Energy storage configuration method and system for power system coordinated by source, grid, load and carbon, and medium

Abstract

The application discloses a source, net, load, carbon coordinated power system energy storage configuration method, system and medium, belongs to the technical field of power system planning, the method comprises the following steps: obtaining the operation parameters and energy storage configuration scheme parameters of a power system; a multi-dimensional evaluation index system containing power sources, power grids, loads and carbon dimensions is constructed; power flow and carbon flow calculation is performed according to the operation parameters and energy storage configuration scheme parameters, power distribution data and carbon emission distribution characteristic data are obtained; the carbon emission distribution characteristic data is introduced into the power grid coordinated dispatching model as a carbon flow constraint, and an operation dispatching scheme is solved; and the optimal energy storage configuration scheme is determined according to the operation dispatching scheme and the multi-dimensional evaluation index system. Through the implementation of the application, the problem that carbon benefits cannot be accurately quantified and are disconnected from operation dispatching in the prior art can be solved, and the scientificity of energy storage configuration is improved.

Description

Technical Field

[0001] This application relates to the field of power system planning technology, and in particular to a method, system and medium for configuring energy storage in a power system that integrates source, grid, load and carbon. Background Technology

[0002] Energy storage, as a key vehicle for regulating the spatiotemporal imbalance of power sources and loads and enhancing system flexibility, has become a core technological path supporting the low-carbon transformation of the power system through large-scale application. The energy storage configuration decision directly determines the economic benefits of energy storage investment, the safety and reliability of grid operation, and the actual contribution to carbon emission reduction. Driven by the dual carbon goals, how to scientifically determine energy storage configuration schemes has become a critical technical issue that urgently needs to be addressed in the field of power system planning.

[0003] Currently, the mainstream approach to energy storage configuration decisions is primarily based on techno-economic analysis, employing an evaluation system encompassing initial investment, operation and maintenance costs, and energy utilization efficiency to compare different options. In recent years, some studies have begun to incorporate carbon emission factors into the evaluation framework, using macro-statistical methods or carbon emission factor analysis to roughly assess the environmental benefits of energy storage. However, existing methods have significant shortcomings in quantifying and applying carbon benefits: their characterization of carbon emissions remains at the level of macro-statistics or static emission coefficients, failing to reflect the impact of electricity transmission and distribution within the network on carbon emission attribution. This makes it difficult to accurately measure the low-carbon value of energy storage and to effectively interact with system operation and scheduling, hindering the realization of carbon reduction effects in actual operation. This fundamental deficiency leads existing evaluation systems to either focus solely on traditional techno-economic indicators while neglecting the long-term low-carbon value of energy storage, or to use crude estimations of carbon benefits, failing to form a closed loop of evaluation and optimization.

[0004] In summary, the core problem with existing technologies lies in the lack of an energy storage configuration method that can accurately quantify carbon benefits and embed carbon indicators into the operation and scheduling process as constraints. Carbon indicators typically remain at the level of ex-post accounting or static comparison, leading to a disconnect between carbon benefit evaluation and system operation, and incomplete and unreliable decision-making basis. Therefore, there is an urgent need for an energy storage configuration method that coordinates source, grid, load, and carbon, achieving accurate quantification of carbon benefits and seamless scheduling through carbon flow constraints embedded in scheduling and multi-dimensional evaluation, thereby improving the scientific rigor and robustness of energy storage configuration decisions. Summary of the Invention

[0005] This application provides a power system energy storage configuration method that integrates source, grid, load, and carbon, aiming to solve the technical problems in the prior art where carbon benefits cannot be accurately quantified and are disconnected from operation and scheduling, thereby achieving accurate quantification of carbon benefits and deep integration with scheduling, and thus improving the scientific nature and robustness of energy storage configuration.

[0006] Firstly, this application provides a method for configuring energy storage in a power system that integrates source, grid, load, and carbon emissions, including: Obtain the operating parameters of the power system and the parameters of the energy storage configuration scheme; Based on the operating parameters of the power system and the parameters of the energy storage configuration scheme, power flow calculation is performed to obtain the power distribution data of the power grid; based on the power distribution data and the carbon emission intensity of the generator sets, carbon flow calculation is performed to obtain the carbon emission distribution characteristic data of the power grid. The carbon emission distribution characteristic data is introduced into the power grid collaborative scheduling model as a carbon flow constraint, and combined with the power grid physical constraints to solve the operation scheduling scheme. Based on the aforementioned operation scheduling scheme and multi-dimensional evaluation index system, the optimal energy storage configuration scheme is determined; wherein, the multi-dimensional evaluation index system includes power source dimension index, power grid dimension index, load dimension index and carbon dimension index. According to the optimal energy storage configuration scheme, the power system is configured with energy storage.

[0007] This application constructs a multi-dimensional evaluation index system by acquiring power system operating parameters and energy storage configuration parameters. It integrates the carbon dimension with traditional techno-economic dimensions into the evaluation framework, fundamentally addressing the one-sidedness of existing evaluation index systems that "emphasize techno-economic aspects while neglecting low-carbon value." Power distribution data of the power grid is obtained through power flow calculations, and then carbon flow calculations are performed based on carbon flow theory to obtain the carbon emission distribution characteristics of the power grid. This transforms carbon emissions from macroscopic statistics into finely trackable nodal carbon potential and branch carbon flow density, upgrading carbon benefit evaluation from "post-hoc static accounting" to "precise quantification at the nodal and branch levels." Carbon emission distribution characteristics are introduced as carbon flow constraints into the power grid collaborative scheduling model and combined with the solution of power grid physical constraints, deeply integrating carbon benefit evaluation with operation and scheduling. Carbon indicators are no longer limited to static comparisons but are directly embedded in the scheduling optimization process, ensuring that the value of carbon emission reduction can be realized in actual operation. The optimal energy storage configuration scheme is determined based on the operation and scheduling scheme and the multi-dimensional evaluation index system, quantifying the multi-dimensional indicators into a single score, forming a quantitatively comparable decision-making basis. Compared to existing technologies that rely on macroscopic statistical methods or static emission coefficients, where carbon benefit evaluation and operation scheduling are independent, evaluation models use globally fixed weights, and decision-making mechanisms are limited to a single scenario, this application achieves precise quantification of carbon benefits through carbon flow theory, achieves deep integration of evaluation and scheduling through carbon flow constraint embedding, achieves comprehensive characterization of energy storage comprehensive benefits through a multi-dimensional evaluation index system, and achieves scientific quantification of decision-making basis through comprehensive scoring. It fundamentally solves the basic problems of incomplete and unreliable decision-making basis in existing technologies, and provides a comprehensive, accurate, and robust scientific basis for energy storage configuration decisions.

[0008] Further, the step of performing power flow calculations based on the operating parameters of the power system and the parameters of the energy storage configuration scheme to obtain power distribution data of the power grid includes: Based on the operating parameters of the power system, determine the network topology of the power grid, the load data of each node in the power grid, and the output data of each generator unit at its node; Based on the parameters of the energy storage configuration scheme, determine the access node of the energy storage and the preset charging and discharging strategy of the energy storage at the access node; Based on the network topology, the power output data of each generator unit node, the load data of each node in the power grid, and the charging and discharging power corresponding to the preset charging and discharging strategy of the energy storage at the access node are used as the power boundary conditions of each node to perform power flow calculation and obtain the active power and active power flow direction of each branch in the power grid. The active power and active power flow direction of each branch in the power grid are used as the power distribution data.

[0009] By using the output of each generator unit, the load of each node, and the charging and discharging status of the energy storage access node as power boundary conditions, the power flow calculation results can accurately reflect the active power and active power flow direction of each branch of the power grid after energy storage participates in regulation. This power distribution data accurately reflects the power flow state of the power grid after energy storage participates in regulation, providing accurate input for subsequent carbon flow calculations. The accurate branch power direction ensures the correctness of the carbon flow tracking path and avoids the deviation in carbon flow density calculation caused by misjudgment of power flow direction.

[0010] Furthermore, the step of calculating carbon flow based on the power distribution data and the carbon emission intensity of the generator sets to obtain the carbon emission distribution characteristic data of the power grid includes: Based on the output data of each generator unit at the node, the load data of each node in the power grid, and the charging and discharging status of the energy storage at the access node, the node injection power of each node in the power grid is determined. Based on the carbon emission intensity of the generator set and the nodal injection power of each node in the power grid, and based on the principle of proportional sharing, the nodal carbon potential of each node in the power grid is calculated. Based on the node carbon potential and the active power flow direction of each branch in the power grid, the branch carbon flow density of each branch in the power grid is determined. The node carbon potential and the branch carbon flux density are used as the carbon emission distribution characteristic data.

[0011] By calculating nodal carbon potential using a proportional sharing principle, the carbon emission intensity on the generation side is allocated to each node according to the direction and magnitude of active power flow. This allows for precise quantification of the carbon emission intensity of electricity consumption at each node, solving the problem of not being able to track carbon emissions from the generation side to the load side. Simultaneously, the branch carbon flow density is determined based on the nodal carbon potential and the active power flow direction of the branches, revealing the transmission path and distribution density of carbon emissions in the transmission network, enabling the identification of high-carbon branches. The nodal carbon potential and branch carbon flow density together constitute carbon emission distribution characteristic data, providing directly quantifiable input parameters for subsequently embedding carbon flow as a constraint into the scheduling model, transforming carbon emissions from a macroscopic statistic into a fine-grained characteristic quantity at the node and branch levels.

[0012] Furthermore, the step of incorporating the carbon emission distribution characteristic data as a carbon flow constraint into the power grid coordinated dispatch model, and combining it with the power grid physical constraints to obtain the operation and dispatch scheme, includes: The aforementioned power grid collaborative scheduling model is constructed, which is used to optimize unit output and energy storage charging and discharging strategies. Determine the set of key branches in the power grid coordinated dispatch model, and impose an upper limit constraint on the carbon flow density of the key branches on the set of key branches; Based on the node carbon potential, the charging and discharging priority of energy storage in the grid collaborative scheduling model is controlled; wherein, when the node carbon potential is lower than a first threshold, the charging priority of energy storage is increased, and when the node carbon potential is higher than a second threshold, the discharging priority of energy storage is increased. Apply power grid physical constraints to the power grid collaborative scheduling model, solve the power grid collaborative scheduling model, and obtain the operation scheduling scheme; wherein, the operation scheduling scheme includes the unit output timing sequence and the energy storage charging and discharging timing sequence.

[0013] This application transforms carbon emission distribution characteristic data into quantifiable carbon flow constraints, directly embedding them into the grid collaborative dispatch model. This solves the problem in existing technologies where carbon benefit evaluation and operation dispatch are independent, and the value of carbon emission reduction cannot be realized. Specifically, by imposing upper limits on the carbon flow density of key branch sets, the dispatch scheme is forced to actively control the transmission paths of high-carbon electricity, preventing excessive accumulation of carbon emissions in local branches. Simultaneously, the charging and discharging priorities of energy storage are dynamically adjusted based on the node carbon potential, prioritizing charging during low-carbon periods and discharging during high-carbon periods, thus achieving the spatiotemporal transfer of carbon emissions. The joint solution of these two types of carbon flow constraints and grid physical constraints ensures that the dispatch results not only meet traditional safety constraints but also proactively optimize the spatial distribution and temporal transfer of carbon emissions. Carbon benefits are transformed from "ex-post evaluation indicators" to "ex-pre-optimization constraints," with evaluation results directly guiding operational decisions. This forms a complete closed loop of carbon benefit quantification, constraint, and optimization, fundamentally solving the problem of the disconnect between carbon benefit evaluation and actual operation.

[0014] Furthermore, the multi-dimensional evaluation index system includes power supply dimension indicators, power grid dimension indicators, load dimension indicators, and carbon dimension indicators, including: The power supply metrics include the proportion of clean energy supply and energy utilization efficiency. The power grid dimension indicators include steady-state safety margin and the proportion of branches with high carbon flow density. The load dimension indicators include steady-state energy sufficiency and energy deficit rate; The carbon dimension indicators include demand-side carbon flow guidance potential and energy storage carbon transfer efficiency.

[0015] This application constructs a comprehensive benefit evaluation index system encompassing four dimensions: power source, grid, load, and carbon. This addresses the problem of existing evaluation systems that focus solely on technical and economic indicators while neglecting the long-term low-carbon value of energy storage. Specifically, the power source dimension indicators measure the proportion of clean energy and energy utilization efficiency, reflecting the level of low-carbonization in the energy structure; the grid dimension indicators assess operational safety margins and the proportion of high-carbon-density branches, identifying areas with dense carbon emission transmission; the load dimension indicators characterize power supply reliability and supply-demand balance capabilities; and the carbon dimension indicators specifically quantify the demand-side carbon flow guidance potential and energy storage carbon transfer efficiency, directly characterizing the unique contribution of energy storage to carbon emission reduction. These four dimensions complement each other, enabling energy storage benefit evaluation to comprehensively cover multiple objectives, including technology, economy, safety, and low carbon, providing a complete evaluation basis for subsequent comprehensive scoring and avoiding decision-making biases caused by missing evaluation dimensions.

[0016] Furthermore, determining the optimal energy storage configuration scheme based on the operation scheduling scheme and the multi-dimensional evaluation index system includes: Based on the operation scheduling scheme, the values ​​of each dimension index in the multi-dimensional evaluation index system are calculated, and the values ​​of each dimension index are comprehensively scored. The optimal energy storage configuration scheme is determined based on the comprehensive scoring results.

[0017] By quantifying the comprehensive benefits of energy storage configuration schemes across four dimensions—source, grid, load, and carbon—into a comparable comprehensive score, the multi-dimensional evaluation problem is transformed into a single-objective optimization problem, providing a direct and unified decision-making basis for subsequent scheme comparison.

[0018] Furthermore, the step of comprehensively scoring the values ​​of the various dimensions of indicators and determining the optimal energy storage configuration scheme based on the comprehensive scoring results includes: The subjective weight of each indicator is determined by comparing each indicator pairwise and constructing a judgment matrix. The objective weight of each indicator is determined by calculating the information entropy of each indicator value. The subjective weights and objective weights are weighted and fused together to obtain the comprehensive weights of each indicator; Based on the comprehensive weight of each indicator, the indicator values ​​of the energy storage configuration scheme under each indicator are weighted and summed to obtain a comprehensive score of the energy storage configuration scheme. The energy storage configuration scheme with the highest comprehensive score is determined as the optimal energy storage configuration scheme for a single scenario.

[0019] By incorporating expert experience through the Analytic Hierarchy Process (AHP) to determine subjective weights, the evaluation results reflect decision-making orientations under different scenarios (e.g., increasing the weight of carbon indicators in high-energy-density scenarios). Objective weights are determined based on the data dispersion of indicator values ​​using the entropy weight method, avoiding human bias and ensuring an objective reflection of indicator differentiation. The subjective and objective weights are then weighted and integrated to obtain a comprehensive weight, balancing decision-making orientation with data patterns. Based on this comprehensive weight, the indicator values ​​are weighted and summed, quantifying multi-dimensional indicators into a single comprehensive score. This makes the merits and demerits of energy storage configuration schemes in a single scenario readily apparent, resolving the issues of arbitrary subjective weight settings or detachment from actual decision-making needs in existing evaluation methods. This ensures the scientific rigor and credibility of the comprehensive score, providing a quantitative basis for selecting the optimal scheme.

[0020] Furthermore, this application also includes: Set up multiple running scenarios and assign corresponding scenario probability weights to each running scenario; For each operating scenario, a comprehensive score for each energy storage configuration scheme is determined for each operating scenario; The comprehensive scores under each operating scenario are weighted and summed according to the probability weight of the corresponding scenario to obtain the cross-scenario comprehensive score of each energy storage configuration scheme. The energy storage configuration scheme with the highest comprehensive score across scenarios is determined as the globally optimal energy storage configuration scheme.

[0021] This application comprehensively covers various potential future operating conditions by pre-setting multiple typical operating scenarios (such as high-proportion renewable energy access, load intensive operation, and low-carbon emission reduction) and assigning probability weights. A complete evaluation process is independently executed for each scenario to obtain a comprehensive score for each energy storage configuration scheme under that scenario. Then, a cross-scenario comprehensive score is calculated based on scenario probability weighting, and the scheme with the highest score is selected as the globally optimal configuration. This step solves the problem that existing decision-making methods are limited to a single scenario and cannot cope with the uncertainty of future operating environments. It elevates the decision-making result from "single-scenario optimal" to "multi-scenario robust," improving the reliability and engineering practicality of energy storage configuration decisions.

[0022] Secondly, this application provides a power system energy storage configuration system that integrates source, grid, load, and carbon, including: a parameter acquisition module, an analysis module, a scheme generation module, an evaluation module, and a configuration module; The parameter acquisition module is used to acquire the operating parameters of the power system and the parameters of the energy storage configuration scheme; The analysis module is used to perform power flow calculations based on the operating parameters of the power system and the parameters of the energy storage configuration scheme to obtain the power distribution data of the power grid; and to perform carbon flow calculations based on the power distribution data and the carbon emission intensity of the generator sets to obtain the carbon emission distribution characteristic data of the power grid. The scheme generation module is used to introduce the carbon emission distribution characteristic data as carbon flow constraints into the power grid collaborative scheduling model, and solve it in combination with the power grid physical constraints to obtain the operation scheduling scheme. The evaluation module is used to determine the optimal energy storage configuration scheme based on the operation scheduling scheme and the multi-dimensional evaluation index system; wherein, the multi-dimensional evaluation index system includes power source dimension index, power grid dimension index, load dimension index and carbon dimension index. The configuration module is used to configure energy storage for the power system according to the optimal energy storage configuration scheme.

[0023] This application achieves full automation from parameter input to optimal solution selection through a modular system architecture and the collaborative work of five modules. The system can be directly deployed on a power system planning platform, receiving operating parameters and energy storage configuration schemes, automatically performing power flow-carbon flow coupling analysis, scheduling optimization, and comprehensive scoring, and outputting the optimal energy storage configuration scheme, providing an efficient and reusable decision-making tool for engineering applications.

[0024] Thirdly, this application provides a computer-readable storage medium, comprising: a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the source-grid-load-carbon coordinated power system energy storage configuration method as described in the first aspect. Attached Figure Description

[0025] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a flowchart of one embodiment of the source-grid-load-carbon coordinated power system energy storage configuration method provided in this application; Figure 2 This is a schematic diagram of the IEEE standard 30-node model topology according to an embodiment provided in this application; Figure 3 This is a schematic diagram of the structure of a power system energy storage configuration system that integrates source, grid, load, and carbon in some embodiments of this application.

[0027] Labeling Explanation: 100, Parameter Acquisition Module; 200, Analysis Module; 300, Solution Generation Module; 400, Evaluation Module; 500, Configuration Module. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0030] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0032] In the planning and operation decisions of new power systems, the scientific nature of energy storage configuration directly affects the economy, security, and low-carbon characteristics of the power grid. Existing methods characterize carbon benefits at the level of macro-statistics or static emission coefficients, failing to accurately quantify the impact of energy storage on the spatiotemporal distribution of carbon emissions, and also making it difficult to integrate carbon benefits with operation and dispatch, resulting in a lack of scientific and reliable basis for energy storage configuration. To address this, this application provides a power system energy storage configuration method that coordinates source, grid, load, and carbon emissions.

[0033] Please refer to Figure 1 , Figure 1 This is a flowchart of an embodiment of the source-grid-load-carbon coordinated power system energy storage configuration method provided in this application. The method includes steps S1 to S5, each step as follows: Step S1: Obtain the operating parameters of the power system and the parameters of the energy storage configuration scheme; Step S2: Based on the operating parameters of the power system and the parameters of the energy storage configuration scheme, perform power flow calculation to obtain the power distribution data of the power grid; based on the power distribution data and the carbon emission intensity of the generator units, perform carbon flow calculation to obtain the carbon emission distribution characteristic data of the power grid. Step S3: Introduce carbon emission distribution characteristic data as carbon flow constraints into the power grid collaborative scheduling model, and solve it in combination with power grid physical constraints to obtain the operation scheduling scheme; Step S4: Determine the optimal energy storage configuration scheme based on the operation and scheduling plan and the multi-dimensional evaluation index system; wherein, the multi-dimensional evaluation index system includes power source dimension index, grid dimension index, load dimension index and carbon dimension index. Step S5: Configure energy storage for the power system according to the optimal energy storage configuration scheme.

[0034] The operating parameters of the power system refer to the basic set of data describing the static structure and dynamic operating state of the power grid, including: the network topology of the power grid (connection relationships between nodes and branches, branch impedance, ground admittance, etc.), load data of each node (time-series values ​​of active and reactive power), output data of each generator unit at the node (unit type, connection node, output upper and lower limits, ramp rate, carbon emission intensity, etc.), and the upper limit of available output of new energy sources (wind power, photovoltaic). These parameters are necessary inputs for power flow calculation and carbon flow calculation.

[0035] Energy storage configuration parameters refer to the technical specifications and operational boundary data of the energy storage equipment to be evaluated, including: the access node, rated power, rated capacity, charge / discharge efficiency, upper and lower limits of state of charge (SOC), initial SOC, and service life. These parameters are used to establish power and energy constraints for energy storage in the scheduling model and are the basis for evaluating the overall benefits of energy storage configuration schemes.

[0036] Carbon flow constraints refer to using carbon emission distribution characteristic data (node ​​carbon potential, branch carbon flow density) as additional constraints on the scheduling model, enabling the scheduling results to proactively optimize the spatiotemporal distribution of carbon emissions while satisfying traditional physical constraints. Node carbon potential refers to the generation-side carbon emission intensity corresponding to a unit of electricity consumption at each node in the power grid, reflecting the spatial distribution of carbon emissions. Branch carbon flow density refers to the amount of carbon emissions carried per unit of transmitted power in each branch, equal to the node carbon potential of the node from which the branch flows, used to track the propagation path of carbon emissions in the transmission network.

[0037] In practical operation, to verify the applicability of the proposed comprehensive benefit evaluation method under different operating conditions and energy storage configurations, an IEEE standard 30-node system was used as the example network model, and its topology is as follows: Figure 2 As shown, Figure 2 This is a schematic diagram of the IEEE standard 30-node model topology according to an embodiment of this application. The system includes 30 nodes, with generator sets connected to nodes 1, 2, 13, 22, 23, and 27. Node 22 is a wind turbine, node 23 is a photovoltaic turbine, and the rest are thermal power units. The reference carbon potential vector for the generator sets is... The carbon potential of the new energy unit is set to 0. The remaining network parameters adopt the parameters of the IEEE 30 standard example.

[0038] Five energy storage configuration schemes, A through E, are set up, all with a fixed access node 10. The rated energy storage capacity of each scheme is... Rated capacity The charge / discharge efficiency, SOC range, and initial SOC are shown in Table 1. Table 1 Parameter settings for five energy storage configuration schemes Among them, Scheme E and Scheme D have the same physical scale, but Scheme E introduces additional carbon costs into its scheduling objectives.

[0039] Specifically, in some embodiments, in step S2, power flow calculations are performed based on the power system's operating parameters and energy storage configuration parameters to obtain the power distribution data of the power grid, including: Based on the operating parameters of the power system, determine the network topology of the power grid, the load data of each node in the power grid, and the output data of each generator unit at its node; Based on the parameters of the energy storage configuration scheme, determine the access node of the energy storage and the preset charging and discharging strategy of the energy storage at the access node; Based on the network topology, the power output data of each generator node, the load data of each node in the power grid, and the charging and discharging power corresponding to the preset charging and discharging strategy of energy storage at the access node are used as the power boundary conditions of each node to perform power flow calculation and obtain the active power and active power flow direction of each branch in the power grid. The active power and active power flow direction of each branch in the power grid are used as power distribution data.

[0040] Power flow calculation is based on the network topology of the power grid, load data of each node, output data of each generator node, and the charging and discharging status of energy storage at the access nodes. Power boundary conditions for each node are constructed, and the Newton-Raphson method can be used to solve the node power balance equations to obtain the active power and active flow direction of each branch. The Newton-Raphson method is an iterative algorithm that linearizes the node power deviation equations and repeatedly corrects the voltage magnitude and phase angle until convergence. It has a second-order convergence speed and is suitable for power flow calculations in large power systems.

[0041] Power boundary conditions refer to the net injected active power (or the balance node with a given voltage amplitude) given for each node in power flow calculations. For generator nodes, the net injected power is the unit output minus the load (if any) of that node, plus or minus the energy storage charging and discharging; for pure load nodes, the net injected power is the negative load value; for energy storage access nodes, the net injected power also needs to be added to the energy storage discharging power or subtracted from the charging power.

[0042] The direction of active power flow refers to the sign of the active power transmitted in a branch. It is defined that the power value is positive when flowing from node i to node j, and negative otherwise. The direction of power flow determines the propagation path of carbon flow tracing.

[0043] Specifically, in some embodiments, in step S2, carbon flow calculation is performed based on power distribution data and generator carbon emission intensity to obtain carbon emission distribution characteristic data of the power grid, including: Based on the output data of each generator unit at the node, the load data of each node in the power grid, and the charging and discharging status of energy storage at the access node, the node injection power of each node in the power grid is determined. Based on the carbon emission intensity of generator sets and the nodal injection power of each node in the power grid, and based on the principle of proportional sharing, the nodal carbon potential of each node in the power grid is calculated. Based on the nodal carbon potential and the active power flow direction of each branch in the power grid, the branch carbon flow density of each branch in the power grid is determined. The nodal carbon potential and branch carbon flux density are used as characteristic data for carbon emission distribution.

[0044] Carbon flow calculation involves determining the nodal injection power (node ​​injection power = node generator output - node load - energy storage charging power + energy storage discharging power) of each node based on the output data of each generator unit, the load data of each node, and the charging and discharging status of energy storage at the access node. Then, based on the proportional sharing principle, the nodal carbon potential of each node is calculated using the injected power and the carbon emission intensity of the generator units. Finally, based on the nodal carbon potential and the active power flow direction of each branch, the branch carbon flow density (branch carbon flow density equals the nodal carbon potential of the node from which the branch flows) is determined. The proportional sharing principle is a fundamental assumption of carbon flow calculation, namely that the power flowing into a node is completely mixed at the node, and each outflowing branch carries carbon emissions in the same proportion.

[0045] In an optional embodiment, step S2 involves performing power flow calculations based on the operating parameters of the power system and the parameters of the energy storage configuration scheme to obtain the power distribution data of the power grid, specifically including the following sub-steps.

[0046] Based on the operating parameters of the power system, the following information is extracted: Network topology and electrical parameters: set of nodes, set of branches Impedance and admittance of each branch; Load on each node: Node During the period active load ; Unit output data: Unit At the node contribution And new energy sources (wind power, photovoltaics) at the node Available output limit ; Determine the energy storage access nodes based on the parameters of the energy storage configuration scheme. and energy storage period charging power and discharge power Charging power represents the electrical energy absorbed from the grid, while discharging power represents the electrical energy injected into the grid. Only one of these values ​​is non-zero at any given time.

[0047] Construct the node power balance equations, substitute the above data into the node power balance relationship, and apply the equations to each node. Establish the equation: Where the left side of the formula represents the nodes. The net injected power is generated by the unit output. New energy power output Energy storage and discharge (Only when) (existing at the time) minus energy storage charging (only when) (existing at the time) and load Composition; the right side is the slave node. Flow out to all adjacent nodes The sum of the active power of the branches, of which Indicates a branch The active power, a positive value indicates that the power comes from Flow direction A negative value indicates the opposite direction.

[0048] Based on the nodal power balance equations, the active power of each branch is solved by inputting the grid topology, electrical parameters, unit output constraints, nodal loads, upper limit of available output from new energy sources, and energy storage charging and discharging power. A power flow algorithm (such as the Newton-Raphson method) is then used. After obtaining the branch power, apply safety constraints to each branch: in This represents the maximum transmission capacity of the branch. This constraint ensures that the operating scheme meets the grid's transmission capacity and steady-state security requirements. The active power of each branch will be calculated from the solution. and its corresponding active power flow direction (by The positive or negative sign of the carbon flow (determined by the power distribution) is used as power distribution data for subsequent carbon flow calculations.

[0049] In another optional embodiment, step S2 involves calculating carbon flow based on power distribution data and generator carbon emission intensity to obtain carbon emission distribution characteristic data of the power grid, specifically including the following sub-steps.

[0050] Based on the unit output, load data, and energy storage charging and discharging status, calculate the values ​​for each node. node injection power : in For the Kronecker function (when If the value is 1, then the value is 0.

[0051] The proportional sharing principle is a fundamental assumption in carbon flow calculations: all active power flowing into a node is completely mixed at the node, and the power flowing out of the node from each branch, as well as the node's load, carries carbon emissions in the same proportion. Based on this principle, for each node... Its node carbon potential The following relationship must be satisfied: in, For generator sets Carbon emission intensity (unit: ), new energy units take 0; To reach the node via branch The set of adjacent nodes to which active power is injected; For the node Flow to Node The active power of the branch (positive value, from power distribution data); For nodes The node carbon potential.

[0052] It should be noted that for radial power grids or systems with clearly defined power flow directions, the calculation can be performed recursively from the power source nodes to the load nodes: first, identify the power source nodes without injection branches (whose carbon potential equals the unit's carbon emission intensity), and then calculate the carbon potential of the downstream nodes sequentially along the power flow direction. For ring networks, matrix inversion or iterative methods can be used to solve the problem.

[0053] Obtain the nodal carbon potential of each node. Then, the carbon flow rate of the branch is further calculated. For the branch... If the time period The meritorious trend (Power from) Flow direction If the carbon flow rate of that branch is... Defined as the carbon emission flow rate carried by the active power flow of this branch: Branch carbon flux density Defined as the ratio of carbon flow rate to active power flow rate: According to the principle of proportional sharing, the carbon flux density of a branch is equal to the nodal carbon potential of the node from which the branch flows. For a branch... If the trend direction is from Flow direction (Right now Then the branch carbon flux density of that branch. for: If the current direction is opposite (from the node) Flow to Node Then the branch carbon flux density satisfies: The unit of branch carbon flux density is Within the statistical period, branch roads carbon flow This can be obtained by accumulating carbon flow over time: Similarly, load nodes During the period The carbon flow rate can be expressed as: in For nodes During the period The load power.

[0054] All nodes in time period node carbon potential Branch carbon flux density of all branches This data is integrated into a dataset to serve as a characteristic dataset of carbon emission distribution. This dataset will be used in subsequent steps to introduce carbon flow constraints into the grid coordinated dispatch model.

[0055] Specifically, in some embodiments, in step S3, carbon emission distribution characteristic data is introduced as a carbon flow constraint into the power grid coordinated dispatch model, and combined with the solution of power grid physical constraints, an operation dispatch scheme is obtained, including: A grid collaborative scheduling model is constructed to optimize unit output and energy storage charging and discharging strategies. Determine the set of key branches in the power grid coordinated dispatch model and impose an upper limit constraint on the carbon flow density of the key branches. Based on the node carbon potential, the charging and discharging priority of energy storage in the grid collaborative scheduling model is controlled; specifically, when the node carbon potential is lower than the first threshold, the charging priority of energy storage is increased, and when the node carbon potential is higher than the second threshold, the discharging priority of energy storage is increased. Apply physical constraints to the power grid in the power grid collaborative scheduling model, solve the power grid collaborative scheduling model, and obtain the operation scheduling scheme; the operation scheduling scheme includes the unit output timing and the energy storage charging and discharging timing.

[0056] The grid coordinated dispatch model refers to a mathematical programming model that aims to minimize the total system operating cost (or maximize overall benefits), uses unit output and energy storage charging and discharging strategies as decision variables, and comprehensively considers grid physical constraints (power balance, branch capacity, energy storage SOC, etc.) and carbon flow constraints (branch carbon flow density upper limit, carbon potential driving priority). This model achieves coordinated optimization of traditional power dispatch and carbon emission reduction targets.

[0057] The critical branch set refers to the set of branches in the power grid with dense carbon emission transmission and a significant impact on carbon emission reduction targets. It can be determined in advance based on carbon flow calculation results or planning requirements (such as environmental capacity and carbon emission hotspots).

[0058] The upper limit constraint on carbon flow density in branches requires that the carbon flow density of each branch in the set of critical branches not exceed a preset threshold. This constraint forces the scheduling scheme to actively control the transmission path of high-carbon electricity and avoid excessive accumulation of carbon emissions in local branches.

[0059] The first and second thresholds are two preset parameters used to determine the carbon potential of a node. The first threshold represents the threshold for low carbon potential. When the carbon potential of a node is below this value, the scheduling model should prioritize energy storage charging (absorbing low-carbon electricity). The second threshold represents the threshold for high carbon potential. When the carbon potential of a node is above this value, energy storage discharging (replacing high-carbon electricity) should be prioritized.

[0060] In an optional embodiment, step S3 introduces carbon emission distribution characteristic data as carbon flow constraints into the power grid collaborative scheduling model, and combines it with the power grid physical constraints to obtain the operation scheduling scheme, specifically including the following steps.

[0061] Under operating scenario s and time period t, the active power output of the unit is... Energy storage charging power Energy storage and discharge power Energy storage state of charge and new energy output A collaborative scheduling model is constructed using these as decision variables. The objective function of this model is typically to minimize the system operating cost (including fuel cost, energy storage degradation cost, etc.), but a carbon cost term can also be introduced depending on demand.

[0062] Nodal carbon potential based on carbon emission distribution characteristics data Branch carbon flux density Two types of carbon flow constraints are added to the scheduling model: Upper limit constraint on branch carbon flux density to determine the set of critical branches It can be pre-set according to carbon emission reduction targets or environmental capacity for each key branch. Apply constraints: in, This is the preset carbon flux density threshold.

[0063] Energy storage charging and discharging priority control, based on energy storage access nodes node carbon potential Introduce triggering rules: when In this case, increase the priority of energy storage charging (e.g., reduce charging costs or set a lower limit for charging power in the objective function). when In this case, the priority of energy storage discharge should be increased (e.g., by increasing discharge revenue in the objective function or setting a lower limit for discharge power).

[0064] in , The first and second thresholds are preset for the running scenario s.

[0065] Add conventional power grid physical constraints to the scheduling model: The node power balance constraint states that the net injected power of each node in each time period equals the sum of the outflow power of the branches: Branch power flow safety constraints, meaning that the transmission power of each branch does not exceed its thermal stability limit: Energy storage power and energy constraints, i.e., charging and discharging power not exceeding the rated value: Dynamic changes in state of charge: in Rated energy storage capacity (MWh), , These are the charging and discharging efficiencies, respectively.

[0066] Safe range of state of charge: Under the premise of satisfying all the above constraints, and with economic efficiency as the primary objective, carbon flux density exceeding limits and safety risks can optionally be included as objectives or penalties. Linear programming, mixed-integer linear programming, or heuristic algorithms are used to solve the model to obtain the unit output for each time period. Energy storage charging and discharging power , and energy storage state of charge The obtained unit output time series and energy storage charging and discharging time series are combined to form an operation scheduling scheme for subsequent multi-dimensional evaluation.

[0067] Specifically, in some embodiments, in step S4, the multi-dimensional evaluation index system includes power supply dimension indicators, power grid dimension indicators, load dimension indicators, and carbon dimension indicators, including: Power supply metrics include the proportion of clean energy supply and energy utilization efficiency; Grid-related indicators include steady-state safety margin and the proportion of branches with high carbon flow density; Load-related indicators include steady-state energy adequacy and energy deficit rate; Carbon-related indicators include demand-side carbon flow guidance potential and energy storage carbon transfer efficiency.

[0068] Specifically, to comprehensively evaluate the overall benefits of energy storage systems in new power systems, an evaluation index system covering multiple aspects of "source-grid-load-carbon" is constructed, following these principles: Systematic principle: Covering the entire chain of power supply, power grid, and load, and specifically incorporating the benefits of energy storage from a carbon perspective to ensure that the impact of carbon flow at each stage is quantified.

[0069] Quantifiability principle: All indicators are based on mathematical models constructed from actual engineering parameters to avoid subjective qualitative evaluation.

[0070] Dynamic Adaptability Principle: To address differences in scenarios such as renewable energy penetration rate, peak-valley load difference, and carbon price fluctuations, the thresholds and weights of indicators are allowed to be dynamically adjusted.

[0071] The evaluation index system consists of four dimensions: power source, power grid, load, and carbon. Each dimension has specific evaluation indicators, as shown in Table 2. Table 2. Multi-dimensional evaluation index system for source-grid-load-carbon

[0072] The construction of the above indicator system lays the foundation for subsequent quantitative calculations. The specific definitions and calculation formulas for each indicator are given below.

[0073] 1. Evaluation metrics for power supply: clean energy supply share This refers to the degree of clean energy utilization in the system's energy supply type, defined as the ratio of energy supplied by clean energy to the total energy supplied by the system. The calculation formula is: In the formula For clean energy power generation, The total energy supplied to the system, expressed in MWh. and Let t represent the power of the wind turbine and the photovoltaic system. Power of thermal power units, in MW. For time intervals.

[0074] Energy utilization efficiency This is the ratio of system output energy to input energy. Output energy is the sum of load consumption energy and energy storage charging energy, minus energy storage discharging energy. Input energy is the total power supply of the system, calculated using the following formula: In the formula To output energy to the system, The energy input to the system, that is, the total energy supplied by the system. . Let be the total load power at time t. and These represent the energy storage charging and discharging power, in MW.

[0075] It should be noted that, depending on the characteristics of different energy types, the above evaluation indicators can be calculated using either power or energy. The difference lies in the energy's inertia or response speed. Low energy inertia or high response speed means that the energy can complete input and output simultaneously, and vice versa. When the energy's input and output are completed synchronously, power can be used. Conversely, energy is extracted. .

[0076] 2. Evaluation indicators for the power grid dimension: Steady-state safety margin quantifies the safety margin between the power of each branch and its transmission capacity under normal grid operation, reflecting the grid's overload resistance under fault-free conditions. By using the ratio of the actual power of a branch to its capacity limit, weak links in the grid are identified, providing safety boundary constraints for energy storage configuration and operation scheduling. For the u-th branch, its safety margin is defined as: in The maximum allowable transmission power for each branch. This represents the actual transmission power of each branch. When... At that time, the branch power did not exceed the limit, and there was a safety margin. This means that the branch power is over the limit and an emergency adjustment is needed.

[0077] For the entire power grid, the margin of the weakest branch can be used. Weighted average margin Over-limit branch road ratio As a system-level steady-state safety margin indicator.

[0078] Margin of the weakest branch: Weighted average margin (weighted by branch capacity): Percentage of over-limit branch roads: in, It is the total number of branch roads. To exceed the limit of branches, This is an indicator function; it takes the value 1 if the condition is true, and 0 otherwise.

[0079] High carbon flux density branch ratio Definition: The proportion of branches with carbon flow density exceeding a preset threshold to the total number of branches.

[0080] The proportion of branches with high carbon flow density is used to quantify the spatial concentration of carbon emissions in the power grid. By comparing the actual carbon flow density of branches with the planned target, the rationality of the power grid's carbon emission distribution is dynamically assessed, reflecting the proportion of branches whose carbon flow density exceeds the planned threshold. This indicator can identify areas of dense carbon emission transmission, providing a priority reference for power grid low-carbon transformation, energy storage site selection, and carbon flow optimization. The proportion of branches with carbon flow density exceeding a preset threshold is calculated using the following formula: in The number of branches where the carbon flux density exceeds a threshold. branch road During the period Carbon flux density (unit: tCO) MW h)), The carbon flow density limit defined for the planning objectives is determined based on the regional carbon emission reduction targets or environmental capacity.

[0081] 3. Load-related evaluation indicators: Steady-state energy sufficiency This metric is used to evaluate the degree of matching between various energy supplies and corresponding load demands during steady-state system operation. It is defined as the root-square variance (i.e., the three-variance) of the matching degree for each energy source. First, the matching degree for each energy source is calculated... Matching degree When supply Not less than the demand If the supply-demand ratio is 1, then the matching degree is set to 1; otherwise, the ratio of supply to demand is used. Then, the expected value of all matching degrees is calculated. Finally, the steady-state energy sufficiency is obtained by averaging the cubes of the differences between each matching degree and the expected value, and then taking the cube root. The specific formula is as follows: in, For the set of output energy types (such as electrical energy, heat energy, etc.), The quantity of the type of energy output; and Energy Supply and load demand (units can be MWh or MW); For energy The degree of matching; This represents the expected value of the energy matching degree. The optimal energy supply is when the system's energy supply matches the load demand; when the energy supply is lower than the load demand, it leads to energy waste or a poor user experience. The three-square deviation of the energy matching degree reflects both the quantity and quality of energy supply.

[0082] The energy deficit ratio measures the degree of energy shortage in a system's supply to users, and is defined as the ratio of the sum of the deficits of all types of loads to the sum of the demands of all types of loads. First, the deficit ratio for each type of energy source is calculated. The amount of the shortfall When supply Less than demand When the demand exceeds the supply, the deficit is the difference between demand and supply; otherwise, the deficit is zero. Then, sum up all energy deficits and divide by the total demand to obtain the energy deficit rate. The specific formula is as follows: in, The total number of energy types, and Energy Supply and load demand (units can be MWh or MW), For energy The smaller the value of this indicator, the lower the degree of energy shortage in the system and the higher the reliability of energy supply.

[0083] 4. Carbon dimension evaluation indicators: Demand-side carbon flow guidance potential This indicator measures the ability of users to proactively adjust their electricity consumption behavior in response to carbon potential signals, and the synergistic optimization role of energy storage. It reflects the contribution of the combined operation of demand-side and energy storage to overall carbon emission reduction by quantifying the effect of user load flexibility on system carbon flow. The calculation formula consists of two parts: the first part is the carbon reduction brought about by the user's own load adjustment, which is the sum of the products of load adjustment and carbon potential difference in each time period; the second part is the amplification effect of energy storage on user adjustment, which is the sum of the product of energy storage charging and discharging power and carbon potential difference multiplied by the energy storage adjustment efficiency coefficient. The specific formula is as follows: in, For users in time periods The load regulation capacity (MW, a positive value indicates a reduction in electricity consumption or a shift in load), For time period The difference between the node carbon potential and the baseline carbon potential (tCO) MW h)), The energy storage regulation efficiency coefficient (valued between 0 and 1, reflecting the utilization rate of energy storage's regulation potential for users, determined by energy storage capacity, response speed, etc.) For energy storage during the period The charging and discharging power (discharging is positive, charging is negative, in MW). Users can reduce the proportion of electricity consumption during high carbon potential periods by adjusting the time of day or reducing electricity consumption; energy storage amplifies the carbon reduction effect of user adjustments by storing electrical energy during low carbon potential periods and discharging it during high carbon potential periods.

[0084] Energy storage carbon transfer efficiency This indicator quantifies the ability of energy storage systems to optimize the spatiotemporal distribution of carbon flow through a "low-storage, high-discharge" strategy, reflecting the efficiency of energy storage in utilizing carbon potential differences to transfer carbon emissions across time periods. By capturing the carbon potential difference between charging during low-carbon-potential periods and discharging during high-carbon-potential periods, carbon emissions are transferred from high-carbon-potential periods to low-carbon-potential periods, thereby reducing the overall carbon emission intensity of the system. The calculation method is as follows: the discharge amount in each period is multiplied by the cumulative difference between the discharge carbon potential and the charging carbon potential in that period, divided by the product of the system's average carbon emission intensity and the total discharge amount, and then multiplied by 100%. The specific formula is as follows: in, and Energy storage time period node carbon potential during charging and discharging MW h)), For energy storage during the period The discharge amount (MWh), The average carbon emission intensity of the system during the statistical period. MW h)), This represents the total discharge of energy stored over one cycle (MWh). The higher this ratio, the better the energy storage is at transferring carbon emissions through carbon potential difference.

[0085] It should be noted that in the comprehensive benefit evaluation of energy storage configuration schemes, in addition to the four core evaluation indicators of "source-grid-load-carbon", economic benefit indicators can also be used as optional calculation items or auxiliary evaluation indicators as needed to supplement the analysis of the economic differences of different energy storage schemes, or as reference data when scoring the comprehensive score.

[0086] Initial investment cost This refers to the one-time capital investment during the construction phase of an energy storage system, including energy storage capacity cost and power cost. The calculation formula is as follows: in, The unit capacity investment cost of energy storage (ten thousand yuan / MWh) The rated capacity of energy storage (MWh) The unit power investment cost of energy storage (ten thousand yuan / MW) Rated charge and discharge power of energy storage .

[0087] Operation and maintenance costs refer to the annual maintenance, repair, and management costs of an energy storage system throughout its entire lifecycle. They are typically calculated based on the rated capacity, using the following formula: In the formula This represents the unit capacity operation and maintenance cost coefficient for energy storage.

[0088] Replacement cost is used to estimate the expense of replacing energy storage equipment when it needs to be replaced due to end of its lifespan or performance degradation. It takes into account the time value of money and is calculated at present value. in, The percentage decrease in energy storage recovery costs (reflecting cost reductions resulting from technological advancements), For the discount rate, This refers to the number of times the energy storage system can be replaced (usually calculated by dividing the project's operating years by the energy storage lifespan and rounding down). The energy storage lifespan is measured in years.

[0089] The operating cost of thermal power units mainly includes fuel costs, which are usually expressed as a quadratic function of active power output, and can also be simplified to a linear model: In the formula Let represent the fuel cost of the g-th thermal power unit at time t. Where: In the formula This represents the coefficient of the linear term in the fuel cost of the g-th generating unit. This represents the constant term coefficient of the fuel cost of the g-th unit.

[0090] The energy arbitrage profit quantification method for energy storage systems utilizes the price difference gained from charging during low-price periods and discharging during high-price periods. The calculation formula is as follows: in, For time period The spot market price of electricity (ten thousand yuan / MWh), and Time periods The discharge power and charging power (MW) of energy storage The time interval is (h). The above economic benefit indicators can be flexibly selected according to specific application scenarios; in the comprehensive comparison of energy storage configuration schemes, they can be used in combination with the four-dimensional core indicators to form a more comprehensive evaluation system, or they can be used alone for economic analysis.

[0091] Specifically, in some embodiments, in step S4, the optimal energy storage configuration scheme is determined based on the operation scheduling scheme and the multi-dimensional evaluation index system, specifically including: Based on the operation and scheduling plan, the values ​​of each dimension index in the multi-dimensional evaluation index system are calculated, and the values ​​of each dimension index are comprehensively scored. The optimal energy storage configuration scheme is determined based on the comprehensive scoring results.

[0092] The operation and scheduling scheme refers to the power output sequence of generating units, the charging and discharging power sequence of energy storage, and the state of charge (SOC) sequence of energy storage for each time period obtained by solving the grid collaborative scheduling model. This scheme is an optimization result aimed at minimizing system operating costs (or maximizing overall benefits) under the premise of satisfying grid physical constraints (power balance, branch capacity, energy storage SOC range, etc.) and carbon flow constraints (upper limit of branch carbon flow density, priority of carbon potential drive).

[0093] Specifically, in some other embodiments, in step S4, the values ​​of each dimension index are comprehensively scored, and the optimal energy storage configuration scheme is determined based on the comprehensive scoring result, including: Step S41: Determine the subjective weight of each indicator by comparing each indicator pairwise and constructing a judgment matrix; Step S42: Determine the objective weight of each indicator by calculating the information entropy of each indicator value; Step S43: Combine the subjective weights and objective weights to obtain the comprehensive weights of each indicator; Step S44: Based on the comprehensive weight of each indicator, the indicator values ​​of the energy storage configuration scheme under each indicator are weighted and summed to obtain the comprehensive score of the energy storage configuration scheme. The energy storage configuration scheme with the highest comprehensive score is determined as the optimal energy storage configuration scheme for a single scenario.

[0094] The subjective weights were determined using the Analytic Hierarchy Process (AHP). Experts were invited to conduct pairwise comparisons and scoring of each indicator at the criterion level (source, network, load, carbon) and the indicator level, constructing a judgment matrix. After consistency testing, the subjective weights of each indicator were obtained. These subjective weights reflect the evaluation objectives and expert experience.

[0095] The objective weights are determined using the entropy weighting method. Based on the actual values ​​of each energy storage configuration scheme for each indicator, the objective contribution of the indicator to the scheme differentiation is reflected by calculating the information entropy and the difference coefficient. The larger the difference coefficient, the higher the objective weight.

[0096] The fusion weight refers to the comprehensive weight obtained by linearly weighting subjective weights and objective weights according to a certain ratio (fusion coefficient α). The fusion weight takes into account both subjective orientation and data objectivity, making the evaluation results more reasonable and stable.

[0097] The comprehensive score is obtained by standardizing each indicator value, multiplying it by the corresponding fusion weight, and summing the results. The higher the score, the better the overall benefit of the solution in that scenario.

[0098] The optimal energy storage configuration scheme for a single scenario refers to calculating a comprehensive score for all candidate energy storage configuration schemes under a specific operating scenario (such as high proportion of new energy access, dense load, low carbon emission reduction, etc.) and selecting the scheme with the highest score.

[0099] In practice, to scientifically assess the comprehensive benefits of the "source-grid-load-carbon" multi-stage process, a weight allocation method that integrates subjective and objective factors is adopted. By combining expert experience with data objectivity, the constructed evaluation indicators are weighted and comprehensively evaluated.

[0100] In step S41, based on multi-dimensional evaluation indicators, different operating scenarios are considered, and expert scoring is organized to calculate subjective weights. Let the scenario number be... In each scene The following hierarchical model is constructed: the target layer is "comprehensive energy storage benefits"; the criterion layer consists of four dimensions: "source, grid, load, and carbon"; and the indicator layer comprises specific evaluation indicators for each dimension. Experts in energy planning, grid operation, and low-carbon technologies are invited to discuss the model in conjunction with specific scenarios. The typical characteristics and evaluation focus are compared and scored pairwise for the relative importance of the criteria layer and the indicator layer. The 1-9 scale method (1 indicates equal importance, 3 indicates slightly important, 5 indicates significantly important, 7 indicates strong importance, 9 indicates extreme importance, and 2, 4, 6, and 8 are the median values ​​of adjacent judgments) is used to quantify and form a judgment matrix, as shown in Table 3.

[0101] Table 3 Judgment Matrix Quantization Criteria In the scene Let the judgment matrix of a certain layer be... ,in Let the order of the judgment matrix be denoted by . Indicates the first in this layer The element is relative to the first element The importance ratio of each element. First, calculate the geometric mean of the elements in each row. Then, the geometric mean of each row is normalized to obtain the weight vector of that layer. ,in .

[0102] To ensure the logical consistency of expert judgments, a consistency check is performed on the judgment matrix. The largest eigenvalue is calculated. ,in Representing vectors The Each component. Then, the consistency index is calculated. and consistency ratio ,in For order The corresponding random consistency index. When If the judgment matrix passes the consistency check, it is considered to have passed; otherwise, the scores for that scenario need to be adjusted and recalculated until the consistency requirements are met. After passing the consistency check, in the scenario... The following is the criterion layer weight vector: And the local weights of the indicator layer under each criterion dimension. Let the indicator layer have a total of... There are 1 indicator, with indicator number 1. For those belonging to the first Indicators of each criterion dimension In the scene The local weights under are denoted as Then the contextualized global subjective weight of this indicator is: The final subjective weight vector for all indicators in scenario s is: In step S42, to reflect the objective distinguishing ability of each evaluation indicator to differentiate schemes under different operating scenarios, the objective weights are calculated using the entropy weight method within the scenario. Under scenario s, different energy storage configuration schemes are selected as samples to construct the original data matrix. ,in Let J be the number of solutions in scenario s, and J be the number of metrics. This represents the evaluation result of the i-th solution on the j-th indicator in scenario s. To eliminate the influence of units, the original data is standardized and normalized according to the indicator type: For positive indicators: For negative indicators: If a certain metric takes the same value in all solutions under scenario s, then let .

[0103] For the standardized data, calculate the weight of scheme i under index j: Based on this, calculate the information entropy of index j under scenario s. and coefficient of difference : The larger the difference coefficient, the higher the distinguishing power and the greater the information content of the indicator in scenario s. Therefore, its objective weight should be relatively higher. Thus, the objective weight of indicator j in scenario s is... for: The final objective weight vector for all indicators in scenario s is: In step S43, a linear weighting method is used to fuse subjective and objective weights within the scene. The subjective weight vector for each indicator in scene s is... The objective weight vector is Then the comprehensive weight vector It can be represented as: In the formula, This represents the fusion coefficient for subjective weights in scenario s, which can be adjusted based on the planning orientation and data availability of that scenario. After obtaining the comprehensive weights of each indicator in scenario s, the standardized values ​​of each indicator are weighted and summed with the comprehensive weights. i Overall evaluation score in scenario s: The energy storage configuration scheme with the highest comprehensive score is determined as the optimal energy storage configuration scheme for this single scenario. The above comprehensive score results can be used for comparison of multiple schemes, and can also provide a quantitative basis for optimization scheduling and strategy adjustment in different scenarios.

[0104] Specifically, in some embodiments, step S4 further includes: Set up multiple running scenarios and assign corresponding scenario probability weights to each running scenario; For each operating scenario, determine the comprehensive score of each energy storage configuration scheme under the operating scenario; The comprehensive scores under each operating scenario are weighted and summed according to the probability weight of the corresponding scenario to obtain the cross-scenario comprehensive score of each energy storage configuration scheme. The energy storage configuration scheme with the highest comprehensive score across scenarios is determined as the globally optimal energy storage configuration scheme.

[0105] The cross-scenario comprehensive score refers to the weighted sum of the comprehensive scores for each of the multiple operating scenarios, calculated based on the probability of each scenario occurring. This score is used to select the globally optimal energy storage configuration under different operating conditions.

[0106] In the implementation, to verify the applicability of this method under different operating conditions, three typical operating scenarios were set up. Scenario parameters were determined by the system load ratio. New energy scale ratio Carbon potential ratio The power output of renewable energy is represented by time-of-use pricing. The output of renewable energy is set at the upper limit of available output, and downward adjustments are allowed during optimized dispatching. The key parameter settings for the three types of operating scenarios are shown in Table 4.

[0107] Table 4 Key parameter settings for three types of operating scenarios in Used for overall scaling of a typical daily load curve; Used for overall scaling of the new energy output curve; Used for carbon flow tracking and carbon-related index calculation; time-of-use pricing is used to drive differences in optimal scheduling and peak shaving / valley filling behaviors.

[0108] For each scenario and its corresponding configuration scheme, a typical 24-hour daily calculation was performed based on the collaborative scheduling and verification model. The solution was obtained under the premise of satisfying constraints such as power balance, branch safety, and energy storage SOC, and the index values ​​of each dimension were calculated. The parameters of the energy storage configuration schemes (A to E) are shown in Table 1, and the index calculation results for each scenario are shown in Tables 5, 6, and 7, respectively.

[0109] Table 5 Scenario 1 (High Proportion of New Energy Access) Indicator Calculation Table 6 Calculation Results of Indicators for Scenario 2 (Load Intensity) Table 7 Calculation Results of Indicators for Scenario 3 (Low-Carbon Emission Reduction) For the three scenarios, experts in energy planning, power grid operation, and low-carbon technology were organized to conduct pairwise comparative scoring to form judgment matrices for the criterion layer and the indicator layer, and these matrices were then tested for consistency. The weights of the criterion layer and the local weights of the indicator layer for each scenario are shown in Tables 8 and 9, respectively.

[0110] Table 8 Subjective Weight Allocation of Criteria Layer in Each Scenario Table 9 Subjective Weight Allocation for Each Scenario Indicator Layer The weights of the criteria layer and the local weights of the indicator layer are multiplied step by step to obtain the global subjective weights of the indicators in each scenario, as shown in Table 10, which are used for subsequent subjective-objective fusion and comprehensive scoring.

[0111] Table 10 Global Subjective Weight Allocation for Each Scenario Based on the evaluation results of the indicators in Tables 5 to 7, the indicators were standardized, and then the information entropy and difference coefficient were calculated to obtain the global objective weight of the indicators in each scenario, as shown in Table 11.

[0112] Table 11 Global Objective Weight Allocation for Each Scenario A linear weighting method is used to fuse subjective and objective weights, and the fusion coefficient is obtained. α =0.5, that is, the comprehensive weight = 0.5 × subjective weight + 0.5 × objective weight. The fusion weights of the indicators in each scenario are calculated as shown in Table 12.

[0113] Table 12 Fusion Weights for Each Scenario The standardized values ​​of each indicator are weighted and summed with the fusion weights to obtain the comprehensive score of each energy storage configuration scheme in each scenario, as shown in Table 13.

[0114] Table 13 Overall Scores for Each Scenario As shown in Table 13, under the three typical operating scenarios, Scheme E has the highest comprehensive score.

[0115] Let the scene set be The probability weights for each scenario are: (satisfy ). Regarding the plan Overall score within the scenario The scores are weighted and aggregated to obtain a comprehensive score across different scenarios: , choose The largest option is recommended: , In this embodiment, it is assumed that the three scenarios occur with equal probability, that is... Based on the scores for each scenario in Table 13, the cross-scenario comprehensive score for each solution was calculated, and the results are shown in Table 14.

[0116] Table 14 Scenario Overall Score and Recommended Solution Table 14 shows that Scheme E has the highest comprehensive score across scenarios (0.805), and is therefore determined to be the globally optimal energy storage configuration scheme. The corresponding energy storage configuration parameters for this scheme (rated charge / discharge power) are as follows: Rated capacity Charge and discharge efficiency The output of the planning results (such as SOC operating boundary, etc.) can be directly used for energy storage planning, or it can be used as a fixed input for subsequent operation scheduling and verification models.

[0117] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of a power system energy storage configuration system that integrates source, grid, load, and carbon in some embodiments of this application. The system includes: a parameter acquisition module 100, an analysis module 200, a scheme generation module 300, an evaluation module 400, and a configuration module 500. The parameter acquisition module 100 is used to acquire the operating parameters of the power system and the parameters of the energy storage configuration scheme. Analysis module 200 is used to perform power flow calculations based on the operating parameters of the power system and the parameters of the energy storage configuration scheme, to obtain the power distribution data of the power grid and the carbon emission intensity of the generator units, to perform carbon flow calculations, and to obtain the carbon emission distribution characteristic data of the power grid. The scheme generation module 300 is used to introduce carbon emission distribution characteristic data as carbon flow constraints into the power grid collaborative scheduling model, and combine it with the power grid physical constraints to obtain the operation scheduling scheme. The evaluation module 400 is used to determine the optimal energy storage configuration scheme based on the operation scheduling scheme and the multi-dimensional evaluation index system. The multi-dimensional evaluation index system includes power source dimension index, grid dimension index, load dimension index and carbon dimension index. Configuration module 500 is used to configure energy storage in the power system according to the optimal energy storage configuration scheme.

[0118] It is understood that the above system item embodiments correspond to the method item embodiments of this application, and can realize the source, grid, load and carbon coordinated power system energy storage configuration method provided by any of the above method item embodiments of this application.

[0119] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, 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 any creative effort.

[0120] For ease of description and brevity, the system implementation embodiments of this application include all the implementation methods in the above-described source, grid, load, and carbon synergy power system energy storage configuration method embodiments, and will not be repeated here.

[0121] Based on the above embodiments of the power system energy storage configuration method with source, grid, load, and carbon coordination, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the power system energy storage configuration method with source, grid, load, and carbon coordination provided in any of the above method embodiments of this application.

[0122] The above are preferred embodiments of this application. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for configuring energy storage in a power system that integrates source, grid, load, and carbon emissions, characterized in that: include: Obtain the operating parameters of the power system and the parameters of the energy storage configuration scheme; Based on the operating parameters of the power system and the parameters of the energy storage configuration scheme, power flow calculation is performed to obtain the power distribution data of the power grid; based on the power distribution data and the carbon emission intensity of the generator sets, carbon flow calculation is performed to obtain the carbon emission distribution characteristic data of the power grid. The carbon emission distribution characteristic data is introduced into the power grid collaborative scheduling model as a carbon flow constraint, and combined with the power grid physical constraints to solve the operation scheduling scheme. Based on the aforementioned operation scheduling scheme and multi-dimensional evaluation index system, the optimal energy storage configuration scheme is determined; wherein, the multi-dimensional evaluation index system includes power source dimension index, power grid dimension index, load dimension index and carbon dimension index. According to the optimal energy storage configuration scheme, the power system is configured with energy storage.

2. The power system energy storage configuration method for source-grid-load-carbon synergy according to claim 1, characterized in that, The step of performing power flow calculations based on the operating parameters of the power system and the parameters of the energy storage configuration scheme to obtain power distribution data of the power grid includes: Based on the operating parameters of the power system, determine the network topology of the power grid, the load data of each node in the power grid, and the output data of each generator unit at its node; Based on the parameters of the energy storage configuration scheme, determine the access node of the energy storage and the preset charging and discharging strategy of the energy storage at the access node; Based on the network topology, the power output data of each generator unit node, the load data of each node in the power grid, and the charging and discharging power corresponding to the preset charging and discharging strategy of the energy storage at the access node are used as the power boundary conditions of each node to perform power flow calculation and obtain the active power and active power flow direction of each branch in the power grid. The active power and active power flow direction of each branch in the power grid are used as the power distribution data.

3. The power system energy storage configuration method for source-grid-load-carbon synergy according to claim 2, characterized in that, The step of calculating carbon flow based on the power distribution data and the carbon emission intensity of the generator sets to obtain the carbon emission distribution characteristics data of the power grid includes: Based on the output data of each generator unit at the node, the load data of each node in the power grid, and the charging and discharging status of the energy storage at the access node, the node injection power of each node in the power grid is determined. Based on the carbon emission intensity of the generator set and the nodal injection power of each node in the power grid, and based on the principle of proportional sharing, the nodal carbon potential of each node in the power grid is calculated. Based on the node carbon potential and the active power flow direction of each branch in the power grid, the branch carbon flow density of each branch in the power grid is determined. The node carbon potential and the branch carbon flux density are used as the carbon emission distribution characteristic data.

4. The power system energy storage configuration method for source-grid-load-carbon synergy according to claim 3, characterized in that, The step of incorporating the carbon emission distribution characteristic data as a carbon flow constraint into the power grid collaborative scheduling model, and combining it with the power grid physical constraints to obtain the operation scheduling scheme, includes: The aforementioned power grid collaborative scheduling model is constructed, which is used to optimize unit output and energy storage charging and discharging strategies. Determine the set of key branches in the power grid coordinated dispatch model, and impose an upper limit constraint on the carbon flow density of the key branches on the set of key branches; Based on the node carbon potential, the charging and discharging priority of energy storage in the grid collaborative scheduling model is controlled; wherein, when the node carbon potential is lower than a first threshold, the charging priority of energy storage is increased, and when the node carbon potential is higher than a second threshold, the discharging priority of energy storage is increased. Apply power grid physical constraints to the power grid collaborative scheduling model, solve the power grid collaborative scheduling model, and obtain the operation scheduling scheme; wherein, the operation scheduling scheme includes the unit output timing sequence and the energy storage charging and discharging timing sequence.

5. The power system energy storage configuration method for source-grid-load-carbon synergy according to claim 1, characterized in that, The multi-dimensional evaluation index system includes power supply dimension indicators, power grid dimension indicators, load dimension indicators, and carbon dimension indicators, including: The power supply metrics include the proportion of clean energy supply and energy utilization efficiency. The power grid dimension indicators include steady-state safety margin and the proportion of branches with high carbon flow density. The load dimension indicators include steady-state energy sufficiency and energy deficit rate; The carbon dimension indicators include demand-side carbon flow guidance potential and energy storage carbon transfer efficiency.

6. The power system energy storage configuration method for source-grid-load-carbon synergy according to claim 1, characterized in that, The step of determining the optimal energy storage configuration scheme based on the operation scheduling scheme and the multi-dimensional evaluation index system includes: Based on the operation scheduling scheme, the values ​​of each dimension index in the multi-dimensional evaluation index system are calculated, and the values ​​of each dimension index are comprehensively scored. The optimal energy storage configuration scheme is determined based on the comprehensive scoring results.

7. The power system energy storage configuration method for source-grid-load-carbon synergy according to claim 6, characterized in that, The process of comprehensively scoring the values ​​of the various indicators and determining the optimal energy storage configuration scheme based on the comprehensive scoring results includes: The subjective weight of each indicator is determined by comparing each indicator pairwise and constructing a judgment matrix. The objective weight of each indicator is determined by calculating the information entropy of each indicator value. The subjective weights and objective weights are weighted and fused together to obtain the comprehensive weights of each indicator; Based on the comprehensive weight of each indicator, the indicator values ​​of the energy storage configuration scheme under each indicator are weighted and summed to obtain a comprehensive score of the energy storage configuration scheme. The energy storage configuration scheme with the highest comprehensive score is determined as the optimal energy storage configuration scheme for a single scenario.

8. The power system energy storage configuration method for source-grid-load-carbon synergy according to claim 7, characterized in that, Also includes: Set up multiple running scenarios and assign corresponding scenario probability weights to each running scenario; For each operating scenario, determine the comprehensive score of each energy storage configuration scheme under the operating scenario; The comprehensive scores under each operating scenario are weighted and summed according to the probability weight of the corresponding scenario to obtain the cross-scenario comprehensive score of each energy storage configuration scheme. The energy storage configuration scheme with the highest comprehensive score across scenarios is determined as the globally optimal energy storage configuration scheme.

9. A power system energy storage configuration system that integrates source, grid, load, and carbon emissions, characterized in that: include: The module includes a parameter acquisition module, an analysis module, a solution generation module, an evaluation module, and a configuration module. The parameter acquisition module is used to acquire the operating parameters of the power system and the parameters of the energy storage configuration scheme; The analysis module is used to perform power flow calculations based on the operating parameters of the power system and the parameters of the energy storage configuration scheme to obtain the power distribution data of the power grid; and to perform carbon flow calculations based on the power distribution data and the carbon emission intensity of the generator sets to obtain the carbon emission distribution characteristic data of the power grid. The scheme generation module is used to introduce the carbon emission distribution characteristic data as carbon flow constraints into the power grid collaborative scheduling model, and solve it in combination with the power grid physical constraints to obtain the operation scheduling scheme. The evaluation module is used to determine the optimal energy storage configuration scheme based on the operation scheduling scheme and the multi-dimensional evaluation index system; wherein, the multi-dimensional evaluation index system includes power source dimension index, power grid dimension index, load dimension index and carbon dimension index. The configuration module is used to configure energy storage for the power system according to the optimal energy storage configuration scheme.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the power system energy storage configuration method of source-grid-load-carbon coordination as described in any one of claims 1-8.

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