Distribution area distributed photovoltaic bearing capacity optimization method, system and device and medium

Abstract

The invention discloses a transformer area distributed photovoltaic bearing capacity optimization method, system and device and a medium, and relates to the technical field of photovoltaic bearing capacity analysis, and the method comprises the steps: determining the constraint condition of a transformer area based on the characteristic analysis result of the transformer area, the constraint conditions comprise a voltage fluctuation constraint condition, a line loss constraint condition and an equipment protection constraint condition; target sub-regions are determined according to the constraint conditions, and the target sub-regions are sub-regions with unqualified bearing capacity in the sub-regions included in the transformer area; and based on the distributed photovoltaic bearing capacity optimization strategy corresponding to the target sub-region, optimizing the target sub-region. According to the invention, the technical problem of low optimization effect of distributed photovoltaic system access in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic load-bearing capacity analysis, and in particular to a method, system, device and medium for optimizing distributed photovoltaic load-bearing capacity in a metropolitan area. Background Art

[0002] At present, more and more distributed photovoltaic systems are connected to the distribution network. In particular, the widespread application of small-capacity and low-voltage distributed photovoltaic systems has brought the advantages of renewable energy generation to the power system and reduced its dependence on traditional energy.

[0003] However, when a large-scale, small-capacity, low-voltage distributed photovoltaic system is connected to a substation, the carrying capacity of the distribution network is limited by many factors, such as voltage fluctuations, line losses, and equipment protection. The volatility of photovoltaic power generation can easily lead to voltage instability, especially when the load does not match the power generation. Excessive voltage fluctuations in the grid will affect the normal operation of the equipment. At the same time, due to the imbalance of power flow, the line loss will also increase significantly, resulting in energy waste and line overload. In addition, the load carrying capacity of equipment such as transformers and circuit breakers in the distribution network is also affected by photovoltaic access. If the equipment load is too high, the equipment will need frequent protection, reducing the service life of the equipment and increasing maintenance costs. Therefore, it is urgent to optimize the access to distributed photovoltaic systems to reduce the impact of the instability and intermittency of photovoltaic power generation.

[0004] In the existing technology, the optimization of distributed photovoltaic system access mostly focuses on the optimization of a single indicator, such as only controlling voltage fluctuations or only considering the adjustment of load distribution. There is a lack of systematic and global optimization methods, resulting in a low optimization effect on the access of distributed photovoltaic systems. Summary of the invention

[0005] The present invention provides a method, system, device and medium for optimizing the distributed photovoltaic carrying capacity in a substation area, which can solve the technical problem of low optimization effect on the access of distributed photovoltaic systems existing in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for optimizing distributed photovoltaic carrying capacity in a metropolitan area, the method comprising:

[0008] Based on the characteristic analysis results of the substation area, determining the constraint conditions of the substation area, wherein the constraint conditions include voltage fluctuation constraint conditions, line loss constraint conditions and equipment protection constraint conditions;

[0009] Determine a target sub-area according to the constraint condition, wherein the target sub-area is a sub-area whose carrying capacity is unqualified among the sub-areas included in the station area;

[0010] The target sub-area is optimized based on the distributed photovoltaic carrying capacity optimization strategy corresponding to the target sub-area.

[0011] In a second aspect, an embodiment of the present invention provides a system for optimizing distributed photovoltaic carrying capacity in a metropolitan area, the system comprising:

[0012] A constraint condition determination unit, used to determine the constraint conditions of the substation based on the characteristic analysis results of the substation, wherein the constraint conditions include voltage fluctuation constraint conditions, line loss constraint conditions and equipment protection constraint conditions;

[0013] A target sub-region determining unit, configured to determine a target sub-region according to the constraint condition, wherein the target sub-region is a sub-region whose carrying capacity is unqualified among the sub-regions included in the station area;

[0014] The optimization unit is used to optimize the target sub-area based on the distributed photovoltaic carrying capacity optimization strategy corresponding to the target sub-area.

[0015] In the third aspect, an embodiment of the present invention also provides a distributed photovoltaic carrying capacity optimization device in an area, comprising: a processor and a memory; the memory is used to store instructions; the processor is used to operate according to the instructions to implement the steps of the aforementioned distributed photovoltaic carrying capacity optimization method in an area.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed, the steps of the aforementioned method for optimizing the distributed photovoltaic carrying capacity of an area are implemented.

[0017] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the aforementioned method for optimizing the distributed photovoltaic carrying capacity of a substation.

[0018] The beneficial effects brought by the technical solution provided by the embodiment of the present invention include:

[0019] Based on the characteristic analysis results of the substation, the constraints of the substation are determined, including voltage fluctuation constraints, line loss constraints and equipment protection constraints. The key factors affecting the operation stability of the substation can be accurately identified, and the operation status of the substation under different loads and photovoltaic power generation scenarios can be dynamically reflected. The voltage fluctuation is effectively controlled, the line loss is optimized, and the equipment is ensured to operate within the safe load range. This not only improves the photovoltaic access carrying capacity of the substation, but also significantly improves the overall safety and efficiency of the power grid, ensuring the long-term stability of the substation power grid under various operating conditions;

[0020] The target sub-area is determined according to the constraint conditions, wherein the target sub-area is a sub-area with unqualified carrying capacity among the various sub-areas contained in the substation, ensuring balanced optimization of voltage fluctuation, line loss and equipment load in each sub-area after photovoltaic access; based on the distributed photovoltaic carrying capacity optimization strategy corresponding to the target sub-area, the target sub-area is optimized, which not only significantly reduces voltage fluctuations and ensures voltage stability, but also improves the system's carrying capacity for large-scale small-capacity photovoltaic access, but also significantly reduces line energy loss, improves power transmission efficiency, and extends the service life of low-voltage lines, reducing the maintenance cost of the power grid.

[0021] The present invention solves the technical problem of low optimization effect on access to a distributed photovoltaic system in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for optimizing distributed photovoltaic carrying capacity in a metropolitan area according to the present invention;

[0024] Figure 2 for Figure 1 Detailed flow chart of step S20;

[0025] Figure 3 for Figure 2 Detailed flow chart of step S201;

[0026] Figure 4 This is a functional module diagram of an embodiment of a distributed photovoltaic load capacity optimization system in a metropolitan area according to the present invention;

[0027] Figure 5 It is a structural schematic diagram of a distributed photovoltaic carrying capacity optimization device in an area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] 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 described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] In a first aspect, an embodiment of the present invention provides a method for optimizing the distributed photovoltaic carrying capacity in an area.

[0031] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of a method for optimizing the distributed photovoltaic carrying capacity of a metropolitan area according to the present invention. Figure 1 As shown in the figure, the optimization method of distributed photovoltaic carrying capacity in the area includes:

[0032] Step S10, based on the characteristic analysis result of the substation, determining the constraint conditions of the substation, wherein the constraint conditions include voltage fluctuation constraint conditions, line loss constraint conditions and equipment protection constraint conditions;

[0033] In some specific embodiments, based on the characteristic analysis results of the substation, determining the voltage fluctuation constraint conditions of the substation includes:

[0034] Based on the total active power and total reactive power of the substation at different times, the voltage fluctuation deviation in the substation at different times is calculated by a first preset formula;

[0035] Select the maximum voltage deviation from the voltage fluctuation deviations in the substation at different times;

[0036] Determine that the voltage fluctuation constraint condition of the substation is that the voltage fluctuation deviation of the substation is less than or equal to the maximum voltage deviation;

[0037] Among them, the first preset formula is as follows:

[0038]

[0039] In the formula, ΔV(t) represents the voltage fluctuation deviation in the substation at time t, P(t) represents the total active power of the substation at time t, R is the line resistance, Q(t) represents the total reactive power of the substation at time t, X is the line reactance, V nom is the rated voltage.

[0040] In this embodiment, based on the power flow formula, the expression of voltage fluctuation is used as the first preset formula, and based on the total active power and total reactive power of the substation at different times, the voltage fluctuation deviation ΔV(t) in the substation at different times is calculated by the first preset formula.

[0041] Select the maximum voltage deviation ΔV from the voltage fluctuation deviation in the substation at different times allow The voltage fluctuation constraint condition of the substation is determined as follows: the voltage fluctuation deviation of the substation is less than or equal to the maximum voltage deviation, that is, the voltage fluctuation constraint condition of the substation is: ΔV(t)≤ΔVallow .

[0042] Further, in one embodiment, after combining and simulating the load clustering results and distributed photovoltaic power generation at different times to obtain the voltage fluctuation deviation of the substation at different times, the voltage fluctuation deviation is updated to obtain the updated voltage fluctuation deviation, including:

[0043] Based on the active power and reactive power of the energy storage system at time t, the voltage fluctuation deviation is updated by a second preset formula to obtain an updated voltage fluctuation deviation;

[0044] Among them, the second preset formula is as follows:

[0045]

[0046] Where, ΔV adj (t) represents the updated voltage fluctuation deviation in the substation at time t, ΔV(t) represents the voltage fluctuation deviation in the substation at time t, P stor (t) represents the active power of the energy storage system at time t, R is the line resistance, Q stor (t) represents the reactive power of the energy storage system at time t, X is the line reactance, V nom is the rated voltage.

[0047] In this embodiment, after obtaining the voltage fluctuation deviation of the substation at different times, a second constraint is imposed on the voltage fluctuation deviation to ensure that the load fluctuation will not cause the voltage to exceed the allowable range when the photovoltaic is connected. Specifically, based on the active power and reactive power of the energy storage system at time t, the second preset formula is used The voltage fluctuation deviation can be updated to obtain an updated voltage fluctuation deviation.

[0048] In some specific embodiments, based on the characteristic analysis results of the substation, determining the line loss constraint condition of the substation includes:

[0049] Based on the total active power and total reactive power of the substation at different times, the line loss of the substation at different times is calculated by a third preset formula;

[0050] Select the maximum line loss from the line losses in the area at different times;

[0051] Determine that the line loss constraint condition of the substation area is that the line loss in the substation area is less than or equal to the maximum line loss;

[0052] Among them, the third preset formula is as follows:

[0053]

[0054] Where L(t) represents the line loss of the substation at time t, P(t) represents the total active power of the substation at time t, Q(t) represents the total reactive power of the substation at time t, R is the line resistance, and V(t) represents the voltage of the substation at time t.

[0055] In this embodiment, based on the total active power and total reactive power of the substation at different times, the third preset formula is used to calculate the total active power and total reactive power of the substation at different times. The line loss of the substation at different times is calculated.

[0056] In another embodiment, based on the line current I(t) at time t, the formula L(t)=I 2 (t)R is used to calculate the line loss of the substation at time t, where R is the line resistance.

[0057] Select the maximum line loss from the line losses within the station area at different times.

[0058] The dispatch of controllable loads reduces the load flow and line loss, and determines the line loss constraint condition of the substation area that the line loss in the substation area is less than or equal to the maximum line loss, that is, the line loss constraint condition of the substation area is: L(t)≤L max .

[0059] In some specific embodiments, based on the characteristic analysis result of the substation, determining the equipment protection constraint condition of the substation includes:

[0060] Based on the measured data, the current of the access point of the distributed photovoltaic system at different times and the load current of the access point of the distributed photovoltaic system at different times are obtained;

[0061] Calculate the sum of the current of the distributed photovoltaic system access point at different times plus the load current of the distributed photovoltaic system access point at the corresponding time, and use the sum as the total current of the distributed photovoltaic system access point at different times;

[0062] The equipment protection constraint condition of the substation is determined as follows: the total current of the distributed photovoltaic system access point at different times is less than or equal to the maximum load current of the transformer in the substation.

[0063] In this embodiment, the current I of the access point of the distributed photovoltaic system at different times provided by the measurement data is calculated. PV (t) plus the load current I of the distributed photovoltaic system access point at the corresponding time load (t), and the obtained sum is taken as the total current of the distributed photovoltaic system access point at different times, that is, I 总 (t) = I load (t)+I PV (t). The equipment protection constraint condition of the substation is determined as follows: the total current of the distributed photovoltaic system access point at different times is less than or equal to the maximum load current I of the transformer in the substationmax , that is, the equipment protection constraint condition of the substation is: I 总 (t)≤I max .

[0064] Step S20, determining a target sub-area according to the constraint condition, wherein the target sub-area is a sub-area whose carrying capacity is unqualified among the sub-areas included in the station area;

[0065] In some specific embodiments, referring to Figure 2 , Figure 2 for Figure 1 Detailed flow chart of step S20 in FIG. Figure 2 As shown, step S20 includes:

[0066] Step S201, dividing the substation area according to electrical distance, voltage sensitivity and geographical location to obtain multiple sub-areas, wherein the priority of division according to electrical distance is higher than that of division according to voltage sensitivity, and the priority of division according to voltage sensitivity is higher than that of division according to geographical location;

[0067] Step S202, based on the preset objective function and the constraint conditions, a voltage fluctuation value, a line loss value and a current when the equipment is running are obtained for each of the sub-areas through a multi-objective optimization genetic algorithm;

[0068] Step S203, determining as a target sub-area a sub-area where the voltage fluctuation value is greater than a first preset threshold, and / or the line loss value is greater than a second preset threshold, and / or the current when the device is running is greater than a third preset threshold.

[0069] In this embodiment, the power network of the substation is represented as an undirected graph G = (N, E), where N is a node set representing each load node and photovoltaic access point in the substation; E is an edge set representing the power lines between each node. According to the grid topology analysis of the substation grid structure, and taking into account the electrical distance, voltage sensitivity and geographical location, the substation is divided into several sub-areas, where the priority of division according to electrical distance is greater than the priority of division according to voltage sensitivity, and the priority of division according to voltage sensitivity is greater than the priority of division according to geographical location. That is, it is prioritized to ensure that nodes with similar electrical characteristics are in the same sub-area, then ensure that nodes with adjacent geographical locations are in the same sub-area, and finally ensure that nodes with similar voltage sensitivity are in the same sub-area.

[0070] Based on the preset objective function and the voltage fluctuation constraints, line loss constraints and equipment protection constraints of the substation, the voltage fluctuation value, line loss value and current of the equipment during operation in each sub-area are obtained through a multi-objective optimization genetic algorithm.

[0071] The preset objective functions include: a first objective function, a second objective function and a third objective function;

[0072] Among them, the first objective function Where f1 represents the square sum of voltage fluctuations, V actual,i (t) represents the actual voltage of the load node in the i-th sub-region at time t, V nom,i represents the rated voltage in the i-th sub-area;

[0073] The second objective function In the formula, f2 represents the sum of line losses in the ith sub-area, T represents the operation time of the substation, and E i represents the load node power set in the ith sub-region, including the total active power and total reactive power in the ith sub-region, I ij (t) represents the current of line j in the i-th sub-area at time t, R ij represents the resistance of line j in the ith sub-region;

[0074] The third objective function Where f3 represents the adjustment function when the equipment load exceeds the maximum load limit of the transformer, I load,i (t) represents the load current at time t in the i-th sub-area, I PV,i (t) represents the current at the access point of the distributed photovoltaic system in the i-th sub-region at time t, I max,i It represents the maximum load current of the transformer in the i-th sub-area, and max() is the maximum value function.

[0075] Multi-objective Optimization Genetic Algorithm (MOGA) is an optimization algorithm that simulates the biological evolution process to find multiple optimal solutions. It is mainly used in multi-objective decision-making problems and can find the optimal balance point between multiple decision variables and multiple objective functions.

[0076] The basic principle of the MOGA algorithm is to simulate the evolutionary process in nature, generate and update a set of candidate solutions through operations such as crossover, mutation, and selection, and select a set of optimal solutions. The specific steps are as follows:

[0077] 1. Initialize the population: Randomly generate a set of initial candidate solutions, called the population. Each individual in the population is a set of values ​​of the decision variable.

[0078] 2. Evaluate fitness: For each individual, calculate its fitness value through the preset objective function. The fitness value represents the quality of the individual in the current state and can be defined according to the specific problem.

[0079] 3. Crossover and mutation: A new group of individuals is generated through crossover and mutation operations. The crossover operation simulates the mating between individuals, crossing the chromosomes of two individuals to generate two new individuals. The mutation operation simulates the mutation of individual genes, generating a new individual by randomly changing the chromosomes of individuals.

[0080] 4. Selection: Select individuals with higher fitness from the population as the parents of the next generation of the population. Common selection strategies include roulette selection, tournament selection, etc.

[0081] 5. Repeat steps 2 to 4 until the stopping condition is met. The stopping condition can be reaching a specified number of iterations, or reaching a certain degree of convergence, etc. Through these operations, individuals with better objective function values ​​are gradually generated.

[0082] After obtaining the voltage fluctuation value, line loss value and current when the equipment is running for each of the sub-areas, the sub-area whose voltage fluctuation value is greater than the first preset threshold, and / or the line loss value is greater than the second preset threshold, and / or the current when the equipment is running is greater than the third preset threshold is determined as the target sub-area.

[0083] In some specific embodiments, referring to Figure 3 , Figure 3 for Figure 2 Detailed flow chart of step S201 in FIG. Figure 3 As shown, step S201 includes:

[0084] Step S2011, dividing the substation area based on the electrical distance to obtain a plurality of preliminary sub-areas of the substation area;

[0085] In some specific embodiments, step S2011 includes:

[0086] Calculate the electrical distance between every two distributed photovoltaic system access points in the grid topology structure of the substation area, and obtain multiple electrical distances in the grid topology structure of the substation area;

[0087] Determine an electrical distance among the multiple electrical distances that is less than or equal to the electrical distance threshold as a target electrical distance;

[0088] The areas where the two distributed photovoltaic system access points corresponding to the target electrical distance are located are divided into the same sub-area to obtain a plurality of preliminary sub-areas.

[0089] In this embodiment, the electrical distance d between each two distributed photovoltaic system access points in the grid topology of the substation is calculated. ij , and obtain multiple electrical distances in the topological structure of the power grid in the substation area. R ijrepresents the resistance in the line between the distributed photovoltaic system access point i and the distributed photovoltaic system access point j, X ij Represents the reactance in the line between distributed photovoltaic system access point i and distributed photovoltaic system access point j.

[0090] Exemplarily, if the first electrical distance between the distributed photovoltaic system access point 1 and the distributed photovoltaic system access point 2 is less than or equal to the electrical distance threshold; the second electrical distances between the distributed photovoltaic system access point 4 and the distributed photovoltaic system access point 1 and the distributed photovoltaic system access point 2 are all less than the electrical distance threshold; the three electrical distances between the distributed photovoltaic system access point 5 and the distributed photovoltaic system access point 1, the distributed photovoltaic system access point 2 and the distributed photovoltaic system access point 4 are also less than the electrical distance threshold; and the fourth electrical distance between the distributed photovoltaic system access point 1 and the distributed photovoltaic system access point 3 is greater than the electrical distance threshold, then the first electrical distance, the second electrical distance and the third electrical distance are determined as the target electrical distances, and the area where the distributed photovoltaic system access point 1, the distributed photovoltaic system access point 2, the distributed photovoltaic system access point 4 and the distributed photovoltaic system access point 5 corresponding to the target electrical distance are located is divided into the same sub-area. Correspondingly, the area where the distributed photovoltaic system access point 3 and the area where the distributed photovoltaic system access point 1, the distributed photovoltaic system access point 2, the distributed photovoltaic system access point 4 and the distributed photovoltaic system access point 5 are located is not the same sub-area.

[0091] By analogy, the station area can be divided into a plurality of preliminary sub-areas, that is, a plurality of preliminary sub-areas of the station area can be obtained.

[0092] Step S2012, updating the multiple preliminary sub-regions based on voltage sensitivity to obtain multiple updated preliminary sub-regions;

[0093] In some specific embodiments, step S2012 includes:

[0094] For any preliminary sub-region, the voltage sensitivity of each distributed photovoltaic system access point in the region is calculated by a fourth preset formula;

[0095] From the various voltage sensitivities, determine a voltage sensitivity within a preset voltage range as a target voltage sensitivity;

[0096] Determine the area where the distributed photovoltaic system access point corresponding to the target voltage sensitivity is located as the updated preliminary sub-area;

[0097] And so on, multiple updated preliminary sub-regions are obtained;

[0098] Among them, the fourth preset formula is as follows:

[0099]

[0100] Where, ΔV PV (t) represents the voltage sensitivity of the area when the distributed photovoltaic system is connected at time t, P PV (t) represents the active power of the distributed photovoltaic system when it is connected at time t, R loc Indicates the resistance of the line at the access point of the distributed photovoltaic system, Q PV (t) represents the reactive power of the distributed photovoltaic system when it is connected at time t, X loc Represents the reactance of the line at the access point of the distributed photovoltaic system, V nom is the rated voltage.

[0101] In this embodiment, the same preliminary sub-region where the distributed photovoltaic system access point 1, the distributed photovoltaic system access point 2, the distributed photovoltaic system access point 4 and the distributed photovoltaic system access point 5 are located is marked as the first preliminary sub-region. Through the fourth preset formula:

[0102]

[0103] The voltage sensitivity of each distributed photovoltaic system access point in the first preliminary sub-area is calculated.

[0104] From the voltage sensitivity of each distributed photovoltaic system access point (distributed photovoltaic system access point 1, distributed photovoltaic system access point 2, distributed photovoltaic system access point 4 and distributed photovoltaic system access point 5) in the first preliminary sub-area, a voltage sensitivity within a preset voltage range is determined as a target voltage sensitivity.

[0105] Exemplarily, if only the voltage sensitivity of distributed photovoltaic system access point 5 is not within the preset voltage range, the voltage sensitivities at distributed photovoltaic system access point 1, distributed photovoltaic system access point 2 and distributed photovoltaic system access point 4 are determined as target voltage sensitivities.

[0106] It is determined that the area where the distributed photovoltaic system access points (distributed photovoltaic system access point 1, distributed photovoltaic system access point 2 and distributed photovoltaic system access point 4) corresponding to the target voltage sensitivity are located is the updated first preliminary sub-area.

[0107] And so on, multiple updated preliminary sub-regions are obtained.

[0108] Step S2013, based on the geographical location of each distributed photovoltaic system access point in each of the preliminary sub-regions, optimizing the updated multiple preliminary sub-regions to obtain multiple sub-regions of the optimized station area.

[0109] In some specific embodiments, step S2013 includes:

[0110] For any updated preliminary sub-region, calculate the distance between the geographical locations of every two distributed photovoltaic system access points in the region to obtain multiple geographical location distances;

[0111] From the various geographical location distances, determine the geographical location distance within a preset geographical distance range as the target geographical location distance;

[0112] Determine the area where the distributed photovoltaic system access point corresponding to the target geographical location is located as the sub-area of ​​the optimized substation area;

[0113] And so on, multiple sub-areas of the optimized stage area are obtained.

[0114] In this embodiment, the updated first preliminary sub-region is the region where the distributed photovoltaic system access point 1, the distributed photovoltaic system access point 2 and the distributed photovoltaic system access point 4 are located. For the updated first preliminary sub-region, the distance between the geographical locations of every two distributed photovoltaic system access points in the first preliminary sub-region is calculated to obtain multiple geographical location distances.

[0115] If, among the geographical location distances, the distance between the geographical locations of distributed photovoltaic system access point 1 and distributed photovoltaic system access point 2 is within the preset geographical distance range, and the distances between distributed photovoltaic system access point 4 and the geographical locations of distributed photovoltaic system access point 1 and distributed photovoltaic system access point 2 are not within the preset geographical distance range, then the geographical location distance between distributed photovoltaic system access point 1 and distributed photovoltaic system access point 2 is determined to be the target geographical location distance, and the area where the distributed photovoltaic system access points (distributed photovoltaic system access point 1 and distributed photovoltaic system access point 2) corresponding to the target geographical location distance are located is determined to be the first sub-area of ​​the optimized substation.

[0116] In another embodiment, if the distance between the geographical locations of the distributed photovoltaic system access point 1 and the distributed photovoltaic system access point 2 is within a preset geographical distance range, the distance between the geographical locations of the distributed photovoltaic system access point 2 and the distributed photovoltaic system access point 4 is within a preset geographical distance range, and the distance between the geographical locations of the distributed photovoltaic system access point 1 and the distributed photovoltaic system access point 4 is not within the preset geographical distance range, then the geographical location distance between the distributed photovoltaic system access point 1 and the distributed photovoltaic system access point 2 and the geographical location distance between the distributed photovoltaic system access point 2 and the distributed photovoltaic system access point 4 are determined to be the target geographical location distance, then the area where the distributed photovoltaic system access point 1 and the distributed photovoltaic system access point 2 are located and the area where the distributed photovoltaic system access point 2 and the distributed photovoltaic system access point 4 are located are determined to be sub-areas of the optimized substation, that is, based on the geographical location of each distributed photovoltaic system access point in the updated first preliminary sub-area, after optimizing the updated first preliminary sub-area, the updated first preliminary sub-area is divided into 2 sub-areas.

[0117] By analogy, multiple sub-areas of the optimized station area can be obtained.

[0118] Step S30: optimizing the target sub-area based on the distributed photovoltaic carrying capacity optimization strategy corresponding to the target sub-area.

[0119] In some specific embodiments, step S30 includes:

[0120] For a first target sub-region where the voltage fluctuation value is greater than a first preset threshold, identifying a target load node in the first target sub-region where the voltage sensitivity is less than a voltage sensitivity threshold;

[0121] The distributed photovoltaic system access point in the first target sub-area is replaced by the target load node.

[0122] In this embodiment, for the first target sub-area where the voltage fluctuation value is greater than the first preset threshold, a new photovoltaic access point location is determined to better disperse the load and reduce the voltage impact on the original power grid, and the photovoltaic power distribution of the access point is adjusted according to the power grid topology to ensure that the optimized access does not affect other parts of the power grid. Specifically, the target load node whose voltage sensitivity is less than the voltage sensitivity threshold in the first target sub-area is identified, and the distributed photovoltaic system access point in the first target sub-area is preferentially replaced with the target load node.

[0123] In some specific embodiments, step S30 further includes:

[0124] For the second target sub-area where the line loss value is greater than the second preset threshold, the load in the second target sub-area is evenly distributed to other lines or nodes.

[0125] In this embodiment, for the second target sub-area where the line loss value is greater than the second preset threshold, the load distribution strategy is optimized, the power flow within the second target sub-area is readjusted, the power flow of the high-load line is reduced, the load distribution within the second target sub-area is balanced, and the line loss is reduced.

[0126] Specifically, by changing the power distribution, the load in the second target sub-area is evenly distributed to other lines or nodes, and load scheduling is used to optimize power flow, ensure load dispersion, and avoid line load imbalance. By accurately calculating and scheduling line losses, line energy losses are significantly reduced, power transmission efficiency is improved, the service life of low-voltage lines is extended, and the maintenance cost of the power grid is reduced.

[0127] In some specific embodiments, step S30 further includes:

[0128] For a third target sub-area where the current of the equipment when running is greater than a third preset threshold, calculate the load peak and average load of the equipment in the third target sub-area;

[0129] Determine that the load device when the load peak value and / or average load is greater than the maximum discharge current is the device to be optimized;

[0130] Starting a discharge mode for an energy storage system within a preset distance from the device to be optimized;

[0131] The load peak is The average load is T is the running time, unit: h, I sb (t) represents the instantaneous load value at time t.

[0132] In this embodiment, in the third target sub-area where the equipment protection mechanism is imperfect, that is, the current when the equipment is running is greater than the third preset threshold, energy storage equipment is added to balance supply and demand through the charging and discharging function of the energy storage system, reduce equipment load pressure, and avoid equipment overload or frequent activation of the protection mechanism.

[0133] Specifically, the load peak and average load of the equipment in the third target sub-area are calculated, where the load peak is The average load is T is the running time, unit: h, I sb (t) represents the instantaneous load value at time t. The equipment whose load peak value and / or average load is greater than the maximum discharge current is determined as the equipment to be optimized.

[0134] An energy storage system is deployed near high-load equipment, that is, within a preset distance centered on the equipment to be optimized, and the discharge mode of the energy storage system within the preset distance from the equipment to be optimized is turned on, thereby balancing the supply and demand of the power grid and reducing the load on the equipment to be optimized.

[0135] Furthermore, when the distributed photovoltaic system generates excess power, the energy storage system is charged, and by adjusting the charging and discharging operations of the energy storage system, it is ensured that the equipment load remains within a safe range, avoiding frequent activation of the protection mechanism.

[0136] In this embodiment, based on the characteristic analysis results of the substation, the constraint conditions of the substation are determined, and the constraint conditions include voltage fluctuation constraint conditions, line loss constraint conditions and equipment protection constraint conditions. The key factors affecting the operation stability of the substation can be accurately identified, and the operation status of the substation under different loads and photovoltaic power generation scenarios can be dynamically reflected. The voltage fluctuation is effectively controlled, the line loss is optimized, and the equipment is ensured to operate within the safe load range. This not only improves the photovoltaic access carrying capacity of the substation, but also significantly improves the overall safety and efficiency of the power grid, ensuring the long-term stability of the substation power grid under various operating conditions.

[0137] The target sub-area is determined according to the constraint conditions, wherein the target sub-area is a sub-area with unqualified carrying capacity among the sub-areas contained in the substation, ensuring the balanced optimization of voltage fluctuation, line loss and equipment load in each sub-area after photovoltaic access; based on the distributed photovoltaic carrying capacity optimization strategy corresponding to the target sub-area, the target sub-area is optimized, which not only significantly reduces voltage fluctuations, ensures voltage stability, and improves the system's carrying capacity for large-scale small-capacity photovoltaic access, but also significantly reduces line energy loss, improves power transmission efficiency, and extends the service life of low-voltage lines, reducing the maintenance cost of the power grid. Through this embodiment, the technical problem of low optimization effect on distributed photovoltaic system access in the related technology is solved.

[0138] In a second aspect, an embodiment of the present invention further provides a system for optimizing the distributed photovoltaic carrying capacity in a substation area.

[0139] In one embodiment, referring to Figure 4 , Figure 4 This is a functional module diagram of an embodiment of the distributed photovoltaic load capacity optimization system in the substation area of ​​the present invention. Figure 4 As shown in the figure, the distributed photovoltaic carrying capacity optimization system in the area includes:

[0140] A constraint condition determination unit 10, used to determine the constraint conditions of the substation based on the characteristic analysis results of the substation, wherein the constraint conditions include voltage fluctuation constraint conditions, line loss constraint conditions and equipment protection constraint conditions;

[0141] A target sub-region determining unit 20, configured to determine a target sub-region according to the constraint condition, wherein the target sub-region is a sub-region whose carrying capacity is unqualified among the sub-regions included in the station area;

[0142] The optimization unit 30 is used to optimize the target sub-area based on the distributed photovoltaic carrying capacity optimization strategy corresponding to the target sub-area.

[0143] Optionally, in one embodiment, the constraint condition determination unit 10 is used to:

[0144] Based on the total active power and total reactive power of the substation at different times, the voltage fluctuation deviation in the substation at different times is calculated by a first preset formula;

[0145] Select the maximum voltage deviation from the voltage fluctuation deviations in the substation at different times;

[0146] Determine that the voltage fluctuation constraint condition of the substation is that the voltage fluctuation deviation of the substation is less than or equal to the maximum voltage deviation;

[0147] Among them, the first preset formula is as follows:

[0148]

[0149] In the formula, ΔV(t) represents the voltage fluctuation deviation in the substation at time t, P(t) represents the total active power of the substation at time t, R is the line resistance, Q(t) represents the total reactive power of the substation at time t, X is the line reactance, V nom is the rated voltage.

[0150] Optionally, in one embodiment, the constraint condition determination unit 10 is further configured to:

[0151] Based on the active power and reactive power of the energy storage system at time t, the voltage fluctuation deviation is updated by a second preset formula to obtain an updated voltage fluctuation deviation;

[0152] Among them, the second preset formula is as follows:

[0153]

[0154] Where, ΔV adj (t) represents the updated voltage fluctuation deviation in the substation at time t, ΔV(t) represents the voltage fluctuation deviation in the substation at time t, P stor (t) represents the active power of the energy storage system at time t, R is the line resistance, Q stor (t) represents the reactive power of the energy storage system at time t, X is the line reactance, V nom is the rated voltage.

[0155] Optionally, in one embodiment, the constraint condition determination unit 10 is further configured to:

[0156] Based on the total active power and total reactive power of the substation at different times, the line loss of the substation at different times is calculated by a third preset formula;

[0157] Select the maximum line loss from the line losses in the area at different times;

[0158] Determine that the line loss constraint condition of the substation area is that the line loss in the substation area is less than or equal to the maximum line loss;

[0159] Among them, the third preset formula is as follows:

[0160]

[0161] Where L(t) represents the line loss of the substation at time t, P(t) represents the total active power of the substation at time t, Q(t) represents the total reactive power of the substation at time t, R is the line resistance, and V(t) represents the voltage of the substation at time t.

[0162] Optionally, in one embodiment, the constraint condition determination unit 10 is further configured to:

[0163] Based on the measured data, the current of the access point of the distributed photovoltaic system at different times and the load current of the access point of the distributed photovoltaic system at different times are obtained;

[0164] Calculate the sum of the current of the distributed photovoltaic system access point at different times plus the load current of the distributed photovoltaic system access point at the corresponding time, and use the sum as the total current of the distributed photovoltaic system access point at different times;

[0165] The equipment protection constraint condition of the substation is determined as follows: the total current of the distributed photovoltaic system access point at different times is less than or equal to the maximum load current of the transformer in the substation.

[0166] Optionally, in one embodiment, the target sub-region determining unit 20 is configured to:

[0167] Divide the substation area according to electrical distance, voltage sensitivity and geographical location to obtain multiple sub-areas, wherein the priority of division according to electrical distance is higher than that of division according to voltage sensitivity, and the priority of division according to voltage sensitivity is higher than that of division according to geographical location;

[0168] Based on the preset objective function and the constraint conditions, a voltage fluctuation value, a line loss value and a current when the equipment is running are obtained for each of the sub-areas through a multi-objective optimization genetic algorithm;

[0169] Determine as a target sub-area a sub-area in which the voltage fluctuation value is greater than a first preset threshold, and / or the line loss value is greater than a second preset threshold, and / or the current when the device is running is greater than a third preset threshold.

[0170] Optionally, in one embodiment, the target sub-region determining unit 20 is configured to:

[0171] Divide the substation area based on electrical distance to obtain multiple preliminary sub-areas of the substation area;

[0172] updating the plurality of preliminary sub-regions based on voltage sensitivity to obtain a plurality of updated preliminary sub-regions;

[0173] Based on the geographical location of each distributed photovoltaic system access point in each of the preliminary sub-areas, the updated multiple preliminary sub-areas are optimized to obtain multiple sub-areas of the optimized station area.

[0174] Optionally, in one embodiment, the target sub-region determining unit 20 is configured to:

[0175] Calculate the electrical distance between every two distributed photovoltaic system access points in the grid topology structure of the substation area, and obtain multiple electrical distances in the grid topology structure of the substation area;

[0176] Determine an electrical distance among the multiple electrical distances that is less than or equal to the electrical distance threshold as a target electrical distance;

[0177] The areas where the two distributed photovoltaic system access points corresponding to the target electrical distance are located are divided into the same sub-area to obtain a plurality of preliminary sub-areas.

[0178] Optionally, in one embodiment, the target sub-region determining unit 20 is configured to:

[0179] For any preliminary sub-region, the voltage sensitivity of each distributed photovoltaic system access point in the region is calculated by a fourth preset formula;

[0180] From the various voltage sensitivities, determine a voltage sensitivity within a preset voltage range as a target voltage sensitivity;

[0181] Determine the area where the distributed photovoltaic system access point corresponding to the target voltage sensitivity is located as the updated preliminary sub-area;

[0182] And so on, multiple updated preliminary sub-regions are obtained;

[0183] Among them, the fourth preset formula is as follows:

[0184]

[0185] Where, ΔV PV(t) represents the voltage sensitivity of the area when the distributed photovoltaic system is connected at time t, P PV (t) represents the active power of the distributed photovoltaic system when it is connected at time t, R loc Indicates the resistance of the line at the access point of the distributed photovoltaic system, Q PV (t) represents the reactive power of the distributed photovoltaic system when it is connected at time t, X loc Represents the reactance of the line at the access point of the distributed photovoltaic system, V nom is the rated voltage.

[0186] Optionally, in one embodiment, the target sub-region determining unit 20 is configured to:

[0187] For any updated preliminary sub-region, calculate the distance between the geographical locations of every two distributed photovoltaic system access points in the region to obtain multiple geographical location distances;

[0188] From the various geographical location distances, determine the geographical location distance within a preset geographical distance range as the target geographical location distance;

[0189] Determine the area where the distributed photovoltaic system access point corresponding to the target geographical location is located as the sub-area of ​​the optimized substation area;

[0190] And so on, multiple sub-areas of the optimized stage area are obtained.

[0191] Optionally, in one embodiment, the objective function includes: a first objective function, a second objective function and a third objective function;

[0192] Among them, the first objective function Where f1 represents the square sum of voltage fluctuations, V actual,i (t) represents the actual voltage of the load node in the i-th sub-region at time t, V nom,i represents the rated voltage in the i-th sub-area;

[0193] The second objective function In the formula, f2 represents the sum of line losses in the ith sub-area, T represents the operation time of the substation, and E i represents the load node power set in the i-th sub-area, I ij (t) represents the current of line j in the i-th sub-area at time t, R ij represents the resistance of line j in the ith sub-region;

[0194] The third objective function Where f3 represents the adjustment function when the equipment load exceeds the maximum load limit of the transformer, I load,i (t) represents the load current at time t in the i-th sub-area, IPV,i (t) represents the current at the access point of the distributed photovoltaic system in the i-th sub-region at time t, I max,i It represents the maximum load current of the transformer in the i-th sub-area, and max() is the maximum value function.

[0195] Optionally, in one embodiment, the optimization unit 30 is used to:

[0196] For a first target sub-region where the voltage fluctuation value is greater than a first preset threshold, identifying a target load node in the first target sub-region where the voltage sensitivity is less than a voltage sensitivity threshold;

[0197] The distributed photovoltaic system access point in the first target sub-area is replaced by the target load node.

[0198] Optionally, in one embodiment, the optimization unit 30 is used to:

[0199] For the second target sub-area where the line loss value is greater than the second preset threshold, the load in the second target sub-area is evenly distributed to other lines or nodes.

[0200] Optionally, in one embodiment, the optimization unit 30 is used to:

[0201] For a third target sub-area where the current of the equipment when running is greater than a third preset threshold, calculate the load peak and average load of the equipment in the third target sub-area;

[0202] Determine that the load device when the load peak value and / or average load is greater than the maximum discharge current is the device to be optimized;

[0203] Starting a discharge mode for an energy storage system within a preset distance from the device to be optimized;

[0204] The load peak is The average load is T is the running time, unit: h, I sb (t) represents the instantaneous load value at time t.

[0205] Among them, the functional implementation of each module in the above-mentioned distributed photovoltaic carrying capacity optimization system corresponds to the steps in the above-mentioned distributed photovoltaic carrying capacity optimization method embodiment, and its functions and implementation processes will not be repeated here one by one.

[0206] In a third aspect, the embodiment of the present invention further provides a distributed photovoltaic load capacity optimization device in a metropolitan area, the structure of which is as follows: Figure 5 As shown, it includes: a memory and a processor, and the processor is used to read and execute the computer program stored in the memory to implement the aforementioned method for optimizing the distributed photovoltaic carrying capacity in a substation.

[0207] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed, the aforementioned method for optimizing the distributed photovoltaic carrying capacity of a substation is implemented.

[0208] In a fifth aspect, an embodiment of the present invention provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the various processes of the above-mentioned distributed photovoltaic carrying capacity optimization method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0209] Finally, it should be noted that: in some processes described in the embodiments of the present invention, multiple operations or steps appearing in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present invention or may be executed in parallel, and the sequence number of the operation is only used to distinguish between different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0210] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing distributed photovoltaic carrying capacity in a metropolitan area, characterized in that: The method comprises: Based on the characteristic analysis results of the substation area, determining the constraint conditions of the substation area, wherein the constraint conditions include voltage fluctuation constraint conditions, line loss constraint conditions and equipment protection constraint conditions; Determine a target sub-area according to the constraint condition, wherein the target sub-area is a sub-area whose carrying capacity is unqualified among the sub-areas included in the station area; The target sub-area is optimized based on the distributed photovoltaic carrying capacity optimization strategy corresponding to the target sub-area.

2. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 1, characterized in that: Based on the characteristic analysis results of the substation, determine the voltage fluctuation constraints of the substation, including: Based on the total active power and total reactive power of the substation at different times, the voltage fluctuation deviation in the substation at different times is calculated by a first preset formula; Select the maximum voltage deviation from the voltage fluctuation deviations in the substation at different times; Determine that the voltage fluctuation constraint condition of the substation is that the voltage fluctuation deviation of the substation is less than or equal to the maximum voltage deviation; Among them, the first preset formula is as follows: In the formula, ΔV(t) represents the voltage fluctuation deviation in the substation at time t, P(t) represents the total active power in the substation at time t, R is the line resistance, Q(t) represents the total reactive power in the substation at time t, X is the line reactance, V nom is the rated voltage.

3. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 2, characterized in that: After calculating the voltage fluctuation deviation in the substation at different times through the first preset formula based on the total active power and total reactive power of the substation at different times, the voltage fluctuation deviation is updated to obtain the updated voltage fluctuation deviation, including: Based on the active power and reactive power of the energy storage system at time t, the voltage fluctuation deviation is updated by a second preset formula to obtain an updated voltage fluctuation deviation; Among them, the second preset formula is as follows: Where, ΔV adj (t) represents the updated voltage fluctuation deviation in the substation at time t, ΔV(t) represents the voltage fluctuation deviation in the substation at time t, P stor (t) represents the active power of the energy storage system at time t, R is the line resistance, Q stor (t) represents the reactive power of the energy storage system at time t, X is the line reactance, V nom is the rated voltage.

4. The method for optimizing distributed photovoltaic carrying capacity in a metropolitan area according to claim 1, characterized in that: Based on the characteristic analysis results of the substation area, determine the line loss constraints of the substation area, including: Based on the total active power and total reactive power of the substation at different times, the line loss of the substation at different times is calculated by a third preset formula; Select the maximum line loss from the line losses in the area at different times; Determine that the line loss constraint condition of the substation area is that the line loss in the substation area is less than or equal to the maximum line loss; Among them, the third preset formula is as follows: Where L(t) represents the line loss of the substation at time t, P(t) represents the total active power of the substation at time t, Q(t) represents the total reactive power of the substation at time t, R is the line resistance, and V(t) represents the voltage of the substation at time t.

5. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 1, characterized in that: Based on the characteristic analysis results of the substation, determine the equipment protection constraints of the substation, including: Based on the measured data, the current of the access point of the distributed photovoltaic system at different times and the load current of the access point of the distributed photovoltaic system at different times are obtained; Calculate the sum of the current of the distributed photovoltaic system access point at different times and the load current of the distributed photovoltaic system access point at the corresponding time, and use the sum as the total current of the distributed photovoltaic system access point at different times; The equipment protection constraint condition of the substation is determined as follows: the total current of the distributed photovoltaic system access point at different times is less than or equal to the maximum load current of the transformer in the substation.

6. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 1, characterized in that: The step of determining the target sub-region according to the constraint condition comprises: Divide the substation area according to electrical distance, voltage sensitivity and geographical location to obtain multiple sub-areas, wherein the priority of division according to electrical distance is higher than that of division according to voltage sensitivity, and the priority of division according to voltage sensitivity is higher than that of division according to geographical location; Based on the preset objective function and the constraint conditions, a voltage fluctuation value, a line loss value and a current when the equipment is running are obtained for each of the sub-areas through a multi-objective optimization genetic algorithm; Determine as a target sub-area a sub-area in which the voltage fluctuation value is greater than a first preset threshold, and / or the line loss value is greater than a second preset threshold, and / or the current when the device is running is greater than a third preset threshold.

7. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 6, characterized in that: The substation area is divided according to electrical distance, voltage sensitivity and geographical location to obtain multiple sub-areas, including: Divide the substation area based on electrical distance to obtain multiple preliminary sub-areas of the substation area; updating the plurality of preliminary sub-regions based on voltage sensitivity to obtain a plurality of updated preliminary sub-regions; Based on the geographical location of each distributed photovoltaic system access point in each of the preliminary sub-areas, the updated multiple preliminary sub-areas are optimized to obtain multiple sub-areas of the optimized station area.

8. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 7, characterized in that: The step of dividing the station area based on the electrical distance to obtain a plurality of preliminary sub-areas of the station area includes: Calculate the electrical distance between every two distributed photovoltaic system access points in the grid topology structure of the substation area, and obtain multiple electrical distances in the grid topology structure of the substation area; Determine an electrical distance among the multiple electrical distances that is less than or equal to the electrical distance threshold as a target electrical distance; The areas where the two distributed photovoltaic system access points corresponding to the target electrical distance are located are divided into the same sub-area to obtain a plurality of preliminary sub-areas.

9. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 7, characterized in that: The step of updating the multiple preliminary sub-regions based on voltage sensitivity to obtain the updated multiple preliminary sub-regions includes: For any preliminary sub-region, the voltage sensitivity of each distributed photovoltaic system access point in the region is calculated by a fourth preset formula; From the various voltage sensitivities, determine a voltage sensitivity within a preset voltage range as a target voltage sensitivity; Determine the area where the distributed photovoltaic system access point corresponding to the target voltage sensitivity is located as the updated preliminary sub-area; And so on, multiple updated preliminary sub-regions are obtained; Among them, the fourth preset formula is as follows: Where, ΔV PV (t) represents the voltage sensitivity of the area when the distributed photovoltaic system is connected at time t, P PV (t) represents the active power of the distributed photovoltaic system when it is connected at time t, R loc Indicates the resistance of the line at the access point of the distributed photovoltaic system, Q PV (t) represents the reactive power of the distributed photovoltaic system when it is connected at time t, X loc Represents the reactance of the line at the access point of the distributed photovoltaic system, V nom is the rated voltage.

10. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 7, characterized in that: The step of optimizing the updated multiple preliminary sub-regions based on the geographical location of each distributed photovoltaic system access point in each preliminary sub-region to obtain multiple sub-regions of the optimized station area includes: For any updated preliminary sub-region, calculate the distance between the geographical locations of every two distributed photovoltaic system access points in the region to obtain multiple geographical location distances; From the various geographical location distances, determine the geographical location distance within a preset geographical distance range as the target geographical location distance; Determine the area where the distributed photovoltaic system access point corresponding to the target geographical location is located as the sub-area of ​​the optimized substation area; And so on, multiple sub-areas of the optimized stage area are obtained.

11. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 6, characterized in that: The objective function includes: a first objective function, a second objective function and a third objective function; Among them, the first objective function Where f1 represents the square sum of voltage fluctuations, V actual,i (t) represents the actual voltage of the load node in the i-th sub-region at time t, V nom,i represents the rated voltage in the i-th sub-area; The second objective function In the formula, f2 represents the sum of line losses in the ith sub-area, T represents the operation time of the substation, and E i represents the load node power set in the i-th sub-area, I ij (t) represents the current of line j in the i-th sub-area at time t, R ij represents the resistance of line j in the ith sub-region; The third objective function Where f3 represents the adjustment function when the equipment load exceeds the maximum load limit of the transformer, I load,i (t) represents the load current at time t in the i-th sub-area, I PV,i (t) represents the current at the access point of the distributed photovoltaic system in the i-th sub-region at time t, I max,i It represents the maximum load current of the transformer in the i-th sub-area, and max() is the maximum value function.

12. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 1, characterized in that: The optimizing strategy for distributed photovoltaic carrying capacity corresponding to the target sub-area, optimizing the target sub-area, comprises: For a first target sub-region where the voltage fluctuation value is greater than a first preset threshold, identifying a target load node in the first target sub-region where the voltage sensitivity is less than a voltage sensitivity threshold; The distributed photovoltaic system access point in the first target sub-area is replaced by the target load node.

13. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 1, characterized in that: The optimizing strategy for distributed photovoltaic carrying capacity corresponding to the target sub-area, optimizing the target sub-area, comprises: For the second target sub-area where the line loss value is greater than the second preset threshold, the load in the second target sub-area is evenly distributed to other lines or nodes.

14. The method for optimizing distributed photovoltaic load capacity in a metropolitan area according to claim 1, characterized in that: The optimizing strategy for distributed photovoltaic carrying capacity corresponding to the target sub-area, optimizing the target sub-area, comprises: For a third target sub-area where the current of the equipment when running is greater than a third preset threshold, calculate the load peak and average load of the equipment in the third target sub-area; Determine that the load device when the load peak value and / or average load is greater than the maximum discharge current is the device to be optimized; Starting a discharge mode for an energy storage system within a preset distance from the device to be optimized; The load peak is The average load is T is the running time, unit: h, I sb (t) represents the instantaneous load value at time t.

15. A distributed photovoltaic load capacity optimization system in a metropolitan area, characterized in that: The system comprises: A constraint condition determination unit, used to determine the constraint conditions of the substation based on the characteristic analysis results of the substation, wherein the constraint conditions include voltage fluctuation constraint conditions, line loss constraint conditions and equipment protection constraint conditions; A target sub-region determining unit, configured to determine a target sub-region according to the constraint condition, wherein the target sub-region is a sub-region whose carrying capacity is unqualified among the sub-regions included in the station area; The optimization unit is used to optimize the target sub-area based on the distributed photovoltaic carrying capacity optimization strategy corresponding to the target sub-area.

16. The distributed photovoltaic load capacity optimization system in the area according to claim 15, characterized in that: The target sub-region determining unit is used to: Divide the substation area according to electrical distance, voltage sensitivity and geographical location to obtain multiple sub-areas, wherein the priority of division according to electrical distance is higher than that of division according to voltage sensitivity, and the priority of division according to voltage sensitivity is higher than that of division according to geographical location; Based on the preset objective function and the constraint conditions, a voltage fluctuation value, a line loss value and a current when the equipment is running are obtained for each of the sub-areas through a multi-objective optimization genetic algorithm; Determine as a target sub-area a sub-area in which the voltage fluctuation value is greater than a first preset threshold, and / or the line loss value is greater than a second preset threshold, and / or the current when the device is running is greater than a third preset threshold.

17. A distributed photovoltaic load capacity optimization device in a metropolitan area, characterized in that: include: A processor and a memory; the memory is used to store a computer program; The processor is used to operate according to the computer program to implement the steps of the method for optimizing the distributed photovoltaic carrying capacity in an area as described in any one of claims 1-14.

18. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed, the steps of the method for optimizing the distributed photovoltaic carrying capacity of a substation as described in any one of claims 1-14 are implemented.

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