A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches

By Ruilong Deng, Zaiyue Yang, Mo-Yuen Chow, Jiming Chen. IEEE Transactions on Industrial Informatics, 2015

860 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c9a5a571bc61f30f66b4abda1479202ee0a11631

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids (2019, methodology): “In summary, there are three approaches to demand response: 1) reducing demand by using local DERs; 2) reducing demand through shifting controllable loads; and 3) designing efficient generator setpoints to reduce the total generation [7].”
Enabling Massive IoT Toward 6G: A Comprehensive Survey (2021, background): “Demand response aims to control the customers’ usage patterns in response to electricity tariffs or other incentives and reduce the demand during system contingencies or at peak time periods [126].”
Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols (2016, background): “Moreover, service providers can calculate billing information for their customers and initiate necessary actions, such as warning messages [81], [159] With the help of DRM, service providers can communicate with smart devices at customer premises to reduce the load on the distribution grid during pe”
Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions (2017, background): “to the data collectors which connect NANs to a WAN [2].”
Energy Management and Optimization Methods for Grid Energy Storage Systems (2018, background): “The goal of the EMS is to minimize the electricity bill or to maximize the grid benefit [142], [208]–[211].”
Defending Against False Data Injection Attacks on Power System State Estimation (2017, background): “By injecting biased state estimates, FDI attacks could manipulate electricity price in power market to arbitrage ﬁnancial proﬁt, or worse still, mislead system operator and result in harmful commands to cause regional blackout [13].”