Emission-aware Energy Storage Scheduling for a Greener Grid

Jha, Rishikesh; Lee, Stephen; Iyengar, S. S.; Hajiesmaili, Mohammad H.; Irwin, David; Shenoy, Prashant

doi:10.1145/3396851.3397755

Cited by 14 publications

(4 citation statements)

References 28 publications

(39 reference statements)

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“…In [13] , innovative scalable and privacy-preserving optimization methods are proposed, which allow large-scale energy communities to offer ancillary services through the sharing of residential PV-battery systems. In [14] , the authors focus on the issue of optimally exploiting the energy storage systems deployed in a PCG, where the objective is to reduce the energy consumption and the carbon emissions. They formulate an optimization problem that aims to find an emission-aware schedule of distributed energy storage and helps to evaluate how distributed storage enables utilities to reduce their reliance on inefficient and carbon-intensive power plants, thereby reducing the overall carbon emissions.…”

Section: Related Workmentioning

confidence: 99%

Cascade computing model to optimize energy exchanges in prosumer communities

Scarcello

Giordano

Mastroianni

et al. 2022

Heliyon

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Recently, the increasing availability of renewable energy plants has changed the market of electrical energy. The concept of energy community enables prosumers to exploit and exchange the energy produced locally and reduce the need for external energy sources. This can help to obtain significant cost savings and increase the percentage of green energy. In this paper, we present the Cascade model, which aims to achieve a twofold goal: compute an energy schedule that satisfies the needs of single prosumers, and maximize the energy sharing at the community level, thus minimizing the overall cost. The Cascade model partitions the prosumers in groups: at each step, an optimization problem is solved for all the users of a group. The solution enables defining a super-user that summarizes the energy requirements of the groups considered before. Then, a new group is considered in the next step, and so on, until all the groups have been processed. This approach enables preventing the exponential increase in computing complexity that is inevitable when all the prosumers are considered together, using the model referred to as Unified. Experimental results show that the Cascade model leads to a great reduction of computing time, while the overall cost closely approximates the optimal solution ensured by the Unified model.

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Section: Related Workmentioning

confidence: 99%

Cascade computing model to optimize energy exchanges in prosumer communities

Scarcello

Giordano

Mastroianni

et al. 2022

Heliyon

View full text Add to dashboard Cite

show abstract

“…Specically, data centers can control: when to use, store, or net meter solar energy; when and how to charge batteries, e.g., from the grid or solar, and when to discharge them; and if and when to use grid energy and how much. Renewable energy research has shown that such control decisions can directly optimize carbon emissions [30].…”

Section: Local Energy: Visibility and Controlmentioning

confidence: 99%

Enabling Sustainable Clouds

Bashir

Guo

Hajiesmaili

et al. 2021

Proceedings of the ACM Symposium on Cloud Computing

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Cloud platforms' growing energy demand and carbon emissions are raising concern about their environmental sustainability. The current approach to enabling sustainable clouds focuses on improving energy-eciency and purchasing carbon osets. These approaches have limits: many cloud data centers already operate near peak eciency, and carbon osets cannot scale to near zero carbon where there is little carbon left to oset. Instead, enabling sustainable clouds will require applications to adapt to when and where unreliable low-carbon energy is available. Applications cannot do this today because their energy use and carbon emissions are not visible to them, as the energy system provides the rigid abstraction of a continuous, reliable energy supply. This vision paper instead advocates for a "carbon rst" approach to cloud design that elevates carbon-eciency to a rst-class metric. To do so, we argue that cloud platforms should virtualize the energy system by exposing visibility into, and software-dened control of, it to applications, enabling them to dene their own abstractions for managing energy and carbon emissions based on their own requirements. CCS CONCEPTS• Computer systems organization ! Cloud computing; Special purpose systems.

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“…Specifically, data centers can control: when to use, store, or net meter solar energy; when and how to charge batteries, e.g., from the grid or solar, and when to discharge them; and if and when to use grid energy and how much. Renewable energy research has shown that such control decisions can directly optimize carbon emissions [29].…”

Section: Local Energy System: Visibility and Controlmentioning

confidence: 99%

Enabling Sustainable Clouds: The Case for Virtualizing the Energy System

Bashir,

Guo,

Hajiesmaili

et al. 2021

Preprint

Self Cite

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Cloud platforms' growing energy demand and carbon emissions are raising concern about their environmental sustainability. The current approach to enabling sustainable clouds focuses on improving energy-efficiency and purchasing carbon offsets. These approaches have limits: many cloud data centers already operate near peak efficiency, and carbon offsets cannot scale to near zero carbon where there is little carbon left to offset. Instead, enabling sustainable clouds will require applications to adapt to when and where unreliable low-carbon energy is available. Applications cannot do this today because their energy use and carbon emissions are not visible to them, as the energy system provides the rigid abstraction of a continuous, reliable energy supply. This vision paper instead advocates for a "carbon first" approach to cloud design that elevates carbon-efficiency to a first-class metric. To do so, we argue that cloud platforms should virtualize the energy system by exposing visibility into, and software-defined control of, it to applications, enabling them to define their own abstractions for managing energy and carbon emissions based on their own requirements.

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Emission-aware Energy Storage Scheduling for a Greener Grid

Cited by 14 publications

References 28 publications

Cascade computing model to optimize energy exchanges in prosumer communities

Cascade computing model to optimize energy exchanges in prosumer communities

Enabling Sustainable Clouds

Enabling Sustainable Clouds: The Case for Virtualizing the Energy System

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