IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v77y2014icp372-381.html
   My bibliography  Save this article

Solving non-convex economic dispatch problem via backtracking search algorithm

Author

Listed:
  • Modiri-Delshad, Mostafa
  • Rahim, Nasrudin Abd

Abstract

This paper presents BSA (backtracking search algorithm) for solving of ED (economic dispatch) problems (both convex and non-convex) with both the valve-point effects in the generator cost function and the transmission network loss considered. BSA is a new evolutionary algorithm for solving of numerical optimization problems; it uses a single control parameter and two crossover and mutation strategies for powerful exploration of the problem's search space. Four test systems (with 3, 6, 20, and 40 generators) are the case studies verifying the method's robustness and effectiveness. The results confirm that compared with existing well-known methods and especially in large-scale test systems, the proposed algorithm is the better approach to solving ED problems.

Suggested Citation

  • Modiri-Delshad, Mostafa & Rahim, Nasrudin Abd, 2014. "Solving non-convex economic dispatch problem via backtracking search algorithm," Energy, Elsevier, vol. 77(C), pages 372-381.
  • Handle: RePEc:eee:energy:v:77:y:2014:i:c:p:372-381
    DOI: 10.1016/j.energy.2014.09.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544214010652
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2014.09.009?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. Mohammadi-ivatloo, Behnam & Rabiee, Abbas & Soroudi, Alireza & Ehsan, Mehdi, 2012. "Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch," Energy, Elsevier, vol. 44(1), pages 228-240.
    2. Basu, M. & Chowdhury, A., 2013. "Cuckoo search algorithm for economic dispatch," Energy, Elsevier, vol. 60(C), pages 99-108.
    3. Cai, Jiejin & Li, Qiong & Li, Lixiang & Peng, Haipeng & Yang, Yixian, 2012. "A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects," Energy, Elsevier, vol. 38(1), pages 346-353.
    4. Niknam, Taher & Mojarrad, Hassan Doagou & Nayeripour, Majid, 2010. "A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch," Energy, Elsevier, vol. 35(4), pages 1764-1778.
    5. Niknam, Taher & Mojarrad, Hasan Doagou & Meymand, Hamed Zeinoddini & Firouzi, Bahman Bahmani, 2011. "A new honey bee mating optimization algorithm for non-smooth economic dispatch," Energy, Elsevier, vol. 36(2), pages 896-908.
    6. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
    7. Basu, M., 2014. "Teaching–learning-based optimization algorithm for multi-area economic dispatch," Energy, Elsevier, vol. 68(C), pages 21-28.
    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. Hu, Zhongbo & Dai, Canyun & Su, Qinghua, 2022. "Adaptive backtracking search optimization algorithm with a dual-learning strategy for dynamic economic dispatch with valve-point effects," Energy, Elsevier, vol. 248(C).
    2. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.
    3. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.
    4. Yuan Shiyi & Fu Jianwen & Cui Feng & Zhang Xin, 2020. "Truck and Trailer Routing Problem Solving by a Backtracking Search Algorithm," Journal of Systems Science and Information, De Gruyter, vol. 8(3), pages 253-272, June.
    5. Guojiang Xiong & Jing Zhang & Xufeng Yuan & Dongyuan Shi & Yu He & Yao Yao & Gonggui Chen, 2018. "A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    6. Adarsh, B.R. & Raghunathan, T. & Jayabarathi, T. & Yang, Xin-She, 2016. "Economic dispatch using chaotic bat algorithm," Energy, Elsevier, vol. 96(C), pages 666-675.
    7. Zaman, Forhad & Elsayed, Saber M. & Ray, Tapabrata & Sarker, Ruhul A., 2016. "Evolutionary algorithms for power generation planning with uncertain renewable energy," Energy, Elsevier, vol. 112(C), pages 408-419.
    8. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Elattar, Ehab & Ginidi, Ahmed R., 2022. "An Amalgamated Heap and Jellyfish Optimizer for economic dispatch in Combined heat and power systems including N-1 Unit outages," Energy, Elsevier, vol. 246(C).
    9. 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.
    10. Dai, Canyun & Hu, Zhongbo & Su, Qinghua, 2022. "An adaptive hybrid backtracking search optimization algorithm for dynamic economic dispatch with valve-point effects," Energy, Elsevier, vol. 239(PE).
    11. Zheng, J.H. & Chen, J.J. & Wu, Q.H. & Jing, Z.X., 2015. "Reliability constrained unit commitment with combined hydro and thermal generation embedded using self-learning group search optimizer," Energy, Elsevier, vol. 81(C), pages 245-254.
    12. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    13. Shilaja, C. & Ravi, K., 2017. "Optimization of emission/economic dispatch using euclidean affine flower pollination algorithm (eFPA) and binary FPA (BFPA) in solar photo voltaic generation," Renewable Energy, Elsevier, vol. 107(C), pages 550-566.
    14. Vitayasak, Srisatja & Pongcharoen, Pupong & Hicks, Chris, 2017. "A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm," International Journal of Production Economics, Elsevier, vol. 190(C), pages 146-157.
    15. Secui, Dinu Calin, 2016. "A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 113(C), pages 366-384.
    16. Jayabarathi, T. & Raghunathan, T. & Adarsh, B.R. & Suganthan, Ponnuthurai Nagaratnam, 2016. "Economic dispatch using hybrid grey wolf optimizer," Energy, Elsevier, vol. 111(C), pages 630-641.

    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. Secui, Dinu Calin, 2016. "A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 113(C), pages 366-384.
    2. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    3. Guojiang Xiong & Jing Zhang & Xufeng Yuan & Dongyuan Shi & Yu He & Yao Yao & Gonggui Chen, 2018. "A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    4. de Athayde Costa e Silva, Marsil & Klein, Carlos Eduardo & Mariani, Viviana Cocco & dos Santos Coelho, Leandro, 2013. "Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch problem," Energy, Elsevier, vol. 53(C), pages 14-21.
    5. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Roosta, Alireza & Amiri, Babak, 2012. "A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch," Energy, Elsevier, vol. 42(1), pages 530-545.
    6. Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    7. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    8. Al-Bahrani, Loau Tawfak & Chandra Patra, Jagdish, 2018. "Multi-gradient PSO algorithm for optimization of multimodal, discontinuous and non-convex fuel cost function of thermal generating units under various power constraints in smart power grid," Energy, Elsevier, vol. 147(C), pages 1070-1091.
    9. Yaşar, Celal & Özyön, Serdar, 2011. "A new hybrid approach for nonconvex economic dispatch problem with valve-point effect," Energy, Elsevier, vol. 36(10), pages 5838-5845.
    10. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Azizipanah-Abarghooee, Rasoul, 2013. "An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties," Energy, Elsevier, vol. 50(C), pages 232-244.
    11. Cai, Jiejin & Li, Qiong & Li, Lixiang & Peng, Haipeng & Yang, Yixian, 2012. "A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects," Energy, Elsevier, vol. 38(1), pages 346-353.
    12. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.
    13. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.
    14. Loau Al-Bahrani & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2021. "Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    15. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    16. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    17. Azizipanah-Abarghooee, Rasoul & Niknam, Taher & Roosta, Alireza & Malekpour, Ahmad Reza & Zare, Mohsen, 2012. "Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method," Energy, Elsevier, vol. 37(1), pages 322-335.
    18. Ghasemi, Mojtaba & Ghavidel, Sahand & Ghanbarian, Mohammad Mehdi & Gharibzadeh, Masihallah & Azizi Vahed, Ali, 2014. "Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm," Energy, Elsevier, vol. 78(C), pages 276-289.
    19. Vo, Dieu Ngoc & Ongsakul, Weerakorn, 2012. "Economic dispatch with multiple fuel types by enhanced augmented Lagrange Hopfield network," Applied Energy, Elsevier, vol. 91(1), pages 281-289.
    20. Özyön, Serdar & Temurtaş, Hasan & Durmuş, Burhanettin & Kuvat, Gültekin, 2012. "Charged system search algorithm for emission constrained economic power dispatch problem," Energy, Elsevier, vol. 46(1), pages 420-430.

    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:77:y:2014:i:c:p:372-381. 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.