Real-Time Energy Management Optimization for Smart Household

Yao, Leehter; Shen, Jin-Yi; Lim, Wei Hong

doi:10.1109/ithings-greencom-cpscom-smartdata.2016.31

Cited by 24 publications

(16 citation statements)

References 14 publications

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“…Yao et al [20] devised an energy management system based on dynamic user responses and the ability of shifting start time of home appliances based on the current pricing tarrif. The authors presented a home energy management system using Mixed Integer Linear Programing (MILP) technique, aiming to solve both load scheduling problem of home appliances and the energy dispatch problem of utility grid under a single optimization framework in real time.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Predictive Home Appliance's Management Algorithm in Smart Grids

Mardini¹,

Khamayseh²,

Mouftah³

2021

Advances in Energy Research

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Author Contributions: WM and YK contributed the conception and design of this research and worked on implementing the algorithms and obtaining the simulations results. WM outlined the model and the methodology that will be used to develop the solution. YK worked on the algorithms and details needed to achieve the solution. HM discussed the related work area and provided the required background for the work and discussed the results and compared them to the previous solutions. All authors contributed to the manuscript revision, read, and approved the submitted version.

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Section: Literature Reviewmentioning

confidence: 99%

Novel Predictive Home Appliance's Management Algorithm in Smart Grids

Mardini¹,

Khamayseh²,

Mouftah³

2021

Advances in Energy Research

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“…Simulation results show that the total energy cost of the community can be reduced by as much as 11.4% in specific scenarios. Yao et al (2016) devised an energy management system based on dynamic user responses and the ability of shifting start time of home appliances based on the current pricing tarrif. The authors presented a home energy management system using Mixed Integer Linear Programing (MILP) technique, aiming to solve both load scheduling problem of home appliances and the energy dispatch problem of utility grid under a single optimization framework in real time.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Predictive Home Appliance's Management Algorithm in Smart Grids

2020

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“…Specifically, for time t = 1, ..., T , the scheduling for the PV-ESS system to store surplus PV generated energy is to minimize the cost of purchasing energy from main grid while maintaining a stable energy content for the user [24,3]. A real intelligent system needs to perform in an online fashion [12,23], i.e., it needs to make decision on scheduling every time t without given full information about future electricity price, PV generated energy and load demand.…”

Section: Zhenhuan Yang Yiming Ying and Qilong Minmentioning

confidence: 99%

“…The scheduling and dispatching cost in micro-grids was studied in [12] as an online linear programming problem. The work [23] focused on the problem of home energy management using mixed integer linear programming in an online fashion. These papers consider the optimization problem in real-time which have not addressed on regret analysis of the algorithms.…”

Section: Zhenhuan Yang Yiming Ying and Qilong Minmentioning

confidence: 99%

Online optimization for residential PV-ESS energy system scheduling

Yang¹,

Ying²,

Min³

2019

Mathematical Foundations of Computing

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This paper studies a residential PV-ESS energy system scheduling problem with electricity purchase cost, storage degradation cost and surplus PV generated cost [2]. This problem can be viewed as an online optimization problem in time t ∈ [1, T ] with switching costs between decision at t − 1 and t. We reformulate the problem into a single variable problem with s = (s 1 , ..., s T) T , which denotes the storage energy content. We then propose a new algorithm, named Average Receding Horizon Control (ARHC) to solve the PV-ESS energy system scheduling problem. ARHC is an online control algorithm exploiting the prediction information with W-steps look-ahead. We proved an upper bound on the dynamic regret for ARHC of order O(nT /W), where n is the dimension of decision space. This bound can be converted to a competitive ratio of order 1 + O(1/W). This result overcomes the drawback of the classical algorithm Receding Horizon Control (RHC), which has been proved [11] that it may perform bad even with large look ahead W. We also provide a lower bound for ARHC of order O(nT /W 2) on the dynamic regret. ARHC is then used to study a real world case in residential PV-ESS energy system scheduling.

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Real-Time Energy Management Optimization for Smart Household

Cited by 24 publications

References 14 publications

Novel Predictive Home Appliance's Management Algorithm in Smart Grids

Novel Predictive Home Appliance's Management Algorithm in Smart Grids

Novel Predictive Home Appliance's Management Algorithm in Smart Grids

Online optimization for residential PV-ESS energy system scheduling

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