Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach

By Sabita Maharjan, Quanyan Zhu, Yan Zhang, Stein Gjessing, Tamer Basar. IEEE Transactions on Smart Grid, 2013

743 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c5fd1605484f6b15b344695749c9178423185fb1

How researchers see this paper

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

In Civil and Structural Engineering

1. Cheng Wang

In Control and Systems Engineering

1. C. Long
2. Chau Yuen
3. Wayes Tushar
4. Mengmeng Yu
5. Yan Zhang
6. H. Vincent Poor
7. David B. Smith
8. Seung Ho Hong

In Electrical and Electronic Engineering

1. Chau Yuen
2. Yan Zhang
3. C. Long
4. H. Vincent Poor
5. Wayes Tushar
6. Sabita Maharjan
7. Jiawen Kang
8. Mengmeng Yu

Citations in the corpus

• A Real-Time Demand-Response Algorithm for Smart Grids: A Stackelberg Game Approach (2016, methodology): “References [4], [5], [19], and [20] used the Stackelberg game theory to model the electricity-trading process between the service provider and users, aiming to maximize the service provider’s benefit by optimizing the day-ahead prices.”
• Three-Party Energy Management With Distributed Energy Resources in Smart Grid (2014, background): “We note that the natural logarithm ln(·) has been extensively used for designing the utility [37], and has also recently been shown to be suitable for designing the utility for power consumers [28].”
• Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things (2018, methodology): “We use the backward induction method to solve the SE for the above formulated game [23].”
• Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model (2019, methodology): “They are logarithmic utility function [14]–[17], [25] and quadratic utility function [18], [19], [24].”
• Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches (2018, background): “[1], [32], [33], [34], [35], [38] [36], [37]”
• A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms (2015, methodology): “A Stackelberg game model has also been used for the interaction between operators and consumers in [221].”
• Privacy-Preserving Energy Trading Using Consortium Blockchain in Smart Grid (2019, background): “, solar energy and smart grid, makes it possible for individuals to store, share, and transfer energy supplies [1], [2].”
• Energy Sharing Management for Microgrids With PV Prosumers: A Stackelberg Game Approach (2017, methodology): “The ln(·) function has been widely used in economics for modeling the preference ordering of users and for decision making, and also recently been shown to be suitable for designing the utility for power consumers [23].”
• A Secure Charging Scheme for Electric Vehicles With Smart Communities in Energy Blockchain (2019, background): “[47], and [48], the natural logarithmic function has been extensively accepted in modeling the utilities of energy buyers.”
• Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach (2014, background): “, solar and wind power), residential users can get easy access to power from multiple utility companies [15]–[17], which indeed makes the interaction between utility companies and residential users an extremely challenging problem.”
• Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “) [141], [142], [143] that can help overcome this limitation.”
• Permissioned Blockchain and Edge Computing Empowered Privacy-Preserving Smart Grid Networks (2019, background): “Smart Grid Network (SGN) is a platform on which all electrical appliances, smart meters, and other energy-related embedded systems are joined [4].”
• Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks (2019, background): “Completing those applications with rigorous delay constraints usually needs to consume extensive computational resources [7], [8].”
• A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches (2015, background): “With such complicated interactions, some sophisticated hierarchical games shall be leveraged to shed light on the multiseller–multibuyer demand response problem, such as Stackelberg games [92], [93], where power utilities play a noncooperative game and users play an evolutionary game, converging to ”
• Energy Storage Sharing in Smart Grid: A Modified Auction-Based Approach (2015, neutral): “not new in the smart grid paradigm and have been discussed in [11], [15], and [16].”
• An overview of Fog computing and its security issues (2016, background): “The work in [21] addressed the DRM problem in a network of multiple utility companies and consumers where every entity is concerned about maximizing its own benefit.”
• Energy Peer-to-Peer Trading in Virtual Microgrids in Smart Grids: A Game-Theoretic Approach (2020, methodology): “Some of these studies involve where the utility companies attempt to maximize their revenue while the consumers are interested in maximizing their own pay-offs [28].”