Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

By Hamed Mohsenian Rad, Alberto Leon-Garcia. IEEE Transactions on Smart Grid, 2010

1,917 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/607b89e4b956da6125e4003b0901e3095a9e11b4

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Citations in the corpus

Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers (2018, methodology): “Optimal energy consumption scheduling based on linear programming is used to minimize the electricity cost and waiting time for each home appliance that operates with a real-time pricing tariff [112].”
Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach (2014, background): “The DAU will first receive the price information from different utilities and broadcast such information to the users.”
An Optimal Power Scheduling Method for Demand Response in Home Energy Management System (2013, methodology): “Index Terms—Demand response, energy management system, genetic algorithm, inclining block rate, real-time pricing, smart grid.”
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].”
Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home (2013, background): “It is effective in reducing the monetary expense charged to end users since different electricity rates can be applied at different time periods in the popular real-time pricing model [11]–[14].”
Optimal demand response based on utility maximization in power networks (2011, background): “There exists a large literature on demand response, see, e.g., [1], [2], [3], [4], [5], [6], [7], [8], [9], [10].”
Optimal Operation of Residential Energy Hubs in Smart Grids (2012, background): “In [9], an optimal and automatic residential load scheduling framework is presented which attempts to achieve a trade-off between minimizing the electricity payment and minimizing waiting time for the operation of each appliance in households considering real-time pricing combined with block rates.”
A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches (2015, background): “The authors in [28] find that the electricity price has high statistical correlations with the prices on yesterday, the day before yesterday, and the same day last week.”
Optimal Smart Home Energy Management Considering Energy Saving and a Comfortable Lifestyle (2016, methodology): “In a similar way, Mohsenian-Rad and Leon-Garcia [3] proposed an optimization algorithm for minimization of users’ electricity bills considering their comfort levels as the problem constraints.”
Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization (2012, neutral): “Cold/hot water temperature.”
The effect of time-of-use tariffs on the demand response flexibility of an all-electric smart-grid-ready dwelling (2016, background): “[18] indicate an overall 25% reduction.”
A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms (2015, methodology): “A Mixed Integer Linear Programming (MILP) method is used in [165]”
Vehicle-to-Aggregator Interaction Game (2012, methodology): “Different from the work in [3]–[6], [24]–[27], here, we use game theory to analyze a V2G market through understanding the vehicle-to-aggregator interactions to provide frequency regulation service to power grids.”
Smart-grid technologies and progress in Europe and the USA (2011, background): “The advanced metering infrastructure (AMI) contributes to all characteristics of the smart grid, such as enabling active participation by consumers, accommodating all generation and storage options, optimizing assets and operating efficiently, operating resiliently against physical and cyber attacks”