Ramp-Rate Control of DRES employing Supercapacitors in Distribution Systems
- 7:45AM Oct 05, 2021
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Kyriaki-Nefeli D. Malamaki, Francisco Jesús Matas-Díaz, Manuel Barragán-Villarejo, Andrei Mihai Gross, Georgios C. Kryonidis, Jose L. Martinez-Ramos, Charis Demoulias: ‘Ramp-Rate Control of DRES employing Supercapacitors in Distribution Systems’ in 2021 International Conference on Smart Energy Systems and Technologies (SEST), pp. 1-6, 2021
The increased penetration of Distributed Renewable Energy Sources (DRES) has posed several challenges into the electric power systems, the most important of which is the instability caused by the intermittent and stochastic nature of the primary sources (wind, sun). Since the System Operators have started to specify limits for the restriction of the Ramp-Rate (RR) at the Point of Common Coupling (PCC) of the DRES with the grid, several algorithms have been proposed to mitigate the power fluctuations using energy storage systems (ESS). Some drawbacks are: high computational effort in the calculation of RR, increased ESS size/decreased ESS operational life, etc. In this paper a new RR Limitation (RRL) algorithm is proposed to address gaps in the current state-of-the-art. This algorithm is based on the two-point calculation of RR and is performed considering the connection of a Supercapacitor (SC) at the DC-link of a DRES converter. The relationship between the SC voltage and the degree to which the RRL is achieved is established, which is something missing from the current state of the art and is essential if the RRL is to be treated as a new tradeable AS. The RR control is validated in a real experimental testbed. Finally, this control is modelled in Simulink in order to perform investigations on the influence that several parameters have on the achieved RRL at the DRES PCC. This is important, since the evaluation of the RRL impact at DRES level will allow for further investigations to evaluate the RRL impact at distribution system level and defer costs related to the installation of large-scale ESS.
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