Real-Time Demand Response Model

By Antonio J. Conejo, Juan M. Morales, Luis Baringo. IEEE Transactions on Smart Grid, 2010

999 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/24ddd74e0ccfb192672f70a8801da64a8261d5ee

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In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, background): “Therefore, in the Internet of Energy realm, main open issues regard [112]: (i) the composition, deployment and management of distributed services over pervasive SG-based infrastructures; (ii) the design of efficient and user-friendly mechanisms, that allow end-users to be capable to control the serv”
The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming (2014, background): “To access price signals, twoway communication link between the consumer and supplier is necessary that AMI (Advanced Metering Infrastructure) system provides it [6,7].”
MOD-DR: Microgrid optimal dispatch with demand response (2017, background): “, minimum run time and notification time, both are viable for practical deployment [20,33,37,29,64].”
Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach (2013, background): “However, traditional power grids are not able to meet these demands and requirements because of their inflexible designs and lack of prompt communications between the supply and the demand sides.”
A day-ahead electricity pricing model based on smart metering and demand-side management (2012, background): “In this model, it is assume that each consumer is equipped with a smart meter with a two-way communication [15].”
A bilevel model for electricity retailers' participation in a demand response market environment (2013, background): “…for controlling a part of the loadhas recently appeared, based e.g. on statistical models for the forecasting of the conditional load response to varying prices (Corradi et al., in press), or conversely on the optimisation problem of a load exposed to dynamic market prices (Conejo et al., 2010).”
A Real-Time Demand-Response Algorithm for Smart Grids: A Stackelberg Game Approach (2016, background): “, 10 or 15 min [8], [9]), and then manages the energy consumption of each device in response to the received RTP.”
Risk-Constrained Profit Maximization for Microgrid Aggregators With Demand Response (2015, background): “For example, the LC contract 1 allows the MG TABLE II SYSTEM PARAMETERS IN THE BASE CASE α β cLLt e pen t cw P grid eRt ($/MWh) ($/MWh) ($/MWh) (MW) ($/MWh) 0.9 0.1 1000 5 35 20 55 aggregator to curtail maximum of 2 MW of load for every hour in the operating day with the capacity price is 6 $/MW,…”
A Multiagent-Based Hierarchical Energy Management Strategy for Multi-Microgrids Considering Adjustable Power and Demand Response (2018, methodology): “A DR model based on real time price (RTP) has been developed by [8] and DR has been used to mitigate the impacts of EVs on smart distribution systems by [9] and [10].”
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].”
Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization (2012, neutral): “Probability of scenario .”