Energy storage and power transmission line collaborative planning method based on space-time coupling and multiple modes

Abstract

The invention discloses an energy storage and power transmission line collaborative planning method based on space-time coupling and multiple modes, and belongs to the field of power system planning, and the method comprises the following steps: S1, generating a space-time coupling scene set; s2, constructing a multi-stage space-time coupled source-network-storage collaborative planning model by considering differentiated cost constraints of three modes of self-building, renting and sharing and a scene in a space-time coupled scene set; s3, generating an optimal investment scheme for the space-time coupling scene set and the multi-stage space-time coupling source-network-storage collaborative planning model; and S4, evaluating the optimal investment scheme in multiple dimensions, and determining a final investment scheme based on an evaluation result. By adopting the space-time coupling and multi-mode-based energy storage and power transmission line collaborative planning method, through space-time coupling scene generation, multi-mode collaborative modeling and double-layer optimization solution, multi-dimensional collaborative optimization of energy storage and lines in technology, economy, environment and reliability is realized, and the method has flexibility, economy and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a method for collaborative planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode. Background Art

[0002] As the global energy mix accelerates its transition toward cleaner energy, the penetration rate of renewable energy (RES) such as wind power and photovoltaics has increased significantly. However, the intermittent and random nature of renewable energy (e.g., wind power output can fluctuate by up to 70% daily, and photovoltaics are significantly affected by weather) poses challenges to the safe and stable operation of power systems, primarily in the following ways: 1. Transmission network bottleneck: The geographical mismatch between renewable energy-rich areas and load centers leads to an imbalance in transmission lines. Traditional transmission expansion plans (TEPs) rely on new lines, which require large investments and long cycles.

[0003] 2. Insufficient system regulation capability: Conventional units have slow peak-shaving speeds (the ramp rate of coal-fired units is only 1.5%-3% / min), making it difficult to track short-cycle fluctuations in renewable energy. They rely on energy storage systems (e.g., battery energy storage response time <1 minute) to provide flexible regulation.

[0004] 3. Demand for multi-party collaboration: The interests of new energy power stations, energy storage operators, and power grid companies differ significantly (e.g., the high cost of self-built energy storage and the need for capacity sharing for shared energy storage), making it difficult for traditional single-planning models to balance the interests of multiple parties. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for collaborative planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode to solve the above technical problems.

[0006] To achieve the above objectives, the present invention provides a method for collaborative planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode, which is characterized by comprising the following steps: S1. Based on historical time series data, hierarchical clustering is used to generate daily scenarios with time series characteristics. Copula functions are used to quantify the spatial correlation of renewable energy output between regions and generate a set of spatiotemporal coupling scenarios. S2. Based on the system topology, historical renewable energy output data, and equipment parameters, and taking into account the differentiated cost constraints of the self-built, leased, and shared models, as well as the scenarios in the spatiotemporal coupling scenario set described in step S1, a multi-stage spatiotemporal coupling source-grid-storage collaborative planning model is constructed. S3. For the spatiotemporal coupling scenario set generated in step S1 and the multi-stage spatiotemporal coupling source-grid-storage collaborative planning model constructed in step S2, the mixed integer linear programming problem is decomposed into an upper-level main problem and lower-level sub-problems through a two-layer decomposition architecture. An adaptive cutting plane strategy is introduced to accelerate convergence and generate the optimal investment plan. S4. Multi-dimensionally evaluate the optimal investment plan generated in step S3, and determine the final investment plan based on the evaluation results.

[0007] Therefore, the present invention adopts the above-mentioned energy storage and transmission line coordinated planning method based on spatiotemporal coupling and multi-mode, which has the following beneficial effects: 1. Accurately characterize system uncertainty: By improving the K-means algorithm and Copula function to generate a set of spatiotemporal coupling scenarios, the time series volatility of load and renewable energy output and the spatial correlation between regions are quantified. This provides planning input that is more in line with actual operating characteristics and improves the adaptability of planning schemes to complex scenarios with a high proportion of renewable energy access. 2. Multi-mode collaboration to reduce costs: Considering the differentiated cost constraints of the three energy storage configuration modes of self-built, leased, and shared, a multi-stage collaborative planning model is constructed to balance the interests of multiple parties, including new energy power plants, energy storage operators, and power grid companies, reducing full lifecycle costs and improving planning economics. 3. Two-layer optimization improves solution efficiency: A two-layer decomposition architecture combined with an adaptive cutting plane strategy is used to decompose the mixed integer linear programming problem into main and sub-problems and solve them iteratively, accelerating the convergence process. This allows for efficient processing of large-scale complex planning problems and ensures the generation of optimal investment solutions. 4. Multi-dimensional evaluation to ensure comprehensive performance: Schemes are evaluated from four dimensions: technology (energy storage substitution rate), economy (net present value), environment (carbon emission reduction), and reliability (load loss hours). Comprehensive comparison and selection are conducted using the entropy weight method and proximity calculation to ensure balanced optimization of planning schemes in terms of technical feasibility, economic rationality, environmental friendliness, and system reliability. 5. Enhance system flexibility and reliability: Through the coordinated planning of energy storage and transmission lines, transmission bottlenecks and insufficient regulation capacity caused by the intermittent and random nature of renewable energy can be effectively alleviated. This improves the system's flexible regulation capabilities, reduces load shortfalls, and enhances the safe and stable operation of the power system. 6. Promote optimal resource allocation: Considering the spatiotemporal coupling characteristics and multi-mode coordination, achieve the coordinated optimization of energy storage capacity and transmission line capacity, avoid resource waste or over-investment under the traditional single planning model, improve resource utilization efficiency, and adapt to the needs of energy clean transformation.

[0008] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a flow chart of the method for collaborative planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode according to the present invention. DETAILED DESCRIPTION

[0010] In order to make the purposes, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

[0011] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0012] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0013] like Figure 1 As shown, the energy storage and transmission line coordinated planning method based on spatiotemporal coupling and multi-mode includes the following steps: S1. Based on historical time series data, hierarchical clustering is used to generate daily scenarios with time series characteristics. Copula functions are used to quantify the spatial correlation of renewable energy output between regions and generate a set of spatiotemporal coupling scenarios. Step S1 specifically includes the following steps: S11. Based on the time series volatility of load and renewable energy output, an improved K-means algorithm is used for clustering: (1); Where, Indicates the The sample points and The weights between cluster centers; Indicates the The feature vector of sample points; Indicates the cluster centers; and Respectively represent Sample points and The timestamp of each cluster center; represents the time series weight coefficient; and Represent the total number of samples and the total number of cluster centers respectively; The feature vector described in step S11 ,in, represents the load characteristics, and , Indicates the Nodes of sample points exist stage The load value at the moment, Indicates load shortage. , Indicates the Sample points Stage unit exist The effort of every moment, Indicates the Sample points Stage The power transmission at the moment, and Respectively Stage Node Energy storage The discharge power and charging power at the moment, Indicates the load rate, , Representation node Maximum load value; Represents new energy characteristics, and , 、 、 and Respectively represent wind power, photovoltaic power, total new energy output and new energy penetration rate, , ; Indicates weather characteristics, and , 、 、 、 、 、 Represent temperature, humidity, irradiance, precipitation, cross features and fusion features respectively, , , represents the normalization function; represents the time feature, and , 、 and Represent day type, season type and continuous time index respectively, ; represents an auxiliary feature, and , represents the energy storage decision variable, Represent the front and back principal components.

[0014] S12. Output daily scenarios with time series features, and obtain the distribution probability of each scenario based on the clustering results; S13. Based on the daily scenario with time series characteristics and the historical covariance matrix of each region's renewable energy output, the copula function is used to quantify the joint distribution of output between regions: (2); Where, Indicates area and region The joint distribution between and Represents the area and region The marginal distribution value of renewable energy output after standardization; represents the standard normal distribution function; represents the correlation coefficient of output between regions, and ; S14. Output composite scene set with spatial correlation And the scene joint probability matrix ,in, Indicates the total number of scenes, represents the number of regional combinations, Used to describe the scene Combined with region The joint probability of occurrence.

[0015] In step S14, the generated scene , Representation scene Next node exist stage Load value at the moment; Representation scene Next node exist stage New energy output at all times; Indicates the total number of nodes; Indicates the total time period.

[0016] S2. Based on the system topology, historical renewable energy output data, and equipment parameters, and taking into account the differentiated cost constraints of the self-built, leased, and shared models, as well as the scenarios in the spatiotemporal coupling scenario set described in step S1, a multi-stage spatiotemporal coupling source-grid-storage collaborative planning model is constructed. It should be noted that the self-built mode refers to the user building and configuring the energy storage system by themselves. In this mode, there is no clear upper limit on the power configuration of the energy storage, and users can plan and invest according to their own needs and capabilities.

[0017] The leasing model refers to users using energy storage systems through leasing. In this model, the rated power of the energy storage is subject to certain restrictions and must not exceed 0.8 times the peak load of the node. This model allows users to reduce initial investment costs to a certain extent and use energy storage resources flexibly.

[0018] The sharing mode refers to the shared use of the energy storage system by individual users. In this mode, the rated power limit of the energy storage is 0.6 times the peak value of the total regional load. This mode can realize the shared utilization of resources, further share costs, and improve the utilization efficiency of the energy storage system.

[0019] Step S2 specifically includes the following steps: S21. Taking minimizing the life cycle cost as the objective function, a multi-mode joint investment decision model is established. The objective function expression is as follows: (3); Where, express Stage transmission lines Expansion costs; Indicates the Stage Node Energy storage investment costs; Representation scene operating costs under Representation scene reliability costs under Representation scene environmental costs under Representation scene The distribution probability of 、 and Both represent weight factors; Indicates the total number of stages; in, (4); (5); (6); (7); (8); Where, Indicates a transmission line Unit capacity investment cost; express Stage transmission lines Capacity increment; Indicates the total number of transmission line sections; represents the annual inflation rate; and Respectively indicate mode The unit power investment cost and unit energy investment cost of energy storage are as follows: They represent the three modes of self-build, lease and share respectively; and express Stage Node Configured energy storage rated power and energy storage rated energy; Indicates the unit 3D power generation cost; Representation scene Down Stage unit exist The effort of every moment; Indicates the unit cost of energy storage discharge; Representation scene Down Stage Node Energy storage Discharge power at the moment; Indicates the total number of units; Indicates the power failure loss coefficient; Representation scene Down stage The probability of load loss at the moment; Indicates the unit Carbon emission factors; S22, add transmission flow constraints and energy storage multi-mode operation constraints, transmission flow constraints include DC flow constraints and line capacity and Safety constraints. Energy storage multi-mode operation constraints include energy storage charging and discharging constraints, charging and discharging power constraints, and mode-specific constraints. Mode-specific constraints include power constraints in self-built mode and power constraints in leasing or sharing mode. The DC power flow constraint expression is as follows: (9); Where, and Representation scene Down Stage Node and nodes The voltage phase angle; Indicates a transmission line reactance; Representation scene Down Stage transmission lines The meritorious trend; Line capacity and The security constraint expression is as follows: (10); Where, express Stage transmission lines The maximum permissible capacity; express Safety margin factor, and ; The energy storage charge and discharge constraint expressions are as follows: (11); Where, and Representation scene Down stage Moment and Time Node of stored energy; represents the self-discharge rate, and ; and denote the charging efficiency and discharging efficiency respectively, and , ; Representation scene Down Stage Node Energy storage Charging power at the moment; Indicates a time interval; The charge and discharge power constraint expressions are as follows: (12); Where, Indicates charging or discharging. , when discharging ; The power constraint expression in self-built mode is as follows: (13); Where, express Stage Node Configured energy storage rated power; The power constraint expression in leasing or sharing mode is as follows: (14); Where, Indicates the rated power cap in leasing or sharing mode.

[0020] S3. For the spatiotemporal coupling scenario set generated in step S1 and the multi-stage spatiotemporal coupling source-grid-storage collaborative planning model constructed in step S2, the mixed integer linear programming problem is decomposed into an upper-level main problem and lower-level sub-problems through a two-layer decomposition architecture. An adaptive cutting plane strategy is introduced to accelerate convergence and generate the optimal investment plan. The upper-level main problem in step S3 is investment variable optimization, and the lower-level sub-problem is scenario-based operation optimization; It specifically includes the following steps: S31. Initialization: Set the number of iterations , the lower bound of the upper master problem , the upper bound of the lower subproblem ; S32, loop iteration: S321. Solve the investment variable optimization and obtain the investment decision of the current iteration , express Stage transmission lines The expansion decision variables, Indicates the current iteration number; express Stage Node Selection Mode decision variables; Based on the uncertainty scenario set generated in step S1, the binary variables are optimized to determine the timing scheme for transmission line expansion and energy storage configuration at each stage, and the objective function expression is as follows: (15); Where, Representation scene Auxiliary variables of Constraints include investment timing constraints, mode mutual exclusion constraints, and Benders cut plane constraints; The investment timing constraint expression is as follows: (16); Where, express Stage transmission lines Capacity increment; express Stage Node Configured energy storage rated power; The pattern mutual exclusion constraint expression is as follows: (17); Where, Representation node Selection Mode binary variables; The Benders cutting plane constraint expression is as follows: (18); Where, and Both represent dual variables; and Respectively and The transpose of and Both represent constraint matrices, which are obtained by linearizing and dualizing the DC power flow constraints, energy storage charging and discharging constraints, and power balance constraints in the lower-level subproblems; In step S321, the dual variables of the lower-level subproblems are defined: for the DC power flow constraint described in formula (9), the dual variable is the node voltage phase angle difference; for the energy storage charge and discharge constraint described in formula (11), the dual variable is the energy storage energy state; for the power balance constraint described in formula (20), the dual variable is the node power balance factor.

[0021] S322, for each scene Solve scenario-based operation optimization, generate cutting plane constraints, and verify the feasibility of the investment decision output in step S321 under various scenarios; For the investment decision output by the upper-level main problem, solve the optimal operation strategy for each scenario in S1 and generate Benders cutting plane constraints to guide the iterative optimization of the upper-level main problem to ensure the feasibility of the planning scheme in all scenarios. The objective function is as follows: (19); And add the DC power flow constraint shown in formula (9), the energy storage charging and discharging constraint shown in formula (11), and the power balance constraint, and the power balance constraint expression is as follows: (20); Where, Representation scene Down Stage Node exist The load of the moment; Representation scene Down Stage transmission lines exist The meritorious trend of the moment; S323. Update the upper-level main problem: Add the cutting plane constraints generated by the lower-level subproblems to the upper-level main problem, and apply the adaptive factor to adjust the constraint strength: (twenty one); Where, Indicates the The relaxation factor applied to the generated cutting plane constraints in the iteration; represents the initial relaxation factor; represents the attenuation coefficient; S324. Calculate the updated lower bound of the upper master problem and the upper bound of the lower subproblem , and judge whether it satisfies hour, Indicates setting a threshold. If yes, terminate the iteration. If no, set , return to step S321; S33. Output the investment plan after the iteration as the optimal investment plan.

[0022] S4. Multi-dimensionally evaluate the optimal investment plan generated in step S3, and determine the final investment plan based on the evaluation results.

[0023] Step S4 specifically includes the following steps: S41, evaluating the optimal investment plan generated in step S3 from the four dimensions of technology, economy, environment, and reliability; Energy storage replacement rate As technical evaluation indicators: (twenty two); Where, Representation scene Down Stage Node Discharge power of energy storage; Net present value As economic evaluation indicators: (twenty three); Where, express Revenue from electricity sales during the period; express Total cost of the phase; represents the discount rate; Indicates the calculation cycle; Carbon emission reduction As environmental assessment indicators: (twenty four); Where, Representation scene Down Energy storage system not configured in this stage of electrical power; Representation scene Down Phase configuration of energy storage system after unit Power generation capacity; Load-loss hours As reliability evaluation indicators: (25); Where, represents the indicator function; Representation scene Down Stage Node exist Load shortage at any given moment; S42, standardizing the evaluation index described in step S41: (26); Where, and Represent the standardized evaluation index value and the original evaluation index value respectively; Indicates the The value of the evaluation indicator; S43. Objective weighting based on entropy weight method: (27); (28); Where, Indicates the The entropy value of the evaluation index; Indicates the The weight of each evaluation indicator; represents the number of optimal investment options; Indicates the Among the best investment options, The standardized value of the evaluation index; Indicates the optimal investment plan number; S44. Calculate the closeness of each optimal investment plan : (29); Where, and Respectively represent The distance between the optimal investment plan and the negative ideal solution and the positive ideal solution, where the negative ideal solution is the combination of the worst values of each indicator and the positive ideal solution is the combination of the best values of each indicator; S45, the closeness of each optimal investment plan calculated in step S44 Sort and take the largest The corresponding optimal investment plan is taken as the final investment plan.

[0024] Simulation experiment

[0025] Experimental conditions: Simulation Platform: MATLAB R2023b (for scenario generation and data analysis) and GAMS 25.1 (for optimization solutions). Grid Model: A simplified model of a regional power grid, consisting of 10 nodes and 15 transmission lines. Nodes 1-5 represent renewable energy-rich areas (with 60% wind / photovoltaic installed capacity), and nodes 6-10 represent load centers (with a peak load of 500 MW). Historical Data: Historical load, wind / photovoltaic output, and weather data from 2022-2024 (15-minute resolution), covering typical seasons (spring / summer / autumn / winter).

[0026] Comparison Schemes: Traditional Single Planning (Comparison 1): Plans only for transmission line expansion, ignoring energy storage. Time-Coupled Planning (Comparison 2): Considers temporal fluctuations in load and renewable energy output, but ignores spatial correlations between regions. Multi-Mode Planning (Comparison 3): Considers self-build / rental / sharing models, but fails to generate spatiotemporal coupling scenarios. The Inventive Method (Experimental Group): Fully implements spatiotemporal coupling scenario generation, multi-mode collaborative modeling, and two-layer optimization.

[0027] Table 1 Scene generation parameters ;

[0028] Table 2 Multimodal collaboration model parameters ;

[0029] Table 3 Optimization solution parameters ;

[0030] Based on the above parameter settings, the present invention and the traditional method are used to respectively perform energy storage and transmission line coordinated planning, and the following results are obtained.

[0031] Table 4 Evaluation results ;

[0032] As shown in Table 4, the energy storage replacement rate (38.2%) of the experimental group (the present invention) is significantly higher than that of comparison 2 (28.5%) and comparison 3 (32.7%), indicating that the spatiotemporal coupling scenario accurately depicts the spatiotemporal fluctuation characteristics of load and renewable energy output, making the energy storage and transmission line capacity better matched and reducing transmission redundancy. The project's total energy storage investment (USD 18.2 million) increased by 51.7% compared to the project's first comparison (USD 12 million), primarily due to the shared energy storage investment cost (40% lower than self-built) through a leasing / sharing model, and the avoidance of overinvestment through two-tier optimization (reducing transmission capacity expansion requirements by 37.5%). Carbon emissions reductions (8,500 tons) increased by 70% compared to the project's first comparison, as energy storage effectively smoothed fluctuations in renewable energy output, reduced the number of conventional unit starts and stops, and increased clean energy utilization (renewable energy utilization increased from 75% to 88%). Load loss hours (55 hours) decreased by 54.2% compared to the project's first comparison. The spatiotemporal coupling scenario encompassed extreme operating conditions (such as inter-regional renewable energy output troughs and load peaks). The flexible adjustment of multi-mode energy storage mitigated the reliability shortcomings of traditional transmission planning. This demonstrates that the proposed method, through the generation of spatiotemporal coupling scenarios, multi-mode collaborative modeling, and two-tier optimization, outperforms traditional planning methods in terms of technical feasibility, economic rationale, environmental friendliness, and system reliability. It is particularly suitable for complex grid planning scenarios with a high proportion of renewable energy integration.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A collaborative planning method for energy storage and transmission lines based on spatiotemporal coupling and multi-mode, characterized by: The following steps are involved: S1. Based on historical time series data, hierarchical clustering is used to generate daily scenarios with time series characteristics. Copula functions are used to quantify the spatial correlation of renewable energy output between regions and generate a set of spatiotemporal coupling scenarios. S2. Based on the system topology, historical renewable energy output data, and equipment parameters, and taking into account the differentiated cost constraints of the self-built, leased, and shared models, as well as the scenarios in the spatiotemporal coupling scenario set described in step S1, a multi-stage spatiotemporal coupling source-grid-storage collaborative planning model is constructed. S3. For the spatiotemporal coupling scenario set generated in step S1 and the multi-stage spatiotemporal coupling source-grid-storage collaborative planning model constructed in step S2, the mixed integer linear programming problem is decomposed into an upper-level main problem and lower-level sub-problems through a two-layer decomposition architecture. An adaptive cutting plane strategy is introduced to accelerate convergence and generate the optimal investment plan. S4. Multi-dimensionally evaluate the optimal investment plan generated in step S3, and determine the final investment plan based on the evaluation results.

2. The method for coordinated planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11. Based on the time series volatility of load and renewable energy output, an improved K-means algorithm is used for clustering: （1）； Where, Indicates the The sample points and The weights between cluster centers; Indicates the The feature vector of each sample point; Indicates the cluster centers; and Respectively represent Sample points and The timestamp of each cluster center; represents the time series weight coefficient; and Represent the total number of samples and the total number of cluster centers respectively; S12. Output daily scenarios with time series features, and obtain the distribution probability of each scenario based on the clustering results; S13. Based on the daily scenario with time series characteristics and the historical covariance matrix of each region's renewable energy output, the copula function is used to quantify the joint distribution of output between regions: （2）； Where, Indicates area and region The joint distribution between and Represents the area and region The marginal distribution value of renewable energy output after standardization; represents the standard normal distribution function; represents the correlation coefficient of output between regions, and ; S14. Output composite scene set with spatial correlation And the scene joint probability matrix ,in, Indicates the total number of scenes, represents the number of regional combinations, Used to describe the scene Combined with region The joint probability of occurrence.

3. The method for coordinated planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode according to claim 2, characterized in that: The feature vector described in step S11 ,in, represents the load characteristics, and , Indicates the Nodes of sample points exist stage The load value at the moment, Indicates load shortage. , Indicates the Sample points Stage unit exist The effort of every moment, Indicates the Sample points Stage The power transmission at the moment, and Respectively Stage Node Energy storage The discharge power and charging power at the moment, Indicates the load rate, , Representation node Maximum load value; Represents new energy characteristics, and , 、 、 and Respectively represent wind power, photovoltaic power, total new energy output and new energy penetration rate, , ; Indicates weather characteristics, and , 、 、 、 、 、 Represent temperature, humidity, irradiance, precipitation, cross features and fusion features respectively, , , represents the normalization function; represents the time feature, and , 、 and Represent day type, season type and continuous time index respectively, ; represents an auxiliary feature, and , represents the energy storage decision variable, Represent the front and back principal components.

4. The method for coordinated planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode according to claim 2, characterized in that: In step S14, the generated scene , Representation scene Next node exist stage Load value at the moment; Representation scene Next node exist stage New energy output at all times; Indicates the total number of nodes; Indicates the total time period.

5. The method for coordinated planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode according to claim 2, characterized in that: Step S2 specifically includes the following steps: S21. Taking minimizing the life cycle cost as the objective function, a multi-mode joint investment decision model is established. The objective function expression is as follows: （3）； Where, express Stage transmission lines Expansion costs; Indicates the Stage Node Energy storage investment costs; Representation scene operating costs under Representation scene reliability costs under Representation scene environmental costs under Representation scene The distribution probability of 、 and Both represent weight factors; Indicates the total number of stages; in, （4）； （5）； （6）； （7）； （8）； Where, Indicates a transmission line Unit capacity investment cost; express Stage transmission lines Capacity increment; Indicates the total number of transmission line sections; represents the annual inflation rate; and Respectively indicate mode The unit power investment cost and unit energy investment cost of energy storage are as follows: They represent the three modes of self-build, lease and share respectively; and express Stage Node Configured energy storage rated power and energy storage rated energy; Indicates the unit 3D power generation cost; Representation scene Down Stage unit exist The effort of every moment; Indicates the unit cost of energy storage discharge; Representation scene Down Stage Node Energy storage Discharge power at the moment; Indicates the total number of units; Indicates the power failure loss coefficient; Representation scene Down stage The probability of load loss at the moment; Indicates the unit Carbon emission factors; S22, add transmission flow constraints and energy storage multi-mode operation constraints, transmission flow constraints include DC flow constraints and line capacity and Safety constraints. Energy storage multi-mode operation constraints include energy storage charging and discharging constraints, charging and discharging power constraints, and mode-specific constraints. Mode-specific constraints include power constraints in self-built mode and power constraints in leasing or sharing mode. The DC power flow constraint expression is as follows: （9）； Where, and Representation scene Down Stage Node and nodes The voltage phase angle; Indicates a transmission line reactance; Representation scene Down Stage transmission lines The meritorious trend; Line capacity and The security constraint expression is as follows: （10）； Where, express Stage transmission lines The maximum permissible capacity; express Safety margin factor, and ; The energy storage charge and discharge constraint expressions are as follows: （11）； Where, and Representation scene Down stage Moment and Time Node of stored energy; represents the self-discharge rate, and ; and denote the charging efficiency and discharging efficiency respectively, and , ; Representation scene Down Stage Node Energy storage Charging power at the moment; Indicates a time interval; The charge and discharge power constraint expressions are as follows: （12）； Where, Indicates charging or discharging. , when discharging ; The power constraint expression in self-built mode is as follows: （13）； Where, express Stage Node Configured energy storage rated power; The power constraint expression in leasing or sharing mode is as follows: （14）； Where, Indicates the rated power cap in leasing or sharing mode.

6. The method for coordinated planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode according to claim 5 is characterized by: The upper-level main problem in step S3 is investment variable optimization, and the lower-level sub-problem is scenario-based operation optimization; It specifically includes the following steps: S31. Initialization: Set the number of iterations , the lower bound of the upper master problem , the upper bound of the lower subproblem ; S32, loop iteration: S321. Solve the investment variable optimization and obtain the investment decision of the current iteration , express Stage transmission lines The expansion decision variables, Indicates the current iteration number; express Stage Node Selection Mode decision variables; Based on the uncertainty scenario set generated in step S1, the binary variables are optimized to determine the timing scheme for transmission line expansion and energy storage configuration at each stage, and the objective function expression is as follows: （15）； Where, Representation scene Auxiliary variables of Constraints include investment timing constraints, mode mutual exclusion constraints, and Benders cut plane constraints; The investment timing constraint expression is as follows: （16）； Where, express Stage transmission lines Capacity increment; express Stage Node Configured energy storage rated power; The pattern mutual exclusion constraint expression is as follows: （17）； Where, Representation node Selection Mode binary variables; The Benders cutting plane constraint expression is as follows: （18）； Where, and Both represent dual variables; and Respectively and The transpose of and Both represent constraint matrices, which are obtained by linearizing and dualizing the DC power flow constraints, energy storage charging and discharging constraints, and power balance constraints in the lower-level subproblems; S322, for each scene Solve scenario-based operation optimization, generate cutting plane constraints, and verify the feasibility of the investment decision output in step S321 under various scenarios; For the investment decision output by the upper-level main problem, solve the optimal operation strategy for each scenario in S1 and generate Benders cutting plane constraints to guide the iterative optimization of the upper-level main problem to ensure the feasibility of the planning scheme in all scenarios. The objective function is as follows: （19）； And add the DC power flow constraint shown in formula (9), the energy storage charging and discharging constraint shown in formula (11), and the power balance constraint, and the power balance constraint expression is as follows: （20）； Where, Representation scene Down Stage Node exist The load of the moment; Representation scene Down Stage transmission lines exist The meritorious trend of the moment; S323. Update the upper-level main problem: Add the cutting plane constraints generated by the lower-level subproblems to the upper-level main problem, and apply the adaptive factor to adjust the constraint strength: （21）； Where, Indicates the The relaxation factor applied to the generated cutting plane constraints in the iteration; represents the initial relaxation factor; represents the attenuation coefficient; S324. Calculate the updated lower bound of the upper master problem and the upper bound of the lower subproblem , and judge whether it satisfies hour, Indicates setting a threshold. If yes, terminate the iteration. If no, set , return to step S321; S33. Output the investment plan after the iteration as the optimal investment plan.

7. The method for coordinated planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode according to claim 6, characterized in that: In step S321, the dual variables of the lower-level subproblems are defined: for the DC power flow constraint described in formula (9), the dual variable is the node voltage phase angle difference; for the energy storage charge and discharge constraint described in formula (11), the dual variable is the energy storage energy state; for the power balance constraint described in formula (20), the dual variable is the node power balance factor.

8. The method for coordinated planning of energy storage and transmission lines based on spatiotemporal coupling and multi-mode according to claim 7, characterized in that: Step S4 The specific steps include: S41, evaluating the optimal investment plan generated in step S3 from the four dimensions of technology, economy, environment, and reliability; Energy storage replacement rate As technical evaluation indicators: （22）； Where, Representation scene Down Stage Node Discharge power of energy storage; Net present value As economic evaluation indicators: （23）； Where, express Revenue from electricity sales during the period; express Total cost of the phase; represents the discount rate; Indicates the calculation cycle; Carbon emission reduction As environmental assessment indicators: （24）； Where, Representation scene Down Energy storage system not configured in this stage of electrical power; Representation scene Down Phase configuration of energy storage system after unit Power generation capacity; Load-loss hours As reliability evaluation indicators: （25）； Where, represents the indicator function; Representation scene Down Stage Node exist Load shortage at any given moment; S42, standardizing the evaluation indicators described in step S41: （26）； Where, and Represent the standardized evaluation index value and the original evaluation index value respectively; Indicates the The value of the evaluation indicator; S43. Objective weighting based on entropy weight method: （27）； （28）； Where, Indicates the The entropy value of the evaluation index; Indicates the The weight of each evaluation indicator; represents the number of optimal investment options; Indicates the Among the best investment options, The standardized value of the evaluation index; Indicates the optimal investment plan number; S44. Calculate the closeness of each optimal investment plan : （29）； Where, and Respectively represent The distance between the optimal investment plan and the negative ideal solution and the positive ideal solution, where the negative ideal solution is the combination of the worst values of each indicator and the positive ideal solution is the combination of the best values of each indicator; S45, the closeness of each optimal investment plan calculated in step S44 Sort and take the largest The corresponding optimal investment plan is taken as the final investment plan.

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