MILP Formulation for Dynamic Demand Response of Electrolyzers
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Date
2022
Publication Type
Conference Paper
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yes
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Abstract
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.
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Publication status
published
Book title
14th International Symposium on Process Systems Engineering
Journal / series
Volume
49
Pages / Article No.
391 - 396
Publisher
Elsevier
Event
14th International Symposium on Process Systems Engineering (PSE 2021+)
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Methods
Software
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Date created
Subject
Electrolysis; Demand response; Mixed-integer linear programming
Organisational unit
09696 - Bardow, André / Bardow, André