  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.
