Big-Data Analytics for Electric Grid and Demand-Side Management

Yin, Rongxin; Ghatikar, Girish; Piette, Mary Ann

doi:10.2172/1773709

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…Though progress was made in furthering the ability of the grid to communicate with end users, industrial customers are still not a main focus of advancements in smart metering. Smart meters have the potential to provide what could be considered “big data” that, once subjected to proper analytics, can provide predictive analysis and potential for all customers 50 …”

Section: Five Improvement Areasmentioning

confidence: 99%

“…These data are visualized in Figure 13 showing provide predictive analysis and potential for all customers. 50 In the aim of grid-responsive smart manufacturing, smart metering is essential for proper signaling, measurement, and analysis of grid response potential. AMI and HANs provide the load profiles and data required to understand facility and machine energy use for response capabilities.…”

Section: Rate Structures Incentives and Utilitiesmentioning

confidence: 99%

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Grid‐responsive smart manufacturing: A perspective for an interconnected energy future in the industrial sector

Billings

Powell

2022

AIChE Journal

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With the growing amount of renewable energy sources, the grid has become responsible for accounting for intermittency and the flexibility needed to utilize dynamic sources. Expensive peaking plants and energy storage systems have been proposed as ways to mitigate those problems. There is a large group of energy consumers that can respond to grid conditions. Historically, these consumers have been residential and commercial users, but with modern innovations and practices, industrial consumers have the potential to become a major player in this space. Grid‐responsive smart manufacturing can be used to utilize modern tools in manufacturing innovation as enablers for grid response. These modern tools already exist but are not widely used for industrial grid‐side energy management. This article defines grid‐responsive smart manufacturing, identifies five major barriers to its widespread implementation, and portrays the path to getting industrial users to be key players in grid stability and flexibility.

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Section: Five Improvement Areasmentioning

confidence: 99%

Section: Rate Structures Incentives and Utilitiesmentioning

confidence: 99%

Grid‐responsive smart manufacturing: A perspective for an interconnected energy future in the industrial sector

Billings

Powell

2022

AIChE Journal

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“…As a demand response measure, we implemented a Precooling + GTA strategy. A GTA strategy raises the cooling temperature setpoint globally across all the zones in a building during a demand response event [36]. Figure 4 illustrates the cooling setpoint schedules used in two cooling strategies: Base and Precooling + GTA.…”

Section: Description Of Cooling Strategiesmentioning

confidence: 99%

How Can Floor Covering Influence Buildings’ Demand Flexibility?

et al. 2021

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Although the thermal mass of floors in buildings has been demonstrated to help shift cooling load, there is still a lack of information about how floor covering can influence the floor’s load shifting capability and buildings’ demand flexibility. To fill this gap, we estimated demand flexibility based on the daily peak cooling load reduction for different floor configurations and regions, using EnergyPlus simulations. As a demand response strategy, we used precooling and global temperature adjustment. The result demonstrated an adverse impact of floor covering on the building’s demand flexibility. Specifically, under the same demand response strategy, the daily peak cooling load reductions were up to 20–34% for a concrete floor whereas they were only 17–29% for a carpet-covered concrete floor. This is because floor covering hinders convective coupling between the concrete floor surface and the zone air and reduces radiative heat transfer between the concrete floor surface and the surrounding environment. In hot climates such as Phoenix, floor covering almost negated the concrete floor’s load shifting capability and yielded low demand flexibility as a wood floor, representing low thermal mass. Sensitivity analyses showed that floor covering’s effects can be more profound with a larger carpet-covered area, a greater temperature adjustment depth, or a higher radiant heat gain. With this effect ignored for a given building, its demand flexibility would be overestimated, which could prevent grid operators from obtaining sufficient demand flexibility to maintain a grid. Our findings also imply that for more efficient grid-interactive buildings, a traditional standard for floor design could be modified with increasing renewable penetration.

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Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Johra

Pereira

et al. 2023

Applied Energy

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Big-Data Analytics for Electric Grid and Demand-Side Management

Cited by 7 publications

References 24 publications

Grid‐responsive smart manufacturing: A perspective for an interconnected energy future in the industrial sector

Grid‐responsive smart manufacturing: A perspective for an interconnected energy future in the industrial sector

How Can Floor Covering Influence Buildings’ Demand Flexibility?

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

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