Power distribution network distributed power supply multi-scenario collaborative planning method and device

Abstract

This invention provides a multi-scenario collaborative planning method for distributed power sources in power distribution networks. First, it acquires actual operating data, load distribution information, and renewable energy access information of the power distribution network. Based on the load distribution information and renewable energy access information, it determines the operating scenarios of the power distribution network and identifies sensitive factors. Then, based on the actual operating data, operating scenarios, sensitive factors, and a pre-established collaborative planning optimization model, it determines the energy storage alternative adaptation scheme for the power distribution network. This model includes an objective function and an uncertainty function. The objective function includes uncertainty parameters. The uncertainty function is used to calculate the impact of source-load-storage uncertainties on the uncertainty parameters. This invention synchronously couples the multi-dimensional fluctuation uncertainties of source, load, and storage to the objective function, fully adapting to the actual operating characteristics of power distribution networks under multiple operating conditions and multi-variable random fluctuations, effectively improving the feasibility, safety, and life-cycle economic benefits of energy storage configuration schemes.

Description

Technical Field

[0001] This invention relates to the field of distribution network planning technology, and in particular to a method and apparatus for multi-scenario collaborative planning of distributed power sources in distribution networks. Background Technology

[0002] With the large-scale integration of distributed photovoltaic, wind power, and other new energy sources into the distribution network, the operation scenarios of the distribution network are becoming increasingly complex, and the load characteristics are showing significant differences. At the same time, problems such as the absorption difficulties caused by the volatility and intermittency of new energy output, excessive peak-valley load differences, and the imbalance between the economic efficiency and security of grid operation are becoming increasingly prominent. As a core device for smoothing load fluctuations, improving the absorption capacity of new energy sources, and ensuring the stable operation of the distribution network, the cost, technical characteristics, and adaptability to distribution network scenarios directly determine the feasibility and effectiveness of planning schemes.

[0003] As a core regulatory resource for smoothing fluctuations, peak shaving and valley filling, improving the absorption of new energy sources, and delaying grid capacity expansion, distributed energy storage can replace the rigid transformation of traditional power grids. The rationality of its configuration scheme directly determines the safe operation level of the distribution network and the investment benefits throughout its entire life cycle.

[0004] Currently, the planning of distribution networks under distributed power sources and diversified energy storage often fails to adequately consider the complexity of actual operation of the distribution network, resulting in planning schemes that do not conform to the actual situation. Summary of the Invention

[0005] This application provides a method and apparatus for collaborative planning of distributed power sources in multiple scenarios in a distribution network, in order to solve the technical problem in related technologies that do not adequately consider the complexity of the actual operation of the distribution network, resulting in planning schemes that do not conform to the actual situation.

[0006] This invention provides a multi-scenario collaborative planning method for distributed generation in power distribution networks, including: Acquire actual operating data, load distribution information, and new energy access information of the power distribution network; Based on load distribution information and new energy access information, determine the operating scenarios of the distribution network and identify the sensitive factors under the operating scenarios; Based on actual operating data, operating scenarios, sensitive factors under operating scenarios, and pre-established collaborative planning and optimization models, determine the energy storage alternative adaptation scheme for the distribution network; The collaborative planning optimization model includes an objective function and an uncertainty function; the objective function includes uncertainty parameters; and the uncertainty function is used to calculate the impact of source-load-storage uncertainty on the uncertainty parameters.

[0007] In one possible implementation, the objective function includes an upper-level objective and a lower-level objective; the upper-level objective includes a first uncertainty parameter; the lower-level objective includes a second uncertainty parameter; based on actual operating data, operating scenarios, sensitive factors within those scenarios, and a pre-established collaborative planning optimization model, an energy storage alternative adaptation scheme for the distribution network is determined, including: Based on the sensitive factors in the operating scenario, determine the first uncertainty parameter and the second uncertainty parameter; By inputting actual operating data and operating scenarios into the collaborative planning and optimization model, an energy storage alternative adaptation scheme for the distribution network is obtained.

[0008] In one possible implementation, the upper-level objective is to maximize the overall safety performance of the distribution network; the lower-level objective is to minimize the annual cost difference between the planned scheme and the benchmark scheme; based on the sensitive factors in the operating scenario, the first uncertainty parameter and the second uncertainty parameter are determined, including: The sensitive factors are input into the uncertainty function to determine the source-load-storage uncertainty matrix; where each row of the source-load-storage uncertainty matrix corresponds to one of the three main entities, and each column corresponds to a sensitive factor. Based on the source-load-storage uncertainty matrix, determine the first uncertainty parameter and the second uncertainty parameter.

[0009] In one possible implementation, the first uncertainty parameter and the second uncertainty parameter are determined based on the source-load storage uncertainty matrix, including: The first uncertainty parameter is determined based on the matrix elements corresponding to the source and load entities within the source-load-storage uncertainty matrix. The second uncertainty parameter is determined based on the matrix elements corresponding to the energy storage entity and cost-sensitive factors within the source-load-storage uncertainty matrix.

[0010] In one possible implementation, before inputting actual operating data and operating scenarios into the collaborative planning and optimization model to obtain the energy storage alternative adaptation scheme for the distribution network, the method further includes: Obtain the energy storage duration requirements for the operational scenario; Determine the type of energy storage battery based on the required energy storage duration; By inputting actual operational data and scenarios into the collaborative planning and optimization model, energy storage alternatives for the distribution network are obtained, including: By inputting the type of energy storage battery, actual operating data, and operating scenarios into the collaborative planning and optimization model, an energy storage alternative adaptation scheme for the distribution network is obtained.

[0011] In one possible implementation, load distribution information includes peak load data, load time-series curves, transformer area load capacity, electric vehicle penetration rate, and peak-valley load difference; renewable energy access information includes distributed photovoltaic installed capacity, wind power output time-series data, renewable energy penetration rate, and historical renewable energy consumption data; based on the load distribution information and renewable energy access information, the operating scenario of the distribution network is determined, including: The load distribution characteristics are determined based on peak load data, load time-series curves, transformer area load capacity, electric vehicle penetration rate, and load peak-valley difference. Based on the installed capacity of distributed photovoltaic power, wind power output time series, renewable energy penetration rate and historical data of renewable energy consumption, the characteristics of renewable energy access are determined. Based on load distribution characteristics and new energy access characteristics, the operation scenarios of the distribution network are determined.

[0012] In one possible implementation, the sensitive factors in the runtime scenario are identified, including: Obtain multiple preset factors; Based on load distribution characteristics and new energy access characteristics, sensitive factors for the operating scenario are selected from multiple preset factors.

[0013] In one possible implementation, after determining the energy storage alternative adaptation scheme for the distribution network, the method further includes: Based on the safety operation indicators of the energy storage alternative adaptation scheme, determine the sensitivity coefficients of each sensitive factor; Based on the sensitivity coefficients of each sensitive factor and the safety constraints of the distribution network, the critical thresholds of the sensitive factors under the energy storage alternative adaptation scheme are determined.

[0014] In one possible implementation, when the output of the collaborative planning optimization model indicates that no energy storage alternative solution exists, the method further includes: The first factor is selected from the sensitive factors, which is the most sensitive factor that has the greatest impact on the distribution network planning; Based on the first factor, determine the direction for optimizing and adjusting the distribution network.

[0015] This invention provides a multi-scenario collaborative planning device for distributed power sources in a distribution network, comprising: The acquisition module is used to acquire actual operating data, load distribution information, and new energy access information of the power distribution network. The determination module is used to determine the operating scenario of the distribution network based on load distribution information and new energy access information, and to determine the sensitive factors under the operating scenario; The optimization module is used to determine the energy storage alternative adaptation scheme for the distribution network based on actual operating data, operating scenarios, sensitive factors under the operating scenarios, and pre-established collaborative planning optimization models. The collaborative planning optimization model includes an objective function and an uncertainty function; the objective function includes uncertainty parameters; and the uncertainty function is used to calculate the impact of source-load-storage uncertainty on the uncertainty parameters.

[0016] Compared to traditional technologies, this invention provides a method and apparatus for multi-scenario collaborative planning of distributed power sources in distribution networks. It acquires actual operating data, load distribution information, and renewable energy access information of the distribution network. Based on the load distribution information and renewable energy access information, it determines the operating scenarios of the distribution network and identifies sensitive factors within those scenarios. Based on the actual operating data, operating scenarios, sensitive factors within those scenarios, and a pre-established collaborative planning optimization model, it determines energy storage alternatives and adaptation schemes for the distribution network. The collaborative planning optimization model includes an objective function and an uncertainty function. The objective function includes uncertainty parameters. The uncertainty function is used to calculate the impact of source-load-storage uncertainties on the uncertainty parameters. This invention synchronously couples the multi-dimensional fluctuation uncertainties of source, load, and storage to the objective function, fully adapting to the actual operating characteristics of distribution networks under multiple operating conditions and multi-variable random fluctuations, effectively improving the feasibility, safety, and life-cycle economic benefits of energy storage configuration schemes. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the implementation of the multi-scenario collaborative planning method for distributed power sources in a power distribution network provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of the multi-scenario collaborative planning device for distributed power sources in the power distribution network provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart illustrating the implementation of the multi-scenario collaborative planning method for distributed power sources in a distribution network provided in this embodiment of the invention. Figure 1 As shown, the method includes: S110, acquires actual operating data of the power distribution network, load distribution information and new energy access information; S120, based on load distribution information and new energy access information, determine the operation scenario of the distribution network and identify the sensitive factors under the operation scenario; S130, based on actual operating data, operating scenarios, sensitive factors under operating scenarios, and pre-established collaborative planning optimization models, determine the energy storage alternative adaptation scheme for the distribution network; The collaborative planning optimization model includes an objective function and an uncertainty function; the objective function includes uncertainty parameters; and the uncertainty function is used to calculate the impact of source-load-storage uncertainty on the uncertainty parameters.

[0020] In this embodiment of the invention, the actual operation data of the power distribution network is divided into five categories: basic data of power grid equipment, load time-series operation data, distributed new energy operation data, benchmark parameter data of energy storage industry, and regional constraints and economic cost data. Specifically, the basic data of power grid equipment can be topology and node parameters, line equipment parameters, distribution transformer parameters, etc.

[0021] Load distribution information includes: peak load data, load time series curves, transformer area load capacity, electric vehicle penetration rate, and load peak-valley difference; renewable energy access information includes: distributed photovoltaic installed capacity, wind power output time series, renewable energy penetration rate, and renewable energy consumption historical data; specifically, data collection can be completed through distribution network SCADA systems, distribution automation terminals, transformer area smart meters, and distributed renewable energy monitoring platforms.

[0022] In this embodiment of the invention, load distribution characteristics are extracted from load data, and renewable energy access characteristics are extracted from photovoltaic / wind power data. The two types of characteristics are fused to determine typical operating scenarios of the distribution network. Sensitive factors specifically include energy storage cost factors, load factors, renewable energy factors, and grid security factors. Among them, energy storage cost factors can be the annual value of the full life cycle cost of different types of energy storage; load factors can be peak load rate, overload load difference, load fluctuation coefficient, peak duration, etc.; renewable energy factors can be renewable energy output fluctuation coefficient, renewable energy penetration rate, and grid absorption rate constraints, etc.; grid security factors can be node voltage deviation threshold, line current carrying capacity limit, and grid expansion and renovation unit price, etc.

[0023] For example, sensitive factors can be quantified in the following ways: Combining the operating mechanism of power distribution networks, parameter standards of the energy storage industry, and the output characteristics of new energy sources, we have developed specific quantitative calculation formulas and evaluation standards for different types of sensitive factors. All quantitative indicators have been uniformly normalized and mapped to the 0-1 value range to facilitate unified calculation by the model. 1. Differentiated Energy Storage Cost Factors: Using the annual cost over the entire lifecycle as the core quantitative indicator, and based on the annual equivalent total cost, the system comprehensively calculates the average annual equivalent initial investment cost, annual operation and maintenance cost, average annual amortization cost of equipment replacement, and average annual deductible income from the residual value at the end of the period for different types of energy storage. Specifically, lithium iron phosphate batteries focus on quantifying the annual cost under short-term cycle conditions, lithium-ion / sodium-ion batteries consider the annual cost under medium-frequency peak-shaving conditions, and flow batteries focus on the average annual comprehensive cost under large-capacity, long-term energy storage conditions. The calculation formula is as follows: in, AC This represents the annual value of the energy storage's total lifecycle cost. C ann,invest The average annual cost is calculated based on the initial investment. Cope For annual operation and maintenance costs of energy storage, C res This is the residual value of the equipment at the end of the period. r The discount rate is... T This refers to the entire lifespan of energy storage equipment. Energy storage costs directly affect the initial investment; the greater the initial investment, the higher the annual value of the total lifespan cost.

[0024] 2. Load Amplitude Factor: A combination of peak load rate and load exceeding the allowable load rate is used to quantify the load volume and supply-demand gap in different scenarios. The peak load rate is the ratio of the actual load in a single time period to the maximum allowable load of the area; the load exceeding the allowable load rate (generally 80%) is the difference between the maximum load and the load corresponding to the maximum allowable load rate, used to accurately calculate the peak-shaving load that energy storage needs to handle in different application scenarios. The peak load rate and the load exceeding the allowable load rate directly determine the basic energy storage configuration capacity. In older residential areas, the greater the peak-valley difference during the evening peak hours, the higher the required short-to-medium-term peak-shaving energy storage capacity and the greater the investment required for energy storage.

[0025] 3. Load Timing Characteristics: Two quantitative indicators, load fluctuation coefficient and peak duration, are introduced, and typical load curve characteristics are classified and calibrated. The severity of load fluctuation is quantified through time-series variance. Combining the time-series distribution characteristics of three typical curve types—evening peak, midday peak, and duck curve—three time-series levels are classified: stable load, short-term fluctuation, and long-term mismatch, to suit different energy storage durations in various scenarios. The duration of peak load determines the appropriate energy storage battery type. Short-term midday peak loads only require low-cost lithium iron phosphate batteries for peak shaving; for evening peak loads lasting 3-6 hours, sodium-ion and lithium-ion batteries are optimally suited; in duck curve scenarios, new energy sources and loads are mismatched throughout the day, necessitating long-term power transfer using flow batteries. Time-series characteristics are a prerequisite for determining the appropriate energy storage type.

[0026] In some embodiments, the objective function includes an upper-level objective and a lower-level objective; the upper-level objective is provided with a first uncertainty parameter; the lower-level objective is provided with a second uncertainty parameter; and the energy storage alternative adaptation scheme for the distribution network is determined based on actual operating data, operating scenarios, sensitive factors under the operating scenarios, and a pre-established collaborative planning optimization model, including: determining the first uncertainty parameter and the second uncertainty parameter based on sensitive factors under the operating scenarios; and inputting the actual operating data and operating scenarios into the collaborative planning optimization model to obtain the energy storage alternative adaptation scheme for the distribution network.

[0027] In this embodiment of the invention, a collaborative planning model for distributed power sources and multi-element energy storage is constructed, with the safe operation of the distribution network as a hard constraint and the optimal economic performance of the power grid throughout its entire life cycle as the objective function. The specific architecture is as follows: Objective function setting: a two-layer objective architecture. The upper layer aims to maximize the safety of the distribution network operation, while the lower layer aims to minimize the annual comprehensive cost over the entire life cycle, under the premise of satisfying all safety constraints of the upper layer. It considers energy storage configuration and construction schemes and power grid construction schemes, and determines the economics of replacing energy storage through comparative analysis.

[0028] Upper-level objective: Maximize the overall security performance of the distribution network, i.e., max F 1. This objective quantifies the power grid safety level from two core dimensions: voltage deviation and line overload.

[0029] in, N The total number of distribution network nodes. M This represents the total number of transmission lines in the power distribution network. U i,t for t Actual voltage at time node U N The node's rated voltage; P line,j,t for t Real-time line transmission power, P line,j,max This represents the maximum allowable transmission power of the line.

[0030] Lower-level objective: Minimize the difference in annual costs between the planned solution and the benchmark solution, i.e., Min F 2.

[0031] Min F 2=∆C=C G -C ES Where ∆C represents the annual comprehensive cost difference; C G The total annual cost over the entire lifecycle of a distributed power generation and multi-energy storage synergistic planning scheme; C ES The annual comprehensive cost for the entire life cycle of the preset benchmark reference scheme (a traditional pure power grid investment and transformation scheme that relies solely on line expansion and transformer capacity increase).

[0032] The constraints of the collaborative planning optimization model can include various types of energy storage power capacity constraints, load supply and demand balance constraints, maximum absorption constraints of new energy sources, time-series constraints of differentiated load characteristics, grid topology and grid connection node constraints, threshold constraints of various sensitive factors, etc., which fully conform to the actual operation boundaries of multiple scenarios.

[0033] In this embodiment of the invention, based on the above objective function, a first uncertainty parameter and a second uncertainty parameter are introduced. The first uncertainty parameter is obtained by extracting only the sensitive factor elements corresponding to the source and load subjects within the matrix and performing calculations. It is used to quantify the disturbance amplitude of distributed new energy output fluctuations and regional load random fluctuations on grid safety indicators such as node voltage and line transmission power. It is superimposed as a correction term into the upper-level safety indicator calculation formula to simulate extreme random operating conditions such as sudden load increases, sudden drops in photovoltaic power generation, and large deviations in wind power output. The corrected safety indicators serve as hard constraints on the model to ensure that the planned energy storage configuration will not experience voltage over-limit or line overload problems under source-load uncertainty disturbances. Specifically, when the first uncertainty parameter is too large, it indicates a higher degree of random disturbances such as sudden load increases and drastic fluctuations in renewable energy output. The node voltage offset and line transmission overload, after parameter correction, are simultaneously amplified, significantly reducing the overall safety performance value of the distribution network calculated by the upper-level objective function. The model will automatically tighten the upper limits of energy storage configuration power and capacity, and even increase the regulation of energy storage charging and discharging to offset the safety risks caused by load fluctuations. If the parameter is too high, causing all energy storage configuration combinations to fail to meet voltage and line current carrying safety constraints, the upper-level feasible solution set is directly cleared, and the model determines that there is no alternative energy storage solution. When the first uncertainty parameter is too small, it indicates stable operation of load and renewable energy output with weak fluctuations. The deviation of the corrected grid safety indicators is extremely small, the overall safety performance value calculated by the upper-level objective function is higher, and the grid safety constraints are relaxed. The model can loosen the restrictions on the scale of energy storage configuration and fully rely on the lower-level economic objectives to seek the optimal energy storage capacity and grid connection scheme. When the first uncertainty parameter is equal to 1... When the source load has no additional random fluctuations, the upper objective function reverts to the traditional static security calculation mode with a fixed typical time series. The security verification is completed only based on the steady-state load and the output of new energy sources, ignoring the potential over-limit risks brought about by random fluctuations in the source load during actual operation. As a result, the planned energy storage configuration has insufficient security margin.

[0034] The second uncertainty parameter is obtained by solving the elements corresponding to the energy storage entity and energy storage cost-sensitive factors within the matrix. It is used to characterize the life-cycle cost deviation caused by fluctuations in cost indicators such as initial investment, annual operation and maintenance, replacement amortization, equipment life, and discount rate of various energy storage systems. Substituting it into the lower-level annual comprehensive cost difference calculation formula, the total annual cost of the energy storage synergy scheme is corrected, accurately taking into account the long-term fluctuations in energy storage equipment cost, cycle loss, and operation and maintenance expenditures. This allows for an objective comparison of the true economics of the energy storage scheme with the traditional grid capacity expansion and transformation scheme, thereby determining the feasibility boundary of energy storage substitution and transformation. Specifically, the uncorrected total annual static cost of the energy storage synergy scheme is first obtained by weighted summing of the various energy storage configuration capacities and the static annualized cost without fluctuations. Then, this total static cost is multiplied by the second uncertainty parameter to obtain the corrected total annual comprehensive cost of the energy storage synergy scheme after taking into account the random fluctuations of various costs throughout the entire life cycle of energy storage. The second uncertainty parameter being greater than 1 indicates that the overall cost of energy storage has increased, raising the annual comprehensive cost; being less than 1 indicates that the overall cost of energy storage has decreased, reducing the annual comprehensive cost; and being equal to 1 indicates that the cost has no fluctuation and is consistent with the traditional static calculation results.

[0035] In this embodiment of the invention, the model employs a double-layer nested intelligent optimization algorithm for iterative solution. The upper-layer safety optimization layer prioritizes traversing different combinations of energy storage power, capacity, and grid connection points. It verifies all operating conditions by combining the full-time voltage and line load indicators after correction by the first uncertainty parameter, eliminating energy storage configurations with voltage overruns or line overloads, and retaining only feasible solutions that meet the dynamic safety boundary to pass to the lower layer. Within the feasible solution set, the lower-layer economic optimization layer takes the minimum difference between the annual cost of the corrected energy storage scheme and the annual cost of traditional grid upgrades as the optimization objective, iteratively adjusting the energy storage installed capacity, the ratio of multiple types of energy storage, and the time-sharing charging and discharging operation strategy until the double-layer objective values ​​are stably converged. After convergence, the model performs a dual judgment: on the one hand, it verifies that all grid safety indicators meet the standards under all source-load fluctuation conditions in the full-time sequence; on the other hand, it compares whether the total annual cost of the energy storage scheme after correction by the second uncertainty parameter is lower than the benchmark upgrade scheme relying solely on line and transformer capacity expansion. When both conditions are met, a standardized energy storage alternative adaptation scheme is output.

[0036] Among them are: energy storage alternative adaptation schemes, energy storage combinations adapted to different scenarios, optimal energy storage power capacity, grid connection nodes and topology, time-sharing charging and discharging coordination strategies, economic and safety critical thresholds for various sensitive factors, and a comparison table of investment returns throughout the entire life cycle.

[0037] In some embodiments, the upper-level objective is to maximize the overall safety performance of the distribution network; the lower-level objective is to minimize the annual cost difference between the planned scheme and the benchmark scheme; based on the sensitive factors in the operating scenario, the first uncertainty parameter and the second uncertainty parameter are determined, including: inputting the sensitive factors into the uncertainty function to determine the source-load-storage uncertainty matrix; wherein, each row in the source-load-storage uncertainty matrix corresponds to one of the main entities of source, load, and storage, and each column corresponds to a sensitive factor; based on the source-load-storage uncertainty matrix, the first uncertainty parameter and the second uncertainty parameter are determined.

[0038] In this embodiment of the invention, for the currently defined typical operating scenarios of the distribution network, the sensitive factors selected in the scenario are retrieved and normalized to the range of 0 to 1. These factors include indicators such as load peak rate, load fluctuation coefficient, new energy output fluctuation coefficient, annual value of the full life cycle cost of various energy storage, node voltage deviation threshold, line current carrying constraint, and energy storage replacement amortization cost. The complete set of standardized sensitive factor quantified values ​​is then input into the preset uncertainty function module.

[0039] Next, the source-load-storage uncertainty matrix is ​​generated. The matrix consists of three rows. The first row represents the source-side entities (distributed photovoltaic and decentralized wind power), the second row represents the load-side entities (residential load, electric vehicle charging load, and seasonal temporary load), and the third row represents the storage-side entities (multi-element energy storage devices such as lithium iron phosphate, sodium ion, and vanadium redox flow). Each column of the matrix corresponds to each of the previously input sensitive factors. Each cell of the matrix is ​​filled with the standard deviation of the fluctuation and the random upward and downward fluctuation range of the corresponding entity under the dimension of that sensitive factor, based on the statistical data obtained from historical operation data. This quantifies the uncertainty strength of the source, load, and storage under different sensitive factors, thus generating the complete source-load-storage uncertainty matrix.

[0040] After constructing the source-load-storage uncertainty matrix, the matrix is ​​split and calculated by region. The values ​​of all sensitive factor columns corresponding to the source main row and load main row of the matrix are extracted separately and weighted to quantify the comprehensive disturbance magnitude of load and new energy random fluctuations on the power grid security index. The first uncertainty parameter used for the upper-level security target is obtained. Then, the cell values ​​corresponding to the storage main row and all cost-related sensitive factor columns of the matrix are extracted separately and comprehensively calculated to quantify the overall fluctuation level of various energy storage investment, operation and maintenance, replacement and other cost indicators. The second uncertainty parameter used for the lower-level economic target is obtained.

[0041] In some embodiments, determining a first uncertainty parameter and a second uncertainty parameter based on the source-load-storage uncertainty matrix includes: determining the first uncertainty parameter based on the matrix elements corresponding to the source and load entities within the source-load-storage uncertainty matrix; and determining the second uncertainty parameter based on the matrix elements corresponding to the energy storage entity and cost-sensitive factors within the source-load-storage uncertainty matrix.

[0042] In this embodiment of the invention, for the first uncertainty parameter, all cell elements in the first row (source side) and the second row (load side) of the matrix are first completely extracted. The cells store quantitative values ​​such as the variance of new energy output fluctuation, random deviation of load peak, load time-series fluctuation amplitude, and reverse power flow fluctuation amount obtained based on historical operating data statistics. All elements related to the energy storage entity in the third row are not used. Then, differentiated weights are configured according to the degree of impact of each sensitive factor on grid security. Sensitive factors related to new energy output fluctuation, sudden increase in evening peak load, and line overload are assigned high weights, while indicators with small fluctuations in stable load are assigned low weights. The values ​​of all elements in the two rows are multiplied by their corresponding weights and then summed to obtain the first uncertainty parameter.

[0043] For the second uncertainty parameter, only the entire data of the third row of the matrix for the energy storage entity is selected. Then, the matrix elements corresponding to cost-sensitive factors such as the average annual cost of initial investment in energy storage, annual operation and maintenance costs, battery replacement amortization, equipment residual value at the end of the period, and discount rate are screened from all columns in this row. The column data corresponding to non-cost-sensitive factors such as load amplitude, new energy fluctuations, and voltage thresholds are directly removed. The weights of each item are set according to the proportion of the full life cycle cost structure of short-term lithium iron phosphate, medium- and long-term sodium-ion, and long-term flow batteries. The initial investment in energy storage has the highest weight, followed by operation and maintenance and replacement costs, and residual value deduction has a negative weight. All the screened cost matrix elements are matched with their corresponding weights to complete the weighted fusion calculation and output the second uncertainty parameter.

[0044] In some embodiments, before inputting actual operating data and operating scenarios into the collaborative planning and optimization model to obtain an energy storage alternative adaptation scheme for the distribution network, the method further includes: obtaining the energy storage duration requirement of the operating scenario; determining the energy storage battery type based on the energy storage duration requirement; correspondingly, inputting actual operating data and operating scenarios into the collaborative planning and optimization model to obtain an energy storage alternative adaptation scheme for the distribution network includes: inputting the energy storage battery type, actual operating data, and operating scenario into the collaborative planning and optimization model to obtain an energy storage alternative adaptation scheme for the distribution network.

[0045] In this embodiment of the invention, a complete set of calibration data for the current planned distribution network operation scenario is retrieved, including quantitative indicators such as scenario load time-series curves, renewable energy output time-series, annual load duration statistics, peak-valley mismatch time intervals, overload duration, and the duration required for storing surplus photovoltaic power during the day. Specific duration features are extracted for five typical scenarios: for the electric vehicle scenario in older residential areas, the duration of evening peak load is extracted; for the short-term heavy load and temporary tourist load scenario in urban areas, the duration of a single annual load surge is extracted; for the high photovoltaic absorption scenario in rural areas, the duration of time-series mismatch between midday photovoltaic power generation and evening load demand is extracted; and for the voltage regulation scenario in remote mountainous areas, the duration of daily energy storage required to ensure voltage stability is extracted.

[0046] Based on the load peak shaving gap and the total surplus electricity from renewable energy sources, and combined with the upper limit of the rated charging and discharging power of energy storage, the required continuous operating time of energy storage for each scenario is calculated using a time-series power balance formula, forming a standardized threshold for energy storage duration requirements. If the load peak lasts only 0-4 hours, it is determined as short-term energy storage demand; if the load adjustment range is 2-4 hours, it is determined as medium- to long-term energy storage demand; if renewable energy transfers across the day and the continuous peak shaving duration exceeds 4 hours throughout the day, it is determined as long-term energy storage demand. In older residential areas with limited installation space, large-capacity long-term energy storage cannot be configured, so the weight of long-term energy storage demand will be appropriately reduced; in rural areas with ample space and continuous demand throughout the day, the lower limit of long-term energy storage duration requirement will be forcibly raised; in remote mountainous areas without expansion conditions, energy storage is required for all-day voltage regulation, so the standard for energy storage operating time will be extended accordingly. Finally, a clear range of energy storage duration requirements for each scenario is output, serving as a rigid criterion for selecting energy storage batteries.

[0047] Energy storage duration requirements can specifically include short-term energy storage requirements, medium- and long-term energy storage requirements, long-term energy storage requirements, and energy storage requirements for composite and coupled scenarios.

[0048] For short-term energy storage (0-4 hours, instantaneous load shaving, and smoothing of renewable energy power), lithium iron phosphate (LFP) batteries are suitable. These batteries offer millisecond-level fast response, high power density, and are ideal for short-term charge-discharge cycles within a day. They meet the needs of short-term peak shaving in older residential areas during evening rush hours, seasonal load smoothing in tourist areas, and smoothing of instantaneous photovoltaic power output fluctuations. Sodium-ion and flow batteries are not used to avoid resource waste caused by excessively high initial investment in long-term energy storage equipment. For medium- to long-term energy storage (2-4 hours, continuous load transfer, and low-temperature distribution area regulation), sodium-ion batteries are suitable. They offer excellent low-temperature performance, lower raw material costs, and a cycle life suitable for continuous peak shaving conditions of 2-4 hours per day. They are often used in older residential areas in northern regions and for seasonal load transfer scenarios in urban areas. If there are combined short-term instantaneous fluctuations with medium- to long-term peak shaving, a hybrid configuration of LFP and sodium-ion batteries is used. For long-term energy storage needs (over 4 hours, including daytime renewable energy consumption and all-day voltage support), vanadium redox flow batteries are ideal, offering advantages such as power and capacity decoupling, ultra-long cycle life of over 10,000 cycles, and intrinsic safety. They perfectly adapt to the timing mismatch of high-penetration solar power in rural areas and the all-day end-point power supply voltage regulation scenarios in remote mountainous regions. They can store surplus solar power during the day for extended periods and achieve stable discharge throughout the day, enabling on-site renewable energy consumption. This overcomes the shortcomings of lithium-ion and sodium-ion batteries, such as insufficient large-capacity, long-term cycle life and high overall cost. If the same feeder simultaneously experiences both short-term load fluctuations and long-term renewable energy consumption needs, the system will not select a single type of energy storage. Instead, it will automatically output a multi-energy storage combination scheme based on the proportion of electricity demanded in both scenarios. The short-term adjustment portion will be configured with lithium iron phosphate batteries, while the long-term power transfer portion will be configured with flow batteries, forming a hybrid energy storage collaborative system. Various energy storage technical parameters and charge / discharge constraints will be simultaneously input into the subsequent collaborative planning and optimization model.

[0049] In this embodiment of the invention, the matched energy storage battery type parameters, the actual operation data of the distribution network in all dimensions, and the operation scenarios that have been divided and bound with exclusive sensitive factors are first uniformly input into the collaborative planning optimization model. The model loads the first and second uncertainty parameters calculated in advance to complete the initialization of the objective function, and then a double-nested intelligent optimization algorithm is used to solve the problem iteratively. The upper-level safety optimization layer prioritizes traversing various combinations of energy storage configurations with different power, capacity, and grid connection points. Throughout the process, it performs safety checks on all operating conditions using full-time node voltage and line load indicators corrected by the first uncertainty parameter. Energy storage configurations that may cause voltage overruns or line overloads are directly eliminated. Only feasible solutions that can meet the dynamic safety boundary under random source-load fluctuations are passed down to the lower-level economic optimization layer. The lower-level economic optimization layer iteratively optimizes within this feasible solution set, with the core optimization objective being to minimize the difference between the annual comprehensive cost of the energy storage synergy scheme corrected by the second uncertainty parameter and the annual cost of the benchmark scheme for traditional grid capacity expansion and renovation. It continuously iterates and adjusts the overall installed capacity of energy storage, the ratio of short-term / medium-term / long-term multi-type energy storage, and the energy storage time-segmented charging and discharging synergy operation strategy. This process is repeated until the calculated values ​​of the upper-level safety objective and the lower-level economic objective are stable and converge, and no longer show significant fluctuations. After the iterative convergence is completed, the model automatically performs a dual feasibility judgment. The first judgment verifies that all power grid safety indicators meet the specifications under the random fluctuation of all time periods and all sources and loads. The second judgment compares whether the total annual cost of the energy storage scheme after correction by the second uncertainty parameter is lower than the traditional transformation scheme that simply relies on line expansion and distribution transformer capacity increase. When both judgment conditions are met at the same time, the model directly outputs the distribution network energy storage alternative adaptation scheme.

[0050] In some embodiments, load distribution information includes peak load data, load time-series curves, transformer area load capacity, electric vehicle penetration rate, and peak-valley load difference; renewable energy access information includes distributed photovoltaic installed capacity, wind power output time-series, renewable energy penetration rate, and historical renewable energy consumption data; based on the load distribution information and renewable energy access information, the operating scenario of the distribution network is determined, including: determining load distribution characteristics based on peak load data, load time-series curves, transformer area load capacity, electric vehicle penetration rate, and peak-valley load difference; determining renewable energy access characteristics based on distributed photovoltaic installed capacity, wind power output time-series, renewable energy penetration rate, and historical renewable energy consumption data; and determining the operating scenario of the distribution network based on the load distribution characteristics and renewable energy access characteristics.

[0051] In this embodiment of the invention, five types of load information are retrieved for quantitative analysis: peak load data, load time-series curves, transformer area load capacity, electric vehicle penetration rate, and peak-valley difference. The peak rate is calculated by comparing the peak load with the transformer area's rated capacity to determine the transformer area's overload level. Peak-valley difference levels are categorized based on the peak-valley difference. Peak periods, durations, and variances are extracted from the time-series curves to differentiate load time-series types. The charging load impact intensity is assessed by combining the electric vehicle penetration rate. The quantitative results related to transformer area load volume, fluctuation patterns, and new load impacts are integrated to form a complete and standardized load distribution characteristic. New energy access characteristics are extracted based on distributed photovoltaic installed capacity, wind power output time-series data, new energy penetration rate, and historical new energy consumption data. Penetration levels are categorized by the ratio of total new energy installed capacity to transformer area load. Wind and solar power output time-series data are analyzed to identify output patterns and statistically analyze the duration of source-load time-series mismatch. Historical curtailment and reverse power flow data are combined to determine the degree of consumption bottlenecks. A unified new energy access characteristic label is generated by comprehensively considering the scale of new energy installed capacity, output fluctuation patterns, and consumption constraint strength.

[0052] The extracted load distribution characteristics are cross-matched and comprehensively compared with the new energy access characteristics. Based on the preset quantitative threshold, five typical scenarios are automatically divided: electric vehicle capacity expansion in old residential areas, short-term peak overload in urban areas, temporary load surge in tourist areas, end-point power supply in remote mountainous areas, and high-penetration new energy consumption in rural areas.

[0053] In this embodiment of the invention, the power distribution network is divided into three typical scenarios: urban, remote mountainous areas, and rural areas. The urban scenario includes three types of operating conditions: The first type is the scenario of increasing electric vehicle load in old residential areas. These residential areas are old, have low initial power distribution capacity, and limited space for installing new power distribution equipment. The continuous increase in electric vehicle penetration will cause the peak load rate to be greater than 1.1 during the evening peak from 18:00 to 22:00, the peak-valley difference to exceed the rated capacity by 40%, and the electric vehicle penetration rate to exceed 30%, leading to the problem of heavy overload of distribution transformers. The construction of power distribution facilities is difficult and costly. Therefore, energy storage is used to replace transformer capacity expansion and short-term peak shaving, thereby replacing or delaying the capacity expansion of transmission and distribution facilities. The second type is the scenario of short-term overload during daily peak hours in urban areas. In summer or during the Spring Festival, there are 1 to 3 hours of peak load rate exceeding 1.2 on a single day, and the number of days with heavy load is less than 30 per year. In the short term, peak loads create a demand for grid reinforcement, but regional site resources are scarce and the return on investment for permanent grid upgrades is low, making it difficult to quickly implement grid expansion projects. Temporary energy storage can be put into operation ahead of schedule to slow down grid expansion. The third scenario is a surge in temporary loads during the summer tourism season, with peak loads reaching 1.8 times the normal level only during holidays. For short-term load surges, which occur infrequently throughout the year, expanding the capacity of distribution substations is too costly and yields poor returns. Short-term energy storage can be used to smooth seasonal peak demand and temporarily increase the capacity limit of distribution substations to meet electricity needs. In remote mountainous areas, where reliable power supply to the end of distribution lines is crucial, voltage deviations at the end of lines exceed ±7%, and line load rates are consistently high while the overall load volume is small. Long-distance grid expansion and maintenance are costly and yield low returns. Energy storage operating in valley charging and discharging modes ensures continuous and stable power supply and voltage quality at the end of the lines. In rural areas, where renewable energy penetration is high but absorption is limited, distributed photovoltaic penetration exceeds 80%, and midday photovoltaic output can reach 60% of the total regional load, forming a typical duck curve timing pattern. There is insufficient space for local renewable energy absorption, and reverse power flow can easily induce voltage exceedance issues. Configuring long-term energy storage at distribution substations and line sides to transfer surplus daytime photovoltaic power enhances local renewable energy absorption capacity, replacing traditional grid expansion and renovation solutions.

[0054] In some embodiments, determining sensitive factors in an operational scenario includes: acquiring multiple preset factors; and selecting sensitive factors in an operational scenario from the multiple preset factors based on load distribution characteristics and new energy access characteristics.

[0055] In this embodiment of the invention, a complete library of standardized preset sensitive factors is pre-retrieved and loaded. These preset factors are multi-dimensional indicators pre-defined based on the operating mechanism of the distribution network and various operating conditions. They are divided into four categories: The first category is load-related preset factors, including peak load rate, peak-valley load difference, load fluctuation coefficient, peak duration, electric vehicle penetration rate, and seasonal load increment; the second category is new energy-related preset factors, covering distributed photovoltaic penetration rate, wind power output fluctuation coefficient, duration of wind and solar power output and load mismatch, historical curtailment rate, and frequency of reverse power flow; the third category is energy storage cost-related preset factors, including the annual value of the full life cycle cost of various types of energy storage, the initial investment unit price of energy storage, annual operation and maintenance costs, battery replacement amortization costs, discount rate, and energy storage charging and discharging efficiency; the fourth category is grid security-related preset factors, including node voltage allowable deviation, maximum line current carrying limit, unit cost of grid expansion, and distribution transformer load limit. All preset factors are equipped with unified quantitative calculation formulas and are normalized in the 0~1 range to form a complete and callable set of preset factors.

[0056] Based on the obtained load distribution characteristics and renewable energy access characteristics, combined with the operating conditions of the current distribution network scenario, all preset factors are screened in layers. Irrelevant factors that have no significant impact on the grid operation and energy storage planning of this scenario are eliminated, and only indicators with a prominent impact are retained as sensitive factors specific to this scenario: For old residential areas with load characteristics of high peak load, high electric vehicle penetration rate, and long evening peak duration, load-related preset factors such as load peak rate, electric vehicle penetration rate, peak-valley difference, and load fluctuation coefficient are retained, along with indicators related to distribution transformer load limit and short-term energy storage investment cost; For rural photovoltaic penetration scenarios, preset factors such as renewable energy penetration rate, wind and solar power output mismatch duration, curtailment rate, line voltage deviation, and long-term energy storage life cycle cost are selected first.

[0057] For example, for end-point power supply scenarios in remote mountainous areas, key indicators such as line voltage deviation, grid expansion and maintenance costs, and annual energy storage maintenance costs are selected; for short-term peak loads in urban areas and temporary tourist load scenarios, the main preset factors such as short-term load peak rate, seasonal load increment, and short-term energy storage investment costs are retained.

[0058] During the screening process, the influence weight of indicators will be distinguished. Only preset factors that can significantly change the grid security boundary and the economics of energy storage solutions will be included in the set of sensitive factors for the scenario. Irrelevant indicators with small fluctuation amplitude and almost no disturbance to the planning results will be completely eliminated. Finally, a list of sensitive factors will be output as the input basis for the subsequent construction of the source-load-storage uncertainty matrix and the calculation of the two types of uncertainty parameters.

[0059] In some embodiments, after determining the energy storage alternative adaptation scheme for the distribution network, the method further includes: determining the sensitivity coefficient of each sensitive factor based on the safety operation indicators of the energy storage alternative adaptation scheme; and determining the critical threshold of the sensitive factor under the energy storage alternative adaptation scheme based on the sensitivity coefficient of each sensitive factor and the safety constraints of the distribution network.

[0060] In this embodiment of the invention, the current energy storage configuration scale, grid topology, and charging / discharging strategy are used as the baseline operating conditions. The normalized values ​​of each scenario-specific sensitive factor are individually disturbed in turn, while keeping all other sensitive factors, energy storage parameters, and source-load data unchanged. The changes in safety operation indicators such as distribution network node voltage deviation, line load rate, and distribution transformer overload degree after each disturbance are repeatedly simulated and calculated. The sensitivity coefficient of the sensitive factor is calculated by the ratio of the change in safety indicators to the disturbance amplitude of the corresponding sensitive factor. The larger the coefficient value, the more significant the change in the safety operation state of the power grid will be due to a small fluctuation in the sensitive factor, and the stronger the impact on the safety margin of the energy storage scheme. The smaller the coefficient value, the weaker the interference of the fluctuation in the indicator on the safety operation condition. After traversing all scenario sensitive factors, a complete list of sensitivity coefficients of sensitive factors is formed, clearly distinguishing between high-impact and low-impact sensitive indicators.

[0061] Using safety constraints such as permissible voltage deviation, maximum line current carrying capacity, and transformer rated load as boundary conditions, for core sensitive factors with high sensitivity coefficients, a forward deduction is performed using a linear sensitivity mapping relationship. The values ​​of these sensitive factors are gradually relaxed or tightened until the simulation results just touch the safety constraint red line. The corresponding value of the sensitive factor at this point is the critical threshold. For sensitive factors with low sensitivity and weak impact, industry-standard upper and lower limits are taken within the standard safety range as the critical threshold. Finally, a set of critical thresholds for sensitive factors adapted to this energy storage scheme is obtained. These thresholds represent the safe value boundaries for each indicator to prevent voltage exceedance and line overload.

[0062] In some embodiments, when the output of the collaborative planning optimization model is that there is no energy storage alternative solution, the method further includes: selecting a first factor from the sensitive factors, wherein the first factor is the most sensitive factor that has the greatest impact on the distribution network planning; and determining the optimization adjustment direction of the distribution network based on the first factor.

[0063] In this embodiment of the invention, after the double-layer nested collaborative planning optimization model converges iteratively, it is found through dual judgment that it cannot simultaneously meet the dynamic security constraints of the power grid and the economic requirements of the energy storage scheme. The model determines that there is no feasible energy storage alternative under the current operating conditions, and then initiates the supporting optimization and adjustment analysis process. The first step involves the system retrieving the sensitivity coefficients corresponding to all previously calculated scenario-sensitive factors. The sensitivity coefficient value directly characterizes the strength of the impact of fluctuations in a single sensitive factor on the distribution network energy storage planning results. The system automatically compares the sensitivity of all sensitive factors and selects the indicator with the highest value and the most significant disturbance to the planning safety boundary and scheme cost, defining it as the first factor. For example, in rural high photovoltaic consumption scenarios, the first factor is usually the new energy penetration rate; in old residential area scenarios, it is often the peak load rate; and in remote mountainous areas, it is often the energy storage operation and maintenance cost. The second step involves deriving targeted and feasible optimization and adjustment directions for the distribution network based on the indicator attributes of the first factor, the current exceeding operating conditions, and the existing construction conditions of the distribution network. If the first factor is a load-related indicator (peak load rate, electric vehicle penetration rate), then load-side optimization solutions such as promoting orderly charging of electric vehicles, staggered peak power consumption, and adding flexible load regulation are output. If the first factor is a new energy-related indicator (photovoltaic penetration rate, power output fluctuation coefficient), then adjustment ideas such as adding energy storage voltage regulation devices to distributed photovoltaic systems, limiting new photovoltaic installations, and configuring new energy output stabilization equipment are given. If the first factor is an energy storage cost-related indicator (energy storage investment, replacement amortization cost), then optimization paths such as replacing with low-cost energy storage battery types, constructing energy storage in batches to reduce one-time investment, and selecting long-cycle energy storage to reduce replacement expenditures are proposed. If the first factor is a grid safety-related indicator (line current carrying capacity, voltage deviation threshold), then grid reinforcement and transformation schemes such as local line expansion, replacing with large-capacity distribution transformers, and adding voltage regulation devices are clarified. Finally, a clear optimization and adjustment direction centered on the first factor is output, providing quantitative and feasible transformation basis for staff to transform the distribution network and adjust the load and new energy access scale.

[0064] Figure 2 This is a schematic diagram of the structure of the multi-scenario collaborative planning device for distributed power sources in a power distribution network provided in an embodiment of the present invention. Figure 2 As shown, the multi-scenario collaborative planning device for distributed power sources in a distribution network includes: The acquisition module 210 is used to acquire actual operating data, load distribution information and new energy access information of the power distribution network; The determination module 220 is used to determine the operating scenario of the distribution network based on load distribution information and new energy access information, and to determine the sensitive factors under the operating scenario; The optimization module 230 is used to determine the energy storage alternative adaptation scheme of the distribution network based on actual operating data, operating scenarios, sensitive factors under the operating scenarios, and a pre-established collaborative planning optimization model. The collaborative planning optimization model includes an objective function and an uncertainty function; the objective function includes uncertainty parameters; and the uncertainty function is used to calculate the impact of source-load-storage uncertainty on the uncertainty parameters.

[0065] Optionally, the objective function includes an upper-level objective and a lower-level objective; the upper-level objective is set with a first uncertainty parameter; the lower-level objective is set with a second uncertainty parameter; the optimization module 230 is used for: Based on the sensitive factors in the operating scenario, determine the first uncertainty parameter and the second uncertainty parameter; By inputting actual operating data and operating scenarios into the collaborative planning and optimization model, an energy storage alternative adaptation scheme for the distribution network is obtained.

[0066] Optionally, the upper-level objective is to maximize the overall security performance of the distribution network; the lower-level objective is to minimize the annual cost difference between the planned scheme and the benchmark scheme; optimization module 230 is used for: The sensitive factors are input into the uncertainty function to determine the source-load-storage uncertainty matrix; where each row of the source-load-storage uncertainty matrix corresponds to one of the three main entities, and each column corresponds to a sensitive factor. Based on the source-load-storage uncertainty matrix, determine the first uncertainty parameter and the second uncertainty parameter.

[0067] Optional, optimization module 230, used for: The first uncertainty parameter is determined based on the matrix elements corresponding to the source and load entities within the source-load-storage uncertainty matrix. The second uncertainty parameter is determined based on the matrix elements corresponding to the energy storage entity and cost-sensitive factors within the source-load-storage uncertainty matrix.

[0068] Optionally, optimization module 230 is also used for: Obtain the energy storage duration requirements for the operational scenario; Determine the type of energy storage battery based on the required energy storage duration; By inputting the type of energy storage battery, actual operating data, and operating scenarios into the collaborative planning and optimization model, an energy storage alternative adaptation scheme for the distribution network is obtained.

[0069] Optionally, load distribution information includes peak load data, load time-series curves, transformer area load capacity, electric vehicle penetration rate, and peak-valley load difference; renewable energy access information includes distributed photovoltaic installed capacity, wind power output time-series, renewable energy penetration rate, and historical renewable energy consumption data; the determination module 220 is used for: The load distribution characteristics are determined based on peak load data, load time-series curves, transformer area load capacity, electric vehicle penetration rate, and load peak-valley difference. Based on the installed capacity of distributed photovoltaic power, wind power output time series, renewable energy penetration rate and historical data of renewable energy consumption, the characteristics of renewable energy access are determined. Based on load distribution characteristics and new energy access characteristics, the operation scenarios of the distribution network are determined.

[0070] Optionally, module 220 is used for: Obtain multiple preset factors; Based on load distribution characteristics and new energy access characteristics, sensitive factors for the operating scenario are selected from multiple preset factors.

[0071] Optionally, the multi-scenario collaborative planning device for distributed generation in the distribution network also includes an analysis module, used for: Based on the safety operation indicators of the energy storage alternative adaptation scheme, determine the sensitivity coefficients of each sensitive factor; Based on the sensitivity coefficients of each sensitive factor and the safety constraints of the distribution network, the critical thresholds of the sensitive factors under the energy storage alternative adaptation scheme are determined.

[0072] Optionally, the analysis module is also used for: The first factor is selected from the sensitive factors, which is the most sensitive factor that has the greatest impact on the distribution network planning; Based on the first factor, determine the direction for optimizing and adjusting the distribution network.

[0073] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0074] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A multi-scenario collaborative planning method for distributed generation in a power distribution network, characterized in that, include: Acquire actual operating data, load distribution information, and new energy access information of the power distribution network; Based on the load distribution information and the new energy access information, the operating scenario of the distribution network is determined, and the sensitive factors under the operating scenario are identified. Based on the actual operating data, the operating scenario, the sensitive factors under the operating scenario, and the pre-established collaborative planning optimization model, determine the energy storage alternative adaptation scheme for the distribution network; The collaborative planning optimization model includes an objective function and an uncertainty function; the objective function includes an uncertainty parameter; and the uncertainty function is used to calculate the impact of source-load-storage uncertainty on the uncertainty parameter.

2. The method for multi-scenario collaborative planning of distributed power sources in distribution networks according to claim 1, characterized in that, The objective function includes an upper-level objective and a lower-level objective; the upper-level objective is set with a first uncertainty parameter; the lower-level objective is set with a second uncertainty parameter; based on the actual operating data, the operating scenario, the sensitive factors under the operating scenario, and the pre-established collaborative planning optimization model, the energy storage substitution adaptation scheme for the distribution network is determined, including: Based on the sensitive factors in the operating scenario, determine the first uncertainty parameter and the second uncertainty parameter; The actual operating data and the operating scenario are input into the collaborative planning and optimization model to obtain the energy storage alternative adaptation scheme for the distribution network.

3. The method for multi-scenario collaborative planning of distributed power sources in a distribution network according to claim 2, characterized in that, The upper-level objective is to maximize the overall security performance of the distribution network; the lower-level objective is to minimize the annual cost difference between the planning scheme and the benchmark scheme. Based on the sensitive factors in the described operating scenario, the first uncertainty parameter and the second uncertainty parameter are determined, including: The sensitive factors are input into the uncertainty function to determine the source-load-storage uncertainty matrix; wherein, each row of the source-load-storage uncertainty matrix corresponds to one of the main entities, namely source, load, and storage, and each column corresponds to a sensitive factor; Based on the source-load-storage uncertainty matrix, the first uncertainty parameter and the second uncertainty parameter are determined.

4. The multi-scenario collaborative planning method for distributed power sources in a distribution network according to claim 3, characterized in that, Based on the source-load uncertainty matrix, the first uncertainty parameter and the second uncertainty parameter are determined, including: The first uncertainty parameter is determined based on the matrix elements corresponding to the source and load entities within the source-load uncertainty matrix. The second uncertainty parameter is determined based on the matrix elements corresponding to the energy storage entity and cost-sensitive factors within the source-load-storage uncertainty matrix.

5. The method for multi-scenario collaborative planning of distributed power sources in a distribution network according to claim 1, characterized in that, Before inputting the actual operating data and the operating scenario into the collaborative planning and optimization model to obtain the energy storage alternative adaptation scheme for the distribution network, the method further includes: Obtain the energy storage duration requirement for the aforementioned operating scenario; The type of energy storage battery is determined based on the required energy storage duration. The step of inputting the actual operating data and the operating scenario into the collaborative planning and optimization model to obtain the energy storage alternative adaptation scheme for the distribution network includes: The energy storage battery type, the actual operating data, and the operating scenario are input into the collaborative planning and optimization model to obtain an energy storage alternative adaptation scheme for the distribution network.

6. The multi-scenario collaborative planning method for distributed power sources in a distribution network according to claim 1, characterized in that, The load distribution information includes peak load data, load time-series curves, transformer area load capacity, electric vehicle penetration rate, and peak-valley load difference; the renewable energy access information includes distributed photovoltaic installed capacity, wind power output time-series data, renewable energy penetration rate, and historical renewable energy consumption data; based on the load distribution information and the renewable energy access information, the operating scenario of the distribution network is determined, including: The load distribution characteristics are determined based on the load peak data, the load time series curve, the load capacity of the transformer area, the electric vehicle penetration rate, and the load peak-valley difference. Based on the distributed photovoltaic installed capacity, the wind power output time series, the new energy penetration rate, and the historical data of new energy consumption, the characteristics of new energy access are determined. The operating scenario of the distribution network is determined based on the load distribution characteristics and the new energy access characteristics.

7. The multi-scenario collaborative planning method for distributed power sources in a distribution network according to claim 6, characterized in that, Identify the sensitive factors in the aforementioned operating scenario, including: Obtain multiple preset factors; Based on the load distribution characteristics and the new energy access characteristics, sensitive factors for the operating scenario are selected from the multiple preset factors.

8. The method for multi-scenario collaborative planning of distributed power sources in a distribution network according to any one of claims 1-7, characterized in that, After determining the energy storage alternative adaptation scheme for the distribution network, the method further includes: Based on the safety operation indicators of the energy storage alternative adaptation scheme, the sensitivity coefficients of each sensitive factor are determined; Based on the sensitivity coefficients of each sensitive factor and the safety constraints of the distribution network, the critical threshold of the sensitive factor under the energy storage alternative adaptation scheme is determined.

9. The method for multi-scenario collaborative planning of distributed power sources in a distribution network according to any one of claims 1-7, characterized in that, When the output of the collaborative planning optimization model indicates that no energy storage alternative solution exists, the method further includes: Select a first factor from the aforementioned sensitive factors, wherein the first factor is the sensitive factor with the highest impact on the distribution network planning; Based on the first factor, the direction for optimizing and adjusting the power distribution network is determined.

10. A multi-scenario collaborative planning device for distributed power sources in a power distribution network, characterized in that, include: The acquisition module is used to acquire actual operating data, load distribution information, and new energy access information of the power distribution network. The determination module is used to determine the operating scenario of the distribution network based on the load distribution information and the new energy access information, and to determine the sensitive factors under the operating scenario; The optimization module is used to determine the energy storage alternative adaptation scheme for the distribution network based on the actual operating data, the operating scenario, the sensitive factors under the operating scenario, and the pre-established collaborative planning optimization model. The collaborative planning optimization model includes an objective function and an uncertainty function; the objective function includes an uncertainty parameter; and the uncertainty function is used to calculate the impact of source-load-storage uncertainty on the uncertainty parameter.

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