Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks: A Comparison of Implementations

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Fältnamn	Indikatorer	Metadata
000		03979naa a2200433 4500
001		oai:DiVA.org:kth-281489
003		SwePub
008		201019s2020 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2814892 URI
024	7	a https://doi.org/10.3390/en131642272 DOI
040		a (SwePub)kth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a García Veloso, Cesaru KTH,Skolan för elektroteknik och datavetenskap (EECS),Tech Univ Catalonia, Barcelona Sch Ind Engn, Barcelona 08028, Spain.4 aut0 (Swepub:kth)u1oe1457
245	1 0	a Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks :b A Comparison of Implementations
264		c 2020-08-15
264	1	b MDPI,c 2020
338		a print2 rdacarrier
500		a QC 20201019
520		a The global proliferation of plug-in electric vehicles (PEVs) poses a major challenge for current and future distribution systems. If uncoordinated, their charging process may cause congestion on both network transformers and feeders, resulting in overheating, deterioration, protection triggering and eventual risk of failure, seriously compromising the stability and reliability of the grid. To mitigate such impacts and increase their hosting capacity in radial distribution systems, the present study compares the levels of effectiveness and performances of three alternative centralized thermal management formulations for a real-time agent-based charge control algorithm that aims to minimize the total impact upon car owners. A linear formulation and a convex formulation of the optimization problem are presented and solved respectively by means of integer linear programming and a genetic algorithm. The obtained results are then compared, in terms of their total impact on the end-users and overall performance, with those of the current heuristic implementation of the algorithm. All implementations were tested using a simulation environment considering multiple vehicle penetration and base load levels, and equipment modeled after commercially available charging stations and vehicles. Results show how faster resolution times are achieved by the heuristic implementation, but no significant differences between formulations exist in terms of network management and end-user impact. Every vehicle reached its maximum charge level while all thermal impacts were mitigated for all considered scenarios. The most demanding scenario showcased over a 30% reduction in the peak load for all thermal variants.
650	7	a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Annan elektroteknik och elektronik0 (SwePub)202992 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Other Electrical Engineering, Electronic Engineering, Information Engineering0 (SwePub)202992 hsv//eng
653		a plug-in electric vehicles
653		a radial low voltage networks
653		a real-time control
653		a centralized thermal management
653		a active distribution networks
653		a user impact minimization
700	1	a Rauma, Kalleu TU Dortmund Univ, Inst Energy Syst Energy Efficiency & Energy Econ, D-44227 Dortmund, Germany.4 aut
700	1	a Fernandez, Julianu Univ Victoria, Inst Integrated Energy Syst, Victoria, BC V8W 2Y2, Canada.4 aut
700	1	a Rehtanz, Christianu TU Dortmund Univ, Inst Energy Syst Energy Efficiency & Energy Econ, D-44227 Dortmund, Germany.4 aut
710	2	a KTHb Skolan för elektroteknik och datavetenskap (EECS)4 org
773	0	t Energiesd : MDPIg 13:16q 13:16x 1996-1073
856	4	u https://doi.org/10.3390/en13164227y Fulltext
856	4	u https://www.mdpi.com/1996-1073/13/16/4227/pdf
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281489
856	4 8	u https://doi.org/10.3390/en13164227

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