A method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device of a power distribution network

Abstract

The application discloses a method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device of a power distribution network, and belongs to the technical field of power system operation and planning. The method comprises the following steps: obtaining historical data of distributed new energy and loads in a power distribution network, and generating an annual time-based operation data set of the power distribution network; based on the annual time-based operation data set of the power distribution network, calculating statistical correlation sensitivity of the new energy and the loads of the power distribution network to each subarea and node, and obtaining candidate installation sites of the electromagnetic interconnection device according to the statistical correlation sensitivity ranking; considering the candidate installation sites, constructing a site selection and capacity determination model of the multi-terminal electromagnetic interconnection device under the constraints of power grid operation and planning, and solving the model to obtain the number of ports, the connected nodes and the capacity of the interconnection device. The application can be applied to the site selection and capacity determination of the multi-terminal electromagnetic interconnection device under various power distribution networks, and comprehensively improves the operation economy and new energy consumption of the power distribution network.

Description

Technical Field

[0001] This invention relates to a method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, belonging to the field of power system operation and planning technology. Background Technology

[0002] The penetration rate of distributed renewable energy in distribution networks has been increasing in recent years. Due to geographical factors and grid conditions, some distribution networks and their sub-regions have limited capacity, and their transformers are experiencing heavy or even overloaded power transmission, leading to difficulties in absorbing some distributed renewable energy output and curtailment of wind and solar power, resulting in significant resource waste. Meanwhile, due to industrial and commercial development and improved living standards, other distribution networks and their sub-regions have larger loads, and their transformers transmit large amounts of power at certain operating times. This allows for the cross-regional absorption of distributed renewable energy through multi-terminal interconnection when curtailment of wind and solar power occurs in a particular sub-region. Electromagnetic multi-terminal interconnection devices have advantages such as low cost, simple structure, and high reliability, making them particularly suitable for multi-regional interconnection scenarios in distribution networks. However, the operational performance of such devices is highly dependent on appropriate deployment location and capacity configuration. Inappropriate site selection and capacity allocation schemes not only fail to fully utilize their cross-regional power exchange capabilities but may also lead to wasted investment. Therefore, it is necessary to conduct in-depth research on their site selection and capacity allocation issues.

[0003] Currently, there has been some research on the site selection and capacity determination methods for flexible interconnected devices. Existing methods mainly fall into two categories: mathematical programming methods and heuristic intelligent algorithms. Among them, mathematical programming methods often involve huge computational costs and are difficult to solve when dealing with high-dimensional, nonlinear, and complex models; while heuristic algorithms, although widely used, are prone to getting trapped in local optima and require a large number of repetitive simulation calculations, resulting in a long optimization process. In addition, regarding site selection strategies, existing research has proposed methods based on branch power sensitivity analysis, which ranks sites by calculating the power flow sensitivity of individual branches. However, this method is mainly applicable to two-end interconnected devices and cannot effectively solve the site selection problem in multi-end, cross-regional scenarios. In terms of optimization models, existing research usually uses the number of device ports as a preset condition or uses fixed parameters, making it difficult to obtain a globally optimal configuration scheme. In terms of data foundation, some studies use typical days or limited scenarios for analysis, failing to fully consider the random fluctuation characteristics of renewable energy output and load over the entire year, affecting the reliability and adaptability of the planning results.

[0004] Therefore, there is an urgent need for a method for the location and capacity determination of electromagnetic interconnection devices that can comprehensively consider the characteristics of new energy sources and load time-series fluctuations and is applicable to multi-zone and multi-terminal interconnection scenarios. Summary of the Invention

[0005] The technical problem to be solved by this invention is how to determine the number of ports, the installation location and capacity of each port of a multi-terminal electromagnetic interconnection device in order to improve the level of new energy consumption, given the background of large-scale integration of distributed new energy into the power distribution network.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, wherein the multi-terminal electromagnetic interconnection device is an electromagnetic multi-terminal interconnection device with a number of ports greater than or equal to 3, and the site selection and capacity determination method includes the following steps: Step S1: Obtain historical data of distributed new energy sources and loads in the distribution network, and generate an annual hourly operation dataset of the distribution network; Step S2: Based on the annual hourly operation dataset of the distribution network, calculate the statistical correlation sensitivity of the new energy and load of the distribution network to each zone and node. The statistical correlation sensitivity includes the up-power sensitivity and the down-power sensitivity. Based on the statistical correlation sensitivity, obtain the candidate installation locations of the electromagnetic interconnection device. Step S3: Considering candidate installation locations, under the constraints of power grid operation and planning, construct and solve the location and capacity model of the multi-terminal electromagnetic interconnection device to obtain the number of interconnection device ports, connection nodes and capacity.

[0007] The aforementioned method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, wherein step S1 includes: Step S11: Obtain historical data of each distributed new energy power station and load in the distribution network; Step S12: Determine the optimal state number for each distributed renewable energy power station and load; Step S13: Define the value range of each state of each distributed new energy power station and load, convert the historical data sequence into a state sequence, and statistically analyze the empirical distribution function of the measured data within the corresponding range of each state. Step S14: Generate the transition probability matrix and the cumulative transition probability matrix based on the original state sequence; Step S15: Randomly generate the initial state of each sequence, and use Monte Carlo sampling based on the cumulative transition probability matrix to generate the simulated state sequence of each distributed new energy power station and load. Step S16: Within the range of values ​​corresponding to each state, the simulated state sequence of each distributed renewable energy power station and load is converted into a time series of specific values ​​using a random power generation method based on the distribution function, until the hourly output and load of each distributed renewable energy power station for the whole year are obtained, and power flow calculation is performed. The power flow calculation results constitute the annual hourly operation dataset of the distribution network.

[0008] In the aforementioned method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a distribution network, in step S2, based on the annual hourly operation dataset, the statistical correlation sensitivity of each zone in the distribution network is calculated. The statistical correlation sensitivity includes the power transmission sensitivity, which characterizes the statistical correlation strength between the total renewable energy output of the zone and the power transmitted to the zone, and the power transmission sensitivity, which characterizes the statistical correlation strength between the total active power load of the zone and the power transmitted to the zone. Based on the ranking results of the power transmission sensitivity and the power transmission sensitivity, candidate power transmission zones and candidate power transmission zones are determined respectively. Within the candidate power supply partition and the candidate power supply partition, the voltage comprehensive sensitivity of each node is calculated. The voltage comprehensive sensitivity characterizes the comprehensive influence of the renewable energy output of the partition on the voltage amplitude and phase angle of each node. Based on the ranking results of the voltage comprehensive sensitivity, candidate access nodes are determined respectively. In step S3, the candidate access node is used as the decision space, the number of ports of the multi-terminal electromagnetic interconnection device is used as the optimization variable, the total cost is minimized, and the grid operation constraints and planning constraints including port number restrictions are considered. The multi-terminal electromagnetic interconnection device location and capacity model is constructed and solved to obtain the optimal number of ports, the connection nodes of each port and the capacity configuration.

[0009] The candidate up-send partition is the partition that ranks first k1 in the up-send power sensitivity ranking, and the candidate down-send partition is the partition that ranks first k2 in the down-send power sensitivity ranking, where k1 and k2 are preset positive integers.

[0010] The aforementioned method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, wherein the transmitted power sensitivity... S up,i Characterizing the first i The total output of all new energy sources in the first zone is related to the first... i Sensitivity of power transmitted to the strip: ; ; ; ; .

[0011] Where N1 represents the first data point in the annual hourly operation data of the distribution network. i Each partition contains a set of times when power is transmitted. P up,ij Indicates the first i Sections in j The power transmitted at any given moment. P new,ij Indicates the first i Sections in jThe total contribution of new energy sources at any given moment.

[0012] The aforementioned method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, wherein the power transmission sensitivity is... S down,i Characterizing the first i The sum of all active loads in the partition is relative to the first... i Sensitivity of power transmission in the segmented area: ; ; ; ; .

[0013] Where N2 represents the number of data points in the annual hourly operation data of the distribution network. i The partition has a set of times for sending down power. P down,ij Indicates the first i Sections in j Downlink power at any given time P load,ij Indicates the first i Sections in j The total active load at any given moment.

[0014] The aforementioned method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, wherein the voltage comprehensive sensitivity... SV im Characterizing the total output of new energy sources in relation to the first i Nodes in a strip partition m Combined sensitivity of voltage amplitude and phase angle: ; ; ; ; ; ; ; .

[0015] Where N represents the annual hourly operation dataset of the distribution network, V imj Indicates the first i Nodes in a strip partition m existj Voltage amplitude at time 10:00 imj Indicates the first i Nodes in a strip partition m exist j The voltage phase angle at a given moment.

[0016] The aforementioned method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, wherein step S3 includes: Taking into account both the investment and operating costs of multi-terminal electromagnetic interconnection devices, the objective function is to minimize the total cost: ; in, C total Represents the total cost. C inv This indicates the investment cost of a multi-terminal electromagnetic interconnection device. C ope Indicates the operating cost of the power grid; Based on the power balance of distribution network nodes, the constraints on the power curtailment of new energy sources, the upper and lower limits of branch power flow, and the voltage amplitude constraints of nodes, grid operation constraints are constructed; based on the port number limit and capacity upper and lower limits of multi-terminal electromagnetic interconnection devices, planning constraints for multi-terminal electromagnetic interconnection devices are constructed; the planning constraints include the port number limit: ;in, x mul Indicates the number of ports of the electromagnetic interconnect device. X mulmax Indicates the maximum allowed number of ports for the electromagnetic interconnect device; Artificial intelligence algorithms, such as particle swarm optimization, are used to solve the established location and capacity model to obtain the number of interconnected device ports, connection nodes, and installed capacity.

[0017] In the aforementioned method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, the calculation formula for each investment cost in step S3 is as follows: ; in, C fix The annualized fixed investment cost for interconnected devices, l 1 represents the annualized cost coefficient per unit capacity. S mul This refers to the capacity of a multi-terminal electromagnetic interconnection device.

[0018] The formula for calculating power grid operating costs is: ; in, C loss It is the cost of network loss. C newIt is the cost of curtailing renewable energy. l 2 and l 3 represents the network loss cost coefficient and the renewable energy curtailment cost coefficient, respectively. Mb Represents the set of distribution network branches. r ij For nodes in the distribution network i To the node j The resistance of the branch circuit I ij,t For nodes in the distribution network i To the node j The side road is t Current at any moment for t The total amount of power curtailed in the power distribution network at any given time. Nk Represents the total number of nodes in the distribution network. N This represents the number of runtimes considered. for t Time Node j The amount of abandoned power from new energy sources.

[0019] The aforementioned method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a distribution network, in step S3, the formulas for distribution network node power balance, renewable energy curtailment power constraint, branch power flow upper and lower limit constraint, and node voltage amplitude constraint are as follows: ; ; ; ; ; ; ; ; ; In the formula: , They represent t Time flows to nodes j The active and reactive power; , They represent t Flowing nodes at all times j The active and reactive power; , They represent t Injecting nodes at all times j The active and reactive power; x ij For nodes in the distribution network i To the nodej The reactance of the branch circuit; , They are respectively t Time Node i and nodes j The voltage; , , for t Time Node j New energy sources, electromagnetic interconnection devices, and the active power of loads; , , They are respectively t Time Node j New energy sources, electromagnetic interconnection devices, and reactive power of loads. , Representing nodes respectively i Upper and lower limits of voltage; Represents a node i To the node j The upper limit of branch current.

[0020] The formula for limiting the number of ports and constraining the upper and lower limits of capacity in multi-terminal electromagnetic interconnection devices is as follows: ; ; in, x mul Indicates the number of ports of the electromagnetic interconnect device. X mulmax This indicates the maximum number of ports in the electromagnetic interconnect device.

[0021] The beneficial effects achieved by this invention are as follows: This invention considers the impact of distributed renewable energy and load fluctuations in each zone of the distribution network on its upstream and downstream power transmission, as well as the impact of distributed renewable energy on the voltage of each node within the zone, and screens out candidate installation locations for multi-terminal electromagnetic interconnection devices. Based on this, by optimizing the installation cost of the interconnection devices and the operating cost of the power grid, the number of interconnection device ports, the installation locations of each port, and the overall capacity are determined. This ensures that the electromagnetic interconnection devices not only have the best operational efficiency but also reduce the impact of distributed renewable energy and load fluctuations on the distribution network. Compared with existing technologies, the outstanding features of this invention are: firstly, by using statistical correlation sensitivity based on hourly operating data throughout the year for two-level screening at both the zone and node levels, it can accurately identify the zones with the most urgent need for inter-regional power mutual assistance, effectively narrowing the optimization search space; secondly, for the first time, the number of ports of the multi-terminal electromagnetic interconnection device is included as an optimization variable in the site selection and capacity determination model, which can automatically determine the optimal number of ports according to actual needs, achieving the best balance between investment costs and operational efficiency. Attached Figure Description

[0022] Figure 1 This is an overall flowchart of the location and capacity determination method for a multi-terminal electromagnetic interconnection device in a power distribution network according to the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0024] The multi-terminal electromagnetic interconnection device involved in this invention is an electromagnetic multi-terminal interconnection device, and the number of its ports is an integer greater than or equal to 3.

[0025] Example 1

[0026] like Figure 1 As shown, this invention discloses a method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, comprising the following steps: Step S1: Obtain historical data of distributed new energy sources and loads in the distribution network to generate an annual hourly operation dataset of the distribution network; the time resolution of the annual hourly operation dataset of the distribution network can be adjusted according to actual needs, such as using a 15-minute resolution or a higher time resolution to improve planning accuracy.

[0027] Step S2: Based on the annual hourly operation dataset of the distribution network, calculate the statistical correlation sensitivity of renewable energy and load to each zone and node. The statistical correlation sensitivity includes the sensitivity of power transmitted upstream and downstream. Candidate installation locations for electromagnetic interconnection devices are obtained by ranking the statistical correlation sensitivities. The statistical correlation sensitivity of this invention is directly calculated based on the annual hourly operation data. Its mathematical essence is the cosine similarity of two variable time series, reflecting the statistical correlation strength between renewable energy output and inter-regional power transmission in actual operation. This sensitivity definition method does not rely on model assumptions such as load growth factors, and has a stronger adaptability to the actual random fluctuations of renewable energy and load, especially suitable for medium- and long-term planning scenarios.

[0028] Step S3: Considering candidate installation locations, under the constraints of power grid operation and planning, construct and solve the location and capacity model of the multi-terminal electromagnetic interconnection device to obtain the number of interconnection device ports, connection nodes and capacity.

[0029] In step S2, based on the annual hourly operation dataset, the statistical correlation sensitivity of each zone in the distribution network is calculated. The statistical correlation sensitivity includes the power transmission sensitivity, which characterizes the statistical correlation strength between the total renewable energy output of the zone and the power transmitted to the zone, and the power transmission sensitivity, which characterizes the statistical correlation strength between the total active power load of the zone and the power transmitted to the zone. Based on the ranking results of the power transmission sensitivity and the power transmission sensitivity, candidate power transmission zones and candidate power transmission zones are determined respectively. Within the candidate power supply partition and the candidate power supply partition, the voltage comprehensive sensitivity of each node is calculated. The voltage comprehensive sensitivity characterizes the comprehensive influence of the renewable energy output of the partition on the voltage amplitude and phase angle of each node. Based on the ranking results of the voltage comprehensive sensitivity, candidate access nodes are determined respectively. In step S3, the candidate access node is used as the decision space, the number of ports of the multi-terminal electromagnetic interconnection device is used as the optimization variable, the total cost is minimized, and the grid operation constraints and planning constraints including port number restrictions are considered. The multi-terminal electromagnetic interconnection device location and capacity model is constructed and solved to obtain the optimal number of ports, the connection nodes of each port and the capacity configuration.

[0030] Step S1 includes: Step S11: Obtain historical data of each distributed new energy power station and load in the distribution network; Step S12: Determine the optimal state number for each distributed renewable energy power station and load; Step S13: Define the value range of each state of each distributed new energy power station and load, convert the historical data sequence into a state sequence, and statistically analyze the empirical distribution function of the measured data within the corresponding range of each state. Step S14: Generate the transition probability matrix and the cumulative transition probability matrix based on the original state sequence; Step S15: Randomly generate the initial state of each sequence, and use Monte Carlo sampling based on the cumulative transition probability matrix to generate the simulated state sequence of each distributed new energy power station and load. Step S16: Within the range of values ​​corresponding to each state, the simulated state sequence of each distributed renewable energy power station and load is converted into a time series of specific values ​​using a random power generation method based on the distribution function, until the hourly output and load of each distributed renewable energy power station for the whole year are obtained, and power flow calculation is performed. The power flow calculation results constitute the annual hourly operation dataset of the distribution network.

[0031] Step S2 includes: Step S21: Calculate and sort the power sensitivity of the total output of new energy sources in each zone of the distribution network to its power transmission. Step S22: Calculate and sort the loads in each zone of the distribution network and their sensitivity to the power they transmit to. Step S23: Select the top k1 zones in terms of power transmission sensitivity as candidate power transmission zones for the multi-terminal electromagnetic interconnection device, and select the top k2 zones in terms of power transmission sensitivity as candidate power transmission zones for the multi-terminal electromagnetic interconnection device. Here, k1 and k2 are preset positive integers, which can be determined based on the actual scale of the distribution network and the maximum number of ports of the multi-terminal electromagnetic interconnection device. For example, when the maximum number of ports of the multi-terminal electromagnetic interconnection device is X, the values ​​of k1 and k2 should satisfy k1 + k2 ≤ X to ensure that the total number of candidate zones does not exceed the maximum number of ports that the device can connect to. If a zone has both high power transmission sensitivity and high power transmission sensitivity (i.e., the zone needs to transmit power during some periods and receive power during other periods), this zone may be selected as both a candidate power transmission zone and a candidate power transmission zone. In this case, overlapping zones are counted only once, and the total number of candidate zones can be equal to k1 + k2 - the number of duplicate zones.

[0032] Step S24: For each node in the candidate power transmission zone, calculate and sort the comprehensive voltage sensitivity of the zone's renewable energy output to the voltage of each node. Based on the actual situation, select the top M1 nodes as candidate access nodes for the multi-terminal electromagnetic interconnection device. For example, if the comprehensive voltage sensitivity of the M1th candidate node is more than 10 times that of the M1+1th candidate node, or if the cost of constructing the supporting lines for the M1+1th port is more than twice the construction cost of the multi-terminal electromagnetic interconnection device, then the top M1 candidate nodes are selected as candidate access nodes for the multi-terminal electromagnetic interconnection device.

[0033] Step S25: For each node in the candidate distribution zone, using the same method as in step S24, calculate and sort the comprehensive voltage sensitivity of the zone load to the voltage of each node. Based on the actual situation, select the top M2 nodes as candidate access nodes for the multi-terminal electromagnetic interconnection device. For example, if the comprehensive voltage sensitivity of the M2th candidate node is greater than 10 times that of the (M2+1)th candidate node, or if the cost of constructing the supporting lines for the (M2+1)th port is greater than twice the construction cost of the multi-terminal electromagnetic interconnection device, then the top M2 candidate nodes are selected as candidate access nodes for the multi-terminal electromagnetic interconnection device. Here, M1 and M2 are both preset positive integers.

[0034] The power transmission sensitivity in step S21 S up,i Characterizing the first i The total output of all new energy sources in the first zone is related to the first... i The sensitivity of the power transmitted by the strip is calculated using the following formula: ; ; ; ; ; Where N1 represents the first data point in the annual hourly operation data of the distribution network. i Each partition contains a set of times when power is transmitted. P up,ij Indicates the first i Sections in j The power transmitted at any given moment. P new,ij Indicates the first i Sections in j The total active power output of new energy sources at any given time. The larger this sensitivity value, the stronger the statistical correlation between the new energy output and the power transmitted to other regions in that region. In other words, the more urgent the demand for new energy transmission in that region is, and the more necessary it is to transfer the surplus power of that region to other regions through multi-terminal electromagnetic interconnection devices.

[0035] The downlink power sensitivity in step S24 is: S down,i Characterizing the first i The sum of all active loads in the partition is relative to the first... i The sensitivity of the power transmitted by the strip zone is calculated using the following formula: ; ; ; ; ; Where N2 represents the number of data points in the annual hourly operation data of the distribution network. i The partition has a set of times for sending down power. P down,ij Indicates the first i Sections in j Downlink power at any given time P load,ij Indicates the first i Sections in j The total active load at any given time. The larger this sensitivity value, the stronger the statistical correlation between the load of the zone and the power transmitted downstream. In other words, the more the load absorption demand of the zone needs to be met by cross-zone power transfer, and the more necessary it is to receive power from other zones through multi-terminal electromagnetic interconnection devices.

[0036] The node voltage comprehensive sensitivity in step S24 SV im Characterizing the total output of new energy sources in relation to the first i Nodes in a strip partition mThe combined sensitivity of voltage amplitude and phase angle is calculated using the following formula: ; ; ; ; ; ; ; ; ; Where N represents the annual hourly operation dataset of the distribution network, V imj Indicates the first i Nodes in a strip partition m exist j Voltage amplitude at time 10:00 imj Indicates the first i Nodes in a strip partition m exist j The voltage phase angle at a given moment.

[0037] Step S3 includes: Step S31: Taking into account both the investment and operating costs of the multi-terminal electromagnetic interconnection device, the objective function is constructed to minimize the total cost: ; in, C total Represents the total cost. C inv This indicates the investment cost of a multi-terminal electromagnetic interconnection device. C ope Indicates the operating cost of the power grid; Step S32: Based on the power balance of distribution network nodes, the power curtailment of new energy sources, the upper and lower limits of branch power flow, and the voltage amplitude of nodes, construct the power grid operation constraints; based on the port number limit and capacity upper and lower limits of multi-terminal electromagnetic interconnection devices, construct the planning constraints of multi-terminal electromagnetic interconnection devices. Step S33: The established model is solved using artificial intelligence algorithms such as particle swarm optimization (PSO). During the solution process, the number of ports is treated as a discrete variable, the number of connected nodes at each port as integer variables, and the capacity of each port as a continuous variable. Joint optimization is performed to obtain the optimal number of ports, the number of connected nodes, and the installed capacity of the interconnected device. In PSO, each particle represents a configuration scheme, including the number of ports, the number of connected nodes, and the capacity. By iteratively updating the position and velocity of the particles, the algorithm eventually converges to the global optimum. The mutual support needs between different distribution network zones vary, and therefore the required optimal number of ports also differs. If the number of ports is preset too few, the potential for cross-regional power mutual support cannot be fully utilized; if the number of ports is preset too many, unnecessary investment costs will increase. This invention, through automatic optimization of the number of ports, can achieve the best balance between investment costs and operational efficiency based on actual needs.

[0038] In step S31, the formulas for calculating each cost are as follows: ; in, C fix The annualized fixed investment cost for interconnected devices, l 1 represents the annualized cost coefficient per unit capacity. S mul This refers to the capacity of a multi-terminal electromagnetic interconnection device.

[0039] The formula for calculating power grid operating costs is: ; in, C loss It is the cost of network loss. C new It is the cost of curtailing renewable energy. l 2 and l 3 represents the network loss cost coefficient and the renewable energy curtailment cost coefficient, respectively. Mb Represents the set of distribution network branches. r ij For nodes in the distribution network i To the node j The resistance of the branch circuit I ij,t For nodes in the distribution network i To the node j The side road is t Current at any moment for t The total amount of power curtailed in the power distribution network at any given time. Nk Represents the total number of nodes in the distribution network. N This represents the number of runtimes considered. for t Time Node j The amount of abandoned power from new energy sources.

[0040] In step S32, the formulas for power balance at distribution network nodes, power curtailment constraints for renewable energy, upper and lower limits of branch power flow constraints, and node voltage amplitude constraints are as follows: ; ; ; ; ; ; ; ; ; In the formula: , They represent t Time flows to nodes j The active and reactive power; , They represent t Flowing nodes at all times j The active and reactive power; , They represent t Injecting nodes at all times j The active and reactive power; x ij For nodes in the distribution network i To the node j The reactance of the branch circuit; , They are respectively t Time Node i and nodes j The voltage; , , for t Time Node j New energy sources, electromagnetic interconnection devices, and the active power of loads; , , They are respectively t Time Node j New energy sources, electromagnetic interconnection devices, and reactive power of loads. , Representing nodes respectively i Upper and lower limits of voltage; Represents a node i To the node j The upper limit of branch current.

[0041] The formula for limiting the number of ports and constraining the upper and lower limits of capacity in multi-terminal electromagnetic interconnection devices is as follows: ; .

[0042] in, x mul Indicates the number of ports of the electromagnetic interconnect device. X mulmax This indicates the maximum number of ports in the electromagnetic interconnect device. Unlike existing technologies that treat the number of ports as a fixed parameter, this embodiment uses the number of ports... x mul As an optimization variable, the optimization algorithm automatically evaluates the total cost under different port number configurations during the optimization process: with fewer ports, the investment cost is lower, but the cross-regional power exchange capability is limited, which may lead to higher power curtailment costs; with more ports, the cross-regional exchange capability is enhanced, but the investment cost increases accordingly. The optimization algorithm finds the optimal number of ports that minimizes the total cost through a global search. This optimal number of ports is determined by the actual exchange needs and investment costs of each region, rather than being preset by humans.

[0043] Example 2

[0044] Taking a real power distribution network as an example, this network comprises three zones, each connected to a distributed photovoltaic power station, with total installed capacities of 8MW, 5MW, and 3MW respectively. The method of this invention is used for the site selection and capacity determination of multi-terminal electromagnetic interconnection devices. The specific process is as follows: (1) Obtain historical data of photovoltaic power plants and loads in each zone, and generate an annual hourly operation dataset based on Markov chain Monte Carlo simulation, totaling 8760 time points.

[0045] (2) Based on the annual hourly operation dataset, the uplink power sensitivity and downlink power sensitivity of each partition were calculated. The results show that the uplink power sensitivity of partition 1 is 0.89, the uplink power sensitivity of partition 3 is 0.72, and the downlink power sensitivity of partition 2 is 0.85. Considering the maximum number of ports, partition 1 was selected as the candidate uplink partition, and partition 2 was selected as the candidate downlink partition.

[0046] (3) In partition 1, the overall voltage sensitivity of each node is calculated. Node 3 has the highest sensitivity (0.76), followed by node 4 (0.68). In partition 2, node 7 has the highest sensitivity (0.81), followed by node 8 (0.72). Nodes 3, 4, 7, and 8 are selected as candidate access nodes.

[0047] (4) Using nodes 3, 4, 7 and 8 as candidate access nodes, the number of ports is used as an optimization variable with a value range of 3-4. The particle swarm algorithm is used to solve the addressing and capacity model.

[0048] (5) The optimized result is: 3 ports, with port 1 connected to node 3 of partition 1, port 2 connected to node 7 of partition 2, and port 3 connected to node 4 of partition 1, for a total capacity of 5.5 MVA. The total annualized cost of this scheme is RMB 1.52 million, and the renewable energy consumption rate is 95.1%.

[0049] (6) To verify the effectiveness of the port number optimization, this embodiment compares with another scheme with a fixed number of 3 ports, where the ports are connected to node 3 of partition 1, node 7 of partition 2, and node 8 of partition 2, respectively. The total annualized cost is RMB 1.65 million and the renewable energy absorption rate is 89.5%. The method of this invention achieves balanced power distribution among ports by using the number of ports as an optimization variable, reducing the total cost by 7.9% and increasing the renewable energy absorption rate by 5.6 percentage points.

[0050] 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.

Claims

1. A method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network, characterized in that, The multi-terminal electromagnetic interconnection device is an electromagnetic multi-terminal interconnection device, and its number of ports is an integer greater than or equal to 3. The addressing and sizing method includes the following steps: S1. Obtain historical data of distributed new energy sources and loads in the distribution network, and generate an annual hourly operation dataset of the distribution network. S2. Based on the annual hourly operation dataset of the distribution network, calculate the statistical correlation sensitivity of the distribution network's new energy sources and loads to each zone and node. The statistical correlation sensitivity includes the power transmission sensitivity and the power transmission sensitivity. Based on the statistical correlation sensitivity, obtain the candidate installation locations of the electromagnetic interconnection device. S3. Considering candidate installation locations, under the constraints of power grid operation and planning, construct and solve the location and capacity model of the multi-terminal electromagnetic interconnection device to obtain the number of interconnection device ports, connection nodes and capacity.

2. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 1, characterized in that, Step S1 includes: Step S11: Obtain historical data of each distributed new energy power station and load in the distribution network; Step S12: Determine the optimal state number for each distributed renewable energy power station and load; Step S13: Define the value range of each state of each distributed new energy power station and load, convert the historical data sequence into a state sequence, and statistically analyze the empirical distribution function of the measured data within the corresponding range of each state. Step S14: Generate the transition probability matrix and the cumulative transition probability matrix based on the original state sequence; Step S15: Randomly generate the initial state of each sequence, and use Monte Carlo sampling based on the cumulative transition probability matrix to generate the simulated state sequence of each distributed new energy power station and load. Step S16: Within the range of values ​​corresponding to each state, the simulated state sequence of each distributed renewable energy power station and load is converted into a time series of specific values ​​using a random power generation method based on the distribution function, until the hourly output and load of each distributed renewable energy power station for the whole year are obtained, and power flow calculation is performed. The power flow calculation results constitute the annual hourly operation dataset of the distribution network.

3. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 1, characterized in that, In step S2, based on the annual hourly operation dataset, the statistical correlation sensitivity of each zone in the distribution network is calculated. The statistical correlation sensitivity includes the power transmission sensitivity, which characterizes the statistical correlation strength between the total renewable energy output of the zone and the power transmitted to the zone, and the power transmission sensitivity, which characterizes the statistical correlation strength between the total active power load of the zone and the power transmitted to the zone. According to the ranking results of the power transmission sensitivity and the power transmission sensitivity, candidate power transmission zones and candidate power transmission zones are determined respectively. Within the candidate power transmission zones and candidate power transmission zones, the voltage comprehensive sensitivity of each node is calculated. The voltage comprehensive sensitivity characterizes the comprehensive influence of the renewable energy output of the zone on the voltage amplitude and phase angle of each node. Based on the ranking results of the comprehensive voltage sensitivity, candidate access nodes are determined respectively; In step S3, the candidate access node is used as the decision space, the number of ports of the multi-terminal electromagnetic interconnection device is used as the optimization variable, the total cost is minimized, and the grid operation constraints and planning constraints including port number restrictions are considered. The multi-terminal electromagnetic interconnection device location and capacity model is constructed and solved to obtain the optimal number of ports, the connection nodes of each port and the capacity configuration.

4. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 3, characterized in that, The candidate up-send partition is the partition that ranks first k1 in the up-send power sensitivity ranking, and the candidate down-send partition is the partition that ranks first k2 in the down-send power sensitivity ranking, where k1 and k2 are preset positive integers.

5. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 1, characterized in that, The upload power sensitivity S up,i Characterizing the first i The total output of all new energy sources in the first zone is related to the first... i Sensitivity of power transmitted to the strip: ； in: , , , N1 represents the first data point in the annual hourly operation data of the distribution network. i Each partition contains a set of times when power is transmitted. P up,ij Indicates the first i Sections in j The power transmitted at any given moment. P new,ij Indicates the first i Sections in j The total contribution of new energy sources at any given moment.

6. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 1, characterized in that, The downfeed power sensitivity is S down,i Characterizing the first i The sum of all active loads in the partition is relative to the first... i Sensitivity of power transmission in the segmented area: ； in: , , , N2 represents the central data point of the annual hourly operation data of the distribution network. i The partition has a set of times for sending down power. P down,ij Indicates the first i Sections in j Downlink power at any given time P load,ij Indicates the first i Sections in j The total active load at any given moment.

7. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 1, characterized in that, The voltage overall sensitivity SV im Characterizing the total output of new energy sources in relation to the first i Nodes in a strip partition m Combined sensitivity of voltage amplitude and phase angle: ； ； ； ； ； ； ； ； ； Where N represents the annual hourly operation dataset of the distribution network, V imj Indicates the first i Nodes in a strip partition m exist j Voltage amplitude at time 10:00 imj Indicates the first i Nodes in a strip partition m exist j The voltage phase angle at a given moment.

8. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 1, characterized in that, Step S3 includes: Taking into account both the investment and operating costs of multi-terminal electromagnetic interconnection devices, the objective function is to minimize the total cost: ； in, C total Represents the total cost. C inv This indicates the investment cost of a multi-terminal electromagnetic interconnection device. C ope Indicates the operating cost of the power grid; Based on the power balance of distribution network nodes, the constraints on the power curtailment of new energy sources, the upper and lower limits of branch power flow, and the voltage amplitude constraints of nodes, grid operation constraints are constructed; based on the port number limit and capacity upper and lower limits of multi-terminal electromagnetic interconnection devices, planning constraints for multi-terminal electromagnetic interconnection devices are constructed; the planning constraints include the port number limit: ;in, x mul Indicates the number of ports of the electromagnetic interconnect device. X mulmax Indicates the maximum allowed number of ports for the electromagnetic interconnect device; The established location and capacity model is solved using artificial intelligence algorithms to obtain the number of interconnected device ports, connection nodes, and installed capacity.

9. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 8, characterized in that, In step S3, the formulas for calculating the various investment costs are as follows: ； in, C fix The annualized fixed investment cost for interconnected devices, λ 1 represents the annualized cost coefficient per unit capacity. S mul For the capacity of multi-terminal electromagnetic interconnection devices; The formula for calculating power grid operating costs is: ； in, C loss It is the cost of network loss. C new It is the cost of curtailing renewable energy. λ 2 and λ 3 represents the network loss cost coefficient and the renewable energy curtailment cost coefficient, respectively. Mb Represents the set of distribution network branches. r ij For nodes in the distribution network i To the node j The resistance of the branch circuit I ij,t For nodes in the distribution network i To the node j The side road is t Current at any moment for t The total amount of power curtailed in the power distribution network at any given time. Nk Represents the total number of nodes in the distribution network. N This represents the number of runtimes considered. for t Time Node j The amount of abandoned power from new energy sources.

10. The method for site selection and capacity determination of a multi-terminal electromagnetic interconnection device in a power distribution network according to claim 7, characterized in that, In step S3, the formulas for power balance at distribution network nodes, power curtailment constraints for renewable energy, upper and lower limits of branch power flow constraints, and node voltage amplitude constraints are as follows: ； ； ； ； ； ； ； ； ； In the formula: , They represent t Time flows to nodes j The active and reactive power; , They represent t Flowing nodes at all times j The active and reactive power; , They represent t Injecting nodes at all times j The active and reactive power; x ij For nodes in the distribution network i To the node j The reactance of the branch circuit; , They are respectively t Time Node i and nodes j The voltage; , , for t Time Node j New energy sources, electromagnetic interconnection devices, and the active power of loads; , , They are respectively t Time Node j New energy sources, electromagnetic interconnection devices, and reactive power of loads; , Representing nodes respectively i Upper and lower limits of voltage; Represents a node i To the node j The upper limit of branch current; The formula for limiting the number of ports and constraining the upper and lower limits of capacity in multi-terminal electromagnetic interconnection devices is as follows: ； ； in, x mul Indicates the number of ports of the electromagnetic interconnect device. X mulmax This indicates the maximum number of ports in the electromagnetic interconnect device.

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