A data-driven scheduling model of virtual power plant using Wasserstein distributionally robust optimization

Distributed energy resources (DER) can be efficiently aggregated by aggregators to sell excessive electricity to spot market in the form of Virtual Power Plant (VPP). The aggregator schedules DER within VPP to participate in day-ahead market for maximizing its profits while keeping the static operating envelope provided by distribution system operator (DSO) in real-time operation. Aggregator, however, needs to make a decision of its offer for biding under the uncertainties of market price and wind power. This paper proposes a two-stage data-driven scheduling model of VPP in day-ahead (DA) and real time (RT) market. In DA market, in order to determine VPP output for biding, a piece-wise affine formulation of VPP profits combing with CVaR for avoiding market price risk is constructed firstly, and then a data-driven distributionally robust model using a Wasserstein ambiguity set is constructed under uncertainties of market price and wind forecast errors. A set of data-driven linearization power constraints are applied in both DA and RT operation when the parameters of distribution network are unknown or inexact. The model then is reformulated equivalently to a mixed 0-1 convex programming problem. The proposed scheduling model is tested on the IEEE 33-bus distribution network showing that under same 1000-sample dataset in training, proposed DRO model has over 85% of reliability while the stochastic optimization has only 69% under the market risk, which means the proposed model has a better out-of-sample performance for uncertainties.

Automated summary

This paper proposes a data-driven scheduling model for Virtual Power Plants (VPP) that efficiently aggregates Distributed Energy Resources (DER) and maximizes profits under market risks and uncertainties.