Abstract: The present invention relates to system and method for providing near real-time DR events and price signals to the customer end-points to optimally manage the available DR resources. The system utilizes bottom up load forecasting for accurate individualized forecasts for customer loads in the presence of dynamic pricing signals. For better efficiency and reliability of grid operation the system utilizes advanced machine learning and robust optimization techniques for real-time and “personalized” DR-offer dispatch.

Abstract: The present invention demonstrates a highly distributed demand response optimization and management system for real-time (DROMS-RT) power flow control to support large scale integration of distributed renewable generation into the grid. The system is a cloud-based platform that reduces critical peak power safely and securely. The arrangement is provided with a control and communications platform to allow highly dispatchable demand response (DR) services in timeframes suitable for providing ancillary services to the transmission grid. The services are substantially more efficient than other forms of ancillary service options currently available to manage the intermittency associated with large-scale renewable integration.

Abstract: The present invention relates to a signal processing technique for characterization of baseline noise, and for determining load reduction in presence of baseline noise. The method utilizes sparse signal processing algorithm to recover demand resource response signal and a plurality of SNR enhancement strategies are then applied to demand resource response signal for enhancing the signal to noise ratio.