research-article

Day-Ahead Optimization of Production Schedules for Saving Electrical Energy Costs

Authors:

Thomas Stueber,

Florian Heimgaertner,

Michael MenthAuthors Info & Claims

e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems

Pages 192 - 203

https://doi.org/10.1145/3307772.3328302

Published: 15 June 2019 Publication History

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Abstract

The integration of weather-dependent renewable energy sources leads to an increased volatility of electrical energy supply. As a result, considerable intra-day price spreads can be observed at the spot markets for electrical energy. To benefit from variable energy prices, enterprises can use price forecasts for cost-optimized load scheduling. Thereby energy costs can be reduced by shifting energy-intensive processes to times with lower energy prices.

In this work, we propose a method to model an industrial unit including devices, storage units, dependencies, restrictions, and production targets as a mixed integer linear program (MILP). Along with a time series of energy prices, the MILP is used to compute optimal run times for the devices while complying with the specified restrictions.

We use the model of a cement plant as an example. We show potential savings compared to default schedules over individual day, weeks, or over the year 2018. We propose optimization with look-ahead, point out its benefits compared to optimization without look-ahead, and show the influence of storages sizes and price variance on the savings potential.

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Scarcello LGiordano AMastroianni C(2022)Edge Computing Parallel Approach for Efficient Energy Sharing in a Prosumer CommunityEnergies10.3390/en1513454315:13(4543)Online publication date: 21-Jun-2022
https://doi.org/10.3390/en15134543
Xia CLi WChang XZhao TZomaya A(2022)Lightweight Online Scheduling for Home Energy Management Systems Under UncertaintyIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31536527:4(887-898)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2022.3153652
Giordano AMastroianni CScarcello LSpezzano G(2020)IoT-Based Energy Sharing Model for Sizing Storage Systems in Energy Communities2020 6th IEEE International Energy Conference (ENERGYCon)10.1109/ENERGYCon48941.2020.9236520(503-508)Online publication date: 28-Sep-2020
https://doi.org/10.1109/ENERGYCon48941.2020.9236520

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Day-Ahead Optimization of Production Schedules for Saving Electrical Energy Costs

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e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems

June 2019

589 pages

ISBN:9781450366717

DOI:10.1145/3307772

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 15 June 2019

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Bundesministerium für Wirtschaft und Energie

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e-Energy '19

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SIGEnergy

e-Energy '19: The Tenth ACM International Conference on Future Energy Systems

June 25 - 28, 2019

AZ, Phoenix, USA

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Scarcello LGiordano AMastroianni C(2022)Edge Computing Parallel Approach for Efficient Energy Sharing in a Prosumer CommunityEnergies10.3390/en1513454315:13(4543)Online publication date: 21-Jun-2022
https://doi.org/10.3390/en15134543
Xia CLi WChang XZhao TZomaya A(2022)Lightweight Online Scheduling for Home Energy Management Systems Under UncertaintyIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31536527:4(887-898)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2022.3153652
Giordano AMastroianni CScarcello LSpezzano G(2020)IoT-Based Energy Sharing Model for Sizing Storage Systems in Energy Communities2020 6th IEEE International Energy Conference (ENERGYCon)10.1109/ENERGYCon48941.2020.9236520(503-508)Online publication date: 28-Sep-2020
https://doi.org/10.1109/ENERGYCon48941.2020.9236520

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A Day-Ahead Renewables-Based Power Scheduling System for Internet of Energy

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Charge migration efficiency optimization in hybrid electrical energy storage (HEES) systems

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