Real-time management model for residential multi-class appliances

Ihsane, Imane; Miègeville, Laurence; Ait-Ahmed, Nadia; Guérin, Patrick M.

doi:10.1109/appeec.2017.8308911

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…For more clarification, we categorized the reviewed articles in Table 1. [10] GA [11] LP [12] Minority Game [13] NLP [14] LP [15] LP [16] LP [17] LP [18] Non-convex [19] DLC [20] MILP [21] ML [22] PSO [23] LP [24] NLP [25] LP [26] LP [27] MINP [28] MILP [29] LP [30] Problem-Solving approach [31] BPSO…”

Section: Introductionmentioning

confidence: 99%

“…They have categorized such loads in different categories and then apply a suitable objective function with proper constraints to schedule them [ 29 ]. In [ 30 , 31 ], the authors have presented a prioritized model based on the appliance categories to minimize their costs. For more clarification, we categorized the reviewed articles in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

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Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization

Samadi

Fattahi

Schriemer

et al. 2020

Sensors

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The Energy-efficiency of demand management technologies and customer’s experience have emerged as important issues as consumers began to heavily adopt these technologies. In this context, where the electrical load imposed on the smart grid by residential users needs to be optimized, it can be better managed when customer’s comfort parameters are used, such as thermal comfort and preferred appliance usage time interval. In this paper a multi-layer architecture is proposed that uses a multi-objective optimization model at the energy consumption level to take consumer comfort and experience into consideration. The paper shows how our proposed Clustered Sequential Management (CSM) approach could improve consumer comfort via appliance use scheduling. To quantify thermal comfort, we use thermodynamic solutions for a Heating Ventilation and Air Conditioner (HVAC) system and then apply our scheduling model to find the best time slot for such thermal loads, linking consumer experience to power consumption. In addition to thermal loads, we also include non-thermal loads in the cost minimization and the enhanced consumer experience. In this hierarchal algorithm, we classified appliances by their load profile including degrees of freedom for consumer appliance prioritization. Finally, we scheduled consumption within a Time of Use (ToU) pricing model. In this model, we used Mixed Integer Linear Programming (MILP) and Linear Programming (LP) optimization for different categories with different constraints for various loads. We eliminate the customer’s inconvenience on thermal load considering ASHRAE standard, increase the satisfaction on EV optimal chagrining constrained by minimum cost and achieve the preferred usage time for the non-interruptible deferrable loads. The results show that our model is typically able to achieve cost minimization almost equal to 13% and Peak-to-Average Ratios (PAR) reduction with almost 45%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization

Samadi

Fattahi

Schriemer

et al. 2020

Sensors

View full text Add to dashboard Cite

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New Evaluation Metrics for Electrical Demand Forecasting: Application to the Residential Sector

Ihsane

Miègeville

Ait-Ahmed

et al. 2018

2018 AEIT International Annual Conference

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An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid

et al. 2020

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Electricity demand is increasing, as a result of increasing consumers in the electricity market. By growing smart technologies such as smart grid and smart energy management systems, customers were given a chance to actively participate in demand response programs (DRPs), and reduce their electricity bills as a result. This study overviews the DRPs and their practices, along with home energy management systems (HEMS) and load management techniques. The paper provides brief literature on HEMS technologies and challenges. The paper is organized in a way to provide some technical information about DRPs and HEMS to help the reader understand different concepts about the smart grid, and be able to compare the essential concerns about the smart grid. The article includes a brief discussion about DRPs and their importance for the future of energy management systems. It is followed by brief literature about smart grids and HEMS, and a home energy management system strategy is also discussed in detail. The literature shows that storage devices have a huge impact on the efficiency and performance of energy management system strategies.

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Real-time management model for residential multi-class appliances

Cited by 3 publications

References 15 publications

Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization

Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization

New Evaluation Metrics for Electrical Demand Forecasting: Application to the Residential Sector

An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid

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