Abstract Industrial demand-side flexibility is essential for power systems with high shares of variable renewables, yet static grid fees and full load hours incentives can suppress flexible electrification by penalizing peaks and rewarding uniform consumption. This paper introduces an open, extensible, regulation-aware optimization platform that integrates market and emission data with a mixed-integer scheduling model of a bi-valent industrial dryer capable of using electricity and gas. The platform is demonstrated through a representative use case from the energy intensive paper manufacturing sector operating under the current reduced static grid fee pursuant. Because the reduction is contingent on meeting a minimum full load hours threshold, the scope for exploiting the bivalent flexibility potential remains limited. As an alternative, a proposed dynamic grid fee regime featuring time-varying low- and high grid fee windows is evaluated. Any reduction of power consumption from the grid during the high grid fee window does not count against meeting the full load hours threshold. With dynamic fees, the optimization concentrates electric operation within low grid fee windows, increasing electrified heat, reducing emissions, and maintaining competitive costs. Notably, during summer months, these windows align well with periods of lower grid carbon intensity, reinforcing cost–emission co-benefits. However, this alignment deteriorates in winter due to higher variability in renewable generation patterns and constrained window timing, limiting incentive effectiveness. The findings demonstrate that tariff parameterization materially shapes realizable industrial flexibility and that regulation-aware optimization can translate latent technical potential into sustained, temporally targeted electrification aligned with system conditions. The platform enables reproducible, policy-relevant scenario analysis and can aid plant operators for scheduling flexible assets and tariff designers in testing tariff parameters against realistic operational responses.
Marcel Prinz (Fri,) studied this question.