Smart Households Demand Response Management with Micro Grid

TL;DR

This paper presents an incentive-based demand response optimization model using neural networks to efficiently schedule household appliances, reducing peak usage and improving power factors in smart grid environments.

Contribution

It introduces a novel multi-objective optimization method based on NAR-NN considering utility and PV energy sources for household demand response.

Findings

Noticeable improvement in power factor

Reduction in customers' bills

Effective appliance scheduling during peak hours

Abstract

Nowadays the emerging smart grid technology opens up the possibility of two-way communication between customers and energy utilities. Demand Response Management (DRM) offers the promise of saving money for commercial customers and households while helps utilities operate more efficiently. In this paper, an Incentive-based Demand Response Optimization (IDRO) model is proposed to efficiently schedule household appliances for minimum usage during peak hours. The proposed method is a multi-objective optimization technique based on Nonlinear Auto-Regressive Neural Network (NAR-NN) which considers energy provided by the utility and rooftop installed photovoltaic (PV) system. The proposed method is tested and verified using 300 case studies (household). Data analysis for a period of one year shows a noticeable improvement in power factor and customers bill.