Heat pumps in low voltage distribution grids by energy storage

Kusch, Wolfgang; Stadler, Ingo; Bhandari, Ramchandra

doi:10.1109/iesc.2015.7384386

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…In the end, each customer is assigned one coincidence factor and one power value for each feeder level, respectively (Figure 5). After determining the active power for each grid customer and feeder level, reactive power is calculated by using the following power factors: 0.99 (leading) for EVs, 1.00 for PVMs, and 0.90 (lagging) [8,36,40,41].…”

Section: Determination Of Grid Loadsmentioning

confidence: 99%

Estimation of Grid Reinforcement Costs Triggered by Future Grid Customers: Influence of the Quantification Method (Scaling vs. Large-Scale Simulation) and Coincidence Factors (Single vs. Multiple Application)

Thormann

Kienberger

2022

Energies

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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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Section: Determination Of Grid Loadsmentioning

confidence: 99%

Estimation of Grid Reinforcement Costs Triggered by Future Grid Customers: Influence of the Quantification Method (Scaling vs. Large-Scale Simulation) and Coincidence Factors (Single vs. Multiple Application)

Thormann

Kienberger

2022

Energies

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“…Furthermore, these HP load profiles are uniformly scaled according to an average domestic space heating demand of 14,316 kWh/a/household and an average domestic warm water demand of 2995 kWh/a/household [57,58] assuming a coefficient of performance of 3.0. Reactive power profiles are derived by applying a constant power factor of 0.9 (lagging) [23,24,26]. Finally, considering a certain HP-penetration, the individual HP-loads of FHs are aggregated for the supplying PCC (e.g., Figure 2) in accordance with the spatial determination, defined in the previous step.…”

Section: Electric Heat Pump Loadsmentioning

confidence: 99%

“…Consequently, an annual average PF including all the modeled time series of EV charging is detected (0.971, leading) and applied for the SIA-and SCA-approach. Due to missing measurement data regarding electrical HPs, a PF of 0.9 (lagging) [23,24,26] is assessed, analogous to the modeled time series (Section 2.2.3). For a combined aggregation of various consumer classes in the SCA-approach, an average PF weighted by individual numbers of consumer classes is applied in a simplified manner (e.g., the combined consideration of two HOs, two EVs and one HP results in a PF of 0.993, lagging).…”

Section: Load Approaches Analyzing Temporal Interactions Between Varimentioning

confidence: 99%

Evaluation of Grid Capacities for Integrating Future E-Mobility and Heat Pumps into Low-Voltage Grids

Thormann

Kienberger

2020

Energies

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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.

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