Abstract
The restructuration of the regulated electricity market provides competitiveness among market participants. In this competitive framework, maximum profit can be achieved by implementing proper bidding strategy for power producers. Nowadays, the advent of generation from renewable source gained as new perspective for bidding strategy. As this renewable generation is sporadic in nature and possesses a lot of uncertainty, so power producers face an inevitable problem. Considering this fact into account, here MFO algorithm is proposed to frame bidding strategy for power producers, which maximizes their profit. In addition, the uncertainty characterization of wind renewable source is designed by employing Weibull PDF. The efficacy of the proposed approach is analyzed by IEEE 30-bus system with other approaches. From the comparative analysis, it is found superiority of the proposed approach for solving the strategic bidding problem of power producers, and the impact of wind generation on single-sided bidding problem reduces market clearing price as well as power dispatch of thermal generators.
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References
David AK, Wen F (2000) Strategic bidding in competitive electricity markets: a literature survey. In: Proceedings of IEEE PES summer meeting, vol 4, pp 2168–2173
Kumar JV, Kumar DV (2014) Generation bidding strategy in a pool based electricity market using shuffled frog lea** algorithm. Appl Soft Comput 21:407–414
Sudhakar AVV, Karri C, Laxmi AJ (2019) Optimal bidding strategy in deregulated power market using invasive weed optimization. Applications of artificial intelligence techniques in engineering, Springer, Singapore, pp 421–429
Kumar JV, Pasha SJ, Kumar DV (2010) Strategic bidding in deregulated market using particle swarm optimization. In: 2010 annual IEEE India conference (INDICON), December. IEEE, pp 1–6
Kumar JV, Kumar DV, Edukondalu K (2013) Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market. Appl Soft Comput 13(5):2445–2455
Sahoo A, Hota PK (2019) Moth flame optimization algorithm based optimal strategic bidding in deregulated electricity market. In: TENCON-2019, IEEE region 10 conference, October. IEEE, pp 2105–2110
Sahoo A, Mahapatra AA (2016) Optimal strategic bidding and financial risk assessment in restructured electricity market using particle swarm optimisation. In: 2016 ICRAIE, December. IEEE, pp 1–6
Dawn S, Tiwari PK, Goswami AK (2018) Efficient approach for establishing the economic and operating reliability via optimal coordination of wind–PSH–solar-storage hybrid plant in highly uncertain double auction competitive power market. IET Renew Power Gener 12(10):1189–1202
Singh SN, Erlich I (2008) Strategies for wind power trading in competitive electricity markets. IEEE Trans Energy Convers 23(1):249–256
Dai T, Qiao W (2015) Optimal bidding strategy of a strategic wind power producer in the short-term market. IEEE Trans Sustain Energy 6(3):707–719
**ao Y, Wang X, Wang X, Dang C, Lu M (2016) Behavior analysis of wind power producer in electricity market. Appl Energy 171:325–335
Sharma KC, Bhakar R, Tiwari HP (2014) Strategic bidding for wind power producers in electricity markets. Energy Convers Manage 86:259–267
Mirjalili S (2015) Moth flame optimization algorithm-a novel nature inspired heuristic paradigm. Knowl Based Syst 89:228–249
Damodaran SK, Sunil Kumar TK (2018) Hydro-thermal-wind generation scheduling considering economic and environmental factors using heuristic algorithms. Energies 11(2):353
Sharma KC, Jain P, Bhakar R (2013) Wind power scenario generation and reduction in stochastic programming framework. Electr Power Compon Syst 41(3):271–285
Azadeh A, Ghaderi SF, Nokhanandan BP, Shaikhalishah M (2012) A new GA approach for optimal bidding strategy view point of profit maximization of a generation company. Expert Syst Appl 39:1565–1574
Rashedi E, Nezamabadi Pour H, Sarazdi S (2009) A gravitational search algorithm. Inf Sci 179:2232–2248
‘University of Massachusetts Amherst, Wind Energy Center’. Available at http://www.umass.edu/windenergy/resoursedata.php/
Sahoo A, Hota PK (2021) Impact of renewable energy sources on modelling of bidding strategy in a competitive electricity market using improved whale optimization algorithm. IET Renew Power Gener 15(4):839–853
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Sahoo, A., Hota, P.K. (2022). Single-Sided Bidding Considering Uncertainty of Renewable Source in a Day-Ahead Energy Market. In: Gupta, O.H., Sood, V.K., Malik, O.P. (eds) Recent Advances in Power Systems. Lecture Notes in Electrical Engineering, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-16-6970-5_16
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DOI: https://doi.org/10.1007/978-981-16-6970-5_16
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