Urban Wind as a Pathway to Positive Energy Districts

Abstract

The increasing demand for decarbonized urban environments has intensified interest in integrating renewable energy systems within cities. This review investigates the potential of urban wind energy as a promising technology in the development of Positive Energy Districts, supporting the transition toward climate-neutral urban areas. A systematic analysis of recent literature is presented, covering methodologies for urban wind resource assessment, including Geographic Information Systems (GIS)-based mapping, wind tunnel experiments, and Computational Fluid Dynamics simulations. The study also reviews available small-scale wind technologies, with emphasis on building-integrated wind turbines, and evaluates their contribution to local energy self-sufficiency. The integration of urban wind systems with energy storage, Power-to-Heat solutions, and smart district networks is discussed within the PED framework. Despite technical, economic, and social challenges, such as low wind speeds, turbulence, and public acceptance, urban wind energy offers temporal complementarity to solar power and can enhance district-level energy resilience. The review identifies key technological and methodological gaps and proposes strategic directions for optimizing urban wind deployment in future sustainable city planning.

1. Introduction

In the face of global climate challenges, cities and urban centers have emerged as pivotal agents of change, driven by the urgent imperative to reduce greenhouse gas emissions and enhance resilience against environmental disruptions. Cities play a central role in the global climate transition because they consume 78% of the world’s energy and are responsible for over 60% of greenhouse gas (GHG) emissions [1]. Furthermore, the influence of cities is projected to expand significantly, as urban populations are expected to grow by 2.2 billion by 2050, representing nearly 70% of the world’s inhabitants. This surge will escalate the demand for energy, infrastructure, and essential resources [2]. Considering these facts, improving residents’ quality of life and supporting cities in their transition towards climate neutrality are essential. In recent years, energy transition has become a priority [3]. As a cornerstone of comprehensive, sustainable urban development, the concept of Positive Energy Districts (PEDs)—also known as climate-positive neighborhoods—has been introduced. PEDs build on a lineage of influential ideas, such as net-zero energy buildings, nearly zero-energy structures, energy-neutral districts, and climate-positive communities, and evolve these principles into a broader, neighborhood-scale approach [4].

The concept of PEDs was formally introduced in 2018 and later incorporated into key European initiatives, including the “Renovation Wave Strategy” and the Horizon Europe Framework Programme through the Driving Urban Transition Partnership [5]. Nowadays, PEDs are considered a comprehensive, multisectoral approach that integrates multiple energy systems—including electricity, heating, cooling, and storage—alongside coordinated interactions among users, buildings, regional grids, transport networks, and Information and Communication Technology (ICT) infrastructure. They serve as a strategic framework for harmonizing climate and energy objectives [6]. The concept of PEDs builds on earlier models of nearly zero, net zero, or positive-energy buildings. It expands these principles by emphasizing energy reciprocity between buildings and broader urban decarbonization efforts, fostering integrated energy flows and collective sustainability at the district scale [7]. However, implementing PEDs in established city areas poses challenges due to technological constraints, fragmented ownership, heritage protections, and the need to preserve architectural character. Given that roughly 75% of the EU’s building stock is energy-inefficient, retrofitting existing districts is a critical priority. As noted by Terés-Zubiaga et al. [8], accelerating energy savings in the building sector is most effectively achieved through district-level renovations, which target clusters of buildings for simultaneous upgrades.

Advancing the energy transition is a critical priority in meeting sustainable development goals, positioning PEDs as a hallmark of decarbonized urban planning. Such districts incorporate various renewable energy (VRE) sources, Power-to-X (P2X) systems, as well as short-term and long-term energy storage [9,10]. To support a transition, energy modeling tools have become essential for analyzing consumption patterns and carbon emissions across urban areas. By simulating diverse scenarios, district energy simulations empower urban planners and decision-makers to craft context-specific decarbonization strategies. This involves optimizing the mix of renewable and non-renewable energy sources, identifying cost-effective pathways to reduce emissions, and planning for the integration of advanced technologies and infrastructure [11]. Driving this shift is a move away from building-level energy strategies toward a broader focus on urban-scale performance. This evolution is reinforced by near-zero energy design policies that emphasize on-site renewable energy generation and a balanced energy footprint.

Designing an effective and sustainable PED requires a systemic, holistic methodology that considers technological complexity alongside environmental and socio-economic dimensions [12]. Optimizing design and performance is the first step toward developing positive-energy buildings and districts. In this context, sustainable architecture encompasses the deliberate reduction in environmental impact by efficiently using building materials, energy, and spatial resources. Consequently, building-integrated concepts have gained prominence, wherein renewable energy technologies are seamlessly incorporated into architectural elements. Although energy generation in NZEDs typically relies on solar photovoltaics (PV), solar thermal systems, and both shallow and deep geothermal energy, micro- and small-scale wind turbines are also promising technologies. They can be widely deployed to complement PV systems, particularly during periods of low solar availability [13,14]. In this context, urban wind turbines can be considered a clean, safe, and environmentally sustainable way to harness wind energy in urban environments. Its implementation can take various forms, including integrating rotors into existing buildings, placing them in adjacent urban spaces, or incorporating them into the design of future architectural developments (see Figure 1). As cities face increasing energy demands, building-integrated wind turbines (BIWTs) offer a strategic, sustainable solution to reduce the carbon footprint of urban structures [15,16].

Urban wind energy is increasingly promoted for its untapped potential and its ability to generate power close to the point of use, offering a decentralized and cost-effective alternative to long-distance electricity transmission. However, its implementation faces several challenges. Micro- and small wind turbines in urban settings tend to be less efficient and economically viable due to lower average wind speeds and higher turbulence compared to open or offshore areas. Buildings further complicate wind flow by causing frequent and unpredictable changes in wind direction, which can reduce performance. Additional concerns include noise, safety, structural vibrations, visual impact, wildlife risks (particularly for birds and bats), and public acceptance. Despite these limitations, urban wind remains a promising complement to other renewable sources, particularly when integrated thoughtfully into the built environment.

Implementing wind turbines in the urban environment requires a framework that accounts for building data, wind speed characteristics, and turbine characteristics. Such a framework consists of a few main steps illustrated in Figure 2. The process begins with identifying and collecting data on buildings and specific district locations suitable for urban wind energy harvesting. Next, annual mean wind speed statistics are gathered for each sub-domain, corresponding to the representative height of each building height group. In the third step, the technical specifications and performance characteristics of the potentially considered wind turbines are analyzed. The final step involves designing and integrating the turbines into the urban environment, ensuring compatibility with the specific buildings, grid, and other relevant factors.

Within the conceptual framework outlined above, this review aims to systematically delineate the principal stages involved in urban wind resource assessment and to evaluate the current state of small-scale wind turbine technologies tailored for urban deployment. Particular emphasis is placed on the interdependencies between assessment methodologies and turbine integration strategies, especially in the context of their potential implementation within PEDs. Given the emergent nature of this research domain and its relevance to accelerating the transition toward sustainable and climate-neutral urban environments, a comprehensive and rigorous examination is both timely and essential.

This review is structured as follows. Section 2 presents the applied methodology, including the criteria and process used for literature selection. Section 3 discusses the main approaches to urban wind resource assessment, encompassing GIS-based mapping, wind tunnel experiments, and computational fluid dynamics (CFD) simulations. Section 4 reviews the available urban wind energy technologies, with a focus on horizontal-and vertical-axis wind turbines and their integration into buildings. Section 5 presents selected case studies of urban wind projects illustrating practical implementations and challenges. Finally, Section 6 summarizes the key findings and outlines future research directions.

2. Methodology

This review aims to provide a comprehensive overview of current research on urban wind assessment and technologies in the context of developing Positive Energy Districts. To ensure transparency and reliability, a systematic approach was used to select and analyze literature. The literature review process involved several stages:

• Titles and abstracts were first reviewed to evaluate their relevance to the research topic.
• Full-text articles were then examined to identify scientific and technical contributions, methodologies, and key findings pertinent to the study.
• The extracted findings were categorized into thematic areas and incorporated into the corresponding sections of the review.

Relevant publications were retrieved from major scientific databases, including Scopus, Web of Science, ScienceDirect, and EBSCO, using combinations of keywords such as urban wind, urban wind energy assessment, Geographic Information Systems, GIS, wind tunnel experiments, Computational Fluid Dynamics, CFD, urban wind energy technologies, building-integrated wind turbines, and Positive Energy Districts. The literature retrieved from these databases was supplemented with information obtained from carefully selected websites relevant to the topic. The following criteria were applied to refine the results:

• peer-reviewed journal articles and conference proceedings were preferred—120 (98.4% of all references);
• articles published in the last 10 years were primarily considered—110 (91.7%), including 79 articles published in the period 2020–2025 and 31 articles published in the period 2015–2019;
• articles from well-established editorial sources were mainly cited, including Elsevier, MDPI, Wiley, Springer Nature, Frontiers, EDP Sciences, IOPscience, and IntechOpen.

3. Overview of Urban Wind Energy Assessment

Wind speed distribution is strongly influenced by local topography and urban morphology. Architectural structures introduce surface roughness, disrupting airflow and reducing wind velocity—most significantly in densely built environments and least over open water. As wind moves from open terrain into urban zones, it encounters building-induced resistance, forming a transitional layer where flow slows and turbulence intensifies due to ground friction [18,19]. Moreover, urban wind conditions play a crucial role in residential comfort, affecting ventilation, air quality, heat exchange through building envelopes, and rain exposure on facades. High wind speeds can cause discomfort for pedestrians, particularly during colder seasons. Consequently, urban planning prioritizes comfort, often leading to reduced wind speeds that limit the potential for urban wind energy utilization [20,21]. Therefore, airflow characteristics around urban buildings refer to the wind flow patterns resulting from the interaction between built structures and surrounding climatic conditions. Understanding these dynamics is crucial for assessing and optimizing urban wind energy utilization, as well as for the effective design and placement of wind turbines. Several literature reviews have addressed the complexities of urban wind flow, providing valuable insights. Among other insights, Arnfield [22] provided a thorough overview of current research in the field, highlighting key studies on airflow and turbulence at various scales within urban environments. The author drew on existing literature to distinguish between two essential atmospheric layers: the Urban Canopy Layer (UCL), which spans from ground level up to rooftop height, and the Urban Boundary Layer (UBL), which extends above the rooftops and is shaped by the urban landscape. Both layers play a significant role in assessing the potential for urban wind energy harvesting. The urban wind profile, mainly composed of the UBL and the UCL, is presented in Figure 3.

Furthermore, both individual buildings and groups of buildings significantly impact the surrounding airflow. In urban environments, wind behavior varies depending on wind direction, architectural geometry, and atmospheric conditions. The interaction between built structures and prevailing wind generates localized acceleration zones and turbulence, shaping the overall wind dynamics within the city [24]. Representative examples of these flow disturbances are illustrated in Figure 4. It can be observed that, among other factors, vortices, wind protection, the Venturi effect, and channeling significantly impact wind conditions. On the other hand, in realistic urban settings, small wind turbines operate amid both buildings and vegetation, especially tall street trees. Buildings tend to enhance wind turbine performance by increasing turbulence and mean wind velocity through roof-induced acceleration effects. In contrast, trees reduce available wind energy by extracting momentum from the flow. Most research to date has focused on building-induced flow effects while neglecting vegetation’s impact [25]. Consequently, accurate assessment of wind conditions for energy applications requires site-specific, year-long measurements at the intended installation height of the wind turbine. Reliance on data from local meteorological stations often leads to erroneous estimates of turbine performance, as such stations typically record at a fixed height (usually 10 m) over flat, open terrain—conditions that differ markedly from most urban environments. From this perspective, this review examines the methods for assessing urban wind resources and then discusses the available urban technologies with potential for integrating urban wind into PED energy systems.

The Atmospheric Boundary Layer (ABL) is the lowest part of the troposphere, typically extending up to 1 km, where wind patterns are significantly influenced by the roughness and characteristics of the Earth’s surface. Wind measurements in the upper regions of the ABL, as well as at altitudes beyond it, are commonly obtained by tracking weather balloons with Doppler radar, satellite observations, or ground-based Sound Detection and Ranging (SODAR) systems. However, these methods typically offer low spatial resolution. At lower altitudes, particularly below 100 m, point-based wind measurements are generally performed using anemometers mounted on tall masts. While such masts enable long-term, continuous data collection, their installation is both logistically challenging and costly, and they provide only a limited number of lateral sensing positions once deployed [27]. This poses a significant constraint for applications that demand high spatial resolution across multiple locations, such as wind assessments near buildings or at turbine sites. Ground-based Light Detection and Ranging (LIDAR) systems can extend wind measurement capabilities to altitudes of approximately 1 to 2 km. Still, they are expensive, offer limited spatial resolution, and yield spatially averaged data over relatively broad sensing volumes [28].

Wind resource assessment in urban areas represents a special case of such studies. Assessing urban wind resources is crucial for optimizing wind energy generation, as it demands precise insights into the complex dynamics of urban airflow. On the other hand, characterizing wind potential in urban environments presents significant challenges due to the complex influence of built structures on atmospheric flow. Buildings frequently induce flow separation, reduce wind speeds, and generate intense turbulence both above and around their surfaces. Therefore, while on-site measurements offer direct insights into wind resources, their application in urban environments is limited by the low spatial and temporal resolution of complex wind patterns and the inability to control boundary conditions. Additionally, installing measurement equipment in dense urban settings can lead to significant inaccuracies due to interference with building topography [29]. On the other hand, wind tunnel experiments, although valuable for simulating controlled flow fields, are often cost-prohibitive—particularly when high-resolution data is required [30]. To overcome these challenges, mapping urban wind resources using Geographic Information Systems (GIS) and analyzing wind behavior using Computational Fluid Dynamics (CFD) has emerged as a practical, scalable approach for assessing wind potential in urban areas [31,32].

3.1. GIS-Based Mapping

Mapping urban wind resources using GIS represents a robust spatial analysis methodology that integrates meteorological, topographical, and urban morphological data to identify optimal locations for wind energy deployment within cities. GIS platforms enable the synthesis of wind speed and direction data with detailed urban features, including building footprints and heights, terrain roughness, vegetation cover, and land use constraints. By interpolating and downscaling wind data to the urban scale, GIS-based analyses can produce high-resolution maps of wind speed, power density, and turbine suitability across various elevations, such as rooftops, façades, and open spaces [33,34]. These maps are further refined using exclusion criteria, including noise-sensitive zones, heritage areas, and aviation corridors, to delineate feasible installation sites. Additionally, GIS facilitates the extraction of morphological indicators—such as frontal area index and ventilation pathways—which are critical for evaluating the influence of urban form on wind flow and turbine siting potential [35]. Beyond resource assessment, GIS outputs can be integrated with urban energy demand datasets, offering planners a decision-support framework to align local renewable energy supply with district-level consumption patterns [36]. Despite challenges such as the turbulent and variable nature of urban wind, the limited availability of fine-scale measurement data, and the computational intensity of CFD integration, GIS-based mapping remains a cost-effective and scalable approach for translating complex wind dynamics into actionable insights.

Examples of research performed using GIS-based mapping. Analyzing literature reveals numerous studies that use GIS methods for wind resource assessment; however, most focus on large-scale areas rather than cities. Wang et al. [37] introduced a GIS-based methodology for municipal renewable energy planning, which was experimentally applied in Kawamata Town, Fukushima, Japan. The proposed approach involved identifying local issues, evaluating and visualizing the renewable energy potential (wind, solar, biomass), comparing sites based on criteria such as solar radiation and land use, and conducting scenario analysis. Two planning scenarios were compared to evaluate the optimal placement of facilities within and outside evacuation zones. The results offered valuable insights into local energy potential and support interactive, post-earthquake planning aligned with the town’s energy development goals. Yildiz [38] assessed the wind energy potential of Balıkesir Province in Turkey using GIS tools. The author analyzed wind speed data from 32 meteorological stations at 10 m above ground level and extrapolated to 100 m based on land cover characteristics. A wind speed map was generated for the region, and the calculated wind energy potential for Balıkesir and its districts was categorized by wind speed ranges and compared with Turkey’s official Wind Energy Potential Atlas (REPA). The results highlighted Balıkesir’s promising capacity for wind energy development and offer a replicable methodology for regional planning and investment analysis. Wong et al. [39] presented a detailed urban ventilation study in Hong Kong using the building frontal area index as a roughness parameter. The index was calculated from 3D building data across different land use types, revealing notably high values in commercial and industrial zones, primarily due to the presence of high-rise buildings. To assess city-scale ventilation, the study employed an innovative overlay of Least Cost Path (LCP) segments, identifying four dominant airflow routes from the east and northeast. Validation with in situ wind measurements confirmed the model’s accuracy. Rustemli et al. [40] developed a provincial-scale wind power site selection map using GIS, incorporating both environmental risks and criteria. A PCE-FWS 20 wind measurement station was installed at Bitlis Eren University’s Rahva campus, collecting meteorological data over one year at 5 s intervals, with averages taken every 20 min. Statistical analysis, including Weibull distribution modeling and graphical techniques, was performed in MATLAB to assess wind energy potential. Monthly wind speed and power density were charted, confirming that the Rahva campus is situated in a favorable zone for wind energy development. Sanchez-Lozano et al. [41] demonstrated that GIS platforms are highly effective tools for addressing complex spatial localization challenges and for generating foundational territorial databases. In the specific case analyzed, the coastal region of Murcia was identified as a particularly suitable area for onshore wind farm development, with 19.94% of the territory deemed viable after applying relevant constraints. Diaz-Cuevas et al. [42] discussed a decision support model that integrates GIS with multi-criteria decision-making methods to identify optimal sites for wind turbine installation at the regional scale. Focusing on the province of Cadiz—an area with an established wind energy infrastructure—the study examined two scenarios based on major and minor constraints, analyzed already developed wind energy regions, and assessed unsuitable zones for turbine deployment. Mentis et al. [43] evaluated the onshore wind power potential across Africa using advanced wind energy technology and GIS-based analysis. The authors estimated the theoretical, geographical, and technical capacities by mapping wind speeds at 80 m hub height and applying wind power curves. Results highlighted countries with high wind energy yields—such as South Africa, Sudan, Algeria, Egypt, and Morocco—while others, like Equatorial Guinea, Gabon, and Liberia, showed minimal potential. The findings offered a comparative overview and support strategic planning for wind energy development on the African continent. Khodakarami et al. [44] presented a comprehensive framework for identifying optimal wind farm locations in the Kurdistan Region of Iraq by integrating remote sensing, GIS, and multicriteria decision-making methods. The authors found that approximately 21% of the region (8277 km²) offers excellent or good wind energy potential, with an estimated capacity exceeding 48,000 MW and 3332 viable sites each exceeding 3 MW. The study projected an annual output of 42.9 TWh, potentially saving 5.8 million tons of natural gas and reducing CO₂ emissions by 16 million tons.

3.2. Wind Tunnel Experiments

Another method for assessing urban wind resources is to use experiments in wind tunnels. Such experiments can be considered as a foundational methodology for investigating airflow interactions at the urban scale and for validating computational or GIS-based predictions. These experiments typically involve placing scaled models of urban layouts or building clusters within a wind tunnel configured to simulate the atmospheric boundary layer. Specially developed setups enable direct measurement and visualization of flow fields in realistic urban configurations, including key flow parameters such as velocity fields, turbulence intensity, aerodynamic forces, and flow separation phenomena [45,46]. In wind tunnel experiments, measurements are performed using various techniques, including hot-wire and hot-film anemometry (HWA, HFA), Irwin probes, thermistor anemometers, sand erosion methods, laser Doppler anemometry (LDA), infrared thermography (IR), and particle image velocimetry (PIV). Each of these techniques operates on distinct physical principles and requires a specialized experimental setup [47]. Wind tunnel testing is considered one of the most effective techniques for investigating the aerodynamic behavior of buildings with diverse geometries and for analyzing localized urban airflow dynamics. It is particularly valuable for examining complex urban morphologies, including variations in building density, height, and floor area ratio [48,49]. On the other hand, despite their strengths, wind tunnel experiments face several limitations, including challenges in accurately scaling Reynolds numbers, replicating the complexity of urban environments, and mitigating instrumentation interference. Nonetheless, when combined with CFD or GIS-based modeling, wind tunnel data can serve as essential ground truth for calibrating simulations of wake interactions, turbulence, and micro-scale flow behavior.

Examples of research performed using wind tunnel experiments. Several recent studies have demonstrated the versatility of wind tunnel testing in urban contexts. Liu et al. [33] highlighted the importance of morphometric evaluations for urban canopy modeling, especially when assessing buildings still in the design phase or estimating roughness height in complex urban areas where traditional anemometric methods fall short. Using wind tunnel experiments, the authors investigated how the planar area, frontal area, shape, and layout of buildings affect their aerodynamic behavior, specifically the drag coefficient. The results revealed consistent patterns across all four parameters, with notable peaks in drag coefficient linked to shape and layout, as well as planar and frontal densities. Li et al. [50] investigated urban wind behavior in a typical area of Nanjing, China, using a spatial partitioning method to divide the complex urban landscape into subspaces. Each subspace was characterized by three spatial indices: openness, area, and shape. From these, 24 subspaces (12 pairs) with distinct spatial features were selected for correlation analysis and used to guide the placement of 45 measurement points in a wind tunnel experiment. The experiment measured the mean wind velocity at four wind directions and found that wind speed ratios across the subspaces remained consistent regardless of wind direction, indicating that spatial characteristics—particularly openness—play a more significant role in shaping wind behavior than directional changes. A plane diagram and a photo of the model analyzed by the authors in the wind tunnel are shown in Figure 5.

Ricci et al. [51] investigated the urban boundary layer in a district of Livorno, Tuscany, using wind tunnel experiments with a 1:300 scale physical model. Two measurement campaigns were conducted: one focused on the urban canopy layer within a curved street canyon, and the other focused on the evolution of the boundary layer with fetch above the urban model. Key parameters—friction velocity, roughness length, and zero-plane displacement—were analyzed through vertical velocity profiles. Results showed a sharp increase in friction velocity and roughness length at the transition from sea to urban terrain, followed by a gradual decrease inland. At the same time, the zero-plane displacement rises slightly with building height. Dar et al. [52] investigated the impact of subtle changes in the roof-edge geometry on the performance and wake behavior of a horizontal-axis wind turbine mounted on a cube-shaped building. An idealized urban canopy model was set up inside the test section to simulate UBL. Three edge designs—sharp, rounded, and fenced—were tested in wind tunnel experiments. Results showed that turbine power output varies significantly with position and roof-edge shape, with a rounded edge offering the most consistent performance. Furthermore, the roof edge shape also affected wake dynamics: as observed, the fence design produced the highest wake recovery and expansion rates, which decreased with smoother edges. Michalek and Zacho [53] conducted wind tunnel experiments at Aerospace Research and Test Establishment to analyze flow and pollutant dispersion over a scaled model of an industrial area and adjacent residential zone. A ground-level emission source was introduced, and airflow was measured using a hot-wire anemometer, while pollutant concentrations were tracked with a flame ionization detector. The collected data validated a newly developed computational model for simulating emission flow and dispersion in urban environments, thereby enhancing its reliability for future applications. Ishida et al. [54] conducted wind tunnel experiments to investigate the effects of wind pressure on low-rise buildings situated near a high-rise structure (see Figure 6). By testing 72 wind directions in 5° increments, researchers evaluated the impact of wind direction on pressure coefficients around surrounding buildings. At a 30° wind angle, the most extreme pressure values were recorded: a positive peak 1.4 times higher and a negative peak three times greater than in scenarios without a nearby high-rise. These findings highlight that accelerated wind flows caused by tall buildings can significantly increase wind loads on adjacent low-rise structures, potentially leading to unexpected damage.

Several papers concentrate on investigating the aerodynamic flow behavior around a single building using wind tunnel testing. In this context, Hemida et al. [55] examined rooftop airflow over a high-rise building using wind tunnel measurements of velocity and pressure across four wind directions and two roof geometries. Surface pressure patterns on flat roofs aligned with findings from low-rise building studies, exhibiting consistent contours for each wind direction. A separation bubble formed at 0°, while cone vortices dominated at 30° and 45°. The optimal wind direction for small wind turbine installation was 45°, offering significant wind amplification with low turbulence. Additionally, roof decking improved flow conditions by reducing directional sensitivity while maintaining the beneficial speed-up effect. The two building models tested in the wind tunnel are presented in Figure 7.

Furthermore, Sari and Cho [56] investigated the aerodynamic behavior of square and top-rounded building models equipped with BIWTs, focusing on optimizing turbine placement relative to wind velocity variations. Wind tunnel experiments and CFD simulations were conducted using three scaled models (1:150) representing urban conditions in Seoul, South Korea. The atmospheric boundary layer wind tunnel, where the experiments were conducted, is shown in Figure 8. Key findings reveal that aligning turbines with streamlines on flat rectangular roofs significantly enhances wind speed compared to elevated placements. Additionally, buildings with rounded corners generate wind velocities up to 34% higher than those with sharp edges.

There are also papers related to the aerodynamic characteristics of single wind turbines, for example, Szczerba et al. [57] investigated the performance of a vertical-axis H-type wind turbine, focusing on how blade pitch angle affects its aerodynamic characteristics. The scope of the work encompassed a wide range of aerodynamic phenomena. The authors sought to capture a comprehensive set of parameters and variables essential to understanding the complex aerodynamic processes. Using a modified NACA0015 airfoil, wind tunnel tests over a Reynolds number range of 50,000 to 300,000 were conducted. A full-scale four-bladed turbine model was tested to analyze the power coefficient distribution as a function of tip speed ratio at varying pitch angles. The results highlighted the impact of angle-of-attack variations and resulting aerodynamic forces, especially in horizontal configurations with vertical rotation.

3.3. CFD in Urban Wind Resource Assessments

CFD plays a pivotal role in analyzing wind behavior in urban environments by providing high-resolution, three-dimensional simulations of airflow, turbulence, and wake phenomena. Its ability to capture detailed wind dynamics makes it an essential tool for assessing urban wind resources. CFD enables high-resolution reconstruction of urban airflow across both spatial and temporal dimensions, offering a cost-effective and reliable alternative. Advances in hardware capabilities and the widespread adoption of commercial CFD software have further enhanced the efficiency and accessibility of CFD-based wind analysis [31,32]. Depending on the study’s scale, complexity, and specific objectives, various CFD methodologies are applied to tailor the analysis effectively, including

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RANS (Reynolds-Averaged Navier–Stokes). This is the most widely used approach for urban wind simulations due to its computational efficiency. This method relies on the Reynolds decomposition to separate the flow into mean and fluctuating components, introducing unknown Reynolds stresses that are resolved using turbulence closure models, such as the k-ε, k-ω, and SST k-ω models. RANS is considered an industry standard for urban wind flow and pollution dispersion studies due to its computational efficiency. However, it has limitations, particularly in accurately representing vertical wind speed gradients and turbulence in the ABL [58,59].

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LES (Large Eddy Simulation). This offers higher fidelity by resolving large-scale turbulent structures. LES is increasingly applied in detailed studies at the pedestrian level and for siting urban wind turbines, though it demands significant computational resources. LES models use low-pass filters to solve the Navier–Stokes equations by focusing on small-scale turbulent motions. LES offers a balance between accuracy and computational efficiency, producing results with higher accuracy than RANS [60,61].

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Hybrid RANS–LES Methods. These approaches, such as Detached Eddy Simulation (DES), strike a balance between accuracy and computational cost by combining the strengths of RANS and LES [62,63].

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Coupled Mesoscale–CFD Models. These integrate regional-scale models (e.g., WRF) with CFD domains to enable downscaling from mesoscale to microscale, effectively bridging the gap between broader atmospheric dynamics and localized urban wind behavior [64,65].

Examples of research performed using CFD assessments. As CFD has proven to be a robust and scalable approach for analyzing wind flow and evaluating its potential for energy harvesting, many authors have used it in their studies. In this context, Liu et al. [66] assessed the use of meteorological station data to simulate wind distribution in urban communities. A full-scale urban model (2–20 km) was built, bridging micro- and mesoscale modeling, and solved using steady RANS equations. Two ground roughness methods were tested, with one providing better near-ground wind data. Compared to a micro-scale model focused solely on the community, the full-scale model yielded wind velocities closer to rooftop measurements—20% higher than observed, versus nearly double the value in the micro-scale model. Despite its complexity, the full-scale model was recommended for accurately predicting urban wind. Zhang et al. [67] explored air ventilation assessment (AVA) in urban environments influenced by twisted wind profiles, which occur due to hilly terrain, such as that surrounding Hong Kong. Unlike conventional wind profiles used in most wind tunnel experiments, twisted wind profiles involve directional changes with height. The research employed an LES-based CFD model aligned with best practice guidelines and conducted six simulation cases. The CFD results, validated against existing wind-tunnel data, showed that twisted wind profiles significantly affect the vertical distribution of wind speed and AVA outcomes. These insights enabled a more accurate analysis of wind-structure interactions, supporting the development of improved urban planning strategies for sustainable city development. Tominaga [68] conducted CFD simulations of total power generation from VAWT installed on the Niigata Institute of Technology campus. While rooftop wind velocity predictions showed significant deviations, the accuracy at the turbine site, 4 m above ground, was acceptable. Initial power generation estimates, based on the turbine’s rated output, exceeded actual measurements by 56%. However, when using empirically derived power curves, the predicted energy output closely matched the observed values, with an error of only 3%. These findings emphasize that both wind velocity accuracy and precise power curve characterization are critical for reliable power generation forecasting in urban wind applications. Juan et al. [69] evaluated the wind energy potential in dense high-rise building configurations. Using CFD simulations validated by wind tunnel experiments, the authors analyzed how building arrangement and height differences affect mean wind velocity and power density in areas between and above buildings. Key findings showed that narrow upstream passages, larger streamwise spacing, and equal building heights allow enhancing wind power density between upstream buildings. Conversely, wider passages, shorter spacing, and lower downstream building heights are favorable for energy harvesting between downstream structures. As was finally concluded, horizontally mounted vertical-axis wind turbines are the most effective solution for capturing wind energy in the discussed urban passages. Tabrizi et al. [70] conducted CFD simulations in urban settings, revealing that turbine performance was highly dependent on architectural and aerodynamic parameters, including building height and geometry, roof configuration, prevailing wind direction, and turbine hub elevation. Li et al. [71] evaluated the reliability of CFD simulations in urban environments by classifying spaces into main streets, inner streets, and non-street areas, and comparing simulation results with wind tunnel measurements. Using the standard k-ε turbulence model, the CFD effectively captured general wind flow patterns, although prediction accuracy varied with urban morphology. Alignment between street orientation and wind direction improved consistency, while variations in street width and building height introduced significant deviations. Specifically, transitions from narrow to wide streets led to underestimations (main streets: −79% to −88%; inner streets: −82% to −93%), whereas transitions from wide to narrow led to overestimations (main streets: 44% to 211%; inner streets: 44% to 211%). These findings highlighted the importance of accounting for morphological influences and identified thresholds for deviation when applying CFD in urban wind studies. Furthermore, Baumann-Stanzed et al. [72] conducted a study to evaluate the wind energy yield of a roof-mounted small wind turbine using CFD simulations with the MISKAM model, while also addressing implications for rooftop air dispersion. The model accurately reproduced wind conditions at 10 m above the roof (approximately 0.5 building heights), although it underestimated speeds closer to the surface. Simulations demonstrated that changes in adjacent building configurations can alter rooftop wind speeds by up to ±10%, depending on the direction of the upper urban canopy layer’s flow. While such variations were negligible for energy yield at sites with low mean wind speeds (~3.5 m/s), they might be significant in windier urban contexts. These findings highlighted the importance of considering local structural influences when siting turbines and designing urban meteorological monitoring networks. On the other hand, Chang et al. [73] emphasized the importance of quantitative data in designing urban ventilation corridors to address challenges from urban expansion and environmental degradation. Using Changchun as a case study, the authors combined CFD for detailed wind environment analysis with GIS for spatial evaluation. CFD simulations identified wind speed and direction patterns at 30 m height, revealing optimal paths for ventilation corridors—primarily aligned with prevailing winds and the south–north axis. Mortezazadeh et al. [74] presented a novel approach for predicting wind power potential of rooftop wind turbine clusters in urban environments, specifically in downtown Montreal, Canada. By combining CFD using the City Fast Fluid Dynamics (CityFFD) method with nondimensional analysis and machine learning via a random forest model, the method enabled accurate long-term wind resource estimation. Validation against local weather station data confirmed the reliability of CityFFD. The technique was applied to a dense urban area with over 250 buildings to project wind energy potential for the period 2020–2049. Simoes and Estanqueiro [75] introduced a practical, cost-effective methodology for assessing urban wind resources, centered on the creation of an Urban Digital Terrain Model (U-DTM) that integrates terrain and built structures. Designed for scalability and ease of use, the approach enabled the harnessing of city-scale wind potential. Validated against empirical wind measurements, the methodology demonstrated acceptable deviations, confirming its reliability for identifying suitable locations for small wind turbine installations. By treating the urban landscape as complex orography, the U-DTM served as input for standard wind assessment tools (e.g., WAsP, WindSim), significantly reducing the computational demands typically associated with CFD simulations of urban environments. To enhance accuracy, synthetic wind data from mesoscale models were used, and correction factors were derived through targeted CFD-Urban simulations. The resulting wind resource maps provided a refined, scalable solution for urban energy planning, particularly in the context of smart city development.

3.4. Comparison of the Methods Discussed for Urban Wind Resource Assessment

The methods for urban wind resource assessment discussed above vary in several aspects, including complexity, accuracy, cost, and required analysis time. Based on these descriptions,

Table 1. Comparison of methods for urban wind resource assessment.

Method	Strengths	Limitations	References
GIS-based mapping	- The possibility of integration of spatial data (morphology, land use, terrain, meteorology) - High-resolution, city-wide suitability maps - Efficient for preliminary screening and decision support - The possibility of incorporating multi-criteria analysis (technical, social, economic)	- Limited accuracy for turbulence and wake effects - Dependent on the quality and availability of input data - Requires validation with CFD or field/wind tunnel data	[33,34,35]
Wind tunnel experiments	- High accuracy in controlled conditions - The possibility of capturing complex flow phenomena (vortices, wake effects) - Useful for validating CFD models and testing turbine prototypes - Physical ground truth	- Scaling issues (including Reynolds number mismatch) - Limited to specific layouts (not city-wide) - Time- and cost-intensive - Not suitable for long-term climatic analysis	[76,77]
CFD assessment	- High spatial and temporal resolution - The possibility of capturing detailed turbulence and flow around complex geometries - Flexible for scenario testing (urban layouts, turbine design, climate conditions, etc.) - Can be coupled with GIS for city-wide simulations	- Time- and cost-intensive in the most complex analyses - Expertise and careful validation are required (with experiments or field data) - Sensitive to boundary conditions and model assumptions - Limited for large-scale, long-term assessments	[78,79]

summarizes the key strengths and limitations of each approach.

4. Overview of Urban Wind Energy Technologies

Wind turbines convert the kinetic energy of wind into electrical power or mechanical work. In urban environments, wind profiles are typically more turbulent due to the influence of buildings, street trees, and other structural obstacles, presenting distinct challenges for the development of effective wind energy systems. Recent trends emphasize increasing turbine size, efficiency, and reliability to improve cost-effectiveness, achieve esthetic integration with architectural elements, and optimize the use of available wind resources. Among the various turbine technologies, two primary types commonly employed in urban settings are horizontal-axis wind turbines (HAWTs) and vertical-axis wind turbines (VAWTs).

4.1. Horizontal and Vertical-Axis Turbines

Both HAWTs and VAWTs are commercially available as small wind turbines. HAWTs feature blades that rotate around a horizontal axis, and during operation, wind flows through the blades, converting kinetic energy into rotational shaft energy. To maintain optimal performance, the rotor must be aligned with the wind direction, which can be achieved either passively with a tail or actively with a yaw motor. However, HAWTs are highly sensitive to changes in wind direction and turbulence. This sensitivity necessitates frequent repositioning, which can reduce efficiency. Additional challenges in urban deployment include risks to birds and aircraft, esthetic concerns, manufacturing constraints, and maintenance difficulties. Blade size also limits their scalability in cities. In contrast, VAWTs offer a promising alternative. They feature blades that rotate around a vertically oriented shaft. One of their key advantages is that they do not require alignment with the wind direction, making them particularly well-suited for urban environments where wind patterns are highly variable. Additionally, VAWTs allow for the generator and gearbox to be positioned at ground level, simplifying maintenance and enabling easier integration into building structures. Given the turbulent and inconsistent nature of urban wind, VAWTs outperform HAWTs in small-scale applications by tolerating greater fluctuations in wind speed and direction. Their lower installation height makes them ideal for densely populated areas where conventional wind turbines are impractical. Typically, the electricity generated by VAWTs is consumed locally, reinforcing their role in decentralized energy systems [80,81]. The various examples of small-scale HAWTs and VAWTs are shown in Figure 9. These turbines differ in their primary use of either lift or drag forces. Wind turbines that operate on lift force feature blades that rotate at much higher speeds than the wind itself. As a result, the blades of these turbines are slender in shape (see Figure 9a). For wind turbines that operate based on drag force, the blades move more slowly than the wind that drives them. The only way to increase the force acting on the rotor is to enlarge the blade surface area (see Figure 9b).

Considering the widely used micro- and small-scale wind turbines, including the HAWT (propeller-type), the Darrieus, and the H-rotor types, the most significant differences between them are summarized in

Table 2. Summary of the most important differences between HAWT (propeller type), the Darrieus, and the H-rotor wind turbines [81].

Parameter	H-Rotor Wind Turbine	Darrieus Turbine	HAWT
Blade profile	Simple	Complicated	Complicated
Blade area	Moderate	Large	Small
Blade load	Moderate	Low	High
Yaw mechanism	No	No	Required
Tower	Yes	No	Yes
Noise	Low	Moderate	High
Generator position	On ground	On ground	On top of tower
Self-starting	No	No	Yes
Foundation	Moderate	Simple	Expensive
Overall structure	Simple	Simple	Complex

. In some respects, the Darrieus turbine and the H-rotor share similar characteristics, which justifies grouping them under the broader category of VAWTs. However, regarding specific aspects, these two designs differ and are often compared to highlight their distinct operational and structural features. The primary distinction between VAWTs and HAWTs lies in the VAWT’s omnidirectional capability—it can harness wind from any direction without the need for reorientation. As a result, VAWTs eliminate the need for a yaw system, which is both costly and prone to mechanical failure. Additionally, the vertical axis design allows the generator to be positioned at the base of the tower, simplifying installation, operation, and maintenance. In contrast, HAWT blades must be self-supporting, as they are anchored only at the root. H-rotors, a subtype of VAWTs, use support arms typically connected at the blade center, which provide structural stability but also introduce additional mass and complexity to the turbine design. Furthermore, HAWTs typically produce a relatively stable torque during operation. In contrast, VAWTs experience inherent torque ripple due to the continuously varying angle of attack between the blades and the apparent wind. This fluctuation can negatively impact the fatigue life of drivetrain components and degrade the quality of power output. However, increasing the number of blades to three or more significantly reduces torque ripple, enhancing both mechanical durability and performance consistency [81]. Further progress in wind turbine technology is an important factor in determining the efficiency of wind energy utilization [83]. In this regard, among other scientists, Gao et al. [84] proposed a novel hybrid wind turbine blade design that combines variable geometry, lift, and drag mechanisms. Chen et al. [85] developed an enhanced hybrid VAWT design incorporating a spoiler on the inner rotor, which increased the average torque coefficient by 7.4% and the peak power coefficient by 32.4%. Similarly, Ahmad et al. [86] introduced an innovative straight-bladed Double-Darrieus hybrid VAWT that demonstrated improved power coefficients and efficient energy conversion, even at low wind speeds, thereby extending the applicability of such systems from small- to large-scale power generation projects. In addition to technological development, conducting both qualitative and quantitative tests of various wind turbine types under multidirectional wind conditions is of great interest, as these represent the actual operating conditions experienced by turbines in urban environments.

4.2. Building-Integrated Wind Turbines

Building-integrated wind turbines (BIWTs) are designed to seamlessly integrate into a building’s architecture, enabling on-site energy production. Unlike traditional devices, BIWTs are mounted on rooftops, façades, or other structural components, turning urban buildings into functional energy generators without compromising their esthetic appeal or structural purpose. In this context, BIWTs generally fall into two categories: systems that incorporate one or a few large-scale turbines positioned on the rooftop, between adjacent buildings, or within a specially designed structural void inside the building, and configurations that utilize numerous micro-turbines positioned along architectural features such as corners or roof ridges [80]. The second approach is generally more practical and cost-effective, particularly for retrofitting existing buildings, as it typically requires no major structural modifications. However, its total energy output is significantly lower than that of large turbines due to limited installation space—primarily confined to rooftops and building edges [17]. Possible locations for BIWTs to exploit flow acceleration are illustrated in Figure 10.

The installed capacity of BIWTs depends on several factors, including not only wind conditions in the city and the system design, but also the building’s size and height. Therefore, BIWTs can be classified as follows:

-

Micro systems designated for use in residential buildings or smaller commercial premises with low energy requirements (power rating 0.01 to 0.4 kW).

-

Small systems suitable for residential buildings, schools, or smaller commercial facilities (power rating from 0.4 to 2.5 kW).

-

Medium systems used in taller buildings or industrial facilities with good wind orientation (power rating from 2.5 to 50 kW).

-

Commercial turbines suitable in megastructures with optimized aerodynamic design (from 50 kW) [88].

Furthermore, BIWT systems primarily utilize VAWTs, which are compact and well-suited for operating efficiently in the turbulent wind conditions typical of urban environments [88]. The examples of BIWTs, HAVTs, and VAWTs integrated with the Center of Energy AGH building and small-scale wind turbines installed on the roof of the AGH Faculty of Foundry Engineering building are shown in Figure 11.

4.3. Integrating Urban Wind into PED Energy Systems

The central tenet of PEDs is achieving a net-positive energy balance at the district scale, whereby the total annual energy generation from RES exceeds the cumulative energy demand of all buildings and infrastructure within the district. In this context, urban wind energy emerges as an underutilized option. Unlike photovoltaics, which dominate NZEB energy supply due to their planar scalability and declining costs, wind energy offers unique spatial and temporal advantages that are especially relevant at the district level. The morphological complexity of urban landscapes—characterized by high-rise buildings, street canyons, and open corridors—creates localized wind acceleration zones and turbulence patterns that can be harnessed through site-specific deployment of small-scale wind turbines. VAWTs, rooftop-mounted HAWTs, and façade-integrated systems are particularly suited for such environments, offering modularity, low noise profiles, and esthetic adaptability. From this perspective, integrating urban wind into NZEBs contributes to PED objectives in several ways. Firstly, wind energy exhibits temporal complementarity with solar irradiance, often peaking during periods of low solar availability, such as nighttime and winter months. This diurnal and seasonal offset enhances the continuity of local renewable generation, reduces reliance on battery storage and grid imports, and improves the match between generation and demand profiles. Secondly, wind integration increases the diversity of energy vectors within the district, enabling more robust energy management strategies and enhancing resilience against climatic variability and grid disturbances. In this context, urban wind is not merely an auxiliary technology but a strategic enabler of PED performance, contributing to climate-neutral urban development and the decarbonization of the built environment.

To fully capitalize on the fluctuating nature of urban wind and solar resources, PEDs should be integrated with advanced energy storage systems capable of temporal load shifting and seasonal balancing. Short-term electricity storage—typically via lithium-ion batteries—supports intra-day demand management and grid stabilization. However, for PEDs aiming to achieve year-round energy positivity, seasonal storage becomes indispensable. Technologies such as hydrogen-based power-to-gas (P2G) systems, compressed air energy storage (CAES), and large-scale flow batteries offer promising pathways for storing excess electricity generated from wind and solar sources during periods of high production and releasing it during seasonal deficits. Also important is the integration of seasonal thermal energy storage (STES), which enables the capture and retention of surplus thermal energy—derived mainly from Power-to-Heat (P2H) systems—for use during colder months. Underground thermal energy storage (UTES) systems, including borehole thermal energy storage (BTES), aquifer thermal energy storage (ATES), and high-temperature packed bed thermal energy storage (PBTES), can be embedded within the district’s subsurface infrastructure to provide long-duration heating support. These systems not only enhance the self-sufficiency of NZEBs but also reduce peak heating loads and improve the overall energy efficiency of district heating networks [89,90]. The general idea of integrating urban wind systems with other RES, energy storage, and P2H solutions into the district network is shown in Figure 12.

5. Case Studies of Urban Wind Projects

Case studies from various global cities illustrate both the technical feasibility and the strategic considerations required for the successful implementation of urban wind projects. One notable example is the Bahrain World Trade Center, which integrates three large-scale HAWTs between its twin towers. The building was completed in 2008. The wind turbines have a rotor diameter of 29 m and a total capacity of 225 kW. They generate between 1100 MWh and 1300 MWh annually. CFD simulations and real-world monitoring confirmed that the building’s aerodynamic design effectively channels wind, enabling it to meet up to 15% of its energy demand from wind alone. The total cost of the wind turbines accounted for approximately 3.5% of the project’s total cost [91,92]. Another example is the Hess Tower in Houston, Texas, USA, a 147 m tall, 20-story building that integrates 10 VAWTs on its rooftop, making it a prominent example of a BIWT project. The building was established in 2009. The installation attracted public attention when a turbine component, compromised by strong winds, detached and fell, an incident widely reported in the local media. This event underscored the importance of comprehensive risk assessments when integrating BIWTs into high-rise structures, particularly in extreme wind conditions and lightning exposure [93]. In London, the Strata SE1 Tower incorporated rooftop wind turbines designed to supply a portion of the building’s electricity. Completed in 2011, the building is a 148 m tall, 43-story residential tower featuring three rooftop HAWTs, each with a capacity of 19 kW. While the building received multiple architectural awards, post-installation assessments revealed lower-than-expected performance due to urban turbulence and wake effects. Public expectations favored continuous turbine operation, but the site’s generally low wind conditions limited their performance [94]. Also in 2011, the Viikki Environment House office building in Helsinki, Finland, was completed. It features four WS-030B turbines mounted on the roof edge and is recognized for its energy efficiency. The above examples of BIWTs integrated into the urban environment are shown in Figure 13.

The experimental BIWT installation at the Center of Energy AGH in Kraków features two turbines—one HAWT and one VAWT—mounted on the 8th and 7th floors, respectively (see Figure 11a). Oriented toward the northwest, the system is optimized for prevailing wind from that direction. The HAWT, manufactured by Ventus Energy, has a 2.2 m diameter and a rated power of 1.5 kW. It operates efficiently over a range of wind speeds from 2.3 m/s to 10 m/s. The VAWT, manufactured by Hipar, has a height of 1.5 m and a diameter of 1.0 m. The rated power of this turbine is 0.65 kW. The start-up wind speed is 1.2 m/s, while the nominal wind speed is 12 m/s. A dedicated monitoring and control infrastructure is used to control wind turbines. It includes a dedicated automation system, a meteorological station, multiple anemometers for analyzing upstream and downstream wind, and sensors for measuring vibration, torque, noise, and rotational speed. The system supports grid connection, battery storage, and artificial load operation, with configurable safety thresholds for wind speed, turbine RPM, and vibration levels [95,96]. Wang et al. [97] investigated the ambient dynamic responses of a rooftop VAWT installed atop a 24-story building (see Figure 14). Structural vibrations of the turbine and its support were analyzed using ambient measurements processed through an automated stochastic subspace identification algorithm with fast clustering for mode detection. The analysis revealed modal behaviors influenced by both turbine operation and building dynamics. Blade rotation speed was identified as the dominant contributor to vibration responses. Notably, certain tower vibration frequencies (e.g., 3.6 Hz in X and 3.8 Hz in Y) align with the building’s second bending modes, indicating structural coupling. In contrast, the building’s first mode vibrations showed minimal impact. These findings underscore the importance of distinguishing building-associated and non-building-associated modes for effective condition monitoring and structural health management of rooftop VAWTs.

Besides the existing examples of BIWTs, there are proposals for novel urban wind turbine installations, and numerous studies analyze existing buildings and locations to assess their potential. For example, Rotterdam’s Windwheel concept, still in development, proposes a hybrid structure that combines wind energy, solar panels, and water recycling. Its design includes a ring-shaped turbine system optimized for omnidirectional urban wind. This project exemplifies the integration of multiple sustainable technologies into a single architectural vision, pushing the boundaries of urban energy self-sufficiency [98]. On the other hand, Saleh et al. [99] evaluated the integration of three VAWT designs—helical, IceWind, and a hybrid model—on residential buildings in Çeşme, Türkiye, where average wind speeds reach 7 m/s. Using SolidWorks and ANSYS Fluent for design and analysis, the turbines achieved outputs of 350 W (helical), 430 W (IceWind), and 590 W (combined). Simulations on a five-story building with 42 turbines showed annual energy consumption reductions of 18.45%, 22.93%, and 30.88%, respectively. Economic analysis revealed payback periods of 12.89, 10.60, and 10.49 years. The results highlight the potential of VAWTs as a sustainable, cost-effective energy solution for urban areas. Park et al. [17] investigated a novel BIWT system that utilizes the building’s exterior surface—an area typically unused in conventional designs. The system combined a specially designed guide vane to enhance wind capture and acceleration with a rotor optimized for specific conditions. Through detailed design analysis and CFD simulations, the optimal configuration was identified. Performance tests confirmed that the integrated guide vane and rotor significantly increase wind speed and power output, demonstrating the system’s potential as an efficient, eco-friendly solution for urban energy generation. A comparison of the installation area between the proposed system and conventional ones is shown in Figure 15.

Kim et al. [93] presented a new wind resource assessment method for BIWTs, specifically designed for the deployment of turbines on a planned 555 m skyscraper in central Seoul, Korea. The approach combined ground-based remote sensing, numerical weather prediction with an urban canopy model, and CFD to evaluate wind conditions at high altitudes. Despite the advanced modeling, results showed that Seoul’s urban wind resource is weak, yielding a low turbine capacity factor of just 7%. Krasniqi et al. [100] explored the under-researched field of urban wind energy, focusing on its feasibility and effectiveness in city environments. At the University of Prishtina’s Technical Faculties Laboratory, both a 300 W horizontal-axis and a 300 W vertical-axis wind turbine were installed alongside a meteorological station. The authors presented two years of wind and power production data (2019–2020), compared theoretical and actual outputs, and evaluated Kosovo’s energy challenges, including pollution from outdated power plants. Chen et al. [101] presented a systematic, interdisciplinary review of wind-powered building skin research over the past decade using Citespace software. It highlighted that studies on wind-optimized building design primarily focus on rooftops (37.5%), with less focus on openings, corners, inter-building spaces, and high-rise upper sections. Diaz et al. [102] conducted a comprehensive assessment of wind energy potential across 32 provinces in the Dominican Republic, analyzing both meteorological conditions and influencing factors. Their findings identified five cities as particularly suitable for wind energy development, with average wind speeds ranging from 4.83 to 6.63 m/s and prevailing wind directions from the northeast at 60–90°. Using Weibull distribution parameters, the estimated annual energy production for a single small wind turbine is 1145 kWh/year. If ten 2-blade Darrieus H-type vertical axis wind turbines were installed in each province, the total projected annual energy production would reach 423,936 kWh/year. A hybrid analysis combining technical, environmental, and socio-economic factors revealed a favorable balance, with strengths and opportunities accounting for 69.3% of the overall potential, while weaknesses and threats represented 30.7%. Tzen et al. [103] conducted a comprehensive study on the design, economic feasibility, and performance analysis of a 6 kW distributed wind turbine system for power generation in urban areas in Greece. The selected site exhibited a notably high average wind speed of 11.6 m/s, which significantly enhanced the turbine’s energy yield and cost-effectiveness. Their analysis encompassed technical design parameters, expected energy output, and financial metrics such as payback period and LCOE, demonstrating that small-scale wind installations can be viable in regions with strong and consistent wind resources. Abdelsalam et al. [104] investigated the aerodynamic performance of a Savonius wind turbine rotor integrated within a building tunnel, focusing on how tunnel geometry and placement affect energy output. Specifically, the influence of tunnel width relative to the building and the tunnel’s location within the architectural model was analyzed to identify configurations that maximize wind capture and power generation. The simulations revealed that vortex formation plays a critical role in rotor efficiency. Numerical results were validated against experimental measurements taken from turbine installations both within a duct and under free-stream conditions. The optimal configuration—placing the tunnel away from the building’s center—yielded the highest power coefficient, resulting in a 104% increase over the free-stream setup. Deltenre et al. [105] proposed a methodology for comparing energy production and return on investment between rooftop-mounted PV panels and wind turbines. The analysis focused on relatively tall buildings (≥60 m) located in areas with favorable wind conditions (annual mean wind speed ≥5 m/s). Using a brute-force approach, the authors applied the methodology to a case study in the Brussels Region. The results indicated that on tall rooftops, where available space is limited by other installations and where average wind speed is 5 m/s, small building-integrated wind turbines (BIWTs) could generate more energy than PV panels. However, their return on investment remained lower than that of PV installations. On the other hand, Bereziartua-Gonzalez et al. [106] investigated the socio-technical challenges and opportunities of integrating small wind turbines into urban environments, with a strong emphasis on design. Through a semi-systematic literature review, the authors examined factors affecting public acceptance and performance—such as esthetics, noise, safety, and energy democratization. Then, they proposed a human-centered design framework that encourages collaboration among designers, engineers, and social scientists, and promotes citizen participation in the development process. Enhancing urban wind energy from a citizen-focused perspective was also discussed by Bao et al. [107]. The authors emphasized that industrial design plays a vital role in aligning turbine systems with residents’ needs and preferences. Adopting a user-centered approach enhances public acceptance, improves energy efficiency, and reduces overall costs.

6. Future of Urban Wind in PEDs: Challenges and Opportunities

Wind has been used for passive ventilation and mechanical power since antiquity. Today, as a plentiful renewable energy source, it plays an increasingly vital role in addressing challenges tied to rapid urbanization and climate resilience—enhancing thermal comfort, supplying clean energy, improving air quality, and cutting carbon emissions. Yet, despite its rising significance, the integration of wind into urban planning and design remains a relatively underexplored field of study [108]. However, the idea of using urban wind systems in NZEDs and PEDs is gaining popularity. In such districts, urban wind offers a locally available, weather-dependent energy source that supports decarbonization by replacing grid electricity and fossil-based heating. When paired with storage and islandable microgrids, it enhances resilience. Considering further development of urban wind in the context of NZEDs and PEDs, the following aspects should be taken into account:

• Technical barriers and challenges

Urban environments generate turbulent, multidirectional inflows that can decrease turbine performance unless they are matched to the specific conditions. Effective assessment requires combining long-term boundary data with CFD/LES modeling and validating the results through measurements or wind tunnel tests. At the district scale, forecasting and grid integration are complicated by intermittency and correlated errors across multiple small units.

• Economic viability

Urban wind economics are highly site- and design-specific. Distributed wind systems typically exhibit a higher levelized cost of energy (LCOE) than utility-scale wind projects. For a representative 1.5 kW turbine and a 20 kW installation, the estimated LCOE is approximately 8.0 ¢/kWh and 24.0 ¢/kWh, respectively [109]. However, the LCOE for urban wind systems can be even higher, primarily due to lower capacity factors and increased integration costs. On the other hand, wind energy can play a vital role in local development by enhancing municipal income and strengthening energy security. Nevertheless, high upfront investment costs and dependence on imported components expose the supply chain to potential vulnerabilities. To address these challenges, strategic measures such as diversified financing mechanisms, coordinated industrial policies, and comprehensive infrastructure planning are essential [110].

• Social acceptance and safety

Despite its many advantages, wind energy often faces concerns among local communities. Public acceptance depends on noise, esthetics, and safety—especially for installations located in public spaces. Aero-acoustic design, operational controls, and precise siting help mitigate low-frequency noise and blade-passing tones. Acceptance also improves with community involvement. Co-benefits include local employment, start-ups in building-integrated wind, and citizen energy initiatives [111].

• Regulatory and institutional barriers

Current standards and planning frameworks often fail to meet the specific needs of urban areas. Another challenge concerns integrating wind energy into the power grid. A promising solution—both technically and economically—is the use of energy storage systems and hybrid systems that integrate wind and solar energy. However, effectively managing these systems remains complex due to the unpredictable and variable duration of favorable and unfavorable wind conditions [112]. On the other hand, an interesting option is leveraging wind energy for hydrogen production [113], storage in Compressed-Air Energy Storage (CAES) systems [114] or convert to heat in P2H systems for seasonal heat storage [115].

• Further optimization of urban wind turbine technology

Next-generation wind energy technologies, such as omnidirectional and closely spaced VAWTs for directional tolerance and higher areal power density, modern generator designs improving efficiency and integration potential, as well as architectural accelerators like ducts and diffusers to concentrate flow in constrained spaces, may be considered as key opportunities for further development of urban wind systems [116,117].

• Implementation of IoT, Digital Twins, and AI

Small wind turbines are increasingly recognized for their potential in Internet of Things (IoT) applications, particularly in powering wide areas and low-energy devices in smart city environments [118]. Another interesting option is Digital twins (DTs) technology that can serve as virtual counterparts to physical turbines. Digital models allow real-time monitoring and performance optimization by replicating turbine behavior under varying conditions. Through predictive simulations, DTs help identify potential failures early and refine maintenance strategies, enhancing reliability, safety, and operational efficiency [119]. AI and model predictive control (MPC) support coordinated dispatch of wind energy, transforming small wind variability into predictable district-level performance [120].

• Hybrid installations utilizing renewable energy resources

Wind and solar energy show temporal complementarity. While solar and wind energy each offer distinct advantages, their intermittent output and location-specific constraints have driven growing interest in hybrid systems. By integrating these resources, hybrid solutions enhance overall efficiency and reliability, offering a more resilient approach to renewable energy generation. Hybrid PV–wind–battery microgrids reduce storage needs, smooth net load, and improve reliability. Strategic co-siting of urban wind at high-value micro-sites can offset PV shortfalls, especially in winter, when managed by responsive control systems [121,122].

Future research should address the key gaps identified in this review. Efforts are needed to improve the validation and cross-comparison of CFD and hybrid GIS–CFD models to enhance the reliability of urban wind resource assessments. Further development of integrated methodologies that combine GIS data, wind tunnel experiments, and numerical simulations is essential to achieve multi-scale, more realistic evaluations. Additionally, establishing standardized procedures and performance indicators for assessing urban wind potential would enable consistent comparisons across studies. Finally, greater attention should be paid to the long-term performance, environmental impact, and techno-economic feasibility of building-integrated wind systems under real urban conditions.

7. Conclusions

Urban wind energy, while currently underutilized, holds significant potential as a key component of distributed renewable energy systems within Positive Energy Districts. Its strategic integration into the built environment can enhance energy diversification, operational stability, and resilience while reducing dependence on centralized power infrastructure.

As discussed in this review, recent advancements in Computational Fluid Dynamics (CFD), Geographic Information Systems (GIS), and hybrid assessment methodologies have substantially improved the precision of site selection and performance forecasting. Moreover, building-integrated wind turbines (BIWTs) have proven particularly suitable for dense urban environments due to their ability to operate effectively under turbulent flow conditions.

The temporal complementarity between wind and solar resources offers additional benefits, enabling more consistent renewable energy generation and more effective energy balancing when combined with energy storage and Power-to-Heat systems.

Given the current state of the art, future research should focus on optimizing turbine designs specifically for urban applications, enhancing predictive modeling tools, and addressing socio-technical and regulatory barriers to enable broader adoption of urban wind technologies. Integrating urban wind energy into Positive Energy District strategies can be pivotal in advancing urban decarbonization and achieving climate-neutral, resilient cities.