Coordinated Optimization of Building Morphological Parameters Under Urban Wind Energy Targets: A Review

Abstract

Against the backdrop of global energy crises and accelerated urbanization, urban wind energy has garnered increasing attention through its integration with building environments. This study investigates the synergistic optimization of architectural parameters (including floor layouts, three-dimensional forms, and roof configurations) with wind capture efficiency. By employing parameterized design and multi-scale flow field analysis, we systematically examine how architectural morphology modulates wind fields and enhances energy performance. Our key findings reveal the following: spatially arranged floor plans significantly influence wind speed distribution; three-dimensional form openings effectively enhance wind velocity while reducing wind-induced vibration responses; and roof configurations and floor layouts demonstrate the highest contribution to wind energy efficiency, with curved roofs showing particularly notable power generation improvements in low-wind environments. The building “density + layout angle + roof form” collaborative strategy has been validated for practical implementation. Current limitations include simulation errors in complex geometries, efficiency bottlenecks in vertical axis turbines, and inadequate lifecycle assessments. Future efforts should focus on multi-field coupled simulations, integrated turbine–architecture design, and enhanced low-carbon evaluation systems to facilitate the transformation of buildings into distributed energy production entities.

1. Introduction

In the context of the global energy crisis and accelerated urbanization, the construction sector accounts for 40% of the energy consumption [1]. With the acceleration of urbanization, the urban population is increasing rapidly, the scale of urban construction continues to expand, and the energy consumption of buildings continues to rise. The dependence on traditional energy sources further aggravates the contradiction between energy supply and demand. Meanwhile, urban wind energy, as a renewable energy with abundant reserves, has great development potential [2]. From a resource distribution perspective, cities contain numerous high-rise buildings whose tops typically experience higher wind speeds, providing physical conditions for wind energy utilization. Furthermore, the urgent demand for clean energy in urban development has made urban wind energy utilization a crucial research direction for addressing energy challenges. As shown in Figure 1, installed capacities of renewable energy sources, like solar and wind power, show an upward trend, reflecting the general direction of clean energy development. Urban wind energy utilization aligns with this trend but must overcome its own challenges. However, complex urban wind environments lead to an uneven distribution of wind resources. For instance, in densely built-up areas, airflow is obstructed and disturbed by structures, forming complex turbulence that hinders effective wind capture and utilization. In open areas, while wind speeds remain relatively stable, wind energy density may be lower. Additionally, architectural interference significantly impacts wind energy utilization. In some cases, excessive wind speed fluctuations cause wind turbines to frequently adjust blade angles, increasing equipment wear and reducing power generation efficiency. Although vertical axis wind turbines (VAWTs) offer advantages in urban environments—resisting turbulence, being low-noise, and being easy to maintain due to ground installation—they demonstrate significantly lower power generation efficiency compared to horizontal axis wind turbines (HAWTs). Furthermore, VAWTs face technical limitations such as installation space constraints and turbulence effects, resulting in the problem of economic insufficiency in practical applications [3]. From an economic perspective, urban wind energy projects currently face high upfront investment costs, including the acquisition, installation, and maintenance of wind power generation equipment. The relatively low power generation efficiency leads to a prolonged return on investment cycle, which restricts large-scale commercial adoption of urban wind energy. The core challenges in urban wind energy utilization lie in the complex modulation of wind fields by building configurations. The passage effect caused by different planar layouts, turbulence variations induced by three-dimensional forms, and wind acceleration effects generated by roof designs all require precise optimization.

To address these challenges systematically and bridge the gaps in existing reviews, this paper constructs an integrated framework centered on four key dimensions: (1) Multi-scale collaborative optimization, connecting urban climatic context, block-level wind field regulation, and building-specific morphological design through a coupled “city–block–building” simulation framework. (2) Building upon this foundation, the framework emphasizes the quantified synergy of morphological parameters, systematically investigating and quantifying the combined effects of key architectural parameters—floor layout, three-dimensional form, and roof configuration—on wind energy capture efficiency. (3) A turbine–architecture adaptation system, developing scenario-specific matching strategies for wind turbine selection and layout based on the interplay between different building forms and resulting wind field characteristics. (4) Full lifecycle and interdisciplinary integration, expanding the evaluation beyond mere operational efficiency to encompass economic viability and environmental impacts across the entire lifecycle, from material production and construction to operation and decommissioning.

Guided by this framework, this paper first reviews the technological evolution and core bottlenecks in the field, establishing the methodological foundation (Section 2). It then synthesizes the current understanding of how building morphology parameters influence wind capture (Section 3) and derives practical application strategies for wind turbines in diverse urban building environments (Section 4). A critical discussion contrasting urban wind energy with traditional wind farms and re-evaluating key findings follows (Section 5), before concluding with the main contributions and future directions (Section 6). This structured approach aims to provide a comprehensive and actionable review for advancing urban wind energy utilization.

In the current era of sustainable development, the energy efficiency of urban buildings has become a critical issue. Buildings not only serve as living and working spaces but also constitute major energy consumers. Among various renewable energy sources, wind power stands out as a key direction for urban building energy transition due to its clean and renewable characteristics. However, the complex urban environment presents multiple challenges for wind energy utilization in buildings, particularly the intricate relationship between architectural forms and wind capture efficiency that is difficult to precisely quantify. This study focuses on quantifying the relationship between building morphology and wind capture efficiency, which is crucial for improving urban building wind energy utilization. Architectural forms vary widely, from simple rectangular structures to complex conical or setback designs, each exerting unique influences on surrounding airflow patterns that affect wind energy capture efficiency. To thoroughly analyze this complex relationship, some research has developed a three-level coupled simulation framework integrating “city–block–building” dynamics [5]. The city-scale simulation is conducted using the WRF (Weather Research and Forecasting) model to simulate meteorological conditions across large regions, providing accurate boundary conditions for subsequent block and building-scale simulations. During the research process, overcoming two major technical bottlenecks—multi-scale simulation accuracy and low wind speed power generation efficiency—has been identified as a critical task. Regarding multi-scale simulation accuracy, through in-depth research on the “Pearl River City” case, researchers [6] have validated their approach by continuously optimizing and calibrating simulation models while analyzing extensive field monitoring data, ultimately achieving highly accurate simulations. Another major challenge in urban wind energy utilization lies in low-wind-efficiency conditions. In urban environments, buildings often obstruct airflow, resulting in generally lower wind speeds across most areas. Traditional HWATs struggle with low wind efficiency under these conditions of low wind speed and high turbulence. Research has shown that designing ventilation openings in supertall buildings can effectively amplify wind speed, thereby reducing wind-induced vibration responses [7]. When the aperture ratio reaches 15%, the wind speed amplification effect increases by 27% while the wind vibration response decreases by 18%. This aperture design alters airflow patterns around buildings, accelerating wind flow through open areas to create high-speed zones that deliver enhanced wind energy for turbines. Simultaneously, a reduced vibration response minimizes operational noise and energy loss during turbine operation, thereby improving equipment stability and extending service life.

This study focuses on the quantitative collaborative optimization of architectural form parameters and wind energy capture efficiency, aiming to provide scientific and effective solutions for urban wind energy utilization. Unlike existing reviews that often focus on single scales or isolated parameters, this research innovatively constructs a systematic framework that integrates four core dimensions to fill critical gaps. First, using systematic integration of multi-scale optimization, it connects urban, block, and building scales via coupled simulations, e.g., quantifying how V-shaped layouts at λ_p = 0.76 enhance mid-row roof wind speed by 22% [8]. Second, through quantified synergy of morphological parameters, it proposes the “density+layout angle+roof form” strategy and critical thresholds, like a 15% aperture ratio, for dual benefits of speed amplification and vibration reduction [7]. Third, the turbine–architecture adaptation system allows for developing scenario-specific matching, e.g., dome roofs (R = 1.5 W) for H-Darrieus turbines in high-rises [9]. Fourth, through full lifecycle and interdisciplinary integration, it expands beyond operational efficiency to include economic barriers (65% equipment cost [10]) and environmental gaps (unaccounted material emissions [11]), proposing LCA-BIM-CFD integration and “Urban Building Wind Energy Design Codes”.

In the field of urban wind energy and building morphology optimization, there are several relevant review studies. For instance, Toja-Silva et al. focused on the application of computational fluid dynamics (CFD) technology in urban wind field simulation [12]; Micallef et al. discussed the impact of building interference on wind fields [13]; and Liu et al. reviewed the development of new turbine technologies [14]. However, these studies mostly focused on single technologies, local issues, or the relationship between individual elements and wind environments. They did not involve the collaborative optimization of building morphological parameters, multi-scale correlation mechanisms, or the adaptation system between buildings and turbines. In contrast, the current review breaks through these limitations. It realizes multi-scale collaborative integration by constructing a three-tier “city–block–building” coupled framework, systematically clarifies the quantitative synergy rules of “floor layout–3D form roof design”, establishes a “morphology–turbine–scenario” matching matrix, and expands to full lifecycle and interdisciplinary integration. For the first time, it achieves the collaborative optimization of full morphological elements of buildings and wind energy utilization, providing a more comprehensive theoretical framework and engineering guidance for the large-scale application of urban wind energy. The research framework is illustrated in Figure 2. This integrated framework represents an attempt to achieve collaborative optimization of all building morphological elements for wind energy utilization, providing a more comprehensive theoretical basis and engineering guidance.

2. Technology Evolution and Main Research Methods

2.1. Technology Evolution

2.1.1. Technology Evolution Context

From the perspective of time, urban architectural wind energy utilization technology has experienced three key development stages from basic theoretical exploration to intelligent integration (as shown in

Table 1. Evolution of wind energy utilization technology in urban buildings.

Stage	Research Emphasis	Represents Research and Achievements	Technical Bottlenecks
Basic theory exploration stage	Wind tunnel test standardization, single-building wind energy assessment, vertical axis turbine application prospect analysis	Li et al. [15] established the wind load test standard for Chinese buildings through wind tunnel tests. They found that the turbulence intensity at the top of Jinmao Tower decreased from 9% to 18% compared with the suburban environment, and the wind load could be reduced by about 20% due to the shielding of surrounding building groups. Zhang [16] measured that the wind speed at the top of high-rise buildings was more than 4 times that at the surface. Dai Kaishan et al. [1] pointed out the application potential of vertical axis turbines in urban buildings.	Parametric design depends on empirical adjustment; VAWT efficiency is lower than HAWT.
Multi-scale simulation integration phase	Multi-scale simulation, architectural form optimization (Villeroi roof, cave design)	Zhang [5] used WRF-CFD model simulation of Typhoon “Von Bia” and found the proportion of the strong wind area was 22.44%. Qiu et al. [15] found that for a rectangular building array at λp = 0.76, the maximum wind speed occurred at the roof and passages of the first-row buildings, but turbulence intensity exceeded 0.16 in central rows, making them unsuitable for Class A turbines. The average wind speed ratio of a Venturi roof with optimized volume increased by 21%, and the power density of generation increased by 32.8%, which significantly improved the utilization efficiency of wind energy [16].	Parametric design relies on empirical adjustment; Lack of intelligent algorithm drive; VAWT efficiency is lower than HAWT.
Intelligent optimization and multi-energy complementation stage	Data-driven design, BIM-CFD integrated platform application, multi-energy complementary system	Elshaer et al. [17] found that the double-sided chamfer design reduced wind load by more than 30%, and the flow velocity coefficient at 0° wind direction reached 1.8. Skvorc et al. [18] found that when the VAWT + photovoltaic cooperative system was adopted, the efficiency of a Shenzhen application increased by 47%.	Lack of lifecycle carbon emission assessment; A certain simulation error in complex form architecture.

).

Basic theory exploration stage: This period mainly focuses on wind tunnel test standardization and wind energy resource assessment of a single building. The “Pearl River City” project optimizes the design of building openings through wind environment simulation and combines the principles of fluid mechanics with VAWTs (as shown in Figure 3) [6]. Its wind energy utilization efficiency improvement design is based on Li et al. [19], and the wind load test standard was established by Zhang Yunpeng [20]. The field measurements of wind speed in high-rise buildings and their engineering validation demonstrate that the project generates 70,000 kWh of annual electricity, providing practical references for “individual building wind energy feasibility assessments” at this stage.

Multi-scale Simulation Integration Phase: As CFD technology matured during this period, research shifted toward multi-scale simulation and architectural form optimization. Francisco Toja-Silva et al. [12] conducted a review on CFD applications in urban wind energy development, analyzed the impact of various turbulence models and building shapes on airflow patterns, and proposed optimization strategies. Their work offered comparative references among different modeling approaches in urban wind energy development [21]. Hou et al. [22] used CFD simulation for neighborhood-scale simulations and found that when the building density λ_p = 26%, wind speeds at roof heights exceeding 1.45 H in mid-row buildings could recover to over 95% of incoming wind speeds with turbulence intensity below 6%. This provides quantitative evidence for wind turbine layout within building clusters. Breakthroughs in morphological optimization technology have made Venturi roofs and fenestration designs key design strategies. Through the integration of ANSYS CFX 14 with wind spectrum software WAsP 12, simulations of the roof wind field at Bunnings Warehouse in Australia revealed that the CFD model accurately captured wind speed acceleration zones and recirculation areas around the building [23]. When southwest winds dominated, the vertical wind component at the roof edge was 32% higher than in the central area. The central roof zone, characterized by low turbulence intensity (I < 8%) and wind speeds recovering to 87% of incoming flow velocity, was identified as the optimal installation location for HAWTs. David Cazzaro and colleagues focused on optimizing VAWT layouts by developing an optimizer that considers arbitrary shapes and obstacles. While their emphasis lay on layout optimization, elements like wake modeling provided valuable references for designing Venturi roof structures that account for wind turbine operating environments [24]. Research on the design of openings in super high-rise buildings showed that when the opening rate was 15%, the wind speed amplification effect increased by 27%, and the wind vibration response reduced by 18%, realizing the synergistic optimization of wind energy capture and structural wind resistance [7]. Sina Hassanli et al. [25] analyzed the influence of building opening layout on urban wind energy capture and optimization of flow field characteristics by a wind tunnel experiment and Particle Image Velocimetry (PIV) technology, demonstrating the role of architectural aperture design in wind energy utilization and flow field optimization from multiple perspectives. However, parameterized design at this stage still relies on empirical adjustments. While VAWTs excel in urban environments with advantages, like turbulence resistance and low noise levels, their power generation efficiency remains significantly lower than HAWTs. The persistent technical and economic challenges stemming from this efficiency gap have yet to be effectively resolved [2].

Intelligent Optimization and Multi-energy Complementary Stage: During this period, artificial intelligence and multi-energy complementary technologies drive related research into the stage of precision optimization. In terms of data-driven design, Elshaer et al. [17] researched corner aerodynamic optimization based on LES and genetic algorithms, demonstrating that a 20% perimeter constraint double-surface chamfer design reduced the windward response by over 30%, with a corner velocity amplification factor reaching 1.8 at 0° wind direction, validating the effectiveness of local geometric adjustments in suppressing wind loads. A collaborative design system combining VAWTs and solar photovoltaic panels was developed by Skvorc et al. [18]; after being applied in the Shenzhen Qianhai Financial District, it improved the comprehensive power generation efficiency by 47% compared to a single system. Current research is further integrating intelligent algorithms with multi-energy complementary technologies to promote the development of building wind energy utilization towards higher efficiency and larger scale.

2.1.2. Core Technical Bottlenecks

At present, research in the field of urban wind energy utilization faces multiple technical bottlenecks, which are interwoven with each other and significantly restrict in-depth theoretical research and the promotion of engineering applications. As shown in

Table 2. Core technical bottlenecks of urban wind energy utilization.

Type of Bottleneck	Presentation	Related Studies
Complex wind environment simulation	Traditional CFD has large separation flow simulation errors for special building forms (the deviation between measured and simulated is up to 15%). The wake turbulence intensity increases by 25% due to the dense building group, and it is difficult to describe the dynamic flow field using the existing model. There is no effective solution for wind field interference in multi-row building groups, and the wind energy assessment error exceeds 20%.	The starting height of the conical building vortex falls to 0.85 H, and the frequency increases [26]. A decrease in building density will lead to a significant increase in turbulence intensity [22].
Low wind speed power generation efficiency	VAWT efficiency is 20–30% lower than HAWT efficiency, and the power is only 12–15% of the rated value when the wind speed is <3 m/s. When the depth of a building balcony is >1.5 m, the wind speed increases by 12%, but the turbulence intensity increases by 15%. The aerodynamic deformation of turbine blades leads to an energy loss of 8–12%, and the wind energy amplification coefficient decreases by 20% when the parapet wall is >0.5 m.	VAWT power output is low at low wind speeds [2]. The depth of the balcony intensifies the attenuation of wind energy power efficiency [27]. There is still energy loss after the Venturi roof is optimized [16].
Economics and lifecycle assessment	The power generation cost is 0.2 CNY/kWh (China), the return on investment exceeds 10 years, and the equipment, operation, and maintenance costs account for 83%. There is a lack of carbon emission assessment of the whole chain of material production, construction, operation and maintenance, and decommissioning. The existing research only focuses on the operation stage; it does not include the impact of building form on the cost.	Micro-siting research does not cover the whole cycle [28]. Lifecycle assessment needs to be introduced, but carbon footprint accounting is missing [29].

, the summary is as follows.

(1)

It is difficult to simulate the wind environment with a complex morphology.

In the simulation of complex wind environments, traditional computational fluid dynamics (CFD) methods have obvious limitations in capturing separation flows in special shapes, such as Venturi roofs and terraced buildings. It is pointed out that conventional turbulence models have difficulty in accurately describing the complex separation flow and vortex shedding phenomenon caused by building morphology, especially for curved roofs and dense building groups. The simulation error of the flow field can reach 15–20% [12], which is consistent with research using wind tunnel tests [26], and the conclusions of the simulation deviation of the cone-shaped building vortex shedding phenomenon are consistent. Through high-resolution CFD simulations of vertical axis turbine arrays between urban buildings, it was found that the superposition of wake flows from multiple building rows could cause a 30% surge in turbulence intensity [30]. However, traditional models showed prediction errors exceeding 22% for such dynamic disturbances, further highlighting the inadequacies of existing simulation methods in characterizing wind fields within dense multi-row building clusters. Although the proposed comprehensive urban vertical wind speed estimation method has improved simulation accuracy for individual buildings, it still lacks effective solutions to wind field interference effects in multi-row dense building clusters [31]. These technical gaps directly lead to expanded errors in wind energy potential assessment, as reported by Maryam Zabarjad Shiraz et al. [32]. The evaluation method combining CFD and meteorological data shows that in high building density (HBD) urban clusters, the wake effect of surrounding buildings leads to a deviation of more than 60% in roof wind energy density prediction, and a refined flow field correction model should be introduced for building layout.

(2)

Low wind speed and high turbulence wind environments result in low wind energy development efficiency.

In terms of low wind speed power generation efficiency, VAWTs have become the preferred solution for the urban environment due to their anti-turbulence characteristics [1], but the problem that their efficiency is 20–30% lower than the efficiency of HAWTs has long existed [2]. Ishugah et al. [2] pointed out that the power output of an existing VAWT is significantly lower than the rated value in a low wind speed environment, and its energy conversion efficiency needs to be improved urgently under urban turbulence conditions. Through CFD simulation and experimental verification, it is found that the torque coefficient of a Savonius-type VAWT under low wind speeds (2–4 m/s) is 18–25% lower than the theoretical value, which is mainly limited by the interaction between blade aerodynamic design and turbulence [33]. CFD simulations show that when the balcony depth of urban buildings exceeds 1.5 m, the average wind speed on the roof can increase by 12%, but the turbulence intensity increases by 15% simultaneously, further exacerbating the efficiency attenuation under low wind speeds [27]. Although the Wind Booster guide vane optimization method proposed by Marco A. Moreno-Armendariz et al. [34] can improve torque by 35%, the engineering application of diffuser-enhanced vanes is still limited by cost and durability problems [15]; therefore, no mature engineering application scheme has been formed.

(3)

Lack of full lifecycle assessment.

The imperfections in economic and lifecycle assessment systems are the key constraints that hinder large-scale urban wind energy adoption. Current benchmark projects like Bahrain World Trade Center demonstrate power generation costs of 0.7–1.05 CNY/kWh, with investment payback periods exceeding 100 years, primarily due to low turbine efficiency and high building integration costs. Even with optimized designs, the levelized cost of electricity still exceeds 0.5 CNY/kWh, resulting in typical payback periods surpassing 20 years [10]. The main reason is that equipment acquisition (65%) and operation and maintenance costs (18% annually) remain high. Lifecycle assessment (LCA) studies of small wind turbines reveal that infrastructure components (masts and foundations) account for 45% of total emissions [11]. However, existing research predominantly focuses on operational phases while neglecting the material consumption impacts of architectural design, resulting in biased cost analyses. More critically, current studies severely lack comprehensive lifecycle assessments that cover material production (e.g., blade composite degradation), construction installation (e.g., high-altitude work costs), operation maintenance (e.g., equipment fatigue from turbulence), and decommissioning processes (e.g., photovoltaic–turbine system recycling). The micro-siting studies by Ovgor et al. [31] only focused on the economy of the operation stage; they did not include the impact of building form on material consumption and construction difficulty. The review of net-zero energy buildings by Wu et al. [32] emphasized that LCA methods should be introduced into the environmental benefit assessment of wind energy systems, but only a few empirical projects (such as Qianhai, Shenzhen) have disclosed power generation efficiency data, and the full chain carbon footprint accounting is still in a blank state [6]. Economic analyses of small HAWTs also reveal that current Levelized Cost of Energy (LCOE) calculations fail to account for additional installation costs from building integration, resulting in estimated return on investment cycle errors exceeding 20% [35]. This oversight in evaluation frameworks creates a fragmented technical–economic analysis, making it difficult to establish replicable commercialization models.

In summary, overcoming these bottlenecks requires deep interdisciplinary collaboration. This involves enhancing wind field simulation accuracy through improved CFD models and intelligent algorithms, optimizing the synergy between wind turbine aerodynamic design and architectural forms, and establishing a comprehensive lifecycle-based techno-economic evaluation framework. These efforts collectively provide systematic solutions for urban wind energy utilization in engineering practice.

2.2. Wind Turbine Fundamentals

2.2.1. Horizontal Axis Wind Turbines (HAWTs)

Horizontal axis wind turbines (HAWTs) have blades that rotate around a horizontal axis, operating on the principle of aerodynamic lift. They have significant efficiency advantages in environments with stable and high wind speeds (>6 m/s), with a power coefficient of up to 0.42 [20]. Such turbines are suitable for row layouts in low building density areas (λ_p < 0.4), where the roof wind speed of front-row buildings is relatively high and turbulence is evenly distributed. Their power generation efficiency can serve as a benchmark reference (based on performance when the building spacing is 1.5 H) [15].

However, HAWTs are extremely sensitive to wind direction changes and require a steering device to keep the blades aligned with the wind direction. In complex urban turbulent environments, they are prone to frequent blade adjustments due to wind speed fluctuations, increasing equipment wear [20]. Meanwhile, their installation requires high support to avoid building obstruction, resulting in poor adaptability in high building density areas or building clusters, and they are easily affected by wake effects, leading to efficiency reduction [2,15].

2.2.2. Vertical Axis Wind Turbines (VAWTs)

Vertical axis wind turbines (VAWTs) have blades that rotate around a vertical axis. They are suitable for complex urban wind environments due to their characteristics of turbulence resistance, low noise, and easy maintenance with ground installation [2]. Based on structural design differences, they can be mainly divided into the following types. The Savonius type is composed of two semi-cylindrical blades forming an S-shaped structure, which is directly driven to rotate by wind force. It has a large starting torque and can start at low wind speeds of 2–4 m/s, with high reliability and low maintenance costs. However, it has relatively low efficiency (power coefficient usually less than 0.2) and is suitable for low wind speed areas and scenarios with high reliability requirements. The Darrieus type adopts a Φ or Δ-shaped multi-blade design, and its operation relies on aerodynamic lift. When the tip speed ratio (TSR) is 4–6, its power coefficient can reach 0.4–0.45, with efficiency close to that of horizontal axis wind turbines. It is suitable for areas with an average wind speed of 6–12 m/s, but it requires external force assistance to start. The H-Darrieus type’s blades are arranged in H-shaped symmetry, integrating lift principles and drag designs, with a starting wind speed as low as 1 m/s, low noise, a small rotation radius, and high space utilization, making it suitable for building roofs, narrow areas, or environments with unstable wind speeds. The Helix type is designed as an S-shaped spiral based on the Savonius structure, composed of three disc-shaped blades, with a starting wind speed as low as 2.5 m/s. Its modular installation method is flexible, making it suitable for building integration scenarios with unstable wind speeds or limited space.

2.2.3. Selection Considerations and Urban Suitability

When selecting wind turbines in urban environments, multiple factors, such as wind field characteristics, building morphology, and technical economy, need to be comprehensively considered. In terms of wind speed and turbulence adaptation, in urban core areas with low wind speeds (≤3 m/s) and high turbulence (TI ≥ 20%), vertical axis wind turbines (such as the Savonius type) have more advantages due to their low starting wind speed and turbulence resistance [2,34]; however, in suburban or open areas with high wind speeds (>6 m/s) and low turbulence, horizontal axis wind turbines can give full play to their efficiency advantages [20]. In terms of building density and layout matching, row layouts in low building density areas (λ_p < 0.4) are suitable for deploying horizontal axis wind turbines; in V-shaped layouts in medium- and high-density areas (λ_p > 0.6), vertical axis wind turbines can better adapt to turbulent environments to improve stability, with power generation efficiency 30% higher than that of row layouts [6,15].

In terms of installation and maintenance costs, vertical axis wind turbines have significantly lower maintenance costs than horizontal axis wind turbines due to ground installation and simple structure, and they are more suitable for integration with buildings (such as roofs and facade openings) [2]. In contrast, horizontal axis wind turbines require high support installation, which is easily affected by building obstructions in high-density areas, increasing installation and maintenance costs [15].

2.3. Core Analytical Framework: Multi-Scale Coupled Simulation

The comprehensive analysis presented in this review is underpinned by a systematic multi-scale coupled simulation framework, integrating “city–block–building” dynamics [5]. This framework serves as the primary methodological backbone for investigating the interplay between urban form, building morphology, and wind energy potential. It enables a holistic assessment, linking macro-climatic conditions derived from urban-scale modeling (e.g., using the WRF model) with microenvironmental flow characteristics at the block and building scales (simulated via tools like ENVI-met and CFD, often validated by wind tunnel tests). The following sections detail the specific methods and tools employed within this framework at each scale.

Table 3. Comparison of multi-scale simulation methods.

Scale	Simulated Target	Core Tools/Models	Typical Size	Resolution Ratio	Core Output Indicators	Typical Application Scenarios
Urban scale	Macro-climate boundary conditions	WRF model	10 km × 10 km	100–1000 m	Annual average wind speed and wind rose chart	Urban wind energy resources survey
Block scale	Microenvironment flow field of building group	ENVI-met	500 m × 500 m	0.5–2 m	Wind speed amplification coefficient of a building group	Analysis of street narrow tube effect
Building scale	Pneumatic optimization of a single building	CFD (k-ε + LES) + wind tunnel test	100 m × 100 m	0.01–0.1 m	Turbulence intensity and wind pressure distribution on the roof	Turbine positioning optimization

summarizes and compares the corresponding research methods at the three levels.

2.3.1. Urban-Scale Simulation

At the urban scale, the WRF model generates boundary conditions such as wind speed, wind direction, and atmospheric stability within a 10 km × 10 km area by inputting regional meteorological data. This effectively identifies localized wind speed attenuation caused by the urban heat island effect. The model plays a crucial role in assessing building wind environments in typhoon-prone areas, enabling precise delineation of high-wind zones (>15 m/s) and building vulnerability zones (such as sharp edges of large structures and windward sides within 200 m of high-rise buildings). It provides macro-climatic foundations for wind-resistant design strategies, including architectural form optimization and shelter corridor planning [5]. When simulating the typhoon wind field in some coastal cities, the WRF model combined with actual meteorological observation data can accurately predict the range of strong wind areas in advance, which provides strong support for urban disaster prevention and mitigation [36]. CFD numerical simulation technology, with its capability to perform three-dimensional dynamic analysis of complex flow fields, has become a core tool bridging architectural design and wind energy utilization. By constructing turbulence models such as the Reynolds-averaged Navier–Stokes (RANS) model and large eddy simulations (LESs), it quantifies critical parameters like wind speed amplification coefficients and turbulence intensity under varying densities, configurations, and morphologies, providing data-driven support for wind energy optimization at the building complex scale. In urban-scale applications, researchers typically combine WRF models with CFD models to balance simulation scope and accuracy, leveraging their respective strengths.

2.3.2. Block-Scale Simulation

In the context of accelerating urbanization, building clusters in high-density urban built environments have become a critical factor influencing wind field distribution. These areas, characterized by compact building spacing (typically less than 1.5 times the building height) and complex spatial configurations, create significant ducting effects and vortex shedding when the airflow is obstructed by structures, resulting in highly uneven wind energy distribution. Achieving efficient wind energy capture through precise wind environment analysis has become a core challenge in green building development. Researchers have quantified the relationship between building density and wind energy distribution using CFD simulations. Parameters such as architectural layout patterns, building spacing, height-to-width ratios, and wind direction significantly impact both wind energy distribution and concentration [37,38].

When integrating urban-scale wind environments with building microenvironments, researchers predominantly employ ENVI-met simulation technology (with resolution up to 0.5 m × 0.5 m × 0.5 m) to comprehensively evaluate the impact of underlying surface factors like vegetation and terrain on airflow patterns. Furthermore, emerging technological tools, such as artificial intelligence and machine learning, are utilized to optimize wind energy system designs, refine urban wind function parameters, predict turbulence distribution, and develop wind turbine start–stop strategies. These advancements have significantly expanded the technical boundaries of the “simulation–optimization–verification” closed-loop methodology [31,39]. The quantitative conclusions at the meso-scale provide direct guidance for the planning and design of high-density urban areas, and the optimization of building spacing and arrangement is also conducive to the wind energy accumulation of the block wind field.

2.3.3. Building-Scale Simulation

The architectural scale utilizes a hybrid approach combining CFD simulations and wind tunnel tests to achieve high-precision micro-scale flow field modeling. Wind tunnel models require careful consideration of scale ratios and pressure sensor accuracy to ensure data reliability. Comparative studies on wind tunnel experiments with different scale ratios have been conducted, with CFD validation revealing average wind speed simulation errors of ≤1.35% under these conditions. This achieves flow field similarity and meets the cost control requirements for model fabrication and testing in engineering practice [40]. Recent studies have quantified the impact of building corner shapes on wind energy density and turbulence intensity through wind tunnel tests and RSM modeling. The findings reveal that circular corner designs can reduce roof turbulence intensity by 20% compared to sharp-angle designs. This architectural scale optimization strategy enhances wind energy utilization efficiency in high-density building clusters, providing a definitive evaluation basis for rooftop wind turbine installation [41].

In terms of wind energy research at the building scale, the facade opening form and roof form of high-rise buildings [7,9], integrated deflector plates on the exterior surface [42], the solar panel layout [18,43], etc., have become key research focuses in the development of wind energy in high-density urban areas. In new constructions, facade openings, specialized roof designs, and exterior surface deflector plate designs have emerged as primary approaches for integrated wind energy architecture. For existing building renovations, optimizing parapet walls and designing eave wind deflector panels have become the main research directions.

The three-tier coupling pathway achieves full-chain integration through data transmission and collaborative analysis. Climate boundary conditions from the urban scale drive neighborhood-scale simulations, which then generate building cluster flow field characteristics (e.g., turbulence intensity and wind speed amplification coefficients) as input parameters for individual building optimization. This ultimately forms a closed-loop system of “macro-climate assessment→meso-optimization layout→micro-form design”. The application process and effectiveness evaluation of this cross-scale integration technology in large-scale urban energy planning projects provide valuable experience for subsequent similar initiatives [44].

The current research still faces two technical bottlenecks. The first is the error in complex flow field simulation (±15%), especially in the simulation of wake interference and typhoon transient wind load [45]. Second, there are bottlenecks in wind energy utilization for low wind speed zones (<3 m/s) due to architectural layout constraints: When the building spacing exceeds 19 m or the windward side width is less than 30 m, the wind speed increase within the passage is insufficient by 42%. Additionally, turbulence intensity exceeding 0.25 often forms in areas below half a building’s total height, resulting in a 35% reduction in the wind turbine startup speed compliance rate [46]. At the same time, the LCA method is introduced to include carbon emissions in material production, construction and installation, decommissioning, and recycling into the evaluation system, so as to promote urban wind energy utilization from single technology optimization to a systematic solution of “low-carbon performance + economic cost + social acceptance” [13,32].

3. Influence of Architectural Morphological Parameters on Wind Energy Capture

3.1. Plan Layout: Group Coordination and Flow Field Control

The spatial configuration of urban building clusters significantly influences wind energy distribution and capture efficiency by altering airflow patterns and turbulence characteristics. In linear street layouts, buildings aligned along prevailing wind directions create distinct channel effects. Through high-rise building array simulations (Figure 4), Yu-Hsuan Juan et al. demonstrated that when the upstream building spacing is w = 0.15 B (building side width), the rooftop wind energy density in the upstream channel increases by 200% compared to open areas. Conversely, when the spacing reaches 0.75 B, the downstream channels’ wind energy density surges by 207% [47]. In low-density urban areas (planning area density λ_p < 0.4), the wind speed between the first row of buildings can be amplified to 1.31 times that of the empty field condition, the wind power density exceeds 200 W/m², and the turbulence intensity meets the installation requirements of class A wind turbines (Iref = 0.16). Therefore, it is suitable to arrange wind turbines on the top of the first row of buildings in this area [15]. However, the turbulence intensity of the rear building is increased by 40% due to the wake of the front building, resulting in a decrease of 15% in the efficiency of the wind turbine [21].

The reverse V-shaped layout (with openings facing the incoming airflow) demonstrates 21.6% higher total lateral exhaust energy than the staggered configuration when operating at a 0° wind direction (as shown in Figure 5). This design fundamentally enhances wind speed concentration efficiency by optimizing the angle of the incoming airflow separation point (approximately 30°). For two adjacent buildings with a height difference of h/H < 0.5 and a spacing of D > 30 m, the target building’s roof wind speed amplification coefficient can reach 1.05. This configuration proves particularly suitable for urban scenarios requiring balanced wind energy capture and optimal building spacing [38]. The staggered layout reduces wake turbulence interference between adjacent buildings by displacing the building by 0.5 W (where W is the building width). Iris Loche et al. simulated 40 balcony designs and found that narrow shallow balconies with a depth of 0.5 m and a width of 2 m can reduce wind field turbulence intensity on short-axis facades by 12% while increasing the near-window wind speed by 10% [48]. In addition, the research on the corner optimization of buildings shows that a rounded corner design can increase the wind power density of a roof by 365% compared with sharp corners, and reduce the turbulence intensity to less than 0.12, providing a morphological optimization reference for wind energy capture in compact urban areas [8]. In addition, wind tunnel tests by Jooss et al. [49] revealed that installing vertical axis turbines at the building’s center boosts power generation efficiency by 23% compared to edge positions. When facing a 45° wind direction, turbine performance improves by 43% over zero-angle conditions, demonstrating how location and wind orientation significantly affect urban turbine efficiency. The “Pearl River City” supertall building exemplifies this through its four wind-guiding openings (miniature V-shaped structures) combined with a vertical axis turbine design. At a 300 m elevation, it achieves wind speeds 67% higher than ground level (from 3 m/s to ≥5 m/s), where its aerodynamic guidance logic aligns with urban wind amplification principles in spatial planning [6]. When the building density is λ_p = 0.76, the wind speed at the top of the middle row can reach 5.2 m/s, which is 22% higher than that of the front row, and the turbulence intensity is relatively stable [22]. VAWTs are prioritized for installation in such areas to improve power generation stability by utilizing their advantages against turbulence. The improvement effects of wind energy with different building densities and layouts are shown in

Table 4. Comparison of wind energy improvement effects with different building densities and layouts.

Building Density (λp)	Recommended Layout Mode	Wind Speed Amplification Coefficient	Applicable Turbine Type	Generating Efficiency
λp < 0.4	row	1.2–2.1	HAWT	Base value (building spacing 1.5 H)
0.4 ≤ λp ≤ 0.6	staggered, stepped	1.5–1.7	Hybrid	20–25% higher than the row layout or equal height roof
λp > 0.6	V form	1.8–2.0	VAWT	A 30% increase over the row layout

.

Building spacing and windward width are key parameters for regulating the wind energy distribution. CFD simulations and wind tunnel tests show that when the building spacing is 19 m (approximately 0.11 H, H = 180 m), the channel exhibits an optimal wind speed amplification effect (reaching 16.6 m/s, 42% higher than free flow velocity). The turbulence intensity remains below 0.15, achieving synergistic optimization of wind energy concentration and flow field stability [46]. A high-density urban area (HBD) with dense buildings leads to a lower average wind speed at the turbine location in the low-density area (LBD) and an 87% decrease in power generation in winter, which verifies the significant impact of surrounding building shading on wind energy capture [32].

Research on cross-shaped high-rise residential complexes reveals that at 45° wind angles, vortex acceleration zones form at a building’s concave corners, generating wind speeds approximately 1.3 times higher than the incoming airflow. Turbulence intensity significantly increases when the height-to-width ratio (W/H) between adjacent buildings is less than 0.5. By implementing staggered layouts or concave corner chamfering, turbulence interference can be reduced, lowering the turbulent intensity by about 20% [8,50]. The opening design on the top of high-rise buildings can increase the wind speed at the front edge of the roof by 25%, but the multi-cavity layout easily causes airflow disorder. It is suggested that the proportion of the single-cavity area should be controlled at 10–15% to balance the utilization of wind energy and flow field stability [51]. These studies provide a quantitative basis for the layout of building groups in urban areas with different densities. From the front efficient zone of the low-density row layout to the advantage zone of the high-density V-shaped layout, combined with spacing optimization and complex morphological treatment, a wind energy capture strategy covering multiple scenarios is formed.

These studies systematically reveal the intrinsic relationship between building cluster layouts and wind energy capture efficiency, establishing a comprehensive technical framework covering layout pattern selection, density adaptation, and spacing optimization. Through differentiated row–column, V-shaped, and staggered layout designs, the research achieves precise utilization of urban wind resources at low, medium, and high densities. Using CFD simulations and wind tunnel tests, the studies quantify how building spacing and channel dimensions regulate wind fields. Strategies such as chamfer treatment and optimized openings for complex structures effectively balance wind energy utilization with flow field stability. These findings provide data-driven scientific evidence for urban planning, shifting building layout design from experience-based approaches to precision optimization. This advancement enhances both urban wind energy capture efficiency and equipment operational stability, laying theoretical and practical foundations for the deep integration of green buildings and renewable energy systems.

3.2. Three-Dimensional Form: Body Optimization and Aerodynamic Control

3.2.1. Height-to-Width Ratio and Turbulence Modulation Mechanism

As a key parameter determining the wind field characteristics of buildings, the nonlinear relationship between the building height-to-width ratio (H/W) and wind energy utilization efficiency has been thoroughly investigated through CFD simulations and wind tunnel tests. CFD simulations reveal that when the building height-to-width ratio increases from 2 to 4, the wind speed variation coefficient (Cr) at 1–3 m heights along the roof corners increases from 0.8 to 1.2. In addition, the turbulence intensity is reduced by about 20% compared with the central area of the roof, forming an ideal range of “low turbulence and high wind speed” [52,53]. This principle becomes particularly evident in super high-rise buildings. When the height-to-width ratio (H/W) exceeds 8, the vortex zone at the top reduces the wind energy capture efficiency by 10%. However, when the height-to-width ratio is maintained between 5 and 6, the roof wind speed of a 150 m super high-rise building increases by 17% compared to a 100 m structure. The turbulence intensity decreases exponentially with height (decreasing by 12% for every 50 m), creating an optimal “high-altitude, high-speed, low-disturbance” environment for wind turbine placement [54]. This is consistent with the amplification effect of air intake velocity (maximum wind speed ratio 3.5) in a 309 m building in the area, indicating that reasonable control of high width ratio can strengthen the “urban canyon” effect and the “wind pocket” effect of the concave surface of the building [55].

By optimizing the roof profile, the wind speed can be increased by 12–15% [56]. The differentiated design of typical building shapes further enhances the benefits of optimizing height-to-width ratios. For conical structures (as shown in Figure 6, left), the tapered profile with a narrower top and a wider bottom (top width/height: 0.7) effectively suppresses crosswind-induced vortex shedding by altering airflow separation points. Aerodynamic studies indicate that as airflow passes through conical structures, the narrowing cross-section thins the boundary layer, shifting the separation point to the mid-upper section. This reduces the frequency of Karman vortex street formation. Wind tunnel tests show that this design decreases wind loads by 20–25% and narrows standard deviations of roof wind speeds across floors by 40%, significantly improving wind speed stability at different heights. In typhoon-prone areas, this design demonstrates exceptional wind resistance. For instance, the conical shape of Xiamen Shimao Strait Towers, as verified by pressure sensor arrays, reduces the crosswind vibration response by 30% and decreases the turbine blade load fluctuations caused by turbulence by 25%, effectively extending equipment lifespan [26]. The wake length of convex building groups is about 20% shorter than that of concave building groups, and the low wind speed area on the leeward side is reduced by 15%, which is conducive to improving the wind energy capture efficiency of surrounding buildings [57]. In addition, LES simulation results show that wind deflection can reduce the base torque of super high-rise square columns by 24%, and the wind load distribution law provides a methodological reference for wind field research of special-shaped buildings (such as conical and tunnel type) [58]. That is, by modifying the turbulence model and wind profile setting, the influence of a complex wind environment on buildings can be effectively simulated.

The stepped recessed design of terraced buildings creates multi-tiered eaves and recessed spaces by progressively shifting floor plans backward (as shown in Figure 6, right). Its core advantage lies in balancing wind speed variations across different elevation levels. Field measurements reveal that traditional rectangular buildings experience up to a 25% wind speed difference between the upper and lower floors, whereas terraced structures can control this variation to under 10% by maintaining a controlled recess ratio (typically ≤0.1 W building width per floor). Numerical simulations demonstrate that terraced buildings achieve a 12% lower solar heat gain coefficient compared to flat-roofed structures, realizing synergistic optimization between wind energy utilization and thermal environmental performance [59]. Research reveals that the stepped recessed design of terraced buildings alters the architectural height and spatial configuration, explaining the principle behind wind field homogenization in such structures [60]. This three-dimensional spatial wind field homogenization effect enables multi-unit layered layouts. For instance, installing micro-Savonius turbines at each terrace edge layer utilizes their omnidirectional activation characteristics to capture airflow from different heights. This transforms building facades into three-dimensional wind energy harvesting interfaces, increasing power generation by over 25% compared to single-roof configurations.

It is worth noting that through CFD simulations of wind speed and turbulence characteristics in building clusters with varying density (26–14%), it was found that high-density (<1.5 H) areas are more suitable for wind turbine installation, with priority recommended for the tops of intermediate rows of buildings [22]. This conclusion parallels the vertical structure of terraced buildings, where their progressively recessed design can be viewed as “vertically stacked multi-row micro-scale structures.” The wind speed variations across different floors show similarities to those between rows in horizontal building clusters, providing new insights for studying wind energy utilization under various terraced building configurations. Through CFD simulations of wind fields in building complexes with different densities (λ_p = 0.76, 0.33, 0.11), it was found that the first-row building passage and the front part of the roof exhibit higher wind power density and acceptable turbulence intensity [15]. This conclusion provides valuable reference for analyzing wind energy distribution across different floors in terraced buildings (similar to vertical arrangements of multi-row structures), facilitating a more comprehensive investigation into the wind energy distribution characteristics of such architectural forms.

Convex-curve buildings can be used to actively regulate the urban micro-climate through parametric modeling. Taking convex-curving building clusters (center angle 60°) as an example, Reynolds-averaged Navier–Stokes (RANS) numerical simulation was conducted in the high-density urban area of Central, Hong Kong [9]. The dome-shaped roof design (as seen in the International Finance Centre) optimizes airflow through rounded edges, achieving 1.6 times higher wind speed than straight-line buildings while delivering 402 W/m² of wind power density, a 167% increase from the 150 W/m² recorded for linear structures. This configuration suppresses airflow separation, maintaining turbulent intensity below 12% to meet IEC 61400-2 standards for wind turbine installation. The curved surface optimization mechanism reduces separation by utilizing curvature radius gradients (typically R = 1.2–1.5 W) on arc roofs and rose-shaped facades. These designs guide boundary layer airflow along smooth surfaces, shifting separation points from sharp edges to more posterior areas, thereby minimizing turbulence intensity [61].

Further research shows that the wind speed amplification coefficient of the high wind speed zone (HVA) on the side of a convex-curved building group can reach 1.5–1.8, and the turbulence intensity is about 12–15%, which provides a suitable field for the installation of turbines near the ground with “high speed and low disturbance” [57]. Also, Cao and Xing [42] investigated the integrated design of wind energy systems with high-rise residential facades, optimizing wind collection through wind pressure and narrow tube effects. Green building ventilation technology is employed to verify power generation efficiency at elevator lobbies, unit platforms, and other locations. The optimized wind energy harvesting approach can be applied to further refine concave–convex curved architectural surfaces, providing new optimization directions and validation methods for wind energy utilization in such buildings.

3.2.2. Angle and Surface Treatment Optimization

The precise control of architectural corner angles and surface details serves as the key approach to addressing urban turbulence challenges. Millimeter-scale morphological adjustments enable targeted interventions in localized airflow patterns. For rectangular building corners, physical models incorporating varying chamfer ratios and channel designs have been developed. Notably, an optimized design with a 20% chamfer ratio (0.2 W width) demonstrated multiple synergistic effects. Wind tunnel tests and CFD simulations revealed a 30% reduction in windward wind loads, a 22% decrease in base moment, a 0.6 H-to-0.4 H advance in separation flow recapturing points, a 40% shortening of wake length, and an equivalent 60% extension of effective airflow length within turbine installation zones [60,62]. This optimization achieved a 25% improvement in the uniformity of wind speed at the roof edge, a 15% reduction in turbulence intensity, and a decrease in turbine startup load fluctuations from ±30% to ±10%, significantly extending the bearing life of horizontal axis fans. Through engineering practice at Guangzhou Pearl River Tower, the synergistic effect of corner chamfering and channel design amplified tunnel wind speeds by 60%, validating the efficiency enhancement mechanism between micro-scale treatments and macroscopic configurations [58].

The “parapet wall height + roof configuration” synergy demonstrates adaptability across architectural types. In high-rise buildings, when parapet walls exceed 0.3 m in height, the wind amplification coefficient decreases at a rate of 0.4 per 10 cm (from 2.1 to 1.7 at 0.3 m→0.5 m). When combined with dome-shaped roofs (curvature R = 1.5 W), this design enhances edge wind speeds by 45% while reducing turbulence intensity by 18%, creating an “acceleration→stabilization” interface. For multi-story buildings, a 0.5 m solid parapet wall reduces corner suction by 40%. Perforated parapet walls (30% perforation rate) utilize Bernoulli’s effect to minimize vortices, elevating roof turbine wind resistance ratings from Class 10 to Class 12. Field applications in Taiwan’s typhoon-prone Pingtan Island, Fujian Province, have achieved a 35% increase in turbine survival rates [63,64].

The advanced development of facade systems and passage effects has opened new pathways for wind energy utilization. The double-skin facade system employs “breathing” vertical openings that reduce the cross-wind vibration response by 20% while enhancing the internal passage wind speed by 18% through cavity effects, creating a stable airflow field suitable for vertical axis turbines. After implementing this system at Australia’s Eureka Tower, the annual power generation increased by 35% compared to traditional facades, with turbine noise reduced by 15 dB, achieving multiple “load reduction+efficiency enhancement + noise reduction” objectives [65,66]. Cao Maoqing et al. [42] integrated CFD simulations into the architectural facade design phase. By applying VENT ventilation performance analysis technology, flow guide plates and wind collection ducts were incorporated into high-rise residential facades. Through the synergistic effects of Bernoulli’s principle and ductwork principles, this approach increased wind energy utilization efficiency by over 30% in public areas, such as elevator lobbies. In an 18-story residential prototype case, 12 VAWTs consistently delivered rated power daily, validating the effectiveness of the “simulation→design→construction” integrated process.

When controlling complex turbulent flows, refined solutions can effectively overcome traditional limitations. The cross-shaped building complex concave vortex zone (with wind speed amplified by 1.4 times at the downwind side of 45°) can suppress 30% of a turbulence surge through a 0.5 W staggered layout or a 0.1 W corner radius treatment, maintaining turbine efficiency above 85% [67]. Through simulations of 14 cavity configurations, it was found that the design of openings at the top of high-rise buildings (with single cavities accounting for 12%) optimized by CFD modeling achieved a 25% increase in the leading-edge wind speed while keeping airflow turbulence below 15% [51]. The single-cavity configuration on the roof’s leading edge amplified the wind speed by 25%, and multi-cavity layouts (>2) increased the airflow turbulence by 35% compared to single cavities. These advancements create conditions for integrating Unmanned Aerial Vehicle (UAV) landing pads with wind turbines. These technologies drive architectural forms to transition from “passive adaptation” to “active regulation”.

Current research has established a comprehensive design system encompassing “macro-scale orientation determination, meso-scale layout refinement, and micro-level optimization”. Low-density urban areas achieve “uniform airflow patterns” through conical setbacks; high-density districts utilize curved facades to create “high-speed low-drag zones”; and supertall buildings harness “high-altitude wind energy belts” via height-to-width ratios and channel effects. Data indicate that integrating architectural flow guidance with innovative wind energy harvesters (e.g., hybrid generators boost efficiency by 50%) could theoretically increase urban wind energy utilization efficiency by 40–60%. Optimized layouts reduce turbulence intensity by 18%, while bladeless technology decreases structural loads, indirectly lowering wind-related construction costs by 20–30% [2,14]. In the future, with advancements in AI-assisted optimization algorithms and 3D printing technologies, three-dimensional form design will more accurately align with wind resource characteristics. This transformation will propel buildings from “energy consumers” to “energy producers,” providing core technological support for achieving carbon neutrality goals. In practical engineering applications, Hong Kong’s Central District high-density urban area has demonstrated the feasibility of multi-scale collaborative optimization through rounded building designs and curved roof configurations, which increased wind power density by 150% [9,61]. This integration not only improves energy efficiency but also enhances structural safety through wind load optimization. For example, the setback design of Shanghai Tower reduces the difference in turbine efficiency between different floors to 8%, realizing three-dimensional energy utilization [59]. Technological progress is promoting the deep integration of architectural design and the urban wind environment, providing a new paradigm for sustainable development.

3.3. Roof Form: Geometric Innovation and Multi-Field Coupling

3.3.1. Airflow Acceleration Effect on Roof Geometry and Influence of Ancillary Structures

The geometric morphology of the roof is the core element that regulates the near-roof flow field and realizes efficient wind energy capture. Its curved surface characteristics and parameter optimization directly determine the airflow acceleration effect and wind load distribution. To evaluate the urban wind energy potential in Hong Kong’s high-density Central District, CFD simulations combined with field measurements were employed [9]. By considering architectural geometry and roof configurations, the study analyzed annual average wind speed, wind energy density, and turbulence intensity to determine optimal turbine installation locations and distances around high-rise buildings and rooftops, providing strategic guidance for urban wind energy utilization. The dome-shaped roofs demonstrated unique advantages in typhoon-prone areas through precise curvature design, as highlighted by Hassan Hemida and colleagues [68]. For buildings with flat or shallow roofs (slope angle of 15°), the pressure difference between the windward and leeward sides showed no significant variation. Among these, single-slope roofs with a slope angle of 30° exhibited optimal ventilation performance, while steep-slope roofs at 30° significantly increased the pressure difference. This occurs because the larger building projection area creates stronger backflow zones behind the structure, resulting in lower pressure on the leeward side. Through wind tunnel tests, the pressure coefficient of an elliptical plane hyperbolic paraboloid roof was studied and compared with a circular roof. It was found that the elliptical roof had better aerodynamic efficiency, and the results provided a reference for the design of this kind of roof [69]. A simulation using the Reynolds-averaged Navier–Stokes (RANS) model revealed that roof slopes within the 0° to 40° range played a critical role in enhancing turbulence and improving ventilation across different canyon height-to-width ratios, particularly in narrow canyons [70]. Notably, turbulence-driven ventilation exhibited a single linear relationship with the square root of the friction coefficient.

Juan et al. [9] conducted an assessment of wind energy potential at the tops of several supertall buildings in China. Long-term monitoring data from the Hong Kong International Financial Centre (case shown in Figure 7, left) revealed that during Category 12 typhoons, the shutdown time of wind turbines on dome roofs was 15% less than that of flat roofs. The wind energy density in the edge zones under dominant northeast winds reached 120 W/m², representing a 35% increase compared to flat roofs in the same area. This effectively validates the energy efficiency stability of curved roof designs in extreme wind environments.

The Venturi roof is specifically designed for low wind speed environments (≤3 m/s) (Figure 8). By employing a throat width-to-height ratio of 0.6 and an inclination angle of 19.5°, it utilizes the Venturi effect to increase the inlet wind speed to 4.1 m/s, achieving a 1.375-time improvement in power generation efficiency. The design features a throat length-to-building width ratio of 1.5:1, ensuring that an accelerated airflow covers two-thirds of the roof area before reaching its peak. Combined with guide vanes (height 0.2 H, angle 30°) that constrain lateral airflow, this configuration enhances wind speed uniformity at the throat center by 22% while reducing turbulence intensity by 18% [67,71].

Ye et al. conducted parametric studies on the edge morphology of Venturi-type roofs (as shown in Figure 9) and the planar configurations of four columns beneath them. Their simulation results indicated that under winter conditions with an average wind speed of 2.8 m/s m/s, the inverted-radius edge design achieved 45% higher annual power generation compared to traditional flat roofs. Additionally, through aerodynamic guidance via the throat structure, the wind-induced vibration response at the top crosswind direction was reduced by 15%, achieving dual breakthroughs in “high-efficiency power generation under low wind speeds” and “structural wind resistance safety” [16].

The micro-scale design of ancillary structures significantly influences roof airflow patterns. Components such as balconies, canopies, and parapet walls reshape the wind distribution at millimeter-to-meter scales by modifying near-wall boundary conditions. The depth and density of balconies exhibit a pronounced bidirectional effect: when the balcony depth reaches 1.5 m with 30% density, the edge bypass effect enhances the average rooftop wind speed by 12%. However, when the density exceeds 40%, overlapping wake effects from adjacent balconies increase turbulent intensity by 15%, resulting in a 9% decrease in turbine blade efficiency within the 3–5 m/s wind speed range [73]. Wind tunnel tests have confirmed that the edge shapes of cubic building roofs (right-angle edges, rounded corners, and solid parapet walls) significantly affect turbine power performance [74]. Sharp edges and solid parapet walls cause significant variations in power output with position changes, while rounded edges mitigate this effect. In cases with solid parapet walls, turbine performance is the worst. Additionally, roof edge shapes influence wake recovery and expansion rates, which decrease as edge smoothness increases.

The slope of a double-pitch canopy and the overhang parameters are key variables for wind energy concentration. Optimization results from CFD simulations and wind tunnel tests indicate that a 20° slope combined with a 0.5 W overhang design can create a “convergence zone” in the front third of the canopy. This configuration achieves a 60% higher wind speed than standard canopies while maintaining only 8% turbulence intensity, making it an ideal installation area for vertical axis wind turbines [75]. Through wind tunnel experiments, one study investigated how multiple parameters of the parapet wall fence shape (height, porosity, inclination angle, curvature) affect airflow patterns in cubic buildings and turbine power output [76]. The findings revealed that inwardly inclined fences at 60° or outwardly curved fences could enhance turbine performance. Increased porosity improved performance when flow paths exceeded one rotor diameter. Furthermore, rotor equivalent velocity was found to be a more suitable reference speed for performance evaluation compared to hub height velocity, with airflow velocity distribution uniformity exceeding 85% in this region, effectively reducing load fluctuations on turbine blades. The influence of canopy height, eave, and slope on wind energy in buildings was studied by Wang et al. [75]. The study showed that the slope of a double-slope ridge canopy had a threshold effect on wind energy capture (Figure 10), and the wind energy amplification coefficient reached 3.58 when the pitched canopy angle was 20°. This model can guide “form + energy efficiency” balance in parametric designs and provide a reference method for evaluating rooftop wind energy.

The matching between the height of a parapet wall and the roof shape of a high-rise building directly affects the integrity of the flow field. When the parapet height increases from 0.5 m to 1 m, the maximum amplification coefficient Cw,max of the rooftop wind energy reduces from 1.9 to 1.6. At the same time, the height of the wind turbine hub needs to increase from 1.13 H to 1.16 H (approximately 4.8 m) to mitigate turbulent effects, which may lead to higher installation costs. Therefore, it is recommended that the supporting parapet wall height be limited to ≤1.0 m to balance wind energy capture efficiency with engineering costs [37]. Multi-story buildings adopt a solid parapet wall of 0.5 m, which can reduce corner suction by 40% by blocking airflow separation at the corner. A perforated parapet wall with a 30% opening rate can reduce suction and avoid vortex agglomeration by exchanging the momentum of the airflow through the pores [64]. Through full-scale experiments, it was found that the end ridge tiles of gable walls generated the highest net upward tensile force [77]. The average wind speed on the tile surface was 55% higher than the incoming wind velocity at the roof center. Moreover, the load model based on surface wind speed requires the inclusion of pressure adjustment factors to avoid underestimating wind loads, revealing the micro-acting mechanisms between ancillary structures and roof wind loads. Monte Carlo simulation evaluations revealed significant differences in wind vulnerability between L-shaped, T-shaped, and C-shaped non-rectangular roof cladding panels compared to rectangular roofs [78]. The most vulnerable areas were identified as the ridge sections, concave corner edges, and valley regions when wind direction primarily impacted concave (L-shaped/T-shaped) or convex (C-shaped) angles. The Internal Pressure Model (IPM-2), which incorporates internal pressure modeling of roof openings, demonstrated superior accuracy in simulating pressure variations. These findings provide quantifiable references for optimizing wind resistance design through coordinated planning between parapet walls and roof configurations.

In conclusion, these studies elucidate how optimizing the slope and overhang parameters of double-slope canopies, along with designing building parapet walls, enhances wind energy utilization efficiency. This provides a critical technical framework for developing precision designs in integrated building–wind energy systems. Future research should integrate architectural characteristics across climate zones with turbine types to conduct multi-parameter coupled empirical studies. Such efforts will advance the engineering application of the “building wind environment + energy equipment” collaborative optimization model, thereby systematically improving renewable energy utilization efficiency within low-carbon building systems.

3.3.2. Multi-Field Coupling Optimization and System Integration Technology

Multi-field coupling technology overcomes the limitations of single-energy utilization, enabling deep integration of wind and solar energy with structural safety to create efficient building energy systems. Research demonstrates that 3 × 3 array panels reduce average wind loads by 57% through mutual shading, while 0.5–2.0 m high parapet walls decrease negative peak loads by 33–41%. This provides structural safety guidance for photovoltaic–wind turbine hybrid system layouts. For instance, when panel spacing is set at 1 m, the “array interference effect significantly reduces wind loads” conclusion from one study can be referenced to optimize airflow in pathways [79]. To address the 2% decrease in surrounding airflow density caused by summer temperature rise in photovoltaic panels, a 0.2 m heat dissipation gap between panels and a 45° inclined deflector grille can increase the hot air discharge speed by 1.5 times while maintaining the air density at the turbine inlet above 1.2 kg/m³, thus avoiding annual power generation loss (approximately 8%) caused by density reduction [80].

The design of openings in super high-rise buildings enhances wind energy utilization efficiency while improving the structural wind vibration response. The core mechanism involves the “jet effect” induced by openings and pressure redistribution. When the opening rate reaches 15% with openings located in the first third of a roof’s leading edge, a high-speed jet forms behind the openings, amplifying the wind speed by 27% while reducing the structural wind vibration response by 18% [7].

The Double-Surface Facade (DSF) system (Figure 11) utilizes optimized cavity airflow patterns to transform building facades into dual carriers for wind energy capture and wind resistance design. The DSF system with strategic openings (25% opening rate, 1.5 m spacing) enhances internal airflow velocity by 15% through the Bernoulli effect. When the incoming wind direction forms an angle of ±45° with the corridor axis, the wind speed amplification factor in the DSF corridor reaches 1.5–1.8 times, aligning quantitatively with theoretical calculations. Its vertically oriented openings (height of 0.8 H; width of 0.2 W) disrupt the vortex shedding cycle on the facade surface, reducing crosswind-induced response by 20% and lowering the Strouhal number from 0.18 to 0.15 [65]. Field tests at Melbourne’s Eureka Tower in Australia show that the system achieves 35% higher power generation efficiency in low-wind zones (2–4 m/s) compared to open-air installations, making it a core solution for urban areas with weak winds. The DSF system requires maintaining an opening-to-height ratio (d/H) between 0.1 and 0.15 to prevent standing wave formation within the cavity.

The Shanghai Center Tower features 270 VAWTs (500-watt, 3 m in diameter and 3.6 m in height) installed on its roof (Figure 12). The building’s 120° torsional design achieves coordinated airflow between the cavity and rooftop acceleration zones by aligning the torsional curtain walls’ angle with the roof’s throat inclination. This configuration reduces lateral wind-induced vibration displacement while increasing turbine density, creating a positive synergy between “wind vibration control” and energy generation [81].

By integrating genetic algorithms (GAs), large eddy simulation (LES), and artificial neural networks (ANNs), one study optimized corner designs in high-rise buildings to reduce wind loads [17]. This approach provides a framework for the performance optimization of building structures under wind field effects, enabling the consideration of corner design impacts on wind fields and other physical fields within multi-physics coupling scenarios. A CFD numerical simulation of urban areas was carried out by scSTREAM 2021 software to analyze the influence of spatial morphological parameters, such as building density, the floor area ratio, and average height, on the roof wind field, providing data support for the optimization of building morphology and wind energy utilization at the urban scale [60]. Yuan and Zhang [82] used a CFD simulation to analyze the effect of wind energy utilization on a rectangular flat roof, and the proposed indicators, such as the wind speed increase coefficient and turbulence intensity, can be used to evaluate the efficiency of wind energy utilization in multi-physical field coupling.

In addition, wind tunnel experiments showed that the operation of VAWTs significantly changes the wind speed distribution in the roof flow field [83]. When a VAWT is installed at the edge of a roof, under the condition of an inflow wind at 0°, the wind speed at H = 0.07 m decreases by 23%, and the central installation extends the attenuation area to H = 0.1 m.

Furthermore, architectural form parameters can be deeply integrated with regional climate characteristics and functional requirements (commercial, super high-rise, residential). This creates a comprehensive technical framework covering “energy efficiency enhancement, risk mitigation, and economic adaptability”. Through multi-objective coordination, the system achieves systematic optimization between architectural form and energy efficiency, providing replicable solutions for urban buildings under the “dual carbon” goals. This approach drives a critical shift in roof design from passively adapting to wind environments to actively regulating energy flows.

4. Application Strategies of Wind Turbines in Different Building Environments

Urban building morphology significantly affects the selection, layout, and efficiency of wind turbines by changing the characteristics of the wind field. This chapter summarizes the application strategies of wind turbines in different environments based on different building forms and strengthens the collaborative optimization of buildings and energy equipment.

4.1. High-Rise Building Form: Utilization of High-Altitude Wind Speed and Structural Coupling Optimization

Generally, the top wind speed of high-rise buildings is higher due to the “height effect” [84]. As the core carrier of wind energy capture, the roof and facade need to achieve the synergistic goal of “efficient power generation + structural optimization” through the deep coupling of architectural form parameters and turbine selection and layout.

As a critical interface for wind energy capture, the roof configuration directly determines local wind field characteristics and turbine compatibility.

Table 5. Roof morphology of a high-rise building—turbine-type matching matrix table.

Roof Type	Typical Wind Speed Characteristics	Recommended Turbine Type	Installation Height/Position	Efficiency Data
Flat roof	The turbulence is uniform, and the wind speed is low	Savonius VAWT	1.5–1.79 H	Advantages of low Reynolds number startup [33,85,86]
Dome/arch	Edge high speed zone	VAWT	Roof center, height 1.3 H	Efficiency increased by more than 40% [87]
Venturi roof	The laryngeal acceleration effect is significant	VAWT	The constricted section of the larynx	Efficiency improved by 1.375 times [89]
V-shaped auxiliary roof	Convergence of guide plates accelerates	VAWT	Channel center	Wind speed increased by 63% [71,72]

summarizes the compatibility matrix between high-rise roof configurations and turbine types. Flat roofs, with their uniform turbulent distribution, are best suited for Savonius vertical axis turbines that operate at low starting wind speeds. These turbines perform optimally under low Reynolds numbers, with the optimal installation height being 1.5 to 1.79 times the building height to minimize the impact of roof separation flow [33,85,86]. Through experimental comparison of the aerodynamic characteristics of three types of micro-turbines, one paper verified their starting advantages under low Reynolds numbers and provided a basis for selecting flat roof scenarios [32]. The edge of a dome/arch roof forms high-speed zones due to the curvature effect, as proposed by Abohela et al. [87]. Through CFD simulations of six roof configurations, one study revealed that arched roofs generated 56.1% more electricity than flat roofs, confirming the acceleration effect of curved surfaces. For low wind speed urban areas, the Venturi roof design with a throat width-to-height ratio of 0.6 achieved a 1.375-time increase in power generation efficiency at 2.5 m/s wind speeds, establishing it as the core solution for low-wind environments [16]. The V-shaped auxiliary roof structure forms a converging channel through the side guide plates. CFD and experimental verification can improve the local wind speed by 63%, which significantly enhances the efficiency of vertical axis wind turbines [71]. It provides a feasible solution for low wind speed areas. A large-eddy simulation was used to determine the optimal turbine installation height and roof chamfer parameters [88]. It is recommended to install vertical axis turbines in this area to reduce turbulent interference. Furthermore, a 20% chamfer rate should be adopted at the building corner to improve the uniformity of the wind speed at the front edge of the roof by 25% and reduce the load fluctuation in the turbine.

Vertical axis turbines are widely used in turbulent building environments. Figure 13 is drawn to show the four common types of VAWTs, and

Table 6. Main characteristics and applicable sites of different types of VAWTs.

Turbine Type	Design Feature	Function	Applicable Sites
Savonius	The S-shaped structure is composed of two semi-cylindrical blades, which are rotated by wind	- Large starting torque: Can be started at a low wind speed (2–4 m/s); suitable for light wind environments. - Low efficiency: The power coefficient is usually less than 0.2, and the wind energy utilization rate decreases significantly at high speed. - High reliability: simple structure, low maintenance cost, strong wind resistance.	Low wind speed areas; Scenarios with high reliability requirements.
Darrieus	Double or multi-blade with a Φ shape or Δ design, driven by aerodynamic lift through symmetrical wings	- High efficiency: When the tip speed ratio (TSR) is 4–6, the power coefficient can reach 0.4–0.45, which is close to that of HWATs. - High wind speed adaptability: Suitable for an environment with average wind speed of 6–12 m/s; especially stable output at high wind speeds. - Starting difficulty: An external force (e.g., motor) is required to assist starting.	High wind speed zone.
H-Darrieus	The blades are arranged in an H-shaped symmetry, combined with the Dario lift principle and Savonius drag design, and the blades have equal cross-section airfoils	- Excellent starting performance: The starting wind speed is as low as 1 m/s, and it can be started in a complex airflow. - Low noise: Horizontal plane rotation design; noise is lower than traditional fans. - High space utilization: Small radius of rotation; can be installed intensively.	Building roofs or narrow areas; areas with unstable wind speed; environmentally sensitive areas.
Helix	Based on Savonius, it adopts the S-shaped spiral structure composed of three disc-shaped blades and generates torque by driving the blades to rotate through wind force	- Low noise, low maintenance cost, starting wind speed as low as 2.5 m/s, modular and flexible installation.	Unstable wind speed zone; limited area space; suitable for building integration.
Flexible composite blade turbine	The composite blade, which combines flexible canvas and grid, can automatically adjust its shape with the wind direction	- Low noise: The tip velocity ratio is low, reducing air vibration and noise. - High adaptability: In an environment where the wind direction changes frequently, the “open/close” operation cycle of the blade is optimized to capture wind energy. - Low cost: Lightweight materials; easy installation and maintenance.	More turbulent areas; integrated with the urban landscape.

is used to present the main characteristics and applicable sites for different types of VAWTs.

A vertical axis turbine installed at the corner of the high-rise building can reduce the standard deviation of the lift coefficient by 30.9%, reduce the energy of vortex-induced vibration, and reduce the peak power spectral density by more than 30%. It has both the function of power generation and the effect of structural load reduction [91]. Wind tunnel tests confirmed the turbine’s effectiveness in reducing aerodynamic loads on buildings, providing theoretical support for facade integration. Buildings with 15% opening rates utilize the narrow tube effect to enhance the wind speed by 27%, making them suitable for installing small HWATs. This design also reduces the wind-induced vibration response by 18%, thereby optimizing power generation stability in high-turbulence environments [64]. Wind tunnel tests on the Pearl River Tower validated the wind speed amplification effect at building openings, providing empirical evidence for turbine layout design in open-arched structures [92]. Additionally, the floor-to-height ratio of high-rise buildings and the distance between adjacent structures (≥3 H) significantly influence rooftop wind fields. When sufficient spacing is maintained, the area of high-speed zones along roof edges expands; therefore, decentralized layouts are recommended to avoid overlapping wake effects from multiple fans. For low-speed zones formed by the “wind barrier effect” in the mid-slopes of supertall buildings, flow-guiding devices can be installed through sky gardens or perforated layers. These enhance wind speeds by 10–15% before matching them with micro-HWATs, achieving gradient utilization of vertical spaces. Figure 14 showcases several common diffuser configurations that serve as references for architectural facade openings, improving wind energy utilization efficiency. Through the meticulous design of roof curvature and chamfer angles, combined with coordinated facade openings and building cluster layouts, high-rises can fully harness upper-air wind resources. This approach not only boosts wind capture efficiency but also reduces structural wind loads, offering a comprehensive solution for renewable energy development in high-density cities.

4.2. Multi-Story Building Clusters: Wind Field Regulation and Roof Microenvironment Optimization in Blocks

In multi-story building clusters, wind fields exhibit distinct characteristics of low wind speeds (annual average ≤ 5 m/s) and high turbulence intensities (TIs ≥ 20%) due to surface friction and high building density. To effectively harness wind energy, it is essential to implement neighborhood-scale wind field regulation and optimize rooftop microenvironments, thereby overcoming traditional layout constraints and achieving efficient adaptation of small, distributed wind turbines.

Table 7. Wind field characteristics of different multi-story building areas.

Region	Wind Field Characteristics	Recommended Turbine Type	Key Design Parameters	Energy Efficiency Data
Front row building (row arrangement)	High wind speed (high 45%), low turbulence	HAWT (30–50 kW)	The spacing is more than 3 H (to avoid wake)	The efficiency in the arrangement outside the leeward area is improved by 12% [94]
Intermediate row building (V-shaped)	Wind speed increased by 22%; turbulence decreased by 18%	VAWT	The opening angle is 45°	The starting wind speed is less than 2.5 m/s [88]
Gabled roof (20°)	Wind speed increased by 12%; turbulence-sensitive	VAWT	Depth 1.5 m, density ≤ 30%	Power fluctuation ≤10% [95]
The balcony area	Wind speed increased by 10–15%	Not installed directly	Depth ≤ 1.2 m, quantity ≤ 4 per building	The roof wind power density is greater than 20% [96]

summarizes the wind field characteristics of different building zones.

The row-and-column layout, as a typical model of multi-story building complexes, forms a natural acceleration flow channel due to the narrow tube effect in its architectural passage (width/height = 0.3). Empirical studies in Shenzhen demonstrate a significant correlation between urban form parameters and wind environments. For example, a 10% increase in building density (BD) reduces the surface wind speed by an average of 0.8 m/s [88]. In southeastern high-climate-quality zones, characterized by higher vegetation coverage and lower building density, wind speeds are 1.2–1.5 m/s higher than those in central-western low-climate-quality zones. Optimizing ventilation corridor layouts can enhance dominant wind penetration, reducing turbulence intensity in sensitive areas by 18–25%. In urban wind energy potential assessments, when the current building density is λ_p = 0.5, rooftop wind speeds can reach 7.2 m/s, representing a 45% increase compared to open areas [94]. This makes it suitable for deploying horizontal axis wind turbine arrays with a rated power of 30–50 kW, achieving an annual power generation of 1.8–2.2 MWh per unit. However, in high-density urban areas (λ_p > 0.6), mid-row buildings are significantly affected by the wake from front-row structures, resulting in an average wind speed decrease of 15% and a 20% increase in turbulence intensity. To address this, V-shaped configurations (with an opening angle of 45°) improve airflow conditions by altering wind path trajectories, as demonstrated by Maryam Zabarjad Shiraz et al. [32]. It is pointed out that when a turbine is arranged at the 3D height of the upstream edge of a building, the operating power coefficient increases by 25%, which is due to the acceleration in the building airflow and the reduction in wake shielding, providing a reference for the layout of turbines in complex wind fields.

The regulatory effect of roof morphology on the microenvironment is reflected in the design of slope and ancillary structures. Through CFD simulations of flat, gable, and pyramid roofs, it was found that a gable roof (slope 20°) and a pyramid roof have 25% lower turbulence intensity under low wind speeds compared to flat roofs, providing a stable operating environment for Savonius turbines with diffusers [95]. Wind tunnel tests further confirmed that 15° and 30° pitched roofs can reduce the total force coefficient of turbines by approximately 10–15%. Under high wind speeds (e.g., 15 m/s), the rotational speed of turbines on inclined roofs decreases by 20% compared to flat roofs, thereby extending the safe operating boundary of the equipment [96]. Experimental results in low wind speed ranges demonstrated that the impact of roof inclination on turbine rotational speed increases progressively with slope elevation.

The depth and density of balconies, a typical element of multi-story buildings, need to be precisely controlled. CFD simulations by Chris Patrick [97] indicated that balcony geometry parameters (e.g., depth and quantity) influence wind fields. Buildings with fewer balconies (e.g., ≤4 per unit) exhibit higher rooftop wind power density, while densely packed balconies may intensify turbulence. The specific quantitative relationships require further investigation in conjunction with building layouts. CFD simulation was also used to study the influence of balcony geometry and position on single-sided natural ventilation and explain the effect of balcony geometric parameters on the surrounding environment from different angles [98]. The influence of balconies on the wind speed and turbulence intensity at the roof edge was found to be similar to the work by Chris Patrick [97], which together reflect the importance of balcony design parameters to the architectural wind environment. Using CFD and the actuator disc model, it was found that the displacement layout of a balcony (horizontal spacing ≥2 m) can reduce turbulence intensity by 10%. Combined with the low-speed characteristics of small vertical axis fans, power fluctuation can be controlled within 10% while maintaining the wind speed gain [99], so as to achieve a balance between wind energy capture and flow field stability.

For multi-story building clusters, wind energy development recommends establishing a coordinated system of “block layout + roof configuration + equipment selection”: Front-row buildings utilize the row convergence effect to arrange horizontal axis turbines, while middle rows adopt V-shaped layouts for vertical axis turbines. The combination of sloping roofs and pyramid-shaped roofs with diffuser designs breaks through low wind speed bottlenecks, while balcony areas optimize airflow through density control. The Berlin case demonstrates that this strategy can achieve an average annual power generation of 2.18 MWh per unit, meeting approximately 5% of the household electricity demand [100]. It is worth noting that the review by N. Aravindhan et al. [56] pointed out that VAWTs were significantly more acceptable than HAWTs in residential areas due to their low noise (≤55 dB). In the future, we can explore the integrated design of balconies and wind turbines, such as integrating blades into balcony railings, so as to balance energy efficiency and beauty [97]. Through the coordinated optimization of blocks and roofs, multi-story building clusters are expected to become an important node of the urban distributed energy network, providing a practical path for low-carbon city construction.

While vertical axis wind turbines (VAWTs) offer superior architectural integration within dense urban cores, their inherent efficiency deficit—typically 20–30% lower than horizontal axis wind turbines (HAWTs) under comparable conditions [2,101]—presents a significant limitation for maximizing energy yield. In contrast, clusters of low-power HAWTs demonstrate substantial potential for efficient wind energy harvesting in semi-urban and rural building environments.

The effectiveness of HAWT clusters in these lower-density contexts stems from several key advantages. HAWTs inherently achieve superior peak power coefficients (Cp ≈ 0.42) [20,101], allowing them to better exploit the generally stronger and more directionally consistent wind resources found outside dense urban cores. Furthermore, the typically larger spacing between buildings (>3 H) in these environments [46,94] allows wind fields to recover significantly downstream of structures. This drastically reduces the complex wake interactions that severely degrade HAWT performance in dense clusters, enabling the effective deployment of multiple HAWTs across suitable buildings without excessive mutual power loss. Strategic siting with building morphology is crucial; for example, optimized zones can be created on gabled/pitched roofs (20–30° slope) [75,89], considering factors like turbine installation height and parapet height (preferably ≤ 0.5 m) [37,64]. Noise constraints, a concern for HAWTs in cities, are often less stringent in semi-urban and rural areas, and they can be effectively managed through adequate siting distances (≥1.5 H) from sensitive receptors [56]. Finally, the higher per-turbine energy yield achieved by HAWTs in these favorable regimes helps offset their typically higher costs. Indeed, deploying clusters across multiple suitable buildings can leverage economies of scale, improving the overall levelized cost of energy (LCOE) and contributing significantly to local, distributed generation [100].

Therefore, clusters of low-power HAWTs represent a viable and efficient solution for wind energy capture in semi-urban and rural built environments. They effectively complement VAWT-focused strategies necessary for dense urban cores by leveraging HAWTs’ superior aerodynamic efficiency where building density and turbulence constraints are relaxed.

4.3. Building Components: Functional Composite Design

Building components create complex wind patterns in urban environments through their unique geometric profiles. These structures can either intensify turbulence through airflow separation or generate localized high-speed zones via structural design. To optimize wind energy utilization efficiency, it is essential to transcend conventional flat roof layouts. This requires optimizing building forms and integrating functional composite designs into ancillary structures. Innovative ancillary designs focus on functional integration, transforming architectural components into wind energy enhancement devices.

Taking the double canopy as an example, the design of a 20° slope and a 0.5 W eave length makes the front one-third of the area of an awning form a significant airflow contraction effect. The wind speed amplification factor reaches 1.6, and the wind energy capture efficiency is 60% higher than that of a flat awning [57,69]. This design not only fulfills the functional requirements of architectural shading and rain protection but also creates efficient wind-capturing zones under low wind speeds through optimized geometry, making it suitable for installing small horizontal axis wind turbines. Its power generation efficiency improves by 40% compared to flat roofs at wind speeds of 3–5 m/s. Wang et al. [102] investigated how different roof eave deflector configurations (height, slope, length, and position) affect wind energy potential in rooftop canopies (Figure 15). The study found that setting up windward eave canopies significantly enhances rooftop wind energy accumulation, with wider canopies yielding better results. The optimal model revealed a 20° positive slope where the deflector upper edge aligns with the roof edge. The authors recommend installing turbines at lower heights in the windward zone, while higher installation heights become necessary as the windward eaves are moved further back.

A considerable breakthrough is the integrated design of an exhaust pipe for super high-rise residential buildings (Figure 16). By embedding Savonius–Darrieus composite blades into the exhaust pipe outlet and utilizing the Venturi effect to form an accelerated flow field, the power generation can reach three times that of a traditional independent system [103]. Kwok and Hu [39] examined the development of building wind energy systems in urban environments, highlighting innovative approaches such as integrated exhaust duct design for super high-rise residential buildings. These systems deeply integrate energy equipment with architectural ventilation infrastructure, aligning with the innovative direction of building wind energy system development. From a macro perspective, this design demonstrates both innovation and cutting-edge features. By merging energy devices with building ventilation systems, it not only addresses startup efficiency challenges in low-wind urban environments but also pioneers a composite design philosophy, where “functional building components serve as energy devices”.

From the functional integration of rooftop canopies to the systematic coordination of exhaust ducts, wind energy utilization fundamentally represents a deep synergy between “architectural form, wind field characteristics, and equipment performance”. Structural optimization employs geometric innovations to shape favorable wind fields, while auxiliary structures enhance localized energy efficiency through multifunctional configurations. Together, these elements overcome technical bottlenecks in complex wind environments. This design strategy not only applies to new constructions but also provides innovative approaches for the low-carbon retrofitting of existing buildings. By optimizing morphological elements, such as curved curtain walls, recessed balconies, and functional canopies, ordinary buildings can be transformed into composite carriers that combine aesthetic value with energy efficiency. This drives urban transformation from “building energy consumption” to “building energy productivity”.

4.4. New Wind Turbine Technologies: Architectural Integration Case Studies

The urban wind environment is affected by building density, terrain, and climate, showing characteristics of low wind speed, high turbulence, and multi-scale flow. It is necessary to build a full-chain technology system covering “evaluation + design + application” through multi-scale flow field simulation, integrated design of buildings and fans, and empirical analysis, so as to improve the scientificity and economy of development.

The refined assessment of urban wind fields relies on the synergy of multi-scale simulation technologies. At the macro level, through coupled analysis of IEC Kaimal turbulent spectrum models and measured wind field data, we can quantify the turbulent energy distribution characteristics of urban wind fields, thereby providing statistically significant climatic background data for wind energy resource evaluation [104]. At the meso-scale urban block level, ventilation potential is analyzed through GIS spatial computation and architectural form parameter analysis. When the building density (BD) exceeds 50% and the surface velocity fraction (SVF) is below 0.5, the ground roughness length (RL) can reach over 2.0 m, creating significant airflow obstruction zones. The narrow tube effect causes local wind speed attenuation of 22–30% [88]. The micro-architectural scale was simulated by CFD large eddy simulation and 1:300 scale wind tunnel test [84,105] to quantify wind speed distribution and turbulence intensity on roofs and facades. It is noteworthy that the non-uniformity of the building height layout significantly affects simulation accuracy. Case studies of high-rise residential buildings in Shenzhen reveal that when building height differences exceed 20 m, traditional RANS models exhibit up to 15% simulation errors in leeward turbulence intensity, necessitating corrections through LES, model adjustments, or 1:150 scale wind tunnel tests [106]. In refined building wind field assessments, steady RANS models combined with the realizable k-ε turbulence model can maintain rooftop wind speed simulation deviations below 8% when simulating rooftop wind fields under urban atmospheric boundary layer (UABL) inflow conditions and it is validated by LiDAR measurements. Research indicates that flat-roof installations achieve optimal turbine heights of 1.51–1.79 times the building height, where turbulence intensity drops below 0.12, reducing turbulent effects by approximately 25% compared to near-roof areas. This micro-siting method provides quantitative guidance for rooftop wind turbine installation in urban environments through a dual-layer strategy: “avoiding high-turbulence zones + identifying wind-speed-amplification zones” [86]. Field measurements in Taiwan’s Tamsui region demonstrate that when horizontal wind speeds exceed 8 m/s, turbulence intensity (TI) converges to 30% and remains stable. In such conditions, small vertical axis wind turbines achieve over 40% efficiency, with 90% of the power output concentrated in wind fields with vertical angles of ≤45°. Furthermore, high turbulence intensity increases power output by 20% during low wind speed periods but reduces it by 10% during high wind speed periods (7–10 m/s). This phenomenon directly relates to the adaptability of the small VAWT composite blade design in turbulent environments [107]. The empirical study by Zhang Yunpeng [20] shows that although horizontal axis turbines have significant efficiency advantages (power coefficient up to 0.42) when the wind speed is >6 m/s, they are highly sensitive to the wind direction, while VAWTs have better comprehensive efficiency stability in urban turbulent environments due to their omnidirectional adaptability and low starting wind speed characteristics.

The deep integration of architecture and ventilation systems is the key to overcoming low wind speed limitations. The omnidirectional guide vane VAWT integrated at facade openings of high-rise buildings achieves a 2-fold increase in inlet wind speed through 30° inclined guide vanes, with torque output rising by 206%. This effectively resolves startup challenges in weak-wind environments [108]. In a Montreal building test case, the roof diffuser cover design accelerates the ambient wind speed from 3 m/s to 4.5 m/s through flow diversion, and the power generation efficiency increased by 35%, which verifies the adaptability of this structure to low wind speed areas [109]. In the multi-energy complementary system, the high stiffness characteristics of carbon fiber blades optimize the coupling effect of the equipment. The blades form a complementary power generation mode with supercapacitors and photovoltaic panels through multi-layer material composite cooperation [110]. A wing-shaped blade airfoil called S1048 adopts a combination structure of a straight edge and a curved section (the straight edge provides a larger moment arm, and the curved section reduces the negative torque of reflux) [111]. Compared with the traditional semi-circular blade, the power coefficient is increased by 14%; therefore, the double-layer blade can start automatically at a wind speed of 3 m/s [35]. The aerodynamic characteristics of three different VAWTs were compared, and the Rose-shaped turbine was proven to be the optimal model compared with the Tulip turbine and the Aeroleaf turbine [112].

CFD simulations of high-rise buildings in Hong Kong show that when the building spacing is ≥2.0 H, the rooftop wind energy density can be enhanced by 1.3–5.4 times, forming a climate zone comparison with the Shenzhen case [113]. In the technical and economic evaluation, a wind energy assessment framework for urbanized regions is proposed, and the proportion of equipment operation and maintenance costs (18% annually) is included in the full lifecycle model [9]. The optimized return on investment cycle can be shortened to 8 years. The equipment coupling design (Figure 17) in the project, which combines wind turbines with parking lots, intuitively demonstrates the practicability of this framework [114]. Through the coordinated optimization of building morphology and equipment spacing, it not only improves wind energy capture efficiency but also reduces operation and maintenance costs, providing an engineering demonstration for the economy of multi-energy complementary systems.

The case studies provide practical references and data support for technology implementation. In the Berlin case, a single 3 kW VAWT generates an average of 2.18 MWh annually, while the city’s total installed capacity can cover 4.95% of the household electricity demand. Economic evaluations indicate that under debt financing, the Levelized Cost of Energy (LCOE) must be combined with subsidy policies to enhance investment feasibility [98]. The application paradigm of HAWTs in low wind speed verification zones holds a greater reference value [81]. Taking the Aventa AV-7 (6.5 kW as an example, simulations in Germany’s Suburban II coastal area (with an average annual wind speed of 5 m/s) show that when coupled with redox flow energy storage, the annual power generation reaches 2.8 MWh, representing a 133% increase compared to VAWT units of equivalent power. However, the balance between wind resistance safety and energy efficiency in engineering practice cannot be overlooked. Long-term monitoring data from the Pearl River Tower show that amplifying the wind speed by 60% leads to a 65% increase in local wind pressure in the turbine installation area. This requires reinforcement through carbon fiber reinforced composite (CFRP) materials and an intelligent pitch control system [58]. Such challenges drive the research into “CFD simulation + structure + equipment” multidisciplinary collaborative evolution. For example, through CFD simulations of a load distribution under different working conditions, combined with a topology optimization algorithm to generate a lightweight support structure, the goal of reducing material consumption by 20% and increasing strength by 15% was achieved [62].

Technical standardization is a key link in promoting large-scale applications. The case of 12 cities in the Netherlands shows that the proposed framework for urban wind energy potential assessments provides policymakers with a quantifiable tool, achieving an accuracy of ±10% in evaluating annual power generation on high-rise building rooftops [66]. To address the current lack of urban turbulence and building interference assessments in existing regulations, researchers recommend developing “Urban Wind Turbine Installation Guidelines” by integrating IEC standards with urban characteristics. These guidelines should specify noise limits (≤55 dB), safety distances (≥1.5 times the building height), and wind-resistant design standards (withstanding 50-year possibility wind speeds), focusing on equipment compatibility and safety under complex wind field conditions [56,115].

From precise evaluations of multi-scale simulations to innovative building–turbine integration, and then to evidence-driven standardization, urban wind energy development is transitioning from fragmented technical experiments to systematic implementation. Future efforts should further integrate meteorological data, Building Information Modeling (BIM), and smart control technologies to establish a comprehensive “evaluation + design + application” system. This will facilitate cities’ transformation from energy consumers to renewable energy production units, providing efficient and cost-effective solutions for achieving the “dual carbon goals”.

5. Discussion: Urban Wind Energy’s Distinctive Development Pathways

5.1. Technical and Applicability Differentiation from Traditional Wind Energy

Urban wind energy diverges fundamentally from conventional wind farms in four aspects (as shown in

Table 8. Urban wind energy vs. traditional wind energy: key contrasts.

Aspect	Urban Wind Energy	Traditional Wind Energy
Wind Resource	Low speed (3–6 m/s), high turbulence (TI > 20%)	Stable, high-speed (>7 m/s) flow, low turbulence
Technical Focus	Building morphology–flow interaction (e.g., V-shaped layouts, curved roofs)	Turbine efficiency optimization and site selection
Constraints	Space limitations (rooftops/facades), noise sensitivity, structural safety	Land availability, long-distance transmission, grid integration
Evaluation	LCA with building integration costs, multi-criteria (efficiency + safety + comfort)	Primary focus on amount of electricity generated

), reflecting its unique technical logic and application scenarios:

Resource Characteristics: Urban environments are dominated by low wind speeds (typically 3–6 m/s) and high turbulence intensities (TIs > 20%) due to building obstructions, street canyons, and surface friction [2,12]. This contrasts sharply with traditional wind farms, which rely on stable, high-speed flows (>7 m/s) in open, low-turbulence areas [20,101].

Technical Focus: In urban contexts, building morphology functions as an active flow control tool rather than a passive constraint. For example, curved roofs with R = 1.5 W boost edge wind velocity by 45% [87]. Traditional wind energy, by contrast, prioritizes turbine aerodynamics and wind farm layout optimization to reduce wake losses [101].

Evaluation Systems: Urban wind energy requires a lifecycle assessment (LCA) that integrates architectural costs, such as 65% of the total expenses attributed to equipment [12], and multi-field constraints, including structural safety (wind-induced vibration) and noise limits (≤55 dB for residential areas) [56]. Traditional wind energy evaluation focuses primarily on electricity generation costs [11].

5.2. Re-Evaluation of Key Findings

The key findings of the review can be summarised on the comparative impact evaluation of building morphological parameters on wind energy capture performance (as shown in

Table 9. Comparative impact of building morphological parameters on wind energy capture performance.

Parameter Category	Optimal Configuration	Key Performance Indicator	Optimization Effect	Applicable Scenario
Planar Layout	V-shaped layout (λp = 0.76)	Mid-row roof wind speed/turbulence intensity	↑ 22% speed/↓ 18% turbulence	High-density clusters (λp > 0.6)
	Determinant layout (λp < 0.4)	Front-row wind power density	> 200 W/m2 (meets Class A turbine criteria)	Low-density areas
	Staggered layout (0.5 W offset)	Wake turbulence interference	↓ 20% turbulence intensity	Medium-/high-density clusters
3D Forms	Conical building (top width/H = 0.7)	Wind load/roof speed std. deviation	↓ 20–25% load/↓ 40% speed fluctuation	Typhoon-prone supertall buildings
	Stepped terrace (recess ratio ≤ 0.1 W)	Inter-floor speed difference	Controlled to <10% (vs. 25% in rectangular bldgs.)	Commercial complexes
	Convex-curved cluster (60° angle)	Edge wind speed amplification	1.5–1.8 times	High-density urban areas
	20% corner chamfering	Roof-edge speed uniformity/turbulence intensity	↑ 25% uniformity/↓ 15% turbulence	High-rise corner zones
Parameter Category	Optimal Configuration	Key performance indicator	Optimization effect	Applicable scenario
	Dome/arch roof (R = 1.5 W)	Edge zone wind speed	↑ 45% (vs. flat roofs)	High-rise buildings
	Double-slope canopy (20° pitch)	Wind amplification factor (F)	Up to 3.58	Low-rise buildings
Synergistic Strategy	High-density: V-shape + curved roof	Average wind speed gain	↑ 20%	High-density urban cores
	Supertall openings (15% aperture)	Speed amplification/wind-induced vibration	↑ 27% speed/↓ 18% vibration response	Supertall buildings

Note: in the table, ↑ means increase, while ↓ means reduce.

). However, some further re-evaluation can be given.

Parameter Synergy: The “density + ayout angle + roof form” collaborative strategy—such as 20% wind speed gains in high-density areas using V-shaped layouts combined with curved roofs [8]—highlights that architectural parameters must be optimized collectively, not in isolation. Isolated adjustments (e.g., roof design without layout optimization) fail to maximize wind energy capture (Section 3).

Wind turbine Limitations: Despite advantages in turbulence resistance and omnidirectional operation, vertical axis wind turbines (VAWTs) are 20–30% less efficient than horizontal axis wind turbines (HAWTs) (Cp < 0.35 vs. HAWTs’ 0.42) [2,101]. Morphological adjustments alone cannot resolve this gap; innovations like diffuser-enhanced designs [106,107,108,109] or hybrid H-Darrieus turbines [107,108,109,110] require deeper integration with building forms (Section 4.1).

5.3. Methodological Challenges

Simulation Accuracy: Errors persist in complex flow simulations, with deviations up to ±15% for conical buildings [26], primarily due to inadequate modeling of multi-row wake interference and dynamic turbulence [30]. Machine learning-enhanced large eddy simulation (LES) models offer a promising solution to refine micro-flow predictions [31].

Scalability Gaps: Validated strategies, such as the 15% aperture ratio for supertall buildings that amplifies wind speed by 27% [7], lack city-scale applicability. The “city–block–building” multi-scale framework needs to be tested in diverse urban typologies (e.g., high-density vs. suburban areas) to ensure generalizability.

6. Conclusions

This review establishes that the coordinated optimization of building morphological parameters, mainly planar layouts, 3D forms, and roof configurations, significantly enhances urban wind energy capture.

V-shaped layouts in high-density areas elevate mid-row wind speeds by 22% while reducing turbulence by 18%, 20% corner chamfering improves roof flow uniformity by 25%, and specialized designs like Venturi roofs boost power output by 1.38 times at low wind speeds (≤3 m/s). The “city–block–building” multi-scale simulation framework quantifies interactions between urban morphology and wind resources but requires broader validation across diverse city typologies. Vertical axis turbines (VAWTs) offer superior adaptability to turbulent urban environments due to omnidirectional operation, yet their efficiency remains 20–30% lower than horizontal axis turbines (HAWTs), necessitating deeper integration with architectural features.

Scalability hinges on full lifecycle economic viability—equipment acquisition constitutes 65% of the costs, while current assessments overlook emissions from material production (45% of the small turbine lifecycle carbon footprint). Future efforts must develop integrated LCA-BIM-CFD platforms and establish Urban Building Wind Energy Design Codes addressing noise limits (≤55 dB) and safety standards. Critical research gaps include simulation inaccuracies and scalability challenges. Advancing machine learning-enhanced turbulence models and turbine–architecture co-design (e.g., hybrid blades/functional building components) will accelerate the transition of buildings from energy consumers to distributed renewable energy producers.