A planning method and device for an offshore integrated energy island
By constructing a cross-source consistent entropy model and an uncertain energy flow propagation model, the capacity configuration of the marine integrated energy island is optimized, solving the problems of uncertainty risk and equipment modeling deviation in the planning of marine energy islands, and achieving the comprehensive optimization of economy and safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ELECTRIC POWER RES INST OF STATE GRID ZHEJIANG ELECTRIC POWER COMAPNY
- Filing Date
- 2026-03-05
- Publication Date
- 2026-06-05
AI Technical Summary
Existing methods for planning integrated marine energy islands are insufficient to accurately depict the multi-scale uncertainties and risks brought about by environmental factors such as wind and waves. Equipment modeling parameters fail to fully consider the impact of long-term severe sea conditions, resulting in a disconnect between planning and operation, a lack of self-correction capabilities, and difficulty in maintaining economic efficiency and safety.
By constructing a cross-source consistency entropy model to quantify data credibility, establishing a physical mechanism model and performing parameter inversion, introducing wind speed and wave height as exogenous disturbances, constructing an uncertain energy flow propagation model, optimizing capacity configuration schemes, and combining construction, operation, and risk loss costs to form a comprehensive objective function.
It improves the accuracy of planning models, enabling early prediction and avoidance of high-risk scenarios, achieving a comprehensive optimization of economy and safety, and avoiding the drawbacks of traditional methods that ignore operational risks.
Smart Images
Figure CN122155256A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of resource allocation, and in particular relates to a planning method and apparatus for an integrated marine energy island. Background Technology
[0002] With the global energy structure shifting towards cleaner energy sources, the large-scale development and utilization of offshore renewable energy sources such as deep-sea wind power has become an important direction. To effectively collect, utilize, and transmit offshore renewable energy over long distances, the concept of integrated offshore energy islands has emerged. These islands typically integrate multiple energy conversion and storage devices, such as wind power, energy storage, electrolytic hydrogen production, and ammonia production, forming a multi-energy coupled system of "electricity-hydrogen-ammonia-storage." However, the deep-sea environment is complex and variable, with highly uncertain wind speeds and waves, posing significant challenges to the planning, design, and stable operation of energy islands.
[0003] Existing marine energy system planning methods typically suffer from the following shortcomings: planning models are mostly based on deterministic assumptions, making it difficult to accurately characterize the multi-scale uncertainties and risks brought about by environmental factors such as wind and waves; equipment modeling parameters mostly use theoretical or empirical values, failing to fully consider the impact of long-term severe sea conditions on the actual operating characteristics of equipment, resulting in significant deviations between the model and the actual situation; the planning phase is disconnected from the operation phase, making it difficult for the capacity configuration scheme determined during planning to maintain optimal economy and safety in actual dynamic operation; and the system lacks the ability to self-correct and evolve based on long-term operational feedback. Summary of the Invention
[0004] In view of this, the present invention discloses a planning method and apparatus for an integrated marine energy island, which can solve the shortcomings of related technologies.
[0005] To achieve the above objectives, the present invention discloses the following technical solution: According to a first aspect of the present invention, a planning system for an integrated marine energy island is proposed, comprising: Collect multi-source heterogeneous operation data and marine environmental data of the integrated marine energy island, and calculate the credibility weight of each data source by constructing a cross-source consistency entropy model; Based on the aforementioned credible weights, physical mechanism models are established for wind power, energy storage, electrolytic hydrogen production, and ammonia production equipment, respectively. Parameter inversion is then performed based on the weighted minimum deviation criterion to determine the set of equipment parameters that reflect the actual operating environment. Based on the set of equipment parameters, a multimodal energy flow network covering electrical energy, chemical energy, and thermal energy is constructed. Wind speed and significant wave height are introduced as exogenous disturbances into the multimodal energy flow network to establish an uncertain energy flow propagation model. The uncertain energy flow propagation model is used to quantify the risk exposure level of different energy links under complex sea conditions. Using the capacity of wind power, energy storage, electrolytic hydrogen production, ammonia production, and transmission channels as decision variables, a comprehensive objective function that takes into account construction costs, operating costs, and risk losses is constructed. The optimal capacity configuration scheme is then obtained, and the marine integrated energy island is planned according to the optimal capacity configuration scheme.
[0006] According to a second aspect of the present invention, a planning device for an integrated marine energy island is provided, characterized in that it comprises: Data Acquisition Unit: Collects multi-source heterogeneous operational data and marine environmental data from the integrated marine energy island, and calculates the credibility weights of each data source by constructing a cross-source consistency entropy model; Inversion Unit: Based on the aforementioned credible weights, physical mechanism models are established for wind power, energy storage, electrolytic hydrogen production, and ammonia production equipment, respectively, and parameter inversion is performed based on the weighted minimum deviation criterion to determine the set of equipment parameters that reflect the actual operating environment. First construction unit: Based on the set of equipment parameters, a multimodal energy flow network covering electrical energy, chemical energy, and thermal energy is constructed, and wind speed and significant wave height are introduced as exogenous disturbances into the multimodal energy flow network to establish an uncertain energy flow propagation model. The uncertain energy flow propagation model is used to quantify the risk exposure level of different energy links under complex sea conditions. Solving Unit: Using the capacity of wind power, energy storage, electrolytic hydrogen production, ammonia production, and transmission channels as decision variables, a comprehensive objective function that takes into account construction costs, operating costs, and risk losses is constructed. The optimal capacity configuration scheme is then obtained, and the marine integrated energy island is planned according to the optimal capacity configuration scheme.
[0007] According to a third aspect of the present invention, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in the second aspect by running the executable instructions.
[0008] According to a fourth aspect of the invention, a computer-readable storage medium is provided having computer instructions stored thereon that, when executed by a processor, implement the steps of the method as described in the second aspect.
[0009] As can be seen from the above technical solutions, the planning system for an integrated marine energy island disclosed in this invention is as follows: On the one hand, by quantifying the credibility of multi-source data through a cross-source consistency entropy model and using this as a basis for equipment parameter inversion, the physical model upon which the planning relies more closely reflects the actual operational characteristics of the complex marine environment, thus improving the accuracy of the planning model. On the other hand, by explicitly introducing exogenous disturbances such as wind speed and wave height into the energy flow model and establishing an uncertain energy flow propagation model, the risk exposure level of different energy links can be quantitatively assessed. This allows the final capacity planning scheme to predict and avoid high-risk scenarios in advance, making the decision more robust. In addition, the constructed comprehensive objective function simultaneously considers construction costs, operating costs, and risk loss costs caused by sea state uncertainty, achieving a comprehensive optimization of economy and safety, avoiding the shortcomings of traditional planning methods that emphasize economy while neglecting operational risks. Attached Figure Description
[0010] Figure 1 This is a flowchart illustrating a planning method for an integrated marine energy island, as provided in an exemplary embodiment. Figure 2 This is a schematic structural diagram of a device provided in an exemplary embodiment; Figure 3 This is a block diagram of a planning device for an integrated marine energy island, provided as an exemplary embodiment. Detailed Implementation
[0011] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.
[0012] It should be noted that in other embodiments, the corresponding methods are not necessarily performed in the order shown and described in this invention. The method comprises steps. In some other embodiments, the method may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.
[0013] With the global energy structure shifting towards cleaner energy sources, the large-scale development and utilization of offshore renewable energy sources such as deep-sea wind power has become an important direction. To effectively collect, utilize, and transmit offshore renewable energy over long distances, the concept of integrated offshore energy islands has emerged. These islands typically integrate multiple energy conversion and storage devices, such as wind power, energy storage, electrolytic hydrogen production, and ammonia production, forming a multi-energy coupled system of "electricity-hydrogen-ammonia-storage." However, the deep-sea environment is complex and variable, with highly uncertain wind speeds and waves, posing significant challenges to the planning, design, and stable operation of energy islands.
[0014] Existing marine energy system planning methods typically suffer from the following shortcomings: planning models are mostly based on deterministic assumptions, making it difficult to accurately characterize the multi-scale uncertainties and risks brought about by environmental factors such as wind and waves; equipment modeling parameters mostly use theoretical or empirical values, failing to fully consider the impact of long-term severe sea conditions on the actual operating characteristics of equipment, resulting in significant deviations between the model and the actual situation; the planning phase is disconnected from the operation phase, making it difficult for the capacity configuration scheme determined during planning to maintain optimal economy and safety in actual dynamic operation; and the system lacks the ability to self-correct and evolve based on long-term operational feedback.
[0015] To address the shortcomings of related technologies, this invention proposes a planning method and apparatus for an integrated marine energy island.
[0016] Figure 1 This is a flowchart illustrating a planning system method for an integrated marine energy island, as provided in an exemplary embodiment. Figure 1 As shown, the method may include the following steps: Step 101: Collect multi-source heterogeneous operation data and marine environmental data of the integrated marine energy island, and calculate the credibility weight of each data source by constructing a cross-source consistency entropy model.
[0017] Collect multi-source heterogeneous operational data and marine environmental data from the offshore integrated energy island. The operational data includes at least the wind turbine output power. State of charge of energy storage devices Operating current of the electrolytic hydrogen production unit The marine environmental data mentioned above includes at least wind speed. and significant wave height .
[0018] Based on the aforementioned multi-source heterogeneous operational data, a cross-source consistency entropy model is constructed to quantify the degree of observation deviation from different data sources at the same time, and the consistency entropy of the i-th type of data source is calculated. The calculation formula is: ; ; in, This represents the observation value of the i-th type of data source at time k. This represents the weighted average of the multi-source observations at that moment; By incorporating a credibility correction factor into the sea state classification, the credibility weight of the data source is calculated using the following formula: ; The sea state sensitivity coefficient β is determined by calibration using statistical results of measurement errors under different sea state conditions in historical operational data, and its expression is: ; in, To measure the standard deviation under stable sea conditions; The standard deviation is measured under high sea states; Confidentiality Correction Factor The exponential decay function of the ratio of significant wave height to design limiting wave height is expressed as: ; in, For significant wave height; To design the ultimate wave height.
[0019] Step 102: Based on the aforementioned credible weights, establish physical mechanism models for wind power, energy storage, electrolytic hydrogen production, and ammonia production equipment, respectively, and perform parameter inversion based on the weighted minimum deviation criterion to determine the set of equipment parameters that reflect the actual operating environment.
[0020] Based on the credibility weight obtained in step 101 Mechanism models for wind power, electrolytic hydrogen production, and energy storage devices were established, and parameters were inverted using the weighted minimum deviation criterion.
[0021] The output power model of the wind turbine is as follows: ; in, Where A is the air density; A is the swept area of the wind turbine. λ is the power coefficient; λ is the tip speed ratio; The pitch angle; This refers to the generator efficiency.
[0022] The hydrogen production rate model is as follows: ; in, The mass of hydrogen produced per unit time; For electrolysis efficiency Electrolysis power; It is Faraday's constant; Single-slot voltage; The SOC dynamic equation of the energy storage device is ; It is in a charged state; This refers to the charging power. This refers to the discharge power. For charge and discharge efficiency; This is the rated energy storage capacity; For time step.
[0023] The ammonia production rate model is ; ; This indicates the amount of ammonia produced per unit time. The molar mass of ammonia; This indicates the amount of hydrogen produced per unit time. For synthesis efficiency; Mass of hydrogen; Power required for ammonia synthesis; The energy consumption coefficient per unit of ammonia production.
[0024] The total power balance is: ; Power for the system and other auxiliary equipment.
[0025] Step 103: Based on the set of equipment parameters, construct a multimodal energy flow network covering electrical energy, chemical energy, and thermal energy, and introduce wind speed and significant wave height as exogenous disturbances into the multimodal energy flow network to establish an uncertain energy flow propagation model. The uncertain energy flow propagation model is used to quantify the risk exposure level of different energy links under complex sea conditions.
[0026] Based on the device parameters output in step 102, a multimodal energy flow network covering various energy forms such as electrical energy, chemical energy, and thermal energy is constructed. The energy flow network uses devices and energy states as nodes, and energy transmission and conversion relationships as directed edges. Efficiency, capacity, and operational constraints are labeled on each node and edge. Among these, power variables correspond to operating costs, capacity variables correspond to investment costs, and power balance corresponds to risk costs.
[0027] Meanwhile, by defining the impact of node failure on the system's load supply capacity, a node vulnerability index is constructed to characterize the sensitivity of the system structure to disturbances.
[0028] Based on the inversion-corrected equipment parameters, a multimodal energy flow network covering electrical energy, chemical energy, and thermal energy is constructed, and its nodal energy flow balance relationship is as follows: ; ; Capacity constraints are ; in, The k-th type of energy output for node j; This corresponds to the energy transmission or conversion efficiency; This represents the maximum capacity.
[0029] The network power is then expressed as ; By introducing sea state variables such as wind speed and wave height as exogenous disturbances into the energy flow network, an uncertain energy flow propagation model is established to describe the transmission, amplification, or attenuation process of environmental disturbances within a multi-energy coupled system. This model quantifies the risk exposure levels of different energy links under complex sea state conditions.
[0030] The results of this analysis will be used to guide the focus of constraints on high-risk links during the capacity planning phase.
[0031] wind speed and significant wave height As an exogenous disturbance, an energy flow network is introduced to establish an uncertain energy flow propagation model: ; in, Power link disturbance; and These are the wind speed and wave height propagation coefficients, respectively.
[0032] Step 104: Using the capacity of wind power, energy storage, electrolytic hydrogen production, ammonia production, and transmission channels as decision variables, construct a comprehensive objective function that takes into account construction costs, operating costs, and risk losses, and solve for the optimal capacity configuration scheme, so as to plan the marine integrated energy island according to the optimal capacity configuration scheme.
[0033] Using wind power installed capacity, energy storage capacity, electrolysis hydrogen production capacity, ammonia production capacity, and transmission line capacity as decision variables, a comprehensive objective function is constructed that simultaneously considers construction costs, operating costs, and risk losses. By solving this optimization model, the optimal capacity configuration scheme that achieves a balance between economy and safety is obtained.
[0034] Using wind power capacity, energy storage capacity, electrolysis hydrogen production capacity, ammonia production capacity, and transmission channel capacity as decision variables, the comprehensive objective function described in the planning is constructed as follows: ; Among them, construction cost item Defined as: ; This is the construction cost coefficient per unit of wind power installed capacity; This represents the construction cost coefficient per unit of energy storage capacity. The construction cost coefficient per unit of electrolytic hydrogen production capacity; The construction cost coefficient per unit ammonia production capacity; Construction cost coefficient per unit of transmission channel capacity.
[0035] Operating cost item Defined as the expected value of system operating costs within the planning period: ; And it satisfies the following capacity constraints: ; Risk loss item It is used to characterize the expected loss caused by system load shortage under uncertain sea state conditions, and is defined as: ; in: ; in, The total length of the planning period or statistical time window; This indicates that at time t, the installed capacity of wind power... The actual output power of the wind power determined; This represents the charging and discharging power of the energy storage device at time t, with a positive value indicating discharging and a negative value indicating charging; This represents the power consumption of the electrolytic hydrogen production unit at time t; This indicates the electrical power transmitted to the outside through the transmission channel; This represents the amount of wind and electricity curtailed at time t due to capacity or safety constraints. This represents the equivalent load demand of the integrated marine energy island at time t, including external transmission load, hydrogen-ammonia conversion load, etc. This represents the maximum energy supply capacity of the system at time t under given capacity configuration and operating conditions.
[0036] Indicates the amount of load shortage; Here, is the sea state risk amplification function, used to characterize the amplifying effect of wind speed and wave height uncertainties on load shortage risk. Its expression is: ; To calculate the average wind speed, and This is the risk sensitivity coefficient, representing the incremental economic loss caused by a unit change in risk.
[0037] ; This represents the actual economic loss at time t. For sea state risk; This represents the risk level associated with wind speed fluctuations.
[0038] By solving the above optimization model, a capacity configuration scheme for a comprehensive marine energy island that achieves the optimal balance between economy and operational safety is obtained.
[0039] In this embodiment, on the one hand, the reliability of multi-source data is quantified through a cross-source consistency entropy model, and equipment parameters are inverted based on this. This makes the physical model on which the planning relies more closely resemble the actual operating characteristics of the complex marine environment, improving the accuracy of the planning model. On the other hand, exogenous disturbances such as wind speed and wave height are explicitly introduced into the energy flow model to establish an uncertain energy flow propagation model. This model can quantitatively assess the risk exposure level of different energy links, enabling the final capacity planning scheme to predict and avoid high-risk scenarios in advance, making the decision more robust. In addition, the constructed comprehensive objective function simultaneously considers construction costs, operating costs, and risk loss costs caused by sea state uncertainty, achieving a comprehensive optimization of economy and safety, avoiding the drawbacks of traditional planning methods that emphasize economy while neglecting operational risks.
[0040] In one embodiment, the method further includes: treating wind power, energy storage, electrolytic hydrogen production, and ammonia production units as operating entities with independent decision-making capabilities; constructing a multi-entity evolutionary game model based on a payoff function; obtaining a system evolutionary stable operation strategy through a replication dynamic mechanism; mapping the optimal capacity configuration scheme and the evolutionary stable operation strategy to annual, daily, and minute time scales; performing cross-time scale collaborative optimization through master-slave decomposition and coordination constraints; generating a daily-scale reference power trajectory and a minute-scale dynamic correction quantity; introducing a safety index that comprehensively reflects the equipment output margin and sea state safety margin; and adaptively adjusting the solution step size of the minute-scale optimization based on the index.
[0041] Suppose there are N types of operating entities in the system, and let the proportion of operating strategies corresponding to the i-th type of entity be denoted as . ,in , t represents the evolution iteration step or running cycle index.
[0042] The revenue function of the Nth type of operating entity under a given capacity configuration and system state Defined as: ; Among them, output revenue item for: ; This represents the electrical power output of subject i at time t; Indicates the hydrogen production rate of the hydrogen production entity; Indicates the ammonia production rate of the ammonia-producing organism; Equivalent value coefficients for electrical energy, hydrogen energy, and ammonia energy.
[0043] Operating and degradation costs for: ; The operating power or processing power of subject i; This refers to the amount of equipment lifespan depletion or condition degradation. This is the unit operating cost coefficient; This represents the lifespan degradation cost coefficient.
[0044] Risk penalty items for: ; in, The load shortage contribution caused by the operational decisions of subject i; Based on the above definition of the profit function, a replication dynamic approach is adopted to evolve and update the multi-agent operating strategy. The update rule is as follows: ; in, The proportion of strategies for subject i in the next evolution step; This represents the average revenue level of the system in its current state.
[0045] When the following conditions are met: ; It is believed that the system's operating strategy has reached an evolutionary stable state.
[0046] The strategy evolution process must satisfy the following capacity constraints: ; in This is the optimal capacity configuration result for the corresponding subject obtained in step 104.
[0047] The capacity planning results and operational strategies are mapped to multiple time scales, including years, days, and minutes. Through master-slave decomposition and coordination constraint methods, the decision results at different scales are ensured to be consistent with each other, avoiding local optima that lead to overall failure.
[0048] On an annual scale, based on capacity planning results and incorporating long-term average operating strategy weights, an annual-scale principal optimization problem is constructed, with its objective function defined as follows: ; in, The annual-scale capacity decision vector; The number of representative operating days within a year; This represents the evolutionary stable policy vector; Let d be the daily-scale operating cost function for the d-th representative day.
[0049] The annual-scale principal problem is used to determine a stable and feasible combination of capacity and strategy across years, and to issue constraint boundaries to the daily-scale.
[0050] Daily-scale operational reference trajectory generation, under given capacity constraints With strategy weights Below, a daily-scale operational optimization model is constructed, and the system reference power trajectory is obtained by solving it. ,satisfy: ; in, ; Establish a minute-scale dynamic correction model. Introduce a power correction factor at the minute scale. The actual power consumption of the system is: ; The optimization objective for minute-scale optimization is defined as follows: ; These represent time scales of year, day, and minute, respectively. Represents a set of time indices on both day and minute scales; Represents the capacity decision vector; Represents the daily-scale power scheduling vector; This indicates the power correction amount on a minute scale; This represents the reference power trajectory issued by the daily-scale scheduling; This represents a minute-scale security penalty function; This represents the safety weight coefficient.
[0051] A safety index that comprehensively reflects the equipment output margin and sea state safety margin is introduced. The dynamic optimization solution step size is adaptively adjusted based on this index to ensure that the system always meets safety constraints under extreme sea state conditions.
[0052] The overall safety margin index of the system at time t is defined as follows: ; in, The power margin and sea state margin are weighted sums, rather than simply multiplied. This represents the maximum permissible wave height.
[0053] Incorporate the safety margin metric into the step size update rule for minute-scale optimization: ; in, Adaptive update step size optimized for minute-scale; Used as the reference step size; This is the minimum allowable step size, used to avoid numerical stagnation.
[0054] The minute-scale power correction is updated as follows: ; in, Iterative indexing optimized for minute-scale; For minute-scale objective function In one embodiment, the method further includes: constructing a planning-operation-feedback self-evolutionary update mechanism based on the long-term average deviation between the actual operating power of the system and the reference power, and triggering the recalculation of the operating strategy or capacity planning when the deviation exceeds a threshold.
[0055] A unified quantification method for operational deviation is defined, and the system's operational power at time t is: The system operating deviation is defined as the deviation between the reference power and the actual executed power: ; Right now: ; During the operating cycle, power deviation is statistically analyzed, and an average deviation index is defined: ; This indicator is used to characterize the long-term consistency between planning results and actual operation.
[0056] It represents the degree of matching between the planning results and the operational status.
[0057] When the following conditions are met: ; This indicates that the minute-by-minute adjustment has exceeded the system's remaining capacity margin, triggering a capacity or policy reconfiguration. It also indicates the current capacity configuration. or strategy weights It does not match the actual operational needs.
[0058] At this point, the following actions will be taken: either recalculate the strategy evolution or re-optimize the capacity planning.
[0059] Figure 2 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 2At the hardware level, the device includes a processor 202, an internal bus 204, a network interface 206, memory 208, and non-volatile memory 210, and may also include other hardware required for its functions. One or more embodiments of the present invention can be implemented in software, for example, the processor 202 reads the corresponding computer program from the non-volatile memory 210 into memory 208 and then runs it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.
[0060] Please refer to Figure 3 A planning device for an integrated marine energy island can be applied to, for example... Figure 3 The device shown, in order to implement the technical solution of the present invention, includes: The acquisition unit 301 is used to collect multi-source heterogeneous operation data and marine environmental data of the marine integrated energy island, and to calculate the credibility weight of each data source by constructing a cross-source consistency entropy model. The inversion unit 302 is used to establish physical mechanism models for wind power, energy storage, electrolytic hydrogen production, and ammonia production equipment based on the credibility weights, and to perform parameter inversion based on the weighted minimum deviation criterion to determine the set of equipment parameters that reflect the actual operating environment. The first construction unit 303 is used to construct a multimodal energy flow network covering electrical energy, chemical energy, and thermal energy based on the set of equipment parameters, and to introduce wind speed and significant wave height as exogenous disturbances into the multimodal energy flow network to establish an uncertain energy flow propagation model. The uncertain energy flow propagation model is used to quantify the risk exposure level of different energy links under complex sea conditions. Solver 304 is used to construct a comprehensive objective function that takes into account construction costs, operating costs and risk losses, using the capacity of wind power, energy storage, electrolytic hydrogen production, ammonia production and transmission channels as decision variables, and solve for the optimal capacity configuration scheme, so as to plan the marine integrated energy island according to the optimal capacity configuration scheme.
[0061] Optionally, the cross-source consistency entropy model is used to quantify the degree of observation deviation from different data sources at the same time; the acquisition unit 301 is specifically used for: Calculate the consistency entropy of the i-th type of data source. The calculation formula is: ; ; in, This represents the observation value of the i-th type of data source at time k. This represents the weighted average of the multi-source observations at that moment; By incorporating a credibility correction factor into the sea state classification, the credibility weight of the data source is calculated using the following formula: ; The sea state sensitivity coefficient β is determined by calibration using statistical results of measurement errors under different sea state conditions in historical operational data, and its expression is: ; in, To measure the standard deviation under stable sea conditions; The standard deviation is measured under high sea states; Confidentiality Correction Factor The exponential decay function of the ratio of significant wave height to design limiting wave height is expressed as: ; in, For significant wave height; To design the ultimate wave height.
[0062] Optionally, the comprehensive objective function is: ; Among them, construction cost item Defined as: ; This is the construction cost coefficient per unit of wind power installed capacity; This represents the construction cost coefficient per unit of energy storage capacity. The construction cost coefficient per unit of electrolytic hydrogen production capacity; The construction cost coefficient per unit ammonia production capacity; Construction cost coefficient per unit of transmission channel capacity.
[0063] Optionally, the device further includes: The second building unit 305 is used to treat wind power, energy storage, electrolytic hydrogen production, and ammonia production units as operating entities with independent decision-making capabilities, construct a multi-entity evolutionary game model based on the payoff function, and obtain a stable system evolution operation strategy through a replication dynamic mechanism. The mapping unit 306 is used to map the optimal capacity configuration scheme and the evolutionary stable operation strategy to the year, day and minute time scales, and to perform cross-time scale collaborative optimization through master-slave decomposition and coordination constraints to generate the daily scale reference power trajectory and the minute scale dynamic correction amount. The adjustment unit 307 is used to introduce a safety index that comprehensively reflects the equipment output margin and sea state safety margin, and to adaptively adjust the solution step size of the minute-scale optimization based on the index.
[0064] Optionally, in the multi-agent evolutionary game model, the payoff function of the i-th type of operating agent... Defined as: ; in, For output and revenue items, For operating and degradation costs, As a risk penalty item, the evolution and update of the operating strategy follow the replication dynamic rules: ; when It will eventually reach an evolutionary stable state.
[0065] Furthermore, the cross-timescale collaborative optimization includes: On an annual scale, we construct an annual principal optimization problem with capacity planning results as decision variables and including the expected daily operating costs. At the daily scale, given capacity constraints and evolution-stabilized policy weights, the daily-scale operational optimization model is solved to generate the system reference power trajectory. ,satisfy: ; On a minute-by-minute scale, a power correction is introduced to adjust the actual system power. It is dynamically optimized by minimizing the amount of correction and safety penalties.
[0066] Furthermore, the device also includes: Trigger unit 308 is used to construct a self-evolving update mechanism of planning-operation-feedback based on the long-term average deviation between the actual operating power and the reference power of the system. When the deviation exceeds a threshold, it triggers the recalculation of the operating strategy or capacity planning. The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.
[0067] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0068] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0069] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0070] For any other form of computer-readable medium (or computer-readable storage medium) as described above, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above embodiments, thereby realizing the technical solution of the present invention.
[0071] The present invention also proposes a computer program that, when executed by a processor, implements one or more of the embodiments described above, thereby realizing the technical solution of the present invention. This computer program may be specifically recorded on the above-described or other computer-readable media, and the present invention does not impose any limitations on this.
[0072] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0073] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.
[0074] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.
[0075] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."
[0076] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.
Claims
1. A planning method for an integrated marine energy island, characterized in that, include: Collect multi-source heterogeneous operation data and marine environmental data of the integrated marine energy island, and calculate the credibility weight of each data source by constructing a cross-source consistency entropy model; Based on the aforementioned credible weights, physical mechanism models are established for wind power, energy storage, electrolytic hydrogen production, and ammonia production equipment, respectively. Parameter inversion is then performed based on the weighted minimum deviation criterion to determine the set of equipment parameters that reflect the actual operating environment. Based on the set of equipment parameters, a multimodal energy flow network covering electrical energy, chemical energy, and thermal energy is constructed. Wind speed and significant wave height are introduced as exogenous disturbances into the multimodal energy flow network to establish an uncertain energy flow propagation model. The uncertain energy flow propagation model is used to quantify the risk exposure level of different energy links under complex sea conditions. Using the capacity of wind power, energy storage, electrolytic hydrogen production, ammonia production, and transmission channels as decision variables, a comprehensive objective function that takes into account construction costs, operating costs, and risk losses is constructed. The optimal capacity configuration scheme is then obtained, and the marine integrated energy island is planned according to the optimal capacity configuration scheme.
2. The method according to claim 1, characterized in that, The cross-source consistency entropy model is used to quantify the degree of deviation in observations from different data sources at the same time. The calculation of the trust weights of each data source by constructing a cross-source consistency entropy model includes: Calculate the consistency entropy of the i-th type of data source. The calculation formula is: ； ； in, This represents the observation value of the i-th type of data source at time k. This represents the weighted average of the multi-source observations at that moment; By incorporating a credibility correction factor into the sea state classification, the credibility weight of the data source is calculated using the following formula: ； The sea state sensitivity coefficient β is determined by calibration using statistical results of measurement errors under different sea state conditions in historical operational data, and its expression is: ； in, To measure the standard deviation under stable sea conditions; The standard deviation is measured under high sea states; Confidentiality Correction Factor The exponential decay function of the ratio of significant wave height to design limiting wave height is expressed as: ； in, For significant wave height; To design the ultimate wave height.
3. The method according to claim 1, characterized in that, The comprehensive objective function is: ； Among them, construction cost item Defined as: ； This is the construction cost coefficient per unit of wind power installed capacity; This represents the construction cost coefficient per unit of energy storage capacity. The construction cost coefficient per unit of electrolytic hydrogen production capacity; The construction cost coefficient per unit ammonia production capacity; Construction cost coefficient per unit of transmission channel capacity.
4. The method according to claim 1, characterized in that, The method further includes: Wind power, energy storage, electrolytic hydrogen production, and ammonia production units are regarded as operating entities with independent decision-making capabilities. A multi-entity evolutionary game model based on the payoff function is constructed, and a dynamic replication mechanism is used to obtain the system's evolutionary stable operation strategy. The optimal capacity configuration scheme and the evolutionary stable operation strategy are mapped to yearly, daily and minute time scales. Cross-time scale collaborative optimization is carried out through master-slave decomposition and coordination constraints to generate daily-scale reference power trajectory and minute-scale dynamic correction amount. A safety index that comprehensively reflects the equipment output margin and sea state safety margin is introduced, and the solution step size of the minute-scale optimization is adaptively adjusted according to the index.
5. The method according to claim 4, characterized in that, In the multi-agent evolutionary game model, the payoff function of the i-th type of operating agent... Defined as: ； in, For output and revenue items, For operating and degradation costs, As a risk penalty item, the evolution and update of the operating strategy follow the replication dynamic rules: ； when It will eventually reach an evolutionary stable state.
6. The method according to claim 4, characterized in that, The cross-timescale collaborative optimization includes: On an annual scale, we construct an annual principal optimization problem with capacity planning results as decision variables and daily operating cost expectations. At the daily scale, given capacity constraints and evolution-stabilized policy weights, the daily-scale operational optimization model is solved to generate the system reference power trajectory. ,satisfy: ； On a minute-by-minute scale, a power correction is introduced to adjust the actual system power. It is dynamically optimized by minimizing the amount of correction and safety penalties.
7. The method according to claim 1, characterized in that, The method further includes: Based on the long-term average deviation between the actual operating power and the reference power of the system, a self-evolving update mechanism of planning-operation-feedback is constructed. When the deviation exceeds the threshold, the operation strategy or capacity planning is recalculated.
8. A planning device for an integrated marine energy island, characterized in that, include: Data Acquisition Unit: Collects multi-source heterogeneous operational data and marine environmental data from the integrated marine energy island, and calculates the credibility weights of each data source by constructing a cross-source consistency entropy model; Inversion Unit: Based on the aforementioned credible weights, physical mechanism models are established for wind power, energy storage, electrolytic hydrogen production, and ammonia production equipment, respectively, and parameter inversion is performed based on the weighted minimum deviation criterion to determine the set of equipment parameters that reflect the actual operating environment. First construction unit: Based on the set of equipment parameters, a multimodal energy flow network covering electrical energy, chemical energy, and thermal energy is constructed, and wind speed and significant wave height are introduced as exogenous disturbances into the multimodal energy flow network to establish an uncertain energy flow propagation model. The uncertain energy flow propagation model is used to quantify the risk exposure level of different energy links under complex sea conditions. Solving Unit: Using the capacity of wind power, energy storage, electrolytic hydrogen production, ammonia production, and transmission channels as decision variables, a comprehensive objective function that takes into account construction costs, operating costs, and risk losses is constructed. The optimal capacity configuration scheme is then obtained, and the marine integrated energy island is planned according to the optimal capacity configuration scheme.
9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in any one of claims 1-7 by executing the executable instructions.
10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.