As the penetration of renewable energy increases in the generation mix, the problem of power dispatchability becomes more critical. The co-location of storage systems with wind energy is a promising solution to shift power delivery from periods of high wind resource availability to periods of high electricity demand. Producing baseload power from wind farms all or most of the time is an example of such dispatchability. In this work, we present an optimization-based dispatch strategy to produce baseload power. At every time step, an optimization problem is solved to decide the storage operation, maximize revenues on the electricity market and reach a given reliability target. In order to reduce the impact of forecast uncertainties on the reliability of the power delivery, a robust formulation of the dispatch optimization is used, based on a pessimistic version of the forecast. The proposed method is evaluated for 18 offshore sites with a 100 MW wind farm and storage system, for one year of operation. By using a robust dispatch strategy, the reliability increases by up to 0.9 points, with a minor impact on revenues (+2% on average), compared to the reference dispatch strategy using a regular forecast. Our study demonstrates the feasibility of providing a reliable baseload power from wind energy in the presence of forecast uncertainty.

Control co-design is a promising approach for wind turbine design due to the importance of the controller in power production, stability, load alleviation, and the resulting coupled effects on the sizing of the turbine components. However, the high computational effort required to solve optimization problems with added control design variables is a major obstacle to quantifying the benefit of this approach. In this work, we propose a methodology to identify if a design problem can benefit from control co-design. The estimation method, based on post-optimum sensitivity analysis, quantifies how the optimal objective value varies with a change in control tuning.

The performance of the method is evaluated on a tower design optimization problem, where fatigue load constraints are a major driver, and using a linear quadratic regulator targeting fatigue load alleviation. We use the gradient-based multi-disciplinary optimization framework Cp-max. Fatigue damage is evaluated with time-domain simulations corresponding to the certification standards. The estimation method applied to the optimal tower mass and optimal cost of energy show good agreement with the results of the control co-design optimization while using only a fraction of the computational effort.

Our results additionally show that there may be little benefit to using control co-design in the presence of an active frequency constraint. However, for a soft–soft tower configuration where the resonance can be avoided with active control, using control co-design results in a taller tower with reduced mass.

For scenarios of high penetration of renewable energy, it becomes increasingly relevant to improve the dispatchability of supply for wind and solar power plants. Baseload power plants, required to produce a minimum power production at all times, are discussed in this context. The baseload constraint can be satisfied with renewable sources when combined with a storage system but at a high cost. This work studies the design drivers of such a storage system when consisting of short and long-term storage. The capacities of the short-term and long-term storage components are calculated as part of a linear optimization problem with the objective of minimizing the cost of baseload, using a metric based on a net present value formulation. Our analysis, based on 10 locations in Northern Europe, highlights a high sensitivity of optimal storage sizing to storage cost assumptions. In addition, the cost of baseload is found to be correlated to the share of renewable power produced above baseload, but not to the correlation between price and wind power, suggesting arbitrage plays a minor role in the business case.

Control co-design is a promising approach for wind turbine design due to the importance of the controller in power production, stability and load alleviation. However, the high computational effort required to solve optimization problems with added control design variables is a major obstacle to quantify the benefit of this approach. In this work, we propose a methodology to identify if a design problem can benefit from control co-design. The estimation method, based on post-optimum sensitivity analysis, quantifies how the optimal objective value varies with a change in control tuning.

The performance of the method is evaluated on a tower design optimization problem, where fatigue load constraints are a major driver, and using a Linear Quadratic Regulator targeting fatigue load alleviation. We use the gradient-based multi-disciplinary optimization framework Cp-max. Fatigue damage is evaluated with time-domain simulations corresponding to the certification standards. The estimation method applied to the optimal tower mass and optimal levelized cost of energy show good agreement with the results of the control-co design optimization, while using only a fraction of the computational effort.

Our results additionally show that there may be little benefit to use control co-design in the presence of an active frequency constraint. However, for a soft-soft tower configuration where the resonance can be avoided with active control, using control co-design results in a higher tower with reduced mass.

This work compares nodal, spline and interpolation parametrization schemes for wind turbine blade planform design. The comparison is done on a power coefficient maximization problem, where the aerodynamic properties of the blade are computed using the Blade Element Method. The problem is solved using a gradient-based interior-point method with analytic gradients. We show the variation in planform design for each parametrization scheme when the degrees of freedom of the parametrization varies. We compare how the power coefficient converges with increasing degrees of freedom for each scheme. Our results shows that the B´ezier spline, the Piecewise Cubic Hermite Interpolation Polynomial (PCHIP) and Lagrange interpolation schemes present the best grid convergence out of all studied schemes.

In wind turbine optimization, the standard power regulation strategy follows a constrained trajectory based on the maximum power coefficient. It can be updated automatically during the optimization process by solving a nested maximization problem at each iteration. We argue that this model does not take advantage of the load alleviation potential of the regulation strategy and additionally requires significant computational effort. An alternative approach is proposed, where the rotational speed and pitch angle control points for the entire operation range are set as design variables, changing the problem formulation from nested to one-level. The nested and one-level formulations are theoretically and numerically compared on different aerodynamic blade design optimization problems for AEP maximization. The aerodynamics are calculated with a steady-state blade element momentum method. The one-level approach increases the design freedom of the problem and allows introducing a secondary objective in the design of the regulation strategy. Numerical results indicate that a standard regulation strategy can still emerge from a one-level optimization. Second, we illustrate that novel optimal regulation strategies can emerge from the one-level optimization approach. This is demonstrated by adding a thrust penalty term and a constraint on the maximum thrust. A region of minimal thrust tracking and a peak-shaving strategy appear automatically in the optimal design.

This work presents and compares two formulations for the co-design optimization of a wind turbine blade under non-linear transient loads: the Nested Analysis and Design (NAND) and the Simultaneous Analysis and Design (SAND) approaches. Analytic sensitivies are used in order to ensure the convergence of the optimization within reasonable computational resources. The two formulations are compared on a mass minimization problem with dynamic constraints, solved with the interior-point method in IPOPT, for a gust input and a turbulent input. Results shows that the NAND and SAND approaches converge towards the same optimum with similar performances. The SAND approach benefits from a simpler design sensitivity analysis and a sparse jacobian of the constraints.

This report presents a non-linear dynamic aero-servo-structural optimization framework applied to wind turbine blades under unsteady loads. The performances of the optimization with the Nested Analysis and Design (NAND) approach and the Simultaneous Analysis and Design (SAND) approach are investigated and compared.
The optimal blade design is described with a focus on the impact of the coupling
between control and structure introduced in the analysis model. The aerodynamic model for the loads is based on the Blade Element Momentum theory. The structural model for the blade is based on the nite element method and on a simplied cross section analysis of the internal blade structure. The optimization problem aims at reducing the mass of the blade within constraints on the power, the tip displacement and the pitch angle, by varying the chord and the control parameters. The optimization with the NAND approach is run using a Sequential Quadratic Programming Algorithm whereas the Interior-point algorithm is used for the SAND approach.
This study shows that adding the control strategy as a design variable allows a relaxation of the structural constraints and further mass reduction. The SAND approach was found to be less robust and less efficient than the NAND approach.