No abstract

No abstract

No abstract

One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

Optimizing the turbine layout in a wind farm is crucial to minimize wake interactions between turbines, which can lead to a significant reduction in power generation. This work is motivated by the need to develop wake interaction models that can accurately capture the wake losses in an array of wind turbines, while remaining computationally tractable for layout optimization studies. Among existing wake interaction models, the sum of squares (SS) model has been reported to be the most accurate. However, the SS model is unsuitable for wind farm layout optimization using mathematical programming methods, as it leads to non-linear objective functions. Hence, previous work has relied on approximated power calculations for optimization studies. In this work, we propose a mechanistic linear model for wake interactions based on energy balance, with coefficients determined based on publicly available data from the Horns Rev wind farm. A series of numerical tests was conducted using test cases from the wind farm layout optimization literature. Results show that the proposed model is solvable using standard mathematical programming methods, and resulted in turbine layouts with higher efficiency than those found by previous work.

No abstract

The performance of photovoltaic (PV) arrays are affected by the operating temperature, which is influenced by thermal losses to the ambient environment. The factors affecting thermal losses include wind speed, wind direction, and ambient temperature. The purpose of this work is to analyze how the aforementioned factors affect array efficiency, temperature, and heat transfer coefficient/thermal loss factor. Data on ambient and array temperatures, wind speed and direction, solar irradiance, and electrical output were collected from a PV array mounted on a CanmetENERGY facility in Varennes, Canada, and analyzed. The results were compared with computational fluid dynamics (CFD) simulations and existing results from PVsyst. The findings can be summarized into three points. First, ambient temperature and wind speed are important factors in determining PV performance, while wind direction seems to play a minor role. Second, CFD simulations found that temperature variation on the PV array surface is greater at lower wind speeds, and decreases at higher wind speeds. Lastly, an empirical correlation of heat transfer coefficient/thermal loss factor has been developed.

Wind farm energy production optimization has received significant attention in recent years. Much of this effort had been focused on optimizing positions of wind turbines within a wind farm domain during the design and planning stage. Optimization of wind turbine positions can reduce wake interactions of upstream turbines. In addition to optimizing turbine positions to reduce wake interactions, prior studies have shown that optimizing yaw and pitch angles can improve energy production as upstream wakes yaw away from downstream turbines. However, yaw angle optimization at the wind farm level has been difficult due to lack of low-fidelity wake model for simulating yawed wakes. Recently, an analytical wake model capable of simulating yawed turbine wakes had been developed, which enable wind farm-scale yaw optimization. In this work, a binary quadratic programming model problem formulation has been developed to optimize yaw angles of wind farms. Yaw optimization of two position-optimized layouts available in the literature were performed to study the potential of yaw optimization. In particular, we set out to understand how layout and wind farm density affect yaw optimization potential. An optimized layout of 39 turbines with 40m rotor diameter in a 2km by 2km domain was used in this study. For this wind farm, yawing optimization only improved power production by ∼1.5% under favorable wind directions. However, as wind farm power density increases by increasing rotor diameter to 60m and 80m, power production improved by ∼5% and ∼10% respectively, under favorable wind directions. Finally, another yaw optimization was performed on an optimized layout with 48 turbines of 82m rotor diameter in a 4km by 4km domain using the proposed model formulation. Under favorable wind directions, yaw angle optimization improved performance by ∼4%. The results show that yaw optimization can improve power production in the same order of magnitude as layout optimization, and that it should be considered in addition and/or in tandem to layout optimization.