- Conference Paper
Electrolyzers can reduce their electricity costs through demand response (DR) by adapting their production rate to time-varying market prices. Although the production rate can often be adapted rapidly, exploiting the full DR potential of an electrolyzer requires to consider slow temperature dynamics, leading to challenging mixed-integer dynamic optimization problems. In this contribution, we propose a dynamic ramping reformulation for real-time scheduling optimization of electrolyzers considering these slow temperature dynamics. Starting from a nonlinear dynamic model, the limits of the temperature gradient are derived to guarantee that the optimization result is feasible on the original model. The limits are then approximated conservatively by piece-wise affine functions leading to a mixed-integer linear program (MILP). Varying the number of piece-wise affine segments allows to explicitly balance model conservativeness against computational burden. We apply our reformulation to a validated alkaline electrolyzer model from literature. Our dynamic temperature ramping approach reduces production costs by 15.9 % compared to nominal operation. A quasi-steady-state optimization, which is restricted to production rates with steady-state temperatures in the allowed range, only leads to 12.8 % improvement. The proposed formulation achieves optimization runtimes below one minute, which is sufficiently fast for real-time scheduling. Show more
Book title14th International Symposium on Process Systems Engineering
Journal / seriesComputer Aided Chemical Engineering
Pages / Article No.
SubjectElectrolysis; Demand response; Mixed-integer linear programming
Organisational unit09696 - Bardow, André / Bardow, André
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