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Optimisation algorithms for the charge dispatch of plug-in vehicles based on variable tariffs

机译:基于可变关税的插电式汽车充电调度优化算法

摘要

Plug-in vehicles powered by renewable energies are a viable way to reduce local and total emissions and could also support a highly efficient grid operation. Indirect control by variable tariffs is one option to link charging or even discharging time with the grid load and the renewable energy production. Algorithms are required to develop tariffs and evaluate grid impacts of variable tariffs for electric vehicles (BEV) as well as to schedule the charging process optimisation. Therefore a combinatorial optimisation algorithm is developed and an algorithm based on graph search is used and customised. Both algorithms are explained and compared by performance and adequate applications. The developing approach and the correctness of the quick combinatorial algorithm are proved within this paper. For vehicle to grid (V2G) concepts, battery degradation costs have to be considered. Therefore, common life cycle assumptions based on the battery state of charge (SoC) have been used to include degradation costs for different Li-Ion batteries into the graph search algorithm. An application of these optimisation algorithms, like the onboard dispatcher, which is used in the German fleet test "Flottenversuch Elektromobiliue4t". Grid impact calculations based on the optimisation algorithm are shown.
机译:由可再生能源驱动的插电式车辆是减少局部排放和总排放的可行方法,并且还可以支持高效的电网运行。通过可变关税间接控制是将充电甚至放电时间与电网负荷和可再生能源生产联系起来的一种选择。需要算法来制定电价并评估电动汽车(BEV)的可变电价对电网的影响,以及安排充电过程的优化。因此,开发了组合优化算法,并使用和定制了基于图搜索的算法。通过性能和适当的应用来解释和比较这两种算法。文中证明了快速组合算法的开发方法和正确性。对于车辆到电网(V2G)的概念,必须考虑电池退化成本。因此,基于电池充电状态(SoC)的常见生命周期假设已用于将不同锂离子电池的降级成本纳入图形搜索算法。这些优化算法的应用,例如车载调度器,用于德国舰队测试“ Flottenversuch Elektromobili ue4t”。显示了基于优化算法的网格影响计算。

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