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There has been an increasing trend in the electric power system from acentralized generation-driven grid to a more reliable, environmental friendly,and customer-driven grid. One of the most important issues which the designersof smart grids need to deal with is to forecast the fluctuations of powerdemand and generation in order to make the power system facilities moreflexible to the variable nature of renewable power resources and demand-side.This paper proposes a novel two-tier scheme for forecasting the power demandand generation in a general residential electrical gird which uses thedistributed renewable resources as the primary energy resource. The proposedforecasting scheme has two tiers: long-term demand/generation forecaster whichis based on Maximum-Likelihood Estimator (MLE) and real-time demand/generationforecaster which is based on Auto-Regressive Integrated Moving-Average (ARIMA)model. The paper also shows that how bulk generation improves the adequacy ofproposed residential system by canceling-out the forecasters estimation errorswhich are in the form of Gaussian White noises.

Optimal Two-Tier Forecasting Power Generation Model in Smart Grids