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CN-121984105-A - Fishing light complementary photovoltaic power generation integrated management system

CN121984105ACN 121984105 ACN121984105 ACN 121984105ACN-121984105-A

Abstract

The invention provides a light field cooperative processing module of a fishing light complementary photovoltaic power generation integrated management system, which optimizes the layout of a photovoltaic array according to illumination requirements and environmental data, ensures illumination uniformity and power generation efficiency of a cultivation area, enables an energy scheduling processing module to realize intelligent power distribution and scheduling based on power generation and load prediction, improves energy utilization efficiency, enables a water environment processing module to accurately control cultivation equipment to operate according to water environment data and energy scheduling instructions, maintains water environment stability, enables a data processing center module to gather multi-source data and iteratively optimizes system parameters based on historical data, and achieves cooperative control and self-adaptive adjustment of each module, thereby comprehensively improving energy output, cultivation environment regulation precision and overall operation efficiency of the fishing light complementary system.

Inventors

  • Niu Jiateng

Assignees

  • 华能烟台八角热电有限公司

Dates

Publication Date
20260505
Application Date
20251128

Claims (10)

  1. 1. A fishing light complementary photovoltaic power generation integrated management system is characterized by comprising: the light field cooperative processing module is configured to generate optimized layout parameters of the photovoltaic array according to the acquired illumination demand data and the environment illumination data; the energy scheduling processing module is configured to generate an energy scheduling instruction according to the obtained photovoltaic power generation prediction data and the cultivation load prediction data; the water environment processing module is configured to generate a water body control signal according to the acquired aquaculture water body environment data and the energy scheduling instruction; The data processing center module is connected with the light field cooperative processing module, the energy scheduling processing module and the water environment processing module, and is configured to collect and store data acquired by each module and generated data, and iteratively optimize the optimized layout parameters, the energy scheduling instructions and the water body control signals based on historical data.
  2. 2. The system of claim 1, wherein the light field co-processing module comprises a ray tracing unit and a multi-objective optimization unit; The ray tracing unit is used for simulating shading distribution of the photovoltaic array based on solar track data; the multi-objective optimization unit is used for solving the optimized layout parameters through an optimization algorithm according to the shading distribution and the illumination demand data.
  3. 3. The system of claim 2, wherein the multi-objective optimization unit employs a non-dominant ranking genetic algorithm to target both the uniformity of the lighting of the farm and the photovoltaic power generation.
  4. 4. The system of claim 1, wherein the energy scheduling processing module comprises a prediction unit and a scheduling unit; the prediction unit is used for predicting the photovoltaic power generation power and the cultivation load based on the numerical weather forecast data; And the scheduling unit is used for generating the energy scheduling instruction according to the prediction result and controlling the distribution of the photovoltaic electric energy to the cultivation load, the energy storage system or the power grid.
  5. 5. The system of claim 4, wherein the energy scheduling instructions include a scheduling priority score, and wherein the step of calculating the scheduling priority score comprises: acquiring a photovoltaic power generation power prediction sequence and a cultivation load power prediction sequence in a scheduling period; Calculating a power deviation sequence of each period based on the photovoltaic power generation power prediction sequence and the cultivation load power prediction sequence, wherein the power deviation is a difference value between power generation power and load power; Acquiring a real-time energy storage electric quantity sequence of an energy storage system; obtaining an optimal energy storage electric quantity value; Generating a scheduling priority score based on the power deviation sequence, the real-time stored energy power sequence, the optimal stored energy power value, the maximum stored energy capacity value, the rated power value, and the predefined weight coefficient and attenuation coefficient by a multi-factor evaluation, wherein the multi-factor evaluation comprises calculating an overall impact metric of the power deviation, calculating an overall impact metric of the stored energy state deviation, calculating a metric of the power variation stability, and weighting and combining the metrics by using the weight coefficient, and simultaneously applying attenuation coefficient adjustment, wherein the obtained scheduling priority score is used for indicating the priority degree of energy scheduling.
  6. 6. The system of claim 5, wherein the optimal stored energy power value is determined by: Acquiring a historical energy storage electric quantity data sequence and a historical load energy data sequence; Calculating the statistical characteristics of the historical energy storage electric quantity data sequence, wherein the statistical characteristics comprise a historical energy storage electric quantity mean value and a historical energy storage electric quantity standard deviation; Calculating statistical characteristics of the historical load energy data sequence, including a historical load energy mean value and a maximum load energy value; calculating candidate optimal energy storage electric quantity through fluctuation analysis and deviation enhancement processing based on the statistical characteristics of the historical energy storage electric quantity data sequence, the statistical characteristics of the historical load energy data sequence, the reference energy value, the adjustment coefficient and the enhancement coefficient, wherein the fluctuation analysis comprises calculating a fluctuation index based on the historical energy storage electric quantity standard deviation and the historical load energy deviation, and the deviation enhancement processing comprises nonlinear conversion of the historical energy deviation; and comparing the candidate optimal energy storage electric quantity with the upper limit value of the energy storage capacity, and selecting a smaller value as an optimal energy storage electric quantity value.
  7. 7. The system of claim 4, wherein the scheduling unit dynamically adjusts the power output priority using a model predictive control strategy.
  8. 8. The system of claim 1, wherein the aqueous environment treatment module comprises a sensing unit and an execution unit; the sensing unit is used for monitoring dissolved oxygen, temperature and flow rate of the culture water body; The execution unit is used for driving the oxygenation equipment and the circulating water pump according to the water body control signal.
  9. 9. The system of claim 8, wherein the execution unit optimizes oxygenation and circulation strategy based on a digital twin model.
  10. 10. The system of claim 1, wherein the data processing center module uses a deep learning algorithm to fuse multi-source data and generate a cooperative control strategy.

Description

Fishing light complementary photovoltaic power generation integrated management system Technical Field The invention relates to the technical field of combination of photovoltaic power generation and aquaculture, in particular to a fishing light complementary photovoltaic power generation integrated management system. Background The fishing light complementary system is used as a comprehensive utilization mode combining photovoltaic power generation and aquaculture, the development of the fishing light complementary system is derived from continuous pursuit of efficient utilization of renewable energy and modernization transformation of agriculture, and the photovoltaic power generation facilities are deployed above the aquaculture area to realize intensive development of land resources and ecological energy collaboration, so that the application field is expanded to scenes such as large-scale fishing farms, distributed photovoltaic power stations and the like, and aims to promote clean energy output and guarantee stable cultivation production activities. However, in the prior art, because the photovoltaic power generation and the cultivation environment management often adopt independent subsystems, the integral collaborative optimization mechanism is lacking, so that the shading effect of the photovoltaic array influences the illumination distribution uniformity of a cultivation area, and the links of energy scheduling and water environment control are disjointed, so that the system energy utilization efficiency is insufficient and the cultivation environment regulation precision is limited, and the system is a main defect of restricting the improvement of the comprehensive performance of the fishing light complementary system. Disclosure of Invention In view of the above, the invention provides a fishing light complementary photovoltaic power generation integrated management system to solve the technical defects in the prior art. Specifically, the invention provides a fishing light complementary photovoltaic power generation integrated management system, which comprises: the light field cooperative processing module is configured to generate optimized layout parameters of the photovoltaic array according to the acquired illumination demand data and the environment illumination data; the energy scheduling processing module is configured to generate an energy scheduling instruction according to the obtained photovoltaic power generation prediction data and the cultivation load prediction data; the water environment processing module is configured to generate a water body control signal according to the acquired aquaculture water body environment data and the energy scheduling instruction; The data processing center module is connected with the light field cooperative processing module, the energy scheduling processing module and the water environment processing module, and is configured to collect and store data acquired by each module and generated data, and to iteratively optimize and optimize layout parameters, energy scheduling instructions and water control signals based on historical data. In some embodiments, the light field co-processing module includes a ray trace unit and a multi-objective optimization unit; the ray tracing unit is used for simulating shading distribution of the photovoltaic array based on the solar track data; The multi-objective optimization unit is used for solving and optimizing layout parameters through an optimization algorithm according to shading distribution and illumination demand data. In some embodiments, the multi-objective optimization unit employs a non-dominant ranking genetic algorithm to target both the uniformity of the lighting of the farm and the photovoltaic power generation as optimization objectives. In some embodiments, the energy scheduling processing module includes a prediction unit and a scheduling unit; The prediction unit is used for predicting the photovoltaic power generation power and the cultivation load based on the numerical weather forecast data; The scheduling unit is used for generating an energy scheduling instruction according to the prediction result and controlling the distribution of the photovoltaic electric energy to the cultivation load, the energy storage system or the power grid. In some embodiments, the energy scheduling instructions include a scheduling priority score, and the step of calculating the scheduling priority score includes: acquiring a photovoltaic power generation power prediction sequence and a cultivation load power prediction sequence in a scheduling period; Calculating a power deviation sequence of each period based on the photovoltaic power generation power prediction sequence and the cultivation load power prediction sequence, wherein the power deviation is a difference value between the power generation power and the load power; Acquiring a real-time energy storage electric quantity sequence of an energy stor