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Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources

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  • Sood, Yog Raj
  • Singh, Randhir

Abstract

In the competitive electricity market it becomes very much important to give special consideration for development of renewable energy sources (RESs) due to environmental and other social problems related with conventional generations. So this paper presents an optimal model of congestion management with special emphasis for promotion of RES in competitive electricity market. This paper presents a generalized optimal model of congestion management for deregulated power sector that dispatches the pool in combination with privately negotiated bilateral and multilateral contracts while maximizing social benefit. This model determines the locational marginal pricing (LMP) based on marginal cost theory. It also determines the size of non-firm transactions as well as pool demand and generations. Both firms as well as non-firm transactions are considered in this model. The proposed model has been applied to IEEE-30 bus test system with addition of some RES for analysis of the proposed model. The RES supplies its power to load either through the firm transaction or through power pool. The power from RES is not subjected to any curtailment in proposed model of congestion management.

Suggested Citation

  • Sood, Yog Raj & Singh, Randhir, 2010. "Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources," Renewable Energy, Elsevier, vol. 35(8), pages 1828-1836.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:8:p:1828-1836
    DOI: 10.1016/j.renene.2010.01.002
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    Citations

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    Cited by:

    1. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    2. Nesamalar, J. Jeslin Drusila & Venkatesh, P. & Raja, S. Charles, 2016. "Energy management by generator rescheduling in congestive deregulated power system," Applied Energy, Elsevier, vol. 171(C), pages 357-371.
    3. Banshwar, Anuj & Sharma, Naveen Kumar & Sood, Yog Raj & Shrivastava, Rajnish, 2019. "Market-based participation of energy storage scheme to support renewable energy sources for the procurement of energy and spinning reserve," Renewable Energy, Elsevier, vol. 135(C), pages 326-344.
    4. Sadhan Gope & A. K. Goswami & P. K. Tiwari, 2020. "Transmission congestion management with integration of wind farm: a possible solution methodology for deregulated power market," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 287-296, April.
    5. Navon, Aviad & Kulbekov, Pavel & Dolev, Shahar & Yehuda, Gil & Levron, Yoash, 2020. "Integration of distributed renewable energy sources in Israel: Transmission congestion challenges and policy recommendations," Energy Policy, Elsevier, vol. 140(C).
    6. Banshwar, Anuj & Sharma, Naveen Kumar & Sood, Yog Raj & Shrivastava, Rajnish, 2017. "Real time procurement of energy and operating reserve from Renewable Energy Sources in deregulated environment considering imbalance penalties," Renewable Energy, Elsevier, vol. 113(C), pages 855-866.
    7. Suganthi, S.T. & Devaraj, D. & Ramar, K. & Hosimin Thilagar, S., 2018. "An Improved Differential Evolution algorithm for congestion management in the presence of wind turbine generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 635-642.
    8. Ye Liu & Guohe Huang & Yanpeng Cai & Cong Dong, 2011. "An Inexact Mix-Integer Two-Stage Linear Programming Model for Supporting the Management of a Low-Carbon Energy System in China," Energies, MDPI, vol. 4(10), pages 1-30, October.

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