Method and device for analyzing influence of distributed photovoltaic power generation on power distribution network line loss

Abstract

The invention discloses a method and device for analyzing the influence of distributed photovoltaic power generation on power distribution network line loss, and belongs to the technical field of power grids. The method comprises the following steps: based on photovoltaic power generation historical data, establishing a joint probability distribution function through a kernel density estimation method and a Joe estimation function so as to accurately describe space-time correlation and uncertainty of photovoltaic output; generating a typical daily curve through sampling and inverse transformation of the joint probability distribution function; based on a typical daily curve, an energy storage adjustment strategy based on loss responsivity is introduced, a multi-target adaptive evolutionary algorithm based on heuristic mutation is used to dynamically optimize energy storage charge and discharge behaviors, and a power distribution network model is constructed; calculating the active power loss of each line and element of the power distribution network, and obtaining a network loss calculation result by considering the influence of the grid-connected point comprehensive power on the line loss after photovoltaic access. According to the method, the line loss of the power distribution system can be effectively and rapidly analyzed, and the adverse effect of distributed power generation access fluctuation on the line loss can be reduced through real-time energy storage adjustment.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and particularly relates to an analysis method and device for the impact of distributed photovoltaic power generation on the line loss of a distribution network. Background Art

[0002] With the continuous improvement of global environmental awareness and the rapid iteration of new energy technologies, distributed photovoltaic power generation, as a clean, efficient, and renewable energy technology, is obtaining unprecedented wide application and promotion in the power grid field. Under the guidance of the country's "dual carbon" strategy, the development of distributed power sources shows a booming trend, which not only becomes an important path for energy transformation but also a key measure to achieve the carbon neutrality goal. The large-scale access of distributed photovoltaics is fundamentally changing the network structure of traditional distribution networks, gradually evolving them from a single radial power supply mode into a complex network system with multiple power sources and multiple nodes. This structural transformation means that the distribution network is no longer a passive system that transmits energy unidirectionally but an intelligent grid with bidirectional energy flow and dynamic regulation capabilities. The intermittent and fluctuating output of distributed photovoltaics brings significant randomness and uncertainty to the net load of the distribution system. This poses higher challenges to the operation stability of the distribution network, making it face a series of technical problems such as voltage fluctuations, voltage over-limit, three-phase voltage imbalance, harmonic pollution, and increased line losses.

[0003] With the rapid progress of energy storage technology, an energy storage system with the characteristics of rapid regulation and flexible energy management has emerged, providing a new paradigm for the more economic, safe, and efficient operation and control of distributed photovoltaics connected to the distribution network. Thanks to technological progress and economies of scale, the cost of energy storage systems has been continuously decreasing, and their application scope is showing a rapid expansion trend. From the initial power peak shaving and frequency modulation to the current wide application in distributed power generation and microgrid construction, energy storage has become an important link connecting new energy, the power grid, and users. Although the academic and industrial circles have conducted multi-angle research on the impact of photovoltaics connected to the distribution network, deeply coupling distributed photovoltaics with energy storage systems and systematically evaluating their impact on the line loss of the distribution network is still a research field that urgently needs to be broken through.

[0004] With its unique advantages of rapid power regulation and flexible energy management, the energy storage system can significantly improve the operation characteristics of the distribution network through an active charge and discharge strategy. It can not only optimize the load balance degree and voltage distribution of the line but also improve the economy of distributed photovoltaic power generation connected to the grid. Therefore, in-depth research on the impact of distributed photovoltaic power generation considering energy storage regulation on the line loss of the distribution network not only has important theoretical significance but also will provide key technical support for the construction of future intelligent grids. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems in the above-related technologies to some extent.

[0006] To this end, the object of the present invention is to provide an analysis method and device for the impact of distributed photovoltaic power generation on the line loss of a distribution network, which can effectively and quickly analyze the line loss of the distribution system.

[0007] To solve the above technical problems, the present invention is implemented as follows: An embodiment of the present invention provides an analysis method for the impact of distributed photovoltaic power generation on the line loss of a distribution network. The content of the method includes: Based on the historical data of photovoltaic power generation, a joint probability distribution function is established through the kernel density estimation method and the Joe estimation function to accurately characterize the spatio-temporal correlation and uncertainty of photovoltaic output; through sampling and inverse transformation of the joint probability distribution function, a typical daily curve considering the correlation and uncertainty of photovoltaic output is generated. Based on the typical daily curve, a energy storage regulation strategy based on loss responsiveness is introduced, and a multi-objective adaptive evolutionary algorithm based on heuristic mutation is used to dynamically optimize the charging and discharging behavior of the energy storage. With the goal of minimizing line loss, a distribution network model is constructed. Calculate the active power loss of each line and component of the distribution network, considering the impact of the combined power at the grid connection point after photovoltaic access on the line loss, and obtain the network loss calculation result.

[0008] In addition, according to the analysis method for the impact of distributed photovoltaic power generation on the line loss of a distribution network of the present invention, the following additional technical features may also be provided: In some embodiments, the kernel density estimation method uses a Gaussian kernel function for density estimation.

[0009] In some embodiments, the calculation of active power loss uses the root mean square current method, and based on the 24-hour hourly load data, the active power loss of each line and component of the distribution network is calculated.

[0010] In some embodiments, the combined power at the grid connection point after photovoltaic access is the coupling of the load power and the photovoltaic output.

[0011] In some embodiments, the content of the multi-objective adaptive evolutionary algorithm based on heuristic mutation includes: Initialization: Use heuristic mapping to generate an initial population to ensure that the population is evenly and purposefully distributed in the search space; and initialize the individual positions in the initial population. Evaluation and screening: Sort the initial population according to superiority and density. Iterative update: Use the sorted optimal solution to guide the global search to expand the coverage range of the solution space; perform local fine search through adaptive boundaries to improve the quality of the solution. Iteration termination: After reaching the maximum number of iterations, the optimal solution set is output for optimizing the energy storage charge and discharge strategy.

[0012] In some of these embodiments, the specific content of the evaluation and screening includes: Calculate the individual objective function value, and screen the dominant solutions based on the ranking of advantages and disadvantages; Perform density ranking on the dominant solutions, and retain the individuals with uniform distribution and excellent performance.

[0013] In some of these embodiments, the iterative update includes an exploration stage and a development stage; The exploration stage conducts a global search: guided by the current optimal solution, update the positions of the first N / 2 individuals to enhance the global convergence; N where is the population size; The development stage conducts a local search: introduce an adaptive boundary for local fine search to improve the accuracy of the solution.

[0014] In some of these embodiments, the content of the energy storage regulation strategy based on the loss responsiveness includes: Take the line loss responsiveness as the core basis for energy storage regulation, quantify the dynamic response characteristics of the system loss to the node power fluctuation, optimize the net power at the connection point through the energy storage charge and discharge strategy, and reduce the line loss; Construct an energy storage system constraint model to ensure the safety and effectiveness of energy storage regulation.

[0015] In some of these embodiments, the energy storage system constraint model includes the charge balance constraint, the power limit constraint, and the charge safety constraint.

[0016] The embodiment of the present invention also provides an analysis device for the impact of distributed photovoltaic power generation on the line loss of the distribution network, including a processor and a memory. A software program is stored on the memory, and when the processor runs the software program, it can implement the content of the analysis method for the impact of distributed photovoltaic power generation on the line loss of the distribution network described in any one of the above.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: In the embodiments of the present invention, the provided analysis method for the impact of distributed photovoltaic power generation on the line loss of the distribution network considers the distribution network with distributed photovoltaic access, analyzes the impact of factors such as photovoltaic configuration, climate conditions, and load types on the line loss of the distribution network, proposes the energy storage regulation basis based on the line loss response degree, and realizes energy storage regulation by using the multi-objective adaptive evolutionary algorithm based on heuristic mutation, which can reduce the line loss of the distribution network operation and contribute to the improvement of the economy of the distribution network operation; the example results show that the access of distributed photovoltaic is beneficial to the reduction of the line loss of the network operation, and its improvement degree has a certain correlation with the photovoltaic capacity, photovoltaic installation location, and switching time. Therefore, the configuration optimization of photovoltaic will be beneficial to the reduction of the line loss of the distribution network and at the same time optimize the economic benefits of the distribution network operation; under the action of energy storage regulation, the line loss of the distribution network under different conditions is further reduced, and the energy storage regulation amount obtained by this method is calculated based on the response degree of the line loss without the need for power flow calculation. Therefore, it is beneficial to be applied in real time in the actual distribution network operation; in addition, in the future, the strategy for realizing the collaborative configuration optimization of photovoltaic and energy storage considering the energy storage conditions will be further studied with the aim of optimizing the line loss.

[0018] The analysis device for the impact of distributed photovoltaic power generation on the line loss of the distribution network of the present invention includes the analysis method for the impact of distributed photovoltaic power generation on the line loss of the distribution network described above, and thus has at least all the features and advantages of the analysis method for the impact of distributed photovoltaic power generation on the line loss of the distribution network, which will not be elaborated here. The additional aspects and advantages of the present invention will be partly given in the following description, partly become obvious from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the analysis method for the impact of distributed photovoltaic power generation on the line loss of the distribution network disclosed in an embodiment of the present invention; Figure 2 It is a topological diagram of the IEEE 33-node distribution system considering distributed photovoltaic disclosed in an embodiment of the present invention; Figure 3 It is an annual actual output data diagram of distributed photovoltaic disclosed in an embodiment of the present invention; Figure 4 It is a distributed scenario sampling diagram of distributed photovoltaic disclosed in an embodiment of the present invention; Figure 5 It is a comparison diagram of the improvement of the line loss of the distribution network under different photovoltaic penetration rates disclosed in an embodiment of the present invention; Figure 6 It is a comparison diagram of the impact of the change in photovoltaic output at different positions on the improvement of the line loss disclosed in an embodiment of the present invention; Figure 7 It is a comparison diagram of the impact of the change in photovoltaic output at different switching times on the improvement of the line loss disclosed in an embodiment of the present invention; Figure 8 Line loss comparison chart of switching scheme 1 and scheme 3 disclosed in an embodiment of the present invention under typical scenarios; Figure 9 Distributed typical scenario diagram of distributed photovoltaic under different climates disclosed in an embodiment of the present invention; Figure 10 Line loss improvement comparison chart under different climate conditions disclosed in an embodiment of the present invention; Figure 11 Line chart of power normalization values for different types of loads disclosed in an embodiment of the present invention; Figure 12 Loss comparison chart of distributed photovoltaic connected to the distribution network under different loads disclosed in an embodiment of the present invention; Figure 13 Output comparison chart of industrial load and distributed photovoltaic typical scenario disclosed in an embodiment of the present invention; Figure 14 Loss comparison chart of distributed photovoltaic connected to the distribution network under industrial load disclosed in an embodiment of the present invention; Figure 15 Loss comparison chart of distributed photovoltaic connected to the distribution network under residential load disclosed in an embodiment of the present invention. Specific embodiments

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Next, the embodiments of the present invention will be described in detail in conjunction with the accompanying drawings through specific embodiments and their application scenarios.

[0022] In some embodiments of the present invention, an analysis method for the impact of distributed photovoltaic power generation considering energy storage regulation on the line loss of the distribution network is provided: First, the joint probability distribution function is used to accurately describe the non-linear spatio-temporal correlation of photovoltaic output, and the scenario method is used to accurately depict and simulate the distributed photovoltaic output, providing data support for subsequent analysis; Second, the root mean square current method, which is more practical for actual applications, is adopted for the line loss calculation model to improve the practicality of the line loss analysis results and mechanisms.

[0023] To achieve efficient and accurate analysis of distribution network line losses, the present invention proposes an energy storage regulation optimization algorithm based on the responsiveness of node complex power change. This method significantly improves the calculation efficiency by quantifying the dynamic response characteristics of system losses to node power fluctuations, providing a reliable theoretical basis for real-time regulation. In the simulation experiment of the IEEE 33-node distribution system, multi-dimensional factors such as the capacity configuration, spatial distribution, operating status of distributed photovoltaic, as well as regional climate and load characteristics are comprehensively evaluated, the mechanism of the impact of photovoltaic grid connection on distribution network line losses is systematically revealed, and the significant effect of the energy storage system in improving line losses is demonstrated.

[0024] In some embodiments of the present invention, for the impact analysis of distributed photovoltaic power generation on distribution network line losses, a systematic calculation method is proposed, and its overall process is as Figure 1 shown. First, based on the historical operation data of photovoltaic power generation (including voltage V, current I, and power P), the probability density function of photovoltaic output is constructed by the kernel density estimation method, and the Joe estimation function is used to establish a joint probability distribution model to accurately describe the spatio-temporal correlation and uncertainty of photovoltaic output. This function can effectively capture the positive and negative correlation relationships between variables, thus more realistically reflecting the output characteristics of distributed photovoltaic. Then, through sampling and inverse transformation of the joint probability distribution function, a typical daily curve considering the correlation and randomness of photovoltaic output is generated, providing data support for subsequent line loss analysis. Finally, with the daily scenario as the calculation period, the actual operation state of the distribution network is simulated, and an energy storage regulation strategy based on loss responsiveness is introduced. Through a multi-objective adaptive evolutionary algorithm based on heuristic mutation, the charge and discharge behavior of the energy storage is dynamically optimized with the goal of minimizing line losses, realizing the efficient and economic operation of the distribution network. This method not only fully considers the volatility and spatial correlation of photovoltaic output, but also effectively improves the stability and economy of the distribution network through energy storage regulation, providing a new technical path for line loss analysis under the background of distributed photovoltaic access.

[0025] The loss of the distribution network is the cumulative sum of the power losses of each electrical component in the grid over time. However, in actual operation, the loss power of electrical components changes dynamically over time and is difficult to accurately describe by an explicit expression. Therefore, mathematical statistics methods are adopted and combined with certain assumptions to design a calculation model for line losses.

[0026] Assume that the equivalent resistance of the grid line is and the equivalent current of the line is , then the active power loss of this line is: (1) Among them, is a correction coefficient, reflecting the subtle differences in actual operation.

[0027] To obtain a more comprehensive energy loss, it is necessary to integrate the instantaneous loss within 24 hours: (2) In practical applications, the current is an instantaneous variable, and its accurate measurement poses technical challenges. By dividing the observation time into tiny time segments, an approximate equivalence can be achieved. A commonly used method in engineering practice is to utilize the hourly load data of a typical day and assume that the current remains relatively stable within each hour. The total loss of the distribution network can be expressed as the accumulation of the equivalent resistance losses of all lines and devices: (3) In the formula: and are the equivalent resistances of different components such as lines and transformers respectively; and are the total number of lines and the total number of transformers respectively; is the calculation period (hours). , are the correction factors of each component respectively; and are the equivalent currents of the corresponding components respectively.

[0028] To obtain these equivalent currents, statistical calculations can be performed based on the hourly power data for 24 hours: (4) In the formula: and are the active and reactive powers at time t respectively; is the voltage at the corresponding time.

[0029] In the practice of energy conservation and loss reduction in the distribution network, traditional technical paths mainly focus on multiple key dimensions: (1) Optimize the power supply radius: By reducing the length of low-voltage lines, the power loss caused by line impedance during current transmission can be reduced.

[0030] (2) Reactive power compensation strategy: Adjust the power factor to reduce the adverse impact of reactive power on system losses.

[0031] (3) Load balance management: By balancing the three-phase load, the negative impact of three-phase imbalance on system losses can be eliminated.

[0032] (4) Transformer capacity configuration: Reasonably select the transformer capacity to optimize the operating efficiency of the transformer.

[0033] With the widespread access of distributed photovoltaic power generation, the current and voltage distributions of the distribution network become more complex. A photovoltaic power station can be regarded as a controllable load source with time-varying characteristics. At the point of common coupling, the original load and photovoltaic output can be comprehensively coupled: (5) In the formula, is the comprehensive power, and are the load power and photovoltaic output respectively.

[0034] From the perspective of distribution network losses, select a specific time section and compare the line losses before and after the photovoltaic access, as shown in formula (6): (6) In the formula, , are the total number of system nodes and the number of photovoltaic accesses respectively; and are the load power and node voltage of node respectively, and are the photovoltaic output and voltage of the photovoltaic respectively.

[0035] The loss change can be expressed as: (7) It can be seen from the above formula that the influence of the photovoltaic access system shows complex non-linear characteristics, and the photovoltaic access position and capacity have different degrees of influence on the line losses: (1) Access capacity: Reasonably selecting the photovoltaic capacity can effectively reduce the system network losses.

[0036] (2) Penetration critical point: When the photovoltaic penetration rate is too high, problems such as reverse power flow, power backfeeding, increased power grid line losses, and voltage over-limit risks may be caused (3) Access position sensitivity: When the access position is close to the power source side, the voltage support for the end load is limited, and the effect of improving the line losses is weak; when the access position is close to the end of the line, the voltage support effect is significant, and the effect of improving the network losses is more obvious.

[0037] The present invention analyzes multiple factors affecting losses, such as the photovoltaic access capacity, photovoltaic access position, photovoltaic connection and disconnection conditions, distribution network topology structure, regional climate characteristics, and load type diversity.

[0038] In the context of distributed photovoltaic power generation accessing the distribution network, the present invention proposes a line loss assessment method. By introducing the line loss improvement rate index, the influence of distributed photovoltaic on the distribution network losses is quantified, as shown in formula (8): (8) Among them, Represents the line loss improvement rate. The smaller the value, the better the improvement degree of line loss.

[0039] Energy storage technology plays a key role in modern distribution networks. Its unique advantages include fast response speed, high energy conversion efficiency, long service life, and low requirements for geographical environment. These characteristics enable the energy storage system to flexibly adapt to grid demands and achieve efficient energy management. Specifically, the energy storage system can absorb electric energy for charging during low electricity consumption periods and release electric energy during high electricity consumption periods, thus effectively realizing the function of peak shaving and valley filling.

[0040] For the 10kV medium-voltage distribution network, the present invention focuses on the battery energy storage system (ESS), and selects to deploy energy storage simultaneously on the network side of the distributed photovoltaic (DG) connection point to effectively regulate the uncertainty of photovoltaic output.

[0041] To suppress line loss, the present invention introduces the loss responsiveness as the core calculation basis for energy storage regulation. For a radial distribution network, the change in system power loss caused by multiple nodes, that is, the line loss, can be approximately expressed as: (9) In the formula, represents the resistance of the line; represents the power change amount of the grid point; and are respectively the conjugate value of the power change at the head node of the line and the conjugate value of the power change amount; is the conjugate value of the voltage at the head node of the line ; is the conjugate value of the voltage at the grid connection node. is the operation of taking the real part of the complex number to ensure that the calculation result is the active power loss.

[0042] The net power change amount at the grid connection point is: (10) In the formula, is the power change of the energy storage system; is the change in photovoltaic power generation output; is the change in load power.

[0043] Furthermore, the overall grid loss can be obtained by the superposition of the influences of all line losses, as follows: (11) In the formula, represents the set of all grid lines.

[0044] The loss responsiveness Reflect the influence degree of line power change on system loss: (12) To ensure the reduction of system loss, the energy storage regulation needs to meet: (13) Among them, represents the energy storage point and the line The equivalent impedance between them.

[0045] Energy storage system constraint model: (1) Charge balance constraint: (14) Among them, represents The remaining charge at time and represents The charging power or discharging power of the energy storage battery at time; and Represent the charging efficiency and discharging efficiency of the battery respectively.

[0046] (2) Power limit constraint: (15) Among them, and Represent the maximum allowable charging power and discharging power of the energy storage battery.

[0047] (3) Charge safety constraint: In the formula, and Represent the lower limit value and upper limit value of the remaining charge of the energy storage respectively.

[0048] To accurately realize the dynamic distribution network optimization of energy storage regulation, this study proposes a multi-objective adaptive evolutionary algorithm based on heuristic mutation. This algorithm effectively balances global exploration and local development through an intelligent search strategy, providing an efficient solution for complex distribution network optimization problems.

[0049] An improved heuristic mapping method is used to generate the initial population, and its mapping equation is defined as: (16) In the formula, \alpha \in \left [ {0,1} \right ] is the heuristic parameter; is the mapping value of the current iteration.

[0050] Generation location of the evolved individual: (17) In the formula, is the vector generated by the heuristic mapping function, which has the same dimension as ; and are the upper and lower bounds of the optimization variables, and is the population size.

[0051] Individual position representation: (18) Wherein, is the position of the th individual in the th dimension, and is the population dimension.

[0052] Ranking of superiority and inferiority is a key technology in multi-objective optimization, which is used to effectively filter the fitness levels of different individuals. For two solutions , , their superiority and inferiority relationship is defined as: if is superior to in all objective functions, then is said to be superior to : (19) Wherein, is the value of the

[0053] th objective function. Density ranking evaluates the distribution characteristics of the solution set, and the calculation process is divided into three steps: (20) In the formula, and are the adjacent values of the th dimension of the and th solution; and

[0054] Secondly, calculate the objective space density: (21) In the formula, and are the adjacent values of the th solution in the th objective function; and are the upper and lower bounds of the corresponding function values.

[0055] Finally, calculate the comprehensive density: (22) The individual in the exploration stage is updated to: (23) In the formula, is the updated position in the exploration stage; the current optimal solution; is the scaling factor.

[0056] The individual in the exploitation stage is updated to : (24) Adaptive boundary: (25) Among them, , is the current iteration number, is the maximum iteration number.

[0057] The multi-objective adaptive evolutionary algorithm based on heuristic mutation proposed by the present invention mainly consists of population initialization, heuristic mutation exploration stage, exploitation stage, and solution evaluation mechanism based on non-dominated sorting and density sorting. Different from traditional random initialization, this algorithm introduces a heuristic mapping (H) to generate the initial population, which can distribute individuals more evenly and purposefully in the search space, laying a good foundation for subsequent evolution. During the population evolution process, the non-dominated sorting mechanism is used to identify the solution set with relative advantages, and individuals that perform excellently in all indicators are selected through strict comparison of multi-objective functions. Further, the comprehensive density is used as the secondary screening criterion in the dominant solution set, which not only considers the distribution in the search space but also integrates the distribution characteristics of the objective space, comprehensively measuring the diversity and representativeness of the solutions. This mechanism ensures that the evaluation of solutions not only depends on the performance of the objective function but also requires good distribution characteristics in the solution space. The algorithm designs a dynamic balance mechanism for the exploration and exploitation stages: the exploration stage mainly focuses on global search, guiding the population to converge to the optimal solution region; the exploitation stage focuses on local detailed search to improve the accuracy of the solutions. Through the adaptive scaling coefficient, the dynamic adjustment of the search strategy is realized, effectively balancing global exploration and local exploitation.

[0058] Adopting the foregoing solution of the present invention, the IEEE 33-bus system is selected as the simulation platform, and the multi-dimensional impact of distributed photovoltaic power generation on the distribution system loss is deeply explored in the Matlab environment. The system topology is as Figure 2As shown, the bus voltage is set to 1.02 p.u., and the reference voltage is 10 kV. The photovoltaic power generation system is selected to be connected to the grid from nodes 18, 22, and 33, and the measured data of the annual grid-connected photovoltaic output of a certain park is used as the research sample. By performing per-unit conversion on the original data (as Figure 3 shown), combined with the scenario generation method innovatively proposed in the present invention, 100 typical scenarios of photovoltaic output are finally obtained (as Figure 4 shown).

[0059] Next, the influence mechanism of distributed photovoltaic access to the distribution network on system losses is mainly analyzed. The photovoltaic penetration rate is defined as the ratio of the photovoltaic installed capacity to the distribution transformer capacity, which is an important parameter for quantifying the scale of distributed photovoltaic access. By introducing the photovoltaic penetration rate index, the influence of photovoltaic power generation with different capacities on the operating characteristics of the distribution network can be systematically evaluated. The mathematical expression of the photovoltaic penetration rate can be described as: (26) where represents the photovoltaic installed capacity at node ; represents the distribution transformer capacity of node .

[0060] The present invention focuses on systematically analyzing the regulation effect of photovoltaic power generation on distribution network losses from three dimensions: photovoltaic penetration rate, access location, and switching time.

[0061] First, through the generated scenario data, the changes in line losses under five different photovoltaic penetration rates (10%, 20%, 40%, 60%, 80%, and 100%) are tested. The research results (as Figure 5 ) reveal a non-linear law: as the photovoltaic penetration rate increases, the line losses show a trend of first decreasing and then increasing. This indicates that appropriately increasing the photovoltaic output is beneficial to reducing line losses, but excessive penetration may lead to reverse power flow, increase the network current carrying capacity, and thus cause an increase in losses. After introducing energy storage regulation, not only the network losses are further improved, but also the increase in line losses caused by surplus photovoltaic output can be effectively suppressed.

[0062] Second, by analyzing the changes in photovoltaic output of three different nodes (18, 22, 33), the influence of the access location on line losses is studied (as Figure 6 ). The results show that node 18 is located at the end of the network, and the change in its photovoltaic capacity has the most significant impact on network losses, and the network loss improvement rate changes from less than 1 to greater than 1; the increase in photovoltaic output of nodes 22 and 33 is generally beneficial to reducing network losses; energy storage regulation helps to reduce network losses to a certain extent, but limited by safety conditions, its loss reduction ability is limited in the case of reverse photovoltaic output.

[0063] Finally, the effects of three different photovoltaic switching time schemes (6:00 - 22:00, 8:00 - 20:00, 10:00 - 18:00) on the distribution network losses were studied (as Figure 7 ). The results show that the network loss improvement degrees of Scheme 1 and Scheme 2 are similar; the network loss improvement effect of Scheme 3 is significantly weaker; after introducing energy storage regulation, the network loss of Scheme 3 is improved to a certain extent; overly shortening the photovoltaic switching time will significantly reduce the network loss improvement effect.

[0064] Figure 8 Furthermore, the loss changes of Scheme 1 and Scheme 3 under typical daily load and photovoltaic output scenarios when the photovoltaic capacity factor is 0.8 were shown. The comparison results show that there are problems in Scheme 3 such as too late input and too early cut-off; the effect of photovoltaic improving network loss is weakened due to improper switching time; energy storage has a certain loss reduction effect when the photovoltaic output is insufficient.

[0065] Due to the uncertain impact of climate change on distributed photovoltaic power generation, the present invention divides the annual photovoltaic output data into quarters and performs K-means clustering to construct quarterly typical scenarios and analyzes the occurrence probabilities of each scenario. The research results (as Figure 9 ) clearly show the significant differences in photovoltaic output between seasons: the photovoltaic output values in summer and autumn are significantly higher than those in winter and spring, and the photovoltaic output in winter is at the lowest level.

[0066] Based on the typical scenarios, the present invention further evaluates the loss improvement rate of photovoltaic output connected to the distribution network. Through weighted calculation of each typical scenario, seasonal comprehensive evaluation results are obtained (as Figure 10 ). The results show that the access of distributed photovoltaic in summer and autumn has the most significant improvement on line losses, while the improvement degree of output in winter is relatively the smallest; under different climate conditions, the energy storage system always plays a key regulating role and can continuously and effectively improve the line loss improvement effect.

[0067] The diversity of load types adds more complexity to the analysis of distribution network losses. The present invention examines four typical load types: industrial, agricultural, commercial, and residential, and deeply analyzes their differential effects on distribution network losses. The load power of the distribution network approximately follows a normal distribution, but there are significant differences in the power expectation values and standard deviations of different loads at different times. Figure 11 The normalized curves of the active power expectations of the typical daily loads of the four load types are shown, revealing the diversity of load characteristics.

[0068] Through the loss assessment of three scenarios: no photovoltaic access, distributed photovoltaic access, and energy storage regulation, the significant impact of load types on network losses is revealed (as Figure 12 ). The losses under industrial loads are the highest, and the losses under agricultural loads are the lowest. Figure 13The daily typical scenarios of industrial load and photovoltaic are compared, clearly showing the significant differences between the peak periods of photovoltaic output and load peak.

[0069] As Figure 14 shown, when the time-sequence characteristics of photovoltaic output and load are seriously mismatched, the loss reduction effect of photovoltaic will be greatly reduced. Taking residential load as an example (such as Figure 15 ), when the power is low at noon, the large-capacity photovoltaic output is likely to cause a large amount of surplus power, resulting in reverse power flow and increasing network losses instead. This phenomenon highlights the importance of the coordination between the photovoltaic power generation system and the load. In this context, the energy storage system demonstrates its great value as a flexible regulation resource, which can dynamically balance the photovoltaic output according to the real-time situation of the network, effectively reduce network losses, and improve the overall economic benefits.

[0070] For the parts not described in detail in the present invention, reference can be made to the prior art or the well-known technology in the art.

[0071] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention, and all of them belong to the protection scope of the present invention.

Claims

1. An analysis method for the impact of distributed photovoltaic power generation on the line loss of a distribution network, characterized in that, The content of the method includes: Based on the historical data of photovoltaic power generation, a joint probability distribution function is established through the kernel density estimation method and the Joe estimation function to accurately characterize the spatio-temporal correlation and uncertainty of photovoltaic power output; through sampling and inverse transformation of the joint probability distribution function, a typical daily curve considering the correlation and uncertainty of photovoltaic power output is generated; Based on the typical daily curve, a energy storage regulation strategy based on loss responsiveness is introduced, and a multi-objective adaptive evolutionary algorithm based on heuristic mutation is used to dynamically optimize the charge and discharge behavior of the energy storage. With the goal of minimizing line losses, a distribution network model is constructed; Calculate the active power losses of each line and component in the distribution network, and consider the influence of the combined power at the grid connection point after photovoltaic access on the line losses to obtain the network loss calculation results.

2. The analysis method for the impact of distributed photovoltaic power generation on the line loss of the distribution network according to claim 1, characterized in that The kernel density estimation method uses a Gaussian kernel function for density estimation.

3. The analysis method for the impact of distributed photovoltaic power generation on the line loss of the distribution network according to claim 1, characterized in that, The calculation of active power losses adopts the root mean square current method. Based on the 24-hour hourly load data, the active power losses of each line and component in the distribution network are calculated.

4. The analysis method for the influence of distributed photovoltaic power generation on the line loss of the distribution network according to claim 1, characterized in that, The combined power at the grid connection point after photovoltaic access is the coupling of the load power and the photovoltaic power output.

5. The analysis method for the influence of distributed photovoltaic power generation on the line loss of a distribution network according to claim 1, wherein The content of the multi-objective adaptive evolutionary algorithm based on heuristic mutation includes: Initialization: A heuristic mapping is used to generate an initial population to ensure that the population is evenly and purposefully distributed in the search space; and the positions of the individuals in the initial population are initialized; Evaluation and screening: Sort the initial population in terms of superiority and inferiority and density; Iterative update: Use the sorted optimal solutions to guide the global search to expand the coverage of the solution space; conduct local fine search through adaptive boundaries to improve the quality of the solutions; Iterative termination: After reaching the maximum number of iterations, output the optimal solution set for optimizing the energy storage charge and discharge strategy.

6. The analysis method for the impact of distributed photovoltaic power generation on the line loss of the distribution network according to claim 5, characterized in that, The specific content of the evaluation and screening includes: Calculate the individual objective function values and screen the dominant solutions based on the superiority and inferiority sorting; Conduct density sorting on the dominant solutions and retain the individuals with uniform distribution and excellent performance.

7. The analysis method for the influence of distributed photovoltaic power generation on the line loss of a distribution network according to claim 5, wherein The iterative update includes an exploration stage and a development stage; In the exploration stage, a global search is carried out: guided by the current optimal solution, the positions of the first N N / 2 individuals are updated to enhance the global convergence; N where N is the population size; In the development stage, local search is carried out: Adaptive boundaries are introduced for local fine search to improve the accuracy of the solutions.

8. The analysis method for the influence of distributed photovoltaic power generation on the line loss of the distribution network according to claim 1, characterized in that, The content of the energy storage regulation strategy based on loss responsiveness includes: Taking the line loss responsiveness as the core basis for energy storage regulation, quantifying the dynamic response characteristics of system losses to node power fluctuations, optimizing the net power at the grid connection point through the energy storage charge and discharge strategy, and reducing line losses; Construct a constraint model for the energy storage system to ensure the safety and effectiveness of energy storage regulation.

9. The analysis method for the impact of distributed photovoltaic power generation on the line loss of a distribution network according to claim 8, characterized in that, The energy storage system constraint model includes power balance constraints, power limit constraints, and energy safety constraints.

10. An analysis device for the impact of distributed photovoltaic power generation on the line loss of a distribution network, comprising a processor and a memory, and a software program is stored on the memory, characterized in that, When the processor runs the software program, it can implement the content of the analysis method for the impact of distributed photovoltaic power generation on the line losses of the distribution network as described in any one of claims 1 to 9.

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