Marine ranching wind-solar-diesel-storage multi-energy complementary scheduling method and system

Abstract

The invention belongs to the technical field of marine ranching, and provides a wind-solar-diesel-storage multi-energy complementary scheduling method and system for a marine ranching, and the method comprises the steps: building a rolling optimization model with the optimal whole life cycle cost as a target; wherein the decision variables of the rolling optimization model comprise the output of each renewable energy power generation unit, the charging and discharging power of the energy storage unit, the output power of the diesel generator and the start-stop state of the diesel generator; the target function of the rolling optimization model is to minimize the comprehensive decision cost in a rolling optimization window, and the comprehensive decision cost comprises the instant fuel cost generated based on the current decision, the long-term operation depreciation cost based on the equipment health state and the dynamic risk punishment cost for guaranteeing the breeding safety; through a dynamic scheduling strategy with the breeding process safety as the highest constraint, stable operation of the system is guaranteed, meanwhile, the start-stop frequency of the diesel generator is remarkably reduced, the service life of equipment is prolonged, and noise and pollutant emission are reduced.

Description

Technical Field

[0001] This invention belongs to the field of marine ranching technology, and in particular relates to a method and system for multi-energy complementary scheduling of wind, solar, diesel and storage in marine ranching. Background Technology

[0002] Marine ranching refers to an artificial fishing ground set up in a specific sea area for the planned cultivation and management of fishery resources. First, a suitable habitat for the growth and reproduction of marine life is created. Then, the attracted organisms, together with artificially stocked organisms, form an artificial fishing ground. Relying on a complete set of systematic fishery facilities and management systems, various marine organisms are brought together to establish a controllable marine ranch. Its main purpose is to ensure the stable and sustainable growth of aquatic resources, which are the foundation of fishery production.

[0003] As marine ranches expand into deeper and more remote areas, their energy supply faces multiple challenges. Existing wind-solar-diesel-storage complementary power supply systems mostly adopt traditional centralized control, which has the following significant problems: the intermittent output of wind and solar power generation is difficult to match with the periodic power demand of aquaculture equipment in real time, resulting in frequent start-stop of diesel generators, which not only increases operating costs but also causes grid frequency fluctuations, affects equipment lifespan, and results in low energy coordination efficiency. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a multi-energy complementary scheduling method and system for marine ranching, combining wind, solar, diesel, and storage. This invention employs a dynamic scheduling strategy with aquaculture process safety as the highest constraint, which significantly reduces the start-stop frequency of diesel generators, extends equipment lifespan, and reduces noise and pollution emissions while ensuring stable system operation.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, the present invention provides a multi-energy complementary scheduling method for marine ranching, including: Obtain relevant data on the wind-solar-diesel-storage complementary power supply system; Based on the relevant data obtained, a rolling optimization model is constructed with the safety of aquaculture processes as the highest constraint and the optimal life-cycle cost as the objective. The decision variables of the rolling optimization model include the output of each renewable energy power generation unit, the charging and discharging power of the energy storage unit, the output power of the diesel generator, and the start-stop status of the diesel generator. The objective function of the rolling optimization model is to minimize the comprehensive decision cost within the rolling optimization window. The comprehensive decision cost includes the immediate fuel cost based on the current decision, the long-term operating depreciation cost based on the equipment health status, and the dynamic risk penalty cost used to ensure aquaculture safety. Under the constraints of aquaculture process safety, real-time power balance, and equipment physical and long-term operation, the scheduling strategy is obtained by solving the rolling optimization model.

[0006] Furthermore, the objective function is: ; in, For immediate fuel costs; Equipment depreciation costs; This is the cost of risk penalties.

[0007] Furthermore, the immediate fuel cost Depreciation cost of the equipment and the aforementioned risk penalty cost They are respectively: ; ; ; in, This refers to the unit price of fuel. This refers to the output power of the diesel engine. This indicates the diesel engine is in start / stop mode. This refers to the no-load fuel consumption rate. Fuel consumption rate for power generation; This refers to the cost coefficient per unit of energy used for battery life. The charging and discharging power of the battery; This is the cost coefficient for a single start-stop cycle of the diesel engine. diesel engine in Current state; For diesel engines Current state; For at any time The total power required for all aerators to operate at full capacity; For at any time The algorithm optimizes the allocation of electrical power to the aerator system. This represents the risk coefficient.

[0008] Furthermore, the safety constraint of the aquaculture process is as follows: when the detected dissolved oxygen level is lower than the preset safety threshold, all aerators are forced to operate at the required maximum power.

[0009] Furthermore, the real-time power balance constraint is: ; in, Real-time output power of photovoltaics; The charging and discharging power of the battery; This refers to the output power of the diesel generator. Basic load power; This refers to the electrical power allocated to the aerator system.

[0010] Furthermore, the physical and long-term operational constraints of the equipment include: the upper limit of the output of each power generation unit shall not exceed its confident available power; the charging and discharging power and state of charge of the energy storage unit shall conform to the optimal adjustable power range and its safety range of the battery; and the output power of the diesel generator shall be within its technical output range and meet the minimum continuous operating time requirement. The physical and long-term operational constraints of the equipment include: 0≤ ≤ ; ≤ ≤ ; ≤ ≤ ; ; in, Photovoltaic power; This represents the maximum photovoltaic power. The charging and discharging power of the battery; This is the maximum charging power; This represents the maximum discharge power. The minimum percentage of battery charge that the battery is allowed to reach; This represents the battery percentage. The maximum percentage of battery charge that the battery is allowed to reach; This indicates the diesel engine is in start / stop mode. This refers to the output power of the diesel engine. This represents the minimum stable power of the diesel engine. This represents the maximum permissible power of the diesel engine.

[0011] Secondly, the present invention also provides a multi-energy complementary scheduling system for marine ranching, including: The data acquisition module is configured to acquire relevant data from the wind-solar-diesel-storage complementary power supply system. The optimization model determination module is configured to: construct a rolling optimization model based on the acquired relevant data, with the safety of aquaculture processes as the highest constraint and the optimal life-cycle cost as the objective; wherein, the decision variables of the rolling optimization model include the output of each renewable energy power generation unit, the charging and discharging power of the energy storage unit, the output power of the diesel generator, and the start-stop status of the diesel generator; the objective function of the rolling optimization model is to minimize the comprehensive decision cost within the rolling optimization window, wherein the comprehensive decision cost includes the immediate fuel cost based on the current decision, the long-term operating depreciation cost based on the equipment health status, and the dynamic risk penalty cost used to ensure aquaculture safety; The scheduling module is configured to solve the rolling optimization model to obtain the scheduling strategy under the constraints of aquaculture process safety, real-time power balance, and equipment physical and long-term operation.

[0012] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-energy complementary scheduling method for marine ranching, including wind, solar, diesel, and storage, as described in the first aspect.

[0013] Fourthly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the steps of the multi-energy complementary scheduling method for marine ranching, wind, solar, diesel and storage described in the first aspect.

[0014] Fifthly, the present invention also provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the steps of the multi-energy complementary scheduling method for marine ranching, including wind, solar, diesel, and storage, as described in the first aspect.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a rolling optimization model with the goal of optimizing the entire life cycle cost. The decision variables of the rolling optimization model include the output of each renewable energy generation unit, the charging and discharging power of the energy storage unit, the output power of the diesel generator, and the start-stop status of the diesel generator. The objective function of the rolling optimization model is to minimize the comprehensive decision cost within the rolling optimization window. The comprehensive decision cost includes the immediate fuel cost based on the current decision, the long-term operating depreciation cost based on the equipment health status, and the dynamic risk penalty cost for ensuring aquaculture safety. Under the constraints of aquaculture process safety, real-time power balance, and equipment physical and long-term operating constraints, the rolling optimization model is solved to obtain a scheduling strategy. Through a dynamic scheduling strategy with aquaculture process safety as the highest constraint, the system's stable operation is ensured while significantly reducing the start-stop frequency of the diesel generator, extending equipment lifespan, and reducing noise and pollution emissions. Attached Figure Description

[0016] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0017] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] Multi-energy complementarity: By coordinating the joint operation of wind power, solar power, diesel power generation and energy storage equipment, a stable energy supply and efficient utilization can be achieved; Modular design: Functional modules can be quickly combined and replaced through interfaces to adapt to the energy needs of marine ranches of different sizes.

[0021] Example 1: As marine ranches expand into deep-sea areas, their energy supply faces multiple challenges. Existing wind-solar-diesel-storage complementary power supply systems mostly employ traditional centralized control, which suffers from the following significant problems: the intermittent output of wind and solar power generation cannot be matched in real time with the periodic power demands of aquaculture equipment, leading to frequent start-stop cycles of diesel generators. This not only increases operating costs but also causes grid frequency fluctuations, affecting equipment lifespan and resulting in low energy coordination efficiency. Fixed-configuration energy equipment is difficult to adapt to the varying power loads of ranches of different sizes. Small ranches suffer from equipment redundancy and waste, while large ranches face insufficient power supply capacity and lack flexible alternatives in case of equipment failure, resulting in insufficient system scalability. Environmental factors such as high humidity, salt spray corrosion, and communication delays in the ocean cause traditional control systems to lag in monitoring the status of energy equipment, making it impossible to respond promptly to sudden operating conditions (such as strong winds causing wind power overload or sudden drops in sunlight leading to insufficient energy storage), easily causing local power outages or equipment damage, and exhibiting poor environmental adaptability. Existing technologies, lacking modular coordination mechanisms and intelligent control capabilities, struggle to achieve stable and efficient energy supply in isolated operation modes.

[0022] To solve at least one of the above problems, such as Figure 1 As shown, this embodiment provides a multi-energy complementary scheduling method and system for marine ranching, including: S1. Multi-source data acquisition (multi-source data acquisition module): The system integrates a meteorological monitoring unit (for real-time sensing of environmental parameters such as wind speed, light intensity, and temperature), equipment status sensors (for monitoring generator speed, battery charging and discharging status, and inverter operating parameters), and a load monitoring device (for collecting real-time power consumption data from aquaculture equipment and lighting systems). Through a distributed sensor network designed for high-humidity, salt-fog marine environments, a comprehensive information acquisition system for energy supply and demand is constructed: key monitoring points employ a positive-pressure micro-circulation sealed chamber structure to effectively block salt-fog corrosion; a heterogeneous communication architecture combining wired fiber optics and wireless LoRa / 5G is established to achieve redundant backup and automatic switching of data transmission links; and a virtual sensor fault-tolerant mechanism is adopted, so that when physical sensors fail, monitoring values ​​are reconstructed in real-time based on a digital twin model and data from associated equipment, ensuring reliable monitoring and control in harsh environments and providing continuous and stable real-time data support for intelligent regulation.

[0023] S2, Intelligent Control (Intelligent Control Module): S2.1 Data Processing: This embodiment introduces environmental modal data (salt spray concentration, wave height) and process modal data (water temperature, dissolved oxygen, and work plan). First, the environmental modal data (including but not limited to salt spray concentration and wave height) and process modal data (including but not limited to water temperature, dissolved oxygen, and work plan) are preprocessed, including data cleaning, filtering, and normalization. Then, a spatiotemporal registration algorithm unifies data from different sources and with different sampling frequencies to the same time reference and coordinate framework. Finally, a feature-level fusion method is used to generate a multimodal fusion feature vector. This vector is a structured dataset, and its typical feature dimensions include, but are not limited to: [normalized salt spray concentration, normalized wave height, normalized water temperature, normalized dissolved oxygen, work plan identifier...]. This embodiment uses the multimodal fusion feature vector as the sole data source to evaluate the real-time status and capabilities of each unit within the system in parallel, and outputs quantitative parameters that can be directly used for scheduling.

[0024] First, the relationship between determining environmental interference factors and assessing power generation capacity is discussed. Environmental interference factors are defined as coefficients that quantify the negative impact of harsh marine environments on the output power of wind and solar power generation equipment. Specifically, they include the photovoltaic power reduction factor (K_salt) and the wind turbine power reduction factor (K_wave). The determination process involves extracting registered salt spray concentration and wave height information from the fused feature vector. By querying the pre-defined salt spray-photovoltaic efficiency reduction model (a lookup table or empirical formula based on experimental data describing the relationship between salt spray concentration and efficiency loss) and the wave height-wind turbine output loss model (a lookup table or rule set based on wind turbine safety operation procedures describing the relationship between wave height and output loss), the photovoltaic power reduction factor (K_salt) and the wind turbine power reduction factor (K_wave) are determined respectively. Furthermore, the unit pre-stores reference power curves for wind and solar power generation equipment, measured under ideal standard test conditions and provided by the equipment manufacturer. The reduction factor is multiplied by the ideal power obtained from querying the reference power curve based on natural conditions (sunlight, wind speed) to ultimately determine the confident usable power after taking into account the adverse effects of the marine environment. The environmental disturbance factor (i.e., the reduction factor) here is a specific application of the aforementioned data fusion results on the power generation side.

[0025] Secondly, there is the relationship between the generation of aquaculture process parameters and load priority assessment. Definition of aquaculture process parameters: In this embodiment, aquaculture process parameters refer to a structured instruction set obtained from dissolved oxygen-load priority decisions, used to guide load scheduling. This instruction set dynamically defines the priority status of various loads and their mandatory power requirements, achieving a leap from traditional power consumption analysis to ensuring core process requirements. Assessment process: Process modal data such as dissolved oxygen, water temperature, and operational plans are extracted from the fused feature vector. Judgments and decisions are made based on the dissolved oxygen-load priority decision method, which includes safety-driven, planning-driven, and optimization-driven approaches. Specifically: Safety-driven (generating rigid supply guarantee instructions): When the dissolved oxygen level approaches the safety threshold, the model combines water temperature to predict emergency oxygenation power and identifies the aerator as the highest priority rigid supply guarantee load; this instruction will force subsequent scheduling to unconditionally prioritize the power supply of this load. 3. **Plan-Driven (Generate Required Plan Instructions):** Based on the work plan, loads related to timed tasks (such as feeding, circulation pump start / stop) are identified as high-priority planned required loads, and the expected power demand is allocated to ensure the orderly operation of the production process. 4. **Optimization-Driven (Manage Adjustable Load Range):** The remaining loads are classified as adjustable loads, and flexible adjustments and capacity reservations are made with reference to data such as water temperature to achieve economically optimized system operation.

[0026] After accurately assessing the system's power supply capacity (source) and power demand (load), the real-time power difference between the two is clearly defined. To efficiently and safely balance this difference and further improve the system's power supply reliability and renewable energy absorption rate, a precise assessment of the energy storage unit (battery), which serves as the core of energy buffering and regulation, is necessary. Based on this, and after comprehensively analyzing the battery's health status, internal resistance changes, and environmental temperature and humidity stresses, a two-step calculation is performed: First, the maximum safe charge / discharge power boundary of the battery is determined based on the State of Health (SOH) and internal resistance to ensure hardware safety. This boundary is calculated using the following core formula: ; ; in, Maximum charging power (W) indicates the battery's charging capacity over time. Maximum safe charging power at that time; Maximum discharge power (W) indicates the battery's discharge power over time. Maximum safe discharge power at that time; The maximum voltage (V) of the battery indicates the voltage value of the battery when it is fully charged; The open-circuit voltage (V) indicates the battery's voltage over time. The open-circuit voltage value at that time, that is, the voltage of the battery when it is not under load; The minimum voltage (V) of the battery represents the voltage value of the battery when it is fully discharged. The internal resistance (Ω) represents the battery's resistance over time. The internal resistance value at that time is a measure of the internal resistance of the battery; The "Status-of-Health" parameter represents the health status of the battery. It is a dimensionless parameter with a value range of 0-1 (0 is not allowed in this embodiment). 1 indicates that the battery is fully healthy, and 0 indicates that the battery is completely unhealthy.

[0027] Based on the ambient temperature, a preset temperature-lifetime optimized power table is consulted to determine the optimal charge / discharge power range recommended for delaying battery aging. The steps for establishing the temperature-lifetime optimized power table are as follows: Step 1: Model Establishment: Accelerated Aging Test: In a controlled laboratory environment, battery samples of the same model are placed at a series of different constant ambient temperatures (e.g., 0°C, 10°C, 25°C, 35°C, 45°C, etc.). Cycle Life Test: At each temperature point, battery samples are subjected to different constant charge / discharge rates (e.g., 0.2C, 0.5C, 1.0C, etc.) for cyclic charge / discharge tests, and their capacity decay and internal resistance growth are continuously monitored. 3. Data Correlation and Analysis: Record the number of complete cycles experienced by the battery when its capacity decays to 80% of the rated capacity (i.e., SOH=80%) under each test condition (temperature T, charge / discharge rate C-rate); establish a three-dimensional relationship model of temperature-rate-cycle life through data fitting. Step 2: Query Table Generation: Based on the above model, a query table that can be directly called is generated. Define a lifespan target: Set a desired battery lifespan target (e.g., ensure at least N cycles). Generate an optimized power table: For each temperature point, in the temperature-rate-cycle lifespan model, find the maximum allowable charge / discharge rate that satisfies the condition of ≥N cycles. Multiply this rate by the battery's rated capacity to obtain the recommended optimal charge / discharge power range for that temperature to meet the lifespan target. Finally, solidify this data into a two-dimensional lookup table. Step 3: Real-time application: Data input: Extract registered and normalized ambient temperature information from the multimodal fusion feature vector. Query operation: Input the current ambient temperature value into the preset temperature-lifespan optimized power table. Output result: The table outputs a recommended optimal charge / discharge power range corresponding to the current temperature. Finally, take the intersection of the battery's maximum safe charge / discharge power boundary and the optimal charge / discharge power range obtained from the temperature-lifespan optimized power table to output an optimal adjustable power range that balances lifespan.

[0028] S2.2 Strategy Generation: This embodiment introduces a dynamic collaborative scheduling mechanism with the safety of aquaculture processes as the highest constraint and the optimization of the entire life cycle cost as the objective. To transform this macro-level objective into real-time control commands, this embodiment constructs an online rolling optimization model. The rolling optimization model employs an optimization algorithm (such as a rolling window algorithm or other rolling optimization algorithms) to perform collaborative optimization simulations of multiple energy devices within a rolling time window [t, t+T] based on predicted data. Its core decision-making process can be fully described as follows: S2.2.1 Optimization Objective: Minimize the overall decision-making cost within the scrolling window: This objective function aims to achieve the optimal real-time decision-making agent model in terms of total lifecycle cost. ; in, For immediate fuel costs; Equipment depreciation costs; This is the cost of risk penalties.

[0029] Instant fuel cost This mainly refers to the fuel consumption of diesel generators, expressed by the formula: ; in, This refers to the unit price of fuel. This refers to the output power of the diesel engine. This indicates the diesel engine is in start / stop mode. This refers to the no-load fuel consumption rate. Both the power generation fuel consumption rate and the generator's own equipment determine the generator's fuel consumption rate.

[0030] Equipment depreciation cost Long-term asset depreciation is quantified into short-term costs in real time, including the battery life cost per kilowatt-hour calculated based on battery health status and depth of charge / discharge, and the diesel engine start-stop loss cost set to avoid damage from high salt spray environments. The formula is as follows: ; in, The battery life cost coefficient is determined by the battery health parameter (SOH) and state parameter (SOC) of the battery. The charging and discharging power of the battery; The cost coefficient for a single start-stop cycle of a diesel engine is determined by the diesel engine equipment itself. diesel engine in Current state; For diesel engines Status at any given moment.

[0031] Risk penalty cost Transforming the absolute constraints of aquaculture safety into economic language that optimization algorithms can understand: ; in, For at any time The total power required for all aerators to operate at full capacity; For at any time The algorithm optimizes the allocation of electrical power to the aerator system. The risk factor is set to an extremely high value to ensure that when dissolved oxygen levels approach a critical value, the algorithm will prioritize oxygenation load regardless of economic cost. The formula operates as follows: the algorithm determines that the provided oxygenation power is greater than or equal to the maximum demand, i.e. ≥ hour, - ≤0→max(0, negative)=0→ = 0 = 0, the risk cost is zero, and the algorithm will not be subject to any economic penalty. Conversely, when the algorithm decides not to increase oxygen production at full capacity in order to save fuel or protect equipment, that is... < , - =X>0→max(0,X)=X→ = X, due to great, This will increase dramatically, causing the total cost J to rise sharply. This means that the algorithm's decision to try to save electricity will immediately incur a huge virtual penalty, while fuel costs and equipment depreciation costs may only be tens or hundreds of yuan at the same time. Under the objective of minimizing the total cost, the algorithm will immediately correct itself. Raise to The value reduces the penalty to 0.

[0032] S2.2.2 Decision Variables and Rigid Constraints: While minimizing the cost function, the decision must strictly satisfy the following constraints, defining the decision variable [photovoltaic power]. Battery charging and discharging power diesel engine output power Diesel engine start / stop status Feasible domain: Aquaculture process safety constraints (highest priority condition constraints): If ,but ≡ ;in, The amount of dissolved oxygen monitored; The minimum dissolved oxygen level required for cultured organisms to survive; This is a buffer value for early warning to ensure a safe dissolved oxygen level. This inequality means that when the detected dissolved oxygen level is lower than the safe threshold, the system must operate the aerator at maximum capacity, suspend all economic optimizations, and do everything possible to ensure the survival of organisms.

[0033] Real-time power balance constraint (extended instantaneous balance): i.e., real-time output power of photovoltaics. +Battery charging and discharging power +Diesel generator output power =Basic load power +Required oxygenation load In general, the total power generated is equal to the total power consumed. ; Equipment physical and long-term operation constraints: Photovoltaic output constraint: 0≤ ≤ Battery operating constraints: ≤ ≤ ,Right now The battery power is always greater than or equal to the maximum charging power. And less than or equal to the maximum discharge power The rigid physical limitations that protect the battery from damage caused by current surges; ≤ ≤ This refers to the maximum and minimum percentage of battery capacity that the battery is allowed to reach. These two sets of constraints are related to the dynamic levelized cost of battery life. The combined effect forms a tiered protection system for the long-term operation of diesel generators: the first line of defense (economic guidance): when near or At that time, it will be achieved by significantly increasing The value guides the algorithm to reduce charging / discharging from an economic cost perspective, actively protecting the battery and achieving optimal and most economical protection. The second line of defense (hard braking): If the economic guidance fails to completely prevent this (e.g., full discharge is required in safe mode), then... ≤ ≤ and power constraints ≤ ≤ This will serve as an inviolable physical bottom line, forcibly terminating charging and discharging to ensure battery safety.

[0034] Long-term operating constraints of diesel generators: Power limitations: ; in, This represents the minimum stable power of the diesel engine. This represents the maximum permissible power of the diesel engine. This inequality constraint defines that when the diesel engine is running, its output power must be between a minimum and a maximum value determined by the technical characteristics of the equipment.

[0035] Minimum continuous operating time (to avoid salt spray damage): Based on this cost model, the strategy generation unit can dynamically generate the following intelligent collaborative instructions: For photovoltaic systems: During dissolved oxygen crises, the inverter is instructed to operate at reduced capacity to ensure power quality and frequency stability, rather than simply curtailing solar power; For energy storage systems: Based on real-time calculations... It dynamically adjusts its charging and discharging power commands. The formula for calculating the cost per kilowatt-hour of electricity is as follows: That is, the cost per kilowatt-hour lifespan coefficient α batt × in The amount of electricity (kWh) flowing through the battery during a given period is considered. If the cost per kilowatt-hour of deep charging and discharging exceeds the overall cost of starting and stopping the diesel engine, the battery is instructed to operate within its healthy range, and the diesel engine is started. Simultaneously, for the diesel generator: considering the start-stop penalty cost... With minimum running time constraint The system intelligently schedules its start-up and shutdown operations to minimize damaging start-ups and shutdowns in high-salt-spray environments. Ultimately, by continuously solving the aforementioned rolling optimization problem, each local decision simultaneously addresses short-term economic efficiency, equipment long-term effectiveness, and production safety. This fundamentally achieves a leap from rigid power balance to ensuring biosafety and maximizing the system's lifetime value.

[0036] S2.3 Fault Tolerance: This embodiment constructs an integrated proactive resilience protection system encompassing prediction, defense, and reconfiguration. Specifically, the LSTM neural network continuously receives operational characteristic sequences from each device, including IGBT junction temperature data of the photovoltaic inverter, charging and discharging internal resistance sequences of the battery, and start-up time sequences of the diesel generator. Using a trained degradation model (the trained LSTM neural network model), it calculates the device's health index and predicted remaining lifespan, performing a non-disruptive preventative switchover before a fault occurs, overturning the traditional reactive switchover approach. In the defense phase, when the health index of any device falls below a preset threshold, a non-disruptive switchover from the warning device to the backup device is completed within 20ms by controlling a static transfer switch and implementing power ramp control. When a severe fault causes partial paralysis, the power grid topology can be dynamically reconstructed. Specifically, firstly, intelligent circuit breakers and fault indicators quickly locate and isolate the faulty section. Then, based on the Dijkstra algorithm, the optimal power supply path to critical loads (such as aerators) is calculated within the remaining power grid topology. Finally, through a remotely controlled intelligent switch sequence, the smallest power supply island containing surviving power sources and critical loads is reconstructed within 200ms. Priority is given to restoring lifeline power to core aquaculture equipment such as aerators, ensuring the safety of aquaculture organisms. All processes are immutably recorded using blockchain technology, enabling precise fault tracing and system self-evolution and upgrades. No pre-set, manually fixed schemes are required, resulting in higher reliability. Therefore, this fundamentally guarantees the continuous and safe operation of marine ranches under extreme conditions.

[0037] This embodiment prioritizes the safety of aquaculture processes and aims for optimal system lifecycle costs. It also features an energy management agent with predictive, defensive, and reconfigurable immunity capabilities. Through data processing, it understands its own capabilities and the external environment. Through strategy generation, it makes optimal decisions (balancing safety, cost, and efficiency). Finally, through fault tolerance, it ensures absolute safety (guaranteeing that the strategy can be executed safely under any circumstances). Ultimately, it achieves true intelligence, resilience, and long-term operation of the marine ranch energy system.

[0038] S3, Multi-functional Complementary Execution (Multi-functional Complementary Execution Module): S3.1 Renewable Energy Access: The output of wind turbines and photovoltaic arrays is integrated through intelligent distribution cabinets, and a hybrid energy storage power smoothing device based on supercapacitors and batteries is configured. This device monitors the instantaneous deviation between the total wind and solar power and the target reference value in real time. It adopts a frequency division control strategy to preferentially allocate high-frequency fluctuation components to supercapacitors with millisecond-level response for rapid throughput, while allocating low-frequency fluctuation components to batteries for continuous balancing. This ensures that the grid-connected output formed by the superposition of the compensated power and the original power can closely track the smoothed target power curve, thereby effectively suppressing the intermittent fluctuations of wind and solar power and ensuring stable and reliable power quality when connected to the DC or AC bus.

[0039] S3.2 Diesel Generator Coordination: By adding an electronic speed governor and soft starter, flexible access and intelligent control of the diesel generator are achieved. Its core lies in the fact that, based on the real-time power command issued by the central dispatcher, the electronic speed governor dynamically adjusts the fuel injection system's fuel supply, thereby precisely controlling the diesel engine's speed and torque to ensure that its output power is consistent with the command value. When renewable energy generation drops sharply, the unit receives a power increase command and quickly increases fuel injection to make the generator operate at a higher load to compensate for the power deficit. When the total system load decreases or renewable energy recovers, it receives a power decrease command and smoothly reduces the load by decreasing the fuel supply until the preset minimum technical output is reached. Only after combining the minimum operating time based on marine environmental reliability can a safe shutdown procedure be executed, thereby achieving the optimal balance between on-demand power supply and fuel economy.

[0040] S3.3 Energy Storage Management: The charging and discharging process of the battery is controlled by a bidirectional converter. Energy is stored when there is excess energy and released when supply and demand are imbalanced, working in conjunction with a strategy generation unit to achieve peak shaving and valley filling. The process involves issuing charging commands in advance during off-peak hours or low-cost periods of wind and solar power surplus when peak loads are predicted and grid supply costs are high. At this time, the bidirectional converter operates in rectification mode, storing excess energy in the battery. When peak loads arrive or dissolved oxygen levels are critical and rigid loads need to be guaranteed, a discharging command is immediately issued. The bidirectional converter switches to inverter mode, precisely releasing energy according to the commanded power, thereby reducing peak grid load and ensuring the safety of core processes. Throughout this process, the optimal adjustable power range, calculated to balance battery life, is always followed to avoid shortening battery life due to excessive charging and discharging, truly achieving a balance between economy and reliability, and improving grid stability.

[0041] The multi-energy complementary execution system, acting as the intelligent executor of the system, achieves precise energy regulation and efficient utilization through the precise coordination of three major units: renewable energy access, diesel power generation collaboration, and energy storage management. It not only smoothly integrates intermittent renewable energy sources such as wind and solar power, ensuring their priority and efficient utilization, but also, through flexible connections and intelligent start-stop strategies, transforms diesel generators into on-demand supplementary power sources, significantly reducing fuel consumption and maintenance costs. Simultaneously, energy storage management, acting as the system's stabilizer and emergency buffer, flexibly performs peak shaving and valley filling and ensures core power supply during faults. These three elements work together to construct an economical, efficient, and robust energy supply system, fundamentally guaranteeing the stable, low-carbon, and long-term operation of the marine ranch energy system.

[0042] In this embodiment, each functional module is connected through an electrical interface and a communication protocol. Energy equipment can be added or removed according to the scale of the ranch (such as adding wave energy power generation modules or expanding energy storage capacity). Equipment replacement and system upgrades are convenient, reducing initial investment and subsequent maintenance costs.

[0043] This embodiment, based on environmental and process data, accurately quantifies wind and solar power generation capacity, the health status of the energy storage system, and load demand characteristics. Through a dynamic scheduling strategy prioritizing aquaculture safety, it significantly reduces the start-stop frequency of diesel generators, extends equipment lifespan, and reduces noise and pollution emissions while ensuring stable system operation. Simultaneously, by intelligently coordinating the energy storage system and power generation equipment, it effectively smooths fluctuations in wind and solar power generation, improves power quality, and ensures continuous and reliable power supply for aquaculture production activities, ultimately achieving the dual goals of efficient energy utilization and aquaculture safety assurance.

[0044] This embodiment features an innovative distributed sensor network architecture and fault-tolerant mechanism, constructing a monitoring system with high environmental adaptability. The system employs positive pressure sealing protection and a heterogeneous communication architecture, maintaining stable and reliable monitoring capabilities even in harsh marine environments such as high temperature, high humidity, and high salt spray. Simultaneously, based on an integrated prediction-defense-reconstruction protection concept, a comprehensive fault response system from the equipment level to the system level has been established. Through intelligent prediction and early warning, seamless switching protection, and dynamic reconstruction and recovery functions, it ensures rapid fault isolation and continuous power supply to lifeline loads, fundamentally avoiding power outages caused by equipment failure or environmental factors, and providing all-weather, highly reliable operational support for the marine ranch energy system.

[0045] This embodiment achieves coordinated operation of multiple energy devices through a unified control platform, simplifies on-site wiring and debugging processes, is compatible with existing marine ranch monitoring systems, and improves the overall level of intelligence.

[0046] In other embodiments, a modular design is adopted, with each unit connected via an industrial-grade Ethernet bus. Multi-source data acquisition modules are distributed at energy equipment sites (such as wind turbine towers, photovoltaic supports, diesel engine rooms, and energy storage containers), collecting real-time meteorological, equipment status, and load data through a distributed sensor network. Corrosion-resistant sealing structures and heterogeneous communication technology ensure data reliability, and a virtual sensor fault-tolerant mechanism is provided. Data is collected in real-time and uploaded to the intelligent control module, providing comprehensive real-time data support for the system. The intelligent control module consists of a data processing unit, a strategy generation unit, and a fault-tolerant unit, integrated in a central control cabinet with a built-in high-performance embedded processor. This intelligent control module is the intelligent hub of the marine ranch energy system, constructing an energy management intelligent agent with the highest requirement of aquaculture process safety, the basic requirement of optimal system lifecycle cost, and possessing a predictive-defense-reconstruction immune system. The module achieves comprehensive understanding through its data processing unit, leveraging multimodal data fusion and dynamic capability assessment mechanisms to accurately grasp the environmental degradation capabilities of wind and solar power generation, the health status boundaries of batteries, and the rigid load demand centered on dissolved oxygen levels. Based on this understanding, the strategy generation unit makes optimal decisions within the framework of minimizing the total lifecycle cost using a rolling optimization algorithm, dynamically coordinating photovoltaic derating, battery charging and discharging, and diesel engine start-stop to achieve economical dispatch under safety constraints. Finally, the fault-tolerant unit ensures the system's uninterrupted operation under extreme conditions through triple protection of equipment health prediction, seamless switching, and grid topology self-reconfiguration, prioritizing the restoration of lifeline power supply to core aquaculture equipment. These three units form a closed loop of perception-decision-immunity, jointly achieving a fundamental leap from passive power supply to proactive protection, and from short-term balance to long-term optimization. The multi-energy complementary execution module consists of a renewable energy access unit, a diesel power generation coordination unit, and an energy storage management unit. Its distribution cabinets, inverters, converters, and other equipment are all designed for moisture and corrosion resistance and installed in a sealed chamber, adapting to long-term operation in the marine environment. The multi-energy complementary execution module, as the system's intelligent actuator, achieves precise energy scheduling and efficient utilization through the coordinated operation of the three main units. The renewable energy access unit employs a hybrid energy storage power smoothing device. By monitoring the deviation between the total wind and solar power and the target reference value in real time, it uses a frequency-division control strategy to distribute high-frequency fluctuation components to the supercapacitor for rapid throughput, while low-frequency fluctuation components are balanced by the battery, ensuring that the grid-connected output closely tracks the smoothed target power curve. The diesel generator coordination unit achieves flexible access through an electronic speed governor and a soft starter. It dynamically adjusts the fuel injection quantity according to the power command from the central dispatcher, rapidly increasing output power when renewable energy is insufficient and smoothly reducing load and performing safe shutdowns during off-peak hours. The energy storage management unit receives charging and discharging commands from the strategy unit through a bidirectional converter. It stores electrical energy in rectification mode when there is an energy surplus and switches to inverter mode to release electrical energy when supply and demand are unbalanced, strictly adhering to the optimal adjustable power range while considering battery life.The three major units work closely together to build a stable and reliable energy supply system, ensuring the economical and efficient operation of the marine ranch energy system.

[0047] During operation, when wind and solar power are abundant, power is preferentially supplied to the load through the renewable energy access unit. This unit integrates wind and solar power output through an intelligent distribution cabinet and is equipped with a hybrid energy storage power smoothing device: it monitors the instantaneous deviation between the total wind and solar power and the target reference value in real time, and uses a frequency division control strategy to distribute high-frequency fluctuation components to the supercapacitor for rapid throughput, while low-frequency fluctuation components are balanced by the battery, ensuring stable and reliable grid-connected power quality. Excess energy is stored in the energy storage system in rectification mode through a bidirectional converter. The charging process strictly follows the optimal adjustable power range calculated by the data processing unit, taking into account battery life. When renewable energy is insufficient or the load suddenly increases, the data processing unit performs fusion analysis on environmental mode (salt spray concentration, wave height) and process mode (water temperature, dissolved oxygen, operational plan) data, outputting the environmentally discounted reliable available wind and solar power, the optimal adjustable power range of the battery, and a rigid supply load indicator. The strategy generation unit, based on a full lifecycle cost optimization model, performs multi-scenario simulation calculations within a forward-looking rolling time window to dynamically generate collaborative commands: It activates the diesel generator collaborative unit to supplement power supply, adjusts fuel injection quantity via an electronic speed governor to maintain an output power accuracy of ±2% relative to the command value; simultaneously, it controls the energy storage management unit to release electrical energy, and the two-phase converter switches to inverter mode to discharge precisely at the commanded power. When the fault-tolerant unit detects equipment anomalies through equipment health trend prediction (LSTM neural network analysis of IGBT junction temperature, battery internal resistance, and other parameters), or detects extreme weather through environmental sensors, it immediately activates the "prediction-defense-reconfiguration" protection system: first, it disconnects the abnormal equipment within 20ms via a smart circuit breaker; then, based on the Dijkstra algorithm, it calculates the optimal power supply path in the remaining power grid; and reconfigures the power grid topology within 200ms via a remote-controlled smart switch sequence, activating backup energy paths (such as independent power supply from the energy storage system). Simultaneously, all fault data is immutably stored using blockchain technology and alarm signals are sent to the remote monitoring center.

[0048] This embodiment allows for module configuration adjustments based on ranch needs: small ranches can be simplified to a combination of wind, solar, and energy storage, while large ranches can be expanded to a multi-energy power supply system combining wind, solar, diesel, energy storage, and wave energy. All modules support plug-and-play functionality, significantly improving system adaptability.

[0049] This embodiment, for start-up or small-scale marine ranches, can use basic wind, solar, and energy storage modules to build the energy framework, significantly reducing initial investment. As the scale of aquaculture expands or processes are upgraded, there is no need to modify the core architecture; simply adding diesel generator co-generation units or future wave / tidal energy access units via interfaces can achieve smooth capacity expansion, perfectly matching the dynamic energy needs of ranch development. When any device (such as a sensor or inverter) malfunctions or needs upgrading, maintenance personnel can directly replace the faulty module as a whole, similar to replacing a board in a server rack, greatly shortening on-site diagnosis and repair time and reducing the risks to aquaculture production caused by equipment downtime. The data processing unit deeply integrates environmental and process modal data, knowing not only how much electricity is available but also its quality and the true load demand. It outputs the confident available power after environmental factor reduction, the optimal adjustable power considering battery life, and the rigid supply load based on dissolved oxygen levels, laying the foundation for precise scheduling. The strategy generation unit performs global optimization decisions based on accurate data. It prioritizes the dispatch of renewable energy and smooths out fluctuations through energy storage systems. When renewable energy is insufficient, it intelligently starts and stops diesel generators and sets their minimum economic operating time to avoid frequent start-stop operations in inefficient ranges, thereby significantly reducing fuel consumption, maintenance costs, and noise pollution. At the same time, through intelligent peak shaving and valley filling by the energy storage system, it further reduces dependence on diesel generators and maximizes the absorption capacity of renewable energy.

[0050] From the positive pressure micro-circulation sealed chamber of the distributed sensors to the moisture-proof and corrosion-resistant design of the execution module equipment, a physical isolation barrier against salt spray corrosion is formed. The heterogeneous communication architecture (wired fiber optic + wireless LoRa / 5G) ensures that the system can maintain uninterrupted communication through redundant links even if any single communication link is interrupted due to severe weather. The integrated predictive-defense-reconstruction protection system built by the fault-tolerant unit is the core of the system's reliability. It can perform non-intrusive preventative switching before a fault occurs based on equipment health trend predictions, turning passive into proactive. When a severe, unforeseen fault causes partial paralysis, the system can self-repair like a living organism, dynamically reconstructing the power grid topology and prioritizing the restoration of power to core lifeline loads such as aerators, ensuring the safety of aquaculture organisms and greatly improving the system's anti-interference and continuous power supply capabilities during isolated operation. Integrating the monitoring, strategy generation, and fault management of various energy devices such as wind, solar, diesel, and storage into a unified software platform greatly simplifies on-site wiring and debugging processes and provides maintenance personnel with a single, user-friendly operating view, improving the overall level of intelligent management. The system's use of open communication protocols and standardized hardware interfaces gives it strong compatibility. It can not only seamlessly connect to the existing monitoring system of the ranch, but also reserve plug-and-play access ports for future new energy forms (such as tidal energy, hydrogen energy, etc.). It is not only a solution to the current problem, but also a scalable hardware foundation for the future upgrade of marine ranch energy system, protecting the long-term investment value of users.

[0051] Example 2: This embodiment provides a multi-energy complementary scheduling system for marine ranching, including: The data acquisition module is configured to acquire relevant data from the wind-solar-diesel-storage complementary power supply system. The optimization model determination module is configured to: construct a rolling optimization model based on the acquired relevant data, with the safety of aquaculture processes as the highest constraint and the optimal life-cycle cost as the objective; wherein, the decision variables of the rolling optimization model include the output of each renewable energy power generation unit, the charging and discharging power of the energy storage unit, the output power of the diesel generator, and the start-stop status of the diesel generator; the objective function of the rolling optimization model is to minimize the comprehensive decision cost within the rolling optimization window, wherein the comprehensive decision cost includes the immediate fuel cost based on the current decision, the long-term operating depreciation cost based on the equipment health status, and the dynamic risk penalty cost used to ensure aquaculture safety; The scheduling module is configured to solve the rolling optimization model to obtain the scheduling strategy under the constraints of aquaculture process safety, real-time power balance, and equipment physical and long-term operation.

[0052] The working method of the system is the same as that of the multi-energy complementary scheduling method of marine ranching wind, solar, diesel and storage in Example 1, and will not be repeated here.

[0053] Example 3: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-energy complementary scheduling method for marine ranching, including wind, solar, diesel, and storage, as described in Embodiment 1.

[0054] Example 4: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, it implements the steps of the multi-energy complementary scheduling method for marine ranching, including wind, solar, diesel, and storage as described in Embodiment 1.

[0055] Example 5: This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the multi-energy complementary scheduling method for marine ranching, including wind, solar, diesel, and storage, as described in Embodiment 1.

[0056] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A multi-energy complementary scheduling method for marine ranching, characterized in that, include: Obtain relevant data on the wind-solar-diesel-storage complementary power supply system; Based on the relevant data obtained, a rolling optimization model is constructed with the safety of aquaculture processes as the highest constraint and the optimal life-cycle cost as the objective. The decision variables of the rolling optimization model include the output of each renewable energy power generation unit, the charging and discharging power of the energy storage unit, the output power of the diesel generator, and the start-stop status of the diesel generator. The objective function of the rolling optimization model is to minimize the comprehensive decision cost within the rolling optimization window. The comprehensive decision cost includes the immediate fuel cost based on the current decision, the long-term operating depreciation cost based on the equipment health status, and the dynamic risk penalty cost used to ensure aquaculture safety. Under the constraints of aquaculture process safety, real-time power balance, and equipment physical and long-term operation, the scheduling strategy is obtained by solving the rolling optimization model.

2. The multi-energy complementary scheduling method for marine ranching based on wind, solar, diesel, and storage as described in claim 1, characterized in that, The objective function is: ； in, For immediate fuel costs; Equipment depreciation costs; This is the cost of risk penalties.

3. The multi-energy complementary scheduling method for marine ranching based on wind, solar, diesel, and storage as described in claim 2, is characterized in that... The immediate fuel cost Depreciation cost of the equipment and the aforementioned risk penalty cost They are respectively: ； ； ； in, This refers to the unit price of fuel. This refers to the output power of the diesel engine. This indicates the diesel engine is in start / stop mode. This refers to the no-load fuel consumption rate. Fuel consumption rate for power generation; This refers to the cost coefficient per unit of energy used for battery life. The charging and discharging power of the battery; This is the cost coefficient for a single start-stop cycle of the diesel engine. diesel engine in Current state; For diesel engines Current state; For at any time The total power required for all aerators to operate at full capacity; For at any time The algorithm optimizes the allocation of electrical power to the aerator system. This represents the risk coefficient.

4. The multi-energy complementary scheduling method for marine ranching based on wind, solar, diesel, and storage as described in claim 1, characterized in that, The safety constraint of the aquaculture process is as follows: when the detected dissolved oxygen level is lower than the preset safety threshold, all aerators are forced to operate at the required maximum power.

5. The multi-energy complementary scheduling method for marine ranching based on wind, solar, diesel, and storage as described in claim 1, characterized in that, The real-time power balance constraint ensures that the sum of the output of each power generation unit is balanced with the sum of all load demands in real time; the real-time power balance constraint is: ； in, Real-time output power of photovoltaics; The charging and discharging power of the battery; This refers to the output power of the diesel generator. Basic load power; This refers to the electrical power allocated to the aerator system.

6. The multi-energy complementary scheduling method for marine ranching based on wind, solar, diesel, and storage as described in claim 1, characterized in that, The physical and long-term operational constraints of the equipment include the upper limit of the output of each power generation unit not exceeding its confident available power, the charging and discharging power and state of charge of the energy storage unit must conform to the optimal adjustable power range and its safe range of the battery, and the output power of the diesel generator must be within its technical output range and meet the minimum continuous operating time requirement. The physical and long-term operational constraints of the equipment include: 0≤ ≤ ； ≤ ≤ ； ≤ ≤ ； ； in, Photovoltaic power; This represents the maximum photovoltaic power. The charging and discharging power of the battery; This is the maximum charging power; This represents the maximum discharge power. The minimum percentage of battery charge that the battery is allowed to reach; This represents the battery percentage. The maximum percentage of battery charge that the battery is allowed to reach; This indicates the diesel engine is in start / stop mode. This refers to the output power of the diesel engine. This represents the minimum stable power of the diesel engine. This represents the maximum permissible power of the diesel engine.

7. A multi-energy complementary dispatching system for marine ranching, characterized in that: include: The data acquisition module is configured to acquire relevant data from the wind-solar-diesel-storage complementary power supply system. The optimization model determination module is configured to: construct a rolling optimization model based on the acquired relevant data, with the safety of aquaculture processes as the highest constraint and the optimal life-cycle cost as the objective; wherein, the decision variables of the rolling optimization model include the output of each renewable energy power generation unit, the charging and discharging power of the energy storage unit, the output power of the diesel generator, and the start-stop status of the diesel generator; the objective function of the rolling optimization model is to minimize the comprehensive decision cost within the rolling optimization window, wherein the comprehensive decision cost includes the immediate fuel cost based on the current decision, the long-term operating depreciation cost based on the equipment health status, and the dynamic risk penalty cost used to ensure aquaculture safety; The scheduling module is configured to solve the rolling optimization model to obtain the scheduling strategy under the constraints of aquaculture process safety, real-time power balance, and equipment physical and long-term operation.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the multi-energy complementary scheduling method for marine ranching, including wind, solar, diesel, and storage as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the program, it implements the steps of the multi-energy complementary scheduling method for marine ranching, including wind, solar, diesel, and storage as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the multi-energy complementary scheduling method for marine ranching, including wind, solar, diesel, and storage, as described in any one of claims 1-6.

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