Wind Energy Planning Research Articles

Effects of wind turbine dimensions on the collision risk of raptors: A simulation approach based on flight height distributions

Wind energy development is a key component of climate change mitigation. However, birds collide with wind turbines, and this additional mortality may negatively impact populations. Collision risk could be reduced by informed selection of turbine dimensions, but the effects of turbine dimensions are still unknown for many species.As analyses of mortality data have several limitations, we applied a simulation approach based on flight height distributions of six European raptor species. To obtain accurate flight height data, we used high-frequency GPS tracking (GPS tags deployed on 275 individuals). The effects of ground clearance and rotor diameter of wind turbines on collision risk were studied using the Band collision risk model.Five species had a unimodal flight height distribution, with a mode below 25 m above ground level, while Short-toed Eagle showed a more uniform distribution with a weak mode between 120 and 260 m. The proportion of positions within 32–200 m ranged from 11 % in Marsh Harrier to 54 % in Red Kite.With increasing ground clearance (from 20 to 100 m), collision risk decreased in the species with low mode (−56 to −66 %), but increased in Short-toed Eagle (+38 %). With increasing rotor diameter (from 50 to 160 m) at fixed ground clearance, the collision risk per turbine increased in all species (+151 to +558 %), while the collision risk per MW decreased in the species with low mode (−50 % to −57 %).These results underpin that wind turbine dimensions can have substantial effects on the collision risk of raptors. As the effects varied between species, wind energy planning should consider the composition of the local bird community to optimise wind turbine dimensions. For species with a low mode of flight height, the collision risk for a given total power capacity could be reduced by increasing ground clearance, and using fewer turbines with larger diameter.

More than deliberation is needed: Potential for agonistic moments in community wind energy planning

Although community project planning is widely understood as crucial to equitable wind energy infrastructure planning, involved members of the public nevertheless perceive such participatory interventions as merely pseudo-participatory. Drawing on agonistic planning literature, we argue that this disposition towards tokenism can only be tackled with a (re)politization of community project planning practices. This includes an explicit (re)integration and cultivation of dissent and the potential overthrow of traditionally consensus-oriented formats that follow the deliberative paradigm. For radically political community energy project planning, however, public discourses must be fluid and participants must be open towards dissent, which largely contradicts the typical postures of a deliberative citizen who is supposed to argue in a rational and objective way, using the best arguments to convince others. To examine the feasibility of agonistic approaches for community wind energy planning, we conducted a quantitative discourse analysis on the data set of a large regional survey of an on-going wind energy planning project in Switzerland. We focused on estimating the degree of hegemony of public wind energy discourses and the willingness of residents to engage in participatory settings that can facilitate radically political community project planning (e.g., substantive participation settings). Our results show that for planning individual wind energy projects, the potential for agonistic planning approaches is low, not only because the discourses are already too hegemonic, but also because there is no real willingness to engage in radically political community wind energy project planning. In the context of early, comprehensive, and integrated community planning, however, agonistic approaches could provide the ground for open and innovative participatory planning of renewable energies.

Innovative hybrid NARX-RNN model for predicting wind speed to harness wind power in Pakistan

The surge in wind energy utilization, particularly within regions characterized by consistent wind patterns, has attracted substantial attention. Pakistan has several wind corridors with potential energy extraction, primarily located in Balochistan and Sindh. This study aims to predict wind speeds and assess wind energy feasibility across Pakistan using a novel hybrid NARX-RNN model. We analyzed wind data from five distinct zones over seven years to identify the most promising site for wind energy. Our hybrid model demonstrated superior performance, achieving a 20 % reduction in RMSE and a 25 % reduction in MAPE compared to recent leading-edge methods. The model maintained robust performance across various wind speed ranges, with notable improvements in high wind speed conditions. We identified Sujawal as the most suitable location due to its consistently high wind speeds, minimal directional variation, and strong economic profile. This study provides valuable insights for sustainable wind energy production in Pakistan, offering a more comprehensive and accurate prediction model crucial for effective wind energy planning and operation.

Increases of Offshore Wind Potential in a Warming World

AbstractOffshore wind farms, a rapidly expanding sector within wind energy, are playing a significant role in achieving global carbon neutrality, and this trend is to continue. Here, we utilize ERA5 reanalysis to correct offshore wind speed trends predicted by CMIP6 models. This approach led to enhanced projections for changes in offshore Wind Power Density (WPD) under four Shared Socioeconomic Pathways (SSPs) scenarios. Throughout the 21st century, global offshore WPD is projected to follow an upward trend across all SSP scenarios. Notably, Europe stands out with the most substantial increase in offshore WPD among regions with higher current installations, projected to reach up to 26% under 4°C global warming. Our study uncovers a notable increase of global offshore WPD in a warmer climate, which offers valuable insights for the strategic planning of future global wind energy.

Wind Speed Prediction in Jerusalem Using Machine Learning Algorithms: A Case Study of Using ANFIS and KNNR

Wind energy is acknowledged for its status as a renewable energy source that offers several advantages, including its low cost of electricity generation, abundant availability, high efficiency, and minimal environmental impact. The prediction of wind speed using machine learning algorithms is crucial for various applications, such as wind energy planning and urban development. This paper presents a case study on wind speed prediction in Palestine Jerusalem city using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and K-Nearest Neighbors Regression (KNNR) algorithms. The study evaluates their performance using multiple metrics, including root mean square (RMSE), bias, and coefficient of determination R<sup>2</sup>. ANFIS demonstrates good accuracy with lower RMSE (0.196) and minimal bias (0.0003). However, there is room for improvement in capturing overall variability (R<sup>2</sup> = 0.15). In contrast, KNNR exhibits a higher R<sup>2</sup> (0.4093), indicating a better fit, but with a higher RMSE (1.4209). These results demonstrated the potential of machine learning algorithms in wind speed prediction, which can lead to optimize the wind energy generation at specific site, and reducing the cost of energy production. This study provides insights into the applicability of ANFIS and KNNR in wind speed prediction for Jerusalem and suggests future research directions. The outcomes have practical implications for wind energy planning, urban development, and environmental assessments in similar regions.

Spatial planning offshore wind energy farms in California for mediating fisheries and wildlife conservation impacts

Achieving a blue economy will require reconciling the value of emerging ocean uses with their impacts on the seascape and sectors with historical access to marine resources and areas. To meet this challenge, we developed an analytical framework for conducting marine spatial planning through tradeoff analysis, and applied it to prospective offshore wind energy development in the ∼974 km2 Morro Bay, California, USA Wind Energy Area (WEA). We generated spatial data layers estimating MW power production and impacts on fisheries value and marine wildlife conservation (seabird and cetacean populations) from wind farm development. We then quantified each sector's response to plans of development across the WEA and inside three leases recently acquired by the energy industry for prospective development. Finally, we integrated the sector response data into an analytical framework for mitigating sector tradeoffs with novel spatial planning solutions (maps of wind farm size, location, and configuration) that optimally maximize value to the emergent energy sector (MW power) while minimizing impacts to historical (fisheries and wildlife) sectors. We found that western sites in the WEA had the highest potential power production concurrent with the lowest impact on the historical sectors, revealing the eastern lease to be less efficient at optimally balancing the sector's objectives relative to the development of the central or western leases or the optimal spatial plans identified in the tradeoff analysis. Within a lease, tradeoff analysis found spatial planning able to generate out-sized savings in fisheries value with only modest losses in MW power – for example, by avoiding development in just 5% of the eastern lease to preserve nearly half its fisheries value and still generate 95% its total power potential. Small-scale development opportunities (e.g., a pilot project) with significant power potential and no fisheries impact were also identified, in this case by placing turbines in an area in the western lease with no fisheries value and high power production potential. These plans would also have a relatively low impact on the wildlife conservation sectors, due to decreases in vulnerability levels of both seabird and cetacean populations to turbines going from east to west across the WEA. Our results can inform site evaluation and permitting processes for wind energy development in the Morro Bay WEA. We also expect the tradeoff analysis framework we developed to provide a simple and actionable analytical tool for supporting marine spatial planning of offshore wind energy and other emerging blue economy activities from a balanced perspective that values emerging uses of marine resources alongside existing socio-economic and conservation interests.

An Enhanced Long-Term Wind Speed Prediction Using Dynamic Unified Ensemble Learning and Data Assimilation Techniques: A Case Study in Tamil Nadu, India

Reliable wind speed prediction is essential for effective grid management since wind energy is a key component of renewable energy generation. However, controlling wind speed just right is no easy feat since the wind flow changes constantly. This paper presents an innovative method for predicting wind speeds in the future. It uses state-of-the-art data assimilation methods and a dynamic unified ensemble learning model. For efficient wind energy planning and monitoring, WSP accuracy is crucial. Data from a single site limited the accuracy of WSPs in previous research. An improved accuracy of long-term wind speed predictions is achieved by the proposed model via the integration of data assimilation and ensemble learning. To increase forecast accuracy, the model uses sophisticated data assimilation methods like the Kalman filter to combine data from many sources. Specifically, the model employs the Stacked CNN + BiLSTM with Data Assimilator (SCBLSTM+DA) technique, which integrates Wind Speed (WS) data from adjacent areas with the CNN + BiLSTM-based Ensemble Learning Model (ELM) and the Four-Dimensional Variational and Ensemble Kalman Filter (4DVar/EnKF) Data Assimilation method. Using real-world wind speed data from nine meteorological stations in Tamil Nadu, India, we find that current prediction models, including both classical statistical and cutting-edge machine learning models, perform better. Further, unlike standalone models, the suggested model shows less susceptibility to changes in prediction time scales. Promising a solution to improve long-term wind speed predictions accuracy, this study has significant consequences for wind energy management and production.

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

AbstractWind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations.

Distribution of common pipistrelle (Pipistrellus pipistrellus) activity is altered by airflow disruption generated by wind turbines.

The mechanisms underlying bat and bird activity peaks (attraction) or losses (avoidance) near wind turbines remain unknown. Yet, understanding them would be a major lever to limit the resulting habitat loss and fatalities. Given that bat activity is strongly related to airflows, we hypothesized that airflow disturbances generated leeward (downwind) of operating wind turbines-via the so-called wake effect-make this area less favorable for bats, due to increased flight costs, decreased maneuverability and possibly lower prey abundance. To test this hypothesis, we quantified Pipistrellus pipistrellus activity acoustically at 361 site-nights in western France in June on a longitudinal distance gradient from the wind turbine and on a circular azimuth gradient of wind incidence angle, calculated from the prevailing wind direction of the night. We show that P. pipistrellus avoid the wake area, as less activity was detected leeward of turbines than windward (upwind) at relatively moderate and high wind speeds. Furthermore, we found that P. pipistrellus response to wind turbine (attraction and avoidance) depended on the angle from the wake area. These findings are consistent with the hypothesis that changes in airflows around operating wind turbines can strongly impact the way bats use habitats up to at least 1500 m from the turbines, and thus should prompt the consideration of prevailing winds in wind energy planning. Based on the evidence we present here, we strongly recommend avoiding configurations involving the installation of a turbine between the origin of prevailing winds and important habitats for bats, such as hedgerows, water or woodlands.

Machine Learning-Driven Wind Energy Forecasting for Sustainable Development

The growing need for energy, in addition to the depletion of fossil fuel supply, has underlined the importance of renewable energy for long-term growth. Renewable energy stands out among these, but its broad usage is hampered by the inherent uncertainty of wind power generation. This study uses machine learning to predict wind energy yield. Several regression models were used, including decision tree regression, linear regression, and random forest regression. The results emphasize the random forest regression, which has a high R-squared score, suggesting strong predictive ability. The paper also contains wind power output projections, which provide insights for optimal wind energy planning and usage. Overall, this attempt gives vital insights to improving the effective use of renewable energy, advancing the cause of sustainable development.

Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances

In the last decade, many artificial intelligence (AI) techniques have been used to solve various problems in sustainable energy (SE). Consequently, an increasing volume of research has been devoted to this topic, making it difficult for researchers to keep abreast of its developments. This paper analyzes 18,715 articles—about AI techniques used for SE—indexed in Scopus and published from 2013 to 2022, which were retrieved and selected following a novel iterative methodology. Besides calculating basic bibliometric indicators, we used clustering techniques and a co-occurrence analysis of author keywords to discover and characterize dominant themes in the literature. As a result, we found eight dominant themes in SE (solar energy, smart grids and microgrids, fuel cells, hydrogen, electric vehicles, biofuels, wind energy, and energy planning) and nine dominant techniques in AI (genetic algorithms, support vector machines, particle swarm optimization, differential evolution, classical neural networks, fuzzy logic controllers, reinforcement learning, deep learning, and multi-objective optimization). Each dominant theme is discussed in detail, highlighting the most relevant work and contributions. Finally, we identified the AI techniques most widely used in each SE area to solve its specific problems.

Wind resource droughts in China

With the rising share of wind energy in power generation, the occurrence of low-wind-power events (termed ‘wind resource droughts’) are becoming critical in understanding the national electricity supply and the security of power systems. We use hourly wind speed data (2428 meteorological stations; in the years 2010–2020) to analyze the occurrence of wind resource droughts in seven onshore wind energy planning regions over China. We find that wind resource droughts tend to occur in warm season (summer and autumn) in most regions (i.e., Northeast China, East China, Central China, South China and Tibetan Plateau). In these regions, the number of moderate wind resource drought events (wind capacity factor below 10%) in the warm season (summer and autumn) was about 3–13 times higher than in the cold season (spring and winter). By contrast, for North China and Northwest China, the wind resource droughts mainly occurred in the autumn and winter. Averaged over 11 years, Northeast China experienced the most moderate wind resource droughts with 30 d yr−1, while Northwest China had only 1 d yr−1. Some of these wind resource drought events occurred consecutively, in which Northeast China experienced nine wind resource drought events that lasted for at least five days (where the longest reached 9 d) across the 11 years from 2010 to 2020; North China and South China exhibited one five-day wind resource drought events; while Northwest China, East China, Central China and Tibetan Plateau had none. Moreover, we found that increasing the aggregated area of wind resource can reduce the volatilities of wind energy. Therefore, improving the cross-regional transmission capacity can substantially help reduce the number of wind resource drought events. These findings should assist decision-makers to establish the counterplan to mitigate the energy shortages and instability in power supply caused by the uncertainty of wind resource droughts.

Optimizing Wind Farm Layouts with Genetic Algorithms (Enhancing Efficiency in Wind Energy Planning and Utilization in Bosnia and Herzegovina)

This paper proposes a genetic algorithm-based approach to optimize wind farm layouts in Bosnia and Herzegovina, a country with a high potential for wind energy production. The primary objective is to maximize the total power output derived from the wind farm while concurrently minimizing the wake effects resulting from turbine interactions. This balance is pivotal in ensuring the optimal utilization of wind energy resources. In this study, three different wind data scenarios are considered: a single wind direction with overall average velocity, the most prevalent wind direction with overall average velocity, and an all-encompassing wind direction analysis incorporating a weighted objective function. The results of the study suggest that the genetic algorithm is highly effective in identifying optimal solutions across each scenario. This serves as a testament to the algorithm's accuracy, robustness, and applicability in tackling real-world problems, thereby marking a significant step forward in the realm of wind farm optimization. The limitations of this research include the use of a simplified wake model and a fixed turbine type. The implications of this research include the potential for increasing the efficiency and profitability of wind farms in Bosnia and Herzegovina, as well as informing future research on more complex and realistic optimization problems.

Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast

Abstract. Uncertainty quantification of long-term modeled wind speed is essential to ensure stakeholders can best leverage wind resource numerical data sets. Offshore, this need is even stronger given the limited availability of observations of wind speed at heights relevant for wind energy purposes and the resulting heavier relative weight of numerical data sets for wind energy planning and operational projects. In this analysis, we consider the National Renewable Energy Laboratory's 21-year updated numerical offshore data set for the US East Coast and provide a methodological framework to leverage both floating lidar and near-surface buoy observations in the region to quantify uncertainty in the modeled hub-height wind resource. We first show how using a numerical ensemble to quantify the uncertainty in modeled wind speed is insufficient to fully capture the model deviation from real-world observations. Next, we train and validate a random forest to vertically extrapolate near-surface wind speed to hub height using the available short-term lidar data sets in the region. We then apply this model to vertically extrapolate the long-term near-surface buoy wind speed observations to hub height so that they can be directly compared to the long-term numerical data set. We find that the mean 21-year uncertainty in 140 m hourly average wind speed is slightly lower than 3 m s−1 (roughly 30 % of the mean observed wind speed) across the considered region. Atmospheric stability is strictly connected to the modeled wind speed uncertainty, with stable conditions associated with an uncertainty which is, on average, about 20 % larger than the overall mean uncertainty.

Implementation of hybrid wind speed prediction model based on different data mining and signal processing approaches

Accurate estimation of wind speed (WS) data, which greatly influences meteorological parameters, plays a vital role in the safe operation and optimization of the power system and water resource management. The study's main aim is to combine artificial intelligence and signal decomposition techniques to improve WS prediction accuracy. Feed-forward back propagation neural network (FFBNN), support vector machine (SVM) and Gaussian processes regression (GPR) models, discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were used to forecast the WS values ​​1 month ahead in Burdur meteorology station. Statistical criteria such as Willmott's index of agreement, mean bias error, mean squared error, determination coefficient, Taylor diagram and regression analysis, and graphical indicators were used to evaluate the prediction success of the models. As a result of the study, it was determined that both wavelet transform and EMD signal processing increased the WS prediction performance of the stand-alone ML model. The best performance was obtained with the hybrid EMD-Matern 5/2 kernel GPR with test (R2:0.802) and validation (R2:0.606). The most successful model structure was obtained using input variables with a delay of up to 3 months. The study's results contribute to wind energy-related institutions in terms of practical use, planning, and management of wind energy.

Sustainable wind energy planning through ecosystem service impact valuation and exergy: A study case in south-central Chile

Wind energy has great potential to drive Chile's energy sector towards a more sustainable model; however, it could induce landscape changes impacting ecosystem services. Accordingly, this study aimed to identify the best areas for wind energy development in Biobío and Ñuble regions, by integrating metrics from suitability analysis, ecosystem service valuation and exergy. The trade-offs between ecosystem services and wind electricity generation were assessed through biophysical indicators. Then, a new indicator based on ecosystem services impacts and exergy concept was developed to determine the most sustainable sites for wind energy development. Results showed that deploying wind energy could have the highest adverse effects on erosion regulation services, with an increase of 40%–50% in soil loss for Ñuble and Biobío. Moreover, the crop and timber provision were also reduced by 10%, while water-related services were favored by an increment of 10% in water savings. The proposed indicator took negative values in all suitable zones under analysis (−25 to −307 GJex/GWh), indicating that ecosystem services losses exceeded gains. Results suggest that seven zones are potential sites for wind energy development based on their impacts which were up to −39 GJex/GWh. This work provides evidence for promoting sustainable wind energy planning by locating the projects in zones with the lowest detrimental ecosystem services impacts.

Spatial Distribution and Trends of Wind Energy at Various Time Scales over the South China Sea

In this study, the spatial distribution and trends of wind energy (as measured by wind and wind power density) were investigated from 1979 to 2021 across various time scales over the South China Sea (SCS)by utilizing ERA5 reanalysis data. The results indicate that the SCS possesses abundant wind energy. In addition, due to the fact that the East Asian monsoon dominates the SCS, the wind energy exhibits obvious seasonal changes. It is in winter and autumn that the winter monsoon (i.e., the northeast wind) prevails over the SCS. Here, the wind energy is abundant and reaches its maximum in December. In summer, the summer monsoon (i.e., the southwest wind) prevails over the SCS. Here, the wind energy is abundant over the southwestern SCS. In spring, however, the wind energy is poor. The annual mean wind energy shows a decreasing trend along the northern coast and an increasing trend over the central SCS. The trends of seasonal mean wind energy in winter, spring, and summer demonstrate a similar pattern to the annual mean wind energy. With respect to the intensity of the trends, they are strongest in winter, followed by spring and autumn, and weakest in summer. The trend of wind energy in autumn almost demonstrates the opposite pattern in comparison with the other seasons, i.e., both decreasing and increasing trends over the northern and southern SCS, respectively. The decreasing trend of wind energy along the northern coast of the SCS occurs in February, April, July, September, and November, whereas the increasing trend over the central SCS appears from the period of December to June. The spatial distribution and trends of wind energy over the SCS can help with issuing a more informed recommendation with respect to offshore wind energy planning.

Achieving energy transition through spatial planning - A case of onshore wind energy planning procedures optimization in China

Wind energy is expected to be an effective solution to mitigate climate change and environmental pollution with broad resource distribution, decarbonization characteristics, and mature technologies. However, the rapid expansion of wind energy may also bring about new issues in space allocation and land use conflicts. Integration research of wind energy into spatial planning framework is imperative. Based on the dynamic change theories and conventional energy planning process, this paper proposed a planning framework with four main phases: 1) preparation, analysis, and orientation; 2) model design; 3) optimization and systematization; 4) implementation and supervision. Under the new framework, the connotation of wind energy planning and existing issues have been re-examined, and the planning procedures have been reorganized to correspond to the Chinese spatial planning system and energy development targets. Principles, priority, and systematization measures were discussed to optimize the planning system from the organizational perspective. The newly constructed framework connected the wind energy development plan and spatial planning into well-organized procedures with definite principles, targets, implementation guidelines, and supervision mechanisms at each planning level. The outcome of the research can be utilized as a guideline strategically positioning of planning goals and content.

基于文献计量和遗传算法的土地利用优化研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 基于文献计量和遗传算法的土地利用优化研究进展 DOI: 10.5846/stxb202112243644 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 第二次青藏高原综合考察研究（2019QZKK0608）；国家自然科学基金（42171088）；国家自然科学基金（41901316）；国家自然科学基金（42171250） Land Use Optimization using Genetic Algorithms: Bibliometric Analysis. Author: Affiliation: Fund Project: Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019QZKK0608); National Natural Science Foundation of China (Grant No. 42171088); National Natural Science Foundation of China (Grant No. 41901316); National Natural Science Foundation of China (Grant No. 42171250) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以Web of Science数据库中1999-2022年（截止2022年1月22日）关于遗传算法与土地利用的164篇英文和27篇中文文献为数据源，基于文献计量方法，对每年中英文发文量、文献来源国家、中英文文献学科方向、中英文载文期刊进行分析，探究基于遗传算法的土地利用优化的总体研究概况，然后利用CiteSpace软件对作者和研究机构合作网络、关键词共现网络和文献共被引网络进行可视化，分析研究热点。研究发现，①基于遗传算法的土地利用优化研究整体呈上升趋势，中国（包括港澳台地区）、美国、伊朗是发文量前三国家，研究属于环境、地理和规划的交叉领域。②研究者间形成了2个较大规模的合作网络和众多中小规模的合作网络，处于核心地位的研究机构来自发文量前十的国家。③绿色基础设施是重要的研究对象，多目标优化是主要的优化模型，与其它技术，如粒子群算法的结合和对比为重要的研究方向。④风力能源规划、自然环境保护、海绵城市规划、生物多样性、交通规划、规划支持系统构建是重要的研究内容，主要的研究方法包括带约束的多目标优化和空间优化模型。 Abstract:To explore research progress of land use optimization based on genetic algorithm, we took 164 English and 27 Chinese papers on genetic algorithms and land use in the Web of Science database from 1999 to 2022 (as of January 22, 2022) as the data source. Based on bibliometric methods, we analyzed the time distribution of literature publications from 1999 to 2022, countries of published papers, the categories of published Chinese and English papers, and the journals of published papers. Moreover, the author cooperation, institute cooperation keyword coexist network and the literature co-citation network was visualized and research hotspots are explored. The study found that (1) judging from the number of papers published each year, the research on land use optimization based on genetic algorithm was rising as a whole. The whole development can be divided into three stages: 1999—2006, 2007—2015, and 2016 to the present. During the different stages, the number of published papers in English had increased significantly, while the number of papers in Chinese had been relatively stable. (2) Judging from the countries of published literature, China, the United States, and Iran were the top three in the world in terms of the number of published papers, and the top 10 countries in terms of the number of published papers were all located in regions with relatively high economic level. (3) The land use optimization research based on genetic algorithm belonged to the interdisciplinary field of environmental science, geography and planning. (4) From the perspective of authors of the papers, the current research was mainly conducted in small or medium-sized teams of less than 10 people, and there were only two large-sized team. Among them, the team with Bo Huang as the core was in a leading position in the domain. From the perspective of research institutions, three research groups had been formed in East Asia, the Middle East and North America , and they cooperated with each other, forming a huge cooperation network. (5) An important research area of land use optimization based on genetic algorithm was green infrastructure. The commonly used modeling method was multi-objective optimization model, which is compared and combined with particle swarm optimization. (6) The hot research areas of land use optimization based on genetic algorithm were southern China and the county scale. Key research directions included transportation planning, wind energy planning, biodiversity, nature conversation, lid planning and planning support system. Research methods included constrained multi-objective optimization and spatially explicit optimization. 参考文献 相似文献 引证文献

Wind Power Harnessing in Sudan, Opportunities and Challenges

The African continent is endowed with large renewable potential, varying in types across diverse geographic areas. Wind resources are of the highest quality in the north, the east, and the southern regions [1]. This study concentrates on Sudan as one of African countries; that possess a good potential in wind power which can be integrated to support national grid as well as future regional power grid. This study uses windPRO 3.0.651 package [2] which is a software of wind energy project design and planning. In this study a comprehensive analysis for wind power in Sudan was done to verify the wind power potential in Sudan.