A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

By John S. Vardakas, Nizar Zorba, Christos V. Verikoukis. IEEE Communications Surveys and Tutorials, 2015

927 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a9d5f22387d0baaa903e388090f60c78f79b4020

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In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems (2017, background): “response applications [10] as well as work on distributed solution of multi-period formulations for model predictive”
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, background): “[13] revised various optimisation models for the optimal control of DR strategies, along with”
Review of Internet of Things (IoT) in Electric Power and Energy Systems (2018, background): “Innovative pricing schemes [29] and strategic business models need to be implemented by the utilities to extract maximum”
An overview of Demand Response: Key-elements and international experience (2017, methodology): “In a detailed review study, Varkadas et al. examined DR types, requirements and enabling technologies, presenting also some real examples from around the world, as well as optimization methods for DR applications with a broad review of relevant literature studies [17].”
5G network-based Internet of Things for demand response in smart grid: A survey on application potential (2020, background): “…system with DR can be transformed from Eq. to where P GDR is the generation power from onsite standby energies, and • The price-based DR is to influence users’ power consumption by time-varying prices [44], including time-of-use (TOU), critical peak pricing (CPP), and real time pricing (RTP) [45].”
Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols (2016, background): “Another group of surveys has focused on SG pricing methods [91], energy efficiency [92], and power demand forecasting [93], [94], as well as the control of renewable energy in the SG [95].”
Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers (2018, background): “RESIDENTIAL DR PROGRAM IN HEMS Recently, many researchers are showing interest in residential DR program, which is crucial in persuading residential customers to decrease their daily electrical consumption voluntarily by allocating available resources and managing load appliances [29], [30].”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “ational cost reduction, elimination of blackouts, and reduction in emission of CO2 [179].”
Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions (2017, background): “Other areas which have been surveyed in the domain of SG include energy efficiency [58], power demand forecasting [59], load balancing [60], optimization and pricing methods [61], and renewable energy [62].”
A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach (2018, background): “Owing to the modern advanced information and communication technologies in smart grid systems, demand response (DR) has become an effective method for improving grid reliability and reducing energy costs due to the ability to react quickly to supply-demand mismatches by adjusting flexible loads on t”
A Review of Power System Flexibility With High Penetration of Renewables (2019, background): “Studies such as [30], [44], compare different DR programs,”
On-Line Building Energy Optimization Using Deep Reinforcement Learning (2017, background): “Therein, both centralized and decentralized solutions exist, but they fail to consider on-line solutions for large-scale, real databases [6].”