h1

000959463 001__ 959463
000959463 005__ 20231103180607.0
000959463 0247_ $$2HBZ$$aHT030335231
000959463 0247_ $$2Laufende Nummer$$a42471
000959463 0247_ $$2datacite_doi$$a10.18154/RWTH-2023-05624
000959463 020__ $$a978-3-948234-31-7
000959463 037__ $$aRWTH-2023-05624
000959463 041__ $$aEnglish
000959463 082__ $$a621.3
000959463 1001_ $$0P:(DE-82)IDM02637$$aDe Din, Edoardo$$b0$$urwth
000959463 245__ $$aMulti-timescale framework for the voltage control of active distribution grids$$cvorgelegt von Edoardo De Din, M.Sc.$$honline, print
000959463 250__ $$a1. Auflage
000959463 260__ $$aAachen$$bE.ON Energy Research Center, RWTH Aachen University$$c2023
000959463 300__ $$a1 Online-Ressource : Illustrationen, Diagramme
000959463 3367_ $$02$$2EndNote$$aThesis
000959463 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd
000959463 3367_ $$0PUB:(DE-HGF)3$$2PUB:(DE-HGF)$$aBook$$mbook
000959463 3367_ $$2BibTeX$$aPHDTHESIS
000959463 3367_ $$2DRIVER$$adoctoralThesis
000959463 3367_ $$2DataCite$$aOutput Types/Dissertation
000959463 3367_ $$2ORCID$$aDISSERTATION
000959463 4900_ $$aE.On Energy Research Center : ACS, Automation of complex power systems$$v117
000959463 502__ $$aDissertation, RWTH Aachen University, 2023$$bDissertation$$cRWTH Aachen University$$d2023$$gFak06$$o2023-02-23
000959463 500__ $$aDruckausgabe: 2023. - Auch veröffentlicht auf dem Publikationsserver der RWTH Aachen University
000959463 5203_ $$lger
000959463 520__ $$aMotivation, Goal and Task of the Dissertation: Electric distribution grids are facing significant grid management challenges due to the increasing installation of Distributed Generator (DG)s and Energy Storage System (ESS)s. As compared to the past, Low Voltage (LV) and Medium Voltage (MV) grids are becoming active distribution grids, requiring advanced solutions to manage the resources installed in the grid while respecting the standards. One of the possible effects produced by this transformation are voltage fluctuations, which can occasionally lead to exceeding the undervoltage or overvoltage limits. For this reason, control algorithms that are able to manage the installed resources in a smart and less invasive way represent an innovative approach to operate active distribution grids. A particular aspect of the voltage control algorithms is that they can have different objectives depending on the timescale and on the structure of the control. This dissertation aims at presenting a possible framework to combine control algorithms with different timescales, showing the beneficial contribution of such control framework. Moreover, distributed algorithms have been recently proven to be a better option for the implementation of the voltage control algorithms that work in a closed-loop with the measurements (defined as online or feedback controls). Therefore, distributed formulation of the online control layers of the framework has been carried out. The work aims satisfying the following requirements: 1. Interconnection of voltage control with different timescales: The proposed framework is used to interface voltage control algorithms with different timescales. 2. Distributed and flexible: The controls with a feedback approach are formulated in a distributed and flexible configuration. 3. Modular and scalable: The modularity is guaranteed by a full distributed implementation of the controls. Scalability for the proposed distributed algorithms has been also analyzed. Major Scientific Contributions: The three requirements defined above have been fulfilled considering three steps, which also define the major scientific contribution of the dissertation. The first step addresses the first requirement, defining the proposed multi-timescale framework in its centralized formulation. In the framework, three different control levels have been defined: the scheduling, an offline control to perform a day-ahead scheduling of the ESSs; the Model Predictive Control (MPC) to track the references produced by the scheduling within a prediction horizon while compensating for any mismatch; the online voltage control that takes action if a rapid voltage variation occurs between two MPC iterations. The scheduler uses the day-ahead forecast information to calculate an operation plan for the ESSs to target the State of Charge (SoC) objective while maintaining the voltage within the limits. The MPC is used as an intermediate layer to track the operation plan for the ESSs and compensate for the errors of the forecast using real measurements from the field in a closed loop. The final stage of the framework prevents sudden voltage violations by modifying the control set-points calculated by the MPC.The second step considers the implementation of a full distributed algorithm to solve the MPC. The procedure is based on the reformulation of the original problem in a constraint-coupled setup and on the adoption of a recently presented algorithm to distribute such optimization setup. Condition limits for the convergence of the proposed distributed MPC have been proposed and proven by simulation tests. Additional simulations tests have been carried out to analyze the scalability of the proposed control.This second step address the both the second and third requirements for the MPC.In the third step, second and third requirements are addressed for the online voltage control. This step considers the implementation of the distributed formulation of the lower layer of the framework, which features the smaller timescale. The formulation, which extend a recently presented algorithm, is obtained by applying the duality theory and by exploiting the sparse structure of the control matrix. The test of the algorithm has been performed by implementing it with containers, which allow a more versatile implementation of the control while reducing the customizationrequired for hardware implementation. Tests with simulated grid are performed to demonstrate the characteristics of the control strategy and its scalability.$$leng
000959463 588__ $$aDataset connected to Lobid/HBZ
000959463 591__ $$aGermany
000959463 653_7 $$aSpannungsregelung
000959463 653_7 $$aVerteilungsnetz
000959463 653_7 $$adistributed control
000959463 653_7 $$adistributed energy resources
000959463 653_7 $$adistribution grid
000959463 653_7 $$amodel predictive control
000959463 653_7 $$amodellprädiktive Regelung
000959463 653_7 $$averteilte Energieressourcen
000959463 653_7 $$averteilte Steuerung
000959463 653_7 $$avoltage control
000959463 7001_ $$0P:(DE-82)IDM01565$$aMonti, Antonello$$b1$$eThesis advisor$$urwth
000959463 7001_ $$0P:(DE-82)IDM05905$$aUlbig, Andreas$$b2$$eThesis advisor$$urwth
000959463 8564_ $$uhttps://publications.rwth-aachen.de/record/959463/files/959463.pdf$$yOpenAccess
000959463 8564_ $$uhttps://publications.rwth-aachen.de/record/959463/files/959463_source.zip$$yRestricted
000959463 909CO $$ooai:publications.rwth-aachen.de:959463$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000959463 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000959463 9141_ $$y2023
000959463 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM02637$$aRWTH Aachen$$b0$$kRWTH
000959463 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM01565$$aRWTH Aachen$$b1$$kRWTH
000959463 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-82)IDM05905$$aRWTH Aachen$$b2$$kRWTH
000959463 9201_ $$0I:(DE-82)616310_20140620$$k616310$$lLehrstuhl für Automation of Complex Power Systems$$x0
000959463 9201_ $$0I:(DE-82)080052_20160101$$k080052$$lE.ON Energy Research Center$$x1
000959463 961__ $$c2023-09-06T11:54:47.639064$$x2023-06-06T11:34:06.938409$$z2023-09-06T11:54:47.639064
000959463 9801_ $$aFullTexts
000959463 980__ $$aI:(DE-82)080052_20160101
000959463 980__ $$aI:(DE-82)616310_20140620
000959463 980__ $$aUNRESTRICTED
000959463 980__ $$aVDB
000959463 980__ $$abook
000959463 980__ $$aphd