Storage-transmission combined planning method and device considering weak regulation link identification, equipment and storage medium

Abstract

The application provides a kind of storage-conveying combined planning method, device and equipment considering weak link identification of regulation capacity and storage medium. It relates to the technical field of new energy storage and transportation planning. The method accurately identifies weak nodes and bottleneck lines for peak regulation by constructing adjustment capacity supply and demand imbalance tracing and weak link evaluation index. And it analyzes the dynamic relationship between energy storage configuration and line expansion, and constructs a combined planning model considering the trade-off between economy and flexibility. The effectiveness and rationality of the method are verified by matching the Garver-6 system example with actual operation data. The example shows that: on the one hand, the evaluation index can accurately identify weak nodes and bottleneck lines for peak regulation, providing guidance for subsequent planning. On the other hand, the proposed planning method avoids waste of energy storage investment and redundancy of line expansion, and significantly improves the weak links of the power grid.

Description

Technical Field

[0001] This application relates to the field of new energy storage and transmission planning technology, and in particular to a storage-transmission joint planning method, device, equipment and storage medium that takes into account the identification of weak links in regulation capacity. Background Technology

[0002] The demand and supply of regulation capacity in high-proportion renewable energy power systems exhibit complex spatiotemporal distribution characteristics, showing a trend towards diversification and decentralization. Therefore, it is necessary to conduct in-depth research on spatiotemporal assessment methods for regulation capacity, tracing the origins of regulation capacity demand to determine when and where it arises, thereby accurately identifying weak links in regulation capacity and providing a solid foundation for the precise allocation of energy storage and efficient expansion of power lines.

[0003] Existing research on regulation capacity assessment methods mainly focuses on two dimensions: resource regulation capacity assessment and line transmission capacity assessment. Regarding resource regulation capacity assessment, the literature "Xu Fengliang, Wang Keqian, Wang Wenhao, et al. Source-grid-storage coordinated expansion planning of medium-voltage distribution system considering operational flexibility [J]. China Electric Power, 2024, 57(7)" constructs net load adaptability rate, net load volatility rate, and line load margin balance indicators to characterize the operational flexibility level of the power system. The literature "Mi Weiming, Ye Peng, Zhang Mingli, et al. A novel distribution system flexibility assessment method based on cloud model [J]. Power System Technology, 2023, 48(6):2532-2540" addresses the problem that existing indicators and assessment methods in regulation capacity assessment research cannot intuitively and comprehensively reflect the system's regulation capacity, and proposes a power system flexibility assessment method based on a cloud model. The paper "Zhang Feng, Fan Hengjian, Deng Hui, et al. Analysis of the adequacy of flexible regulation capability of power system based on mathematical morphology [J / OL]. China Electric Power, 1-15 [2025-03-17]." uses the electric-gas-hydrogen coupling as a carrier to propose a flexible operation domain model of hybrid integrated energy distribution network and quantitatively evaluate the flexibility of distribution network operation. The paper "Flexibility supply and demand balance of ultra-high proportion renewable energy power system [J]. Automation of Electric Power System, 2022, 46(16):3-16." analyzes the operation characteristics of ultra-high proportion renewable energy power system, studies the basic principles and challenges of flexibility supply and demand balance, and proposes an analysis system for flexibility supply and demand balance of ultra-high proportion renewable energy power system. Regarding the assessment of line transmission capacity, the literature "Lin Zhiyu, Li Huaqiang, Su Yunche, et al. Power grid assessment and extended planning method considering flexibility carrying capacity [J]. Power System Protection and Control, 2021, 49(5):46-57." focuses on the impact of power grid line transmission carrying capacity on the supply and demand transmission of regulation capacity, and proposes the concept and measurement index of flexibility carrying capacity; the literature "Zang Yanxue, Bian Xiaoyan, Liang Siqi, et al. Flexibility assessment and optimal dispatch method of new energy power system considering line transmission capacity [J]. Power System Protection and Control, 2023, 51(11):15-26." proposes the evaluation index of insufficient system regulation capacity from the two perspectives of regulation capacity resource margin and line regulation capacity transmission margin by measuring the regulation capacity demand of nodes and its allocation on lines.The literature "Xinyin D. Flexibility evaluation of a new distribution system based on co-operative game-Gaussian cloud model[J]. Electrical Engineering, 2024, 107(4): 1-16." comprehensively considers four perspectives: power supply side, grid measurement, load side, and energy storage side. It combines the entropy weight method and the coefficient of variation method with the idea of ​​cooperative game theory and assigns values. It also constructs a flexibility evaluation index system using the Gaussian cloud model. The literature "Zhang Haibo, Hu Yukang, Li Zhengrong, et al. Optimal configuration of energy storage considering the risk of insufficient flexibility in high-load-density areas[J]. Power System Technology, 2023, 47(12): 4926-4936." constructs an overall regulation capacity evaluation index to quantify the global regulation capacity of the system and constructs a local regulation capacity evaluation index to quantify the impact of the line as a transmission channel on the supply and demand balance of regulation capacity. However, the above studies still have significant limitations: In terms of resource regulation capacity, most studies evaluate the regulation capacity level of the system from the global system level, lacking the evaluation of the spatiotemporal distribution of regulation capacity, and cannot pinpoint when and where the regulation capacity demand arises; In terms of line transmission capacity, existing studies mostly analyze from static cross-sections, pursuing the optimal overall load margin of the lines, and cannot identify which specific line caused the network congestion.

[0004] Regarding the optimization and allocation of regulation capacity resources, scholars have conducted extensive research on energy storage optimization, storage-transmission joint planning, and source-grid-storage coordination planning. The literature "Zou Shih-hao, Cao Yong-ji, Zhang Heng-xu, et al. Grid-storage joint planning considering the operational flexibility of carbon capture power plants [J]. High Voltage Engineering, 2024, 50(11): 5164-5173." analyzes the flexible operation mechanism of carbon capture power plants, constructs a power system flexibility supply model and quantitative indicators, and establishes a grid-storage two-layer optimization model considering the flexibility of carbon capture power plants; the literature "Huang Ling-ling, Bian Ya-jie, Fu Zhang-jie, et al. Coordination planning method for multi-dimensional flexibility resources of transmission networks considering dynamic changes in network transmission flexibility [J / OL]. Proceedings of the CSEE, 1-15." focuses on... Based on the dynamic changes in power flow in the transmission network, a network transmission flexibility index reflecting the dynamic matching degree between the transmission demand of flexible resources and the network transmission capacity was constructed. Based on this, a two-stage coordinated optimization model for multiple flexible resources in the transmission network considering a unified power flow controller was established. The literature "Chen Zhanpeng, Hu Yan, Tai Nengling, et al. Source-grid joint planning of renewable energy power systems considering flexibility and economy [J]. Electric Power Automation Equipment, 2022, 42(09):94-101." comprehensively considers two types of flexible resources, power sources and lines, and establishes a two-layer joint planning model for power systems that balances flexibility and economy. The literature "Li Junhui, Chen Guohang, Ma Teng, et al. Peak-shaving optimization control strategy for flow battery energy storage systems with high wind power penetration [J]. Power Generation Technology, 2024, 45(03):434-447." addresses the peak-shaving economic problem brought about by systems with high wind power penetration and proposes a peak-shaving optimization control method for flow battery energy storage that balances economy and technology. The literature “Xiao Juanxia, ​​Li Yong, Han Yu, et al. Distribution network elasticity enhancement strategy considering the spatiotemporal characteristics and flexibility resource synergy optimization of typhoons [J]. Journal of Electrical Engineering, 2024, 39(23):7430-7446.”, “Liu Fang, Lu Rongxin, Xu Yun, et al. Planning-operation coordination method for supporting the dual flexibility of new distribution systems with intelligent hybrid energy storage soft switching [J / OL]. Power System Technology, 1-16 [2025-07-02].” and “Wang Jie, Xu Lijun, Li Xiaozhu, et al. Coordinated optimization of distribution network intelligent soft switching and multi-type shared energy storage considering the risk of insufficient flexibility [J / OL]. Power Generation Technology, 1-11 [2025-07-02].” jointly planned energy storage and intelligent soft switching, established a planning-operation joint optimization model considering the dual flexibility control potential of “node-network”, and used a hybrid algorithm to solve it.The literature "Huan Zhenglin, Liu Jie, Xu Shenzhi, et al. Coordinated planning of source-load-storage flexibility resources for high proportion of new energy access [J]. Power Grid and Clean Energy, 2022, 38(7): 107-117." quantifies the flexibility demand and the flexibility supply potential considering network constraints at the node and system levels, and incorporates the constructed evaluation index as a constraint into the source-load-storage coordinated planning model, and plans multiple types of flexibility resources in a holistic way. The literature "

[17] Yu F, Hao B, Yongxiang C, et al. Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration [J]. Scientific Reports, 2024, 14(1): 7658-7658." comprehensively considers demand-side flexibility resources such as electric vehicles and air conditioning, and proposes an optimal configuration model for demand-side flexibility resources to enhance renewable energy consumption. However, the above methods do not consider the dynamic influence mechanism between energy storage configuration and transmission line expansion in the planning process, which often leads to the problems of wasted energy storage investment and redundant line expansion. For example, relying solely on peak-shaving demand to configure energy storage may overlook transmission channel bottlenecks, while expanding lines only to address transmission congestion may mask the hidden dangers of insufficient node regulation capacity. This planning model is difficult to adapt to the increased system complexity after a high proportion of renewable energy is integrated into the grid. Summary of the Invention

[0005] This application provides a storage-transmission joint planning method, device, equipment, and storage medium that takes into account the identification of weak links in regulation capacity. It improves the model in the evaluation and planning stages, identifies weak links in the power grid through the constructed evaluation indicators, and carries out joint expansion planning of energy storage and transmission lines based on the weak links. This will effectively solve the problems of power system regulation capacity supply and demand imbalance, waste of flexibility resources investment, and redundancy of line expansion, and is conducive to the consumption of renewable energy and the safe operation of the power grid.

[0006] Firstly, this application provides a storage-transmission joint planning method that takes into account the identification of weak links in regulation capability, including:

[0007] Acquire wind and solar power data and load data for the planning target year, as well as initial power grid topology and thermal power unit parameters;

[0008] The current system operation status is simulated by using multi-period DC power flow to obtain the load shedding, wind and solar curtailment and unit operation status of the power grid in each period;

[0009] The imbalance between supply and demand caused by insufficient peak-shaving capacity and insufficient transmission channels;

[0010] Based on the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels, the degree of insufficient regulation capacity and the line full-load time ratio of different nodes are calculated. Nodes with insufficient regulation capacity greater than the first set threshold are identified as weak nodes, and lines with a line full-load time ratio greater than the second set threshold are identified as weak lines. Energy storage configuration candidate set and line expansion candidate set are formed.

[0011] Based on the dynamic relationship between energy storage configuration and transmission line expansion, a joint planning model for energy storage and transmission is established with the goal of minimizing costs. Based on the candidate set of energy storage configurations and candidate set of line expansions, a set of joint planning schemes for energy storage and transmission lines is generated. The joint planning model for energy storage and transmission lines is solved under set constraints. During the solution process, all energy storage and transmission planning schemes in the set of joint planning schemes for energy storage and transmission lines are traversed, and the scheme with the best economic efficiency is selected from the set of joint planning schemes for energy storage and transmission lines.

[0012] In one possible design, the imbalance between supply and demand caused by insufficient peak-shaving capacity and insufficient transmission channels is calculated, including:

[0013] The formula for calculating the load shedding power caused by insufficient forward peak-shaving capacity is as follows:

[0014]

[0015] In the formula, P loadcurG (t) represents the load shedding power caused by insufficient positive peak-shaving capacity of the system at time t; P NL (t) represents the net load power of the system at time t; P gmax (t) represents the maximum technical output of the system's adjustable unit at time t;

[0016] The formula for calculating the power curtailment caused by insufficient negative peak-shaving capacity is as follows:

[0017]

[0018] In the formula, P rescurG (t) represents the power of wind and solar power curtailed due to insufficient negative peak-shaving capacity at time t; P gmin (t) represents the minimum technical output of the system's adjustable unit at time t;

[0019] The formula for calculating the load shedding power due to insufficient transmission channel capacity is as follows:

[0020]

[0021] In the formula, P loadcurL,i (t) represents the load shedding power of load node i at time t due to insufficient transmission channel capacity; P load,i(t) represents the load demand of node i, P gen,i (t) represents the local power generation of node i, P ji (t) represents the power flow of the transmission line from node j to node i, N i Let i be the set of nodes connected to node i.

[0022] The formula for calculating the power curtailment caused by insufficient transmission channel capacity is as follows:

[0023]

[0024] In the formula, P rescurL,j (t) represents the power curtailment of wind and solar power at renewable energy node j at time t due to insufficient transmission channel capacity; P res,j (t) represents the renewable energy generation at node j, N j Let j be the set of nodes connected to node j.

[0025] In one possible design, the dynamic relationship between energy storage configuration and transmission line expansion is determined as follows:

[0026] A power transfer distribution factor is established to characterize the change in unit power injected at any node k; wherein the formula for calculating the power transfer distribution factor is:

[0027]

[0028] In the formula, PTDF k,ij ΔP represents the change in line power flow when a power source is injected at node k; k ΔP represents the power change at node k. ij x represents the change in power flow in branch ij; ik Let x be the element in the i-th row and k-th column of the reactance matrix; jk Let x be the element in the j-th row and k-th column of the reactance matrix; ij The reactance value on line ij;

[0029] Based on the power transmission distribution factor, the power flow of each line in the power grid is corrected using the following formula:

[0030] P C-ij (t)=P ij (t)+PTDF k,ij P E (t) (6)

[0031] In the formula, P c-ij (t) represents the power flow value at time t after line ij is corrected; P ij (t) represents the original power flow of line ij at time t, P E (t) represents the energy storage charging and discharging power at time t.

[0032] In one possible design, based on the supply-demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels, the degree of insufficient regulation capacity at different nodes and the ratio of full-load duration of lines are calculated, including:

[0033] The average shortfall A in the upward adjustment capability of computing nodes i,up and the average deficiency of downward adjustment capacity A i,dn The calculation formula is:

[0034]

[0035] In the formula, P loadcur,i (t) represents the load shedding amount of node i at time t, T up,ins P represents the sum of periods where upward adjustment capacity is insufficient. rescur,i (t) represents the amount of wind and solar power curtailment at node i at time t, where T is the amount of curtailment. dn,ins The sum of periods where downward adjustment capacity is insufficient, where T is the total duration;

[0036] Based on the average insufficient amount A of the node's upward adjustment capability i,up and the average deficiency of downward adjustment capacity A i,dn The dispersion B of insufficient upward adjustment capability of nodes is calculated using the following formula. i,u The degree of dispersion of the amount of downward adjustment capability B i,dn :

[0037]

[0038] In the formula, t1 is the moment when the upward adjustment capability is insufficient, and t2 is the moment when the downward adjustment capability is insufficient;

[0039] According to A i,up A i,dn B i,u and B i,dn The comprehensive index H of insufficient regulation capacity is calculated using the following formula. i :

[0040]

[0041] In the formula, H i,up H is an indicator of insufficient upward adjustment capability of nodes. i,dn This is an indicator of insufficient downward adjustment capability of nodes.

[0042] The line load factor is calculated using the following formula:

[0043]

[0044] In the formula, L ij(t) represents the load factor of line ij during time period t; P ij (t) represents the active power flow through line ij during time period t; P ij,max Let be the active power transmission limit of line ij;

[0045] A line with a load factor of 100% is defined as a fully loaded line. The full-load duration ratio of a line is determined using the following formula:

[0046]

[0047] In the formula, μ ij T represents the ratio of the full-load duration of line ij within the operating cycle. ij,light T ij,heavy These represent the off-peak and full-peak periods for line ij, respectively.

[0048] In one possible design, nodes with insufficient regulation capacity exceeding a first set threshold are identified as weak nodes, and lines with a full-load duration exceeding a second set threshold are identified as weak lines. This forms a candidate set for energy storage configurations and a candidate set for line expansion, including:

[0049] Based on the comprehensive index H of insufficient adjustment capabilities of each node in the system i Based on the set first threshold H th When H i >H th When node i is included in the candidate node set for energy storage configuration, for each candidate node, the required energy storage power and capacity range are calculated based on its average insufficient regulation capacity, generating an energy storage configuration scheme. This scheme is then discretized according to a set step size to form multiple configuration combinations. All configuration combinations of candidate nodes constitute the energy storage configuration candidate set Ω. ess ;

[0050] Based on the ratio of full-load duration of each line in the system, μ ij Based on the set second threshold μ th When μ ij >μ th At that time, line ij is included in the candidate line set for line expansion; for each candidate line, its full load duration ratio μ is used as the basis for selection. ij and trend P ij The required range of expansion lines is calculated, and the expansion schemes are discretized according to a set step size. The expansion schemes of all candidate lines constitute the line expansion candidate set Ω. line .

[0051] In one possible design, the objective function of the storage and transportation joint planning model is expressed as:

[0052] minf = f line +f ess +f rescur+f loadcur +f g (13)

[0053] In the formula, minf is the objective function; f line f is the equivalent annual investment cost of the transmission line; ess The equivalent annual investment cost for energy storage; f recur The cost of abandoning wind and solar power annually; f lodcur Annual load shedding penalty cost; f g For unit operating costs;

[0054] The formula for calculating the equivalent annual investment cost of the transmission line is as follows:

[0055]

[0056] In the formula, i is the discount rate; n line C is the economic service life of the line; line,ij The investment cost for constructing a new line on branch line ij; Let p be the 0-1 decision variable for building the p-th line on branch ij; p is the total number of expanded lines.

[0057] The formula for calculating the equivalent annual investment cost of energy storage is as follows:

[0058]

[0059] In the formula, n ess Indicates the service life of energy storage; These represent the investment costs per unit power and per unit capacity for configuring energy storage at weak node i, respectively. These are the energy storage power and capacity configured for node i, respectively.

[0060] The formula for calculating the annual cost of wind and solar power curtailment penalties is as follows:

[0061]

[0062] In the formula, C res To avoid the cost of abandoning wind and light, P windcur,j (t) represents the amount of wind curtailment at wind node j at time t, P suncur,k (t) represents the amount of solar power wasted at photovoltaic node k at time t, Ω wind For the set of wind power nodes, Ω sun Let N be the set of photovoltaic nodes, and N be the time length.

[0063] The formula for calculating the annual load shedding penalty cost is as follows:

[0064]

[0065] In the formula, C loadFor load shedding costs, P loadcur,i (t) represents the load shedding amount at load node i at time t, Ω load For the set of load nodes;

[0066] The formula for calculating the operating cost of the unit is as follows:

[0067]

[0068] In the formula, a m b m c m P is a parameter for generator cost. g,m (t) represents the power of thermal power unit m at time t, N g This represents the number of thermal power units.

[0069] In one possible design, the constraints set include thermal power unit output and ramp rate constraints, node power balance constraints, branch power flow constraints, branch power limit constraints, energy storage operation constraints, upper limit constraints for newly built lines, and wind and solar curtailment and load shedding capacity constraints.

[0070] The constraints on the output and climbing rate of the thermal power unit are expressed as follows:

[0071]

[0072] In the formula, This represents the minimum output of the thermal power unit. This represents the maximum output of the thermal power unit. and These represent the downward and upward ramp rates of the thermal power unit, respectively.

[0073] The node power balance constraint is expressed as follows:

[0074] P g,i (t)+P wind,i (t)+P sun,i (t)+P ess,i (t)=P load,i (t) (21)

[0075] In the formula, P g,i (t) represents the output of the thermal power unit at node i at time t, P wind,i (t) represents the power output of the wind turbine at node i at time t, P sun,i (t) represents the photovoltaic power output of node i at time t, P ess,i (t) represents the power of the energy storage device at node i at time t, P load,i (t) represents the active load of node i at time t;

[0076] The branch power flow constraint is expressed as:

[0077] Bθ(t)=P g (t)+P wind (t)+P sun (t)+P ess (t)-P load (t) (22)

[0078] In the formula, B is the nodal admittance matrix of the system, θ t P is the voltage phase angle vector at node t. g (t) represents the output power vector of the thermal power unit at time t; P wind (t) represents the output power vector of the wind turbine at time t; P sun (t) represents the output power vector of the photovoltaic unit at time t; P ess (t) represents the output power vector of the energy storage device at time t; P load (t) represents the load power vector at time t;

[0079] The branch power non-exceeding constraint is expressed as follows:

[0080] -P ij,max ≤P ij (t)≤P ij,max (twenty three)

[0081] The energy storage operation constraints are expressed as follows:

[0082]

[0083] In the formula, and These are the minimum and maximum power values ​​of the energy storage device, respectively; P ess (t) represents the actual power value of the energy storage at time t; and These are the minimum and maximum values ​​of the energy storage device capacity, respectively; E ess (t) represents the actual energy storage capacity at time t; δ SOCmin and δ SOCmax These represent the minimum and maximum states of charge of energy storage, δ SOC (t) represents the state of charge of the stored energy at time t; δ SOC(0) and δ SOC(24) These represent the state of charge of the stored energy at the start and end of each day, respectively.

[0084] The upper limit constraint for the newly built line is expressed as follows:

[0085]

[0086] In the formula, sgn() is a symbolic function indicating whether a certain line needs to be expanded; Nmax The maximum number of new lines; l is the l-th branch, n l For the number of construction cycles of the l-th planned line;

[0087] The constraints on wind and solar curtailment and load shedding capacity are expressed as follows:

[0088]

[0089] In the formula, P windcur,i (t) represents the amount of wind curtailed at node i at time t, P suncur,i (t) represents the amount of light discarded by node i at time t.

[0090] Secondly, this application provides a storage-transportation joint planning device that considers the identification of weak links in regulation capability, the device comprising:

[0091] The data acquisition module is configured to acquire wind and solar data and load data for the planning target year, as well as the initial power grid topology and thermal power unit parameters;

[0092] The situation simulation module is configured to simulate the current system's operating situation using multi-period DC power flow, and obtain the load shedding, wind and solar curtailment, and unit operation status of the power grid at each time period;

[0093] The imbalance calculation module is configured to calculate the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels;

[0094] The candidate set generation module is configured to calculate the degree of insufficient regulation capacity and the line full load time ratio of different nodes based on the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels. Nodes with insufficient regulation capacity greater than a first set threshold are identified as weak nodes, and lines with a line full load time ratio greater than a second set threshold are identified as weak lines. The module then forms a candidate set for energy storage configuration and a candidate set for line expansion.

[0095] The joint planning module is configured to generate a set of joint planning schemes for energy storage and transmission lines based on the energy storage configuration candidate set and the line expansion candidate set. It establishes a joint planning model for energy storage and transmission lines with the goal of minimizing costs, and solves the joint planning model for energy storage and transmission lines under set constraints. During the solution process, it traverses all energy storage and transmission planning schemes in the set of joint planning schemes for energy storage and transmission lines, and selects the scheme with the best economic efficiency from the set of joint planning schemes for energy storage and transmission lines.

[0096] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the storage-transmission joint planning method considering the identification of weak links in regulation capability as described in the first aspect above and various possible designs of the first aspect.

[0097] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the storage-transmission joint planning method for identifying weak links in regulation capability as described in the first aspect and various possible designs of the first aspect.

[0098] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the storage-transmission joint planning method for identifying weak links in regulation capability as described in the first aspect and various possible designs of the first aspect.

[0099] The storage-transportation joint planning method, apparatus, equipment, and storage medium provided in this application, which take into account the identification of weak links in regulation capability, have at least the following beneficial effects:

[0100] 1) This application calculates the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels, thereby tracing the source of supply and demand imbalance. This can clarify the main reasons for the supply and demand imbalance in the power system. The proposed indicators of insufficient regulation capacity and line full load time ratio can accurately identify weak nodes and weak lines in the power grid.

[0101] 2) This application has significant advantages in terms of economic efficiency, avoids investment waste and redundant expansion, and significantly improves the economic efficiency of power grid operation;

[0102] 3) This application has the best effect on improving weak links in the power grid. Compared with existing methods, the comprehensive index of insufficient regulation capacity is significantly reduced and the ratio of full-load time of lines is significantly reduced, which further proves the superiority of this application in improving the overall performance of the power grid. Attached Figure Description

[0103] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0104] Figure 1 A flowchart illustrating a storage-transmission joint planning method that considers the identification of weak points in regulation capability, provided as an embodiment of this application;

[0105] Figure 2The impact of the energy storage configuration provided in this application at the source end and the load end on the power flow of the line; wherein, (a) the impact of the operation of the energy storage configuration at the source end on the power flow; (b) the impact of the operation of the energy storage configuration at the load end on the power flow;

[0106] Figure 3 The evaluation and planning solution flowchart provided for the embodiments of this application;

[0107] Figure 4 Power flow curves of typical daytime branches provided in embodiments of this application;

[0108] Figure 5 A schematic diagram illustrating the dynamic optimization process of energy storage configuration and line expansion provided in the embodiments of this application;

[0109] Figure 6 The diagram illustrates the improvement effects of different planning schemes provided in the embodiments of this application on weak links; wherein, (a) is a comparison of the comprehensive index of insufficient adjustment capacity of weak nodes (MW); and (b) is a comparison of the full-load market (duration) ratio of weak lines (%).

[0110] Figure 7 A structural diagram of a storage-transmission integrated planning device that takes into account the identification of weak links in regulation capability, provided in an embodiment of this application.

[0111] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0112] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0113] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0114] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0115] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with 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. The embodiments of this application will now be described with reference to the accompanying drawings.

[0116] Driven by dual carbon targets, renewable energy continues to develop rapidly, but the power system faces the dual risks of power curtailment and power shortages due to insufficient regulation capacity. Insufficient regulation capacity is mainly caused by system peak-shaving capacity and transmission bottlenecks. Accurately identifying weak links in regulation capacity and efficiently allocating flexible resources and transmission lines are crucial to ensuring the economic and security of a high-proportion renewable energy power system. Therefore, this application provides a storage-transmission joint planning method that considers the identification of weak links in regulation capacity. First, it proposes a method for tracing the supply-demand imbalance of power system regulation capacity to clarify the causes of grid regulation capacity deficits. Second, it constructs an index for the degree of regulation capacity insufficiency and an evaluation index for the ratio of line full load time, thereby achieving accurate location of weak links in grid regulation capacity. Finally, it proposes a storage-transmission joint planning method that considers the identification of weak links in regulation capacity, and uses the Garver-6 node to verify the effectiveness and rationality of the method.

[0117] like Figure 1 As shown, the storage-transmission joint planning method that takes into account the identification of weak links in regulation capacity includes the following steps S100-S500.

[0118] S100: Obtain wind and solar data and load data for the planning target year, as well as the initial power grid topology and thermal power unit parameters.

[0119] S200: The current system's operating status is simulated using multi-period DC power flow to obtain the load shedding, wind and solar curtailment, and unit operation status of the power grid at different times.

[0120] In this embodiment, step S200 performs a simulation based on the relevant data obtained in step S100. The simulation method used can be an existing method, which will not be elaborated here. The simulation data obtained can be used in the calculation of subsequent steps.

[0121] S300: Calculates the imbalance between supply and demand caused by insufficient peak-shaving capacity and insufficient transmission channels.

[0122] In this embodiment, the purpose of calculating the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient power transmission channels is to trace the source of the supply and demand imbalance in the system.

[0123] In some embodiments, step S300 may be implemented by the following steps S301-S302.

[0124] S301: Analysis of supply and demand imbalance caused by insufficient peak-shaving capacity.

[0125] Power systems with high renewable energy penetration face the challenge of insufficient peak-shaving capacity. The root cause lies in the fact that the random output characteristics of fluctuating power sources such as wind and solar power generate a two-way disturbance effect on the system's net load curve. During peak net load periods, a sharp increase in system net load causes the positive peak-shaving demand to exceed the maximum technical output limit of conventional units. At this time, conventional units cannot track load fluctuations by adjusting their output upwards, thus triggering load shedding to maintain power balance.

[0126]

[0127] In the formula, P loadcurG (t) represents the load shedding power caused by insufficient positive peak-shaving capacity of the system at time t; P NL (t) represents the net load power of the system at time t; P gmax (t) represents the maximum technical output of the system's adjustable unit at time t.

[0128] During periods of low net load, the system's net load drops sharply, causing the negative peak-shaving demand to fall below the minimum technical output limit of conventional units. At this time, constrained by the unit's ramp-up rate and start-up / shutdown characteristics, the system is forced to adopt measures to curtail wind and solar power to absorb excess generating capacity.

[0129]

[0130] In the formula, P RescurG (t) represents the power of wind and solar power curtailed due to insufficient negative peak-shaving capacity at time t; P gmin (t) represents the minimum technical output of the system's adjustable unit at time t.

[0131] S302: Analysis of supply and demand imbalance caused by insufficient power transmission channel capacity.

[0132] The achievement of power balance at power system nodes is strictly constrained by the transmission capacity boundary. If the power demand injected into a node exceeds the transmission limit of the associated transmission channel, it will trigger a double network congestion effect: for receiving-end load nodes, their power demand cannot obtain sufficient power injection through the interconnection channel, thus triggering load shedding; for sending-end renewable energy power plant nodes, their power generation is constrained by the network transmission bottleneck and cannot be fully transmitted, forcing the system to adopt wind and solar curtailment measures, the expression of which is:

[0133]

[0134] In the formula, P loadcurL (t) represents the load shedding power of load node i at time t due to insufficient transmission channel capacity; P load,i (t) represents the load demand of node i, Pgen,i (t) represents the local power generation of node i, P ji (t) represents the power flow of the transmission line from node j to node i, N i Let i be the set of nodes connected to node i.

[0135]

[0136] In the formula, P rescurL,j (t) represents the power curtailment of wind and solar power at renewable energy node j at time t due to insufficient transmission channel capacity; P res,j (t) represents the renewable energy generation at node j; N j Let j be the set of nodes connected to node j.

[0137] S400: Based on the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels, calculate the degree of insufficient regulation capacity of different nodes and the line full-load time ratio. Nodes with insufficient regulation capacity greater than the first set threshold are identified as weak nodes, and lines with a line full-load time ratio greater than the second set threshold are identified as weak lines. This forms a candidate set for energy storage configuration and a candidate set for line expansion.

[0138] The purpose of step S400 is to identify the weak links in the power system supply and demand. In some embodiments, it can be implemented through the following steps S401-S403.

[0139] S401: Calculate the evaluation index of weak nodes.

[0140] In this embodiment, nodes that frequently experience wind and solar power curtailment or load shedding due to insufficient local regulation capacity and the inability to receive adequate external power transmission support are defined as weak nodes. The average insufficiency A in the upward and downward regulation capacity of these nodes is defined. i,up A i,dn Equation (7) can reflect the severity of the deficiency in regulatory capacity.

[0141]

[0142] In the formula, P loadcur,i (t) represents the load shedding amount of node i at time t, T up,ins P represents the sum of periods where upward adjustment capacity is insufficient. rescur,i (t) represents the amount of wind and solar power curtailment at node i at time t, where T is the amount of curtailment. dn,ins The sum of the periods when downward adjustment capacity is insufficient, where T is the total duration.

[0143] The degree of dispersion B of insufficient upward and downward adjustment capability of nodes i,up B i,dn This helps determine whether the deficit in regulatory capacity is concentrated or dispersed, and its expression is shown in equation (8):

[0144]

[0145] In the formula, t1 is the moment when the upward adjustment capability is insufficient, and t2 is the moment when the downward adjustment capability is insufficient.

[0146] Evaluating weak nodes requires comprehensive consideration of both the average level and the dispersion of the regulatory capacity deficit. The comprehensive index H for insufficient regulatory capacity of weak nodes is... i It can be represented as:

[0147]

[0148] In the formula, H i,up H is an indicator of insufficient upward node adjustment capability. i,dn This is an indicator of insufficient downward adjustment capability of nodes.

[0149] When the overall index of insufficient node regulation capacity is high, it indicates that the node has a large gap in regulation capacity, the deviation between the actual system operation state and the ideal average operation state increases, the node is more likely to experience "short-term concentrated" insufficient regulation capacity, and the node needs to be equipped with energy storage.

[0150] S402: Evaluation index for weak lines.

[0151] A high proportion of wind and solar power grid connection significantly increases the uncertainty of net load, leading to uncertainty in system power flow. To ensure system supply and demand balance, each branch line needs to maintain a certain margin. Line load factor can be expressed as the ratio of active power flow to transmission capacity:

[0152]

[0153] In the formula, L ij (t) represents the load factor of line ij during time period t; P ij (t) represents the active power flow through line ij during time period t; P ij,max Let be the active power transmission limit of line ij.

[0154] In this embodiment, a line with a load rate of 100% is referred to as a fully loaded line. A fully loaded line will cause a supply and demand imbalance in the system, resulting in load shedding or curtailment of wind and solar power. This embodiment constructs a line full-load duration ratio index to identify weak lines in the system:

[0155]

[0156] In the formula, μ ij T represents the ratio of the full-load duration of line ij within the operating cycle. ij,light T ij,heavy These represent the off-peak and full-peak periods for line ij, respectively. ijThe larger the value, the more severe the transmission congestion on the line, and the more the line needs to be expanded.

[0157] S403: Generation of candidate sets based on weak links.

[0158] Step S403 aims to construct a candidate set of energy storage configurations and a candidate set of line expansions based on the weak nodes and weak lines identified above, thereby reducing the solution domain for subsequent optimization planning.

[0159] The comprehensive index H, which indicates insufficient adjustment capability of each node in the system, is calculated. i Set the threshold H. th When H i >H th When node i is selected, it is included in the candidate node set for energy storage configuration. For each candidate node i, the required energy storage power and capacity range are calculated based on its average insufficient regulation capacity, generating an energy storage configuration scheme. This scheme is then discretized with a certain step size to form multiple configuration combinations. All configuration combinations of candidate nodes constitute the energy storage configuration candidate set Ω. ess .

[0160] By calculating the ratio of full-load duration μ of each line in the system ij Set threshold μ th When μ ij >μ th At that time, line ij is included in the candidate line set for line expansion. For each candidate line ij, based on its full load duration ratio μ ij and trend P ij The required range of expansion lines is calculated, and the expansion schemes are discretized with a certain step size. All expansion schemes for candidate lines constitute the expansion candidate set Ω. line .

[0161] S500: Based on the dynamic relationship between energy storage configuration and transmission line expansion, a joint planning model for energy storage and transmission is established with the goal of minimizing costs. Based on the candidate sets of energy storage configuration and transmission line expansion, a set of joint planning schemes for energy storage and transmission lines is generated. The joint planning model for energy storage and transmission lines is solved under the set constraints. During the solution process, all energy storage and transmission planning schemes in the set of joint planning schemes for energy storage and transmission lines are traversed, and the scheme with the best economic efficiency is selected from the set of joint planning schemes for energy storage and transmission lines.

[0162] In some embodiments, the dynamic relationship between energy storage configuration and transmission line expansion is determined through an impact analysis of energy storage configuration on transmission line expansion. Specifically, in a power network containing energy storage, the charging and discharging behavior of energy storage essentially constitutes a flexible adjustment mechanism for node injected power. This bidirectional power adjustment capability, by changing the distribution of net injected power at nodes, will trigger a dynamic reconfiguration process of the power flow distribution in the power grid. To study the impact of changes in net injected power at nodes on line power flow, this embodiment introduces the Power Transfer Distribution Factor (PTDF). PTDF represents the change in active power flow on line ij caused by a unit change in injected power at any node k, as shown in the following equation:

[0163]

[0164] In the formula, PTDF k,ij ΔP represents the change in line power flow when a power source is injected at node k; k ΔP represents the power change at node k. ij x represents the change in power flow in branch ij; ik Let x be the element in the i-th row and k-th column of the reactance matrix; jk Let x be the element in the j-th row and k-th column of the reactance matrix; ij Let be the reactance value on line ij.

[0165] The location and capacity determination of energy storage in the power grid will make certain corrections to the power flow of various lines in the power grid. The calculation formula is as follows:

[0166] P C-ij (t)=P ij (t)+PTDF k,ij P E (t) (6)

[0167] In the formula, P c-ij (t) represents the power flow value at time t after line ij is corrected; P ij (t) represents the original power flow of line ij at time t, P E (t) represents the energy storage charging and discharging power at time t.

[0168] After the correction, the power flow values ​​for each line will change, and the expansion plan for the transmission lines will also change accordingly. For example... Figure 2 As shown, Figure 2 (a) shows the power flow of the line when energy storage is configured at the source end. When wind power is generated in large quantities, sufficient transmission channels are needed to transmit wind power to the outside, and the lines must be expanded. If appropriate energy storage is configured at the wind power nodes, the peak shaving and valley filling effect of energy storage can be utilized, and the supply and demand balance of the system regulation capacity can be met without expansion.

[0169] Figure 2 (b) shows the power flow of the line when the energy storage is configured at the load end. During the off-peak period, the energy storage can increase external transmission through charging. During the peak period, the energy storage can reduce the peak load of the node through discharging, thereby changing the power flow of the line and affecting the expansion of the transmission line.

[0170] Energy storage deployment and transmission line expansion are mutually constraining, with their dynamic relationship primarily manifested in spatiotemporal coupling and economic trade-offs. Energy storage exhibits spatiotemporal migration characteristics; its site selection and deployment can modify power flow in transmission lines, thus dynamically adjusting the timing and scale of transmission line expansion. Furthermore, energy storage deployment and transmission line expansion have a cost-complementary relationship. When planning energy storage and transmission, an economic analysis is needed to achieve the optimal cost balance between energy storage investment, line expansion, and wind / solar curtailment. Therefore, the dynamic relationship between energy storage deployment and transmission line expansion must be considered during planning.

[0171] In some embodiments, in step S500, to accurately configure energy storage and efficiently expand transmission lines, a combined energy storage and transmission planning model based on weak nodes and weak lines is established. The objective function of this model comprises five parts: the equivalent annual investment cost of transmission lines, the equivalent annual investment cost of energy storage, the annual wind and solar curtailment penalty cost, the annual load shedding penalty cost, and the annual unit operating cost. The objective function is expressed as follows:

[0172] minf = f line +f ess +f rescur +f loadcur +f g (13)

[0173] In the formula, minf is the objective function; f line f is the equivalent annual investment cost of the transmission line; ess The equivalent annual investment cost for energy storage; f recur The cost of abandoning wind and solar power annually; f lodcur Annual load shedding penalty cost; f g This refers to the unit's operating costs.

[0174] The formula for calculating the equivalent annual investment cost of a transmission line is as follows:

[0175]

[0176] In the formula, i is the discount rate; n line C is the economic service life of the line; line,ij The investment cost for constructing a new line on branch line ij; Let p be a 0-1 decision variable for building the p-th line on branch ij, where p is the total number of expanded lines.

[0177] The formula for calculating the equivalent annual investment cost of energy storage is as follows:

[0178]

[0179] In the formula, n ess Indicates the service life of energy storage; These represent the investment costs per unit power and per unit capacity for configuring energy storage at weak node i, respectively. These are the energy storage power and capacity configured for node i.

[0180] The formula for calculating the annual penalty cost of wind and solar power curtailment is:

[0181]

[0182] In the formula, C res To avoid the cost of abandoning wind and light, P windcur,j (t) represents the amount of wind curtailment at wind node j at time t, P suncur,k (t) represents the amount of solar power wasted at photovoltaic node k at time t, Ω wind For the set of wind power nodes, Ω sun Let N be the set of photovoltaic nodes, and N be the time length.

[0183] The formula for calculating the annual load shedding penalty cost is as follows:

[0184]

[0185] In the formula, C load For load shedding costs, P loadcur,i (t) represents the load shedding amount at load node i at time t, Ω load This is the set of load nodes.

[0186] The formula for calculating the unit operating cost is:

[0187]

[0188] In the formula, a m b m c m P is a parameter for generator cost. g,m (t) represents the power of thermal power unit m at time t. N g This represents the number of thermal power units.

[0189] The constraints of this model are mainly limited by the following constraints: power output and ramp rate constraints of thermal power units, nodal power balance constraints, branch power flow constraints, branch power limit constraints, energy storage operation constraints, upper limit constraints of newly built lines, wind and solar curtailment, and load shedding capacity constraints.

[0190] The constraints on the output and ramp rate of thermal power units are expressed as follows:

[0191]

[0192] In the formula, This represents the minimum output of the thermal power unit. This represents the maximum output of the thermal power unit. and These represent the downward and upward ramp rates of the thermal power unit, respectively.

[0193] The node power balance constraint is expressed as:

[0194] P g,i (t)+P wind,i (t)+P sun,i (t)+P ess,i (t)=P load,i (t) (21)

[0195] In the formula, P g,i (t) represents the output of the thermal power unit at node i at time t, P wind,i (t) represents the power output of the wind turbine at node i at time t, P sun,i (t) represents the photovoltaic power output of node i at time t, P ess,i (t) represents the power of the energy storage device at node i at time t, P load,i (t) represents the active load of node i at time t.

[0196] Branch flow constraints are represented as:

[0197] Bθ(t)=P g (t)+P wind (t)+P sun (t)+P ess (t)-P load (t) (22)

[0198] In the formula, B is the nodal admittance matrix of the system, θ t P is the voltage phase angle vector at node t. g (t) represents the output power vector of the thermal power unit at time t; P wind (t) represents the output power vector of the wind turbine at time t; P sun (t) represents the output power vector of the photovoltaic unit at time t; P ess (t) represents the output power vector of the energy storage device at time t; P load (t) is the load power vector at time t.

[0199] The branch power limit constraint is expressed as:

[0200] -P ij,max ≤P ij (t)≤P ij,max(23) Energy storage operation constraints are expressed as follows:

[0201]

[0202] In the formula, and These are the minimum and maximum power values ​​of the energy storage device, respectively; P ess (t) represents the actual power value of the energy storage at time t; and These are the minimum and maximum values ​​of the energy storage device capacity, respectively; E ess (t) represents the actual capacity of the energy storage at time t. δ SOCmin and δ SOCmax These represent the minimum and maximum states of charge of energy storage, δ SOC (t) represents the state of charge of the stored energy at time t. δ SOC(0) and δ SOC(24) These represent the state of charge of the stored energy at the start and end of each day, respectively.

[0203] The upper limit constraint for newly built lines is expressed as follows:

[0204]

[0205] In the formula, sgn() is a symbolic function indicating whether a certain line needs to be expanded; N max The maximum number of new lines; l is the l-th branch, n l This refers to the number of construction cycles for the l-th planned line.

[0206] Wind and solar curtailment and load shedding capacity constraints are expressed as follows:

[0207]

[0208] In the formula, P windcur,i (t) represents the amount of wind curtailed at node i at time t, P suncur,i (t) represents the amount of light discarded by node i at time t.

[0209] In some embodiments, such as Figure 3 The diagram shown is a flowchart of the evaluation and planning solution provided in an embodiment of this application. This integrated storage-transmission planning, which takes into account the identification of weaknesses in regulation capacity, can be implemented through the following steps 1 to 5.

[0210] Step 1: Input wind and solar data and load data for the target year of the plan, and provide the initial power grid topology and thermal power unit parameters.

[0211] Step 2: Simulate the current system's operational status using multi-period DC power flow to obtain the load shedding, wind and solar curtailment, and unit operation status of the power grid at each time period.

[0212] Step 3: Calculate the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient power transmission channels using equations (1)-(4) to trace the source of the supply and demand imbalance in the system.

[0213] Step 4: After tracing the causes of the supply-demand imbalance in the system, identify the weak links in the system's supply and demand. Calculate the degree of inadequacy H of the adjustment capacity of the i-th node using equation (8). i Determine whether it is greater than the first set threshold H. th This is used to identify weak nodes that urgently need energy storage. The line full-load duration ratio μ is calculated using equation (10). ij Determine whether it is greater than the second set threshold μ. th This allows for the identification of vulnerable power lines that urgently need expansion. A candidate set for energy storage configurations and a candidate set for power line expansion are then created.

[0214] Step 5: Consider the dynamic relationship between energy storage configuration and transmission line expansion, form a set of joint planning schemes for energy storage and transmission lines, and under constraints such as energy storage operation constraints, consider the energy storage configuration cost, line expansion cost and load shedding penalty cost, wind and solar curtailment penalty cost and thermal power operation cost, traverse all energy storage-transmission planning schemes, and select the most economical scheme from them.

[0215] To verify the effectiveness and rationality of the planning method proposed in this application, an improved Garver-6 node system was matched based on measured load, wind power, and photovoltaic data of a power grid. This regional power grid includes two conventional thermal power units located at nodes 1 and 3, respectively; an 800MW photovoltaic power plant is connected to node 4; and a 1200MW wind farm is connected to node 6. Relevant parameters for the example are shown in Table 1.

[0216] Table 1 Relevant Parameters

[0217]

[0218] Tracing the origins of supply and demand imbalances.

[0219] Through multi-period operation simulation, the results in Table 2 show that: 1) The existing power grid suffers from a severe supply-demand imbalance, with a total system load shedding of 2.556 × 10⁵ MWh and total wind and solar power curtailment reaching 1.036 × 10⁵ MWh. The safety of power grid operation is seriously threatened, and expansion planning and construction are urgently needed. 2) The main reason for the supply-demand imbalance is insufficient transmission channel capacity. Insufficient peak shaving has a relatively small impact on the supply-demand imbalance. After tracing the cause of the supply-demand imbalance, the next step is to identify the weak links.

[0220] Table 2 Results of tracing the causes of supply and demand imbalance events

[0221]

[0222] Results of weak link identification.

[0223] Using the weakness identification index constructed using the method described in this application, nodes 2, 4, and 6 were identified as weak nodes in the current 6-node system. Node 4 showed the most severe weakness, with a comprehensive regulation capacity deficiency index reaching 851.02 MWh. Node 6 was the second weakest, with a comprehensive regulation capacity deficiency index of 509.21 MWh. This is because nodes 4 and 6 are connected to photovoltaic and wind power renewable energy sources, and their strong volatility led to supply and demand imbalances. Finally, node 2 had a comprehensive regulation capacity deficiency index of 182.57 MWh. As a hub node, node 2 connects to many branches, making it more susceptible to fluctuations in power flow, thus resulting in insufficient regulation capacity at this node. Figure 4 It is evident that lines 1-2, 2-3, 2-4, and 2-6 are all operating at full capacity, thus classifying these branches as weak points. With this, the weak nodes and lines in the power grid have been identified using the proposed weak link evaluation method, providing a basis for subsequent precise energy storage configuration and efficient line expansion.

[0224] The impact of different energy storage configurations on line expansion.

[0225] The impact of different energy storage configuration schemes on line expansion costs is analyzed. Line expansion costs are adjusted dynamically with changes in energy storage capacity. The impact of energy storage configuration schemes on line expansion costs is shown in Table 3. The dynamic optimization process of energy storage capacity and line expansion costs is as follows: Figure 5 As shown.

[0226] Table 3. Impact of Energy Storage Configuration on Line Expansion Costs

[0227]

[0228] exist Figure 5 In the diagram, the red dot represents the optimal solution for the joint planning of energy storage capacity configuration and transmission lines. At this point, the energy storage capacity is 600 MWh, the line expansion cost is 12 million yuan, and the total cost is 56.6 million yuan. The trend in the diagram shows that there is a dynamic complementary relationship between energy storage capacity configuration and line expansion cost. Appropriate energy storage configuration can effectively reduce the need for line expansion, thereby optimizing system resource allocation.

[0229] Therefore, in the integrated planning of energy storage and transmission, if the dynamic complementary relationship between energy storage capacity and line expansion is not fully considered, it may lead to unnecessary investment waste and expansion redundancy, resulting in significant planning costs for the power system. For example, insufficient energy storage capacity may force the system to rely on excessive line expansion, increasing infrastructure investment; while excessive energy storage capacity may avoid line expansion, but may lead to increased total costs due to excessive energy storage investment, failing to achieve optimal economic efficiency.

[0230] Economic comparison analysis of different planning schemes.

[0231] To verify the effectiveness of the planning method proposed in this application, the following five planning schemes were designed for comparative analysis:

[0232] Option 1: No energy storage or transmission lines planned;

[0233] Option 2: Based on the weak nodes identified by the method in this application, only energy storage is configured;

[0234] Option 3: Based on the weak lines identified by the method of this application, only line expansion is required;

[0235] Option 4: First, expand the power lines using a step-by-step backward planning method, and then plan energy storage at renewable energy nodes;

[0236] Option 5: Storage-transmission joint planning method that takes into account the identification of weak links (the method in this application).

[0237] The economic comparison results of the five planning schemes are shown in Table 4, and the planning results of the five planning schemes are shown in Table 5.

[0238] Table 4. Economic Comparison Results of Different Planning Schemes

[0239] plan 1 2 3 4 5 Total cost / 10,000 yuan 17080 14609 10192 9831 9294 Thermal power cost / 10,000 yuan 1508 1998 1882 1561 1586 Load shedding penalty / 10,000 yuan 14185 6181 2985 0 24 Penalty for abandoning scenic views / 10,000 yuan 1387 0 925 215 143 Line expansion cost 10,000 yuan 0 0 4400 3100 2400 Energy storage configuration cost / 10,000 yuan 0 6430 0 5055 5141

[0240] Table 5. Planning Results of Different Planning Schemes

[0241]

[0242] As shown in Tables 4 and 5, Scheme 5 has the lowest total cost compared to the other four schemes, indicating that the energy storage-transmission planning scheme based on weak links can maximize the economic benefits of the power grid. Scheme 1, which does not include energy storage configuration or line expansion, has the highest total cost. The load shedding and wind / solar curtailment caused by insufficient system peak-shaving capacity and transmission congestion are severe. Scheme 2 only configures energy storage at weak nodes, significantly reducing wind / solar curtailment. This is because the energy storage charges during periods of low net load and discharges during periods of high net load, narrowing the peak-to-valley difference and alleviating the problem of insufficient regulation capacity. However, due to the lack of transmission line expansion, some energy storage lacks sufficient charging and discharging space, and the insufficient system regulation capacity is mainly caused by transmission congestion. Scheme 3 only expands weak lines. Compared to Scheme 2, the load shedding phenomenon is significantly reduced, indirectly reflecting that transmission congestion is the main cause of insufficient system regulation capacity. Option 4 does not address energy storage and transmission planning for weak links, but instead deploys energy storage at renewable energy nodes. It requires two fewer expansion lines compared to Option 3 because energy storage enables the spatial and temporal migration of power, and the step-by-step backward expansion method optimizes line investment costs, thus reducing the need for expansion. Comparing Option 5 and Option 4, Option 5 addresses energy storage and transmission planning for weak links, avoiding wasted investment in flexibility resources and redundant transmission line expansion. Therefore, Option 5 significantly reduces load shedding and wind / solar curtailment compared to Option 4, and is more economically viable.

[0243] Comparative analysis of technical indicators of different planning schemes.

[0244] To verify the effectiveness of precise energy storage configuration and efficient line expansion in the planning scheme presented in this paper, this embodiment analyzes the improvement effect of different planning schemes on weak links. Table 6 shows a comparative analysis of the technical indicators of weak links under different planning schemes. Figure 6 This diagram illustrates the improvement effects of different planning schemes on weak links.

[0245] Table 6 Comparison of Technical Indicators of Weak Links in Different Planning Schemes

[0246]

[0247] From Table 6 and Figure 5 It is evident that Scheme 5 has advantages over the other four schemes in all indicators. The adjustment capacity of node 2 is close to 0, and the full load time ratio of each weak line is also significantly reduced. This shows that configuring based on weak links during the planning process can not only improve economic efficiency, but also alleviate the supply and demand imbalance of weak links, reduce the probability of transmission congestion events, and avoid investment waste. This verifies the effectiveness of the "precise identification-collaborative optimization" mechanism.

[0248] In summary, this embodiment addresses the problem of power system regulation capacity supply-demand imbalance after a high proportion of renewable energy is integrated into the grid, proposing a joint planning method for energy storage and transmission lines based on weak links in regulation capacity. By using a power system regulation capacity supply-demand imbalance tracing method and constructing an insufficiency index and a line full-load duration ratio evaluation index, weak nodes and weak lines in the power grid are accurately identified. The effectiveness and rationality of the proposed method are verified through a case study of a Garver-6 node system. The main conclusions are as follows:

[0249] 1) The imbalance source tracing method provided in this application can clearly identify the main causes of power system supply and demand imbalance, and the proposed indicators of insufficient regulation capacity and line full load time ratio can accurately identify weak nodes and weak lines in the power grid.

[0250] 2) The storage-transmission joint planning method proposed in this application, which takes into account the identification of weak links, has obvious advantages in terms of economy. Compared with method 4, it saves a total of RMB 5.37 million in costs, avoids investment waste and expansion redundancy, and significantly improves the economic efficiency of power grid operation.

[0251] 3) By comparing the technical indicators of different planning schemes, the method proposed in this application has the best effect on improving the weak links of the power grid. Compared with Scheme 4, the comprehensive index of insufficient regulation capacity decreased by 78.1%, and the ratio of full-load time of lines decreased by 63.5%, which further proves the superiority of the storage and transmission planning method that takes into account the identification of weak links in improving the overall performance of the power grid.

[0252] This application also provides a storage-transportation joint planning device that takes into account the identification of weak links in regulation capability, such as... Figure 7 As shown, the storage-transmission integrated planning device that takes into account the identification of weak links in regulation capability includes:

[0253] Data acquisition module 701 is configured to acquire wind and solar data and load data for the planning target year, as well as initial power grid topology and thermal power unit parameters;

[0254] The situation simulation module 702 is configured to simulate the current system's operating situation using multi-period DC power flow, and obtain the load shedding, wind and solar curtailment, and unit operation status of the power grid at each time period;

[0255] The imbalance calculation module 703 is configured to calculate the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels;

[0256] The candidate set generation module 704 is configured to calculate the degree of insufficient regulation capacity and the line full load time ratio of different nodes based on the supply and demand imbalance caused by insufficient peak regulation capacity and insufficient transmission channels. Nodes with insufficient regulation capacity greater than a first set threshold are identified as weak nodes, and lines with a line full load time ratio greater than a second set threshold are identified as weak lines. The module then forms a candidate set for energy storage configuration and a candidate set for line expansion.

[0257] The joint planning module 705 is configured to generate a set of joint planning schemes for energy storage and transmission lines based on the energy storage configuration candidate set and the line expansion candidate set. It establishes a joint planning model for energy storage and transmission lines with the goal of minimizing costs, and solves the joint planning model for energy storage and transmission lines under set constraints. During the solution process, it traverses all energy storage and transmission planning schemes in the set of joint planning schemes for energy storage and transmission lines, and selects the scheme with the best economic efficiency from the set of joint planning schemes for energy storage and transmission lines.

[0258] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0259] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0260] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0261] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0262] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the storage-transmission joint planning method that takes into account the identification of weak links in the regulation capability described in the above embodiments.

[0263] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the storage-transmission joint planning method that takes into account the identification of weak links in the regulation capability in the above embodiments.

[0264] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0265] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0266] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0267] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0268] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0269] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0270] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0271] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0272] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0273] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0274] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A storage-transmission joint planning method that considers the identification of weak links in regulation capability, characterized in that, The method includes: Acquire wind and solar power data and load data for the planning target year, as well as initial power grid topology and thermal power unit parameters; The current system operation status is simulated by using multi-period DC power flow to obtain the load shedding, wind and solar curtailment and unit operation status of the power grid in each period; The imbalance between supply and demand caused by insufficient peak-shaving capacity and insufficient transmission channels; Based on the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels, the degree of insufficient regulation capacity and the line full-load time ratio of different nodes are calculated. Nodes with insufficient regulation capacity greater than the first set threshold are identified as weak nodes, and lines with a line full-load time ratio greater than the second set threshold are identified as weak lines. Energy storage configuration candidate set and line expansion candidate set are formed. Based on the dynamic relationship between energy storage configuration and transmission line expansion, a joint planning model for energy storage and transmission is established with the goal of minimizing costs. Based on the candidate set of energy storage configurations and candidate set of line expansions, a set of joint planning schemes for energy storage and transmission lines is generated. The joint planning model for energy storage and transmission lines is solved under set constraints. During the solution process, all energy storage and transmission planning schemes in the set of joint planning schemes for energy storage and transmission lines are traversed, and the scheme with the best economic efficiency is selected from the set of joint planning schemes for energy storage and transmission lines. The dynamic relationship between energy storage configuration and transmission line expansion is determined in the following way: Establish a power transfer distribution factor to characterize any node. k Injecting a unit power change; wherein, the power transfer distribution factor is calculated using the following formula: （5） In the formula, PTDF k,ij Represents a node k The change in line power flow when a power source is injected; P k For nodes k The change in power; P ij branch road ij The amount of change in the tidal current; x ik For the reactance matrix, the first i line, number k Column elements; x jk For the reactance matrix, the first j line, number k Column elements; x ij For the line ij The reactance value on it; Based on the power transmission distribution factor, the power flow of each line in the power grid is corrected using the following formula: （6） In the formula, P c-ij (t) For the line ij After revision t The trend value at any given moment; P ij (t) for t Timetable ij The original trend, P E (t) for t Energy storage charging and discharging power at all times; Based on the supply-demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels, the degree of insufficient regulation capacity at different nodes and the ratio of full-load duration of lines are calculated, including: Average shortfall in the upscaling capability of computing nodes A i,up and the average amount of downward adjustment capability is insufficient A i,dn The calculation formula is: （7） In the formula, P loadcur, i ( t ) is a node i exist t The load shedding amount at any given time. T up,ins The sum of periods in which upward adjustment capacity is insufficient. P rescur, i ( t ) is a node i exist t The amount of wind and solar energy abandoned at any given moment. T dn,ins The sum of periods where downward adjustment capacity is insufficient, where T is the total duration; Average insufficient amount based on node upward adjustment capability A i,up and the average amount of downward adjustment capability is insufficient A i,dn The dispersion of insufficient upward adjustment capability of nodes is calculated using the following formula. B i,up and the degree of dispersion of insufficient downward adjustment capability B i,dn : （8） In the formula, t 1 When there is insufficient upward adjustment capability, t 2 This is a moment when the downward adjustment capability is insufficient; according to A i,up , A i,dn , B i,up and B i,dn The comprehensive index of insufficient adjustment capacity is calculated using the following formula. H i : （9） （10） In the formula, H i,up This is an indicator of insufficient upward adjustment capability of nodes. H i,dn This is an indicator of insufficient downward adjustment capability of nodes. The line load factor is calculated using the following formula: （11） In the formula, for t Time-of-day routes ij Load rate; P ij ( t )for t Time period through the line ij The meritorious trend; For the line ij The active power transmission limit; A line with a load factor of 100% is defined as a fully loaded line. The full-load duration ratio of a line is determined using the following formula: （12） In the formula, μ ij For the line during the operating cycle ij The ratio of full load time, T ij,light , T ij,heavy The lines are respectively ij Off-peak hours and peak hours; Nodes with insufficient regulation capacity exceeding a first set threshold are identified as weak nodes, and lines with a full-load duration exceeding a second set threshold are identified as weak lines. This forms a candidate set for energy storage configurations and a candidate set for line expansion, including: Based on the comprehensive index of insufficient adjustment capabilities of each node in the system H i ， Based on the set first threshold H th ,when H i >H th At that time, the node i The candidate nodes for energy storage configuration are included in the set. For each candidate node, the required energy storage power and capacity range are calculated based on its average deficiency in regulation capacity. Energy storage configuration schemes are generated and discretized at a set step size to form multiple configuration combinations. All configuration combinations of candidate nodes constitute the energy storage configuration candidate set Ω. ess ; Based on the full load duration ratio of each line in the system μ ij ， Based on the set second threshold μ th ,when μ ij > μ th At that time, the line ij The set of candidate lines to be included in the line expansion; for each candidate line, based on its full-load duration ratio. μ ij and trends P ij The required range of expansion lines is calculated, and the expansion schemes are discretized according to a set step size. The expansion schemes of all candidate lines constitute the line expansion candidate set Ω. line .

2. The storage-transmission joint planning method considering the identification of weak links in regulation capability according to claim 1, characterized in that, The calculation of supply and demand imbalances caused by insufficient peak-shaving capacity and insufficient transmission channels includes: The formula for calculating the load shedding power caused by insufficient forward peak-shaving capacity is as follows: （1） In the formula, P loadcurG( t ) for the system in t The load shedding power caused by insufficient positive peak-shaving capacity at all times; P NL ( t ) for the system in t Net load power at any given time; P gmax( t For the system's adjustable units in t Maximum technical output at any given moment; The formula for calculating the power curtailment caused by insufficient negative peak-shaving capacity is as follows: （2） In the formula, For the system in t The amount of wind and solar power curtailed due to insufficient negative peak-shaving capacity at all times; P gmin ( t For the system's adjustable units in t Minimum technical output at any given moment; The formula for calculating the load shedding power due to insufficient transmission channel capacity is as follows: （3） In the formula, P loadcurL,i ( t ) is the load node i exist t The load shedding power caused by insufficient transmission channel capacity at all times; P load,i (t) For nodes i The load demand, P gen,i (t) For nodes i Local power generation, P ji (t) For nodes j To the node i Power flow of transmission lines, N i For nodes i A set of connected nodes; The formula for calculating the power curtailment caused by insufficient transmission channel capacity is as follows: （4） In the formula, P rescurL,j ( t ) for renewable energy nodes j exist t The power curtailment caused by insufficient transmission channel capacity at all times; P res,j (t) For nodes j New energy power generation, N j For nodes j A set of connected nodes.

3. The storage-transmission joint planning method considering the identification of weak links in regulation capability according to claim 1, characterized in that, The objective function of the storage and transportation joint planning model is expressed as: （13） In the formula, min f The objective function is... f line The equivalent annual investment cost of the transmission line; f ess The equivalent annual investment cost for energy storage; f recur The cost of penalties for abandoning wind and solar power for years; f lodcur The annual load reduction penalty cost; f g For unit operating costs; The formula for calculating the equivalent annual investment cost of the transmission line is as follows: （14） In the formula, i The discount rate; n line The economic service life of the line; C line,ij For the side road ij The investment cost of building a new line; xp line,ij For the side road ij New p 0-1 decision variables for each route; p This represents the total number of expansion lines; The formula for calculating the equivalent annual investment cost of energy storage is as follows: （15） In the formula, n ess Indicates the service life of energy storage; , Representing weak nodes i Investment costs per unit power and per unit capacity for configuring energy storage; Pi ess , Ei ess They are nodes i Configured energy storage power and capacity; The formula for calculating the annual cost of wind and solar power curtailment penalties is as follows: （16） In the formula, C res To avoid the cost of abandoning the scenery, P windcur,j ( t ) is a wind power node j exist t The amount of wind curtailed at any given moment. P suncur,k ( t ) for photovoltaic nodes k exist t The amount of light discarded at any given time, Ω wind For the set of wind power nodes, Ω sun A collection of photovoltaic nodes. N The duration; The formula for calculating the annual load shedding penalty cost is as follows: （17） In the formula, C load To cover load shedding costs, P loadcur,i ( t ) is the load node i exist t The load shear rate at any given time, Ω load For the set of load nodes; The formula for calculating the operating cost of the unit is as follows: （18） In the formula, a m , b m , c m For generator cost parameters, P g,m ( t ) for thermal power units m exist t Power at any moment N g This represents the number of thermal power units.

4. The storage-transmission joint planning method considering the identification of weak links in regulation capability according to claim 3, characterized in that, The constraints set include thermal power unit output and ramp rate constraints, nodal power balance constraints, branch power flow constraints, branch power limit constraints, energy storage operation constraints, upper limit constraints for newly built lines, and wind and solar curtailment and load shedding capacity constraints. The constraints on the output and climbing rate of the thermal power unit are expressed as follows: （19） （20） In the formula, Pmin g,m represents the minimum output of the thermal power unit. Pmax g,m represents the maximum output of the thermal power unit; and R+ g and m represent the downward and upward ramp rates of the thermal power unit, respectively. The node power balance constraint is expressed as follows: （21） In the formula, P g,i ( t ) represents a node i exist t The output of thermal power units at any given time. P wind,i ( t ) represents a node i exist t It is constantly providing power to the wind turbines. P sun,i ( t ) represents a node i exist t The output of photovoltaic units at all times P ess,i ( t ) represents a node i exist t The power of the energy storage device at any given time. P load,i ( t ) represents a node i exist t Active load at any given moment; The branch power flow constraint is expressed as: （22） In the formula, B Let be the nodal admittance matrix of the system. θ t For nodes t Voltage phase angle vector at any given moment; P g ( t ) for thermal power units t Output power vector at any time; P wind ( t ) for wind turbine t Output power vector at any time; P sun ( t ) for photovoltaic units t Output power vector at any time; P ess ( t ) for energy storage devices t Output power vector at any time; P load ( t )for t Load power vector at any given time; The branch power non-exceeding constraint is expressed as follows: （23） The energy storage operation constraints are expressed as follows: （24） In the formula, Pmin ess and Pmax ess These are the minimum and maximum power values ​​of the energy storage device, respectively. P ess ( t For energy storage t The actual power value at that moment; Emin ess and Emax ess These are the minimum and maximum values ​​of the energy storage device capacity, respectively. E ess ( t For energy storage t The actual capacity value at any given time; δ SOCmin and δ SOCmax These represent the minimum and maximum states of charge for energy storage, respectively. δ SOC ( t For energy storage t The state of charge at any given moment; δ SOC(0) and δ SOC(24) These represent the state of charge of the stored energy at the start and end of each day, respectively. The upper limit constraint for the newly built line is expressed as follows: （25） In the formula, sgn() is a symbolic function that indicates whether a certain line needs to be expanded; N max This refers to the maximum number of newly added lines; l For the first l A side road, n l For the first l Number of construction cycles for each of the candidate routes; The constraints on wind and solar curtailment and load shedding capacity are expressed as follows: （26） In the formula, P windcur,i ( t ) is a node i exist t The amount of wind curtailed at any given moment. P suncur,i ( t ) is a node i exist t The amount of light discarded at any given moment.

5. A storage-transmission joint planning device that considers the identification of weak links in regulation capability, characterized in that, The apparatus for implementing the storage-transportation joint planning method as described in any one of claims 1-4, which considers the identification of weak points in regulation capability, comprises: The data acquisition module is configured to acquire wind and solar data and load data for the planning target year, as well as the initial power grid topology and thermal power unit parameters; The situation simulation module is configured to simulate the current system's operating situation using multi-period DC power flow, and obtain the load shedding, wind and solar curtailment, and unit operation status of the power grid at each time period; The imbalance calculation module is configured to calculate the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels; The candidate set generation module is configured to calculate the degree of insufficient regulation capacity and the line full load time ratio of different nodes based on the supply and demand imbalance caused by insufficient peak-shaving capacity and insufficient transmission channels. Nodes with insufficient regulation capacity greater than a first set threshold are identified as weak nodes, and lines with a line full load time ratio greater than a second set threshold are identified as weak lines. The module then forms a candidate set for energy storage configuration and a candidate set for line expansion. The joint planning module is configured to generate a set of joint planning schemes for energy storage and transmission lines based on the energy storage configuration candidate set and the line expansion candidate set. It establishes a joint planning model for energy storage and transmission lines with the goal of minimizing costs, and solves the joint planning model for energy storage and transmission lines under set constraints. During the solution process, it traverses all energy storage and transmission planning schemes in the set of joint planning schemes for energy storage and transmission lines, and selects the scheme with the best economic efficiency from the set of joint planning schemes for energy storage and transmission lines.

6. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the storage-transmission joint planning method as described in any one of claims 1-4, which takes into account the identification of weak links in regulation capability.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the storage-transmission joint planning method as described in any one of claims 1-4, taking into account the identification of weak links in regulation capability.

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