IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i9p2354-d355500.html
   My bibliography  Save this article

Investigations of Various Market Models in a Deregulated Power Environment Using ACOPF

Author

Listed:
  • Aruna Kanagaraj

    (Department of EEE, Anna University, Chennai 600025, Tamil Nadu, India)

  • Kumudini Devi Raguru Pandu

    (Department of EEE, Anna University, Chennai 600025, Tamil Nadu, India)

Abstract

A bi-level electricity market clearing process was developed for energy and reserve allocation in the day-ahead market using AC Optimal Power Flow (ACOPF). An energy-consuming entity (ECE) which does not want its cleared demand to be curtailed, even if any contingency occurs, purchases power from the reserve market at a higher rate. The proposed model helps the ECE to secure a reserve market allocation at the price of the energy market in the real-time market settlement. Various market models were formulated for the evaluation of locational marginal pricing (LMP) in the energy market and locational contingency marginal reserve pricing (LCMRP) in the reserve market. The impact of wind farms on LMP, LCMRP, and negative LMP was analyzed. The increase in demand requirement in the deregulated environment was balanced in the proposed models by the thermal–wind coordination dispatch. The market models were illustrated with the IEEE 30 bus system.

Suggested Citation

  • Aruna Kanagaraj & Kumudini Devi Raguru Pandu, 2020. "Investigations of Various Market Models in a Deregulated Power Environment Using ACOPF," Energies, MDPI, vol. 13(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2354-:d:355500
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/9/2354/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/9/2354/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reddy, S. Surender, 2017. "Optimal scheduling of thermal-wind-solar power system with storage," Renewable Energy, Elsevier, vol. 101(C), pages 1357-1368.
    2. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    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. Diego Larrahondo & Ricardo Moreno & Harold R. Chamorro & Francisco Gonzalez-Longatt, 2021. "Comparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Power," Energies, MDPI, vol. 14(15), pages 1-15, July.
    2. Zorana Božić & Dušan Dobromirov & Jovana Arsić & Mladen Radišić & Beata Ślusarczyk, 2020. "Power Exchange Prices: Comparison of Volatility in European Markets," Energies, MDPI, vol. 13(21), pages 1-15, October.
    3. Waldemar Niewiadomski & Aleksandra Baczyńska, 2021. "Advanced Flexibility Market for System Services Based on TSO–DSO Coordination and Usage of Distributed Resources," Energies, MDPI, vol. 14(17), pages 1-31, September.

    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. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. Amirinia, Gholamreza & Mafi, Somayeh & Mazaheri, Said, 2017. "Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS," Renewable Energy, Elsevier, vol. 113(C), pages 915-929.
    5. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
    6. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data," Energies, MDPI, vol. 10(9), pages 1-24, September.
    7. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization," Renewable Energy, Elsevier, vol. 191(C), pages 459-492.
    8. Botelho, D.F. & de Oliveira, L.W. & Dias, B.H. & Soares, T.A. & Moraes, C.A., 2022. "Integrated prosumers–DSO approach applied in peer-to-peer energy and reserve tradings considering network constraints," Applied Energy, Elsevier, vol. 317(C).
    9. Stylianos A. Papazis, 2022. "Integrated Economic Optimization of Hybrid Thermosolar Concentrating System Based on Exact Mathematical Method," Energies, MDPI, vol. 15(19), pages 1-22, September.
    10. Han, Qinkai & Wang, Tianyang & Chu, Fulei, 2022. "Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    11. Rodriguez-Hernandez, O. & Jaramillo, O.A. & Andaverde, J.A. & del Río, J.A., 2013. "Analysis about sampling, uncertainties and selection of a reliable probabilistic model of wind speed data used on resource assessment," Renewable Energy, Elsevier, vol. 50(C), pages 244-252.
    12. Xu, Jiuping & Wang, Fengjuan & Lv, Chengwei & Huang, Qian & Xie, Heping, 2018. "Economic-environmental equilibrium based optimal scheduling strategy towards wind-solar-thermal power generation system under limited resources," Applied Energy, Elsevier, vol. 231(C), pages 355-371.
    13. Mostafaeipour, Ali, 2010. "Productivity and development issues of global wind turbine industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1048-1058, April.
    14. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    15. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.
    16. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    17. Muhammad Fitra Zambak & Catra Indra Cahyadi & Jufri Helmi & Tengku Machdhalie Sofie & Suwarno Suwarno, 2023. "Evaluation and Analysis of Wind Speed with the Weibull and Rayleigh Distribution Models for Energy Potential Using Three Models," International Journal of Energy Economics and Policy, Econjournals, vol. 13(2), pages 427-432, March.
    18. Höltinger, Stefan & Salak, Boris & Schauppenlehner, Thomas & Scherhaufer, Patrick & Schmidt, Johannes, 2016. "Austria's wind energy potential – A participatory modeling approach to assess socio-political and market acceptance," Energy Policy, Elsevier, vol. 98(C), pages 49-61.
    19. Shen, Xin & Chen, Jin-Ge & Zhu, Xiao-Cheng & Liu, Peng-Yin & Du, Zhao-Hui, 2015. "Multi-objective optimization of wind turbine blades using lifting surface method," Energy, Elsevier, vol. 90(P1), pages 1111-1121.
    20. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.

    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:gam:jeners:v:13:y:2020:i:9:p:2354-:d:355500. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.