A source side and network side energy storage collaborative planning method and device, electronic equipment and storage medium

Abstract

This invention discloses a method, device, electronic equipment, and storage medium for collaborative planning of source-side and grid-side energy storage, belonging to the field of power system energy storage planning technology. The method includes: acquiring dynamic prediction data of the target power grid system and performing cluster analysis to generate a set of operating scenarios including typical and extreme scenarios; constructing and solving a peak-shaving upper-layer energy storage planning model based on a first time resolution with the goal of minimizing comprehensive economic cost, to obtain the preliminary energy storage configuration capacity of the source-side and grid-side; using the preliminary energy storage configuration capacity as the operating boundary, constructing and solving a frequency regulation lower-layer energy storage planning model at a higher time resolution, ultimately obtaining a collaborative planning strategy for source-side and grid-side energy storage, achieving hierarchical coordination and optimization. By implementing this invention, the problem of energy storage capacity mismatch caused by computational dimensionality disasters or perceptual distortion due to single-time-scale modeling in existing technologies, when considering multiple system regulation needs, can be solved.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage planning technology, specifically to a method, device, electronic equipment, and storage medium for coordinated planning of source-side and grid-side energy storage. Background Technology

[0002] With the large-scale integration of a high proportion of renewable energy sources, the power system exhibits significant fluctuations and uncertainties on both the source and load sides. Coordinating the planning of energy storage on both the source and grid sides to achieve efficient reuse of energy storage resources across the entire system has extremely important engineering application value for improving system power supply reliability and promoting the consumption of renewable energy.

[0003] However, current source-side and grid-side energy storage collaborative planning technologies face severe time-scale coupling conflicts when considering multiple system regulation needs. The root cause of this problem lies in the fact that the grid's peak-shaving demand focuses on macroscopic energy transfer over a longer timescale, while frequency regulation demand requires rapid power response within an extremely short timescale. Existing planning methods typically rely on a single time resolution for global modeling: using a coarse time resolution directly smooths out high-frequency dynamic power fluctuations, preventing the model from accurately sensing the system's frequency regulation gap; using a fine, high time resolution globally leads to a severe curse of dimensionality when dealing with annual forecast data, making the model unsolvable. This one-way compromise on time scales prevents existing models from effectively coordinating regulation needs at different time granularities, resulting in calculated configuration capacities that easily deviate from actual complex operating conditions, leading to severe capacity mismatches in energy storage systems when facing real-world operating environments. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for coordinated planning of energy storage on the source side and the grid side. It can solve the problem of energy storage capacity mismatch caused by computational dimension disaster or perception distortion due to single time scale modeling when taking into account multiple system adjustment needs.

[0005] An embodiment of the present invention provides a source-side and grid-side energy storage collaborative planning method, comprising: Acquire dynamic forecast data of the target power grid system; Clustering calculations are performed based on dynamic prediction data to generate a set of simulated running scenarios that include typical and extreme scenarios; Based on the simulated operation scenario set and the preset first time resolution, with the optimization objective of minimizing the overall economic cost of the system, an upper-level energy storage planning model oriented towards the system's peak-shaving needs is constructed; the upper-level energy storage planning model is solved to generate the preliminary energy storage configuration capacity on the source side and the grid side. The initial energy storage configuration capacity is set as the operating boundary parameter, and a lower-level energy storage planning model oriented towards system frequency regulation needs is constructed based on the operating boundary parameter and the preset second time resolution; wherein, the second time resolution is smaller than the first time resolution; the lower-level energy storage planning model is solved to generate a collaborative energy storage planning strategy between the source side and the grid side.

[0006] Furthermore, dynamic forecast data of the target power grid system is obtained, including: Acquire renewable energy output forecast data and system load forecast data for the target power grid system; among which, renewable energy output forecast data includes wind farm output forecast data and photovoltaic power plant output forecast data. The forecast data of new energy power output and the forecast data of system load are used as the dynamic forecast data of the target power grid system.

[0007] Furthermore, clustering operations are performed based on the dynamic prediction data to generate a set of simulated operating scenarios that include both typical and extreme scenarios, including: A preset spatial clustering algorithm is used to identify noise points in the dynamic prediction data and extract the noise point dataset. The remaining data obtained after removing noisy points from the dynamic prediction data will be used as the regular dataset. Cluster the noise point dataset to generate extreme scenarios; Clustering operations are performed on regular datasets to generate typical scenarios; Extreme scenarios are combined with typical scenarios to generate a set of simulated running scenarios.

[0008] Furthermore, a pre-defined spatial clustering algorithm is used to identify noise points in the dynamic prediction data, and a noise point dataset is extracted, including: Obtain the neighborhood radius parameter and the minimum sample density threshold; For each data point in the dynamic prediction data, the search neighborhood corresponding to the current data point is determined based on the neighborhood radius parameter, and the actual number of samples in the search neighborhood is counted. After completing the sample statistics operation for all data points in the dynamic prediction data, the data points whose actual sample number is less than the minimum sample density threshold are marked as target noise points. All target noise points are aggregated to generate a noise point dataset.

[0009] Furthermore, based on the simulated operation scenario set and the preset first time resolution, and with the optimization objective of minimizing the overall economic cost of the system, an upper-level energy storage planning model oriented towards the system's peak-shaving needs is constructed, including: Obtain the peak-shaving cost and peak-shaving constraints of the target power grid system; wherein, the peak-shaving cost includes system operating cost, energy storage planning investment cost, new energy curtailment penalty cost, and load shedding penalty cost; the peak-shaving constraints include power balance constraints, thermal power unit operating constraints, hydropower unit operating constraints, new energy power plant operating constraints, energy storage operating constraints, and system reserve constraints. The system's comprehensive economic cost is generated by summing up each cost in the peak-shaving cost. Based on peak-shaving constraints, simulated operation scenarios, and a preset first time resolution, an upper-level energy storage planning model is constructed with the goal of minimizing the overall economic cost of the system.

[0010] Furthermore, the initial energy storage configuration capacity is set as the operating boundary parameter, and based on the operating boundary parameter and the preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation requirements is constructed, including: Obtain the frequency regulation cost and frequency regulation constraints of the target power grid system; wherein, the frequency regulation cost includes the energy storage frequency regulation operation cost and the system frequency regulation penalty cost; the frequency regulation constraints include system frequency deviation constraints, frequency regulation power response constraints, and energy storage dynamic state of charge constraints; The initial energy storage configuration capacity is converted into an upper limit constraint on energy storage capacity, and the upper limit constraint on energy storage capacity is used as the operating boundary parameter. The overall frequency modulation cost of the system is generated by summing up each cost in the frequency modulation cost. Based on frequency regulation constraints, operating boundary parameters, and a preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation needs is constructed with the goal of minimizing the overall system frequency regulation cost.

[0011] Furthermore, after solving the lower-level energy storage planning model and generating energy storage collaborative planning strategies for the source and grid sides, the process also includes: Obtain multiple operational scenarios to be verified for the target power grid system, as well as preset operational safety evaluation indicators; The energy storage collaborative planning strategy is substituted into the multiple operating scenarios to be verified to perform power flow calculations and obtain the actual operating status data of the system under each operating scenario to be verified. Calculate the operational safety verification result of the target power grid system based on the actual operating status data of the system. When the operational safety verification results meet the operational safety evaluation indicators, the energy storage collaborative planning strategy will be determined as the final energy storage construction scheme for the target power grid system. When the operational safety verification result fails to meet the operational safety evaluation indicators, the energy storage collaborative planning strategy is marked as a high-risk strategy, and the system early warning information corresponding to the high-risk strategy is output.

[0012] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0013] One embodiment of the present invention provides a source-side and grid-side energy storage collaborative planning device, including: a data acquisition module, a scenario clustering module, an upper-layer peak-shaving planning module, and a lower-layer frequency regulation planning module; The data acquisition module is used to acquire dynamic prediction data of the target power grid system; The scenario clustering module is used to perform clustering calculation operations based on dynamic prediction data to generate a set of simulated running scenarios that include typical scenarios and extreme scenarios. The upper-level peak-shaving planning module is used to construct an upper-level energy storage planning model for system peak-shaving needs based on the simulated operation scenario set and the preset first time resolution, with the optimization objective of minimizing the overall economic cost of the system; and to solve the upper-level energy storage planning model to generate the preliminary energy storage configuration capacity on the source side and the grid side. The lower-level frequency regulation planning module is used to set the initial energy storage configuration capacity as the operating boundary parameter, and to construct a lower-level energy storage planning model oriented towards the system frequency regulation requirements based on the operating boundary parameter and the preset second time resolution; wherein, the second time resolution is smaller than the first time resolution; and to solve the lower-level energy storage planning model to generate a collaborative energy storage planning strategy between the source side and the grid side.

[0014] Based on the above method embodiments, the present invention provides corresponding electronic device embodiments.

[0015] An embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements any one of the source-side and grid-side energy storage collaborative planning methods described in the above-described method embodiments.

[0016] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0017] One embodiment of the present invention provides a storage medium storing a computer program thereon, wherein, when the computer program is running, it controls the device where the storage medium is located to execute any one of the source-side and grid-side energy storage collaborative planning methods described in the above-described method embodiments.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method, apparatus, electronic device, and storage medium for source-side and grid-side energy storage collaborative planning. The method acquires dynamic prediction data of the target power grid system and performs cluster analysis based on the dynamic prediction data to generate a set of simulated operating scenarios including typical and extreme operating scenarios. Based on the simulated operating scenario set and a preset first time resolution, an upper-level energy storage planning model oriented towards system peak-shaving needs is constructed with the optimization objective of minimizing the overall economic cost of the system. The upper-level energy storage planning model is solved to generate preliminary energy storage configuration capacities for the source and grid sides. Using the preliminary energy storage configuration capacity as operating boundary parameters, a lower-level energy storage planning model oriented towards system frequency regulation needs is constructed in conjunction with a preset second time resolution, where the second time resolution is smaller than the first time resolution. The lower-level energy storage planning model is solved to generate a source-side and grid-side energy storage collaborative planning strategy, achieving hierarchical collaborative optimization of peak shaving and frequency regulation.

[0019] This invention constructs a two-layer solution architecture with progressive time scales. The upper layer solves for the initial peak-shaving capacity at a larger first time resolution and passes the operational boundary parameters downwards. The lower layer solves for the final frequency regulation planning strategy at a smaller second time resolution. This mechanism successfully separates and logically coordinates the regulation requirements at different time granularities, avoiding the computational dimensionality curse caused by using a fine resolution globally, and compensating for the deficiency of a single coarse resolution in detecting rapid power fluctuations. It solves the problem of energy storage capacity mismatch caused by unilateral compromise of time scales. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a source-side and grid-side energy storage collaborative planning method provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of a source-side and grid-side energy storage collaborative planning device provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, to address the problem of energy storage capacity mismatch caused by computational dimensionality disasters or perception distortions due to single-time-scale modeling in existing technologies when considering multiple system adjustment needs, an embodiment of the present invention provides a source-side and grid-side energy storage collaborative planning method, which includes at least the following steps: Step S1: Obtain dynamic prediction data of the target power grid system; In a preferred embodiment, acquiring dynamic prediction data of the target power grid system includes: Acquire renewable energy output forecast data and system load forecast data for the target power grid system; among which, renewable energy output forecast data includes wind farm output forecast data and photovoltaic power plant output forecast data. The forecast data of new energy power output and the forecast data of system load are used as the dynamic forecast data of the target power grid system.

[0024] Specifically, this involves acquiring dynamic forecast data for the target power grid system. The target power grid system possesses dual physical attributes: power supply from the source side and power consumption from the grid side. With the large-scale grid connection of a high proportion of renewable energy equipment, the target power grid system exhibits significant fluctuations in power output on the source side and uncertainties in power consumption on the load side. To accurately perceive the energy supply and demand evolution of the target power grid system over its future operating cycle, it is essential to collect baseline information reflecting dynamic energy fluctuations in advance.

[0025] In a preferred embodiment, acquiring dynamic forecast data of the target power grid system includes: acquiring renewable energy output forecast data and system load forecast data of the target power grid system; wherein, renewable energy output forecast data includes wind farm output forecast data and photovoltaic power station output forecast data; and using the renewable energy output forecast data and system load forecast data as dynamic forecast data of the target power grid system.

[0026] In detail, renewable energy output forecast data is mainly used to characterize the renewable energy generation potential and actual output status on the source side. Wind farm output forecast data reflects the estimated active power output of wind turbine generators affected by changes in external meteorological wind speed at the corresponding forecast time point. Photovoltaic power plant output forecast data reflects the estimated active power generated by photovoltaic arrays affected by solar irradiance conditions. Meanwhile, system load forecast data represents the estimated scale of overall electricity demand on the grid side at the corresponding forecast time point. By summarizing and integrating wind farm output forecast data, photovoltaic power plant output forecast data, and system load forecast data, a comprehensive dataset containing the power supply status on the source side and the electricity consumption trend on the grid side can be constructed. This comprehensive dataset is then established as the dynamic forecast data for the target power grid system. In addition to dynamic forecast data, network topology information of the target power grid system, basic operating parameters of various conventional generator units, and reserve capacity demand indicators of the target power grid system are also acquired simultaneously, providing complete physical operational constraint boundary support for subsequent model building steps.

[0027] Performing the above data acquisition operations can comprehensively and accurately quantify the random characteristics of power generation on the source side and the characteristics of power consumption changes on the grid side of the target power grid system. This provides an absolutely reliable underlying data foundation for subsequent multi-timescale scenario simulation calculations and collaborative optimization of energy storage capacity, thereby effectively preventing the hidden risks of the final energy storage planning strategy deviating from the actual physical conditions caused by missing input information.

[0028] Step S2: Perform clustering calculations based on the dynamic prediction data to generate a set of simulated running scenarios that include typical and extreme scenarios; In a preferred embodiment, clustering calculations are performed based on dynamic prediction data to generate a set of simulated running scenarios that includes typical and extreme scenarios, including: A preset spatial clustering algorithm is used to identify noise points in the dynamic prediction data and extract the noise point dataset. The remaining data obtained after removing noisy points from the dynamic prediction data will be used as the regular dataset. Cluster the noise point dataset to generate extreme scenarios; Clustering operations are performed on regular datasets to generate typical scenarios; Extreme scenarios are combined with typical scenarios to generate a set of simulated running scenarios.

[0029] In a preferred embodiment, a preset spatial clustering algorithm is used to identify noise points in the dynamic prediction data and extract a noise point dataset, including: Obtain the neighborhood radius parameter and the minimum sample density threshold; For each data point in the dynamic prediction data, the search neighborhood corresponding to the current data point is determined based on the neighborhood radius parameter, and the actual number of samples in the search neighborhood is counted. After completing the sample statistics operation for all data points in the dynamic prediction data, the data points whose actual sample number is less than the minimum sample density threshold are marked as target noise points. All target noise points are aggregated to generate a noise point dataset.

[0030] Specifically, clustering operations are performed based on dynamic forecast data to generate a set of simulated operating scenarios, including typical and extreme scenarios. Directly performing simulation optimization calculations on the year-round dynamic forecast data faces a computational bottleneck due to the excessive data scale, making the model difficult to solve. Furthermore, considering the severe impact of frequent extreme weather events on the power supply and demand balance of the target power grid system, it is essential to accurately extract representative typical operating conditions and extreme abnormal operating conditions from massive historical forecast sequences. By using clustering operations to obtain the probability distribution characteristics corresponding to typical and extreme scenarios, a set of simulated operating scenarios that highly replicates the actual operation of the target power grid system is constructed.

[0031] In a preferred embodiment, clustering calculations are performed on the dynamic prediction data to generate a set of simulated running scenarios that includes typical and extreme scenarios. This includes: using a preset spatial clustering algorithm to identify noise points in the dynamic prediction data and extracting a noise point dataset; using the remaining data obtained after removing the noise point dataset from the dynamic prediction data as a regular dataset; performing clustering operations on the noise point dataset to generate extreme scenarios; performing clustering operations on the regular dataset to generate typical scenarios; and merging the extreme scenarios and typical scenarios to generate a set of simulated running scenarios.

[0032] Spatial clustering algorithms can adaptively discover cluster structures of arbitrary shapes and identify outliers based on the density differences of data points distributed in multidimensional space. The outliers identified by spatial clustering algorithms physically correspond to the abrupt operational characteristics of the target power grid system under extreme weather conditions. These outliers are extracted to form a noise point dataset. The continuous and dense feature data points remaining after removing these outliers from the dynamic prediction data represent the normal operational fluctuation patterns of the target power grid system, constituting a regular dataset. By performing clustering operations on the noise point dataset, a small number of extreme scenarios representing severe boundary conditions are extracted. Clustering algorithms are then used to cluster the regular dataset, aggregating typical scenarios that reflect the main operational modes of the target power grid system. Effectively combining the extreme scenarios with the typical scenarios creates a set of simulated operational scenarios that balances high-probability normal fluctuations with low-probability extreme shocks.

[0033] In a preferred embodiment, a preset spatial clustering algorithm is used to identify noise points in the dynamic prediction data and extract a noise point dataset, including: obtaining a neighborhood radius parameter and a minimum sample density threshold; for each data point in the dynamic prediction data, determining the search neighborhood corresponding to the current data point based on the neighborhood radius parameter, and counting the actual number of samples within the search neighborhood; after completing the sample counting operation for all data points in the dynamic prediction data, marking data points whose actual sample number is less than the minimum sample density threshold as target noise points; and aggregating all target noise points to generate a noise point dataset.

[0034] To quantify the data density around each data point, a neighborhood radius parameter is pre-defined to delineate the multidimensional feature space search range centered on the current test data point. A minimum sample density threshold is used to define the minimum number of samples necessary to form an effective data cluster. Each independent data point within the dynamic prediction data is traversed, and the total number of surrounding related data points within the preset search range is calculated. After the distribution density analysis of all data points is completed, isolated data points whose total number of surrounding related data points does not reach the lower limit of the minimum sample density threshold are identified one by one and judged as target noise points representing abnormal operating conditions. Finally, all identified target noise points are aggregated to construct a dedicated noise point dataset.

[0035] Performing the above clustering calculation and scene extraction operations can effectively eliminate redundant time series information and greatly reduce the computational dimensionality of the subsequent collaborative planning model. At the same time, it ensures that the generated scene set accurately retains the key physical operating boundary characteristics of the target power grid system in response to normal fluctuations and extreme disturbances.

[0036] Step S3: Based on the simulated operation scenario set and the preset first time resolution, construct an upper-level energy storage planning model for system peak-shaving needs with the optimization objective of minimizing the overall economic cost of the system; solve the upper-level energy storage planning model to generate the preliminary energy storage configuration capacity on the source side and the grid side. In a preferred embodiment, based on a set of simulated operating scenarios and a preset first time resolution, and with the goal of minimizing the overall economic cost of the system, an upper-level energy storage planning model oriented towards the system's peak-shaving needs is constructed, including: Obtain the peak-shaving cost and peak-shaving constraints of the target power grid system; wherein, the peak-shaving cost includes system operating cost, energy storage planning investment cost, new energy curtailment penalty cost, and load shedding penalty cost; the peak-shaving constraints include power balance constraints, thermal power unit operating constraints, hydropower unit operating constraints, new energy power plant operating constraints, energy storage operating constraints, and system reserve constraints. The system's comprehensive economic cost is generated by summing up each cost in the peak-shaving cost. Based on peak-shaving constraints, simulated operation scenarios, and a preset first time resolution, an upper-level energy storage planning model is constructed with the goal of minimizing the overall economic cost of the system.

[0037] Specifically, based on the simulated operation scenario set and the preset first time resolution, an upper-level energy storage planning model oriented towards the system's peak-shaving needs is constructed with the optimization objective of minimizing the overall economic cost of the system. The upper-level energy storage planning model is then solved to generate preliminary energy storage configuration capacities on the source and grid sides. In actual operation, the peak-shaving demand of the target power grid system mainly manifests as addressing the source-load energy mismatch problem over a relatively long timescale. To achieve spatiotemporal energy shifting at the macroscopic level of the entire grid, a reference time step size reflecting the large-scale energy transfer characteristics must be established. The preset first time resolution represents an hourly time granularity, discretizing the entire day's scheduling simulation cycle into multiple continuous macroscopic time periods, thereby filtering out high-frequency, small power disturbances and focusing on the long-cycle load peak-valley difference filling task.

[0038] In a preferred embodiment, based on a set of simulated operating scenarios and a preset first time resolution, an upper-level energy storage planning model oriented towards system peak-shaving needs is constructed with the goal of minimizing the overall economic cost of the system. This includes: obtaining the peak-shaving cost and peak-shaving constraints of the target power grid system; wherein the peak-shaving cost includes system operating cost, energy storage planning investment cost, renewable energy curtailment penalty cost, and load shedding penalty cost; and the peak-shaving constraints include power balance constraints, thermal power unit operating constraints, hydropower unit operating constraints, renewable energy power plant operating constraints, energy storage operating constraints, and system reserve constraints.

[0039] The overall economic cost of the system is generated by summing up each cost in the peak-shaving cost. Specifically, the objective function of the upper-level energy storage planning model oriented towards the system's peak-shaving demand is constructed as follows: In the formula, For the overall economic cost of the system; For system operating costs; Planning investment costs for energy storage; The cost of punitive measures for abandoning new energy sources; Penalty cost for loss of load; This is the set of runtime segments for the upper-level planning model; A collection of thermal power units; Cost of a single start-up and shutdown of a thermal power unit; These are the start-up and shutdown status variables of the thermal power unit during the corresponding time period; The unit operating cost of thermal power units; This represents the output power of the thermal power unit during the corresponding time period; To plan for a multi-functional energy storage system; The discount rate for funds; The design service life of energy storage equipment; The unit capacity investment cost of energy storage equipment; For the construction capacity of energy storage equipment; The penalty cost for abandoning new energy sources per unit; A collection of photovoltaic power plants; This represents the amount of solar power curtailed by the photovoltaic power plant during the corresponding time period. For wind farms; This represents the wind curtailment power of the wind farm during the corresponding time period; The cost of penalty per unit of load loss; This represents the power loss during the corresponding time period.

[0040] At the physical constraint level, the power balance constraint ensures real-time energy supply and demand parity across the entire network, and the formula is as follows: In the formula, The actual grid-connected power of a photovoltaic power plant after taking into account the charging and discharging of its internal energy storage; The actual grid-connected power after the wind farm's metering and internal energy storage charging and discharging processes; A collection of hydroelectric power units; This represents the output power of the hydropower unit during the corresponding time period; The target power grid system has an existing energy storage collection; The discharge power of energy storage during the corresponding time period; The charging power of energy storage during the corresponding time period; This represents the original load demand of the target power grid system during the corresponding time period.

[0041] The operating constraints of thermal power units and hydropower units define the ultimate power output boundaries and cross-time ramp rate limits of conventional generator units, and the formulas are as follows: In the formula, This is the upper limit of the output of thermal power units; and These are the downward ramp coefficient and upward ramp coefficient of thermal power units based on the first time resolution, respectively. This is the upper limit of the hydropower unit's output. and These are the downward ramp coefficient and upward ramp coefficient of the hydropower unit based on the first time resolution, respectively.

[0042] The operational constraints of renewable energy power plants take into account the effect of source-side energy storage configuration on smoothing the actual grid connection curve of renewable energy, and the formula is as follows: In the formula, This represents the maximum available grid-connected power of the photovoltaic power plant. This represents the maximum available grid-connected power of the wind farm. To provide power to the pure photovoltaic power generation equipment of the photovoltaic power station during the corresponding time period; To provide power to the wind farm's pure wind power generation equipment during the corresponding time period; and These refer to the discharge power and charging power of the source-side energy storage configured inside the photovoltaic power station. and These refer to the discharge power and charging power of the source-side energy storage configured inside the wind farm.

[0043] The constraints for energy storage operation define the temporal continuity attributes and safety physical limits of the charging and discharging behavior of energy storage devices, and are constructed using the following formula: In the formula, and These are Boolean variables representing the energy storage discharge state and charging state, respectively; The maximum charge and discharge power limit for energy storage devices; The amount of electricity stored for energy storage during the corresponding time period; The energy conversion efficiency of energy storage devices during charging and discharging; and These are the lower and upper limits of the storage capacity of energy storage devices, respectively. This refers to the amount of electricity stored by the energy storage device at the beginning of the planning period. This refers to the amount of electricity stored by the energy storage device at the end of the planned cycle.

[0044] The system reserve constraint aims to reserve sufficient uplink and downlink regulation margins for the target power grid system to resist random shocks caused by prediction errors. The formula is as follows: In the formula, The reserve requirement ratio coefficient preset for the target power grid system; The reserve requirement ratio coefficient preset for the target power grid system; This represents the lower limit of the output of thermal power units. This represents the lower limit of the output of the hydropower unit.

[0045] Based on peak-shaving constraints, simulated operation scenarios, and a preset first time resolution, an upper-level energy storage planning model is constructed with the goal of minimizing the overall economic cost of the system. The extracted objective function and the set of equality and inequality constraints are integrated and embedded with a time-representative set of simulated operation scenario data to assemble a rigorous upper-level optimization model that characterizes the large-scale energy transfer patterns of the power grid. Subsequently, a preset mathematical solution tool is used to perform global optimization iterative calculations on the upper-level energy storage planning model, outputting the values ​​of each decision variable that satisfy the optimal economic indicators, and extracting the preliminary energy storage configuration capacity to be deployed at source-side nodes and grid-side nodes.

[0046] By implementing the above-mentioned upper-level planning model construction and solution steps, the economic costs of coordinating the power generation scheduling of conventional units across the entire network and the spatiotemporal transfer of energy from energy storage devices can be balanced under a macro-scale long-term scheduling framework. This provides the target power grid system with a benchmark capacity allocation architecture that meets basic peak-shaving tasks and has the lowest infrastructure costs, thereby completely avoiding the waste of funds and resources caused by excessive and blind investment in energy storage.

[0047] Step S4: Set the initial energy storage configuration capacity as the operating boundary parameter, and construct a lower-level energy storage planning model oriented towards system frequency regulation requirements based on the operating boundary parameter and the preset second time resolution; wherein, the second time resolution is smaller than the first time resolution; solve the lower-level energy storage planning model to generate a collaborative energy storage planning strategy between the source side and the grid side.

[0048] In a preferred embodiment, the initial energy storage configuration capacity is set as the operating boundary parameter, and based on the operating boundary parameter and a preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation requirements is constructed, including: Obtain the frequency regulation cost and frequency regulation constraints of the target power grid system; wherein, the frequency regulation cost includes the energy storage frequency regulation operation cost and the system frequency regulation penalty cost; the frequency regulation constraints include system frequency deviation constraints, frequency regulation power response constraints, and energy storage dynamic state of charge constraints; The initial energy storage configuration capacity is converted into an upper limit constraint on energy storage capacity, and the upper limit constraint on energy storage capacity is used as the operating boundary parameter. The overall frequency modulation cost of the system is generated by summing up each cost in the frequency modulation cost. Based on frequency regulation constraints, operating boundary parameters, and a preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation needs is constructed with the goal of minimizing the overall system frequency regulation cost.

[0049] In a preferred embodiment, after solving the lower-level energy storage planning model to generate a collaborative energy storage planning strategy between the source side and the grid side, the method further includes: Obtain multiple operational scenarios to be verified for the target power grid system, as well as preset operational safety evaluation indicators; The energy storage collaborative planning strategy is substituted into the multiple operating scenarios to be verified to perform power flow calculations and obtain the actual operating status data of the system under each operating scenario to be verified. Calculate the operational safety verification result of the target power grid system based on the actual operating status data of the system. When the operational safety verification results meet the operational safety evaluation indicators, the energy storage collaborative planning strategy will be determined as the final energy storage construction scheme for the target power grid system. When the operational safety verification result fails to meet the operational safety evaluation indicators, the energy storage collaborative planning strategy is marked as a high-risk strategy, and the system early warning information corresponding to the high-risk strategy is output.

[0050] Specifically, the initial energy storage configuration capacity is set as the operating boundary parameter. Based on the operating boundary parameter and a preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation needs is constructed; the second time resolution is smaller than the first time resolution. The lower-level energy storage planning model is solved to generate a collaborative energy storage planning strategy for the source side and the grid side. To compensate for the technical deficiency that the macroscopic time scale cannot perceive rapid power fluctuations, a second time resolution with finer granularity is introduced for modeling. The second time resolution is usually set at the minute level, specifically used to capture instantaneous regulation gaps caused by sudden changes in renewable energy output on the source side and drastic load fluctuations on the grid side within the target power grid system. After clarifying the initial energy storage configuration capacity at the macroscopic level, the obtained initial capacity value is directly converted into a rigid capacity capping boundary for lower-level frequency regulation optimization.

[0051] In a preferred embodiment, the initial energy storage configuration capacity is set as the operating boundary parameter, and a lower-level energy storage planning model oriented towards system frequency regulation requirements is constructed based on the operating boundary parameter and a preset second time resolution. This includes: obtaining the frequency regulation cost and frequency regulation constraints of the target power grid system; wherein the frequency regulation cost includes the energy storage frequency regulation operation cost and the system frequency regulation penalty cost; the frequency regulation constraints include system frequency deviation constraints, frequency regulation power response constraints, and energy storage dynamic state of charge constraints; converting the initial energy storage configuration capacity into an upper limit constraint on energy storage capacity, and using the upper limit constraint on energy storage capacity as the operating boundary parameter; and summing each cost in the frequency regulation cost to generate the comprehensive system frequency regulation cost.

[0052] Specifically, the objective function and capacity boundary constraints for system frequency regulation requirements are constructed as follows: In the formula, For the overall frequency modulation cost of the system; For the operating cost of energy storage frequency regulation; The system frequency adjustment penalty cost; The frequency regulation capacity allocated to the corresponding energy storage devices in the lower-level planning model.

[0053] At the physical constraint level, the frequency regulation power response constraint aims to ensure that the target power grid system has sufficient upward and downward transient power response capabilities to mitigate rapid changes in net load. First, the net load evolution of the target power grid system is calculated: Subsequently, frequency modulation power response constraints related to the up and down adjustment capabilities are constructed: In the formula, For discrete running time steps divided based on the second time resolution; The net load power of the target power grid system at the corresponding time step; The original load demand of the target power grid system at the corresponding time step; This represents the grid-connected power of the photovoltaic power plant at the corresponding time step; This represents the grid-connected power of the wind farm at the corresponding time step; A frequency regulation energy storage unit additionally configured to meet rapid adjustment needs; This is a Boolean variable representing the discharge state of the energy storage device at the corresponding time step. This represents the discharge power of the energy storage device at the corresponding time step. This represents the amount of electricity stored by the energy storage device at the corresponding time step. The upward ramp coefficient of thermal power units based on the second time resolution; This represents the output power of the thermal power unit at the corresponding time step. The upward gradient coefficient of the hydropower unit based on the second time resolution; This represents the output power of the hydropower unit at the corresponding time step; This is a Boolean variable representing the charging state of the energy storage device at the corresponding time step. This refers to the charging power of the energy storage device at the corresponding time step. The downward ramp coefficient for thermal power units based on the second time resolution; The downward ramp coefficient of the hydropower unit is based on the second time resolution.

[0054] Meanwhile, the system frequency deviation constraint is responsible for limiting the frequency deviation of the target power grid system after power disturbances to within a pre-set safe allowable range, preventing frequency collapse accidents. The energy storage dynamic state of charge constraint follows the same physical deduction rules as the upper-level planning model, continuing to constrain the energy conservation and capacity limits of the energy storage devices at the second time resolution to avoid equipment damage. Based on the frequency regulation constraint, operating boundary parameters, and the preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation needs is constructed with the optimization objective of minimizing the overall system frequency regulation cost. By combining the above objective functions and constraint groups, a global optimization iteration is carried out to obtain a source-side and grid-side energy storage collaborative planning strategy accurate to the minute level.

[0055] In a preferred embodiment, after solving the lower-level energy storage planning model to generate energy storage collaborative planning strategies for the source and grid sides, the method further includes: acquiring multiple operational scenarios to be verified for the target power grid system and preset operational safety evaluation indicators; substituting the energy storage collaborative planning strategy into the multiple operational scenarios to be verified for power flow calculation, and acquiring the actual operating status data of the system under each operational scenario to be verified; calculating the operational safety verification result of the target power grid system based on the actual operating status data of the system; when the operational safety verification result meets the operational safety evaluation indicators, determining the energy storage collaborative planning strategy as the final energy storage construction scheme for the target power grid system; when the operational safety verification result does not meet the operational safety evaluation indicators, marking the energy storage collaborative planning strategy as a high-risk strategy, and outputting system early warning information corresponding to the high-risk strategy.

[0056] Obtaining the theoretically optimal solution does not directly equate to absolute reliability at the engineering level. To demonstrate the feasibility of the energy storage planning and configuration scheme obtained from the solution in a real physical architecture, a closed-loop post-safety verification operation must be carried out. The operating scenarios to be verified cover extreme and severe conditions such as multiple bus faults and short circuits, as well as sudden disconnections of transmission lines. The output energy storage collaborative planning strategy is distributed and applied to the underlying network topology nodes involved in the operating scenarios to perform full-element power flow calculations. The power flow calculations can accurately output actual system operating status data, including node voltage amplitude and phase angle, as well as the active and reactive power flow distribution of the lines. The obtained actual system operating status data is rigorously compared with preset operating safety evaluation indicators. If the actual system operating status data are all within the safety red lines defined by the evaluation indicators, the obtained strategy is confirmed to have engineering practical value and is directly approved as the final energy storage construction scheme. Conversely, if any limit exceedance occurs, it indicates a potential local energy overload, and the output process must be immediately stopped, the corresponding strategy is labeled as high-risk, and an operation and maintenance alarm is triggered.

[0057] By performing the above-mentioned two-layer time scale decomposition and the one-way closed-loop verification operation of the post-safety defense line, the dilemma of optimization dimension disaster and the separation of adjustment needs caused by traditional single time granularity modeling has been completely reversed. It not only ensures the economic benefits of long-cycle energy transfer, but also takes into account the frequency stability of short-cycle transient power fluctuations, and finally outputs a set of highly adaptable energy storage construction drawings that fully meet the harsh physical conditions of the power grid.

[0058] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0059] like Figure 2 As shown, an embodiment of the present invention provides a source-side and grid-side energy storage collaborative planning device, including: a data acquisition module, a scenario clustering module, an upper-layer peak-shaving planning module, and a lower-layer frequency regulation planning module; The data acquisition module is used to acquire dynamic prediction data of the target power grid system; The scenario clustering module is used to perform clustering calculation operations based on dynamic prediction data to generate a set of simulated running scenarios that include typical scenarios and extreme scenarios. The upper-level peak-shaving planning module is used to construct an upper-level energy storage planning model for system peak-shaving needs based on the simulated operation scenario set and the preset first time resolution, with the optimization objective of minimizing the overall economic cost of the system; and to solve the upper-level energy storage planning model to generate the preliminary energy storage configuration capacity on the source side and the grid side. The lower-level frequency regulation planning module is used to set the initial energy storage configuration capacity as the operating boundary parameter, and to construct a lower-level energy storage planning model oriented towards the system frequency regulation requirements based on the operating boundary parameter and the preset second time resolution; wherein, the second time resolution is smaller than the first time resolution; and to solve the lower-level energy storage planning model to generate a collaborative energy storage planning strategy between the source side and the grid side.

[0060] In a preferred embodiment, the data acquisition module acquires dynamic prediction data of the target power grid system, including: Acquire renewable energy output forecast data and system load forecast data for the target power grid system; among which, renewable energy output forecast data includes wind farm output forecast data and photovoltaic power plant output forecast data. The forecast data of new energy power output and the forecast data of system load are used as the dynamic forecast data of the target power grid system.

[0061] In a preferred embodiment, the scene clustering module performs clustering calculations based on dynamic prediction data to generate a set of simulated running scenarios that includes typical and extreme scenarios, including: A preset spatial clustering algorithm is used to identify noise points in the dynamic prediction data and extract the noise point dataset. The remaining data obtained after removing noisy points from the dynamic prediction data will be used as the regular dataset. Cluster the noise point dataset to generate extreme scenarios; Clustering operations are performed on regular datasets to generate typical scenarios; Extreme scenarios are combined with typical scenarios to generate a set of simulated running scenarios.

[0062] In a preferred embodiment, the scene clustering module uses a preset spatial clustering algorithm to identify noise points in the dynamic prediction data and extract a noise point dataset, including: Obtain the neighborhood radius parameter and the minimum sample density threshold; For each data point in the dynamic prediction data, the search neighborhood corresponding to the current data point is determined based on the neighborhood radius parameter, and the actual number of samples in the search neighborhood is counted. After completing the sample statistics operation for all data points in the dynamic prediction data, the data points whose actual sample number is less than the minimum sample density threshold are marked as target noise points. All target noise points are aggregated to generate a noise point dataset.

[0063] In a preferred embodiment, the upper-level peak-shaving planning module, based on a set of simulated operating scenarios and a preset first time resolution, constructs an upper-level energy storage planning model oriented towards system peak-shaving needs, with the goal of minimizing the overall economic cost of the system. This model includes: Obtain the peak-shaving cost and peak-shaving constraints of the target power grid system; wherein, the peak-shaving cost includes system operating cost, energy storage planning investment cost, new energy curtailment penalty cost, and load shedding penalty cost; the peak-shaving constraints include power balance constraints, thermal power unit operating constraints, hydropower unit operating constraints, new energy power plant operating constraints, energy storage operating constraints, and system reserve constraints. The system's comprehensive economic cost is generated by summing up each cost in the peak-shaving cost. Based on peak-shaving constraints, simulated operation scenarios, and a preset first time resolution, an upper-level energy storage planning model is constructed with the goal of minimizing the overall economic cost of the system.

[0064] In a preferred embodiment, the lower-level frequency regulation planning module sets the initial energy storage configuration capacity as the operating boundary parameter, and constructs a lower-level energy storage planning model oriented towards system frequency regulation requirements based on the operating boundary parameter and a preset second time resolution, including: Obtain the frequency regulation cost and frequency regulation constraints of the target power grid system; wherein, the frequency regulation cost includes the energy storage frequency regulation operation cost and the system frequency regulation penalty cost; the frequency regulation constraints include system frequency deviation constraints, frequency regulation power response constraints, and energy storage dynamic state of charge constraints; The initial energy storage configuration capacity is converted into an upper limit constraint on energy storage capacity, and the upper limit constraint on energy storage capacity is used as the operating boundary parameter. The overall frequency modulation cost of the system is generated by summing up each cost in the frequency modulation cost. Based on frequency regulation constraints, operating boundary parameters, and a preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation needs is constructed with the goal of minimizing the overall system frequency regulation cost.

[0065] In a preferred embodiment, the source-side and grid-side energy storage collaborative planning device further includes: a safety verification module; The security verification module is used to acquire multiple operational scenarios to be verified for the target power grid system and preset operational security evaluation indicators. The energy storage collaborative planning strategy is substituted into the multiple operating scenarios to be verified to perform power flow calculations and obtain the actual operating status data of the system under each operating scenario to be verified. Calculate the operational safety verification result of the target power grid system based on the actual operating status data of the system. When the operational safety verification results meet the operational safety evaluation indicators, the energy storage collaborative planning strategy will be determined as the final energy storage construction scheme for the target power grid system. When the operational safety verification result fails to meet the operational safety evaluation indicators, the energy storage collaborative planning strategy is marked as a high-risk strategy, and the system early warning information corresponding to the high-risk strategy is output.

[0066] It should be noted that the embodiments of the device described above correspond to the embodiments of the present invention described above, and can realize the source-side and grid-side energy storage collaborative planning method of any one of the present invention described above. Furthermore, the embodiments of the device described above are merely illustrative. The modules described as separate components may or may not be physically separate, and 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 achieve the purpose of this embodiment according to actual needs. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort.

[0067] Based on the above-described method embodiments of the present invention, a corresponding embodiment of an electronic device is provided.

[0068] An embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the source-side and grid-side energy storage collaborative planning method according to any one of the present invention, or, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments.

[0069] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0070] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0071] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0072] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0073] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments; Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute any one of the above-described source-side and grid-side energy storage collaborative planning methods of the present invention.

[0074] The aforementioned storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0075] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0076] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for coordinated planning of energy storage on the source side and the grid side, characterized in that, include: Acquire dynamic forecast data of the target power grid system; Clustering calculations are performed based on dynamic prediction data to generate a set of simulated running scenarios that include typical and extreme scenarios; Based on the set of simulated operation scenarios and the preset first time resolution, an upper-level energy storage planning model oriented towards the system's peak-shaving needs is constructed with the goal of minimizing the overall economic cost of the system. Solve the upper-level energy storage planning model to generate the preliminary energy storage configuration capacity on the source side and the grid side; The initial energy storage configuration capacity is set as the operating boundary parameter, and a lower-level energy storage planning model oriented towards system frequency regulation requirements is constructed based on the operating boundary parameter and the preset second time resolution; wherein, the second time resolution is smaller than the first time resolution; Solve the lower-level energy storage planning model to generate a collaborative energy storage planning strategy between the source side and the grid side.

2. The source-side and grid-side energy storage collaborative planning method as described in claim 1, characterized in that, Obtain dynamic forecast data for the target power grid system, including: Acquire renewable energy output forecast data and system load forecast data for the target power grid system; among which, renewable energy output forecast data includes wind farm output forecast data and photovoltaic power plant output forecast data. The forecast data of new energy power output and the forecast data of system load are used as the dynamic forecast data of the target power grid system.

3. The source-side and grid-side energy storage collaborative planning method as described in claim 2, characterized in that, Clustering operations are performed based on dynamic prediction data to generate a set of simulated running scenarios that include both typical and extreme scenarios, including: A preset spatial clustering algorithm is used to identify noise points in the dynamic prediction data and extract the noise point dataset. The remaining data obtained after removing noisy points from the dynamic prediction data will be used as the regular dataset. Cluster the noise point dataset to generate extreme scenarios; Clustering operations are performed on regular datasets to generate typical scenarios; Extreme scenarios are combined with typical scenarios to generate a set of simulated running scenarios.

4. The source-side and grid-side energy storage collaborative planning method as described in claim 3, characterized in that, A pre-defined spatial clustering algorithm is used to identify noise points in the dynamic prediction data, and a noise point dataset is extracted, including: Obtain the neighborhood radius parameter and the minimum sample density threshold; For each data point in the dynamic prediction data, the search neighborhood corresponding to the current data point is determined based on the neighborhood radius parameter, and the actual number of samples in the search neighborhood is counted. After completing the sample statistics operation for all data points in the dynamic prediction data, the data points whose actual sample number is less than the minimum sample density threshold are marked as target noise points. All target noise points are aggregated to generate a noise point dataset.

5. The source-side and grid-side energy storage coordinated planning method as described in claim 4, characterized in that, Based on the simulated operation scenario set and the preset first time resolution, and with the optimization objective of minimizing the overall economic cost of the system, an upper-level energy storage planning model oriented towards the system's peak-shaving needs is constructed, including: Obtain the peak-shaving cost and peak-shaving constraints of the target power grid system; wherein, the peak-shaving cost includes system operating cost, energy storage planning investment cost, new energy curtailment penalty cost, and load shedding penalty cost; the peak-shaving constraints include power balance constraints, thermal power unit operating constraints, hydropower unit operating constraints, new energy power plant operating constraints, energy storage operating constraints, and system reserve constraints. The system's comprehensive economic cost is generated by summing up each cost in the peak-shaving cost. Based on peak-shaving constraints, simulated operation scenarios, and a preset first time resolution, an upper-level energy storage planning model is constructed with the goal of minimizing the overall economic cost of the system.

6. The source-side and grid-side energy storage collaborative planning method as described in claim 5, characterized in that, The initial energy storage configuration capacity is set as the operating boundary parameter. Based on the operating boundary parameter and the preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation requirements is constructed, including: Obtain the frequency regulation cost and frequency regulation constraints of the target power grid system; wherein, the frequency regulation cost includes the energy storage frequency regulation operation cost and the system frequency regulation penalty cost; the frequency regulation constraints include system frequency deviation constraints, frequency regulation power response constraints, and energy storage dynamic state of charge constraints; The initial energy storage configuration capacity is converted into an upper limit constraint on energy storage capacity, and the upper limit constraint on energy storage capacity is used as the operating boundary parameter. The overall frequency modulation cost of the system is generated by summing up each cost in the frequency modulation cost. Based on frequency regulation constraints, operating boundary parameters, and a preset second time resolution, a lower-level energy storage planning model oriented towards system frequency regulation needs is constructed with the goal of minimizing the overall system frequency regulation cost.

7. The source-side and grid-side energy storage collaborative planning method as described in claim 1, characterized in that, After solving the lower-level energy storage planning model and generating a collaborative energy storage planning strategy between the source and grid sides, the following steps are also included: Obtain multiple operational scenarios to be verified for the target power grid system, as well as preset operational safety evaluation indicators; The energy storage collaborative planning strategy is substituted into the multiple operating scenarios to be verified to perform power flow calculations and obtain the actual operating status data of the system under each operating scenario to be verified. Calculate the operational safety verification result of the target power grid system based on the actual operating status data of the system. When the operational safety verification results meet the operational safety evaluation indicators, the energy storage collaborative planning strategy will be determined as the final energy storage construction scheme for the target power grid system. When the operational safety verification result fails to meet the operational safety evaluation indicators, the energy storage collaborative planning strategy is marked as a high-risk strategy, and the system early warning information corresponding to the high-risk strategy is output.

8. A source-side and grid-side energy storage collaborative planning device, characterized in that, include: Data acquisition module, scene clustering module, upper-layer peak shaving planning module, and lower-layer frequency regulation planning module; The data acquisition module is used to acquire dynamic prediction data of the target power grid system; The scenario clustering module is used to perform clustering calculation operations based on dynamic prediction data to generate a set of simulated running scenarios that include typical scenarios and extreme scenarios. The upper-level peak-shaving planning module is used to construct an upper-level energy storage planning model for system peak-shaving needs based on the simulated operation scenario set and the preset first time resolution, with the optimization objective of minimizing the overall economic cost of the system. Solve the upper-level energy storage planning model to generate the preliminary energy storage configuration capacity on the source side and the grid side; The lower-level frequency regulation planning module is used to set the initial energy storage configuration capacity as the operating boundary parameter, and to construct a lower-level energy storage planning model oriented towards system frequency regulation requirements based on the operating boundary parameter and a preset second time resolution; wherein, the second time resolution is smaller than the first time resolution; Solve the lower-level energy storage planning model to generate a collaborative energy storage planning strategy between the source side and the grid side.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the source-side and grid-side energy storage collaborative planning method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the source-side and grid-side energy storage collaborative planning method as described in any one of claims 1 to 7.

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