In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …

This paper provides a review and analysis of trends related to demand response (DR). The
authors have considered six different topics for the analysis of DR trends: Users, Network …

Incentive-based demand response is playing an increasingly important role in ensuring the
safe operation of the power grid and reducing system costs, and advances in information …

Incentive-based integrated demand response (IDR) has been recognized as a powerful tool
to mitigate supply-demand imbalance in integrated energy systems with high penetration of …

We study baseline-based demand response (DR) programs. In such programs, customers
get rebates based on how much they reduce electricity consumption during DR events …

Through ad vanced information communication and management system, virtual power
plant (VPP) can realize the aggregation and coordination optimization of distributed energy …

Load aggregators (LAs) purchase demand response (DR) capacity from users by incentive
compensation and then resell them by bidding in the day-ahead DR market to gain profits …

We study a demand response (DR) problem from utility (also referred to as operator)'s
perspective with realistic settings, in which the utility faces uncertainty and limited …

The utilization of aggregated demand-side flexibility via demand response (DR) has become
a promising pathway for the integration of renewable energy resources in power systems …

We propose a network-constrained Stackelberg game framework to set energy prices for
flexible consumers in a distribution grid. In this set-up, an aggregator acts as the leader …