Abstract
For a pool-based electricity power market, the bidding strategy of individual generators is a key factor that influence the market clearing price. In this paper, the profit function and risk indexes for individual generators are derived. The bidding strategies of generators are selected based on different preference of profit and risks. Three typical strategy, aggressive, normal and conservative, are chosen as the bidding strategy for analysis. Simulations results on different scenarios show obvious effects of bidding strategies on the market clearing results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arroyo JM, Conejo AJ (2002) Optimal response of a power generator to energy, AGC, and reserve pool-based markets. Power Eng Rev 22(4):76–77
Dai T, Qiao W (2016) Finding equilibria in the pool-based electricity market with strategic wind power producers and network constraints. IEEE Trans Power Syst 32(1):1
Wen F, David AK (2002) Coordination of bidding strategies in energy and spinning reserve markets for competitive suppliers using a genetic algorithm. In: 2000 Power Engineering Society Summer Meeting (Cat. No. 00CH37134), vol 4. IEEE, Seattle, WA, pp 2174–2179
Zhang CH, Zhang L (2017) Calculation of nodal price based on modified DC-OPF and its application in loss allocation. In: Proceedings of the 2017 IEEE Region 10 Reference. Penang, pp 1942–1946
Feng H, Yang ZL, Zheng YX et al (2018) Intelligent agent based bidding simulation method for multi-input decision factors of power supplier. Autom Electr Power Syst 42(23):72–80
Fernandezblanco R, Arroyo JM, Alguacil N (2017) On the solution of revenue- and network-constrained day-ahead market clearing under marginal pricing—part I: an exact bilevel programming approach. IEEE Trans Power Syst (99):1
Hao R, Ai Q, Jiang ZQ (2018) Bi-level game strategy for multi-agent with incomplete information in regional integrated energy system. Autom Electr Power Syst 42(04):194–201
Vrettos E, Oldewurtel F, Andersson G (2015) Robust energy-constrained frequency reserves from aggregations of commercial buildings. IEEE Trans Power Syst 31(6):4272–4285
Li SD, Shi QS, Zhao WH et al (2016) A multi-objective optimization based bidding model with vehicle-to-grid reserve provision considered. Autom Electr Power Syst 40(02):77–83
Xu Q, Ji Y, Huang Q et al (2018) Bi-level optimized dispatch strategy of electric supply-demand balance considering risk-benefit coordination. IET Smart Grid 1(4):169–176
Liu G, Starke M, **ao B et al (2017) Robust optimization based microgrid scheduling with islanding constraints. IET Gener Transm Distrib 11(7):1820–1828
Geng YL, Ming Z, Juan Y et al (2004) Research on procurement strategy of reactive power as auxiliary service in power markets. In: IEEE International Conference on Electric Utility Deregulation, vol 1. IEEE, Hong Kong, China, pp 349–354 (2004)
Liu YY, Chen TE, Wang JX, Li Y et al (2017) Bidding method for wind generation company in nodal power market. In: The 6th International Conference on Renewable Power Generation, pp 19–21
Acknowledgements
This work was supported by Science and Technology Project of Guangdong Power Grid Corporation [No. 036000KK52170010 (GDKJXM20173410)].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Feng, S., Zhang, X., Bai, Y., Cheng, L., Bao, YQ., Wu, M. (2020). Influence of Risk Favor on the Market Clearing of a Competitive Electricity Market. In: Xue, Y., Zheng, Y., Rahman, S. (eds) Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control. PMF PMF 2019 2021. Lecture Notes in Electrical Engineering, vol 584. Springer, Singapore. https://doi.org/10.1007/978-981-13-9779-0_76
Download citation
DOI: https://doi.org/10.1007/978-981-13-9779-0_76
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9778-3
Online ISBN: 978-981-13-9779-0
eBook Packages: EnergyEnergy (R0)