Smart Households Demand Response Management with Micro Grid
This work addresses energy efficiency and cost savings for households and utilities in smart grid systems, but it appears incremental as it applies a known neural network method to a specific domain.
The paper tackles the problem of scheduling household appliances to minimize energy usage during peak hours by proposing an Incentive-based Demand Response Optimization (IDRO) model, resulting in noticeable improvements in power factor and customer bills based on 300 case studies over one year.
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.