Abstract
This paper proposes a pricing strategy for the Market by using a Stackelberg game-based bi-level programming model. In the model, the wholesale price and the retail price is optimized by the Market to increase its profit. In the model, the Energy Provider (EP) optimize optimizes the adjustment coefficient to increase the trading probability with the Utility Company (UC). Besides, the Market can sell the power to the UC directly. Then, the UCs determine their retail price by non-cooperative game to increase their profit. The simulation results reveal that the Market determines the optimal price to maximize profits. On the other hand, the proposed price strategy of EP can increase the trading probability, which promotes more UCs to trade with EP, thus increasing its benefit. Finally, UCs get a bash equilibrium through the non-cooperative game. The research proves that the proposed strategy is a win–win strategy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Medina J, Muller N, Roytelman I (2010) Demand response and distribution grid operations: opportunities and challenges. IEEE Trans Smart Grid 1(2):193–198
Metke AR, Ekl RL (2010) Security technology for smart grid networks.IEEE Trans Smart Grid 1(1):99–107
Moslehi K, Kumar R (2010) A reliability perspective of the smart grid. IEEE Trans Smart Grid 1(1):57–64
Wei W et al (2019) Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty. IEEE Access 7:171405–171410
Zeng F, Bie Z, Liu S, Yan C, Li G (2020) Trading model combining electricity, heating, and cooling under multi-energy demand response. J Mod Power Syst Clean Energy 8(1):133–141
Kamyab F et al (2016) Demand response program in smart grid using supply function bidding mechanism. IEEE Trans Smart Grid 7(3):1277–1284
Hupez M et al (2018) Cooperative demand-side management scenario for the low-voltage network in liberalised electricity markets. IET Gener Transm Distrib 12(22):5990–5999
Yang Z, Ni M, Liu H (2020) Pricing strategy of multi-energy provider considering integrated demand response. IEEE Access. Digital Object Identifier. https://doi.org/10.1109/ACCESS.2020.3016005
Yang J, Dai Y, Ma K, Liu H, Liu Z (2021) A pricing strategy based on potential game and bargaining theory in smart grid. IET Gener Transm Distrib 15:253–263
Acknowledgements
This work was supported by the National Natural Science Foundation of China (NSFC) (62001327), the Scientific Research Project of Tianjin Educational Committee (2022KJ010), and the Foundation of the Key Laboratory of System Control and Information Processing, Ministry of Education (Scip202217).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, X., Ma, H., Yin, Y., Li, Z. (2024). A Pricing Strategy for Smart Grid Based on PSO and Distributed Iterative Algorithm. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_49
Download citation
DOI: https://doi.org/10.1007/978-981-99-7502-0_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7555-6
Online ISBN: 978-981-99-7502-0
eBook Packages: EngineeringEngineering (R0)