Modelling a Robust Bidding Strategy Problem for Price-Maker Gencos Considering the Uncertainty of Competitors' Bids

Document Type : Research Paper

Author

Assistant Professor, Department of Industrial Engineering, Faculty of Technical and Engineering, Garmsar University, Garmsar, Iran

Abstract

Day-ahead power market is one of the most common electricity sales markets in deregulated electricity networks. Power generation companies (Gencos) need to submit a bid to participate in this market. This bid includes an output-price proposal staircase that shows the Gencos attitude to different levels of generation. The bidding strategy problem responds to this requirement of Gencos. In this paper, the bidding strategy problem has been studied from the perspective of price-maker Gencos as a bi-level programming problem where the first level includes a self-scheduling problem from the point of view of the Genco and the second level includes a market settlement problem from the perspective of the market operator. Hence, robust optimization has been used as a tool to deal with uncertainty. Due to the existence of uncertainty in the second level sub-problem and the importance of investigating its effects on the solution of the first level sub-problem, the problem has been reformulated as a single-level integrated model and Then, a robust optimization approach that can be used in the presence of correlation between uncertainty factors has been used. Finally, the performance of the proposed robust model has been evaluated during a Monte Carlo simulation process. The simulation results show that the robust model lead to solutions with lower profit compared to the deterministic model. However, due to increasing the chance of acceptance of a robust solution in the market settlement process, it increases the average profit of the strategic Genco in the long run.

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References

  • Kirschen, D.S. and G. Strbac, Fundamentals of power system economics. 2018: John Wiley & Sons.
  • Conejo, A.J., M. Carrión, and J.M. Morales, Decision making under uncertainty in electricity markets. Vol. 1. 2010: Springer.
  • Ci-ling, Z. and X. Pei-yuan. Stochastic non-linear Nash Game in multi-zone electricity markets. in 2008 China International Conference on Electricity Distribution. 2008.
  • Dai, T. and W. Qiao. Optimal bidding strategy of a strategic wind power producer in the short-term market. in 2016 IEEE Power and Energy Society General Meeting (PESGM). 2016. IEEE.
  • Gountis, V.P. and A.G. Bakirtzis, Bidding strategies for electricity producers in a competitive electricity marketplace. IEEE Transactions on Power Systems, 2004. 19(1): p. 356-365.
  • Mozdawar, A. and B. Khaki. Strategic bidding in pay as bid power market by combined probabilistic and game theory procedures. in 2009 IEEE Bucharest PowerTech. 2009. IEEE.
  • Ma, X., et al., Towards the development of risk-constrained optimal bidding strategies for generation companies in electricity markets. Electric power systems research, 2005. 73(3): p. 305-312.
  • Kumar, J.V. and D.V. Kumar. Risk-constrained bidding strategy for Generation Companies in an open electricity market using Fuzzy Adaptive Particle Swarm Optimization. in 2011 International Conference on Energy, Automation and Signal. 2011. IEEE.
  • Langary, D., N. Sadati, and A.M. Ranjbar, Direct approach in computing robust Nash strategies for generating companies in electricity markets. International Journal of Electrical Power & Energy Systems, 2014. 54: p. 442-453.
  • Li, G., J. Shi, and X. Qu, Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review. Energy, 2011. 36(8): p. 4686-4700.
  • Mozdawar, A., et al. Game based strategic bidding in pay as bid markets considering incomplete information and risk factor. in 2009 International Conference on Power Engineering, Energy and Electrical Drives. 2009. IEEE.
  • Boonchuay, C. and W. Ongsakul, Risk-constrained optimal bidding strategy for a generation company using self-organizing hierarchical particle swarm optimization. Applied Artificial Intelligence, 2012. 26(3): p. 246-260.
  • Takano, Y., N. Ishii, and M. Muraki, A sequential competitive bidding strategy considering inaccurate cost estimates. Omega, 2014. 42(1): p. 132-140.
  • Da-Wei, H. and H. Xue-Shan. Study on generation companies' bidding strategy based on hybrid intelligent method. in 2009 Ninth International Conference on Hybrid Intelligent Systems. 2009. IEEE.
  • Ma, X. and T. Shi. Credibility theory based approach to build optimal bidding strategies with risk management for generation companies. in 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century. 2008. IEEE.
  • Li, Y.-p. and X.-g. Li. Power supplier’s risk types and optimal asset management reckon in biding failure. in 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. 2008. IEEE.
  • Jain, A., et al. Bacteria foraging optimization algorithm based strategic bidding in electricity markets. in 2014 Eighteenth National Power Systems Conference (NPSC). 2014. IEEE.
  • Baringo, L. and A.J. Conejo, Offering strategy of wind-power producer: A multi-stage risk-constrained approach. IEEE Transactions on Power Systems, 2015. 31(2): p. 1420-1429.
  • Zareian Jahromi et al., Robust multi-objective optimization model in sustainable closed loop supply chain design, Industrial engineering researches in production systems, 2014, 2(3): p. 93-111. (In Persian)
  • Rafiei, M., Mohammadi Talab, A., Presenting a mathematical model with a robust optimization approach for the design of a dynamic cellular manufacturing system considering multi-tasking machines, Industrial engineering researches in production systems, 2016, 4(9): p. 281-295. (In Persian)
  • Jalilvand-Nejad, A., R. Shafaei, and H. Shahriari, A Genco self-scheduling problem with correlated prices using a new robust optimization approach. International Journal of Production Research, 2017. 55(11): p. 3249-3265.
  • Rahimiyan, M. and L. Baringo, Strategic bidding for a virtual power plant in the day-ahead and real-time markets: A price-taker robust optimization approach. IEEE Transactions on Power Systems, 2015. 31(4): p. 2676-2687.
  • Thatte, A.A., D.E. Viassolo, and L. Xie. Robust bidding strategy for wind power plants and energy storage in electricity markets. in 2012 IEEE Power and Energy Society General Meeting. 2012. IEEE.
  • Zugno, M. and A.J. Conejo, A robust optimization approach to energy and reserve dispatch in electricity markets. European Journal of Operational Research, 2015. 247(2): p. 659-671.
  • Grigg, C., et al., The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee. IEEE Transactions on power systems, 1999. 14(3): p. 1010-1020.
  • Kardakos, E.G., C.K. Simoglou, and A.G. Bakirtzis, Optimal bidding strategy in transmission-constrained electricity markets. Electric Power Systems Research, 2014. 109: p. 141-149.
  • Wood, A.J., B.F. Wollenberg, and G.B. Sheblé, Power generation, operation, and control. 2013: John Wiley & Sons.
  • Taha, H.A. and H.A. Taha, Operations research: an introduction. Vol. 7. 2003: Prentice hall Upper Saddle River, NJ.
  • Mulvey, J.M., R.J. Vanderbei, and S.A. Zenios, Robust optimization of large-scale systems. Operations research, 1995. 43(2): p. 264-281.
  • Rahmani, D., et al., A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Applied Mathematical Modelling, 2013. 37(20-21): p. 8957-8971
Journal of Industrial Engineering Research in Production Systems
Volume 11, Issue 23
March 2024
Pages 5-19

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