Robust Optimal Scheduling of Multi-Energy Virtual Power Plants with Incentive Demand Response and Ladder Carbon Trading: A Hybrid Intelligence-Inspired Approach

Yongyu Dai (), Zhengwei Huang, Yijun Li and Rongsheng Lv
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Yongyu Dai: College of Economics, Fuyang Normal University, Fuyang 236037, China
Zhengwei Huang: College of Economics and Management, China Three Gorges University, Yichang 443002, China
Yijun Li: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Rongsheng Lv: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China

Energies, 2025, vol. 18, issue 18, 1-26

Abstract: Aiming at the uncertainty in load demand and wind-solar power output during multi-energy virtual power plant (VPP) scheduling, this paper proposes a robust optimal scheduling method incorporating incentive-based demand response (IDR). By integrating robust optimization theory, a ladder-type carbon trading mechanism, and IDR compensation strategies, a comprehensive scheduling model is established with the objective of minimizing the operational cost of the VPP. To enhance computational efficiency and adaptability, we propose a hybrid approach that combines the Column-and-Constraint Generation (C&CG) algorithm with Karush–Kuhn–Tucker (KKT) condition linearization to transform the robust optimization model into a tractable form. A robustness coefficient is introduced to ensure the adaptability of the scheduling scheme under various uncertain scenarios. The proposed framework enables the VPP to select the most economically and environmentally optimal dispatching strategy across different energy vectors. Extensive multi-scenario simulations are conducted to evaluate the performance of the model, demonstrating its significant advantages in enhancing system robustness, reducing carbon trading costs, and improving coordination among distributed energy resources. The results indicate that the proposed method effectively improves the risk resistance capability of multi-energy virtual power plants.

Keywords: multi-energy virtual power plant; robust optimization; incentive demand response; carbon trading; column-and-constraint generation algorithm; hybrid intelligence; energy scheduling (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: 2025
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