Study of Two-Stage Economic Optimization Operation of Virtual Power Plants Considering Uncertainty

Hao Sun, Yanmei Liu, Penglong Qi, Zhi Zhu, Zuoxia Xing () and Weining Wu
Additional contact information
Hao Sun: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Yanmei Liu: Material Branch, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110004, China
Penglong Qi: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Zhi Zhu: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Zuoxia Xing: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Weining Wu: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China

Energies, 2024, vol. 17, issue 16, 1-23

Abstract: In a highly competitive electricity spot market, virtual power plants (VPPs) that aggregate dispersed resources face various uncertainties during market transactions. These uncertainties directly impact the economic benefits of VPPs. To address the uncertainties in the economic optimization of VPPs, scenario analysis is employed to transform the uncertainties of wind turbines (WTs), photovoltaic (PV) system outputs, and electricity prices into deterministic problems. The objective is to maximize the VPP’s profits in day-ahead and intra-day markets (real-time balancing market) by constructing an economic optimization decision model based on two-stage stochastic programming. Gas turbines and electric vehicles (EVs) are scheduled and traded in the day-ahead market, while flexible energy storage systems (ESS) are deployed in the real-time balancing market. Based on simulation analysis, under the uncertainty of WTs and PV system outputs, as well as electricity prices, the proposed model demonstrates that orderly charging of EVs in the day-ahead stage can increase the revenue of the VPP by 6.1%. Additionally, since the ESS can adjust the deviations in day-ahead bid output during the intra-day stage, the day-ahead bidding strategy becomes more proactive, resulting in an additional 3.1% increase in the VPP revenue. Overall, this model can enhance the total revenue of the VPP by 9.2%.

Keywords: virtual power plants; electricity market; electric vehicles; energy storage system; stochastic planning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/16/3940/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/16/3940/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:16:p:3940-:d:1452730

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().