• Energy Research

Wake effects in a wind farm (WF) include the wind velocity deficit and added turbulence. The wind velocity deficit may bring significant loss of the wind power and the added turbulence may cause extra fatigue load on the wind turbines (WTs). Inclusion of the wake effects in the wind farm control design can increase the total captured power by derating the upwind WTs. However, this may increase the turbulence and cause more fatigue load on the downwind WTs. This paper proposes an optimized active power dispatch strategy for WFs to maximize the total captured power while maintaining the fatigue load of the shafts and the towers within a certain range from the values using traditional strategy, which adopts maximum power point tracking (MPPT) control for each WT. A WT derating control strategy is included in the WT controller and the fatigue load for the tower and shaft is evaluated offline at a series of turbulence intensity, mean wind speed and active power reference to form a lookup table, which is used for the WF control. The proposed strategy is compared with WT MPPT control strategy and WF MPPT control strategy. The simulation results show the effectiveness of the proposed strategy.

Wake effects in a wind farm (WF) include the wind velocity deficit and added turbulence. The wind velocity deficit may bring significant loss of the wind power and the added turbulence may cause extra fatigue load on the wind turbines (WTs). Inclusion of the wake effects in the wind farm control design can increase the total captured power by derating the upwind WTs. However, this may increase the turbulence and cause more fatigue load on the downwind WTs. This paper proposes an optimized active power dispatch strategy for WFs to maximize the total captured power while maintaining the fatigue load of the shafts and the towers within a certain range from the values using traditional strategy, which adopts maximum power point tracking (MPPT) control for each WT. A WT derating control strategy is included in the WT controller and the fatigue load for the tower and shaft is evaluated offline at a series of turbulence intensity, mean wind speed and active power reference to form a lookup table, which is used for the WF control. The proposed strategy is compared with WT MPPT control strategy and WF MPPT control strategy. The simulation results show the effectiveness of the proposed strategy.

Decommissioning is usually the last stage of the offshore wind farm life cycle. Due to the challenges of the decommissioning process, such as the impact on the marine environment, severe weather conditions, vessel limitations and lack of operational experience, the decommissioning strategy should be planned to avoid complications, which ultimately can cause radical changes to the levelized cost of energy (LCoE) and the wind farm owner's business case. Instead of dismantling, repowering may be a sustainable alternative solution to extend the lifetime of a wind farm. In this paper, the research is focused on optimization of offshore wind farm repowering, which is one option for the wind farm owner at end of life for the offshore wind farm. The LCoE is used as the evaluation index to identify whether it is economical to invest in such a way. In an optimized repowering strategy, different types of wind turbines are selected to replace the original wind turbines to reconstruct the wind farm, which is demonstrated to be better than the refurbishment approach which replaces the old wind turbines with the same type. The simulations performed in this research reveal that the reconstructed wind farm, which consists of multiple types of wind turbine, has a smaller LCoE (10.43%) than the refurbishment approach, which shows the superiority of the proposed method. This research contributes an optimization tool to the wind industry, which consequently drives down the cost of energy produced by offshore wind turbines.

Decommissioning is usually the last stage of the offshore wind farm life cycle. Due to the challenges of the decommissioning process, such as the impact on the marine environment, severe weather conditions, vessel limitations and lack of operational experience, the decommissioning strategy should be planned to avoid complications, which ultimately can cause radical changes to the levelized cost of energy (LCoE) and the wind farm owner's business case. Instead of dismantling, repowering may be a sustainable alternative solution to extend the lifetime of a wind farm. In this paper, the research is focused on optimization of offshore wind farm repowering, which is one option for the wind farm owner at end of life for the offshore wind farm. The LCoE is used as the evaluation index to identify whether it is economical to invest in such a way. In an optimized repowering strategy, different types of wind turbines are selected to replace the original wind turbines to reconstruct the wind farm, which is demonstrated to be better than the refurbishment approach which replaces the old wind turbines with the same type. The simulations performed in this research reveal that the reconstructed wind farm, which consists of multiple types of wind turbine, has a smaller LCoE (10.43%) than the refurbishment approach, which shows the superiority of the proposed method. This research contributes an optimization tool to the wind industry, which consequently drives down the cost of energy produced by offshore wind turbines.

The high penetration of wind energy in electrical power systems presents challenges for all operators. For the wind farm (WF) planners, one of these challenges is optimizing its layout with a set of constraints. This paper proposes a bi-hierarchy optimization scheme to determine the capacity and layout of a grid-connected WF. The environmental impacts involved by the installation of a WF have been taken into consideration in the problem. The first-layer model optimizes the WF capacity and configuration with minimized comprehensive generation cost of wind energy and two sets of constraints. The sound pressure level ( SPL ) limit of the noise emitted by the wind turbines (WTs) is handled to be one of the constraints of the first-layer model. The second-layer model determines the generation schedule of other conventional generators. A Gaussian wake model is applied to calculate the effective wind speed for each WT. For the simulations, the WF is supposed to be integrated in the IEEE 30-bus test system. The wild goats algorithm (WGA) and the quadratic programming (QP) method are used to solve the problem. The simulation results validate the effectiveness of the proposed model and prove that environmental influences of WFs should not be ignored during the planning stage.

The high penetration of wind energy in electrical power systems presents challenges for all operators. For the wind farm (WF) planners, one of these challenges is optimizing its layout with a set of constraints. This paper proposes a bi-hierarchy optimization scheme to determine the capacity and layout of a grid-connected WF. The environmental impacts involved by the installation of a WF have been taken into consideration in the problem. The first-layer model optimizes the WF capacity and configuration with minimized comprehensive generation cost of wind energy and two sets of constraints. The sound pressure level ( SPL ) limit of the noise emitted by the wind turbines (WTs) is handled to be one of the constraints of the first-layer model. The second-layer model determines the generation schedule of other conventional generators. A Gaussian wake model is applied to calculate the effective wind speed for each WT. For the simulations, the WF is supposed to be integrated in the IEEE 30-bus test system. The wild goats algorithm (WGA) and the quadratic programming (QP) method are used to solve the problem. The simulation results validate the effectiveness of the proposed model and prove that environmental influences of WFs should not be ignored during the planning stage.

With the increasing size of wind farm, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of the wind turbines’ (WT) locations will influence the capital investment and contribute to the wake losses which incur the reduction of energy production. As a consequence, the optimized placement of the wind turbines may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using Levelized Production Cost (LPC) as the objective function. The optimization procedure is performed by Particle Swarm Optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of wind turbines within wind farms and extendible for different wind conditions and capacity of wind farms.

With the increasing size of wind farm, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of the wind turbines’ (WT) locations will influence the capital investment and contribute to the wake losses which incur the reduction of energy production. As a consequence, the optimized placement of the wind turbines may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using Levelized Production Cost (LPC) as the objective function. The optimization procedure is performed by Particle Swarm Optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of wind turbines within wind farms and extendible for different wind conditions and capacity of wind farms.