A method and related device for the management and emergency response of an integrated offshore wind, solar and energy storage system.

Abstract

This invention relates to the field of integrated energy security, specifically to a management and emergency response method and related device for an integrated offshore wind, solar, and energy storage system. The method involves: using a collaborative sensing network to perceive the state of each energy unit within the integrated offshore wind, solar, and energy storage system, acquiring multi-source heterogeneous sensing data; transmitting the multi-source heterogeneous sensing data to a decision-making end, constructing an energy digital twin based on the data; calculating the energy supply and demand balance based on the energy digital twin; simultaneously simulating preset risk scenarios based on real-time environmental data, and outputting risk warnings; generating optimized decision-making schemes using a multi-timescale optimization algorithm based on the energy supply and demand balance and risk warnings; and distributing the optimized decision-making schemes to the execution end, which then implements rescue operations according to the optimized decision-making schemes. This invention addresses the problems of decentralized operation and maintenance, delayed risk warnings, and low emergency response efficiency in integrated offshore wind, solar, and energy storage systems.

Description

Technical Field

[0001] This invention relates to the field of integrated energy security, specifically to a method and related apparatus for the management and emergency support of an integrated offshore wind, solar and energy storage system. Background Technology

[0002] Offshore wind, solar and energy storage integrated energy systems have become an important direction for the development of new energy due to their advantages such as high resource utilization and strong environmental adaptability.

[0003] However, the current integrated offshore wind, solar and energy storage system still faces multiple challenges in operation and maintenance, safety and resource allocation, such as blind spots in perception, delayed early warning and inefficient emergency response.

[0004] In existing technologies, monitoring methods are mostly limited to a single field or a single device. For example, relying solely on manual inspections or fixed sensors to monitor land-based equipment, or using a single sea-based device to monitor offshore facilities, results in inconsistent data formats and transmission protocols across different energy units, creating data silos. Regarding risk warning, existing systems primarily focus on fault diagnosis of single devices, making it difficult to accurately simulate the impact of wind power outages and solar power curtailment on the energy supply and demand balance under extreme weather conditions. In terms of emergency response, when major failures such as wind turbine fires or submarine cable breaks occur, existing models rely on manual dispatch, and the allocation of emergency resources such as drones, unmanned vessels, and rescue supplies lacks autonomy and timeliness.

[0005] In summary, the above solutions have the following problems: First, they are difficult to achieve comprehensive state perception across the sky, sea, and land, and cannot provide complete data support; second, they lack risk warning capabilities, and cannot provide early warning of risks, often only responding passively after a failure occurs; third, they have low emergency response efficiency, making it difficult for emergency forces at sea, on land, and in the air to coordinate quickly, resulting in cumbersome rescue procedures and potentially causing significant economic losses and safety risks. Summary of the Invention

[0006] To address the problems mentioned in the prior art, this invention proposes a management and emergency support method and related devices for an integrated offshore wind, solar and energy storage system. This method overcomes the problems in operation, maintenance, safety and scheduling of the integrated offshore wind, solar and energy storage system. It constructs a multi-source heterogeneous sensing data and collaborative sensing network to achieve multi-energy flow coordination and risk early warning, and establishes a cross-domain resource autonomous scheduling and emergency response mechanism. This solves the problems of decentralized operation and maintenance, delayed risk early warning and low emergency response efficiency in the integrated offshore wind, solar and energy storage system.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: This invention proposes a management and emergency response method for an integrated offshore wind, solar, and energy storage system, comprising: S1. Based on the collaborative sensing network, the state of each energy unit in the integrated offshore wind, solar and energy storage system is sensed, and multi-source heterogeneous sensing data is obtained. S2. Transmit the multi-source heterogeneous sensing data to the decision-making end, and construct an energy digital twin based on the multi-source heterogeneous sensing data; S3. Based on the energy digital twin, calculate the energy supply and demand balance; at the same time, based on real-time environmental data, simulate preset risk scenarios and output risk warnings; S4. Based on the energy supply and demand balance and risk warning, an optimized decision scheme is generated using a multi-timescale optimization algorithm; the optimized decision scheme is then sent to the execution end, which implements the rescue according to the optimized decision scheme.

[0008] As a further improvement of the present invention, the collaborative sensing network in S1 includes a space-based satellite module, an air-based UAV module, a sea-based unmanned vessel module, a land-based robot module, and a fixed sensor module. The state perception includes state perception of wind power, photovoltaics, energy storage, and loads; It can also perceive the status of surrounding sea areas, weather, sea conditions, and water quality.

[0009] As a further improvement of the present invention, the airborne UAV module includes a high-definition camera, an infrared thermal imager and a gas sensor, used to collect data on equipment surface defects, temperature anomalies and gas leaks; The sea-based unmanned vessel module includes acoustic detection equipment, an underwater camera, and a water quality sensor, used to collect underwater structure and water quality data. The land-based robot module includes a robotic arm and inspection sensors, used for inspecting land-based stations and handling simple faults. The fixed sensor module includes a vibration sensor, a light sensor, a voltage and current sensor, and a seawater desalination load monitoring unit.

[0010] As a further improvement of the present invention, the energy digital twin in S2 includes standardization processing of the received multi-source heterogeneous sensing data; The parameters of the energy digital twin are continuously optimized through machine learning algorithms and iteratively updated by combining real-time sensing data to construct the energy digital twin.

[0011] As a further improvement of the present invention, in step S3, based on the energy digital twin, the energy supply and demand balance is calculated; simultaneously, based on real-time environmental data, a preset risk scenario is simulated, and a risk warning is output, including: The process of calculating the energy supply and demand balance includes: based on the energy digital twin, analyzing the output characteristics of wind power and photovoltaic power and the absorption characteristics of energy storage and load, and establishing a balance equation; based on the balance equation, calculating the distribution of active power and reactive power in real time to obtain the energy supply and demand balance. Pre-set risk scenarios such as extreme weather, equipment failure, and cyberattacks; based on energy digital twins and real-time environmental data, simulate the impact of risk events on the source supply and demand balance and identify the cross-domain propagation path of risks. Based on the cross-domain propagation path, probability of occurrence, scope of impact, and degree of loss, risks are divided into four risk levels, and corresponding risk warnings are issued for each risk level.

[0012] As a further improvement of the present invention, the multi-timescale optimization algorithm in S4 adopts a hierarchical optimization architecture: the upper layer is a medium- and long-term optimization layer, which formulates emergency plans and energy dispatch plans based on meteorological forecast data and load forecast data; The middle layer is a short-term optimization layer that adjusts energy allocation strategies based on real-time wind and solar power prediction results; The lower layer is the real-time optimization layer, which generates energy storage charging and discharging control commands based on real-time operating data.

[0013] As a further improvement to the present invention, after performing S4, S5 is also included: During the rescue operation, the execution end transmits execution status data and on-site working condition data back to the decision-making end in real time. The decision-making end updates the energy digital twin based on the transmitted data, dynamically adjusts and optimizes the decision-making plan, and simultaneously sends it back to the execution end, forming a closed loop.

[0014] This invention proposes a management and emergency support system for an integrated offshore wind, solar, and energy storage system, comprising: The acquisition module is used to perform state perception on each energy unit in the integrated offshore wind, solar and energy storage system based on the collaborative sensing network, and to acquire multi-source heterogeneous sensing data. The construction module transmits the multi-source heterogeneous sensing data to the decision-making end and constructs an energy digital twin based on the multi-source heterogeneous sensing data; The output module calculates the energy supply and demand balance based on the energy digital twin; at the same time, it simulates preset risk scenarios based on real-time environmental data and outputs risk warnings. The decision output module is used to generate an optimized decision scheme based on the energy supply and demand balance and risk warning using a multi-timescale optimization algorithm; the optimized decision scheme is then sent to the execution end, which implements the rescue according to the optimized decision scheme.

[0015] This invention proposes a management and emergency support device for an integrated offshore wind, solar, and energy storage system. The device comprises a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the aforementioned management and emergency support method for the integrated offshore wind, solar, and energy storage system.

[0016] This invention proposes a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for the management and emergency response of an integrated offshore wind, solar, and energy storage system.

[0017] Compared with the prior art, the present invention achieves the following technical effects: This invention constructs a collaborative sensing network, integrating space-based satellite modules, airborne UAV modules, sea-based unmanned surface vessel modules, land-based robot modules, and fixed sensor modules to achieve integrated three-dimensional monitoring across land, sea, and air. This breaks through the limitations of traditional monitoring methods and data silos, solving the problem that existing technologies often limit monitoring to a single domain or single device, such as relying solely on manual inspections or fixed sensor monitoring. It provides comprehensive data support for subsequent system management. Furthermore, this invention constructs an energy digital twin to calculate the energy supply and demand balance and simulates preset risk scenarios to output risk warnings. This upgrades from equipment-level fault diagnosis to system-level risk warning, enabling proactive identification of cross-domain risk propagation paths and providing sufficient time for risk mitigation. It addresses the limitations of existing technologies that focus on single-device fault diagnosis and cannot accurately simulate the impact of wind power outages and solar power curtailment on energy supply and demand balance under extreme weather conditions, thus achieving proactive response.

[0018] This invention generates optimized decision-making schemes by employing multi-timescale optimization algorithms and distributes these schemes to the execution end for rescue operations. Combined with a three-level response mechanism involving equipment, stations, and regions, it achieves autonomous scheduling and cross-domain linkage of emergency resources in fault scenarios. It can quickly dispatch drone swarms, unmanned vessels, and land-based rescue forces to carry out joint rescue operations, ensuring continuous power supply to critical loads. This significantly improves emergency response efficiency and system resilience, avoiding the problems of existing emergency models that rely on manual dispatch and lack autonomy and timeliness. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 This is a schematic diagram of the sensing network structure of the present invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0021] See Figure 1 This embodiment proposes a management and emergency response method for an integrated offshore wind, solar, and energy storage system, including: S1. Based on the collaborative sensing network, the state of each energy unit in the integrated offshore wind, solar and energy storage system is sensed, and multi-source heterogeneous sensing data is obtained. S2. Transmit the multi-source heterogeneous sensing data to the decision-making end, and construct an energy digital twin based on the multi-source heterogeneous sensing data; S3. Based on the energy digital twin, calculate the energy supply and demand balance; at the same time, based on real-time environmental data, simulate preset risk scenarios and output risk warnings; S4. Based on the energy supply and demand balance and risk warning, an optimized decision scheme is generated using a multi-timescale optimization algorithm; the optimized decision scheme is then sent to the execution end, which implements the rescue according to the optimized decision scheme.

[0022] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments: Step 1, such as Figure 2 As shown, the sensing network in this embodiment includes a space-based satellite module, an air-based UAV module, a sea-based unmanned vessel module, a land-based robot module, and a fixed sensor module. Through these modules, multi-source heterogeneous sensing data can be acquired.

[0023] Specifically, the space-based satellite module uses high-resolution remote sensing and meteorological satellites to acquire real-time meteorological and sea condition data such as wind speed, wind direction, wave height, and rainfall over a wide area of ​​sea, while simultaneously monitoring the macroscopic location and layout of offshore wind farms and photovoltaic power stations. The air-based UAV module consists of a formation of 6 to 8 multi-rotor UAVs equipped with high-definition visible light cameras, infrared thermal imagers, and hydrogen leak sensors. Following a preset path, they cruise and photograph offshore wind turbine blades and photovoltaic array panels, identifying defects such as blade cracks and hot spots on modules using infrared thermal imagers, and monitoring hydrogen concentration around the energy storage power station using gas sensors. The sea-based unmanned surface vessel module is equipped with side-scan sonar, underwater high-definition cameras, and pH sensors, conducting navigation monitoring along the wind turbine foundations and submarine cable routes. Sonar is used to detect structural damage to the underwater portion of the wind turbine foundation, underwater cameras are used to observe surface wear on submarine cables, and pH sensors collect real-time water quality data. The land-based robot module uses a wheeled inspection robot equipped with a robotic arm, temperature sensors, and voltage sensors to inspect the battery packs, inverters, and distribution cabinets of the onshore energy storage power station and the island microgrid. The robotic arm can perform simple switching operations and mark faulty equipment. The fixed sensor module includes a triaxial vibration sensor deployed on the wind turbine foundation, light intensity and temperature sensors for the photovoltaic array, voltage and current sensors for the energy storage power station, load monitoring sensors for the island microgrid, and flow and pressure sensors for the seawater desalination plant. All sensors collect data at a sampling frequency of 1Hz to achieve full-time monitoring.

[0024] It should be noted that this embodiment uses a communication network to achieve data transmission, including a space-based satellite communication sub-network, an air-based UAV communication sub-network, a sea-based wireless communication sub-network, a land-based wired communication sub-network, and edge communication nodes. The space-based satellite communication subnetwork uses the BeiDou satellite communication system to achieve data transmission between sea-based unmanned surface vessels, sensors on remote islands, and the decision-making level, with a communication rate of up to 2Mbps. The air-based UAV communication subnetwork uses a WiFi 6 self-organizing network, with UAVs exchanging data via millimeter-wave communication, achieving a communication latency of less than 50ms. The sea-based wireless communication subnetwork combines surface 4G / 5G communication with underwater acoustic communication. Surface devices transmit data via the 4G / 5G network, while underwater devices interact with surface unmanned surface vessels via acoustic communication modules. The land-based wired communication subnetwork uses single-mode fiber optic communication, with a transmission rate of up to 10Gbps, ensuring high-speed data transmission between fixed sensors and the decision-making level. Edge communication nodes are deployed on key islands and offshore platforms, using edge computing technology to preprocess collected data, such as filtering out abnormal data and compressing image data, reducing transmission bandwidth consumption. Simultaneously, it enables protocol conversion and data forwarding across multiple communication subnetworks, improving network anti-interference capabilities. Step 2: The energy digital twin construction unit in this embodiment uses the Unity3D engine and Python data analysis library to standardize the collected multi-source heterogeneous sensing data and construct a digital twin model covering offshore wind turbines, photovoltaic arrays, energy storage power stations and island microgrid loads. The model update frequency is 10Hz to achieve real-time synchronization with the physical entities. Based on an energy digital twin, the nodal voltage method is used to analyze the output characteristics of wind power and photovoltaics, as well as the absorption characteristics of energy storage and load. An energy flow balance equation is established, and the active and reactive power distribution of the system is calculated in real time to understand the energy supply and demand matching status and obtain the energy supply and demand balance status. Based on the acquired real-time environmental data, including preset typhoons, wind turbine fires, submarine cable breaks, and other typical risk scenarios, combined with meteorological early warning data obtained from space-based satellite modules, the energy digital twin simulates the impact of different wind speeds on wind turbine output during typhoons, analyzes the power supply gap caused by photovoltaic power curtailment and wind turbine shutdown, and identifies the propagation path of risks from equipment failure to system collapse based on fault tree analysis. The risks are divided into four levels according to the scope of impact and the degree of loss.

[0025] Step 3: When the wind turbine blade temperature exceeds the preset threshold, a major risk warning is triggered and pushed to the decision-making terminal. The collaborative optimization decision-making unit adopts a hierarchical optimization algorithm. The upper medium- and long-term optimization layer formulates a wind turbine feathering angle adjustment plan, an energy storage power station pre-charging strategy, and a diesel backup power start-up plan based on the meteorological forecast data for the next 7 days. The middle short-term optimization layer allocates the grid connection ratio of wind power and photovoltaic power and the energy storage charging and discharging time based on the wind and solar power forecast results for the next 24 hours. The lower real-time optimization layer adjusts the output power of the energy storage converter through a PID control algorithm based on second-level operating data to smooth out fluctuations in wind and solar power output.

[0026] Step 4: This embodiment constructs a three-level response mechanism involving equipment, site, and area. When a fixed sensor detects that the vibration acceleration of a wind turbine exceeds the threshold of 10 m / s², it can automatically send a command to cut off the grid connection switch of the wind turbine and isolate the faulty unit. If at the same time, the infrared thermal imager detects that the temperature of the wind turbine nacelle exceeds 150°C, the built-in carbon dioxide fire extinguishing system in the nacelle is immediately activated, and two drones in the site are dispatched to carry fire extinguishing bombs to the fire location to carry out fire extinguishing operations. The land-based robot module blocks the site entrance and sets up warning signs.

[0027] By updating the status of emergency resources in real time, available emergency resources are displayed; according to the dispatch instructions from the decision-making end, the drone formation of the island base is arranged to take off to provide reinforcement, and the unmanned boats in the port carry fire extinguishers, first aid supplies and temporary power supply equipment to the faulty wind farm. At the same time, the output ratio of the surrounding photovoltaic power stations is adjusted to transmit the excess power to the island microgrid to ensure the power supply for residents and military facilities. The land command center monitors the progress of the rescue in real time through digital twins to achieve coordinated processing of the sea, land and air.

[0028] Based on the same inventive concept, this invention also provides a management and emergency support system for an integrated offshore wind, solar and energy storage system. Since the principle of this management and emergency support system for an integrated offshore wind, solar and energy storage system is similar to that of the aforementioned management and emergency support method for an integrated offshore wind, solar and energy storage system, the implementation of this management and emergency support system for an integrated offshore wind, solar and energy storage system can refer to the implementation of the management and emergency support method for an integrated offshore wind, solar and energy storage system, and the repeated parts will not be described again.

[0029] In practical implementation, the management and emergency support system for the integrated offshore wind, solar, and energy storage system provided in this embodiment of the invention specifically includes: The acquisition module is used to perform state perception on each energy unit in the integrated offshore wind, solar and energy storage system based on the collaborative sensing network, and to acquire multi-source heterogeneous sensing data. The construction module transmits the multi-source heterogeneous sensing data to the decision-making end and constructs an energy digital twin based on the multi-source heterogeneous sensing data; The output module calculates the energy supply and demand balance based on the energy digital twin; at the same time, it simulates preset risk scenarios based on real-time environmental data and outputs risk warnings. The decision output module is used to generate an optimized decision scheme based on the energy supply and demand balance and risk warning using a multi-timescale optimization algorithm; the optimized decision scheme is then sent to the execution end, which implements the rescue according to the optimized decision scheme.

[0030] Accordingly, this invention also provides a control and emergency support device for an integrated offshore wind, solar and energy storage system, including a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the control and emergency support method for the integrated offshore wind, solar and energy storage system provided in this invention.

[0031] For more detailed information on the above methods, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0032] Accordingly, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described method for the management and emergency support of an integrated offshore wind, solar and energy storage system as provided in the embodiments of the present invention.

[0033] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems, devices, and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0034] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0035] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0036] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 limitations, 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.

[0037] The above provides a detailed description of the management and emergency response methods, systems, equipment, and storage media for the integrated offshore wind, solar, and energy storage system provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for management and control and emergency guarantee of an offshore wind-solar-storage integrated energy system, characterized in that, include: S1. Based on the collaborative sensing network, the state of each energy unit in the integrated offshore wind, solar and energy storage system is sensed, and multi-source heterogeneous sensing data is obtained. S2. Transmit the multi-source heterogeneous sensing data to the decision-making end, and construct an energy digital twin based on the multi-source heterogeneous sensing data; S3. Based on the energy digital twin, calculate the energy supply and demand balance; at the same time, based on real-time environmental data, simulate preset risk scenarios and output risk warnings; S4. Based on the energy supply and demand balance and risk warning, an optimization algorithm with multiple time scales is used to generate an optimized decision scheme. The optimized decision-making plan is then sent to the execution end, which implements the rescue operation based on the optimized decision-making plan.

2. The management and emergency protection method of the offshore wind-solar-storage integrated energy system according to claim 1, characterized in that, The collaborative sensing network in S1 includes a space-based satellite module, an air-based unmanned aerial vehicle module, a sea-based unmanned vessel module, a land-based robot module, and a fixed sensor module. The state perception includes state perception of wind power, photovoltaics, energy storage, and loads; It can also perceive the status of surrounding sea areas, weather, sea conditions, and water quality.

3. The management and emergency protection method for the offshore wind-solar-storage integrated energy system according to claim 2, characterized in that, The airborne UAV module includes a high-definition camera, an infrared thermal imager, and a gas sensor, which are used to collect data on equipment surface defects, temperature anomalies, and gas leaks. The sea-based unmanned vessel module includes acoustic detection equipment, an underwater camera, and a water quality sensor, used to collect underwater structure and water quality data. The land-based robot module includes a robotic arm and inspection sensors, used for inspecting land-based stations and handling simple faults. The fixed sensor module includes a vibration sensor, a light sensor, a voltage and current sensor, and a seawater desalination load monitoring unit.

4. The management and emergency protection method for the offshore wind-solar-storage integrated energy system according to claim 1, characterized in that, The energy digital twin in S2 includes standardized processing of the received multi-source heterogeneous sensing data; The parameters of the energy digital twin are continuously optimized through machine learning algorithms and iteratively updated by combining real-time sensing data to construct the energy digital twin.

5. The management and emergency protection method for the offshore wind-solar-storage integrated energy system according to claim 1, characterized in that, In step S3, the energy supply and demand balance is calculated based on the energy digital twin. Simultaneously, based on real-time environmental data, it simulates preset risk scenarios and outputs risk warnings, including: The process of calculating the energy supply and demand balance includes: based on the energy digital twin, analyzing the output characteristics of wind power and photovoltaic power and the absorption characteristics of energy storage and load, and establishing a balance equation; based on the balance equation, calculating the distribution of active power and reactive power in real time to obtain the energy supply and demand balance. Pre-set risk scenarios such as extreme weather, equipment failure, and cyberattacks; based on energy digital twins and real-time environmental data, simulate the impact of risk events on the source supply and demand balance and identify the cross-domain propagation path of risks. Based on the cross-domain propagation path, probability of occurrence, scope of impact, and degree of loss, risks are divided into four risk levels, and corresponding risk warnings are issued for each risk level.

6. The management and emergency protection method for the offshore wind-solar-storage integrated energy system according to claim 1, characterized in that, The multi-timescale optimization algorithm in S4 adopts a hierarchical optimization architecture: the upper layer is the medium- and long-term optimization layer, which formulates emergency plans and energy dispatch plans based on meteorological forecast data and load forecast data; The middle layer is a short-term optimization layer that adjusts energy allocation strategies based on real-time wind and solar power prediction results; The lower layer is the real-time optimization layer, which generates energy storage charging and discharging control commands based on real-time operating data.

7. The management and emergency protection method for the offshore wind-solar-storage integrated energy system according to claim 1, characterized in that, After S4, S5 is also included: During the rescue operation, the execution end transmits execution status data and on-site working condition data back to the decision-making end in real time. The decision-making end updates the energy digital twin based on the transmitted data, dynamically adjusts and optimizes the decision-making plan, and simultaneously sends it back to the execution end, forming a closed loop.

8. A management and control and emergency guarantee system for an offshore wind-solar-storage integrated energy system, characterized in that, include: The acquisition module is used to perform state perception on each energy unit in the integrated offshore wind, solar and energy storage system based on the collaborative sensing network, and to acquire multi-source heterogeneous sensing data. The construction module transmits the multi-source heterogeneous sensing data to the decision-making end and constructs an energy digital twin based on the multi-source heterogeneous sensing data; The output module calculates the energy supply and demand balance based on the energy digital twin. Simultaneously, based on real-time environmental data, it simulates preset risk scenarios and outputs risk warnings; The decision output module is used to generate optimized decision schemes based on the energy supply and demand balance and risk warning, using multi-timescale optimization algorithms. The optimized decision-making plan is then sent to the execution end, which implements the rescue operation based on the optimized decision-making plan.

9. A management and control and emergency guarantee device for an offshore wind-solar-storage integrated energy system, characterized in that, It includes a processor and a memory, wherein when the processor executes a computer program stored in the memory, it implements the control and emergency support method for the integrated offshore wind, solar and energy storage system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer programs, wherein the computer programs, when executed by a processor, implement the control and emergency support method for the integrated offshore wind, solar and energy storage system as described in any one of claims 1 to 7.

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