research-article

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

Authors:

John S. Vardakas,

Christos V. VerikoukisAuthors Info & Claims

IEEE Communications Surveys & Tutorials, Volume 17, Issue 1

Pages 152 - 178

https://doi.org/10.1109/COMST.2014.2341586

Published: 01 January 2015 Publication History

Abstract

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

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Zhao TZhou MMo YWang JLuo JPan XChen M(2023)Optimizing Demand Response in Distribution Network with Grid Operational ConstraintsProceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3575813.3595206(299-313)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3575813.3595206
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A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

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Published In

cover image IEEE Communications Surveys & Tutorials

IEEE Communications Surveys & Tutorials Volume 17, Issue 1

Firstquarter 2015

492 pages

ISSN:1553-877X

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Copyright © 2014.

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IEEE Press

Publication History

Published: 01 January 2015

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Fattahi AGhodsi SDasu SAhmadi R(2024)Flattening Energy-Consumption Curves by Monthly Constrained Direct Load Control ContractsOperations Research10.1287/opre.2021.063872:2(570-590)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1287/opre.2021.0638
Fattahi ADasu SAhmadi R(2023)Peak-Load Energy Management by Direct Load Control ContractsManagement Science10.1287/mnsc.2022.449369:5(2788-2813)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1287/mnsc.2022.4493
Zhao TZhou MMo YWang JLuo JPan XChen M(2023)Optimizing Demand Response in Distribution Network with Grid Operational ConstraintsProceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3575813.3595206(299-313)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3575813.3595206
Hassan MRehmani MDu JChen J(2023)Differentially Private Demand Side Management for Incentivized Dynamic Pricing in Smart Grid¹1.
A preliminary version has been published by 2020 IEEE International Conference on Communications (ICC 2020), June, 2020, Dublin, Ireland entitled Differentially Private Dynamic Pricing for Efficient Demand Response in Smart Grid.
IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315747235:6(5724-5737)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3157472
dos Santos SSoares JBarroso Gde Athayde Prata B(2023)Demand response application in industrial scenariosExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119393215:COnline publication date: 15-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119393
Yang JWang ETrivedi RZhao TZha HPelachaud CTaylor MFaliszewski PMascardi V(2022)Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement LearningProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3536010(1436-1445)Online publication date: 9-May-2022
https://dl.acm.org/doi/10.5555/3535850.3536010
Vasudevan NVenkatraman VRamkumar ASheela AThampi SEl-Alfy ETrajkovic L(2021)Real-time day ahead energy management for smart home using machine learning algorithmJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18988641:5(5665-5676)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-189886
Reijsbergen DYang ZMaw ADinh TZhou J(2021)Transparent Electricity Pricing with PrivacyComputer Security – ESORICS 202110.1007/978-3-030-88428-4_22(439-460)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1007/978-3-030-88428-4_22
Amasyali KOlama MChen YDel Barrio ABarros FHu XKavak H(2020)Genetic algorithm for demand responseProceedings of the 2020 Spring Simulation Conference10.5555/3408207.3408265(1-12)Online publication date: 19-May-2020
https://dl.acm.org/doi/10.5555/3408207.3408265
Clausen AKoenig GKlingert SGhatikar GSchwartz PBates N(2019)An Analysis of Contracts and Relationships between Supercomputing Centers and Electricity Service ProvidersWorkshop Proceedings of the 48th International Conference on Parallel Processing10.1145/3339186.3339209(1-8)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3339186.3339209
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