While an area-wide implementation of electric vehicles (EVs) and electric heat pumps (HPs) will contribute to a decarbonization of the energy system, they represent new challenges for existing low-voltage (LV) power grids. Hence, this study investigates potential grid congestions on the basis of three contrasting load approaches applied to four different grid regions. Within the three load approaches, temporal characteristics of various grid customer classes (EVs, HPs, households etc.) are derived from highly resolved realistic load profiles. In accordance with classic grid planning, firstly a static load approach is analyzed by applying the modeled coincidence for each consumer class individually. Secondly, this static approach is modified by including combined coincidence factors, taking temporal consumer class interactions into account. Finally, both static load approaches are compared with detailed annual time series analyses by means of load flow simulations using real-life LV grid data. The evaluation of inadmissible voltage characteristics and thermal congestions identifies future grid extension needs depending on the considered grid region. In addition, the variation of the applied load approach highlights the need to consider consumer-specific temporal behavior. In fact, by neglecting temporal interactions between conventional and future grid customers, the classic grid planning approach overestimates future grid extension needs. To counteract an oversizing of future grid structures, this paper presents a combined consideration of EVs’ and HPs’ coincidence as well as resulting grid consequences on the LV level.
The integration of future grid customers, e.g., electric vehicles, heat pumps, or photovoltaic modules, will challenge existing low-voltage power grids in the upcoming years. Hence, distribution system operators must quantify future grid reinforcement measures and resulting costs early. On this account, this work initially evaluates different methods to quantify future grid reinforcement needs, applied by the current state of research. Thereby, it indicates the significance of large-scale grid simulations, i.e., simulating several thousand low-voltage grids, to quantify grid reinforcements accurately. Otherwise, a selected area’s total grid reinforcement costs might be misjudged significantly. Due to its fast application, deterministic grid simulations based on coincidence factors are most commonly used in the current state of research to simulate several thousand grids. Hence, in the second step, recent studies’ approaches to applying grid customers’ coincidence factors are evaluated: While simplified approaches allow fast simulation of numerous grids, they underestimate potential grid congestion and grid reinforcement costs. Therefore, a fully automated large-scale grid simulation tool is developed in this work to allow the simulation of multiple grids applying grid customers’ coincidence factors appropriately. As a drawback, the applied deterministic framework only allows an estimation of future grid reinforcement costs. Detailed determination of each grid’s grid reinforcement costs requires time-resolved grid simulations.
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