Maximization Of Power Production Research Articles

Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity

A new multiple wake model is developed for wind farm power prediction and wind farm control. First, numerical simulations are conducted for two wind turbines under different layout sets, and the characteristics of mean velocity and turbulence intensity in multiple wakes are systematically investigated. A new multiple wake model considering the local effective turbulence on the rotor and the wake interaction effects is proposed. The proposed model can favorably predict the mean velocity and turbulence intensity distributions in multiple wake regions, as well as the power production in wind farm comparing with numerical simulations and field measurements. Finally, the new proposed multiple wake model is applied to wind farm modelling and optimization framework, which enables the maximization of wind farm power production by wake steering control. The wind sector width of 2° with the wind speed bin of 0.5 m/s is proposed for the lookup-table-based wake steering optimization. The proposed values reduce the prediction error of annual energy production gain from 34.5% to 3.2% comparing with the conventional values of 5° and 1 m/s. In addition, the yaw offset limit of ±15° is recommended to satisfy both the maximization of power production and the safety requirement of International Electrotechnical Commission (IEC) standard.

A new mathematical programming approach to optimize wind farm layouts

The optimal placement of turbines in a wind farm is critical to the maximization of power production. In this paper, we develop a new mathematical programming approach for wind farm layout optimization. We use Jensen's wake decay model to represent multi-turbine wake effects. We develop mixed integer linear and quadratic optimization formulations and apply them to several example layout cases in the literature. Compared to previous approaches, our models produce layouts that tend to be more symmetric and that generate slightly more power. Our formulations solve quickly, allowing a decision maker to efficiently explore the impact of different turbine densities in a wind farm.

Aerodynamic Shape Optimization of Wind Turbine Blades Using a Parallel Genetic Algorithm

An aerodynamic shape optimization methodology based on Genetic Algorithm and Blade Element Momentum theory is developed for rotor blades of horizontal axis wind turbines Optimization studies are performed for the maximization of power production at a specific wind speed, rotor speed and rotor diameter. The potential flow solver with a boundary layer model, XFOIL, provides sectional aerodynamic loads. The sectional chord length, the sectional twist and the blade profiles at root, mid and tip regions of the blade are taken as design variables. The blade sections may be defined by the NACA four digit airfoil series or by arbitrary airfoil profiles defined by a Bezier curve. The sectional flow computations required by Genetic algorithm, which is inherently parallel, are performed in a parallel computing environment with 512 cores. Message Passage Interface (openMPI), is employed in parallel computations. Validation studies are first performed.

Maximizing power production in a stack of microbial fuel cells using multiunit optimization method

This study demonstrates real-time maximization of power production in a stack of two continuous flow microbial fuel cells (MFCs). To maximize power output, external resistances of two air-cathode membraneless MFCs were controlled by a multiunit optimization algorithm. Multiunit optimization is a recently proposed method that uses multiple similar units to optimize process performance. The experiment demonstrated fast convergence toward optimal external resistance and algorithm stability during external perturbations (e.g., temperature variations). Rate of the algorithm convergence was much faster than in traditional maximum power point tracking algorithms (MPPT), which are based on temporal perturbations. A power output of 81-84 mW/L(A) (A = anode volume) was achieved in each MFC.

RESERVOIR OPERATION ANI EVALUATION OF DOWNSTREAM FLOW AUGMENTATION1

ABSTRACT: Operation of a storage‐based reservoir modifies the downstream flow usually to a value higher than that of natural flow in dry season. This could be important for irrigation, water supply, or power production as it is like an additional downstream benefit without any additional investment. This study addresses the operation of two proposed reservoirs and the downstream flow augmentation at an irrigation project located at the outlet of the Gandaki River basin in Nepal. The optimal operating policies of the reservoirs were determined using a Stochastic Dynamic Programming (SDP) model considering the maximization of power production. The modified flows downstream of the reservoirs were simulated by a simulation model using the optimal operating policy (for power maximization) and a synthetic long‐term inflow series. Comparing the existing flow (flow in river without reservoir operation) and the modified flow (flow after reservoir operation) at the irrigation project, the additional amount of flow was calculated. The reliability analysis indicated that the supply of irrigation could be increased by 25 to 100 percent of the existing supply over the dry season (January to April) with a reliability of more than 80 percent.