IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v34y2009i10p1539-1551.html
   My bibliography  Save this article

Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques

Author

Listed:
  • Sadegheih, A.

Abstract

This paper proposes a new hybrid algorithm Meta-heuristic for the problem of network planning systems. The main goal of this paper is, to develop an efficient optimization tool which will minimise the cost functions of the stated optimization problems in network planning systems. The following are the objectives of the research: to investigate the capabilities of genetic algorithm, simulated annealing and tabu search for the defined optimization tasks; to develop a hybrid optimization algorithm which will produce improved iterations compared to those found by GA, SA, and TS algorithms. The performance of the hybrid algorithm is illustrated and six hybrid algorithms are developed, to improve the iterations obtained. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. It is advantageous to use exact DC load flow constraint equations based on the modified form of Kirchhoff's Second Law because the iterative process for line addition is not required. Hence, the computation time is decreased. Finally, the hybrid VI shows to be a very good option for network planning systems given that it obtains much accentuated reductions of iteration, which is very important for network planning.

Suggested Citation

  • Sadegheih, A., 2009. "Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques," Energy, Elsevier, vol. 34(10), pages 1539-1551.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:10:p:1539-1551
    DOI: 10.1016/j.energy.2009.06.047
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544209002692
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2009.06.047?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Koeppel, Gaudenz & Andersson, Göran, 2009. "Reliability modeling of multi-carrier energy systems," Energy, Elsevier, vol. 34(3), pages 235-244.
    2. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    3. Al-Muhawesh, Tareq A. & Qamber, Isa S., 2008. "The established mega watt linear programming-based optimal power flow model applied to the real power 56-bus system in eastern province of Saudi Arabia," Energy, Elsevier, vol. 33(1), pages 12-21.
    4. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    5. Bogdan, Željko & Cehil, Mislav & Kopjar, Damir, 2007. "Power system optimization," Energy, Elsevier, vol. 32(6), pages 955-960.
    6. Chicco, Gianfranco & Mancarella, Pierluigi, 2009. "Matrix modelling of small-scale trigeneration systems and application to operational optimization," Energy, Elsevier, vol. 34(3), pages 261-273.
    7. Mařík, Karel & Schindler, Zdenek & Stluka, Petr, 2008. "Decision support tools for advanced energy management," Energy, Elsevier, vol. 33(6), pages 858-873.
    8. Dimopoulos, George G. & Frangopoulos, Christos A., 2008. "Optimization of energy systems based on Evolutionary and Social metaphors," Energy, Elsevier, vol. 33(2), pages 171-179.
    9. Bresesti, Paola & Calisti, Roberto & Cazzol, Maria Vittoria & Gatti, Antonio & Provenzano, Dario & Vaiani, Andrea & Vailati, Riccardo, 2009. "The benefits of transmission expansions in the competitive electricity markets," Energy, Elsevier, vol. 34(3), pages 274-280.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuan, Guanghui & Yang, Weixin, 2019. "Study on optimization of economic dispatching of electric power system based on Hybrid Intelligent Algorithms (PSO and AFSA)," Energy, Elsevier, vol. 183(C), pages 926-935.
    2. Fitiwi, Desta Z. & Olmos, L. & Rivier, M. & de Cuadra, F. & Pérez-Arriaga, I.J., 2016. "Finding a representative network losses model for large-scale transmission expansion planning with renewable energy sources," Energy, Elsevier, vol. 101(C), pages 343-358.
    3. Sadegheih, A., 2010. "A novel formulation of carbon emissions costs for optimal design configuration of system transmission planning," Renewable Energy, Elsevier, vol. 35(5), pages 1091-1097.
    4. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    5. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
    6. Kim, M.K. & Park, J.K. & Nam, Y.W., 2011. "Market-clearing for pricing system security based on voltage stability criteria," Energy, Elsevier, vol. 36(2), pages 1255-1264.
    7. Ara, A. Lashkar & Kazemi, A. & Niaki, S.A. Nabavi, 2011. "Optimal location of Hybrid Flow Controller considering modified steady-state model," Applied Energy, Elsevier, vol. 88(5), pages 1578-1585, May.
    8. Sadegheih, A., 2011. "Optimal design methodologies under the carbon emission trading program using MIP, GA, SA, and TS," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 504-513, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    2. Stoppato, Anna & Cavazzini, Giovanna & Ardizzon, Guido & Rossetti, Antonio, 2014. "A PSO (particle swarm optimization)-based model for the optimal management of a small PV(Photovoltaic)-pump hydro energy storage in a rural dry area," Energy, Elsevier, vol. 76(C), pages 168-174.
    3. Lin, Q.G. & Huang, G.H., 2010. "An inexact two-stage stochastic energy systems planning model for managing greenhouse gas emission at a municipal level," Energy, Elsevier, vol. 35(5), pages 2270-2280.
    4. Destro, Nicola & Benato, Alberto & Stoppato, Anna & Mirandola, Alberto, 2016. "Components design and daily operation optimization of a hybrid system with energy storages," Energy, Elsevier, vol. 117(P2), pages 569-577.
    5. Liu, Tianhao & Tian, Jun & Zhu, Hongyu & Goh, Hui Hwang & Liu, Hui & Wu, Thomas & Zhang, Dongdong, 2023. "Key technologies and developments of multi-energy system: Three-layer framework, modelling and optimisation," Energy, Elsevier, vol. 277(C).
    6. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2011. "Influence analysis of building types and climate zones on energetic, economic and environmental performances of BCHP systems," Applied Energy, Elsevier, vol. 88(9), pages 3097-3112.
    7. Marianov, Vladimir & Serra, Daniel & ReVelle, Charles, 1999. "Location of hubs in a competitive environment," European Journal of Operational Research, Elsevier, vol. 114(2), pages 363-371, April.
    8. Schreiner, Lena & Madlener, Reinhard, 2022. "Investing in power grid infrastructure as a flexibility option: A DSGE assessment for Germany," Energy Economics, Elsevier, vol. 107(C).
    9. Chiara Gruden & Irena Ištoka Otković & Matjaž Šraml, 2020. "Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    10. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    11. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Xiong, Wen & Wang, Li, 2017. "Typical scenario set generation algorithm for an integrated energy system based on the Wasserstein distance metric," Energy, Elsevier, vol. 135(C), pages 153-170.
    12. Thibaud Deguilhem & Juliette Schlegel & Jean-Philippe Berrou & Ousmane Djibo & Alain Piveteau, 2024. "Too many options: How to identify coalitions in a policy network?," Post-Print hal-04689665, HAL.
    13. Anurag Agarwal, 2009. "Theoretical insights into the augmented-neural-network approach for combinatorial optimization," Annals of Operations Research, Springer, vol. 168(1), pages 101-117, April.
    14. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    15. Lai, Sau Man & Hui, Chi Wai, 2009. "Feasibility and flexibility for a trigeneration system," Energy, Elsevier, vol. 34(10), pages 1693-1704.
    16. Helena Ramalhinho-Lourenço & Olivier C. Martin & Thomas Stützle, 2000. "Iterated local search," Economics Working Papers 513, Department of Economics and Business, Universitat Pompeu Fabra.
    17. Jayasekara, Saliya & Halgamuge, Saman K., 2013. "Mathematical modeling and experimental verification of an absorption chiller including three dimensional temperature and concentration distributions," Applied Energy, Elsevier, vol. 106(C), pages 232-242.
    18. Marti, Rafael, 1998. "A tabu search algorithm for the bipartite drawing problem," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 558-569, April.
    19. Mohammad Javad Feizollahi & Igor Averbakh, 2014. "The Robust (Minmax Regret) Quadratic Assignment Problem with Interval Flows," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 321-335, May.
    20. Nha Vo‐Thanh & Hans‐Peter Piepho, 2023. "Generating designs for comparative experiments with two blocking factors," Biometrics, The International Biometric Society, vol. 79(4), pages 3574-3585, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:34:y:2009:i:10:p:1539-1551. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.