A method for accommodating distributed photovoltaic power in a distribution network

Abstract

The present invention discloses a method for distributed photovoltaic accommodation in a distribution network; the optimal photovoltaic accommodation path from the photovoltaic access point to each load node is obtained through simulation, and the daily load change curve is fitted and predicted by collecting the load change data of the load nodes. By comparing the actual electricity consumption data with the predicted daily load change curve, the electricity consumption gap is judged, and the electricity consumption gap is supplemented through the optimal photovoltaic accommodation path, so as to improve the photovoltaic accommodation rate, reduce the electricity consumption gap, and reasonably plan the photovoltaic power.

Description

Technical Field

[0001] The present invention relates to the field of power grids, and particularly to a method for accommodating distributed photovoltaic power in a distribution network. Background Art

[0002] With the intensive construction of photovoltaic distributed power sources, a large number of photovoltaic grid connections have put pressure on the operation of rural power grids. Due to the limited accommodation of grid-connected photovoltaic power, the cost of accommodating through grid expansion is relatively high, especially for the reconstruction of power grids in some remote areas.

[0003] Existing photovoltaic power accommodation technologies mainly affect the local power sales and accommodation capabilities in photovoltaic application areas by improving the accommodation technology. The main factors include the rural power grid structure, power load characteristics, and photovoltaic output characteristics. The power sales models for photovoltaic power accommodation mainly include direct grid connection and sales. Since the current electricity market transactions are still in the stage of partial research-based trial opening, the pricing research for new energy electricity transactions is in a qualitative research stage, lacking in-depth quantitative analysis. For the classification of new energy grid connection prices, it mainly includes fixed electricity price systems, quota systems, and competitive grid connection systems. The current power accommodation and sales methods for distributed power sources such as photovoltaics are still in the initial research stage. Especially for the accommodation of individual photovoltaic power stations in remote areas, due to the very weak power grids and low electricity loads in these areas, the power of photovoltaics cannot be fully predicted, with volatility and intermittency, resulting in a low current photovoltaic power accommodation rate, and there are also price interferences from human factors. When it is the peak electricity consumption period, the stored photovoltaic power cannot quickly supplement the electricity gap, and the photovoltaic power cannot be evenly transmitted, resulting in the electricity gap not being supplemented and the situation of electricity surplus in some cases. Therefore, reasonable allocation of photovoltaic power is required to ensure the rational application of photovoltaic power accommodation technology.

[0004] For example, a "Method and Device for Accommodating Photovoltaic Power in a Micro Energy Grid" disclosed in a Chinese patent document with the publication number CN109038645A first clusters the air source heat pumps in the micro energy grid, then solves the pre-constructed photovoltaic power accommodation model based on the clustering results to obtain the Pareto optimal solution set; finally, determines the optimal scheduling strategy for the air source heat pumps and conducts photovoltaic power accommodation, improving the utilization rate of air source heat pumps in the micro energy grid, with a large amount of photovoltaic power accommodation, increasing the number of schedulable air source heat pumps, ensuring the heating demand and heating cost of users. At the same time, by clustering air source heat pumps with different parameters, the scheduling of each air source heat pump is transformed into the scheduling of air source heat pump clustering clusters, thus simplifying the difficulty of the scheduling algorithm, reducing the phenomenon of light abandonment in the micro energy grid during the peak photovoltaic power generation period, reducing energy waste, increasing the peak shaving capacity of the micro energy grid, being able to ensure the safe and stable operation of the large power grid, while saving energy and heating costs, improving the heating quality, and reducing the pollution gases generated by coal combustion. However, this device and method still cannot accurately provide electricity for the electricity gap. Summary of the Invention

[0005] The present invention mainly aims at the problems of low photovoltaic accommodation rate and inaccurate regulation of photovoltaic power in the existing stage; it provides a method for distributed photovoltaic accommodation in a distribution network; by means of simulation, the optimal photovoltaic accommodation path from the photovoltaic access point to each load node is obtained, and the daily load change curve is fitted and predicted by collecting the load change data of the load nodes. By comparing the actual electricity consumption data with the predicted daily load change curve, the electricity consumption gap is judged, and the electricity consumption gap is supplemented through the optimal photovoltaic accommodation path, so as to improve the photovoltaic accommodation rate, reduce the electricity consumption gap and reasonably plan the photovoltaic power.

[0006] The above technical problems of the present invention are mainly solved by the following technical solutions:

[0007] A method for distributed photovoltaic accommodation in a distribution network, the distributed photovoltaic accommodation method includes the following steps:

[0008] Step S1, collect the positions of photovoltaic access points in the distribution network and the positions of load nodes in the distribution network, and construct a network diagram of photovoltaic access points and load nodes; obtain the optimal photovoltaic accommodation path;

[0009] Step S2, perform capacity simulation on each access load node at the position of the photovoltaic access point to determine the photovoltaic installation capacity of each access load node in the whole network;

[0010] Step S3, obtain the historical load change data and historical power generation of power plants of the load nodes in the distribution network; construct a predicted daily load change curve;

[0011] Step S4, compare the predicted daily load change curve with the actual daily load change curve of users to determine the regulation time and regulation object;

[0012] Step S5, output photovoltaic power from the photovoltaic access point along the optimal photovoltaic accommodation path to the regulation object within the regulation time.

[0013] By means of simulation, the optimal photovoltaic accommodation path from the photovoltaic access point to each load node is obtained, and the daily load change curve is fitted and predicted by collecting the load change data of the load nodes. By comparing the actual electricity consumption data with the predicted daily load change curve, the electricity consumption gap is judged, and the electricity consumption gap is supplemented through the optimal photovoltaic accommodation path, so as to improve the photovoltaic accommodation rate, reduce the electricity consumption gap and reasonably plan the photovoltaic power.

[0014] Preferably, the steps for obtaining the optimal photovoltaic accommodation path in step S1 are as follows:

[0015] Step S21, import the network diagram of photovoltaic access points and load nodes into the simulation software, set the photovoltaic access point and load nodes as different nodes, divide different nodes into different groups by regions, and each group contains multiple nodes;

[0016] Step S22: For each group, referring to the distribution network path information database between nodes, taking any photovoltaic access point as the reference node, retrieve the movement paths of the reference node passing through different nodes within the group along each distribution network path, and calculate the movement costs of each path.

[0017] Step S23: Set the node with the minimum movement cost as the nearest node relative to this reference node; at the same time, compare the nodes with the minimum movement cost among each path except the nearest node as the first comparison nodes; again, take the nearest node as the second reference node, and calculate the node with the minimum movement cost from the second reference node as the second comparison node.

[0018] Step S24: Compare the movement cost from the reference node to the first comparison node and the movement cost from the second reference point to the second comparison node, and select the path with the smaller movement cost as the second photovoltaic accommodation path.

[0019] Step S25: Set the path from the reference node to the nearest node as the optimal photovoltaic accommodation path.

[0020] By comparing the costs of transportation paths, the optimal photovoltaic accommodation path and the second photovoltaic accommodation path are delimited, providing two paths for photovoltaic accommodation, and ensuring that the path for initially inputting photovoltaic power from the reference node to the load node is the path with the minimum transportation cost, saving costs.

[0021] Preferably, after the regulation ends, determine whether there is a second regulation object within the group. If so, output the surplus photovoltaic power of the distribution network load node to the second regulation object through the second photovoltaic accommodation path. Compare the path costs of the second photovoltaic accommodation path and the optimal photovoltaic accommodation path and select the one with the lower cost to supplement the load power consumption gap, ensuring the fastest speed of supplementing the power and reducing costs.

[0022] Preferably, the predicted daily load change curve in Step S3 is obtained according to the following steps:

[0023] Step S51: Collect the historical data of the daily load changes at the distribution network load nodes in the past three years.

[0024] Step S52: Fit the daily load changes of users according to the date to form the date-load change curves of different years respectively.

[0025] Step S53: Perform weighted superposition in the order of years from small to large according to the ratio of 0.3, 0.3, and 0.4 to obtain the date-load change curve model.

[0026] Step S54: Collect the historical data of the daily load changes at the distribution network load nodes in the past seven days.

[0027] Step S55: Fit and form seven-day temperature-load change curves according to the corresponding relationship between temperature and daily load change of users respectively;

[0028] Step S56: Perform weighted average on the seven-day temperature-load change curves. The process of weighted average is as follows:

[0029]

[0030] where, is the average load, and α1, α2, α3, α4, α5, α6, α7 are the weighting coefficients from the first day to the seventh day of the seven days;

[0031] The sum of α1, α2, α3, α4, α5, α6, α7 is 1; where,

[0032] α1 < α2 < α3 < α4 < α5 < α6 < α7

[0033] The temperature-load change curve obtained after weighted average is the temperature-load change curve model.

[0034] Temperature and weather changes are the most important factors affecting peak power consumption. High-temperature and high-cold weather will both lead to a sharp increase in stage power consumption. Therefore, when making the predicted daily load change curve, it is necessary to comprehensively formulate the power consumption of similar temperatures to obtain the most accurate prediction data. Moreover, since the change of temperature is continuous, the data in the time period closest to the current day is the most valuable for reference. Therefore, when weighting the data, the important factor to consider is that the days closest to the current day have the highest weight. The data within seven days and within three years are collected for weighted average to obtain the closest data, and the load of the current day is predicted based on this data to meet the required standard.

[0035] Preferably, the regulated object and regulation time are obtained according to the following steps:

[0036] Step S61: Collect the highest temperature data of the current day and the daily load change of users in the morning;

[0037] Step S62: The computer compares the temperature-load change curve model with the date-load change curve model, and obtains the predicted daily load change curve through iteration of the ant colony algorithm;

[0038] Step S63: Take the points where the load before and after the peak point is 80% of the peak load, and the corresponding time period in between is the adjustable time period; within the adjustable time period; when the actual daily load of the user exceeds the predicted daily load of the current day, the user is selected as the regulated object.

[0039] Selecting specific regulated objects and regulation criteria, and adjusting the regulated objects within the regulation time can ensure the improvement of the efficiency of PV consumption.

[0040] Preferably, the time period when the load of the user's daily load change curve is greater than the load of the user's predicted daily load change curve is selected as the first regulation time t1; within the adjustable time period, the time period when the load of the user's daily load change curve is greater than or equal to 85% of the peak load of the user's predicted daily load change curve is the second regulation time t2;

[0041] Calculate the regulation time t:

[0042] t = t1 + t2 - t0

[0043] where t0 is the overlapping time of the first regulation time t1 and the second regulation time t2.

[0044] The load within the peak value is the largest, and within the same region, the peak electricity consumption of users is similar. Therefore, the peak period of load use can be determined by predicting the change of the curve, the best regulation time can be determined, and the load can also be stored in advance according to the predicted peak data to ensure the electricity consumption during the peak period. By overlapping the two peak times, the final regulation time is obtained. Within this regulation time, the data is close to the peak value, and timely regulation of users exceeding the predicted curve can effectively relieve the electricity peak.

[0045] Preferably, the steps of simulating the photovoltaic accommodation capacity are as follows:

[0046] Step S71: Collect the active power of the distributed photovoltaic, the active power of the distribution network load node, and the reactive power of the distribution network load node; determine the active power per unit capacity of the distributed photovoltaic according to the initial active power of the distributed photovoltaic and the installed capacity of the distributed photovoltaic; determine the non-initial active power of the distributed photovoltaic according to the active power per unit capacity of the distributed photovoltaic, the initial installed capacity of the distributed photovoltaic, the number of distributed photovoltaic capacity growth times, and the distributed photovoltaic capacity growth coefficient;

[0047] Step S72: Determine the reactive power of the distribution network load node according to the reactive power of the root node of the distribution network feeder, the proportion of the distribution network reactive power loss in the load reactive power, and the number of distribution network load nodes;

[0048] Step S73: Establish a scatter diagram of the distributed photovoltaic accommodation capacity according to the maximum value of the node voltage and the installed capacity of the distributed photovoltaic; Step S74: Determine the maximum value of the installed capacity of the distributed photovoltaic among the intersections of the allowable voltage threshold of the distribution network and the scatter diagram of the distributed photovoltaic accommodation capacity as the maximum installed capacity of the distributed photovoltaic;

[0049] Step S75: Determine the minimum value of the installed capacity of the distributed photovoltaic among the intersections of the allowable voltage threshold of the distribution network and the scatter diagram of the distributed photovoltaic accommodation capacity as the minimum installed capacity of the distributed photovoltaic;

[0050] Step S76: Determine the maximum node voltage and the distributed PV installation capacity corresponding to the maximum node voltage based on the active power of the distributed PV, the active power of the distribution network load nodes, and the reactive power of the distribution network load nodes.

[0051] Determine the maximum PV capacity supplement value for supplementing the power consumption gap based on the distributed PV installation capacity.

[0052] Preferably, when the PV accommodation amount during the regulation time period exceeds 90% of the PV capacity of its reference node, an early warning signal is sent to the background through the wireless network. Avoiding the situation where the PV capacity is too low to supplement the power, and timely early warning and reminder can reduce the problems caused by too low PV power and inability to supplement the power gap in time.

[0053] The beneficial effects of the present invention are:

[0054] Obtain the optimal PV accommodation path from the PV access point to each load node through simulation, and fit and predict the daily load change curve by collecting the load change data of the load nodes. Judge the power consumption gap by comparing the actual power consumption data and the predicted daily load change curve, and supplement the power consumption gap through the optimal PV accommodation path. At the same time, judge the path costs of the optimal PV accommodation path and the second PV accommodation path to ensure that the PV power fills the power consumption gap through the loop with the minimum cost, improve the PV accommodation rate, reduce the power consumption gap, and reasonably plan the PV power. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of this method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] It should be understood that the embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0057] The technical solution of the present invention will be further specifically described below through embodiments.

[0058] A method for accommodating distributed PV in a distribution network needs to perform PV accommodation according to the following steps:

[0059] First step, determine the positions of the distribution network load nodes and the PV access points of the distribution network, and construct a network diagram;

[0060] In the simulation software, based on the network diagram, set the load nodes and PV access points as different nodes, divide the area groups, and each group contains multiple nodes;

[0061] Taking any photovoltaic access point as a reference node, retrieve the different movement paths of the reference node passing through the group according to each distribution network path, and calculate the movement costs of each path.

[0062] Set the node with the minimum movement cost as the nearest node relative to the reference node; at the same time, compare the nodes with the minimum movement cost in each path except the nearest node as the first comparison nodes; again, take the nearest node as the second reference node, and calculate the node with the minimum movement cost from the second reference node as the second comparison node.

[0063] Compare the movement cost from the reference node to the first comparison node and the movement cost from the second reference point to the second comparison node, and select the path with the smaller movement cost as the second photovoltaic accommodation path.

[0064] The path from the reference node to the nearest node is set as the optimal photovoltaic accommodation path.

[0065] In the second step, for each access load node at the photovoltaic access point position, perform capacity simulation to determine the photovoltaic installation capacity of each access load node in the whole network.

[0066] Collect the active power of distributed photovoltaics, the active power of distribution network load nodes, and the reactive power of distribution network load nodes; determine the active power per unit capacity of distributed photovoltaics according to the initial active power and installation capacity of distributed photovoltaics; determine the non-initial active power of distributed photovoltaics according to the number of distributed photovoltaic capacity growth times, the active power per unit capacity of distributed photovoltaics, the distributed photovoltaic capacity growth coefficient, and the initial installation capacity of distributed photovoltaics.

[0067] Determine the reactive power of the distribution network load nodes according to the reactive power of the root node of the distribution network feeder, the proportion of distribution network reactive power loss to load reactive power, and the number of distribution network load nodes.

[0068] Establish a scatter diagram of distributed photovoltaic accommodation capacity according to the maximum node voltage and the distributed photovoltaic installation capacity.

[0069] Determine the maximum value of the distributed photovoltaic installation capacity among the intersection points of the allowable voltage threshold of the distribution network and the scatter diagram of distributed photovoltaic accommodation capacity as the maximum distributed photovoltaic installation capacity.

[0070] Determine the minimum value of the distributed photovoltaic installation capacity among the intersection points of the allowable voltage threshold of the distribution network and the scatter diagram of distributed photovoltaic accommodation capacity as the minimum distributed photovoltaic installation capacity.

[0071] Determine the maximum node voltage and the distributed PV installation capacity corresponding to the maximum node voltage according to the active power of the distributed PV, the active power of the distribution network load node, and the reactive power of the distribution network load node.

[0072] The steps of simulating the PV accommodation capacity are as follows:

[0073] When the PV accommodation amount within the regulation time period exceeds 90% of the PV capacity of its reference node, an early warning signal is sent to the background through the wireless network.

[0074] In the third step, obtain the historical data of the load change of the distribution network load node and the historical power generation of the power plant; construct the predicted daily load change curve;

[0075] Since the temperature changes are similar in the same month and date in general between adjacent years, the data of the most recent three historical years are selected as the reference standard;

[0076] The date-load change curves of different years are respectively fitted according to the date corresponding to the user's daily load change; since the temperature data closest to the current year are often similar to the temperature changes in the current year, when performing weight changes, the most recent year should have the largest weight, and since the years are gradually increasing, the proportions need to be weighted and superimposed according to the ratio of 0.3, 0.3, and 0.4 to obtain the date-load change curve model. And since the weights add up to 1, the unit of the original curve will not be changed.

[0077] Collect the historical data of the daily load change at the distribution network load node in the past seven days; since the temperature change is a continuous process and there will be no data mutations, the temperature data within seven days are often more valuable as a reference than the data on the same date in different years;

[0078] The seven-day temperature-load change curves are respectively fitted according to the temperature corresponding to the user's daily load change;

[0079] Perform weighted averaging on the seven-day temperature-load change curves. The process of weighted averaging is as follows:

[0080]

[0081] Where is the average load, and α1, α2, α3, α4, α5, α6, and α7 are the weighting coefficients from the first day to the seventh day of the seven days;

[0082] The sum of α1, α2, α3, α4, α5, α6, and α7 is 1; where

[0083] α1 < α2 < α3 < α4 < α5 < α6 < α7

[0084] The temperature-load change curve obtained after weighted average is the temperature-load change curve model. Since the change in temperature is a continuous curve, the closer it gets to the current day, the greater the reference value of the temperature data. At the same time, in order not to change the weight unit of the load data and ensure that this data can be superimposed and compared with the standardized daily load curve, the sum of the weights should be 1.

[0085] Step 4: Compare the predicted daily load change curve with the actual user's daily load change curve to determine the regulation time and regulation object; when specifically judging the daily load change, the following principles should be followed:

[0086] Collect the highest temperature data of the current day and the user's daily load change in the morning; since the morning is the beginning of the day and the temperature in the morning is usually not at its peak, it is the most accurate to use the temperature data and load change in the morning for comparison.

[0087] The computer compares the temperature-load change curve model with the date-load change curve model, and obtains the predicted daily load change curve through the iteration of the ant colony algorithm;

[0088] Take the points where the load before and after the peak point is 80% of the peak load, and the corresponding time period is the adjustable time period; within the adjustable time period, when the actual load used by the user on the current day exceeds the predicted daily load of the current day, the user is selected as the regulation object.

[0089] Select the time period when the load of the user's daily load change curve is greater than the load of the user's predicted daily load change curve as the first regulation time t1; within the adjustable time period, the time period when the load of the user's daily load change curve is greater than or equal to 85% of the peak load of the user's predicted daily load change curve is the second regulation time t2;

[0090] Calculate the regulation time t:

[0091] t = t1 + t2 - t0

[0092] where t0 is the overlapping time of the first regulation time t1 and the second regulation time t2.

[0093] Step 5: Within the regulation time, output photovoltaic power along the optimal photovoltaic consumption path to the regulation object that needs to supplement power. After the regulation of the first regulation object is completed, judge whether there is a second regulation object in the group. If there is, output the surplus photovoltaic power of the distribution network load node to the second regulation object through the second photovoltaic consumption path.

Claims

1. A method for accommodating distributed photovoltaic power in a distribution network, characterized in that: The distributed PV accommodation method includes the following steps: Step S1: Collect the positions of PV access points and load nodes in the distribution network, and construct a network diagram of PV access points and load nodes; calculate the moving costs from each PV access point to the load nodes, and select the path with the minimum moving cost as the optimal PV accommodation path; Step S2: Conduct capacity simulation on each access load node at the PV access point position to determine the PV installation capacity of each access load node in the whole network; Step S3: Obtain the historical data of load changes and historical power generation of power plants at the load nodes of the distribution network; construct a predicted daily load change curve; Step S4: Compare the predicted daily load change curve with the actual daily load change curve of users to determine the regulation time and regulation object; Step S5: Output PV power from the PV access point along the optimal PV accommodation path to the regulation object within the regulation time.

2. A method for accommodating distributed photovoltaics in a distribution network according to claim 1, characterized in that: The steps for obtaining the optimal PV accommodation path in Step S1 are as follows: Step S21: Import the network diagram of PV access points and load nodes into the simulation software, set the PV access points and load nodes as different nodes, and divide the different nodes into different groups by region, with multiple nodes in a single group; Step S22: For each group, referring to the distribution network path information database between nodes, taking any PV access point as the reference node, retrieve the moving paths of the reference node passing through different nodes in the group along each distribution network path, and calculate the moving costs of each path; Step S23: Assume that the node with the minimum moving cost is the nearest node relative to the reference node; at the same time, compare the nodes with the minimum moving cost in each path except the nearest node as the first comparison nodes; take the nearest node as the second reference node again, and calculate the node with the minimum moving cost from the second reference node as the second comparison node; Step S24: Compare the moving cost from the reference node to the first comparison node and the moving cost from the second reference point to the second comparison node, and select the path with the smaller moving cost as the second PV accommodation path; Step S25: Set the path from the reference node to the nearest node as the optimal PV accommodation path.

3. A method for accommodating distributed photovoltaic power in a distribution network according to claim 1 or 2, characterized in that: After the regulation ends, determine whether there is a second regulation object in the group. If so, output the surplus PV power of the distribution network load node to the second regulation object through the second PV accommodation path.

4. A method for accommodating distributed photovoltaic power in a distribution network according to claim 1, characterized in that: The predicted daily load change curve in Step S3 is obtained according to the following steps: Step S51: Collect the historical data of daily load changes at the load nodes of the distribution network in the past three years; Step S52: Fit the daily load changes of users according to the date to form date-load change curves for different years respectively; Step S53: Perform weighted superposition in the order of years according to the ratios of 0.3, 0.3, and 0.4 to obtain a date-load change curve model; Step S54: Collect the historical data of daily load changes at the load nodes of the distribution network in the past seven days; Step S55: Fit the daily load changes of users according to the temperature to form seven-day temperature-load change curves respectively; Step S56: Conduct weighted average on the seven-day temperature-load change curves. The process of weighted average is as follows: wherein, is the average load, and α1, α2, α3, α4, α5, α6, α7 are the weighting coefficients from the first day to the seventh day of the seven days; the sum of α1, α2, α3, α4, α5, α6, α7 is 1; wherein, α1＜α2＜α3＜α4＜α5＜α6＜α7 Step S57: The temperature-load change curve obtained after weighted average is the temperature-load change curve model.

5. A method for accommodating distributed photovoltaic power in a distribution network according to claim 4, characterized in that: The regulated object and regulation time are obtained according to the following steps: Step S61: Collect the highest temperature data of the day and the daily load change of the user in the morning. Step S62: The computer compares the temperature-load change curve model with the date-load change curve model, and obtains the predicted daily load change curve through iteration of the ant colony algorithm. Step S63: Take the points where the load before and after the peak point is 80% of the peak load, and the corresponding time period in between is the adjustable time period; within the adjustable time period, when the actual load used by the user on the current day exceeds the predicted daily load of the current day, the user is selected as the regulated object.

6. A method for accommodating distributed PV power in a distribution network according to claim 5, characterized in that: Select the time period when the load of the user's daily load change curve is greater than the load of the user's predicted daily load change curve as the first regulation time t1; within the adjustable time period, the time period when the load of the user's daily load change curve is greater than or equal to 85% of the peak load of the user's predicted daily load change curve is the second regulation time t2. Calculate the regulation time t: t = t1 + t2 - t0 where t0 is the overlapping time of the first regulation time t1 and the second regulation time t2.

7. A method for accommodating distributed photovoltaic power in a distribution network according to claim 1, characterized in that: The steps for simulating the photovoltaic accommodation capacity are as follows: Step S71: Collect the active power of the distributed photovoltaic, the active power of the distribution network load node, and the reactive power of the distribution network load node; determine the active power per unit capacity of the distributed photovoltaic according to the initial active power of the distributed photovoltaic and the installed capacity of the distributed photovoltaic; determine the non-initial active power of the distributed photovoltaic according to the active power per unit capacity of the distributed photovoltaic, the initial installed capacity of the distributed photovoltaic, the number of distributed photovoltaic capacity growth times, and the distributed photovoltaic capacity growth coefficient. Step S72: Determine the reactive power of the distribution network load node according to the reactive power of the root node of the distribution network feeder, the proportion of the distribution network reactive power loss in the load reactive power, and the number of distribution network load nodes. Step S73: Establish a scatter plot of the distributed photovoltaic accommodation capacity according to the maximum node voltage and the installed capacity of the distributed photovoltaic. Step S74: Determine the maximum value of the distributed photovoltaic installed capacity among the intersection points of the distribution network voltage allowable threshold and the distributed photovoltaic accommodation capacity scatter plot as the maximum distributed photovoltaic installed capacity. Step S75: Determine the minimum value of the distributed photovoltaic installed capacity among the intersection points of the distribution network voltage allowable threshold and the distributed photovoltaic accommodation capacity scatter plot as the minimum distributed photovoltaic installed capacity. Step S76: Determine the maximum node voltage and the corresponding distributed photovoltaic installed capacity according to the active power of the distributed photovoltaic, the active power of the distribution network load node, and the reactive power of the distribution network load node.

8. A method for accommodating distributed PV power in a distribution network according to claim 2, characterized in that: When the photovoltaic accommodation amount within the regulation time period exceeds 90% of the photovoltaic capacity of its reference node, an early warning signal is sent to the background through the wireless network.

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