The objective of this study is to design and optimize the layout of the offshore wind farms to maximize the power at a specific location. The energy production of the downstream wind turbines decreases because of the reduced wind speed and increased level of turbulence caused by the wakes formed by the upstream wind turbines. Therefore, the overall power efficiency is lowered due to the wake interference among wind turbines. This paper focuses on using the application of a Gaussian-based wake model and different optimization algorithms like the differential evolution particle swarm optimization (DPSO). The Gaussian wake model uses an exponential function to evaluate the velocity deficit, in contrast to the Jensen wake model that assumes a uniform velocity profile inside the wake. The layout optimization framework has been created for the energy production in order to provide reference for specific conditions and constraints at the Gulf of Maine and other typical projects in the future.


With the growing requirement of energy and environmental protection, the sustainable energy like wind energy has been significantly concerned in recent years. In this case, the investigations about wind farm optimization have been concerned by lots of researchers. In wind farms, one of the most critical power reduction is caused by the wake and turbulence from the blades of previous turbines. Generally, this phenomenon would drop the power production and mechanical performance of turbines. The layout optimization of wind farms according to the wake has been an essential concern for both onshore and offshore wind energy applications.

Figure 1 indicates the annual average offshore wind speeds (m/s) in the United States. From this diagram, the Gulf of Maine have one of the greatest wind energy potential on the east coast. The Gulf of Maine locates very close to the cities such as Portland and Boston with magnificent electricity requirement. So, it is considerably valuable to investigate how to develop wind power in the Gulf of Maine.

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