Simulation Analysis of Onshore and Offshore Wind Farms’ Generation Potential for Polish Climatic Conditions

Abstract

Currently, Poland is witnessing a dynamic development of the offshore wind energy sector, which will be a key component of the national energy mix. While many international studies have addressed wind energy deployment, there is a lack of research that compares the energy and economic performance of both onshore and offshore wind farms under Polish climatic and spatial conditions, especially in relation to turbine spacing optimization. This study addresses that gap by performing a computer-based simulation analysis of three onshore spacing variants (3D, 4D, 5D) and four offshore variants (5D, 6D, 7D, 9D), located in central Poland (Stęszew, Okonek, Gostyń) and the Baltic Sea, respectively. The efficiency of wind farms was assessed in both energy and economic terms, using WAsP Bundle software and standard profitability evaluation metrics (NPV, MNPV, IRR). The results show that the highest NPV and MNPV values among onshore configurations were obtained for the 3D spacing variant, where the energy yield leads to nearly double the annual revenue compared to the 5D variant. IRR values indicate project profitability, averaging 14.5% for onshore and 11.9% for offshore wind farms. Offshore turbines demonstrated higher capacity factors (36–53%) compared to onshore (28–39%), with 4–7 times higher annual energy output. The study provides new insight into wind farm layout optimization under Polish conditions and supports spatial planning and investment decision making in line with national energy policy goals.

1. Introduction

1.1. State of Wind Energy in Poland

Renewable energy sources are expanding globally at a much faster pace than conventional ones, with wind energy showing the highest growth rate among them [1,2,3,4]. Over the past few years, wind power has emerged as one of the most cost-effective forms of electricity generation when evaluated using the Levelised Cost of Electricity (LCOE) metric, driven by reductions in installation costs and ongoing technological advancements [5]. Wind energy saw the most rapid expansion of all RESs in Poland during the period 2005–2016.

By the end of 2005, Europe aimed to have approximately 47,000 wind turbines installed. In the previous year, the average capacity of individual units was around 1.3 MW for onshore installations and 2.1 MW for offshore ones. By 2030, it is assumed that the average size of a wind turbine will be 2 MW for onshore turbines and 10 MW for offshore turbines, and to reach the target of 300 GW, 90,000 turbines will be needed (75,000 for onshore turbines and 15,000 for offshore turbines) [6]. In order to achieve these goals, scientific work is being undertaken to develop methods for more efficient design of wind investments. In 2024, a new planning method initially designed for offshore wind farms was presented in [7] to accelerate the development of efficient onshore wind farms.

The combination of supportive policies and incentives for renewable energy development has led to substantial growth in the installed capacity of renewable sources, such as wind energy, both in Poland and other countries. However, in Poland, photovoltaic power has surpassed wind turbine power due to the introduction of targeted support programs. The share of RES technologies in Poland is shown in Figure 1 [8].

In Poland, the development of onshore wind energy is much more advanced compared to offshore energy. The northern coastline offers the most favourable wind conditions. Nonetheless, challenges related to site availability, limited transmission infrastructure, and potential risks to the stability and reliability of the Polish power system—such as outages or operational interruptions—must be considered [9]. According to the latest data from the Energy Market Agency, at the end of September 2023, the Polish power system had a total generating power of 64.4 GW, of which 27 GW were renewable sources. The National Power System in Poland is based on coal-fired power plants. More than 20 GW of existing generation capacity is expected to be decommissioned by 2035.

Photovoltaics had the largest share in renewable energy generation, with an installed power of 15.6 GW, followed by wind farms (9.1 GW), biomass power plants (0.98 GW), hydropower plants (0.97 GW), and biogas plants (0.29 GW). Photovoltaics recorded the largest increase in power on the renewable energy market in Poland, increasing its value by over 4.4 GW in the year ending September 2023. For comparison, the power of wind farms increased by just over 1 GW in the same period.

Offshore energy in Poland is beginning to develop. The first wind farm projects in the Baltic Sea are in the preparation or implementation phase, and the first launch is expected in 2027. Offshore wind energy offers greater energy production and more consistent output compared to onshore farms. This is due to higher wind speeds at sea and the absence of land-based obstacles that reduce air flow. Additionally, offshore wind farms typically employ different types of turbines, which have higher rated power, larger rotor diameters, and are often taller than those used on land. The currently developed maritime spatial development plan initially designates several locations with a total area of approximately 2.5 thousand km². The Polish Exclusive Economic Zone (EEZ), which can extend up to 200 nautical miles from the coastline, is estimated to have a generation capacity of at least 10–12 GW. Depending on the scenario, this area could deliver up to 50 TWh of electricity annually—or even 80 TWh with 20 GW of installed capacity—representing nearly one-third of Poland’s current yearly electricity demand.

Poland has adopted two key strategic documents that set the directions for energy policy until 2040—the National Energy and Climate Plan and the Energy Policy of Poland until 2040. The latest updates of the documents from 2023 predict a dynamic increase in installed power, both in the onshore and offshore sectors. The plan assumes doubling the power of wind installations compared to the current state, by 2040, to a total of 19 GW. In the case of offshore energy, the installed power is to reach 6 GW in 2030 and increase by another 6 GW every 5 years [10]. Figure 2 presents a comparative overview of the strategic directions for wind energy development.

Currently, nine wind projects being implemented in the Baltic Sea have obtained a location permit (Figure 3).

Table 1. Current offshore wind farm projects.

Project Name	Investors	Area [km2]	Power [MW]	Planned First Energy Extraction
Phase I
MFW Baltic II	Equinor/Polenergia	122	720	2028
MFW Baltic III	Equinor/Polenergia	116	720	2028
Baltica 2	PGE Baltica/Orstead	189	1498	2027
Baltica 3	PGE Baltica/Orstead	131	1045.5	2026
FEW Baltic-2	RWE Renewables	42	350	2026
Baltic Power	PKN Orlen/Northland	131	1200	2026
BC-Wind	Ocean Winds	91	399	2028
Phase II
MFW Baltic I	Equinor/Polenergia	128	1560	after 2030
Baltica I	PGE Baltica/Orstead	108	896	after 2030

summarizes the current offshore wind farm projects of Phase I and II. The first phase of the support system (pre-auction) concerns financial support granted for investments in the form of covering the negative balance, which means the difference between the market price of energy and the price of generating electricity at sea. In accordance with the regulation of the Minister of Climate and Environment, the maximum price for energy generated and fed into the grid is PLN 319.60/MWh. The second phase of the support system will be based on an auction system, and the first auctions are planned for 2025. The maximum price of energy under Phase II was proposed by the Ministry of Climate at PLN 471.83/MWh. On 30 May 2023, the Ministry of Infrastructure presented new areas where offshore wind farms will be developed.

The projects listed in Table 1 represent the key pillars of Poland’s offshore wind development strategy, with Phase I focusing on early implementation supported by a fixed-price mechanism and Phase II transitioning to market-based auctions. The combined capacity of nearly 6 GW planned in Phase I is expected to significantly contribute to Poland’s decarbonization goals and energy security. The geographic dispersion of these projects along the Baltic coast ensures diversification of supply, while the involvement of international energy companies underscores the growing investor confidence in the Polish offshore sector. The phased approach also enables infrastructure scaling and gradual integration of offshore generation into the national transmission system.

1.2. Literature Review

The global expansion of wind energy is developing in two primary directions: onshore and offshore wind power systems. Onshore technologies can be divided into three main categories based on their installed capacity and purpose. Small-scale wind turbines, with rated power up to 50 kW, are typically used in off-grid applications, remote areas, or hybrid systems, where they supply energy to individual households or specific circuits. In domestic settings, turbines in the 3–5 kW range are the most commonly used. Medium-scale turbines, with capacities between 200 and 600 kW, are usually connected to the power grid and are often operated by small businesses or private owners. Large-scale wind installations involve individual turbines exceeding 1 MW or multi-turbine wind farms, which are built to feed electricity into the grid and meet the technical requirements of grid operators. Offshore wind power involves the deployment of large turbines in open-sea environments, where wind conditions are generally stronger and more stable than on land. These systems are typically mounted on fixed foundations anchored to the seabed, although growing attention is being given to floating offshore wind technologies, which enable the installation of turbines in deeper waters, further expanding the spatial potential of offshore development.

In future, wind energy will develop on a global scale, covering more and more locations, with a simultaneous increase in the number of turbines and the construction of larger and more extensive wind farms. A thorough overview of the various aspects influencing wind energy expansion was provided in [12], highlighting that global growth in this sector is possible if key conditions such as public support, economic feasibility, policy support, and mitigation of possible adverse effects are properly addressed. The authors emphasize the importance of the sustainable disposal of dismantled equipment at the end of its life cycle for social acceptance.

In the literature on the subject, we find many articles on the use of onshore and offshore wind energy [13,14,15,16].

In [17], the authors introduced a method for evaluating the environmental impact of alternative energy sources, such as wind turbines, throughout their life cycle. The assessment considers various factors, including technical specifications of the turbines, network attributes, and associated engineering infrastructure. The researchers formulated waste management strategies utilizing SimaPro software in conjunction with the Ecoindicator’99 method, aiming to minimize the environmental burden associated with wind turbine life cycles. The most significant reduction in harmful environmental impact can be achieved during the dismantling and disposal of the wind turbine life cycle, while the strongest adverse environmental impact occurs during the WT manufacturing stage. Nevertheless, as shown in [18], although the construction and manufacturing of onshore wind farms involve certain energy inputs and greenhouse gas emissions, these are compensated for during the plant’s operational lifetime, which supports their viability in the shift toward low-emission energy systems.

The profitability of offshore wind energy depends not only on the design of the turbine itself but also on the careful planning of entire wind farms. Since wind turbines are usually deployed in large wind farms comprising many units, the wake effects caused by upstream turbines lead to increased turbulence within the farm. To mitigate these effects, wind farm flow dynamics are modelled in advance, allowing for optimization of turbine orientation and positioning to reduce the impact of wake interactions. In 2023, the authors of [19] conducted a comparison of three wind farm simulation tools—FLORIS, FLORIDyn, and WFSim—which represent low- to medium-fidelity approaches and are used to assess wake flow behaviour within wind farms. The key factors influencing the total loss of the aerodynamic wake in a wind farm include the proper layout and configuration of the turbines. A new approach to the design of offshore wind farm clusters is introduced in [20] through the EERA-DTOC modelling tool. The costs associated with the implementation of offshore wind farm investments are usually much higher compared to onshore wind farms; therefore, the optimization of infrastructure in such projects is crucial. In [21], the authors applied a nonlinear programming (NLP) approach incorporating integer variables to optimize energy production revenues while accounting for wake-induced losses, electrical transmission losses, and constraints related to cable routing.

A key issue in the context of calculating airflow losses is the modelling of the aerodynamic wake on terrain with a complex relief. The authors of [22] presented a microscale airflow model to estimate the average wind speed and direction and fluctuations in a hilly terrain, using a CFD (computational fluid dynamics) model of the terrain topography effect and a mesoscale model of atmospheric effects (atmospheric stability).

Study [23] presents a production forecast and its validation for a real-world onshore wind farm located in Galicia, a complex and wind-rich area in northwest Spain, using high-resolution WRF model simulations in both horizontal and vertical dimensions. It was proposed that the WRF model could serve as an effective tool for forecasting wind energy output in operational wind farms situated in areas with complex topography.

The mesoscale Weather Research and Forecasting (WRF) model was also applied to analyse wake effects and power generation at the Zhangbei wind power base, which comprises 1880 turbines across 16 wind farms situated in varying wind and terrain conditions [24]. The study demonstrated that the behaviour of wind farm wakes is strongly influenced by factors such as wind direction, wind speed category, and terrain characteristics. In particular, wake effects can persist for distances of 20–35 km or more in isolated wind farms, whereas in mountainous regions, they typically dissipate within approximately 6 km. The study identified suboptimally coordinated wind farms with reduced performance, demonstrating that insufficient spacing between upwind and downwind farms can lead to substantial power losses due to wake effects, highlighting the importance of accurate spacing and layout in contemporary wind farm design.

In Poland, a clear slowdown in onshore wind farm investments has been observed for many years due to restrictive distance regulations. Since the introduction of the Distance Act in 2016, the slowdown in the development of wind projects has been abnormal compared to the rest of Europe [25]. Nevertheless, based on the statistical analysis, prospects for a positive development of the wind sector in the coming years were found, provided that the distribution network is modernized and private investors support it. The work [25] took into account the availability of wind resources in Poland, the development of technology, environmental aspects, and legal and social aspects. The 2024 report of the Polish Wind Energy Association (PWEA) suggests the necessary legislative changes and indicates the role of the onshore sector in the energy transformation in Poland [26]. The authors of the report showed that by reducing the required distance of wind farms from residential buildings from 700 m (the distance required by law) to 500 m (the distance suggested in the report), the potential area for the development of onshore wind projects may increase twice. In the context of Poland, the study presented in [9] emphasized that constructing smaller, geographically dispersed wind farms and linking them in pairs to form a virtual power plant may offer greater benefits both for the national energy system and for electricity producers. The authors of the work [27] conducted an analysis of potential areas in Poland for the development of onshore wind energy. The model encompasses a high-resolution GIS-based assessment of potential locations for wind turbines. The study took into account several scenarios of the distance of the power plant from selected land-use objects, with applicable legal standards, and estimated the potential of the installed capacity of onshore wind farms to be at least 47 GW, which is several times higher than the assumptions of PEP2040 (13.9 GW).

The solution for the further development of wind projects in Poland may be their location in industrial areas [28]. The Polish Wind Energy Association estimated the technical potential of the wind sector in such areas to be 16.9 GW, assuming no regulatory and economic constraints. Looking to 2030, with low regulatory risk and taking into account conflicts with other forms of land use, this potential drops to 1.6 GW of installed power, which still constitutes a significant contribution to the implementation of current political goals—11% of the total power of wind installations planned for 2030.

An interesting example of the use of wind energy is the implementation of the Energy to Heat project, which involves the construction of a wind and photovoltaic farm to power the KR2 boiler house in Wałcz (Poland) using a direct power line.

Offshore wind energy is today considered to be the most energy-efficient and safe source of renewable energy. The new regulations have raised expectations for the imminent revitalization of Poland’s wind energy sector. In 2022, authors in [29] focused on the accelerated changes in the renewable energy sources and the related legislation, especially emphasizing the prospect of building offshore wind farms in Poland. The development of offshore wind power involves several technical and logistical challenges, such as the lack of comprehensive design standards for support structures, demanding installation processes and higher operation and maintenance expenses. These factors contribute to a levelized cost of energy that remains nearly twice as high as that of onshore wind power [30]. In work [31], different offshore foundation options were discussed, including some new approaches that may contribute to reducing the levelized cost of energy.

Studies [32,33] examine the prospects for offshore wind energy development in the Baltic Sea, addressing not only technical and economic factors but also considering potential environmental consequences. In addition to the units permanently connected to the seabed in 2022, authors in work [34] analysed floating wind turbines in the Baltic Sea, assessing to what extent the implementation of this technology is technically and economically justified in this region. In [35], the authors examined the technical intricacies of floating wind turbine systems, focusing on the key challenges and enabling solutions required for the advancement of self-sustaining offshore wind farm technologies. Consequently, the study compiled a set of O&M challenges along with a range of enabling technologies that have already been successfully adopted in other industrial sectors. A practical study on floating wind turbines was carried out in [36], where the authors analysed the spatial feasibility of this technology in South Africa. They concluded that approximately 2% (246,105.4 km²) of the country’s Exclusive Economic Zone (EEZ) is technically suitable for floating wind deployment, offering an estimated potential of 142.61 GW of installed capacity.

In 2023, study [5] examined how offshore wind energy contributes to improving the Polish power system’s capability to meet national electricity demand. The authors estimated that the initial phase of offshore wind development, involving 6 GW of installed capacity, could generate around 29.2 TWh annually, covering approximately 17% of national electricity consumption. According to the authors, offshore wind energy in the Baltic Sea region exhibits a significantly higher capacity factor than onshore installations—55.6% compared to 30.1%—demonstrating a notable performance advantage of offshore solutions.

In the same year, the authors of [37] presented a detailed overview of the procedures required for constructing a wind farm in the Baltic Sea. The outlined activities included selecting a location that does not interfere with maritime routes, lies reasonably close to shore, and has suitable seabed depths. Further technical work involved conducting bathymetric surveys with multi-beam echo sounders to map the seafloor, utilizing side-scan sonar to detect underwater objects, performing magnetometer surveys to locate and remove metallic items, and applying sub-bottom profiling techniques to assess whether the subsurface geological layers are adequate for establishing stable foundations.

Wind energy can also be an energy-efficient and economically justified complement to other modern energy conversion technologies. The work in [38] emphasizes the possibility of an effective and purposeful combination of offshore wind farm capabilities with hydrogen generation systems using modular electrolyzers, which can ensure the dynamic development of both technologies. The topic was further explored in 2023 in study [39], where the authors highlighted that the growing scale and promising economics of offshore wind energy make it a viable option for hydrogen generation. The paper offers a detailed assessment of the levelized cost of hydrogen (LCOH), taking into account the geographic locations of 23 prospective offshore wind farm projects in the Baltic Sea. In addition, it includes a comparison of hydrogen production expenses from both offshore and onshore wind sources projected for the years 2030 and 2050.

The social dimension of wind energy development in Poland has gained prominence, particularly in light of growing public expectations regarding renewable energy investments. In a recent study by Chomać-Pierzecka [40], a regionally targeted survey conducted in Poland revealed that 68% of respondents support the development of offshore wind energy, with a 5% increase in support year-over-year, indicating a dynamic rise in public acceptance. A key motivation behind this support was economic optimism, as 65% of participants believed that offshore wind projects could lead to lower electricity prices. Additionally, 53% of respondents associated these investments with positive effects on regional labour markets, particularly in terms of job creation. Notably, concerns regarding landscape changes were not expressed by respondents in 2024, suggesting a shift in public perception.

Public acceptance is also influenced by broader economic pressures. As noted by Chomać-Pierzecka et al. [41], the COVID-19 pandemic and rising EU ETS emission allowance costs exacerbated the electricity price crisis in Poland, strengthening the case for expanding wind energy capacity. In a comparative analysis with the Baltic States (Lithuania, Latvia, Estonia), the authors emphasized that Poland could benefit from adopting tested offshore practices from neighbouring countries. Their research, supported by a questionnaire on the Polish energy market, pointed to wind energy as a strategic tool for price stabilization.

Despite these encouraging trends, there remain important gaps in public engagement and policy frameworks. Chomać-Pierzecka et al. [42] observed that the Polish literature and planning documents often treat social aspects as secondary, with little focus on individual energy consumers or public sentiment toward local investments. The Distance Act remains a major barrier to onshore wind development in Poland, which severely restricts potential siting options and has not been adequately evaluated from a social perspective. The authors argue that public consultation should be treated as a fundamental part of wind farm planning, especially in light of Poland’s urgent need to diversify its energy mix in response to the war in Ukraine and fossil fuel disruptions.

Complementing these national studies, the work of Forastiero et al. [43], though focused on Uruguay, provides relevant international insights. The authors identified numerous potential social and environmental risks of offshore wind development, such as disruptions to marine ecosystems, impacts on fishing communities and tourism, and increased noise and maritime traffic. They stress the need for comprehensive environmental and social impact assessments (ESIAs) and call for transparent public dialogue as a prerequisite for long-term sustainability. These recommendations align well with Poland’s emerging offshore sector, where large-scale investments in the Baltic Sea require greater emphasis on social license to operate.

Together, these findings underline that technical and economic modelling of wind farm efficiency should be complemented by systematic investigation of public perception, social impacts, and communication strategies, particularly in countries like Poland where public trust and participation are critical to the success of the energy transition.

In summary, numerous studies have addressed the technological, environmental, and economic aspects of wind energy development across different scales and geographies. However, there remains a lack of integrated analyses that simultaneously consider wind farm layout design, spatial constraints, economic indicators, and national energy policy objectives, particularly within the specific context of Poland. This highlights the need for research that not only models technical performance but also evaluates practical investment outcomes under real legal and geographic conditions. In response to this gap, the following section outlines the specific aim and novelty of the present study.

1.3. Aim and Novelty of the Research

While many international studies have addressed the design, performance, or optimization of wind farms, relatively few have provided a detailed comparative assessment of both onshore and offshore wind farms located within a specific national context, particularly considering spatial, economic, and legislative constraints. In the case of Poland, most existing research has either focused on single-location modelling, general feasibility analyses, or national-level energy projections without addressing the technical and economic implications of turbine layout design (i.e., inter-turbine spacing) under real environmental conditions.

The primary aim of this study is to fill this identified gap by conducting a simulation-based performance and economic assessment of multiple turbine spacing configurations for both onshore and offshore wind farms situated in Poland. Using WAsP software, this study evaluates seven distinct layout variants (three onshore (3D, 4D, 5D) and four offshore (5D, 6D, 7D, 9D)), reflecting real geographic and meteorological conditions in three Polish communes (Stęszew, Okonek, Gostyń) and in the Baltic Sea. The turbine spacing was expressed as a multiple of rotor diameter (D), and its impact on wake losses, capacity factor, energy production, net present value (NPV), and internal rate of return (IRR) was analysed.

The novelty of this research lies in its integrated approach, which combines technical modelling of wake effects and energy yield with a financial evaluation of investment profitability, all while taking into account Poland’s unique planning, spatial, and legal constraints. Unlike many previous studies, this work explicitly considers how the restrictive distance regulations for onshore wind farms (the so-called “10H rule”), the availability of coastal areas in the Polish Exclusive Economic Zone (EEZ), and the current national energy strategy (PEP2040) influence the optimal design of wind farm layouts.

To the best of the authors’ knowledge, this is the first study to simultaneously analyse and compare the energy and economic performance of multiple wind farm layout variants in both onshore and offshore settings under Polish conditions, with a focus on practical implications for policy, investment, and spatial planning. The findings are especially relevant in the context of Poland’s accelerating energy transition, providing stakeholders with concrete data to inform turbine layout decisions that balance energy yield, cost-efficiency, and legal compliance. The results also offer valuable insights for revising development strategies, particularly as Poland aims to double its installed wind power capacity and expand offshore wind infrastructure in the coming decades.

2. Materials and Methods

2.1. Computer Simulation Tools

WAsP (Wind Atlas Analysis and Application Program) was used to assess the energy potential of selected land and sea areas. WAsP is widely recognized as a benchmark software tool used in the assessment of wind resources and the design of wind energy projects. Developed by the Technical University of Denmark (DTU), the WAsP software enables a wide range of calculations including energy production forecasts for individual turbines and wind farms, evaluation of wake effects and overall farm efficiency, assessment of site-specific wind conditions in line with IEC standards (such as average wind speed, turbulence intensity, shear, extreme wind events, and flow angles), as well as optimal turbine placement and mapping of wind potential and turbulence. The program implements air flow calculation modules for various types of terrain, taking into account its topographic characteristics. WAsP offers two advanced microscale wind flow models that enable extrapolation of wind conditions both horizontally and vertically: the linear IBZ wind flow model and the CFD wind flow model. The software also enables detailed simulation of wind farm operations and local wind conditions by incorporating several specialized models. These include a stability model that utilizes inputs from ERA5 reanalysis data to capture local atmospheric stability, wake effect models PARK1 and PARK2 for assessing aerodynamic interactions among turbines within the farm, as well as a forest model that applies displacement height parameters and obstacle representations to accurately describe wind behaviour in areas affected by nearby vegetation or structures.

2.2. Input Data for Simulation Analysis

Input data used in WAsP for wind resource assessment and energy yield calculations can originate from various sources. Wind climatology data may be obtained from measurements taken at nearby meteorological masts or derived from mesoscale modelling outputs. Terrain elevation data are typically acquired directly from satellite measurements such as those from space shuttle missions or other datasets. Meanwhile, land cover classification and identification of surrounding obstacles affecting wind conditions are usually extracted from topographic maps, spatial databases, or satellite imagery sources like Google Earth.

The input data for modelling using WAsP software, containing meteorological data of wind speed and terrain data, were obtained from the Global Wind Atlas (GWA) database, which provides high-resolution wind climatology data globally. The GWA data are based on state-of-the-art mesoscale atmospheric modelling, primarily utilizing the ERA5 reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 is one of the most comprehensive and widely validated reanalysis datasets, incorporating a vast array of observational data and employing advanced data assimilation techniques to reconstruct historical atmospheric states with high accuracy. The Global Wind Atlas applies a microscale modelling approach, specifically the Wind Atlas Analysis and Application Program (WAsP) methodology, to downscale the ERA5 mesoscale data to a finer spatial resolution (approximately 250 m grid size), accounting for local terrain effects such as topography, surface roughness, and obstacles. This approach enables the generation of localized wind resource maps that are suitable for preliminary energy yield assessments.

However, it should be acknowledged that, in the present study, no direct validation of the GWA wind speed data against site-specific meteorological mast measurements or other local observational datasets was performed for the selected Polish locations. Despite this, the use of GWA data is justified by its global coverage, methodological rigor, and prior validation studies that confirm its suitability for wind resource assessment in complex terrains and offshore areas. Future work could incorporate local measurement campaigns to further refine the input data and improve the fidelity of the simulation results.

Orography maps are generated based on the integration of NASA’s SRTM topography database with the Viewfinder Panoramas digital terrain model. Terrain roughness data are obtained from the European Space Agency’s CCI-LC database. The generalized wind climate in WAsP consists of wind rose and Weibull distribution data defined for five heights above ground level and five terrain roughness classes.

2.3. Onshore Wind Farm Modelling

2.3.1. Selecting a Location for Wind Farms

The analysis covered the area of the Wielkopolska province (central Poland), with a total area of 29,826 km². According to the PWEA report [44] published in June 2024, the Wielkopolska region has one of the highest development potentials for onshore wind energy in Poland. For the purposes of determining possible areas for the construction of wind farms, development areas, industrial facilities, surface waters, protected areas, forests, and motorways and roads were excluded. The buffer distance of wind farms from individual facilities was taken into account based on current legal regulations, specified in the Distance Act and data included in [44,45]. The map of excluded areas was prepared in the QGIS program, based on the database of topographic objects of the BDOT10k geoportal [46]. Location assumptions taking into account spatial constraints are summarized in

Table 2. List of areas excluding or limiting the possibility of constructing wind farms along with the adopted distance buffer.

Factor Excluding or Limiting the Construction of Wind Farms	The Adopted Distance Buffer
Residential development	700 m
Other development	-
Industrial facilities	-
Communication network (roads, carriageways, highways, railways)	200 m
High-voltage power lines	300 m
Water network (rivers, streams, canals, drainage ditches)	-
National parks	2500 m
Nature reserves	500 m
Natura 2000 areas	-
Landscape parks	-

.

The buffer distances presented above reflect current Polish legal regulations and planning practices. These constraints are essential for ensuring compliance with environmental, safety, and social acceptance criteria.

After taking into account the excluded areas and spatial restrictions, a map of areas available for the construction of wind farms was created (Figure 4). Areas marked in green are excluded areas, and areas marked in white are available areas. Based on the received map, three areas were selected in the municipalities of Stęszew, Okonek, and Gostyń. These areas are characterized by a large area of available land, without a significant number of obstacles, elevations and buildings. In addition, the plots are located in close proximity to each other, which minimizes the costs associated with running cable routes. Areas available for the construction of wind farms in the selected municipalities were plotted in the form of polygons, based on a map generated in the QGIS program. The boundary coordinates of the selected areas were entered into the Google Earth application, and then the distances of the polygon boundaries from the limiting objects were measured. The top view of the selected plots is shown in Figure 5.

The careful selection of these sites enabled a consistent comparative analysis of different spacing configurations under similar wind and terrain conditions. Data containing the wind speed distribution at a height of 100 m and maps of orography and terrain roughness were obtained from the publicly available Global Wind Atlas (GWA) database. Data files downloaded from GWA are the input data for computational models in the WAsP program. As a result of the calculations, a wind resource map was obtained in the form of a rectangle with dimensions of 25 km × 25 km.

Table 3. Selected wind and terrain characteristics of selected wind farm areas.

Wind Farm	Ground Area	Wind Speed	Power Density	Terrain Height	Roughness of the Terrain	Roughness Class
	[km2]	[m/s]	[W/m2]	[m]	[m]	[-]
FW Stęszew	3.5	8.42	585	82.8	0.239	2
FW Okonek	5.46	7.18	347	139.2	0.692	3
FW Gostyń	3.68	7.57	433	113.3	0.185	2

presents selected average wind and terrain characteristics. In all the analysed areas, winds from the west (sector 270°) dominate.

As shown above, the Stęszew location demonstrates the highest average wind speed and power density, suggesting potentially greater energy yield compared to the other two sites.

The wind roses presented in Figure 6 confirm the dominance of westerly winds across all three locations, which supports the selection of a standardized alignment of wind turbines in the simulations. Terrain maps showing the surface shape and roughness are presented in Figure 7, Figure 8 and Figure 9.

Terrain shape and roughness significantly influence wind speed profiles and turbulence intensity. These factors were integrated into the WAsP simulation to increase the accuracy of energy yield estimations.

2.3.2. Wind Turbine Model

The wind turbine model available in the NREL database, based on research conducted by the International Energy Agency, with a rated power of 3.4 MW was adopted. The technical data are summarized in

Table 4. Technical data of the IEA 3.4 MW turbine reference models.

Model Name	IEA 3.4 MW 130 RWT
Nominal power	3370 kW
Nominal wind speed	9.8 m/s
Start wind speed	4 m/s
Cut-off wind speed	25 m/s
Rotor diameter	130 m
Tower height	110 m
Power regulation	Pitch
IEC class	IIIA

, and the power and torque characteristics are shown in Figure 10.

The 3.4 MW turbine model selected represents a modern and widely used onshore configuration, suitable for comparative analysis of spacing variants in Polish conditions.

2.4. Offshore Wind Farm Modelling

2.4.1. Selecting a Location for Wind Farms

The wind farm was located in one of the nine water areas designated in the Spatial Development Plan of Polish Maritime Areas (SDPPMA) based on the wind potential criterion. Average wind speeds in the Exclusive Economic Zone range from 9.2 to 9.6 m/s. The highest wind speeds were recorded in the area of the Baltica 1 project, located 80 km from the coastline, at the height of the city of Łeba. The area has an area of approx. 108 km², and the depth of the seabed is approx. 40 m. Figure 11 shows the boundaries of the Polish Exclusive Economic Zone together with the water areas designated in SDPPMA and the average wind speeds, measured at a height of 100 m, occurring in these areas.

2.4.2. Wind Turbine Model

Two wind turbine models were used in the simulation analysis. Currently, offshore wind turbine powers reach 15 MW (Vestas V236-15.0, which is to be delivered to the Baltic Power project, implemented by PKN Orlen Poland and Northland Power). Therefore, the first model has a rated power of 15 MW and a rotor diameter of 240 m (based on IEA data). The second model, implemented by the Technical University of Denmark DTU, achieves a rated power of 10 MW with a rotor diameter of 178 m. The simulation assumptions are summarized in

Table 5. Initial assumptions of the offshore wind farm project.

Parameter	Value	Unit
Maximum installed power	1500	MW
Minimum installed power	400	MW
Water area	108	km2
Turbine model	DTU 10 MW 178 RWT IEA 15 MW 240 RWT	-
Distance between turbines	5D, 6D, 7D, 9D	-

. Detailed technical data and power and torque curves of selected wind turbine models are presented in

Table 6. Technical data of DTU and IEA turbine models.

Model name	DTU 10 MW 178 RWT	IEA 15 MW 240 RWT
Nominal power	10 MW	15 MW
Nominal wind speed	11.4 m/s	10.6 m/s
Cut-in wind speed	4 m/s	3 m/s
Cut-off wind speed	25 m/s	25 m/s
Rotor diameter	178.3 m	240 m
Tower height	119 m	150 m
Power regulation	Pitch	Pitch
IEC class	IA	IB

and Figure 12.

The power and torque curves of both offshore turbines illustrate different performance thresholds, particularly in lower-wind-speed regions. These differences are considered in the production simulations and economic assessment.

2.5. Economic Evaluation Tools

In order to assess the profitability of the investment, selected economic indicators were taken into account:

▪

Net present value (NPV)—the difference between the discounted stream of positive cash flows generated over the entire life cycle of the project and the value of costs incurred in its implementation. The NPV method takes into account the variability of the value of money over time by applying a discount rate. The value of the discount rate reflects the weighted average cost of capital, taking into account the costs of debt and equity capital. A positive NPV value indicates the profitability of the project. If the NPV is negative, the project should be rejected. NPV is defined by the following formula:

$$\begin{array}{c}NPV={\displaystyle \sum _{k=1}^N}P{V}_{d,k}·C{F}_k-C{F}_0\\ {PV}_{d,k}={\displaystyle \frac{1}{{\left(1+d\right)}^k}}\end{array}$$

(1)

where PV_d,k is the discount factor at discount rate d and k period; CF_k is the positive net cash flow; and CF₀ is the investment outlay.

▪

Modified net present value (MNPV)—the NPV criterion assumes that the financial surpluses obtained in subsequent years will be reinvested at an interest rate equal to the discount rate. In reality, the values may differ. For this purpose, the reinvestment rate should be assumed and included in the calculation:

$$\begin{array}{c}MNPV=P{V}_{d,k}{\displaystyle \sum _{k=1}^N}F{V}_{r,N-k}·C{F}_k-C{F}_0\\ {FV}_{r,N-k}={\left(1+r\right)}^{N-k}\end{array}$$

(2)

where FV_r,N-k is the capitalization ratio at reinvestment rate r and N-k is the period, where N is the total duration of the project.

▪

Profitability ratio (PI) is the ratio of the present value of positive cash flows to the investment outlay. If the PI is greater than 100%, the project can be subjected to further analysis. If this value is less than 100%, the project is not profitable:

$$PI={\displaystyle \frac{{\sum }_{k=1}^{N}P{V}_{d,k}·C{F}_k}{C{F}_0}}$$

(3)

▪

Internal rate of return (IRR)—the discount rate at which the NPV is zero. The calculated IRR value should be compared to the adopted discount rate. If the IRR is greater than the discount rate, the project should be subjected to further analysis. Otherwise, the project should be rejected. The IRR value can be calculated based on the following relationship:

$${\displaystyle \sum _{k=1}^N}P{V}_{IRR,k}·C{F}_k-C{F}_0=0$$

(4)

where PV_IRR,k is the discount factor at a discount rate equal to the IRR value and k is this period.

▪

Modified rate of return (MIRR)—in order to take into account income from periodic capitalization of interest, as in the case of the NPV method, the reinvestment rate r should be taken into account:

$$MIRR=\sqrt[N]{{\displaystyle \frac{{\sum }_{k=1}^{N}F{V}_{r,N-k}·C{F}_k}{C{F}_0}}}-1$$

(5)

where FV_r,N-k is the capitalization ratio at reinvestment rate r and N-k is this period.

▪

Normal and discounted payback period (DPP)—the normal payback period, PP, informs about the number of periods after which the nominal financial surpluses will be equal to the incurred outlay. The normal payback period method ignores the variability of the value of money over time. In order to realistically assess the payback period of the investment, the discount factor should be taken into account, using the discounted payback period DPP criterion:

$$\begin{array}{c}{\displaystyle \sum _{k=1}^n}C{F}_k=C{F}_0\\ {\displaystyle \sum _{k=1}^n}P{V}_{d,k}·C{F}_k=C{F}_0\end{array}$$

(6)

where n is the payback period.

These financial indicators provide a comprehensive assessment of investment profitability, enabling a multidimensional evaluation of the wind farm layouts in both onshore and offshore contexts.

3. Results

3.1. Energy Analysis of Onshore Wind Farms

Wind farm operation modelling was performed for each of the selected areas in three variants of wind unit spacing, expressed in multiples of the rotor diameter—3D, 4D, 5D. Views of individual variants are presented in Figure 13, Figure 14 and Figure 15. The visualizations illustrate the spatial density of turbines and the extent to which the selected area is utilized in each spacing variant. The 3D configuration makes the most use of available land but increases wake interactions.

The turbulence losses (wake losses) were determined for each turbine in the system, and therefore so was the annual net energy production for each wind turbine and the entire farm (gross production minus energy losses).

Table 7. Selected energy indicators calculated on the basis of the performed simulation.

	Distance	Number of Turbines	Cp Netto	Energy Net	Energy Gross	Wake Total	Wake for Single Turbine	Installed Power	Energy from 1 MW of Installed Power
	[-]	[-]	[%]	[GWh/year]	[GWh/year]	[GWh/year]	[GWh/year]	[MW]	[GWh/MW]
FW Stęszew
1	3D	47	28.62	496.424	702.524	140.435	2.99	159.80	3.52
2	4D	25	35.07	298.305	373.657	48.986	1.96	85.00	3.97
3	5D	18	39.06	222.099	268.985	17.511	0.97	61.20	4.11
FW Okonek
4	3D	48	28.72	492.673	674.232	116.372	2.42	163.20	3.42
5	4D	32	33.61	352.649	442.718	43.431	1.36	108.80	3.67
6	5D	24	36.63	278.050	337.055	22.246	0.93	81.60	3.86
FW Gostyń
7	3D	44	31.12	478.161	640.736	99.314	2.26	149.60	3.62
8	4D	27	36.47	318.056	393.301	33.195	1.23	91.80	3.92
9	5D	20	39.05	243.578	291.325	15.528	0.78	68.00	4.06

summarizes the calculated energy parameters of wind farms. As shown in Table 7, increasing the distance between turbines leads to a clear reduction in wake losses and an improvement in net capacity factors. However, this comes at the cost of reducing the number of turbines and, consequently, the total installed capacity. The most compact variant (3D) achieves the highest gross energy output but also experiences the greatest wake losses, especially in the Stęszew location.

Figure 16 shows views of the most and least obscured turbines in a given wind farm.

The visualization confirms that turbines located in central or downstream positions within the layout are significantly more affected by turbulence, while edge-positioned turbines experience less obstruction. This visual distinction helps understand the variation in wake loss values reported in the next table.

Table 8. Total, minimum, and maximum wake losses in individual wind farms.

Distance	FW Stęszew			FW Okonek			FW Gostyń
	Wake Total [%]	Wake Min. [%]	Wake Max. [%]	Wake Total [%]	Wake Min. [%]	Wake Max. [%]	Wake Total [%]	Wake Min. [%]	Wake Max. [%]
3D	19.99	8.84	27.51	17.26	5.39	25.73	15.5	6.87	23.21
4D	13.11	4.16	13.11	9.81	3.92	12.97	8.44	3.39	12.67
5D	6.51	3.67	8.89	6.6	2.72	8.54	5.33	2.65	7.3

compares the extent of wake losses in different spacing variants. As expected, the tightest configuration (3D) exhibits the highest total and maximum wake effects, while the 5D layout significantly reduces turbulence-related losses across all three sites.

The numerical trends are confirmed graphically in the following Figure 17, Figure 18, Figure 19 and Figure 20, which illustrate annual energy production and wake loss behaviour for each farm under varying spacing arrangements.

Figure 20 highlights the trade-off between turbine count and efficiency: as spacing increases, the number of turbines and total installed power decrease, but the energy yield per megawatt improves. This effect is particularly visible in the shift from 3D to 5D, indicating the importance of balancing land use with performance optimization.

3.2. Energy Analysis of Offshore Wind Farms

The analysis was carried out for four variants of the distance between turbines, expressed as a multiple of the rotor diameter (5D, 6D, 7D, 9D) for the assumed installed power (range from 400 MW to 1500 MW) in the designated area. Depending on the distance between turbines and the rotor diameter of the analysed model, the number of wind units was changed. The turbines were arranged in a regular grid along the dominant wind direction in accordance with the wind rose. The terrain maps are illustrated in Figure 21. Views of the turbines are presented in Figure 22.

The annual electricity production by the wind farm was estimated taking into account losses resulting from the wind speed deficit in the area of neighbouring turbines. The calculations were performed in the same way as for land areas. The terrain roughness for sea areas was assumed at a value of 0, and the loss coefficient WDC (wake decay constant) was 0.06. The loss coefficient WDC determines the rate of expansion of the generated aerodynamic wake and is determined semi-empirically based on the surface roughness. The calculation results for the individual variants are presented in

Table 9. Selected energy indicators calculated for individual variants of the offshore wind farm.

	Distance	Number of Turbines	Cp	Energy Net	Energia Gross	Average Energy per Turbine Net	Installed Power	Energy from 1 MW of Installed Power	Wake Total
	[-]	[-]	[%]	[GWh]	[GWh]	[GWh]	[MW]	[GWh/MW]	[%]
DTU 10 MW 178 RWT
1	5D	146	36.09	5655.8	7600.5	38.7	1460.0	3.9	18.39
2	6D	106	41.18	4386.3	5518.1	41.4	1060.0	4.1	12.82
3	7D	66	45.97	2887.3	3435.8	43.7	660.0	4.4	7.84
4	9D	43	49.04	1943.0	2238.5	45.2	430.0	4.5	4.80
IEA_15 MW_240_RWT
5	5D	73	45.26	4869.0	5981.5	66.7	1095.0	4.4	10.73
6	6D	53	49.01	3676.1	4342.7	69.4	795.0	4.6	7.16
7	7D	40	51.12	2834.7	3277.5	70.9	600.0	4.7	5.14
8	9D	27	53.22	1952.4	2212.4	72.3	405.0	4.8	3.21

.

Figure 23 shows the distributions of electricity generation and losses depending on the distance between turbines.

Figure 24 presents the characteristics of net electricity production and the number of wind turbines in the farm for both variants of the proposed turbine models. The energy yields from 1 MW of installed power are on average higher (by (0.3–0.5) GWh) when using 15 MW turbines.

3.3. Economic Analysis of Onshore Wind Farms

The individual variants of the Stęszew, Okonek, and Gostyń wind farms were subjected to economic analysis in order to indicate the most profitable variant. Estimating the investment costs in the implementation of wind projects is crucial in the context of determining the minimum price offered by the investor during the auction. CAPEX costs are largely dependent on the wind turbine technology. The CAPEX cost price was assumed to be USD 1370/kW (USD/PLN exchange rate at 3.94). The price was assumed based on the NREL onshore wind energy cost forecast analysis [47]. Wind turbine technology has a key impact on costs; therefore, it allows for a more accurate estimation of investment costs. Operating costs of wind farms are significantly lower than the incurred investment costs and usually amount to about 25% of the annual investment revenues. Operating costs per MW were assumed to be USD 44/kW [47].

Investment revenues depend mainly on the productivity of the wind farm. Net energy production values obtained from modelling were adopted. Another factor influencing revenues is the price of electricity, established through an auction. Farms put into operation by 1 July 2016 could count on additional revenue from the sale of green certificates. The analysis assumed revenue from the sale of energy through an auction and a reference energy price of PLN 324/MWh based on the most up-to-date data from the Energy Regulatory Office [48].

Table 10. Cost and revenue structure of the analysed wind farms.

Parameter	Unit	Stęszew			Okonek			Gostyń
		3D	4D	5D	3D	4D	5D	3D	4D	5D
Installed power	MW	159.8	85	61.2	163.2	108.8	81.6	149.6	91.8	68
CAPEX	PLN/MW	5,397,800
OPEX	PLN/MW	173,360
Energy price	PLN/GWh	324,000
CAPEX total	mln PLN	862.57	458.81	330.35	880.92	587.28	440.46	807.51	495.52	367.05
OPEX total	mln PLN	27.70	14.74	10.61	28.29	18.86	14.15	25.93	15.91	11.79
Net energy production	GWh/year	496.42	298.31	222.10	492.67	352.65	278.05	478.16	318.06	24,58
Energy sales	mln PLN/year	160.84	96.65	71.96	159.63	114.26	90.09	154.92	103.05	78.92

summarises the structure of costs and revenues of individual variants of the analysed wind farms in the first year of the project life cycle.

In order to analyse the cash flows, the investment duration was assumed to be 20 years (average real life of onshore wind farms). The discount rate was assumed at 8% and the reinvestment rate at 12%, and the value of income tax at 19% was also taken into account. Expenditures were depreciated linearly at a rate of 5% over the entire investment period. In addition, an increase of 2.5% in energy prices and cash costs was assumed in accordance with NBP inflation year-on-year. A graphical presentation of cash flows over the project duration is presented in Figure 25, Figure 26 and Figure 27.

Table 11. Criteria for assessing the profitability of onshore wind farm investments.

	Assessment Criteria	FW Stęszew			FW Okonek			FW Gostyń
		3D	4D	5D	3D	4D	5D	3D	4D	5D
1	NPV [mln PLN]	371.5	292.4	230.8	339.8	293.5	257.7	384.9	304.4	247.6
2	MNPV [mln PLN]	672.8	475.9	367.9	637.8	508.6	428.2	676.1	499.8	397.7
3	PI	143.1%	163.7%	169.9%	138.6%	150.0%	158.5%	147.7%	161.4%	167.5%
4	IRR	13.1%	15.4%	16.1%	12.6%	13.9%	14.9%	13.7%	15.2%	15.8%
5	MIRR	11.2%	11.9%	12.1%	11.0%	11.4%	11.7%	11.3%	11.8%	12.0%
6	PP	7.18	6.30	6.08	7.40	6.86	6.50	6.96	6.39	6.16
7	DPP	10.89	8.99	8.56	11.42	10.17	9.41	10.40	9.17	8.73

presents the results of the economic analysis using selected economic evaluation indicators.

As shown by the calculated economic indicators, all analysed variants of onshore wind farms in the communes of Stęszew, Okonek, and Gostyń demonstrate positive profitability. The highest IRR values were recorded for variants with 5D spacing, ranging from 14.9% to 16.1%, which indicates a favourable internal return on investment. However, when considering the MNPV and NPV values, the 3D spacing variants generate the highest cumulative profits due to higher installed capacity, despite higher wake losses. These results confirm the importance of balancing turbine density with wake effect losses and capital costs. The final choice of the optimal variant may therefore depend on the investor’s preferences regarding risk, available area, and investment strategy.

3.4. Economic Analysis of the Offshore Wind Farms

In the analysis of the distribution of costs and revenues, the unit CAPEX costs were considered separately for a 10 MW and 15 MW turbine, due to the differences in price depending on the technology. The analysis of the costs of the wind farm consisting of 10 MW units was prepared based on the PWEA report from 2024 [49], in which a turbine of the same power was used, located 45 km from the onshore transformer station. The distance of the farm from the coastline is crucial in the context of the costs incurred, and a linear increase in the CAPEX value (to approx. PLN 1.6 million) is estimated with an increase in the distance from the transformer station by another 50 km. Therefore, the values assumed in the report were scaled to the analysed wind farm, which is located 80 km from the coastline. The unit investment costs were assumed to be PLN 12.76 million/MW (at the assumed EUR/PLN exchange rate of 4.29). Costs for a 15 MW turbine were estimated based on an analysis conducted by NREL [47]. A model scenario with technical parameters identical to the model adopted in the work was selected, with a power of 15 MW, a rotor diameter of 240 m, and a tower height of 150 m. The unit investment cost was estimated at PLN 13.23 million/MW (assuming the USD/PLN exchange rate of 3.94). Unit operating costs were assumed in both cases to be PLN 0.51 million/MW, based on the estimates of PWEA [49]. An annual increase in the value of operating fees by 2.5% was assumed in accordance with the NBP inflation assumptions. The revenues of offshore wind farms are shaped primarily by energy production by power plants and energy sales prices. The amount of net energy produced in individual analysis variants was assumed based on the conducted energy analysis. The energy sale price was assumed to be PLN 471.83/MWh, i.e., the maximum energy sale price in the auction system for projects qualified for Phase II of the offshore investment support system [50]. The analysed area of the Bałtyk I project implementation by the Polenergia group has already qualified for Phase II of support; therefore, the corresponding maximum energy price was adopted in order to accurately reflect the current conditions for providing financial support. The selling price of energy from offshore wind farms is to be indexed annually to the average annual consumer price index; therefore, an annual price increase of 2.5% was assumed in accordance with the NBP inflation target.

Table 12. Cost and revenue structure of the analysed wind farms.

Parameter	Unit	DTU 10 MW 178 RWT				IEA 15 MW 240 RWT
		5D	6D	7D	9D	5D	6D	7D	9D
Installed power	MW	1460	1060	660	430	1095	795	600	405
CAPEX	mln PLN/MW	12.767				13.231
OPEX	mln PLN/MW	0.51
Energy price	PLN/GWh	319,600
CAPEX total	mln PLN	18,640.331	13,533.39	8426.451	5489.961	14,487.81	10,518.55	7938.528	5358.506
OPEX total	mln PLN	744.6	540.6	336.6	219.3	558.45	405.45	306	206.55
Net energy production	GWh/year	5655.8	4386.3	2887.3	1943	4869	3676.1	2834.7	1952.4
Sales revenue	mln PLN/year	1807.59368	1401.861	922.7811	620.9828	1556.132	1174.882	905.9701	623.987

presents the unit and total costs of implementing the offshore wind farm project in the studied variants and the revenues generated from the investment in the first year of the project’s life.

The cash flow analysis assumed an investment duration of 25 years, which is the average real lifespan of offshore wind farms. A discount rate of 6.5% and a reinvestment rate of 7.1% were assumed, and an income tax value of 19% was also taken into account. Expenditures were depreciated linearly at a rate of 4% over the entire investment period. In addition, a 2.5% year-on-year increase in energy prices and operating fees was assumed in accordance with NBP inflation. After discounting the nominal values, the cash flow results for the entire life of the project were obtained (Figure 28 and Figure 29).

The values of selected economic indicators allowing the assessment of the profitability of the investment are presented in

Table 13. Criteria for assessing the profitability of offshore wind farm investments.

	Assessment Criteria	DTU 10 MW 178 RWT				IEA 15 MW 240 RWT
		5D	6D	7D	9D	5D	6D	7D	9D
1	NPV [mld PLN]	6.254	6.174	4.755	3.459	8.348	6.884	5.547	3.971
2	MNPV [mld PLN]	8.391	7.861	5.881	4.223	10.255	8.337	6.673	4.750
3	PI	132.4%	144.0%	154.5%	160.8%	157.6%	165.4%	169.9%	174.1%
4	IRR	9.5%	10.5%	11.3%	11.8%	11.6%	12.2%	12.5%	12.9%
5	MIRR	8.0%	8.4%	8.7%	8.9%	8.8%	9.0%	9.1%	9.2%
6	PP	10.18	9.45	8.87	8.56	8.71	8.56	8.15	7.97
7	DPP	16.05	14.30	13.04	12.38	12.70	12.38	11.54	11.19

.

3.5. Comparison of Onshore and Offshore Wind Farms

3.5.1. Comparison in Terms of Wind Conditions

Table 14. Weibull distribution parameters for the analysed wind farm locations.

Location	Parameters of the Weibull Distribution	10 m	50 m	100 m	150 m	200 m
MFW Bałtyk	k [-]	1.99	2.13	2.29	2.24	2.11
	A [m/s]	8.3	9.8	10.6	11.3	11.6
FW Stęszew	k [-]	1.74	2.05	2.43	2.45	2.22
	A [m/s]	4.9	7.1	8.5	9.8	10.5
FW Okonek	k [-]	1.96	2.25	2.6	2.65	2.44
	A [m/s]	4	6.4	7.8	8.09	10
FW Gostyń	k [-]	1.7	2	2.37	2.37	2.17
	A [m/s]	4.8	7	8.4	9.6	10.3

presents the Weibull distribution parameters for the analysed wind farm locations for five different heights above ground level. Each of the obtained distributions is characterized by the scale coefficient A and shape coefficient k, reflecting the average wind speed and its variability. Figure 30 illustrates the Weibull distributions in the form of graphs. The offshore farm is characterized by higher coefficients k and A, which indicate higher average wind speeds with lower variability. This is also confirmed by the obtained probability density distributions. Wind speeds change to a lesser extent with height above sea level.

The parameter describing the wind speed distribution is the vertical wind speed profile, which reflects the change in wind speed with height above ground level. It depends on ground parameters, such as surface shape or terrain roughness coefficient. Graphs of the wind speed gradient for the analysed locations are shown in Figure 31. The sea area is characterized by higher wind speed values and higher dynamics of speed changes with increasing height. The profiles of the analysed land locations are comparable.

Table 15. Parameters of the statistical description of turbulence in the analysed areas.

	Turbulence Intensity TI [-]			Variance σ2 [m2/s2]			Coefficient α [-]
	u	v	w	u	v	w
FW Stęszew	0.129	0.0919	0.0672	0.72	0.367	0.196	0.138
FW Okonek	0.14	0.099	0.0717	0.764	0.382	0.2	0.126
FW Gostyń	0.129	0.0918	0.0671	0.687	0.35	0.187	0.142
MFW Bałtyk	0.066	0.0476	0.0348	0.335	0.172	0.092	0.091

presents the statistical parameters of the turbulence description, calculated in the WAsP program for the sector of the dominant wind speed (270° for each location) at the height of the rotor. The list includes the turbulence intensity (TI) and the variance σ² of the individual components of the wind speed vector, the averaged value of the horizontal wind speed at the rotor level, and the coefficient α. The values of the TI and σ² parameters are approximately two times lower in the sea area than in the land areas.

3.5.2. Comparison in Terms of Energy Factors

In order to compare the efficiency of onshore and offshore wind farms, selected energy parameters were collected, and then their values obtained for individual spacing variants were averaged. The results were compared for offshore turbines with a capacity of 10 MW and 15 MW and a 3.4 MW onshore turbine. The annual net energy production by a single wind turbine and the entire farm, total losses resulting from turbine shading (wake losses), the installed power of the farms, and the wind farm capacity utilization factor (Cp) were taken into account. The results are presented in Figure 32.

3.5.3. Comparison in Terms of Economic Factors

Investments in the form of offshore and onshore wind farms were compared by averaging the values of selected economic indicators. Only projects considered profitable were taken into account, so the variant of an offshore wind farm with a 10 MW unit with 5D spacing was rejected. The results are presented in Figure 33.

3.6. Limitations and Uncertainty

Although the WAsP model is a widely accepted and industry-standard tool for wind resource and energy yield assessment, it is subject to limitations inherent to computational modelling. In this study, input data (wind speed, orography, surface roughness) were derived from the Global Wind Atlas and reanalysis datasets (ERA5, WRF), which, while widely validated in the literature, are not a substitute for long-term local wind measurements. As such, there may be uncertainty in wind speed estimates due to terrain complexity, data resolution, or atmospheric conditions not captured at fine scale.

While a full sensitivity analysis was not performed due to data and resource limitations, the modelling assumptions were deliberately conservative and based on reputable sources. A preliminary sensitivity estimate shows that a ±5% variation in average wind speed—a realistic range for reanalysis-derived data—may lead to an approximate ±15% change in annual energy production due to the cubic relationship between wind speed and power output. This nonlinearity should be considered in investment risk analyses.

Furthermore, a direct validation of results against operational production data was not possible, as the study focused on prospective not yet constructed sites. However, the comparative nature of the analysis—evaluating different turbine spacing strategies under consistent assumptions—provides relevant insights for early-stage decision making. The methodology and assumptions used are consistent with prior studies, and the underlying data sources have been validated in the international literature.

In future work, the authors plan to enhance the study with localized wind measurement data (e.g., from meteorological masts or SCADA systems of existing wind farms) and to expand the analysis with Monte Carlo or parametric uncertainty simulations.

The economic analysis presented in this study incorporates key site-specific cost drivers such as the distance to shore and water depth. The offshore wind farm location considered corresponds to a water depth of approximately 40 m, consistent with data from the PWEA report [49]. Costs related to the distance to shore were scaled accordingly based on this source. Additionally, the NREL report [47]—which informed part of the economic assumptions—considers offshore wind farms at comparable depths (greater than 30 m) and describes the relevant foundation and technology options.

However, the current model does not explicitly account for variations in seabed conditions, foundation types (e.g., monopile, jacket), or other site-specific engineering challenges, which can substantially impact capital expenditures. These simplifications stem from data availability constraints and the focus on providing a comparative baseline rather than a detailed project-level cost assessment. This may be a topic for future analysis.

4. Discussion

Based on the conducted computer simulations and comparative analyses, the following conclusions may be drawn.

▪

Figure 17, Figure 18 and Figure 19 show the distribution of net electricity production along with losses for the analysed onshore wind farms. A clear dependence of the level of wake losses on the applied distance between wind turbines is observed. For a distance of 3D, the loss values are at the level of 15–20%. When the distance is increased to 4D, the losses decrease by almost two times. For a distance of 5D, the losses are marginal and amount to 5–6%. The use of larger distances between turbines also has a direct impact on increasing the C_p factor, the values of which range from 28% (for a 3D distance) to 39% (for a 5D distance). This observed reduction in wake losses with increased inter-turbine spacing corresponds with results from previous computational studies [51], confirming the adverse effect of close turbine placement on wind capture efficiency. The simulations performed in this study reinforce these findings under Polish wind conditions and with specific land constraints.

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Figure 20 shows the distribution of losses compared to the number of wind turbines in the wind farm. The number of wind turbines does not always translate proportionally to the value of generated losses. For the 4D distance, in the Stęszew variant, the smallest number of turbines was used, i.e., 25, while the sum of losses reached the highest value among the analysed cases (approx. 49 GWh/year). In the Okonek variant, with the number of turbines equal to 32, losses were estimated at 44 GWh/year. Significant differences in loss generation can also be observed in the 3D spacing variant between the Stęszew and Okonek farms. In the case of the Stęszew object, losses amount to about 140 GWh/year, and in the case of Okonek, losses are 116 GWh/year with a similar number of wind turbines. The Gostyń farm in this variant is characterized by the smallest losses of about 100 GWh/year, but the number of wind turbines is significantly smaller, which has a direct impact on the level of mutual shading of units. Analysing the level of loss generation in relation to the number of turbines, the Gostyń wind farm is the most advantageous, and the Stęszew wind farm is the least advantageous. The discrepancies in the results of the losses result primarily from the distribution of wind turbines in a given area. The Okonek wind farm is located on the largest area of approx. 5.46 km². The wind turbines are grouped, and the individual groups are distributed at distances from each other. A similar layout grid was used for the Gostyń wind farm, but the total area is approximately 3.68 km². In turn, the Stęszew wind farm is characterized by a dense layout of turbines in two larger groups within an area of approximately 3.5 km². This is an unfavourable layout as it translates into a higher level of losses in relation to the number of used wind turbines.

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The Stęszew and Gostyń wind farms are characterized by a similar level of energy production in relation to 1 MW of installed power. The Okonek farm is less efficient in this respect. The capacity utilization factors in this case take the lowest values (28.7–36.6%). This may be due to the characteristics of the terrain. The Okonek farm is located in an area classified as roughness class 3, which means forested, rural, and cultivated areas with a lot of bushes. The average roughness coefficient in this case is 0.692 m. For comparison, the areas of the Stęszew and Gostyń farms are classified as roughness class 2, which should be interpreted as cultivated areas with a small number of trees and bushes and buildings, located about 500 m away. The average roughness coefficient values are 0.239 m and 0.185 m, respectively. The higher roughness coefficient of the terrain has a negative impact on the vertical profile of the wind speed distribution and is associated with the occurrence of obstacles that obscure wind turbines. As a result, areas with a higher roughness class have a negative impact on the efficiency of the power plant. These findings are in line with the work of Barthelmie and Jensen [52], who emphasized the strong influence of surface roughness on turbulence intensity and wind speed gradients. The present analysis provides a more localized and detailed evaluation of these effects for typical rural and semi-forested areas in Poland.

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The highest NPV values are characteristic of the 3D variants. Similarly, MNPV values also reach higher values with smaller spacing. This is related to the larger number of turbines distributed on the surface of a given farm and, consequently, higher installed power. The energy generated by the power plants in the 3D variant allows annual revenues almost twice as high as those of the 5D variant to be obtained. The net present value ranges from PLN 230–257 million in the 3D variant to PLN 292–304 million in the 4D variant and PLN 339–384 million in the 5D variant. After taking into account the capitalization of interest with an assumed rate of 10%, the revenue increases by approx. 60–90% depending on the case considered.

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After taking into account the PI criterion, it can be stated that the profitability of the investment increases with the increase in the turbine spacing. In each of the analysed cases, PI > 100%, so each of them should be considered profitable. The highest profitability coefficient is distinguished by the Stęszew wind farm in the 5D variant, which is 169.9%. This means that each PLN 1 of the incurred outlay generates almost PLN 1.70 of the current value of the stream of positive cash flows. The lowest profitability coefficient is 138.6% (Okonek in the 3D variant).

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The (M)IRR values in the analysed cases behave inversely to (M)NPV and take higher values for larger turbine spacings. All (M)IRR results are higher than the limit rate of 8%, so each of them can be considered profitable.

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The PP and DPP indicators assume more favourable values for larger turbine spacings. The typical payback period includes nominal cash flows and ranges from 6 to 7.5 years. After taking into account the time value of money, this period is extended by 2–3 years.

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If the investor takes into account only the net present value when assessing profitability, the highest positive flows above the investment outlay are generated by wind projects with higher installed power, i.e., in the 3D spacing variant. These are profits of the order of PLN 340–385 million generated over the entire project life cycle. After correcting the NPV value by a reinvestment rate of 10%, the profits increase to the order of PLN 640–676 million. The remaining assessment criteria taken into account indicate an inverse relationship. Both the profitability index and the (M)IRR increase with the increase in the spacing of wind units, which indicates greater investment efficiency from the financial point of view, as well as a higher margin of financial security. This also means a faster payback period of about 2 years for the difference between the 3D and 5D variants. Therefore, when assessing the profitability of an investment, one should not be guided only by the net present value.

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In the case of offshore wind farms, energy production increases with decreasing distance between units, which is a result of greater total installed power (Figure 23). Reducing distance causes greater wake losses and causes turbines to operate with lower power utilization factors. The wind farm using a turbine model with a rated power of 10 MW shows lower operating efficiency. The power utilization factor for this variant reaches values in the range of 36–49%, while the farm with turbines of 15 MW achieves a power utilization factor of 45–53%.

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As can be seen (Figure 24), at distances equal to 7D and 9D, the differences between energy production are marginal, while the number of turbines is about 1.6 times higher in the case of the 10 MW model. For distances equal to 5D and 6D, energy production by 10 MW units is higher by 16% and 19%, respectively, compared to the farm with 15 MW units. The number of turbines needed to achieve this level of energy generation is twice as high in the case of the 10 MW model. The installed power is therefore disproportionately high in relation to the expected energy yields. Energy yields from 1 MW of installed power are on average higher by (0.3–0.5) GWh when using 15 MW turbines. This confirms general observations from offshore wind studies presented in [53] indicating that larger turbines typically achieve higher specific yields. The novel aspect here is the detailed quantitative assessment of this effect in the context of the Polish Exclusive Economic Zone, using real design parameters from the Baltic Power project.

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The smaller the distance between turbines, the higher the initial investment cost (Figure 28 and Figure 29). This is related to the previously adopted assumptions of limiting the wind farm area; therefore, at smaller distances, the number of generating units is significantly higher, which translates directly into investment costs. The fastest return on investment, that is, within 11–12 years, can be expected with a spacing variant equal to 9D. The payback time increases with decreasing spacing. This results from lower investment costs as well as the efficiency of the farm operation. Wind farms with units with a larger spacing are characterized by higher power utilization factors and a lower percentage of losses resulting from shielding the turbines.

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For the offshore wind farm, all calculated NPV values are positive, so each project can be accepted for further analysis. The highest NPV value, in the case of a farm with a 10 MW turbine, is assumed by the 5D spacing variant, for which positive flows amount to over PLN 6 billion over the entire project life cycle. For the project with a 15 MW turbine, the highest NPV is over PLN 8 billion. For the largest values of spacing between turbines, NPV values were estimated at PLN 3.5 billion and almost PLN 4 billion for the variants with 10 MW and 15 MW turbines, respectively. Assuming reinvestment of annual revenues at a rate of 7.1%, the adjusted NPV values increase and show a decreasing trend with increasing spacing. For farms with a 10 MW turbine, the MNPV is over PLN 8 billion for the 5D spacing, and for a 15 MW turbine, it is over PLN 10 billion. For the 9D variants, MNPV values increase to PLN 4.2 billion and PLN 4.75 billion for the 10 MW and 15 MW turbines, respectively. Comparing both variants of the applied wind turbine technology, it can be stated that the use of the 15 MW turbine is a more profitable option. NPV values are higher in this case by 10–25% depending on the variant, and the differences in NPV increase with the number of generating units.

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The PI profitability indices increase with the increase in the spacing between turbines. The highest profitability level is achieved by the 9D variants and amounts to 160.8% and 174.1% for 10 MW and 15 MW turbines, respectively. Reducing the spacing to five times the rotor diameter causes the PI value to decrease by 28.4 and 16.5 percentage points, respectively, for the 10 MW and 15 MW turbines, compared to the values determined for the 9D spacing. It can be concluded that the 9D variants, despite the lowest NPV, are economically more efficient after taking into account the revenue-to-cost ratio. The PI coefficients assume higher values for the variants with a 15 MW turbine in each spacing variant, which indicates their higher efficiency from an economic point of view.

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The IRR and MIRR values should be higher than the assumed cut-off rate of 6.5% for the project to be considered profitable. All determined M(IRR) values are higher, so each project can be subjected to further analysis. M(IRR) follows a similar trend to the PI indicator and increases with increasing distance between turbines. IRR values range between 9.5% and 11.8% for the 10 MW turbine variant and between 11.6% and 12.9% for the 15 MW turbine variant. Projects using a larger turbine are therefore characterized by a higher rate of return. The obtained IRR values are higher by 3% to 6.4% than the base discount rate of 6.5%. Therefore, they provide a high margin of financial security in the event of an increase in the loan rate.

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No significant differences were found in the analysis of the typical payback period. Such a period is about 8.5–10 years for a farm with 10 MW turbines and about 8 years for a farm with 15 MW turbines. After taking into account the change in the value of money over time, the payback period is significantly extended to 12–16 years for an investment with a 10 MW turbine and 11–13 years for a 15 MW turbine. Considering the average service life of offshore wind farms, which is 25 years, it can be stated that the investments will pay off in a relatively short time, regardless of the adopted variant.

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The use of a larger turbine is more profitable from an economic point of view. Newer technology allows for higher incomes in a shorter period of time, is more effective in relation to the investment outlays incurred, and provides a higher level of financing security. The most profitable from an NPV point of view are variants with a higher density of wind units and, consequently, higher installed powers. Larger-scale projects generate higher annual energy yields, so despite lower operating efficiency, they allow for higher revenues throughout the project’s life. In both variants of the used wind turbine technology, NPV is approximately twice as high for the 5D spacing variants compared to the 9D variants. The remaining indicators indicate higher profitability for the 9D spacing variants. Both the PI and M(IRR) profitability coefficients assume a growing trend with increasing distance between turbines. The payback period also decreases for projects with a higher spacing variant. The differences in the values of PI, M(IRR) and DPP coefficients are not significant enough in relation to the differences in the possible generated revenues. Additionally, if financial benefits from interest capitalization are taken into account, the revenue may increase by approx. PLN 2 billion for variants with a 5D spacing.

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Based on the obtained results (Figure 32), it can be concluded that offshore wind farms are characterized by higher capacity utilization factors. The Cp coefficient values are on average 49.65%, 43.07%, and 34.26% for an offshore turbine with a power of 15 MW or 10 MW and an onshore turbine with a power of 3.4 MW, respectively. Offshore turbines are able to generate about four to seven times more energy per year than an onshore turbine. This is due to the higher power, construction technology, and operating conditions of turbines at sea and on land. A larger power turbine (15 MW) generates an average of almost 70 GWh per year, while a 10 MW turbine generates about 42 GWh per year, which is about 40% less annual energy production. Looking at the overall operation of wind farms, it can be stated that offshore wind farms produce an average of about 10 times more energy than an onshore farm. This is a natural consequence of significantly higher installed power. The installed power of offshore wind farms is about seven and nine times higher for farms with 15 MW and 10 MW turbines, respectively, compared to the installed power of an onshore farm. In addition, attention should be paid to the generation of losses resulting from the shading of turbines. The average percentage shares of losses are 8%, 14%, and 15%, respectively, for a wind farm with 15 MW, 10 MW, and 3.4 MW turbines. A 15 MW offshore unit generates almost two times the losses of a 10 MW unit. An offshore farm with 10 MW units has a very similar percentage value of average losses to an onshore farm. This is due to the fact that offshore farms are arranged in a regular grid, and the average number of turbines used in offshore farms is about three times greater than in the case of onshore farms. Turbines in onshore farms are usually grouped in an irregular form and placed on adjacent plots, which increases the distance between individual groups. Such a turbine arrangement and a smaller number of units reduce mutual interactions between turbines and consequently limit losses.

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Offshore wind farms generate an average revenue stream that is nearly 20 times higher than that of onshore wind farms, which is a natural consequence of the larger scale of the projects (Figure 33). The IRR values for both offshore and onshore wind farms are similar and average 12.5%, 11.2%, and 14.5%, respectively, for 15 MW and 10 MW offshore turbines and a 3.4 MW onshore turbine. The IRR indicates the maximum cost of capital that the investor can accept without incurring losses. The discount rate assumed for calculating offshore investments is 6.5%, and for onshore wind farms, it is 8%. In all three cases, the IRR significantly exceeds the adopted limit value, which indicates the profitability of the investment while maintaining a margin of financial safety. Onshore wind farm investments are characterized by a faster payback period.

5. Conclusions

At the end of 2023, the installed power of onshore wind farms in Poland was about 9.3 GW, making Poland one of the largest wind energy markets in Central and Eastern Europe. The government plans to further increase the installed power, with the goal of reaching 14 GW by 2030.

Currently, large-power onshore wind energy in Poland is developing dynamically, although it faces certain regulatory and administrative challenges. The introduction of the “10H” Act in 2016 significantly limited the development of new onshore wind projects by introducing a minimum distance of 10 times the turbine height from residential buildings. However, in 2023, work began on changes to this regulation, the aim of which is to facilitate the construction of new wind farms by easing distance requirements. However, location restrictions, such as the currently applicable rule of maintaining a minimum distance of 700 m from residential buildings, still largely inhibit the development of wind energy in Poland. The number of free areas is small, and the location of power plants on many plots located next to each other is not a desirable situation, as it requires the consent of the owners of a larger amount of land, which complicates the administrative process and is also financially disadvantageous.

Wind conditions in a given area have a direct impact on the efficiency of the power plant. This can be seen from the obtained values of the capacity utilization factor, which are on average 34% in land areas and up to 49% in sea areas.

The weather conditions in the Baltic Sea are more favourable in terms of wind speed, which can be concluded from the obtained Weibull distribution graphs. The vertical profile of wind speed also indicates better wind conditions in the maritime areas. Its course indicates a more rapid increase in wind speed with height in the case of maritime areas.

The development of large-power offshore wind energy in Poland is in the phase of intensive development and planning, which is crucial for the country’s energy transformation and for increasing the share of renewable sources in the energy mix. By 2030, Poland plans to install 5.9 GW of offshore wind farms, and by 2040, at least 11 GW. Offshore wind farms will be located in the Baltic Sea, which is characterized by relatively shallow waters and favourable wind conditions, which are conducive to the construction and operation of such installations.

Several key projects are already in the advanced planning stage, such as Baltic Power (Orlen and Northland Power), Polenergia and Equinor, and PGE and Ørsted, where the first environmental decisions and contracts for difference (CFD) have already been granted. These projects involve the construction of farms with a total power of approximately 5.9 GW.

To enable the construction and servicing of offshore wind farms, Poland plans to modernize and expand key ports such as Gdynia, Gdańsk, and Świnoujście. These ports will function as assembly, service, and logistics bases.

In the case of offshore wind farms, the water areas designated for the construction of power plants have been designated in the Spatial Development Plan for Polish Maritime Areas, and the factors limiting the possibility of erecting wind farms include aspects such as the distance from the coastline (approx. 22 km, in accordance with the location of the Exclusive Economic Zone); the depth of foundations; and the location of military training grounds, fishing corridors, and maritime communication routes. In addition, the implementation of offshore wind investments requires a number of permits in the Polish legal context, such as environmental and geological studies, building permits, and concessions for energy production. The key document is the permit for the construction of artificial islands, structures, and devices in Polish maritime areas, which has so far been issued for 10 projects implemented in the Baltic Sea.

The number of wind turbines that can be installed in a limited area is determined by the calculated value of wake losses. A key aspect in minimizing losses due to shielding of turbines is their appropriate arrangement. For this purpose, three variants of onshore turbine spacing were distinguished (3D, 4D, and 5D), as well as four variants of offshore turbine spacing (5D, 6D, 7D, and 9D). In the case of onshore farms, it is stated that at a distance of 3D, losses amount to 15–20%. With an increase in the distance up to four rotor diameters, losses decrease by almost two times. At a five-fold diameter, losses are at the level of 5–6%. In the case of offshore farms, an analogous relationship can be observed.

Wake losses increase from 5% to 18% with increasing unit density for a 10 MW turbine and from 3% to 11% for a 15 MW turbine. Wake losses are lower for a 15 MW turbine.

The key factor determining the revenue from the sale of energy is its reference price. Currently, the maximum price for the sale of energy from offshore installations in the first phase of support (pre-auction phase) has been set at PLN 319/MWh, while the price proposed for the auction in the second phase of support is set at a maximum of PLN 471.83/MWh. The reference price of energy from onshore installations is currently PLN 324/MWh. In the case of onshore turbines, the net present value (NPV) increases with decreasing distance, which should be associated with higher installed power. Projects with a smaller spacing generate significantly higher revenue, but variants with a larger spacing can be considered more effective, taking into account the ratio of potential revenue to the investment outlay incurred.

CAPEX and OPEX costs are much higher for offshore investments and amount to approximately PLN 13 million/MW and PLN 510 thousand/MW per year, respectively. The cost of building onshore farms is less than PLN 5.5 million/MW, and operating costs are PLN 170 thousand/MW per year. The scale of revenues is much higher, amounting to PLN 4–8 billion of positive cash flows from offshore investments and PLN 250–400 million in the case of onshore investments. Additionally, the profitability of offshore farm projects may be affected by the form of project financing and support provided at the national and EU level, such as the conditions of support provided under the National Reconstruction Plan.