Quantitative impact assessment of transmission congestion and demand side management on electricity producers’ market power

Anupam Mittal; Kanwardeep Singh

doi:10.1515/ijeeps-2023-0316

Article

Quantitative impact assessment of transmission congestion and demand side management on electricity producers’ market power

Anupam Mittal and Kanwardeep Singh

Published/Copyright: February 2, 2024

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information

From the journal International Journal of Emerging Electric Power Systems Volume 26 Issue 2

Abstract

This paper presents the impact of transmission congestion and demand side management on Electricity Producers’ market power using the quantitative investigation of pool-based market clearing mechanism. The mathematical problem formulation for pool-based market clearing consists of maximization of social welfare objective function, which incorporates maximization of demand benefit obtained from flexible demand bids and minimization of Electricity Producers’ real and reactive power costs obtained from actual and strategic supply bids. In this problem formulation, the nonlinear modeling of power system is used for real and reactive power flow equality constraints, transmission line capability inequality constraints (for incorporating the impact of transmission congestion), and generator capability curve equality constraints. The impacts of demand side management have been incorporated in the problem by considering single-period and multi-period demand flexibilities, and distributed generation placement at appropriate locations. The formulated optimization problem has been solved using nonlinear programming, which provides the nodal prices as byproducts of the solution. The electricity producers’ capability to exercise market power has been analyzed from their revenue and surplus indices, which have been obtained from nodal prices. Various case studies have been simulated on IEEE 30-bus system for quantitative determination of electricity producers’ market power. The results obtained are very interesting and demonstrate demand side strategies to tackle the electricity producers’ market power with actual and strategic bidding under normal and congested system conditions.

Keywords: demand bid; demand side management (DSM); distributed generation; electricity market; locational market power; transmission congestion

Corresponding author: Anupam Mittal, Electrical Engineering Department, IK Gujral Punjab Technical University, Kapurthala, 144603, India, E-mail: eranumtech@gmail.com

Acknowledgments

Ms. Anupam Mittal is grateful to I.K. Gujral Punjab Technical University, Kapurthala for giving her an opportunity to pursue Ph.D. Degree.

Research ethics: The authors are committed to the research ethics and followed the same in the present research work.
Author contributions: The authors accept responsibility for the present research work and research findings.
Competing interests: The authors state no conflict of interest.
Research funding: None declared.
Data availability: The data used in this work have been cited in the paper.

References

1. David, AK, Wen, F. Market power in electricity supply. In: 2002 IEEE power engineering society winter meeting. Conference proceedings (Cat. No. 02 CH37309), New York; 2002.Search in Google Scholar

2. U. S. Department of Justice. (Jointly issued by the U. S. Federal Trade Commission), 1992 horizontal merger guidelines (with April 8, 1997, revisions to Section 4 on efficiencies). Washington, D.C.; 1997:1–37 pp.Search in Google Scholar

3. Stoff, S. Power systems economics: designing markets for power. Piscataway, NJ: IEEE Press Wiley; 2002.Search in Google Scholar

4. Christie, RD, Wollenberg, BF, Wangensteen, I. Transmission management in the deregulated environment. IEEE Proc 2000;88:170–95. https://doi.org/10.1109/5.823997.Search in Google Scholar

5. Karthikeyan, SP, Raglend, IJ, Kothari, DP. A review on market power in the deregulated electricity market. Electr Power Energy Syst 2013;48:139–47. https://doi.org/10.1016/j.ijepes.2012.11.024.Search in Google Scholar

6. He, H, Xu, Z, Cheng, GH. Impacts of transmission congestion on market power in electricity market. In: Conference: power systems conference and exposition. IEEE PES; 2004:1–6 pp.Search in Google Scholar

7. Wang, P, Xiao, Y, Ding, Y. Nodal market power assessment in electricity markets. IEEE Trans Power Syst. 2004;19:1373–8. https://doi.org/10.1109/TPWRS.2004.831695.Search in Google Scholar

8. Gan, D, Bourcier, DV. Locational market power screening and congestion management: experience and suggestions. IEEE Power Eng Rev 2002;22:70. https://doi.org/10.1109/MPER.2002.4311689.Search in Google Scholar

9. Bompard, E, Pa, YC, Napoli, R, Jiang, CW. Assessing the market power due to the network constraints in competitive electricity markets. Electr Power Syst Res 2006;76:953–61. https://doi.org/10.1016/j.epsr.2005.12.004.Search in Google Scholar

10. Singh, K, Padhy, NP, Sharma, JD. Social welfare maximization considering reactive power and congestion management in the deregulated environment. Electr Power Compon Syst 2010;38:50–71. https://doi.org/10.1080/15325000903273312.Search in Google Scholar

11. Dai, Y, Ni, Y, Shen, C, Wen, F, Han, Z, Wu, FF. A study of reactive power marginal price in electricity market. Electr Power Syst Res 2001;57:41–8. https://doi.org/10.1016/s0378-7796(00)00119-x.Search in Google Scholar

12. Rahimi, F, Ipakchi, A. Demand response as a market resource under the smart grid paradigm. IEEE Trans Smart Grid 2010;1:82–8. https://doi.org/10.1109/TSG.2010.2045906.Search in Google Scholar

13. Pina, A, Silva, C, Ferrao, P. The impact of demand side management strategies in the penetration of renewable electricity. Energy 2012;41:128–37. https://doi.org/10.1016/j.energy.2011.06.013.Search in Google Scholar

14. Kirschen, DS, Strbac, G. Fundamentals of power system economics. West Sussex, U.K.: Wiley; 2004.10.1002/0470020598Search in Google Scholar

15. Su, CL, Kirschen, DS. Quantifying the effect of demand response in electricity markets. IEEE Trans Power Syst 2009;24:1199–207. https://doi.org/10.1109/tpwrs.2009.2023259.Search in Google Scholar

16. Singh, K, Padhy, NP, Sharma, J. Influence of price responsive demand shifting bidding on congestion and LMP in pool-based day-ahead electricity markets. IEEE Trans Power Syst 2011;26:886–96. https://doi.org/10.1109/tpwrs.2010.2070813.Search in Google Scholar

17. Ye, Y, Papadaskalopoulos, D, Strbac, G. Investigating the ability of demand shifting to mitigate electricity producers’ market power. IEEE Trans Power Syst 2017;33:3800–11. https://doi.org/10.1109/TPWRS.2017.2782703.Search in Google Scholar

18. Strbac, G. Demand side management: benefits and challenges. Energy Pol 2008;36:4419–26. https://doi.org/10.1016/j.enpol.2008.09.030.Search in Google Scholar

19. Devine, MT, Bertsch, V. Role of demand response in mitigating market power – a quantitative analysis using a stochastic market equilibrium model. ESRI WP635; 2019. http://aei.pitt.edu/id/eprint/102247.Search in Google Scholar

20. Green, R. Nodal pricing of electricity: how much does it cost to get it wrong? J Regul Econ 2007;31:125–49. https://doi.org/10.1007/s11149-006-9019-3.Search in Google Scholar

21. Lee, CY, Tuegeh, M. Virtual visualization of generator operation condition through generator capability curve. Energies 2021;14:185. https://doi.org/10.3390/en14010185.Search in Google Scholar

22. Abhyankar, N, Lin, J, Liu, X, Sifuentes, F. Economic and environmental benefits of market-based power-system reform in China: a case study of the southern grid system. Resour Conserv Recycl 2020;153:1–11. https://doi.org/10.1016/j.resconrec.2019.104558.Search in Google Scholar

23. Rao, SS. Engineering optimization: theory and practice. Hoboken, New Jersey: John Wiley & Sons; 2009.Search in Google Scholar

24. Waltz, RA, Plantenga, TD. Knitro user’s manual: version 6.0: iena Optimization, Inc.; 2009. Available from: www.ziena.com.Search in Google Scholar

25. Singla, A, Singh, K, Yadav, YK. Optimization of distributed solar photovoltaic power generation in day-ahead electricity market incorporating irradiance uncertainty. J Mod Power Syst Clean Energy 2021;9:545–60. https://doi.org/10.35833/mpce.2019.000164.Search in Google Scholar

26. Zimmerman, RD, Murillo-Sanchez, CE, Thomas, RJ. MATPOWER: steady-state operations, planning and analysis tools for power systems research and education. IEEE Trans Power Syst 2011;26:12–19. https://doi.org/10.1109/tpwrs.2010.2051168.Search in Google Scholar

27. Gaur, K, Kumar, H, Agarwal, RPK, Baba, KVS, Soonee, SK. Analysing the electricity demand pattern. In: 2016 National Power Systems Conference (NPSC), Bhubaneswar, India; 2016:1–6 pp.10.1109/NPSC.2016.7858969Search in Google Scholar

28. Singh, K, Yadav, VK, Padhy, NP, Sharma, J. Congestion management considering optimal placement of distributed generator in deregulated power system networks. Electr Power Compon Syst 2013;42:13–22. https://doi.org/10.1080/15325008.2013.843218.Search in Google Scholar

Received: 2023-09-02

Accepted: 2024-01-16

Published Online: 2024-02-02

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/ijeeps-2023-0316

Keywords for this article

demand bid; demand side management (DSM); distributed generation; electricity market; locational market power; transmission congestion