Thomas Stueber
Michael Menth
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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Index Terms
- Day-Ahead Optimization of Production Schedules for Saving Electrical Energy Costs
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