Decentralized Provision of Renewable Predictions within a Virtual Power Plant

TL;DR

This paper introduces a decentralized algorithm for renewable output prediction in virtual power plants, reducing data communication and privacy risks while maintaining prediction accuracy and social surplus.

Contribution

It proposes a novel decentralized prediction algorithm for VPPs, with proven convergence and bounded error, improving privacy and scalability over centralized methods.

Findings

Decentralized scheme reduces data communication needs.

Prediction accuracy approaches centralized performance as participants increase.

Variance of prediction gap decreases with more consumers and higher uncertainty.

Abstract

The mushrooming of distributed energy resources turns end-users from passive price-takers to active market participants. To manage those massive proactive end-users efficiently, virtual power plant (VPP) as an innovative concept emerges. It can provide some necessary information to help consumers improve their profits and trade with the electricity market on behalf of them. One important information that is desired by the consumers is the prediction of renewable outputs inside this VPP. Presently, most VPPs run in a centralized manner, which means the VPP predicts the outputs of all the renewable sources it manages and provides the predictions to every consumer who buys this information. We prove that by providing predictions, the social total surplus can be improved. However, when more consumers and renewables participate in the market, this centralized scheme needs extensive data communication and may jeopardize the privacy of individual stakeholders. In this paper, we propose a decentralized prediction provision algorithm in which consumers from each subregion only buy local predictions and exchange information with the VPP. Convergence is proved under a mild condition, and the demand gap between centralized and decentralized schemes is proved to have zero expectation and bounded variance. Illustrative examples show that the variance of this gap decreases with more consumers and higher uncertainty, and validate the proposed algorithm numerically.