Mobile energy storage power interconnection-oriented distribution area matching method and system

Abstract

This invention discloses a method and system for matching distribution transformer substations for mobile energy storage and power supply matching, relating to the field of distribution network operation and planning technology. The method includes: determining the duration of voltage exceeding the upper limit for each substation based on the low-voltage side voltage sequence of the substation's distribution transformers; identifying photovoltaic backfeed substations by combining distributed photovoltaic installed capacity; determining the number of households experiencing voltage exceeding the limit in each substation based on the substation user voltage data sequence, and identifying substations experiencing voltage exceeding the lower limit; constructing candidate combinations based on the two types of substations; using the latitude and longitude coordinates of the substation distribution transformers and road network data for road constraint screening to obtain effective candidate combinations; calculating time-period matching score, distance matching score, and supply-demand adaptation matching score for each effective candidate combination; and then calculating a comprehensive matching score, outputting the priority matching result according to the ranking result. This invention prioritizes matching substation combinations with severe voltage exceeding limits under limited resource constraints, providing a decision-making basis for the phased construction of charging and discharging infrastructure.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network operation and planning technology, and particularly relates to a method and system for matching photovoltaic backfeeding and voltage lower limit-crossing distribution areas. Under the constraint of limited charging and discharging resources, it provides a decision-making basis for the priority construction and site selection of autonomous driving mobile energy storage charging and discharging infrastructure by quantitatively matching photovoltaic backfeeding distribution areas and voltage lower limit-crossing distribution areas. Background Technology

[0002] In recent years, distributed photovoltaic (PV) power generation has experienced explosive growth, with a large number of PV systems connected to low-voltage distribution areas. During periods of abundant sunshine and low load, the PV power that cannot be absorbed locally creates a reverse flow, causing the operating voltage of distribution transformers to exceed the upper limit. Conversely, during evening peak hours or seasonal heavy load scenarios, users at the end of long feeders are affected by line voltage drops, making it easy for their supply voltage to exceed the lower limit. These two types of voltage exceeding problems often coexist within the same power supply area and are naturally complementary—PV reverse-feeding distribution areas need to absorb surplus power, while heavily loaded low-voltage (voltage exceeding the lower limit) distribution areas need to supplement power. Through power mutual assistance, peak shaving and valley filling can be achieved, synergistically improving voltage quality.

[0003] However, photovoltaic backfeeding areas and heavily loaded low-voltage areas are spatially dispersed, making it difficult to address both simultaneously using traditional on-site remediation methods. New, flexible resources such as autonomous mobile energy storage can achieve energy transfer across areas, providing a feasible solution to these problems. However, the construction and renovation of charging and discharging infrastructure are constrained by resources such as manpower and funding, typically adopting a phased construction approach. In the initial stages of construction, the number of available charging and discharging resources is limited, making it difficult to cover all problematic areas. Relying solely on experience for average site selection can easily lead to dispersed remediation resources, preventing areas with severe voltage exceedances from receiving priority remediation.

[0004] In the existing technology, there is a lack of matching research for the above two types of transformer substations. A quantitative evaluation method that can comprehensively consider time coordination (matching during voltage over-limit periods), spatial accessibility (travel distance constraints), and power adaptability (matching surplus and demand) has not yet been formed, making it difficult to provide a scientific basis for the priority deployment and phased expansion of charging and discharging infrastructure.

[0005] Therefore, under the constraint of limited resources, it is necessary to establish a quantitative matching method for photovoltaic backfeeding areas and voltage-below-limit areas by combining historical operating data, to prioritize the identification of combinations of areas that need key treatment, and to achieve efficient utilization of limited construction resources. Summary of the Invention

[0006] To address the aforementioned deficiencies in existing technologies, the present invention aims to provide a method and system for matching distribution transformer areas for mobile energy storage and power mutual assistance. This system aims to solve the technical problem that, under the constraint of limited charging and discharging resources, the lack of effective means to quantitatively evaluate and prioritize the matching of photovoltaic backfeeding distribution transformer areas and distribution transformer areas with voltage exceeding the lower limit results in the inability to prioritize the treatment of distribution transformer areas with dispersed governance resources and severe voltage exceeding the limit.

[0007] This invention solves the above-mentioned technical problems through the following technical solution: a transformer substation matching method for mobile energy storage power exchange, comprising:

[0008] Collect operational status data for the distribution area, including at least: voltage sequence of the low-voltage side of the distribution transformer in the area, voltage data sequence of users in the area, distributed photovoltaic installed capacity, latitude and longitude coordinates of the distribution transformer in the area, and road network data;

[0009] Based on the voltage sequence of the low-voltage side of the distribution transformer in the distribution area, the duration of voltage exceeding the upper limit of each distribution area is determined. According to the sorting result of the duration of voltage exceeding the upper limit of each distribution area, a first threshold is determined. Combined with the distributed photovoltaic installed capacity, the distribution areas whose duration of voltage exceeding the upper limit exceeds the first threshold and are connected to distributed photovoltaic are identified as photovoltaic backfeeding distribution areas, and a set of photovoltaic backfeeding distribution areas is constructed.

[0010] Based on the voltage data sequence of the transformer area users, the number of households in each transformer area whose voltage exceeds the limit is determined. According to the sorting result of the number of households in each transformer area whose voltage exceeds the limit, a second threshold is determined. Transformer areas whose number of households whose voltage exceeds the limit exceeds the second threshold are identified as transformer areas whose voltage exceeds the lower limit, and a set of transformer areas whose voltage exceeds the lower limit is constructed.

[0011] Candidate distribution area combinations are constructed based on the set of photovoltaic backfeeding areas and the set of voltage lower limit-crossing areas. Road constraints are used to screen each candidate distribution area combination using the latitude and longitude coordinates of the distribution transformers and the road network data to obtain effective candidate combinations that meet traffic requirements. For each effective candidate combination, time period matching score, distance matching score, and supply and demand adaptation matching score are calculated, and then a comprehensive matching score is calculated. The priority matching result of photovoltaic backfeeding areas and voltage lower limit-crossing areas is output according to the ranking result of the comprehensive matching score.

[0012] This invention identifies problematic transformer substations requiring remediation by constructing sets of photovoltaic backfeeding areas and sets of substations with voltage exceeding the lower limit. It eliminates inaccessible combinations through road constraints to ensure the feasibility of matching results. Furthermore, it calculates matching scores based on time period, distance, and supply-demand compatibility, and then synthesizes these scores to obtain a comprehensive matching score, outputting priority matching results in order of score. This solution, under the condition of limited charging and discharging infrastructure resources, prioritizes the allocation of limited resources to the substation combinations with the highest comprehensive matching degree, avoiding the resource dispersion problem caused by relying on experience-based average distribution, and achieving precise allocation of remediation resources.

[0013] This invention quantitatively evaluates candidate transformer substation combinations from three dimensions: time-period matching score, distance matching score, and supply-demand adaptation matching score. These dimensions are time synergy (the degree of matching during voltage over-limit periods), spatial accessibility (travel distance between substations), and power adaptability (the degree of matching between surplus photovoltaic power and the power demand for power management). A comprehensive matching score is then calculated. This mechanism transforms the substation matching problem into a quantifiable and rankable mathematical problem, providing a scientific and reproducible decision-making basis for the priority deployment of charging and discharging infrastructure, and overcoming the technical deficiency of existing technologies that lack quantitative matching methods.

[0014] Before the matching calculation, this invention uses the latitude and longitude coordinates of the transformer substation and road network data to screen each candidate transformer substation combination based on road constraints, eliminating combinations that do not meet the traffic requirements and retaining only valid candidate combinations for subsequent matching degree calculation. This ensures that the final output priority matching result has actual accessibility, avoiding invalid matching where autonomous driving mobile energy storage vehicles cannot reach the target transformer substation due to traffic constraints such as road level, height restrictions, and weight restrictions, thus guaranteeing the feasibility of the technical solution.

[0015] This invention provides a clear priority ranking for the phased construction of charging and discharging infrastructure by outputting the priority matching results between photovoltaic backfeed areas and voltage lower limit areas. These results can be used to guide the priority deployment of limited resources in the initial stage (selecting the combination of areas with the highest comprehensive matching score) and also serve as the basis for subsequent phased expansion deployment (selecting combinations with higher scores in sequence), thus conforming to the actual construction pattern of phased advancement of charging and discharging infrastructure.

[0016] This invention collects operational status data such as the low-voltage side voltage sequence of the distribution transformer in the distribution area, the voltage data sequence of users in the distribution area, and the installed capacity of distributed photovoltaic power generation. It then identifies photovoltaic backfeeding distribution areas based on the duration of voltage exceeding the upper limit and the installed capacity of distributed photovoltaic power generation, and identifies distribution areas exceeding the lower limit based on the number of users experiencing voltage exceeding the upper limit. This achieves objective and accurate identification of problematic distribution areas. This identification method does not rely on subjective judgment, ensuring the accuracy and reliability of the basic data for subsequent matching calculations.

[0017] Furthermore, the duration of voltage exceeding the upper limit is obtained by accumulating the sampling intervals corresponding to the sampling points in the low-voltage side voltage sequence of the distribution transformer in the substation that are greater than the preset upper voltage threshold.

[0018] This invention quantifies the severity of voltage exceeding the upper limit problem into a duration index. By accumulating the sampling interval, it accurately calculates the total duration of voltage exceeding the upper limit for each transformer area, providing an objective and quantifiable basis for identifying photovoltaic backfeed transformer areas. It is also applicable to data acquisition scenarios with different sampling frequencies.

[0019] Furthermore, the number of households experiencing voltage exceedance is obtained by accumulating the product of the number of users whose voltage values ​​obtained from the user voltage data sequence of the transformer area are lower than the preset lower voltage threshold at each sampling time within the statistical period and the sampling interval.

[0020] This invention quantifies the severity of the voltage lower limit problem as the "number of users per hour" index, and considers both the number of users experiencing voltage lower limits and the duration of the voltage lower limit problem, comprehensively reflecting the scope and severity of the low voltage problem, and providing a scientific basis for the identification of transformer substations experiencing voltage lower limits.

[0021] Furthermore, the first threshold is determined by sorting the duration of voltage exceeding the upper limit of each transformer area from largest to smallest and then using the percentile method; the second threshold is determined by sorting the number of households in each transformer area when the voltage exceeds the upper limit from largest to smallest and then using the percentile method.

[0022] The percentile method is used to dynamically determine the identification threshold. Based on the actual data distribution, the most problematic transformer areas are adaptively selected to avoid misjudgment or omission that may be caused by a fixed threshold, and to ensure that the identification results are comparable and consistent in different regions and time periods.

[0023] Furthermore, the road constraint filtering includes:

[0024] The latitude and longitude coordinates of the photovoltaic backfeeding area and the voltage lower limit crossing area are projected onto the nodes in the road network, respectively, as the starting node and the ending node;

[0025] The shortest path algorithm is used to calculate the shortest travel distance from the starting node to the ending node, and it is determined whether the path corresponding to the shortest travel distance meets the preset travel requirements.

[0026] If the preset access requirements are not met, the corresponding candidate station combination will be removed.

[0027] This invention introduces road network constraints to ensure that the matched combination of energy storage areas is actually accessible, avoiding invalid matching where autonomous mobile energy storage vehicles cannot reach the target energy storage area due to road level, height restrictions, weight restrictions, and other obstacles, thus ensuring the feasibility and implementability of the matching results.

[0028] Furthermore, the time period matching score is determined in the following way:

[0029] Suppose the statistical period contains D natural days, and each natural day contains M sampling times; calculate the weighted average time of voltage exceeding the upper limit event in the photovoltaic backfeeding area on the d-th natural day. Then, the weighted average time of the photovoltaic backfeeding area within the statistical period is calculated. :

[0030] ; ;

[0031] in, Indicates the index of sampling time within a natural day; This indicates that the photovoltaic backfeeding area is on the dth natural day. The voltage value of the low-voltage side of the distribution transformer at each sampling time; This indicates the preset upper voltage threshold. This represents an indicator function, which takes the value 1 if the condition within the parentheses is true, and 0 otherwise.

[0032] Calculate the weighted average time of voltage lower limit events in the transformer area on the d-th natural day. Then, the weighted average time of voltage exceeding the lower limit in the distribution area within the statistical period is calculated. :

[0033] ; ;

[0034] in, This indicates the voltage level exceeding the lower limit on the dth natural day. The number of users whose voltage exceeds the lower limit at each sampling time;

[0035] Calculate the time interval between photovoltaic backfeed areas and voltage lower limit crossing areas. :

[0036] ;

[0037] The time interval is used to determine the time period matching score. :

[0038] ;

[0039] in, This indicates the shortest time required for a single charge and discharge cycle of mobile energy storage; Indicates the maximum allowed duration; This indicates the preset maximum matching score.

[0040] This invention quantifies the temporal synergy between periods when the voltage of a photovoltaic backfeeding area exceeds the upper limit and periods when the voltage of a photovoltaic backfeeding area exceeds the lower limit. A two-step method of "daily averaging followed by overall averaging" reduces the impact of random fluctuations across different days, making the weighted average more representative. A circular time interval calculation accurately reflects the shortest time difference between the two periods within a one-day cycle. A segmented scoring mechanism intuitively demonstrates the impact of temporal synergy on mutual assistance feasibility, providing a quantitative basis in the time dimension for calculating the comprehensive matching score.

[0041] Furthermore, the distance matching score is determined in the following way:

[0042] Set the maximum allowed passage distance as The distance matching score is calculated based on the shortest travel distance between the photovoltaic backfeeding area and the voltage lower limit area. :

[0043] ;

[0044] in, This indicates the shortest travel distance between photovoltaic backfeeding areas and areas with voltage below the lower limit. This indicates the preset maximum matching score.

[0045] This invention quantifies the spatial accessibility between stations, with higher scores for closer distances, reflecting the priority principle of "mutual assistance based on proximity". The score decreases linearly with the increase of the shortest travel distance, making the form simple and the physical meaning of the parameters clear, which is convenient for setting the maximum allowable travel distance according to the actual road network. The introduction of a full matching score value makes the distance matching score have a unified dimension with other matching scores, which is convenient for subsequent weighted summation calculation of the comprehensive matching score.

[0046] Furthermore, the supply and demand matching score is determined in the following way:

[0047] Determine the distributed photovoltaic installed capacity of the photovoltaic backfeeding area and the number of hours its voltage exceeds the upper limit, as well as the number of households in the photovoltaic backfeeding area whose voltage exceeds the lower limit for the time limit;

[0048] Set the experience coefficient , Calculate the supply-demand ratio using the following formula. :

[0049] ;

[0050] in, This indicates the distributed photovoltaic installed capacity of the photovoltaic backfeeding area; This indicates the number of hours the voltage in the photovoltaic backfeeding area exceeds the upper limit; This indicates the number of households in the voltage range that exceeds the lower voltage limit.

[0051] Based on the supply-demand ratio, determine the supply-demand matching score. :

[0052] ;

[0053] in, , , This represents the preset matching score, and is the maximum matching score. > > .

[0054] This invention quantitatively matches the surplus electricity that can be absorbed by photovoltaic (PV) power distribution areas with the electricity demand for pollution control in areas where voltage exceeds the lower limit. The surplus electricity can be absorbed is represented by the product of PV installed capacity and the number of hours the voltage exceeds the upper limit, while the electricity demand for pollution control is represented by the number of households experiencing voltage exceedances. The physical meaning is clear. An empirical coefficient is introduced, allowing for calibration based on actual operating data and improving model adaptability. A segmented scoring mechanism intuitively reflects the impact of supply-demand balance on the mutual assistance effect; the highest score is achieved when the supply-demand ratio is close to 1, prioritizing the matching of distribution area combinations with the most balanced supply and demand.

[0055] Furthermore, the comprehensive matching score is obtained by weighted summation of the time-period matching score, distance matching score, and supply-demand matching score:

[0056] ;

[0057] in, This indicates the overall matching score; Indicates the time period matching score; Indicates the distance matching score; This indicates the score for supply and demand matching; , , Each represents the corresponding non-negative weight coefficient. .

[0058] This invention integrates the evaluation indicators of three dimensions—time period matching, distance matching, and supply-demand matching—into a single quantitative indicator, facilitating unified ranking and comparison of different candidate transformer area combinations. The weighted summation method is concise, and the weight coefficients can be flexibly preset according to the planning side's emphasis on different matching dimensions, or calibrated through historical governance effects, exhibiting good adjustability and adaptability, and providing a scientific ranking basis for outputting priority matching results.

[0059] Based on the same concept, the present invention also provides a transformer substation matching system for mobile energy storage power matching, including a memory, a processor, and a computer program or instructions stored in the memory, wherein the processor executes the computer program or instructions to implement the transformer substation matching method for mobile energy storage power matching as described above. Attached Figure Description

[0060] To more clearly illustrate the technical solution of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1This is a flowchart of the transformer substation matching method for mobile energy storage power exchange in an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the distribution of each station area in an embodiment of the present invention. Detailed Implementation

[0063] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0065] Example 1

[0066] like Figure 1 As shown in the figure, this embodiment provides a method for matching distribution transformers for mobile energy storage power supply, which includes the following steps:

[0067] Step S1: Collect the operating status data of the transformer area.

[0068] In this embodiment, the statistical period is taken as the historical operating data of the most recent year, the sampling interval is set to 15 minutes, and there are 96 sampling times per day. The collected operating status data of all transformer substations within the target power supply area includes at least:

[0069] Voltage sequence of low-voltage side of distribution transformer in the distribution area: For distribution area h, the voltage value of the low-voltage side of its distribution transformer is collected at each sampling time within the statistical period. This data is used for subsequent identification of photovoltaic backfeed distribution areas and calculation of time period matching score.

[0070] Voltage data sequence for users in a transformer area: For users within transformer area h, the user voltage value is collected at each sampling time within the statistical period. This data is used to calculate the number of users whose voltage exceeds the limit, and then identify transformer areas whose voltage exceeds the lower limit.

[0071] Distributed photovoltaic installed capacity: Collect the total installed capacity of distributed photovoltaic power connected to each distribution area. This data is used to help identify photovoltaic backfeeding distribution areas and, together with the duration of voltage exceeding the upper limit, characterizes the surplus electricity that can be absorbed.

[0072] Distribution transformer coordinates: Collect the geographical coordinates of each distribution transformer in the distribution area, including longitude and latitude. This data is used to project the location of the distribution area onto the road network nodes, and to perform road constraint screening and distance matching score calculation.

[0073] Road network data: Collect road network data within the target power supply area, including at least: road node coordinates, road segment connections, road segment length, road classification (e.g., arterial road, secondary arterial road, local road), height restrictions, weight restrictions, etc. This data is used to construct a weighted road network, calculate the shortest travel distance between substations, and determine whether the path meets the traffic requirements.

[0074] The collected data is stored in a database in structured tabular form for easy retrieval and processing in subsequent steps. Those skilled in the art will understand that, in practical applications, the length of the statistical period and the sampling interval can be appropriately adjusted according to the data acquisition conditions. The above embodiments are merely illustrative and do not constitute a limitation on the scope of protection of this invention.

[0075] Step S2: Determine the duration of voltage exceeding the upper limit for each distribution transformer based on the voltage sequence of the low-voltage side of the distribution transformer. Determine the first threshold based on the sorting result of the duration of voltage exceeding the upper limit for each distribution transformer. Combined with the distributed photovoltaic installed capacity, identify the distribution transformers whose duration of voltage exceeding the upper limit exceeds the first threshold and are connected to distributed photovoltaic as photovoltaic backfeeding distribution transformers, and construct a set of photovoltaic backfeeding distribution transformers.

[0076] This step, based on the low-voltage side voltage sequence of the distribution transformer and the distributed photovoltaic installed capacity collected in step S1, identifies the distribution areas with photovoltaic backfeeding and constructs a set of photovoltaic backfeeding distribution areas.

[0077] Set voltage upper limit threshold This is used to determine whether the voltage on the low-voltage side of the distribution transformer in the distribution area exceeds the upper limit. In this embodiment, the upper voltage threshold is... (Per-unit value). Those skilled in the art can set this upper limit threshold voltage according to the distribution network voltage qualification standard or actual operating requirements.

[0078] For transformer area h, based on the low-voltage side voltage sequence of the transformer area's distribution transformer collected in step S1, the duration of voltage exceeding the upper limit within the statistical period is calculated. The duration of voltage exceeding the upper limit is calculated by accumulating the sampling intervals corresponding to the sampling points in the low-voltage side voltage sequence of the distribution transformer in the substation that exceed the upper voltage threshold. The specific calculation formula is as follows:

[0079] (1)

[0080] in: t represents the total number of sampling points within the statistical period; t represents the index of the sampling time (or sampling point) within the statistical period. Δt represents the low-voltage side voltage value of the distribution transformer in area h at the t-th sampling time; Δt is the sampling interval, which is 15 minutes in this embodiment. This is an indicator function; it takes the value 1 if the condition within the parentheses is true, and 0 otherwise.

[0081] Calculate the voltage over-limit duration for all transformer substations within the target power supply area according to the formula (1) above. The duration of voltage exceeding the upper limit in each distribution area. Sort the data from largest to smallest to obtain a sorted sequence. Then, determine the first threshold using the percentile method based on the sorted results. In this embodiment, the minimum duration of voltage exceeding the upper limit corresponding to the top 15% of transformer areas is selected as the first threshold. That is, if there are a total of N s If the number of stations is 0.15 × N, then take the 0.15 × Nth position in the sorting. s The duration for which the voltage of a given transformer area exceeds the upper limit is used as the first threshold. Those skilled in the art can adjust this percentage according to actual governance needs, for example, selecting the top 10% or the top 20%.

[0082] Based on the distributed photovoltaic installed capacity information collected in step S1, areas that simultaneously meet the following two conditions are identified as photovoltaic backfeeding areas:

[0083] Condition 1: The voltage in transformer area h exceeds the upper limit for a duration that exceeds the first threshold, i.e. ;

[0084] Condition 2: Access to distributed photovoltaic (PV) systems, i.e., the installed capacity of distributed PV systems in transformer area h. .

[0085] The photovoltaic (PV) backfeeding areas that meet the above conditions are designated as PV backfeeding areas, and a set of PV backfeeding areas is constructed: .

[0086] To facilitate comparative analysis of the severity of photovoltaic backfeeding areas in subsequent examples, this embodiment sorts the areas by the duration of voltage exceeding the upper limit from largest to smallest, defining the sorting position as the "backfeeding severity ranking". The smaller the ranking position, the longer the duration of voltage exceeding the upper limit and the higher the backfeeding severity; the larger the ranking position, the shorter the duration of voltage exceeding the upper limit and the lower the backfeeding severity.

[0087] Step S3: Determine the number of households in each distribution area whose voltage exceeds the limit based on the user voltage data sequence. Determine the second threshold based on the sorting result of the number of households in each distribution area whose voltage exceeds the limit. Identify the distribution areas whose number of households whose voltage exceeds the limit exceeds the second threshold as distribution areas with voltage below the limit, and construct a set of distribution areas with voltage below the limit.

[0088] This step, based on the voltage data sequence of the transformer substations collected in step S1, identifies the transformer substations with voltage below the lower limit and constructs a set of transformer substations with voltage below the lower limit.

[0089] Set voltage lower limit threshold This is used to determine whether the user's voltage has exceeded the lower limit. In this embodiment, the lower voltage limit threshold is set to... Those skilled in the art can set this threshold according to the distribution network voltage qualification standards or actual operating requirements.

[0090] For transformer area h, based on the voltage data sequence of users in the transformer area collected in step S1, the number of users whose voltage exceeds the limit within the statistical period is counted. The number of households experiencing voltage exceeding the limit is calculated by summing the product of the number of households whose voltage values, obtained from the user voltage data sequence of the transformer area, are lower than the preset lower voltage threshold at each sampling time within the statistical period, and the product of the sampling interval. The specific calculation formula is as follows:

[0091] (2)

[0092] in: The set of users within the h-th grid area; This represents the voltage value of the k-th user at the t-th sampling time within the transformer area h, i.e., the voltage value of the k-th user at the t-th sampling time within the transformer area h.

[0093] Calculate the number of households in the target power supply area when the voltage exceeds the limit for all transformer substations according to the above formula (2). Its physical meaning is the sum of the product of the number of users whose voltage exceeds the lower limit and the duration within the statistical period, which can comprehensively reflect the severity and scope of the low voltage problem.

[0094] Number of households in each transformer area experiencing voltage over-limit times Sort the data from largest to smallest to obtain a sorted sequence. Then, determine the second threshold using the percentile method based on the sorted result. In this embodiment, the minimum number of households experiencing voltage exceedances corresponding to the top 10% of transformer substations is selected as the second threshold. That is, if there are a total of N s If the number of stations is 0.1 × N, then take the 0.1 × Nth position in the sorting. s The number of households whose voltage exceeds the limit in the corresponding transformer area is used as the second threshold. Those skilled in the art can adjust this percentage according to actual governance needs, for example, the top 5% or the top 15% can be selected.

[0095] Transformer areas where the number of households exceeding the second threshold when voltage exceeds the limit are identified as transformer areas where voltage exceeds the lower limit, i.e., satisfying the following conditions: Transformer areas that meet the above conditions are categorized as voltage-below-the-lower-limit transformer areas, and a set of voltage-below-the-lower-limit transformer areas is constructed. .

[0096] To facilitate comparative analysis of the severity of voltage exceeding the lower limit in subsequent examples, this embodiment sorts each transformer area by the number of households experiencing voltage exceeding the limit from largest to smallest, defining the sorting position as "overload severity sorting". The smaller the sorting position, the larger the number of households experiencing voltage exceeding the limit and the higher the overload severity; the larger the sorting position, the smaller the number of households experiencing voltage exceeding the limit and the lower the overload severity.

[0097] Step S4: Construct candidate distribution area combinations based on the photovoltaic backfeeding distribution area set and the voltage lower limit crossing distribution area set. Use the latitude and longitude coordinates of the distribution transformers and road network data to screen each candidate distribution area combination for road constraints, and obtain effective candidate combinations that meet the traffic requirements.

[0098] This step is based on the photovoltaic backfeeding distribution area set constructed in step S2. The set of voltage lower limit distribution areas constructed in step S3 Candidate combinations of transformer stations are constructed, and road network data is used for constraint screening to obtain effective candidate combinations that meet traffic requirements, providing a basis for subsequent matching degree calculation.

[0099] Utilizing photovoltaic backfeed distribution area and voltage lower limit distribution area collection Construct a set of candidate transformer area combinations:

[0100] (3)

[0101] For any candidate combination of transformer areas Perform the following road constraint filtering:

[0102] Using the latitude and longitude coordinates of the distribution transformer in step S1, the photovoltaic power is fed back to the distribution area. and voltage lower limit area The coordinates are projected onto the nearest nodes in the road network, serving as the start and end nodes, respectively. The road network is abstracted as a graph: road intersections are nodes, road segments are edges, and the edge weight is the segment length (in kilometers). In this embodiment, the segment length is used as the edge weight.

[0103] The shortest path algorithm is used to calculate the shortest travel distance from the starting node to the ending node. In this embodiment, Dijkstra's algorithm is used, which sums the lengths of each segment on the shortest path to obtain the shortest travel distance. .

[0104] Check the preset traffic requirements (e.g., road grade, height restriction, weight restriction, etc.) of the path corresponding to the shortest travel distance (i.e., the shortest travel path) to determine whether they meet the requirements for the autonomous mobile energy storage vehicle to pass. If no path meets the preset traffic requirements, then the combination is... From the candidate transformer area combination set Eliminate from the list. After road constraint filtering, the effective candidate combination set R is obtained: .

[0105] Step S5: For each valid candidate combination, calculate the time period matching score, distance matching score, and supply and demand matching score, and then calculate the comprehensive matching score.

[0106] For each candidate combination in the effective candidate combination set R obtained in step S4, the time period matching score, distance matching score and supply and demand adaptation matching score are calculated in sequence, and the weighted sum is used to obtain the comprehensive matching score.

[0107] The time-period matching score is used to quantify the temporal synergy between periods when the voltage of a photovoltaic backfeeding area exceeds the upper limit and periods when the voltage of a photovoltaic backfeeding area exceeds the lower limit. In this embodiment, the following steps are used for calculation:

[0108] The statistical period consists of D calendar days (partial days less than one calendar day are discarded), and each calendar day contains M sampling times. In this embodiment, M=96, corresponding to a 15-minute sampling interval. For photovoltaic backfeeding areas, the weighted average time of voltage exceeding the upper limit event on the d-th calendar day (d=1,2,…,D) is calculated. Then, the weighted average time of the photovoltaic backfeeding area within the statistical period is calculated. :

[0109] (4)

[0110] (5)

[0111] in, Indicates the index of sampling time within a natural day; This indicates that the photovoltaic backfeeding area is on the dth natural day. The voltage value of the low-voltage side of the distribution transformer at each sampling time; This indicates the preset upper voltage threshold. This represents an indicator function that takes the value 1 if the condition within the parentheses is true, and 0 otherwise.

[0112] For transformer substations where voltage exceeds the lower limit, calculate the weighted average time of voltage exceeding the lower limit event on the d-th natural day. Then, the weighted average time of voltage exceeding the lower limit in the distribution area within the statistical period is calculated. :

[0113] (6)

[0114] (7)

[0115] in, This indicates the voltage level exceeding the lower limit on the dth natural day. The number of users whose voltage exceeds the lower limit at each sampling time.

[0116] Considering a calendar day as the cycle, calculate the shortest interval between two weighted average moments on the cycle period, that is, the time interval between the photovoltaic backfeeding area and the voltage lower limit crossing area. :

[0117] (8)

[0118] Let the shortest time required for a single charge and discharge cycle of mobile energy storage be... The maximum allowed duration is In this embodiment, the sampling interval is 15 minutes. (Corresponding to 1 hour) (Corresponding to 6 hours). Time period matching score. Calculate using the following formula:

[0119] (9)

[0120] in, This represents the preset maximum matching score. In this embodiment, it is taken as... .

[0121] Distance matching score is used to quantify the spatial accessibility between photovoltaic backfeed areas and areas with voltage exceeding the lower limit. A maximum permissible passage distance is set. In this embodiment, we take The shortest travel distance calculated based on step S4. Calculate the distance matching score using the following formula. :

[0122] (10)

[0123] The score is 0 when the shortest passage distance is greater than or equal to the maximum allowed passage distance. The closer the distance, the higher the score, showing a linear decreasing relationship.

[0124] Supply and demand matching scores are used to quantify the surplus electricity that can be absorbed by photovoltaic backfeeding areas. Electricity demand for the treatment of areas with voltage exceeding the lower limit The degree of matching. The amount of surplus electricity that can be absorbed is determined based on the distributed photovoltaic installed capacity of the photovoltaic backfeeding area and the number of hours its voltage exceeds the upper limit; the amount of electricity required for treatment is determined based on the number of households in the area with voltage exceeding the lower limit for the duration of voltage exceedance.

[0125] Set the experience coefficient , In this embodiment, take Calculate the supply-demand ratio using the following formula. :

[0126] (11)

[0127] in, The distributed photovoltaic installed capacity of the photovoltaic backfeeding area is obtained in step S1; The number of hours the voltage of the photovoltaic backfeeding area exceeds the upper limit is calculated using formula (1) in step S2; The number of households whose voltage exceeds the lower limit in the voltage distribution area is calculated using formula (2) in step S3.

[0128] Based on the supply-demand ratio, the supply-demand matching score is determined using the following formula. :

[0129] (12)

[0130] in, , , This represents the preset matching score, and is the maximum matching score. > > ≥0. In this embodiment, , , .

[0131] The time-period matching score, distance matching score, and supply-demand matching score are weighted and summed to obtain the comprehensive matching score:

[0132] (13)

[0133] in, This indicates the overall matching score; , , Each represents the corresponding non-negative weight coefficient. In this embodiment, based on the importance that the planning side attaches to time period, distance, and supply-demand matching, the following is taken: , , The weighting coefficients can also be calibrated based on historical governance results.

[0134] Step S6: Output the priority matching results of photovoltaic backfeeding areas and voltage lower limit areas according to the sorting results of the comprehensive matching scores.

[0135] This step, based on the comprehensive matching score of each valid candidate combination calculated in step S5, outputs the priority matching results of photovoltaic backfeeding areas and voltage lower limit areas according to the comprehensive matching score ranking, providing a basis for site selection for the construction of autonomous driving mobile energy storage charging and discharging equipment and supporting infrastructure. The specific implementation process is as follows:

[0136] For all valid candidate combinations calculated in step S5 Overall matching score Sort the data from largest to smallest to obtain a sorted sequence. Based on actual construction resource constraints, select the preferred matching result from the sorted sequence. Specific methods include the following two:

[0137] One-to-one matching: In the case of extremely limited resources at the initial stage of construction, the photovoltaic backfeeding area with the highest score and the area with voltage below the lower limit are selected as the one-to-one matching results.

[0138] (14)

[0139] The matching result is a priority service area combination for autonomous driving mobile energy storage charging and discharging equipment (such as mobile energy storage vehicles), and the charging and discharging infrastructure is preferentially deployed in an appropriate location between the photovoltaic backfeeding area and the voltage lower limit area.

[0140] Multiple combination priority matching: When resources are relatively abundant or phased construction is required, several combinations with the highest comprehensive matching scores are selected as priority matching results. For example, the top K combinations in the sorted sequence are selected. These combinations correspond to the priority deployment order of charging and discharging infrastructure according to their comprehensive matching scores from high to low, serving as the basis for phased construction decisions.

[0141] Based on the output priority matching results, determine the priority site selection and phased construction sequence for autonomous driving mobile energy storage charging and discharging equipment and supporting infrastructure:

[0142] Priority deployment locations: For the selected priority service area combination, charge and discharge infrastructure will be deployed between the photovoltaic backfeed area and the voltage lower limit area (such as near the passageway node or the midpoint between the two areas) to facilitate power exchange between mobile energy storage vehicles.

[0143] Phased construction sequence: When multiple priority matching results are selected, they are arranged in descending order of comprehensive matching score as the construction tasks for the first phase, the second phase, and so on, to ensure that limited resources are prioritized for the combination of transformer substations with the highest comprehensive matching degree.

[0144] To verify the effectiveness of the method of this invention, a simulation experiment was conducted on 53 transformer substations within a certain power supply area. The specific distribution of each substation is as follows: Figure 2 As shown, Figure 2The origin is the center of the power supply area. Thirteen photovoltaic (PV) reverse-feeding substations (numbered PV1~PV13) and forty substations exceeding the lower voltage limit (numbered HP1~HP40) were identified. Two simple strategies were selected for benchmark comparison:

[0145] Baseline Method 1: A strategy of matching based solely on geographical distance. Among all combinations of photovoltaic backfeeding areas and voltage lower limit crossing areas that satisfy road accessibility constraints, the pair of areas with the shortest travel distance is selected and denoted as the combination. ;

[0146] Baseline Method 2: A strategy that matches based solely on the severity of voltage exceedances. The system selects the photovoltaic backfeeding area set with the longest duration of voltage exceedances (i.e.,...). The transformer substation with the largest number of households when the voltage exceeds the limit is selected from the set of substation substations with the lowest voltage limit. The two largest (largest) transformer areas are paired together and denoted as a combination. .

[0147] Table 1 shows a comparison of preferred matching combinations determined by different methods.

[0148]

[0149] Note: The sorting of backfeeding degree and overload degree is from largest to smallest. The larger the sort number, the lower the degree. For example, the backfeeding degree of PV8 photovoltaic backfeeding area is ranked 13 (that is, the duration of voltage exceeding the upper limit is ranked 13th among all areas), and the overload degree of HP15 photovoltaic backfeeding area is ranked 9 (that is, the number of households exceeding the voltage limit is ranked 9th among all areas).

[0150] As shown in Table 1, under the same set of distribution areas and data conditions, the preferred matching combination obtained by the method of this invention is photovoltaic backfeeding area PV8 and voltage lower limit exceeding area HP15 (ranked 13 in backfeeding degree and 9 in heavy load degree), based on the comprehensive matching score. =95.23, significantly higher than the combination PV5–HP14 (overall matching score 87.24) selected only based on the shortest passage distance and the combination PV1–HP30 (overall matching score 82.73) selected only based on the severity of the violation.

[0151] Looking at the sub-indicators, the time period matching score corresponding to the method of this invention is... =92.25, significantly better than the benchmark method 1's 60.86 and the benchmark method 2's 65.92; the method of the present invention also maintains a high level in distance matching score and supply and demand adaptation matching score, at 95.54 and 97.48 respectively, which better matches the surplus photovoltaic power and the power gap in the voltage lower limit area while taking into account the feasibility of travel distance.

[0152] This embodiment demonstrates that, given the same candidate set of photovoltaic backfeeding areas and voltage exceeding the lower limit areas, the comprehensive matching degree calculation and ranking method proposed in this invention can prioritize the selection of a pair of areas that are more coordinated in terms of peak-shifting time, spatial distance, and power supply and demand. Compared to benchmark strategies that rely solely on a single indicator (distance or severity of exceeding the limit), the method of this invention is more suitable as a decision-making basis for the site selection and phased commissioning of autonomous driving mobile energy storage charging and discharging equipment and supporting infrastructure.

[0153] Example 2

[0154] This invention also provides a transformer substation matching system for mobile energy storage power exchange. The system includes a memory, a processor, and a computer program or instructions stored in the memory. The processor executes the computer program or instructions to implement the transformer substation matching method for mobile energy storage power exchange in Embodiment 1 of this invention.

[0155] Although not shown, the system includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) or loaded from a storage portion into random access memory (RAM). The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit, graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in the RAM. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0156] The processor and memory described above are used together to execute programs / instructions stored in the memory. When the program / instructions are executed by the computer, they can implement the methods, steps, or functions described in the above embodiments.

[0157] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for matching distribution transformer areas for power exchange in mobile energy storage, characterized in that, include: Collect operational status data for the distribution area, including at least: voltage sequence of the low-voltage side of the distribution transformer in the area, voltage data sequence of users in the area, distributed photovoltaic installed capacity, latitude and longitude coordinates of the distribution transformer in the area, and road network data; Based on the voltage sequence of the low-voltage side of the distribution transformer in the distribution area, the duration of voltage exceeding the upper limit of each distribution area is determined. According to the sorting result of the duration of voltage exceeding the upper limit of each distribution area, a first threshold is determined. Combined with the distributed photovoltaic installed capacity, the distribution areas whose duration of voltage exceeding the upper limit exceeds the first threshold and are connected to distributed photovoltaic are identified as photovoltaic backfeeding distribution areas, and a set of photovoltaic backfeeding distribution areas is constructed. Based on the voltage data sequence of the transformer area users, the number of households in each transformer area whose voltage exceeds the limit is determined. According to the sorting result of the number of households in each transformer area whose voltage exceeds the limit, a second threshold is determined. Transformer areas whose number of households whose voltage exceeds the limit exceeds the second threshold are identified as transformer areas whose voltage exceeds the lower limit, and a set of transformer areas whose voltage exceeds the lower limit is constructed. Candidate distribution area combinations are constructed based on the set of photovoltaic backfeeding areas and the set of voltage lower limit-crossing areas. Road constraints are used to screen each candidate distribution area combination using the latitude and longitude coordinates of the distribution transformers and the road network data to obtain effective candidate combinations that meet traffic requirements. For each effective candidate combination, time period matching score, distance matching score, and supply and demand adaptation matching score are calculated, and then a comprehensive matching score is calculated. The priority matching result of photovoltaic backfeeding areas and voltage lower limit-crossing areas is output according to the ranking result of the comprehensive matching score.

2. The transformer substation matching method for mobile energy storage power exchange according to claim 1, characterized in that, The duration of voltage exceeding the upper limit is obtained by accumulating the sampling intervals corresponding to the sampling points in the low-voltage side voltage sequence of the distribution transformer in the substation that are greater than the preset upper voltage threshold.

3. The transformer substation matching method for mobile energy storage power exchange according to claim 1, characterized in that, The number of households experiencing voltage exceedances is obtained by accumulating the product of the number of users whose voltage values ​​are lower than a preset lower voltage threshold obtained from the user voltage data sequence of the transformer area at each sampling time within the statistical period and the sampling interval.

4. The transformer substation matching method for mobile energy storage power exchange according to claim 1, characterized in that, The first threshold is determined by sorting the duration of voltage exceeding the upper limit of each transformer area in descending order and then using the percentile method; the second threshold is determined by sorting the number of households in each transformer area when the voltage exceeds the upper limit in descending order and then using the percentile method.

5. The transformer substation matching method for mobile energy storage power exchange according to claim 1, characterized in that, The road constraint filtering includes: The latitude and longitude coordinates of the photovoltaic backfeeding area and the voltage lower limit crossing area are projected onto the nodes in the road network, respectively, as the starting node and the ending node; The shortest path algorithm is used to calculate the shortest travel distance from the starting node to the ending node, and it is determined whether the path corresponding to the shortest travel distance meets the preset travel requirements. If the preset access requirements are not met, the corresponding candidate station combination will be removed.

6. The transformer substation matching method for mobile energy storage power exchange according to claim 1, characterized in that, The time period matching score is determined in the following way: Suppose the statistical period contains D natural days, and each natural day contains M sampling times; calculate the weighted average time of voltage exceeding the upper limit event in the photovoltaic backfeeding area on the d-th natural day. Then, the weighted average time of the photovoltaic backfeeding area within the statistical period is calculated. : ； ； in, Indicates the index of sampling time within a natural day; This indicates that the photovoltaic backfeeding area is on the dth natural day. The voltage value of the low-voltage side of the distribution transformer at each sampling time; This indicates the preset upper voltage threshold. This represents an indicator function, which takes the value 1 if the condition within the parentheses is true, and 0 otherwise. Calculate the weighted average time of voltage lower limit events in the transformer area on the d-th natural day. Then, the weighted average time of voltage exceeding the lower limit in the distribution area within the statistical period is calculated. : ； ； in, This indicates the voltage level exceeding the lower limit on the dth natural day. The number of users whose voltage exceeds the lower limit at each sampling time; Calculate the time interval between photovoltaic backfeed areas and voltage lower limit crossing areas. : ； The time interval is used to determine the time period matching score. : ； in, This indicates the shortest time required for a single charge and discharge cycle of mobile energy storage; Indicates the maximum allowed duration; This indicates the preset maximum matching score.

7. The transformer substation matching method for mobile energy storage power exchange according to claim 1, characterized in that, The distance matching score is determined in the following way: Set the maximum allowed passage distance as The distance matching score is calculated based on the shortest travel distance between the photovoltaic backfeeding area and the voltage lower limit area. : ； in, This indicates the shortest travel distance between photovoltaic backfeeding areas and areas with voltage below the lower limit. This indicates the preset maximum matching score.

8. The transformer substation matching method for mobile energy storage power exchange according to claim 1, characterized in that, The supply and demand matching score is determined in the following way: Determine the distributed photovoltaic installed capacity of the photovoltaic backfeeding area and the number of hours its voltage exceeds the upper limit, as well as the number of households in the photovoltaic backfeeding area whose voltage exceeds the lower limit for the time limit; Set the experience coefficient , Calculate the supply-demand ratio using the following formula. : ； in, This indicates the distributed photovoltaic installed capacity of the photovoltaic backfeeding area; This indicates the number of hours the voltage in the photovoltaic backfeeding area exceeds the upper limit; This indicates the number of households in the voltage range that exceeds the lower voltage limit. Based on the supply-demand ratio, determine the supply-demand matching score. : ； in, , , This represents the preset matching score, and is the maximum matching score. > > .

9. The distribution area matching method for mobile energy storage power exchange according to any one of claims 1 to 8, characterized in that, The comprehensive matching score is obtained by weighted summation of the time-period matching score, distance matching score, and supply-demand matching score: ； in, This indicates the overall matching score; Indicates the time period matching score; Indicates the distance matching score; This indicates the score for supply and demand matching; , , Each represents the corresponding non-negative weight coefficient. .

10. A distribution network matching system for mobile energy storage power exchange, comprising a memory, a processor, and a computer program or instructions stored in the memory, characterized in that, The processor executes the computer program or instructions to implement the distribution area matching method for mobile energy storage power exchange as described in any one of claims 1 to 9.

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