Optimal demand response based on utility maximization in power networks

By Na Li, Lijun Chen, Steven H. Low. IEEE Power & Energy Society General Meeting, 2011

999 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/423aa0de1d4089f07841bea04b7fa30fb83df2b6

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In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach (2014, background): “We assume that the whole operation time in one day is divided into time slots, and let denote the set of operation time slots.”
Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms (2011, methodology): “In Section VII we will present examples of optimal policies obtained by solving the LSPD problem for a linear storage model, and by solving the SPD problem for both linear and non-linear storage models.”
A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms (2015, background): “The key assumption of the analysis presented in [212] is that each residence is equipped with different appliances of diverse power demands.”
Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing (2012, methodology): “While the energy consumption of practical electric appliances can be modeled from physical consideration, the corresponding customer’s satisfaction is usually encapsulated using the concept of utility function [5], [6], [11].”
Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: areview (2017, methodology): “To quantify the improvement in battery's health, a battery health cost function C(T) is formulated based on common life-limiting factors of chemical battery is shown in the following equation [97].”
A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches (2015, background): “For these appliances, users only concern about whether their tasks will be finished within a time period, which means that their aggregate energy consumption should not be less than a threshold before a deadline [28], [29].”
Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications (2012, neutral): “Demand response market models (such as in [26]– [30]).”
A survey of distributed optimization (2019, background): “…and Stous-trup (2016) and He et al. (2019) studied the coordination be-tween distributed generators and energy storages, while the authors of Li, Chen, and Low (2011) ; Wu, Lian, Sun, Yang, and Hansen (2017) and Qin, Wan, Yu, Li, and Li (2019) studied the coordination between distributed…”
Distributed Optimal Energy Management in Microgrids (2015, positive): “We model the battery cost as [22]: C b (p b ) α b t∈T p b (t) 2 − β b T −2 t=0 p b (t + 1)p b (t) + γ b t∈T min(E b (t) − δ b ¯ E b , 0) 2 , (10) where p b (p b (t), t ∈ T ) is the charging/discharging vector, α b , β b , γ b , and δ b are positive constants. α b , β b , and γ b trade off among…”
Demand Response Management via Real-Time Electricity Price Control in Smart Grids (2013, background): “The last thread of work is concerned with setting the real-time retail electricity price based on the maximization of the aggregate surplus of users and retailers subject to the supply-demand matching (e.g., [11]–[15]).”