An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization. Issue 6 (15th May 2021)

• Record Type: Journal Article
• Authors: Zheng, Yingying; Celik, Berk; Suryanarayanan, Siddharth; Maciejewski, Anthony A.; Siegel, Howard Jay; Hansen, Timothy M.
• Is Part Of: IET smart grid. Volume 4:Issue 6(2021)
• Page Start: 612
• Page End: 622
• Publication Date: 2021-05-15
• Subjects: optimisation -- neural nets -- smart power grids -- power engineering computing -- demand side management -- pricing -- resource allocation -- scheduling -- profitability -- parallel programming -- data handling -- electricity supply industry; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/stg2.12042
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract: 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.