Abstract
Optimizing a manufacturing company's in-house energy demand amidst fluctuating electricity prices and uncertainties in renewable energy supply as well as volatile manufacturing planning situations is a challenging task. To tackle this issue, a novel approach is developed for scheduling the energy supply in manufacturing systems with the objective of reducing energy costs. The approach employs Quantum Annealing to determine the optimal mix of in-house generation, purchased electricity, and energy storage. The effectiveness and scalability of the approach are demonstrated through the validation using two simplified use cases, showcasing its potential in solving complex energy supply optimization problems.
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