Review of Design Schemes and AI Optimization Algorithms for High-Efficiency Offshore Wind Farm Collection Systems

Abstract

The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the collector system constitutes a substantial part of the investment cost in wind farms, the design and optimization of this system are pivotal to enhancing the economic viability of offshore wind farms. A thorough examination of collector system design and optimization methodologies is essential to elucidate the critical aspects of collector system design and to assess the comparative merits and drawbacks of various optimization techniques, thereby facilitating the development of collector systems that offer superior economic performance and heightened reliability. This paper conducts a review of the evolving trends in collector system research, with a particular emphasis on topology optimization models and algorithms. It juxtaposes the economic and reliability aspects of collector systems with varying topologies and voltage levels. Building on this foundation, the paper delves into the optimization objectives and variables within optimization models. Furthermore, it provides a comprehensive overview and synthesis of AI-driven optimization algorithms employed to address the optimization challenges inherent in offshore wind farm collector systems. The paper concludes by summarizing the existing research limitations pertaining to offshore wind farm collector systems and proposes innovative directions for future investigative endeavors. The overarching goal of this paper is to enhance the comprehension of offshore wind farm collector system design and optimization through a systematic analysis, thereby fostering the continued advancement of offshore wind power technology.

1. Introduction

Confronted with the formidable challenges of global warming and the energy crisis, wind energy, as an emerging source of energy characterized by mature technology, broad applicability, and significant potential for future growth, has emerged as one of the viable solutions to these issues [1,2]. As onshore wind power generation approaches saturation, nations have begun to turn their attention to offshore wind power development. However, the intricacies of the offshore environment and the challenges associated with the design and maintenance of offshore wind farms have become critical factors constraining the advancement of this sector.

The electrical system is the heart of offshore wind farm design, encompassing wind turbines, a power collection system, and a transmission system. Within this framework, the collector system is tasked with the critical role of aggregating the electricity generated by the wind turbines and conveying it to the transmission system and grid connection via submarine cables. The reliability of this system is therefore paramount for the seamless operation of offshore wind farms. Typically, the investment cost of the collector system accounts for about 15–30% of the total investment cost of offshore wind farms [3]. As the scale and distance of offshore wind farms expand, this proportion may increase further. Consequently, optimizing the topological design of the collector system can significantly reduce the levelized cost of electricity (LCOE) for offshore wind power and enhance the overall efficiency of offshore wind farms.

The substantial design challenges inherent in collector systems pose a significant barrier to the development of offshore wind farms [4]. However, the current models and algorithms struggle to accommodate the expanding scale of these wind farms. Despite the notable progress in the design and optimization of collector systems over recent years, the operational stability of offshore wind farms remains a critical issue, particularly with the proliferation of offshore wind power projects. Existing optimization models often overlook certain dimensions due to the system’s excessive complexity. Moreover, conventional optimization methods prove ineffective when scaled up to larger-capacity wind farms.

Enhancing the optimization techniques for collector systems is crucial. This paper offers a comprehensive review of the current methods for designing collector systems, encompassing AC and DC topologies, optimization models, and AI-driven optimization algorithms. It also furnishes valuable references and succinct information pertinent to the design and construction of offshore wind power. The goal is to provide novel insights that can drive the advancement and further enhance the efficiency of offshore wind power.

To offer a comprehensive overview of the existing scholarly work on collector systems for offshore wind farms, a bibliometric analysis was conducted. Figure 1 illustrates the research findings in this domain through CiteSpace [5]. The literature included spans the last decade, sourced from a search for “offshore wind farm collector system” on the Web of Science platform. The keywords within these documents are highlighted with various colors in the figure. As depicted in Figure 1, recent research in the realm of offshore wind farm collector systems has concentrated on several pivotal areas, including modeling, cost analysis, algorithm development, and design optimization. The exploration of these topics significantly influences the advancement of offshore wind energy. This analysis mirrors the pressing challenges faced in the evolution of offshore wind power and broadly outlines the research trends within the field. It positively contributes to accurately discerning the research trajectory.

Table 1. Comparison between this article and other reviews.

Review Document	Main Content	Main Purpose	Related Fields
[6]	Analyzes the structure of offshore wind farm collection systems, the impact of submarine cables, and the economic optimization of collection systems. Discusses key factors affecting the economic cost and reliability of the collection system.	Provides insights into the economic optimization of offshore wind farm collection systems to offer references for the design and operation of offshore wind farms.	Collection system structure, submarine cables, economic analysis
[7]	Investigates the layout of offshore wind farm collection systems and the related configuration design, analyzing the economic and reliability differences between various design schemes. Proposes suitable collection system schemes for specific cases and forecasts future trends.	Provides optimized design solutions for collection systems, improving the economic and reliability performance of offshore wind farms.	Structure, layout, economic and reliability analysis
[8]	Summarizes the offshore wind power collection system, including the overall concept of the AC and DC collection systems, and compares circuit breaker configuration schemes. Provides an overview of the system design for the economic and reliability optimization of collection systems.	Provides a complete overview of offshore wind farm collection systems, promotes an understanding of current issues, and points out future research directions.	Structure, optimization design, economic and reliability analysis
[9]	Summarizes the structure and optimization planning methods of offshore wind farm collection systems, investigates wind farm selection and collection and transmission methods, and presents methods for reliability evaluation.	Supports low-carbon economic development, promotes the optimization planning of collection systems, and maximizes environmental and economic benefits.	Overall offshore wind farm structure, collection and transmission system methods
This review	Compares the AC and DC collection systems in terms of voltage selection, optimization models, and algorithms to current optimization design methods.	Summarizes the optimization design methods for offshore wind farm collection systems to provide references for the design of collection systems.	Collection system optimization design

offers a comparative analysis of the key elements of this paper with other literature reviews on offshore wind farm collector systems. The literature review detailed in Table 1 includes the basic components of a collector system, an assessment of the economic implications of submarine cables, and an exploration of the current market state and technological advancements in collector systems for offshore wind farms over the past decade. Notably, Madariaga et al. [6] conducted an exhaustive review of the current status of offshore wind farms and evaluated their feasibility and economic viability. Tan et al. [7] conducted an extensive survey of collector system topologies and switching configurations used in offshore wind farms worldwide, with a particular focus on anticipated design trends for high-capacity offshore wind farms. Sun et al. [8] provided a comprehensive overview of alternating current (AC) and direct current (DC) topologies and circuit breaker configuration options for offshore wind farms. Wang et al. [9] provided a summary of the collector system structure and its reliability assessment.

Nevertheless, scant research efforts have been made to provide a holistic review of the design and optimization of offshore wind farm collector systems. Consequently, this paper endeavors to offer an exhaustive and methodical synthesis of the extant literature in this domain. The principal contributions of this paper are delineated as follows:

(1) This paper elucidates the research hotspots and trends in this field through a three-dimensional visualization and analysis of the literature’s keywords over the past decade, utilizing CiteSpace. This approach addresses a gap in the current body of reviews.

(2) This paper conducts a thorough comparison of the merits and demerits of AC and DC collector systems across various topologies and voltage levels. It highlights that DC collector systems and 66 kV AC collector systems are poised to become focal points for future research and application.

(3) This paper scrutinizes different optimization models of collector systems, encompassing optimization objectives and variables. It identifies the lacunae in current research and proposes future research directions, such as the impact of variable prioritization on model optimization efficacy, which has not been addressed in previous reviews.

(4) Given the limited review of algorithms in this field, this paper offers an extensive overview of AI-based optimization algorithms employed in existing studies. It categorizes these algorithms into deterministic and heuristic groups for a comprehensive comparison, thereby providing a reference for the potential integration of algorithms to enhance optimization outcomes.

(5) This paper introduces innovative proposals for forthcoming research in the domain of offshore wind farm collector systems, offering researchers fresh perspectives to foster the advancement of offshore wind energy.

It is important to highlight that the literature reviewed in this paper predominantly spans the domain of offshore wind farm collector systems over the past decade, with a particular emphasis on topologies, voltage levels, reliability, optimization models, and algorithms. Additionally, a selection of valuable earlier works has been incorporated to bolster the arguments presented in this paper. This paper largely overlooks studies that lack rigorous validation and those that are exclusively focused on offshore wind-related policies or onshore wind energy.

Figure 2 illustrates the overarching framework and pivotal components of this paper. The foundation of this review is to ascertain the current research trends in the offshore wind power sector by visualizing and analyzing the keywords from the literature of the past decade using CiteSpace in Section 1. Building on this, Section 2 compares AC and DC collector systems with varying topologies and voltage ratings, evaluating their economic, stability, and reliability metrics. Section 3 discusses the research on optimization models, encompassing optimization objectives and variables, and highlights the research gaps in this area. Section 4 compares AI-based optimization algorithms for addressing these issues, reviewing the algorithmic processes, information encoding, and other technical aspects. Finally, Section 5 presents conclusions, identifying the research shortcomings and proposing future research directions.

2. Offshore Wind Farm Collector System Design Scheme

The study of offshore wind power collection system design schemes primarily concentrates on two critical aspects: the selection of voltage levels and the choice between AC and DC modes. These factors significantly influence the economic viability and reliability of the collection system. Depending on the varying capacities and distances from the shore, there are diverse design options for the collector system. This chapter compiles and contrasts the existing design alternatives.

2.1. Collector System Topology

2.1.1. AC Collector System Topology

As depicted in Figure 3, the conventional topologies for submarine cable arrangements in AC power collection systems encompass a variety of configurations, including chain, single-sided ring, double-sided ring, composite ring, and star-shaped layouts. For offshore wind farms with varying capacities and equipment specifications, it is essential to select an appropriate topology structure tailored to their unique requirements [10,11,12,13,14,15,16].

Variations in economic and reliability aspects are observed across different topologies. The comparative analysis of system reliability and economy under various topologies can be conducted using methodologies such as the block enumeration method and the minimum path method, while factoring in the potential failure of electrical components. Among the prevalent collector system topologies, the unidirectional ring topology has been identified as the most reliable for offshore wind farms, as referenced in [10,11]. In the context of long-distance, high-capacity offshore wind farm applications, the double-sided toroidal collector system topology demonstrates significant advantages in terms of combined economic efficiency and reliability, as detailed in [12,13]. For the chain topology, which is optimal in terms of economy, a well-designed switch configuration scheme can also result in desirable reliability levels, as discussed in [14]. Regarding the ring topology, which is widely utilized in offshore wind farms for its high reliability, the availability rate can be determined using the fault tree analysis by integrating both probabilistic and deterministic approaches. This provides quantitative metrics for reliability assessment, as outlined in [15], thereby enabling the optimization of system configurations, such as the switch configuration scheme under the ring topology, and enhancing the overall system stability, as further elaborated in [16].

Voltage instability is a critical factor impacting the stability of power systems [17]. Voltage stability can be quantified through voltage deviation. In the context of offshore wind farm collection systems, voltage deviation refers to the deviation of the actual voltage at the booster station from its rated value, leading to either overvoltage or undervoltage conditions. Excessive voltage deviation can significantly compromise the stability of the power system. The mathematical expression for this is

$$∆U\%={\displaystyle \frac{U-U_N}{U_N}}\times 100\%$$

(1)

In this context, $U$ denotes the actual voltage of the booster station, while $U_N$ signifies its nominal voltage. Reference [18] investigates the voltage deviation across the aforementioned five AC collection system configurations under varying wind speeds. The findings reveal that the maximum voltage deviation at the nominal wind speed is 1.6%, which is significantly lower than the upper limit of 10% as stipulated by the standard. Concurrently, the voltage deviations across different configurations are found to be nearly identical.

It is imperative to highlight that the criteria for assessing the stability of power systems also encompass aspects such as power angle stability and frequency stability. Presently, there is a scarcity of literature that examines the collection system topology from these perspectives. Nevertheless, such studies are crucial for the stability of power systems, warranting further exploration in this domain.

Certain novel AC collector system configurations possess distinct advantages over conventional designs. Notably, an innovative double-sided toroidal topology that facilitates the consolidation of multiple substations can significantly enhance the economic viability of offshore wind farms. This is achieved by optimizing the allocation of transformer capacity across various substations, as referenced in [12].

2.1.2. DC Collector System Topology

DC collector system configurations predominantly encompass series, parallel, series-parallel, and matrix interconnections, as depicted in Figure 4. A novel radial DC collector system topology, specifically designed for irregularly arranged wind turbine generators (WTGs), has garnered extensive research attention, as illustrated in Figure 5. Further investigation is warranted to assess its economic viability and reliability. An alternative topology has been proposed to mitigate unit overvoltage by incorporating auxiliary connection paths, thereby maintaining the operation of fault-free units post-fault. This design effectively prevents the disconnection of the series branch from the collector system due to overvoltage induced by bypassed faulty units on fault-free units during severe faults in traditional series-parallel topologies, significantly enhancing the operational efficiency of offshore wind farms [18].

In contrast to AC collector schemes, the implementation of DC collector systems can substantially decrease the construction costs of the collector system [19,20,21]. However, DC collector systems necessitate sophisticated equipment such as DC circuit breakers, DC wind turbines, and DC/DC converters, which are technologically underdeveloped for offshore wind farms that pose greater operational and maintenance challenges. At the present time, there are no commercially viable DC collector systems in operation. Consequently, the stability and reliability of DC collector systems necessitate further exploration.

2.2. Collector System Voltage Level Selection

Offshore wind farms that have been commercially deployed predominantly utilize a 35 kV AC collection system. This configuration employs on-site step-up transformers to elevate the terminal voltage of offshore wind turbines to 35 kV, subsequently using submarine cables to aggregate at the offshore substation.

However, with the ongoing increase in individual turbine capacity and the expansion of wind farm scale, coupled with the trend of offshore wind farms moving towards deeper waters, the limitations of the 35 kV AC collection system are becoming increasingly evident. These limitations include a weak power transmission capacity, a reduced number of wind turbine groups that can be connected, and significant power losses. If the cable’s cross-sectional area is enlarged, both the cable cost and construction expenses will substantially increase. Concurrently, considering the high construction and operational costs associated with offshore wind farms, the continued use of this scheme could severely impact the economic feasibility of such installations.

To address these challenges, a higher voltage collection scheme is often implemented. For offshore wind farms with large-capacity units of a similar scale, the 66 kV AC collection system, due to its more flexible topological line design, requires significantly fewer cables than the 35 kV AC collection system, thereby also reducing investment and construction costs. The wiring diagram is depicted in Figure 6. References [22,23] provide an in-depth comparison between the 35 kV and 66 kV collection systems. Figure 6 illustrates the investment costs, operational and maintenance costs, and the cost of energy (COE) for 35 kV and 66 kV collection systems at different single machine capacities for wind farms located 15 km and 40 km offshore. Reference [22] evaluates the economic viability of 35 kV and 66 kV collection systems from the perspective of capital expenditure (CAPEX), annual operational and maintenance costs (OPEX), and the cost of energy (COE) for primary electrical equipment and cables. The voltage levels, wind turbine capacities, and total capacities of the wind farms for the six comparative designs are presented in

Table 2. Comparative cases.

Case	Voltage	Wind Turbine Capacity	Total Wind Farm Capacity
Case 1	35 kV	6 MW	288 MW
Case 2	66 kV	6 MW	288 MW
Case 3	35 kV	8 MW	288 MW
Case 4	66 kV	8 MW	288 MW
Case 5	35 kV	10 MW	300 MW
Case 6	66 kV	10 MW	300 MW

. Figure 7 displays the various costs, and the results indicate that, when considering the investment cost and operational and maintenance cost of the cables, the economic viability of the 66 kV AC collection system consistently outperforms the 35 kV scheme.

For offshore wind farms situated at greater distances, the loss in high-voltage direct current (HVDC) transmission schemes is considerably lower than that in alternating current (AC) transmission schemes [24,25,26,27]. Consequently, all existing long-distance wind farms utilize HVDC transmission schemes. Besides its inherent limitations, such as low current-carrying capacity, the 35 kV AC collection system also entails additional construction costs for offshore booster stations when integrated with high-voltage direct current transmission systems. In the 66 kV AC collection system design proposed by the European TenneT company, Arnhem, Netherlands, the offshore booster station, which has some functional overlap with the offshore converter station, has been eliminated. In this scheme, the generator-side voltage is increased to 66 kV and then directly connected to the offshore converter station through the collection system [28,29]. Compared to the traditional 35 kV collection system, this scheme can transmit the same power with cables of smaller cross-sectional areas, thereby significantly reducing the investment and laying costs of submarine cables. Furthermore, the higher voltage reduces system losses [24,30,31,32], and the reduction in the number of circuits facilitates maintenance, lowering the overall construction investment cost of the offshore wind farm collection system [33,34]. Concurrently, shorter collection lines and fewer cables greatly diminish the risk of damage to 66 kV collection system cables from ship anchors and equipment.

Reference [33] compares the aforementioned scheme with the 35 kV collection scheme in a case study applied to an 800 MW wind farm with a 90 km offshore distance. The results indicate that the 66 kV collection system, due to the higher voltage level of the wind turbine units, has higher costs in areas such as wind turbine step-up transformers, switchgear, and wind turbine foundations compared to the 35 kV system; although the unit price of 35 kV submarine cables is lower than that of 66 kV cables, there are more instances of submarine cables, and the cable routes are more complex, leading to higher costs for submarine cables; at the same time, due to the elimination of the offshore booster station, the investment cost in this aspect will be significantly lower than the 35 kV system. Overall, the scheme saves CNY 320 million in overall expenditure compared to the original 35 kV system, offering economic benefits.

The world’s first 66 kV AC collection system was commercially implemented in the Blyth offshore wind farm project in the UK. Concurrently, the 66 kV AC offshore wind farm located in Yuhuan County, Zhejiang Province, China, has also been successfully connected to the grid. Despite the current scarcity of operational data for 66 kV wind turbine units, the scheme still exhibits a promising future, and an increasing number of offshore wind projects worldwide are expected to adopt the 66 kV collection system.

2.3. Comparison of Collector System Design Options

Table 3. Comparison of different collection system topologies.

Topology	Collection System	Cost	Reliability
Chain	AC	Low	Low
Ring	AC	High	High
Star	AC	Medium	Medium
Series-parallel	DC	Relatively low	Relatively low
MI	DC	Relatively high	Relatively high

presents a comparative analysis of the cost and reliability of collector systems across various topologies.

Table 4. Comparison of collection systems at different voltage levels.

Comparison Scheme	AC 35 kV	AC 66 kV	DC Series	DC Parallel
Offshore Platform Size	Large	Medium	Small	Small
System Efficiency	Low	Medium	High	High
Operational Control Difficulty	Simple	Average	Difficult	Average
Engineering Application Status	Common	Rare	None	None

delineates four distinct design options for voltage level collector systems: AC 35 kV, AC 66 kV, DC series, and DC parallel. The AC 35 kV configuration is well suited for large-scale platforms, characterized by low efficiency, straightforward control, and broad applicability [23,28,29,30]. Conversely, the AC 66 kV configuration is tailored for medium-sized platforms, offering medium efficiency, moderate control complexity, and limited applications. Both DC series and shunt systems are deemed appropriate for small-scale platforms, boasting high efficiency but marred by challenging control mechanisms and the absence of engineering implementations to date. While the DC system exhibits commendable efficiency, its intricate control requirements constrain its widespread application.

It is important to highlight that the economic aspects of the DC collector systems detailed in Table 4 are contingent upon the voltage level. The selection of voltage is influenced by the offshore distance of the wind farm, necessitating different voltage choices for wind farms situated at varying offshore distances. Reference [21] indicates that for small- to medium-sized wind farms positioned more than 80 km from the shoreline, a collector system voltage of ±100 kV or higher is economically advantageous. Should collector system voltages extend to ±140 kV, DC collector systems would emerge as the optimal configuration across all sizes and offshore distances. The voltage of the parallel collector system corresponds to the output voltage of the DC turbines, whereas the series collector system arranges the turbines in clusters in series before converging them in parallel. The requisite turbine output voltage can be deduced from the wind farm’s capacity and the voltage level of the collector system. Given the scarcity of international application examples of DC wind turbines and DC collector systems, further investigation into their practical effectiveness is warranted.

Table 5. Comparison of AC and DC collection systems.

Characteristic	DC Collection System	AC Collection System	Comparison
Topology	Includes parallel, series, and series-parallel types, reducing power loss and easy to expand	Typically uses chain, ring, or star topology, mature technology	DC systems are suitable for far-offshore wind farms, enabling long-distance transmission
Cable Type and Losses	Low operational losses in DC cables, suitable for long-distance transmission	Uses AC cables, relatively higher losses	DC cables may increase insulation costs under high load
Converter Requirements	Requires DC/DC converters, may use modular multilevel converters (MMC)	Requires AC/DC and DC/AC converters	DC converter technology is evolving, impacting efficiency and cost
Voltage Level and Scalability	Can adopt higher voltage levels like 66 kV, improving transmission capacity and scalability	Typically uses 35 kV, limited scalability	Higher voltage levels reduce the number of subsea cables, lowering costs
Economic Efficiency	Initial investment may be high, but long-term operational losses are low	Lower initial investment but higher long-term operational and maintenance costs	DC systems show increasing economic potential as technology matures
Reliability and Maintenance	Maintenance technology for DC equipment is developing, with potential reliability improvements	Mature technology, but high maintenance costs	Reliability and maintenance of DC systems are key research focuses
Environmental Impact	Generally lower electromagnetic interference, smaller environmental impact	Requires consideration of electromagnetic constraints	DC systems are better suited for environmentally sensitive areas
Technical Innovation and Future Trends	Active innovation, including DC circuit breakers and converters	Innovations mainly focus on efficiency improvement and cost reduction	DC systems are a major focus for future technological innovation
Market Acceptance and Investment Risk	Market acceptance is gradually increasing, with relatively high investment risks	High market acceptance and lower investment risk	DC systems are gaining market acceptance and technological maturity
System Integration Capability	Integration capability is improving, especially with HVDC and smart grids	High compatibility with existing grid infrastructure	DC systems offer more integration options and flexibility
Adaptability to Renewable Energy	Strong adaptability, suitable for integration with multiple renewable energy sources	Average adaptability, requires energy storage and balancing	DC systems are better suited for building multi-energy complementary systems

presents a thorough comparison between AC and DC collector systems, revealing that DC systems possess notable advantages for deep and remote offshore wind farms, particularly in terms of low-loss, long-distance transmission capabilities and enhanced transmission capacity and scalability at higher voltage levels [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]. DC systems employ diverse topologies that minimize power losses and are readily scalable. Although DC/DC converters are necessary, technological advancements are enhancing their efficiency and reducing associated costs. DC cables exhibit low losses, but the cost of insulation may escalate under high loads. The economic viability of DC systems is improving as the technology evolves, with a focus on reliability and maintenance in ongoing research and a minimal environmental footprint. Technological innovation is dynamic, market acceptance is progressively growing, and system integration is increasing, particularly in multi-energy complementary systems. Conversely, AC systems, while technologically mature, suffer from high long-term operational losses and limited scalability. The DC collector system represents a pivotal direction for the future development of offshore wind power technology.

China’s current industry standard for wind farms states that the voltage level of AC collector lines for offshore wind farms should be 35 kV~66 kV [35]. This is in line with the actual choice of completed projects and the development trend in the short term. At the same time, it should be determined comprehensively according to the scale of the wind farm and the expected expansion scale, the capacity of wind turbines, the reliability of the wind farm operation, the length of the collector line, and the operation, maintenance, and repair of the wind farm. For the specific topology of the collector system, the designer should find the best solution under the above grid code requirements. Since there is no application example of a DC collector system, there is no specific grid code requirement at present [36,37].

3. Optimization Models

Optimizing the collector systems in offshore wind farms is a complex, multidimensional challenge that encompasses various objectives such as economic feasibility, reliability, and environmental impact. The prevalent models utilized in this context include physical and mathematical models. The intricacy of offshore wind farm collector systems poses significant difficulties in constructing physical models or simulating them effectively. Mathematical models, on the other hand, utilize empirical equations to abstractly represent the system and assess variables through the application of specific metrics, thereby determining the superiority of optimization outcomes. This approach offers the advantage of reducing the simulation complexity of the system and providing precise quantitative analysis, prediction, and optimization of the behavior of complex systems, making it highly effective for optimizing collector systems.

This chapter will delve into the analysis of the offshore wind farm collector system model, focusing on optimization objectives and variable selection.

3.1. Optimization Goals

The optimization objective function typically aims to minimize investment while fulfilling the reliability requirements of the collector system [38], thereby accounting for the cost of wind farm kWh and ensuring its long-term stable operation.

3.1.1. Economic Objectives

The crux of the economic optimization objective lies in the composition of the total cost.

Table 6. Comparison of economic models for offshore wind farm collection systems.

Reference	Objective Function	Total Cost Components	Advantages
[39]	Minimize submarine cable length, thereby minimizing the total lifecycle cost	Construction cost, maintenance cost, and decommissioning cost model	Considers the entire lifecycle and converts it into a multi-traveling salesman problem to optimize cable layout
[40]	Minimize the net present value of the total lifecycle cost of the offshore wind farm collection system	Initial investment cost, operating cost, maintenance cost, and outage loss cost	Includes initial investment cost, operating cost, maintenance cost, and outage loss cost throughout the system’s lifecycle
[41]	Minimize investment cost while meeting collection system reliability requirements	Costs include wind turbine transformers at the tower base, medium-voltage submarine cables in the collection system, offshore substation electrical equipment, and high-voltage transmission cables	Effectively solves the coupling effects between system structure, power flow, and short-circuit current
[42]	Minimize total cost, including annual investment cost of MV cables and the cost of transformers installed at each wind turbine (WT)	Sum of the annual investment cost of medium-voltage cables (CCB) and transformer costs installed at each wind turbine (CWTT)	More realistic handling of transformer and submarine cable costs
[43]	Minimize the total cost of offshore wind farm planning, including WT micro-siting and subsea cable network design	Wind turbine maintenance cost, foundation cost, cable investment cost, and long-term power loss cost; does not consider costs for WT, transformers, and switchgear	Integrates important practical elements such as seabed geography, substation scalability, and turbine reliability
[44]	Minimize cable construction costs, power losses, and expected wind curtailment costs	Costs of cables, power losses, and curtailed wind power	Approximate power loss calculation, ensures cable failure “N − 1” standard
[45]	Minimize total investment cost for wind farm electrical connections	Submarine cable costs, offshore substation investment cost, turbine output transformer and related switchgear costs	Achieves an optimal balance between cost and reliability
[46]	Maximize the comprehensive assessment of investment cost and reliability	Investment cost, reliability index	Efficient optimization considering both economic efficiency and reliability

presents a comparison of several economic optimization models for offshore wind farm collector systems under investigation. These diverse models offer various perspectives for consideration, such as the resolution of system architecture and the interplay between power flow and short-circuit current, thereby enhancing traditional optimization models and adopting a more holistic approach to the overall optimization of the collector system. The cost component encompasses primarily the expenses associated with the converter cabinet, the submarine cable and its installation, and the substation equipment. While the majority of these models center on cost minimization as their primary goal, there are notable variations in the specific cost elements considered.

Reference [39] presents an objective function designed to minimize the total lifecycle cost of submarine cables by reformulating the problem into a multi-traveling salesman problem for optimized cable layout, while taking into account construction, maintenance, and retirement costs. Reference [40] sets its objective function to minimize the net present value of the total lifecycle cost of the offshore wind farm collection system, encompassing initial investment, operation, maintenance, and power outage losses. Reference [41] concentrates on minimizing investment costs while ensuring system reliability, effectively managing interactions between system structure, power flow, and short-circuit current, and includes costs such as transformers at the base of wind turbine towers, medium voltage submarine cables, electrical equipment in offshore substations, and high voltage transmission cables. Reference [42] aims to minimize total costs, including annual investment costs for medium voltage cables and transformers on wind turbines, with a focus on accurately accounting for the actual costs of these components. Reference [43] extends the objective function to the total cost of offshore wind farm planning, integrating practical factors like seabed geography, substation scalability, and turbine reliability, and covers costs such as wind turbine maintenance, foundation, cable investment, and long-term power loss, excluding wind turbines, transformers, and switchgear costs. Reference [44] aims to minimize cable construction costs, power losses, and expected wind power reduction costs, characterized by approximating power losses and ensuring “N − 1” standard compliance for cable faults. Reference [45] seeks to minimize the total investment cost of electrical connections in wind farms, balancing cost and reliability. Finally, the objective function of reference [46] is to maximize the comprehensive evaluation of investment cost and reliability, achieving efficient optimization between economic efficiency and system reliability.

Several models have developed comprehensive lifecycle cost frameworks that encompass operational expenses, maintenance expenditures, and costs associated with downtime, in addition to the initial investment outlay [47,48,49]. The traditional total cost is depicted in Equation (2):

$$C_{all}=C_{cab}+C_{con}+C_{loss}$$

(2)

where $C_{all}$ is the total cost of topology optimization of the collector system for offshore wind farms, $C_{cab}$ is the investment cost of the cable, $C_{con}$ is the construction cost, and $C_{loss}$ is the price of electricity corresponding to the loss of power in the submarine cable itself during the operation of the wind farm.

3.1.2. Reliability Objectives

The power collection system of an offshore wind farm is pivotal for the transmission of electrical energy, and its reliability assessment is critical for the smooth operation of the offshore wind farm [50]. Consequently, it is imperative to evaluate the reliability of the offshore wind farm’s collection system using accurate models. Such assessments not only ensure the seamless functioning of the offshore wind farm but also substantially enhance its economic benefits. However, the complexity of analyzing the collection system’s reliability is significantly magnified due to the multitude of system components and intricate marine environmental factors.

Table 7 presents a comparison of reliability optimization models proposed in various literature. Given the high-dimensional complexity of the collection system optimization problem, numerous factors influence the reliability of offshore wind farms. Existing studies have concentrated on the failure rates of components, such as converters, topology structures, and switch configurations. Reference [51] introduces a reliability assessment analysis method for offshore wind farms that accounts for environmental impacts on failures. This method enhances the accuracy and computational efficiency of the model by integrating a protection zone model, an equivalent power unit model, and a common cause failure (CCF) analysis. Reference [50] proposes a comprehensive reliability assessment method for the collection system based on the full probability enumeration of component state spaces. Several studies have put forward reliability analysis methods for key components within the collection system. Reference [52] presents a reliability model for wind power converters, taking into account their influence on the overall reliability of offshore wind farms. Scholars have examined the impact of different switch configurations in the system, which are influenced by the use of equipment such as vacuum circuit breakers and isolation switches. They have proposed an improved model [53] and provided reliability assessment algorithms for both traditional and fully configured switch schemes [54].

Taking into account the influence of the intricate marine environment is a significant challenge for the reliability of offshore wind farm collection systems. Studies referenced as [55,56] have put forth reliability assessment models that account for wind speed fluctuations, and certain researchers have integrated the effects of extreme weather events, such as typhoons, into reliability assessment models using time series Monte Carlo methods and Markov processes, as seen in [57,58]. However, as offshore wind power evolves towards larger scales and higher capacities, traditional reliability analysis methods struggle to keep up. A comprehensive approach that amalgamates multi-state Markov processes with universal generating functions has been proposed in [59], encompassing models for multi-state wind turbine output, wind turbine reliability, and inter-array cable reliability, while also alleviating computational demands through network splitting strategies. Furthermore, to address the need for reliability analysis under stochastic scenarios and special emergency conditions, a progressive contingency incorporation (PCI) method has been employed to enhance optimization efficiency when integrating emergency assessment schemes, as detailed in [60].

Table 7. Comparison of reliability models for offshore wind farm collection systems.

Reference	Research Content	Reliability Impact Factors	Model Advantages
[50]	Improves the reliability of the offshore wind farm transmission system’s electrical main connections	Component state space, component failure rate	Combines multiple reliability assessment methods, improving assessment accuracy and effectiveness
[51]	Accurately quantifies the reliability of offshore wind farms, considering environmental impacts	Environmental impact, failure modes, protection zone model, equivalent power unit model, common cause failure (CCF) analysis	Resolves computational complexity through protection zone and equivalent power unit models; accounts for environmental impact through CCF analysis
[52]	Analyzes the impact of converter faults on wind farm reliability	Converter faults, wind turbine wake effect	Considers the impact of wind turbine converter faults on overall reliability
[53]	Minimizes the impact of faults in the collection system	Switch configuration schemes, cable failure rate, equipment failure rate	Proposes fundamental rules for switch configurations, reducing decision variables and improving solving efficiency
[57]	Establishes a reliability assessment model for offshore wind farms and VSC-HVDC systems considering the impact of severe weather	Wind speed correlation, VSC-HVDC equipment faults, severe weather conditions	Integrates the impact of severe weather and equipment failures, enhancing practicality and accuracy
[59]	Assesses the availability of large-scale offshore wind farms, including their collection systems	Multi-state wind turbine output, wind turbine reliability, array cable reliability model	Combines multi-state Markov processes and the universal generating function (UGF), reducing computational burden while considering wind turbine output dependency
[61]	Assesses the reliability of DC collection systems	Collection system topology, wind turbine capacity, electrical equipment failure rate	The UGF-based assessment method improves computational efficiency, suitable for multi-state systems

Table 7. Comparison of reliability models for offshore wind farm collection systems.

Reference	Research Content	Reliability Impact Factors	Model Advantages
[50]	Improves the reliability of the offshore wind farm transmission system’s electrical main connections	Component state space, component failure rate	Combines multiple reliability assessment methods, improving assessment accuracy and effectiveness
[51]	Accurately quantifies the reliability of offshore wind farms, considering environmental impacts	Environmental impact, failure modes, protection zone model, equivalent power unit model, common cause failure (CCF) analysis	Resolves computational complexity through protection zone and equivalent power unit models; accounts for environmental impact through CCF analysis
[52]	Analyzes the impact of converter faults on wind farm reliability	Converter faults, wind turbine wake effect	Considers the impact of wind turbine converter faults on overall reliability
[53]	Minimizes the impact of faults in the collection system	Switch configuration schemes, cable failure rate, equipment failure rate	Proposes fundamental rules for switch configurations, reducing decision variables and improving solving efficiency
[57]	Establishes a reliability assessment model for offshore wind farms and VSC-HVDC systems considering the impact of severe weather	Wind speed correlation, VSC-HVDC equipment faults, severe weather conditions	Integrates the impact of severe weather and equipment failures, enhancing practicality and accuracy
[59]	Assesses the availability of large-scale offshore wind farms, including their collection systems	Multi-state wind turbine output, wind turbine reliability, array cable reliability model	Combines multi-state Markov processes and the universal generating function (UGF), reducing computational burden while considering wind turbine output dependency
[61]	Assesses the reliability of DC collection systems	Collection system topology, wind turbine capacity, electrical equipment failure rate	The UGF-based assessment method improves computational efficiency, suitable for multi-state systems

3.2. Optimization Variables

Table 8. Comparison of optimization variables in models for offshore collection systems.

Variables	Number of Wind Turbines	Position of Wind Turbines	Number of Substations	Position of Substations	Topological Connection	Cable Length	Cable Type
Variable Type	Integer	Continuous	Integer	Continuous	Discrete	Discrete	Discrete
Reference [39]				√	√		√
Reference [41]			√	√	√		√
Reference [42]		√			√		√
Reference [44]		√		√	√
Reference [45]					√	√
Reference [46]			√	√	√	√
Reference [47]	√	√	√	√	√	√	√

compiles the optimization variables from a selection of existing models. The optimization variables in the majority of these models encompass critical parameters such as the number and positioning of substations and turbines, network topology, and the length and type of cables. The optimization of these parameters is intrinsically linked to the operational efficiency and cost management of offshore wind farms. The table delineates the categories of these variables, which include continuous, discrete, and integer types. The varying shades of color in the table visually represent the degree of interdependence among the variables. The deeper the hue, the greater the influence of other variables on a particular variable, suggesting a reduced level of independence in the optimization process.

From the optimization perspective of the collector system, it is evident that the quantity and positioning of wind turbines are dictated by the environmental conditions of the offshore wind farm and are not influenced by other variables. Conversely, the configuration of substations and their topological connections exhibit an increasing degree of correlation and are predominantly governed by the wind turbines. In contrast, the length and type of submarine cables are primarily determined by factors such as topology, with only a limited number of unique solutions available for cable selection once the topology is established. Moreover, the interconnections among these variables are not typically characterized by non-linearity or interactivity.

The intricacy of these multi-dimensional and interrelated variables substantially amplifies the challenge of constructing models for offshore wind farm collector systems. This complexity renders the allocation of computational resources for optimization through simulation impractical. Several recent studies have conducted thorough analyses to integrate new variables, such as power loss, into the optimization of the collector system. This approach has resulted in an even greater increase in the dimensionality and complexity of the variables compared to conventional models.

Considering the high-dimensional complexity and the multitude of variables inherent in the optimization of offshore wind farm collector systems, along with the inherent correlations and mutual constraints among these optimization variables, it is clear that the order in which the dimensions are addressed can significantly influence the accuracy and efficiency of the optimization process. However, the sequence in which variables are optimized in existing studies is often not emphasized within the algorithmic approach or is left to the discretion of the researcher. To date, no study has addressed the question of overall efficiency and accuracy under different optimization orders, which represents a significant gap in the current research landscape.

4. AI-Based Optimization Algorithms

The optimization challenges inherent in collector systems for offshore wind farms are characterized by their high dimensionality and complexity. Consequently, AI-driven optimization algorithms have been extensively utilized in the design, operation, and maintenance of these systems. Researchers leverage these algorithms to simulate and analyze a wide array of operating conditions, subsequently fine-tuning the parameters of the collector system to achieve the highest possible energy transfer efficiency and minimize system losses. These algorithms are adept at handling complex system constraints, such as cable length, voltage levels, and switching configurations, to ensure system stability and reliability [62]. Simultaneously, intelligent optimization algorithms reduce maintenance costs and extend the operational life of the equipment by predicting system performance, thereby enhancing the economic feasibility and environmental sustainability of the entire wind farm [63].

This chapter is designed to offer a comprehensive overview of the topology optimization process, including an examination of how information is encoded and a comparative analysis of existing algorithms.

4.1. Topology Optimization Process

As depicted in Figure 8, the solution to the optimization model for the offshore wind farm collector system encompasses three primary components: the input component, the algorithmic component, and the output component.

The input section primarily encompasses data pertinent to the offshore wind farm, including the positioning of the turbines, the location of the substation, the direction of the wind, and other relevant information. The algorithmic component initially encodes the wind farm data and maps it to the search space, which is essential for the efficient execution of algorithmic operations. Subsequently, the constructed model must be solved, and the superiority of the solution must be evaluated for iterative optimization. In this process, a number of factors must be taken into account, including the objective function and the constraints. In conclusion, the optimization results are presented in the form of a collector system topology and system parameters, thereby concluding the optimization process.

4.2. Information Coding Method

The initial step involves encoding the optimization variables as input data to be integrated into the optimization algorithm, extracting the dimensional information for each position within the algorithm, and mapping the model’s solution space to the algorithm’s search space. The encoding method must adhere to principles of completeness, soundness, and non-redundancy.

The utilization of conventional coding techniques, especially binary coding, faces considerable challenges when optimizing collector systems for offshore wind farms. Although these coding methods are effective in certain simple scenarios, their application in wind farm collector systems that involve multidimensional data, such as coordinate positions, electrical connections, and turbine models, becomes problematic. This difficulty arises from the inaccuracy in representing continuous variables and capturing the intricate details of these systems, resulting in a significant expansion of the solution space. This phenomenon is commonly known as the “curse of dimensionality”. It is thus clear that more flexible and efficient coding methods are necessary to overcome these challenges and to facilitate more precise simulation and optimization of the collector systems in offshore wind farms.

To tackle the complexities arising from a variety of variable types, such as continuous, discrete, and integer, researchers commonly utilize a suite of coding techniques to more efficiently encode multidimensional data within wind farm collection systems. Among these techniques, combinatorial coding stands out for its flexibility and efficiency in information representation. It achieves this by amalgamating various coding elements, including coordinates and numerical values. Additionally, an innovative approach known as chain-list coding has been developed. This method delineates the interconnectivity between individual wind turbines and offshore booster stations within a wind farm by projecting the spanning tree structure onto the chain-list coding space. The merit of this chain-list coding technique lies in its direct representation of information in the form of a spanning tree, which offers a more succinct and accessible format for topology optimization.

Table 9. Comparison of different coding methods.

Reference	Coding Method	Code Content	Matching Algorithm
[64]	combination code	X-axis and Y-axis coordinates of N substations, turbine connection index information for N fixed areas, and turbine connection index information for one adaptive area	PSO
[65]	combination code	Whether the turbines are connected to each other by cables	GA
[66]	linked list code	Complete representation of the spanning tree structure, including turbine connections, current direction, and number of cable-mounted turbines	GA
[67]	combination code	Coordinate information of the X-axis and Y-axis of N power stations, all cable connections, and cable types of all connections	PSO

offers a comparative analysis of the existing literature on information encoding for offshore wind farm collection systems. In reference [64], a combinatorial coding approach is introduced that delivers a detailed depiction of the coordinate information of N substations on the x-y axis, along with the turbine connection metrics within N fixed regions and 1 adaptive region, tailored for particle swarm optimization. Reference [65] employs combinatorial industrial coding to signify the connectivity between wind turbines via cables, with genetic algorithms subsequently applied to refine the transmission network topology for offshore wind farms. Reference [66] utilizes chain-list coding to provide an exhaustive representation of the spanning tree structure, including wind turbine connections, wind direction, and the count of wind turbines mounted on cables. Additionally, a genetic algorithm is harnessed to optimize the topology of the offshore wind farm collection system. In reference [67], a combinatorial coding method is proposed that encompasses the x-y axis coordinate information of N generating stations, all cable connections, and the corresponding cable models for these connections. This method is integrated with a particle swarm optimization algorithm to optimize the topology of the offshore wind farm collection system.

The innovative coding techniques are adept at processing data across multiple dimensions simultaneously, which allows optimization algorithms to more comprehensively account for the design and operational parameters of the wind farm collector system. This methodology can effectively mitigate the constraints imposed by the mathematical model, aligning it more closely with the actual system and ultimately yielding a superior solution. Concurrently, as the algorithm approaches the optimal solution, the significant jumps in the solution space, which are a result of the random iterations inherent in these coding methods, will be considerably less pronounced compared to those of a single coding method. This can substantially enhance the efficiency and quality of the algorithm when solving complex problems.

4.3. Topology Optimization Algorithms

The intricate submarine topography of offshore wind farms, coupled with the impact of numerous constraints including cable capacity, cable crossings, and restricted zones, presents challenges in the optimization of collector system topology design, such as high dimensionality, nonlinearity, and ambiguous clustering boundaries.

Table 10 presents a comparison of the commonly utilized algorithms for addressing the optimization issues of offshore wind farm collector systems in recent years. These algorithms can be broadly categorized into two groups: deterministic algorithms and heuristic algorithms. Some novel studies have also integrated the strengths of these two algorithm types to enhance optimization performance [68]. In a recent study, Zhang et al. [69] assisted the clustering of turbine clusters based on a large language model (LLM), which enabled the LLM to understand the optimization objective through the chain cueing method, and used the LLM to partition the large-scale offshore wind farm into a number of small regions in order to reduce the dimensionality of the optimization problem and to improve the speed and quality of the solution. This is the first time that LLM has been applied to the field of offshore wind farm collector system design, which will provide a brand new idea for the design of future wind farms. In the latest study, Xu et al. [70] improved the population generation method of the genetic algorithm based on the dynamic edge-weighted minimum spanning tree algorithm to solve the problem in order to expand the search solution space of the algorithm and improve the quality of the solution. This is also a new mixed optimization algorithm.

Within the realm of heuristic algorithms, Gonzalez et al. [62] introduced the integration of a Genetic Algorithm with the Multi-Travelling Salesman Problem (MTSP) to enhance the topology of power pooling systems. Zhao et al. [46] employed an enhanced Genetic Algorithm to bolster the economic and reliability aspects of power pooling system topologies for offshore wind farms. Zuo et al. [68] implemented a two-tier optimization framework utilizing Deterministic Fuzzy C-mean clustering and Genetic Algorithm (GA) to achieve a balance between economic efficiency and output stability. Wu et al. [71] combined an improved Fuzzy C-mean clustering algorithm (FCM) with the Prim algorithm to enhance planning efficiency and the identification of optimal solutions. Chen et al. [72] leveraged a PSO-FCM algorithm for wind turbine clustering, alongside BIP and Benders decomposition algorithms for optimizing wind turbine allocation and cable topology. Song et al. [73] proposed a hybrid optimization framework that combines Binary Particle Swarm Optimization (BPSO) with Improved Monte Carlo Tree Search (IMCTS), accounting for floating characteristics and various factors. Wei et al. [41] addressed the dimensional curse problem in large-scale wind farms by employing a fuzzy clustering algorithm, a Single-Parent Genetic Algorithm (SPGA), and a Multi Traveller Problem (MTSP) model. Srikakulapu et al. [74] amalgamated Ant Colony Optimization (ACO) with MTSP for optimizing collector system topologies. Liu et al. [75] improved search efficiency by simplifying problem size based on a partitioning strategy. Wang et al. [66] proposed an improved genetic algorithm to enhance optimality search and convergence, which is particularly suited for collector system topology optimization.

Within the realm of deterministic algorithms, Zuo et al. [43] introduced a hybrid integer programming approach for the comprehensive optimization of offshore wind farm planning. X. Shen et al. [44] enhanced the dual-ring topology of an offshore wind farm collector system using a mixed integer quadratic programming (MIQP) algorithm, incorporating an “N − 1” criterion to bolster system reliability and economic viability. Chen et al. [72] integrated fuzzy C-mean clustering (FCM) with binary integer programming (BIP) for the generation of network models and automated allocation of wind turbines. Li et al. [76] merged FCM with Delaunay triangulation and dynamic edge weights, a method well suited for the optimization of complex collector system topologies. Li et al. [77] took into account restricted areas, employing Delaunay triangulation and minimum spanning tree (MST) algorithms to optimize cable routing. Huang et al. [78] leveraged GIS techniques and obstacle avoidance algorithms to enhance the utility of path optimization. Shen X et al. [79] utilized mixed integer linear programming (MILP) to minimize cable investment costs and power losses. R. Chen et al. [80] employed simulated annealing (SA) algorithms to optimize cable topology, thereby improving grouping and topological efficiency. Collectively, these algorithms offer efficient planning and design methodologies for offshore wind farm collector systems, facilitated by precise mathematical modeling and sophisticated optimization techniques.

A thorough analysis of these algorithms highlights the distinct advantages and traits of heuristic and deterministic approaches in optimizing the collector systems of offshore wind farms. Heuristic methods, including genetic algorithms, particle swarm optimization, and ant colony optimization (as referenced in [42,46,65,68,69,71,73,76]), are particularly adept at addressing large-scale, multi-objective, and dynamically changing issues. Their flexibility and adaptability enable them to swiftly identify an approximate optimal solution, which is crucial in scenarios with intricate constraints and multimodal problems. Nonetheless, these algorithms may necessitate numerous iterations and substantial computational time to ensure a solution, with the solution’s precision being influenced by parameter configurations and the number of iterations.

In contrast, deterministic algorithms, such as mixed integer programming and graph-theoretic methods (as detailed in [43,44,64,67,75,81,82]), offer precise solutions that ensure the discovery of a globally optimal or deterministically optimal solution. They have demonstrated accuracy and reliability, particularly in optimizing cable investment costs and power loss. These algorithms construct an exact mathematical model to describe the problem, making them well suited for structured and well-defined issues. However, they may face challenges in computational efficiency when tackling large-scale problems.

Nevertheless, the design of offshore wind farm collector systems is contingent upon a multitude of complex conditions, encompassing those related to the marine environment and construction limitations. As a result, it is often essential to offer decision-makers a spectrum of optimal solutions when devising the collector system. Existing algorithms, which solely pursue the global optimal solution, fall short of meeting the necessary criteria. Multimodal algorithms offer innovative prospects in this regard. These optimization algorithms are capable of concurrently identifying multiple optimal solutions across various dimensions, as opposed to delivering a single optimal solution. This capability enables decision-makers to weigh multiple options concurrently, which holds significant value in practical engineering construction. Reference [83] used a multimodal algorithm to solve this problem for the first time, effectively solving the problem that existing algorithms can only find a single optimal solution. However, this area still needs more research to improve the performance of the algorithms.

Table 10. Comparison of optimization algorithms for offshore wind farm collection systems.

Reference	Optimization Algorithm	Algorithm Type	Algorithm Description	Algorithm Steps	Algorithm Features
[41]	Fuzzy Clustering Algorithm + Single Parent Genetic Algorithm (SPGA) + MTSP Model	Layered Optimization	Layered optimization model with substation layer, turbine layer, and cable layer	1. Substation partitioning via fuzzy clustering 2. Turbine string optimization via SPGA 3. Turbine connection via MTSP model	Solves dimensional curse for large-scale wind farms
[42]	Genetic Algorithm + Multiple Traveling Salesman Problem (MTSP)	Genetic Algorithm	Offshore wind farm collection system optimization design	1. Cost model development 2. Optimization model design 3. Genetic algorithm implementation	Combines MTSP and genetic algorithm, considers cable cross-section, optimizes collection system design
[43]	Mixed Integer Programming	Two-layer Optimization Framework	Comprehensive joint optimization of offshore wind farm planning	1. Wind farm layout optimization 2. Collection system optimization 3. Two-layer optimization framework implementation	Considers the interaction between wind farm layout and collection system for holistic optimization
[44]	Mixed Integer Quadratic Programming (MIQP)	MIQP	Optimized planning for dual-loop topology of offshore wind farm collection systems	1. Dual-loop topology design 2. Cross-avoidance constraints 3. k-degree central tree model	Meets the “N − 1” criterion, improving system reliability and economy
[46]	Genetic Algorithm	Improved Genetic Algorithm	Offshore wind farm power collection system topology optimization	1. Region partitioning 2. Optimization model establishment 3. Genetic algorithm implementation	Improves the genetic algorithm to speed up convergence, optimizing both economy and reliability
[72]	Fuzzy C-Means Clustering (FCM) + Binary Integer Programming (BIP)	Mixed Integer Programming	Network model generation based on FCM and automatic turbine allocation via BIP	1. Wind turbine clustering using FCM 2. Wind turbine reallocation via BIP 3. Cable layout optimization using Minimum Spanning Tree (MST) algorithm	Takes network reliability into account
[68]	Deterministic FCM + Genetic Algorithm (GA)	Mixed Optimization Algorithm	Two-layer optimization framework combining deterministic and heuristic algorithms	1. Outer layer: wind turbine grouping and substation location optimization 2. Inner layer: cable connection optimization using GA and Clarke and Wright’s savings algorithm	Balances economic efficiency and output stability
[69]	Genetic Algorithm + DMST	Mixed Optimization Algorithm	Improves genetic algorithm with DMST for topology optimization.	1. Initial population with DMST 2. Genetic operations (encoding, crossover, mutation) 3. Evolution until termination	Lifecycle cost minimization with cable selection and crossing avoidance constraints.
[73]	Binary Particle Swarm Optimization (BPSO) + Improved Monte Carlo Tree Search (IMCTS)	Mixed Optimization Algorithm	Two-layer optimization framework with BPSO in the upper layer and IMCTS in the lower	1. Upper: connection between turbines and offshore substations via BPSO 2. Lower: turbine-to-turbine cable topology optimization via IMCTS	Considers floating characteristics and environmental factors
[74]	Ant Colony Optimization (ACO) + MTSP	Mixed Optimization Algorithm	Combines ACO and MTSP for collection system topology optimization	1. Initialize parameters 2. Build initial solution 3. Optimize using ACO and MTSP 4. Update solution 5. Check termination conditions	ACO seeks optimal paths; MTSP solves multiple traveling salesmen problems, suitable for complex collection system topology
[75]	Divide-and-Conquer Strategy	Divide-and-Conquer	Collection system topology optimization using divide-and-conquer	1. Subdivision 2. Minimum spanning tree optimization 3. Traversal search 4. Topology scheme generation	Simplifies problem scale and improves search efficiency, suitable for large-scale wind farms
[76]	FCM + Delaunay Triangulation	Graph Theory Algorithm	Network model generation based on FCM and cable layout optimization using Delaunay triangulation	1. Turbine clustering using FCM 2. Delaunay triangulation 3. Dynamic edge weight adjustment 4. Iterative optimization	Combines fuzzy clustering and triangulation for dynamic edge weight adjustment, suitable for complex collection system topology optimization
[77]	Delaunay Triangulation + Minimum Spanning Tree (MST)	Graph Theory Algorithm	Collection system topology design considering restricted areas	1. Delaunay triangulation 2. Edge classification 3. Form minimum tree	Considers environmental restrictions to optimize cable paths and avoid restricted areas
[78]	GIS Technology + Minimum Bounding Box Method	Obstacle Avoidance Path Optimization Algorithm	Collection system topology optimization considering obstacle areas	1. Obstacle area layering 2. Obstacle avoidance path optimization 3. Fuzzy comprehensive evaluation	Combines GIS technology with obstacle avoidance algorithms to improve practicality in path optimization considering obstacle areas
[79]	Mixed Integer Linear Programming (MILP)	MILP	Large-scale offshore wind farm collection system planning	1. FCM clustering 2. List potential connection cables 3. MILP model formulation 4. Solve problem	Extracts techniques from the DNEP problem to optimize cable investment costs and power losses
[80]	Simulated Annealing (SA)	Simulated Annealing	Grouped optimization design of large offshore wind farm collection system topology	1. Turbine grouping 2. Initial topology generation 3. Simulated annealing optimization	Simulated annealing algorithm improves grouping and topology optimization quality and efficiency
[71]	Improved Fuzzy C-Means Clustering Algorithm (FCM) + Prim Algorithm	Clustering and Graph Theory Algorithms	Improved FCM for turbine clustering and Prim for cable layout	1. Turbine clustering using improved FCM algorithm 2. Cable layout optimization via Prim algorithm	Enhances planning efficiency and optimization capability
[82]	Genetic Algorithm	Improved Genetic Algorithm	Genetic algorithm for offshore wind farm collection system topology optimization	1. Coding and initial population 2. Fitness calculation 3. Selection, crossover, and mutation 4. Next generation population generation	Improved genetic algorithm enhances optimization and convergence for collection system topology

Table 10. Comparison of optimization algorithms for offshore wind farm collection systems.

Reference	Optimization Algorithm	Algorithm Type	Algorithm Description	Algorithm Steps	Algorithm Features
[41]	Fuzzy Clustering Algorithm + Single Parent Genetic Algorithm (SPGA) + MTSP Model	Layered Optimization	Layered optimization model with substation layer, turbine layer, and cable layer	1. Substation partitioning via fuzzy clustering 2. Turbine string optimization via SPGA 3. Turbine connection via MTSP model	Solves dimensional curse for large-scale wind farms
[42]	Genetic Algorithm + Multiple Traveling Salesman Problem (MTSP)	Genetic Algorithm	Offshore wind farm collection system optimization design	1. Cost model development 2. Optimization model design 3. Genetic algorithm implementation	Combines MTSP and genetic algorithm, considers cable cross-section, optimizes collection system design
[43]	Mixed Integer Programming	Two-layer Optimization Framework	Comprehensive joint optimization of offshore wind farm planning	1. Wind farm layout optimization 2. Collection system optimization 3. Two-layer optimization framework implementation	Considers the interaction between wind farm layout and collection system for holistic optimization
[44]	Mixed Integer Quadratic Programming (MIQP)	MIQP	Optimized planning for dual-loop topology of offshore wind farm collection systems	1. Dual-loop topology design 2. Cross-avoidance constraints 3. k-degree central tree model	Meets the “N − 1” criterion, improving system reliability and economy
[46]	Genetic Algorithm	Improved Genetic Algorithm	Offshore wind farm power collection system topology optimization	1. Region partitioning 2. Optimization model establishment 3. Genetic algorithm implementation	Improves the genetic algorithm to speed up convergence, optimizing both economy and reliability
[72]	Fuzzy C-Means Clustering (FCM) + Binary Integer Programming (BIP)	Mixed Integer Programming	Network model generation based on FCM and automatic turbine allocation via BIP	1. Wind turbine clustering using FCM 2. Wind turbine reallocation via BIP 3. Cable layout optimization using Minimum Spanning Tree (MST) algorithm	Takes network reliability into account
[68]	Deterministic FCM + Genetic Algorithm (GA)	Mixed Optimization Algorithm	Two-layer optimization framework combining deterministic and heuristic algorithms	1. Outer layer: wind turbine grouping and substation location optimization 2. Inner layer: cable connection optimization using GA and Clarke and Wright’s savings algorithm	Balances economic efficiency and output stability
[69]	Genetic Algorithm + DMST	Mixed Optimization Algorithm	Improves genetic algorithm with DMST for topology optimization.	1. Initial population with DMST 2. Genetic operations (encoding, crossover, mutation) 3. Evolution until termination	Lifecycle cost minimization with cable selection and crossing avoidance constraints.
[73]	Binary Particle Swarm Optimization (BPSO) + Improved Monte Carlo Tree Search (IMCTS)	Mixed Optimization Algorithm	Two-layer optimization framework with BPSO in the upper layer and IMCTS in the lower	1. Upper: connection between turbines and offshore substations via BPSO 2. Lower: turbine-to-turbine cable topology optimization via IMCTS	Considers floating characteristics and environmental factors
[74]	Ant Colony Optimization (ACO) + MTSP	Mixed Optimization Algorithm	Combines ACO and MTSP for collection system topology optimization	1. Initialize parameters 2. Build initial solution 3. Optimize using ACO and MTSP 4. Update solution 5. Check termination conditions	ACO seeks optimal paths; MTSP solves multiple traveling salesmen problems, suitable for complex collection system topology
[75]	Divide-and-Conquer Strategy	Divide-and-Conquer	Collection system topology optimization using divide-and-conquer	1. Subdivision 2. Minimum spanning tree optimization 3. Traversal search 4. Topology scheme generation	Simplifies problem scale and improves search efficiency, suitable for large-scale wind farms
[76]	FCM + Delaunay Triangulation	Graph Theory Algorithm	Network model generation based on FCM and cable layout optimization using Delaunay triangulation	1. Turbine clustering using FCM 2. Delaunay triangulation 3. Dynamic edge weight adjustment 4. Iterative optimization	Combines fuzzy clustering and triangulation for dynamic edge weight adjustment, suitable for complex collection system topology optimization
[77]	Delaunay Triangulation + Minimum Spanning Tree (MST)	Graph Theory Algorithm	Collection system topology design considering restricted areas	1. Delaunay triangulation 2. Edge classification 3. Form minimum tree	Considers environmental restrictions to optimize cable paths and avoid restricted areas
[78]	GIS Technology + Minimum Bounding Box Method	Obstacle Avoidance Path Optimization Algorithm	Collection system topology optimization considering obstacle areas	1. Obstacle area layering 2. Obstacle avoidance path optimization 3. Fuzzy comprehensive evaluation	Combines GIS technology with obstacle avoidance algorithms to improve practicality in path optimization considering obstacle areas
[79]	Mixed Integer Linear Programming (MILP)	MILP	Large-scale offshore wind farm collection system planning	1. FCM clustering 2. List potential connection cables 3. MILP model formulation 4. Solve problem	Extracts techniques from the DNEP problem to optimize cable investment costs and power losses
[80]	Simulated Annealing (SA)	Simulated Annealing	Grouped optimization design of large offshore wind farm collection system topology	1. Turbine grouping 2. Initial topology generation 3. Simulated annealing optimization	Simulated annealing algorithm improves grouping and topology optimization quality and efficiency
[71]	Improved Fuzzy C-Means Clustering Algorithm (FCM) + Prim Algorithm	Clustering and Graph Theory Algorithms	Improved FCM for turbine clustering and Prim for cable layout	1. Turbine clustering using improved FCM algorithm 2. Cable layout optimization via Prim algorithm	Enhances planning efficiency and optimization capability
[82]	Genetic Algorithm	Improved Genetic Algorithm	Genetic algorithm for offshore wind farm collection system topology optimization	1. Coding and initial population 2. Fitness calculation 3. Selection, crossover, and mutation 4. Next generation population generation	Improved genetic algorithm enhances optimization and convergence for collection system topology

5. Conclusions

This paper offers an exhaustive review and synthesis of the design and optimization techniques utilized in the collector systems of offshore wind farms. The review spans a variety of subjects, including system topology, the choice between AC/DC, collection voltage selection, optimization frameworks, and algorithmic development. Based on an analysis of the extant literature, this paper draws the following conclusions:

(1) For the 35 kV AC collector system for offshore wind farms, which is commonly used at present, the selection of the topology must take into account the size of the wind farm, the distance from the coast, and so on. However, as the capacity of wind farms increases, the use of the 66 kV AC collector system will become a trend. Research results show that the economics of 66 kV systems are always better compared to 35 kV systems. With the further development of technology, the 66 kV AC collector system may become the main program.

(2) As offshore wind farms extend further from the coast and scale up, an increasing number of collection systems are expected to adopt a direct current (DC) configuration in place of the traditional alternating current (AC) setup. This shift will substantially decrease losses within the collection system and enhance the efficiency of offshore wind farms. Moreover, the DC configuration can more effectively integrate with the evolving DC transmission infrastructure, thereby simplifying system complexity.

(3) The optimization of collector systems in offshore wind farms constitutes a multifaceted and intricate optimization challenge, characterized by complex interdependencies among variables. Existing optimization methodologies can be broadly categorized into deterministic and heuristic algorithms, each with its own limitations. To achieve superior outcomes, it is imperative to integrate the principles of both algorithm types to address the issue and to simulate the optimization results. Additionally, further research is required to understand the impact of variable prioritization on optimization outcomes.

(4) The unimodal problem-solving approach is constrained by the unique attributes of the offshore environment and construction conditions. To overcome these limitations, a multimodal and multi-objective solution strategy is imperative, and there remains a significant gap in research within this area.

Despite the fact that this paper comprehensively reviews the relevant content of the design and optimization of offshore wind farm collector systems, there are still some shortcomings.

(1) Existing studies on optimization algorithms almost always use different examples for validation, which makes it impossible to accurately assess the differences in effect between different algorithms.

(2) This paper ignores the relevant content of collector system control and protection. Some of the existing research is outstanding in its contribution to these areas [84]. In fact, this will affect the grid-connected operation of offshore wind farms.

To address the above research shortcomings and gaps, this paper proposes new directions for future research in this area:

(1) Further validate the stability, reliability, etc., of DC collector systems and promote the practical application of DC collector systems.

(2) Unify the test cases of algorithms to facilitate the comparison of different algorithms in terms of optimization effect, required computational power, and other performances.

(3) Considering the optimization of variable priority, investigate the impact of the order of optimizing variables on the optimization results of the power collection system.

(4) Combine multimodal algorithms to solve the problem to meet the needs of practical engineering.