Optimization of Power Scheduling for Energy Management in Smart Homes

Sun, Huo-Ching; Yan, Huaxiang

doi:10.1016/j.proeng.2012.06.225

Cited by 21 publications

(6 citation statements)

References 11 publications

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“…A smart grid is defined as a network able to incorporate the actions of the elements involving that network, including producers and consumers, to distribute electricity in an efficient, sustainable, profitable and safe way. In this area, Sun and Huang (2012) reviewed different optimisation methodologies for the energy management in smart homes. Also, Honarmand et al (2014) and Bracco et al (2015) presented mathematical formulations for the management of energy generation for transport purposes.…”

Section: Introductionmentioning

confidence: 99%

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

Silvente

Kopanos

Dua

et al. 2018

Chemical Engineering Research and Design

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This work presents a Mixed Integer Linear Programming (MILP) approach based on a combination of a rolling horizon and stochastic programming formulation. The objective of the proposed formulation is the optimal management of the supply and demand of energy and heat in microgrids under uncertainty, in order to minimise the operational cost. Delays in the starting time of energy demands are allowed within a predefined time windows to tackle flexible demand profiles. This approach uses a scenario-based stochastic programming formulation. These scenarios consider uncertainty in the wind speed forecast, the processing time of the energy tasks and the overall heat demand, to take into account all possible scenarios related to the generation and demand of energy and heat. Nevertheless, embracing all external scenarios associated with wind speed prediction makes their consideration computationally intractable. Thus, updating input information (e.g., wind speed forecast) is required to guarantee good quality and practical solutions. Hence, the two-stage stochastic MILP formulation is introduced into a rolling horizon approach that periodically updates input information.

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Section: Introductionmentioning

confidence: 99%

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

Silvente

Kopanos

Dua

et al. 2018

Chemical Engineering Research and Design

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“…The authors analyzed the buildings of different categories such as offices and commercial residential. Sun et al present a detailed discussion about the smart home energy management based on various optimization techniques such as fuzzy logic, neural networks, heuristic methods, and evolutionary approach in . Various LM techniques have been discussed and compared at residential level in .…”

Section: Dynamic Pricing‐based Scheduling Schemesmentioning

confidence: 99%

An overview of load management techniques in smart grid

Mahmood

Javaid

Khan

et al. 2015

Int. J. Energy Res.

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SUMMARYLoad management (LM) is supposed to have a vital role in future energy management systems. This article presents an overview and comparison of LM techniques along with related technologies and implementation challenges in smart grid. The article also covers consumer and utility concerns in context of LM to enhance readers' intuition about the topic. Two major categories of LM techniques, incentive-based and dynamic pricing-based schemes, have been discussed and compared. Most commonly used incentive-based direct load control is elaborated in detail. Dynamic pricing-based energy consumption scheduling schemes, featuring peak load reduction and consumers' energy cost minimization at residential level, are also emphasized. Furthermore, the article includes a description of dynamic pricing-based home energy management and associated optimization techniques as well as comparison of the latest schemes.

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“…There are many studies that model the household load scheduling problem as a deterministic optimization problem [5,6]. In [7], a detailed home energy management system structure is developed to determine the optimal day-ahead appliances scheduling under hourly pricing and peak power-limiting.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization for Household Load Scheduling with Uncertain Parameters

Wang

Fang

et al. 2018

Applied Sciences

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Home energy management systems (HEMS) face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers' comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy.

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Optimization of Power Scheduling for Energy Management in Smart Homes

Cited by 21 publications

References 11 publications

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

An overview of load management techniques in smart grid

Robust Optimization for Household Load Scheduling with Uncertain Parameters

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