A Multi-Time Scale Dispatch Strategy Integrating Carbon Trading for Mitigating Renewable Energy Fluctuations in Virtual Power Plants

Wanling Zhuang, Junwei Liu, Jun Zhan, Honghao Liang, Cong Shen (), Qian Ai and Minyu Chen
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Wanling Zhuang: Shenzhen Power Supply Company, Shenzhen 518001, China
Junwei Liu: Shenzhen Power Supply Company, Shenzhen 518001, China
Jun Zhan: Shenzhen Power Supply Company, Shenzhen 518001, China
Honghao Liang: Shenzhen Power Supply Company, Shenzhen 518001, China
Cong Shen: Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
Qian Ai: Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
Minyu Chen: Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2025, vol. 18, issue 10, 1-26

Abstract: Under the “dual-carbon” strategic framework, the installed capacity of renewable energy sources has continuously increased, while that of conventional generation units has progressively decreased. This structural shift significantly diminishes the operational flexibility of power generation systems and intensifies grid imbalances caused by renewable energy volatility. To address these challenges, this study proposes a carbon-aware multi-timescale virtual power plant (VPP) scheduling framework with coordinated multi-energy integration, which operates through two sequential phases: day-ahead scheduling and intraday rolling optimization. In the day-ahead phase, demand response mechanisms are implemented to activate load-side regulation capabilities, coupled with information gap decision theory (IGDT) to quantify renewable energy uncertainties, thereby establishing optimal baseline schedules. During the intraday phase, rolling horizon optimization is executed based on updated short-term forecasts of renewable energy generation and load demand to determine final dispatch decisions. Numerical simulations demonstrate that the proposed framework achieves a 3.76% reduction in photovoltaic output fluctuations and 3.91% mitigation of wind power variability while maintaining economically viable scheduling costs. Specifically, the intraday optimization phase yields a 1.70% carbon emission reduction and a 7.72% decrease in power exchange costs, albeit with a 3.09% increase in operational costs attributable to power deviation penalties.

Keywords: IGDT robust optimization; virtual power plant; two-stage optimization; multi-energy optimization; uncertainty management (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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