Robust Bidding Strategy for Thermal Power Generation Company in Competitive Electricity Market

Document Type : Original Article

Authors

1 PhD student, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

2 Professor, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

Abstract

The aim of this research is to deal with uncertainty in order to obtain the optimal bidding strategy for power generation companies to determine the price and power selling in day-ahead electricity market to maximize profit. This strategy includes the electricity price and the amount of electric power that should be offered to electricity market. The proposed model has two parts. The first part suggests a special method for obtaining the bid prices and in the second part by modeling a self-scheduling problem, different values of power are proposed for each bidding price to the electricity market. A mathematical modeling approach is applied in this research by using a mixed-integer non-linear programming model which is implemented in Lingo software in a case study of thermal generation unit to investigate the efficiency of the proposed model. The proposed model, empowers the decision makers to make robust decisions by applying fuzzy methods against uncertainty of electricity market prices to achieve the optimal solution which also has the capability to adjust the robustness level. Finally, a sensitivity analysis is applied to validate and evaluate the performance of the proposed model under different uncertainty situation which indicates the robustness of model. Also, the resistance of the model in high variations of uncertain parameter is illustrated.

Keywords

References

[1] Haddad, H., Taghizadeh Yazdi, M., Zandieh, M., Heydari Dehui, J., Razavi Haji Agha, H. (2021). Presenting a Bi-Level programming approach for Unit commitment in Iran with minimization of greenhouse gas emission. Modern Research in Decision Making, 6(4), 55-74.
[2] Mardani, M., Fatemi Ardestani, S.F., Ghazizadeh, M.S., (2018), Essays In Iranian Wholesale Electricity Market, the Ph.D. degree Graduate Sharif University of Technology, Tehran, Iran. (In Persian)
[3] Shamsini Ghiasvand, F., Afsha, K., Bigdeli, N., (2015), optimal bidding strategy of power producers in pay-as-bid markets, , Imam Khomeini International University, the Degree of Master Graduate Ggazvin, Iran. (In Persian)
[4] Marzban, E., Mohammadi, M. (2021). Future Scenarios for power Management in Iran. Management Research in Iran, 20(3), 177-204.
[5] Aien, M., Rashidinejad, M., Fotuhi-Firuzabad, M., (2015), GENCO’s Decision Making Considering Power Market Uncertainties, the Ph.D. degree Graduate University of Advanced Technology, Energy Department, kerman, Iran. (In Persian)
[6] Vatani, B., Chowdhury, B., Dehghan, S., & Amjady, N. (2018). A critical review of robust self-scheduling for generation companies under electricity price uncertainty. International Journal of Electrical Power and Energy Systems, 97, 428–439. 
[7] Jalilvand Nejad, A., Shafaei, R., (2016), Developing a Robust Model to Manage Power Selling in The Competitive Market, the Ph.D. degree Graduate, K. N. Toosi University of Technology, Tehran, Iran.  (In Persian)
[8] Hamta, N., Ehsanifar, M., Moghaddasi, A. (2018). An Investigation of Iranian Entrepreneurs’ Decision Making Logic Based On Effectuation Theory. Modern Research in Decision Making, 2(4), 255-273.
[9] Valipour Khatir, M., Azar, A., Amini, M. (2021). Developing Performance Based Budgeting Model: Organizational Excellence Approach. Management Research in Iran, 21(2), 179-198.
[10] Kayedpour, F., Amiri, M., olfat, L., Pishvaee, M. (2021). Supply chain configuration based on dynamic pricing and robust optimization. Modern Research in Decision Making, 6(3), 156-187.
[11] Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030–1081.
[12] Itaba, S., & Mori, H. (2017). A Fuzzy-Preconditioned GRBFN Model for Electricity Price Forecasting. Procedia Computer Science, 114, 441–448. 
[13] Sheikh-el-eslami, M. K., & Seifi, H. (2006). Short-term electricity price forecasting using a fuzzy stochastic predictor. IEEE Power Engineering Society General Meeting, 1–5.
[14] Nazari Farsani, V., Sheikh-El-Eslami, M.K., (2019), A New Approach for Interval Forecasting of Electricity Price based on Deep Learning, the Degree of Master Graduate Tarbiat Modares University, Tehran, Iran. (In Persian)
[15] Nowotarski, J., & Weron, R. (2014). Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices. International Conference on the European Energy Market, EEM, 1, 0–4. 
[16] Nowotarski, J., & Weron, R. (2015). Computing electricity spot price prediction intervals using quantile regression and forecast averaging. Computational Statistics, 30(3), 791–803. 
[17] Parvaresh, M., Mohtavi Pour, S.S., (2017), Anew optimal bidding Strategy for Generation compahies (GENCOS), the Degree of Master University Of Guilan, Guilan, Iran. (In Persian)
[18] Esmaeily, A., Ahmadi, A., Raeisi, F., Ahmadi, M. R., Esmaeel Nezhad, A., & Janghorbani, M. (2017). Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate. Energy, 122, 182–193. 
[19] Yazdaninejad, M., & Amjady, N. (2017). Risk-minimizing stochastic self-scheduling model for microgrid in day-ahead electricity market. International Transactions on Electrical Energy Systems, 27(5). 1-18.
[20] Khaloie, H., Abdollahi, A., Rashidinejad, M., & Siano, P. (2019). Risk-based probabilistic-possibilistic self-scheduling considering high-impact low-probability events uncertainty. International Journal of Electrical Power and Energy Systems, 110, 598–612. 
[21] Jiao, P. H., Chen, J. J., Qi, B. X., Zhao, Y. L., & Peng, K. (2019). Electricity price driven active distribution network planning considering uncertain wind power and electricity price. International Journal of Electrical Power and Energy Systems, 107, 422–437. 
[22] Goroohi Sardou, I., & Ansari, M. (2020). Risk-constrained self-scheduling of a generation company considering natural gas flexibilities for wind energy integration. Journal of Renewable and Sustainable Energy, 12. 1-14
[23] Nojavan, S., Zare, K., & Mohammadi-Ivatloo, B. (2017). Robust bidding and offering strategies of electricity retailer under multi-tariff pricing. Energy Economics, 68, 359–372. 
[24] Nojavan, S., & Zare, K. (2018). Interval optimization based performance of photovoltaic/wind/FC/electrolyzer/electric vehicles in energy price determination for customers by electricity retailer. Solar Energy, 171, 580–592. 
[25] Mazzi, N., Kazempour, J., & Pinson, P. (2018). Price-Taker Offering Strategy in Electricity Pay-as-Bid Markets. IEEE Transactions on Power Systems, 33(2), 2175–2183. 
[26] Jabari, F., Mohammadi-ivatloo, B., & Mohammadpourfard, M. (2019). Robust optimal self-scheduling of potable water and power producers under uncertain electricity prices. Applied Thermal Engineering, 162. 1-13
[27] Nojavan, S., Nourollahi, R., Pashaei-Didani, H., & Zare, K. (2019). Uncertainty-based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange. International Journal of Electrical Power and Energy Systems, 105(January 2018), 237–248. 
[28] Yazdaninejad, M., Amjady, N., & Dehghan, S. (2020). VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach. IEEE Transactions on Smart Grid, 11(4), 3632–3645. 
[29] Najafi, A., Pourakbari-Kasmaei, M., Jasinski, M., Lehtonen, M., & Leonowicz, Z. (2022). A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems. Energy, 238. 1-15
[30] Dehghan, E., Shafiei, M., Amiri, M., & Jabbarzadeh, A. (2018). Hybrid robust , stochastic and possibilistic programming for closed-loop supply chain network design. Computers & Industrial Engineering 123, 220–231. 
[31] Farhadi, H., Zangiabadi, M., Mansouri, H., (2014), Methods For Solving Fuzzy Two-Level Programming Problems, the Degree of Master Graduate Shahrood University

Files

Share

How to cite

Statistics