Research on the Resource-Allocation-Optimization Strategy for Offshore Wind Power Construction Considering Complex Influencing Factors

Abstract

The construction process of offshore wind farms involves multiple complexities, which is very complex to be scheduled manually, and the coordinating and optimized scheduling not only decreases project construction costs but also increases the construction speed. The impact of meteorological conditions on offshore wind power construction has been considered, and optimizing resource-allocation strategies under complex influencing factors has been analyzed. Then, a comprehensive strategy optimization index system is developed, which includes key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. Additionally, an offshore wind power resource-allocation-optimization model is formulated based on discrete event simulation (DES). A statistical analysis of each optimization index was performed using this model to assess the impact of resource-allocation strategies. The simulation results demonstrate that the model can not only simulate the construction process of offshore wind farms and monitor the state of wind turbines, personnel, and meteorological conditions in real time but also accurately calculate key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. This strategy effectively enhances resource-allocation efficiency during the wind farm installation phase and improves the overall construction process efficiency.

1. Introduction

In light of the increasingly severe challenges posed by global climate change and the urgent need for energy transformation, offshore wind power, as an environmentally friendly and sustainable energy solution, is gaining increasing attention [1,2,3]. With many countries setting ambitious “peak carbon and carbon neutrality” targets, offshore wind power is widely recognized as a key contributor to the future clean energy system due to its immense energy potential and vast development prospects [4,5,6,7,8,9]. However, the construction cost of offshore wind power has been increasing. Moreover, offshore wind power construction costs are significantly higher compared to land-based wind turbine installation costs of the same scale, with offshore wind power costing more than twice as much as land-based installations [10,11,12]. The installation cost of offshore wind power is heavily influenced by weather conditions [13,14]. The cost is greatly affected by both weather conditions and the availability of personnel for construction. Therefore, conducting simulation research on resource-allocation strategies is of great significance for offshore wind power construction under complex influencing factors to accelerate wind farm development and enhance the economic benefits of these projects.

The optimization of resource-allocation strategies for offshore wind power construction is a crucial means to achieve cost reduction and improve the efficiency of offshore wind farms. Consequently, many scholars, both domestically and internationally, have conducted extensive research in this area. Zhao et al. [15] proposed a cloud-edge-end collaboration framework enhanced by Internet of Things (IoT) technology. To assess the economic feasibility of the novel foundation applied to offshore wind projects, a full life cycle economic assessment model for offshore wind projects was developed. Wu et al. [16], using a 300 MW offshore wind farm as a case study, evaluated the whole-life-cycle economic benefits of the new pile-friction ring composite foundation by calculating the construction cost and key economic indicators during the operational phase of the wind farm. Santos et al. [17,18] proposed a maritime project vessel planning method based on meteorological uncertainty, using a discrete simulation and genetic algorithms, which ensures the stable operation of maritime projects while saving costs. Scholz et al. [19] proposed a mathematical model based on which an optimal ship construction scheduling table is calculated using weather forecast data to optimize the delivery of components for dockside WTGs; Abderrahim et al. [20] addressed the issue of planning the overall installation of an offshore wind farm by developing a mathematical model to generate a mid-term planning schedule for project evaluation, aimed at reducing the total project costs. Venkitachalam et al. [21] conducted a construction planning simulation of the installation process for submarine cables in wind farms, and to enhance the realism of the simulation, uncertain variables were incorporated into the model to investigate their impact on the installation process.

These studies have provided a valuable reference for resource allocation in offshore wind power construction. Significant progress has been made in promoting the development of offshore wind farms, yet systematic research in this field remains insufficient. In particular, the optimization of offshore wind power construction strategies is particularly crucial in addressing weather uncertainties. Therefore, this study aims to conduct a comprehensive assessment of the complex factors influencing the resource-allocation process in offshore wind power construction and to perform an in-depth analysis of this process. To optimize resource allocation in offshore wind farm construction, this study establishes an evaluation index system and constructs a planning model. The model simulates the construction progress and completion of each task, considering the impact of weather and human resources. By doing so, it aims to enhance construction efficiency and reduce costs.

2. Optimization of Strategies Under Complex Influencing Factors

Offshore wind power resource-allocation-strategy optimization refers to the pre-planning of offshore wind farm construction projects, considering meteorological conditions, as well as human and material resources [22,23]. Generally, the allocation of materials, personnel, and other resources is coordinated by the construction center, and it monitors weather conditions, the weather duration window, and available working hours to ensure that they meet the requirements for offshore construction. Based on these judgments, the appropriate process is selected. Only when weather conditions are favorable and resource allocation is sufficient can the most suitable process be selected for executing offshore construction tasks.

2.1. Key Parameters of the Process

The construction process of offshore wind farms is complex, and different processes require varying allocations of resources. Taking wind turbine installation as an example, 21 processes are needed, ranging from lifting the ship at the entry point to lowering it and removing the legs. These 21 processes are categorized into four major types based on the construction method: transportation, lifting, inverted transportation, and others. They are further refined based on factors, such as the average construction time, minimum interruptible time, delay, preceding process, personnel requirements, construction cost, and other relevant dimensions. The process classification and descriptions are shown in

Table 1. Process classification and description.

Events	Duration	Direct Predecessor	Interruptibility	Min Interruption Time	Personnel Required	Cost of Process	Attributes
A1	T A1	A0	Yes	1	4	0.15M ¥	Transportation
A2	T A2	A0, A1	No	/	6	0.20M ¥	Lifting
…	…	…	…	…	…	…	…
Z1	T Z1	Y1	Yes	2	4	0.15M ¥	Other

.

A logical sequence exists between the processes. For example, the transportation of the tower must be completed before it can be barged and lifted. Furthermore, the interruptibility of the process plays a critical role; if an interruptible process is delayed by external factors, it must be completed in sections based on minimum working hours. Delays occur randomly, and the Poisson distribution is typically used to model the probability of process delays.

2.2. Construction Resources

Construction equipment selection is constrained by meteorological conditions. The transportation of towers, nacelles, hubs, blades, and other construction materials is permitted only when the ship’s wind and wave resistance meets the transportation requirements under the current meteorological conditions. In the “lifting” process, the selection of cranes faces similar restrictions as ships. The key parameters of the vessel and crane in the simulation are listed in the

Table 2. Vessel and crane performance parameters.

	Equipment	Vessel	Crane
Attributes
Load Capacity/(kg)		—	—
Wave Resistance/(m/s)		$H_S$	$H_S$
Wind Resistance/m		$V_S$	$V_S$
Cost		$G_A$	$G_A$
Maximum Speed (m/s)		—

, where $H_S$ denotes the wave height restriction, $V_S$ denotes the wind speed restriction, and $G_A$ represents the construction costs.

In addition, the number of construction personnel also influences the efficiency of resource allocation, and different processes will be assigned the appropriate number of laborers to maintain a dynamic balance between resource-allocation efficiency and personnel costs. In offshore wind power construction, workers’ daily working hours significantly impact the progress of the project and the efficiency of resource utilization. Workers’ hours are affected by weather, waves, wind, and other environmental factors. Under suitable weather conditions, extending working hours can accelerate progress, though workers’ fatigue and safety must be carefully considered. The construction strategy optimization must account for manpower utilization. Manpower allocation should be dynamically adjusted based on conditions and schedule requirements to adapt to the evolving environment.

2.3. Meteorological Conditions

The weather at sea is complex and unpredictable, and the installation of offshore wind turbines is significantly influenced by weather conditions. When rainfall exceeds 10 mm/day or wind speed exceeds 6 m/s, all aerial operations and lifting activities are halted. High temperatures also impact construction, while wave conditions frequently influence ship-related processes. The project schedule depends on timely completion. Therefore, considering the project schedule, five key meteorological variables must be considered, including rainfall, wind speed at a height of 10 m, wind speed at a height of 100 m, temperature, and wave height. The execution of a process at time $t_0$ depends on whether the minimum working hours required for the process are available, sufficient resources at time $t_s$, and whether all five meteorological conditions are simultaneously met within the resource limit $R_S$. We can define an indicator function I(_t) as follows:

$$I_{\left(t\right)}=\left\{\begin{array}{c}1R\left(t\right)>R_s\\ 0R\left(t\right)\le R_s\end{array}\right.,t\in [t_0,t_s]$$

(1)

3. Resource-Allocation-Optimization Indicators

3.1. Minimum Working Hours

In offshore wind projects, the minimum working hours are a key scheduling parameter that determines the minimum time that each process can work after it has started. This metric has a direct impact on construction interruptibility and efficiency. For processes that can be started and stopped flexibly, such as material transportation, the minimum working hours are typically shorter. This means that if unfavorable conditions are encountered, these processes can be adjusted quickly without significant resource wastage. For processes that require continuous operation, such as blade lifting, longer minimum working hours are necessary to ensure construction quality and safety. In this way, the construction time can be effectively managed by the construction team to ensure that requirements are met while maximizing resource efficiency.

3.2. Window Period Utilization

The construction window is the time period suitable for construction under specific climatic and maritime conditions [24]. Given the complexity and unpredictability of the offshore environment, the construction window is usually short, making the effective use of this window critical to improving the construction efficiency and ensuring that projects are completed on time.

$$UCWP={\displaystyle \frac{T_a}{T_t}}\times 100\%$$

(2)

where $T_a$ is the total time actually spent on construction during the construction window, and $T_t$ is the total time suitable for construction as determined based on climatic, sea state, and other environmental factors.

3.3. Rate of Optimization of Resource Allocation

The resource-allocation-optimization rate measures the efficiency improvements resulting from optimized resource allocation. It reflects the ability to improve construction efficiency through the effective allocation of limited resources. Offshore wind power construction usually faces complex environmental conditions and high risks, making the effective allocation and scheduling of resources crucial.

$$RAOR{\displaystyle \frac{{\eta }_b-{\eta }_a}{\eta a}}\times 100\%$$

(3)

where ${\eta }_a$ represents the post-optimization resource-use efficiency, which is the efficiency after implementing optimal resource allocation, and ${\eta }_b$ represents pre-optimization resource-use efficiency, which is the efficiency prior to optimization.

3.4. Cost–Benefit Ratio

The cost–benefit ratio (CBR) is used to measure the relationship between the actual cost of a project and the expected cost [25]. The formula for calculating the cost–benefit ratio is as follows:

$$CBR={\displaystyle \frac{C_A}{C_E}}$$

(4)

where $C_A$ represents the actual cost, and $C_E$ represents the expected cost.

The actual cost is calculated as the product of the construction time planned by the simulation system created for a specific offshore wind power project and the daily cost. The expected cost, on the other hand, is the product of the project completion time estimated by the project manager and the daily cost.

This ratio reflects the effectiveness of cost control in the project. If the cost–benefit ratio is less than 1, this indicates that the actual cost is lower than the expected cost, resulting in better economic efficiency. Conversely, if the ratio is greater than 1, this indicates that the actual cost exceeds the expected cost, leading to worse economic efficiency.

4. Resource-Allocation-Optimization Simulation System

The construction state of offshore wind farms is simulated using the discrete event-simulation method, resource-allocation efficiency, the quantity of construction resources, and other relevant characteristics. Historical meteorological data serve as the operating environment for offshore wind power construction, and the actual construction process serves as the driving rule to simulate the offshore wind power construction. This simulation portrays the real-time state of wind turbine generators (WTGs), construction equipment, and personnel, and statistically measures the efficiency and economic indexes of offshore wind power. The main relationships and interactions within this model are depicted in the accompanying Figure 1.

4.1. Weather Module of System

The latitude and longitude coordinates of an offshore wind power construction site are known, and weather data from the past ten years for the site are used as simulation constraints. For the hoisting process, wind limit conditions are set at 6 m/s, with the rainfall limit at 3 mm and temperature limit at 36 °C. The daily working hours of workers are set from 7:00 a.m. to 6:00 p.m. Construction cannot take place beyond these hours, and if the remaining time in a day is insufficient to complete the process, construction will be halted. The weather design module within the simulation system is presented in Figure 2.

4.2. Process Module of System

Due to the complexity of offshore wind farm construction, the minimum working hours of the processes may vary considerably. The working hours for lifting, inverting, and processes of type “Other” are determined based on on-site construction experience, while the working hours for transportation processes depend on the distance between construction coordinates, the set speed of the trucks, and relevant regulations. The minimum working hours are shown in the Figure 3.

As shown in Figure 4, the simulation system provides a daily construction schedule. By integrating the historical meteorological data of the area and the minimum working time required for each process, it makes reasonable allocations of resources, such as labor and equipment, to minimize downtime. This approach helps to reduce project costs while maintaining orderly progress of the project. According to the simulation data, the utilization rate of the project’s window period (UCWP) reached 91.6%.

4.3. Simulation Result

Figure 5 illustrates the specific construction data for the offshore wind power project in January and February. The installation of one wind turbine unit was initially planned to take 36 days. Through the simulation by the DES system, taking into account the attributes of each process in the early stages of the project, in select process simulations, resource optimization and weather factors were considered. Ultimately, after excluding the remaining effects, a time schedule for each step of the wind turbine installation was obtained. The simulation system predicted that the project construction would only take 23 days, saving 13 days compared to the original construction plan, which significantly reduced labor costs and equipment rental expenses. The efficiency of resource-allocation optimization, RAOA, increased by 36%.

The cost structure of offshore wind projects is complex, and the project cost structure is defined by the system as labor costs, equipment lease, operational costs, and other expenses. According to the report “Cost Composition of Offshore Wind Power Construction (2022)”, construction and installation costs account for about 35% of the total cost, while equipment lease operational costs account for about 50% of the construction and installation costs, with an installed capacity of 100 MW for the offshore wind power project. The daily equipment lease operational cost is 1.4 million yuan. Other costs include sea area acquisition, offshore booster stations, high-voltage sea cables, interest, and insurance expenses, which account for about 10%. These additional costs amount to 496,000 yuan.

In summary, the daily cost for the offshore wind power project is approximately 1.93 million yuan. The expected construction time is 36 days, while the simulation system indicates that it only takes 23 days. From this, the CBR is calculated to be 0.638. A CBR value less than 1 indicates that the actual cost of the project is lower than the expected cost, suggesting that the project has good economic benefits.

5. Conclusions

The construction of offshore wind farms is characterized by high investment costs, long construction periods, high construction difficulty, and vulnerability to environmental factors, making it challenging to plan the construction process and coordinate resource allocation in advance for offshore wind power projects. This paper conducts an in-depth study of the optimization of resource-allocation strategies for offshore wind power construction and establishes a resource-allocation-strategy model based on a discrete event-simulation platform. This model accounts for various complex influencing factors while finely managing the construction process. It addresses the difficulty of establishing a precise long-term construction plan during the pre-construction phase of offshore wind farms and provides guidance for the allocation of project construction resources.

The model can objectively and effectively evaluate the required manpower, material resources, and time needed for each task, generating predictive results that optimize resource scheduling during wind farm installation and improve overall efficiency. Data show that the resource-allocation efficiency and economic benefits can be significantly improved by implementing the optimization strategy. The utilization rate of the window period reached 91.6%, the optimization rate of resource allocation increased by 36%, and the cost–benefit ratio was 0.638, indicating that the actual cost of the project is lower than expected, resulting in better economic outcomes. A scientific and effective solution is provided by this study for optimizing resource-allocation strategies in offshore wind power construction, which holds significant theoretical and practical value for accelerating wind farm construction and enhancing economic benefits.