An Informatics Approach to Demand Response Optimization in Smart Grids

TLDR

Smart grids with real‑time smart meters generate vast data that, when ingested, processed, and analyzed, enable demand forecasting, peak‑load response, and sustainable consumer energy use through energy informatics. The approach employs pattern mining, machine learning on complex events, integrated semantic information, low‑latency distributed stream processing, cloud‑based scalability, and privacy safeguards to support a smarter power grid. Applied in the DOE‑sponsored Los Angeles Smart Grid Demonstration, this architecture delivers an agile, adaptive grid for Los Angeles.

Abstract

Power utilities are increasingly rolling out “smart” grids with the ability to track consumer power usage in near real-time using smart meters that enable bidirectional communication. However, the true value of smart grids is unlocked only when the veritable explosion of data that will become available is ingested, processed, analyzed and translated into meaningful decisions. These include the ability to forecast electricity demand, respond to peak load events, and improve sustainable use of energy by consumers, and are made possible by energy informatics. Information and software system techniques for a smarter power grid include pattern mining and machine learning over complex events and integrated semantic information, distributed stream processing for low latency response, Cloud platforms for scalable operations and privacy policies to mitigate information leakage in an information rich environment. Such an informatics approach is being used in the DoE sponsored Los Angeles Smart Grid Demonstration Project, and the resulting software architecture will lead to an agile and adaptive Los Angeles Smart Grid.