An Optimal Power Scheduling Method for Demand Response in Home Energy Management System

Zhao, Zhiyang; Lee, Won Cheol; Shin, Yoan; Song, Kyung‐Bin

doi:10.1109/tsg.2013.2251018

Cited by 596 publications

(115 citation statements)

References 16 publications

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“…Constraint (19) establishes the relationships between the state of charge (SOC), discharging and charging powers, and battery float losses. Constraint (20) places a lower bound on the SOC. Constraint (21) places an upper bound on the float losses that is activated when a threshold is reached.…”

Section: Batteries (Ess) Modelmentioning

confidence: 99%

A realistic energy optimization model for smart‐home appliances

2019

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Summary Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. The control of smart appliances can be based on optimization models, which should be realistic and efficient. However, increased realism also implies an increase in solution time. Many of the optimization models in the literature have limitations on the types of appliances considered and/or their reliability. This paper proposes a home energy management scheduling model that is more realistic and efficient. We develop a mixed integer linear optimization model that minimizes the energy cost while maintaining a given level of user comfort. Our main contribution is the variety of specific appliance models considered and their integration into a single model. We consider the use of energy in appliances and electric vehicles (EVs) and take into account renewable local generation, batteries, and demand response. Our models of a shower, a fridge, and a hybrid EV consider both the electricity consumption and the conventional fuel cost. We present computational results to validate the model and indicate how it overcomes the limitations of other models. Our results, compared with the best competitors, provide cost savings ranging from 8% to 389% over a horizon of 24 hours.

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Section: Batteries (Ess) Modelmentioning

confidence: 99%

A realistic energy optimization model for smart‐home appliances

2019

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“…However, in this work, our aim is to reduce the delay time for smart home users to increase their comfort. Now, DF of appliance "a" is taken into consideration as explained in [61,68,74,75]:…”

Section: Delay Factor (Df)mentioning

confidence: 99%

“…If the start time is et a , the DF would be 1. The sum of DFs for all appliances with the waiting time cost can be modelled as [61,68,74,75]:…”

Section: Delay Factor (Df)mentioning

confidence: 99%

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Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

et al. 2018

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Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort..

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“…Based on the advanced technologies of HEMS architecture, there have been many works that focus on energy scheduling strategies in smart house [5,6,7,8]. Most of the existing works take into account centralized management with the evolutionary algorithm to handle either single or multiple objective optimization.…”

Section: Introductionmentioning

confidence: 99%

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Bui

Jung

Camacho

2018

Sensors

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Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems.

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An Optimal Power Scheduling Method for Demand Response in Home Energy Management System

Cited by 596 publications

References 16 publications

A realistic energy optimization model for smart‐home appliances

A realistic energy optimization model for smart‐home appliances

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

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