An aggregator-based resource allocation in the smart grid using an artificial neural network and sliding time window optimization

The success of an efficient and effective aggregator-based residential demand response system in the smart grid relies on the day-ahead customer incentive pricing (CIP) and the load shifting protocols. An artificial neural network model is designed to generate the day-ahead CIP for the aggregator based on historical data. Load scheduling is proposed as a day-ahead optimization problem that is solved using a blocked sliding window technique using parallel computing. With the assumptions made, the proposed algorithm improved the aggregator performance by reducing the overall simulation time from 275 to 45 min and increasing the aggregator forecast profits and customer savings by 11.85% and 35.99% compared to the previous genetic algorithm-based approach.

Automated summary

The success of an efficient and effective aggregator-based residential demand response system in the smart grid depends on the day-ahead customer incentive pricing (CIP) and load shifting protocols. By utilizing an artificial neural network model to generate day-ahead CIP and proposing load scheduling as an optimization problem, significant improvements in aggregator performance were observed.