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M. Baricchio
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East/West (E/W) vertical bifacial photovoltaic (PV) modules can achieve higher profits than the conventional North/South (N/S) tilted configuration depending on the design choices and external conditions. In this study a model based on 2D view factor concept is developed to estimate the power generated by a large-scale bifacial PV farm, considering the non-uniformity of the incident irradiance and the spectral impact. A validation using measured data is performed, focusing on the non-uniformity of the rear irradiance. This model is used to compare the profitability between E/W vertical and N/S tilted PV farm configurations, considering higher prices during noon with respect to morning/evening periods. The results identify the ratio between these two price values as the key variable that influences the comparison between the PV farm configurations. Specifically, a sufficiently high price ratio ensures the higher profitability of E/W vertical modules, however, the exact value is dependent on the location and the design variables. In general, higher row-to-row distance and lower diffuse fraction enhance the profitability of the E/W vertical over the N/S tilted configuration. On the other hand, elevation of the modules, curtailment strategies and hybrid solutions have a minor influence.
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East/West (E/W) vertical bifacial photovoltaic (PV) modules can achieve higher profits than the conventional North/South (N/S) tilted configuration depending on the design choices and external conditions. In this study a model based on 2D view factor concept is developed to estimate the power generated by a large-scale bifacial PV farm, considering the non-uniformity of the incident irradiance and the spectral impact. A validation using measured data is performed, focusing on the non-uniformity of the rear irradiance. This model is used to compare the profitability between E/W vertical and N/S tilted PV farm configurations, considering higher prices during noon with respect to morning/evening periods. The results identify the ratio between these two price values as the key variable that influences the comparison between the PV farm configurations. Specifically, a sufficiently high price ratio ensures the higher profitability of E/W vertical modules, however, the exact value is dependent on the location and the design variables. In general, higher row-to-row distance and lower diffuse fraction enhance the profitability of the E/W vertical over the N/S tilted configuration. On the other hand, elevation of the modules, curtailment strategies and hybrid solutions have a minor influence.
Wake steering represents a viable solution to mitigate the wake effect within a wind farm. New research that considers the effect of the control strategy within the layout optimization is emerging, adopting a co-design approach. This study estimates the potential of this technique within the layout optimization for a wide range of realistic conditions. To capture the benefits of such methods, a genetic algorithm tailored to the layout optimization problem has been developed in this work; hence this is referred to as a layout optimization genetic algorithm (LO-GA). The crossover phase is designed to recognize and exploit the differences and the similarities between parent layouts, whereas the randomness of the mutation is limited to improve the exploration of the design space. New relations have been introduced to calculate the geometric yaw angles based on the reciprocal positions between the turbines. For a base case of 16 turbines located at the Hollandse Kust Noord site, a gain in the annual energy production (AEP) between 0.3 % and 0.4 % is obtained when the co-design approach is adopted. This increases up to 0.6 % for larger farms, saturating after 25 turbines. However, the benefit of the co-design decreases in the case of low power densities or if the wind resource is highly unidirectional. On the other hand, in the case that wake steering is not applied during the operation of the farm, a decrease in the AEP up to 0.6 % is registered for a layout optimized with the co-design method. To prevent the risk related to future decisions on the control strategy, a multi-objective co-design approach is proposed. This is based on the simultaneous optimization of the layout for a scenario in which wake steering is applied versus a case where wake steering is not adopted during the operation of the farm. We have concluded that the solutions obtained with this method ensure an AEP gain higher than 0.3 % for a 16-turbine farm while limiting the loss to below 0.1 % in the case that wake steering is not applied. However, these AEP gains are affected by the size of the wind direction bins adopted in the simulations, enhancing the necessity of taking into account the wind direction errors and the yaw actuation constraints for a realistic evaluation of the co-design approach.
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Wake steering represents a viable solution to mitigate the wake effect within a wind farm. New research that considers the effect of the control strategy within the layout optimization is emerging, adopting a co-design approach. This study estimates the potential of this technique within the layout optimization for a wide range of realistic conditions. To capture the benefits of such methods, a genetic algorithm tailored to the layout optimization problem has been developed in this work; hence this is referred to as a layout optimization genetic algorithm (LO-GA). The crossover phase is designed to recognize and exploit the differences and the similarities between parent layouts, whereas the randomness of the mutation is limited to improve the exploration of the design space. New relations have been introduced to calculate the geometric yaw angles based on the reciprocal positions between the turbines. For a base case of 16 turbines located at the Hollandse Kust Noord site, a gain in the annual energy production (AEP) between 0.3 % and 0.4 % is obtained when the co-design approach is adopted. This increases up to 0.6 % for larger farms, saturating after 25 turbines. However, the benefit of the co-design decreases in the case of low power densities or if the wind resource is highly unidirectional. On the other hand, in the case that wake steering is not applied during the operation of the farm, a decrease in the AEP up to 0.6 % is registered for a layout optimized with the co-design method. To prevent the risk related to future decisions on the control strategy, a multi-objective co-design approach is proposed. This is based on the simultaneous optimization of the layout for a scenario in which wake steering is applied versus a case where wake steering is not adopted during the operation of the farm. We have concluded that the solutions obtained with this method ensure an AEP gain higher than 0.3 % for a 16-turbine farm while limiting the loss to below 0.1 % in the case that wake steering is not applied. However, these AEP gains are affected by the size of the wind direction bins adopted in the simulations, enhancing the necessity of taking into account the wind direction errors and the yaw actuation constraints for a realistic evaluation of the co-design approach.