Stochastic Flexibility Evaluation for Virtual Power Plants by Aggregating Distributed Energy Resources

Abstract

To manage a large amount of flexible distributed energy resources (DERs) in the distribution networks, the virtual power plant (VPP) is introduced into the industry. The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole. Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties. In this paper, we introduce the stochastic power flexibility range (PFR) and time-coupling flexibility (TCF) to describe the power flexibility of VPP. In this model, both operational constraints and the randomness of the DERs' output are incorporated, and a combined model and data-driven solution is proposed to obtain the stochastic PFR, TCF, and cost function of VPP. The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding, considering uncertainties. Finally, a numerical test is performed. The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.

Keywords

• Chance constrained optimization
• combined model and data-driven
• stochastic power flexibility
• virtual power plant