Method for evaluating and analyzing photovoltaic consumption capability of power distribution network

Abstract

The invention discloses a power distribution network photovoltaic consumption capability evaluation and analysis method, and belongs to the field of photovoltaic consumption analysis. Comprising the following steps: obtaining photovoltaic operation data of a power distribution network by relying on a power distribution automation system, a data middle platform and a dispatching control platform; establishing a photovoltaic consumption capability evaluation optimization model of the power distribution network, and solving based on a grey wolf algorithm; dividing a bearing capacity area according to the photovoltaic absorption capacity of the power distribution network; the invention discloses a method for performing visual analysis and display by using a power grid graph. The method specifically comprises the steps of photovoltaic openable capacity analysis, access district recommendation, data checking and correction analysis and the like. And dividing the regional bearing capacity into green, yellow and red grades by combining a grey wolf algorithm result, a real-time load and an equipment operation limit value, and evaluating the bearing potential and risk in a layered manner. The division standard is visual and scientific, and a clear basis is provided for distributed photovoltaic access.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic absorption analysis, and in particular to a method for evaluating and analyzing the photovoltaic absorption capacity of a distribution network. Background Art

[0002] With the construction of new power systems, renewable energy represented by distributed photovoltaics has been connected to the grid on a large scale. However, due to the significant uncertainty of distributed photovoltaic power generation, its large-scale access has led to problems such as uneven voltage distribution in the distribution network, reduced voltage quality, voltage over-limit, and power flow reverse transmission, which has interfered with the operation of the existing power grid and affected the stability of the power grid.

[0003] To address the above issues, it is necessary to integrate the distributed photovoltaic operation data in the existing system, analyze, evaluate and calculate the open capacity of distributed photovoltaic substations and lines in the distribution network, scientifically and rationally analyze and guide the development of distributed photovoltaics to areas with great absorption potential and strong grid carrying capacity, and alleviate the absorption pressure on the grid.

[0004] To this end, the present invention proposes a method for evaluating and analyzing the photovoltaic absorption capacity of a distribution network. Relying on the distribution network and photovoltaic operation data in the existing distribution automation system, data center and dispatching control platform, the present invention can build an optimization model for evaluating the photovoltaic absorption capacity of the distribution network, use the Grey Wolf algorithm for solution and analysis, and use a grid map for visualization analysis and display, so as to assist in the photovoltaic access and absorption of the distribution network. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention proposes a method for evaluating and analyzing the photovoltaic absorption capacity of a distribution network.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for evaluating and analyzing the photovoltaic absorption capacity of a distribution network comprises the following steps:

[0008] Step 1: Relying on the distribution automation system, data center, and dispatching control platform, obtain the photovoltaic operation data of the distribution network;

[0009] Step 2: Establish an optimization model for evaluating the photovoltaic absorption capacity of the distribution network and solve it based on the Grey Wolf Algorithm;

[0010] Step 3: Divide the carrying capacity area according to the photovoltaic absorption capacity of the distribution network;

[0011] Step 4: Use a grid map for visual analysis and display, including PV open capacity analysis, access area recommendation, data verification and correction analysis, etc.

[0012] Furthermore, relying on the distribution automation system, data center, and dispatching control platform, a technical solution for obtaining the photovoltaic operation data of the distribution network is characterized by accessing the main transformer parameters, circuit breaker parameters, medium and low voltage distributed photovoltaic operation data, and main network operation data in the dispatching control platform. Access the substation data, line data, transformer data, and distributed photovoltaic real-time measurement data in the distribution automation system. Access the distributed photovoltaic archives, distributed photovoltaic historical measurement data, and meteorological data in the data center. The data is integrated and analyzed to obtain an overview of the distributed photovoltaics under the distribution network in the region, and the distributed photovoltaic archives are classified according to the "region-substation-line-distribution transformer" division.

[0013] The example of the present invention also provides a technical solution for establishing an optimization model for evaluating the photovoltaic absorption capacity of a distribution network and solving it based on the Grey Wolf Algorithm. The characteristic of the technical solution is that, with reference to relevant literature, the evaluation indicators of the photovoltaic absorption capacity of the distribution network in the example of the present invention are defined as: voltage deviation rate, transformer load rate, abandoned light rate, photovoltaic absorption rate and line load rate.

[0014] The voltage deviation rate refers to the difference between the actual voltage and the target voltage, usually expressed as a percentage. It reflects the voltage stability of the power supply system under given load conditions. In the present invention, it can reflect the power supply voltage deviation in a certain area after photovoltaic power is absorbed. The voltage deviation rate can be calculated by the following formula:

[0015]

[0016] In the formula, f u is the power supply voltage deviation in a certain area; T is the optimization time length; N is the voltage node in the area; U N is the rated voltage at this voltage level; U n (t) is the voltage of node n at time t.

[0017] The transformer load rate refers to the ratio between the actual load of the transformer at a specific moment and its rated load, usually expressed as a percentage. The load rate reflects the efficiency and working status of the transformer. The specific formula is as follows:

[0018]

[0019] In the formula, f T is the load rate of a transformer in a certain area; P L (t) represents all loads under the transformer at time t; P PV (t) represents the actual photovoltaic output of the transformer at time t; S T is the rated capacity of a transformer.

[0020] The abandonment rate means that when a region faces the situation that the local electricity load cannot fully consume the photovoltaic power generation during dispatch, and there is a risk in the operation of the power grid or the power feedback to the upper grid reaches the equipment limit, some photovoltaic power generation power needs to be reduced. This reduction ratio can be expressed by the abandonment rate, that is, the reduction ratio of photovoltaic power generation in the region. The specific formula is as follows:

[0021]

[0022] In the formula, f Q Indicates the abandoned light rate in a certain area; P PV (t) represents the power consumed by photovoltaic equipment in a certain area after optimization algorithm analysis at time t; P PV.N (t) represents the actual power generated by the photovoltaic equipment in a certain area before the optimization algorithm analysis at time t.

[0023] Photovoltaic absorption rate: The reverse flow of power distribution in the distribution area may affect the stability of the power system. At the same time, the absorption capacity of the upper power grid is also limited. Therefore, distributed photovoltaic power should give priority to local absorption. The photovoltaic absorption rate of the distribution area refers to the absorption ratio of local photovoltaic power generation in the area, which is used to evaluate the utilization efficiency of photovoltaic resources. By improving the absorption rate, the pressure on the upper power grid can be effectively reduced and the stability of the system can be enhanced. The specific formula is as follows:

[0024]

[0025] In the formula, f PV is the photovoltaic absorption rate, P l (t) represents the active power of the line at time t.

[0026] Line load rate: It indicates the average load of the line during the optimization period, which can indicate the load condition of the line. The specific formula is:

[0027]

[0028] In the formula, f L is the line load rate; S lN is the maximum transmission capacity of the line; θ is the set of all lines in a certain area; P l (t) represents the active power of the line at time t.

[0029] After completing the establishment of the five sub-indicators, the distribution network photovoltaic absorption capacity evaluation optimization objective function is established. The objective function consists of two sub-goals, namely, the minimum abandonment rate and the minimum voltage deviation rate, and strives to maximize the possible absorption of photovoltaic output under the premise of minimizing the impact on the power grid. The specific formula is:

[0030]

[0031] Where minF is the target function of the photovoltaic absorption capacity of the distribution network; f Q is the actual abandoned light rate; f Q1 is the maximum allowable light abandonment rate; 、 f U is the voltage deviation rate of actual operation; f U1 is the maximum allowable voltage deviation rate, F is the total objective function; F1 represents sub-objective function 1, with the minimum abandonment rate; F2 represents sub-objective function 2, with the minimum voltage deviation rate; a1 and a2 are the weights of the sub-objective functions, both of which are in the interval (0, 1) and add up to 1.

[0032] The constraints of the objective function include active power balance constraint and distribution network voltage deviation rate constraint. The specific formula is:

[0033]

[0034] 0.9U N ≤U(t)≤1.07U N (9) Where P T (t) represents the active power transmitted by the transformer; P loss (t) represents the regional network active power loss at time t, P l,i (t) represents the active power of the i-th line at time t, and U(t) is the distribution network voltage at time t.

[0035] In this step, the Gray Wolf Optimization Algorithm is used to solve the optimization model for evaluating the photovoltaic absorption capacity of the distribution network. The Gray Wolf Optimization Algorithm integrates all the advantages of the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm, and uses nonlinear control parameters to ensure faster convergence speed in later iterations and fewer parameters to be adjusted in the later stages, which has more advantages in solution accuracy. The Gray Wolf Algorithm also shows good robustness in a variety of benchmark optimization problems and practical problems. Assuming that the spatial dimension of the optimal solution for photovoltaic absorption capacity is d, the number of wolves is N, and the position of the i-th wolf is expressed as:

[0036]

[0037] Using a, b, and c to represent the first, second, and third optimal solutions for the entire wolf pack, respectively, the range of the target solution can be expressed as:

[0038] X(t+1)=X p (t)-A·|c·X p (t)-X(t)| (11)

[0039] Where t is the number of iterations, X p(t) is the position of the optimal solution, while X(t) and X(t+1) represent the position changes between adjacent positions of the optimal solution. A is the convergence factor, c is the swing factor, and the calculation formula is:

[0040]

[0041] Where r1 and r2 are random numbers between [0,1]. a is the distance control parameter, expressed as:

[0042]

[0043] Where, T max is the maximum number of iterations. ini and a fin are the initial value and the end value respectively.

[0044] Using the above formula, we get the positions of a, b, and c as:

[0045]

[0046] In the formula, X 1、2、3 (t+1) represent the three new position vectors temporarily generated after the wolf’s position is updated; X a、b、c In (t), Xa is usually considered to be the candidate for the optimal solution, Xb is the candidate for the second-best solution, and Xc is the candidate for the third-best solution. 1、2、3 The value of changes dynamically with the number of iterations, and is usually used to adjust the distance between the current gray wolf position and the target wolf; c1, c2, and c3 are randomization coefficients, which are random numbers in the interval [0,1] and are used to increase the randomness and diversity of the search process; X next (t+1) is the position of the next iteration, X a It is the optimal solution of the Grey Wolf Optimization (GWO) algorithm.

[0047] The example of the present invention also provides a technical solution for dividing the carrying capacity area according to the photovoltaic absorption capacity of the distribution network, which is characterized in that, according to the national "Guidelines for the Assessment of the Carrying Capacity of Distributed Power Sources Connected to the Grid", the photovoltaic absorption capacity of a certain area obtained by the gray wolf algorithm is combined with the real-time load, transformer operating limit, and line operating limit of the area to calculate the reverse load of the equipment, and the formula is as follows:

[0048]

[0049] Where λ is the photovoltaic absorption capacity of a certain region; P pv Indicates the optimized output result of photovoltaic equipment in this area; P L Represents the equivalent power load, which is the total power load minus the output of photovoltaic equipment and other power sources; S T / L Indicates transformer operating limits and line operating limits.

[0050] Based on the results of calculation and analysis, the zoning and stratification are determined. The evaluation levels can be divided into green, yellow and red from low to high. The areas that can be completely consumed on site and have no reverse flow are designated as green areas; the areas where the reverse flow does not exceed 80% of the equipment limit are designated as yellow areas; and the areas where the reverse flow exceeds 80% of the equipment limit are designated as red areas.

[0051] The example of the present invention also provides a method for visual analysis and display using a grid map, which specifically includes technical solutions such as photovoltaic open capacity analysis, access area recommendation, data verification and correction analysis, etc., characterized in that after using the optimization algorithm to calculate the photovoltaic absorption capacity of the region, combined with the construction results of the grid map, the calculation results are visualized and displayed, thereby completing the entire process of the distribution network photovoltaic absorption capacity evaluation and analysis method. Among them, the analysis consists of photovoltaic open capacity analysis, access area recommendation, data verification and correction analysis, etc.

[0052] The analysis of the open capacity of photovoltaic power generation is as follows: combining the real-time and historical measurement and operation data of distributed photovoltaic power generation, gathering and analyzing the real-time status of equipment in various regions and voltage levels, and visually replaying the change process of the carrying capacity in the form of a nebula diagram based on the photovoltaic absorption capacity of the distribution network obtained by the Grey Wolf algorithm. The access area recommendation is as follows: based on a map of power grid resources, through a custom geographic range, the transformer equipment in the geographical area is automatically retrieved, and the photovoltaic carrying capacity is analyzed in combination with the transformer load. According to the transformer load from low to high, it is recommended that the distribution transformer can be connected. The data verification and correction analysis is as follows: according to the historical operation data of photovoltaic users and the current weather conditions, the operation data of the accessed distributed photovoltaic power generation is verified and analyzed, and data anomalies caused by equipment anomalies, collection anomalies, etc. are screened. At the same time, the operation data is corrected based on the historical operation load curve to ensure the accuracy of the basic data.

[0053] Beneficial effects of the present invention:

[0054] 1. Comprehensive data acquisition and integration: Relying on the distribution automation system, data center and dispatch control platform, we can obtain multi-source data including main transformer parameters, real-time photovoltaic measurement data, meteorological data, etc., to form a panoramic view of regional photovoltaic operation, and archive them by "region-station-line-transformer". We have achieved comprehensive integration of distributed photovoltaic data, provided high-quality basic data for optimization calculation, and solved the problem of data dispersion and inconsistency.

[0055] 2. Scientific evaluation indicators and optimization model construction: define multi-dimensional evaluation indicators such as voltage deviation rate and abandoned light rate, and build an optimization model with the goal of minimizing abandoned light rate and voltage deviation rate. Through reasonable constraints, ensure that the model does not affect the stability of the power grid while improving the efficiency of photovoltaic consumption, and solve the problem of single evaluation dimension of traditional methods.

[0056] 3. Efficient solution algorithm: Based on the Grey Wolf Optimization Algorithm (GWO), the photovoltaic absorption capacity optimization model is quickly solved through dynamic convergence factors and balanced global and local search capabilities. This algorithm has significant advantages in accuracy and convergence speed, and solves the problem that traditional algorithms are prone to fall into local optimality.

[0057] 4. Regional division of photovoltaic absorption capacity: Combined with the results of the Grey Wolf algorithm and the real-time load and equipment operation limits, the regional carrying capacity is divided into green, yellow and red levels, and the carrying potential and risk are evaluated in layers. This division standard is intuitive and scientific, providing a clear basis for distributed photovoltaic access.

[0058] 5. Visual analysis and recommendation function: Use the grid one map platform to visualize the evaluation results of photovoltaic absorption capacity in the form of a star cloud map, and accurately guide photovoltaic access planning through data verification and substation recommendation functions. It solves the problem that traditional analysis results are not intuitive and decision support is insufficient. By comprehensively utilizing the existing distribution automation system and big data technology, efficient absorption and reasonable scheduling of photovoltaic power generation can be achieved. This analysis method builds an evaluation and optimization model based on the gray wolf algorithm, which helps to identify and recommend the best access area, reduce the abandonment rate, improve voltage stability, and provide a complete subsequent analysis process based on the grid one map, thereby enhancing the reliability and safety of the power grid. At the same time, the platform provides a scientific basis for the planning and management of distributed photovoltaics, promotes the effective use of renewable energy, and helps the green energy transformation.

[0059] The present invention first relies on the distribution automation system, data middle platform and dispatching control platform to obtain the photovoltaic operation data of the distribution network; then establishes the distribution network photovoltaic absorption capacity evaluation optimization model, and solves it based on the grey wolf algorithm; divides the carrying capacity area according to the photovoltaic absorption capacity of the distribution network; uses a method of visual analysis and display of the power grid map, specifically including photovoltaic open capacity analysis, access area recommendation, data verification and correction analysis, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The present invention is a flow chart of the method.

[0061] Figure 2 This is a flow chart of the gray wolf optimization algorithm used in step 2 of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the examples described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Embodiment 1:

[0064] The specific process of the present invention is as follows:

[0065] Step 1: Relying on the distribution automation system, data center, and dispatching control platform, obtain the distribution network photovoltaic operation data, access the main transformer parameters, circuit breaker parameters, medium and low voltage distributed photovoltaic operation data, and main network operation data in the dispatching control platform. Access the substation data, line data, transformer data, and distributed photovoltaic real-time measurement data in the distribution automation system. Access the distributed photovoltaic archives, distributed photovoltaic historical measurement data, and meteorological data in the data center. Integrate and analyze the data to obtain an overview of the distributed photovoltaic under the distribution network in the region, and classify the distributed photovoltaic archives according to the "region-station-line-transformer" partition.

[0066] In actual operation, data can be divided into station records, operation data, station-line transformer records, distribution transformer real-time measurement, photovoltaic real-time measurement, basic archives, historical measurement and weather data. Station-line transformer records can include: substations, lines and transformers. Distribution transformer real-time measurement can include: current curve, voltage curve and power curve. Photovoltaic real-time measurement data can include: current curve, voltage curve and power curve.

[0067] The basic files may include: photovoltaic user files, power supply points, metering points, and meter information. Historical measurements may include: power curves, current curves, and voltage curves. Weather data may include: daily and hourly weather forecasts, real-time weather information, and real-time weather warning information.

[0068] Step 2: Establish an optimization model for evaluating the photovoltaic absorption capacity of the distribution network, and solve it based on the Grey Wolf Algorithm. With reference to relevant literature, define the evaluation indicators of the photovoltaic absorption capacity of the distribution network in the example of the present invention as follows: voltage deviation rate, transformer load rate, abandoned light rate, photovoltaic absorption rate and line load rate.

[0069] The voltage deviation rate refers to the difference between the actual voltage and the target voltage, usually expressed as a percentage. It reflects the voltage stability of the power supply system under given load conditions. In the present invention, it can reflect the power supply voltage deviation in a certain area after photovoltaic power is absorbed. The voltage deviation rate can be calculated by the following formula:

[0070]

[0071] In the formula, f u is the power supply voltage deviation in a certain area; T is the optimization time length; N is the voltage node in the area; U N is the rated voltage at this voltage level; U n (t) is the voltage of node n at time t.

[0072] The transformer load rate refers to the ratio between the actual load of the transformer at a specific moment and its rated load, usually expressed as a percentage. The load rate reflects the efficiency and working status of the transformer. The specific formula is as follows:

[0073]

[0074] In the formula, f T is the load rate of a transformer in a certain area; P L (t) represents all loads under the transformer at time t; P PV (t) represents the actual photovoltaic output of the transformer at time t; S T is the rated capacity of a transformer.

[0075] The abandonment rate means that when a region faces the situation that the local electricity load cannot fully consume the photovoltaic power generation during dispatch, and there is a risk in the operation of the power grid or the power feedback to the upper grid reaches the equipment limit, some photovoltaic power generation power needs to be reduced. This reduction ratio can be expressed by the abandonment rate, that is, the reduction ratio of photovoltaic power generation in the region. The specific formula is as follows:

[0076]

[0077] In the formula, f Q Indicates the abandoned light rate in a certain area; P PV (t) represents the power consumed by photovoltaic equipment in a certain area after optimization algorithm analysis at time t; P PV.N (t) represents the actual power generated by the photovoltaic equipment in a certain area before the optimization algorithm analysis at time t.

[0078] Photovoltaic absorption rate: The reverse flow of power distribution in the distribution area may affect the stability of the power system. At the same time, the absorption capacity of the upper power grid is also limited. Therefore, distributed photovoltaic power should give priority to local absorption. The photovoltaic absorption rate of the distribution area refers to the absorption ratio of local photovoltaic power generation in the area, which is used to evaluate the utilization efficiency of photovoltaic resources. By improving the absorption rate, the pressure on the upper power grid can be effectively reduced and the stability of the system can be enhanced. The specific formula is as follows:

[0079]

[0080] In the formula, f PV is the photovoltaic absorption rate.

[0081] Line load rate: It indicates the average load of the line during the optimization period, which can indicate the load condition of the line. The specific formula is:

[0082]

[0083] In the formula, f L is the line load rate; SlN is the maximum transmission capacity of the line; θ is the set of all lines in a certain area; P l (t) represents the active power of the line at time t.

[0084] After completing the establishment of the five sub-indicators, the distribution network photovoltaic absorption capacity evaluation optimization objective function is established. The objective function consists of two sub-goals, namely, the minimum abandonment rate and the minimum voltage deviation rate, and strives to maximize the possible absorption of photovoltaic output under the premise of minimizing the impact on the power grid. The specific formula is:

[0085]

[0086] Wherein, F is the total objective function; F1 represents sub-objective function 1, with the minimum abandonment rate; F2 represents sub-objective function 2, with the minimum voltage deviation rate; a1 and a2 are the weights of the sub-objective functions, both of which are in the interval (0, 1) and add up to 1.

[0087] The constraints of the objective function include active power balance constraint and distribution network voltage deviation rate constraint. The specific formula is:

[0088]

[0089] 0.9U N ≤U(t)≤1.07U N (9) Where P T (t) represents the active power transmitted by the transformer; P loss (t) represents the active power loss of the regional network at time t.

[0090] In this step, the Gray Wolf Optimization Algorithm is used to solve the optimization model for evaluating the photovoltaic absorption capacity of the distribution network. The Gray Wolf Optimization Algorithm integrates all the advantages of the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm, and uses nonlinear control parameters to ensure faster convergence speed in later iterations and fewer parameters to be adjusted in the later stages, which has more advantages in solution accuracy. The Gray Wolf Algorithm also shows good robustness in a variety of benchmark optimization problems and practical problems. Assuming that the spatial dimension of the optimal solution for photovoltaic absorption capacity is d, the number of wolves is N, and the position of the i-th wolf is expressed as:

[0091]

[0092] Using a, b, and c to represent the first, second, and third optimal solutions for the entire wolf pack, respectively, the range of the target solution can be expressed as:

[0093] X(t+1)=X p (t)-A·|c·X p (t)-X(t)| (11)

[0094] Where t is the number of iterations, X p (t) is the position of the optimal solution, while X(t) and X(t+1) represent the position changes between adjacent positions of the optimal solution. A is the convergence factor, c is the swing factor, and the calculation formula is:

[0095]

[0096] Where r1 and r2 are random numbers between [0,1], and a is the distance control parameter, expressed as:

[0097]

[0098] Where, T max is the maximum number of iterations, a ini and a fin are the initial value and the end value respectively.

[0099] Using the above formula, we get the positions of a, b, and c as:

[0100]

[0101] In the formula, X next (t+1) is the position of the next iteration, X a It is the optimal solution of GWO.

[0102] In actual operation, the distribution transformer is taken as the smallest unit. According to the photovoltaic output, the reverse load of the distribution transformer is analyzed. At the same time, combined with the grid topology, the load of each level of equipment is analyzed in a hierarchical manner according to the "region-station-line-transformer" hierarchy. Before actual operation, the distribution network topology analysis, photovoltaic connection topology analysis, photovoltaic carrying capacity zoning, and open capacity analysis of stations, lines, and transformers are first carried out.

[0103] In actual operation, the photovoltaic absorption capacity evaluation based on the wolf pack optimization algorithm is solved. The specific solution process is as follows:

[0104] Step 1: Input the distribution area network structure, node load, basic information of each flexible resource, distributed photovoltaic output forecast data, etc.

[0105] Step 2: Initialize the gray wolf population, including the alpha wolf (α), the second wolf (β), the third wolf (δ), and other follower wolves (ω). According to the control capacity and constraints of each flexible resource, the initial output of each flexible resource is randomly generated in each time period as the initial solution of the population.

[0106] Step 3: Calculate the fitness of each gray wolf and determine the optimal solution for the alpha wolf (α), the suboptimal solutions for the second wolf (β) and the third wolf (δ).

[0107] Step 4: Based on the position information of the leader wolf (α), the second wolf (β), and the third wolf (δ), guide the update of other follower wolves (ω), and adjust the position of each gray wolf as follows:

[0108] Update the distance between the gray wolf and the alpha wolf, the second wolf, and the third wolf;

[0109] Based on the above distance, calculate the new position and speed according to the formula.

[0110] Step 5: Perform fitness evaluation for each iteration, update the extreme values ​​of the alpha wolf, the second wolf, and the third wolf, and gradually approach the global optimal solution.

[0111] Step 6: Check whether the iteration termination condition is met. If the termination condition is met, stop the iteration and output the optimal scheduling result; otherwise, continue the iteration and repeat steps 3 to 5.

[0112] Step 3: Divide the carrying capacity area according to the photovoltaic absorption capacity of the distribution network. According to the national "Guidelines for the Assessment of the Carrying Capacity of Distributed Generation Access to the Grid", the photovoltaic absorption capacity of a certain area obtained by the Grey Wolf algorithm is combined with the real-time load, transformer operating limit, and line operating limit of the area to calculate the equipment reverse load. The formula is as follows:

[0113]

[0114] Where P pv Indicates the optimized output result of photovoltaic equipment in this area; P L Represents the equivalent power load, which is the total power load minus the output of photovoltaic equipment and other power sources; S T / L Indicates transformer operating limits and line operating limits.

[0115] Based on the results of calculation and analysis, the zoning and stratification are determined. The evaluation levels can be divided into green, yellow and red from low to high. The areas that can be completely consumed on site and have no reverse flow are designated as green areas; the areas where the reverse flow does not exceed 80% of the equipment limit are designated as yellow areas; and the areas where the reverse flow exceeds 80% of the equipment limit are designated as red areas.

[0116] In practical applications, the color can be refined to distinguish the granularity:

[0117] Green: No send has occurred.

[0118] yellow:

[0119] Light yellow 0<return rate<25%;

[0120] Yellow 25≤Reverse rate<60%;

[0121] Dark yellow 60≤reverse rate<80%.

[0122] red:

[0123] Light red 80≤reverse rate<90;

[0124] Red 90≤Reverse rate<100;

[0125] Deep red 100≤reverse rate.

[0126] Step 4: The method of using a grid map for visualization analysis and display includes analysis of photovoltaic open capacity, recommendation of access areas, data verification and correction analysis, etc. Specifically, based on the grid grid information, geographic location, spatial range rendering and other information in the grid resource map platform, combined with the real-time and historical measurement and operation data of distributed photovoltaics, the real-time situation of equipment in each region and voltage level is analyzed, and the photovoltaic absorption capacity of the distribution network obtained based on the gray wolf algorithm is visualized and replayed in the form of a nebula map to display the change process of carrying capacity. The specific method of access area recommendation is as follows: based on the grid resource map, through the custom geographic range, the transformer equipment in the geographic area is automatically retrieved, and the photovoltaic carrying capacity is analyzed in combination with the transformer load. According to the transformer load from low to high, it is recommended to access the distribution transformer. The specific method of data verification and correction analysis is as follows: according to the historical operation data of photovoltaic users and the current weather conditions, the operation data of the accessed distributed photovoltaics is verified and analyzed, and the data anomalies caused by equipment anomalies, collection anomalies, etc. are screened. At the same time, the operation data is corrected based on the historical operation load curve to ensure the accuracy of the basic data.

[0127] The present invention evaluates and analyzes the photovoltaic absorption capacity of the distribution network, improves the utilization efficiency of photovoltaic resources, demonstrates the potential of the distribution network in accessing distributed photovoltaics, and realizes scientific and reasonable photovoltaic absorption planning and management based on the existing distribution automation system and big data technology. To this end, the present invention proposes a method for evaluating and analyzing the photovoltaic absorption capacity of the distribution network, which improves the stability and security of the power grid.

[0128] 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, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for evaluating and analyzing the photovoltaic absorption capacity of a distribution network, characterized in that: The following steps are involved: Step 1: Relying on the distribution automation system, data center, and dispatching control platform, obtain the distribution network photovoltaic operation data; Step 2: Establish an optimization model for evaluating the photovoltaic absorption capacity of the distribution network and solve it based on the Grey Wolf Algorithm; Step 3: Divide the carrying capacity area according to the photovoltaic absorption capacity of the distribution network; Step 4: Use a grid map for visual analysis and display, including PV open capacity analysis, access area recommendation, data verification and correction analysis.

2. A method according to claim 1, characterized in that In step 1, the data includes main transformer parameters, circuit breaker parameters, medium and low voltage distributed photovoltaic operation data, main network operation data in the dispatching control platform, substation data, line data, transformer data, distributed photovoltaic real-time measurement data in the distribution automation system, and marketing distributed photovoltaic archives, distributed photovoltaic historical measurement data, and meteorological data in the data center. The data are integrated and analyzed to obtain an overview of distributed photovoltaics under the distribution network in the region, and the distributed photovoltaic archives are classified according to the "region-substation-line-distribution transformer" division.

3. A method according to claim 1, characterized in that In step 2, an optimization model for evaluating the photovoltaic absorption capacity of the distribution network is established, in which the evaluation indicators of the photovoltaic absorption capacity of the distribution network are defined as voltage deviation rate, transformer load rate, abandoned light rate, photovoltaic absorption rate and line load rate, and an objective function is established. The objective function consists of two sub-goals: minimizing the abandoned light rate and minimizing the voltage deviation rate, and corresponding constraints are set.

4. A method according to claim 3, characterized in that The specific process in step 2 is as follows: Assuming that the spatial dimension of the optimal solution for photovoltaic absorption capacity is d, the number of wolves is N, and the position of the i-th wolf is expressed as: Where, X i represents the position of the i-th wolf; Using a, b, and c to represent the first, second, and third optimal solutions for the entire wolf pack, respectively, the range of the target solution can be expressed as: X(t+1)=X p (t)-A·|c·X p (t)-X(t)| (11) Where t is the number of iterations, X p (t) is the position of the optimal solution, while X(t) and X(t+1) represent the position changes between adjacent positions of the optimal solution, A is the convergence factor, and c is the swing factor. The calculation formula is: Where r1 and r2 are random numbers between [0,1], and a is the distance control parameter, expressed as: Where, T max is the maximum number of iterations, a ini and a fin are the initial value and the end value respectively; Using the above formula, the positions of a, b, and c are: Where i = 1, 2, 3, X i (t+1) represent the three new position vectors temporarily generated after the wolf position is updated; j = a, b, c, X j In (t), Xa is usually considered as the candidate for the optimal solution, Xb is the candidate for the second-best solution, and Xc is the candidate for the third-best solution; i The value of changes dynamically with the number of iterations, and is usually used to adjust the distance between the current gray wolf position and the target wolf; c1, c2, and c3 are randomization coefficients, which are random numbers in the interval [0,1] and are used to increase the randomness and diversity of the search process; X next (t+1) is the position of the next iteration, X a It is the optimal solution of the Grey Wolf Optimization Algorithm GWO.

5. A method according to claim 1, characterized in that: In step 3, the specific implementation is as follows: the photovoltaic absorption capacity of a certain area obtained by the Grey Wolf algorithm is combined with the real-time load, transformer operating limit and line operating limit of the area to calculate the equipment reverse load. According to the calculation and analysis results, the carrying capacity area is determined by zoning and layering, and the evaluation level can be divided into green, yellow and red from low to high.

6. A method according to claim 1, characterized in that In step 4, a visual analysis and display is performed using a grid map, which specifically includes analysis of the available photovoltaic capacity, recommendation of access areas, and data verification and correction analysis. Based on the grid grid information, geographic location, and spatial range rendering information in the grid resource map platform, and combined with the real-time and historical measurement and operation data of distributed photovoltaics, the real-time status of equipment in each region and voltage level is analyzed, and the capacity change process is visualized and played back in the form of a nebula map. The transformer equipment in the geographical area is automatically retrieved, and the photovoltaic capacity is analyzed in combination with the transformer load. The distribution transformer that can be connected is recommended, and the distributed photovoltaic operation data is verified and analyzed based on the historical operation data of photovoltaic users and the current weather conditions, and data anomalies are screened and corrected.

7. A method according to claim 1, characterized in that The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for evaluating and analyzing the photovoltaic absorption capacity of a distribution network according to any one of claims 1 to 6 is implemented.

Images