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| 001 | 910845 | ||
| 005 | 20240712113118.0 | ||
| 024 | 7 | _ | |a 10.1016/j.apenergy.2022.119060 |2 doi |
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| 037 | _ | _ | |a FZJ-2022-04195 |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Rücker, Fabian |0 0000-0003-2842-879X |b 0 |e Corresponding author |
| 245 | _ | _ | |a Self-sufficiency and charger constraints of prosumer households with vehicle-to-home strategies |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2022 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a In recent years, the market of electric vehicles has been growing strongly. This growth is accompanied bydiscussions on vehicle-to-home strategies that allow households with a photovoltaic system and an electricvehicle both to charge the vehicle with solar energy and to supply energy from the vehicle to the household.However, vehicle-to-home technology is still not yet widely implemented in prosumer households and thereis still little literature about the impact of technological constraints given by the hardware and chargingprotocols on prosumer energy consumption. To close this research gap, we develop heuristic vehicle-to-homecharging strategies that aim to increase self-sufficiency, vehicle availability and traction battery lifetime. Wediscuss charging power constraints due to technical limitations measured in the laboratory and communicationprotocols. We investigate the impact of charging power constraints, bidirectional charger capability andforecasting algorithms on the self-sufficiency of the prosumer household. The simulation model integrates acomprehensive electric vehicle model, photovoltaic system model and historic measurement data of prosumerand driving profiles. We propose and simulate three different exemplary mobility profile scenarios. Themobility scenarios differ in their departure and arrival time distributions and are named Worker, Half-timeWorker and Late Worker. The developed smart charging strategies can increase the self-sufficiency of thehousehold by up to 16.9 percentage points in comparison to charging the vehicle with maximum power uponplug-in. Decreasing the minimum charging power constraint from 4.1 kW to 1.8 kW can increase self-sufficiencyby up to 10.5 percentage points. Smart charging strategies, the use of a bidirectional charger, relaxation ofcharging power constraints and the use of forecasting algorithms increase the self-sufficiency of a prosumerhousehold with a photovoltaic system and an electric vehicle. |
| 536 | _ | _ | |a 1223 - Batteries in Application (POF4-122) |0 G:(DE-HGF)POF4-1223 |c POF4-122 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Schoeneberger, Ilka |0 0000-0003-1205-3049 |b 1 |
| 700 | 1 | _ | |a Wilmschen, Till |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Sperling, Dustin |0 0000-0001-5300-859X |b 3 |
| 700 | 1 | _ | |a Haberschusz, David |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Figgener, Jan |0 0000-0003-2216-9432 |b 5 |
| 700 | 1 | _ | |a Sauer, Dirk Uwe |0 P:(DE-Juel1)172625 |b 6 |
| 773 | _ | _ | |a 10.1016/j.apenergy.2022.119060 |g Vol. 317, p. 119060 - |0 PERI:(DE-600)2000772-3 |p 119060 |t Applied energy |v 317 |y 2022 |x 0306-2619 |
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