Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

By Hamed Mohsenian Rad, Vincent W. S. Wong, Juri Jatskevich, Robert Schober, Alberto Leon-Garcia. IEEE Transactions on Smart Grid, 2010

2,795 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/021d2b357c2d4fb0000f93970732bdef961ac6d4

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In Control and Systems Engineering

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Citations in the corpus

Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach (2014, methodology): “We assume that the whole operation time in one day is divided into time slots, and let denote the set of operation time slots.”
A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms (2015, background): “encouragement of energy-aware consumption patterns and the construction of buildings with high energy efficiency [8].”
Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement Learning (2020, background): “The concept of autonomous demand-side management is first introduced in [6], in which a non-cooperative game is formulated between the utility company and local customers.”
Review of demand response under smart grid paradigm (2011, background): “In reference [48] an approach to solve the energy cost minimization problem to find out the optimal hourly consumption level of a number of customers served by a single utility is proposed.”
Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home (2013, neutral): “The proposed framework attempts to achieve a desired trade-off between minimizing the monetary expense and minimizing the waiting time for the operation of each appliance in a household.”
Internet of Things (IoT), Applications and Challenges: A Comprehensive Review (2020, background): “Game theory was proposed in the research article to formulate energy consumption scheduling where strategies were based upon daily schedules of household applications and their energy loads [57].”
Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints (2015, background): “In this respect, although minimization of hourly demand or of the peak-to-average demand ratio as in [7] and [8] may reduce the peak demand, this may not necessarily be below a certain power threshold that could create congestion and voltage issues (i.”
Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation (2013, methodology): “Automatic demand scheduling has been implemented using particle swarm optimization [75], neural-network-based prediction [77], or game theory approaches [78].”
Distributed Internet-Based Load Altering Attacks Against Smart Power Grids (2011, background): “Home automation and the use of energy consumption scheduling (ECS) functionality in smart meters have recieved increasing attention to implement indirect load control [20]–[23].”
Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design (2012, methodology): “Note thatquadratic functionsare among several practical examples for cost functions that satisfy Assumptions 1-3, a nd are considered throughout this paper [15], [32]: Ck(Lk) = akL 2 k + bkLk + ck, (9) whereak > 0, bk ≥ 0, andck ≥ 0 are fixed parameters.”
An integer linear programming based optimization for home demand-side management in smart grid (2012, methodology): “In this paper, inspired by the scheduling mechanism in [4], we propose a demand-side management method using integer linear programming technique.”
Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues (2015, background): “Several studies apply optimization models [38]–[40] while others use neural networks [41], [42] or game-theory [43]–[46].”