IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v34y2009i9p1401-1412.html
   My bibliography  Save this article

Multi-objective congestion management incorporating voltage and transient stabilities

Author

Listed:
  • Esmaili, Masoud
  • Shayanfar, Heidar Ali
  • Amjady, Nima

Abstract

Congestion in a power system is turned up due to operating limits. To relieve congestion in a deregulated power market, the system operator pays to market participants considering their bids to alter their active powers. After relieving congestion, the network may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. The proposed multi-objective framework for congestion management in this paper simultaneously optimizes competing objective functions of congestion management cost, voltage security, and dynamic security. The voltage stability margin and corrected transient energy margin are employed as indices to be incorporated into the multi-objective congestion management. A fuzzy decision maker is proposed to derive the most efficient solution among Pareto-optimal solutions of multi-objective mathematical programming problem. Results of testing the proposed method on the New-England test system elaborate the efficiency of the proposed method.

Suggested Citation

  • Esmaili, Masoud & Shayanfar, Heidar Ali & Amjady, Nima, 2009. "Multi-objective congestion management incorporating voltage and transient stabilities," Energy, Elsevier, vol. 34(9), pages 1401-1412.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:9:p:1401-1412
    DOI: 10.1016/j.energy.2009.06.041
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544209002631
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2009.06.041?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. Massoud Amin, S. & Gellings, Clark W., 2006. "The North American power delivery system: Balancing market restructuring and environmental economics with infrastructure security," Energy, Elsevier, vol. 31(6), pages 967-999.
    2. Capizzi, G. & Tina, G., 2007. "Long-term operation optimization of integrated generation systems by fuzzy logic-based management," Energy, Elsevier, vol. 32(7), pages 1047-1054.
    3. 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.
    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.
    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. Esmaili, Masoud & Amjady, Nima & Shayanfar, Heidar Ali, 2011. "Multi-objective congestion management by modified augmented [epsilon]-constraint method," Applied Energy, Elsevier, vol. 88(3), pages 755-766, March.
    2. Reddy, S. Surender & Abhyankar, A.R. & Bijwe, P.R., 2011. "Reactive power price clearing using multi-objective optimization," Energy, Elsevier, vol. 36(5), pages 3579-3589.
    3. 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.
    4. Peesapati, Rajagopal & Yadav, Vinod Kumar & Kumar, Niranjan, 2018. "Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations," Energy, Elsevier, vol. 147(C), pages 980-994.
    5. Tabandeh, Abbas & Abdollahi, Amir & Rashidinejad, Masoud, 2016. "Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures," Energy, Elsevier, vol. 104(C), pages 213-228.
    6. Khorramdel, Benyamin & Raoofat, Mahdi, 2012. "Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms," Energy, Elsevier, vol. 45(1), pages 994-1006.
    7. Panda, Mitali & Nayak, Yogesh Kumar, 2022. "Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem," Energy, Elsevier, vol. 254(PC).
    8. Kargarian, A. & Raoofat, M., 2011. "Stochastic reactive power market with volatility of wind power considering voltage security," Energy, Elsevier, vol. 36(5), pages 2565-2571.
    9. 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.
    10. Smail, Houria & Alkama, Rezak & Medjdoub, Abdellah, 2018. "Impact of large scale power plant connection on congestion in the algerian electricity transmission system," Energy, Elsevier, vol. 159(C), pages 115-120.
    11. Peng Wu & Ling Xu & Ada Che & Feng Chu, 2022. "A bi-objective decision model and method for the integrated optimization of bus line planning and lane reservation," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1298-1327, July.
    12. Junkai He & Feng Chu & Feifeng Zheng & Ming Liu, 2021. "A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times," Annals of Operations Research, Springer, vol. 296(1), pages 71-93, January.
    13. Hosseini, Seyyed Ahmad & Amjady, Nima & Shafie-khah, Miadreza & Catalão, João P.S., 2016. "A new multi-objective solution approach to solve transmission congestion management problem of energy markets," Applied Energy, Elsevier, vol. 165(C), pages 462-471.

    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. 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.
    2. Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
    3. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    4. Miloš Božić & Miloš Stojanović & Zoran Stajić & Dragan Tasić, 2013. "A New Two-Stage Approach to Short Term Electrical Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-19, April.
    5. Johansson, Bengt, 2013. "A broadened typology on energy and security," Energy, Elsevier, vol. 53(C), pages 199-205.
    6. 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.
    7. 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.
    8. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    9. Miguel López & Carlos Sans & Sergio Valero & Carolina Senabre, 2018. "Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting," Energies, MDPI, vol. 11(8), pages 1-19, August.
    10. Blanco, Jesús M. & Vazquez, L. & Peña, F., 2012. "Investigation on a new methodology for thermal power plant assessment through live diagnosis monitoring of selected process parameters; application to a case study," Energy, Elsevier, vol. 42(1), pages 170-180.
    11. Sadegheih, A., 2009. "Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques," Energy, Elsevier, vol. 34(10), pages 1539-1551.
    12. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    13. He, Yaoyao & Xu, Qifa & Wan, Jinhong & Yang, Shanlin, 2016. "Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function," Energy, Elsevier, vol. 114(C), pages 498-512.
    14. 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.
    15. 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.
    16. Deihimi, Ali & Showkati, Hemen, 2012. "Application of echo state networks in short-term electric load forecasting," Energy, Elsevier, vol. 39(1), pages 327-340.
    17. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
    18. 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.
    19. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    20. Samuel Atuahene & Yukun Bao & Yao Yevenyo Ziggah & Patricia Semwaah Gyan & Feng Li, 2018. "Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change," Energies, MDPI, vol. 11(10), pages 1-19, October.

    More about this item

    Statistics

    Access and download statistics

    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:9:p:1401-1412. 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.