000917562 001__ 917562
000917562 005__ 20240712112855.0
000917562 0247_ $$2doi$$a10.48550/ARXIV.2205.14598
000917562 0247_ $$2Handle$$a2128/33649
000917562 037__ $$aFZJ-2023-00764
000917562 1001_ $$0P:(DE-Juel1)176974$$aBaader, Florian$$b0
000917562 245__ $$aDemand Response for Flat Nonlinear MIMO Processes using Dynamic Ramping Constraints
000917562 260__ $$barXiv$$c2022
000917562 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1673945959_26809
000917562 3367_ $$2ORCID$$aWORKING_PAPER
000917562 3367_ $$028$$2EndNote$$aElectronic Article
000917562 3367_ $$2DRIVER$$apreprint
000917562 3367_ $$2BibTeX$$aARTICLE
000917562 3367_ $$2DataCite$$aOutput Types/Working Paper
000917562 520__ $$aVolatile electricity prices make demand response (DR) attractive for processes that can modulate their production rate. However, if nonlinear dynamic processes must be scheduled simultaneously with their local multi-energy system, the resulting scheduling optimization problems often cannot be solved in real time. For single-input single-output processes, the problem can be simplified without sacrificing feasibility by dynamic ramping constraints that define a derivative of the production rate as the ramping degree of freedom. In this work, we extend dynamic ramping constraints to flat multi-input multi-output processes by a coordinate transformation that gives the true nonlinear ramping limits. Approximating these ramping limits by piecewise affine functions gives a mixed-integer linear formulation that guarantees feasible operation. As a case study, dynamic ramping constraints are derived for a heated reactor-separator process that is subsequently scheduled simultaneously with its multi-energy system. The dynamic ramping formulation bridges the gap between rigorous process models and simplified process representations for real-time scheduling.
000917562 536__ $$0G:(DE-HGF)POF4-1121$$a1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112)$$cPOF4-112$$fPOF IV$$x0
000917562 588__ $$aDataset connected to DataCite
000917562 650_7 $$2Other$$aOptimization and Control (math.OC)
000917562 650_7 $$2Other$$aFOS: Mathematics
000917562 7001_ $$0P:(DE-Juel1)180103$$aAlthaus, Philipp$$b1$$ufzj
000917562 7001_ $$0P:(DE-Juel1)172023$$aBardow, André$$b2$$ufzj
000917562 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b3$$eCorresponding author$$ufzj
000917562 773__ $$a10.48550/ARXIV.2205.14598
000917562 8564_ $$uhttps://juser.fz-juelich.de/record/917562/files/2205.14598.pdf$$yOpenAccess
000917562 909CO $$ooai:juser.fz-juelich.de:917562$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000917562 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176974$$aForschungszentrum Jülich$$b0$$kFZJ
000917562 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)176974$$aRWTH Aachen$$b0$$kRWTH
000917562 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)176974$$a ETH Zürich$$b0
000917562 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180103$$aForschungszentrum Jülich$$b1$$kFZJ
000917562 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)180103$$aRWTH Aachen$$b1$$kRWTH
000917562 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172023$$aForschungszentrum Jülich$$b2$$kFZJ
000917562 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)172023$$a ETH Zürich$$b2
000917562 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172097$$aForschungszentrum Jülich$$b3$$kFZJ
000917562 9131_ $$0G:(DE-HGF)POF4-112$$1G:(DE-HGF)POF4-110$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1121$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x0
000917562 9141_ $$y2022
000917562 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000917562 920__ $$lyes
000917562 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
000917562 9801_ $$aFullTexts
000917562 980__ $$apreprint
000917562 980__ $$aVDB
000917562 980__ $$aUNRESTRICTED
000917562 980__ $$aI:(DE-Juel1)IEK-10-20170217
000917562 981__ $$aI:(DE-Juel1)ICE-1-20170217