Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

By M. Pedrasa, T. Spooner, Iain MacGill. IEEE Transactions on Smart Grid, 2010

870 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d6647910bfdddeba029448221e33b508b25735a4

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Citations in the corpus

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].”
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].”
A business case for Smart Grid technologies: A systemic perspective (2011, background): “) without the need to perform daily and complex decision making on energy management (see e.g. ADDRESS Project Deliverable D1.1, 2009; Pedrasa et al., 2010).”
Appliance Commitment for Household Load Scheduling (2011, background): “[9] to maximize the owner’s revenue by considering the deviation from the user-desired temperature as an undelivered energy service.”
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].”
A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches (2015, methodology): “The authors in [87] apply the PSO approach to solve the problem of cooperative scheduling of pervasive DERs, due to its easy”
Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation (2013, methodology): “This is the motivation to improve decisionsupport tools by incorporating location management frameworks to learn and predict the user occupation starting from activities and habit estimation [10], [75], [79].”
Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid (2011, methodology): “We compare iHEM with RLC [4], DsT [5], and OCS [7] in terms of savings and peak load reduction in Table III. iHEM provides similar savings with DsT and RLC while OCS is able to provide more savings.”
Demand Response Management via Real-Time Electricity Price Control in Smart Grids (2013, background): “For example, [27, 28] proposed a high-computational-complexity algorithm to determine the optimal energy demand scheduling.”
Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home (2013, background): “which is the typical number of household appliances [47].”
An Optimal Power Scheduling Method for Demand Response in Home Energy Management System (2013, background): “Index Terms—Demand response, energy management system, genetic algorithm, inclining block rate, real-time pricing, smart grid.”