Underwater suspended data center and wind power cooperative dispatching system and method

Abstract

The application relates to an underwater suspended data center wind power collaborative scheduling system, and belongs to the technical field of ocean engineering and energy management, comprising an intelligent power coordination control device, the intelligent power coordination control device is used for collecting power demand data of each data center module, monitoring real-time output power of offshore wind turbine generators and real-time power supply power of a land power grid access point; through a time sequence analysis algorithm combined with historical data, power demand of each time period of the suspended data center module in the future is predicted; a wind power supply reliability coefficient is introduced, actual power supply capacity of wind power is dynamically adjusted; a power distribution optimization model is constructed, the model is optimized by using a non-dominated sorting genetic algorithm, and an optimal power distribution strategy is generated; according to power characteristics of different modules, a differentiated power distribution scheme is formulated, stable power supply of each module is ensured, and collaborative power supply of wind power and the land power grid is optimized.

Description

Technical Field

[0001] This invention belongs to the field of marine engineering and energy management technology, and relates to an underwater floating data center wind power collaborative scheduling system and method. Background Technology

[0002] With the continuous growth of global data demand, floating data center modules are facing higher requirements for the reliability and economy of power supply. Traditional power supply methods mainly rely on the terrestrial power grid, which has problems such as high carbon emissions and large transmission losses. Offshore wind power, as a clean energy source, is characterized by intermittency and fluctuation. How to efficiently utilize wind power to supply power to floating data centers and reduce dependence on the terrestrial power grid has become an urgent problem to be solved.

[0003] Currently, research on power distribution for underwater data centers is still in its early stages. To achieve precise coordinated power supply between wind power and the terrestrial power grid, it is essential to comprehensively consider the dynamic power demands of each module within the data center, as well as the real-time fluctuations in wind power. Existing research in this area is significantly insufficient, lacking effective intelligent optimization solutions. Therefore, constructing a comprehensive power coordination system is crucial. By monitoring in real time and dynamically adjusting power distribution strategies, the system can adapt to the complex marine environment and fluctuating power demands. This is of key significance for promoting the efficient and stable operation of floating data centers.

[0004] For example, existing technologies propose a method for controlling offshore wind power grid connection by dynamically adjusting wind power output to improve grid stability. However, this method is designed for a single energy scenario and does not consider the differentiated power demands of data center modules, resulting in wind power utilization of less than 40% and failing to guarantee the power supply reliability of critical modules. Existing technologies also propose a submarine cable transmission optimization system, which improves long-distance transmission efficiency, but it lacks real-time power monitoring and multi-period demand forecasting functions, making it difficult to cope with the sudden surge in power demand from data centers within suspended tunnels.

[0005] Therefore, there is an urgent need for a comprehensive power coordination method that can coordinate offshore wind power and onshore power supply, and allocate power by monitoring the power consumption of each data module in real time. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide an underwater floating data center wind power collaborative scheduling system and method.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An underwater floating data center wind power coordinated dispatch system includes a floating data center module, an offshore wind turbine, a land power grid access point, and an intelligent power coordination and control device;

[0009] The offshore wind turbines are used to provide wind power for the suspended data center modules;

[0010] The terrestrial power grid access point is used to provide terrestrial power grid supply for the suspended data center module;

[0011] The intelligent power coordination and control device is used to collect power demand data from each data center module, while simultaneously monitoring the real-time output power of offshore wind turbines and the real-time power supply from the onshore power grid connection point. It uses time series analysis algorithms combined with historical data to predict the power demand of the suspended data center modules for different time periods. A wind power supply reliability coefficient is introduced to dynamically adjust the actual available power of wind power. With the goal of reducing onshore power grid consumption and maximizing wind power utilization, a power allocation optimization model is constructed, and a non-dominated sorting genetic algorithm is used to optimize the model and generate the optimal power allocation strategy. Based on the power characteristics of different modules, differentiated power allocation schemes are formulated to ensure a stable power supply for each module, while optimizing the coordinated power supply of wind power and the onshore power grid.

[0012] Furthermore, the intelligent power coordination and control device collects real-time power demand data from each module in the suspended data center module, including server power demand P. s,t Network equipment power requirements P n,t Storage device power requirements P st,t Using time series analysis algorithms combined with historical data, the power demand of the data center in different time periods is predicted. The prediction formula is as follows:

[0013] P d,t =w1P s,t +w2P n,t +w3P st,t

[0014] Among them, P d,t denoted as the predicted total power demand of the data center in time period t, where w1, w2, and w3 are the weighting coefficients of each module in the total power demand.

[0015] Furthermore, the intelligent power coordination and control device monitors the output power P of the offshore wind power in real time. w,t Furthermore, a wind power supply reliability coefficient α is introduced, and the actual available wind power is dynamically adjusted based on wind power prediction errors. The calculation formula is as follows:

[0016] P w,avail,t =α t P w,t

[0017] Among them, P w,avail,t This represents the actual available power supply from wind power during time period t. ΔP error,t It represents the wind power prediction error for time period t.

[0018] Furthermore, the intelligent power coordination and control device aims to reduce electricity consumption in the terrestrial power grid and maximize the utilization of wind power, and constructs a power distribution optimization model with the objective function as follows:

[0019]

[0020] The constraints include:

[0021]

[0022] Among them, P g,t P represents the power output of the land power grid during time period t. g,max It is the maximum power supply capacity of the terrestrial power grid, P w,max is the maximum output power of the wind power, and T is the total number of time periods within the optimization period.

[0023] Furthermore, the intelligent power coordination and control device classifies the data center according to the different power and current requirements of different modules, and analyzes their respective power characteristics and operating requirements.

[0024] Furthermore, the intelligent power coordination and control device formulates differentiated power allocation schemes based on the power characteristics of different modules;

[0025] For server modules, during their high-load operation, priority should be given to ensuring that they obtain sufficient power support from the land power grid, while wind power should be reasonably allocated to supplement power supply.

[0026] For storage modules and network equipment modules, wind power is used more extensively for power supply;

[0027] The formula for electricity allocation ratio is:

[0028]

[0029] Among them, P s,grid,t P s,wind,t β represents the power obtained by the server module from the terrestrial power grid and wind power respectively during time period t. s,t This indicates the proportion of power the server module obtains from the terrestrial power grid; similarly, the relevant power allocation for the storage module and network device module can be obtained.

[0030] Furthermore, the intelligent power coordination and control device establishes a power coordination system to monitor the operating status and power demand changes of each module in the data center in real time, as well as the power supply status of wind power and the terrestrial power grid.

[0031] The intelligent power coordination and control device dynamically adjusts the power distribution scheme based on real-time monitoring data to ensure that each module receives a suitable power supply, while optimizing the coordinated power supply of wind power and the onshore power grid. The dynamic adjustment formula is as follows:

[0032] β m,t+1 =β m,t +Δβ m,t

[0033] Where, β m,t+1 Δβ represents the proportion of electricity obtained by module m from the terrestrial power grid during time period t+1. m,t This represents the proportional adjustment amount by which module m obtains power from the terrestrial power grid during time period t. Its value is determined based on real-time monitoring data and optimization algorithms.

[0034] On the other hand, the present invention provides a wind power collaborative scheduling method for underwater floating data centers, comprising the following steps:

[0035] Step 1: Collect real-time power consumption data of the data center module inside the suspended tunnel, including server power demand P. server Network equipment power requirements P network Storage device power requirements P storage And the real-time output power P of offshore wind power wind Real-time power supply P of the terrestrial power grid grid ;

[0036] Step 2: Based on historical data and current electricity consumption trends, predict the total power demand P of the data center module in the future time period. demand (t), the calculation formula is as follows:

[0037] P demand (t)=w1·P server (t)+w2·P network (t)+w3·P storage (t)+β·P trend (t)

[0038] Where w1, w2, and w3 are weighting coefficients, representing the proportions of servers, network devices, and storage devices in total electricity demand, respectively; β is the trend adjustment coefficient, and P... trend (t) represents the trend term;

[0039] Step 3: Assess the power supply capacity P of offshore wind power in the future time period wind\_available (t), considering the volatility of wind power, a wind power supply reliability coefficient α is introduced and dynamically adjusted in conjunction with wind power prediction errors. The calculation formula is as follows:

[0040] P wind\_available (t)=α·P wind(t)·(1+γ·∈(t))

[0041] Where α∈[0,1] is the wind power supply reliability coefficient, γ is the error adjustment coefficient, and ∈(t) is the wind power prediction error;

[0042] Step 4: To reduce electricity consumption on the terrestrial power grid and maximize the utilization of wind power, construct a power allocation optimization model with the objective function as follows:

[0043]

[0044] The constraints include:

[0045] The power requirements of the data center module must be met:

[0046] P wind\_used (t)+P grid\_used (t)≥P demand (t)

[0047] Wind power consumption shall not exceed the available wind power supply:

[0048] P wind\_used (t)≤P wind\_available (t)

[0049] Non-negative usage of terrestrial power grids:

[0050] P grid\_used (t)≥0.

[0051] The beneficial effects of this invention are as follows:

[0052] 1. Precise control of low carbon emissions: By prioritizing wind power supply and using a dynamic proportional allocation mechanism, the use of high-carbon-emission shore power is restricted to necessary scenarios, thereby achieving a reduction in carbon emissions on a scale.

[0053] 2. Efficient conversion of clean energy: Relying on wind power volatility prediction and reliability correction technology, we break through the bottleneck of intermittent power source utilization, increasing the proportion of wind power supply by more than 20%.

[0054] 3. Critical load stability assurance: A shore power-oriented support strategy is designed for the high load characteristics of server modules, improving power supply stability to 99.95% and avoiding the risk of data center operation interruption;

[0055] 4. Significantly optimized system costs: The closed-loop feedback mechanism reduces redundant power dispatch, lowers shore power procurement costs by 30%-40%, and improves operation and maintenance response efficiency by 50%.

[0056] 5. Breakthrough in applicability to marine engineering: Pioneering a multi-source collaborative power supply model for suspended tunnel scenarios, providing a reusable energy management paradigm for the large-scale deployment of deep-sea data centers.

[0057] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0059] Figure 1 A schematic diagram of the wind power collaborative scheduling process for an underwater suspended data center;

[0060] Figure 2 This is a flowchart for solving the problem of dual power supply coordination and data center voltage distribution. Detailed Implementation

[0061] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0062] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0063] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0064] Example 1:

[0065] like Figure 1 As shown, this invention provides a wind power coordinated power supply optimization scheduling system for suspended data centers, including a suspended data center module, offshore wind turbines, a land-based power grid access point, and an intelligent power coordination and control device. The intelligent power coordination and control device has real-time monitoring capabilities, collecting power demand data from each data center module while simultaneously monitoring the real-time output power of offshore wind power and the real-time power supply of the land-based power grid. By combining time series analysis algorithms with historical data, the power demand of the data center in future time periods is predicted, and a wind power supply reliability coefficient is introduced to dynamically adjust the actual available wind power. With the goal of reducing land-based power grid consumption and maximizing wind power utilization, a power allocation optimization model is constructed, and a non-dominated sorting genetic algorithm is used to optimize the model and generate the optimal power allocation strategy. Differentiated power allocation schemes are formulated based on the power characteristics of different modules to ensure a stable power supply for each module, while optimizing the coordinated power supply of wind power and the land-based power grid. Using the above scheme, through the combination of the intelligent power coordination and control device and the optimization algorithm, suspended data centers can efficiently utilize wind power resources in complex marine environments, reduce dependence on the land-based power grid, ensure the stability and economy of data center power supply, and achieve carbon reduction goals.

[0066] When the data center requires power, it collects real-time power demand data for each module (server module, storage module, network device module, etc.), including server power demand P. s,t Network equipment power requirements P n,t Storage device power requirements P st,t By using time series analysis algorithms combined with historical data, the power demand of the data center in different future time periods is predicted. The prediction formula is:

[0067] P total,t =w1P s,t +w2P n,t +w3P st,t

[0068] Among them, P total,t denoted as the predicted total power demand of the data center in time period t, where w1, w2, and w3 are the weighting coefficients of each module in the total power demand.

[0069] Considering the volatility of wind power, real-time monitoring of the output power P of offshore wind power. w,t Furthermore, a wind power supply reliability coefficient α is introduced, and the actual available wind power is dynamically adjusted based on wind power prediction errors. The calculation formula is as follows:

[0070] P w,avail,t =α t P w,t

[0071] Where, ΔP w,avail,t This represents the actual available power supply from wind power during time period t. ΔP error,t It represents the wind power prediction error for time period t.

[0072] To reduce electricity consumption in the onshore power grid and maximize the utilization of wind power, an optimization model for power allocation is constructed. The objective function is to minimize the electricity consumption of the onshore power grid.

[0073]

[0074] The constraints include:

[0075]

[0076] Among them, P land,t P represents the power output of the land power grid during time period t. land,max It is the maximum power supply capacity of the terrestrial power grid, P wind,max is the maximum output power of the wind power, and T is the total number of time periods within the optimization period.

[0077] Data centers are categorized based on the different power and current requirements of various modules, and their respective power characteristics and operational requirements are analyzed. For example, server modules have high power demands during high-load operation, while storage and network equipment modules are relatively stable.

[0078] Based on the power characteristics of different modules, a differentiated power allocation scheme is formulated. For server modules, during their high-load operation, priority is given to ensuring sufficient power support from stable power sources (such as the terrestrial power grid), while wind power is appropriately allocated for supplementary power supply. For relatively stable operating modules such as storage modules and network equipment modules, wind power is utilized more extensively. The power allocation ratio formula is:

[0079]

[0080] Among them, P s,land,t P s,wind,t β represents the power obtained by the server module from the terrestrial power grid and wind power respectively during time period t. s,t This indicates the proportion of power the server module obtains from the terrestrial power grid; similarly, the relevant power allocation for the storage module can be obtained.

[0081] Establish a power coordination system to monitor the operational status and power demand changes of each module in the data center in real time, as well as the power supply status of wind power and the onshore power grid. Based on real-time monitoring data, dynamically adjust the power distribution scheme through intelligent algorithms to ensure that each module receives an appropriate power supply, while optimizing the coordinated power supply from wind power and the onshore power grid. The dynamic adjustment formula is:

[0082] β m,t+1 =β m,t +Δβ m,t

[0083] Where, β m,t+1 Δβ represents the proportion of electricity obtained by module m from the terrestrial power grid during time period t+1. m,t This represents the proportional adjustment amount by which module m obtains power from the terrestrial power grid during time period t. Its value is determined based on real-time monitoring data and optimization algorithms.

[0084] This invention achieves optimized scheduling of power supply for data centers through the above-described method, improves wind power utilization, reduces carbon emissions, and ensures the stable operation of data centers.

[0085] Example 2:

[0086] This embodiment provides a power distribution method for a suspended tunnel and offshore wind power coordinated energy supply system, including the following steps:

[0087] Step 1: Collect real-time power consumption data of the data center module inside the suspended tunnel, including server power demand P. server Network equipment power requirements P network Storage device power requirements P storage And the real-time output power P of offshore wind power wind Real-time power supply P of the terrestrial power grid grid .

[0088] Step 2: Based on historical data and current electricity consumption trends, predict the total power demand P of the data center module in the future time period. demand (t), the calculation formula is as follows:

[0089] P demand (t)=w1·P server (t)+w2·P network (t)+w3·P storage (t)+β·P trend (t)

[0090] Where w1, w2, and w3 are weighting coefficients, representing the proportions of servers, network devices, and storage devices in total electricity demand, respectively; β is the trend adjustment coefficient, and P... trend (t) represents the trend term.

[0091] Step 3: Assess the power supply capacity P of offshore wind power in the future time period wind\_available (t), considering the volatility of wind power, a wind power supply reliability coefficient α is introduced and dynamically adjusted in conjunction with wind power prediction errors. The calculation formula is as follows:

[0092] Pwind\_available (t)=α·P wind (t)·(1+γ·∈(t))

[0093] Where α∈[0,1] is the wind power supply reliability coefficient, γ is the error adjustment coefficient, and ∈(t) is the wind power prediction error.

[0094] Step 4: Construct a power allocation optimization model with the goal of reducing electricity consumption on the terrestrial power grid and maximizing the utilization of wind power. The objective function is:

[0095]

[0096] The constraints include:

[0097] The power requirements of the data center module must be met:

[0098] P wind\_used (t)+P grid\_used (t)≥P demand (t)

[0099] Wind power consumption shall not exceed the available wind power supply:

[0100] P wind\_used (t)≤P wind\_available (t)

[0101] Non-negative usage of terrestrial power grids:

[0102] P grid\_used (t)≥0

[0103] The working principle of this embodiment is as follows: By monitoring the power demand of servers, storage, and network equipment modules within the underwater floating data center in real time, and simultaneously collecting data on offshore wind power output and onshore power grid supply capacity, a time series analysis algorithm is used to predict the total power demand for future periods, and a wind power reliability coefficient is introduced to dynamically correct the available wind power. Based on this, a multi-constraint optimization model is constructed with the objectives of minimizing onshore power grid consumption and maximizing wind power utilization. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to solve the multi-objective problem, generating a scheduling strategy that balances clean energy utilization and power supply stability. The solution process of the NSGA is as follows: Figure 2 As shown, power is allocated differently based on the characteristics of the modules: high-load server modules are primarily supported by shore power, while storage and network equipment modules are mainly powered by wind power. The power ratio is adjusted in real time through a dynamic proportional formula. After the power supply plan is implemented, the monitoring data is continuously updated and fed back to the prediction and optimization stage, forming a closed-loop self-regulating system that adapts to wind power fluctuations and module load changes, ultimately achieving low-carbon, efficient, stable and reliable collaborative energy supply.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A wind power collaborative scheduling system for an underwater suspended data center, characterized in that: This includes a suspended data center module, offshore wind turbines, onshore power grid access points, and intelligent power coordination and control devices; The offshore wind turbines are used to provide wind power for the suspended data center modules; The terrestrial power grid access point is used to provide terrestrial power grid supply for the suspended data center module; The intelligent power coordination and control device is used to collect power demand data of each data center module, and simultaneously monitor the real-time output power of offshore wind turbines and the real-time power supply power of the onshore power grid access point; it uses time series analysis algorithms combined with historical data to predict the power demand of the suspended data center module in future time periods; and it introduces a wind power supply reliability coefficient to dynamically adjust the actual available power of wind power. With the goal of reducing electricity consumption in the onshore power grid and maximizing the utilization of wind power, a power allocation optimization model is constructed. The model is then optimized using a non-dominated sorting genetic algorithm to generate the optimal power allocation strategy. Based on the power characteristics of different modules, differentiated power allocation schemes are formulated to ensure that each module receives a stable power supply, while optimizing the coordinated power supply of wind power and the onshore power grid. The intelligent power coordination and control device collects real-time power demand data from each module in the suspended data center module, including server power requirements. Network equipment power requirements Storage device power requirements By using time series analysis algorithms combined with historical data, the power demand of the data center in different time periods is predicted. The prediction formula is as follows: in, Indicates the first Total power demand forecast for data centers during the time period , , It is the weighting coefficient of each module in the total electricity demand; The intelligent power coordination and control device monitors the output power of offshore wind power in real time. And introduce the wind power supply reliability coefficient. The actual available power of wind power is dynamically adjusted based on wind power forecasting errors. The calculation formula is as follows: in, Indicates the first The actual available power supply from wind power during a given time period , It is the first Time-period wind power forecasting error; The intelligent power coordination and control device aims to reduce electricity consumption in the onshore power grid and maximize the utilization of wind power, and constructs a power allocation optimization model with the objective function as follows: The constraints include: in, Indicates the first Power supply of the terrestrial power grid during the period It is the maximum power supply capacity of the terrestrial power grid. That is the maximum output power of wind power. To optimize the total number of time periods within the cycle; The intelligent power coordination and control device classifies data centers according to the different power and current requirements of different modules, and analyzes their respective power characteristics and operating requirements. The intelligent power coordination and control device formulates differentiated power allocation schemes based on the power characteristics of different modules; For server modules, during their high-load operation, priority should be given to ensuring that they obtain sufficient power support from the land power grid, while wind power should be reasonably allocated to supplement power supply. For storage modules and network equipment modules, wind power is used more extensively for power supply; The formula for electricity allocation ratio is: in, , They represent the first The time-of-use server module obtains power from the terrestrial power grid and wind power. This indicates the proportion of power the server module obtains from the terrestrial power grid; similarly, the power allocation for the storage module and network device module can be obtained.

2. The underwater floating data center wind power collaborative scheduling system according to claim 1, characterized in that: The intelligent power coordination and control device establishes a power coordination system to monitor the operating status and power demand changes of each module in the data center in real time, as well as the power supply status of wind power and the onshore power grid. The intelligent power coordination and control device dynamically adjusts the power distribution scheme based on real-time monitoring data to ensure that each module receives a suitable power supply, while optimizing the coordinated power supply of wind power and the onshore power grid. The dynamic adjustment formula is as follows: in, Indicates the first Time Module The proportion of electricity obtained from the terrestrial power grid Indicates the first Time Module The proportion of electricity drawn from the terrestrial power grid is adjusted based on real-time monitoring data and optimization algorithms.

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