fu, 2013. "Distributed optimal power flow for smart grid transmission system with renewable energy sources," Energy, Elsevier, vol. 56(C), pages 184-192.
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    Cited by:

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    2. Mateo, C. & Frías, P. & Tapia-Ahumada, K., 2020. "A comprehensive techno-economic assessment of the impact of natural gas-fueled distributed generation in European electricity distribution networks," Energy, Elsevier, vol. 192(C).
    3. Karamanev, Dimitre & Pupkevich, Victor & Penev, Kalin & Glibin, Vassili & Gohil, Jay & Vajihinejad, Vahid, 2017. "Biological conversion of hydrogen to electricity for energy storage," Energy, Elsevier, vol. 129(C), pages 237-245.
    4. Abdi, Hamdi & Beigvand, Soheil Derafshi & Scala, Massimo La, 2017. "A review of optimal power flow studies applied to smart grids and microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 742-766.
    5. 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.
    6. Naserbegi, A. & Aghaie, M. & Minuchehr, A. & Alahyarizadeh, Gh, 2018. "A novel exergy optimization of Bushehr nuclear power plant by gravitational search algorithm (GSA)," Energy, Elsevier, vol. 148(C), pages 373-385.
    7. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "A general model for energy hub economic dispatch," Applied Energy, Elsevier, vol. 190(C), pages 1090-1111.
    8. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "Economic dispatch of multiple energy carriers," Energy, Elsevier, vol. 138(C), pages 861-872.
    9. Majidi, Majid & Nojavan, Sayyad & Zare, Kazem, 2017. "A cost-emission framework for hub energy system under demand response program," Energy, Elsevier, vol. 134(C), pages 157-166.
    10. Quanming Zhang & Zhichao Ren & Ruiguang Ma & Ming Tang & Zhongxiao He, 2019. "Research on Double-Layer Optimized Configuration of Multi-Energy Storage in Regional Integrated Energy System with Connected Distributed Wind Power," Energies, MDPI, vol. 12(20), pages 1-16, October.

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