Predictability and Fairness in Load Aggregation and Operations of Virtual Power Plants

TL;DR

This paper explores the concepts of predictability and fairness in the management of virtual power plants, demonstrating that traditional controllers often fail to ensure these properties, while certain stable controllers can.

Contribution

It introduces a formal notion of predictability and fairness in load aggregation, and shows how incrementally input-to-state stable controllers can guarantee these properties.

Findings

Traditional controllers often fail to ensure predictability and fairness.

iISS controllers can guarantee predictability and fairness under mild conditions.

The approach accounts for non-linear power system models.

Abstract

In power systems, one wishes to regulate the aggregate demand of an ensemble of distributed energy resources (DERs), such as controllable loads and battery energy storage systems. We suggest a notion of predictability and fairness, which suggests that the long-term averages of prices or incentives offered should be independent of the initial states of the operators of the DER, the aggregator, and the power grid. We show that this notion cannot be guaranteed with many traditional controllers used by the load aggregator, including the usual proportional-integral (PI) controller. We show that even considering the non-linearity of the alternating-current model, this notion of predictability and fairness can be guaranteed for incrementally input-to-state stable (iISS) controllers, under mild assumptions.