A network-constructed energy storage power station planning method and system

Abstract

This invention discloses a planning method and system for grid-connected energy storage power stations. Through the coordinated operation of five modules, it achieves end-to-end planning from multi-source data acquisition to engineering scheme implementation, with an overall latency of ≤500ms and support for single-machine / cluster deployment. The five modules include: a data acquisition and processing subsystem, which collects raw data from multiple sources including the power grid, resources, environment, and market; after outlier identification, completion, and standardization, it outputs high-quality standardized data; a planning and analysis subsystem, which selects typical operating conditions based on standardized data and calculates the short-circuit ratio and inertia level at the grid connection point using quantitative formulas to determine the power grid support requirements. This invention is adaptable to various application scenarios, achieving optimal economic performance throughout its entire lifecycle, balancing technical practicality and cost rationality. It can access data from various scenarios such as extreme weather and marine environments, perfectly adapting to different application scenarios such as offshore wind power grid connection points and renewable energy-rich areas, solving the problem of single-scenario adaptability in traditional planning schemes.

Description

Technical Field

[0001] This invention relates to the field of energy storage power station planning technology, and in particular to a grid-type energy storage power station planning method and system. Background Technology

[0002] With the large-scale grid connection of high-proportion renewable energy sources, the power grid exhibits typical characteristics of "weak inertia, weak support, and high volatility." The declining proportion of traditional synchronous generators has led to a significant weakening of the grid's frequency regulation, voltage regulation, and disturbance rejection capabilities. Grid-based energy storage, as a core supporting device with virtual synchronous machine characteristics, can actively provide inertia, voltage support, and fault ride-through capabilities, becoming a key technological path to solve these problems. However, existing grid-based energy storage power station planning methods and systems still have many technical pain points and are difficult to adapt to the changing needs of the power grid, as follows:

[0003] Existing planning schemes fail to deeply integrate grid control logic with preliminary site selection, capacity determination, and equipment configuration. VSG control parameters (damping coefficient, droop coefficient) are mostly passively adjusted after planning is completed, failing to achieve coordinated optimization between planning and control. Furthermore, the lack of pre-designed adaptations for extreme conditions such as grid-connected / islanded switching, black start, and short-circuit faults results in poor transient stability and low fault ride-through success rate under strong disturbances, making it difficult to meet the requirements for safe grid operation. The existing system does not fully consider the impact of special scenarios such as extreme weather (typhoons, high temperatures) and marine environment on equipment selection and planning schemes, resulting in insufficient universality of the schemes; the economic assessment only focuses on the initial investment cost and does not include the full life cycle indicators such as operation and maintenance costs, equipment replacement costs, and carbon benefits, and lacks quantitative simulation accuracy verification standards, making it difficult to balance the feasibility and economy of the planning scheme.

[0004] Therefore, a grid-based energy storage power station planning method and system are proposed to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned shortcomings by providing a planning method and system for grid-type energy storage power stations.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a grid-type energy storage power station planning system, which realizes the entire process planning from multi-source data acquisition to engineering scheme implementation through the coordinated linkage of five major modules, with an overall latency of ≤500ms, and supports single-machine / cluster deployment; the five major modules include: The data acquisition and processing subsystem collects raw data from multiple sources, including power grids, resources, environment, and markets. After outlier identification, completion, and standardization, it outputs high-quality standardized data. The planning and analysis subsystem filters typical operating conditions based on standardized data, calculates the short-circuit ratio and inertia level of the grid connection point through quantitative formulas to determine the grid support requirements, and uses an improved particle swarm optimization algorithm to construct a two-layer optimization model to solve for the optimal location, capacity and access node. The equipment configuration subsystem uses a multi-attribute decision algorithm to adapt to the combination scheme of multiple energy storage media based on the planning results, and determines the core parameters of the energy storage converter, step-up transformer and battery pack through electrical formula quantification. The control strategy subsystem is designed based on equipment parameters, featuring an improved virtual synchronous machine control architecture and full-condition adaptive control logic. It has the functions of grid-connected / islanded switching, fault ride-through, and black start capability assessment. The solution output and evaluation subsystem verifies the technical feasibility of the solution through digital twin simulation, calculates the economic efficiency throughout the entire life cycle, generates engineering documents that can be directly implemented, and forms a planning closed loop.

[0007] Preferably, the data acquisition and processing subsystem includes a multi-source data acquisition module, a data preprocessing module, and a storage and management module. The data preprocessing unit uses outlier identification and data completion algorithms to process the raw data. The multi-source data acquisition module includes a power grid data acquisition unit, a resource and environment data acquisition unit, and a market and load data acquisition unit. The power grid data acquisition unit is equipped with dual-mode communication via fiber optic sensing and a private wireless network. It collects data such as the 35kV-500kV power grid topology, line parameters, short-circuit ratio (SCR) at the grid connection point, and inertia level. It supports IEC61850 protocol access, has a data update frequency of ≤1s, and automatically identifies the weak grid level (SCR < 1.5 indicates an extremely weak grid, and 1.5 ≤ SCR < 2.0 indicates a weak grid). The resource and environmental data acquisition module integrates satellite remote sensing and ground monitoring data, while the market and load data acquisition module captures electricity market transaction data through API interfaces, collects user-side subdivided load time-series curves, and distinguishes between adjustable loads and rigid loads. The storage and management unit adopts an InfluxDB+Redis hybrid storage architecture.

[0008] Preferably, the multi-objective optimization function module included in the planning and analysis subsystem adopts a two-layer optimization core architecture, integrates an improved particle swarm optimization (PSO) algorithm for solving, and calculates the core indicators of the power grid through quantitative formulas to construct constraints. In the two-layer optimization architecture, the upper-layer site selection and capacity determination layer focuses on minimizing the objective function, with constraints including the aforementioned SCR, inertia index, energy storage capacity boundary, and land compliance; the lower-layer operation optimization layer aims to maximize grid support benefits and maximize the renewable energy absorption rate, with constraints including energy storage SOC (20%≤SOC≤80%), charging and discharging power limits, and primary frequency regulation response time (≤100ms). The operating parameters are solved collaboratively using a genetic algorithm and a sequential quadratic programming algorithm.

[0009] The lower layer is the operation optimization layer, which aims to maximize the grid support benefits and the renewable energy consumption rate. The constraints include energy storage SOC (20%~80%), charging and discharging power limits, and primary frequency regulation response time (≤100ms). The planning and analysis subsystem solves the optimization problem through a multi-algorithm fusion strategy, integrating the improved particle swarm optimization (PSO) algorithm, the empire competition algorithm, and the genetic algorithm + sequential quadratic programming algorithm. It adopts a GPU cluster and a distributed computing architecture to achieve parallel solution in multiple scenarios, while avoiding single-node overload through a load balancing mechanism.

[0010] Preferably, the equipment configuration includes a core equipment parameter matching module and an energy storage medium selection module. The energy storage medium selection module supports the coordinated planning of lithium iron phosphate batteries, semi-solid-state batteries, vanadium redox flow batteries, and flywheel energy storage. Based on the scenario requirements and grid support requirements output by the planning and analysis subsystem, the system automatically calculates the capacity ratio and access method of different media to adapt to differentiated requirements such as short-term frequency regulation, high-power support, long-term peak shaving, and millisecond-level inertia support. The core equipment parameter matching module determines key parameters such as the power of the energy storage converter (PCS), the step-up transformer capacity, and the battery pack capacity according to the multi-media combination scheme to ensure that the equipment performance is adapted to the grid control requirements. The upper layer of the dual-layer optimization core module is the site selection and capacity determination layer, which aims to minimize the total life cycle cost (investment + operation and maintenance + replacement + carbon cost). The constraints include short-circuit ratio (SCR ≥ 1.5~2.0), inertia support (≥ 80% of equivalent synchronous machine inertia), capacity boundary and land compliance. The lower layer is the operation optimization layer, which aims to maximize the grid support benefits and maximize the renewable energy absorption rate. The constraints include SOC (20%~80%), charging and discharging power limits and primary frequency regulation response time (≤ 100ms).

[0011] Preferably, the solution output and evaluation subsystem includes a technical evaluation module and an economic evaluation module. The technical evaluation unit conducts dynamic simulation verification of normal and extreme working conditions based on a millimeter-level three-dimensional digital twin constructed by BIM+GIS. The economic assessment module calculates the total life cycle cost, benefits, and investment payback period, and the sensitivity analysis module quantifies the impact of equipment prices, new energy penetration rates, and electricity price adjustments on the project's revenue. The multi-algorithm fusion solution module integrates improved particle swarm optimization (PSO), imperial competition algorithm, and genetic algorithm + sequential quadratic programming algorithm, and adaptively selects the solution algorithm for different planning problems.

[0012] Preferably, the digital twin achieves synchronous linkage between the physical entity and the virtual model through a dynamic parameter mapping module, with a synchronization delay of ≤10ms.

[0013] Preferably, the solution output and evaluation subsystem includes technical evaluation, economic evaluation and engineering implementation functional modules, which generate 3-5 differentiated planning solutions based on the planning results, covering different energy storage medium ratios, access nodes and capacity configurations; The project implementation module transforms the optimal solution into standardized planning reports, engineering construction drawings, equipment procurement lists, and operation and maintenance manuals, supporting export in Word / PDF format and directly connecting with the engineering design and construction phases.

[0014] A planning method for grid-type energy storage power stations, S1: Data acquisition and preprocessing, using the 3σ criterion algorithm to remove abnormal data and linear interpolation to complete missing data, outputting a standardized dataset; S2: Power grid demand analysis and optimization planning, calculate the short-circuit ratio and inertia level of the grid connection point to construct constraints, use the improved particle swarm algorithm to solve the two-level optimization model, and output the basic planning scheme; S3: Equipment configuration and control strategy design, using the entropy weight-TOPSIS method to determine the energy storage medium ratio and core equipment parameters, build an improved virtual synchronous machine control model and formulate full-condition control logic; S4: Scheme evaluation and closed-loop optimization. The root mean square error and mean absolute percentage error are used to verify the simulation accuracy, calculate the cost per kilowatt-hour and investment payback period. If the simulation fails to meet the standards, the parameters are adjusted in reverse and S2-S3 are repeated. S5: Output engineering implementation documents and complete the entire process planning of grid-type energy storage power stations.

[0015] The beneficial effects of this invention are reflected in: This invention integrates the design of network control strategies into the entire planning process in advance, enabling deep linkage between the control strategy subsystem and the planning analysis and equipment configuration subsystems. Based on the planning results and equipment parameters, an improved virtual synchronous machine control architecture is directly built, and adaptive control logic for all operating conditions such as grid-connected and islanded switching, fault ride-through, and black start is formulated in advance. This solves the problem of control and planning disconnect from the planning source, improves the transient stability of the power plant under various operating conditions, and significantly enhances the ability to respond to faults and adapt to extreme operating conditions.

[0016] It can adapt to a variety of application scenarios, achieving optimal economic performance throughout its entire lifecycle while balancing technical practicality and cost rationality. It can access data from various scenarios such as extreme weather and marine environments, perfectly adapting to different application scenarios such as offshore wind power grid connection points and new energy-rich areas, solving the problem of single-scenario adaptability of traditional planning solutions. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention; Figure 2 This is a partial flowchart of the present invention; Detailed Implementation The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1-2 This invention discloses a planning method and system for a grid-type energy storage power station. Through the coordinated operation of five modules, it achieves full-process planning from multi-source data acquisition to engineering scheme implementation, with an overall latency of ≤500ms, and supports single-machine / cluster deployment. The five modules include: The data acquisition and processing subsystem collects raw data from multiple sources, including power grids, resources, environment, and markets. After outlier identification, completion, and standardization, it outputs high-quality standardized data. The planning and analysis subsystem filters typical operating conditions based on standardized data, calculates the short-circuit ratio and inertia level of the grid connection point through quantitative formulas to determine the grid support requirements, and uses an improved particle swarm optimization algorithm to construct a two-layer optimization model to solve for the optimal location, capacity and access node. The equipment configuration subsystem uses a multi-attribute decision algorithm to adapt to the combination scheme of multiple energy storage media based on the planning results, and determines the core parameters of the energy storage converter, step-up transformer and battery pack through electrical formula quantification. The control strategy subsystem is designed based on equipment parameters, featuring an improved virtual synchronous machine control architecture and full-condition adaptive control logic. It has the functions of grid-connected / islanded switching, fault ride-through, and black start capability assessment. The solution output and evaluation subsystem verifies the technical feasibility of the solution through digital twin simulation, calculates the economic efficiency throughout the entire life cycle, generates engineering documents that can be directly implemented, and forms a planning closed loop.

[0019] Preferably, the data acquisition and processing subsystem includes a multi-source data acquisition module, a data preprocessing module, and a storage and management module. The data preprocessing unit uses outlier identification and data completion algorithms to process the raw data. The multi-source data acquisition module includes a power grid data acquisition unit, a resource and environment data acquisition unit, and a market and load data acquisition unit. The power grid data acquisition unit is equipped with dual-mode communication via fiber optic sensing and a private wireless network. It collects data such as the 35kV-500kV power grid topology, line parameters, short-circuit ratio (SCR) at the grid connection point, and inertia level. It supports IEC61850 protocol access, has a data update frequency of ≤1s, and automatically identifies the weak grid level (SCR < 1.5 indicates an extremely weak grid, and 1.5 ≤ SCR < 2.0 indicates a weak grid). The resource and environmental data acquisition module integrates satellite remote sensing and ground monitoring data, while the market and load data acquisition module captures electricity market transaction data through API interfaces, collects user-side subdivided load time-series curves, and distinguishes between adjustable loads and rigid loads. The storage and management unit adopts an InfluxDB+Redis hybrid storage architecture.

[0020] (1) 3σ criterion outlier identification algorithm: For the data sequence {x1,x2,...,x...} n}, calculate the mean μ=Σx i / n、Standard deviation σ=√[Σ(x i -μ)² / (n-1)], when |x i When -μ|>3σ, it is judged as an outlier and removed; (2) Linear interpolation completion formula: x k =x k-1 +(x k+1 -x k-1 )×(t k -t k-1 ) / (t k+1 -t k-1 ), where t is a time series, x k Data to be completed; The storage and management unit adopts an InfluxDB+Redis hybrid storage architecture, with time-series data stored in InfluxDB and hot data stored in Redis. It integrates an AES-256 encrypted data security module, and the multi-source data collection time scale covers from minutes to grades.

[0021] Preferably, the multi-objective optimization function module included in the planning and analysis subsystem adopts a two-layer optimization core architecture, integrates an improved particle swarm optimization (PSO) algorithm for solving, and calculates the core indicators of the power grid through quantitative formulas to construct constraints. In the two-layer optimization architecture, the upper-layer site selection and capacity determination layer focuses on minimizing the objective function, with constraints including the aforementioned SCR, inertia index, energy storage capacity boundary, and land compliance; the lower-layer operation optimization layer aims to maximize grid support benefits and maximize the renewable energy absorption rate, with constraints including energy storage SOC (20%≤SOC≤80%), charging and discharging power limits, and primary frequency regulation response time (≤100ms). The operating parameters are solved collaboratively using a genetic algorithm and a sequential quadratic programming algorithm.

[0022] The lower layer is the operation optimization layer, which aims to maximize the grid support benefits and the renewable energy consumption rate. The constraints include energy storage SOC (20%~80%), charging and discharging power limits, and primary frequency regulation response time (≤100ms). The planning and analysis subsystem solves the optimization problem through a multi-algorithm fusion strategy, integrating the improved particle swarm optimization (PSO) algorithm, the empire competition algorithm, and the genetic algorithm + sequential quadratic programming algorithm. It adopts a GPU cluster and a distributed computing architecture to achieve parallel solution in multiple scenarios, while avoiding single-node overload through a load balancing mechanism.

[0023] It should be noted that: (1) Short-circuit ratio at grid connection point: SCR=S s c / P 9en S s c represents the three-phase short-circuit capacity at the grid connection point (MVA), P 9en The total rated output (MW) of new energy units is given, with the constraint that SCR ≥ 1.5~2.0. (2) Inertia level: H e q=Σ(H i S i ) / S e q, where H i S represents the inertia time constant (s) of each unit. i S is the rated capacity of the unit (MVA). e q represents the system's equivalent capacity (MVA), with the constraint that the inertia increase is ≥ 80% of the equivalent synchronous machine's inertia. (3) Improved Particle Swarm Optimization Algorithm: Adaptive Inertia Weight ω=ω max -(ω max -ω min )×t / T max , where ω max =0.9、ω min =0.4, t is the current iteration number, T max The maximum number of iterations; the objective function is min(C). total )=C invest +C ope ation -C ene fit C invest For the initial investment cost, C ope ation For maintenance costs, C ene f it For profit.

[0024] Preferably, the equipment configuration includes a core equipment parameter matching module and an energy storage medium selection module. The energy storage medium selection module supports the coordinated planning of lithium iron phosphate batteries, semi-solid-state batteries, vanadium redox flow batteries, and flywheel energy storage. Based on the scenario requirements and grid support requirements output by the planning and analysis subsystem, the system automatically calculates the capacity ratio and access method of different media to adapt to differentiated requirements such as short-term frequency regulation, high-power support, long-term peak shaving, and millisecond-level inertia support. The core equipment parameter matching module determines key parameters such as the power of the energy storage converter (PCS), the step-up transformer capacity, and the battery pack capacity according to the multi-media combination scheme to ensure that the equipment performance is adapted to the grid control requirements. The upper layer of the dual-layer optimization core module is the site selection and capacity determination layer, which aims to minimize the total life cycle cost (investment + operation and maintenance + replacement + carbon cost). The constraints include short-circuit ratio (SCR ≥ 1.5~2.0), inertia support (≥ 80% of equivalent synchronous machine inertia), capacity boundary and land compliance. The lower layer is the operation optimization layer, which aims to maximize the grid support benefits and maximize the renewable energy absorption rate. The constraints include SOC (20%~80%), charging and discharging power limits and primary frequency regulation response time (≤ 100ms).

[0025] It should be noted that: Entropy Weight-TOPSIS method: Evaluation matrix X = [x ij ], entropy weight w j =-k×Σ(x ij ×lnx ij ), k=1 / lnn (n is the number of media types), through relative proximity C i =d i - / (d i + +d i - The sorting process determines the medium ratio; (2) Rated power of energy storage converter: P p c s =k×P es k is a redundancy coefficient of 1.2 to 1.5, P es The rated charge and discharge power of the energy storage system; (3) Step-up variable capacity: S t a f o =P p c s / cosφ, where cosφ≥0.95; (4) Battery pack capacity: E at =P es ×T nu / DOD, where T nu For continuous discharge time, DOD ≤ 80%.

[0026] Preferably, the network control module of the control strategy subsystem adopts an improved virtual synchronous machine (VSG) control architecture, introducing an adaptive damping coefficient based on the rate of change of frequency (df / dt) and a dynamic droop coefficient based on the grid connection point voltage (V_PCC) to improve transient stability under strong disturbances. The operating condition adaptation module formulates the control logic for seamless grid-connected / islanded switching, fault ride-through, and black start under all operating conditions, and verifies the adaptability to extreme operating conditions with the digital twin dynamic simulation function; the black start capability assessment function simulates a completely dark grid scenario, calculates the ability of the energy storage power station to provide starting power for gas turbine units and coal-fired units, evaluates the starting power, starting time and voltage / frequency stability, and outputs a black start feasibility solution.

[0027] It should be noted that: (1) The equation of motion of the machine: J×d²θ / dt²=T m -T e -D×dθ / dt, where J is the virtual moment of inertia, θ is the virtual angle of work, and T is the virtual angle of work. m For virtual mechanical torque, T e Where D is the virtual electromagnetic torque, and D is the damping coefficient; (2) Electromagnetic power equation: P e =E9U9sinδ / (X9+X line ), where E9 is the VSG output electromotive force, U9 is the grid voltage, δ is the power angle difference, X9 is the VSG equivalent reactance, X line For line reactance; (3) Adaptive damping coefficient: D=D0+k_D×|df / dt|, where D0 is the basic damping coefficient and k_D is the adjustment coefficient; (4) Dynamic droop coefficient: Active droop coefficient m = m0 × (1 + k m ×Δf), reactive power droop coefficient n=n0×(1+k) n ×ΔU), where Δf is the frequency deviation and ΔU is the voltage deviation; (5) Black start capability assessment: P sta t ≥P unit +P loss , where P sta t For energy storage output startup power, P unit P is the unit's starting power. loss This refers to line and equipment losses.

[0028] Preferably, the solution output and evaluation subsystem includes a technical evaluation module and an economic evaluation module. The technical evaluation unit conducts dynamic simulation verification of normal and extreme working conditions based on a millimeter-level three-dimensional digital twin constructed by BIM+GIS. The economic assessment module calculates the total life cycle cost, benefits, and investment payback period, and the sensitivity analysis module quantifies the impact of equipment prices, new energy penetration rates, and electricity price adjustments on the project's revenue. The multi-algorithm fusion solution module integrates improved particle swarm optimization (PSO), imperial competition algorithm, and genetic algorithm + sequential quadratic programming algorithm, and adaptively selects the solution algorithm for different planning problems.

[0029] The digital twin achieves synchronous linkage between the physical entity and the virtual model through a dynamic parameter mapping module, with a synchronization delay of ≤10ms.

[0030] The solution output and evaluation subsystem includes technical evaluation, economic evaluation and engineering implementation functional modules. Based on the planning results, it generates 3-5 differentiated planning solutions, covering different energy storage medium ratios, access nodes and capacity configurations. The project implementation module transforms the optimal solution into standardized planning reports, engineering construction drawings, equipment procurement lists, and operation and maintenance manuals, supporting export in Word / PDF format and directly connecting with the engineering design and construction phases.

[0031] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0032] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating the relative importance of the features implying a number of technical features. Therefore, features defined with "first" or "second" can explicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory and cannot be implemented, such a combination should be considered non-existent and not within the scope of protection claimed by this invention.

[0033] Additionally, "multiple" refers to two or more.

[0034] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A grid-type energy storage power station planning system, characterized in that, The system achieves full-process planning from multi-source data collection to engineering solution implementation through the coordinated operation of five major modules, with an overall latency of ≤500ms and supports single-machine / cluster deployment. The five modules include: The data acquisition and processing subsystem collects raw data from multiple sources, including power grids, resources, environment, and markets. After outlier identification, completion, and standardization, it outputs high-quality standardized data. The planning and analysis subsystem filters typical operating conditions based on standardized data, calculates the short-circuit ratio and inertia level of the grid connection point through quantitative formulas to determine the grid support requirements, and uses an improved particle swarm optimization algorithm to construct a two-layer optimization model to solve for the optimal location, capacity and access node. The equipment configuration subsystem uses a multi-attribute decision algorithm to adapt to the combination scheme of multiple energy storage media based on the planning results, and determines the core parameters of the energy storage converter, step-up transformer and battery pack through electrical formula quantification. The control strategy subsystem is designed based on equipment parameters, featuring an improved virtual synchronous machine control architecture and full-condition adaptive control logic. It has the functions of grid-connected / islanded switching, fault ride-through, and black start capability assessment. The solution output and evaluation subsystem verifies the technical feasibility of the solution through digital twin simulation, calculates the economic efficiency throughout the entire life cycle, generates engineering documents that can be directly implemented, and forms a planning closed loop.

2. The grid-type energy storage power station planning system according to claim 1, characterized in that, The data acquisition and processing subsystem includes a multi-source data acquisition module, a data preprocessing module, and a storage and management module. The data preprocessing unit uses outlier identification and data completion algorithms to process the raw data. The multi-source data acquisition module includes a power grid data acquisition unit, a resource and environment data acquisition unit, and a market and load data acquisition unit. The power grid data acquisition unit is equipped with dual-mode communication via fiber optic sensing and a private wireless network. It collects data such as the 35kV-500kV power grid topology, line parameters, short-circuit ratio (SCR) at the grid connection point, and inertia level. It supports IEC 61850 protocol access, has a data update frequency of ≤1s, and automatically identifies the weak grid level (SCR < 1.5 indicates an extremely weak grid, and 1.5 ≤ SCR < 2.0 indicates a weak grid). The resource and environmental data acquisition module integrates satellite remote sensing and ground monitoring data, while the market and load data acquisition module captures electricity market transaction data through API interfaces, collects user-side subdivided load time-series curves, and distinguishes between adjustable loads and rigid loads. The storage and management unit adopts an InfluxDB+Redis hybrid storage architecture.

3. The grid-type energy storage power station planning system according to claim 1, characterized in that, The planning and analysis subsystem includes a multi-objective optimization function module that adopts a two-layer optimization core architecture, integrates an improved particle swarm optimization (PSO) algorithm for solving, and calculates core power grid indicators and constructs constraints through quantitative formulas. In the two-layer optimization architecture, the upper-layer site selection and capacity determination layer focuses on minimizing the objective function, with constraints including the aforementioned SCR, inertia index, energy storage capacity boundary, and land compliance; the lower-layer operation optimization layer aims to maximize grid support benefits and maximize the renewable energy absorption rate, with constraints including energy storage SOC (20%≤SOC≤80%), charging and discharging power limits, and primary frequency regulation response time (≤100ms). The operating parameters are solved collaboratively using a genetic algorithm and a sequential quadratic programming algorithm. The lower layer is the operation optimization layer, which aims to maximize the grid support benefits and the renewable energy consumption rate. The constraints include energy storage SOC (20%~80%), charging and discharging power limits, and primary frequency regulation response time (≤100ms). The planning and analysis subsystem solves the optimization problem through a multi-algorithm fusion strategy, integrating the improved particle swarm optimization (PSO) algorithm, the empire competition algorithm, and the genetic algorithm + sequential quadratic programming algorithm. It adopts a GPU cluster and a distributed computing architecture to achieve parallel solution in multiple scenarios, while avoiding single-node overload through a load balancing mechanism.

4. The grid-type energy storage power station planning system according to claim 1, characterized in that, The equipment configuration includes a core equipment parameter matching module and an energy storage medium selection module. The energy storage medium selection module supports the collaborative planning of lithium iron phosphate batteries, semi-solid-state batteries, vanadium redox flow batteries, and flywheel energy storage. Based on the scenario requirements and grid support requirements output by the planning and analysis subsystem, the system automatically calculates the capacity ratio and access method of different media to adapt to differentiated requirements such as short-term frequency regulation, high-power support, long-term peak shaving, and millisecond-level inertia support. The core equipment parameter matching module determines key parameters such as the power of the energy storage converter (PCS), the step-up transformer capacity, and the battery pack capacity according to the multi-media combination scheme to ensure that the equipment performance is adapted to the grid control requirements. The upper layer of the dual-layer optimization core module is the site selection and capacity determination layer, which aims to minimize the total life cycle cost (investment + operation and maintenance + replacement + carbon cost). The constraints include short-circuit ratio (SCR ≥ 1.5~2.0), inertia support (≥ 80% of equivalent synchronous machine inertia), capacity boundary and land compliance. The lower layer is the operation optimization layer, which aims to maximize the grid support benefits and maximize the renewable energy absorption rate. The constraints include SOC (20%~80%), charging and discharging power limits and primary frequency regulation response time (≤ 100ms).

5. The grid-type energy storage power station planning system according to claim 1, characterized in that, The network control module of the control strategy subsystem adopts an improved virtual synchronous machine (VSG) control architecture, which introduces an adaptive damping coefficient based on the rate of change of frequency (df / dt) and a dynamic droop coefficient based on the grid connection point voltage (V_PCC) to improve transient stability under strong disturbances. The operating condition adaptation module formulates the control logic for seamless grid-connected / islanded switching, fault ride-through, and black start under all operating conditions, and verifies the adaptability to extreme operating conditions with the digital twin dynamic simulation function; the black start capability assessment function simulates a completely dark grid scenario, calculates the ability of the energy storage power station to provide starting power for gas turbine units and coal-fired units, evaluates the starting power, starting time and voltage / frequency stability, and outputs a black start feasibility solution.

6. The grid-type energy storage power station planning system according to claim 1, characterized in that, The solution output and evaluation subsystem includes a technical evaluation module and an economic evaluation module. The technical evaluation unit conducts dynamic simulation verification of normal and extreme working conditions based on a millimeter-level three-dimensional digital twin constructed by BIM+GIS. The economic assessment module calculates the total life cycle cost, benefits, and investment payback period, and the sensitivity analysis module quantifies the impact of equipment prices, new energy penetration rates, and electricity price adjustments on the project's profitability. The multi-algorithm fusion solution module integrates improved particle swarm optimization (PSO), imperial competition algorithm, and genetic algorithm + sequential quadratic programming algorithm, and adaptively selects the solution algorithm for different planning problems.

7. A grid-type energy storage power station planning system according to claim 6, characterized in that, The digital twin achieves synchronous linkage between the physical entity and the virtual model through a dynamic parameter mapping module, with a synchronization delay of ≤10ms.

8. The grid-type energy storage power station planning system according to claim 7, characterized in that, The solution output and evaluation subsystem includes technical evaluation, economic evaluation and engineering implementation functional modules. Based on the planning results, it generates 3-5 differentiated planning solutions, covering different energy storage medium ratios, access nodes and capacity configurations. The project implementation module transforms the optimal solution into standardized planning reports, engineering construction drawings, equipment procurement lists, and operation and maintenance manuals, supporting export in Word / PDF format and directly connecting with the engineering design and construction phases.

9. A planning method for a grid-type energy storage power station, characterized in that, The system implementation based on claim 1 includes the following steps: S1: Data acquisition and preprocessing, using the 3σ criterion algorithm to remove outlier data and linear interpolation to complete missing data, outputting a standardized dataset; S2: Power grid demand analysis and optimization planning, calculate the short-circuit ratio and inertia level of the grid connection point to construct constraints, use the improved particle swarm algorithm to solve the two-level optimization model, and output the basic planning scheme; S3: Equipment configuration and control strategy design, using the entropy weight-TOPSIS method to determine the energy storage medium ratio and core equipment parameters, build an improved virtual synchronous machine control model and formulate full-condition control logic; S4: Scheme evaluation and closed-loop optimization. The root mean square error and mean absolute percentage error are used to verify the simulation accuracy, calculate the cost per kilowatt-hour and investment payback period. If the simulation fails to meet the standards, the parameters are adjusted in reverse and S2-S3 are repeated. S5: Output engineering implementation documents and complete the entire process planning of grid-type energy storage power stations.

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