A weak power grid-oriented optical storage and charging micro-grid control method and system

Abstract

The application relates to the technical field of micro-grid control, in particular to a photovoltaic storage and charging micro-grid control method and system for a weak power grid. The method comprises the following steps: acquiring a photovoltaic panel information set, analyzing the coupling influence of light reflection and internal load impact on the photovoltaic panel based on the photovoltaic panel information set, and obtaining a photovoltaic panel dynamic disturbance information set; acquiring a fish farm information set, analyzing the multi-objective coordination and trade-off relationship between fish farming demand and power generation demand based on the fish farm information set and in combination with the photovoltaic panel dynamic disturbance information set, and obtaining an optimized scheduling information set; generating a multi-end coordinated control strategy based on the optimized scheduling information set, and outputting a photovoltaic storage and charging micro-grid control log. The method makes the micro-grid load disturbance analysis more accurate and builds a data foundation for collaborative regulation of breeding and power generation.

Description

Technical Field

[0001] This application relates to the field of microgrid control technology, and in particular to a control method and system for photovoltaic-storage-charging microgrids for weak power grids. Background Technology

[0002] With the development of renewable energy technologies, photovoltaic-storage-charging microgrids are widely used in weak grid areas and in fishery-solar complementary scenarios. Current technologies are based on photovoltaic MPPT, bidirectional energy storage regulation, and load hierarchical management, combined with strategies such as virtual synchronous machines and droop control to achieve grid-connected and off-grid switching and power balance, which can initially meet the basic power supply and photovoltaic consumption needs in fishery-solar complementary scenarios.

[0003] However, existing technologies still have shortcomings: First, in weak grid environments, photovoltaic output is significantly affected by the coupling effects of light fluctuations and load shocks. Existing strategies are not adaptable enough to such dynamic disturbances, affecting system stability. Second, dispatching focuses on a single optimization objective and does not fully consider the temporal and sensitive characteristics of aquaculture loads, lacking a multi-objective coordination mechanism for aquaculture and power generation, storage, and charging needs. Third, multi-terminal coordination strategies fail to achieve real-time adaptation of disturbance information to aquaculture needs, resulting in low power regulation accuracy and poor power supply quality, which restricts efficient operation. Summary of the Invention

[0004] This application provides a control method and system for a photovoltaic-storage-charging microgrid for weak power grids to solve the above-mentioned problems.

[0005] In a first aspect, this application provides a control method for a photovoltaic-storage-charging microgrid oriented towards weak power grids. The method includes: acquiring a photovoltaic panel information set; based on the photovoltaic panel information set, analyzing the coupling effect of light reflection and internal load impact on the photovoltaic panels to obtain a photovoltaic panel dynamic disturbance information set; acquiring a fish farm aquaculture information set; based on the fish farm aquaculture information set, and in conjunction with the photovoltaic panel dynamic disturbance information set, analyzing the multi-objective coordination and trade-off relationship between fish farm aquaculture demand and power generation demand to obtain an optimized scheduling information set; and based on the optimized scheduling information set, generating a multi-terminal coordinated control strategy and outputting a photovoltaic-storage-charging microgrid control log.

[0006] Through the above technical solutions, firstly, by coupling analysis of light reflection and internal load impact, the core disturbance sources affecting photovoltaic stability under weak power grid conditions are accurately identified, providing high-precision feedforward information for control; secondly, through multi-objective collaborative optimization, the contradiction between power generation and aquaculture in the solar-aquaculture farm is resolved, achieving a balance between economic and ecological benefits; finally, through model predictive control, multi-terminal coordination is achieved, ensuring the robust execution of optimized scheduling commands in dynamic environments, and combined with detailed control logs, observability and maintainability are improved, thereby enhancing the overall stability, economy, and sustainability of the solar-storage-charging microgrid under weak power grid conditions.

[0007] Optionally, the step of analyzing the coupling effect of sunlight reflection and internal load impact on the photovoltaic panel based on the photovoltaic panel information set to obtain a photovoltaic panel dynamic disturbance information set includes: the photovoltaic panel information set includes a solar radiation information set and a photovoltaic panel power load information set; based on the solar radiation information set, analyzing the characteristics of sunlight reflection fluctuations on the water surface caused by changes in the incident angle and intensity of sunlight to obtain photovoltaic panel reflection disturbance information; based on the photovoltaic panel power load information set, analyzing the impact characteristics of load switching and changes within the microgrid on the output power parameters of the photovoltaic panel to obtain photovoltaic panel load impact information; and based on the photovoltaic panel reflection disturbance information, combined with the photovoltaic panel load impact information, analyzing the superposition and coupling relationship between reflection fluctuations and load impacts in time and space to obtain the photovoltaic panel dynamic disturbance information set.

[0008] Optionally, the process of constructing the photovoltaic panel load impact information includes: based on the photovoltaic panel power load information set, analyzing the switching sequence and continuous operation requirements of the internal aquaculture load to obtain the steady-state disturbance information of the uninterrupted priority load; based on the photovoltaic panel power load information set, analyzing the random access and high-power demand characteristics of the externally connected fishing vessel charging load to obtain the fishing vessel fast-charging impact information; based on the steady-state disturbance information, combined with the fishing vessel fast-charging impact information, analyzing the power fluctuation and voltage transient characteristics caused by the superposition of aquaculture guarantee needs and sudden charging needs to obtain the photovoltaic panel load impact information.

[0009] Optionally, the process of constructing the steady-state disturbance information includes: based on the photovoltaic power load information set, analyzing the operating parameters and periodic start-stop patterns of each aquaculture equipment to maintain a stable aquaculture environment, and obtaining the steady-state operating characteristics of different aquaculture equipment; based on the steady-state operating characteristics, analyzing the collaborative working modes and temporal overlap relationships of each aquaculture equipment in order to achieve aquaculture goals during the aquaculture operation cycle, and obtaining the collaborative disturbance characteristics between equipment; based on the collaborative disturbance characteristics between equipment, analyzing the continuous impact of the continuous and periodic operation of multiple aquaculture equipment on the power and voltage background values ​​of the microgrid, and obtaining the steady-state disturbance information.

[0010] Optionally, the step of analyzing the superposition and coupling relationship between reflection fluctuations and load impacts in time and space based on the photovoltaic panel reflection disturbance information and the photovoltaic panel load impact information to obtain the photovoltaic panel dynamic disturbance information set includes: analyzing the periodic fluctuation characteristics and step impact characteristics based on the photovoltaic panel reflection disturbance information and the photovoltaic panel load impact information, and the overlapping, interleaving or isolation relationship between the two in the occurrence time period to obtain the disturbance synchronization time window; analyzing the uneven illumination caused by the movement of the water surface reflection area and the local power impact caused by the access of charging piles at different locations and the start and stop of aquaculture equipment clusters based on the photovoltaic panel reflection disturbance information and the photovoltaic panel load impact information, and the overlapping influence range of the two in the physical partition of the photovoltaic array to obtain the disturbance superposition spatial region; and analyzing the dynamic degradation characteristics of photovoltaic output power caused by the combined action of periodic fluctuations and step impacts in the same time window and in the same spatial region to obtain the photovoltaic panel dynamic disturbance information set.

[0011] Optionally, the step of analyzing the multi-objective coordination and trade-off relationship between aquaculture demand and power generation demand based on the fish farm aquaculture information set and the photovoltaic panel dynamic disturbance information set to obtain an optimized scheduling information set includes: the fish farm aquaculture information set includes an aquaculture equipment operation information set and a water environment information set; based on the aquaculture equipment operation information set, analyzing the heat accumulation characteristics generated by power conversion during the continuous and periodic operation of different aquaculture equipment clusters to obtain equipment operation heat accumulation information; based on the water environment information set, analyzing the suitable water temperature threshold range for the target aquaculture fish species, as well as the current water body's heat capacity and heat dissipation characteristics to obtain water body temperature control requirements; based on the equipment operation heat accumulation information and the water body temperature control requirements, analyzing the impact of heat accumulation generated by the operation of aquaculture equipment clusters on water body temperature rise to obtain heat accumulation coupled temperature rise effect; based on the heat accumulation coupled temperature rise effect and the photovoltaic panel dynamic disturbance information set, analyzing the coordinated control rules for time-series management of aquaculture equipment operating power and targeted start-stop of heat dissipation or cooling equipment when photovoltaic output fluctuates due to disturbances to obtain the optimized scheduling information set.

[0012] Optionally, the process of constructing the heat accumulation coupled temperature rise effect includes: based on the heat accumulation information of the equipment operation, analyzing the total heat energy generated per unit time by the continuous and periodic operation of different equipment clusters under different aquaculture operation stages, to obtain the aquaculture heat production power characteristics; based on the water temperature control requirements, analyzing the heat energy threshold required to maintain the water temperature threshold range and the heat capacity and heat dissipation characteristics of the water per unit time, to obtain the water body heat balance power characteristics; based on the aquaculture heat production power characteristics, combined with the water body heat balance power characteristics, analyzing the dynamic matching and imbalance relationship between the heat production capacity and the natural heat dissipation capacity of the aquaculture water body in time series, to obtain the heat production and dissipation power mismatch window; based on the heat production and dissipation power mismatch window, analyzing the expected magnitude and duration of the water temperature exceeding the suitable threshold range due to net heat accumulation during the mismatch period, to obtain the heat accumulation coupled temperature rise effect.

[0013] Optionally, based on the heat-coupled temperature rise effect and combined with the photovoltaic panel dynamic disturbance information set, the analysis of the coordinated control rules for time-series management of the operating power of aquaculture equipment and targeted start-up and shutdown of heat dissipation or cooling equipment under the condition that photovoltaic output fluctuates due to disturbances, to obtain the optimized scheduling information set, includes: based on the heat-coupled temperature rise effect and combined with the disturbance synchronization time window, analyzing the feasibility and shift amount of shifting the operating power of aquaculture equipment during peak heat production periods to outside the disturbance synchronization time window and during periods of sufficient photovoltaic output, to obtain power time-series shift information; based on the power time-series shift information, analyzing the start-up and shutdown timing and operating power level of corresponding heat dissipation and cooling equipment in spatial regions where the expected temperature rise exceeds a threshold and the photovoltaic output in the spatial region is limited due to local disturbances, to obtain heat dissipation equipment start-up and shutdown information; based on the power time-series shift information and combined with the heat dissipation equipment start-up and shutdown information, integrating to form the coordinated control rules that stagger peak times in time and accurately match heat load and power supply capacity in space, to obtain the optimized scheduling information set.

[0014] Optionally, the step of generating a multi-terminal coordinated control strategy based on the optimized scheduling information set and outputting a photovoltaic-storage-charging microgrid control log includes: analyzing the instruction sequence for coordinated adjustment of the photovoltaic array output power, energy storage system charging and discharging power, and fishing boat charging load power based on the power timing shift information and the start / stop information of the heat dissipation equipment, to obtain multi-terminal power coordination instructions; analyzing the compensation amount and adjustment timing required to maintain the stability of microgrid voltage and frequency during the power adjustment process based on the multi-terminal power coordination instructions, to obtain voltage and frequency support instructions; integrating the multi-terminal power coordination instructions and the voltage and frequency support instructions to form the multi-terminal coordinated control strategy that is time-coordinated and coordinated in terms of control objectives; and recording the strategy triggering conditions, response actions of each terminal device, and actual operating parameter deviations based on the execution process of the multi-terminal coordinated control strategy to obtain the photovoltaic-storage-charging microgrid control log.

[0015] Secondly, this application provides a photovoltaic-storage-charging microgrid control system for weak power grids. The system includes: a dynamic disturbance module for acquiring a photovoltaic panel information set, and based on the photovoltaic panel information set, analyzing the coupling effect of light reflection and internal load impact on the photovoltaic panels to obtain a photovoltaic panel dynamic disturbance information set; an optimized scheduling module for acquiring a fish farm aquaculture information set, and based on the fish farm aquaculture information set, combined with the photovoltaic panel dynamic disturbance information set, analyzing the multi-objective coordination and trade-off relationship between fish farm aquaculture demand and power generation demand to obtain an optimized scheduling information set; and a control strategy module for generating a multi-terminal coordinated control strategy based on the optimized scheduling information set and outputting a photovoltaic-storage-charging microgrid control log. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application; Figure 2 A flowchart illustrating a control method for a photovoltaic-storage-charging microgrid oriented towards a weak power grid, provided in one embodiment of this application; Figure 3 This is a schematic diagram of a photovoltaic-storage-charging microgrid control system structure for weak power grids, provided as an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0020] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0021] In the operation of the microgrid for photovoltaic storage and charging in fish farms, existing technologies still have obvious shortcomings. In weak grids, photovoltaic output is easily affected by the coupling interference of light fluctuations and load impacts. The control strategy is not adapted to dynamic disturbances, which can easily affect system stability. The scheduling objective is singular and does not fit the characteristics of aquaculture loads in fish farms. There is a lack of multi-objective coordination mechanism. Multi-terminal coordination is difficult to match demand in real time. Power regulation and power supply quality are poor, which restricts the efficient operation of the system.

[0022] Based on this, this application provides a control method and system for photovoltaic-storage-charging microgrids in weak power grids. It couples and analyzes light reflection and internal load disturbances to accurately pinpoint the core causes of photovoltaic instability in weak power grids and provides accurate feedforward basis. Multi-objective optimization resolves conflicts between aquaculture and photovoltaic farming, and coordinates economic and ecological benefits. Model predictive control enables multi-terminal collaboration, ensuring robust scheduling execution. Combined with logs, it improves operation and maintenance, and comprehensively enhances the stability, economy, and sustainability of photovoltaic-storage-charging microgrids in weak power grids.

[0023] Figure 1 This application provides an application scenario diagram. During the operation of a photovoltaic-storage-charging microgrid in a fish farm, the method provided in this application is applied to locate the photovoltaic disturbance source in a weak grid through coupling analysis, providing precise feedforward for control; multi-objective optimization is used to resolve the conflict between fishing and photovoltaic power generation, and to coordinate economic and ecological benefits; combined with logs, operation and maintenance capabilities are improved, and the overall performance of the photovoltaic-storage-charging microgrid under a weak grid is comprehensively enhanced.

[0024] Specifically, the method provided in this application can be applied to any server. The server interacts with photovoltaic monitoring sensors and aquaculture management platform to obtain photovoltaic panel information provided by the photovoltaic monitoring sensors and aquaculture information provided by the aquaculture management platform. By coupling and analyzing light reflection and internal load impact, the core disturbance sources affecting photovoltaic stability under weak grid conditions are accurately identified, and the photovoltaic-storage-charging microgrid control log is output to the fish farm operation and maintenance personnel. Overall, the stability, economy and sustainability of the photovoltaic-storage-charging microgrid under weak grid conditions are enhanced.

[0025] For specific implementation details, please refer to the following examples.

[0026] Figure 2 This is a flowchart illustrating a control method for a photovoltaic-storage-charging microgrid oriented towards a weak power grid, provided as an embodiment of this application. The method of this embodiment can be applied to servers in the above-mentioned scenarios. Figure 2 As shown, the method includes: S201. Obtain the photovoltaic panel information set. Based on the photovoltaic panel information set, analyze the coupling effect of light reflection and internal load impact on the photovoltaic panel to obtain the photovoltaic panel dynamic disturbance information set.

[0027] A photovoltaic (PV) panel information set can be a collection of information reflecting the operating status of PV panels and their surrounding environmental parameters, with PV monitoring sensors as the data source. A PV panel is a semiconductor device that directly converts solar energy into electrical energy using the photovoltaic effect. Sunlight reflection refers to the reflection phenomenon that occurs after sunlight shines on objects in the surrounding environment or on the surface of the PV panel. Internal load surges can be sudden and significant changes in the electrical load within a PV-storage-charging microgrid over a short period of time; such load fluctuations can impact the voltage and power balance of the microgrid. A PV panel dynamic disturbance information set can be a collection of information used to characterize the fluctuations in the operating status of PV panels under the influence of external environment and internal load.

[0028] Specifically, in aquaculture-solar hybrid fish farms, photovoltaic power generation is subject to dual coupled disturbances from the reflected light field on the water surface and the start-up and shutdown of loads such as aquaculture equipment. Existing methods treat these disturbances in isolation, resulting in large deviations in output prediction and grid fluctuations. By fusing photovoltaic panel operation data and internal load disturbance data, and introducing a coupled correlation analysis method based on projection transformation, the non-uniform irradiance distribution model of reflected light on the water surface is coupled with the grid impedance change model caused by load impact for modeling and simulation. This allows for the analysis of the composite disturbance information set that leads to dynamic distortion of the photovoltaic panel output characteristics.

[0029] S202. Obtain the aquaculture information set of the fish farm. Based on the aquaculture information set of the fish farm and the dynamic disturbance information set of the photovoltaic panel, analyze the multi-objective coordination and trade-off relationship between the aquaculture demand and the power generation demand of the fish farm to obtain the optimized scheduling information set.

[0030] The aquaculture information set can be a collection of information reflecting various electricity demands and environmental control requirements during the aquaculture production process, with the aquaculture management and control platform as the data source. Aquaculture demand refers to the various requirements of the aquaculture farm during the production process, including the stability, continuity, and power output of the power supply to ensure the normal growth of aquatic products and maintain a stable aquaculture environment. Power generation demand refers to the requirements of the photovoltaic-storage-charging microgrid during photovoltaic power generation, including the output power, absorption efficiency, and dispatch allocation, to improve the utilization efficiency of photovoltaic clean energy, reduce dependence on weak grids, and ensure the microgrid's own power balance. The optimized dispatch information set can be a collection of dispatch parameters and strategy guidance information used to guide the multi-terminal coordinated control of the photovoltaic-storage-charging microgrid.

[0031] Specifically, there is a conflict between the goals of generating revenue and ensuring aquaculture in the fishery-solar complementary microgrid: photovoltaic power affects dissolved oxygen in the water, aquaculture electricity consumption impacts grid stability, and the existing dispatching prioritizes power generation over ecology, and power rationing reduces overall benefits. By introducing a multi-objective particle swarm optimization algorithm and coordinating water ecology and power generation stability methods, multiple objectives are dynamically balanced to generate an optimized dispatching information set that takes into account both economic and ecological considerations.

[0032] S203. Based on the optimized scheduling information set, generate a multi-terminal coordinated control strategy and output the control log of the photovoltaic-storage-charging microgrid.

[0033] Multi-terminal coordinated control strategies can be collaborative control strategies formulated for the photovoltaic power generation end, energy storage regulation end, charging load end, and fish farm power consumption end in a photovoltaic-storage-charging microgrid. The photovoltaic-storage-charging microgrid control log can record various operational data, regulation operations, and status changes during the multi-terminal coordinated control process of the photovoltaic-storage-charging microgrid.

[0034] Specifically, in solar-aquaculture integrated fish farms, the key challenge is for multi-terminal devices to respond quickly and without conflict to scheduling targets and resist disturbances. Existing control systems are slow to respond and prone to oscillations. By relying on the scheduling power benchmark, model predictive control is used to generate accurate power commands through rolling optimization. The microgrid controller log service records data from all dimensions, providing complete data support for assessment, fault backtracking, and strategy iteration.

[0035] The approach provided in this embodiment firstly identifies the core disturbance sources affecting photovoltaic stability under weak grid conditions by coupling analysis of light reflection and internal load impact, providing high-precision feedforward information for control. Secondly, through multi-objective collaborative optimization, the contradiction between power generation and aquaculture in the solar-aquaculture farm is resolved, achieving a balance between economic and ecological benefits. Finally, through model predictive control, multi-terminal coordination is achieved, ensuring the robust execution of optimized scheduling commands in dynamic environments. Combined with detailed control logs, observability and maintainability are improved, thereby enhancing the overall stability, economy, and sustainability of the solar-storage-charging microgrid under weak grid conditions.

[0036] In some embodiments, the photovoltaic panel information set includes a solar radiation information set and a photovoltaic panel power load information set. Based on the solar radiation information set, the characteristics of light reflection fluctuations on the water surface caused by changes in the incident angle and intensity of sunlight are analyzed to obtain photovoltaic panel reflection disturbance information. Based on the photovoltaic panel power load information set, the impact characteristics of load switching and changes within the microgrid on the output power parameters of the photovoltaic panel are analyzed to obtain photovoltaic panel load impact information. Based on the photovoltaic panel reflection disturbance information, combined with the photovoltaic panel load impact information, the superposition and coupling relationship between reflection fluctuations and load impacts in time and space is analyzed to obtain a photovoltaic panel dynamic disturbance information set.

[0037] The solar radiation information set can be a set of parameters characterizing the solar radiation-related features of the environment in which the photovoltaic panel is located. The photovoltaic panel power load information set can be a set of parameters reflecting the impact of various loads within the microgrid on the photovoltaic panel's power output. Coupling effects can be the combined effects formed by the superposition and interaction of solar reflection fluctuations and internal load impacts in the temporal and spatial dimensions. The photovoltaic panel reflection disturbance information can be a set of parameters related to the disturbances caused by solar reflection fluctuations on the operation of the photovoltaic panel. The photovoltaic panel load impact information can be a set of parameters related to the impact of internal load impacts on the photovoltaic panel's output power parameters. Reflection fluctuations can be the wave characteristics generated by the reflection of sunlight on the water surface caused by changes in the angle and intensity of sunlight incidence.

[0038] Specifically, in the process of photovoltaic (PV) power generation on the surface of fish farms, if the coupling effect of sunlight reflection and internal load impact is not analyzed, the actual disturbance of the PV panels cannot be accurately captured, leading to deviations in PV output judgment. This results in a lack of scientific basis for subsequent aquaculture and power generation scheduling, damage to the stability of the microgrid power supply, and difficulty in matching the electricity demand of the fish farm. To address these issues: high-precision light sensors (such as those using silicon photodiodes and cosine correctors to measure total solar radiation and diffuse radiation) and intelligent monitoring units for PV panels (such as optimizers integrating current and voltage sensing and communication functions) are deployed at key nodes of the PV array to collect real-time data on the solar incidence angle (e.g., obtained through a dual-axis tracking calculation module), irradiance, and the current and voltage data of the PV strings, constructing a solar information set with spatiotemporal resolution. Simultaneously, the power at the grid connection point is collected at second-level cycles through the SCADA interface of the microgrid energy management system (EMS). Voltage data, combined with smart meters (such as those supporting the DL / T645 protocol) to obtain detailed switching timing and power curves of each branch load (such as aerators, circulating water pumps, feeders, and fishing boat charging piles), forms a photovoltaic panel power load information set. At the data analysis layer, for reflection disturbances, a water surface light reflection calculation model based on Fresnel equations and surface roughness models is applied. Input parameters such as solar altitude angle (e.g., varying from 35 degrees to 65 degrees), azimuth angle, and wind speed (used to estimate the surface slope distribution caused by waves) are input to dynamically simulate the non-reflected light formation on the photovoltaic panel array plane. A uniform irradiance distribution map was generated, and its fluctuation period and amplitude characteristics were extracted to quantify photovoltaic panel reflection disturbance information. For load impact, a hybrid algorithm based on wavelet transform and abrupt change detection was used to process the load power time series data: First, wavelet multi-resolution analysis was used to extract the low-frequency steady-state component characterizing the periodic start-up and shutdown of aquaculture equipment (e.g., aerators operating on a 4-hour cycle); second, CUSUM or sliding window variance detection algorithms were applied to accurately identify the power step abrupt change points caused by random access to fishing boat charging piles (e.g., single pile power demand can reach 80 kW) and their impact intensity. The duration of the disturbance is used to separate and quantify the steady-state disturbance information and the fast-charging impact information of fishing boats, which are then integrated into the load impact information of the photovoltaic panels. Finally, in the coupling analysis layer, a spatiotemporal gridding processing method is introduced: the time axis is divided into minute-level segments, and the photovoltaic array is divided into multiple sub-regions according to the physical layout; through time alignment, the fluctuation curve of the reflection disturbance and the step event of the load impact are superimposed on a unified time axis to identify the "disturbance synchronization time window" (for example, it was found that the peak period of reflection fluctuation from 11:00 to 13:00 every day has a 70% overlap with the peak period of fishing boat charging requests).By spatial mapping, the calculated high-reflection intensity region is superimposed with the high-load impact point (i.e., the location of charging piles and large-scale aquaculture equipment clusters), delineating the "disturbance superposition spatial region" (for example, determining that array C area is simultaneously a high-reflection risk area and the location of two high-power charging piles). Based on this, a dynamic simulation model (such as a detailed electrical model of the photovoltaic array and a grid impedance model built based on Matlab / Simulink) is used as input to simulate and analyze the sudden drop depth, recovery time, and impact on grid connection voltage of photovoltaic output power under coupled disturbances. Finally, a comprehensive set of dynamic disturbance information for photovoltaic panels, including disturbance intensity, spatiotemporal location, and expected impact, is extracted, providing accurate disturbance images for upper-level optimized scheduling.

[0039] The method provided in this embodiment accurately uncovers the coupled disturbance patterns of photovoltaic panels under dual factors, obtaining comprehensive dynamic disturbance information. This provides reliable data support for subsequent multi-objective scheduling of fish farms, enhancing the scientific nature and stability of microgrid operation.

[0040] In some embodiments, based on the photovoltaic panel power load information set, the switching timing and continuous operation requirements of the internal aquaculture load are analyzed to obtain the steady-state disturbance information of the uninterrupted priority load; based on the photovoltaic panel power load information set, the random access and high-power demand characteristics of the externally connected fishing vessel charging load are analyzed to obtain the fishing vessel fast-charging impact information; based on the steady-state disturbance information, combined with the fishing vessel fast-charging impact information, the power fluctuation and voltage transient characteristics caused by the superposition of aquaculture guarantee needs and sudden charging needs are analyzed to obtain the photovoltaic panel load impact information.

[0041] Internal aquaculture load refers to the fixed base load of the microgrid formed by various electrical equipment connected to the aquaculture farm to maintain the aquaculture environment and carry out aquaculture production. Switching sequence refers to the time order and node characteristics of each piece of equipment in the internal aquaculture load being put into or taken out of operation according to aquaculture process requirements. Continuous operation demand refers to the power demand characteristics of some core equipment in the internal aquaculture load that require continuous and uninterrupted operation to ensure the stability of the aquaculture environment. Uninterruptible priority load refers to high-priority electrical loads in the internal aquaculture load that are directly related to the aquaculture water environment and the survival of aquatic organisms and cannot tolerate sudden power outages. Steady-state disturbance information refers to the continuous and regular disturbance characteristics of the photovoltaic panel output power parameters generated by uninterruptible priority loads during continuous and periodic operation. Externally connected fishing vessel charging load refers to the temporary electrical load formed by fishing vessels coming to the fish farm for resupply connecting to the microgrid charging piles. Random connection characteristics refer to the irregular connection time characteristics of fishing vessel charging loads due to the uncertain arrival time of fishing vessels. High-power demand characteristics refer to the power demand characteristics of fishing vessel charging loads that typically require high-power charging to achieve rapid energy replenishment. Fast charging impact information for fishing vessels can be defined as the sudden, abrupt disturbances in the output power parameters of photovoltaic panels caused by random access and high power demand from the fishing vessel's charging load. Aquaculture support needs refer to the basic requirements for the stability and power supply of the microgrid to maintain normal aquaculture operations. Sudden charging demand refers to the temporary, sudden power demand generated when fast charging loads from fishing vessels are connected. Power fluctuation characteristics refer to the deviations of the photovoltaic panel's output power from the baseline value due to various load disturbances. Voltage transient characteristics refer to the temporary, short-term deviations of the photovoltaic panel's output voltage from the rated value due to load disturbances.

[0042] Specifically, during the operation of the microgrid for photovoltaic storage and charging in the fish farm, if the steady-state disturbance of the aquaculture load and the impact of fast charging on fishing boats are not analyzed separately, the full picture of the load impact will not be grasped, leading to deviations in the dynamic disturbance analysis of the photovoltaic panels, causing power and voltage imbalance in the microgrid, affecting the stability of power supply for aquaculture and reducing the adaptability of fast charging on fishing boats. To address the aforementioned issues: Smart meters (e.g., devices using DL / T645 or Modbus protocols) installed on the power distribution circuits of aquaculture equipment and charging piles are used to collect real-time voltage, current, power, and switch status information. Firstly, for the "internal aquaculture load," time-series clustering (e.g., K-Shape algorithm) and association rule mining techniques are employed to analyze the historical operating logs of the equipment. This accurately identifies clusters of "uninterruptible priority loads" (e.g., aerators maintaining dissolved oxygen in the core aquaculture area) and establishes their periodic "switching sequence" model, thereby extracting "steady-state disturbance information" characterizing the power demand baseline. Concurrently, for the "externally accessed fishing boat charging load," the "pile-boat" connection events (e.g., CC / CP signals) reported by the charging pile controller are used as triggers. Combined with Fast Fourier Transform analysis of the power signal's step characteristics, "random access" events are captured in real-time and their "..." are quantified. The system analyzes high-power demand characteristics (e.g., using the sliding window differential method to detect the transient process of power jumping from 0kW to 60kW within seconds), independently generates a sequence of "fishing boat fast charging impact information" events. Finally, it introduces the time-series superposition simulation method commonly used in power transient analysis to align the two on a unified time axis. By establishing a simplified sequence impedance model of the microgrid that includes line impedance, it reproduces the composite scenario of a fast charging impact event (as a current step source) superimposed on a steady-state disturbance background (as a time-varying load source) in a simulation platform (such as MATLAB / Simulink). It simulates and calculates the voltage dynamic response at the point of common coupling, thereby quantitatively analyzing "power fluctuations" and "voltage transient characteristics" (e.g., assessing whether the voltage sag is below 0.85pu and lasts for more than 100ms). All analysis results are structured into a "photovoltaic load impact information" data object containing time tags, disturbance type, amplitude, and predicted voltage impact, for upper-level calls.

[0043] The method provided in this embodiment accurately decomposes the characteristics of two types of load disturbances, fully captures the overall picture of photovoltaic panel load impact, provides accurate data for subsequent dynamic disturbance analysis, ensures the stability of microgrid power and voltage, and improves the adaptability to aquaculture and fast charging loads.

[0044] In some embodiments, based on the photovoltaic power load information set, the operating parameters and periodic start-stop patterns of each aquaculture equipment to maintain a stable aquaculture environment are analyzed to obtain the steady-state operating characteristics of different aquaculture equipment; based on the steady-state operating characteristics, the collaborative working modes and temporal overlap relationships generated by each aquaculture equipment in order to achieve aquaculture goals during the aquaculture operation cycle are analyzed to obtain the collaborative disturbance characteristics between equipment; based on the collaborative disturbance characteristics between equipment, the continuous impact of the superposition of the continuous and periodic operation of multiple aquaculture equipment on the power and voltage background values ​​of the microgrid is analyzed to obtain steady-state disturbance information.

[0045] Operating parameters can be the core performance indicators required by each aquaculture device to maintain a stable aquaculture environment. Periodic start-stop patterns can be the fixed-interval start-stop and fixed-duration operation patterns of aquaculture devices based on aquaculture process requirements. Steady-state operating characteristics can be the inherent operating features exhibited by different aquaculture devices during stable operation phases. Aquaculture operation cycles can be the continuous operation time intervals set by the fish farm to achieve specific aquaculture stage goals. Collaborative working modes can be the cooperative and coordinated operation methods formed by various aquaculture devices to achieve common aquaculture goals. Temporal overlap relationships can be the overlapping and connecting states of different aquaculture devices in terms of operating time. Inter-device collaborative disturbance characteristics can be the disturbance characteristics generated by multiple aquaculture devices operating collaboratively on the microgrid. Microgrid power and voltage background values ​​can be the basic stable values ​​of power and voltage when the microgrid has no sudden load impacts and only carries the normal operating load of the aquaculture devices. Continuous impacts can be the long-term, uninterrupted effects of the continuous and periodic operation of aquaculture devices on the microgrid power and voltage background values.

[0046] Specifically, during the operation of a microgrid for solar power, energy storage, and charging in a fish farm, if steady-state disturbance information is not established, it will be impossible to control the basic load disturbance superimposed on the aquaculture equipment. This will lead to the inability to distinguish between steady-state and sudden shocks, resulting in distorted judgment of photovoltaic load shocks. This will cause the microgrid control strategy to deviate from reality and seriously affect the coordinated stability of aquaculture and power generation. To address the aforementioned issues: For each type of equipment (e.g., an aerator with a rated power of 1.5kW), time series decomposition techniques (e.g., using the STL seasonal decomposition algorithm or Fourier transform for spectral analysis) are employed to process its long-term power curves, removing random interferences such as weather and human operation, and extracting its inherent "steady-state operating characteristics." These characteristics include periodic start-stop patterns triggered by timers or environmental parameter thresholds (e.g., starting when dissolved oxygen is below 5mg / L), typical operating durations (e.g., 2 hours per run), and baseline power. Then, based on the known "aquaculture operation cycle" table, association rule mining (e.g., the Apriori algorithm) and event sequence analysis models (e.g., the PrefixSpan algorithm) are used to analyze the start-stop event sequences of all equipment, automatically identifying and quantifying "cooperative working modes" and "temporal overlap relationships." For example, the rule "within 5 minutes after the feeder starts, there is a 90% probability that the aerator and circulating water pump in area A will start simultaneously, with overlapping operating times" is identified. "Up to 20 minutes", then, all the identified cooperative patterns are input into a clustering analysis algorithm (such as K-means clustering) according to their aggregated power pattern and occurrence time, forming several typical "inter-equipment cooperative disturbance characteristics" clusters. Each cluster represents a load combination pattern that recurs during the breeding cycle and has a specific power profile (such as the total power ladder formed by the superposition of 3 devices). Finally, these characteristic clusters are used as inputs and injected into a simulation framework based on improved power flow calculation (such as forward back substitution) and voltage stability analysis. The simulation calculates the continuous and periodic impact curve formed by the temporal superposition of these regular load clusters on the total active power and key bus voltage at the microgrid point of common coupling (PCC) throughout the entire operating cycle, thereby accurately quantifying and outputting "steady-state disturbance information". This information is essentially a parameterized model that includes the background power fluctuation range (such as 500W-1500W), voltage slow drift period and amplitude, providing a baseline for subsequent advanced control.

[0047] The method provided in this embodiment accurately captures the basic steady-state disturbance characteristics of aquaculture equipment on the microgrid, providing a reliable benchmark for subsequent analysis of sudden load shocks, making the load disturbance analysis of the microgrid more accurate, and laying a solid data foundation for the coordinated regulation of aquaculture and power generation.

[0048] In some embodiments, based on photovoltaic panel reflection disturbance information and photovoltaic panel load impact information, the periodic fluctuation characteristics and step impact characteristics are analyzed, and the overlap, interleaving or isolation relationship between the two in the occurrence time period is obtained to obtain the disturbance synchronization time window; based on photovoltaic panel reflection disturbance information and photovoltaic panel load impact information, the uneven illumination caused by the movement of the water surface reflection area and the local power impact caused by the access of charging piles at different locations and the start and stop of aquaculture equipment clusters are analyzed, and the overlapping influence range of the two in the physical partition of the photovoltaic array is obtained to obtain the disturbance superposition spatial region; based on the disturbance synchronization time window and the disturbance superposition spatial region, the dynamic degradation characteristics of photovoltaic output power caused by the combined effect of periodic fluctuations and step impacts in the same time window and in the same spatial region are analyzed to obtain the photovoltaic panel dynamic disturbance information set.

[0049] Periodic fluctuation characteristics can refer to the regular power fluctuations exhibited by photovoltaic panels due to the reflection of sunlight from the water surface, which change with the angle and intensity of sunlight incidence. Step-impact characteristics can refer to the sudden, step-like power surges experienced by photovoltaic panels due to internal load switching, fast charging by fishing boats, etc. Disturbance synchronization time windows can refer to the time intervals corresponding to the overlapping, interleaving, or isolated occurrence of periodic fluctuation characteristics and step-impact characteristics. Water surface reflection area movement can refer to the change in the position of the area on the water surface reflecting sunlight from the photovoltaic panels due to changes in the angle and intensity of sunlight incidence. Uneven illumination can refer to the inconsistent light intensity received by different locations within the photovoltaic array caused by factors such as the movement of the water surface reflection area. Localized power surges can refer to the power surges in localized areas of the photovoltaic array caused by the connection of charging piles at different locations and the start-up and shutdown of aquaculture equipment clusters. Photovoltaic array physical zoning can refer to the physical zoning of the photovoltaic panel array based on the layout of the fish farm, equipment distribution, etc. Disturbance superposition space area can refer to the overlapping influence range of uneven illumination and localized power surges within the physical zoning of the photovoltaic array. The dynamic degradation characteristics of photovoltaic output power can be the dynamic variation characteristics of photovoltaic panel output power caused by the combined effects of periodic fluctuations and step impacts within the same disturbance synchronization time window and disturbance superposition spatial region.

[0050] Specifically, in the process of building a microgrid for photovoltaic, energy storage, and charging in a fish farm, if the spatiotemporal coupling relationship between reflected fluctuations and load impacts is not analyzed, the actual disturbances to the photovoltaic panels cannot be accurately grasped, leading to inaccurate subsequent scheduling strategies, causing severe fluctuations in photovoltaic output, unstable voltage and frequency in the microgrid, and affecting the operation of aquaculture equipment and the charging safety of fishing boats. To address these issues, the following technique is first applied: time series alignment and Pearson correlation coefficient calculation are used to precisely align the periodic power fluctuation sequence (e.g., sinusoidal fluctuations with a 10-minute period) in the photovoltaic panel reflected disturbance information with the step event marker sequence in the load impact information. The correlation coefficient between the two is then calculated using a sliding time window. When the absolute value of the correlation coefficient consistently exceeds a threshold (e.g., 0.7) within a certain period (e.g., midday 12:00-13:00), this period is determined to be the disturbance synchronization time window. This technique directly quantifies... The probability of temporal resonance between the trough of reflection fluctuations and the peak of load impacts was assessed. Subsequently, spatial overlay analysis and DC power flow tracing algorithms based on a Geographic Information Platform (GIS) were employed. This involved spatially registering and overlaying the uneven illumination raster map output from a water surface reflection model (such as an irradiance distribution model considering Fresnel reflection) with a power grid topology map marked with the locations of charging piles (such as the three 150kW fast charging piles located on the west side of the dock) and aquaculture equipment clusters (such as the aerator group in Area A). Then, forward-backward substitution was used for power flow tracing to determine the impact of each load impact on various parts of the photovoltaic array. The power deficit distribution caused by the partitioning (e.g., the 12 sub-arrays divided according to the combiner box) is analyzed to identify the spatially overlapping disturbance superposition regions where the influence ranges of the two are highly coincident (e.g., the sub-arrays 5 and 6 on the west side are identified as dual influence regions). Finally, for the spatiotemporal coupling points identified above, a real-time multiphysics co-simulation based on MATLAB / Simulink and PLECS tools is initiated to construct a model including the equivalent circuit model of the photovoltaic module with dual diodes in this region, a variable step-size MPPT algorithm (e.g., the disturbance observation method) model, and a model including the line impedance. The microgrid state-space model is injected with identified coupling disturbance time-series data. The simulation runs and extracts the frequency domain characteristics of the output power (by analyzing the oscillation frequency through FFT) and dynamic quality indicators (such as settling time and overshoot). This specifically characterizes and outputs the dynamic degradation characteristics of photovoltaic output power, such as "within the spatiotemporal window, the output power of subarray No. 5 exhibits a 30% amplitude decay accompanied by a continuous oscillation at a frequency of about 2Hz". The entire process transforms the abstract coupling relationship into a set of quantified spatiotemporal features that can be used for precise control through a series of specific and feasible modeling and algorithm techniques.

[0051] The method provided in this embodiment accurately identifies the spatiotemporal characteristics of photovoltaic panel disturbances, providing real data support for subsequent scheduling, making microgrid regulation more targeted, ensuring stable photovoltaic output, maintaining the safe operation of the microgrid, and taking into account both aquaculture and power generation needs.

[0052] In some embodiments, the aquaculture information set includes an aquaculture equipment operation information set and a water environment information set. Based on the aquaculture equipment operation information set, the heat accumulation characteristics generated by electrical energy conversion during the continuous and periodic operation of different aquaculture equipment clusters are analyzed to obtain equipment operation heat accumulation information. Based on the water environment information set, the suitable water temperature threshold range for the target aquaculture fish species, as well as the current water body's heat capacity and heat dissipation characteristics, are analyzed to obtain water body temperature control requirements. Based on the equipment operation heat accumulation information, combined with the water body temperature control requirements, the impact of heat accumulation generated by the operation of the aquaculture equipment cluster on water body temperature rise is analyzed to obtain the heat accumulation coupled temperature rise effect. Based on the heat accumulation coupled temperature rise effect, combined with the photovoltaic panel dynamic disturbance information set, the coordinated control rules for time-series management of aquaculture equipment operating power and targeted start-up and shutdown of heat dissipation or cooling equipment are analyzed when photovoltaic output fluctuates due to disturbances, to obtain an optimized scheduling information set.

[0053] The aquaculture equipment operation information set can be a collection of information reflecting the operational status of various aquaculture equipment in a fish farm. The aquatic environment information set can be a collection of parameters describing the environmental conditions of the aquaculture water. Equipment operation heat accumulation information can be data related to the cumulative heat characteristics generated by electrical energy conversion during the operation of different aquaculture equipment clusters. The target aquaculture species can be the specific fish species planned for aquaculture in the fish farm, whose growth characteristics determine the core requirements for water temperature control. The water temperature threshold range can be the suitable water temperature range for the normal growth and survival of the target aquaculture species. The water heat capacity can be the amount of heat absorbed or released by the aquaculture water to raise or lower the temperature per unit. The water heat dissipation characteristics can be the inherent characteristics of the aquaculture water in dissipating heat through various means. The water temperature control requirements can be the specific requirements and standards for temperature control to maintain a suitable temperature in the aquaculture water. The heat accumulation coupled with temperature rise effect can be the comprehensive effect of the interaction between the heat accumulation of aquaculture equipment and the thermal balance characteristics of the water, leading to a rise in the temperature of the aquaculture water. The fluctuation of photovoltaic output due to disturbance can be the non-stationary change in output power, voltage, and other electrical parameters of the photovoltaic panels due to coupled disturbances. Aquaculture equipment operating power timing management can be a time-based adjustment and planning of aquaculture equipment operating power based on photovoltaic output and water temperature control requirements. Heat dissipation or cooling equipment can be various devices specifically configured in fish farms to control the temperature of aquaculture water. Targeted start-up and shutdown can be precise start-up, shutdown, and speed adjustment operations of heat dissipation and cooling equipment based on the temperature rise of the aquaculture water and the spatial characteristics of photovoltaic output. Coordinated control rules can be principles for the coordinated management of aquaculture and heat dissipation equipment, taking into account photovoltaic output, equipment heat accumulation, and water temperature control requirements.

[0054] Specifically, in the process of coordinated regulation of aquaculture and microgrid power generation, if the heat accumulation of aquaculture equipment and water temperature regulation are not combined with photovoltaic disturbance analysis, the water temperature will exceed the standard and affect aquaculture. The fluctuation of photovoltaic output will also cause microgrid instability. At the same time, the imbalance between aquaculture and power generation demand will cause aquaculture losses and energy waste. To address the aforementioned issues: First, the data acquisition layer utilizes existing Industrial Internet of Things (IIoT) technology: real-time data collection of operating power and start / stop signals from equipment such as aerators and circulating water pumps is generated through smart meters or PLCs (Programmable Logic Controllers) embedded in the aquaculture equipment, forming a "quaculture equipment operation information set." A network of digital temperature sensors (such as PT100 platinum resistance thermometers) deployed in the water continuously collects water temperature data, which, together with suitable temperature thresholds for fish species (e.g., 22-28 degrees Celsius for a particular aquaculture species), forms a "water environment information set." Next, the modeling and analysis layer applies thermodynamics and energy balance principles to establish a dedicated model: based on equipment operating power data, using power-calorific-value conversion coefficients (e.g., according to Joule's law, 1 kilowatt-hour of electrical energy can theoretically be converted into 3600 kilojoules of heat energy), the instantaneous "aquaculture heat production power characteristics" of each equipment cluster and even the entire aquaculture area are calculated. Simultaneously, based on real-time water temperature, water volume, specific heat capacity, and convective and evaporative heat dissipation coefficients obtained by fitting historical data, a dynamic water body model is constructed. The system employs a state-of-the-art thermal balance model to quantify the "water body thermal balance power characteristics" required to maintain the target temperature. Then, the collaborative optimization layer is crucial for technical implementation: it compares the aforementioned heat generation and dissipation power characteristics in real-time over time. When the heat generation power consistently exceeds the dissipation capacity, it is determined to enter a "power mismatch window." Within this window, a temperature rise prediction model based on differential equations is invoked, inputting parameters such as the current heat power difference and water body heat capacity, to predict the water body temperature rise curve and the extent of exceeding the standard over a future period (e.g., the next 30 minutes). Simultaneously, it accesses the "photovoltaic panel dynamic disturbance information set," particularly the "disturbance synchronization time window" and location information related to power output decline. Finally, the decision execution layer utilizes a rule engine and optimization algorithms: for example, when it is predicted that the water temperature in a certain area will rise by 0.5 degrees Celsius in 20 minutes and the photovoltaic output in that area will decrease by 30% due to water surface reflection disturbance, the rule engine triggers a "power time-series shift" action—by sending a Modbus message to the corresponding feeder or water pump controller. The TCP command delays the high-power operation period originally scheduled within the disturbance window until the photovoltaic output recovers. For areas where the temperature may still exceed the standard after the shift, the backup heat dissipation equipment (such as circulating cooling towers) in the zone will be automatically started through the relay output module or intelligent circuit breaker, and the operating frequency of the inverter will be adjusted in stages according to the predicted temperature rise, thereby achieving precise matching and dynamic balance of heat generation, heat dissipation and power supply in time and space.

[0055] The method provided in this embodiment accurately matches the fluctuations in photovoltaic power output with the temperature control needs of aquaculture, avoids abnormal water temperature, ensures the stable operation of the microgrid, achieves multi-objective synergy between aquaculture and power generation, and improves overall operational efficiency.

[0056] In some embodiments, based on the heat accumulation information of equipment operation, the total heat energy generated per unit time by different equipment clusters under different aquaculture operation stages is analyzed to obtain the aquaculture heat production power characteristics; based on the water temperature control requirements, the threshold of heat energy dissipation and replenishment required to maintain the water temperature threshold range and the heat capacity and heat dissipation characteristics of the water per unit time is analyzed to obtain the water body heat balance power characteristics; based on the aquaculture heat production power characteristics, combined with the water body heat balance power characteristics, the dynamic matching and imbalance relationship between the heat production capacity and the natural heat dissipation capacity of the aquaculture water body in time series is analyzed to obtain the power mismatch window; based on the power mismatch window, the expected magnitude and duration of the water temperature exceeding the suitable threshold range due to net heat accumulation during the mismatch period are analyzed to obtain the heat accumulation coupled temperature rise effect.

[0057] Aquaculture operation stages can be defined as different work phases in a fish farm based on aquaculture objectives and fish growth patterns. An equipment cluster can be a collection of multiple aquaculture devices operating in combination within a fish farm to achieve specific aquaculture functions. Aquaculture heat production power characteristics can be the characteristic attribute of the total heat energy generated per unit time by different equipment clusters under different aquaculture operation stages. Water temperature control requirements can be the suitable water temperature threshold range for the target aquaculture fish species, and a set of relevant parameters related to the current water body's heat capacity and heat dissipation characteristics. Water temperature threshold range can be the upper and lower limits of water temperature that ensures the normal survival and growth of the target aquaculture fish species. Water body heat capacity can be the physical characteristic of the temperature change that occurs when aquaculture water absorbs or releases a certain amount of heat. Water body heat dissipation characteristics can be the inherent attribute of aquaculture water exchanging heat with the external environment and dissipating its own heat. Heat energy threshold can be the critical value of heat dissipation and replenishment required per unit time for the water body to maintain the water temperature threshold range. Water body heat balance power characteristics can be the characteristic attribute of the heat energy threshold required for dissipation and replenishment to maintain the water temperature threshold range and the water body's heat capacity and heat dissipation characteristics per unit time. The heat production capacity of aquaculture water can be defined as the ability of various equipment operating during the aquaculture process to increase the temperature of the water. The natural heat dissipation capacity of the water can be defined as the ability of the aquaculture water to dissipate heat through natural heat exchange with the outside environment without human intervention. The power mismatch window can be defined as the time interval during which the heat production capacity and natural heat dissipation capacity of the aquaculture water exhibit a dynamic imbalance in temporal sequence. The mismatch period can be defined as the specific time interval within the power mismatch window during which heat production and heat dissipation of the aquaculture water are in an unbalanced state. Net heat accumulation can be defined as the state in which the heat production of the aquaculture water consistently exceeds the heat dissipation during the mismatch period, leading to the continuous accumulation of heat in the water.

[0058] Specifically, in the process of regulating the microgrid for photovoltaic, energy storage, and charging in fish farms, if the heat accumulation coupling temperature rise effect is not constructed, it is impossible to predict the water temperature changes caused by the imbalance between heat production from aquaculture and heat dissipation from the water body. This can easily lead to the water temperature exceeding the suitable threshold, causing reduced activity, disease, or even death of farmed fish. At the same time, it makes the equipment regulation under the fluctuation of photovoltaic output lose its basis, undermining the stability of both aquaculture and power supply. To address the above problems: First, based on the heat accumulation information of equipment operation, an equipment power-to-heat conversion coefficient model is adopted (for example, by combining laboratory measurements and on-site calibration, it is determined that when a specific model of liquid oxygen aerator is running at its rated power, about 30% of the electrical energy is ultimately dissipated into the water as heat energy). The operating power time-series data of various aquaculture equipment (such as aerators, circulating pumps, and feeders) collected in real time (which can be obtained through smart meters or IoT data acquisition terminals) is accumulated hourly and the efficiency is calculated to obtain the aquaculture heat production power characteristics, that is, a dynamic curve with time as the horizontal axis and the total heat production per unit time (such as kilowatt-hours) as the vertical axis. Simultaneously, based on the need for water temperature regulation, a one-dimensional or two-dimensional dynamic model of water body heat balance is applied (e.g., the aquaculture area is gridded, and simplified simulation is performed using hydrodynamic and heat transfer modules such as MIKE 21 or Delft3D based on energy conservation, or a machine learning regression model trained based on measured data is used). Inputting real-time monitored water volume, specific heat capacity, water surface area, current water temperature, and minute-level ambient temperature, wind speed, relative humidity, and total solar radiation data provided by the meteorological station, the real-time heat dissipation power of the water body through natural processes such as evaporation, convection, and long-wave radiation is calculated. Combined with the upper limit of the suitable temperature range for the target fish species (e.g., 22-28℃), the water body heat balance power characteristics are obtained, i.e., the maximum net heat input power curve allowed to maintain the temperature below the threshold. Then, a sliding time window comparison algorithm is used (e.g., using 15 minutes as a window, calculating the difference between the "heat production power characteristic value" and the "heat balance power characteristic value" on a continuous time series), to analyze the dynamic relationship between these two curves. When the difference is consistently positive, a power mismatch window is identified, and its opening... The start and end times, duration, and cumulative net heat power values ​​were all accurately recorded. Finally, based on the cumulative net heat power of each mismatch window (i.e., the time integral), it was input into a lumped parameter thermal response model (for example, treating the water body as a homogeneous whole, using its total heat capacity parameter, and directly calculating the theoretical temperature rise based on the heat input: ΔT=Q_net / (m*c_p), where m is the mass of the water body and c_p is the specific heat capacity of the water). The model analyzed the expected magnitude and duration of the water temperature exceeding the appropriate threshold range caused by this net heat accumulation without any active heat dissipation intervention (for example, predicting that the water temperature in a certain zone will cumulatively rise from 26.5℃ to 29.2℃ within the next 3 hours, exceeding the upper limit by 1.2℃, and may continue to operate at a high level for 2 hours). This quantitative prediction conclusion, which includes specific magnitude, spatial location, and duration, serves as the output of the heat accumulation coupling temperature rise effect to guide subsequent coordinated regulation.

[0059] The method provided in this embodiment accurately quantifies the coupling relationship between heat production from aquaculture and heat dissipation from water, predicts the magnitude and duration of abnormal water temperature, provides precise basis for microgrid scheduling, ensures stable aquaculture water temperature, improves fish survival rate, and achieves coordinated regulation of aquaculture and power generation.

[0060] In some embodiments, based on the heat accumulation coupling temperature rise effect and combined with the disturbance synchronization time window, the feasibility and shift amount of shifting the operating power of aquaculture equipment during peak heat production periods to outside the disturbance synchronization time window and during periods of sufficient photovoltaic output are analyzed to obtain power time-series shift information. Based on the power time-series shift information, when the expected temperature rise exceeds the threshold in a spatial region and the photovoltaic output in the spatial region is limited due to local disturbances, the start-up and shutdown timing and operating power level of the corresponding heat dissipation and cooling equipment are analyzed to obtain heat dissipation equipment start-up and shutdown information. Based on the power time-series shift information and combined with the heat dissipation equipment start-up and shutdown information, a coordinated control rule that staggers peak times in time and accurately matches heat load and power supply capacity in space is integrated to obtain an optimized scheduling information set.

[0061] Targeted start-up and shutdown of heat dissipation or cooling equipment can be an operational method that adjusts the start-up, shutdown, and power levels of heat dissipation or cooling equipment in specific areas and at specific times based on the expected water temperature rise and the spatial distribution of photovoltaic output. Peak heat production periods refer to the time periods during which the heat energy generated by the aquaculture equipment cluster reaches its peak, easily causing a rapid rise in water temperature. Sufficient photovoltaic output periods refer to the time periods when the photovoltaic array is minimally affected by disturbances, its output power is stable, and it can meet the power requirements of the aquaculture equipment. Power time-series shift information can be information representing the feasibility and specific shift amount of shifting the operating power of the aquaculture equipment from peak heat production periods to periods of sufficient photovoltaic output. Expected temperature rise can be the specific value of the predicted temperature rise of the aquaculture water within a specific time period based on the heat accumulation of the aquaculture equipment and the heat dissipation characteristics of the water. Thresholds can be critical values ​​set for indicators such as water temperature and the degree of photovoltaic output disturbance to maintain the stability of the aquaculture water environment. Limited photovoltaic output due to local disturbances can refer to a state where the output power of a specific physical zone of the photovoltaic array decreases due to disturbances such as light reflection and local load impacts, failing to meet the regional electricity demand. Information on the start / stop of heat dissipation equipment can indicate the timing and operating power level of the equipment in areas where water temperature rises above a threshold and photovoltaic output is limited. Heat load can be the total amount of heat accumulated during the operation of aquaculture equipment and the amount of heat energy dissipated or replenished to maintain a suitable water temperature. Power supply capacity can be the effective electrical output capability of the photovoltaic array to provide to aquaculture equipment, heat dissipation equipment, etc., under specific temporal and spatial conditions.

[0062] Specifically, during the operation of the microgrid for photovoltaic storage and charging in fish farms, if the heat accumulation coupling temperature rise effect and photovoltaic disturbance characteristics are not controlled by the equipment, problems such as the overlap of heat generation peaks and photovoltaic output disturbances, local temperature rise exceeding the threshold and insufficient power supply will occur, directly damaging the aquatic environment of aquaculture, causing the power supply and demand of the microgrid to be unbalanced, and triggering equipment shutdown. To address the aforementioned issues: First, a spatiotemporal correlation matching algorithm based on a rule engine is employed to compare and identify in real time the data chain describing the cumulative temperature rise effect (description of the expected temperature rise magnitude and duration) with the data chain indicating the disturbance synchronization time window during which photovoltaic output is affected. This automatically identifies the overlapping "high-risk spatiotemporal units" (e.g., predicting a 0.8℃ rise in water temperature in aquaculture area A3 between 1 PM and 2 PM, with photovoltaic output expected to decrease by 40% due to strong water surface reflection). To address the identified risks and achieve power time-series shifting, a flexible scheduling technique based on constraint satisfaction problem (CSP) is applied. Various aquaculture equipment (such as feeders and circulating pumps) are abstracted into tasks with attributes such as operating time windows, minimum operating duration, power levels, and tolerance for delays / advancements. Periods with ample photovoltaic output are considered available resource windows. The CSP solver, while satisfying all hard constraints of the aquaculture process, seeks feasible load rearrangement schemes (e.g., successfully scheduling the No. 1 feeder task, originally scheduled to start during a high-risk period, to 3 PM when photovoltaic output is sufficient). (Point execution) Meanwhile, for local temperature control, a closed-loop regulation based on model predictive control (MPC) is deployed. The simplified thermodynamic model of each zone (such as a first-order inertial plus pure time delay model) is used as the internal prediction model, and the data of distributed water temperature sensors and local irradiance meters are used as real-time feedback. The optimal control sequence in the future finite time domain is solved in a rolling manner. For example, when it is predicted that the water temperature of zone B5 will exceed the threshold in 30 minutes and the real-time output of photovoltaic power in this zone is insufficient, the MPC controller will immediately calculate the best time and minimum required power to start the backup cooling water pump in this zone (such as running at 60% of the rated power for 20 minutes after 5 minutes), thereby generating accurate start and stop information of heat dissipation equipment. Finally, a central co-optimizer is responsible for integration and arbitration: it receives the global load shift plan output by the CSP solver and the local heat dissipation demand reported by the MPC controller, performs global coordination through multi-objective optimization algorithm, resolves potential conflicts (such as avoiding shifting high-power equipment to a zone that is planned to start cooling), and generates a unified and conflict-free optimized scheduling information set that can be distributed.

[0063] The method provided in this embodiment accurately matches heat load with photovoltaic power supply capacity, avoids the impact of photovoltaic disturbances on aquaculture, stabilizes water temperature, ensures aquaculture production, and improves the power dispatch efficiency and operational stability of the microgrid.

[0064] In some embodiments, based on power timing shift information and combined with heat dissipation equipment start / stop information, the instruction sequence for coordinated adjustment of photovoltaic array output power, energy storage system charging / discharging power, and fishing boat charging load power is analyzed to obtain multi-terminal power coordination instructions; based on multi-terminal power coordination instructions, the compensation amount and adjustment timing required to maintain microgrid voltage and frequency stability during power adjustment are analyzed to obtain voltage and frequency support instructions; based on multi-terminal power coordination instructions and combined with voltage and frequency support instructions, a multi-terminal coordinated control strategy that is time-coordinated and coordinated in control objectives is formed; based on the execution process of the multi-terminal coordinated control strategy, the strategy triggering conditions, the response actions of each terminal device, and the actual operating parameter deviations are recorded to obtain the photovoltaic-storage-charging microgrid control log.

[0065] The output power of the photovoltaic array can be the actual electrical power transmitted by the photovoltaic array in the microgrid for photovoltaic-storage-charging. The charging and discharging power of the energy storage system can refer to the input power during charging and the output power during discharging of the energy storage system in the microgrid. The charging load power of the fishing boats can refer to the power value corresponding to the electrical load generated by the fishing boats connected to the microgrid during charging. Multi-terminal power coordination commands can refer to the sequence of commands for coordinated adjustment of the power of the photovoltaic array, energy storage system, and fishing boat charging load. Microgrid voltage and frequency stability can refer to the state in which the voltage and frequency of the photovoltaic-storage-charging microgrid are maintained within a preset reasonable range during operation. Compensation amount can refer to the supplementary or adjusted value required to maintain the voltage and frequency stability of the microgrid during power adjustment. Adjustment timing can refer to the time sequence and rhythm of compensation operations to maintain the voltage and frequency stability of the microgrid. Voltage and frequency support commands can refer to the compensation amount and adjustment timing related commands formulated based on multi-terminal power coordination commands to maintain the voltage and frequency stability of the microgrid. Strategy triggering conditions can refer to the specific environment, parameters, and other conditions that trigger the execution of various commands in the multi-terminal coordination control strategy. The response actions of each device at the end of the microgrid can refer to the operational status adjustments made by each device, such as photovoltaic arrays, energy storage systems, and charging piles, after receiving control commands. The actual operating parameter deviation can refer to the difference between the actual operating parameters and the preset parameters of the strategy after each device at the end of the microgrid executes its control actions.

[0066] Specifically, without this step, during the regulation and control of the microgrid for photovoltaic, energy storage, and charging in a weak power grid fish farm, power adjustment will cause voltage and frequency instability, the operation of equipment at each end will lack unified instructions, the demand for aquaculture and power supply will be unbalanced, and without operation records, it will be impossible to review and optimize, ultimately leading to microgrid failure and damage to aquaculture production. To address the aforementioned issues: Utilizing the power time-series shift information and heat dissipation equipment start-up and shutdown information from the optimized scheduling information set, firstly, a multi-objective rolling optimization technique, Model Predictive Control (MPC), is employed. Specifically, this involves using the predicted photovoltaic output curves for several future scheduling cycles (e.g., the next 15 minutes, in 5-minute intervals), the planned power curves of the shifted aquaculture and heat dissipation equipment, and the charging and discharging capacity of energy storage as constraints. The objective functions are then solved online to minimize total operating cost and power fluctuations, resulting in a complete set of time-coherent "multi-terminal power coordination commands," such as "From 10:05 to 10:10, the photovoltaic inverter's power is limited to 80% of its rated value, the energy storage discharges at 50kW, and the power of the No. 1 fishing boat charging pile is increased to 90kW." Next, to address potential grid dynamic issues arising from executing these power commands, an adaptive virtual synchronous machine (VSG) control technique is introduced. The specific processing method is as follows: The aforementioned power command sequence is fed into a real-time digital simulation model of a microgrid containing line parameters and load models to predict the voltage deviation and system frequency change trend of key nodes in advance. Once it is predicted that the frequency deviation will exceed ±0.2Hz at a certain moment, the control algorithm immediately calculates the virtual inertia and damping coefficient that the energy storage converter needs to simulate and generates the corresponding "voltage and frequency support command", such as "at 10:07, the energy storage provides an additional 15kVar of reactive power support". Subsequently, through a time-scale synchronization and command fusion module, the power command and support command are aligned and logically verified on a unified time axis to ensure seamless timing (for example, the energy storage system discharge command and the command to provide virtual inertia must take effect in tandem). Finally, a complete "multi-terminal coordinated control strategy" is packaged and distributed to the controllers of each device. Finally, the entire strategy execution process is based on IEC The 61850 standard SCADA (Supervisory Control and Data Acquisition) provides a comprehensive record. The application of this technology is reflected in the fact that SCADA not only collects the action feedback status of each device (such as "the photovoltaic inverter has executed the power limiting command"), but also captures the actual trajectory of bus voltage and frequency with millisecond-level accuracy through PMU (Synchronous Phasor Measurement Unit). It compares these data with the expected values ​​of the commands, calculates the deviation, and finally stores all strategy triggering conditions, commands, responses and deviation data into a structured database according to timestamps, forming a traceable and analyzable "photovoltaic-storage-charging microgrid control log".

[0067] The method provided in this embodiment accurately coordinates the power adjustment at each end, maintains the stability of the microgrid voltage and frequency, ensures the implementation of the control strategy, and records the operating data to provide a basis for strategy optimization, thus ensuring the dual stability of aquaculture and microgrid operation.

[0068] Figure 3 A schematic diagram of a photovoltaic-storage-charging microgrid control system for weak power grids is provided in one embodiment of this application, as shown below. Figure 3 As shown, a photovoltaic-storage-charging microgrid control system 300 for weak power grids in this embodiment includes: a dynamic disturbance module 301, an optimization scheduling module 302, and a control strategy module 303.

[0069] The dynamic disturbance module 301 is used to acquire a photovoltaic panel information set, and based on the photovoltaic panel information set, analyze the coupling effect of light reflection and internal load impact on the photovoltaic panel to obtain a photovoltaic panel dynamic disturbance information set; the optimization scheduling module 302 is used to acquire a fish farm aquaculture information set, and based on the fish farm aquaculture information set, combined with the photovoltaic panel dynamic disturbance information set, analyze the multi-objective coordination and trade-off relationship between fish farm aquaculture demand and power generation demand to obtain an optimized scheduling information set; the control strategy module 303 is used to generate a multi-terminal coordinated control strategy based on the optimized scheduling information set and output the photovoltaic-storage-charging microgrid control log.

[0070] Optionally, when the dynamic disturbance module 301 analyzes the coupling effect of sunlight reflection and internal load impact on the photovoltaic panel based on the photovoltaic panel information set to obtain the photovoltaic panel dynamic disturbance information set, it is specifically used for: the photovoltaic panel information set including a solar information set and a photovoltaic panel power load information set; based on the solar information set, analyzing the characteristics of sunlight reflection fluctuations on the water surface caused by changes in the incident angle and intensity of sunlight to obtain photovoltaic panel reflection disturbance information; based on the photovoltaic panel power load information set, analyzing the impact characteristics of load switching and changes within the microgrid on the photovoltaic panel output power parameters to obtain photovoltaic panel load impact information; based on the photovoltaic panel reflection disturbance information, combined with the photovoltaic panel load impact information, analyzing the superposition and coupling relationship between reflection fluctuations and load impacts in time and space to obtain the photovoltaic panel dynamic disturbance information set.

[0071] Optionally, the dynamic disturbance module 301, during the construction of the photovoltaic panel load impact information, is specifically used for: analyzing the switching sequence and continuous operation requirements of the internal aquaculture load based on the photovoltaic panel power load information set to obtain the steady-state disturbance information of the uninterrupted priority load; analyzing the random access and high-power demand characteristics of the externally connected fishing vessel charging load based on the photovoltaic panel power load information set to obtain the fishing vessel fast-charging impact information; and analyzing the power fluctuation and voltage transient characteristics caused by the superposition of aquaculture guarantee requirements and sudden charging requirements based on the steady-state disturbance information and the fishing vessel fast-charging impact information to obtain the photovoltaic panel load impact information.

[0072] Optionally, the dynamic disturbance module 301, during the construction of the steady-state disturbance information, is specifically used for: analyzing the operating parameters and periodic start-stop patterns of each aquaculture equipment to maintain a stable aquaculture environment based on the photovoltaic power load information set, to obtain the steady-state operating characteristics of different aquaculture equipment; analyzing the collaborative working modes and temporal overlap relationships of each aquaculture equipment in order to achieve aquaculture goals within the aquaculture operation cycle based on the steady-state operating characteristics, to obtain the collaborative disturbance characteristics between equipment; and analyzing the continuous impact on the power and voltage background values ​​of the microgrid formed by the continuous and periodic operation of multiple aquaculture equipment based on the collaborative disturbance characteristics between equipment, to obtain the steady-state disturbance information.

[0073] Optionally, when the dynamic disturbance module 301 analyzes the superposition and coupling relationship between reflection fluctuations and load impacts in time and space based on the photovoltaic panel reflection disturbance information and the photovoltaic panel load impact information to obtain the photovoltaic panel dynamic disturbance information set, it is specifically used for: analyzing the periodic fluctuation characteristics and step impact characteristics based on the photovoltaic panel reflection disturbance information and the photovoltaic panel load impact information, and the overlapping, interleaving or isolation relationship between the two in the occurrence time period to obtain the disturbance synchronization time window; analyzing the uneven illumination caused by the movement of the water surface reflection area and the local power impact caused by the access of charging piles at different locations and the start and stop of aquaculture equipment clusters based on the photovoltaic panel reflection disturbance information and the photovoltaic panel load impact information, and the overlapping influence range of the two in the physical partition of the photovoltaic array to obtain the disturbance superposition spatial region; and analyzing the dynamic degradation characteristics of photovoltaic output power caused by the combined action of periodic fluctuations and step impacts in the same time window and in the same spatial region to obtain the photovoltaic panel dynamic disturbance information set.

[0074] Optionally, when the optimized scheduling module 302 analyzes the multi-objective coordination and trade-off relationship between fish farm aquaculture demand and power generation demand based on the fish farm aquaculture information set and the photovoltaic panel dynamic disturbance information set to obtain the optimized scheduling information set, it is specifically used for: the fish farm aquaculture information set including aquaculture equipment operation information set and aquatic environment information set; based on the aquaculture equipment operation information set, analyzing the heat accumulation characteristics generated by power conversion during the continuous and periodic operation of different aquaculture equipment clusters to obtain equipment operation heat accumulation information; based on the aquatic environment information set, analyzing the target aquaculture fish species... The appropriate water temperature threshold range, along with the current water body's heat capacity and heat dissipation characteristics, yields the water temperature regulation requirements. Based on the equipment's accumulated heat information and the water temperature regulation requirements, the impact of the accumulated heat generated by the aquaculture equipment cluster's operation on the water temperature rise is analyzed, resulting in the accumulated heat coupled temperature rise effect. Based on the accumulated heat coupled temperature rise effect and the photovoltaic panel's dynamic disturbance information set, the coordinated regulation rules for time-series management of aquaculture equipment operating power and targeted start-stop of heat dissipation or cooling equipment are analyzed under the condition that photovoltaic output fluctuates due to disturbances, resulting in the optimized scheduling information set.

[0075] Optionally, the optimization scheduling module 302, during the construction process of the heat accumulation coupled temperature rise effect, is specifically used for: analyzing the total heat energy generated per unit time by different equipment clusters under different aquaculture operation stages based on the heat accumulation information of the equipment operation, to obtain the aquaculture heat production power characteristics; analyzing the heat energy threshold required to maintain the water temperature threshold range and the heat capacity and heat dissipation characteristics of the water per unit time based on the water temperature control requirements, to obtain the water body heat balance power characteristics; analyzing the dynamic matching and imbalance relationship between the heat production capacity and the natural heat dissipation capacity of the aquaculture water body in time sequence based on the aquaculture heat production power characteristics and the water body heat balance power characteristics, to obtain the power production and dissipation mismatch window; and analyzing the expected magnitude and duration of the water body temperature exceeding the suitable threshold range due to net heat accumulation during the mismatch period based on the power production and dissipation mismatch window, to obtain the heat accumulation coupled temperature rise effect.

[0076] Optionally, when the optimized scheduling module 302 analyzes the timing management of the operating power of the aquaculture equipment and the coordinated control rules for targeted start-stop of heat dissipation or cooling equipment under the condition that the photovoltaic output fluctuates due to disturbances, based on the heat accumulation coupling temperature rise effect and combined with the photovoltaic panel dynamic disturbance information set, to obtain the optimized scheduling information set, it is specifically used for: analyzing the feasibility and shift amount of shifting the operating power of the aquaculture equipment during the peak heat production period to outside the disturbance synchronization time window and during the period of sufficient photovoltaic output, based on the heat accumulation coupling temperature rise effect and combined with the disturbance synchronization time window, to obtain power timing shift information; based on the power timing shift information, analyzing the start-stop timing and operating power level of the corresponding heat dissipation and cooling equipment in the spatial area where the expected temperature rise exceeds the threshold and the photovoltaic output in the spatial area is limited due to local disturbances, to obtain heat dissipation equipment start-stop information; and based on the power timing shift information and combined with the heat dissipation equipment start-stop information, integrating to form the coordinated control rules that stagger peak times in time and accurately match heat load and power supply capacity in space, to obtain the optimized scheduling information set.

[0077] Optionally, when the control strategy module 303 generates a multi-terminal coordinated control strategy based on the optimized scheduling information set and outputs the photovoltaic-storage-charging microgrid control log, it is specifically used for: analyzing the instruction sequence for coordinated adjustment of the photovoltaic array output power, the energy storage system charging and discharging power, and the fishing boat charging load power based on the power timing shift information and the start / stop information of the heat dissipation equipment, to obtain multi-terminal power coordination instructions; analyzing the compensation amount and adjustment timing required to maintain the stability of the microgrid voltage and frequency during the power adjustment process based on the multi-terminal power coordination instructions, to obtain voltage and frequency support instructions; integrating the multi-terminal power coordination instructions and the voltage and frequency support instructions to form the multi-terminal coordinated control strategy that is time-coordinated and coordinated in terms of control objectives; and recording the strategy triggering conditions, the response actions of each terminal device, and the actual operating parameter deviations based on the execution process of the multi-terminal coordinated control strategy to obtain the photovoltaic-storage-charging microgrid control log.

[0078] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A control method for a photovoltaic-storage-charging microgrid for weak power grids, characterized in that, include: A photovoltaic panel information set is obtained. Based on the photovoltaic panel information set, the coupling effect of light reflection and internal load impact on the photovoltaic panel is analyzed to obtain a photovoltaic panel dynamic disturbance information set. A fish farm aquaculture information set is obtained. Based on the fish farm aquaculture information set and the photovoltaic panel dynamic disturbance information set, the multi-objective coordination and trade-off relationship between fish farm aquaculture demand and power generation demand is analyzed to obtain an optimized scheduling information set. Based on the optimized scheduling information set, a multi-terminal coordinated control strategy is generated, and the control log of the photovoltaic-storage-charging microgrid is output.

2. The method according to claim 1, characterized in that, Based on the photovoltaic panel information set, the coupled effects of light reflection and internal load impact on the photovoltaic panel are analyzed to obtain a dynamic disturbance information set for the photovoltaic panel, including: The photovoltaic panel information set includes a solar energy information set and a photovoltaic panel power load information set. Based on the solar information set, the characteristics of light reflection and wave on the water surface caused by changes in the incident angle and intensity of sunlight are analyzed to obtain the photovoltaic panel reflection disturbance information. Based on the photovoltaic panel power load information set, the impact characteristics of load switching and changes within the microgrid on the output power parameters of the photovoltaic panel are analyzed to obtain photovoltaic panel load impact information; Based on the photovoltaic panel reflection disturbance information and combined with the photovoltaic panel load impact information, the superposition and coupling relationship between reflection fluctuations and load impacts in time and space is analyzed to obtain the photovoltaic panel dynamic disturbance information set.

3. The method according to claim 2, characterized in that, The process of constructing the photovoltaic panel load impact information includes: Based on the photovoltaic power load information set, the switching sequence and continuous operation requirements of the internal aquaculture load are analyzed to obtain the steady-state disturbance information of the uninterrupted priority load. Based on the photovoltaic power load information set, the random access and high power demand characteristics of the external fishing boat charging load are analyzed to obtain the fast charging impact information of fishing boats. Based on the steady-state disturbance information and the fast-charging impact information of fishing vessels, the power fluctuation and voltage transient characteristics caused by the superposition of aquaculture security needs and sudden charging needs are analyzed to obtain the photovoltaic panel load impact information.

4. The method according to claim 3, characterized in that, The process of constructing the steady-state disturbance information includes: Based on the photovoltaic power load information set, the operating parameters and periodic start-stop patterns of each aquaculture equipment that maintain a stable aquaculture environment are analyzed to obtain the steady-state operating characteristics of different aquaculture equipment. Based on the aforementioned steady-state operation characteristics, the collaborative working modes and temporal overlap relationships of each aquaculture equipment in order to achieve aquaculture goals during the aquaculture operation cycle are analyzed to obtain the collaborative disturbance characteristics between equipment. Based on the cooperative disturbance characteristics among the devices, the continuous impact of the combined continuous and periodic operation of various aquaculture devices on the power and voltage background values ​​of the microgrid is analyzed to obtain the steady-state disturbance information.

5. The method according to claim 4, characterized in that, Based on the photovoltaic panel reflection disturbance information and combined with the photovoltaic panel load impact information, the superposition and coupling relationship between reflection fluctuations and load impacts in time and space is analyzed to obtain the photovoltaic panel dynamic disturbance information set, including: Based on the photovoltaic panel reflection disturbance information and the photovoltaic panel load impact information, the periodic fluctuation characteristics and step impact characteristics are analyzed, and the overlap, overlap or isolation relationship between the two in the occurrence time is obtained to obtain the disturbance synchronization time window. Based on the photovoltaic panel reflection disturbance information and the photovoltaic panel load impact information, the uneven illumination caused by the movement of the water surface reflection area and the local power impact caused by the access of charging piles at different locations and the start-up and shutdown of the aquaculture equipment cluster are analyzed. The overlapping influence range of the two on the physical partition of the photovoltaic array is obtained to obtain the disturbance superposition space region. Based on the disturbance synchronization time window and the disturbance superposition spatial region, the dynamic degradation characteristics of photovoltaic output power caused by the combined effect of periodic fluctuations and step impacts within the same time window and the same spatial region are analyzed to obtain the dynamic disturbance information set of the photovoltaic panel.

6. The method according to claim 5, characterized in that, Based on the fish farm aquaculture information set and combined with the photovoltaic panel dynamic disturbance information set, the multi-objective coordination and trade-off relationship between fish farm aquaculture demand and power generation demand is analyzed to obtain an optimized scheduling information set, including: The aquaculture information set includes an aquaculture equipment operation information set and a water environment information set. Based on the aforementioned aquaculture equipment operation information set, the heat accumulation characteristics generated by electrical energy conversion during the continuous and periodic operation of different aquaculture equipment clusters are analyzed to obtain equipment operation heat accumulation information. Based on the water environment information set, the suitable water temperature threshold range for the target farmed fish species is analyzed, as well as the current heat capacity and heat dissipation characteristics of the water body, to obtain the water temperature regulation requirements. Based on the heat accumulation information of the equipment operation and combined with the water temperature control requirements, the impact of the heat accumulation generated by the operation of the aquaculture equipment cluster on the water temperature rise is analyzed, and the heat accumulation coupled temperature rise effect is obtained. Based on the heat accumulation coupling temperature rise effect, and combined with the photovoltaic panel dynamic disturbance information set, the coordinated control rules for time-series management of the operating power of aquaculture equipment and targeted start-up and shutdown of heat dissipation or cooling equipment are analyzed when the photovoltaic output fluctuates due to disturbance, thus obtaining the optimized scheduling information set.

7. The method according to claim 6, characterized in that, The process of constructing the heat accumulation coupled temperature rise effect includes: Based on the heat accumulation information of the equipment operation, the total heat energy generated per unit time by the continuous and periodic operation of different equipment clusters under different breeding operation stages is analyzed to obtain the characteristics of breeding heat production power. Based on the water temperature regulation requirements, the threshold range of water temperature and the threshold of heat energy dissipated and replenished required to maintain the heat capacity and heat dissipation characteristics of water per unit time are analyzed to obtain the water body heat balance power characteristics. Based on the aforementioned aquaculture heat production power characteristics and combined with the aforementioned water body heat balance power characteristics, the dynamic matching and imbalance relationship between the heat production capacity and the natural heat dissipation capacity of the aquaculture water body in time series is analyzed to obtain the power mismatch window. Based on the aforementioned power generation mismatch window, the expected magnitude and duration of the water temperature exceeding the appropriate threshold range due to net heat accumulation during the mismatch period are analyzed, thus obtaining the heat accumulation coupled temperature rise effect.

8. The method according to claim 7, characterized in that, Based on the heat accumulation coupling temperature rise effect and combined with the photovoltaic panel dynamic disturbance information set, the system analyzes the coordinated control rules for time-series management of the operating power of aquaculture equipment and targeted start-up and shutdown of heat dissipation or cooling equipment when photovoltaic output fluctuates due to disturbances. This results in the optimized scheduling information set, which includes: Based on the heat accumulation coupling temperature rise effect, and combined with the disturbance synchronization time window, the feasibility and shift amount of shifting the operating power of aquaculture equipment during the peak heat production period to outside the disturbance synchronization time window and during the period of sufficient photovoltaic output are analyzed, and power time sequence shift information is obtained. Based on the power time-series shift information, the start-up and shutdown timing and operating power level of the corresponding heat dissipation and cooling equipment are analyzed when the expected temperature rise exceeds the threshold in the spatial region and the photovoltaic output of the spatial region is limited due to local disturbances, so as to obtain the start-up and shutdown information of the heat dissipation equipment. Based on the power timing shift information and combined with the start / stop information of the heat dissipation equipment, the coordinated control rules that stagger peak times in time and accurately match heat load and power supply capacity in space are integrated to obtain the optimized scheduling information set.

9. The method according to claim 8, characterized in that, The process of generating a multi-terminal coordinated control strategy based on the optimized scheduling information set and outputting the photovoltaic-storage-charging microgrid control log includes: Based on the power timing shift information and the start / stop information of the heat dissipation equipment, the instruction sequence for coordinated adjustment of the output power of the photovoltaic array, the charging and discharging power of the energy storage system and the charging load power of the fishing boat is analyzed to obtain multi-terminal power coordination instructions. Based on the multi-terminal power coordination command, the compensation amount and adjustment sequence required to maintain the voltage and frequency stability of the microgrid during the power adjustment process are analyzed to obtain the voltage and frequency support command. Based on the multi-terminal power coordination command and combined with the voltage frequency support command, a multi-terminal coordinated control strategy that is time-coordinated and coordinated in terms of control objectives is formed. Based on the execution process of the multi-terminal coordinated control strategy, the strategy triggering conditions, the response actions of each terminal device, and the actual operating parameter deviations are recorded to obtain the control log of the photovoltaic-storage-charging microgrid.

10. A control system for a photovoltaic-storage-charging microgrid oriented towards weak power grids, characterized in that, The method applied to any one of claims 1-9 includes: The dynamic disturbance module is used to acquire the photovoltaic panel information set, and based on the photovoltaic panel information set, analyze the coupling effect of light reflection and internal load impact on the photovoltaic panel to obtain the photovoltaic panel dynamic disturbance information set; The optimization scheduling module is used to acquire the aquaculture information set of the fish farm, and based on the aquaculture information set of the fish farm, combined with the dynamic disturbance information set of the photovoltaic panel, analyzes the multi-objective coordination and trade-off relationship between the aquaculture demand and the power generation demand of the fish farm, and obtains the optimization scheduling information set. The control strategy module is used to generate a multi-terminal coordinated control strategy based on the optimized scheduling information set and output the control log of the photovoltaic-storage-charging microgrid.

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