A Spatio-Temporal Analysis of electric appliance end-use demand in the residential sector: Case study of Tours (France)

Falaki, Farinaz; Merabtine, Abdelatif; Martouzet, Denis

doi:10.1016/j.scs.2020.102635

Cited by 10 publications

(7 citation statements)

References 47 publications

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“…Both of these power supply units originally produce DC power. Also, a proportion of the overall load of the building, such as electronic appliances, DC motors, power electronics, batteries, etc., originally consumes DC power [29]. Therefore, it is evident that it would be more beneficial if at least part of the building's power supply was based on DC power distribution, as presented in Figure 4 [30].…”

Section: Main Applications Of Ac/dc Hybrid Smart Microgridsmentioning

confidence: 99%

A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart Microgrids

Fotopoulou¹,

Rakopoulos²,

Stergiopoulos³

et al. 2023

Smart Grids Technology and Applications

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The majority of Medium Voltage (MV) and Low Voltage (LV) power systems are based on and operate using Alternating Current (AC) infrastructures. Yet, modern energy market needs, which promote more decentralized concepts with a high Renewable Energy Sources (RES) penetration rate and storage integration, bring Direct Current (DC) to the forefront. In this sense, AC/DC hybrid smart microgrids constitute a newly-introduced research field with a variety of potential applications that combine the benefits of both AC and DC systems. The purpose of this chapter is to review the advantages and disadvantages of AC/DC hybrid grids and analyze potential applications that would benefit from such infrastructures. Also, the most significant efforts and requirements for the constitution of a solid regulatory framework for AC/DC hybrid grids are presented, to pave the way towards their wider adoption by the market.

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Section: Main Applications Of Ac/dc Hybrid Smart Microgridsmentioning

confidence: 99%

A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart Microgrids

Fotopoulou¹,

Rakopoulos²,

Stergiopoulos³

et al. 2023

Smart Grids Technology and Applications

View full text Add to dashboard Cite

show abstract

“…Residential demands are considered hard to control as end users' energy consumption patterns and preferences vary by demographics and other social-psychological attributes (Hu, 2015;Xu et al, 2018). Compelling evidence has shown that household characteristics and demographics (some referred to as socio-demographics) are linked to pro-environmental energy use behaviors and DR participation (Hayn et al, 2014;Frederiks et al, 2015a;Frederiks et al, 2015b;Hu, 2015;Yamaguchi et al, 2020;Falaki et al, 2021). For example, a review summarized that age, gender, education, employment status, income, household, dwelling size, and homeowner status significantly impacted household energy use (Frederiks et al, 2015b).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of social–Psychological factors and financial incentives in demand response and residential energy behavior

Chen

Cao

et al. 2023

Front. Energy Res.

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Introduction: Demand response programs are promising approaches to balance supply and demand and reduce or shift peak hour loads. However, the impacts of residents’ behavioral patterns and social–psychological factors on these programs are poorly understood.Method: We used an extended model of the theory of planned behavior to understand how social–psychological factors, demographics, and household characteristics influence the demand response behaviors (i.e., adjusting A/C and shifting the time of use) of 826 US residents who participated in our study.Results: We found that low-income households (43.7%) reported as more likely to respond to the behavior of raising A/C thermostat settings when no one is home compared to higher-income residents. Thermal comfort needs (β = 0.34) and participants’ age (β = 0.02) increased the financial incentives requested to accept higher A/C temperatures while someone is home. Thermal comfort needs (β = 0.36) and energy-saving habits (β = −0.11) are the significant predictors of increased incentives requested for higher A/C temperatures when no one is home. Older adults request higher financial incentives for shifting washer and dryer usage than younger residents. Finally, attitudes toward energy saving are the strongest predictor of electricity curtailment behavioral intention, followed by perceived behavioral control, subjective norms, cost and environmental concerns, and energy-saving habits.Discussion: This finding suggests the potential of social–psychological variables in shaping energy consumption behaviors. This study provides insights for designing effective demand response programs and broadly analyzing energy behavioral patterns.

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“…Kavousian et al (2013) analyzed smart meter data from 1628 residential households by constructing a daily peak electricity consumption model and found that weather, floor area and residential location were the main influencing factors for customers' peak electricity demand. Falaki et al (2021) constructed macro and micro models to study the effects of socioeconomic variables on household electricity demand. The main elements found through the study that affect the number of household appliances are the size of the house and the total number of household members.…”

Section: Declaration Of Interestsmentioning

confidence: 99%

Mining research on correlation factors of residential electricity stability based on improved FP-growth algorithm

Pan,

Liu

2023

MEQ

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PurposeOn the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.Design/methodology/approachFirst, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.FindingsCompared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.Originality/valueThis paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.HighlightsThe stability of electricity consumption is important to the stable operation of the grid.An improved FP-growth algorithm is employed to explore the influencing factors.The improved algorithm enables the mining of rules containing specific attribute labels.Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

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A Spatio-Temporal Analysis of electric appliance end-use demand in the residential sector: Case study of Tours (France)

Cited by 10 publications

References 47 publications

A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart Microgrids

A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart Microgrids

Analysis of social–Psychological factors and financial incentives in demand response and residential energy behavior

Mining research on correlation factors of residential electricity stability based on improved FP-growth algorithm

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