Distributed new energy storage regulation method and system

Abstract

The application discloses a kind of distributed new energy energy storage regulation methods and systems, specifically related to energy storage regulation technical field, including distributed new energy power generation region division module, distributed new energy power generation data acquisition module, distributed new energy power generation central control module, distributed new energy energy storage regulation module, distributed new energy power transaction economic benefit module, distributed new energy energy storage correlation analysis module and distributed new energy energy storage optimization module, the energy storage resource regulation data of each monitoring subarea in distributed new energy power generation region is collected by the application, new energy energy storage demand evaluation index and power market transaction economic benefit index are calculated, energy storage resource allocation optimization coefficient is analyzed, the capacity of energy storage system is reasonably configured according to the demand of new energy power generation, the efficient use of energy storage resource is improved, based on data, it is conducive to promoting the entire new energy industry to intelligent, digital direction development.

Description

Technical Field

[0001] This invention relates to the field of energy storage regulation technology, and more specifically, to a distributed new energy storage regulation method and system. Background Technology

[0002] With the increasing severity of environmental problems and concerns about the depletion of traditional energy sources, distributed new energy sources such as solar and wind power have been widely used. Through effective control strategies for distributed new energy sources and energy storage systems, the goals of efficient consumption of new energy sources, economically optimized operation of energy storage systems, and grid stability can be achieved.

[0003] In recent years, with the development of intelligent algorithms, the optimal energy storage control strategy can be found through iterative calculation based on the real-time status of distributed new energy and energy storage systems to achieve optimized system operation. It has good adaptability and optimization ability, and can fully tap the potential of distributed energy storage and improve the overall performance of the system.

[0004] However, in actual use, it still has some shortcomings, such as the lack of intelligence in the processing of large amounts of communication data by the existing distributed new energy storage regulation strategy, the failure to fully consider economic efficiency, resulting in high operating costs and affecting the return on investment of new energy storage.

[0005] Existing distributed renewable energy storage is affected by weather, which leads to large fluctuations and randomness in its output power. Existing control methods have a slow response speed and are difficult to cope with sudden changes in energy storage and fluctuations in power load, thus affecting the control effect. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a distributed new energy storage regulation method and system to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a distributed new energy storage and control system, comprising:

[0008] Distributed new energy power generation area division module: used to divide the distributed new energy power generation area into monitoring sub-areas according to the equal area division method, and to number each monitoring sub-area of ​​the distributed new energy power generation area.

[0009] Distributed new energy power generation data acquisition module: used to collect energy storage resource regulation data of each monitoring sub-region of the distributed new energy power generation area. The distributed new energy power generation data acquisition module includes a new energy power generation data acquisition unit and a new energy power trading data acquisition unit.

[0010] Central control module for distributed new energy power generation: Based on the new energy power generation data collected by the new energy power generation data acquisition unit, calculate the new energy storage demand assessment index for each monitoring sub-region of the distributed new energy power generation area.

[0011] Distributed new energy storage control module: used to obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, compare it with the preset new energy storage demand assessment index, and ensure that the energy storage system prioritizes to provide power to the monitoring sub-region with high power generation demand.

[0012] Distributed New Energy Power Trading Economic Benefit Module: Based on the new energy power trading data collected by the new energy power trading data acquisition unit, the economic benefit index of power market trading in each monitoring sub-region of the distributed new energy power generation area is calculated.

[0013] The distributed renewable energy storage correlation analysis module is used to analyze and obtain the energy storage resource allocation optimization coefficient of the distributed renewable energy power generation area based on the renewable energy storage demand assessment index and the power market transaction economic benefit index of each monitoring sub-area in the distributed renewable energy power generation area.

[0014] Distributed new energy storage optimization module: used to obtain the energy storage resource configuration optimization coefficient of the distributed new energy power generation area, compare it with the preset energy storage resource configuration optimization coefficient, and process it.

[0015] Preferably, the distributed new energy power generation area division module specifically comprises:

[0016] Aerial photography of the distributed renewable energy power generation area was conducted using a drone equipped with lidar to obtain the area of ​​the distributed renewable energy power generation area. The distributed renewable energy power generation area was then divided into monitoring sub-areas according to the method of equal area division, and the monitoring sub-areas of the distributed renewable energy power generation area were numbered sequentially as 1, 2, ... i, ... n.

[0017] Preferably, the distributed new energy power generation data acquisition module specifically comprises:

[0018] New energy power generation data acquisition unit: Collects the actual power generation, planned power generation, power generation efficiency, and power output of new energy in each monitoring sub-region of the distributed new energy power generation area, and labels them as xs respectively. i 、xj i ,xk i 、xg i , where i = 1, 2, ..., n, and i represents the number of the i-th monitoring sub-region;

[0019] New Energy Power Transaction Data Acquisition Unit: Collects equipment operation and maintenance costs and equipment power revenue for each monitoring sub-region of the distributed new energy power generation area, and labels them as dw. i dy i .

[0020] Preferably, the distributed new energy power generation central control module specifically comprises:

[0021] S41: After normalizing the renewable energy generation efficiency and renewable energy generation output power of each monitoring sub-region of the distributed renewable energy generation area, we obtain Δxk. i Δxg i ;

[0022] S42: Calculate the renewable energy power generation capacity index for each monitoring sub-region:

[0023]

[0024] Among them, KG i Let Δxk be the renewable energy generation capacity index for the i-th monitoring sub-region. i Let Δxg represent the renewable energy generation efficiency of the i-th monitoring sub-region. i Represented as the output power of new energy power generation in the i-th monitoring sub-region;

[0025] S43: By collecting historical meteorological data from each monitoring sub-region of the distributed new energy power generation area, including sunlight intensity, rainfall, and snow accumulation, and after normalizing the historical meteorological data, the environmental adaptability of each monitoring sub-region for new energy power generation is calculated.

[0026]

[0027] Among them, HQ i Let Δqt represent the environmental adaptability of new energy power generation in the i-th monitoring sub-region. i Let Δqj represent the light intensity of the i-th monitoring sub-region. i Let Δqx represent the rainfall in the i-th monitoring sub-region. i Let λ represent the snow cover in the i-th monitoring sub-region, e represent the natural constant, and λ1, λ2, and λ3 represent the weighting factors for light intensity, precipitation, and snow cover, respectively, with λ1+λ2+λ3=1;

[0028] S44: The formula for calculating the new energy storage demand assessment index is as follows:

[0029]

[0030] Where, α iLet KG represent the new energy storage demand assessment index for the i-th monitoring sub-region. i HQ is represented as the renewable energy generation capacity index for the i-th monitoring sub-region. i Let ΔKG represent the environmental adaptability of new energy power generation in the i-th monitoring sub-region, ΔHQ represent the mean value of the new energy power generation capacity index D, and ΔHQ represent the mean value of the environmental adaptability of new energy power generation.

[0031] Preferably, the distributed new energy storage and control module specifically comprises:

[0032] S51: Obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, and compare it with the preset new energy storage demand assessment index. If the new energy storage demand assessment index of a certain monitoring sub-region is greater than the preset new energy storage demand assessment index, it indicates that the monitoring sub-region is a region with high power generation demand; otherwise, it indicates that the monitoring sub-region is a region with surplus power generation demand.

[0033] S52: Based on the real-time new energy storage demand assessment index, dynamically adjust the charging and discharging strategy of energy storage to ensure that the energy storage system prioritizes providing power to the monitoring sub-areas with high power generation demand. For areas with surplus power generation demand, the excess new energy power in those areas will be stored or allocated to areas with high demand.

[0034] Preferably, the distributed new energy power trading economic benefit module specifically comprises:

[0035] S61: Calculate the fluctuation of power revenue in each monitored sub-region based on equipment operation and maintenance costs and equipment power revenue.

[0036]

[0037] Among them, WY i Let dw represent the fluctuation in electricity revenue in the i-th monitoring sub-region. i Let dy represent the equipment operation and maintenance cost of the i-th monitoring sub-region. i Let represent the equipment electricity revenue of the i-th monitoring sub-region, and r represent the discount rate;

[0038] When the electricity revenue of the equipment is greater than the equipment operation and maintenance costs, the net revenue is positive, and the greater the fluctuation of the electricity revenue, the smaller the fluctuation of the electricity revenue.

[0039] S62: The formula for calculating the economic benefit index of the electricity market transaction is as follows:

[0040]

[0041] Where, β i Let xs be the economic benefit index of electricity market transactions in the i-th monitoring sub-region.i Let xj represent the actual power generation of new energy sources in the i-th monitoring sub-region. i Let WY represent the planned new energy power generation for the i-th monitoring sub-region. i Let WY represent the fluctuation in electricity revenue in the i-th monitoring sub-region. 预 Represented as the preset fluctuation in electricity revenue, SJ 允 It represents the allowable difference between the actual power generation of new energy sources and the planned power generation of new energy sources.

[0042] Preferably, the formula for calculating the energy storage resource allocation optimization coefficient is as follows:

[0043]

[0044] Where θ represents the energy storage resource allocation optimization coefficient, and α i Let be the new energy storage demand assessment index for the i-th monitoring sub-region, Δα be the mean of the new energy storage demand assessment index, and β be the mean of the new energy storage demand assessment index. i Let Δβ represent the economic benefit index of electricity market transactions in the i-th monitoring sub-region, Δβ represent the mean of the economic benefit index of electricity market transactions, and n represent the number of monitoring sub-regions.

[0045] Preferably, the distributed new energy storage optimization module specifically comprises:

[0046] The optimization coefficient for energy storage resource allocation in the distributed renewable energy generation area is obtained and compared with the preset optimization coefficient. If the optimization coefficient is greater than the preset coefficient, it indicates that the distributed renewable energy generation efficiency is high and the economic benefits in the electricity market are good, and the energy storage resource allocation in the distributed renewable energy generation area should be increased. Conversely, if the optimization coefficient is lower than the preset coefficient, it indicates that the distributed renewable energy generation efficiency is poor, resulting in poor economic benefits in the electricity market, and the energy storage resource allocation in the distributed renewable energy generation area should be reduced.

[0047] Preferably, a distributed new energy storage regulation method includes the following steps:

[0048] Step S01: Distributed new energy power generation area division: This step is used to divide the distributed new energy power generation area into monitoring sub-areas according to the method of equal area division, and to number each monitoring sub-area of ​​the distributed new energy power generation area.

[0049] Step S02: Distributed New Energy Generation Data Acquisition: This step is used to collect energy storage resource regulation data from each monitoring sub-region of the distributed new energy generation area. The distributed new energy generation data acquisition includes a new energy generation data acquisition sub-step and a new energy power transaction data acquisition sub-step. The energy storage resource regulation data includes new energy generation data and new energy power transaction data.

[0050] Step S03: Central control of distributed new energy power generation: used to receive data transmitted from the distributed new energy power generation data acquisition step, and calculate the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area based on the new energy power generation data collected in the new energy power generation data acquisition sub-step.

[0051] Step S04: Distributed new energy storage regulation: This step is used to obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, compare it with the preset new energy storage demand assessment index, and ensure that the energy storage system prioritizes providing power to the monitoring sub-regions with high power generation demand.

[0052] Step S05: Economic Benefits of Distributed New Energy Power Trading: This step receives data transmitted from the distributed new energy power generation data acquisition step and calculates the economic benefit index of power market trading for each monitoring sub-region of the distributed new energy power generation area based on the new energy power trading data acquired in the new energy power trading data acquisition sub-step.

[0053] Step S06: Correlation analysis of distributed new energy storage: This step is used to analyze and obtain the optimization coefficient of energy storage resource allocation in the distributed new energy power generation area based on the new energy storage demand assessment index and the power market transaction economic benefit index of each monitoring sub-area in the distributed new energy power generation area.

[0054] Step S07: Distributed New Energy Storage Optimization: This step involves obtaining the energy storage resource configuration optimization coefficient for the distributed new energy power generation area, comparing it with the preset energy storage resource configuration optimization coefficient, and then processing the result.

[0055] The technical effects and advantages of this invention are as follows:

[0056] 1. This invention provides a distributed new energy storage regulation method and system. It collects energy storage resource regulation data from each monitoring sub-region of a distributed new energy power generation area. Based on the new energy power generation data collected by the new energy power generation data acquisition unit, it calculates the new energy storage demand assessment index for each monitoring sub-region of the distributed new energy power generation area. Based on the new energy power transaction data collected by the new energy power transaction data acquisition unit, it calculates the power market transaction economic benefit index for each monitoring sub-region of the distributed new energy power generation area. Further analysis yields an energy storage resource allocation optimization coefficient, which is compared with a preset energy storage resource allocation optimization coefficient. If the energy storage resource allocation optimization coefficient of the distributed new energy power generation area is greater than the preset energy storage resource allocation optimization coefficient... If the data is high, it indicates that the distributed renewable energy generation is efficient and has good economic benefits in the electricity market, and the allocation of energy storage resources in the current distributed renewable energy generation area should be increased. Conversely, if the data is low, it indicates that the distributed renewable energy generation is inefficient and has poor economic benefits in the electricity market, and the allocation of energy storage resources in the current distributed renewable energy generation area should be reduced. Based on real-time collected renewable energy generation data and electricity trading data, quantitative analysis can ensure that optimization decisions take into account both generation efficiency and economic benefits. According to the characteristics and needs of renewable energy generation, the capacity of energy storage systems can be reasonably allocated to achieve efficient utilization of energy storage resources, support the stable operation of distributed renewable energy generation systems and maximize economic benefits. Based on data, this is conducive to promoting the development of the entire renewable energy industry towards intelligence and digitalization.

[0057] 2. This invention provides a distributed renewable energy storage regulation method and system. Utilizing a distributed renewable energy storage regulation module, it acquires the renewable energy storage demand assessment index for each monitoring sub-region of a distributed renewable energy power generation area and compares it with a preset renewable energy storage demand assessment index. If the renewable energy storage demand assessment index of a monitoring sub-region is greater than the preset index, it indicates that the monitoring sub-region has high power generation demand; conversely, if it is less than the preset index, it indicates that the monitoring sub-region has surplus power generation demand. Based on the real-time renewable energy storage demand assessment index, the charging and discharging strategy of the energy storage is dynamically adjusted to ensure that the energy storage system prioritizes power generation demand. The system provides power to high-demand monitoring sub-regions, while storing or allocating surplus renewable energy to areas with high demand. The energy storage system can dynamically adjust its charging and discharging strategies based on real-time renewable energy storage demand assessment indices. Prioritizing power supply to high-demand monitoring sub-regions ensures stable power supply, helps avoid power fluctuations caused by localized power shortages, and improves the stability and reliability of the entire power system. Storing or allocating surplus power to areas with high demand effectively balances energy differences between different monitoring sub-regions and improves renewable energy utilization efficiency. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the structure of a distributed new energy storage and control system according to the present invention.

[0059] Figure 2 This is a schematic diagram of the distributed new energy power generation data acquisition module of the present invention.

[0060] Figure 3 This is a flowchart illustrating a distributed new energy storage regulation method according to the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1 As shown, the present invention provides a distributed new energy storage control system, including a distributed new energy power generation area division module, a distributed new energy power generation data acquisition module, a distributed new energy power generation central control module, a distributed new energy storage control module, a distributed new energy power trading economic benefit module, a distributed new energy storage correlation analysis module, and a distributed new energy storage optimization module.

[0063] The distributed new energy power generation area division module is connected to the distributed new energy power generation data acquisition module. The distributed new energy power generation data acquisition module is connected to the distributed new energy power generation central control module and the distributed new energy power trading economic benefit module. The distributed new energy power generation central control module is connected to the distributed new energy energy storage regulation module. The distributed new energy power generation central control module and the distributed new energy power trading economic benefit module are connected to the distributed new energy energy storage correlation analysis module. The distributed new energy energy storage correlation analysis module is connected to the distributed new energy energy storage optimization module.

[0064] The distributed renewable energy power generation area division module is used to divide the distributed renewable energy power generation area into monitoring sub-areas according to an equal area division method, and to number each monitoring sub-area. In a large distributed photovoltaic power generation area, dividing the area into monitoring sub-areas allows for the rapid location of affected areas, enabling corresponding measures to be taken for scheduling and management.

[0065] In one possible design, the distributed new energy power generation area division module specifically comprises:

[0066] Aerial photography of the distributed renewable energy power generation area was conducted using a drone equipped with lidar to obtain the area of ​​the distributed renewable energy power generation area. The distributed renewable energy power generation area was then divided into monitoring sub-areas according to the method of equal area division, and the monitoring sub-areas of the distributed renewable energy power generation area were numbered sequentially as 1, 2, ... i, ... n.

[0067] Please see Figure 2 As shown, the distributed new energy power generation data acquisition module is used to collect energy storage resource regulation data of each monitoring sub-region of the distributed new energy power generation area. The distributed new energy power generation data acquisition module includes a new energy power generation data acquisition unit and a new energy power trading data acquisition unit. The energy storage resource regulation data includes new energy power generation data and new energy power trading data.

[0068] In one possible design, the distributed new energy power generation data acquisition module specifically comprises:

[0069] New energy power generation data acquisition unit: Collects the actual power generation, planned power generation, power generation efficiency, and power output of new energy in each monitoring sub-region of the distributed new energy power generation area, and labels them as xs respectively. i 、xj i ,xk i 、xg i , where i = 1, 2, ..., n, and i represents the number of the i-th monitoring sub-region;

[0070] New Energy Power Transaction Data Acquisition Unit: Collects equipment operation and maintenance costs and equipment power revenue for each monitoring sub-region of the distributed new energy power generation area, and labels them as dw. i dy i .

[0071] The distributed new energy power generation central control module is used to receive data transmitted by the distributed new energy power generation data acquisition module, and calculate the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area based on the new energy power generation data collected by the new energy power generation data acquisition unit.

[0072] In one possible design, the distributed new energy power generation central control module specifically comprises:

[0073] S01: After normalizing the renewable energy generation efficiency and renewable energy generation output power of each monitoring sub-region of the distributed renewable energy generation area, Δxk is obtained. i Δxg i ;

[0074] S02: Calculate the renewable energy power generation capacity index for each monitoring sub-region:

[0075]

[0076] Among them, KG i Let Δxk be the renewable energy generation capacity index for the i-th monitoring sub-region. i Let Δxg represent the renewable energy generation efficiency of the i-th monitoring sub-region. i Represented as the output power of new energy power generation in the i-th monitoring sub-region;

[0077] The higher the index, the stronger the new energy power generation capacity under the current new energy power generation efficiency and output power conditions in the monitored sub-region; conversely, the weaker the index, the weaker the new energy power generation capacity under the current new energy power generation efficiency and output power conditions in the monitored sub-region.

[0078] S03: By collecting historical meteorological data from each monitoring sub-region of the distributed new energy power generation area, including sunlight intensity, rainfall, and snow accumulation, and after normalizing the historical meteorological data, the environmental adaptability of each monitoring sub-region for new energy power generation is calculated.

[0079]

[0080] Among them, HQ i Let Δqt represent the environmental adaptability of new energy power generation in the i-th monitoring sub-region. i Let Δqj represent the light intensity of the i-th monitoring sub-region. i Let Δqx represent the rainfall in the i-th monitoring sub-region. i Let λ represent the snow cover in the i-th monitoring sub-region, e represent the natural constant, and λ1, λ2, and λ3 represent the weighting factors for light intensity, precipitation, and snow cover, respectively, with λ1+λ2+λ3=1;

[0081] The higher the light intensity, the higher the efficiency of new energy power generation and the higher the environmental adaptability of new energy power generation. Conversely, the lower the light intensity, the lower the environmental adaptability of new energy power generation. Similarly, the higher the rainfall and snow accumulation, the lower the efficiency of new energy power generation and the lower the environmental adaptability of new energy power generation.

[0082] S04: The formula for calculating the new energy storage demand assessment index is as follows:

[0083]

[0084] Where, α i Let KG represent the new energy storage demand assessment index for the i-th monitoring sub-region. i HQ is represented as the renewable energy generation capacity index for the i-th monitoring sub-region. iLet ΔKG represent the environmental adaptability of new energy power generation in the i-th monitoring sub-region, ΔHQ represent the mean value of the new energy power generation capacity index, and ΔHQ represent the mean value of the environmental adaptability of new energy power generation.

[0085] n represents the number of monitored sub-regions.

[0086] The distributed new energy storage control module is used to obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, compare it with the preset new energy storage demand assessment index, and ensure that the energy storage system prioritizes providing power to the monitoring sub-regions with high power generation demand.

[0087] In one possible design, the distributed new energy storage and control module specifically comprises:

[0088] S01: Obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, and compare it with the preset new energy storage demand assessment index. If the new energy storage demand assessment index of a certain monitoring sub-region is greater than the preset new energy storage demand assessment index, it indicates that the monitoring sub-region is a region with high power generation demand; otherwise, it indicates that the monitoring sub-region is a region with surplus power generation demand.

[0089] S02: Based on the real-time new energy storage demand assessment index, dynamically adjust the charging and discharging strategy of the energy storage system to ensure that the energy storage system prioritizes providing power to the monitoring sub-areas with high power generation demand. For areas with surplus power generation demand, the excess new energy power in those areas will be stored or allocated to areas with high demand.

[0090] The distributed new energy power trading economic benefit module is used to receive data transmitted by the distributed new energy power generation data acquisition module, and calculate the power market trading economic benefit index of each monitoring sub-region of the distributed new energy power generation area based on the new energy power trading data collected by the new energy power trading data acquisition unit.

[0091] In one possible design, the distributed new energy power trading economic benefit module specifically comprises:

[0092] S01: Calculate the fluctuation of power revenue in each monitored sub-region based on equipment operation and maintenance costs and equipment power revenue.

[0093]

[0094] Among them, WY i Let dw represent the fluctuation in electricity revenue in the i-th monitoring sub-region. i Let dy represent the equipment operation and maintenance cost of the i-th monitoring sub-region. iLet represent the equipment electricity revenue of the i-th monitoring sub-region, and r represent the discount rate;

[0095] When the electricity revenue of the equipment is greater than the equipment operation and maintenance costs, the net revenue is positive, and the greater the fluctuation of the electricity revenue, the smaller the fluctuation of the electricity revenue.

[0096] S02: The formula for calculating the economic benefit index of the electricity market transaction is as follows:

[0097]

[0098] Where, β i Let xs be the economic benefit index of electricity market transactions in the i-th monitoring sub-region. i Let xj represent the actual power generation of new energy sources in the i-th monitoring sub-region. i Let WY represent the planned new energy power generation for the i-th monitoring sub-region. i Let WY represent the fluctuation in electricity revenue in the i-th monitoring sub-region. 预 Represented as the preset fluctuation in electricity revenue, SJ 允 It represents the allowable difference between the actual power generation of new energy sources and the planned power generation of new energy sources.

[0099] The distributed new energy storage correlation analysis module is used to analyze and obtain the energy storage resource allocation optimization coefficient of the distributed new energy power generation area based on the new energy storage demand assessment index and the power market transaction economic benefit index of each monitoring sub-area of ​​the distributed new energy power generation area.

[0100] In one possible design, the formula for calculating the energy storage resource allocation optimization coefficient is:

[0101]

[0102] Where θ represents the energy storage resource allocation optimization coefficient, and α i Let be the new energy storage demand assessment index for the i-th monitoring sub-region, Δα be the mean of the new energy storage demand assessment index, and β be the mean of the new energy storage demand assessment index. i Let Δβ represent the economic benefit index of electricity market transactions in the i-th monitoring sub-region, Δβ represent the mean of the economic benefit index of electricity market transactions, and n represent the number of monitoring sub-regions.

[0103] The distributed new energy storage optimization module is used to obtain the energy storage resource configuration optimization coefficient of the distributed new energy power generation area, compare it with the preset energy storage resource configuration optimization coefficient, and process it.

[0104] In one possible design, the distributed new energy storage optimization module specifically comprises:

[0105] The optimization coefficient for energy storage resource allocation in the distributed renewable energy generation area is obtained and compared with the preset optimization coefficient. If the optimization coefficient is greater than the preset coefficient, it indicates that the distributed renewable energy generation efficiency is high and the economic benefits in the electricity market are good, and the energy storage resource allocation in the distributed renewable energy generation area should be increased. Conversely, if the optimization coefficient is lower than the preset coefficient, it indicates that the distributed renewable energy generation efficiency is poor, resulting in poor economic benefits in the electricity market, and the energy storage resource allocation in the distributed renewable energy generation area should be reduced.

[0106] Please see Figure 3 As shown in this embodiment, it should be specifically explained that the present invention provides a distributed new energy storage regulation method, including the following steps:

[0107] Step S01: Distributed new energy power generation area division: This step is used to divide the distributed new energy power generation area into monitoring sub-areas according to the method of equal area division, and to number each monitoring sub-area of ​​the distributed new energy power generation area.

[0108] Step S02: Distributed New Energy Generation Data Acquisition: This step is used to collect energy storage resource regulation data from each monitoring sub-region of the distributed new energy generation area. The distributed new energy generation data acquisition includes a new energy generation data acquisition sub-step and a new energy power transaction data acquisition sub-step. The energy storage resource regulation data includes new energy generation data and new energy power transaction data.

[0109] Step S03: Central control of distributed new energy power generation: used to receive data transmitted from the distributed new energy power generation data acquisition step, and calculate the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area based on the new energy power generation data collected in the new energy power generation data acquisition sub-step.

[0110] Step S04: Distributed new energy storage regulation: This step is used to obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, compare it with the preset new energy storage demand assessment index, and ensure that the energy storage system prioritizes providing power to the monitoring sub-regions with high power generation demand.

[0111] Step S05: Economic Benefits of Distributed New Energy Power Trading: This step receives data transmitted from the distributed new energy power generation data acquisition step and calculates the economic benefit index of power market trading for each monitoring sub-region of the distributed new energy power generation area based on the new energy power trading data acquired in the new energy power trading data acquisition sub-step.

[0112] Step S06: Correlation analysis of distributed new energy storage: This step is used to analyze and obtain the optimization coefficient of energy storage resource allocation in the distributed new energy power generation area based on the new energy storage demand assessment index and the power market transaction economic benefit index of each monitoring sub-area in the distributed new energy power generation area.

[0113] Step S07: Distributed New Energy Storage Optimization: This step involves obtaining the energy storage resource configuration optimization coefficient for the distributed new energy power generation area, comparing it with the preset energy storage resource configuration optimization coefficient, and then processing the result.

[0114] In this embodiment, it should be specifically explained that the present invention collects energy storage resource regulation data from each monitoring sub-region of the distributed new energy power generation area. Based on the new energy power generation data collected by the new energy power generation data acquisition unit, it calculates the new energy storage demand assessment index for each monitoring sub-region of the distributed new energy power generation area. Based on the new energy power transaction data collected by the new energy power transaction data acquisition unit, it calculates the power market transaction economic benefit index for each monitoring sub-region of the distributed new energy power generation area. Further analysis yields the energy storage resource allocation optimization coefficient, which is compared with a preset energy storage resource allocation optimization coefficient. If the energy storage resource allocation optimization coefficient of the distributed new energy power generation area is greater than the preset energy storage resource allocation optimization coefficient, then... This indicates that the distributed renewable energy generation is highly efficient and has good economic benefits in the electricity market, so the allocation of energy storage resources in the current distributed renewable energy generation area should be increased. Conversely, it indicates that the distributed renewable energy generation is inefficient, resulting in poor economic benefits in the electricity market, so the allocation of energy storage resources in the current distributed renewable energy generation area should be reduced. Based on real-time collected renewable energy generation data and electricity trading data, quantitative analysis can ensure that optimization decisions take into account both generation efficiency and economic benefits. According to the characteristics and needs of renewable energy generation, the capacity of energy storage systems can be rationally allocated to achieve efficient utilization of energy storage resources, support the stable operation of distributed renewable energy generation systems and maximize economic benefits. Based on data, this is conducive to promoting the development of the entire renewable energy industry towards intelligence and digitalization.

[0115] This invention utilizes a distributed new energy storage control module. It acquires the new energy storage demand assessment index for each monitoring sub-region of a distributed new energy power generation area and compares it with a preset new energy storage demand assessment index. If the new energy storage demand assessment index of a monitoring sub-region is greater than the preset index, it indicates that the monitoring sub-region has high power generation demand; conversely, if it is less than the preset index, it indicates that the monitoring sub-region has surplus power generation demand. Based on the real-time new energy storage demand assessment index, the charging and discharging strategy of the energy storage is dynamically adjusted to ensure that the energy storage system prioritizes providing power to monitoring sub-regions with high power generation demand. For areas with surplus power generation demand, the excess new energy power is stored or allocated to areas with high demand. This dynamic adjustment of the charging and discharging strategy based on the real-time new energy storage demand assessment index ensures stable power supply, helps avoid power fluctuations caused by local power shortages, and improves the stability and reliability of the entire power system. Storing or allocating excess power to areas with surplus power generation demand effectively balances energy differences between different monitoring sub-regions and improves the efficiency of new energy utilization.

[0116] In conclusion, 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 within the protection scope of the present invention.

Claims

1. A distributed new energy storage and control system, characterized in that, include: Distributed new energy power generation area division module: used to divide the distributed new energy power generation area into monitoring sub-areas according to the equal area division method, and to number each monitoring sub-area of ​​the distributed new energy power generation area; Distributed new energy power generation data acquisition module: used to collect energy storage resource regulation data of each monitoring sub-region of the distributed new energy power generation area. The distributed new energy power generation data acquisition module includes a new energy power generation data acquisition unit and a new energy power trading data acquisition unit. Central control module for distributed new energy power generation: Based on the new energy power generation data collected by the new energy power generation data acquisition unit, calculate the new energy storage demand assessment index for each monitoring sub-region of the distributed new energy power generation area; Distributed new energy storage control module: used to obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, compare it with the preset new energy storage demand assessment index, and ensure that the energy storage system prioritizes to provide power to the monitoring sub-region with high power generation demand; Distributed New Energy Power Trading Economic Benefit Module: Based on the new energy power trading data collected by the new energy power trading data acquisition unit, the power market trading economic benefit index of each monitoring sub-region of the distributed new energy power generation area is calculated; Distributed new energy storage correlation analysis module: used to analyze and obtain the energy storage resource allocation optimization coefficient of the distributed new energy power generation area based on the new energy storage demand assessment index and the power market transaction economic benefit index of each monitoring sub-area of ​​the distributed new energy power generation area; Distributed new energy storage optimization module: used to obtain the energy storage resource configuration optimization coefficient of the distributed new energy power generation area, compare it with the preset energy storage resource configuration optimization coefficient, and process it; The distributed new energy power generation area division module is specifically as follows: Aerial photography of the distributed new energy power generation area was conducted using drones equipped with lidar to obtain the area of ​​the distributed new energy power generation area. The distributed new energy power generation area was then divided into monitoring sub-areas according to the method of equal area division, and the monitoring sub-areas of the distributed new energy power generation area were numbered sequentially as 1, 2, ... i, ... n; The central control module for distributed new energy power generation is specifically as follows: S41: After normalizing the renewable energy generation efficiency and renewable energy generation output power of each monitoring sub-region of the distributed renewable energy generation area, we obtain... , ; S42: Calculate the renewable energy power generation capacity index for each monitoring sub-region: in, Let represent the new energy power generation capacity index of the i-th monitoring sub-region. Let be the new energy power generation efficiency of the i-th monitoring sub-region. Represented as the output power of new energy power generation in the i-th monitoring sub-region; S43: By collecting historical meteorological data from each monitoring sub-region of the distributed new energy power generation area, including sunlight intensity, rainfall, and snow accumulation, and after normalizing the historical meteorological data, the environmental adaptability of each monitoring sub-region for new energy power generation is calculated. ； in, This represents the environmental adaptability of new energy power generation in the i-th monitoring sub-region. Let represent the light intensity of the i-th monitoring sub-region. Let represent the rainfall in the i-th monitoring sub-region. Let represent the snow cover in the i-th monitoring sub-region, and e represent the natural constant. , , These are weighting factors for light intensity, rainfall, and snow accumulation, respectively. + + =1; S44: The formula for calculating the new energy storage demand assessment index is as follows: ； in, Let represent the new energy storage demand assessment index for the i-th monitoring sub-region. Let represent the new energy power generation capacity index of the i-th monitoring sub-region. This represents the environmental adaptability of new energy power generation in the i-th monitoring sub-region. This is represented by the mean value of the new energy power generation capacity index D. This represents the mean value of the environmental adaptability of new energy power generation. The distributed new energy storage and regulation module is specifically as follows: S51: Obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, and compare it with the preset new energy storage demand assessment index. If the new energy storage demand assessment index of a certain monitoring sub-region is greater than the preset new energy storage demand assessment index, it indicates that the monitoring sub-region is a region with high power generation demand; otherwise, it indicates that the monitoring sub-region is a region with surplus power generation demand. S52: Based on the real-time new energy storage demand assessment index, dynamically adjust the charging and discharging strategy of energy storage to ensure that the energy storage system prioritizes providing power to the monitoring sub-areas with high power generation demand. For areas with surplus power generation demand, the excess new energy power in the area will be stored or allocated to areas with high demand. The specific economic benefit module for distributed new energy power trading is as follows: S61: Calculate the fluctuation of power revenue in each monitored sub-region based on equipment operation and maintenance costs and equipment power revenue. ； in, This is represented as the fluctuation in electricity revenue in the i-th monitoring sub-region. Let represent the equipment operation and maintenance cost of the i-th monitoring sub-region. Let represent the equipment electricity revenue of the i-th monitoring sub-region, and r represent the discount rate; When the electricity revenue of the equipment is greater than the equipment operation and maintenance costs, the net revenue is positive, and the greater the fluctuation of the electricity revenue, the smaller the fluctuation of the electricity revenue. S62: The formula for calculating the economic benefit index of the electricity market transaction is as follows: ； in, Let be the economic benefit index of electricity market transactions in the i-th monitoring sub-region. This represents the actual power generation of new energy sources in the i-th monitoring sub-region. Let represent the planned power generation of new energy sources in the i-th monitoring sub-region. This is represented as the fluctuation in electricity revenue in the i-th monitoring sub-region. This is represented as the preset fluctuation in electricity revenue. This is expressed as the allowable difference between the actual power generation from new energy sources and the planned power generation from new energy sources. The formula for calculating the energy storage resource allocation optimization coefficient is as follows: ； in, This is represented as the energy storage resource allocation optimization coefficient. Let represent the new energy storage demand assessment index for the i-th monitoring sub-region. This represents the average of the new energy storage demand assessment index. Let be the economic benefit index of electricity market transactions in the i-th monitoring sub-region. denoted as the mean of the economic benefit index of electricity market transactions, and n represents the number of monitored sub-regions.

2. The distributed new energy storage and control system according to claim 1, characterized in that: The distributed new energy power generation data acquisition module is specifically as follows: New energy power generation data acquisition unit: collects the actual power generation, planned power generation, power generation efficiency, and power output of new energy in each monitoring sub-region of the distributed new energy power generation area, and marks them as follows: , , , , where i = 1, 2, ..., n, and i represents the number of the i-th monitoring sub-region; New Energy Power Transaction Data Acquisition Unit: Collects equipment operation and maintenance costs and equipment power revenue for each monitoring sub-region of the distributed new energy power generation area, and marks them as follows: , .

3. The distributed new energy storage and control system according to claim 1, characterized in that: The distributed new energy storage optimization module is specifically as follows: The optimization coefficient for energy storage resource allocation in the distributed renewable energy generation area is obtained and compared with the preset optimization coefficient. If the optimization coefficient is greater than the preset coefficient, it indicates that the distributed renewable energy generation efficiency is high and the economic benefits in the electricity market are good, and the energy storage resource allocation in the distributed renewable energy generation area should be increased. Conversely, if the optimization coefficient is lower than the preset coefficient, it indicates that the distributed renewable energy generation efficiency is poor, resulting in poor economic benefits in the electricity market, and the energy storage resource allocation in the distributed renewable energy generation area should be reduced.

4. A distributed new energy storage regulation method, using a distributed new energy storage regulation system as described in any one of claims 1-3, characterized in that: Includes the following steps: Step S01: Distributed new energy power generation area division: This step is used to divide the distributed new energy power generation area into monitoring sub-areas according to the method of equal area division, and to number each monitoring sub-area of ​​the distributed new energy power generation area. Step S02: Distributed New Energy Generation Data Acquisition: This step is used to collect energy storage resource regulation data from each monitoring sub-region of the distributed new energy generation area. The distributed new energy generation data acquisition includes a new energy generation data acquisition sub-step and a new energy power transaction data acquisition sub-step. The energy storage resource regulation data includes new energy generation data and new energy power transaction data. Step S03: Central control of distributed new energy power generation: used to receive data transmitted from the distributed new energy power generation data acquisition step, and calculate the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area based on the new energy power generation data collected in the new energy power generation data acquisition sub-step. Step S04: Distributed new energy storage regulation: This step is used to obtain the new energy storage demand assessment index of each monitoring sub-region of the distributed new energy power generation area, compare it with the preset new energy storage demand assessment index, and ensure that the energy storage system prioritizes providing power to the monitoring sub-regions with high power generation demand. Step S05: Economic Benefits of Distributed New Energy Power Trading: This step receives data transmitted from the distributed new energy power generation data acquisition step and calculates the economic benefit index of power market trading for each monitoring sub-region of the distributed new energy power generation area based on the new energy power trading data acquired in the new energy power trading data acquisition sub-step. Step S06: Correlation analysis of distributed new energy storage: This step is used to analyze and obtain the optimization coefficient of energy storage resource allocation in the distributed new energy power generation area based on the new energy storage demand assessment index and the power market transaction economic benefit index of each monitoring sub-area in the distributed new energy power generation area. Step S07: Distributed New Energy Storage Optimization: This step involves obtaining the energy storage resource configuration optimization coefficient for the distributed new energy power generation area, comparing it with the preset energy storage resource configuration optimization coefficient, and then processing the result.

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