Solar energy produced by photovoltaic (PV) systems is steadily growing in importance. One of the major challenges in large-scale deployment of PV systems to the current electrical grid is the intermittent power generation by PV systems, causing increased operating costs and power quality issues. The intermittency is primarily due to variations in cloudiness characterized by a wide range of spatial and temporal scales. At the national level, real-time balancing of supply and demand is commonplace. However, household PV introduces these challenges at the local level. This requires a new generation of distribution grid management, where accurate forecasts of the resulting net load on the distribution level of the grid enable mitigation of mismatch between local supply and demand. Forecasting PV power on timescales ranging from minutes to days thus heavily depends on state-of-the-art weather models, where the forecasts of the occurrence and properties of clouds are the major challenge. The shift to matching local supply and demand are a major theme in the research roadmap of distribution system operators (DSO) such as Alliander, the private partner in this proposal. The aim of this research is: To enable optimal integration of solar energy into the local electricity grid by smart-grid technologies. We achieve this by matching the demands of electrical grid management with detailed meteorological data.
|Effective start/end date||1/10/18 → …|
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