ABSTRACT
However, the distributed wind power closer to the user or load centers, leading to more complex elements of the surrounding environment. The changes of obstacles, surface roughness and topography have increased the difficulty of numerical weather prediction that used for distributed wind power forecast, which has seriously hampered the development of distributed wind power. Microscale weather modeling is an effective way to simulate distributed wind energy prediction. Although encouraging advances in micro-scale flow modeling, including the evolution of different technologies and flavors of LES and CFD (Computational Fluid Dynamics) models, have been made in the last decade, the modeling ability for micro-scale flows associated with real weather at distributed wind farm scales is still very limited. In fact, microscale weather flow models encounter many challenges. Therefore existing micro-scale models have mostly focused on idealized case study, with idealized initial conditions and/or boundary conditions and/or highly simplified atmospheric physics. The Real-Time Four Dimensional Data Assimilation (RTFDDA) weather forecasting system (Liu Y, 2006), built upon WRF (Liu Y, 2008a. The
1 INTRODUCTION
Energy crisis, environmental issues and other factors have accelerated the development of distributed generation, especially the rapid development of distributed wind power. “Twelve Five” period, the development of China’s wind power has adjusted to “centralized” and “distributed”, the distributed wind power is with respect to the concentrated development large-scale wind farms (Wang C, 2005), it is the wind power production and used in the same location or limited to the local area. Distributed wind power generation with flexible, environmentally friendly features is more and more access to the distribution network (Linag Y, 2003), not only to save investment of the high-pressure lines and booster stations, but also to achieve the balance between wind power output and local load (Wang L, 2001; Xu Y, 2011; Dai J, 2011). The installed capacity of distributed wind power in 2015 will reach 500 million kilowatts and in 2020 is expected to reach 15 million kilowatts, accounting for 5% and 7.5% of the target installed capacity, respectively. However, the output with obvious intermittent and random volatility features, will affect the normal operation of the power system (Chen H, 2006; Hu H, 2006; Hadjsaid N, 1999; Puttgen H, 2003), when the large number of distributed wind power access to the distribution network. Studies have shown that accurately
operational…part 1…; Liu Y, 2008b. The operational…part 2…). It has been downscaled to LES scale modeling grids, through the nested-down grid refinements, this modeling system provides a unique ability for simulating real micro-scale weather processes by incorporating realistic mesoscale weather forcing, high-resolution terrain and land use, and physical processes of solar and long wave radiation and cloud microphysics (Liu Y, 2011). The WRF-LES system is employed to forecast the distribution wind in northeast of China, the results show that the model system can effectively improve the accuracy of wind prediction, and the model system has application potential.
