A high-resolution smart home power demand model and future impact on load profile in Germany

Alzate, Efrain Bernal; Mallick, Nayeem H.; Xie, Junhao

doi:10.1109/pecon.2014.7062413

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…Depending on the model the region could be composed of hundreds ( [10], [20], [47]) to hundred of thousands of households ( [14], [32], [45], [52], [57]). Those belonging to the second category aim to model the electricity load profile of different types of houses ( [3], [37], [42], [53], [54], [56]) or study the impact of different energy prices on the residential load profile [22] and, hence, only modelled single houses.…”

Section: B Based On the Sampling Ratementioning

confidence: 99%

A Comprehensive Review of Residential Electricity Load Profile Models

Proedrou¹

2021

IEEE Access

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Section: B Based On the Sampling Ratementioning

confidence: 99%

A Comprehensive Review of Residential Electricity Load Profile Models

Proedrou¹

2021

IEEE Access

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“…They can be divided into two categories: one models the end-use electricity of a region (neighborhoods, districts, cities), Depending on the model, the region could consist of hundreds [40,41] to hundreds of thousands of households [1,[42][43][44][45]. while the other focuses on modeling the electricity load profile of different types of houses [46][47][48][49] or studying the impact of energy prices on the residential load profile [50], Hence modeling single houses.…”

Section: Sampling Ratementioning

confidence: 99%

Bottom-Up Medium-Term Hourly Resolution Household Electricity Load Profile Model

Sayed

2024

Preprint

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Electricity load profiling at the household level is essential for Demand-side management, and Electrical systems designing, Where having reliable electricity is crucial and required. This paper introduces a load profile model that generates hourly load profiles for the entire year. This model is aimed at filling gaps in understanding household electricity use and provides valuable insights into electricity demand modeling. Our research is based on a detailed survey that included household socioeconomics, electricity usage, appliance inventory, and behavior patterns. In this study, We utilized the gathered data to classify households into three distinct socioeconomic groups. This categorization was based on their income levels and lifestyles and developed a probabilistic bottom-up approach to create customized electricity appliance profiles. This approach gives accurate power consumption behavior estimates. Our method integrates probabilistic appliance usage with the impact of socioeconomic and environmental factors like ambient temperature on energy usage. finally validates the result against actual consumption reports. also, We compared our Load Profile Generation (LPG) model with previously estimated load profiles. This comparison highlighted that variations in household power usage, influenced by human behavior, add complexity to the load profile, even when daily energy needs at home are similar.

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“…The smart home model used in this paper is outlined below and built on those presented by the same author in [20]. Variables such as a real-time price and storage systems are now included into the present model.…”

Section: B Smart Home Load Profile Generationmentioning

confidence: 99%

“…The problem with all these models is that they are rarely integrated to represent a more realistic smart home model. In contrast, a different approach is taken here, including a high resolved smart home power demand model presented in [20] into the LV distribution network simulation and interacting with smart PV inverters to support decentralized or centralized reactive power (VAR) control methodologies.…”

Section: Introductionmentioning

confidence: 99%

Smart LV distribution networks: An approach for power flow formulation with smart home models

Alzate

Xie

2015

2015 IEEE 6th International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

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In the case of low voltage (LV) distribution networks, the inclusion of smart homes is expected to increase the systems complexity due to multiplicity of interacting users with autonomous use of energy and the highly penetration of photovoltaic (PV). Consequently, the shift towards smart LV distribution systems implies that the traditional unidirectional power flow formulation and control strategies are not more realistic and flexible enough. At this aim, a systematic approach for modeling and simulation of smart LV distribution networks was developed to address this problem. It integrates a new power flow formulation, a high-resolved power demand model for smart homes and controllable PV inverters, allowing the simulation and analysis of more realistic LV distribution systems. It is used initially to estimate the benefits and future impacts of smart homes in the LV distribution system in Germany. The methodology can be used to evaluate the effectiveness of actual voltage control strategies in diverse scenarios on the smart LV distribution networks. Keywords-LV distribution network, smart home; power flow; high resolved load profile; photovoltaic.I.

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A high-resolution smart home power demand model and future impact on load profile in Germany

Cited by 15 publications

References 10 publications

A Comprehensive Review of Residential Electricity Load Profile Models

A Comprehensive Review of Residential Electricity Load Profile Models

Bottom-Up Medium-Term Hourly Resolution Household Electricity Load Profile Model

Smart LV distribution networks: An approach for power flow formulation with smart home models

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