Data-Driven Evaluation of the Economic Viability of a Residential Battery Storage System Using Grid Import and Export Measurements.
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| Title: | Data-Driven Evaluation of the Economic Viability of a Residential Battery Storage System Using Grid Import and Export Measurements. |
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| Authors: | Gebhard, Tim August1,2 (AUTHOR), Garrido-Zafra, Joaquín1,2 (AUTHOR), Moreno-Muñoz, Antonio1 (AUTHOR) amoreno@uco.es |
| Source: | Energies (19961073). Feb2026, Vol. 19 Issue 4, p1072. 21p. |
| Subject Terms: | *Storage batteries, *Net present value, *Load management (Electric power), *Smart meters, *Cost effectiveness, *Energy storage, *Energy demand management |
| Abstract: | Battery-electric residential storage systems can increase the self-consumption of photovoltaic (PV) generation; however, economical sizing typically requires a high-resolution time series of PV production and household load behind the meter. In practice, such data are often unavailable. This work therefore presents a simulation model for determining the economically optimal residential storage capacity based exclusively on smart-meter data at the point of common coupling (PCC), i.e., hourly import and export time series. Economic performance is assessed using net present value (NPV) over a multi-year evaluation horizon. In addition, technical constraints (SoC limits, power limits, charging/discharging efficiencies) as well as capacity degradation are considered via a semi-empirical aging model. For validation, a reproducible reference scenario is constructed using PVGIS generation data and the standard load profile H23, enabling a direct comparison between the conventional approach (consumption/generation) and the PCC approach (import/export). The results show that the capacity optimum can be reproduced consistently using PCC data, even under smart-meter-like integer kWh quantization. At the same time, large parts of the investigated parameter space indicate that, under the assumed scenarios, foregoing a storage system is often not economically sensible. Sensitivity analyses further highlight the strong impact of load shifting, in particular due to the charging time of electric vehicles. A case study using real PCC measurement data, together with a two-week-window analysis, demonstrates practical applicability and robustness under limited measurement durations. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Battery-electric residential storage systems can increase the self-consumption of photovoltaic (PV) generation; however, economical sizing typically requires a high-resolution time series of PV production and household load behind the meter. In practice, such data are often unavailable. This work therefore presents a simulation model for determining the economically optimal residential storage capacity based exclusively on smart-meter data at the point of common coupling (PCC), i.e., hourly import and export time series. Economic performance is assessed using net present value (NPV) over a multi-year evaluation horizon. In addition, technical constraints (SoC limits, power limits, charging/discharging efficiencies) as well as capacity degradation are considered via a semi-empirical aging model. For validation, a reproducible reference scenario is constructed using PVGIS generation data and the standard load profile H23, enabling a direct comparison between the conventional approach (consumption/generation) and the PCC approach (import/export). The results show that the capacity optimum can be reproduced consistently using PCC data, even under smart-meter-like integer kWh quantization. At the same time, large parts of the investigated parameter space indicate that, under the assumed scenarios, foregoing a storage system is often not economically sensible. Sensitivity analyses further highlight the strong impact of load shifting, in particular due to the charging time of electric vehicles. A case study using real PCC measurement data, together with a two-week-window analysis, demonstrates practical applicability and robustness under limited measurement durations. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19041072 |
