IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v47y2012i1p451-464.html
   My bibliography  Save this article

Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints

Author

Listed:
  • Niknam, Taher
  • Azizipanah-Abarghooee, Rasoul
  • Narimani, Mohammad Rasoul

Abstract

This paper proposes a new method called the enhanced charged system search algorithm in order to solve the reserve constrained dynamic optimal power flow. The optimal power flow (OPF) is applied to develop the corrective strategies and to make the least cost dispatches. The static OPF solutions will usually result in unattractive generation cost in a dynamic environment, so to cope with this problem it is necessary to solve the dynamic optimal power flow which considers time-related constraints along with time-separated limits. Also, some restrictions such as prohibited operating zones and valve-point effects beside the multi-fuel type of generation units should be taken into account in order to get closer to the real condition of the power systems. Furthermore, to ensure secure real-time power system operations, the system operator must schedule sufficient resources to meet energy demand and operating reserve requirements, simultaneously. This problem is a complex optimization problem spontaneously which its complexity is increased with considering all above constraints. This paper utilizes an enhanced charged system search algorithm, which is a newly developed optimization algorithm, to solve the proposed problem. This algorithm is equipped with a novel mutation strategy in order to increase the population diversity and to amend the convergence criteria. For validating the ability of the proposed algorithm, it is applied to two small- and large-scale case studies including 30 and 118 buses test systems.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:47:y:2012:i:1:p:451-464
    DOI: 10.1016/j.energy.2012.07.053
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544212006020
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2012.07.053?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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Yuan, Xiaohui & Su, Anjun & Yuan, Yanbin & Nie, Hao & Wang, Liang, 2009. "An improved PSO for dynamic load dispatch of generators with valve-point effects," Energy, Elsevier, vol. 34(1), pages 67-74.
    6. Fesanghary, M. & Ardehali, M.M., 2009. "A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem," Energy, Elsevier, vol. 34(6), pages 757-766.
    7. 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.
    8. Vaisakh, K. & Srinivas, L.R., 2010. "A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis," Energy, Elsevier, vol. 35(8), pages 3155-3171.
    9. Liao, Gwo-Ching, 2011. "A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power," Energy, Elsevier, vol. 36(2), pages 1018-1029.
    10. Salgado, R.S. & Rangel, E.L., 2012. "Optimal power flow solutions through multi-objective programming," Energy, Elsevier, vol. 42(1), pages 35-45.
    11. 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.
    12. Lin, Guixian & He, Xuming & Portnoy, Stephen, 2012. "Quantile regression with doubly censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 797-812.
    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. Goudarzi, Arman & Viray, Z.N.C. & Siano, Pierluigi & Swanson, Andrew G. & Coller, John V. & Kazemi, Mehdi, 2017. "A probabilistic determination of required reserve levels in an energy and reserve co-optimized electricity market with variable generation," Energy, Elsevier, vol. 130(C), pages 258-275.
    2. Pourakbari-Kasmaei, Mahdi & Rider, Marcos J. & Mantovani, José R.S., 2014. "An unequivocal normalization-based paradigm to solve dynamic economic and emission active-reactive OPF (optimal power flow)," Energy, Elsevier, vol. 73(C), pages 554-566.
    3. Lin, Shin-Yeu & Lin, Ai-Chih, 2014. "RLOPF (risk-limiting optimal power flow) for systems with high penetration of wind power," Energy, Elsevier, vol. 71(C), pages 49-61.
    4. 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.
    5. Adhvaryyu, P.K. & Chattopadhyay, P.K. & Bhattacharya, A., 2017. "Dynamic optimal power flow of combined heat and power system with Valve-point effect using Krill Herd algorithm," Energy, Elsevier, vol. 127(C), pages 756-767.
    6. 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.
    7. Kamankesh, Hamidreza & Agelidis, Vassilios G. & Kavousi-Fard, Abdollah, 2016. "Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand," Energy, Elsevier, vol. 100(C), pages 285-297.
    8. Shin-Yeu Lin & Ai-Chih Lin, 2016. "Risk-Limiting Scheduling of Optimal Non-Renewable Power Generation for Systems with Uncertain Power Generation and Load Demand," Energies, MDPI, vol. 9(11), pages 1-16, October.
    9. 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.
    10. Yu, L. & Li, Y.P. & Huang, G.H., 2016. "A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China," Energy, Elsevier, vol. 98(C), pages 190-203.
    11. Ghasemi, Mojtaba & Ghavidel, Sahand & Akbari, Ebrahim & Vahed, Ali Azizi, 2014. "Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos," Energy, Elsevier, vol. 73(C), pages 340-353.
    12. Zare, Mohsen & Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Amiri, Babak, 2014. "Multi-objective probabilistic reactive power and voltage control with wind site correlations," Energy, Elsevier, vol. 66(C), pages 810-822.
    13. 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.
    14. Ismail Marouani & Tawfik Guesmi & Hsan Hadj Abdallah & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Salem Rahmani, 2022. "Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review," Energies, MDPI, vol. 15(12), pages 1-35, June.
    15. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.
    16. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Seifi, Alireza, 2013. "A new algorithm for combined heat and power dynamic economic dispatch considering valve-point effects," Energy, Elsevier, vol. 52(C), pages 320-332.

    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. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Ghasemi, Mojtaba & Ghavidel, Sahand & Akbari, Ebrahim & Vahed, Ali Azizi, 2014. "Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos," Energy, Elsevier, vol. 73(C), pages 340-353.
    7. Pourakbari-Kasmaei, Mahdi & Rider, Marcos J. & Mantovani, José R.S., 2014. "An unequivocal normalization-based paradigm to solve dynamic economic and emission active-reactive OPF (optimal power flow)," Energy, Elsevier, vol. 73(C), pages 554-566.
    8. 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.
    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. Azizipanah-Abarghooee, Rasoul & Niknam, Taher & Bina, Mohammad Amin & Zare, Mohsen, 2015. "Coordination of combined heat and power-thermal-wind-photovoltaic units in economic load dispatch using chance-constrained and jointly distributed random variables methods," Energy, Elsevier, vol. 79(C), pages 50-67.
    11. 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.
    12. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Seifi, Alireza, 2013. "A new algorithm for combined heat and power dynamic economic dispatch considering valve-point effects," Energy, Elsevier, vol. 52(C), pages 320-332.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Panigrahi, B.K. & Ravikumar Pandi, V. & Das, Sanjoy & Das, Swagatam, 2010. "Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem," Energy, Elsevier, vol. 35(12), pages 4761-4770.
    19. 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.
    20. Yang, Wenqiang & Zhu, Xinxin & Xiao, Qinge & Yang, Zhile, 2023. "Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles," Energy, Elsevier, vol. 282(C).

    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:47:y:2012:i:1:p:451-464. 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.