Optimal Two-Tier Forecasting Power Generation Model in Smart Grids

TL;DR

This paper introduces a two-tier forecasting model for power demand and generation in smart grids, combining long-term MLE-based and real-time ARIMA-based methods to enhance accuracy and system reliability.

Contribution

It presents a novel two-tier forecasting scheme integrating MLE and ARIMA models for improved power prediction in residential smart grids with renewable resources.

Findings

The two-tier model improves forecasting accuracy.

Bulk generation reduces estimation errors.

The scheme enhances grid reliability and flexibility.

Abstract

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