PERFORMANCE TEST OF NEW PV-FORECASTING MODELS IN REALISTIC ENVIRONMENTS

In this paper new methods of PV-forecasting are evaluated in different simulation studies to calculate their performance in terms of economic value. The focus lies on energy management in residential PV-systems with battery storage and micro-grids powered by different sources. The micro-grid simulation was built up without physical models and consists of two diesel generators, (gensets) a PVsystem and a fixed load profile. The results show that the necessary spinning reserve can be reduced by the use of PVpower forecasting. This leads to better utilization of the PV-System as well as cost reductions in comparison to systems without forecast. The total energy costs can be lowered by around 3.3 % to 3.4 % depending on the PV generation profile and the forecast quality. These results apply only for the chosen system setup, whereas for larger PV arrays or higher number of gensets the economic benefit is expected to be higher. The second application investigated in this paper is a residential PV-system with an energy storage. This simulation model was built up with a physical inverter and battery model. An energy management controller optimizing for financial benefits using forecasts resulted in annual electricity costs lowered by 2.1 % to 2.5 % compared to a standard storage battery controller. We assumed that the load profile was assumed to be predicted perfectly. This moderate results arise out of the tariff gap between electricity consumption (duties and taxes are added) and feed-in, leaving little scope for optimization.