Distributed photovoltaic intelligent planning method and related device

Abstract

The invention belongs to the technical field of distributed photovoltaic planning, and discloses a distributed photovoltaic intelligent planning method and a related device. The distributed photovoltaic intelligent planning method comprises the following steps: acquiring power distribution network model data; based on the obtained power distribution network model data, solving the photovoltaic capacity objective function, obtaining a calculation result, and matching the calculation result with power distribution network nodes to obtain a planning result; wherein the photovoltaic capacity objective function takes the photovoltaic maximum capacity which can be accepted by all nodes as an optimization solving objective; in the step of solving, the maximum value of the photovoltaic capacity is firstly set, then the photovoltaic bearing capacity index is analyzed and calculated based on thermal stability, short-circuit current, voltage deviation and harmonic components, constraint conditions are established, and a photovoltaic capacity objective function is solved by adopting a selected optimization solving algorithm. According to the invention, the problem that the operation safety of the power distribution network is threatened in the conventional photovoltaic planning method only based on the feeder capacity can be avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed photovoltaic planning, and in particular relates to a distributed photovoltaic intelligent planning method and related devices. Background Art

[0002] The construction of distributed photovoltaics is an important measure to achieve "carbon peak and carbon neutrality". In recent years, distributed photovoltaics have shown a rapid growth trend. For example, the installed capacity of distributed photovoltaics in the operating area of ​​State Grid Corporation of China has reached 82.108 million kW. It is estimated that the installed capacity of distributed new energy will reach more than 180 million kW in 2025 and more than 350 million kW in 2030.

[0003] The large-scale integration of distributed photovoltaic power into the distribution network increases the intermittency and uncertainty of its output, causing significant fluctuations in the distribution network's power flow. This can lead to insufficient carrying capacity, such as photovoltaic power generation reverse transmission and voltage over-limit. Specifically, the intermittent and uncertain output of distributed photovoltaic power is the root cause of the problem, directly leading to significant fluctuations in the distribution network's power flow. Existing planning methods simply determine the scale of distributed photovoltaic access based on feeder capacity, without fully considering the impact of photovoltaic output characteristics on the distribution network. This results in the distribution network lacking sufficient carrying capacity when faced with situations such as photovoltaic power generation reverse transmission. This insufficient carrying capacity further leads to a series of problems such as voltage over-limit and excessive short-circuit current, ultimately threatening the safe and stable operation of the distribution network. Summary of the Invention

[0004] The present invention aims to provide a distributed photovoltaic intelligent planning method and related apparatus to address one or more of the aforementioned technical problems. The technical solution disclosed in this invention, specifically a distributed photovoltaic intelligent planning technology based on photovoltaic carrying capacity assessment and analysis, can avoid the issues that threaten the operational safety of distribution networks that exist in current photovoltaic planning methods based solely on feeder capacity.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a distributed photovoltaic intelligent planning method, comprising the following steps: Obtain distribution network model data; Solving a photovoltaic capacity objective function based on the acquired distribution network model data to obtain a calculation result; wherein the photovoltaic capacity objective function uses the maximum photovoltaic capacity that can be accommodated by all nodes as an optimization solution target; in the solution step, first setting a maximum photovoltaic capacity, then calculating a photovoltaic carrying capacity index based on thermal stability, short-circuit current, voltage deviation, and harmonic component analysis and establishing constraints, and finally solving the photovoltaic capacity objective function using a selected optimization solution algorithm based on the maximum photovoltaic capacity and the constraints; The calculated results are matched with the distribution network nodes to obtain the maximum photovoltaic capacity of all nodes and serve as the distributed photovoltaic intelligent planning result.

[0006] A further improvement of the technical solution of the present invention is that the expression of the photovoltaic capacity objective function is: ; Where, n is the node number, N is the total number of nodes; For nodes n of PV capacity that can be accommodated.

[0007] A further improvement of the technical solution of the present invention is that, in the step of setting the maximum photovoltaic capacity, the feeder load history curve is first analyzed, and then 60% of the minimum value of the historical maximum load is taken as the maximum photovoltaic capacity.

[0008] A further improvement of the technical solution of the present invention is that in the step of calculating the photovoltaic carrying capacity index based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis and establishing constraint conditions, (1) In the thermal stability analysis, the capacity of distributed power generation can be added in the evaluation area Calculated as follows: ; Where, is the maximum reverse load rate within the preset time period; S e is the actual operating limit of the transformer or line; is the equipment operation margin factor; The calculation expression of each reverse load rate within the preset time period is: ; Where, is the reverse load rate; Provide power for distributed power generation; The equivalent power load at the same moment, that is, the load minus the output of other power sources except distributed power sources; (2) In the short-circuit current analysis, the short-circuit current satisfies the following relationship: ; Where, is the system bus short-circuit current, is the allowable short-circuit current limit; (3) In voltage deviation analysis, the percentage of voltage change is calculated according to the following formula: ; Where, is the percentage of voltage change; 、 are the resistance and reactance components of the grid impedance respectively; The maximum positive and negative reactive power values ​​calculated based on the required values ​​of the power factor at the grid connection point of different types of distributed power sources in GB / T 33593; is the rated voltage of the busbar; (4) In harmonic component analysis: the harmonic current component does not exceed the allowable value specified in "Power Quality Public Grid Harmonics" (GB / T14549-1993); among which, Public connection point i The first distributed photovoltaic h The allowable values ​​of subharmonic current are: ; Where, The minimum short-circuit capacity of the common connection point; is the benchmark short-circuit capacity; For the h Allowable value of subharmonic current; The capacity of the electricity agreement; is the capacity of the power supply equipment; a is the phase superposition coefficient; The sum of the harmonic currents of multiple distributed photovoltaics at the grid connection point is: ; Where, For public connection points h order harmonic current distortion; N wt The number of devices connected to the public connection point; For the i devices h Subharmonic current distortion; For the i The transformation ratio of the equipment transformer; is the margin factor.

[0009] A further improvement of the technical solution of the present invention is that, in the step of solving the photovoltaic capacity objective function based on the maximum photovoltaic capacity and the constraint conditions using the selected optimization solution algorithm, an adaptive legacy mutation algorithm is specifically used for solving the problem. The solution steps are: Perform population initialization and parameter initialization; The fitness function is used to evaluate the population; the fitness value is defined according to the objective function photovoltaic capacity, and the constraints are calculated using the photovoltaic carrying capacity index to determine whether the stopping conditions are met. If so, the population is stopped and the optimal solution is output. Otherwise, cross mutation is continued.

[0010] A second aspect of the present invention provides a distributed photovoltaic intelligent planning system, comprising: Data acquisition module, used to obtain distribution network model data; An optimization solution module is used to solve the photovoltaic capacity objective function based on the obtained distribution network model data to obtain a calculation result; wherein the photovoltaic capacity objective function takes the maximum photovoltaic capacity that can be accommodated by all nodes as the optimization solution target; in the solution step, the maximum photovoltaic capacity is first set, and then the photovoltaic carrying capacity index is calculated based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis and constraint conditions are established. Finally, based on the maximum photovoltaic capacity and the constraint conditions, the photovoltaic capacity objective function is solved using the selected optimization solution algorithm; The matching planning module is used to match the calculated results with the distribution network nodes to obtain the maximum photovoltaic capacity of all nodes and use it as the distributed photovoltaic intelligent planning result.

[0011] A further improvement of the technical solution of the present invention is that the expression of the photovoltaic capacity objective function is: ; Where, n is the node number, N is the total number of nodes; For nodes n of PV capacity that can be accommodated.

[0012] A further improvement of the technical solution of the present invention is that, in the step of setting the maximum photovoltaic capacity, the feeder load history curve is first analyzed, and then 60% of the minimum value of the historical maximum load is taken as the maximum photovoltaic capacity.

[0013] A further improvement of the technical solution of the present invention is that in the step of calculating the photovoltaic carrying capacity index based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis and establishing constraint conditions, (1) In the thermal stability analysis, the capacity of distributed power generation can be added in the evaluation area Calculated as follows: ; Where, is the maximum reverse load rate within the preset time period; S e is the actual operating limit of the transformer or line; is the equipment operation margin factor; The calculation expression of each reverse load rate within the preset time period is: ; Where, is the reverse load rate; Provide power for distributed power generation; The equivalent power load at the same moment, that is, the load minus the output of other power sources except distributed power sources; (2) In the short-circuit current analysis, the short-circuit current satisfies the following relationship: ; Where, is the system bus short-circuit current, is the allowable short-circuit current limit; (3) In voltage deviation analysis, the percentage of voltage change is calculated according to the following formula: ; Where, is the percentage of voltage change; 、 are the resistance and reactance components of the grid impedance respectively; The maximum positive and negative reactive power values ​​calculated based on the required values ​​of the power factor at the grid connection point of different types of distributed power sources in GB / T 33593; is the rated voltage of the busbar; (4) In harmonic component analysis: the harmonic current component does not exceed the allowable value specified in "Power Quality Public Grid Harmonics" (GB / T14549-1993); among which, Public connection point i The first distributed photovoltaic h The allowable values ​​of subharmonic current are: ; Where, The minimum short-circuit capacity of the common connection point; is the benchmark short-circuit capacity; For the h Allowable value of subharmonic current; The capacity of the electricity agreement; is the capacity of the power supply equipment; a is the phase superposition coefficient; The sum of the harmonic currents of multiple distributed photovoltaics at the grid connection point is: ; Where, For public connection points h order harmonic current distortion; N wt The number of devices connected to the public connection point; For the i devices h Subharmonic current distortion; For the i The transformation ratio of the equipment transformer; is the margin factor.

[0014] A further improvement of the technical solution of the present invention is that, in the step of solving the photovoltaic capacity objective function based on the maximum photovoltaic capacity and the constraint conditions using the selected optimization solution algorithm, an adaptive legacy mutation algorithm is specifically used for solving the problem. The solution steps are: Perform population initialization and parameter initialization; The fitness function is used to evaluate the population; the fitness value is defined according to the objective function photovoltaic capacity, and the constraints are calculated using the photovoltaic carrying capacity index to determine whether the stopping conditions are met. If so, the population is stopped and the optimal solution is output. Otherwise, cross mutation is continued.

[0015] In a third aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the distributed photovoltaic intelligent planning method as described in any one of the first aspects of the present invention is implemented.

[0016] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the distributed photovoltaic intelligent planning method as described in any one of the first aspects of the present invention.

[0017] Compared with the prior art, the present invention has the following beneficial effects: In response to the problems existing in the existing photovoltaic planning methods that are based only on feeder capacity, the present invention discloses a distributed photovoltaic intelligent planning method, specifically a distributed photovoltaic intelligent planning technology based on photovoltaic carrying capacity assessment and analysis, which can avoid the problems that threaten the safe operation of the distribution network that exist in the current photovoltaic planning methods that are based only on feeder capacity. Specifically and explanatoryally, in the technical solution disclosed by the present invention, an objective function is determined, and an optimization solution is performed with the distribution network operation safety conditions as constraints, and with the maximum distributed photovoltaic capacity of the node as a constraint condition, to obtain a distributed photovoltaic planning result. The present invention achieves the safe operation of the distribution network while ensuring that the feeder can accommodate the maximum distributed photovoltaic capacity. It should be further emphasized that in the technical solution of the present invention, a technical means with the distribution network operation safety conditions as constraints is proposed based on the analysis of the distribution network thermal stability, short-circuit current, voltage deviation and harmonic components. It avoids the problems of distribution network operation safety caused by the existing planning method relying solely on feeder capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 1 is a flow chart of a distributed photovoltaic intelligent planning method according to an embodiment of the present invention; Figure 2 1 is a schematic diagram of a feeder structure in a specific embodiment of the present invention; Figure 3 is a schematic diagram of a photovoltaic intelligent planning process based on a genetic variation algorithm in a specific embodiment of the present invention; Figure 4 Schematic diagram of a distributed photovoltaic intelligent planning system in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments and technical solutions are only part of the embodiments of the present invention, not all of the embodiments.

[0021] All other embodiments obtained by persons of ordinary skill in the art based on the technical solutions disclosed in the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatuses.

[0022] See also Figure 1 The embodiment of the present invention provides a distributed photovoltaic intelligent planning method, specifically a distributed photovoltaic intelligent planning method based on photovoltaic carrying capacity evaluation and analysis, comprising the following steps: Step 1: Obtain distribution network model data; In the specific exemplary technical solution of the embodiment of the present invention, the following information is obtained from the distribution network operation system: Figure 2 The distribution network topology data shown; at the same time, refer to Figure 2 Parameters of distribution network topology elements (for example, node information, load information, line parameters, etc.) are obtained from the distribution network management system to construct a distribution network model.

[0023] Step 2: Solve the photovoltaic capacity objective function based on the distribution network model data obtained in step 1 to obtain a calculation result; wherein the photovoltaic capacity objective function takes the maximum photovoltaic capacity that can be accommodated by all nodes as the optimization solution target; in the solution step, first set the maximum photovoltaic capacity, then calculate the photovoltaic carrying capacity index based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis and establish constraints, and finally, based on the maximum photovoltaic capacity and the constraints, use the selected optimization solution algorithm to solve the photovoltaic capacity objective function; In the specific exemplary technical solution of the embodiment of the present invention, in the constructed photovoltaic capacity objective function, the maximum photovoltaic capacity that can be accommodated by all nodes is used as the optimization solution target, and the function is expressed as: ; Where, n is the node number; N is the total number of nodes; For nodes n of PV capacity that can be accommodated.

[0024] Step 3: Match the calculated results with the distribution network nodes to obtain the maximum photovoltaic capacity of all nodes (i.e., the distributed photovoltaic intelligent planning results).

[0025] In the technical solutions disclosed in the embodiments of the present invention, distribution network topology data and parameters of topology elements (such as node information, load information, and line parameters) are obtained from the distribution network operation and management systems, respectively, to construct a complete distribution network model. This multi-source data integration approach comprehensively reflects the actual operating conditions of the distribution network, providing a solid foundation for subsequent planning calculations and avoiding planning errors caused by missing or inaccurate data. The constructed photovoltaic capacity objective function optimizes the maximum photovoltaic capacity that all nodes can accommodate, fully considering the potential of distributed photovoltaic integration into the distribution network. While ensuring the safe and stable operation of the distribution network, it maximizes the scale of distributed photovoltaic integration, helps improve the utilization rate of clean energy, and promotes the optimization and adjustment of the energy structure. When solving the photovoltaic capacity objective function, the photovoltaic carrying capacity index is calculated based on thermal stability, short-circuit current, voltage deviation, and harmonic component analysis, and constraints are established. These constraints cover key aspects of distribution network operation and can ensure the safe and stable operation of the distribution network after the integration of distributed photovoltaics. For example, thermal stability constraints can prevent damage to lines and equipment due to overload; short-circuit current constraints can avoid serious damage to equipment caused by short-circuit faults; voltage deviation constraints can ensure power supply quality; and harmonic component constraints can reduce adverse effects on the grid and user equipment. The calculated results are matched to the distribution network nodes to obtain the maximum photovoltaic capacity of all nodes, which is the result of distributed photovoltaic intelligent planning. This node-level planning result is highly accurate and practical, providing specific guidance for the actual integration of distributed photovoltaic systems, allowing planners and operators to optimize the layout and capacity configuration of photovoltaic power plants based on the carrying capacity of different nodes.

[0026] The technical solution of the embodiment of the present invention discloses a distributed photovoltaic intelligent planning method based on photovoltaic carrying capacity assessment and analysis, and constructs a comprehensive photovoltaic carrying capacity assessment system. Compared with traditional distributed photovoltaic planning methods, the present invention focuses more on the actual carrying capacity of the distribution network. Through multi-faceted carrying capacity index analysis and constraint establishment, it achieves an organic combination of distributed photovoltaic access capacity and the safe and stable operation of the distribution network, improves the scientificity and rationality of planning, and ensures the safety of grid operation. During the planning process, the present invention considers the impact of the intermittent and uncertain photovoltaic output on the carrying capacity of the distribution network. By establishing a photovoltaic capacity objective function and constraints, and using an optimization solution algorithm for dynamic solution, this dynamic planning approach can automatically adjust the photovoltaic access capacity according to the real-time operating status of the distribution network and the access requirements of distributed photovoltaics, thereby improving the adaptability and flexibility of planning. The constructed photovoltaic capacity objective function uses the photovoltaic capacity that can be accommodated by the node as a variable, and obtains the maximum photovoltaic capacity that can be accommodated by all nodes by summing them. This objective function construction method can intuitively reflect the acceptance capacity of different nodes for distributed photovoltaics, facilitating the analysis and evaluation of planning results. At the same time, it also provides clear quantitative indicators for the subsequent optimization and adjustment of distributed photovoltaic access solutions. In summary, the distributed photovoltaic intelligent planning method disclosed in the embodiment of the present invention has made significant progress in data acquisition, planning objectives, constraints, planning results, etc., and can provide a scientific, reasonable and efficient solution for the planning of distributed photovoltaic access to the distribution network, and can ensure the safe operation of the power grid.

[0027] In the specific exemplary technical solution of the embodiment of the present invention, based on the above embodiment, the step of setting the maximum photovoltaic capacity includes: analyzing the feeder load history curve, and according to the latest local regulations, taking 60% of the minimum value of the historical maximum load as the maximum photovoltaic capacity, which can ensure the safe operation of the distribution network.

[0028] In a specific exemplary technical solution of an embodiment of the present invention, based on the above embodiments, the steps of calculating the photovoltaic carrying capacity index and establishing the constraint conditions based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis specifically include: 1) Thermal stability assessment: Thermal stability assessment should calculate the reverse load rate based on factors such as grid operation mode, transmission and transformation equipment limits, load conditions, power generation conditions, and distributed power output characteristics. , reverse load rate It should be calculated according to the following formula: ; Where, Provide power for distributed power generation; is the equivalent power load at the same moment, that is, the load minus the output of other power sources except the distributed power source; S eIt is the actual operating limit of the transformer or line.

[0029] Additional distributed power capacity can be added in the assessment area Calculated as follows: ; Where, is the equipment operating margin coefficient, generally taken as 0.8; is the maximum reverse load rate, that is, within one day Maximum value.

[0030] 2) Short-circuit current assessment: The assessment should be conducted based on the current status of the short-circuit current under the maximum operating mode of the system within the assessment scope and the capacity of the distributed power supply to be verified. It should satisfy the following formula: ; Where, is the system bus short-circuit current, is the allowable short-circuit current limit.

[0031] 3) Voltage deviation assessment: It should be calculated based on the capacity of the distributed power supply to be verified and the requirements of GB / T 33593. The calculation formula is as follows: ; Where, 、 are the resistance and reactance components of the grid impedance. In a specific exemplary technical solution, the grid resistance component can be ignored in a high-voltage grid; The maximum positive and negative reactive power values ​​calculated based on the required values ​​of the power factor at the grid connection point of different types of distributed power sources in GB / T 33593; is the rated voltage of the bus in this area.

[0032] 4) Harmonic component verification: The harmonic current component cannot exceed the allowable value specified in "Power Quality Public Grid Harmonics" (GB / T 14549-1993); Public connection point i The first distributed photovoltaic h The allowable values ​​of subharmonic current are: ; Where, The minimum short-circuit capacity of the common connection point; is the benchmark short-circuit capacity; For the h Allowable value of subharmonic current; The capacity of the electricity agreement; is the capacity of the power supply equipment; a is the phase superposition coefficient.

[0033] The harmonic currents of multiple distributed photovoltaics at the grid connection point are: ; Where, For public connection points h order harmonic current distortion; N wt The number of devices connected to the public connection point; For the i devices h Subharmonic current distortion; For the i The transformation ratio of the equipment transformer; is the margin coefficient. According to the Electrical Design Manual for Power Engineering, when h is less than or equal to 5, it is 1; when h is between 5 and 10, it is 1.4; when h is greater than or equal to 10, it is 2.

[0034] In a specific exemplary technical solution of an embodiment of the present invention, based on the above embodiments, the step of solving the photovoltaic capacity objective function using a selected optimization algorithm based on the maximum photovoltaic capacity and the constraint conditions, specifically using an adaptive legacy mutation algorithm for solving, includes the following steps: 1) Population initialization: randomly generate m individuals to form the initial population Where, For the m Initialization individuals; interpretatively, a set of initial solutions is created, which represent possible PV capacity configurations; 2) Initialization parameters: determine the chromosome encoding, establish the population size, and parameters such as crossover and mutation probabilities.

[0035] 3) Evaluate the population using a fitness function: Define the fitness value based on the objective function (PV capacity). Use the PV carrying capacity indicator to calculate the constraints (i.e., thermal stability, short-circuit current, voltage deviation, and harmonic component analysis mentioned above). Determine whether the stopping conditions are met. If so, stop and output the optimal solution; otherwise, continue cross-mutation. For example, the fitness value is defined based on the objective function (perhaps maximizing PV capacity). The fitness value of each individual (solution) is calculated. The constraints are calculated using the PV carrying capacity indicator to determine whether each solution satisfies the system's constraints.

[0036] In the embodiment of the present invention, the process of performing crossover mutation is as follows: for two selected parent individuals, randomly selecting a crossover point to exchange gene segments, and then performing crossover.

[0037] Specifically, for example, Parent 1: 0110|1 Parent 2: 1100| Offspring 1: 01100 Offspring 2: 11001 Generate offspring Xchild is a linear combination of two parents, expressed as: X child =αXparent1+(1-α)Xparent2, α∈[0, 1]; Where, X parent1 For the parent generation 1; X parent2 For the parent generation.

[0038] By probability W m Flip a random bit.

[0039] For example, the original individual is 01101 → after mutation, it is 01001.

[0040] In the technical solution of the embodiment of the present invention, an adaptive legacy mutation algorithm is used for solving the problem, which can reasonably plan the scale of distributed photovoltaic installations.

[0041] See also Figure 2 and Figure 3 In a specific exemplary technical solution of the present invention, a distributed photovoltaic intelligent planning method based on photovoltaic carrying capacity evaluation and analysis is provided, comprising the following steps: (S1) Constructing topological model data: forming training data set and validation data set based on historical measurement data; constructing Figure 2 The distribution network system model and parameters are shown.

[0042] (S2) Constructing photovoltaic capacity objective function: According to Figure 2 The structure is set to accept the capacity and the target value to be solved.

[0043] (S3) Setting the maximum photovoltaic capacity: Analyze the feeder load history curve and, in accordance with the latest local regulations, take 60% of the minimum historical maximum load as the maximum photovoltaic capacity.

[0044] (S4) Calculate the photovoltaic carrying capacity index and establish constraint conditions: Calculate the thermal stability assessment, voltage deviation, short-circuit current and harmonic component verification according to the formula provided in S4, and use them as solution constraints.

[0045] (S5) A legacy mutation algorithm is used for solving the problem; wherein, the algorithm parameters are set, and the photovoltaic capacity is taken as the maximum value, and the thermal stability assessment, voltage deviation, short-circuit current and harmonic component verification are used as constraints for solving the problem.

[0046] (S6) Matching calculation results with nodes: Obtain calculation results, match the results with grid nodes, and obtain the maximum photovoltaic capacity of all nodes.

[0047] The technical solution provided by the embodiments of this invention uses analysis of the distribution network's thermal stability, short-circuit current, voltage deviation, and harmonic components to assess the distribution network's photovoltaic capacity. Based on the assessment results, a legacy variation algorithm is used to rationally plan the distributed photovoltaic capacity. Figure 2 The feeder structure is shown, which includes power input points, lines, transformers, general load nodes, and photovoltaic nodes. Figure 3 The PV intelligent planning algorithm process based on a genetic mutation algorithm provided in an embodiment of the present invention includes: constructing a distribution network model and parameters: establishing a mathematical model of the distribution network and determining relevant network parameters; analyzing feeder load curves and determining the upper limit of feeder PV capacity according to regulations: analyzing the feeder load curves and determining the upper limit of PV capacity that can be installed on the feeder according to regulations and standards; establishing an objective function based on maximum PV capacity: establishing an objective function with the goal of maximizing PV capacity; setting upper and lower bounds of the solution variables and model convergence conditions: determining the upper and lower bounds of the optimization variables and setting the model convergence conditions to ensure the stability and effectiveness of the optimization process; setting the number of iterations, i.e., related algorithm parameters: setting the number of algorithm iterations and other relevant parameters; initializing the population and determining the values ​​of the algorithm parameters: initializing the genetic algorithm population and determining the various parameters required by the algorithm; and evaluating the current population using a fitness function: using a fitness function to evaluate individuals in the current population, measuring their performance and determining whether the optimal individual in the current population meets the preset constraints. If the constraints are met, the process ends. If not, the process proceeds to the next step. Perform selection, crossover, and mutation on the population. Selection involves selecting superior individuals based on their fitness; crossover involves performing a crossover on the selected individuals to generate new individuals; and mutation involves performing mutations on the individuals to increase the diversity of the population. To generate a new generation of populations, perform the above operations, return to the previous step, evaluate the current population using the fitness function, and continue iterating.

[0048] Figure 3 The technical solution shown in the figure first sets the photovoltaic operation constraints, solves the target, and then solves the maximum photovoltaic capacity of the node through the genetic mutation algorithm; it uses the adaptive genetic mutation algorithm to optimize the photovoltaic capacity in the distribution network, aiming to maximize the photovoltaic capacity while meeting various constraints. Table 1 shows the calculated Figure 2 Each node can accept the calculated results of the maximum photovoltaic capacity. At the same time, the embodiment of the present invention provides voltage values ​​for each important node (as shown in Table 2), which are stable within the standard voltage range of 1.05-0.95, realizing that the invention satisfies the constraint conditions based on providing the calculated maximum capacity.

[0049] Table 1. Calculation results of the maximum photovoltaic capacity that can be accommodated by each node

[0050] Table 2 Voltage values ​​of important nodes

[0051] The following are device embodiments of the present invention, which can be used to perform the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0052] See also Figure 4 In an embodiment of the present invention, a distributed photovoltaic intelligent planning system is provided, comprising: Data acquisition module, used to obtain distribution network model data; An optimization solution module is used to solve the photovoltaic capacity objective function based on the obtained distribution network model data to obtain a calculation result; wherein the photovoltaic capacity objective function takes the maximum photovoltaic capacity that can be accommodated by all nodes as the optimization solution target; in the solution step, the maximum photovoltaic capacity is first set, and then the photovoltaic carrying capacity index is calculated based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis and constraint conditions are established. Finally, based on the maximum photovoltaic capacity and the constraint conditions, the photovoltaic capacity objective function is solved using the selected optimization solution algorithm; The matching planning module is used to match the calculated results with the distribution network nodes to obtain the maximum photovoltaic capacity of all nodes and use it as the distributed photovoltaic intelligent planning result.

[0053] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory, wherein the memory is configured to store a computer program, the computer program including program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor is the computing core and control core of the terminal and is suitable for implementing one or more instructions, specifically loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used to perform the operations of the distributed photovoltaic intelligent planning method.

[0054] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device within a computer device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media within the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be high-speed Random Access Memory (RAM) or non-volatile memory, such as at least one disk drive. The processor may load and execute the one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the distributed photovoltaic intelligent planning method described in the above-mentioned embodiment.

[0055] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, optical storage, etc.) containing computer-usable program code.

[0056] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A distributed photovoltaic intelligent planning method, characterized in that: The following steps are involved: Obtain distribution network model data; Solving a photovoltaic capacity objective function based on the acquired distribution network model data to obtain a calculation result; wherein the photovoltaic capacity objective function uses the maximum photovoltaic capacity that can be accommodated by all nodes as an optimization solution target; in the solution step, first setting a maximum photovoltaic capacity, then calculating a photovoltaic carrying capacity index based on thermal stability, short-circuit current, voltage deviation, and harmonic component analysis and establishing constraints, and finally solving the photovoltaic capacity objective function using a selected optimization solution algorithm based on the maximum photovoltaic capacity and the constraints; The calculated results are matched with the distribution network nodes to obtain the maximum photovoltaic capacity of all nodes and serve as the distributed photovoltaic intelligent planning result.

2. A distributed photovoltaic intelligent planning method according to claim 1, characterized in that: The expression of the photovoltaic capacity objective function is: ； Where, n is the node number, N is the total number of nodes; For nodes n of PV capacity that can be accommodated.

3. A distributed photovoltaic intelligent planning method according to claim 1, characterized in that: In the step of setting the maximum photovoltaic capacity, the feeder load history curve is first analyzed, and then 60% of the minimum value of the historical maximum load is taken as the maximum photovoltaic capacity.

4. A distributed photovoltaic intelligent planning method according to claim 1, characterized in that: In the step of calculating the photovoltaic carrying capacity index based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis and establishing constraint conditions, (1) In the thermal stability analysis, the capacity of distributed power generation can be added in the evaluation area Calculated as follows: ； Where, is the maximum reverse load rate within the preset time period; S e is the actual operating limit of the transformer or line; is the equipment operation margin factor; The calculation expression of each reverse load rate within the preset time period is: ； Where, is the reverse load rate; Provide power for distributed power generation; The equivalent power load at the same moment, that is, the load minus the output of other power sources except distributed power sources; (2) In the short-circuit current analysis, the short-circuit current satisfies the following relationship: ； Where, is the system bus short-circuit current, is the allowable short-circuit current limit; (3) In voltage deviation analysis, the percentage of voltage change is calculated according to the following formula: ； Where, is the percentage of voltage change; 、 are the resistance and reactance components of the grid impedance respectively; is the maximum reactive positive and negative value; is the rated voltage of the busbar; (4) In harmonic component analysis: the harmonic current component does not exceed the allowable value; among them, Public connection point i The first distributed photovoltaic h The allowable values ​​of subharmonic current are: ； Where, The minimum short-circuit capacity of the common connection point; is the benchmark short-circuit capacity; For the h Allowable value of subharmonic current; The capacity of the electricity agreement; is the capacity of the power supply equipment; a is the phase superposition coefficient; The sum of the harmonic currents of multiple distributed photovoltaics at the grid connection point is: ； Where, For public connection points h order harmonic current distortion; N wt The number of devices connected to the public connection point; For the i devices h Subharmonic current distortion; For the i The transformation ratio of the equipment transformer; is the margin factor.

5. A distributed photovoltaic intelligent planning method according to claim 1, characterized in that: In the step of solving the photovoltaic capacity objective function based on the maximum photovoltaic capacity and the constraint conditions using the selected optimization solution algorithm, the adaptive legacy mutation algorithm is specifically used for solving the problem. The solution steps are: Perform population initialization and parameter initialization; The fitness function is used to evaluate the population; the fitness value is defined according to the objective function photovoltaic capacity, and the constraints are calculated using the photovoltaic carrying capacity index to determine whether the stopping conditions are met. If so, the population is stopped and the optimal solution is output. Otherwise, cross mutation is continued.

6. A distributed photovoltaic intelligent planning system, characterized in that: include: Data acquisition module, used to obtain distribution network model data; An optimization solution module is used to solve the photovoltaic capacity objective function based on the obtained distribution network model data to obtain a calculation result; wherein the photovoltaic capacity objective function takes the maximum photovoltaic capacity that can be accommodated by all nodes as the optimization solution target; in the solution step, the maximum photovoltaic capacity is first set, and then the photovoltaic carrying capacity index is calculated based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis and constraint conditions are established. Finally, based on the maximum photovoltaic capacity and the constraint conditions, the photovoltaic capacity objective function is solved using the selected optimization solution algorithm; The matching planning module is used to match the calculated results with the distribution network nodes to obtain the maximum photovoltaic capacity of all nodes and use it as the distributed photovoltaic intelligent planning result.

7. A distributed photovoltaic intelligent planning system according to claim 6, characterized in that: The expression of the photovoltaic capacity objective function is: ； Where, n is the node number, N is the total number of nodes; For nodes n of PV capacity that can be accommodated.

8. A distributed photovoltaic intelligent planning system according to claim 6, characterized in that: In the step of setting the maximum photovoltaic capacity, the feeder load history curve is first analyzed, and then 60% of the minimum value of the historical maximum load is taken as the maximum photovoltaic capacity.

9. A distributed photovoltaic intelligent planning system according to claim 6, characterized in that: In the step of calculating the photovoltaic carrying capacity index based on thermal stability, short-circuit current, voltage deviation and harmonic component analysis and establishing constraint conditions, (1) In the thermal stability analysis, the capacity of distributed power generation can be added in the evaluation area Calculated as follows: ； Where, is the maximum reverse load rate within the preset time period; S e is the actual operating limit of the transformer or line; is the equipment operation margin factor; The calculation expression of each reverse load rate within the preset time period is: ； Where, is the reverse load rate; Provide power for distributed power generation; The equivalent power load at the same moment, that is, the load minus the output of other power sources except distributed power sources; (2) In the short-circuit current analysis, the short-circuit current satisfies the following relationship: ； Where, is the system bus short-circuit current, is the allowable short-circuit current limit; (3) In voltage deviation analysis, the percentage of voltage change is calculated according to the following formula: ； Where, is the percentage of voltage change; 、 are the resistance and reactance components of the grid impedance respectively; is the maximum reactive positive and negative value; is the rated voltage of the busbar; (4) In harmonic component analysis: the harmonic current component does not exceed the allowable value; among them, Public connection point i The first distributed photovoltaic h The allowable values ​​of subharmonic current are: ； Where, The minimum short-circuit capacity of the common connection point; is the benchmark short-circuit capacity; For the h Allowable value of subharmonic current; The capacity of the electricity agreement; is the capacity of the power supply equipment; a is the phase superposition coefficient; The sum of the harmonic currents of multiple distributed photovoltaics at the grid connection point is: ； Where, For public connection points h order harmonic current distortion; N wt The number of devices connected to the public connection point; For the i devices h Subharmonic current distortion; For the i The transformation ratio of the equipment transformer; is the margin factor.

10. A distributed photovoltaic intelligent planning system according to claim 6, characterized in that: In the step of solving the photovoltaic capacity objective function based on the maximum photovoltaic capacity and the constraint conditions using the selected optimization solution algorithm, the adaptive legacy mutation algorithm is specifically used for solving the problem. The solution steps are: Perform population initialization and parameter initialization; The fitness function is used to evaluate the population; the fitness value is defined according to the objective function photovoltaic capacity, and the constraints are calculated using the photovoltaic carrying capacity index to determine whether the stopping conditions are met. If so, the population is stopped and the optimal solution is output. Otherwise, cross mutation is continued.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the distributed photovoltaic intelligent planning method according to any one of claims 1 to 5 is implemented.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the distributed photovoltaic intelligent planning method according to any one of claims 1 to 5 is implemented.

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