Energy-efficient integrated marine smart charging pile system and retrofitting method

Abstract

This invention relates to an energy efficiency-driven integrated power supply and storage smart charging pile system and its retrofitting method for offshore wind farms. The system includes: a pile-disc composite monopile foundation, comprising a monopile foundation and a friction disc, the friction disc including an inner ring region and an outer ring region coaxially arranged with the monopile foundation; the inner ring region for placing power supply equipment and counterweights; a smart distribution cabinet; offshore unmanned equipment; an OpenSees computational model for calculating the excess pore water pressure ratio of the pile-disc composite monopile foundation under seismic loading based on different inner and outer ring radius ratios and thicknesses of the friction disc, according to pile-disc-soil interaction; and a multi-factor driven intelligent optimization site selection model. This invention identifies the optimal retrofitting location for offshore wind farms and performs integrated power supply and storage retrofitting on the foundations within the offshore wind farms. While reducing transmission losses, it provides infrastructure support for unmanned offshore maintenance facilities, deep-sea structural platforms, and ocean shipping, while simultaneously achieving zero-carbon construction and operation and improving the utilization efficiency of offshore wind turbines.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of offshore wind power technology, in particular to an energy efficiency driven supply and storage integrated offshore intelligent charging pile system and a reforming method, which comprehensively considers factors such as the distribution of deep-sea power consumption equipment, the endurance and route of offshore unmanned equipment, and optimizes the site selection of the existing offshore wind farm reform, and reforms the foundation structure of the offshore wind turbine to realize local storage and nearby consumption of electric energy. BACKGROUND

[0002] As an important part of marine economy, deep-sea economy, including ocean transportation, deep-sea offshore structure construction and use, and unmanned operation and maintenance facilities, needs electric power to support its normal operation. The existing power supply mode through the land power grid via submarine cable transmission leads to rising costs and unstable power supply due to the increase in transmission distance, and other reliable energy sources need to be considered.

[0003] Wind energy is a typical renewable energy with broad development prospects and application potential, which can provide clean and sustainable power supply and reduce dependence on traditional fossil fuels. However, the area suitable for installing offshore wind turbines in the near sea is limited, and offshore wind turbines show three trends of large-scale, clustering and deep-sea. In the process of deep-sea development of offshore wind turbines, the increase in power transmission distance causes the increase in transmission loss and cost, and the cost of cable laying also increases due to the difficulty and distance of deep-sea laying, resulting in a decline in economic benefits.

[0004] The existing offshore wind turbine group in the offshore wind farm does not have the ability to directly supply power to the surrounding power consumption equipment, and the existing offshore wind farm site selection is concentrated on grid efficiency, grid cost and wind resource distribution, all of which are transmitted back to the mainland power grid through long-distance submarine cable. Compared with grid power transmission, the electric energy generated by the offshore wind farm is not planned to be directly supplied to other marine economic facilities around the offshore wind farm, and there is a lack of local power supply for surrounding economic development activities and other multi-objective considerations. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application aims to solve the technical problem of providing an energy efficiency driven supply and storage integrated offshore intelligent charging pile system and a reforming method. The system and reforming method determine the optimal reform position of the offshore wind farm and reform the pile-disc composite single pile foundation in the offshore wind farm to reduce transmission loss and provide support for deep-sea unmanned operation facilities (including offshore unmanned equipment), deep-sea structure platforms, and ocean transportation on the infrastructure. At the same time, by directly reforming the supply and storage integration of the offshore wind turbine, the dependence on traditional fossil fuels for deep-sea economic development can be reduced, zero-carbon construction and operation can be realized, and the utilization efficiency of the offshore wind turbine can be improved.

[0006] The technical scheme adopted by the present application to solve the technical problem is:

[0007] In a first aspect, the present application provides an energy efficiency driven integrated offshore intelligent charging pile system, which comprises:

[0008] A pile-disc composite single pile foundation comprises a single pile foundation and a friction disc arranged at the lower part of the single pile foundation, the friction disc comprises an inner ring area and an outer ring area coaxially arranged with the single pile foundation, and the inner ring area is used for placing power supply equipment and counterweight blocks;

[0009] An intelligent power distribution cabinet is used for monitoring the real-time output power of offshore wind turbines and the load of power equipment, and dynamically allocating the input power of different power equipment.

[0010] An offshore unmanned device comprises a drone device and an unmanned ship device, a wireless charging transmitting end is integrated at the top of the offshore wind turbine, and a floating wireless charging platform is deployed near the offshore wind turbine, the floating wireless charging platform is electrically connected with the offshore wind turbine, a wireless charging transmitting end is arranged on the floating wireless charging platform, and the unmanned ship device docks and charges at the floating wireless charging platform.

[0011] An OpenSees calculation model is used to calculate the excess pore water pressure ratio of the pile-disc composite single pile foundation under the action of an earthquake under different friction disc inner-outer ring radius ratios R and thicknesses T. eppr ;

[0012] A multi-factor driven intelligent optimization site selection model takes the maximum coverage range of offshore wind farms, the minimum power transmission loss and the minimum comprehensive transformation cost as the target, takes the power supply capacity limitation of the maximum output power of the wind turbine, the transformation cost limitation of a single offshore wind farm, the safety distance limitation of the shipping channel of the offshore wind farm and the shipping freighter, and the endurance constraint limitation of the offshore unmanned device as the constraint, and performs multi-objective solution to determine whether to select scheme A or scheme B for the offshore wind farm to be transformed; when scheme A is selected, the OpenSees calculation model is called to calculate the excess pore water pressure ratio eppr , and eppr the safety factor is calculated to determine the minimum friction disc inner-outer ring radius ratio and thickness that meet the structural safety requirement, and realize the structure-economy collaborative optimization; when scheme B is selected, the number of wind turbines to be transformed is determined according to the power demand of the surrounding power equipment, the annual average growth rate of the power demand of the surrounding power equipment, the added power brought by the transformed wind turbine, and the service life of the surrounding power equipment.

[0013] Further, the inner ring area of the friction disc is provided with a symmetrical sealing cabin layer, a battery layer is arranged in the inner ring area, a honeycomb alloy framework is arranged in the battery layer, and energy storage batteries are embedded in the honeycomb alloy framework, the arrangement of the energy storage batteries matches the spatial distribution characteristics of the power demand of the peripheral power equipment, and all the energy storage batteries are electrically connected together; a protective layer is arranged on the upper and lower surfaces of the energy storage batteries, and all the energy storage batteries are encapsulated into the battery layer by a steel outer cylinder, a steel inner cylinder and the protective layer, and a concrete protective layer is arranged outside the battery layer; the outer ring area is integrally poured with concrete to wrap the inner ring area.

[0014] Further, in the OpenSees calculation model, the model size and the actual size of the pile-disc-soil interaction adopt a scaling relationship of 1:6, the fan body is simplified as a steel rod with a concentrated load at the upper part, the range of the soil body is more than 10 times the diameter of the pile, and the soil body is divided into grids; the inner and outer ring radius ratio and thickness are taken as variables to parameterize the modeling and grid division of the friction disc.

[0015] Further, in the process of minimizing the comprehensive reconstruction cost, the opportunity cost is introduced to balance the difference in construction cost of executing the A scheme or the B scheme, and the opportunity cost considers the annual output value of economic activities of different power equipment, the attenuation coefficient and the economic weight coefficient of different economic development.

[0016] Further, the SSA-NSGA-III algorithm is used for intelligent multi-objective solution.

[0017] In the second aspect, the application provides a method for reconstructing an energy-efficient integrated offshore intelligent charging pile, which comprises the following steps:

[0018] Obtain the position coordinates of the existing offshore wind farm, the driving route of the existing ocean route and the power equipment, wherein the power equipment includes an ocean freighter, an offshore structure platform, an offshore ranch and a data center, the position coordinates of the ocean freighter on the ocean route are selected as the points with the maximum appearance probability of the ocean freighter; obtain the power supply radius of each offshore wind farm and the power demand of each power equipment;

[0019] An OpenSees calculation model of pile-disc-soil interaction is constructed, which is used to calculate the excess pore water pressure ratio of the pile-disc composite single pile foundation under the action of earthquake under different inner and outer ring radius ratios R and thicknesses T of the friction disc eppr ;

[0020] With the objectives of maximizing the coverage of offshore wind farms, minimizing power transmission losses, and minimizing overall retrofit costs, and constrained by factors such as the power supply capacity limitations of the maximum output power of wind turbines, the retrofit cost of a single offshore wind farm, the safe distance between offshore wind farms and shipping lanes, and the endurance constraints of unmanned offshore equipment, a multi-objective solution is used to determine whether to select scheme A or B for the offshore wind farms requiring retrofit. When scheme A is selected, the OpenSees computational model is used to calculate the excess pore water pressure ratio. eppr ,by eppr Calculate the safety factor to determine the minimum ratio of the inner and outer ring radii and the thickness of the friction disc that meets the structural safety requirements, thereby achieving structural-economic synergistic optimization. When option B is selected, set the ratio of the inner and outer ring radii and the thickness of the friction disc, and determine the number of wind turbines that need to be modified based on the power demand of surrounding electrical equipment, the average annual growth rate of the power demand of surrounding electrical equipment, the additional power brought by the modification of the wind turbines, and the service life of surrounding electrical equipment, thereby achieving structural-economic synergistic optimization of the wind turbines.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] 1. This invention achieves integrated power supply and storage by modifying the inner ring area of ​​the friction disk for power supply and storage. The modified inner ring area contains a higher density of energy storage batteries and gravel counterweights to ensure the stability of the inner ring area under wind, wave, and current loads. The electricity of the offshore wind turbine is stored in the basic friction disk of the offshore wind turbine. Compared with existing methods such as storing batteries in the wind turbine nacelle, tower, or externally mounted batteries, this invention utilizes the ring-shaped modification of the friction disk space, without occupying additional space on the upper part of the tower or adding other energy storage devices. It does not change the original aerodynamic shape of the wind turbine. By replacing part of the friction disk structure with higher density energy storage batteries (compared to the unmodified structure), the structure is more stable and has less impact on the original operation of the offshore wind turbine.

[0023] 2. This invention combines a multi-factor driven intelligent optimization site selection model, comprehensively considering multiple factors such as the operating distance of unmanned offshore equipment, the distance between offshore wind farms and nearby marine power equipment (such as marine structural platforms, offshore ranches, data centers, etc.), and structural safety. With the goal of structural-safety-economic synergistic optimization, it forms a more intelligent and comprehensive site selection decision. Based on the modification of the basic structure, it coordinates the site selection decision and the basic structure modification to determine the final modification scheme. This solves the problem of resource misallocation caused by the indiscriminate modification of offshore wind turbines in the existing technology, which in turn restricts other economic development activities in the surrounding area.

[0024] 3. This invention adopts a friction disk with inner and outer partitions, and sets up a number of energy storage chambers in the inner ring area. It breaks through the limitations of existing energy storage methods in terms of energy storage battery capacity, and has the ability to directly supply energy to large-scale power facilities (or equipment), thus broadening the application scenarios. At the same time, for unmanned equipment at sea, wireless charging is used to replenish energy in a timely manner to expand the unmanned operation range and operation time. This innovative approach provides basic support for deep-sea economic development activities, reduces power transmission losses, and improves the utilization efficiency of offshore wind turbines.

[0025] 4. The SSA-NSGA-Ⅲ algorithm is used to solve the problem. When solving multi-objective functions, the SSA algorithm needs to set weight coefficients to balance the relationship between multiple objectives, which leads to insufficient search efficiency and uniform distribution of the solution set when solving high-dimensional functions. NSGA-Ⅲ also suffers from high computational complexity and uneven distribution of the solution set when solving high-dimensional functions. The discoverer-follower-watcher behavior of SSA is introduced into the simulated binary crossover and mutation operation of NSGA-Ⅲ to enhance the search efficiency of the algorithm and ensure that it can quickly and accurately obtain the global optimal solution. Attached Figure Description

[0026] Figure 1 This is a schematic diagram illustrating the location selection principle of the multi-factor driven intelligent optimization location selection model in this invention.

[0027] Figure 2 A schematic diagram of the energy supply modification for offshore charging stations.

[0028] Figure 3 This is a schematic diagram of the structure of a friction disc according to an embodiment of the present invention.

[0029] Figure 4 This is a schematic diagram of the battery layer layer structure in the inner ring region of one embodiment of the present invention.

[0030] Figure 5 This is a schematic diagram of the cross-sectional structure of a friction disk after an energy storage battery is installed in one embodiment of the present invention.

[0031] Figure 6 This is a flowchart illustrating the SSA-NSGA-Ⅲ algorithm.

[0032] In the diagram, 1 is the concrete protective layer, 2 is the battery layer, 3 is the outer ring area, 4 is the wind turbine nacelle, 5 is the tower, 6 is the connection between the tower and the foundation, 7 is the monopile foundation, 8 is the friction disc, 9 is the cable, 10 is the wireless charging transmitter, 11 is the floating wireless charging platform, 21 is the inner lining, 22 is the sealing layer, 23 is the energy storage battery frame, 24 is the steel outer cylinder, 25 is the steel inner cylinder, 201 is the honeycomb alloy frame, and 202 is the energy storage battery. Detailed Implementation

[0033] Specific embodiments of the present invention are given below. These specific embodiments are only used to further illustrate the present invention and do not limit the scope of protection of this application.

[0034] Based on the trend of offshore wind farms becoming more remote and larger, this paper proposes a multi-factor-driven intelligent optimization site selection method for offshore charging piles. The selected offshore wind farms undergo integrated power supply and storage transformation of their turbines, converting them from "power generation units" into "deep-sea energy hubs." This enables the turbines to collect wind energy while directly providing energy support to surrounding electrical equipment via cable 9. The multi-factor-driven intelligent optimization site selection method for offshore charging piles comprehensively considers factors such as the main routes of ocean-going freighters, the distribution of resources around the offshore wind farm, and the operating time and endurance of unmanned offshore equipment. Based on these factors, the optimized site selection ensures that the charging piles can directly provide clean and stable energy to nearby marine structures, ecological development, and data centers, while reducing losses and increased output costs caused by long-distance transmission. The specific implementation mainly considers the following three aspects:

[0035] 1. Integrated Energy Storage Offshore Wind Turbine Retrofit: The existing pile-disc composite monopile foundation will be modified. The existing composite monopile foundation consisting of a friction disc in a circular shape will be modified to a double-ring design with inner and outer rings. The inner ring area will be replaced with an energy storage battery. Wireless charging modules (including a wireless charging transmitter 10 and a floating wireless charging platform 11) will be added to the upper part or around the offshore wind turbine as needed to achieve direct energy storage and local consumption, while ensuring its reliability and stability as a foundation structure.

[0036] 2. Construct a multi-factor driven intelligent optimization site selection model: Based on the remaining power of unmanned operation and maintenance facilities such as drones to identify whether they need to be recharged, the model considers the power consumption of surrounding electrical equipment such as marine ranches and seabed data centers, and combines factors such as the location of offshore wind turbines in offshore wind farms and the routes of passing ships to construct an objective function that considers multiple factors. The SSA-NSGA-Ⅲ algorithm is used for intelligent multi-objective solution to efficiently solve the multi-factor driven intelligent optimization site selection model and ensure the acquisition of the global optimal solution.

[0037] 3. A robust energy supply decision-making mechanism: The system retains a grid connection interface to ensure that during periods of non-local energy supply or when wind turbine power is high, the units can stably feed the generated energy into the grid, just like conventional wind turbines, thus maintaining their fundamental function as the core power generation unit of the offshore wind farm. Simultaneously, not all the electricity generated by the offshore wind farm is first transmitted to distant land; it can be directly supplied to surrounding electrical equipment locally via dedicated lines or wireless methods.

[0038] Figure 1This is a schematic diagram illustrating the site selection principle during the construction of the multi-factor driven intelligent optimization site selection model in this invention. Within the power supply radius of an offshore wind farm, there are multiple electrical devices, such as offshore structural platforms, marine ranches, unmanned vessels, drones, ocean-going cargo ships, data centers, etc. Each offshore wind farm contains at least one offshore wind turbine, and there are several offshore wind farms in the open ocean. Each offshore wind turbine (see...) Figure 2 The structure includes a wind turbine nacelle 4, a tower 5, a composite monopile foundation, and a connection between the tower and the foundation 6. The foundation is a pile-plate composite monopile foundation, which includes a monopile foundation 7 and a friction plate 8 arranged at the bottom of the monopile foundation.

[0039] A floating wireless charging platform 11 is deployed near the offshore wind turbine. The floating wireless charging platform is connected to the offshore wind turbine via a cable 9. A wireless charging transmitter 10 is installed on the floating wireless charging platform. Unmanned surface vessels (USVs) can dock with the floating wireless charging platform for stable charging. A wireless charging transmitter 10 is also integrated on the top of the offshore wind turbine for charging USV equipment.

[0040] The friction disk (see Figures 3-5The system comprises an inner ring region (not shown in the figure) and an outer ring region 3, coaxially arranged with the monopile foundation. The inner ring region includes a battery layer 2 and a concrete protective layer 1. The inner ring region is used to house power supply equipment and counterweights to ensure the overall stress distribution. The layout of the power supply equipment matches the spatial distribution characteristics of the power demand of surrounding electrical equipment. The friction disc is divided into two annular regions: the inner ring region and the outer ring region 3. Concrete protective layers 1 are provided on both the upper and lower surfaces of the battery layer 2 in the inner ring region. The internal space of the inner ring region is symmetrically arranged in sealed compartments to accommodate the energy storage battery. The battery layer 2 is encapsulated as a whole by an inner liner 21, a sealing layer 22, an energy storage battery frame 23, a steel outer cylinder 24, and a steel inner cylinder 25. The inner liner 21 and the sealing layer 22, together with a waterproof membrane, form a protective layer, which is symmetrically distributed above and below the energy storage battery frame 23. The energy storage battery frame 23 is composed of an energy storage battery pack and a honeycomb alloy frame. The honeycomb alloy frame is made of high-strength alloy, and a number of energy storage batteries 202 are embedded in the cells of the honeycomb alloy frame 201. All energy storage batteries are connected in series to form an energy storage battery pack. The honeycomb alloy frame bears the load, ensuring the stability of the overall power supply and protecting the energy storage battery pack from wind and wave loads, shear forces under earthquake loads, and pressure from deep seawater. The energy storage battery pack only provides self-weight stress to limit the horizontal displacement of the foundation. Protective layers are set on the top and bottom of the energy storage battery pack and the honeycomb alloy frame to prevent electrolyte corrosion, ensure airtightness, and prevent salt spray penetration. The concrete protective layer 1 is a 5cm concrete layer used to block seawater intrusion. In addition, a temperature control management unit is set in the energy storage battery pack to ensure the operating temperature of the energy storage battery. The outer ring area is integrally cast with concrete, wrapping the inner ring area from the circumference without covering the location of the concrete protective layer. The friction disk is entirely covered with a waterproof and anti-corrosion coating for seepage and corrosion prevention.

[0041] This invention addresses the issue of stable energy supply in deep-sea areas by implementing an integrated energy supply and storage system for offshore wind farms, driven by multiple factors and featuring intelligent optimization in site selection and structural modification. The core of this invention lies in: firstly, integrating and collecting geographical location information of key electrical equipment such as offshore wind farms, ocean shipping routes, and deep-sea structures, and combining this with data on the endurance and operating time of existing unmanned operation and maintenance equipment, a multi-objective function is constructed, aiming to maximize the coverage of the offshore wind farm, minimize power transmission losses, minimize overall modification costs, and optimize structural safety. An intelligent optimization algorithm combining SSA (Sparrow Search Algorithm) and NSGA-Ⅲ (Third Generation Non-Dominated Sorting Genetic Algorithm) is used to solve the multi-objective function. Simultaneously, OpenSees (Marine Geotechnical Computing Platform) is introduced to calculate the structural safety factor. The resulting Pareto optimal solution set (Pareto front) represents the optimal offshore wind farm site selection for intelligent modification. Two specific modification schemes are set up. The decision variable X and the modification variable S are substituted into three objective functions: maximizing the coverage area of ​​the offshore wind farm, minimizing power transmission loss, and minimizing the overall modification cost. The Pareto optimal solution set, satisfying the constraints, is then obtained. This Pareto optimal solution set provides the 0 and 1 values ​​of the modification variable S, allowing the determination of the modification scheme. Specifically, when an element in the modification variable S is 0, Scheme A is adopted: the inner ring area of ​​a single friction disk is enlarged to expand its energy storage capacity, facilitating support for more electrical equipment. This is then combined with the objective function of structural safety requirements. f 4. Using the OpenSees calculation model, the required area and thickness (i.e., R and T, with the area to be modified determined by the R value) are given. When an element in the modification variable S is 1, Option B is adopted: the system modifies the offshore wind turbine according to the set modification area and thickness (R and T) of the friction disk. These set R and T are selected through safety verification and do not require further safety verification; they can be set directly through the objective function. f 5. The number of wind turbines requiring modification is given. Finally, different modification combinations for multiple offshore wind farms are presented, with each offshore wind farm corresponding to a different modification scheme.

[0042] For low-power-density application scenarios in deep-sea areas (such as small monitoring equipment and intermittently operating unmanned operation and maintenance platforms), after selecting the target offshore wind farm, this invention innovatively proposes to integrate the friction disk and energy storage battery in the pile-disc composite monopile foundation. The friction disk is divided into an inner ring area and an outer ring area. A honeycomb alloy skeleton is set in the inner ring area. Each unit of the honeycomb alloy skeleton constitutes a compartment, which can accommodate the energy storage battery. The energy storage battery is arranged in the unit of the honeycomb alloy skeleton as needed to achieve integrated power supply and storage. This compartmentalized design of the friction disk with inner and outer rings allows for energy storage modification only in the inner ring area compartment, thereby significantly reducing the system modification cost in this low-power-density scenario.

[0043] In this invention, the integrated power supply and storage transformation specifically refers to dividing the friction disc of the pile-disc composite monopile foundation into a coaxial outer ring area and an inner ring area. The inner ring area of ​​the friction disc is then transformed into an integrated power supply and storage system, with energy storage batteries placed as needed within a honeycomb alloy frame. All energy storage batteries are connected in series to form an energy storage battery pack. The energy storage battery pack is used to store the electrical energy generated by the offshore wind turbine. The input and output power are allocated through an intelligent distribution cabinet, which, together with the energy storage battery pack, forms a power supply device. Cables are used to connect the power-consuming equipment, thereby providing power to the surrounding power-consuming equipment.

[0044] In this invention, energy efficiency-driven and intelligent site selection are reflected in the storage, distribution, and overall layout optimization of electricity; specifically, it involves the structural reconstruction of the pile-disc composite monopile foundation, storing the electricity generated by the offshore wind turbine in the modified friction disc, and connecting the electrical equipment via submarine cables, thereby achieving extremely simplified optimization of the energy transmission path at the physical level; after f 1~ f 3. Solve for optimal solutions to achieve overall layout optimization, thereby realizing energy efficiency at the planning level and avoiding resource idleness and sunk costs; based on f 4 or f 5 The solution involves dynamically dividing the inner and outer ring areas into compartments to achieve energy efficiency in operation.

[0045] In this invention, the safety of the inner and outer ring dynamic compartments is achieved through parametric modeling and integration of the pile-soil system in OpenSees. f 4. The ratio of the inner and outer ring radii R and the thickness T of the friction disc are given, taking into account both safety and economy.

[0046] This invention enables the efficient conversion of intermittent electrical energy generated by offshore wind farms into a stable and reliable energy supply, effectively supporting the continuous operation of deep-sea economic activities.

[0047] Example 1:

[0048] This embodiment of the energy efficiency-driven integrated supply and storage marine smart charging pile transformation method includes the integrated supply and storage transformation of the existing pile-panel composite monopile foundation of the offshore wind farm, and the intelligent optimization site selection of the offshore wind farm considering multiple factors and multiple objectives.

[0049] I. Integrated Supply and Storage Transformation

[0050] 1. Dynamic compartmentalized energy storage integration of friction disk

[0051] There are two schemes for energy storage integration retrofitting of pile-disc composite monopile foundations: Scheme A refers to enhancing the energy storage capacity by increasing the ratio of the inner and outer ring radii and the thickness of the friction disc of the pile-disc composite monopile foundation. Specifically, this means increasing the area ratio of the inner ring region to the entire friction disc (achieved through the inner and outer ring radius ratio R) or increasing the overall volume of the friction disc (achieved through the friction disc thickness) to improve the energy storage capacity of the inner ring region. However, this scheme is limited by the maximum output power of the offshore wind turbines and structural safety constraints. Scheme B refers to increasing the number of wind turbines that need to be retrofitted in the offshore wind farm to obtain greater power output. However, its retrofitting cost will be significantly higher than Scheme A. When choosing this scheme, the ratio of the inner and outer ring radii R and the thickness T of the friction disc of the pile-disc composite monopile foundation remain unchanged, and R and T pass the safety verification. Only the number of wind turbines n that need to be retrofitted needs to be given.

[0052] In this invention, the site selection process for offshore wind farms that need to be modified incorporates the development potential of the surrounding area into the site selection decision, and intelligently selects a suitable modification scheme from the above-mentioned schemes A and B.

[0053] 1.1 Dynamic compartmentalized energy storage of friction disc

[0054] Based on the optimized solution set output by the intelligent site selection model, and comprehensively considering the power demand and electricity consumption characteristics of the electrical equipment within the power supply radius of the offshore wind farm, Scheme A is adopted to structurally modify the friction disk. According to the ratio R of the inner and outer ring radii of the friction disk (R ranges from 0.2 to 1.0, with the inner ring area used to house the power supply equipment while ensuring the overall stress distribution, and the area occupied by the power supply equipment determined after modification), an optimization algorithm (such as the SSA algorithm) is employed. Given the optimization range of R (0.2-1.0) and T (0.5-2.5m), the most economical compartment range that satisfies both output power and structural safety is selected, i.e., through the first optimization... f 1~ f 3. After optimization, select the offshore wind farms that meet the constraints and require modification, and then conduct a second evaluation. After optimization, the R and T values ​​that pass the safety calculation are selected as the most economical cabin allocation range.

[0055] If through the first time f 1~ f 3. After optimization, the offshore wind farms that meet the constraints and require modification are selected. Option B is chosen for the wind farm modification. Given the scope of the compartment design, and considering the power demand of surrounding electrical equipment and its average annual growth rate, the following steps are taken: Given the number n of wind turbines that need to be modified.

[0056] Different compartment designs are adopted for different modification schemes. Schemes A and B use the same energy storage modification method for the friction disk. Based on the distribution and load data of electrical equipment, combined with the structural strength analysis of the friction disk and the characteristics of wind, wave and current load distribution, the entire inner ring area of ​​the friction disk is transformed into a new integrated power supply and storage structure that can both store the electrical energy generated by the offshore wind turbine and supply energy to the surrounding electrical equipment.

[0057] Physical experiments yielded the physical and mechanical parameters of the modified friction disc: shear modulus G and elastic modulus E. Using G and E as inputs, along with the ranges for R and T, dynamic compartmentalization calculations were performed. The principle was to select the minimum modification range (R) and friction disc thickness (T) that met the safety requirements of the pile-disc composite monopile foundation structure by combining an optimization algorithm within the finite element method. Shear modulus and elastic modulus are physical properties of the honeycomb alloy skeleton. Once the material used is determined, E and G are also determined. They serve as parameters in the friction disc calculation to determine the ratio of the inner and outer ring radii and the thickness of the friction disc, which are then calculated together. eppr .

[0058] Dynamic compartmentation aims to precisely match the spatial distribution characteristics of the power demand of surrounding electrical equipment, enabling on-demand configuration of energy storage battery capacity. Under the premise of limited cost increase from the modification of the friction disk structure, it can significantly reduce the procurement cost and space occupation of energy storage batteries.

[0059] 1.2 Integrated supply and storage of friction discs

[0060] Meanwhile, in order to cope with the inherent temporal and spatial fluctuations and intermittency of offshore wind energy resources, a power supply decision mechanism is set up to ensure that the power demand of local loads can be met under high and low wind speed conditions, that is, the combined power of wind turbine power generation and energy storage discharge is greater than or equal to the load demand. In order to regulate the input and output of power, a smart distribution cabinet with hardware and software coordination for power allocation is set up. The dynamic adjustment of power is achieved by setting up power monitoring, decision control and hardware collaboration. The implementation path is as follows: (1) Monitoring: Monitor the real-time output power of offshore wind turbines, smart meters monitor the load of electrical equipment, and embedded IC chips report SOC (State of Charge); (2) Control: Set different priorities to realize the dynamic allocation of input power of different electrical equipment; (3) Key hardware: Integrate hardware into the smart distribution cabinet to solve the mutual communication between input and output, report the status of electrical equipment to realize real-time decision-making, power allocation adjustment, power conversion and setting protection measures. The intelligent power distribution cabinet includes a main control chip, a power distribution module consisting of dynamic routing, a power conversion module that converts the 35kV AC power generated by the fan into DC power of the target voltage using a rectifier, and a protection circuit module for achieving safety isolation.

[0061] 2. Wireless charging integration for unmanned marine equipment

[0062] This embodiment employs wireless charging technology to enhance the safety, reliability, and convenience of charging operations for unmanned marine equipment (drones and unmanned surface vessels). It effectively avoids the safety hazards of traditional wired charging interfaces, such as aging, corrosion, and water ingress caused by the high salt spray and humidity of the ocean, and significantly simplifies the charging connection process. Different wireless charging devices are used for different types of unmanned marine equipment. For drones, a wireless charging transmitter is integrated on the top of the offshore wind turbine. The drone docks on the turbine and charges itself via the transmitter, minimizing the impact of external charging mechanisms on the drone's aerodynamic shape and avoiding potential difficulties in charging interface alignment and drone stability risks caused by sea wind disturbances. For unmanned surface vessels, a floating wireless charging platform is deployed near the wind turbine. The platform is connected to the wind turbine via a cable, and a wireless charging transmitter is installed on it. The unmanned surface vessel docks on the platform for stable charging. The floating wireless charging platform is positioned at a certain distance from the wind turbine, a distance that ensures both proximity and safe charging, facilitating stable docking and charging for the unmanned surface vessel.

[0063] II. Intelligent Optimization Site Selection for Offshore Wind Farms Based on Multiple Factors and Objectives

[0064] By establishing a multi-factor driven intelligent optimization site selection model, the power supply requirements of deep-sea electrical equipment, the endurance limitations of offshore unmanned equipment, and the potential for offshore wind farm retrofitting are systematically incorporated into the site selection decision to determine the optimal retrofitting scheme (Scheme A or Scheme B). To evaluate the reliability and safety of retrofitting scheme A, the excess pore water pressure ratio of pile-disk composite monopile foundations with different friction disk inner and outer ring radius ratios and thicknesses is calculated under seismic loading based on a finite element model of pile-disk-soil interaction built in OpenSees (i.e., the OpenSees computational model). eppr And the OpenSees computational model calculates eppr The safety factor serves as a parameter for calculation and is also used as one of the suitability functions for comprehensive evaluation. Finally, the SSA-NSGA-Ⅲ algorithm is employed for a fast and efficient solution of the multi-factor-driven intelligent optimal location selection model; the specific steps are as follows:

[0065] 2.1 Data Collection and Numerical Model: Collect the existing offshore wind farms, the existing ocean shipping routes, and the location coordinates of electrical equipment, including marine structural platforms, marine ranches, and data centers; obtain the power supply radius of each offshore wind farm and the power demand (i.e., the power demand of each electrical equipment); the location coordinates of ocean-going freighters on ocean shipping routes are selected from the points with the highest probability of occurrence of ocean-going freighters.

[0066] An OpenSees computational model of a pile-soil composite monopile foundation was constructed, which is a model of pile-soil interaction and satisfies... The minimum is sufficient; the excess pore water pressure ratio is calculated based on the OpenSees calculation model of pile-soil interaction. eppr The model dimensions are scaled from the actual dimensions at a ratio of 1:6. Seismic loading is selected. eppr The safety factor of the structure, i.e., the anti-liquefaction performance of the foundation structure under seismic load, is evaluated by the (excess pore water pressure ratio). Therefore, in the finite element modeling, the wind turbine body (including the monopile foundation 7, tower 5, and the connection part between the tower and the foundation 6; the monopile foundation 7 is a structure buried in or standing on the seabed to support the tower and the wind turbine nacelle; the connection part between the tower and the foundation connects the tower and the foundation. In actual engineering, the connection is generally made by flange or grouting, but in the finite element simulation, the differences between the monopile foundation 7, tower 5, and the connection part between the tower and the foundation 6 are ignored and simplified to a single steel rod) is simplified to a steel rod with a concentrated load on the upper part. The soil is selected as 22m×16m soil and meshed. Since the friction disk is the main part of the study, in the process of parametric modeling and meshing of the friction disk, it is necessary to consider that the variables under study (thickness and inner and outer ring radius ratio) are replaced by parameters in the modeling process, which is convenient for optimization and non-dominated sorting in the subsequent process.

[0067] 2.2 A multi-factor driven intelligent optimization site selection model is constructed. The objectives are to maximize the coverage of offshore wind farms, minimize power transmission losses, and minimize overall retrofit costs. Constraints include the power supply capacity limit of the wind turbine's maximum output power, the retrofit cost limit of a single offshore wind farm, the safe distance limit of shipping channels between the offshore wind farm and the main shipping routes of cargo ships, and the endurance constraint of unmanned offshore equipment. The multi-objective solution determines whether to select scheme A or B for the retrofit of the offshore wind farm requiring modification. When scheme A is selected, the OpenSees calculation model is used to calculate the excess pore water pressure ratio. eppr ,by eppr Calculate the safety factor to determine the minimum ratio of the inner and outer ring radii and the thickness of the friction disc that meet structural safety requirements, thus completing the structural-economic co-optimization. Simultaneously, constraints are introduced during the construction of the objective function to ensure that the solution set closely approximates the actual situation. The relevant calculation formulas are as follows:

[0068] 2.2.1 Setting variables

[0069] Decision variables for offshore wind farms: ;in ,when When, it indicates the selection of the first. If one offshore wind farm is upgraded, then another will not be upgraded; otherwise, no upgrade will be implemented. N represents the number of offshore wind farms.

[0070] Variables for offshore wind farm retrofitting: ;in ,when When, it indicates the first The offshore wind farm at this location will be upgraded according to Option A. When, it indicates the first Option B was chosen to modify the offshore wind farm at the selected location; Option A represents increasing the ratio and thickness of the inner and outer ring radii of the friction disc, while Option B represents increasing the number of wind turbines that need to be modified.

[0071] Friction disk parameters: The compartmentalization mechanism is determined by the thickness T of the friction disk and the ratio of the inner and outer ring radii R. The modeling simplifies calculations by scaling the pile-disk-soil interaction model and disregarding size effects. Both T and R are continuous variables, and their ranges are given as follows: .

[0072] 2.2.2 Constructing the objective function

[0073] 2.2.2.1 Maximize the coverage of offshore wind farms, that is, maximize the number of electrical devices used within the coverage area of ​​offshore wind farms, thereby maximizing the number of covered electrical devices;

[0074]

[0075] in, This indicates an indicator function, and the set of electrical devices is... The formula does not distinguish between electrical equipment and facilities. It refers to electrical facilities such as marine structural platforms, marine ranches and data centers, as well as electrical equipment including drones and unmanned ships, as electrical equipment in the calculation. This indicates the total number of electrical devices that require power. This represents the total number of candidate offshore wind farms. It is a binary variable indicating whether to select an offshore wind farm. To carry out renovations, and These represent the location coordinates of the offshore wind farm and the location coordinates of the electrical equipment, respectively. This indicates the distance between the power-consuming equipment requiring power and the selected offshore wind farm. Its specific value can be calculated using the distance formula between two points, which will not be elaborated here. The function of the indicator is: when the power-consuming equipment is within the power supply radius of the offshore wind farm, the value is assigned as 1, and otherwise it is assigned as 0. For the first The range of electricity that an offshore wind farm can supply; f The purpose of the 1 function is to quantify the coverage capability of offshore wind farms, transforming complex spatial relationships into calculable numerical targets and providing optimization directions; that is, the closer to 1, the more comprehensive the coverage. Specifically...f The value 1 refers to the proportion of electrical equipment that can be covered by a current offshore wind farm to the total number of electrical equipment. The ultimate goal is to transform the complex spatial relationship between offshore wind farms and the electrical equipment they can cover into a simple numerical relationship. f The larger the value of 1, the wider the range that can be covered.

[0076] 2.2.2.2 Minimize overall renovation costs

[0077]

[0078] Where N represents the total number of candidate offshore wind farms. This represents the theoretical maximum cost, used to normalize the function and map its range to the interval [0,1]. Indicates the first The fixed retrofit cost of an offshore wind farm includes the cost of upgrading and retrofitting basic power supply facilities such as energy storage batteries and smart distribution cabinets. This refers to the cost of connecting different electrical devices, including cable costs and installation costs; among which... It is a piecewise function used to evaluate the basic cost difference between different modification schemes (Scheme A or Scheme B), and its specific expression is as follows:

[0079]

[0080] in, , These represent the basic costs of Scheme A and Scheme B, respectively. Scheme A includes cost differences arising from different construction methods, such as varying R and T values, the number of energy storage batteries added, and installation methods. Scheme B includes cost differences arising from different construction methods depending on the number of wind turbines requiring modification. Scheme B has a significantly higher construction cost than Scheme A, but it can support a significantly larger number of electrical devices, and its future modification potential is also increased. Opportunity cost is introduced as a contributing factor. To balance the differences in construction costs, among which

[0081]

[0082] in, Indicates electrical equipment The economic weighting coefficient is set to different values ​​based on different types of economic development. Indicates electrical equipment Annual output value of economic activities; Attenuation coefficient, controlling the range of influence; The closer a value is to 0, the lower its modification cost. use To quantify the cost differences between different offshore wind farms, the cost is converted from an absolute value to a relative value by using the ratio between the current selected offshore wind farm's cost and the theoretical maximum cost. This removes the dimension and allows it to be optimized together with other functions.

[0083] 2.2.2.3 Minimize power transmission loss

[0084]

[0085] in, This represents the theoretical maximum loss, which is used here as a normalized benchmark for power transmission loss; M represents the total number of electrical devices that require power. Representing the Power requirements of individual electrical equipment; The transmission loss coefficient per unit distance is related to factors such as cable resistance and voltage level. This formula is used to apply the transmission loss coefficient to the location of each selected offshore wind farm. and the location of electrical equipment If the electrical equipment is within the power supply radius, the contribution of losses is calculated. The formula's logic is as follows: First, all offshore wind farms are checked to see if any selected offshore wind farms cover the electrical equipment. Then, for offshore wind farms that do cover the electrical equipment, the losses of each electrical equipment are calculated. Finally, normalization is performed to map the actual losses onto... Within the specified interval, the transmission loss of electrical energy from the selected offshore wind farm to the power-consuming equipment is quantified. Similarly, f 3. The transport loss of the currently selected offshore wind farm is compared with the theoretical maximum loss of all selected offshore wind farms to eliminate the difference in dimensions and normalize the data, which facilitates optimization.

[0086] 2.2.2.4 Structural safety requirements, minimizing the safety factor.

[0087]

[0088] Excess pore water pressure ratio eppr Soil liquefaction is an important indicator, and its calculation formula is as follows:

[0089]

[0090] In the formula, Δu represents the increment of pore water pressure u between two adjacent moments during the earthquake; Effective stress; eppr The calculation process selects the maximum value among all time histories of the detection points in the centrifuge test. The results can be obtained by processing the OpenSees calculation model output with Python, and different values ​​can be calculated by inputting different ratios of the inner and outer ring radii and thicknesses of the friction discs. This leads to different conclusions.eppr ;

[0091] when eppr A value ≥1 indicates soil liquefaction. f 4 represents the safety factor. eppr The smaller, f The larger the value of 4, the stronger the resistance to liquefaction under seismic loads after the renovation, indicating a safer structure. (Based on adaptability...) The solution determines whether to adopt Scheme A or Scheme B for the fan retrofit.

[0092] 2.2.2.5 Number of offshore wind turbine retrofits

[0093]

[0094] When Option B is selected, which involves retrofitting more offshore wind turbines, this function provides the most economical number of turbines that need to be retrofitted. In the formula... To meet the power requirements of surrounding electrical equipment, It represents the average annual growth rate of power demand from surrounding electrical equipment. It refers to the service life of the surrounding electrical equipment. It is the maximum power that the power supply equipment can provide. The additional power generated by modifying the fan The number of wind turbines to be upgraded. Given the average annual growth rate of power demand from surrounding electrical equipment, when... At that time, the most economical number of wind turbines that need to be modified can be obtained.

[0095] 2.3 Setting Constraints

[0096] This invention takes into account practical situations and sets four constraints: First, a power supply capacity limit for the maximum output power of each modified wind turbine, considering its maximum output power limitation. Second, a cost limit for modifying a single offshore wind farm to avoid economically inefficient modifications. Third, to ensure the safety of shipping vessels and offshore wind farms, offshore wind farms must maintain a minimum safe distance from major shipping routes. Fourth, considering the endurance and operating time of unmanned offshore equipment, constraints are added to the supply point settings to better reflect real-world conditions. The specific formulas are as follows:

[0097] 2.3.1 Power supply capacity limitation of the maximum output power of the wind turbine

[0098]

[0099] In other words, for a selected offshore wind farm, the total power of electrical equipment within its power supply radius should be less than or equal to that of the selected offshore wind farm. The maximum power supply that wind turbines and energy storage battery packs can provide ; Indicates the selected number The number of electrical devices covered in an offshore wind farm.

[0100] 2.3.2 Upper limit of retrofit cost for a single offshore wind farm

[0101]

[0102] For the selected offshore wind farm, its fixed retrofit cost And the cost of connecting all different electrical devices The sum should be less than or equal to the artificially set cost ceiling. ;

[0103] 2.3.3 Safe distances for shipping lanes

[0104]

[0105] That is, any selected offshore wind farm (i.e. A safe distance must be maintained between the waterway and the waterway. To avoid offshore wind farms interfering with ship operations or causing risks, It should be selected in accordance with maritime law and other relevant regulations;

[0106] 2.3.4 Endurance Constraints of Unmanned Maritime Equipment

[0107]

[0108]

[0109] In the formula, This is a reduction factor introduced to take into account the battery degradation, sea conditions, and aging of the unmanned marine equipment, and is used to estimate the actual usable endurance. Ports representing the departure points of unmanned maritime equipment; , This refers to the distance between the starting point of the unmanned offshore equipment and the offshore wind farm being modified, and the distance between two adjacent offshore wind farms; This represents the speed at which unmanned maritime equipment travels. This represents the theoretical endurance of type u equipment (drone or unmanned surface vessel). The above formula means that the distance between the port and the offshore wind farm being modified, and the distance between the two offshore wind farms being modified, must be less than the endurance of the offshore unmanned equipment; where... This represents a set of locations for offshore wind farms, their coordinates, and records their spatial information. Decision variables for offshore wind farms This indicates the state of the offshore wind farm during the optimization process, indicating whether the offshore wind farm has been selected to participate in the optimization.

[0110] 3. Optimization solution:

[0111] The SSA-NSGA-Ⅲ algorithm is used for optimization and solution. The specific solution steps are as follows:

[0112] Step 1: Set the maximum number of iterations, crossover probability, and mutation probability. Set the producer ratio (PD), watchdog ratio (SD), and safety threshold (ST) in the SSA algorithm. Use Python to connect the SSA algorithm with the pile-disk composite monopile foundation, inputting the ranges of R and T. ;

[0113] Step 2: Initialize the population in the SSA algorithm using the Sobol sequence to generate... There are 1 individual, and each individual contains two variables, X and S;

[0114] R and T are related to the modification variable S. R and T are randomly assigned values ​​only when S=0; when S=1, R and T are assigned fixed values. Calculate the number of fans to be upgraded;

[0115] For variables X and S, a 0,1 binary encoding method is used. For X, its specific meaning is the set of locations of offshore wind farms. Each offshore wind field exists in two states: selected and unselected. Therefore, X can be viewed as an N-dimensional column vector, where each element is distributed as 0 and 1. This indicates that offshore wind farms have been selected for retrofitting. The number of offshore wind farms ultimately selected for retrofitting can be set manually, and the specific offshore wind farms requiring retrofitting need to be determined through an optimization algorithm. Similarly, the retrofitting variable S can be viewed as an N-dimensional column vector composed of 0s and 1s, which is defined in the three objective functions. When performing multi-objective solutions, the distribution of 0 or 1 in the solution set is selected;

[0116] When choosing At this point, the modification is carried out according to Scheme A. Considering the friction disk variable, research shows that after the thickness and diameter of the friction disk increase to a certain value, further increasing the thickness and diameter can easily lead to a marginal effect on the wind turbine's anti-liquefaction ability. Therefore, this invention studies the compartment modification method with different inner ring area radii under a fixed outer ring radius, that is, using the ratio of inner and outer ring radii and thickness as variables R and T.

[0117] After obtaining the initial population, these individuals serve as parents in the iterative process, and the optimal chromosome values ​​of the parents are calculated and balanced by NSGA-III. The problem of conflicting objectives.

[0118] Step 3: Population Update. The update mechanism from SSA is used to update the positions of producers, followers, and vigilant individuals. Their functions are global search, developing high-quality regions, and avoiding local optima, respectively. Here, we take X as an example to illustrate the update mechanism for individual positions in the population. The position update formula is as follows:

[0119] Producer location update:

[0120]

[0121] Where t represents the current iteration; d represents the dimension of the optimization problem, which in this example represents the number of decision variables and modification variables. Indicates the first The sparrow is at the (t+1)th iteration. Position coordinates in 3D space. It represents the maximum number of iterations. It is a random number. Indicates alarm values ​​and safety thresholds. For random numbers that follow a normal distribution, for A matrix in which every element is 1. Indicates the first The number of sparrow individuals at iteration t is... The position coordinates in 3D space (position before update).

[0122] Follower position update:

[0123]

[0124] in, It is the optimal position occupied by the producer. This represents the current worst-case position. A is a 1×d matrix, where each element is randomly assigned the value 1 or -1, and... . When representing the size of the population, that is, the total number of individuals generated in step 2, When, it indicates that the fitness value is poor. The number of followers is very likely in a poorly optimized state, requiring an expanded search radius. Indicates the first The number of sparrow individuals at iteration t is... The position coordinates in 3D space (position before update). This represents the worst position vector (i.e., the position of the individual with the lowest fitness) among all sparrow individuals in the t-th iteration. The position vector of the producer in the (t+1)th iteration (i.e., the position of the individual with the highest fitness).

[0125] Guardian location update:

[0126]

[0127] in, It is the current globally optimal position. The worst position in the t-th iteration (the position with the lowest fitness among all individuals). Indicates the first The number of sparrow individuals in the t-th iteration is... The position coordinates in 3D space (position before update). β As a step size control parameter, it conforms to a normal distribution of random numbers with a mean of 0 and a variance of 1. It is a random number. Here This is the current fitness value of the sparrow. and These are the current best and worst fitness values ​​globally, respectively. It is the minimum constant set to avoid division by zero errors.

[0128] Step 4: Merge the parent and offspring populations, and eliminate individuals that do not meet the constraints according to the set constraints; proceed to the next step of solving for individuals that meet the constraints.

[0129] Step 5: If the solution set of different modification schemes for offshore wind farms obtained in Step 4 is... In this step, the inner and outer ring radius ratio and thickness values ​​of the friction disc in the pile-disc composite monopile foundation that need to be modified are optimized. Given the range of the inner and outer ring radius ratio R and thickness T of the friction disc, relevant geological parameters and physical properties of the friction disc material (E, G) and wind turbine material parameters are set. Using the OpenSees calculation model and the written Python program, the excess pore water pressure ratio is calculated. eppr In finite element calculations, combined with The optimization process involves using Python for pre- and post-processing of the pile-soil system and calling the OpenSees computational model for calculations, ultimately invoking the fitness function. Select the ratio of the inner and outer ring radii and the thickness of the friction disk that minimizes the structural safety requirements; then use the NSGA-Ⅲ fast sorting mechanism to perform non-dominated sorting and crowding distance sorting on the merged population to obtain the solution set with the Pareto front and large crowding distance, which is the new population (containing X, S, R, T).

[0130] Step 6: Determine if the termination condition is met. If not, the new population will be used as the parent individuals for the next iteration. The next iteration will be performed and the population will be updated using the producer, follower, and watchdog mechanism of the SSA algorithm. Steps 3 to 5 will be repeated. If the termination condition is met, the frontier solution set will be output.

[0131] Step 7: If the solution set of different modification schemes for offshore wind farms obtained in Step 4 is... Then call the function directly. Solve the problem, given the number of wind turbines that need to be modified, and output the frontier solution set;

[0132] The termination condition must satisfy at least one of the following conditions:

[0133] 1) Reach the set maximum number of iterations ;

[0134] 2) If the frontier 1 remains unchanged for 50 consecutive generations, it is determined to be converged, and the optimization is terminated early.

[0135] In this invention, the SSA-NSGA-Ⅲ algorithm outputs the selected offshore wind farm solution set (i.e., X, whether to modify), the offshore wind farm modification scheme solution set (i.e., S, whether to select scheme A or scheme B), the modification parameters of the friction disk (inner and outer ring radius ratio R and thickness T) or the number of wind turbines n that need to be modified.

[0136] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. An energy efficiency driven integrated offshore intelligent charging pile system for storage, characterized in that, The system comprises: A pile-disc composite single pile foundation, comprising a single pile foundation and a friction disc arranged at the lower part of the single pile foundation, the friction disc comprising an inner ring area and an outer ring area coaxially arranged with the single pile foundation, the inner ring area being used for placing power supply equipment and counterweights; An intelligent power distribution cabinet for monitoring real-time output power of the offshore wind turbine and loads of the power-consuming equipment and dynamically allocating input power of different power-consuming equipment; An offshore unmanned equipment, comprising an unmanned aerial vehicle and an unmanned ship, the offshore wind turbine being integrated with a wireless charging transmitting end at the top of the offshore wind turbine, a floating wireless charging platform being arranged near the offshore wind turbine, the floating wireless charging platform being electrically connected with the offshore wind turbine, and the wireless charging transmitting end being arranged on the floating wireless charging platform, the unmanned ship being docked with the floating wireless charging platform for charging; OpenSees computational model for calculating the excess pore water pressure ratio of pile-plate composite single pile foundation under seismic action according to the pile-plate-soil interaction under different friction plate inner and outer ring radius ratio R and thickness T EPPR ; A multi-factor driven intelligent optimization site selection model, aiming to maximize the coverage range of the offshore wind farm, minimize the power transmission loss and minimize the comprehensive transformation cost, and taking the power supply capacity limitation of the maximum output power of the wind turbine, the transformation cost limitation of a single offshore wind farm, the safety distance limitation of the offshore wind farm and the shipping channel of the shipping freighter, and the endurance constraint limitation of the offshore unmanned equipment as constraints, the multi-objective solution is determined to select the offshore wind farm to be transformed by the A or B scheme; Wherein, the A scheme is, calling OpenSees calculation model to calculate the excess pore water pressure ratio EPPR , and EPPR The safety factor is calculated, and the minimum friction disc inner and outer ring radius ratio and thickness meeting the structural safety requirement are determined; the B scheme is, according to the power requirement of the peripheral power utilization equipment, the annual average growth rate of the power requirement of the peripheral power utilization equipment, the new power brought by the modified fan and the service life of the peripheral power utilization equipment, the number of the fans needing to be modified is determined.

2. The energy efficiency driven integrated offshore intelligent charging pile system according to claim 1, characterized in that, The inner ring area of the friction disc is arranged in a symmetrical sealed cabin in layers, a battery layer is arranged in the inner ring area, a honeycomb alloy framework is arranged in the battery layer, energy storage batteries are embedded in the honeycomb alloy framework, the arrangement of the energy storage batteries matches the spatial distribution characteristics of the power demand of the peripheral power-consuming equipment, and all the energy storage batteries are electrically connected together; protective layers are arranged on the upper and lower surfaces of the energy storage batteries, all the energy storage batteries are encapsulated into the battery layer by a steel outer cylinder, a steel inner cylinder and the protective layers, and a concrete protective layer is arranged outside the battery layer; the outer ring area is integrally poured with concrete to wrap the inner ring area.

3. The energy efficiency driven integrated offshore intelligent charging pile system according to claim 1, characterized in that, In the OpenSees calculation model, the model size of the pile-disc-soil interaction adopts a scaling relationship of 1:6 with the actual size, the wind turbine body is simplified as a steel rod with a concentrated load at the upper part, the range of the soil body is more than 10 times the diameter of the pile, and the soil body is divided into grids; the inner and outer ring radius ratio and thickness are taken as variables to parameterize the modeling and grid division of the friction disc.

4. The energy efficiency driven integrated offshore intelligent charging pile system according to claim 1, characterized in that, In the determination process of the minimum comprehensive transformation cost, an opportunity cost is introduced to balance the difference in construction cost of the A scheme or the B scheme, the opportunity cost considering the annual output value, the decay coefficient and the economic weight coefficient of different economic development of the economic activities of different power-consuming equipment.

5. The energy efficiency driven integrated offshore intelligent charging pile system according to claim 1, characterized in that, An SSA-NSGA-III algorithm is used for intelligent multi-objective solution.

6. A method for transforming an energy-efficient, integrated charging pile on a sea, characterized in that, The transformation method comprises: Obtaining the position coordinates of the existing offshore wind farm, the driving route of the existing ocean route and the power-consuming equipment, wherein the power-consuming equipment includes ocean freighters, offshore platforms, offshore ranches and data centers, the position coordinates of the ocean freighter on the ocean route are selected as the points with the maximum appearance probability of the ocean freighter; the power supply radius of each offshore wind farm and the demand for power of each power-consuming equipment are obtained; The OpenSees calculation model of pile-disk-soil interaction is constructed to calculate the excess pore water pressure ratio of pile-disk composite single pile foundation under the action of earthquake under the condition of different friction disk inner and outer ring radius ratio R and thickness T EPPR ; Maximize the coverage range of the offshore wind farm, minimize the power transmission loss and minimize the comprehensive transformation cost as the goal, with the power supply capacity limit of the maximum output power of the wind turbine, the transformation cost limit of a single offshore wind farm, the safety distance limit between the offshore wind farm and the shipping channel of the shipping freighter, and the endurance constraint limit of the offshore unmanned equipment as the constraints, multi-objective solution is carried out to determine whether the offshore wind farm needs to be transformed and which scheme A or B is selected for transformation; Wherein, the A scheme is, calling OpenSees calculation model to calculate the excess pore water pressure ratio EPPR , the B scheme is, setting the inner and outer ring radius ratio and the thickness of the friction disc, and determining the number of wind turbines to be reconstructed according to the power demand of the peripheral power utilization equipment, the annual average growth rate of the power demand of the peripheral power utilization equipment, the newly added power brought by the reconstructed wind turbine and the service life of the peripheral power utilization equipment. EPPR , the B scheme is, setting the inner and outer ring radius ratio and the thickness of the friction disc, and determining the number of wind turbines to be reconstructed according to the power demand of the peripheral power utilization equipment, the annual average growth rate of the power demand of the peripheral power utilization equipment, the newly added power brought by the reconstructed wind turbine and the service life of the peripheral power utilization equipment.

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