The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
A game-theoretic approach for optimal time-of-use electricity pricing
Abstract
Demand for electricity varies throughout the day, increasing the average cost of power supply. Time-of-use (TOU) pricing has been proposed as a demand-side management (DSM) method to influence user demands. In this paper, we describe a game-theoretic approach to optimize TOU pricing strategies (GT-TOU). We propose models of costs to utility companies arising from user demand fluctuations, and models of user satisfaction with the difference between the nominal demand and the actual consumption. We design utility functions for the company and the users, and obtain a Nash equilibrium using backward induction. In addition to a single-user-type scenario, we also consider a scenario with multiple types of users, each of whom responds differently to time-dependent prices. Numerical examples show that our method is effective in leveling the user demand by setting optimal TOU prices, potentially decreasing costs for the utility companies, and increasing user benefits. An increase in social welfare measure indicates improved market efficiency through TOU pricing.
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Cites
Distributed generation: a definition
Ackermann, T., Andersson, G., & Söder, L. (2001) · Electric Power Systems Research · doi:10.1016/s0378-7796(01)00101-8 · Cited by 2,502
Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments
Mohsenian-Rad, A.-H., & Leon-Garcia, A. (2010) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2010.2055903 · Cited by 1,838
The Schur Complement and Its Applications
(2005) · Numerical Methods and Algorithms · doi:10.1007/b105056 · Cited by 1,615
Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid
Samadi, P., Mohsenian-Rad, A.-H., Schober, R., Wong, V. W. S., & Jatskevich, J. (2010) · 2010 First IEEE International Conference on Smart Grid Communications · doi:10.1109/smartgrid.2010.5622077 · Cited by 1,022
The concept of demand-side management for electric utilities
Gellings, C. W. (1985) · Proceedings of the IEEE · doi:10.1109/proc.1985.13318 · Cited by 768
Turning on the Lights: A Meta-Analysis of Residential Electricity Demand Elasticities
Espey, J. A., & Espey, M. (2004) · Journal of Agricultural and Applied Economics · doi:10.1017/s1074070800021866 · Cited by 362
Designing incentive compatible contracts for effective demand management
Fahrioglu, M., & Alvarado, F. L. (2000) · IEEE Transactions on Power Systems · doi:10.1109/59.898098 · Cited by 285
Electric Energy and Power Consumption by Light-Duty Plug-in Electric Vehicles
Wu, D., Aliprantis, D. C., & Gkritza, K. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2010.2052375 · Cited by 279
Quantifying Customer Response to Dynamic Pricing
Faruqui, A., & George, S. (2005) · The Electricity Journal · doi:10.1016/j.tej.2005.04.005 · Cited by 261
Creating a Smarter U.S. Electricity Grid
Joskow, P. L. (2012) · The Journal of Economic Perspectives · doi:10.1257/jep.26.1.29 · Cited by 160
Cited by
Esther, B. P., & Kumar, K. S. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.282 · Cited by 264
An Optimal and Distributed Demand Response Strategy With Electric Vehicles in the Smart Grid
Tan, Z., Yang, P., & Nehorai, A. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2291330 · Cited by 256
Self-citationEEP2P: An energy-efficient and economy-efficient P2P network protocol
Zhou, Z., Xie, M., Zhu, T., Xu, W., Yi, P., Huang, Z., Zhang, Q., & Xiao, S. (2014) · International Green Computing Conference · doi:10.1109/igcc.2014.7039171 · Cited by 19
Metadata
- Authors
- Yang, P., Tang, G., & Nehorai, A.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2013-05
- Volume
- 28
- Issue
- 2
- Pages
- 884-892
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2023409181
- Cited by
- 438
- References
- 16
- FWCI
- 15.67
- Type
- journal article
- Language
- en
- Open access
- closed
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