Method for planning energy storage capacity of micro-grid containing high-permeability distributed power supply

Abstract

The invention discloses a method for planning the energy storage capacity of a micro-grid containing a high-permeability distributed power supply, and relates to the field of micro-grid planning, and the method comprises the steps: generating predicted meteorological data according to the historical environment information of power equipment; acquiring a power generation threshold value and an energy storage threshold value; acquiring environment information of same-environment similar power generation equipment and energy storage devices; generating a same environment power generation equipment cooperation index; combining the type information and the historical environment information of the power equipment to generate a power generation equipment component environment response coefficient and an energy storage device component environment response coefficient; generating predicted power generation data in combination with the predicted meteorological data; generating energy storage device performance prediction data according to the predicted meteorological data and the environmental response coefficient of the energy storage device component; according to the energy storage threshold value and the predicted performance data of the energy storage device, judging whether the energy storage device meets the demand of the micro-grid or not; and according to the energy storage device capacity information and the predicted power generation data, judging whether the energy storage device continues to accommodate electric energy.

Description

Technical Field

[0001] The present invention relates to the field of microgrid planning, and in particular to a method for planning energy storage capacity of a microgrid containing a high-penetration distributed power source. Background Art

[0002] High-penetration distributed power sources refer to the power system in which the installed capacity of distributed power sources accounts for a relatively high proportion, and its power generation accounts for a large share of the total power supply. A microgrid is a small power system that usually includes distributed power sources, energy storage devices and local loads, among which energy storage devices are used in microgrids to balance the intermittency and volatility of distributed power sources.

[0003] With the increasing popularity and widespread application of renewable energy in the field of power production, its inherent intermittent and uncertain characteristics have brought unprecedented challenges to the stable operation of the power supply system. These characteristics make it difficult to evaluate the power of various types of power generation equipment in microgrids in coastal areas. Due to geographical factors in coastal areas, it is inevitable that components of microgrid equipment will age during use, causing a decrease in work efficiency. Since it is impossible to accurately predict the specific performance of equipment after aging, it is difficult to ensure whether it can still meet the power needs of the microgrid, which in turn brings challenges to the planning of microgrids. Summary of the invention

[0004] In order to solve the above technical problems, a method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources is provided. This technical solution solves the problems proposed in the above background technology that the power of various types of power generation equipment in the microgrid is difficult to evaluate, and the specific performance of the equipment after aging cannot be accurately predicted, making it difficult to ensure whether it can still meet the power demand of the microgrid.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources, comprising:

[0007] Collecting type information and historical environment information of power equipment in the microgrid, wherein the power equipment includes power generation equipment and energy storage devices;

[0008] According to the historical environmental information of power equipment, a meteorological observation model is established to generate forecast meteorological data;

[0009] Collect and analyze historical data of power equipment to obtain power generation thresholds and energy storage thresholds;

[0010] Analyze historical environmental information based on the type information of the power equipment to generate environmental information of the same type of power generation equipment in the same environment and environmental information of the same type of energy storage device in the same environment;

[0011] Combine the historical data of power equipment with the environmental information of the same type of power generation equipment in the same environment, obtain and analyze the power generation power of the environmental equipment, and generate the synergy index of the same environmental power generation equipment;

[0012] Combining the type information and historical environmental information of the power equipment, generating environmental response coefficients of components of the power generation equipment and components of the energy storage device;

[0013] Combine the predicted meteorological data, the synergy index of power generation equipment in the same environment and the environmental response coefficient of the components of the power generation equipment to generate predicted power generation data;

[0014] Generate energy storage device performance prediction data based on predicted meteorological data and environmental response coefficients of components of the energy storage device;

[0015] According to the energy storage threshold and the predicted energy storage device performance data, determine whether the energy storage device meets the microgrid demand. If not, maintain the energy storage device. If yes, obtain the energy storage device capacity information.

[0016] Based on the capacity information of the energy storage device and the predicted power generation data, it is determined whether the energy storage device can continue to accommodate electric energy. If so, the electric energy is transmitted to the energy storage device. If not, the electric energy is transferred and the power generation output is limited.

[0017] Preferably, the obtaining of power generation threshold and energy storage threshold specifically includes:

[0018] According to the historical data of the power generation equipment, the steady-state power generation interval data of the power generation equipment is obtained, and the upper limit and lower limit of the steady-state power generation interval data are recorded as the power generation upper limit value and the power generation lower limit value respectively;

[0019] According to historical data of the energy storage device, the capacity upper limit and discharge efficiency of the energy storage device are obtained, and the capacity upper limit of the energy storage device is recorded as a first energy storage threshold;

[0020] Obtain historical data on power demand of the microgrid, combine it with historical data on the energy storage device, obtain the proportion of the energy storage device in the microgrid supply and demand, and determine the minimum energy storage device capacity value required to maintain the stability of the grid supply and demand, which is recorded as the second energy storage threshold;

[0021] The discharge efficiency of the energy storage device is ranked, the fluctuation range of the discharge efficiency is clarified, and the discharge efficiency threshold of the energy storage device is determined, which is recorded as the third energy storage threshold;

[0022] The upper limit value of power generation and the lower limit value of power generation are collectively referred to as power generation thresholds, and the first energy storage threshold, the second energy storage threshold and the third energy storage threshold are collectively referred to as energy storage thresholds.

[0023] Preferably, generating the synergy index of power generation equipment in the same environment specifically includes:

[0024] According to the type information of the power generation equipment, the rated power of each type of power generation equipment is obtained;

[0025] Arrange and analyze the historical environmental information of the power generation equipment, extract the characteristic environmental data value corresponding to each historical environmental information, and record it as environmental characteristic data;

[0026] Combine environmental characteristic data and historical data of power generation equipment to obtain the power generation efficiency of various types of power generation equipment under various characteristic environments;

[0027] Combining the power generation efficiency of each type of power generation equipment under each characteristic environment and the rated power of each type of power generation equipment, the ratio of the power generation efficiency of each type of power generation equipment under the characteristic environment to the rated power is obtained, which is recorded as the correlation of the power generation efficiency of each type of power generation equipment under the characteristic environment;

[0028] Determine the synergy index of power generation equipment in the same environment based on the correlation of power generation efficiency of various types of power generation equipment under characteristic environments;

[0029] Among them, the specific calculation formula for the synergy index of power generation equipment in the same environment is:

[0030]

[0031] In the formula, c represents the synergy index of power generation equipment in the same environment, A represents the characteristic environmental data value corresponding to the environment in which the power generation equipment is located, and α i represents the correlation of power generation efficiency of power generation equipment i with environmental characteristic data, α j represents the correlation of power generation efficiency of power generation equipment j with respect to environmental characteristic data, and m represents the total number of power generation equipment.

[0032] Preferably, the generating of the environmental response coefficients of the components of the power generation equipment and the components of the energy storage device specifically includes:

[0033] According to the type information of the power generation equipment and the energy storage device, obtain and analyze the component information of the power generation equipment and the energy storage device, and obtain the internal structure of the power generation equipment and the energy storage device and the material information of the component parts;

[0034] Collect environmental sensitivity coefficients of materials used in power generation equipment and energy storage device components;

[0035] Classify the components of the power generation equipment according to the internal structure of the power generation equipment and the material information of the components, and generate component information of the power generation equipment of the same category;

[0036] Analyze historical data and historical environmental information of power generation equipment and energy storage devices to obtain data on the overall impact of the environment on the efficiency of power generation equipment;

[0037] According to the environmental sensitivity coefficient of the material of the power generation equipment components, the component information of the same type of power generation equipment is processed, and combined with the historical environmental information, the interference power generation efficiency data of the components of the power generation equipment is generated;

[0038] Combine the disturbed power generation efficiency data of the components of the power generation equipment and the overall impact data of the environment on the efficiency of the power generation equipment to generate the environmental response coefficient of the components of the power generation equipment;

[0039] According to the environmental sensitivity coefficient of the material of the energy storage device components, the historical data of the energy storage device is analyzed and processed to obtain the capacity change data of the energy storage device of each material at different time scales;

[0040] The environmental response coefficients of the components of the energy storage device are generated by combining the capacity change data of energy storage equipment of various materials at different time scales and historical environmental information.

[0041] Preferably, the generating predicted power generation data specifically includes:

[0042] Extract the predicted meteorological features according to the predicted meteorological data, and generate the predicted power generation of the power generation equipment matching the predicted meteorological features;

[0043] Combining the predicted meteorological data and the environmental response coefficients of the components of the power generation equipment, the predicted reduction value of the power generation of each type of power generation equipment is obtained, which is recorded as the first predicted amount;

[0044] Combining the predicted meteorological data with the historical data of the power generation equipment, the predicted power generation of each type of power generation equipment is obtained, which is recorded as the second predicted amount;

[0045] Combine the predicted power generation of power generation equipment matched with the predicted meteorological characteristics and the synergy index of power generation equipment in the same environment to obtain the predicted synergy power generation of each type of power generation equipment, which is recorded as the third predicted amount;

[0046] Based on the lower limit of power generation of the power generation equipment, determine whether the difference between the second predicted amount and the first predicted amount of the equipment is less than the lower limit of power generation. If so, it is considered that the power generation equipment has a fault and cannot meet the power supply demand of the microgrid, and the power generation equipment needs to be repaired. If not, record the difference between the second predicted amount and the first predicted amount of the equipment as the preliminary predicted power generation;

[0047] Based on the upper limit of power generation of the power generation equipment, determine whether the sum of the initial predicted power generation of the equipment and the third predicted power generation is greater than the upper limit of power generation. If not, it is considered that the power generation equipment has excess capacity and exceeds the power supply demand of the microgrid, and the power generation equipment needs to be restricted. If so, record the sum of the initial predicted power generation of the equipment and the third predicted power generation as the predicted power generation data.

[0048] Preferably, generating energy storage device performance prediction data specifically includes:

[0049] Obtain the performance of the current energy storage device based on the historical data of the energy storage device;

[0050] A time node is represented by a daily cycle, and the forecasted meteorological data corresponding to each future time node is generated;

[0051] According to the predicted meteorological data corresponding to each time node and the environmental response coefficient of the components of the energy storage device, the aging degree of the components of the energy storage device of different materials corresponding to each time node is obtained;

[0052] Combined with the aging degree of components of energy storage devices made of different materials corresponding to each time node and the performance of the current energy storage device, the predicted data of energy storage device capacity change is generated;

[0053] According to the historical data of the energy storage device, a capacity efficiency correlation index of the energy storage device is obtained;

[0054] Obtain predicted discharge efficiency of the energy storage device according to the predicted data of capacity change of the energy storage device and the capacity efficiency correlation index of the energy storage device;

[0055] The predicted data on energy storage device capacity changes and the predicted discharge efficiency of the energy storage device are collectively referred to as energy storage device performance prediction data.

[0056] Preferably, judging whether the energy storage device meets the microgrid demand based on the energy storage threshold and the predicted energy storage device performance data specifically includes:

[0057] Analyze and organize the energy storage device performance prediction data to obtain the energy storage device capacity and energy storage device discharge efficiency corresponding to each future time node;

[0058] Based on the second energy storage threshold, determine whether the energy storage device capacity corresponding to each future time node is less than the second energy storage threshold. If not, it means that the energy storage device capacity corresponding to the time node can meet the microgrid demand. Continue to compare according to the order of time nodes. If yes, record the time node data as a capacity node.

[0059] Taking the third energy storage threshold as a benchmark, determine whether the discharge efficiency of the energy storage device corresponding to each future time node is less than the third energy storage threshold. If not, continue to compare according to the order of the time nodes. If yes, record the data of the time node as the discharge efficiency node.

[0060] Compare the time sequence of capacity nodes and discharge efficiency nodes, obtain the time node information close to the current moment, and use this time node as the microgrid demand prediction matching point;

[0061] According to the predicted matching point of microgrid demand, the energy storage device can be inspected and maintained in advance.

[0062] Preferably, judging whether the energy storage device continues to accommodate electric energy based on the energy storage device capacity information and the predicted power generation data specifically includes:

[0063] Analyze and organize the predicted power generation data to obtain the predicted power generation of each power generation equipment corresponding to each future time node;

[0064] Acquire and analyze the location information of power generation equipment and energy storage devices, sort the energy storage devices according to the distance between the power generation equipment and the energy storage devices, and generate information about energy storage devices close to the power generation equipment;

[0065] Acquire the first energy storage threshold of the close-range energy storage device of the power generation equipment by combining the information of the close-range energy storage device of the power generation equipment and the first energy storage threshold of each energy storage device;

[0066] Based on the first energy storage threshold of the close-range energy storage device of the power generation equipment, determine whether the predicted power generation of each power generation equipment corresponding to each future time node is greater than the first energy storage threshold of the close-range energy storage device of the power generation equipment. If not, the electric energy generated by the power generation equipment is transmitted to the close-range energy storage device, and the predicted power generation is compared in the order of the time nodes. If so, the electric energy generated by the power generation equipment is transmitted to the long-distance adjacent energy storage device, and the power output of the power generation equipment with a low power generation equipment synergy index type is adjusted.

[0067] Furthermore, a microgrid energy storage capacity planning system with high penetration rate distributed power sources is proposed, which is used to implement the above-mentioned microgrid energy storage capacity planning method with high penetration rate distributed power sources, including:

[0068] A data acquisition module, which is used to collect type information, historical environmental information, historical data of power equipment, environmental sensitivity coefficients of materials of power generation equipment and energy storage device components, and location information of power generation equipment and energy storage devices in the microgrid, and transmit the data information to the data integration module and the power generation prediction module;

[0069] A data integration module, which is used to summarize and organize the received data, obtain the power generation threshold, energy storage threshold, the synergy index of power generation equipment in the same environment, the environmental response coefficient of the components of the power generation equipment and the environmental response coefficient of the components of the energy storage device by analyzing the summarized and organized data, and transmit the data to the power generation prediction module, the fault alarm module and the energy management module;

[0070] A power generation prediction module, which is used to process the data transmitted by the data acquisition module to generate predicted meteorological data, and to generate a first predicted quantity, a second predicted quantity and a third predicted quantity in combination with the synergy index of power generation equipment in the same environment and the environmental response coefficient of the components of the power generation equipment transmitted by the data integration module, so as to obtain predicted power generation data;

[0071] A fault alarm module, which is used to judge the received data, obtain available data and abnormal data according to the judgment result, transmit the available data to the data integration module and the energy management module, analyze the unavailable data, obtain the time when the equipment abnormality occurs, and send an abnormality reminder to the energy management module;

[0072] The energy management module is used to organize and summarize the received data, and according to the results of the organization and summary, timely plan the power transportation route and the production status of various types of power generation equipment.

[0073] Optionally, the fault alarm module includes:

[0074] a first judgment unit, the first judgment unit being configured to use the received power generation lower limit value as a reference value, judge the difference between the second predicted value and the first predicted value of each type of power generation equipment, generate a judgment result, and transmit the result to the abnormality reminder unit;

[0075] a second judgment unit, the second judgment unit being configured to use the received power generation upper limit value as a reference value, judge the sum of the preliminary predicted power generation of each type of power generation equipment and the third predicted power generation, generate a judgment result, and transmit the result to the abnormality warning unit;

[0076] a third judgment unit, the third judgment unit being configured to use the received second energy storage threshold as a reference value, judge the capacity of the energy storage device corresponding to each future time node, generate a judgment result, and transmit the judgment result to the abnormality reminder unit;

[0077] a fourth judgment unit, the fourth judgment unit being configured to use the received third energy storage threshold as a reference value, judge the discharge efficiency of the energy storage device corresponding to each future time node, generate a judgment result, and transmit the judgment result to the abnormality reminder unit;

[0078] a fifth judgment unit, the fifth judgment unit being configured to use the received first energy storage threshold of the close-range energy storage device of the power generation equipment as a reference value, judge the predicted power generation of each power generation equipment corresponding to each future time node, generate a judgment result, and transmit it to the abnormality reminder unit;

[0079] The abnormality reminder unit is used to process the received data, obtain abnormal data according to the judgment result, and transmit the abnormal data to the energy management module.

[0080] Compared with the prior art, the present invention has the following beneficial effects:

[0081] The present invention proposes a microgrid energy storage capacity planning scheme containing a high-penetration distributed power source. According to the historical environmental information of the power equipment, the predicted meteorological data is generated, the historical data of the power equipment is collected and analyzed, the power generation threshold and the energy storage threshold are obtained, and according to the type information of the power equipment, the environmental information of the same type of power generation equipment in the same environment and the environmental information of the same type of energy storage device in the same environment are generated. The synergy index of the power generation equipment in the same environment is generated by combining the historical data of the power equipment and the environmental information of the same type of power generation equipment in the same environment. The environmental response coefficient of the components of the power generation equipment and the environmental response coefficient of the components of the energy storage device are generated by combining the type information of the power equipment and the historical environmental information. The same environment power generation equipment synergy index and the environmental response coefficient of the power generation equipment components are used to generate predicted power generation data. According to the predicted meteorological data and the environmental response coefficient of the energy storage device components, the energy storage device performance prediction data is generated. According to the energy storage threshold and the predicted energy storage device performance data, it is judged whether the energy storage device meets the needs of the microgrid. According to the energy storage device capacity information and the predicted power generation data, it is judged whether the energy storage device continues to accommodate electrical energy. In this way, the power of various types of power generation equipment in the microgrid affected by the environment can be effectively predicted, the specific performance of the equipment after aging can be accurately predicted, and whether the power equipment can meet the power demand of the microgrid can be accurately judged, thus ensuring the stability of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A flow chart of a method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources proposed by the present invention;

[0083] Figure 2 A flowchart of the steps for obtaining the power generation threshold and the energy storage threshold in the present invention;

[0084] Figure 3 A flowchart of the steps for generating a synergy index of power generation equipment in the same environment in the present invention;

[0085] Figure 4 A flow chart of the steps of generating environmental response coefficients of components of power generation equipment and components of energy storage device in the present invention;

[0086] Figure 5 A flowchart of the steps for generating energy storage device performance prediction data in the present invention;

[0087] Figure 6 A structural flow chart for determining whether the energy storage device continues to store electrical energy in the present invention;

[0088] Figure 7 This is a structural diagram of the microgrid energy storage capacity planning system with high-penetration distributed power sources proposed in the present invention. DETAILED DESCRIPTION

[0089] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0090] Reference Figure 1 As shown, a method for planning energy storage capacity of a microgrid with high penetration rate distributed power sources includes:

[0091] Collect type information and historical environment information of power equipment in the microgrid, where the power equipment includes power generation equipment and energy storage devices;

[0092] According to the historical environmental information of power equipment, a meteorological observation model is established to generate forecast meteorological data;

[0093] Collect and analyze historical data of power equipment to obtain power generation thresholds and energy storage thresholds;

[0094] Analyze historical environmental information based on the type information of the power equipment to generate environmental information of the same type of power generation equipment in the same environment and environmental information of the same type of energy storage device in the same environment;

[0095] Combine the historical data of power equipment with the environmental information of the same type of power generation equipment in the same environment, obtain and analyze the power generation power of the environmental equipment, and generate the synergy index of the same environmental power generation equipment;

[0096] Combining the type information and historical environmental information of the power equipment, generating environmental response coefficients of components of the power generation equipment and components of the energy storage device;

[0097] Combine the predicted meteorological data, the synergy index of power generation equipment in the same environment and the environmental response coefficient of the components of the power generation equipment to generate predicted power generation data;

[0098] Generate energy storage device performance prediction data based on predicted meteorological data and environmental response coefficients of components of the energy storage device;

[0099] According to the energy storage threshold and the predicted energy storage device performance data, determine whether the energy storage device meets the microgrid demand. If not, maintain the energy storage device. If yes, obtain the energy storage device capacity information.

[0100] Based on the capacity information of the energy storage device and the predicted power generation data, it is determined whether the energy storage device can continue to accommodate electric energy. If so, the electric energy is transmitted to the energy storage device. If not, the electric energy is transferred and the power generation output is limited.

[0101] This solution generates predicted meteorological data based on historical environmental information of power equipment, collects and analyzes historical data of power equipment, obtains power generation thresholds and energy storage thresholds, generates environmental information of power generation equipment of the same type in the same environment and environmental information of energy storage devices of the same type in the same environment based on type information of power equipment, generates a synergy index of power generation equipment in the same environment by combining historical data of power equipment and environmental information of power generation equipment of the same type in the same environment, generates environmental response coefficients of power generation equipment components and environmental response coefficients of energy storage device components by combining type information of power equipment and historical environmental information, generates predicted power generation data by combining predicted meteorological data, synergy index of power generation equipment in the same environment and environmental response coefficients of power generation equipment components, generates predicted power generation data based on predicted meteorological data and environmental response coefficients of energy storage device components, determines whether the energy storage device meets the needs of the microgrid based on the energy storage threshold and predicted performance data of the energy storage device, determines whether the energy storage device continues to accommodate electric energy based on capacity information of the energy storage device and predicted power generation data, and in this way, effectively predicts the power of various types of power generation equipment in the microgrid affected by the environment, and accurately predicts the specific performance of the equipment after aging.

[0102] Reference Figure 2 As shown, the power generation threshold and energy storage threshold are obtained, including:

[0103] According to the historical data of power generation equipment, the steady-state interval data of power generation equipment is obtained, and the upper and lower limits of the steady-state interval data of power generation are recorded as the upper limit value of power generation and the lower limit value of power generation respectively. Although the microgrid power generation equipment containing high-penetration distributed power sources is easily affected by the environment, causing the power generation data to fluctuate frequently, in a longer time interval, the power generation data of the power generation equipment will have a steady-state interval tending to balance;

[0104] According to the historical data of the energy storage device, the capacity upper limit and the discharge efficiency of the energy storage device are obtained, and the capacity upper limit of the energy storage device is recorded as the first energy storage threshold;

[0105] Obtain the historical data of power demand of the microgrid, combine it with the historical data of the energy storage device, obtain the proportion of the energy storage device in the supply and demand of the microgrid, determine the minimum capacity value of the energy storage device required to maintain the stability of the power supply and demand of the power grid, and record it as the second energy storage threshold. For a smaller power demand area, there are usually one or more specific energy storage devices in the microgrid to supply power to the area. By analyzing the historical data of power demand of the microgrid and splitting the microgrid into regions, the power demand of each area in the microgrid can be obtained. Through the historical data of the energy storage device, the power transmission of each energy storage device can be obtained. Combined with the power demand of each area in the microgrid and the power transmission of each energy storage device, the proportion of the energy storage device in the supply and demand of the microgrid can be obtained.

[0106] The discharge efficiency of the energy storage device is sorted, the fluctuation range of the discharge efficiency is clarified, and the discharge efficiency threshold of the energy storage device is determined, which is recorded as the third energy storage threshold. The energy storage batteries inside each energy storage device are different, so it is impossible to determine a clear and unified value for the discharge efficiency threshold. By sorting the discharge efficiency of the energy storage device, a discharge efficiency data list is generated, and the discharge efficiency corresponding to the first 10% in the data list is obtained, and the efficiency is used as the discharge efficiency threshold of this type of energy storage device;

[0107] The upper limit value of power generation and the lower limit value of power generation are collectively referred to as power generation thresholds, and the first energy storage threshold, the second energy storage threshold and the third energy storage threshold are collectively referred to as energy storage thresholds.

[0108] Reference Figure 3 As shown, the synergy index of power generation equipment in the same environment is generated, including:

[0109] According to the type information of the power generation equipment, the rated power of each type of power generation equipment is obtained;

[0110] Arrange and analyze the historical environmental information of the power generation equipment, extract the characteristic environmental data value corresponding to each historical environmental information, and record it as environmental characteristic data;

[0111] Combine environmental characteristic data and historical data of power generation equipment to obtain the power generation efficiency of various types of power generation equipment under various characteristic environments;

[0112] Combining the power generation efficiency of each type of power generation equipment under each characteristic environment and the rated power of each type of power generation equipment, the ratio of the power generation efficiency of each type of power generation equipment under the characteristic environment to the rated power is obtained, which is recorded as the correlation of the power generation efficiency of each type of power generation equipment under the characteristic environment;

[0113] Determine the synergy index of power generation equipment in the same environment based on the correlation of power generation efficiency of various types of power generation equipment under characteristic environments;

[0114] Among them, the specific calculation formula for the synergy index of power generation equipment in the same environment is:

[0115]

[0116] In the formula, c represents the synergy index of power generation equipment in the same environment, A represents the characteristic environmental data value corresponding to the environment in which the power generation equipment is located, and α i represents the correlation of power generation efficiency of power generation equipment i with environmental characteristic data, α j represents the correlation of power generation efficiency of power generation equipment j with respect to environmental characteristic data, and m represents the total number of power generation equipment.

[0117] It is understandable that although distributed power sources are susceptible to the corresponding characteristic environment due to their different power generation principles, under the same characteristic environment, other types of distributed power sources will produce corresponding changes in the environment. Taking photovoltaic power generation equipment as an example, the characteristic environment corresponding to photovoltaic power generation equipment is sunlight. The intensity of sunlight will affect the heating of the ground, and then cause local air convection, causing changes in wind force, affecting the production capacity of wind power equipment. The degree to which the characteristic environment corresponding to a single type of distributed power source affects the production capacity of other distributed power sources can be called the correlation of the power generation efficiency of various types of power generation equipment under the characteristic environment. By analyzing and calculating the correlation of the power generation efficiency of various types of power generation equipment under the characteristic environment, the overall impact index of the corresponding power generation equipment of the characteristic environment on the remaining power generation equipment can be obtained, which is called the synergy index of power generation equipment in the same environment.

[0118] Reference Figure 4 As shown, the environmental response coefficients of the components of the power generation equipment and the components of the energy storage device are generated, specifically including:

[0119] According to the type information of the power generation equipment and the energy storage device, obtain and analyze the component information of the power generation equipment and the energy storage device, and obtain the internal structure of the power generation equipment and the energy storage device and the material information of the component parts;

[0120] Collect environmental sensitivity coefficients of materials used in power generation equipment and energy storage device components;

[0121] Classify the components of the power generation equipment according to the internal structure of the power generation equipment and the material information of the components, and generate component information of the power generation equipment of the same category;

[0122] Analyze historical data and historical environmental information of power generation equipment and energy storage devices to obtain data on the overall impact of the environment on the efficiency of power generation equipment;

[0123] According to the environmental sensitivity coefficient of the material of the power generation equipment components, the information of the components of the same category of power generation equipment is processed, and combined with the historical environmental information, the disturbed power generation efficiency data of the components of the power generation equipment are generated. The disturbed power generation efficiency data of the components of the power generation equipment represent the data of the equipment efficiency change caused by the corrosion of the internal components of the power generation equipment due to environmental changes. According to the internal structure of the power generation equipment, the importance of each component of the power generation equipment can be obtained. The importance of each component of the power generation equipment, the environmental sensitivity coefficient of the material of the component and the corresponding value of the historical environmental information of the power generation equipment are multiplied to generate the disturbed power generation efficiency data of the components of the power generation equipment;

[0124] Combine the disturbed power generation efficiency data of the components of the power generation equipment and the overall impact data of the environment on the efficiency of the power generation equipment to generate the environmental response coefficient of the components of the power generation equipment; further determine the efficiency change data caused by the components of the power generation equipment according to the difference between the overall impact data of the environment on the efficiency of the power generation equipment and the disturbed power generation efficiency data of the components of the power generation equipment; and use the ratio of the efficiency change data caused by the components of the power generation equipment further determined to the data corresponding to the historical environmental information as the environmental response coefficient of the components of the power generation equipment;

[0125] According to the environmental sensitivity coefficient of the material of the energy storage device components, the historical data of the energy storage device is analyzed and processed to obtain the capacity change data of the energy storage device of each material at different time scales;

[0126] The environmental response coefficients of the components of the energy storage device are generated by combining the capacity change data of energy storage equipment of various materials at different time scales and historical environmental information.

[0127] It can be understood that the environmental sensitivity coefficient is a quantitative indicator used to measure the sensitivity of materials or components to specific environmental factors. Through the environmental sensitivity coefficient of the material of the power generation equipment components and historical environmental information, combined with the disturbed power generation efficiency data of the components of the power generation equipment and the overall impact data of the environment on the efficiency of the power generation equipment, the degree to which the efficiency of the equipment changes due to the influence of the environment can be obtained, which is called the environmental response coefficient of the components of the power generation equipment. Similarly, the environmental response coefficient of the components of the energy storage device can be obtained.

[0128] Furthermore, the predicted power generation data is generated, including:

[0129] Extract the predicted meteorological features according to the predicted meteorological data, and generate the predicted power generation of the power generation equipment matching the predicted meteorological features;

[0130] Combine the predicted meteorological data and the historical data of power equipment to obtain the predicted power generation of each type of power generation equipment, which is recorded as the second predicted amount. The second predicted amount only considers the environment and historical equipment data, and does not consider the impact of the environment on the equipment. Therefore, it is only a rough estimate of the power generation of future power generation equipment;

[0131] Combine the predicted meteorological data and the environmental response coefficient of the components of the power generation equipment to obtain the predicted reduction value of the power generation of each type of power generation equipment, which is recorded as the first predicted value; obtain the predicted degree of wear of the components of the power generation equipment by the meteorological environment through the predicted meteorological data and the environmental response coefficient of the components of the power generation equipment; perform weighted processing on the second predicted value of each type of equipment according to the predicted degree of wear of the components of the power generation equipment to obtain the predicted reduction value of the power generation;

[0132] Combine the predicted power generation of power generation equipment matching the predicted meteorological characteristics and the synergy index of power generation equipment in the same environment to obtain the predicted synergy power generation of each type of power generation equipment, which is recorded as the third predicted amount. The predicted power generation of power generation equipment matching the predicted meteorological characteristics represents the predicted power generation of the power generation equipment corresponding to the predicted meteorological characteristics. Multiply the predicted power generation of the equipment by the synergy index of power generation equipment in the same environment to obtain the predicted synergy power generation of the remaining equipment under the current meteorological characteristics.

[0133] Based on the lower limit of power generation of the power generation equipment, determine whether the difference between the second predicted amount and the first predicted amount of the equipment is less than the lower limit of power generation. If so, it is considered that the power generation equipment has a fault and cannot meet the power supply demand of the microgrid, and the power generation equipment needs to be repaired. If not, the difference between the second predicted amount and the first predicted amount of the equipment is recorded as the preliminary predicted power generation. The difference between the second predicted amount and the first predicted amount of the equipment indicates that the power generation of the power generation equipment is roughly estimated in the future. Considering that the power generation is reduced due to the influence of the environment on the structure of the power equipment, the difference between the second predicted amount and the first predicted amount cannot be less than the lower limit of power generation. If it is less than the lower limit of power generation, it means that the structure of the equipment cannot meet the needs of the microgrid due to the influence of the environment.

[0134] Based on the upper limit of power generation of the power generation equipment, determine whether the sum of the preliminary predicted power generation and the third predicted power generation of the equipment is greater than the upper limit of power generation. If not, it is considered that the power generation equipment has excess capacity and exceeds the power supply demand of the microgrid, and the power generation equipment needs to be restricted. If so, the preliminary predicted power generation and the sum of the third predicted power generation of the equipment are recorded as predicted power generation data. The preliminary predicted power generation and the sum of the third predicted power generation of the equipment represent the synergistic power generation generated by the power generation equipment on the basis of the preliminary predicted power generation, considering that the power equipment is affected by the characteristic environment. Therefore, the sum of the preliminary predicted power generation and the third predicted power generation cannot be greater than the upper limit of power generation. If it is greater than the upper limit of power generation, it means that the power generation data of the power generation equipment exceeds the demand of the microgrid, making it impossible for the microgrid to achieve supply and demand balance.

[0135] Reference Figure 5 As shown, the energy storage device performance prediction data is generated, including:

[0136] Obtain the performance of the current energy storage device based on the historical data of the energy storage device;

[0137] A time node is represented by a daily cycle, and the forecasted meteorological data corresponding to each future time node is generated;

[0138] According to the predicted meteorological data corresponding to each time node and the environmental response coefficient of the components of the energy storage device, the aging degree of the components of the energy storage device of different materials corresponding to each time node is obtained;

[0139] Combined with the aging degree of components of energy storage devices made of different materials corresponding to each time node and the performance of the current energy storage device, the predicted data of energy storage device capacity change is generated;

[0140] According to the historical data of the energy storage device, a capacity efficiency correlation index of the energy storage device is obtained;

[0141] Obtain predicted discharge efficiency of the energy storage device according to the predicted data of capacity change of the energy storage device and the capacity efficiency correlation index of the energy storage device;

[0142] The predicted data on energy storage device capacity changes and the predicted discharge efficiency of the energy storage device are collectively referred to as energy storage device performance prediction data.

[0143] It is understandable that there is a certain correlation between the capacity and discharge efficiency of the energy storage device. The larger the capacity of the energy storage device, the more energy it can store. The energy storage device will have a certain energy loss during the discharge process, and the internal resistance of the energy storage device will be affected. Therefore, for a large-capacity energy storage device, the heat generated by the battery during discharge will increase, causing the internal resistance of the battery to increase, resulting in a decrease in discharge efficiency.

[0144] Furthermore, based on the energy storage threshold and the predicted energy storage device performance data, it is determined whether the energy storage device meets the microgrid requirements, including:

[0145] Analyze and organize the energy storage device performance prediction data to obtain the energy storage device capacity and energy storage device discharge efficiency corresponding to each future time node;

[0146] Based on the second energy storage threshold, determine whether the energy storage device capacity corresponding to each future time node is less than the second energy storage threshold. If not, it means that the energy storage device capacity corresponding to the time node can meet the microgrid demand. Continue to compare according to the order of time nodes. If yes, record the time node data as a capacity node.

[0147] Taking the third energy storage threshold as a benchmark, determine whether the discharge efficiency of the energy storage device corresponding to each future time node is less than the third energy storage threshold. If not, continue to compare according to the order of the time nodes. If yes, record the data of the time node as the discharge efficiency node.

[0148] Compare the time sequence of capacity nodes and discharge efficiency nodes, obtain the time node information close to the current moment, and use this time node as the microgrid demand prediction matching point;

[0149] According to the predicted matching point of microgrid demand, the energy storage device can be inspected and maintained in advance.

[0150] Reference Figure 6As shown, according to the capacity information of the energy storage device and the predicted power generation data, it is determined whether the energy storage device continues to accommodate electric energy, specifically including:

[0151] Analyze and organize the predicted power generation data to obtain the predicted power generation of each power generation equipment corresponding to each future time node;

[0152] Acquire and analyze the location information of power generation equipment and energy storage devices, sort the energy storage devices according to the distance between the power generation equipment and the energy storage devices, and generate information about energy storage devices close to the power generation equipment;

[0153] Acquire the first energy storage threshold of the close-range energy storage device of the power generation equipment by combining the information of the close-range energy storage device of the power generation equipment and the first energy storage threshold of each energy storage device;

[0154] Based on the first energy storage threshold of the close-range energy storage device of the power generation equipment, determine whether the predicted power generation of each power generation equipment corresponding to each future time node is greater than the first energy storage threshold of the close-range energy storage device of the power generation equipment. If not, the electric energy generated by the power generation equipment is transmitted to the close-range energy storage device, and the predicted power generation is compared in the order of the time nodes. If so, the electric energy generated by the power generation equipment is transmitted to the long-distance adjacent energy storage device, and the power output of the power generation equipment with a low power generation equipment synergy index type is adjusted.

[0155] Further, see Figure 7 As shown, based on the above-mentioned microgrid energy storage capacity planning method with high penetration rate distributed power sources, this scheme proposes a microgrid energy storage capacity planning system with high penetration rate distributed power sources, including:

[0156] Data acquisition module, which is used to collect type information, historical environmental information, historical data of power equipment, environmental sensitivity coefficients of materials of power generation equipment and energy storage device components, and location information of power generation equipment and energy storage devices in the microgrid, and transmit the data information to the data integration module and the power generation prediction module;

[0157] Data integration module, the data integration module is used to summarize and organize the received data, and obtain the power generation threshold, energy storage threshold, synergy index of power generation equipment in the same environment, environmental response coefficient of power generation equipment components and environmental response coefficient of energy storage device components by analyzing the summarized and organized data, and transmit the data to the power generation prediction module, fault alarm module and energy management module;

[0158] The power generation prediction module is used to process the data transmitted by the data acquisition module, generate predicted meteorological data, combine the synergy index of power generation equipment in the same environment and the environmental response coefficient of the components of the power generation equipment transmitted by the data integration module, generate the first predicted quantity, the second predicted quantity and the third predicted quantity, and obtain the predicted power generation data;

[0159] Fault alarm module: The fault alarm module is used to judge the received data, obtain available data and abnormal data according to the judgment results, transmit the available data to the data integration module and the energy management module, analyze the unavailable data, obtain the time when the equipment abnormality occurs, and send abnormality reminders to the energy management module;

[0160] Energy management module: The energy management module is used to organize and summarize the received data, and according to the results of the organization and summary, timely plan the power transportation routes and the production status of various types of power generation equipment.

[0161] Furthermore, the fault alarm module includes:

[0162] A first judgment unit, the first judgment unit is used to use the received power generation lower limit value as a reference value, judge the difference between the second predicted value and the first predicted value of each type of power generation equipment, generate a judgment result, and transmit it to the abnormality reminder unit;

[0163] A second judgment unit, the second judgment unit is used to use the received power generation upper limit value as a reference value, judge the sum of the preliminary predicted power generation of each type of power generation equipment and the third predicted amount, generate a judgment result, and transmit it to the abnormality reminder unit;

[0164] a third judgment unit, the third judgment unit being configured to use the received second energy storage threshold as a reference value, judge the capacity of the energy storage device corresponding to each future time node, generate a judgment result, and transmit the result to the abnormality reminder unit;

[0165] A fourth judgment unit, the fourth judgment unit is used to use the received third energy storage threshold as a reference value, judge the discharge efficiency of the energy storage device corresponding to each future time node, generate a judgment result, and transmit it to the abnormality reminder unit;

[0166] A fifth judgment unit, the fifth judgment unit is used to use the received first energy storage threshold of the close-range energy storage device of the power generation equipment as a reference value, judge the predicted power generation of each power generation equipment corresponding to each future time node, generate a judgment result, and transmit it to the abnormality reminder unit;

[0167] The abnormality reminder unit is used to process the received data, obtain abnormal data according to the judgment result, and transmit the abnormal data to the energy management module.

[0168] To sum up, the advantages of the present invention are: it effectively predicts the power of various types of power generation equipment in the microgrid affected by the environment, accurately predicts the specific performance of the equipment after aging, can accurately determine whether the power equipment can meet the power demand of the microgrid, and ensures the stability of the microgrid.

[0169] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for planning energy storage capacity of a microgrid containing high penetration distributed power sources, characterized in that: include: Collect type information and historical environmental information of power generation equipment and energy storage devices in the microgrid; Based on the historical environmental information of power generation equipment and energy storage devices, a meteorological observation model is established to generate forecast meteorological data; Collect and analyze historical data of power generation equipment and energy storage devices to obtain power generation thresholds and energy storage thresholds; Analyze historical environmental information according to type information of power generation equipment and energy storage device, and generate environmental information of power generation equipment of the same type and environment and environmental information of energy storage device of the same type and environment; Combine the historical data of power generation equipment with the environmental information of the same type of power generation equipment in the same environment, obtain and analyze the power generation power of the environmental equipment, and generate the synergy index of the same environmental power generation equipment; Generate environmental response coefficients of components of power generation equipment and components of energy storage device by combining type information of power generation equipment and energy storage device and historical environmental information; Combine the predicted meteorological data, the synergy index of power generation equipment in the same environment and the environmental response coefficient of the components of the power generation equipment to generate predicted power generation data; Generate energy storage device performance prediction data based on predicted meteorological data and environmental response coefficients of components of the energy storage device; According to the energy storage threshold and the predicted energy storage device performance data, determine whether the energy storage device meets the microgrid demand. If not, maintain the energy storage device. If yes, obtain the energy storage device capacity information. Based on the capacity information of the energy storage device and the predicted power generation data, it is determined whether the energy storage device can continue to accommodate electric energy. If so, the electric energy is transmitted to the energy storage device. If not, the electric energy is transferred and the power generation output is limited.

2. A method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources according to claim 1, characterized in that: The obtaining of the power generation threshold and the energy storage threshold specifically includes: According to the historical data of the power generation equipment, the steady-state power generation interval data of the power generation equipment is obtained, and the upper limit and lower limit of the steady-state power generation interval data are recorded as the power generation upper limit value and the power generation lower limit value respectively; According to historical data of the energy storage device, the capacity upper limit and discharge efficiency of the energy storage device are obtained, and the capacity upper limit of the energy storage device is recorded as a first energy storage threshold; Obtain historical data on power demand of the microgrid, combine it with historical data on the energy storage device, obtain the proportion of the energy storage device in the microgrid supply and demand, and determine the minimum energy storage device capacity value required to maintain the stability of the grid supply and demand, which is recorded as the second energy storage threshold; The discharge efficiency of the energy storage device is ranked, the fluctuation range of the discharge efficiency is clarified, and the discharge efficiency threshold of the energy storage device is determined, which is recorded as the third energy storage threshold; The upper limit value of power generation and the lower limit value of power generation are collectively referred to as power generation thresholds, and the first energy storage threshold, the second energy storage threshold and the third energy storage threshold are collectively referred to as energy storage thresholds.

3. A method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources according to claim 2, characterized in that: The generating of the synergy index of power generation equipment in the same environment specifically includes: According to the type information of the power generation equipment, the rated power of each type of power generation equipment is obtained; Arrange and analyze the historical environmental information of power generation equipment, extract characteristic environmental data values ​​corresponding to each historical environmental information, and record them as environmental characteristic data; Combine environmental characteristic data and historical data of power generation equipment to obtain the power generation efficiency of various types of power generation equipment under various characteristic environments; Combining the power generation efficiency of each type of power generation equipment under each characteristic environment and the rated power of each type of power generation equipment, the ratio of the power generation efficiency of each type of power generation equipment under the characteristic environment to the rated power is obtained, which is recorded as the correlation of the power generation efficiency of each type of power generation equipment under the characteristic environment; Determine the synergy index of power generation equipment in the same environment based on the correlation of power generation efficiency of various types of power generation equipment under characteristic environments; Among them, the specific calculation formula for the synergy index of power generation equipment in the same environment is: In the formula, c represents the synergy index of power generation equipment in the same environment, A represents the characteristic environmental data value corresponding to the environment in which the power generation equipment is located, and α i represents the correlation of power generation efficiency of power generation equipment i with environmental characteristic data, α j represents the correlation of power generation efficiency of power generation equipment j with respect to environmental characteristic data, and m represents the total number of power generation equipment.

4. A method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources according to claim 3, characterized in that: The generating of the environmental response coefficients of the components of the power generation equipment and the components of the energy storage device specifically includes: According to the type information of the power generation equipment and the energy storage device, obtain and analyze the component information of the power generation equipment and the energy storage device, and obtain the internal structure of the power generation equipment and the energy storage device and the material information of the component parts; Collect the environmental sensitivity coefficients of the materials of each component of power generation equipment and energy storage devices; Classify the components of the power generation equipment according to the internal structure of the power generation equipment and the material information of the components, and generate component information of the power generation equipment of the same category; Analyze historical data of power generation equipment and energy storage devices as well as historical environmental information to obtain data on the overall impact of the environment on the efficiency of power generation equipment; According to the environmental sensitivity coefficient of the material of the power generation equipment components, the component information of the same type of power generation equipment is processed, and combined with the historical environmental information, the interference power generation efficiency data of the components of the power generation equipment is generated; Combine the disturbed power generation efficiency data of the components of the power generation equipment and the overall impact data of the environment on the efficiency of the power generation equipment to generate the environmental response coefficient of the components of the power generation equipment; According to the environmental sensitivity coefficient of the material of the energy storage device components, the historical data of the energy storage device is analyzed and processed to obtain the capacity change data of the energy storage device of each material at different time scales; The environmental response coefficients of the components of the energy storage device are generated by combining the capacity change data of energy storage equipment of various materials at different time scales and historical environmental information.

5. A method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources according to claim 4, characterized in that: The generating of predicted power generation data specifically includes: Extract the predicted meteorological features according to the predicted meteorological data, and generate the predicted power generation of the power generation equipment matching the predicted meteorological features; Combining the predicted meteorological data and the environmental response coefficients of the components of the power generation equipment, the predicted reduction value of the power generation of each type of power generation equipment is obtained, which is recorded as the first predicted amount; Combining the predicted meteorological data with the historical data of the power generation equipment, the predicted power generation of each type of power generation equipment is obtained, which is recorded as the second predicted amount; Combine the predicted power generation of power generation equipment matched with the predicted meteorological characteristics and the synergy index of power generation equipment in the same environment to obtain the predicted synergy power generation of each type of power generation equipment, which is recorded as the third predicted amount; Based on the lower limit of power generation of the power generation equipment, determine whether the difference between the second predicted amount and the first predicted amount of the equipment is less than the lower limit of power generation. If so, it is considered that the power generation equipment has a fault and cannot meet the power supply demand of the microgrid, and the power generation equipment needs to be repaired. If not, record the difference between the second predicted amount and the first predicted amount of the equipment as the preliminary predicted power generation; Based on the upper limit of power generation of the power generation equipment, determine whether the sum of the initial predicted power generation of the equipment and the third predicted power generation is greater than the upper limit of power generation. If not, it is considered that the power generation equipment has excess capacity and exceeds the power supply demand of the microgrid, and the power generation equipment needs to be restricted. If so, record the sum of the initial predicted power generation of the equipment and the third predicted power generation as the predicted power generation data.

6. A method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources according to claim 5, characterized in that: The generating of energy storage device performance prediction data specifically includes: Obtain the performance of the current energy storage device based on the historical data of the energy storage device; A time node is represented by a daily cycle, and the forecasted meteorological data corresponding to each future time node is generated; According to the predicted meteorological data corresponding to each time node and the environmental response coefficient of the components of the energy storage device, the aging degree of the components of the energy storage device of different materials corresponding to each time node is obtained; Combined with the aging degree of components of energy storage devices made of different materials corresponding to each time node and the performance of the current energy storage device, the predicted data of energy storage device capacity change is generated; According to the historical data of the energy storage device, a capacity efficiency correlation index of the energy storage device is obtained; Obtain predicted discharge efficiency of the energy storage device according to the predicted data of capacity change of the energy storage device and the capacity efficiency correlation index of the energy storage device; The predicted data on energy storage device capacity changes and the predicted discharge efficiency of the energy storage device are collectively referred to as energy storage device performance prediction data.

7. A method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources according to claim 6, characterized in that: The step of judging whether the energy storage device meets the requirements of the microgrid based on the energy storage threshold and the predicted energy storage device performance data specifically includes: Analyze and organize the energy storage device performance prediction data to obtain the energy storage device capacity and energy storage device discharge efficiency corresponding to each future time node; Based on the second energy storage threshold, determine whether the energy storage device capacity corresponding to each future time node is less than the second energy storage threshold. If not, it means that the energy storage device capacity corresponding to the time node can meet the microgrid demand. Continue to compare according to the order of time nodes. If yes, record the time node data as a capacity node. Taking the third energy storage threshold as a benchmark, determine whether the discharge efficiency of the energy storage device corresponding to each future time node is less than the third energy storage threshold. If not, continue to compare according to the order of the time nodes. If yes, record the data of the time node as the discharge efficiency node. Compare the time sequence of capacity nodes and discharge efficiency nodes, obtain the time node information close to the current time, and use this time node as the microgrid demand prediction matching point; According to the predicted matching point of microgrid demand, the energy storage device can be inspected and maintained in advance.

8. A method for planning energy storage capacity of a microgrid containing high-penetration distributed power sources according to claim 7, characterized in that: The determining, based on the energy storage device capacity information and the predicted power generation data, whether the energy storage device continues to accommodate electric energy specifically includes: Analyze and organize the predicted power generation data to obtain the predicted power generation of each power generation equipment corresponding to each future time node; Acquire and analyze the location information of power generation equipment and energy storage devices, sort the energy storage devices according to the distance between the power generation equipment and the energy storage devices, and generate information about energy storage devices close to the power generation equipment; Acquire the first energy storage threshold of the close-range energy storage device of the power generation equipment by combining the information of the close-range energy storage device of the power generation equipment and the first energy storage threshold of each energy storage device; Based on the first energy storage threshold of the close-range energy storage device of the power generation equipment, determine whether the predicted power generation of each power generation equipment corresponding to each future time node is greater than the first energy storage threshold of the close-range energy storage device of the power generation equipment. If not, the electric energy generated by the power generation equipment is transmitted to the close-range energy storage device, and the predicted power generation is compared in the order of the time nodes. If so, the electric energy generated by the power generation equipment is transmitted to the long-distance adjacent energy storage device, and the power output of the power generation equipment with a low power generation equipment synergy index type is adjusted.

9. A microgrid energy storage capacity planning system containing high penetration distributed power sources, characterized in that: A method for planning energy storage capacity of a microgrid containing a high-penetration distributed power source as claimed in any one of claims 1 to 8, comprising: A data acquisition module, which is used to collect type information, historical environmental information, historical data, environmental sensitivity coefficients of component materials and location information of power generation equipment and energy storage devices in the microgrid, and transmit the data information to the data integration module and the power generation prediction module; A data integration module, which is used to summarize and organize the received data, obtain the power generation threshold, energy storage threshold, the synergy index of power generation equipment in the same environment, the environmental response coefficient of the components of the power generation equipment and the environmental response coefficient of the components of the energy storage device by analyzing the summarized and organized data, and transmit the data to the power generation prediction module, the fault alarm module and the energy management module; A power generation prediction module, which is used to process the data transmitted by the data acquisition module to generate predicted meteorological data, and to generate a first predicted quantity, a second predicted quantity and a third predicted quantity in combination with the synergy index of power generation equipment in the same environment and the environmental response coefficient of the components of the power generation equipment transmitted by the data integration module, so as to obtain predicted power generation data; A fault alarm module, which is used to judge the received data, obtain available data and abnormal data according to the judgment result, transmit the available data to the data integration module and the energy management module, analyze the unavailable data, obtain the time when the equipment abnormality occurs, and send an abnormality reminder to the energy management module; The energy management module is used to organize and summarize the received data, and according to the results of the organization and summary, timely plan the power transportation route and the production status of various types of power generation equipment.

10. A microgrid energy storage capacity planning system with high penetration rate distributed power sources according to claim 9, characterized in that: The fault alarm module comprises: a first judgment unit, the first judgment unit being configured to use the received power generation lower limit value as a reference value, judge the difference between the second predicted value and the first predicted value of each type of power generation equipment, generate a judgment result, and transmit the result to the abnormality reminder unit; a second judgment unit, the second judgment unit being configured to use the received power generation upper limit value as a reference value, judge the sum of the preliminary predicted power generation of each type of power generation equipment and the third predicted power generation, generate a judgment result, and transmit the result to the abnormality warning unit; a third judgment unit, the third judgment unit being configured to use the received second energy storage threshold as a reference value, judge the capacity of the energy storage device corresponding to each future time node, generate a judgment result, and transmit the judgment result to the abnormality reminder unit; a fourth judgment unit, the fourth judgment unit being configured to use the received third energy storage threshold as a reference value, judge the discharge efficiency of the energy storage device corresponding to each future time node, generate a judgment result, and transmit the judgment result to the abnormality reminder unit; a fifth judgment unit, the fifth judgment unit being configured to use the received first energy storage threshold of the close-range energy storage device of the power generation equipment as a reference value, judge the predicted power generation of each power generation equipment corresponding to each future time node, generate a judgment result, and transmit it to the abnormality reminder unit; The abnormality reminder unit is used to process the received data, obtain abnormal data according to the judgment result, and transmit the abnormal data to the energy management module.

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