The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Interactive Dispatch Modes and Bidding Strategy of Multiple Virtual Power Plants Based on Demand Response and Game Theory
Abstract
This paper examines operation models of multiple virtual power plants (multiVPPs), aiming at unified management of the multiVPP through VPP central controller, which reveals the controllability of the VPP as source and load in general. Two operation models will be introduced in this paper: 1) VPP dispatch model; and 2) the game theoretic model for multiVPP dispatch. In the VPP dispatch model, considering interactive coordination between VPP and energy consumers, a demand response model based on time-of-use pricing mechanism and interruptible loads is employed. And then the optimal results of VPP dispatch model are applied in the game theoretic model for multiVPP dispatch. During the process of market competition, the bidding strategy of each VPP is determined by its affordable power output and fuel cost. To solve the inherent variability and unpredictability of the renewable generation sources, the multitime scale rolling scheduling strategy is adopted. Finally, taking the multiVPP, which consists of various distributed generations and battery storage devices, as an example, variables including transferred load, compensation capacity, optimal bidding strategy, and profits for each VPP are obtained. From the analysis of the results, the method serves as a foundation for dispatch of multiVPP.
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Cites
Mohsenian-Rad, A.-H., Wong, V. W. S., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2010.2089069 · Cited by 2,744
Control for Grid-Connected and Intentional Islanding Operations of Distributed Power Generation
Balaguer, I. J., Lei, Q., Yang, S., Supatti, U., & Peng, F. Z. (2011) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2049709 · Cited by 622
Control and Operation of a DC Microgrid With Variable Generation and Energy Storage
Xu, L., & Chen, D. (2011) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2011.2158456 · Cited by 567
Centralized control for optimizing microgrids operation
Tsikalakis, A. G., & Hatziargyriou, N. D. (2011) · 2011 IEEE Power and Energy Society General Meeting · doi:10.1109/pes.2011.6039737 · Cited by 365
Evolutionary programming based optimal placement of renewable distributed generators
Khatod, D. K., Pant, V., & Sharma, J. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2211044 · Cited by 293
Optimal Generation Mix With Short-Term Demand Response and Wind Penetration
De Jonghe, C., Hobbs, B. F., & Belmans, R. (2012) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2011.2174257 · Cited by 240
An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles
Vasirani, M., Kota, R., Cavalcante, R. L. G., Ossowski, S., & Jennings, N. R. (2013) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2259270 · Cited by 234
Stochastic Unit Commitment With Uncertain Demand Response
Wang, Q., Wang, J., & Guan, Y. (2012) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2202201 · Cited by 192
Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication
Yang, H., Yi, D., Zhao, J., & Dong, Z. Y. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2013.2242702 · Cited by 127
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Cited by
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Lu, R., & Hong, S. H. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.12.061 · Cited by 394
Yang, Q., Wang, H., Wang, T., Zhang, S., Wu, X., & Wang, H. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117026 · Cited by 238
Li, S., Wu, W., & Lin, Y. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3166274 · Cited by 54
Gao, Y., Zhou, X., Ren, J., Wang, X., & Li, D. (2018) · Energies · doi:10.3390/en11113072 · Cited by 28
Metadata
- Authors
- Wang, Y., Ai, X., Tan, Z., Yan, L., & Liu, S.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2016-01
- Volume
- 7
- Issue
- 1
- Pages
- 510-519
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2081400663
- Cited by
- 288
- References
- 11
- FWCI
- 7.54
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
