Simulating Residential Energy Demand in Urban and Rural Areas

Thorve, Swapna; Swarup, Samarth; Marathe, Achla; Chungbaek, Youngyun; Nordberg, Eric K.; Marathe, Madhav V.

doi:10.1109/wsc.2018.8632203

Cited by 12 publications

(13 citation statements)

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“…Power rating distributions for dishwashers are obtained from a survey conducted by NIST 62,63 . Power ratings and duration distributions for laundry appliances are derived from literature 27,44 and surveys 63 ; power ratings for appliances in cook activity include electric ovens, microwaves, and electric cooktops (small-and large burners.) Power rating distributions for these appliances are derived from the NIST efficiency study 64 , and durations of appliance usage are obtained from ATUS data, where the maximum limit for cooking activities is capped to three.…”

Section: Synthetic Populations Andmentioning

confidence: 99%

“…44 , Thorve et.al. 27 , Tsuji et.al. 43 Our model is based on the approach adopted in Subbiah et.al.…”

Section: End-usementioning

confidence: 99%

“…This context of household occupancy and occurrence of events is added to an existing model in literature presented in Thorve et.al. 27 in order to schedule these hot water usage events. 43 All the three referenced models have inspired the design of activity models involving use of appliances.…”

Section: Dhwmentioning

confidence: 99%

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High resolution synthetic residential energy use profiles for the United States

et al. 2023

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Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States.

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Section: Synthetic Populations Andmentioning

confidence: 99%

“…44 , Thorve et.al. 27 , Tsuji et.al. 43 Our model is based on the approach adopted in Subbiah et.al.…”

Section: End-usementioning

confidence: 99%

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High resolution synthetic residential energy use profiles for the United States

et al. 2023

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“…In this work, we try to generate the distribution network for a given geographical region (county/town/city) from different open source publicly available information. These data pertain to following sources: (i) transportation network data published by [10], (ii) Geographical location of HV substations from data sets published by [19] and (iii) Residential electric power demand information developed in the models by [17]. Roads The road network represented in the form of a graph R = (V R , L R ), where V R and L R are respectively the sets of nodes and links of the network.…”

Section: Available Datasetsmentioning

confidence: 99%

Creating Realistic Power Distribution Networks using Interdependent Road Infrastructure

Meyur

Marathe

Vullikanti

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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Physical inter-dependencies between networked civil infrastructures such as transportation and power system network are well known. In order to analyze complex non-linear co-relations between such networks, datasets pertaining to such real infrastructures are required. Such data are not readily available due to their sensitive nature. This work proposes a methodology to generate realistic synthetic distribution network for a given geographical region. The generated network is not the actual distribution system but is very similar to the real distribution network. The synthetic network connects high voltage substations to individual residential consumers through primary and secondary distribution networks. The distribution network is generated by solving an optimization problem which minimizes the overall length of network and is subject to the usual structural and power flow constraints. The work also incorporates identification of long high voltage feeders originating from substations and connecting remotely situated customers in rural geographical locations. The proposed methodology is applied to create synthetic distribution networks in Montgomery county of south-west Virginia, USA. The created networks are validated for their structural feasibility and ability to operate within acceptable voltage limits under average load demand scenario.

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“…The synthetic data are not the real-world data; rather, they are generated by mathematical models operating on openly available information and are designed to ensure the generated data are similar to the real-world data, thus allowing them to be used as a proxy for the actual data. Some examples of synthetic power grid data include synthetic transmission networks ( 18 – 21 ), synthetic distribution networks ( 22 – 25 ), and synthetic residential customer energy usage data ( 26 – 28 ).…”

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confidence: 99%

Ensembles of realistic power distribution networks

Meyur

Vullikanti

Swarup

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.

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Simulating Residential Energy Demand in Urban and Rural Areas

Cited by 12 publications

References 0 publications

High resolution synthetic residential energy use profiles for the United States

High resolution synthetic residential energy use profiles for the United States

Creating Realistic Power Distribution Networks using Interdependent Road Infrastructure

Ensembles of realistic power distribution networks

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