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Current state of the art wind power plant (WPP) controllers operate wind turbines (WTs) independently as individual machines, thus dispatching the WTs’ set points in an equal manner to all of them. To achieve optimal WPP control, three aspects need to be addressed: • Maximizing the yield (power production) balanced against turbine mechanical loading and electricity price • Enhancing WPP capability to provide ancillary services (primary, secondary, and tertiary reserves), and • Reducing operating costs (i.e. reduced fatigue load degradation of WTs and O&M requirement) over the lifetime of the WPP. The goal of TotalControl is to move the WPP controller design philosophy from individual optimization of WT operation to a coordinated optimization of the overall WPP performance. The TotalControl project aims to achieve this by developing and validating advanced integrated WPP/WT control schemes conditioned on grid demandsand wind turbine fatigue damage limits. For developing and testing of the different WPP controllers, a range of high-fidelity and medium-fidelity simulation models are used. These models are already available in the consortium, but will be thoroughly validated against full scale measurements in the Lillgrund WPP. Due to the complexity and multi-scale nature of WPP flow dynamics, the high-fidelity CFDbased models are very expensive in simulation time, e.g. requiring supercomputing, and therefore not well suited as control design models.

Designing wind farms in complex terrain is becoming more and more important, especially for countries like China, where a large portion of the territory is featured as complex terrain. Although potential richer wind resources could be expected at complex terrain sites (thanks to the terrain effects), they also expose many challenges for wind farm designers/developers. In this study, we present the FarmOpt methodology for designing wind farms in complex terrain, which combines thestate-of-the-art wind resource assessment methods with engineering wake models adapted for complex terrain and advanced layout optimization algorithms. Various constraints are also modelled and considered in the design optimization problem for maximizing the annual energy production (AEP). A case study is presented to illustrate the effectiveness of the methodology. Further developments of the FarmOpt tool are also briefly introduced.