Distributed new energy storage regulation and control method and system

Abstract

The invention discloses a distributed new energy storage regulation and control method and system, and particularly relates to the technical field of energy storage regulation and control. Comprising 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 regulation and control module and a distributed new energy power transaction economic benefit module, according to the invention, energy storage resource regulation and control data of each monitoring sub-region of a distributed new energy power generation region are collected, a new energy storage demand evaluation index and a power market transaction economic benefit index are calculated, an energy storage resource configuration optimization coefficient is analyzed, and the energy storage resource configuration optimization coefficient is optimized. The capacity of the energy storage system is reasonably configured according to the requirement of new energy power generation, efficient utilization of energy storage resources is improved, and the whole new energy industry is promoted to develop towards the intelligent and digital direction on the basis of data.

Description

Technical Field

[0001] The present invention relates to the field of energy storage control technology, and more specifically, to a distributed new energy storage control method and system. Background Art

[0002] With the increasing severity of environmental problems and concerns about the depletion of traditional energy, distributed new energy sources such as solar energy and wind energy have been widely used. Through effective regulation strategies of distributed new energy and energy storage systems, the goals of efficient new energy consumption, 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 is found through iterative calculation based on the real-time status of distributed new energy and energy storage systems to achieve optimized operation of the system. It has good adaptability and optimization capabilities, 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. For example, the existing distributed new energy storage control strategy is not intelligent enough to process large amounts of communication data and does not 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 the weather, which leads to large volatility and randomness in its output power. Existing control methods have a slow response speed and are unable to cope with sudden changes in energy storage and fluctuations in power load, which in turn affects the control effect. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a distributed new energy storage control method and system for solving the problems raised in the above-mentioned background technology.

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

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

[0009] Distributed renewable energy power generation data acquisition module: used to collect energy storage resource control data of each monitoring sub-area in the distributed renewable energy power generation area. The distributed renewable energy power generation data acquisition module includes a renewable energy power generation data acquisition unit and a renewable energy power transaction data acquisition unit.

[0010] Distributed new energy power generation central control module: Based on the new energy power generation data collected by the new energy power generation data collection unit, the new energy storage demand assessment index of each monitoring sub-area in the distributed new energy power generation area is calculated.

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

[0012] Distributed new energy power transaction economic benefit module: Based on the new energy power transaction data collected by the new energy power transaction data collection unit, the power market transaction economic benefit index of each monitoring sub-area in the distributed new energy power generation area is calculated.

[0013] 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 power market transaction economic benefit index of each monitoring sub-area in the distributed new 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 is specifically:

[0016] The distributed renewable energy power generation area is photographed by a drone equipped with a laser radar to obtain the area of ​​the distributed renewable energy power generation area. The distributed renewable energy power generation area is divided into monitoring sub-areas of equal area, and the monitoring sub-areas of the distributed renewable energy power generation area are numbered 1, 2, ...i, ...n in sequence.

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

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

[0019] New energy power transaction data collection unit: collects the equipment operation and maintenance costs and equipment power income of each monitoring sub-area in the distributed new energy power generation area, marked as dw i ,dy i .

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

[0021] S41: After normalizing the new energy generation efficiency and new energy generation output power of each monitoring sub-area in the distributed new energy generation area, Δxk is obtained. i , Δxg i ;

[0022] S42: Calculate the new energy power generation capacity index of each monitoring sub-area:

[0023]

[0024] Among them, KG i Expressed as the renewable energy power generation capacity index of the i-th monitoring sub-region, Δxk i Expressed as the renewable energy generation efficiency of the i-th monitoring sub-area, Δxg i It is represented as the output power of renewable energy generation in the i-th monitoring sub-area;

[0025] S43: Collect historical meteorological data of each monitoring sub-area in the distributed new energy power generation area, wherein the historical meteorological data includes light intensity, rainfall, and snow accumulation. After normalizing the historical meteorological data, calculate the environmental adaptability of new energy power generation in each monitoring sub-area:

[0026]

[0027] Among them, HQ i Expressed as the environmental adaptability of renewable energy generation in the i-th monitoring sub-region, Δqt i Expressed as the light intensity of the i-th monitoring sub-area, Δqj i Expressed as the rainfall in the i-th monitoring sub-area, Δqx i is represented by the snow accumulation in the i-th monitoring sub-area, e is represented by a natural constant, λ1, λ2, and λ3 are weight factors of light intensity, rainfall, and snow accumulation, respectively, and λ1+λ2+λ3=1;

[0028] S44: The calculation formula of the new energy storage demand assessment index is:

[0029]

[0030] Among them, α iExpressed as the new energy storage demand assessment index of the i-th monitoring sub-area, KG i It is expressed as the renewable energy power generation capacity index of the i-th monitoring sub-region, HQ i It is represented as the environmental adaptability of renewable energy power generation in the i-th monitoring sub-area, ΔKG is represented as the mean value of the renewable energy power generation capacity index D, and ΔHQ is represented as the mean value of the environmental adaptability of renewable energy power generation.

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

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

[0033] S52: Based on the real-time new energy storage demand assessment index, the energy storage charging and discharging strategy is dynamically adjusted to ensure that the energy storage system prioritizes providing electricity to monitored sub-areas with high power generation demand. For areas with surplus power generation demand, the excess new energy power in the area is stored or allocated to areas with high demand.

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

[0035] S61: Calculate the power revenue fluctuation of each monitoring sub-area based on the equipment operation and maintenance costs and the equipment power revenue:

[0036]

[0037] Among them, WY i It is expressed as the power revenue fluctuation of the ith monitoring sub-area, dw i Expressed as the equipment operation and maintenance cost of the i-th monitoring sub-area, dy i It is represented as the equipment power benefit of the i-th monitoring sub-area, and r is the discount rate;

[0038] When the power income of the equipment is greater than the equipment operation and maintenance cost, the net income is positive, and the power income fluctuation value is larger; conversely, the power income fluctuation value is smaller;

[0039] S62: The calculation formula of the power market transaction economic benefit index is:

[0040]

[0041] Among them, β i Expressed as the economic benefit index of electricity market transactions in the ith monitoring sub-region, xsi It is expressed as the actual power generation of renewable energy in the i-th monitoring sub-area, xj i Expressed as the planned renewable energy generation capacity of the i-th monitoring sub-region, WY i Expressed as the power revenue fluctuation of the ith monitoring sub-area, WY 预 Expressed as the preset power revenue fluctuation, SJ 允 It is expressed as the allowable difference between the actual power generation of new energy and the planned power generation of new energy.

[0042] Preferably, the calculation formula for the energy storage resource configuration optimization coefficient is:

[0043]

[0044] Among them, θ represents the optimization coefficient of energy storage resource configuration, α i is represented by the new energy storage demand assessment index of the i-th monitoring sub-area, Δα is represented by the mean of the new energy storage demand assessment index, β i It is represented as the economic benefit index of electricity market transaction in the ith monitoring sub-region, Δβ is represented as the mean of the economic benefit index of electricity market transaction, and n is represented as the number of monitoring sub-regions.

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

[0046] Obtain the energy storage resource configuration optimization coefficient of the distributed renewable energy power generation area and compare it with the preset energy storage resource configuration optimization coefficient. If the energy storage resource configuration optimization coefficient of the distributed renewable energy power generation area is greater than the preset energy storage resource configuration optimization coefficient, it indicates that the distributed renewable energy power generation efficiency is high and the economic benefits of the electricity market are good. The energy storage resource configuration of the current distributed renewable energy power generation area should be increased. Otherwise, it indicates that the distributed renewable energy power generation efficiency is poor, resulting in poor economic benefits in the electricity market. The energy storage resource configuration of the current distributed renewable energy power generation area should be reduced.

[0047] Preferably, a distributed new energy storage control method comprises the following steps:

[0048] Step S01: Distributed renewable energy power generation area division: used to divide the distributed renewable energy power generation area into monitoring sub-areas of equal area, and number each monitoring sub-area of ​​the distributed renewable energy power generation area;

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

[0050] Step S03: Distributed New Energy Power Generation Central Control: used to receive data transmitted in the distributed new energy power generation data collection step, and calculate the new energy storage demand assessment index of each monitoring sub-area in the distributed new energy power generation area based on the new energy power generation data collected in the new energy power generation data collection sub-step;

[0051] Step S04: Distributed New Energy Storage Control: This is used to obtain the new energy storage demand assessment index of each monitoring sub-area in 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 power supply to the monitoring sub-areas with high power generation demand;

[0052] Step S05: Distributed New Energy Power Transaction Economic Benefit: This step receives data transmitted in the distributed new energy power generation data collection step, and calculates the power market transaction economic benefit index of each monitored sub-region in the distributed new energy power generation area based on the new energy power transaction data collected in the new energy power transaction data collection sub-step;

[0053] Step S06: Distributed New Energy Storage Correlation Analysis: This is used to analyze and obtain the energy storage resource configuration 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 in the distributed new energy power generation area;

[0054] Step S07: Distributed new energy storage optimization: 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.

[0055] Technical effects and advantages of the present invention:

[0056] 1. The present invention provides a distributed new energy storage control method and system, which collects energy storage resource control data of each monitoring sub-area in the distributed new energy power generation area, calculates the new energy storage demand assessment index of each monitoring sub-area in the distributed new energy power generation area based on the new energy power generation data collected by the new energy power generation data collection unit, calculates the power market transaction economic benefit index of each monitoring sub-area in the distributed new energy power generation area based on the new energy power transaction data collected by the new energy power transaction data collection unit, further analyzes and obtains the energy storage resource configuration optimization coefficient, and compares it with the preset energy storage resource configuration optimization coefficient. If the energy storage resource configuration optimization coefficient of the distributed new energy power generation area is greater than the preset energy storage resource configuration optimization coefficient, the energy storage resource configuration optimization coefficient is calculated. If the number is high, it indicates that the distributed renewable energy power generation efficiency is high and the economic benefits of the power market are good. The energy storage resource configuration of the current distributed renewable energy power generation area should be increased. On the contrary, it indicates that the distributed renewable energy power generation efficiency is poor, resulting in poor economic benefits in the power market. The energy storage resource configuration of the current distributed renewable energy power generation area should be reduced. Based on the real-time collected renewable energy power generation data and power transaction data, through quantitative analysis, it is ensured that the optimization decision takes into account both power generation efficiency and economic benefits. According to the characteristics and needs of renewable energy power generation, the capacity of the energy storage system is reasonably configured to achieve efficient utilization of energy storage resources, support the stable operation of the distributed renewable energy power generation system and maximize economic benefits. Based on data, it is conducive to promoting the development of the entire new energy industry towards intelligence and digitalization.

[0057] 2. The present invention provides a distributed new energy storage control method and system, which uses a distributed new energy storage control module to obtain the new energy storage demand assessment index of each monitoring sub-area in the distributed new energy power generation area and compare it with the preset new energy storage demand assessment index. When the new energy storage demand assessment index of a certain monitoring sub-area is greater than the preset new energy storage demand assessment index, it indicates that the monitoring sub-area is an area with high power generation demand. Conversely, it indicates that the monitoring sub-area is an area with surplus power generation demand. Then, according to 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 gives priority to the power generation demand. The energy storage system provides electricity 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 is stored or deployed to areas with high demand. The charging and discharging strategy of energy storage can be dynamically adjusted according to the real-time new energy storage demand assessment index. The energy storage system gives priority to providing electricity to the monitoring sub-areas with high power generation demand, ensuring stable power supply, which is conducive to avoiding power fluctuations caused by local power shortages and improving the stability and reliability of the entire power system. For areas with surplus power generation demand, the excess power is stored or deployed to areas with high demand, which can effectively balance the energy differences between different monitoring sub-areas and improve the efficiency of new energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0060] Figure 3 The figure is a flow chart of a distributed new energy storage control method of the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] See also 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, and 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 generation area division module is used to divide the distributed renewable energy generation area into monitoring sub-areas of equal area and number each monitoring sub-area. Within a large distributed photovoltaic power generation area, the division of monitoring sub-areas allows for rapid location of affected areas and the implementation of appropriate scheduling and management measures.

[0065] In one possible design, the distributed renewable energy power generation area division module is specifically as follows:

[0066] The distributed renewable energy power generation area is photographed by a drone equipped with a laser radar to obtain the area of ​​the distributed renewable energy power generation area. The distributed renewable energy power generation area is divided into monitoring sub-areas of equal area, and the monitoring sub-areas of the distributed renewable energy power generation area are numbered 1, 2, ...i, ...n in sequence.

[0067] See also Figure 2 As shown, the distributed new energy power generation data acquisition module is used to collect energy storage resource control data of each monitoring sub-area in 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 transaction data acquisition unit. The energy storage resource control data includes new energy power generation data and new energy power transaction data.

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

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

[0070] New energy power transaction data collection unit: collects the equipment operation and maintenance costs and equipment power income of each monitoring sub-area in the distributed new energy power generation area, marked 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-area in 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 renewable energy power generation central control module is specifically:

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

[0074] S02: Calculate the new energy power generation capacity index of each monitoring sub-area:

[0075]

[0076] Among them, KG i Expressed as the renewable energy power generation capacity index of the i-th monitoring sub-region, Δxk i Expressed as the renewable energy generation efficiency of the i-th monitoring sub-area, Δxg i It is represented as the output power of renewable energy generation in the i-th monitoring sub-area;

[0077] The higher the index, the stronger the renewable energy power generation capacity is under the current renewable energy power generation efficiency and renewable energy power output conditions of the monitoring sub-area. Conversely, the lower the index, the weaker the renewable energy power generation capacity is under the current renewable energy power generation efficiency and renewable energy power output conditions of the monitoring sub-area.

[0078] S03: Collect historical meteorological data of each monitoring sub-area in the distributed new energy power generation area, wherein the historical meteorological data includes light intensity, rainfall, and snow accumulation. After normalizing the historical meteorological data, calculate the environmental adaptability of new energy power generation in each monitoring sub-area:

[0079]

[0080] Among them, HQ i Expressed as the environmental adaptability of renewable energy generation in the i-th monitoring sub-region, Δqt i Expressed as the light intensity of the i-th monitoring sub-area, Δqj i Expressed as the rainfall in the i-th monitoring sub-area, Δqx i is represented by the snow accumulation in the i-th monitoring sub-area, e is represented by a natural constant, λ1, λ2, and λ3 are weight factors of light intensity, rainfall, and snow accumulation, respectively, and λ1+λ2+λ3=1;

[0081] When the light intensity value is greater, the efficiency of new energy power generation is greater, and the environmental adaptability value of new energy power generation is greater. Conversely, the environmental adaptability value of new energy power generation is smaller. When the rainfall value is greater and the snow accumulation is greater, the efficiency of new energy power generation is smaller, and the environmental adaptability value of new energy power generation is smaller. Conversely, the environmental adaptability value of new energy power generation is greater.

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

[0083]

[0084] Among them, α i Expressed as the new energy storage demand assessment index of the i-th monitoring sub-area, KG i It is expressed as the renewable energy power generation capacity index of the i-th monitoring sub-region, HQ iThe new energy power generation environment fitness of the i th monitoring sub-region is represented by ΔKG, the average of the new energy power generation capacity index is represented by ΔKG, and the average of the new energy power generation environment fitness is represented by ΔHQ.

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

[0086] The distributed new energy storage regulation module is configured to obtain the new energy storage demand evaluation index of each monitoring sub-region in the distributed new energy power generation region, compare the new energy storage demand evaluation index with a preset new energy storage demand evaluation index, and ensure that the energy storage system preferentially provides power for the monitoring sub-region with high power generation demand.

[0087] In a possible design, the distributed new energy storage regulation module specifically includes:

[0088] S01: Obtain the new energy storage demand evaluation index of each monitoring sub-region in the distributed new energy power generation region, and compare the new energy storage demand evaluation index with a preset new energy storage demand evaluation index. When the new energy storage demand evaluation index of a certain monitoring sub-region is greater than the preset new energy storage demand evaluation 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: Dynamically adjust the charging and discharging strategy of the energy storage according to the real-time new energy storage demand evaluation index, ensure that the energy storage system preferentially provides power for the monitoring sub-region with high power generation demand, and store or allocate the surplus new energy power of the region with surplus power generation demand to the region with high power generation demand.

[0090] The distributed new energy power transaction economic benefit module is configured to receive data transmitted by the distributed new energy power data acquisition module, and calculate the power market transaction economic benefit index of each monitoring sub-region in the distributed new energy power generation region according to the new energy power transaction data collected by the new energy power transaction data acquisition unit.

[0091] In a possible design, the distributed new energy power transaction economic benefit module specifically includes:

[0092] S01: Calculate the power income fluctuation of each monitoring sub-region by the equipment operation and maintenance cost and the equipment power income.

[0093]

[0094] WY = Σ (dw + dy) / n i WY represents the power income fluctuation of the i th monitoring sub-region, dw represents the equipment operation and maintenance cost of the i th monitoring sub-region, and dy represents the equipment power income of the i th monitoring sub-region. i WY represents the power income fluctuation of the i th monitoring sub-region, dw represents the equipment operation and maintenance cost of the i th monitoring sub-region, and dy represents the equipment power income of the i th monitoring sub-region. iIt is represented as the equipment power benefit of the i-th monitoring sub-area, and r is the discount rate;

[0095] When the power income of the equipment is greater than the equipment operation and maintenance cost, the net income is positive, and the power income fluctuation value is larger; conversely, the power income fluctuation value is smaller;

[0096] S02: The calculation formula of the electricity market transaction economic benefit index is:

[0097]

[0098] Among them, β i Expressed as the economic benefit index of electricity market transactions in the ith monitoring sub-region, xs i It is expressed as the actual power generation of renewable energy in the i-th monitoring sub-area, xj i Expressed as the planned renewable energy generation capacity of the i-th monitoring sub-region, WY i Expressed as the power revenue fluctuation of the ith monitoring sub-area, WY 预 Expressed as the preset power revenue fluctuation, SJ 允 It is expressed as the allowable difference between the actual power generation of new energy and the planned power generation of new energy.

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

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

[0101]

[0102] Among them, θ represents the optimization coefficient of energy storage resource configuration, α i is represented by the new energy storage demand assessment index of the i-th monitoring sub-area, Δα is represented by the mean of the new energy storage demand assessment index, β i It is represented as the economic benefit index of electricity market transaction in the ith monitoring sub-region, Δβ is represented as the mean of the economic benefit index of electricity market transaction, and n is represented as the number of monitoring sub-regions.

[0103] The distributed new energy 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 is specifically:

[0105] Obtain the energy storage resource configuration optimization coefficient of the distributed renewable energy power generation area and compare it with the preset energy storage resource configuration optimization coefficient. If the energy storage resource configuration optimization coefficient of the distributed renewable energy power generation area is greater than the preset energy storage resource configuration optimization coefficient, it indicates that the distributed renewable energy power generation efficiency is high and the economic benefits of the electricity market are good. The energy storage resource configuration of the current distributed renewable energy power generation area should be increased. Otherwise, it indicates that the distributed renewable energy power generation efficiency is poor, resulting in poor economic benefits in the electricity market. The energy storage resource configuration of the current distributed renewable energy power generation area should be reduced.

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

[0107] Step S01: Distributed renewable energy power generation area division: used to divide the distributed renewable energy power generation area into monitoring sub-areas of equal area, and number each monitoring sub-area of ​​the distributed renewable energy power generation area;

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

[0109] Step S03: Distributed New Energy Power Generation Central Control: used to receive data transmitted in the distributed new energy power generation data collection step, and calculate the new energy storage demand assessment index of each monitoring sub-area in the distributed new energy power generation area based on the new energy power generation data collected in the new energy power generation data collection sub-step;

[0110] Step S04: Distributed New Energy Storage Control: This is used to obtain the new energy storage demand assessment index of each monitoring sub-area in 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 power supply to the monitoring sub-areas with high power generation demand;

[0111] Step S05: Distributed New Energy Power Transaction Economic Benefit: This step receives data transmitted in the distributed new energy power generation data collection step, and calculates the power market transaction economic benefit index of each monitored sub-region in the distributed new energy power generation area based on the new energy power transaction data collected in the new energy power transaction data collection sub-step;

[0112] Step S06: Distributed New Energy Storage Correlation Analysis: This is used to analyze and obtain the energy storage resource configuration 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 in the distributed new energy power generation area;

[0113] Step S07: Distributed new energy storage optimization: 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.

[0114] In this embodiment, it should be specifically explained that the present invention collects the energy storage resource control data of each monitoring sub-area in the distributed new energy power generation area, calculates the new energy storage demand assessment index of each monitoring sub-area in the distributed new energy power generation area based on the new energy power generation data collected by the new energy power generation data collection unit, calculates the power market transaction economic benefit index of each monitoring sub-area in the distributed new energy power generation area based on the new energy power transaction data collected by the new energy power transaction data collection unit, further analyzes and obtains the energy storage resource configuration optimization coefficient, and compares it with the preset energy storage resource configuration optimization coefficient. If the energy storage resource configuration optimization coefficient of the distributed new energy power generation area is greater than the preset energy storage resource configuration optimization coefficient, then This indicates that the distributed renewable energy generation efficiency is high and the economic benefits of the electricity market are good. Therefore, the energy storage resource allocation in the current distributed renewable energy generation area should be increased. On the contrary, this indicates that the distributed renewable energy generation efficiency is poor, resulting in poor economic benefits in the electricity market. Therefore, the energy storage resource allocation in the current distributed renewable energy generation area should be reduced. Based on the real-time collected renewable energy generation data and power transaction data, quantitative analysis is performed to ensure that the optimization decision takes into account both power generation efficiency and economic benefits. According to the characteristics and needs of renewable energy power generation, the capacity of the energy storage system is reasonably configured to achieve efficient utilization of energy storage resources, support the stable operation of the distributed renewable energy generation system and maximize economic benefits. Based on data, it is conducive to promoting the development of the entire new energy industry towards intelligence and digitalization.

[0115] The present invention utilizes a distributed new energy storage control module to obtain a new energy storage demand assessment index for each monitoring sub-region of a distributed new energy power generation area and compare it with a preset new energy storage demand assessment index. When the new energy storage demand assessment index of a monitoring sub-region is greater than the preset new energy storage demand assessment index, it indicates that the monitoring sub-region is an area with high power generation demand. Conversely, it indicates that the monitoring sub-region is an area with surplus power generation demand. Then, according to the real-time new energy storage demand assessment index, the energy storage charging and discharging strategy is dynamically adjusted to ensure that the energy storage system prioritizes providing power to the monitoring sub-region with high power generation demand. For areas with surplus power generation demand, the excess new energy power in the area is stored or allocated to areas with high demand. The energy storage charging and discharging strategy can be dynamically adjusted according to the real-time new energy storage demand assessment index. The energy storage system prioritizes providing power to the monitoring sub-region with high power generation demand, ensuring stable power supply, avoiding power fluctuation problems caused by local power shortages, and improving the stability and reliability of the entire power system. For areas with surplus power generation demand, the excess power is stored or allocated to areas with high demand, which can effectively balance the energy differences between different monitoring sub-regions and improve the efficiency of new energy utilization.

[0116] 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 scope of protection of the present invention.

Claims

1. A distributed new energy storage control system, characterized in that: include: Distributed renewable energy power generation area division module: used to divide the distributed renewable energy power generation area into monitoring sub-areas according to the division method of equal area, and number each monitoring sub-area of ​​the distributed renewable energy power generation area; Distributed renewable energy power generation data acquisition module: used to collect energy storage resource control data of each monitoring sub-area in the distributed renewable energy power generation area. The distributed renewable energy power generation data acquisition module includes a renewable energy power generation data acquisition unit and a renewable energy power transaction data acquisition unit. Distributed renewable energy power generation central control module: Based on the renewable energy power generation data collected by the renewable energy power generation data acquisition unit, it calculates the renewable energy storage demand assessment index of each monitoring sub-area in the distributed renewable energy power generation area; Distributed renewable energy storage control module: This module is used to obtain the renewable energy storage demand assessment index for each monitoring sub-area within the distributed renewable energy power generation area and compare it with the preset renewable energy storage demand assessment index to ensure that the energy storage system prioritizes power supply to monitoring sub-areas with high power generation demand. Distributed new energy power transaction economic benefit module: Based on the new energy power transaction data collected by the new energy power transaction data collection unit, the power market transaction economic benefit index of each monitoring sub-region in the distributed new energy power generation area is calculated; Distributed New Energy Storage Correlation Analysis Module: This 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 power market transaction economic benefit index of each monitored sub-area in 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.

2. A distributed new energy storage control system according to claim 1, characterized in that: The distributed new energy power generation area division module is specifically: The distributed renewable energy power generation area is photographed by a drone equipped with a laser radar to obtain the area of ​​the distributed renewable energy power generation area. The distributed renewable energy power generation area is divided into monitoring sub-areas of equal area, and the monitoring sub-areas of the distributed renewable energy power generation area are numbered 1, 2, ...i, ...n in sequence.

3. A distributed new energy storage control system according to claim 1, characterized in that: The distributed new energy power generation data acquisition module is specifically: New energy power generation data acquisition unit: collects the actual power generation, planned power generation, power generation efficiency and output power of new energy in each monitoring sub-area of ​​the distributed new energy power generation area, marked as xs respectively i 、xj i 、xk i 、xg i , where i = 1, 2, ... n, i represents the number of the i-th monitoring sub-area; New energy power transaction data collection unit: collects the equipment operation and maintenance costs and equipment power income of each monitoring sub-area in the distributed new energy power generation area, marked as dw i ,dy i .

4. A distributed new energy storage control system according to claim 1, characterized in that: The distributed new energy power generation central control module is specifically: S41: After normalizing the new energy generation efficiency and new energy generation output power of each monitoring sub-area in the distributed new energy generation area, Δxk is obtained. i , Δxg i ; S42: Calculate the new energy power generation capacity index of each monitoring sub-area: Among them, KG i Expressed as the renewable energy power generation capacity index of the i-th monitoring sub-region, Δxk i Expressed as the renewable energy generation efficiency of the i-th monitoring sub-area, Δxg i It is represented as the output power of renewable energy generation in the i-th monitoring sub-area; S43: Collect historical meteorological data of each monitoring sub-area in the distributed new energy power generation area, wherein the historical meteorological data includes light intensity, rainfall, and snow accumulation. After normalizing the historical meteorological data, calculate the environmental adaptability of new energy power generation in each monitoring sub-area: Among them, HQ i Expressed as the environmental adaptability of renewable energy generation in the i-th monitoring sub-region, Δqt i Expressed as the light intensity of the i-th monitoring sub-area, Δqj i Expressed as the rainfall in the i-th monitoring sub-area, Δqx i is represented by the snow accumulation in the i-th monitoring sub-area, e is represented by a natural constant, λ1, λ2, and λ3 are weight factors of light intensity, rainfall, and snow accumulation, respectively, and λ1+λ2+λ3=1; S44: The calculation formula of the new energy storage demand assessment index is: Among them, α i Expressed as the new energy storage demand assessment index of the i-th monitoring sub-area, KG i It is expressed as the renewable energy power generation capacity index of the i-th monitoring sub-region, HQ i It is represented as the environmental adaptability of renewable energy power generation in the i-th monitoring sub-area, ΔKG is represented as the mean value of the renewable energy power generation capacity index D, and ΔHQ is represented as the mean value of the environmental adaptability of renewable energy power generation.

5. A distributed new energy storage control system according to claim 1, characterized in that: The distributed new energy storage control module is specifically: S51: Obtaining a new energy storage demand assessment index for each monitoring sub-region of the distributed new energy power generation region, and comparing the index with a preset new energy storage demand assessment index. When the new energy storage demand assessment index of a monitoring sub-region is greater than the preset new energy storage demand assessment index, it indicates that the monitoring sub-region is an area with high power generation demand; otherwise, it indicates that the monitoring sub-region is an area with surplus power generation demand. S52: Based on the real-time new energy storage demand assessment index, the energy storage charging and discharging strategy is dynamically adjusted to ensure that the energy storage system prioritizes providing electricity to monitored sub-areas with high power generation demand. For areas with surplus power generation demand, the excess new energy power in the area is stored or allocated to areas with high demand.

6. A distributed new energy storage control system according to claim 1, characterized in that: The distributed new energy power trading economic benefit module is specifically as follows: S61: Calculate the power revenue fluctuation of each monitoring sub-area based on the equipment operation and maintenance costs and the equipment power revenue: Among them, WY i It is expressed as the power revenue fluctuation of the ith monitoring sub-area, dw i Expressed as the equipment operation and maintenance cost of the i-th monitoring sub-area, dy i It is represented as the equipment power benefit of the i-th monitoring sub-area, and r is the discount rate; When the power income of the equipment is greater than the equipment operation and maintenance cost, the net income is positive, and the power income fluctuation value is larger; conversely, the power income fluctuation value is smaller; S62: The calculation formula of the power market transaction economic benefit index is: Among them, β i Expressed as the economic benefit index of electricity market transactions in the ith monitoring sub-region, xs i It is expressed as the actual power generation of renewable energy in the i-th monitoring sub-area, xj i Expressed as the planned renewable energy generation capacity of the i-th monitoring sub-region, WY i Expressed as the power revenue fluctuation of the ith monitoring sub-area, WY 预 Expressed as the preset power revenue fluctuation, SJ 允 It is expressed as the allowable difference between the actual power generation of new energy and the planned power generation of new energy.

7. A distributed new energy storage control system according to claim 1, characterized in that: The calculation formula of the energy storage resource configuration optimization coefficient is: Among them, θ represents the optimization coefficient of energy storage resource configuration, α i is represented by the new energy storage demand assessment index of the i-th monitoring sub-area, Δα is represented by the mean of the new energy storage demand assessment index, β i It is represented as the economic benefit index of electricity market transaction in the ith monitoring sub-region, Δβ is represented as the mean of the economic benefit index of electricity market transaction, and n is represented as the number of monitoring sub-regions.

8. A distributed new energy storage control system according to claim 1, characterized in that: The distributed new energy storage optimization module is specifically: Obtain the energy storage resource configuration optimization coefficient of the distributed renewable energy power generation area and compare it with the preset energy storage resource configuration optimization coefficient. If the energy storage resource configuration optimization coefficient of the distributed renewable energy power generation area is greater than the preset energy storage resource configuration optimization coefficient, it indicates that the distributed renewable energy power generation efficiency is high and the economic benefits of the electricity market are good. The energy storage resource configuration of the current distributed renewable energy power generation area should be increased. Otherwise, it indicates that the distributed renewable energy power generation efficiency is poor, resulting in poor economic benefits in the electricity market. The energy storage resource configuration of the current distributed renewable energy power generation area should be reduced.

9. A distributed new energy storage control method, using a distributed new energy storage control system according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step S01: Distributed renewable energy power generation area division: used to divide the distributed renewable energy power generation area into monitoring sub-areas of equal area, and number each monitoring sub-area of ​​the distributed renewable energy power generation area; Step S02: Distributed New Energy Power Generation Data Collection: This is used to collect energy storage resource control data from each monitoring sub-area of ​​the distributed new energy power generation area. The distributed new energy power generation data collection includes a new energy power generation data collection sub-step and a new energy power transaction data collection sub-step. The energy storage resource control data includes new energy power generation data and new energy power transaction data. Step S03: Distributed New Energy Power Generation Central Control: used to receive data transmitted in the distributed new energy power generation data collection step, and calculate the new energy storage demand assessment index of each monitoring sub-area in the distributed new energy power generation area based on the new energy power generation data collected in the new energy power generation data collection sub-step; Step S04: Distributed New Energy Storage Control: This is used to obtain the new energy storage demand assessment index of each monitoring sub-area in 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 power supply to the monitoring sub-areas with high power generation demand; Step S05: Distributed New Energy Power Transaction Economic Benefit: This step receives data transmitted in the distributed new energy power generation data collection step, and calculates the power market transaction economic benefit index of each monitored sub-region in the distributed new energy power generation area based on the new energy power transaction data collected in the new energy power transaction data collection sub-step; Step S06: Distributed New Energy Storage Correlation Analysis: This is used to analyze and obtain the energy storage resource configuration 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 in the distributed new energy power generation area; Step S07: Distributed new energy storage optimization: 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.

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