Distributed photovoltaic access power distribution network substation overload early warning method

Abstract

The present application relates to a kind of distributed photovoltaic access power distribution network substation heavy overload early warning method.First, the output simultaneous rate coefficient of the output of distribution network regional distributed photovoltaic in greater time period is analyzed, the average coefficient of photovoltaic output in the month is obtained by analyzing the number of sunny days in target month, and the probability that the average value of photovoltaic output simultaneous rate coefficient in each day analysis period is greater than the average value of output coefficient in the month is calculated;Then, based on the development of future regional power grid distributed photovoltaic and load, and considering the capacity of distributed photovoltaic direct current side and the capacity ratio, the value range of component capacity of regional power grid target year distributed photovoltaic development and the critical development capacity of distributed photovoltaic under different scenarios are calculated;Then, the load rate of distribution network substation under different scenarios is analyzed, and the heavy load or overload of substation under different scenarios is early warned;Finally, the regional power grid distributed photovoltaic development efficiency index is proposed to represent the development efficiency of regional distributed photovoltaic.

Description

Technical Field

[0001] This invention relates to a method for early warning of heavy overload in distributed photovoltaic substations connected to a distribution network. Background Technology

[0002] With the integration of a high proportion of renewable energy and the increasing complexity of user electricity consumption behavior, the randomness and volatility of source-load relationships pose significant challenges to power system planning, operation, and dispatch. A key characteristic of the new distribution system dominated by new energy sources is the revolutionary change in power supply structure. The proportion of highly uncertain renewable energy sources such as wind and solar power will increase substantially, while the proportion of highly flexible fossil fuel power sources will decrease significantly. Adapting to the high uncertainty of power sources has become a new requirement for building a resilient power grid.

[0003] Due to the intermittent and fluctuating power output of distributed photovoltaic (PV) systems, the increasing proportion of PV systems connected to the distribution network poses significant challenges to the network's capacity and power quality. High-proportion PV integration has a substantial impact on the safe and stable operation of the distribution network, easily leading to a series of problems such as transformer overload safety risks, grid backflow, and excessively high terminal voltage. Therefore, to ensure the safe and stable operation of the system and improve the PV absorption capacity, it is necessary to study distribution network absorption assessment and analysis methods for large-scale PV integration and distribution network overload early warning methods. Summary of the Invention

[0004] The purpose of this invention is to provide a method for early warning of heavy overload in distribution network substations when distributed photovoltaic (PV) is connected to the distribution network. Based on the development status of distributed PV in the distribution network, this method provides early warning of heavy load or overload in distribution network substations under different distributed PV development scales. It can provide a theoretical basis for power grid planners in the analysis of distributed PV access in the distribution network and has significant engineering practical significance.

[0005] To achieve the above objectives, the technical solution of this invention is: a method for early warning of heavy overload in distributed photovoltaic (PV) substations connected to a distribution network. Based on historical operating data, the method first analyzes the output simultaneity coefficient of distributed PV in the distribution network area during periods of high output. By analyzing the number of sunny days in the target month, the method obtains the average PV output coefficient for that month and calculates the probability that the average PV output simultaneity coefficient for each analyzed period in that month is greater than the average output coefficient for that month. Next, based on the future development of distributed PV and load in the regional power grid and considering the DC-side capacity and capacity ratio of distributed PV, the method calculates the range of component capacity for the target year of distributed PV development in the regional power grid under different scenarios and the critical development capacity of distributed PV. Then, the method analyzes the load rate of substations in the distribution network under different scenarios and provides early warnings for substation overload or heavy load under different scenarios. Finally, the method proposes a regional power grid distributed PV development efficiency index to characterize the regional distributed PV development efficiency.

[0006] In one embodiment of the present invention, the method for analyzing the simultaneous output rate coefficient of distributed photovoltaic power generation in a distribution network area during periods of high output, by analyzing the number of sunny days in a target month to obtain the average photovoltaic output coefficient for that month, and calculating the probability that the average value of the simultaneous output rate coefficient of photovoltaic power generation during the analysis period of each day in that month is greater than the average output coefficient for that month, is implemented as follows:

[0007] The photovoltaic output coefficient during the analysis and research period on sunny days is shown in the following formula:

[0008] α = {α1, α2, ..., α} i ,…,α 16}i∈(1,16) (1)

[0009]

[0010] In the formula, α represents the set of simultaneous rate coefficients of distributed photovoltaic power output during periods of high photovoltaic power output. i P represents the simultaneity coefficient corresponding to the i-th data point of the regional photovoltaic output. PV,i S represents the photovoltaic output of the region corresponding to the i-th data point. inverter,i This represents the AC side capacity of the photovoltaic inverter in the region corresponding to the i-th data point;

[0011] The average value of the photovoltaic output simultaneity rate coefficient within the corresponding time period is calculated as shown in the following formula:

[0012]

[0013] In the formula, α ave This represents the average simultaneity coefficient of distributed photovoltaic power output during periods of high photovoltaic output.

[0014] Based on the number of sunny days in the statistical data, the average photovoltaic output coefficient for the corresponding month is analyzed as follows:

[0015]

[0016] In the formula, α month,ave α represents the average output factor of distributed photovoltaic power in the regional power grid for the current month. ave,j This represents the average photovoltaic output simultaneity coefficient of the j-th sunny day in the current month during the analysis period, and m represents the number of sunny days in this month.

[0017] Then, among the number of sunny days in the month, the average photovoltaic output simultaneity coefficient for each analyzed period is greater than the average output coefficient α for the entire month. month,ave The probability p(α) ave,j >α month,ave As shown in the following formula:

[0018]

[0019] In the formula, N represents the average photovoltaic output simultaneity coefficient during the analysis period of m sunny days in the month, which is greater than the average output coefficient α for the month. month,ave The number of days.

[0020] In one embodiment of the present invention, the specific implementation method for calculating the range of component capacity for the target year of distributed photovoltaic development in the regional power grid and the critical development capacity of distributed photovoltaic under different scenarios, based on the future development of distributed photovoltaic and load in the regional power grid and considering the DC side capacity and capacity ratio of distributed photovoltaic, is as follows:

[0021] The power balance equation for a power distribution network substation connecting to the grid is shown below:

[0022] P sub =P PV -P load -P ESS +P resource (6)

[0023] In the formula, P sub P represents the on-grid power of a substation in a distribution network. PV P represents the distributed photovoltaic output power of the regional power grid. load P represents the regional power grid load. ESS P represents the energy storage charging power in the regional power grid. resource This indicates the output power of other power sources within the regional power grid, excluding distributed photovoltaic power.

[0024] The load factor of the distribution network substation is shown in the following formula:

[0025]

[0026] In the formula, S sub This indicates the rated capacity of the distribution network substation, where 80%≤β≤100% indicates that the substation is heavily loaded, and β>100% indicates that the substation is overloaded;

[0027] Based on the future development of distributed photovoltaic and load in the regional power grid, and considering that the regional distributed photovoltaic output coefficient remains unchanged, the energy storage scale and other power output conditions are assumed to remain unchanged. Equation (6) becomes:

[0028] P sub =α month,ave S inverter -P load -P cont (8)

[0029] In the formula, P contIt represents the combined output power of energy storage and other power sources, and is considered a constant when the scale of energy storage and the output of other power sources remain unchanged.

[0030] The distributed photovoltaic capacity ratio η is taken as 1, that is, the ratio of the photovoltaic DC module capacity to the rated capacity of the inverter AC side is 1. The capacity ratio is shown in the following formula:

[0031]

[0032] In the formula, S PV,DC Indicates the capacity of the photovoltaic DC module;

[0033] Substituting equation (9) into equation (8), we get:

[0034]

[0035] α is obtained based on the analysis of historical operating data. month,ave The substation load P obtained through prediction load P calculated based on the running data cont The relationship between the substation grid load rate and the DC-side capacity and capacity ratio of distributed photovoltaic power can be obtained through the following formula, as shown below:

[0036]

[0037] In the formula, S' PV,DC η' represents the target annual capacity of distributed photovoltaic (PV) development in the region's power grid, and η' represents the capacity ratio to be used in subsequent distributed PV development.

[0038] In one embodiment of the present invention, the specific implementation method for analyzing the load rate of substations in different scenarios and providing early warnings for substation overload or overload in different scenarios is as follows:

[0039] Assume the theoretically exploitable distributed photovoltaic capacity of the corresponding regional power grid is S. PV,DC,N The critical development capacity S of distributed photovoltaic power was calculated based on β = 100%. PV,DC,critical Based on the target year's distributed photovoltaic capacity ratio, regional power grid load, energy storage and other power output, and equation (11), the load factor of the distribution network substations is analyzed:

[0040] Scenario: When P sub ≤0.8S sub When β≤80%, the module capacity S' for the target annual distributed photovoltaic development of the power grid in this region is calculated. PV,DCThe range of values; therefore, under a given capacity ratio, the distributed photovoltaic development capacity that prevents the substation from being overloaded is calculated; as shown in equation (5), the average value of the photovoltaic output simultaneity coefficient during the daily analysis period is greater than the average value of the output coefficient α for the month. month,ave The probability is p(α) ave,j >α month,ave );

[0041] If max{S' PV,DC}<min{S PV,DC,N -S PV,DC ,S PV,DC,critical With the future development of distributed photovoltaic power, the corresponding distribution network substations will not experience heavy grid load.

[0042] If max{S' PV,DC}≥min{S PV,DC,N -S PV,DC ,S PV,DC,critical}, then it is believed that with the future development of distributed photovoltaics, p(α) ave,j >α month,ave The probability of this will cause the corresponding distribution network substation to experience heavy load or overload.

[0043] Scenario: When 0.8s sub ≤P sub ≤S sub When 80% ≤ β ≤ 100%, the module capacity S' for the target annual distributed photovoltaic development of the power grid in this region is calculated. PV,DC The range of values ​​for;

[0044] If max{S' PV,DC}<min{S PV,DC,N -S PV,DC ,S PV,DC,critical With the future development of distributed photovoltaic power, the corresponding distribution network substations will not experience grid overload.

[0045] If max{S' PV,DC}≥min{S PV,DC,N -S PV,DC ,S PV,DC,critical}, then it is believed that with the future development of distributed photovoltaics, p(α) ave,j >α month,ave The probability of this will cause the corresponding power distribution substation to experience overload.

[0046] Scenario: When P sub >S sub When β > 100%, the module capacity S' of the corresponding regional power grid in the target year for distributed photovoltaic development is calculated. PV,DCWithin the range of values, the corresponding distribution network substation will inevitably experience grid overload.

[0047] In one embodiment of the present invention, the regional power grid distributed photovoltaic development efficiency index δ is shown in the following formula:

[0048]

[0049] The larger the index value δ, the higher the efficiency of distributed photovoltaic development in the region.

[0050] Compared with the prior art, the present invention has the following beneficial effects: The method of the present invention is based on the development of distributed photovoltaic power distribution networks and provides early warning of heavy load or overload of distribution substations under different distributed photovoltaic development scales. It can provide a theoretical basis for power grid planners in the analysis of distributed photovoltaic access in distribution networks and has very important engineering practical significance. Attached Figure Description

[0051] Figure 1 This is the daily output characteristic curve of a typical autumn load for distributed photovoltaic systems.

[0052] Figure 2 This represents the daily output coefficient of a typical distributed photovoltaic load.

[0053] Figure 3 The output and load characteristic curves of distributed photovoltaic power generation in substations are shown.

[0054] Figure 4 The load characteristics of the substation after deducting distributed photovoltaic and other power sources. Detailed Implementation

[0055] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0056] This invention provides a method for early warning of heavy overload in distributed photovoltaic (PV) substations connected to a distribution network. Based on historical operating data, it first analyzes the output simultaneity coefficient of distributed PV in the distribution network area during periods of high output. By analyzing the number of sunny days in the target month, it obtains the average PV output coefficient for that month and calculates the probability that the average PV output simultaneity coefficient for each analyzed period of the sunny days in that month is greater than the average output coefficient for that month. Next, based on the future development of distributed PV and load in the regional power grid and considering the DC-side capacity and capacity ratio of distributed PV, it calculates the range of component capacity for the target year of distributed PV development in the regional power grid under different scenarios and the critical development capacity of distributed PV. Then, it analyzes the load rate of substations in the distribution network under different scenarios and provides early warnings for substation overload or heavy load under different scenarios. Finally, it proposes a regional power grid distributed PV development efficiency index to characterize the regional distributed PV development efficiency.

[0057] The following is a detailed implementation process of the method of the present invention.

[0058] 1. With the large-scale development of distributed photovoltaic (PV) power, in the future, under midday operation, there may be scenarios where distributed PV grid connection leads to heavy overload of the main transformer in the distribution network. Typically, the peak PV output period is between 11:00 AM and 3:00 PM. The PV output coefficient during this period on a sunny day is analyzed and studied, as shown in the following formula. Data points are spaced at 15-minute intervals, totaling 16 data points.

[0059] α = {α1, α2, ..., α} i ,…,α 16}i∈(1,16) (1)

[0060]

[0061] In the formula, α represents the set of simultaneous rate coefficients of distributed photovoltaic power output during periods of high photovoltaic power output. i P represents the simultaneity coefficient corresponding to the i-th data point of the regional photovoltaic output. PV,i S represents the photovoltaic output of the region corresponding to the i-th data point. inverter,i This represents the AC side capacity of the photovoltaic inverter in the region corresponding to the i-th data point.

[0062] 2. Calculate the average value of the photovoltaic output simultaneity coefficient during this period, as shown in the following formula.

[0063]

[0064] In the formula, α ave This represents the average simultaneity coefficient of distributed photovoltaic output during periods of high photovoltaic output.

[0065] 3. Based on the number of sunny days in the statistical data, analyze the average photovoltaic output coefficient for the month, as shown in the following formula.

[0066]

[0067] In the formula, α month,ave α represents the average output factor of distributed photovoltaic power in the regional power grid for the current month. ave,j This represents the average photovoltaic output simultaneity coefficient of the j-th sunny day in the current month during the analysis period, and m represents the number of sunny days in this month.

[0068] Then, the average value of the photovoltaic output simultaneity rate coefficient for each day of the month's sunny days is calculated to be greater than the average output coefficient α for the entire month. month,ave The probability p(α) ave,j >α month,ave ), as shown in the following formula.

[0069]

[0070] In the formula, N represents the average photovoltaic output simultaneity coefficient during the analysis period of m sunny days in the month, which is greater than the average output coefficient α for the month. month,ave The number of days.

[0071] 4. The power balance equation for a distribution network substation when connected to the grid is shown in the following formula.

[0072] P sub =P PV -P load -P ESS +P resource (6)

[0073] In the formula, P sub P represents the on-grid power of a substation in a distribution network. PV P represents the distributed photovoltaic output power of the regional power grid. load P represents the regional power grid load. ESS P represents the energy storage charging power in the regional power grid. resource This indicates the output power of other power sources within the regional power grid, excluding distributed photovoltaic power.

[0074] 5. The load rate of the distribution network substation is shown in the following formula.

[0075]

[0076] In the formula, S sub This indicates the rated capacity of the substation in the distribution network. 80% ≤ β ≤ 100% indicates the substation is heavily loaded, and β > 100% indicates the substation is overloaded.

[0077] 6. Based on the future development of distributed photovoltaic power and load in the regional power grid, and considering that the output coefficient of distributed photovoltaic power in the region does not change significantly, equation (6) can be transformed into:

[0078] P sub =α month,ave S inverter -P load -P cont (8)

[0079] In the formula, P cont It represents the combined output power of energy storage and other power sources, and is considered a constant when the scale of energy storage and the output of other power sources do not change significantly.

[0080] 7. The existing distributed photovoltaic capacity ratio η is usually taken as 1, that is, the ratio of the photovoltaic DC module capacity to the rated capacity of the inverter AC side is 1, and the capacity ratio is shown in the following formula.

[0081]

[0082] In the formula, S PV,DC This indicates the capacity of the photovoltaic DC module.

[0083] 8. Substituting equation (9) further into equation (8), we get:

[0084]

[0085] 9. α obtained based on the analysis of historical operating data month,ave The substation load P obtained through prediction load P calculated based on the running data cont The relationship between the substation grid load rate and the DC side capacity and capacity ratio of distributed photovoltaic power can be obtained by the following formula, as shown below.

[0086]

[0087] In the formula, S' PV,DC η' represents the target annual capacity of distributed photovoltaic (PV) development in the region's power grid, and η' represents the capacity ratio to be used in subsequent distributed PV development.

[0088] 10. Assume that the theoretically exploitable distributed photovoltaic capacity of the power grid in this region is S. PV,DC,N The critical development capacity S of distributed photovoltaic power can be calculated based on β = 100%. PV,DC,critical Based on the target year's distributed photovoltaic capacity ratio, regional power grid load, energy storage and other power output, and equation (11), the load factor of the distribution network substations is analyzed:

[0089] Scenario: When P sub ≤0.8S sub When β ≤ 80%, the module capacity S' for the target annual distributed photovoltaic development of the power grid in that region can be calculated. PV,DC The range of values ​​for is given. Therefore, under a given capacity ratio, the distributed photovoltaic development capacity that prevents the substation from being overloaded can be calculated. From equation (5), it can be seen that the average value of the photovoltaic output simultaneity coefficient during the daily analysis period is greater than the average value of the output coefficient α for the month. month,ave The probability is p(α) ave,j >α month,ave ).

[0090] If max{S' PV,DC}<min{S PV,DC,N -S PV,DC ,S PV,DC,critical With the future development of distributed photovoltaic power, this distribution network substation will not experience heavy grid load.

[0091] If max{S' PV,DC}≥min{S PV,DC,N -S PV,DC ,S PV,DC,critical}, then it is believed that with the future development of distributed photovoltaics, p(α) ave,j >α month,ave The probability of this will cause the power distribution substation to experience heavy load or overload.

[0092] Scenario: When 0.8s sub ≤P sub ≤S sub When 80% ≤ β ≤ 100%, the module capacity S' for the target annual distributed photovoltaic development of the regional power grid can be calculated. PV,DC The range of values ​​for .

[0093] If max{S' PV,DC}<min{S PV,DC,N -S PV,DC ,S PV,DC,critical Therefore, with the future development of distributed photovoltaic power, this distribution network substation will not experience grid overload.

[0094] If max{S' PV,DC}≥min{S PV,DC,N -S PV,DC ,S PV,DC,critical}, then it is believed that with the future development of distributed photovoltaics, p(α) ave,j >α month,ave The probability of this will cause the power distribution substation to experience overload.

[0095] Scenario: When P sub >S sub When β > 100%, the module capacity S' of the target annual distributed photovoltaic development of the power grid in that region can be calculated. PV,DC The range of values ​​for this value. Within this range, the power distribution substation will inevitably experience grid overload.

[0096] 11. Therefore, the regional power grid distributed photovoltaic development efficiency index δ is proposed, as shown in the following formula.

[0097]

[0098] The larger the index value δ, the higher the efficiency of distributed photovoltaic development in the region.

[0099] Implementation examples.

[0100] Taking a substation in a provincial power distribution network as an example, this substation has two main transformers, each with a rated capacity of 63MVA, and the total rated capacity of the main transformer is 126MVA. This substation typically experiences distributed photovoltaic (PV) grid connection during midday operation in autumn. Currently, the substation has a distributed PV capacity of 330MW, with a rated AC capacity of 300MW for the distributed PV inverters, resulting in a distributed PV capacity ratio of 1.1. In addition to distributed PV, the substation also has 100MW of other power sources, but no energy storage is configured. Figure 1 The figure shows the output characteristic curves of the distributed photovoltaic system connected to the substation under a typical load day in autumn.

[0101] Based on the data in the above figure, the output coefficient of distributed photovoltaic power generation between 11:00 AM and 3:00 PM can be calculated and analyzed. Figure 2 As shown.

[0102] Therefore, α can be calculated. ave = 0.57. The month containing the typical day has 16 sunny days. Based on the 16 days of operational data, we can obtain: α month,ave =0.62.

[0103] Then, the average value of the photovoltaic output simultaneity rate coefficient for each day of the month's sunny days is calculated to be greater than the average output coefficient α for the entire month. month,ave The probability p(α) ave,j >α month,ave ), as shown in the following formula.

[0104]

[0105] Based on the actual conditions of the substation, the characteristic curves of distributed photovoltaic power and load under the substation are as follows: Figure 3 As shown.

[0106] The load characteristics of the substation after deducting distributed photovoltaic and other power sources are as follows: Figure 4 As shown.

[0107] Based on equations (8) to (10), we can analyze and calculate the following:

[0108]

[0109] Therefore, equation (11) can be transformed into:

[0110]

[0111] To improve the simultaneity rate of distributed photovoltaic (PV) power generation and output during peak midday load periods, the capacity ratio of distributed PV connected to this substation will be set at 1.2, thereby increasing the output coefficient of distributed PV. Therefore, when the substation experiences full grid load, i.e., P... sub=126MW, the critical capacity for further development of distributed photovoltaic power is S PV,DC,critical =114MW. The theoretically exploitable distributed photovoltaic capacity of this region's power grid is S. PV,DC,N =400MW.

[0112] Therefore, when the actual future development capacity of distributed photovoltaic power S' PV,DC When the capacity is ≤65MW, the power distribution substation will not experience heavy load on the grid.

[0113] When the actual developed capacity of distributed photovoltaic power in the future is 65≤S' PV,DC When the capacity is ≤114MW, there is a 56.25% probability that the power distribution substation will experience grid overload.

[0114] The regional power grid's distributed photovoltaic (PV) development efficiency is 1, as shown in the following formula. Therefore, the distributed PV in the area where this substation is located can be fully developed in the future, exhibiting high development efficiency.

[0115]

[0116] The method proposed in this invention is based on the development of distributed photovoltaic (PV) power grids and provides early warning of overload or overload of distribution substations under different distributed PV development scales. It can provide a theoretical basis for power grid planners in the analysis of distributed PV access in distribution networks and has significant engineering practical significance.

[0117] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A distributed photovoltaic access power distribution network substation overload early warning method, characterized in that, Based on historical operational data, this study first analyzes the simultaneous output rate coefficient of distributed photovoltaic (PV) systems in the distribution network area during periods of high output. By analyzing the number of sunny days in the target month, the average PV output coefficient for that month is obtained, and the probability that the average PV output simultaneous output rate coefficient for each analyzed period during the sunny days of that month is greater than the average output coefficient for that month is calculated. Next, based on the future development of distributed PV and load in the regional power grid, and considering the DC-side capacity and capacity ratio of distributed PV, the range of component capacity and critical development capacity of distributed PV in the target year of the regional power grid are calculated under different scenarios. Then, the load rate of substations in the distribution network is analyzed under different scenarios, and early warnings are issued for substation overload or heavy load under different scenarios. Finally, a regional power grid distributed PV development efficiency index is proposed to characterize the regional distributed PV development efficiency. The analysis of the simultaneous output rate coefficient of distributed photovoltaic power generation in the distribution network area during periods of high output involves analyzing the number of sunny days in the target month to obtain the average photovoltaic output coefficient for that month, and calculating the probability that the average photovoltaic output simultaneous output rate coefficient for each day of the analysis period within the number of sunny days in that month is greater than the average output coefficient for the month. The specific implementation method is as follows: The photovoltaic output coefficient during the analysis and research period on sunny days is shown in the following formula: (1) (2) In the formula, denotes a set of simultaneous rate coefficients of distributed photovoltaic output in a period of large photovoltaic output, denotes a simultaneous rate coefficient corresponding to the i th data point of regional photovoltaic output, denotes the regional photovoltaic output corresponding to the i th data point, denotes the regional photovoltaic inverter AC side capacity corresponding to the i th data point; The average value of the photovoltaic output simultaneity rate coefficient within the corresponding time period is calculated as shown in the following formula: (3) In the formula, denotes the average value of the temporal rate coefficient of the distributed photovoltaic output when the photovoltaic output is large. Based on the number of sunny days in the statistical data, the average photovoltaic output coefficient for the corresponding month is analyzed as follows: (4) In the formula, represents the average value of the output coefficient of the regional grid distributed photovoltaic in the month, represents the average value of the simultaneous rate coefficient of photovoltaic output in the analysis period of the jth sunny day in the month, represents the number of sunny days in the month; Then, among the number of sunny days in the month, the average photovoltaic output simultaneity rate coefficient for each analyzed period is greater than the average output coefficient for the entire month. probability As shown in the following formula: (5) In the formula, N represents the average photovoltaic output simultaneity coefficient during the analysis period of m sunny days in the month, which is greater than the average output coefficient for the month. The number of days.

2. The method for early warning of heavy overload in distributed photovoltaic substations connected to a distribution network according to claim 1, characterized in that, Based on the future development of distributed photovoltaic (PV) power and load in the regional power grid, and considering the DC-side capacity and capacity ratio of distributed PV, the following calculations are made regarding the range of component capacity for the target year of distributed PV development in the regional power grid under different scenarios, and the specific implementation methods for the critical development capacity of distributed PV: The power balance equation for a power distribution network substation connecting to the grid is shown below: (6) In the formula, Indicates the on-grid power of the distribution network substation. Indicates the output power of distributed photovoltaic power in the regional power grid. Indicates the regional power grid load. Indicates the energy storage charging power in the regional power grid. This indicates the output power of other power sources within the regional power grid, excluding distributed photovoltaic power. The load factor of the distribution network substation is shown in the following formula: (7) In the formula, This indicates the rated capacity of the distribution network substation, where This indicates that the substation is under heavy load. This indicates that the substation is overloaded. Based on the future development of distributed photovoltaic and load in the regional power grid, and considering that the regional distributed photovoltaic output coefficient remains unchanged, the energy storage scale and other power output conditions are assumed to remain unchanged. Equation (6) becomes: (8) In the formula, It represents the combined output power of energy storage and other power sources, and is considered a constant when the scale of energy storage and the output of other power sources remain unchanged. Distributed photovoltaic capacity ratio The value is set to 1, meaning the ratio of the photovoltaic DC module capacity to the inverter AC side rated capacity is 1. The capacity ratio is shown in the following formula: (9) In the formula, Indicates the capacity of the photovoltaic DC module; Substituting equation (9) into equation (8), we get: (10) Based on the analysis of historical operating data The substation load obtained through prediction Calculated based on runtime data The relationship between the substation grid load rate and the DC-side capacity and capacity ratio of distributed photovoltaic power can be obtained through the following formula, as shown below: (11) In the formula, This indicates the target annual capacity of distributed photovoltaic (PV) modules for the regional power grid. This indicates the capacity ratio value to be used in the subsequent development of distributed photovoltaic power.

3. The method for early warning of heavy overload in distributed photovoltaic substations connected to a distribution network according to claim 2, characterized in that, The specific implementation method for analyzing the load rate of distribution network substations under different scenarios and providing early warnings for substation overload or heavy load under different scenarios is as follows: The theoretically exploitable distributed photovoltaic capacity of the corresponding regional power grid is ,according to Calculation of the critical development capacity of distributed photovoltaic power Based on the target year's distributed photovoltaic capacity ratio, regional power grid load, energy storage and other power output, and equation (11), the load factor of the distribution network substations is analyzed: Scenario 1: When ,Right now At that time, the module capacity for distributed photovoltaic development in the target year of the regional power grid was calculated. The range of values; therefore, under a given capacity ratio, the distributed photovoltaic development capacity that prevents the substation from being overloaded is calculated; as shown in equation (5), the average value of the photovoltaic output simultaneity coefficient during the daily analysis period is greater than the average value of the output coefficient for the month. The probability is ; like Therefore, with the future development of distributed photovoltaic power, the corresponding distribution network substations will not experience heavy grid load. like They believe that with the future development of distributed photovoltaic power, there will be... The probability of this will cause the corresponding power distribution network substation to experience heavy load or overload. Scenario 2: When ,Right now At that time, the module capacity for distributed photovoltaic development in the target year of the regional power grid was calculated. The range of values ​​for; like Therefore, with the future development of distributed photovoltaic power, the corresponding distribution network substations will not experience grid overload. like They believe that with the future development of distributed photovoltaic power, there will be... The probability of this will cause the corresponding power distribution substation to experience grid overload; Scenario 3: When ,Right now At that time, the module capacity for distributed photovoltaic development in the corresponding regional power grid in the target year was calculated. Within the range of values, the corresponding distribution network substation will inevitably experience grid overload.

4. The method for early warning of heavy overload in distributed photovoltaic substations connected to a distribution network according to claim 3, characterized in that, The regional power grid distributed photovoltaic development efficiency index As shown in the following formula: (12) Indicator value The larger the value, the higher the efficiency of distributed photovoltaic development in that region.

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