Multi-objective optimization of clustered wind farms based on potential game approach

The development of offshore wind power has become a pressing energy issue, driven by the need to find new electrical power sources and to reduce the use of fossil fuels. Offshore wind farms can harness tremendous wind resources without annoying citizens and with a comparatively low environmental impact. They are thus becoming a central pillar of a carbon free energy system. However offshore turbines and wind farms are costly to install and maintain, making reliability and cost-effectiveness key issues. This work covers reliability of offshore wind farms as a whole, starting from weather and wind conditions, dealing with wind turbine technology, farm layout, monitoring, safety and maintenance. The thoroughly revised second edition additionally covers turbines of up to 10 MW, turbine design changes, turbine converters, HVDC converter stations and DC links, offshore sub-sea collector and export cables, and the structures supporting large offshore wind farms. Offshore Wind Power is essential reading for scientists, engineers, technicians and advanced students interested or engaged in the design of wind turbines, drivetrain technology and power mechatronics, in academia and industry.

<strong class="journal-contentHeaderColor">Abstract.</strong> Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Actively promoting the development of offshore wind power is an inevitable choice if the People’s Republic of China plans to fulfill its international commitments, respond to climate change, ensure energy security, and improve energy infrastructure. Inevitably, offshore wind power development will conflict with other marine activities, including mariculture and shipping. Therefore, learning how to develop offshore wind power without affecting the environment or conflicting with other marine activities is crucial to the conservation of spatial marine resources. The rapid development of offshore wind power in Liaoning Province has allowed researchers to develop an index system that can be used to evaluate the suitability of offshore wind power development sites by considering costs, environmental protection, and sea management. Spatial analysis and a multi-attribute evaluation method integrating a fuzzy membership function were used to evaluate offshore wind farm placement in Liaoning. The results classified 5%, 18%, 21%, and 56% offshore areas of Liaoning as very suitable, relatively suitable, somewhat unsuitable, and unsuitable for wind power development, respectively. The results of this paper can provide a reference for decision makers who plan for offshore wind farm locations under the constraints of high-intensity development.

Multiple wind farms (WFs) model consists of wind speed model and single WF model. In the multiple WFs integrated power system, the wind speeds at different WFs are correlated to some degree if the distance between WFs is not very large. Hence, a novel MC-Nataf-CD method combining partitions Monte Carlo (MC) sampling, Nataf transformation and Cholesky Decomposition (CD) is proposed to simulate correlated wind speeds with desired cross-correlation coefficients. The wind speeds generated by the proposed wind speed model is closer to both the historical data and the Weibull fitting curve. For the single WF model, wake effects, the forced outage rates (FORs) of wind turbine generators (WTGs) and the reliability of the connected grids inside a WF need to be taken into consideration. Four different single WF models are proposed to reveal the impact of these relevant factors. A statistical method based on state-sampling MC simulation is implemented for the reliability estimation of Roy Billinton Test System (RBTS) with multiple WFs, using both historical and simulated wind speed data. The simulation results and the calculated reliability indices demonstrate that the proposed techniques are effective and accurate in the modeling of correlated multiple WFs.

With the development of offshore wind power towards the deep and distant sea, the DC collection and DC transmission (all-DC) wind farm demonstrates distinct advantages, including the absence of the Ferranti effect and elimination of multi-level voltage conversion requirements. For the protection of the all-DC collection network in offshore wind farms, this paper first analyzes the zero-mode current relationship at line outlets of the collection network and wind turbine outlets during single-pole-to-ground (SPG) faults. Analysis reveals that (1) zero-mode current polarities are opposite between fault and non-fault lines, and (2) zero-mode current polarities are opposite between the upstream and downstream of the fault point. Based on these characteristics, a polarity comparison-based protection and fault localization scheme is proposed. The method implements waveform peak–valley detection through mathematical morphology algorithms, with detection results quantified by similarity algorithms to achieve fault section localization. An all-DC offshore wind farm simulation is built in PSCAD/EMTDC. A variety of simulations under different operating conditions are delivered, demonstrating the validity and effectiveness of the proposed protection and fault localization scheme. The method accurately distinguishes the SPG fault on lines and buses while remaining unaffected by fault resistance.

The construction of offshore wind farms in Northern Europe commenced in the early nineties. In the beginning, only minor test offshore wind farms were built, but from 2002 and onwards, large-scale offshore wind farms have been erected. Construction and operation experiences from these wind farms are now being collected, and this paper discusses construction and operation experiences obtained from selected offshore wind farms. Differences and similarities between the wind industry and the oil and gas industry are discussed with a view to raising the awareness of this new and growing industry among market players in the oil and gas industry. Current status and plans in Europe [1] In the European Union (EU) the development of wind power is very impressive. Over the past five years, 30% of all newly installed electricity generating capacity has been wind power. The installed capacity is now 40.5 GW, and the generated power is enough to supply 21 million households. However, wind power still only covers 2.8% of the total European electricity demand. Only 680 MW of the above-mentioned 40.5 GW capacity are installed off shore. Recent projections of the future development suggest that by 2030 the installed capacity may reach 300 GW of which 150 GW are expected to be installed off shore. Whether the targets for offshore wind power will be met depends crucially on technology development, grid infrastructure and the existence of political incentives to encourage investments. A renewables target of at least 20% of EU energy by 2020 will be a significant driving force behind this development. It will only be possible to meet this target with a strong contribution from wind power. Another driving force behind the wind power development is the increase in fossil fuel prices. Current status and plans in Denmark [2] Denmark has been and is still a frontrunner in the development of on- and offshore wind energy. Only in Denmark, Spain, Germany and Ireland does wind power cover more than 5% of the electricity demand. In Denmark, the actual figure is close to 20%. The installed capacity is 3100 MW of which 420 MW are located off shore (two large-scale wind farms contribute 325 MW). Recent wind farm developments have been located off shore, and future plans aim to expand offshore wind power event further. Currently, investment decisions to erect two offshore wind farms of 200 MW each are in the pipeline. Challenges going off shore The main reasons for going off shore are access to large energy resources and potentially less complicated planning processes. Although the wind resources are vast, the investment in foundations and electrical infrastructure (cables and substations) are dramatically higher than for onshore wind farms, and operation costs increase as well. At first glance, the planning process, including the environmental statements, seems to be easier off shore, but experience shows that there are many stakeholders in the offshore areas, who make the offshore process as difficult as the onshore process.

Numerical algorithms for generating an almost even approximation of the Pareto front in nonlinear multi-objective optimization problems

The non-dominated sorting genetic algorithm II (NSGA-II) is the most intensively used multi-objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to several simple MOEAs analyzed also via mathematical means, no such study exists for the NSGA-II so far. In this work, we show that mathematical runtime analyses are feasible also for the NSGA-II. As particular results, we prove that with a population size larger than the Pareto front size by a constant factor, the NSGA-II with two classic mutation operators and three different ways to select the parents satisfies the same asymptotic runtime guarantees as the SEMO and GSEMO algorithms on the basic OneMinMax and LOTZ benchmark functions. However, if the population size is only equal to the size of the Pareto front, then the NSGA-II cannot efficiently compute the full Pareto front (for an exponential number of iterations, the population will always miss a constant fraction of the Pareto front). Our experiments confirm the above findings.

An uncertainty analysis for offshore wind power investment decisions in the context of the national subsidy retraction in China: A real options approach

China is rich in offshore wind power resources, and these resources can be locally consumed by the economically developed provinces located in the eastern coastal region. The development of offshore wind power can accelerate the energy transition in China and help achieve carbon peak in 2030 and carbon neutrality in 2060. In this article, we analyze the current situation, trends, and challenges of energy development in China, and propose that the development of offshore wind power is an important strategic support for the energy structure transformation in China. The key technologies of offshore wind power development in China are summarized including wind turbine, offshore power transmission, offshore engineering, and operation and maintenance technologies. In view of the bottleneck problems restricting the development of China’s offshore wind power industry, we propose some countermeasures and suggestions to promote the healthy and orderly development of China’s offshore wind power industry from five aspects: exploration and evaluation of offshore wind power resources, understanding of energy transition, overall planning and integrated planning, scientific and technological innovation, and policy support mechanism.

he increase in demand for electricity in the United States motivates a unique opportunity for researching alternative energy sources – the need for electricity is mounting, while traditional sources for electrical generation, such as coal, are depleting and becoming more costly to access. Exploring alternative energy sources, such as wind power, are therefore ideal, as wind is readily available and will not deplete with use. However, the optimization of wind farms is imperative to ensure that wind farm power development is as high as possible, and that prediction of wind farm performance prior to installation is accurate. To meet this goal, the modeling that is being employed within wind farm optimization schemes must be reflective of real-world wind farm conditions, using site-specific parameters to ensure that researchers can accurately predict the potential power development and costs for resulting wind farm designs.

This paper presents an application of non-dominated sorting genetic algorithm-II (NSGA-II) technique for solving shortest path routing problems in computer networks. The problem is formulated as a non-linear constrained multi-objective optimisation problem. NSGA-II is applied to handle shortest path routing problem as a true multi-objective optimisation problem (MOOP) with competing and non-commensurable objectives. A priority-based encoding scheme is employed for population initialisation. Priorities are assigned to all the edges and NSGA-II is implemented to find the optimal solution. It is noted that this approach can find a diverse set of solutions and is converging near the true Pareto-optimal set. Results for a sample test network have been presented to demonstrate the capabilities of the NSGA-II algorithm to generate well-distributed Pareto-optimal solutions of shortest path routing problem in one single run. The results obtained by NSGA-II are compared with single objective weighting factor method for which genetic algorithm (GA) is applied.

Offshore wind energy development in China: Current status and future perspective

Summary1. There has been a rapid increase in the development of renewable energy because of the need to combat climate change. One of the most widely used technologies has been onshore wind farms. These have the potential to affect birds through disturbance or collision, but the extent to which such developments cause general population declines, and therefore are of wider conservation concern, remains largely untested.2. Monitoring data from wind farms located on unenclosed upland habitats in the UK were collated to test whether breeding densities of upland birds were reduced as a result of wind farm construction or during wind farm operation.3. Data were available for ten species although none were raptors. Red grouse Lagopus lagopus scoticus, snipe Gallinago gallinago and curlew Numenius arquata densities all declined on wind farms during construction. Red grouse densities recovered after construction, but snipe and curlew densities did not. Post‐construction curlew densities on wind farms were also significantly lower than reference sites. Conversely, densities of skylark Alauda arvensis and stonechat Saxicola torquata increased on wind farms during construction.4. There was little evidence for consistent post‐construction population declines in any species, suggesting for the first time that wind farm construction can have greater impacts upon birds than wind farm operation.5. The impacts of wind farms were largely unaffected by technical specifications (turbine height, number or total generating power) and therefore are widely applicable.6. Synthesis and applications. This study confirms that regulatory authorities and developers should particularly consider the likely impacts of wind farms on large waders. Greater weight should be given to the effects of construction on wildlife in impact assessments than at present. Mitigation measures during construction, including restricting construction activity to non‐breeding periods, should be considered and tested as a means to reduce these negative effects.

Correlation modeling of multiple wind farms based on piecewise cloud representation and regular vine copulas