A Cloud-Edge-End Collaboration Framework for Fixed-Time Distributed Optimization of Virtual Power Plants

Kai Kang, Nian Shi, Keqi Zhang, Si Cai, Liang Zhang, Xinan Shao, Lei Shu, Renjie Hu and Leimin Wang ()
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Kai Kang: PowerChina Hubei Engineering Co., Ltd., Wuhan 430040, China
Nian Shi: PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China
Keqi Zhang: PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China
Si Cai: PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China
Liang Zhang: PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China
Xinan Shao: PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China
Lei Shu: PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China
Renjie Hu: School of Automation, China University of Geosciences, Wuhan 430074, China
Leimin Wang: School of Automation, China University of Geosciences, Wuhan 430074, China

Mathematics, 2025, vol. 13, issue 11, 1-18

Abstract: As the power grid expands, concerns about system computation speed and information privacy are becoming more critical. While distributed optimization methods protect individual privacy effectively, they struggle with computational efficiency in complex topologies. To address these issues, this paper proposes a cloud–edge–end collaboration framework consisting of a cloud server and multiple edge servers. This framework enables parallel computation of multiple distributed optimization algorithms. Additionally, a distributed fixed-time optimization consensus algorithm is designed for virtual power plants, allowing the convergence time to be predetermined offline. The fixed-time convergence of the algorithm is proven and its effectiveness and superiority are demonstrated through simulation cases.

Keywords: virtual power plant; distributed optimization; cloud–edge–end collaboration framework; fixed-time consensus (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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