A Forward-Looking Assessment of Robotized Operation and Maintenance Practices for Offshore Wind Farms

Abstract

Operation and maintenance (O&M) activities represent a significant share of the levelized cost of energy (LCOE) for offshore wind farms (OWFs), making cost reduction a key priority. Robotic-based solutions, leveraging aerial and underwater vehicles in a cooperative framework, offer the potential to optimize O&M logistics and reduce costs. Additionally, the deployment of persistent autonomous robotic systems can minimize the need for human intervention, enhancing efficiency. This study presents the development of an O&M cost calculator that integrates multiple modules: a weather forecast module to account for meteorological uncertainties, a failure module to model OWF failures, a maintenance module to estimate costs for both planned and unplanned activities, and a power module to quantify downtime-related losses. A forward-looking comparative economic analysis is conducted, assessing the cost-effectiveness of human-based versus robot-based inspection, maintenance, and repair (IMR) activities. The findings highlight the economic viability of robotic solutions in offshore wind O&M, supporting their potential role in reducing operational expenditures and improving energy production efficiency.

1. Introduction

Europe’s greatest advantage in offshore renewable energy is the vast potential offered by our seas, from the North Sea to the Mediterranean, to the Atlantic as well as the seas surrounding the EU’s outer regions. As such, Europe is presented with a major opportunity to ramp up renewable power generation to achieve the objective of reaching carbon neutrality and to install 300 GW of offshore wind capacity by 2050, as proposed by the EU’s publication of the Offshore Renewables Energy Strategy [1].

Just like any other industrial equipment, wind turbines require service and maintenance (also known as operations and maintenance (O&M)), and in the case of offshore wind farms (OWFs), operational expenditure (OPEX) accounts for between 20% to 30% of the levelized cost of anergy (LCOE) during the farm lifetime [2].

O&Ms costs are related to a limited number of cost components and include the following:

• Regular/Preventative Maintenance;
• Unplanned/Corrective Maintenance;
• Spare Parts;
• Repair Labor;
• Others, such as the chartering of heavy-lift vessels and crews.

Moreover, compared to onshore wind farms, even the smallest failures will impact the availability of the offshore wind farm in a major way. The costs for corrective maintenance are a factor of 2 higher than the regular maintenance; as for onshore wind farms, the difference is negligible [3].

The effectiveness of O&M directly impacts the time for which the turbines produce power, and hence, the revenue of the OWF; therefore, it is in our best interest to increase the availability to maximize the production of revenue, or so it would seem, but in fact, as the turbines approach 100% availability, we start to see the cost of achieving said availability increase. In other words, if an owner of an OWF invests too little in O&M, they will incur a penalty in the form of poor performance of the turbines and other components. Conversely, if there is an over-investment in O&M, the owner will start facing diminishing returns as each increment in availability costs more and more [4].

In considering the economic dimensions of offshore wind O&M, it is apparent that innovation and technological advancement play a substantial role in shaping cost structures and efficiency. As we move towards more sophisticated and automated solutions, it is important to keep in mind that while these new technologies promise great benefits, their exact impact on the economics of O&M in the offshore wind industry remains uncertain.

One such innovation on the horizon involves the use of robotic solutions for O&M tasks. This is a groundbreaking development that is set to change the O&M landscape fundamentally. The advantages of these robotic solutions could range from reducing the number of technicians required for maintenance work and increasing the uptime of wind turbines, to improving the safety of operations. However, these advancements also bring with them a new set of challenges and uncertainties.

As these types of robotic solutions are very novel, one of the biggest uncertainties the offshore wind industry is facing is the ability to quantify the impact of these solutions. This uncertainty extends to various factors—cost-effectiveness, operational efficiency, safety improvements, and ultimately, their influence on the overall economics of offshore wind energy. Every day, research is being done with newer and more advanced robots; analytics and software applications are being developed to better understand the economic impact these solutions will bring [5].

The practical implementation of robotized solutions in OWF requires a multi-phase approach involving technological adaptation, infrastructure development, and operational integration. The process typically begins with a feasibility assessment that evaluates the technical, economic, and environmental viability of deploying robotic systems within the specific context of a wind farm. This includes site-specific considerations such as sea conditions, turbine type, and accessibility.

Once feasibility is confirmed, the next phase involves system integration planning. This step focuses on adapting robotic systems—such as ASV, ROV, aerial drones, or climbing robots—to the wind farm’s operational environment. Integration requires ensuring compatibility with existing infrastructure, such as docking stations for recharging and communication systems that enable real-time data exchange between robots and control centers.

The deployment phase involves installing the necessary hardware and software infrastructure, including sensor arrays, control platforms, and automated docking systems. This phase also requires establishing communication protocols and integrating data management systems for real-time monitoring and predictive maintenance.

Finally, the operation and monitoring phase ensures that robotized systems function as intended during real-time O&M activities. Advanced control algorithms enable autonomous task execution, such as blade inspections, structural cleaning, and underwater inspections. Additionally, predictive maintenance algorithms can leverage data collected by robotic systems to optimize scheduling and minimize downtime.

The central purpose of this paper is to investigate the economic impact and quantify the impact of the benefits that these new robotized O&M strategies would have in an OWF. The main objectives of this paper are the following:

• Research already-existing O&M cost calculators (NOWIcob, O2M, etc.), and understand their main components, models, and inputs.
• Develop our own O&M cost calculator composed of several modules, taking into consideration robotics solutions to O&M problems. New contributions by the authors are realized in this section by understanding how to quantify the impact of these solutions, which are, at the moment, not yet sufficiently assessed.
• Through the previous tool, obtain parameters such as the availability and total power generated, which will be used to assess the economics of these robotic solutions, in comparison to regular (traditional) human-based ones.

From the best knowledge of the authors, there are no commercial tools that integrate robots in the simulation of offshore O&M practices; therefore, this paper is a step in this direction. Besides providing a tool that incorporates robotics solutions in the assessment of O&M activities, this paper introduces a “robotized factor”, which is a novel way to differentiate between traditional and robotized solutions.

A 30% reduction in repair times and costs is a preliminary estimate, but further research is needed to validate this assumption. As more offshore wind farms adopt robotic solutions, empirical data will become available to refine the robotization factor and improve the accuracy of our model. The 30% reduction used as the “robotization factor” represents an optimistic, exploratory scenario aligned with the highest projections from the literature. The figure reflects a long-term vision of technological advancement, not current or near-term expected values. There is inherent uncertainty in such projections, and actual realized reductions may vary significantly depending on technology development, deployment, and industry adoption timelines.

The assumptions and enabling conditions that would be necessary to realize such high levels of OPEX reduction, include the following [5]:

• Purpose-built wind farm infrastructure designed for robotic accessibility and operation.
• Fully autonomous resident robotic systems capable of performing all minor repair tasks without human intervention.
• Robust implementation of inspection and maintenance strategies leveraging robotics.
• Lessons learned from early deployments being fed back into the design of future wind farms and turbines.

This paper is divided into five Sections, including the introduction. Section 1 contains an overview of the topic of the paper, the problem we aim to research, and the objectives. In Section 2, a brief review of the current state of the art is offered, where we examine the existing literature related to O&M in the offshore wind sector, with an emphasis on robotic solutions and their value. A description of the methodology and models which have been chosen to research and solve the problem is presented in Section 3. Section 4 includes the results from the simulations, as well as a discussion of these same results. Finally, a conclusion summarizing the main findings of this research is given in Section 5.

2. Literature Review

2.1. Existing Offshore O&M Simulation Tools

As an important first step to understanding how to develop a tool to model O&M costs, we must first analyze existing tools available in the market. During these past two decades, there have been a great number of such tools developed, either as commercial software products (such as ECN O&M, O2M, and NOWIcob) or authored models not publicly available, such as CONTOFAX. Most of these tools model the relationship between availability, costs, and maintenance, all while considering the weather conditions and its inherent variability.

Generally, these tools allow for simulation on a wind farm level, considering different failure types for each available wind turbine. The inputs usually consist of failure rates of the considered subsystems, inspection, maintenance, and repairs (IMRs) policies, power curves for the designated turbines, and weather conditions. Then, for most cases, a stochastic simulation is run (Monte Carlo for example) in the time domain to simulate the uncertainty of the failures of the turbines; afterwards, depending on the failure type, different maintenance parameters (type of vessel, time to repair, etc.) are considered to obtain the availability, OPEX, or any other desired output [6].

The Energy Research Centre of the Netherlands (ECN), in collaboration with Delft University, has been developing tools and methods to better optimize the O&M strategies of offshore wind farms. Two of these tools are the ECN’s O&M Tool and the O&M Cost Estimator (OMCE) [7]. OMCE is a successor to the O&M Tool, as the former was a cost-modelling tool, not a simulation tool, which used mean values from historical data as input. OMCE’s input data include data from supervisory control and data Aacquisitions (SCADAs) systems, condition monitoring, and load measurements. Any data regarding failure rates, vessel usage, spare parts, and weather conditions are analyzed in the context of corrective maintenance (CM), while data from condition monitoring and load measurements are analyzed in the concern of condition-based maintenance (CBM). In total, OMCE includes 5 different “blocks” or ”modules”. These blocks include operation and maintenance, logistics, loads and lifetime, health monitoring, and weather. ECN also splits maintenance actions into different categories depending on the required resources and severity of the failures. This allows the user to prioritize which failures are dealt with first. It is worth noting that this simulation tool is not based on a Monte Carlo analysis. The simulation outputs consist of parameters such as downtime, availability, and O&M costs.

GL Garrad Hassan, a consultancy firm specializing in the field of renewable energy, offers a range of tools and software for the design of wind farms and O&M simulation (O2M tool) [8]. O2M is a simulation tool based on the Monte Carlo approach. Its input data is categorized into four distinct blocks: environment, project description, O&M resources, and failures. Throughout the simulation, different output parameters such as availability; accessibility; and ideal, actual, and lost production are recorded. These and other parameters allow for O&M analysis. It is also possible to simulate different O&M strategies by changing the costs in the simulation model.

SINTEF Energy Research, in partnership with the NOWITECH research center, developed a decision support tool, NOWIcob [9]. This tool is designed to understand the sensitivities of O&M costs due to changes in maintenance and logistic strategies and assists in selecting optimized strategies for a specific wind farm. NOWIcob is based on a Monte Carlo simulation, simulating O&M operations in an offshore wind farm over its complete lifetime. The input parameters are divided into controllable and uncontrollable categories. Controllable inputs are strategy choices that the wind farm operator can directly decide on, such as the choice of vessels, shift durations, and maintenance personnel. Uncontrollable inputs act as stochastic parameters and include weather conditions, failure rates, and electricity prices, which fall outside the direct influence of the wind farm operator. The NOWIcob model is separated into four logical parts: input data, weather simulation, maintenance and Logistics, and results. Each part plays a crucial role in the simulation process, from handling various input parameters to simulating weather conditions, calculating maintenance time, and displaying performance criteria. In [6], other existing O&M tools are reviewed.

While previous studies, such as those by Niemi (2023) [10] and Gutschi et al. (2019) [11], have explored O&M modeling, our study provides an additional contribution by introducing a novel integration of robotics into a comprehensive O&M cost calculator and proposing a framework for estimating robotization impact in offshore wind maintenance—an aspect not explicitly quantified in previous works, therefore allowing for a direct comparison between traditional and robotized strategies.

2.2. Research on the Impact of Robotized Solutions for O&M in OWF

There are already several articles related to the usage of robotized solutions for O&M and analysis of the economic impact that such solutions bring to energy production. Below, some of these articles are reviewed.

A cost-benefit evaluation was performed by the Norwegian University of Science and Technology [12], investigating the potential economic benefits of using remote inspection technology for offshore wind farms. This remote inspection consisted of a robot system inside the turbine nacelle that performed inspections on behalf of an operator located on land. The results demonstrated that with a remote inspection system combined with condition monitoring, the availability and cost of energy were greater compared to condition monitoring alone.

In Norway [13], a field trial was performed, looking to evaluate the effectiveness of remote inspections of wind turbines. The experiment had 31 participants performing inspections with and without the robot in a laboratory environment. It was challenging to remotely operate the robot, and these inspections did not perform as well as the manned ones; however, the total time for the remote inspections was much shorter, and due to the high cost of visiting these offshore wind turbines, it was concluded that there is a considerable potential for cost savings with remote inspections.

A study was performed by the University of Manchester on the impact of drone-based inspections on the LCOE for OWFs [14], with the goal of evaluating the effects of changing inspection technology on the OPEX and revenue, thus defining the link between technology implementation and LCOE. The model was shown to be broadly valid, in that its results were in line with present data, and it was concluded that reducing manual inspection with remotely piloted drones lowered inspection costs by 70% and decreased revenue lost during inspection by 90% due to reduced downtime.

Study [15] reviewed the challenges and opportunities for artificial intelligence and robotics in the offshore wind sector, including the reduction of O&M costs. It hypothesized that to reduce costs, new operational procedures must be developed for robotics and artificial intelligence (RAI) deployment which would result in more frequent interventions and improvements in the quality and time of repairs and inspections, eventually leading to reduced costs for O&M.

A doctoral thesis by Øyvind Netland [16] looked to study the benefits, usability, and feasibility of remote inspection of offshore wind turbines. Several experiments were performed, such as a simulation with the life cycle cost calculator NOWIcob and a series of usability tests, which suggested an improved availability and a reduction in the cost of energy when remote inspections were used. Although in the end an effectiveness equal to that of manned inspection was not reached, it performed well enough to justify continued research on the topic.

A new multi-robot platform for autonomous O&M of OWFs was proposed by the University of Manchester [17], consisting of a fully autonomous and symbiotic integration of an onshore control center, an ASV (autonomous surface vessel), an unmanned aerial system with multiple UAVs (unmanned eerial vehicles) and a blade IMR (inspection, maintenance, and repair) robot. The aim was to significantly reduce the costs and turbine downtime associated with O&M tasks. The system addressed all phases of the O&M of OWFs autonomously, managing a fluid collaboration with the human operators.

A series of technology roadmaps and industry insights highlighting key challenges and priority innovation areas were published by the Offshore Wind Innovation Hub (OWIH) [5]. Their more recent study highlighted that the use of autonomous systems and robotics is expected to play a big role in the reduction of O&M costs. The simulations showed a reduction in OPEX of 9.6–16.7% and LCOE of 1.65–3.51% between 2030–2040 and 11.6–22.6% in OPEX and 2.31–4.97% in LCOE after 2050.

A paper published by Iqbal et. al. from the University of Jeddah [18] looked to analyze the prospective opportunities and challenges those robotic developments brought to renewable energy production, namely offshore wind energy. It concluded that, due to being a recent trend, not much quantitative data was available, but there was a great potential for cost reductions with the new technology being developed.

In the paper [19], a design for a symbiotic system of systems for safe and resilient autonomous robotics in offshore wind farms was presented. It looked for a way to enable cooperation, collaboration, and corroboration between the human operators and the many robots. The result showed a symbiotic system with enhanced run-time operational resilience and safety compliance, with the potential to be highly transferable to other mission scenarios; in other words, it provided a pathway to implementing scalable autonomy as a service.

In [20], a life cycle cost model for floating offshore wind farms was developed as a way to ascertain whether this technology is economically viable. This model took into account several key parameters such as the CAPEX, OPEX, and DECEX, the CAPEX representing the largest contributor to the life cycle cost of wind farms, the OPEX representing the expenditures related to O&M, and DECEX relating to decommissioning costs. The LCOE is calculated using these three parameters and the annual energy produced. The model was tested in a case study in southern Italy, resulting in splits of 18–20% of OPEX in the LCOE.

A report published at the request of TKI Offshore Wind [21] looked to investigate the future potential cost reductions for offshore wind energy. The report compiled a full list of potential innovations using a literature review and input from several experts. The most promising innovations were determined based on their large impact on LCOE and the high chance to materialize soon. One of these important innovations was the use of robots and drones to monitor and repair needs. The report classified this innovation with a low-medium potential of reducing the LCOE but a low reduction effort, making it a safe way to lower future LCOE values.

A study by Fahrni et al. from the University of Exeter [22] looked to explore the feasibility of autonomous robotic subsea intervention systems for targeted inspection and maintenance in offshore environments by reviewing existing systems. It concluded that there are several technologies capable of reducing costs and health and safety risks for personnel, such as autonomously deployed ROVs (remotely operated vehicles) as well as AUVs (Autonomous underwater vehicles) and ASVs (autonomous surface vessels).

A study into UAVs (unmanned aerial vehicles) for inspection of offshore wind turbines (OWT) and their role in the safe and efficient operation of wind farm fleets was performed in [23]. The report tested the potential risks and failures of these UAVs as a way to confirm their feasibility and reliability. It concluded that UAVs, even though their technology is still in its infancy, with few to no articles assessing the RAM (reliability, availability and maintainability), showed great promise in reducing O&M time and cost.

In another paper [24], state-of-the-art research on OWT maintenance, covering strategy selection, repairs, and environmental concerns was reviewed, identifying promising areas concerning cost-effective maintenance strategies. One such area was the remote O&M of OWFs, a promising solution to mitigate issues of restricted accessibility and several maintenance tasks. The devices reviewed were found to be reasonable and effective after comparison with manned inspections.

Several studies have investigated the impact of robotics on O&M efficiency. For instance, a report by the Offshore Renewable Energy (ORE) Catapult indicates that robotic technologies could reduce O&M costs by up to 27.1%, with an associated levelized cost of energy (LCOE) reduction of up to 9.9% when fully deployed in offshore wind farms [5]. Khalid et al. (2023) primarily focus on external inspections, demonstrating the role of UAVs and ROVs in detecting surface-level defects [25]. Additionally, Kabbabe Poleo et al. (2021) estimate that drone-based inspections can reduce inspection costs by up to 70% and decrease downtime by 90% [14]. These studies partially support the assumption that robotic technologies can reduce repair times and costs, though we acknowledge that current systems are primarily used for inspections rather than complex repairs.

While it is true that current robotic applications in offshore wind farms are primarily focused on inspections, emerging technologies such as autonomous drones and robotic arms show promise in performing minor repairs and maintenance tasks. Complex mechanical repairs (e.g., gearbox replacements, generator failures) still require human intervention, but the following robotic applications are increasingly feasible:

• External inspections—UAVs and ROVs are widely used to inspect blades, towers, and substructures [25].
• Blade surface repairs—Robotic systems are being developed to apply coatings and perform minor structural repairs on blades [15].
• Subsea inspections and cleaning—ROVs are being used to inspect and clean underwater structures, reducing the need for divers [5].

The increasing scale and complexity of OWF demand innovative approaches to ensure cost-effective, safe, and reliable O&M. Harsh marine environments, high logistical costs, and the limited accessibility of offshore installations—especially in the case of floating offshore wind turbines—highlight the urgent need for technological solutions that can address these challenges. Robotic systems offer significant advantages, including improving safety, reducing operational costs, and enhancing maintenance efficiency by minimizing the need for human intervention in hazardous environments.

Recent literature underscores the growing importance of robotics and automation in offshore wind O&M. Rinaldi et al. (2021) provide a comprehensive overview of the current status and future trends in offshore wind turbine O&M, demonstrating how robotic technologies can enhance maintenance effectiveness and reduce associated costs [26]. Furthermore, Hu et al. (2024) examine the specific challenges associated with floating offshore wind turbines, emphasizing the potential of autonomous systems to improve both installation and maintenance operations [27]. Additionally, Jiang (2021) presents a thorough technical review of offshore wind turbine installation methods, highlighting how robotic technologies can streamline deployment operations and mitigate limitations inherent in conventional practices [28].

Several studies have explored the potential of robotized solutions for OWF O&M, evaluating their impact on costs, efficiency, and reliability. The literature covers a range of technological approaches, including remote inspections, autonomous robotic systems, drone-based monitoring, and AI-driven maintenance strategies. To provide a structured overview of these findings,

Table 1. Summary of key studies in O&M activities for OWF.

Reference	Technology Studied	Main Findings
[5]	Robotics in Offshore Wind O&M	Robotic technologies could reduce O&M costs by up to 27.1% and LCOE by up to 9.9%.
[10]	Robotics in Offshore Wind O&M	Provided a comprehensive review of O&M trends, highlighting the cost reduction potential of robotics.
[11]	Autonomous Systems for Floating Offshore Wind	Highlighted the potential of autonomous systems to improve installation and maintenance operations of floating wind turbines.
[12]	Remote robotic inspections inside turbine nacelle	Increased availability and reduced O&M costs
[13]	Remote inspection robots	Reduced inspection time but required operator training
[14]	Drone-based turbine inspections	70% lower inspection costs, 90% reduction in downtime
[15]	AI-driven robotic maintenance	Improved intervention frequency and repair quality
[16]	Remote inspection with NOWIcob model	Enhanced availability and reduced energy costs
[17]	Multi-robot autonomous O&M platform (UAVs, ASVs, blade IMR robots)	Fully autonomous system for offshore wind turbine maintenance
[18]	Industry-wide review of robotic applications	High potential for cost savings but limited data available
[19]	Human-robot collaboration for O&M	Increased resilience and safety of robotic solutions
[20]	Life cycle cost modeling for floating OWFs	OPEX accounts for 18–20% of LCOE
[21]	Future cost-reduction pathways	Classified robotics as a safe, low-effort innovation
[22]	Autonomous underwater robotic intervention	Enhanced safety and reduced maintenance costs
[23]	Reliability and efficiency of UAV-based inspections	High feasibility, but RAM (Reliability, Availability, Maintainability) needs further study
[24]	Remote O&M strategies	Found remote inspections to be effective and viable
[25]	Resilience Modeling for Offshore Wind Farms	Introduced a framework for modeling disturbances and maintenance responses, combining traditional and robotic strategies.
[26]	Robotics in Offshore Wind Turbine Installation	Reviewed offshore wind turbine installation methods and the role of robotics in streamlining operations and reducing limitations.
[27]	O&M Strategies for Floating Offshore Wind Turbines	Discussed specific O&M challenges for floating wind turbines and the role of autonomous systems in improving efficiency and cost-effectiveness.
[28]	AI and Robotics in Offshore Wind Sector	Discussed challenges and opportunities for AI and robotics integration, including advancements in blade surface repairs.

summarizes key studies, highlighting the technologies assessed, their main conclusions, and their expected economic impact on O&M costs.

3. Models and Methods

3.1. Assumptions

The simulation tool developed in this research adopts several simplifying assumptions to render the problem tractable given the complexity of the offshore wind farm operation and maintenance process. They are as follows:

• Robotization factor—It represents an optimized future scenario.
• Failure Mode—It only deals with the normal life phase of wind turbine components, characterized by a constant failure rate.
• Availability—For unplanned maintenance, it assumes a fixed delay for the availability of spare parts and crew.
• Cost—It assumes a fixed cost for parts and maintenance.
• Crew Shift—If the repair time extends beyond a shift, it is assumed the crew stays until the fault is fixed.
• Repair—When a component is fixed, it is assumed to be 100% repaired, and the failure data remains the same.
• Weather—The tool considers the weather safe for travel if the weather conditions are safe at the start and end of the maintenance.

These assumptions, while simplifying the actual complexities of offshore wind farm O&M, serve to make the simulation tool computationally manageable and still provide valuable insights into the comparison between traditional and robotized O&M strategies. Future iterations of this tool could explore relaxing these assumptions to capture a more nuanced picture of offshore wind farm O&M.

3.2. O&M Tool

The research tool was developed in Python. The tool is structured modularly, with four unique modules each handling a specific aspect of offshore wind farm operation and maintenance. This structure allows for focused extension and refinement based on the analysis focus.

The research tool uses a Monte Carlo simulation methodology, a computational algorithm that uses repeated random sampling to estimate the probability of different outcomes in a process influenced by random variables. This method is particularly useful for complex systems where outcomes are difficult to predict. In this research, the Monte Carlo simulation models the operation and maintenance (O&M) of an offshore wind farm over its lifetime by generating synthetic weather and failure data. The tool runs multiple iterations, each representing a possible scenario for the wind farm over its lifetime. Each iteration generates a set of weather and failure data, calculates the downtime and costs associated with maintenance, and then calculates the power generated by each turbine. By running many iterations, the tool can provide a range of possible outcomes, along with their associated probabilities.

In our model, traditional and robotized O&M solutions are represented through cost structures, failure rate responses, and downtime recovery processes. The simulation accounts for the specific characteristics of each approach, such as higher intervention costs and longer repair times for traditional O&M versus the reduced intervention costs and faster response times associated with robotized systems.

To simulate combined strategies, we developed hybrid scenarios where both traditional and robotized solutions operate concurrently. In these scenarios, robotic systems handle specific maintenance tasks (e.g., inspections, minor repairs, or underwater inspections) while more complex interventions remain under traditional human-operated O&M procedures. This approach allows the model to evaluate how combining both strategies affects overall costs, downtime, and system availability. Here’s a breakdown of how the several modules work.

3.2.1. Weather Module

The first step involves generating synthetic weather data for the desired lifetime of the wind farm. This step is essential because the weather significantly impacts both turbine operations and maintenance activities. The simulation tool uses a Markov model to forecast weather conditions, which are crucial for planning and executing O&M activities in OWF.

The foundation of the discrete-time Markov chains method lies in determining the probability that a given state will transition into any other state within the system. This approach culminates in the creation of a matrix of probabilities. To achieve this, the model checks the number of times each state occurs in the historical weather data for each month ($N_i$), along with the number of times it transitions into each of the other states ($n_{i,j}$). The probability of state $i$ transitioning into state $j$ ($p_{i,j}$) is then computed by the following equation:

$$p_{i,j}={\displaystyle \frac{n_{i,j}}{N_i}}$$

(1)

These probabilities, arrayed in a matrix format ($P{\left(parameter\right)}_{month}$)—Equation (2)—for both wind speed and wave height, are grouped by month. This arrangement offers the potential to predict future values of these essential parameters, given an initial pair of values.

$$P{\left(parameter\right)}_{month}=\left(\begin{array}{ccc}{p}_{1,1}& \dots & p_{1,n}\\ ⋮& ⋮& ⋮\\ p_{m,1}& \dots & p_{m,n}\end{array}\right)$$

(2)

The simulated values of wind speed and wave height are then juxtaposed with the safety thresholds of the maintenance vessels. This comparison offers valuable insights into the feasibility of transit to the wind farm, allowing for informed decisions about whether additional downtime is required due to adverse weather conditions. Such a decision-making tool is embedded in the Maintenance module of the simulation tool, serving as a crucial component in improving the planning and execution of O&M activities in offshore wind farms.

3.2.2. Failure Module

The core objective of this module is to simulate the incidence and gravity of various failures that could potentially affect the distinct subsystems within a wind turbine. Under the scope of this module, each subsystem within the turbine (for instance, the gearbox) is postulated to be susceptible to three distinct types of failure. These failures are classified according to their severity: minor failures, which allow the turbine to continue operation; major failures, which necessitate the immediate halting of turbine operation; and replacement failures, wherein the damaged component cannot be repaired and must be completely replaced.

For the purposes of this model, the approach followed in [29] is borrowed, and it is assumed each failure mode follows an exponential cumulative probability distribution (Equation (3))

$$F{\left(t\right)}_{mode}=1-e^{-{\lambda }_{i,mode}t}$$

(3)

where ${\lambda }_{i,mode}$ is the failure rate for subsystem $i$, and for failure mode m-minor, M-major, R-replacement. Consequently, a subsystem is deemed to have failed when any of the failure modes transpires. The failure probability of a subsystem is thus represented in Equation (4):

$$F{\left(t\right)}_{subsystem}=1-e^{-{\lambda }_{i}t}$$

(4)

In this equation, ${\lambda }_i$ represents the aggregate failure rate of the three failure modes (minor, major, replacement) within any subsystem (${\lambda }_i={\lambda }_{i,m}+{\lambda }_{i,M}+{\lambda }_{i,R}$).

Given the assumption that the wind turbine is a serialized system of subsystems—a system that fails when any one of its subsystems fails—the failure probability of the complete turbine is as follows:

$$F{\left(t\right)}_{turb}=1-e^{-{\lambda }_{turb}t}$$

(5)

Here, ${\lambda }_{turb}$ denotes the sum of all failure rates for every type of failure across all subsystems, with these failure rates derived from [30] and presented in Appendix A.

According to the exponential reliability theory, the mean time to failure (MTTF) is computed as $-1/{\lambda }_{i,mode}$, $-1/{\lambda }_i$ and $-1/{\lambda }_{turb}$, of subsystem $i$ under a particular failure mode, for each subsystem $i$ and for the complete turbine, respectively. The time to failure of all the subsystems is simulated using Equation (4) where $t$ is replaced by a random number for $F\left(t\right)$. The faulty subsystem is identified as the one with the lowest time to failure. The same procedure is repeated to compute the failure mode of the subsystem under fault, using Equation (3).

Each type of subsystem failure is associated with different repair times, material costs, and personnel requirements, each of which are critical variables in the computation of costs within the maintenance module. Thus, the output of the reliability module provides essential inputs for the subsequent maintenance module, ensuring an integrated and comprehensive approach to modelling and optimizing O&M activities within OWF.

To provide a clearer understanding of the failure classification process, a flowchart is included (Figure 1) illustrating the mechanism of fault simulation. This diagram outlines the steps from subsystem failure detection through severity classification (minor, major, or replacement failures) and subsequent integration into the maintenance module for cost and downtime assessment.

3.2.3. Maintenance Module

The maintenance module of the simulation tool differentiates between planned and unplanned maintenance. Planned maintenance is a scheduled activity that occurs annually, aiming to minimize downtime by avoiding potential vessel delays. Unplanned maintenance is triggered by a failure within the turbine, with data registered based on the affected subsystem and the classification of the repair. A breakdown of the unplanned maintenance process is illustrated in Figure 2.

The tool also incorporates robotic solutions, aiming to reduce downtime and costs. These solutions are applied to specific scenarios involving subsystem failures, offering an alternative to traditional O&M approaches. A significant challenge encountered during this research was the integration of the maintenance strategies with the robotic strategies. The majority of these strategies revolve around the inspection and monitoring of specific components, such as the tower, blades, and transition pieces. This raised questions regarding the integration of these robotic strategies into the existing tool structure. However, a possible solution emerged through the reconsideration of the planned maintenance module. Rather than scheduling a single annual maintenance for the entire turbine, it would be feasible to incorporate several additional planned inspections for the specific components. For instance, annual inspections could be scheduled for the blades and tower, and biannual inspections for the mooring system. These inspection strategies have been integrated into the unplanned maintenance module as well. Thus, by including these inspection activities as part of the tasks within the unplanned maintenance module, it is possible to create a more comprehensive and responsive maintenance strategy.

A ‘robotized factor’ was introduced to differentiate between traditional and robotized solutions (including, UAV, ASV, ROV, etc.) in the simulation tool. This factor reduces repair times and costs by a third when in robotized mode, based on the premise that robotized solutions can work more efficiently and may not be as affected by adverse weather conditions. This difference in downtime becomes a key factor in comparing the efficacy of traditional human O&M strategies with their robotized counterparts.

In summary, the maintenance module differentiates between routine (planned) and reactive (unplanned) maintenance, each affecting wind turbine operation differently. One of the novel parts of this work lies in integrating advanced robotic solutions into both types of maintenance to minimize turbine downtime and costs. We explored additional robot-aided inspections for specific components, alongside their potential application in troubleshooting during unplanned maintenance. This integration is a step towards revolutionizing wind turbine maintenance, and our research examines how these advancements can transform the offshore wind farm landscape.

3.2.4. Power Module

The power module of the simulation tool estimates the energy generated by the wind farm by summing up the energy produced by each turbine. This is done using data from the weather forecast module and the turbine’s power curve. The energy outputs of all turbines are then consolidated to ascertain the total energy production of the wind farm.

The power curve requires wind speed data at the hub height of the turbine. Since the primary wind speed data is measured at a reference height, a method of extrapolation is employed to derive the required wind speed at the hub height (Prandtl law). When the wind speed falls below the turbine’s cut-in wind speed or rises above the cut-out wind speed, the turbine ceases operation and produces no power.

For wind speeds within the bounds of the cut-in and cut-out speeds, the turbine’s output power is computed from the turbine’s unique power curve, as provided by the manufacturer and represented in Figure 3.

The total energy produced by all $N$ turbines of the OWF is as follows:

$$E_{total}={\displaystyle \sum }_{n\_1}^N{\displaystyle \sum }_{i=1}^T{(\mathsf{\Delta }{t}_i{P}_i)}_n$$

(6)

In Equation (6), $\mathsf{\Delta }{t}_i$ is the time interval, $P_i$ is the turbine average output power in this time interval, $N$ is the total number of turbines in the OWF, and $T$ is the total period of time.

3.2.5. Summary

Here’s a summary of how the several modules work:

• Weather simulation—The first step involves generating synthetic weather data for the desired lifetime of the wind farm. This step is essential because the weather significantly impacts both turbine operations and maintenance activities.
• Failure simulation—Next, the tool simulates the occurrence of failures at specific timesteps for each turbine. The occurrence of these failures is based on historical failure data, providing a realistic representation of the types and frequency of failures that might occur in the wind farm.
• Maintenance simulation—For each simulated failure, the tool calculates the response by the O&M team. This includes estimating the costs associated with repairing the failure and the downtime for the turbine while the failure is being addressed. Downtime here refers to the period during which the turbine is non-operational due to failure and subsequent maintenance.
• Power generation—The simulation tool also keeps a record of the periods when each turbine is operational. With the wind speed data generated in the first step, the tool can then calculate the power generated by each turbine during its operational periods.
• Iteration and analysis—The entire simulation is repeated multiple times, with the results of each iteration saved for further analysis. This allows for a thorough examination of both individual iterations and the average values across all iterations.

The strength of this Monte Carlo simulation tool is that it provides a comprehensive analysis of an offshore wind farm’s operation and maintenance. By considering the complex interplay between weather conditions, turbine failures, and maintenance activities, the tool can provide insights into the operational efficiency, downtime, maintenance costs, and power output of an offshore wind farm. An overview of the tool’s structure can be seen in Figure 4.

3.3. Data Collection

An essential part of constructing an O&M tool for an OWF involves the meticulous collection of data which serves as the input to the model. This data spans various domains, each vital for a thorough representation of the real-world scenarios the tool aims to simulate.

One of the primary sources of data is the wind farm itself, which provides crucial information about the specificities of the turbines, their components, and the various subsystems they encompass. In this study, we focused on the Vestas V164-8.0 MW turbine, a widely used model in offshore wind farms. However, due to the limited availability of detailed data about this specific turbine model, especially regarding its components and failure rates, we made some adjustments to our approach. Firstly, we standardized the components of the turbine to be more generic. This allowed us to apply data from a broader range of sources, increasing the robustness of our model. While this approach may not capture the specificities of the Vestas V164-8.0 MW turbine in detail, it provides a reasonable approximation that applies to a wide range of offshore wind turbines. Secondly, we supplemented our data with publicly available information about the Vestas V164-8.0 MW turbine. This included data on its cut-in and cut-out wind speeds, power curve, and hub height.

Environmental data, which includes meteorological conditions like wind speeds and weather forecasts, was collected from reliable meteorological sources and databases that provide historical and real-time weather data. In our research, we used historical weather values from Copernicus ERA5 [32] dating from 1998 to 2020.

The maintenance data, which includes aspects like repair times, material costs, crew availability, and vessel schedules, is collected from industry reports, maintenance logs, and operator insights. Furthermore, data regarding the failure rates of different subsystems of the turbines is gathered from relevant literature and databases, as well as industry reports. A significant part of this input data, particularly failure rates, average repair times, and costs, was obtained from [30]. As for other maintenance data, such as costs relating to vessel chartering and crew and vessels’ maximum travel conditions, we took into consideration data from [33], which compiles data extracted from several reference OWF and related literature. Additional data was obtained from [34,35].

3.4. Economic Analysis

One target of this paper is to conduct an economic analysis comparing the cost-effectiveness and profitability of traditional human-based maintenance and innovative robotic strategies for offshore wind farm operations. This analysis focuses on comparing maintenance costs and lost production due to downtimes for both strategies.

The primary component of this analysis is comparing energy production between the two strategies, considering the downtime associated with each type of maintenance activity. Lost production, which translates directly into lost revenue, is a key factor. The revenue that would have been generated at full capacity can be calculated as the product of the ideal total produced energy and the (unit) price of energy. The revenue that was actually generated can be calculated as the product of the total energy actually generated and the (unit) price of energy. The lost revenue is the difference between these two values.

Maintenance costs, including labor costs, vessel hire costs, spare parts costs, and any additional costs related to repair and maintenance activities, are another critical aspect of the economic analysis. The goal is to determine which strategy results in lower overall maintenance costs without compromising the reliability and performance of the turbines. The O&M cost breakdown is provided in Appendix A.

4. Results and Discussion

4.1. Inputs and General Considerations

The simulation tool was run for a total of 1000 Monte Carlo iterations, providing a robust estimate of the expected outcomes. The inputs and considerations used in the simulation are outlined below:

• Wind farm configuration—The simulated wind farm was composed of three VESTAS V164.8MW turbines, representing a small-scale offshore wind farm.
• Distance from shore—The wind farm was assumed to be located 20 km from the shore. This distance affects the time taken to reach the turbines during the inspections.
• Vessel speed—A vessel speed of 20 km/h was assumed for maintenance operations. This speed affects the time required to reach the wind farm from the shore and return.
• Simulation time frame—The simulation was run for 20 years, representing the typical lifetime of an offshore wind farm.
• Starting weather conditions—The initial weather conditions were set to a wave height of 1.5 m and a wind speed of 10 m/s. These starting conditions would trigger the generation of weather values, based on the Markov Chains simulation.
• Planned maintenance schedule—A planned maintenance inspection was scheduled for each turbine once a year, starting in the months of July and August. For the robotized solutions, an additional inspection was scheduled twice a year.
• Maximum safe weather conditions—The maximum safe weather conditions for maintenance operations were set to a wind speed of 15 m/s and a wave height of 2.5 m. These conditions determine when maintenance operations can be safely carried out.
• Prices and costs—The average repair costs for each type of failure were retrieved from [30], additionally, average technician costs were set to 82,886 €/year. The vessel charter cost was set to 3340 €/day. The vessel fuel costs were set to 1.5 €/L These values were obtained from [34,35]. Additionally, values for failure rates were taken from [30].

4.2. Tool Validation

Before delving into the results of the Monte Carlo simulations, and comparing traditional and robotized solutions, it is essential to validate the simulation tool itself. This validation process ensures that the tool accurately represents the real-world conditions and behaviors it is designed to simulate, thereby increasing confidence in the results it produces. As a note, these validations were not done as part of the whole tool simulation, but in a separate test environment to ensure a reliable output.

Robotized O&M for offshore wind farms is a relatively new research topic, and currently, there is a lack of published models that can be used for direct comparison. Given this limitation, our validation approach focuses on comparing the model’s synthetic outputs with actual data from existing offshore wind farms, ensuring that the results are consistent with observed maintenance trends. This validation was performed to the full extent of the available data, providing a reasonable assessment of the model’s reliability despite the absence of alternative benchmark models.

The weather simulation module is validated by comparing the synthetic weather data it generates to actual weather data for the same location and time period. This comparison allows us to assess how well the module captures the variability and trends in weather conditions that affect offshore wind farm operations. A comparison of such historical and simulated values can be seen in Figure 5.

The comparison showed that the synthetic weather data closely matched the actual data, with the largest deviation in wind speed and wave height for a specific month being 2 m/s and 0.3 m, respectively. These deviations are relatively small and fall within acceptable limits, suggesting that the weather simulation module provides a realistic representation of the weather conditions at the offshore wind farm location.

4.3. Time-Based Availability

This indicator (time availability) correlates the time when the turbine is producing with the time when the turbine could be producing because the meta-ocean conditions allow it. It is defined as the ratio of the annual number of hours the OWF is operating, actually generating power ($h_{op}$), to the total annual number of hours the wind speed is between the cut-in and the cut-out wind speeds ($h_{total}$), as in Equation (7):

$$A_{time}={\displaystyle \frac{h_{op}}{h_{total}}}100$$

(7)

In Figure 6, the variation in time availability throughout the year is depicted for a regular O&M solution and a robotics O&M solution. As anticipated, the time availability during the summer months is significantly higher than during the winter months.

In Figure 7, we can see the computed mean annual time availability, which is 86.1% for regular O&M and 88.0% for robotics O&M. The robotics solution shows a higher time availability compared to the traditional solution. This is due to the faster response times and reduced downtime associated with the robotic solution.

4.4. Power-Based Availability

We also computed the power availability index. It is the ratio of the average power actually generated considering failures and repairs ($P_{avg}^{w/FR})$ to the average expected power not considering failures and repairs ($P_{avg}^{wo/FR}$), as in Equation (8). Figure 8 shows the power availability for regular O&M and robotics O&M:

$$A_{power}={\displaystyle \frac{P_{avg}^{w/FR}}{P_{avg}^{wo/FR}}}100$$

(8)

The mean power-availability values for regular and robotics O&M are, respectively, 83.9% and 84.8%, as seen in Figure 9. Again, the robotics solutions show a higher power availability compared to the traditional solution.

4.5. O&M Costs

While the availability of the wind farm is a crucial factor in assessing the effectiveness of regular and robotics O&M solutions, it is equally important to consider the costs associated with each approach. After all, the ultimate goal is to find a solution that not only maximizes the availability but also minimizes costs, thereby enhancing the overall profitability of the wind farm. In our simulation, the same cost values were used for both regular and robotic O&M solutions, allowing for a direct comparison of costs. Let us break down the costs taken into consideration: vessel charter costs and technician costs are fixed costs, and the vessel fuel cost is a variable cost.

However, two key differences need to be taken into account with the robotic alternative: the additional inspections performed and the reduced cost of repairing failures. The first additional cost translates into an additional amount of vessel fuel cost; however, since the fuel consumption rate for a travel speed of 20 km/h is about 30 L/hour with a fuel cost of 1.5 €/L, and the distance travelled is 20 km, the differences between both types of solutions is marginal at best. The second additional cost translates into a reduced average repair cost (reduced by the “robotized factor” of a third, discussed in the Methodology section). The results obtained are shown in

Table 2. O&M costs comparison.

Costs	Regular O&M (€)	Robotics O&M (€)
Repair	832,364	689,784
Technician	82,886	82,886
Vessel charter	3340	3340
Vessel fuel	16,750	24,850

for the 20-year lifetime of the OWF.

Additionally, since both solutions resulted in different availabilities, there are also indirect costs associated with downtime and lost energy production. Using Equation (6) and the mean power-availability obtained from Figure 9, we can calculate this passive cost, both for regular O&M and robotics solutions. A feed-in tariff of 100 €/MWh was considered.

Although detailed, year-by-year operational data for both traditional and robotized O&M strategies is scarce—particularly for emerging robotic solutions—a comparative chart (Figure 10) has been included to illustrate the aggregated costs over the 20-year simulation period. This visualization highlights key cost components such as repair, technician, and vessel-related expenses, providing insights into the potential economic differences between the two approaches based on modeled data. Notably, the robotized strategy shows significant reductions in repair costs and moderate increases in vessel fuel costs due to additional inspections, aligning with our simulation results. While this comparison is based on modeled estimates rather than empirical data, it serves to highlight the potential cost differences between the two strategies based on current literature and simulation outcomes.

For the regular O&M solution, the repair costs amounted to 832,364 €, which is a significant portion of the total costs. The vessel fuel costs were relatively minor in comparison, at 16,750 €. However, the most substantial cost was the lost revenue due to downtime, which was 12.26 M€. This highlights the significant impact of downtime on the profitability of the wind farm and underscores the importance of minimizing downtime through effective O&M strategies.

In contrast, the robotics O&M solution resulted in lower repair costs, at 689,784 €. This represents a reduction of approximately 17% compared to the regular O&M solution, which suggests that the use of robotic technologies can lead to significant savings in the repair costs. The vessel fuel costs were slightly higher, at 24,850 €, likely due to the additional inspections performed by the robotic solutions. However, this increase in fuel costs is relatively minor compared to the savings in repair costs.

Most notably, the lost revenue costs for the robotics O&M solution were lower, at 11.57 M€. This represents a reduction of approximately 5.6% compared to the regular O&M solution. This suggests that the robotics O&M solution was more effective at minimizing downtime and thus maximizing the power output of the wind farm.

In conclusion, the results of this analysis indicate that the robotics O&M solution can lead to cost savings compared to the regular solution, both in terms of repair and lost revenue costs. This supports the argument for the adoption of robotics technologies in the O&M of offshore wind farms. However, it is important to note that these results are based on a specific set of assumptions and conditions, and the actual cost savings may vary depending on the specific characteristics of the wind farm and the robotics technologies used.

5. Conclusions

This paper was dedicated to assessing the potential impact of robotized solutions on the operation and maintenance (O&M) tasks of offshore wind farms. To facilitate this assessment, a comprehensive simulation tool was developed, designed to model and analyze the various factors influencing the efficiency and effectiveness of O&M tasks. The tool operates in two distinct modes: one for regular (traditional) O&M solutions and another for robotized solutions. This dual-mode operation allows for a direct comparison of the performance and cost-effectiveness of the two approaches under identical conditions. This comparison led to these advantages of robotics O&M solutions:

• Higher availability—The robotics O&M solution showed a higher time availability (88.0%) and power availability (84.8%) compared to regular O&M solutions (86.1% and 83.9%, respectively). Although it might seem small, this seemingly minor improvement has a significant impact. Over the lifespan of the wind farm, which was 20 years, this additional operational time accumulates, so a 2% increase in availability would result in an additional 3500 h of operation. This difference is largely due to robots’ ability to identify faults earlier, leading to reduced repair times and less downtime.
• Lower O&M costs—Despite slightly higher fuel costs due to additional inspections, the robotics solution resulted in lower repair costs (17% lower). This reduction in costs, coupled with a higher power availability, clearly demonstrates the cost-effectiveness of robotic solutions.
• Reduced lost revenue: The robotics solution also resulted in lower lost revenue costs. This is a direct consequence of the higher power availability, as less downtime translates into less lost power and thus less lost revenue. In our case, the reduction was 5.6% in comparison to regular O&M.

It is important to recognize that this is an emerging field of study with a significant lack of specific offshore wind farm data. This data scarcity necessitated certain assumptions and approximations, which present opportunities for future research and refinement of the simulation tool. Certain parameters, such as repair costs and times, were treated as constants, despite their potential stochastic nature.

Moreover, it is acknowledged that different robotic technologies, such as drones, underwater robots, and autonomous surface vessels, exhibit varying levels of performance, applicability, and operational complexity. However, the purpose of this study was not to provide a detailed assessment of each individual technology but rather to introduce a generalized ‘robotized factor’ as a simplified method for evaluating robotized O&M activities in offshore wind farms. This approach enabled a structured comparison between traditional and robotized strategies within a techno-economic framework, allowing for a high-level analysis of their potential cost and time savings.

As the technology matures and more offshore wind farms adopt robotized solutions for O&M, it will be crucial to collect and analyze data from these implementations. This data will not only validate the simulation tool but also provide insights for further refinement and improvement. One such aspect that would benefit from more data is the “robotized factor” used to simulate the difference between human and robotics solutions.