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Multi-objective congestion management incorporating voltage and transient stabilities

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Citations

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    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.

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