Decision Support System for Smart Grid Using Demand Forecasting Models (original) (raw)

Smart Network Inspired Paradigm and Approaches in IoT Applications

Abstract

In recent years, the penetration of renewable energy (RE) into the power system is ever increasing to meet the exponential increase in power demand. The number of prosumers, generating renewable energy in a distributed manner and participating in a power network is also increasing drastically. This has posed serious issues to grid stability, as instantaneous power demand and renewable power generation are inherently intermittent and dynamic in nature. Precise demand–supply balance is critical but essential for maintaining the stability of the grid. In order to accommodate excess penetration of RE and maintain demand–supply balance, a detailed revision in infrastructure and planning or smart grid implementation becomes essential. In this work, we have designed an innovative hybrid ARMA demand forecast model, using historical power demand of Maharashtra state in India. The forecast results of hybrid ARMA model are compared with traditional statistical models. A precise power demand balance is key to smooth, stable and reliable operation of smart grid or modern power network, models designed in this work shall be useful to smart grid: energy management system in decision-making processes related to real-time operation and control of power system.

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