Two-stage stochastic optimization of virtual power plant with wind power and uncertain demand

ABSTRACT

The virtual power plant (VPP) has been recognized as an effective way to facilitate penetration of renewable and distributed energy resources in electricity markets. This paper introduces an adaptive curtailment strategy for a VPP comprising a wind power plant and uncertain demand, and explores the economic advantage of adaptively adjusting wind power curtailment. A two-stage stochastic model is proposed to deal with the uncertainties in wind power generation (WPG), load demand and market prices. In the model, the bid decision is made in the face of uncertainties in the first stage, while the control (curtailment) decision is made based on realized uncertain parameters in the second stage. This paper provides the closed-form optimal curtailment decision and characterizes the optimal bid decision. An efficient binary search algorithm is developed for optimizing the bid decision. By using a distribution-free approach, we show that as the prediction accuracy of WPG improves, the optimal bid decision converges toward the expected minimal power exchange, leading to a decrease in expected operational cost with diminishing marginal return. Numerical experiments based on real-world data demonstrate that compared with the existing greedy strategy and coordinated strategy, the proposed model can decrease the expected operational cost up to 16.9% and 11.0%, respectively.

KEYWORDS:

• Wind power
• adaptive curtailment
• virtual power plant
• bidding strategy
• two-stage stochastic optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15435075.2024.2343008

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant number 71871023, 62203177]; National Science and Technology Innovation 2030 - Major program under Grant [2022ZD0115403]; and Science and Technology Innovation Project of Beijing Institute of Technology.