An optimization management system for distributed photovoltaic power generation

Abstract

The present invention relates to the technical field of power generation management, and specifically discloses an optimization management system for distributed photovoltaic power generation, which is used to solve the problem that the demand for existing water pumps and lighting loads is highly volatile, such as the demand for water pumping during an irrigation cycle and the sudden increase in lighting demand at night. A scheduling system with insufficient dynamics cannot flexibly respond to these load fluctuations, and is prone to power shortage or excessive waste. The present invention predicts and schedules the power demand for water pumping and lighting loads in real time, realizes efficient use of electric energy based on dynamic energy management and multi-energy supply and demand interaction, can reduce the consumption of energy storage batteries, extend the service life of equipment, and maintain the stability of system power supply under load fluctuations, thereby improving the economy, stability and dynamic response capability of the system, and is particularly suitable for remote areas or areas with insufficient grid coverage.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation management, and more specifically, to an optimization management system for distributed photovoltaic power generation. Background Art

[0002] The existing integrated photovoltaic power generation system for water pumping and lighting is mainly due to the urgent need for clean energy in remote areas or areas without grid coverage. Traditional power supply methods are difficult to implement in these areas. The photovoltaic power generation system has become an ideal solution to solve basic needs such as water pumping and lighting due to its renewability, independence and low maintenance cost. Figure 1 As shown, it is usually composed of photovoltaic panels, energy storage batteries, DC / DC converters, inverters, and loads (such as water pump motors, lighting equipment), etc. The photovoltaic panels convert solar energy into electrical energy during the day, directly providing power for the water pump motor to pump water, and the excess electricity is stored in the battery for use at night or on cloudy days to drive lighting equipment or power other household appliances. The existing technology coordinates the energy flow within the system through an energy manager, such as Figure 2 As shown in the figure, since the system power generation and load demand power are both highly random and volatile, it is necessary to rely on the energy management controller to guide the operation of each circuit unit to ensure that the energy flow direction is changed effectively and quickly to achieve energy balance in the system. The battery power can be dynamically allocated according to the load demand to ensure continuous power supply even when the light is unstable. pv is the actual power of the photovoltaic power source, P w is the actual power of the wind turbine, P d is the actual power of the diesel generator, P b is the actual power of the battery, P L is the actual power of the local load. The interaction of multi-energy supply and demand means the dynamic balance and scheduling between photovoltaic power generation, energy storage system, water pumping and lighting loads. In actual use, the demand for water pumps and lighting loads fluctuates greatly, such as the water pumping demand during the irrigation cycle and the sudden increase in lighting demand at night. The scheduling system with insufficient dynamics cannot flexibly respond to these load fluctuations, which is prone to power shortage or excessive waste. In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides an optimization management system for distributed photovoltaic power generation, which is used to solve the large fluctuations in the demand for existing water pumps and lighting loads, such as the water pumping demand during the irrigation cycle and the sudden increase in lighting demand at night. The scheduling system with insufficient dynamics cannot flexibly respond to these load fluctuations, and is prone to power shortage or excessive waste, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An optimization management system for distributed photovoltaic power generation, a processor and a pumping and lighting integrated photovoltaic power generation system connected to the processor in communication, a parameter prediction and scheduling module, a dynamic energy management module, and a multi-energy supply and demand interaction module. The parameter prediction and scheduling module is used to obtain a first photovoltaic power generation parameter and a first load parameter of the pumping and lighting integrated photovoltaic power generation system in real time, and to perform photovoltaic power generation prediction and load demand prediction based on the first photovoltaic power generation parameter and the first load parameter respectively; the dynamic energy management module is used to flexibly adjust the power distribution and set the priority of the pumping load and the lighting load according to the demand through real-time monitoring of the pumping and lighting integrated photovoltaic power generation system; and the actual power P of the photovoltaic power source is collected in real time. pv , Remaining battery capacity E bat and the total load demand D load Set the priority of water pumping load and lighting load: pv (t)+P bat (t)≥D load (t), the key load D is supplied first crit (t); when P pv (t)+P bat (t)<D load (t), then reduce the load or shut down the low priority load D low (t); the specific formula is:

[0006]

[0007] Where: P distrib (t) is the total load distribution power at time t, D crit (t) is the critical load demand at time t, D low (t) is the low priority load demand at time t, P pv (t) is the actual power of the photovoltaic power source at time t, P bat (t) is the output power of the energy storage battery at time t, D load (t) is the total load demand at time t.

[0008] As a further solution of the present invention, the dynamic energy management module is used when there is excess photovoltaic power generation, that is, P pv (t)>D load (t), the energy storage battery is charged first; when the photovoltaic power generation is insufficient, that is, P pv (t)<D load (t), the energy storage battery is given priority to discharge power; and overcharge and discharge are avoided. The minimum power limit of the battery is E min , the maximum power limit is E max, specifically:

[0009]

[0010]

[0011] Where: P bd (t) is the power used for charging at time t, D load (t) is the total load demand at time t, P pv (t) is the actual power of photovoltaic power generation at time t, E bat (t) is the current power of the energy storage battery at time t, E max is the maximum allowable capacity of the battery, E min The minimum allowable battery charge.

[0012] As a further solution of the present invention, the parameter prediction and scheduling module is used to obtain the first photovoltaic power generation parameter and the first load parameter in real time, and perform photovoltaic power generation prediction and load demand prediction based on the first photovoltaic power generation parameter and the first load parameter respectively; the prediction formula for photovoltaic power generation prediction based on the first photovoltaic power generation parameter is:

[0013] P pv (t) = η·A·G(t)·f(T);

[0014] Where: P pv (t) is the actual power of the photovoltaic power source at time t, η is the photovoltaic conversion efficiency, A is the area of ​​the photovoltaic panel, G(t) is the light intensity at time t, and f(T) is the temperature correction factor when the temperature is T;

[0015] The formula for load demand prediction based on the first load parameter is:

[0016] D load (t) = D base +D irrig (t)+D light (t);

[0017] Where: D load (t) is the total load demand at time t, D base is the basic load, D irrig (t) is the irrigation load demand at time t, D light (t) is the lighting load demand at time t.

[0018] As a further solution of the present invention, the multi-energy supply and demand interaction module is used to coordinate the energy flow between photovoltaic power generation, energy storage, water pumping and lighting loads; by obtaining the actual power P of photovoltaic power generation at time t pv (t), current charge E of the energy storage battery bat (t), water pumping load demand Dpump (t) and lighting load demand D light (t), based on the irrigation cycle to set the pumping operation demand and time period to set the pump demand adjustment, the pump demand adjustment formula is:

[0019]

[0020] Where: D pump (t) is the pumping load demand at time t, P pv (t) is the power used for charging at time t, D base (t) is the base load demand at time t;

[0021] The flow of electric energy is dynamically adjusted by constructing a dynamic power distribution model. The formula of the dynamic power distribution model is:

[0022]

[0023] Where: P supply (t) is the actual power supply of the system at time t, P pv (t) is the output power of the photovoltaic power generation system at time t, P bat (t) is the output power of the energy storage battery at time t, P pv (t) is the power used for charging at time t, D load (t) is the total load demand of the system at time t, including pumping load and lighting load, D light (t) is the required power of the lighting load at time t, and γ is the adjustment coefficient, which is used to control the reduction ratio of the lighting load.

[0024] As a further solution of the present invention, the integrated photovoltaic power generation system for pumping and lighting is respectively connected to the parameter prediction and scheduling module, the dynamic energy management module, and the multi-energy supply and demand interaction module; the parameter prediction and scheduling module is connected to the dynamic energy management module; the dynamic energy management module is connected to the multi-energy supply and demand interaction module.

[0025] The technical effects and advantages of the optimization management system of distributed photovoltaic power generation of the present invention are as follows: the present invention realizes efficient utilization of electric energy based on dynamic energy management and multi-energy supply and demand interaction through real-time prediction and scheduling of electricity demand of pumping and lighting loads, which can reduce the consumption of energy storage batteries, extend the service life of equipment, and maintain the stability of system power supply under load fluctuations, thereby improving the economy, stability and dynamic response capability of the system, and is particularly suitable for remote areas or areas with insufficient grid coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of the structure of the integrated photovoltaic power generation system for water pumping and lighting provided by the present invention;

[0027] Figure 2 A schematic diagram of the structure of an independent photovoltaic power generation system using an energy management controller to coordinate the direction of energy flow provided by the present invention;

[0028] Figure 3 A schematic structural diagram of an optimization management system for distributed photovoltaic power generation provided by the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] In the present embodiment, an optimization management system for distributed photovoltaic power generation includes a processor and a pumping and lighting integrated photovoltaic power generation system, a parameter prediction and scheduling module, a dynamic energy management module, and a multi-energy supply and demand interaction module that are communicatively connected to the processor; the pumping and lighting integrated photovoltaic power generation system is connected to the parameter prediction and scheduling module, the dynamic energy management module, and the multi-energy supply and demand interaction module respectively; the parameter prediction and scheduling module is connected to the dynamic energy management module; and the dynamic energy management module is connected to the multi-energy supply and demand interaction module.

[0031] The parameter prediction and scheduling module is used to obtain the first photovoltaic power generation parameter and the first load parameter of the integrated photovoltaic power generation system for water pumping and lighting in real time, and to perform photovoltaic power generation prediction and load demand prediction based on the first photovoltaic power generation parameter and the first load parameter respectively; the first photovoltaic power generation parameter includes the light intensity and temperature at each moment; the first load parameter includes the irrigation load demand and the lighting load demand at each moment;

[0032] The dynamic energy management module is used to flexibly adjust the power distribution and set the priority of the pumping load and lighting load according to the demand through real-time monitoring of the integrated photovoltaic power generation system of pumping and lighting;

[0033] The multi-energy supply and demand interaction module is used to coordinate the energy flow between photovoltaic power generation, energy storage, water pumping and lighting loads.

[0034] Through the photovoltaic power generation and load demand prediction of the parameter prediction scheduling module, it is possible to predict load fluctuations, such as the water pumping demand during the irrigation cycle and the lighting demand at night, and adjust the power distribution in advance, which can effectively reduce power shortages or waste; the dynamic energy management module monitors the operating status of the integrated photovoltaic power generation system for water pumping and lighting in real time, flexibly adjusts the distribution of electricity according to priority settings, and adjusts the power supply priority of water pumping and lighting according to the real-time load demand, ensuring that key loads are met first when power is insufficient; during the peak period of photovoltaic power generation (such as sunny days), water pumping operations are given priority to reduce energy storage consumption, maximize the use of solar energy, improve power generation utilization, and avoid waste caused by not using excess electricity during peak power generation periods; by reasonably allocating the power supply of photovoltaic power generation and energy storage systems, the dependence on energy storage batteries is reduced, which helps to extend battery life and reduce system maintenance and replacement costs. The priority scheduling strategy set in the system ensures that when power is insufficient, key loads (such as nighttime lighting or key irrigation loads) are given priority in power supply. This hierarchical power supply method can maintain power supply stability when loads fluctuate drastically, reducing the risk of downtime of important loads due to power shortages. The multi-energy supply and demand interaction module can achieve dynamic energy flow between photovoltaic power generation, energy storage batteries, pumping and lighting loads to ensure the coordinated work of each module. For example, when photovoltaic power generation is insufficient, electricity is supplemented from the energy storage system to avoid load power shortages. Real-time prediction and scheduling as well as load priority setting can avoid excessive discharge of energy storage batteries when power demand is insufficient, and prevent unnecessary lighting or pumping operations, which can significantly reduce unnecessary energy consumption. Reasonable power distribution and multi-energy interaction reduce the frequent charging and discharging of energy storage equipment, reduce the wear and aging rate of energy storage equipment, extend its service life, and reduce maintenance costs. Load transfer and time-sharing scheduling can reduce the impact of load demand on the system during peak hours, avoid overload operation, and reduce the risk of system failure and equipment replacement costs.

[0035] The parameter prediction and scheduling module is used to obtain the first photovoltaic power generation parameter and the first load parameter in real time, and perform photovoltaic power generation prediction and load demand prediction based on the first photovoltaic power generation parameter and the first load parameter respectively; the prediction formula for photovoltaic power generation prediction based on the first photovoltaic power generation parameter is:

[0036] P pv (t) = η·A·G(t)·f(T);

[0037] Where: P pv (t) is the actual power of the photovoltaic power source at time t, η is the photovoltaic conversion efficiency, A is the area of ​​the photovoltaic panel, G(t) is the light intensity at time t, and f(T) is the temperature correction factor when the temperature is T;

[0038] The formula for load demand prediction based on the first load parameter is:

[0039] D load (t) = D base +D irrig (t)+D light (t);

[0040] Where: D load (t) is the total load demand at time t, D base is the basic load, D irrig (t) is the irrigation load demand at time t, D light (t) is the lighting load demand at time t.

[0041] By real-time monitoring of light intensity and temperature changes, the current power generation situation can be dynamically predicted, and the power generation strategy can be adjusted immediately. It can optimize power generation during the peak period of photovoltaic power generation to avoid over- or under-generation. The load demand forecast takes into account the different needs of water pumping and lighting, and can reasonably allocate power supply priorities. When the demand for lighting suddenly increases during the irrigation cycle or at night, the load peak can be identified in advance and reasonably dispatched to reduce the power supply pressure caused by sudden loads. Accurate power generation and load demand forecasts allow the system to arrange water pumping operations in advance and meet water pumping needs as much as possible during the peak period of photovoltaic power generation, thereby reducing dependence on energy storage batteries and improving energy utilization. Through load demand forecasting, unnecessary power supply waste can be avoided, especially when demand is low. Photovoltaic power generation is used for actual needs to the maximum extent, reducing the loss of energy storage systems and unnecessary energy storage discharge; when there is excess photovoltaic power generation, the energy storage battery is charged in advance according to the forecast, and it is discharged reasonably at night or when there is insufficient light, reducing the frequent charging and discharging of the energy storage battery, avoiding excessive wear of the battery, and effectively extending the battery life; through accurate prediction of power generation and load, the power supply strategy is dynamically adjusted according to the current status, for example, when photovoltaic power generation is insufficient, key loads are given priority, and loads are balanced during peak demand to ensure stable system operation; the fluctuation of photovoltaic power generation and load can be sensed in advance, and the power supply plan can be adjusted to reduce system instability caused by imbalance between supply and demand, and avoid the impact of power outages on the operation of key equipment (such as water pumps and lighting).

[0042] The dynamic energy management module is used to monitor the integrated photovoltaic power generation system of water pumping and lighting in real time, and flexibly adjust the power distribution according to the demand to set the priority of water pumping load and lighting load: by collecting the actual power P of the photovoltaic power source in real time pv 、Battery remaining capacity E bat and the total load demand D load Set the priority of water pumping load and lighting load: pv (t)+P bat (t)≥D load (t), the key load D is supplied first crit(t); when P pv (t)+P bat (t)<D load (t), then reduce the load or shut down the low priority load D low (t); the specific formula is:

[0043]

[0044] Where: P distrib (t) is the total load distribution power at time t, D crit (t) is the critical load demand at time t, D low (t) is the low priority load demand at time t, P pv (t) is the actual power of the photovoltaic power source at time t, P bat (t) is the output power of the energy storage battery at time t, D load (t) is the total load demand at time t;

[0045] When there is excess photovoltaic power generation, P pv (t)>D load (t), the energy storage battery is charged first; when the photovoltaic power generation is insufficient, that is, P pv (t)<D load (t), the energy storage battery is given priority to discharge power; and overcharge and discharge are avoided. The minimum power limit of the battery is E min , the maximum power limit is E max , specifically:

[0046]

[0047]

[0048] Where: P bd (t) is the power used for charging at time t, D load (t) is the total load demand at time t, P pv (t) is the actual power of photovoltaic power generation at time t, E bat (t) is the current power of the energy storage battery at time t, E max is the maximum allowable capacity of the battery, E min The minimum allowable battery charge.

[0049] When the photovoltaic power generation is in excess (i.e. P pv (t)>D loadWhen the power generation is insufficient, the system will give priority to charging the energy storage battery to avoid wasting electricity. When the power generation is insufficient, the system will give priority to discharging from the battery to supplement the insufficient power to ensure the stability of the load power supply. The system sets the minimum and maximum power limits of the energy storage battery to avoid over-charging and discharging of the battery, effectively protect the battery, extend its service life, help improve the health and efficiency of the battery, and reduce the replacement frequency. When the photovoltaic power generation is greater than the total load demand, the excess power is automatically used for energy storage charging to reduce the waste of power during the peak power generation period, which can ensure the maximum utilization of solar energy during the peak power generation period and reduce the overall power loss of the system. By giving priority to using photovoltaic power generation to meet demand during the day, frequent charging of energy storage batteries is avoided. Discharge, especially when there is excess photovoltaic power generation, reduces battery discharge pressure and improves the efficiency of electric energy utilization; reasonable charging and discharging strategies reduce the frequent charging and discharging cycles of energy storage batteries, avoid excessive discharge and overcharging, effectively extend battery life, and reduce battery replacement frequency and maintenance costs; through real-time collection of photovoltaic power generation, battery power and total load demand, it can dynamically adjust power distribution when demand fluctuates (such as peak irrigation or sudden increase in nighttime lighting demand) to avoid insufficient power supply due to fluctuating loads; the dynamic energy management module can flexibly adjust the power supply plan according to the priority of different load demands, ensure the stability of the pumping and lighting systems, and adaptively adjust the system operation when power generation and load demand change to ensure continuous power supply.

[0050] The multi-energy supply and demand interaction module is used to coordinate the energy flow between photovoltaic power generation, energy storage, water pumping and lighting loads; by obtaining the actual power P of photovoltaic power generation at time t pv (t), current charge E of the energy storage battery bat (t), water pumping load demand D pump (t) and lighting load demand D light (t), based on the irrigation cycle to set the pumping operation demand and time period to set the pump demand adjustment, the pump demand adjustment formula is:

[0051]

[0052] Where: D pump (t) is the pumping load demand at time t, P pv (t) is the power used for charging at time t, D base (t) is the base load demand at time t;

[0053] It should be noted that the peak period of photovoltaic power generation refers to the daytime, usually when the sun's angle is close to noon, from about 10 am to 2 pm. The specific time period will vary depending on factors such as geographical location, season, and weather conditions.

[0054] During the peak period of photovoltaic power generation (such as 10 am to 2 pm), the system will give priority to pumping operations and use excess photovoltaic power to drive water pumps, which can reduce dependence on energy storage batteries, make full use of solar energy during peak power generation, and avoid power generation waste; directly meeting the water pump demand during the peak period of photovoltaic power generation instead of supplying power through energy storage batteries can reduce the load of the energy storage system, which not only improves the efficiency of power utilization, but also extends the service life of energy storage batteries; photovoltaic power generation is set to meet the needs of the basic load first, and when the basic load power supply is worry-free, the excess power will be used for water pump operation, and the setting ensures that key loads such as lighting are still given priority when power resources are limited, reducing power supply fluctuations for important loads; by obtaining and analyzing the irrigation cycle, photovoltaic power generation and energy storage conditions in real time, the system allocates sufficient power to the water pump during peak periods to ensure that irrigation needs are met in a timely manner, while avoiding unstable power supply caused by load fluctuations; through the water pump demand adjustment formula, the power demand of the water pump can be flexibly adjusted according to the real-time photovoltaic power generation and basic load conditions The system can meet the needs of water pumps by adjusting the load power supply strategy according to the weather, geographical location and seasons. As photovoltaic power generation is greatly affected by weather, geographical location and seasons, the water pump demand regulation mechanism adjusts the load power supply strategy according to these factors to achieve optimal energy utilization under different conditions. In the period when photovoltaic power generation is sufficient, photovoltaic power is used first to meet the water pump demand, reducing the number and depth of discharge of energy storage batteries, which can effectively extend battery life and reduce maintenance frequency and cost. The system can reduce the waste of electricity during excess power generation, reduce the overall energy consumption of the system, and make the operating cost of the system more economical and efficient. Especially in remote or off-grid areas, this efficient utilization method can greatly save energy consumption costs. The multi-energy supply and demand interactive module arranges pumping operations during the peak period of photovoltaic power generation, realizes dynamic adjustment when the power demand fluctuates greatly, and ensures that the system remains stable under load fluctuations. After meeting the basic load, the excess power is used for water pump operations, which helps to avoid power shortages when the load fluctuates greatly. This strategy of prioritizing key loads significantly reduces the impact of power outages on pumping and lighting operations.

[0055] The flow of electric energy is dynamically adjusted by constructing a dynamic power distribution model. The formula of the dynamic power distribution model is:

[0056]

[0057] Where: P supply (t) is the actual power supply of the system at time t, P pv (t) is the output power of the photovoltaic power generation system at time t, P bat (t) is the output power of the energy storage battery at time t, P pv (t) is the power used for charging at time t, D load (t) is the total load demand of the system at time t, including pumping load and lighting load, D light(t) is the required power of the lighting load at time t, and γ is the adjustment coefficient, which is used to control the reduction ratio of the lighting load.

[0058] When P pv (t)+P bat (t)≥D load At (t), the output power of photovoltaic power generation and energy storage batteries can meet the current total load demand D load (t), directly supply P pv (t)+P bat (t) power to meet all loads;

[0059] When P pv (t)+P bat (t)<D load At (t), when the output power of photovoltaic power generation and energy storage batteries is less than the total load demand, the system power supply is insufficient. In order to ensure that the key load (pumping) is powered first, the system will reduce the power of the lighting load and adjust the lighting load demand D according to the adjustment coefficient γ. light (t) is adjusted and the power supply after reduction is calculated as P pv (t)+P bat (t)-(D light (t)·γ);

[0060] When γ = 1, the system will completely cut the lighting load to ensure that all power is used for the remaining critical loads;

[0061] When 0<γ<1, the system partially reduces the lighting load and maintains partial power supply to the lighting load;

[0062] When γ=0, no lighting load reduction is performed and all loads are allocated power according to normal demand.

[0063] By reducing or shutting down non-critical loads (such as lighting loads) when the power supply is insufficient, the power supply of critical loads (such as pumping) can be prioritized to avoid pumping interruptions caused by insufficient power. The model sets an adjustment coefficient to dynamically adjust the reduction ratio of the lighting load according to demand. For example, during irrigation, the adjustment coefficient is set to a higher value to ensure continuous power supply to the pumping load; and during periods of high lighting demand, the adjustment coefficient is appropriately lowered to maintain lighting power supply; when the output power of the photovoltaic power generation and energy storage system cannot meet the total load demand, the power supply plan can be quickly adjusted by reducing the lighting load to ensure that the critical load is not affected, which is particularly important in the case of a sudden increase in demand or a sharp drop in power generation; through flexible power distribution and regulation, the power supply strategy can be adjusted in time when the load demand fluctuates to avoid power outages or load tripping, ensuring the safety of critical equipment. Continuous operation; when the power is sufficient, the model can distribute all load demands on demand without generating extra energy waste; when the power is insufficient, the power consumption of low-priority loads is reduced to ensure that limited power is used for key needs first, maximizing the use of photovoltaic power generation and energy storage system power to avoid waste; when photovoltaic power generation is sufficient, the power is directly supplied by photovoltaic power to reduce the dependence on energy storage batteries; when power generation is insufficient, the system only uses battery power to supply key loads, reducing the frequency of battery over-discharge and extending the service life of the energy storage system; by partially or completely reducing the lighting load, the system can effectively deal with the problem of short-term power supply shortage, reduce voltage fluctuations and instability, and avoid affecting the equipment; when the total load demand exceeds the power supply capacity, the system will automatically reduce the lighting load to prevent system instability or tripping caused by load overload. This helps maintain the long-term operational stability of the system.

[0064] The embodiment of the present invention can predict the fluctuation of loads, such as the pumping demand during the irrigation cycle and the lighting demand at night, through the photovoltaic power generation and load demand prediction of the parameter prediction and scheduling module, and adjust the power distribution in advance, which can effectively reduce the power supply shortage or waste. The dynamic energy management module monitors the operating status of the integrated photovoltaic power generation system of pumping and lighting in real time, flexibly adjusts the distribution of power according to the priority setting, and adjusts the power supply priority of pumping and lighting according to the real-time load demand, ensuring that the key load is met first when power is insufficient. During the peak period of photovoltaic power generation (such as sunny days), pumping operations are given priority to reduce energy storage consumption, maximize the use of solar energy, improve power generation utilization, and avoid waste caused by not using excess electricity during peak power generation. By reasonably allocating the power supply of photovoltaic power generation and energy storage systems, the dependence on energy storage batteries is reduced, which helps to extend battery life and reduce system maintenance and replacement costs. The priority scheduling strategy set in the system ensures that when power is insufficient, key loads (such as nighttime lighting or key irrigation loads) are given priority in power supply. This hierarchical power supply method can maintain power supply stability when loads fluctuate drastically, reducing the risk of downtime of important loads due to power shortages. The multi-energy supply and demand interaction module can achieve dynamic energy flow between photovoltaic power generation, energy storage batteries, pumping and lighting loads to ensure the coordinated work of each module. For example, when photovoltaic power generation is insufficient, electricity is supplemented from the energy storage system to avoid load power shortages. Real-time prediction and scheduling as well as load priority setting can avoid excessive discharge of energy storage batteries when power demand is insufficient, and prevent unnecessary lighting or pumping operations, which can significantly reduce unnecessary energy consumption. Reasonable power distribution and multi-energy interaction reduce the frequent charging and discharging of energy storage equipment, reduce the wear and aging rate of energy storage equipment, extend its service life, and reduce maintenance costs. Load transfer and time-sharing scheduling can reduce the impact of load demand on the system during peak hours, avoid overload operation, and reduce the risk of system failure and equipment replacement costs. The embodiments of the present invention can predict and dispatch the electricity demand of pumping and lighting loads in real time, and can also realize efficient utilization of electric energy through dynamic energy management and multi-energy supply and demand interaction, thereby reducing the consumption of energy storage batteries, extending the service life of equipment, and maintaining the stability of system power supply under load fluctuations. The economy, stability and dynamic response capability of the entire system are comprehensively improved, and it is particularly suitable for remote areas or areas with insufficient grid coverage.

[0065] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0066] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimization management system for distributed photovoltaic power generation, a processor, a pumping and lighting integrated photovoltaic power generation system connected to the processor in communication, a parameter prediction and scheduling module, a dynamic energy management module, and a multi-energy supply and demand interaction module, characterized in that: The parameter prediction and scheduling module is used to obtain the first photovoltaic power generation parameter and the first load parameter of the integrated photovoltaic power generation system for water pumping and lighting in real time, and to perform photovoltaic power generation prediction and load demand prediction based on the first photovoltaic power generation parameter and the first load parameter respectively; the dynamic energy management module is used to flexibly adjust the power distribution and set the priority of water pumping load and lighting load according to demand through real-time monitoring of the integrated photovoltaic power generation system for water pumping and lighting; the actual power of the photovoltaic power source is collected in real time , Remaining battery power and the total load demand Set the priority of water pumping load and lighting load: When the critical load is supplied first ;when When the load is reduced or the low priority load is turned off ; The specific formula is: ; Where: The total power allocated to the load at time t, is the critical load demand at time t, is the low priority load demand at time t, is the actual power of the photovoltaic power source at time t, is the output power of the energy storage battery at time t, is the total load demand at time t; The parameter prediction and scheduling module is used to obtain the first photovoltaic power generation parameter and the first load parameter in real time, and perform photovoltaic power generation prediction and load demand prediction based on the first photovoltaic power generation parameter and the first load parameter respectively; the prediction formula for photovoltaic power generation prediction based on the first photovoltaic power generation parameter is: ; Where: is the actual power of the photovoltaic power source at time t, is the photovoltaic conversion efficiency, is the photovoltaic panel area, is the light intensity at time t, is the temperature correction factor when the temperature is T; The formula for load demand prediction based on the first load parameter is: ; Where: is the total load demand at time t, As the basic load, is the irrigation load demand at time t, is the lighting load demand at time t.

2. The distributed photovoltaic power generation optimization management system according to claim 1, characterized in that: Dynamic Energy Management Module When there is excess photovoltaic power generation, When the photovoltaic power generation is insufficient, the energy storage battery is charged first; when the photovoltaic power generation is insufficient, When the battery is charged, it will be discharged first, and over-charging and over-discharging should be avoided. The minimum power limit of the battery is , the maximum power limit is , specifically: ; ; Where: is the power used for charging at time t, is the total load demand at time t, is the actual power of photovoltaic power generation at time t, is the current power of the energy storage battery at time t, is the maximum allowable charge of the battery, The minimum allowable battery charge.

3. The distributed photovoltaic power generation optimization management system according to claim 1, characterized in that: The multi-energy supply and demand interaction module is used to coordinate the energy flow between photovoltaic power generation, energy storage, water pumping and lighting loads; by obtaining the actual power of photovoltaic power generation at time t , the current charge of the energy storage battery , water pumping load demand and lighting load requirements ,The pump demand adjustment is set based on the irrigation cycle and the time period. The pump demand adjustment formula is: ; Where: is the pumping load demand at time t, is the power used for charging at time t, is the base load demand at time t; The flow of electric energy is dynamically adjusted by constructing a dynamic power distribution model. The formula of the dynamic power distribution model is: ; Where: is the actual power supply of the system at time t, is the output power of the photovoltaic power generation system at time t, is the output power of the energy storage battery at time t, is the power used for charging at time t, is the total load demand of the system at time t, including pumping load and lighting load, is the required power of the lighting load at time t, It is the adjustment coefficient, which is used to control the reduction ratio of lighting load.

4. The distributed photovoltaic power generation optimization management system according to claim 1, characterized in that: The integrated photovoltaic power generation system for pumping and lighting is connected to the parameter prediction and scheduling module, the dynamic energy management module, and the multi-energy supply and demand interaction module respectively; the parameter prediction and scheduling module is connected to the dynamic energy management module; and the dynamic energy management module is connected to the multi-energy supply and demand interaction module.

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