A new energy high permeability power distribution network intelligent planning method and system

Abstract

This invention discloses an intelligent planning method for distribution networks with high penetration of new energy sources, relating to the field of distribution network technology. The method includes: determining current capacity planning parameters based on distribution network operation data to form a capacity planning scheme; performing simulation calculations on the power quality, safety stability, and carrying capacity of the distribution network based on the current capacity planning scheme to obtain parameters for evaluating these parameters; quantitatively scoring each node and the entire distribution network based on these parameters; and correcting the capacity planning parameters according to the scoring results to obtain an updated capacity planning scheme. Through a three-dimensional coupled evaluation of power quality, safety stability, and carrying capacity, and a convergence determination of the quantitative scoring, this method effectively improves the distribution network's adaptability, safety stability, and absorption capacity for high-proportion new energy sources, demonstrating significant engineering application value and socio-economic benefits.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and more specifically, to a smart planning method and system for power distribution networks with high penetration of new energy sources. Background Technology

[0002] The global energy system is transitioning towards cleaner and lower-carbon energy, a core trend. my country's dual-carbon goals explicitly require a significant increase in the proportion of renewable energy in energy consumption. Wind power, solar power, and other new energy sources, characterized by their cleanliness, low carbon footprint, and renewable resources, have become key drivers of this energy transition. The distribution network, as the last mile of the power system, is a crucial link in the integration and consumption of new energy sources; a high proportion of new energy connections is an inevitable requirement for this transition.

[0003] Distributed photovoltaic and decentralized wind power outputs are significantly affected by natural conditions, exhibiting marked randomness, intermittency, and volatility. Distribution networks present a new operational characteristic of strong uncertainty on both the source and load sides, placing stringent demands on the coordinated adaptation capabilities of grid capacity regulation and power quality regulation. Existing distribution networks mostly adopt a static and conservative strategy of setting the upper limit of renewable energy access capacity. This strategy determines the renewable energy access capacity planning based on a fixed safety factor, without considering changes in the operational phase or making adaptive adjustments to the capacity planning based on grid operation quality. It is difficult to adapt to the actual dynamic operating conditions of distribution networks with high renewable energy penetration, which seriously restricts the safe and stable operation of renewable energy distribution networks. Summary of the Invention

[0004] One objective of this invention is to provide a smart planning method and system for distribution networks with high penetration of new energy sources. By conducting a multi-dimensional comprehensive evaluation of the power quality, safety, stability, and carrying capacity of the distribution network, and revising the capacity planning scheme based on the evaluation results, the invention effectively improves the distribution network's adaptability, safety, stability, and absorption capacity for high-proportion new energy sources. It provides a systematic technical solution for the intelligent upgrading and transformation of distribution networks, offers technical support for the upgrading and transformation of distribution networks with high penetration of new energy sources and the access of new energy sources, promotes the large-scale application of clean energy and the intelligent development of distribution networks, and has significant engineering application value and socio-economic benefits.

[0005] This objective is achieved using the following technical solution:

[0006] A smart planning method for distribution networks with high penetration of new energy sources includes:

[0007] Acquire distribution network operation data, determine current capacity planning parameters based on the distribution network operation data, and formulate a capacity planning scheme; Based on the current capacity planning scheme, simulation calculations are performed on the power quality, safety stability, and carrying capacity of the distribution network to obtain parameters for evaluating the power quality, safety stability, and carrying capacity of the distribution network. Based on parameters for evaluating power quality, safety and stability, and carrying capacity of the distribution network, quantitative scores are performed on each node and the entire distribution network to obtain the scoring results, which include the comprehensive score of each node and the overall distribution network score. Determine whether the scoring result meets the convergence condition. If not, adjust the capacity planning parameters to obtain an updated capacity planning scheme. Return the updated capacity planning scheme as the current capacity planning scheme to continue iterating until the convergence condition is met.

[0008] In distribution networks with high penetration of renewable energy, the output of distributed photovoltaic and wind power is significantly affected by natural conditions such as sunlight and wind speed, exhibiting significant randomness, intermittency, and volatility. Localized renewable energy generation transforms the distribution network from a traditional unidirectional power flow to a bidirectional one, where power flow is no longer solely determined by load but jointly by renewable energy output and load. Traditional renewable energy distribution network planning methods are largely based on historical typical operating conditions, fixed safety factors, and empirical parameters, pre-setting renewable energy access capacity limits, reactive power compensation configuration schemes, and load allocation strategies. Essentially, these methods are static, offline, and unidirectional planning methods. Such methods cannot adapt and correct for fluctuations in renewable energy output, real-time load changes, and grid topology adjustments. They rely solely on single indicators such as load factor and voltage limits for simple post-hoc verification, making it difficult to fully reflect the impact of strong uncertainties on grid operation from both the source and load sides. Furthermore, because traditional planning does not unify and couple the three core constraints of power quality, safety and stability, and carrying capacity, capacity planning, power quality control, safety and stability assessment, and carrying capacity verification operate independently, the planning schemes are difficult to meet the overall operational requirements. They generally suffer from low planning accuracy and local optima with insufficient global performance, making them unsuitable for the operational needs of distribution networks with high renewable energy penetration.

[0009] This invention incorporates a convergence determination mechanism based on a three-dimensional coupled evaluation and quantitative scoring of power quality, safety and stability, and carrying capacity. This allows capacity planning schemes to incorporate multi-dimensional operational constraints. A preliminary capacity planning scheme is generated based on distribution network operation data as input. Simultaneously, the three-dimensional coupled evaluation of power quality, safety and stability, and carrying capacity is integrated, allowing the planning scheme to undergo multi-dimensional operational constraint verification during its generation process. Quantitative scoring transforms multi-dimensional evaluation indicators into quantifiable scores for individual nodes and the entire distribution network, until an optimal scheme satisfying all constraints is generated. This not only evaluates the planning parameters of each node individually but also assesses the overall distribution network. By iteratively adjusting parameters in a targeted manner, a globally optimal solution is generated that satisfies all-dimensional constraints and takes into account both network security and renewable energy consumption. This avoids insufficient network performance caused by local optima. At the same time, the capacity planning scheme is optimized by evaluating power quality, safety and stability, and carrying capacity in three dimensions to solve the problem of low accuracy in traditional planning. It also overcomes the problems of bidirectional power flow fluctuations, complex voltage and harmonic disturbances, and difficulty in accurately quantifying stability margin and consumption space under high renewable energy access, which lead to a disconnect between the planning scheme and actual operating conditions. This achieves precise adaptation of capacity planning to the dynamic operating requirements of the grid with high renewable energy access.

[0010] Furthermore, based on the distribution network operation dataset, capacity planning parameters are calculated using a reliability assessment algorithm to form a capacity planning scheme. By analyzing and calculating the operation data through the reliability assessment algorithm, the capacity planning parameters that meet the requirements for safe and reliable operation of the power grid can be quantitatively determined, thereby improving the planning accuracy and reliability.

[0011] Furthermore, the parameters for evaluating the power quality of the distribution network include voltage flicker and total harmonic distortion (THD). The original values ​​of voltage flicker and THD are obtained from the distribution network operating data through harmonic analysis. Based on these original values, an adaptive filtering algorithm is used to optimize the voltage flicker and THD. Optimizing the original values ​​of voltage flicker and THD through the adaptive filtering algorithm effectively eliminates random noise, measurement errors, high-frequency interference, and non-characteristic harmonic components in the distribution network operating data, avoiding the influence of interference data on the power quality evaluation results. This makes the final voltage flicker and THD values ​​closer to the actual operating level, significantly improving the accuracy and reliability of the power quality evaluation parameters.

[0012] Furthermore, the parameters for evaluating safety and stability include a stability margin coefficient, which is calculated based on distribution network operation simulation data. A simulation model is constructed based on the power grid topology, line parameters, new energy sources, and load models. The current distribution network operation data is input into the model to conduct continuous power flow and static stability simulations to obtain the power grid's stable operating limits. The stability margin coefficient is then calculated based on the relative difference between the current operating point and the stability limit, thereby achieving an accurate evaluation of safety and stability. The stability margin coefficient can quantitatively reflect the safety margin between the distribution network and the stability limit, intuitively characterizing the voltage stability level and safety redundancy of the distribution network under a high proportion of new energy access, and can provide clear quantitative stability constraints for capacity planning.

[0013] Furthermore, the parameters for evaluating carrying capacity include the absorption margin coefficient, which is calculated based on distribution network operation simulation data. A simulation model is constructed according to the power grid topology, equipment parameters, new energy sources, and load models. Current operating data is input into the model to conduct power flow simulation. Under the conditions of satisfying voltage, equipment capacity, and power flow safety constraints, the maximum absorbable power of new energy sources is determined through simulation. Then, the absorption margin coefficient is calculated based on the relative difference between the maximum absorbable power and the current actual output, thereby achieving an accurate evaluation of the carrying capacity of the distribution network. The absorption margin coefficient can quantitatively characterize the maximum acceptance capacity and remaining absorption space of the distribution network for new energy sources, and intuitively reflect the overall carrying capacity level of the distribution network under a high proportion of new energy access, providing clear quantitative constraints on carrying capacity for capacity planning.

[0014] Furthermore, normalized scores are applied to the parameters for evaluating power quality, safety and stability, and carrying capacity of the distribution network, respectively, to obtain comprehensive power quality, safety and stability, and carrying capacity scores for each node. Then, a node comprehensive score is obtained by weighted aggregation based on the weights of power quality, safety and stability, and carrying capacity. The node comprehensive score is then weighted by node importance to obtain the overall distribution network score. Normalized scoring eliminates dimensional differences between parameters of different dimensions, ensuring fair and comparable evaluation results. Weight allocation allows for flexible adaptation to different planning needs, node-by-node scoring accurately identifies weak nodes, and combining node importance considers both the overall and local operational levels of the distribution network, improving the rigor of the evaluation process and providing accurate and reliable support for subsequent capacity planning optimization, thus ensuring the safe and stable operation of the distribution network and the efficient consumption of new energy.

[0015] Furthermore, the weight of safety and stability is greater than the weight of carrying capacity, and the weight of carrying capacity is greater than the weight of power quality. Setting the weight of safety and stability as the highest ensures that while meeting the needs of renewable energy consumption, the capacity planning scheme prioritizes preventing grid instability, voltage collapse, and other safety risks, fundamentally guaranteeing the reliability of the distribution network. The weight of carrying capacity is the second highest, which can focus on improving the distribution network's capacity to accept and consume renewable energy under the premise of ensuring safety and stability, avoiding insufficient renewable energy consumption and capacity waste due to excessive pursuit of safety, and achieving a balance between safety and consumption. The weight of power quality is the lowest, which ensures that power quality issues such as voltage flicker and harmonics are not ignored, and that they do not affect the overall safety and consumption goals, achieving coordinated optimization of safety, consumption, and power quality.

[0016] Furthermore, the convergence condition is that after N consecutive iterations, the change in the overall distribution network score is less than a preset threshold, which can ensure the stability and reliability of the iterative optimization process, avoid invalid calculations, improve optimization efficiency, and ensure that the final capacity planning scheme reaches the comprehensive optimal state.

[0017] Furthermore, based on the scores of each node, the capacity planning parameters are adjusted according to the difference between the preset score and the actual score of the current node, using a positive correlation. Adjusting the capacity planning parameters based on the difference between the preset score and the actual score of each node allows parameter adjustments to directly target and correct node weaknesses, enabling the capacity planning scheme to accurately address weak links and achieve targeted optimization. Optimizing the parameters of each node according to the positive correlation ensures that the optimization direction always moves towards improving the overall distribution network score, guaranteeing stable convergence of the iterative process. The capacity planning parameters include access capacity, reactive power compensation capacity, and load allocation ratio. Access capacity determines the scale of renewable energy access, reflecting the upper limit of carrying capacity. Reactive power compensation capacity determines voltage support capability and harmonic suppression level, directly affecting safety, stability, and power quality. Load allocation ratio determines power flow balance, directly improving equipment capacity and system safety margin. Simultaneous adjustment of these three parameters achieves optimal synergy among multiple objectives: carrying capacity, safety, stability, and power quality.

[0018] A smart planning system for a distribution network with high penetration of new energy sources, comprising:

[0019] The data acquisition module is used to acquire operational data of the power distribution network;

[0020] The capacity planning module is used to determine the current capacity planning scheme in the distribution network based on the operating data.

[0021] The power quality assessment module is used to determine the power quality parameters of each node in the distribution network based on the operating data of the capacity planning scheme in the distribution network.

[0022] The safety and stability assessment module is used to determine the safety and stability parameters of the distribution network based on the operational simulation data of the capacity planning scheme in the distribution network.

[0023] The carrying capacity assessment module is used to determine the carrying capacity parameters of the distribution network based on the operational simulation data of the capacity planning scheme in the distribution network.

[0024] The comprehensive evaluation module is used to determine the scores of each node and the overall distribution network based on the power quality parameters, safety and stability parameters and core carrying capacity parameters of each node in the distribution network. It then adjusts the capacity planning scheme based on the scores of each node and determines convergence based on the overall distribution network score.

[0025] The data acquisition module acquires real-time operational data of the distribution network. The capacity planning module generates an initial capacity planning scheme based on the actual operating status. Then, the power quality assessment module, safety and stability assessment module, and carrying capacity assessment module conduct multi-dimensional quantitative evaluation of the current capacity planning scheme. Finally, the comprehensive assessment module completes the scoring of nodes and the overall distribution network. The access capacity planning scheme is iteratively corrected based on the score difference, and the score change is used as the convergence criterion to obtain the optimal scheme that takes into account power quality, safety and stability, and carrying capacity. This effectively improves the refinement, intelligence, and adaptability of distribution network planning under high-proportion renewable energy access.

[0026] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0027] 1. The present invention provides a smart planning method and system for distribution networks with high penetration of new energy sources. Through the coupled evaluation of power quality, safety and stability, and carrying capacity, the capacity planning scheme is modified based on the evaluation results. This effectively improves the distribution network's adaptability, safety and stability level, and absorption capacity for high-proportion new energy sources. It provides a systematic technical solution for the intelligent upgrading and transformation of distribution networks and has significant engineering application value and social and economic benefits.

[0028] 2. The present invention provides a smart planning method and system for distribution networks with high penetration of new energy sources. By using a reliability assessment algorithm based on the distribution network operation dataset, the initial capacity planning scheme is generated, which improves the accuracy and safety of the initial scheme and avoids the deviation caused by traditional experience values ​​and fixed coefficients. This lays a precise and reliable foundation for subsequent multi-dimensional iterative optimization.

[0029] 3. The present invention provides an intelligent planning method and system for distribution networks with high penetration of new energy sources. By using absorption margin coefficient, stability margin coefficient, voltage flicker value and total harmonic distortion rate of voltage as scoring parameters, the method comprehensively evaluates the operating status of the distribution network from the dimensions of carrying capacity, safety stability and power quality. This makes the scoring results more consistent with the actual operating characteristics of the power grid, and provides an accurate, reliable and comprehensive evaluation basis for the iterative correction of subsequent capacity planning schemes, ensuring the safe and stable operation of the power grid with a high proportion of new energy access. Attached Figure Description

[0030] The accompanying drawings, which are provided to further illustrate embodiments of the invention and constitute a part of this invention, are not intended to limit the scope of the invention.

[0031] Figure 1 This is a flowchart illustrating an intelligent planning method for a distribution network with high penetration of new energy sources according to the present invention.

[0032] Figure 2 This is a digital integrated architecture diagram of a smart planning system for a new energy high-penetration distribution network according to the present invention. Detailed Implementation

[0033] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, where there is no conflict, the embodiments of the present invention and the features thereof can be combined with each other.

[0034] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0035] Example 1

[0036] A smart planning method for distribution networks with high penetration of new energy sources, such as Figure 1 As shown, it includes:

[0037] Acquire distribution network operation data, determine current capacity planning parameters based on the distribution network operation data, and formulate a capacity planning scheme;

[0038] In some embodiments, based on the distribution network operation dataset, capacity planning parameters are calculated using a reliability assessment algorithm to form a capacity planning scheme. The distribution network is partitioned using the margin method and feeder margin is calculated to initially screen the capacity planning parameters. For key nodes, the minimum path method is used to search for the minimum power supply path and calculate the node reliability index. Finally, the fault traversal method is used to perform N-1 fault and capacity limit verification on the schemes that pass the initial screening to form the current capacity planning scheme.

[0039] Based on the current capacity planning scheme, simulation calculations are performed on the power quality, safety stability, and carrying capacity of the distribution network to obtain parameters for evaluating the power quality, safety stability, and carrying capacity of the distribution network. Based on parameters for evaluating power quality, safety and stability, and carrying capacity of the distribution network, quantitative scores are performed on each node and the entire distribution network to obtain the scoring results, which include the comprehensive score of each node and the overall distribution network score.

[0040] Determine whether the scoring result meets the convergence condition. If not, correct the capacity planning parameters to obtain an updated capacity planning scheme. Return the updated capacity planning scheme as the current capacity planning scheme to continue iterating until the convergence condition is met.

[0041] In some embodiments, the convergence condition is that after N consecutive iterations, the change in the overall distribution network score is less than a preset threshold. An iteration count threshold N and a score change threshold are set. Each iteration calculates the overall distribution network score and records the difference between adjacent scores. When the score difference for N consecutive iterations is less than [a certain value], [the system continues to calculate the score]. If convergence is not achieved, the current solution is output. If not, the capacity planning parameters are corrected, an updated solution is generated, and the iteration is repeated until the convergence condition is met. The preset threshold is then used. The value range is 0.1%-1.0%. This threshold takes into account both the accuracy requirements of distribution network capacity planning and the efficiency of iterative optimization, ensuring that the scheme reaches the optimal or near-optimal state after convergence.

[0042] In some embodiments, based on the scores of each node, the capacity planning parameters are adjusted according to the difference between the preset score and the actual score of the current node, using a positive correlation. These capacity planning parameters include access capacity, reactive power compensation capacity, and load allocation ratio. The preset score is taken as 100 points, and the difference between the preset score and the actual score of the current node is used to adjust the capacity planning parameters. It is positively correlated with the magnitude of parameter adjustment. The larger the value, the greater the adjustment range. The smaller the value, the smaller the adjustment range.

[0043] Calculate the difference between the preset score and the actual score of the current node. :

[0044] ;

[0045] in, The target score is set to 100 points. For the first The current actual score of each node.

[0046] Based on the difference Adjust capacity planning parameters based on positive correlation:

[0047] The capacity planning parameters include access capacity, reactive power compensation capacity, and load allocation ratio.

[0048] The capacity correction formula is:

[0049] ;

[0050] in, For the first The capacity of each node after correction For the first The capacity of each node before correction This is the access capacity correction factor. For the first The score difference between each node.

[0051] The reactive power compensation capacity correction formula is as follows:

[0052] ;

[0053] in, For the first After each node is corrected, the reactive power compensation capacity is connected. For the first The reactive power compensation capacity accessed by each node before correction This is the reactive power compensation correction factor.

[0054] The formula for correcting the load allocation ratio is:

[0055] ;

[0056] ;

[0057] in, For the first The percentage of load that each node needs to transfer out. For the first The load power that each node needs to transfer. For the first The current load power of each node For load distribution correction factor,

[0058] The load power transferred from nodes with low ratings and heavy loads is transferred to nodes with high ratings and light loads, thereby achieving a balanced distribution of load among nodes.

[0059] After the correction is completed, an updated capacity planning scheme is generated, and the overall score of the updated distribution network is calculated. And calculate the change in the overall score between two adjacent iterations:

[0060] ;

[0061] in, For the first Rate of change in overall rating across the network in the next iteration For the first Overall score of the distribution network after the next iteration For the first The overall score of the distribution network after the next iteration.

[0062] If the change in score over N consecutive iterations All less than the threshold If the iteration is successful, the adjustment is stopped and the current capacity planning scheme is output. Otherwise, the updated scheme is used as the current scheme, and the iteration continues until the convergence condition is met.

[0063] Example 2

[0064] Based on Example 1, the parameters for evaluating the power quality of the distribution network include voltage flicker and total harmonic distortion (THD). The original values ​​of voltage flicker and THD are obtained from the distribution network operation data through harmonic analysis. Based on the original values, the voltage flicker and THD are optimized using an adaptive filtering algorithm. The original parameters containing noise are extracted from the distribution network operation data through harmonic analysis, and then measurement noise and transient interference are filtered out using an adaptive filtering algorithm to obtain accurate power quality parameters, providing a reliable basis for node scoring.

[0065] For the collected instantaneous voltage value sequence Perform harmonic analysis to calculate the original values ​​of voltage flicker and total harmonic distortion (THD).

[0066] By collecting operational data from the power distribution network, the sampling frequency... Sampling duration The instantaneous voltage value sequence is obtained. ,in One sampling point.

[0067] right Perform a Fast Fourier Transform (FFT) to decompose the fundamental voltage RMS value. and the effective value of the 2nd to 50th harmonic voltage .

[0068] Total harmonic distortion of voltage The original value is calculated using the following formula:

[0069] ;

[0070] in, This is the original value of the total harmonic distortion rate of voltage (including measurement noise and transient interference).

[0071] Based on voltage instantaneous value sequence Calculate the voltage amplitude fluctuation sequence ,in This is the rated voltage.

[0072] right After performing frequency weighting and visual perception correction, the original value of voltage flicker is obtained. .

[0073] ;

[0074] in, Number of flicker-sensitive frequency bands , For the first The flicker visual sensitivity coefficient at different frequency bands.

[0075] Then use the least mean square The adaptive filtering algorithm iteratively filters the original value, updating the filter weights until the error converges. After filtering out noise interference, the optimized voltage flicker value is obtained. and voltage total harmonic distortion .

[0076] In some embodiments, the parameters for evaluating safety and stability include a stability margin coefficient, which is calculated based on distribution network operation simulation data.

[0077] Power flow calculations and operational simulations are performed on the current distribution network to obtain the current voltage amplitude at each node. Current load rate of each branch / transformer / reactive power at each node .

[0078] By gradually increasing the node load or renewable energy output, continuous power flow simulation is performed to find the critical point at which the node is about to become unstable and its voltage is about to collapse, thus obtaining the critical voltage. and critical load power .

[0079] The following formula calculates the stability margin coefficient. :

[0080] ;

[0081] Or load margin:

[0082]

[0083] in, For the first The stability margin coefficient of each node.

[0084] In some embodiments, the parameters for evaluating carrying capacity include the absorption margin coefficient, which is calculated based on distribution network operation simulation data.

[0085] The operation simulation and power flow calculation of the distribution network are performed to obtain the first... The current actual carrying power of each node and the maximum allowable power of the line or transformer The absorption margin coefficient is calculated according to the following formula:

[0086] ;

[0087] in, For the first The absorption margin coefficient of each node, The first result obtained by running the simulation The maximum active power that each node can carry. The first result obtained by running the simulation The current actual active power carried by each node.

[0088] Example 3

[0089] Based on Examples 1 and 2, normalized scores are applied to the parameters for evaluating power quality, safety and stability, and carrying capacity of the distribution network. This yields a comprehensive power quality score, a comprehensive safety and stability score, and a comprehensive carrying capacity score for each node. Then, a node comprehensive score is obtained by weighted aggregation of the power quality, safety and stability, and carrying capacity scores. Finally, the overall distribution network score is obtained by weighted aggregation of the node comprehensive scores based on node importance.

[0090] In some embodiments, the power quality score of the distribution network is obtained by weighting the total harmonic distortion (THD) score and the voltage flicker score. It is calculated using the following formula:

[0091] ;

[0092] in, For the optimized first Harmonic distortion rate of the node (National standard limit for 10kV distribution network).

[0093] The voltage flicker value score It is calculated using the following formula:

[0094] ;

[0095] in, For the optimized first The voltage flicker value of the node. (International root value).

[0096] The comprehensive power quality score for:

[0097] ;

[0098] The overall safety and stability score is:

[0099] ;

[0100] in, For the first Stability margin coefficient of each node.

[0101] The comprehensive load-bearing capacity score is:

[0102] ;

[0103] in, For the first Absorption margin coefficient of each node.

[0104] The scores for each dimension are weighted and aggregated based on the weights of power quality, safety and stability, and load-bearing capacity to obtain the result. Overall score of each node Node's overall score It reflects the overall level of a single node in terms of power quality, safety and stability, and load-bearing capacity.

[0105] ;

[0106] in, As the weight of the overall quality score, As the weight for the safety and stability score, As the weights for bearing capacity, each weight satisfies .

[0107] In some embodiments, the weight of safety stability is greater than the weight of load-bearing capacity, and the weight of load-bearing capacity is greater than the weight of power quality.

[0108] The overall score of the distribution network is obtained by weighting and aggregating the comprehensive scores of all nodes based on their importance. for:

[0109] ;

[0110] in, For the first The importance weight of each node This represents the total number of nodes in the distribution network.

[0111] Example 4

[0112] A smart planning system for power distribution networks with high penetration of new energy sources, such as Figure 1 As shown, it includes:

[0113] The data acquisition module is used to acquire operational data of the power distribution network;

[0114] The capacity planning module is used to determine the current capacity planning scheme in the distribution network based on the operating data.

[0115] The power quality assessment module is used to determine the power quality parameters of each node in the distribution network based on the operating data of the capacity planning scheme in the distribution network.

[0116] The safety and stability assessment module is used to determine the safety and stability parameters of the distribution network based on the operational simulation data of the capacity planning scheme in the distribution network.

[0117] The carrying capacity assessment module is used to determine the carrying capacity parameters of the distribution network based on the operational simulation data of the capacity planning scheme in the distribution network.

[0118] The comprehensive evaluation module is used to determine the scores of each node and the overall distribution network based on the power quality parameters, safety and stability parameters and core carrying capacity parameters of each node in the distribution network. It then adjusts the capacity planning scheme based on the scores of each node and determines convergence based on the overall distribution network score.

[0119] In some embodiments, such as Figure 2 As shown, it also includes a digital integration layer and a high-penetration new energy distribution network intelligent planning strategy layer based on the digital integration layer. The digital integration layer includes a standardized integration scheme module, a data processing module, and a business interface module, providing the system with a standardized and scalable operating environment and data processing capabilities. The intelligent planning strategy layer includes a data acquisition module, a capacity planning module, a power quality assessment module, a safety and stability assessment module, a carrying capacity assessment module, and a comprehensive assessment module, realizing a closed-loop process from data acquisition, capacity planning, multi-dimensional assessment to comprehensive scoring, scheme correction, and iterative convergence.

[0120] In some embodiments, the standardized integration solution module standardizes and encapsulates common basic components, data service components, and common application service components. Relying on container orchestration and isolation technologies such as Kubernetes (k8s) and Docker, it achieves resource isolation and runtime management of components, ensuring compatible access of multi-source heterogeneous data and business scenarios. This allows standardized modules to be flexibly combined to adapt to new energy distribution network planning scenarios of different scales and needs.

[0121] In some embodiments, the data processing module includes a data acquisition module, a data transmission module, a data analysis module, and a data calculation module.

[0122] The data acquisition module acquires operational data such as voltage, current, load, renewable energy output, and equipment status of the power distribution network through sensors, SCADA systems, and distributed energy data acquisition devices.

[0123] The data transmission module adopts a network protocol-based transmission channel and a real-time data subscription and publishing mechanism to achieve secure, efficient, and real-time communication between the virtual power plant's on-site operation data and the business system.

[0124] The data analysis module performs preprocessing on the raw data, such as cleaning, noise reduction, and format conversion, to form standardized data.

[0125] The data computing module, based on preprocessed data, executes core algorithms such as harmonic analysis, power flow calculation, and simulation to provide effective information for planning decisions.

[0126] In some embodiments, the business integration module constructs a unified business scenario implementation architecture based on real-time data, system models, and scenario optimization. Through integration with power grid-related application platforms, it achieves unified access and control of business data, user permissions, and service governance, ensuring the security permissions and governance of business, user, and service data, and enabling collaborative work between this system and the existing power grid system.

[0127] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0128] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A smart planning method for a distribution network with high penetration of new energy sources, characterized in that, include: Acquire distribution network operation data, determine current capacity planning parameters based on the distribution network operation data, and formulate a capacity planning scheme; Based on the current capacity planning scheme, simulation calculations are performed on the power quality, safety stability, and carrying capacity of the distribution network to obtain parameters for evaluating the power quality, safety stability, and carrying capacity of the distribution network. Based on parameters for evaluating power quality, safety and stability, and carrying capacity of the distribution network, quantitative scores are performed on each node and the entire distribution network to obtain the scoring results, which include the comprehensive score of each node and the overall distribution network score. Determine whether the scoring result meets the convergence condition. If not, adjust the capacity planning parameters to obtain an updated capacity planning scheme. Return the updated capacity planning scheme as the current capacity planning scheme to continue iterating until the convergence condition is met.

2. The intelligent planning method for a distribution network with high penetration of new energy sources according to claim 1, characterized in that, Based on the distribution network operation dataset, capacity planning parameters are calculated using a reliability assessment algorithm to form a capacity planning scheme.

3. The intelligent planning method for a distribution network with high penetration of new energy sources according to claim 1, characterized in that, The parameters for evaluating the power quality of the distribution network include voltage flicker and total harmonic distortion (THD). The original values ​​of voltage flicker and THD are obtained by harmonic analysis of the distribution network operation data. Based on the original values, the voltage flicker and THD are optimized using an adaptive filtering algorithm.

4. The intelligent planning method for a distribution network with high penetration of new energy sources according to claim 1, characterized in that, The parameters for evaluating safety and stability include the stability margin coefficient, which is calculated based on distribution network operation simulation data.

5. The intelligent planning method for a distribution network with high penetration of new energy sources according to claim 1, characterized in that, The parameters for evaluating carrying capacity include the absorption margin coefficient, which is calculated based on distribution network operation simulation data.

6. The intelligent planning method for a distribution network with high penetration of new energy sources according to claim 1, characterized in that, The parameters for evaluating power quality, safety and stability, and carrying capacity of the distribution network are normalized and scored respectively to obtain the comprehensive power quality score, comprehensive safety and stability score, and comprehensive carrying capacity score of each node. Then, the node comprehensive score is obtained by weighted aggregation based on the weights of power quality, safety and stability, and carrying capacity. The node comprehensive score is obtained by weighted aggregation based on the node importance.

7. The intelligent planning method for a distribution network with high penetration of new energy sources according to claim 6, characterized in that, The weight of safety and stability is greater than the weight of bearing capacity, and the weight of bearing capacity is greater than the weight of power quality.

8. The intelligent planning method for a distribution network with high penetration of new energy sources according to claim 1, characterized in that, The convergence condition is that after N consecutive iterations, the change in the overall distribution network score is less than a preset threshold.

9. The intelligent planning method for a distribution network with high penetration of new energy sources according to claim 1, characterized in that, Based on the scores of each node, the capacity planning parameters are adjusted according to the difference between the preset score and the actual score of the current node, with a positive correlation. The capacity planning parameters include access capacity, reactive power compensation capacity, and load allocation ratio.

10. A smart planning system for a distribution network with high penetration of new energy sources, characterized in that, include: The data acquisition module is used to acquire operational data of the power distribution network; The capacity planning module is used to determine the current capacity planning scheme in the distribution network based on the operating data. The power quality assessment module is used to determine the power quality parameters of each node in the distribution network based on the operating data of the capacity planning scheme in the distribution network. The safety and stability assessment module is used to determine the safety and stability parameters of the distribution network based on the operational simulation data of the capacity planning scheme in the distribution network. The carrying capacity assessment module is used to determine the carrying capacity parameters of the distribution network based on the operational simulation data of the capacity planning scheme in the distribution network. The comprehensive evaluation module is used to determine the scores of each node and the overall distribution network based on the power quality parameters, safety and stability parameters and core carrying capacity parameters of each node in the distribution network. It then adjusts the capacity planning scheme based on the scores of each node and determines convergence based on the overall distribution network score.

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