@article{YU2026, 
author = {Meng YU and Yan LI and Liangliang ZHU and Xiangyu GUO and Min ZHANG and Chenguan XU},
title = {Optimization Model of Virtual Power Plant Participating in Power Peak Shaving Decision Based on Resource Response Capability and IGDT},
year = {2026},
journal = {Distributed Energy},
volume = {11},
number = {4},
pages = {43-57},
keywords = {virtual power plant (VPP), resource response capability, information gap decision theory (IGDT), decision optimization model, power peak shaving},
url = {https://www.sciopen.com/article/10.16513/j.2096-2185.DE.25100203},
doi = {10.16513/j.2096-2185.DE.25100203},
abstract = {To address the optimization problem of resource aggregation and bidding decision-making for virtual power plant (VPP) participating in power peak shaving, a decision optimization model based on resource response capability and information gap decision theory (IGDT) is proposed. Considering four dimensions: response potential, fluctuation degree, duration, and response speed, an aggregation indicator system for distributed resources is constructed. A multi-objective aggregation optimization model is established, balancing the maximization of expected response revenue and the minimization of deviation penalty risk, to screen the optimal resource portfolio. The market transaction framework and bidding decision mechanism for VPP participating in power peak shaving are designed. The IGDT theory is introduced to characterize the uncertainty of peak shaving compensation prices, and a risk-averse (RA) model is constructed to optimize bidding strategies. The simulation results show that the multi-objective optimization model of VPP aggregation can take into account both economic and risk considerations. It can provide a theoretical method for VPP aggregators to screen resources and reduce the risk of deviation punishment of VPP. The IGDT-based bidding decision optimization model can help avoid the transaction risk caused by the uncertainty of peak compensation price, so that the VPP can obtain reasonable response benefits.}
}