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An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system

Abstract One of the primary technical challenges of integrating high levels of PV generation into standalone off-grid power supply systems is their variable power output characteristics. In dealing with this issue, the integration of reliable PV forecasting techniques and preferably energy storage, are highly effective. Applying a short-term PV forecasting method, together with a compensatory controllable resource, can help in the management of system operation. This study incorporates the development of an energy flow modelling tool that has been used to analyse the benefits of 1-min ahead PV forecasting and battery storage for different system configurations. Based on the five days of 1-min ahead forecasting results analysed, it is found that PV forecasting enables the prosumer to install more than double the PV capacity, compared to the allowed installed PV capacity when no forecasting is employed. This additional PV capacity saves around 24–25% (on average) of diesel fuel per day for the diesel-PV-battery configuration. The outcomes evidently indicate that incorporating 1-min ahead PV forecasting enables a significant increase of PV hosting capacity of the system, without compromising the reliability of the system.
- German Aerospace Center Germany
- Murdoch University Australia
- Murdoch University Australia
PV hosting capacity, Energiesystemanalyse, Standalone off-grid power supply system, PV penetration level, Short-term PV forecasting, Energy flow modelling tool, remote area electricity
PV hosting capacity, Energiesystemanalyse, Standalone off-grid power supply system, PV penetration level, Short-term PV forecasting, Energy flow modelling tool, remote area electricity
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).33 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
