Currently, Offshore Wind Farms (OWFs) are designed to achieve high turbine density so as to reduce costs. However, due to wake interferences, densely packing turbines reduces energy production. Having insight into optimized trade-offs between energy production, capital investment and operational costs would be valuable to OWFs designers. To obtain this insight, the design of OWFs should be formulated as a multi-objective optimization problem. How to best solve a Multi-Objective Wind Farm Layout Optimization Problem (MOWFLOP) is however still largely an open question. It is however known that evolutionary algorithms (EAs) are among the state-of-the-art for solving multi-objective optimization problems. This work studies the different features that an MO Evolutionary Algorithm (MOEA) should have and which Constraint-Handling Techniques (CHTs) are suitable for solving MOWFLOP. We also investigate the relation between problem dimensionality/complexity and the degrees of freedom offered by different turbine-placement grid resolutions. Finally, the influence of problem size on algorithm performance is studied. The performance of two variants of the recently introduced Multi-Objective Gene-pool Optimal Mixing Evolutionary Algorithm (MOGOMEA) is compared with a traditional and a novel version of the Nondominated Sorting Genetic Algorithm II (NSGA-II). Five CHTs were used to assess which technique provides the best results. Results on a case study with different OWF areas demonstrate that one variant of MOGOMEA outperforms the NSGA-II for all tested problem sizes and CHTs.@en

Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases. @en

Estimates are that circa 40GW of offshore wind power capacity is going to be installed throughout Europe by the end of this decade. In this scenario, a pan-European offshore grid network is needed in order to efficiently integrate large amounts of offshore wind into the different European countries' transmission networks. In this paper, the dynamic model of multi-terminal HVDC (MTDC) transmission system composed of voltage-source converters is presented. Afterwards, the dynamic models are used to compare four different methods for controlling the DC voltage inside MTDC networks, viz.: droop control, ratio control, priority control and voltage margin control. Lastly, a case study is performed in a four-node MTDC network and the different control strategies are compared.@en

The aim of this paper is to present the current status of the offshore wind industry and to identify trends in Offshore Wind Projects (OWPs). This was accomplished via a thorough analysis of the key characteristics – commissioning country, installed capacity, number of turbines, water depth, project area, distance to shore, transmission technology and investment cost – of the commissioned and under construction European OWPs. Furthermore, the current status of the several countries outside of Europe was also investigated. The analysis revealed that the European offshore wind power grew on average 36.1% yearly since 2001. Currently, there are 7748 MW installed and 3198 MW under construction distributed among 76 OWPs situated in European waters. These projects are spread among ten countries, with the highest share of offshore projects belonging to the northern European countries. The UK has 46% of the total installed European offshore wind capacity with 26 projects, Germany ranks second with 16, while Denmark is third with 13 projects. These countries constitute 88% of the European offshore capacity. The analysis also showed that, although the installed capacity of the OWPs is growing, the projects׳ area is not increasing at the same pace due to the release of turbines with higher rated capacities which allow projects to increase their power nameplate without proportionally increasing the number of turbines. The average distance to shore and the water depth are both increasing throughout the years. Although the average investment cost per project is rising with the higher distances to shore and water depths, the multi-GW plans of the northern European and Asian countries indicate that the industry will continue to grow. The European Union targets of having 40 GW of offshore wind capacity deployed by 2020 in Europe and 150 GW by 2030 may represent plausible scenarios since the required growth is below the European.@en

The aim of this paper is to present the current status of the offshore wind industry and to identify trends in Offshore Wind Projects (OWPs). This was accomplished via a thorough analysis of the key characteristics – commissioning country, installed capacity, number of turbines, water depth, project area, distance to shore, transmission technology and investment cost – of the commissioned and under construction European OWPs. Furthermore, the current status of the several countries outside of Europe was also investigated. The analysis revealed that the European offshore wind power grew on average 36.1% yearly since 2001. Currently, there are 7748 MW installed and 3198 MW under construction distributed among 76 OWPs situated in European waters. These projects are spread among ten countries, with the highest share of offshore projects belonging to the northern European countries. The UK has 46% of the total installed European offshore wind capacity with 26 projects, Germany ranks second with 16, while Denmark is third with 13 projects. These countries constitute 88% of the European offshore capacity. The analysis also showed that, although the installed capacity of the OWPs is growing, the projects׳ area is not increasing at the same pace due to the release of turbines with higher rated capacities which allow projects to increase their power nameplate without proportionally increasing the number of turbines. The average distance to shore and the water depth are both increasing throughout the years. Although the average investment cost per project is rising with the higher distances to shore and water depths, the multi-GW plans of the northern European and Asian countries indicate that the industry will continue to grow. The European Union targets of having 40 GW of offshore wind capacity deployed by 2020 in Europe and 150 GW by 2030 may represent plausible scenarios since the required growth is below the European.@en

The aim of this paper is to present the current status of the offshore wind industry and to identify trends in Offshore Wind Projects (OWPs). This was accomplished via a thorough analysis of the key characteristics – commissioning country, installed capacity, number of turbines, water depth, project area, distance to shore, transmission technology and investment cost – of the commissioned and under construction European OWPs. Furthermore, the current status of the several countries outside of Europe was also investigated. The analysis revealed that the European offshore wind power grew on average 36.1% yearly since 2001. Currently, there are 7748 MW installed and 3198 MW under construction distributed among 76 OWPs situated in European waters. These projects are spread among ten countries, with the highest share of offshore projects belonging to the northern European countries. The UK has 46% of the total installed European offshore wind capacity with 26 projects, Germany ranks second with 16, while Denmark is third with 13 projects. These countries constitute 88% of the European offshore capacity. The analysis also showed that, although the installed capacity of the OWPs is growing, the projects׳ area is not increasing at the same pace due to the release of turbines with higher rated capacities which allow projects to increase their power nameplate without proportionally increasing the number of turbines. The average distance to shore and the water depth are both increasing throughout the years. Although the average investment cost per project is rising with the higher distances to shore and water depths, the multi-GW plans of the northern European and Asian countries indicate that the industry will continue to grow. The European Union targets of having 40 GW of offshore wind capacity deployed by 2020 in Europe and 150 GW by 2030 may represent plausible scenarios since the required growth is below the European.@en

In the future, floating wind turbines could be used to harvest energy in deep offshore areas where higher wind mean speeds are observed. Currently, several floating turbine concepts are being designed and tested in small scale projects; in particular, one concept allows the turbine to move after installation. This article presents a novel layout optimization framework for wind farms composed of moveable floating turbines. The proposed framework uses an evolutionary optimization strategy in a nested configuration which simultaneously optimizes the anchoring locations and the wind turbine position within the mooring lines for each individual wind direction. The results show that maximum energy production is obtained when moveable wind turbines are deployed in an optimized layout. In conclusion, the framework represents a new design optimization tool for future offshore wind farms composed of moveable floating turbines.

@en

In the future, floating wind turbines could be used to harvest energy in deep offshore areas where higher wind mean speeds are observed. Currently, several floating turbine concepts are being designed and tested in small scale projects; in particular, one concept allows the turbine to move after installation. This article presents a novel layout optimization framework for wind farms composed of moveable floating turbines. The proposed framework uses an evolutionary optimization strategy in a nested configuration which simultaneously optimizes the anchoring locations and the wind turbine position within the mooring lines for each individual wind direction. The results show that maximum energy production is obtained when moveable wind turbines are deployed in an optimized layout. In conclusion, the framework represents a new design optimization tool for future offshore wind farms composed of moveable floating turbines.

@en

In the future, floating wind turbines could be used to harvest energy in deep offshore areas where higher wind mean speeds are observed. Currently, several floating turbine concepts are being designed and tested in small scale projects; in particular, one concept allows the turbine to move after installation. This article presents a novel layout optimization framework for wind farms composed of moveable floating turbines. The proposed framework uses an evolutionary optimization strategy in a nested configuration which simultaneously optimizes the anchoring locations and the wind turbine position within the mooring lines for each individual wind direction. The results show that maximum energy production is obtained when moveable wind turbines are deployed in an optimized layout. In conclusion, the framework represents a new design optimization tool for future offshore wind farms composed of moveable floating turbines.

@en

Lesnicar and Marquardt introduced a modular multilevel converter (MMC) topology back in 2003. Although this topology has received a great deal of attention in recent years by both the research community and industry, hitherto no steady-state model has been developed which accurately captured all the relevant power losses while being computationally light. Hence, the aim of this paper is to introduce a fast MMC loss model which captures the key sources of power losses in steady-state operation. The proposed model was compared to a loss model developed by Marquardt. Both models presented similar results under the same assumptions. However, the proposed model captures the switching losses more realistically and considers the temperature of operation of the electronics, as well as the losses of the inductors and cooling system, in the overall efficiency of the MMC. To validate these new additions, the proposed steady-state model was compared to a dynamic model. Once again, the proposed model was able to capture the different sources of power losses. Nonetheless, results demonstrated that the balancing strategy greatly influences the efficiency of the MMC. Therefore, information regarding the envisioned control strategy is necessary to accurately calculate the efficiency curve of the MMC.@en

Lesnicar and Marquardt introduced a modular multilevel converter (MMC) topology back in 2003. Although this topology has received a great deal of attention in recent years by both the research community and industry, hitherto no steady-state model has been developed which accurately captured all the relevant power losses while being computationally light. Hence, the aim of this paper is to introduce a fast MMC loss model which captures the key sources of power losses in steady-state operation. The proposed model was compared to a loss model developed by Marquardt. Both models presented similar results under the same assumptions. However, the proposed model captures the switching losses more realistically and considers the temperature of operation of the electronics, as well as the losses of the inductors and cooling system, in the overall efficiency of the MMC. To validate these new additions, the proposed steady-state model was compared to a dynamic model. Once again, the proposed model was able to capture the different sources of power losses. Nonetheless, results demonstrated that the balancing strategy greatly influences the efficiency of the MMC. Therefore, information regarding the envisioned control strategy is necessary to accurately calculate the efficiency curve of the MMC.@en

Lesnicar and Marquardt introduced a modular multilevel converter (MMC) topology back in 2003. Although this topology has received a great deal of attention in recent years by both the research community and industry, hitherto no steady-state model has been developed which accurately captured all the relevant power losses while being computationally light. Hence, the aim of this paper is to introduce a fast MMC loss model which captures the key sources of power losses in steady-state operation. The proposed model was compared to a loss model developed by Marquardt. Both models presented similar results under the same assumptions. However, the proposed model captures the switching losses more realistically and considers the temperature of operation of the electronics, as well as the losses of the inductors and cooling system, in the overall efficiency of the MMC. To validate these new additions, the proposed steady-state model was compared to a dynamic model. Once again, the proposed model was able to capture the different sources of power losses. Nonetheless, results demonstrated that the balancing strategy greatly influences the efficiency of the MMC. Therefore, information regarding the envisioned control strategy is necessary to accurately calculate the efficiency curve of the MMC.@en

Lesnicar and Marquardt introduced a modular multilevel converter (MMC) topology back in 2003. Although this topology has received a great deal of attention in recent years by both the research community and industry, hitherto no steady-state model has been developed which accurately captured all the relevant power losses while being computationally light. Hence, the aim of this paper is to introduce a fast MMC loss model which captures the key sources of power losses in steady-state operation. The proposed model was compared to a loss model developed by Marquardt. Both models presented similar results under the same assumptions. However, the proposed model captures the switching losses more realistically and considers the temperature of operation of the electronics, as well as the losses of the inductors and cooling system, in the overall efficiency of the MMC. To validate these new additions, the proposed steady-state model was compared to a dynamic model. Once again, the proposed model was able to capture the different sources of power losses. Nonetheless, results demonstrated that the balancing strategy greatly influences the efficiency of the MMC. Therefore, information regarding the envisioned control strategy is necessary to accurately calculate the efficiency curve of the MMC.@en

Lesnicar and Marquardt introduced a modular multilevel converter (MMC) topology back in 2003. Although this topology has received a great deal of attention in recent years by both the research community and industry, hitherto no steady-state model has been developed which accurately captured all the relevant power losses while being computationally light. Hence, the aim of this paper is to introduce a fast MMC loss model which captures the key sources of power losses in steady-state operation. The proposed model was compared to a loss model developed by Marquardt. Both models presented similar results under the same assumptions. However, the proposed model captures the switching losses more realistically and considers the temperature of operation of the electronics, as well as the losses of the inductors and cooling system, in the overall efficiency of the MMC. To validate these new additions, the proposed steady-state model was compared to a dynamic model. Once again, the proposed model was able to capture the different sources of power losses. Nonetheless, results demonstrated that the balancing strategy greatly influences the efficiency of the MMC. Therefore, information regarding the envisioned control strategy is necessary to accurately calculate the efficiency curve of the MMC.@en