Network-configuration-type energy storage optimal configuration method and system based on source and storage load cooperation

Abstract

The application provides a network-constructing energy storage optimal configuration method and system based on source and load coordination, which comprises the following steps: obtaining source and load topology information of a target power grid system, and calculating node characteristic indexes according to the source and load topology information; combining the node characteristic indexes under the same node two by two, and constructing a node adaptation degree function according to the combination result; selecting matching nodes one by one which meet a preset range, and constructing a network-constructing energy storage system and a grid connection and charge constraint, verifying the virtual impedance excitation characteristic under the corresponding constraint; determining the matching nodes and the source and load island system starting scheme which meet the virtual impedance excitation characteristic, and calculating the charge and discharge capacity of the network-constructing energy storage system and the step-up transformer tap position under the starting scheme; and outputting the configuration parameters of the network-constructing energy storage system. The application can significantly improve the comprehensiveness, reliability and economy of the network-constructing energy storage in the site selection and capacity determination of a complex power system.

Description

Technical Field

[0001] This invention relates to the field of power system automation and smart grid technology, and in particular to a method and system for optimizing the configuration of grid-based energy storage based on source-storage-load coordination. Background Technology

[0002] With the increasing penetration rate of new energy sources, the inertia support capacity and voltage stability of power grid systems face severe challenges. Grid-based energy storage technology, due to its ability to simulate synchronous generator characteristics and autonomously construct grid voltage and frequency, is considered one of the key technologies for improving the stability of weak power grids. In the planning stage of grid-based energy storage, its site selection and capacity determination directly affect the system's support effect on transient disturbances, islanding operation, and black start processes.

[0003] Currently, research on energy storage optimization configuration mainly focuses on grid-connected energy storage, whose site selection and capacity determination typically use node voltage levels, line transmission limits, or economic costs as single optimization objectives. However, existing methods struggle to comprehensively assess the integrated adaptability of nodes in source-storage-load collaborative scenarios, particularly lacking a systematic consideration of multi-dimensional electrical characteristics during transient processes (such as inertial response, disturbance smoothing, and fault ride-through capability). Furthermore, when using energy storage to start thermal power units or construct islanded systems, existing technologies often neglect the matching problem between line capacitive effects and the virtual impedance of energy storage, and rarely incorporate the tap position of step-up transformers as configuration variables into the optimization framework. This leads to configuration schemes facing risks of overvoltage, resonance, or insufficient capacity utilization in practical engineering. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for optimizing the configuration of grid-type energy storage based on source-storage-load coordination, which aims to solve the configuration failure problem caused by the single index and lack of transient matching verification in traditional methods.

[0005] In a first aspect, the present invention provides a method for optimizing the configuration of grid-based energy storage based on source-storage-load coordination, the method comprising: The source-storage-load topology information of the target power grid system is obtained, and node characteristic indicators are calculated based on the source-storage-load topology information. The node characteristic indicators include voltage deviation rate, voltage sensitivity, generator disturbance power, inertia support strength, new energy absorption margin, new energy fluctuation mitigation contribution, power flow transmission quantitative indicators, and fault resistance capability. The node feature indicators under the same node are combined in pairs, and a node fitness function is constructed based on the combination result, so as to obtain the fitness of each node according to the node fitness function. According to the adaptability, matching nodes that meet the preset range are selected one by one, and a grid-type energy storage system connected to each matching node and grid connection and charge constraints are constructed. Under the corresponding constraints, the virtual impedance excitation characteristics of the thermal power unit in the start-up source-load islanding system of the grid-type energy storage system are verified during each charging stage. If the virtual impedance excitation characteristic verification is satisfactory, then the matching node and the source-load islanding system startup scheme that meet the virtual impedance excitation characteristic are determined, and the charging and discharging capacity of the grid-type energy storage system and the tap position of the step-up transformer under the startup scheme are calculated. The configuration parameters of the grid-type energy storage system are output, including the matching nodes that meet the verification of virtual impedance excitation characteristics, the charging and discharging capacity of the grid-type energy storage system, and the tap position of the step-up transformer.

[0006] Secondly, the present invention provides a grid-based energy storage optimization configuration system based on source-storage-load coordination, the system comprising: The node characteristic index acquisition module is used to acquire the source-storage-load topology information of the target power grid system and calculate the node characteristic index based on the source-storage-load topology information. The node characteristic index includes voltage deviation rate, voltage sensitivity, generator disturbance power, inertia support strength, new energy absorption margin, new energy fluctuation smoothing contribution, power flow transmission quantitative index and fault resistance capability. The fitness function construction module is used to combine the node feature indicators under the same node in pairs, and construct the node fitness function based on the combination result, so as to obtain the fitness of each node according to the node fitness function. The node selection module is used to select matching nodes that meet the preset range one by one according to the adaptability, and to construct a grid-type energy storage system connected to each matching node and grid connection and charge constraints, and to verify the virtual impedance excitation characteristics of the thermal power unit in each charging stage of the grid-type energy storage system starting the source-load islanding system under the corresponding constraints. The connector location determination module is used to determine the matching node and source-load islanding system startup scheme that meet the virtual impedance excitation characteristics if the virtual impedance excitation characteristics verification is met, and to calculate the charging and discharging capacity of the grid-type energy storage system and the tap position of the step-up transformer under the startup scheme. The configuration parameter output module is used to output the configuration parameters of the grid-type energy storage system. The configuration parameters include the matching node that meets the verification of virtual impedance excitation characteristics, the charging and discharging capacity of the grid-type energy storage system, and the tap position of the step-up transformer.

[0007] Thirdly, the present invention provides a storage medium that stores one or more programs, which, when executed by a processor, implement the above-described method for optimizing the configuration of grid-type energy storage based on source-storage-load coordination.

[0008] Fourthly, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the above-mentioned source-storage-load coordination-based grid-type energy storage optimization configuration method.

[0009] Compared with the prior art, the present invention has the following advantages: The proposed method for optimizing the configuration of grid-based energy storage based on source-storage-load synergy can significantly improve the comprehensiveness, reliability, and economy of grid-based energy storage in complex power systems by constructing a multi-dimensional node characteristic index system, introducing a node adaptability function screening mechanism, and coupling virtual impedance excitation characteristic verification with dynamic setting of transformer taps. This effectively solves the configuration failure problem caused by the single index and lack of transient matching verification in traditional methods. Specifically: First, compared to existing technologies that only use a few indicators such as voltage deviation or sensitivity, this invention calculates eight dimensions of node characteristic indicators, including voltage deviation rate, voltage sensitivity, generator disturbance power, inertia support strength, new energy absorption margin, new energy fluctuation mitigation contribution, power flow transmission quantification indicators, and fault resistance capability. Based on specific pairwise combinations, it constructs a node fit function, which comprehensively evaluates the overall fit of grid-based energy storage deployment from multiple perspectives, such as static voltage support, transient inertia response, disturbance mitigation capability, and fault ride-through performance, avoiding insufficient support due to one-sided point selection based on a single indicator. Second, this invention not only constructs power balance and state of charge constraints but also further verifies the virtual impedance excitation characteristics of thermal power units in each charging stage of the grid-based energy storage start-up source-load islanding system. By judging the matching relationship between the sum of line capacitive reactance and virtual impedance and generator impedance, it can effectively avoid excitation during unloaded charging of the line. The invention addresses magnetic resonance and overvoltage issues, overcoming the shortcomings of existing technologies that only consider steady-state constraints while neglecting transient electromagnetic matching risks. Furthermore, by using the tap position of the step-up transformer as a collaborative optimization variable, the optimal tap position is dynamically calculated based on the line no-charge voltage, energy storage output voltage setpoint, equivalent virtual reactance, and load reactive power demand. This allows for adaptive matching of voltage regulation requirements at different charging stages, preventing voltage over-limits and improving energy storage capacity utilization. Additionally, the invention verifies whether the discharge capacity of grid-based energy storage meets the time safety requirements of manual switching operations, and calculates additional reserve time as an output parameter when it does not, thereby ensuring power supply continuity throughout the black start process. The final output configuration parameters (matching node, charging / discharging capacity, step-up transformer tap position, and additional reserve time) simultaneously meet multiple engineering requirements, including steady-state economic dispatch, transient electromagnetic compatibility, and dynamic operation safety, significantly improving the configuration effectiveness of grid-based energy storage in source-load synergy scenarios. Attached Figure Description

[0010] Figure 1 This is a flowchart of a grid-based energy storage optimization configuration method based on source-storage-load coordination proposed in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the node parameter selection and fit degree calculation process in this invention. Figure 3 This is a schematic diagram of the startup scheme for a source-load island system, as exemplified by the present invention. Figure 4 This is a schematic diagram of the structure of a grid-type energy storage optimization configuration system based on source-storage-load coordination proposed in an embodiment of the present invention.

[0011] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but does not exclude other elements or objects.

[0013] like Figures 1 to 3 As shown, an embodiment of the present invention proposes a method for optimizing the configuration of grid-type energy storage based on source-storage-load coordination. This method includes steps S101 to S105, wherein: Step S101: Obtain the source-storage-load topology information of the target power grid system, and calculate the node characteristic indicators based on the source-storage-load topology information. The node characteristic indicators include voltage deviation rate, voltage sensitivity, generator disturbance power, inertia support strength, new energy absorption margin, new energy fluctuation mitigation contribution, power flow transmission quantitative indicators, and fault resistance capability. It should be noted that current methods for site selection and verification of grid-connected energy storage systems largely rely on static power flow calculations and stability simulations, which lack flexibility and easily overlook uncertainties such as sudden changes in operating conditions and insufficient inertia support during actual grid-connected operation. In particular, this invention introduces the Gumbel-Softmax algorithm to establish a screening mechanism for grid-connected energy storage site selection based on grid inertia, absorption capacity, and wave suppression capabilities. This solves the problems of low discrete variable solving and multi-objective coordination matching in site selection optimization. Its advantages lie in maintaining the hierarchical nature of discrete decision variables for grid-connected energy storage while enhancing the convergence of the fit set, thus improving the scalability and flexibility of the site selection criteria for grid-connected energy storage.

[0014] Specifically, the voltage deviation rate is calculated according to the following formula: ; in, Let i be the average voltage deviation rate at node i. Let be the actual voltage of node i at time t. The voltage is the rated voltage, and T is the total number of time points counted. Calculate voltage sensitivity using the following formula: ; in, Let be the voltage sensitivity of node i. Let be the reactive power of node i at time t; Calculate the generator disturbance power using the following formula: ; in, Let be the disturbance power at node i. The sum of load regulation capabilities. Let i be the regulation capacity coefficient of the i-th generator in the system. The system frequency change is denoted by n, and n is the total number of generators connected to the target power grid system. The strength of the inertia brace is calculated using the following formula: ; in, For the inertia support strength of the target power grid system, Let be the inertial constant of the i-th synchronous machine. This refers to the rated capacity of a single unit. Let n be the total installed capacity of the units. The virtual inertia of the grid-connected energy storage system represents the maximum output power difference, where n is the total number of generators connected to the target power grid system. This represents the time variation value corresponding to the change in system frequency. The margin for renewable energy consumption is calculated using the following formula: ; in, The new energy consumption margin at node i. This represents the maximum power transmitted outward by the line where node i is located. For the local load of node i, Let i be the network loss of node i. Let i be the installed capacity of new energy sources at node i. The contribution of new energy sources to mitigating fluctuations is calculated using the following formula: ; in, Contribution to fluctuation mitigation , They are the target power grid system at time t, The total power fluctuation caused by sudden changes in new energy output or load. , They are respectively system time t, Local load active power at any given time. Let t be the active power of the grid-type energy storage system in the target power grid system at time t; The quantitative index of power flow transmission is calculated using the following formula: ; in, To quantify the key influence of node i in power flow transmission in a power network, For unit power injected from node s, node When extracted, the power flow through node i is taken out, and N is the total number of nodes in the target power grid system; The fault tolerance capability is calculated using the following formula: ; in, To measure the fault tolerance of a power system in meeting safe operation criteria under preset fault scenarios, the value range is [0,1]. To determine the number of scenarios in which the target power grid system does not experience overload, voltage exceedance, frequency instability, or load loss among all preset unit component failure scenarios, each scenario corresponds to the independent disconnection of one component (such as a line or transformer). This represents the total number of all components in the target power grid system that can be included in the verification.

[0015] Step S102: Combine the node feature indicators under the same node in pairs, and construct a node fitness function based on the combination result, so as to obtain the fitness of each node according to the node fitness function. It should be noted that, in some embodiments, the total set is defined first. ,in, to For the first to fifth basic combinations, Other physical quantities characterizing the strength of power grid nodes, which may be expanded as needed; From the total set, select at least two basic combinations to obtain , ,..., ; Construct the node fitness function according to the following formula: ; in, The suitability of deploying a grid-type energy storage system at node i in a power system is given by x, where x is the number of combinations, ranging from 1 to 26. For the corresponding combination of standards, Let i be the comprehensive ability score of node i under the corresponding combination, where the comprehensive ability score is the product of the scores of each basic combination included under the corresponding combination; For the first to fourth basic combinations, assuming they contain node feature indices a and b, the basic combination score is calculated using the following formula: ; For the fifth basic combination, its basic combination score is... , This is the activation function.

[0016] It should also be noted that this invention constructs a basic combination by pairwise combining the indicators, rather than evaluating the eight indicators as independent dimensions. This is mainly because the actual operational performance of grid-based energy storage is often determined by the coupling relationship between two indicators rather than the absolute value of a single indicator. For example, a low voltage deviation rate alone is insufficient to indicate that a node is suitable for deploying grid-based energy storage. If voltage sensitivity is also very high, even small power fluctuations can trigger significant voltage jumps, and the voltage support capacity of the grid-based energy storage will be quickly saturated. By forcibly pairing closely coupled indicators for evaluation, the evaluation process is compelled to focus on the synergistic effects and constraints between the indicators.

[0017] Step S103: Select matching nodes that meet the preset range one by one according to the adaptability, and construct a grid-type energy storage system connected to each matching node and grid connection and charge constraints, and verify the virtual impedance excitation characteristics of the thermal power unit in each charging stage of the grid-type energy storage system starting the source-load islanding system under the corresponding constraints. It should be noted that after calculating the suitability of all nodes, it is also necessary to screen out all matching nodes that meet the preset range, and select them in descending order from all matching nodes that meet the preset range to ensure that grid-type energy storage is deployed first at the node with the best overall electrical performance.

[0018] Furthermore, in some embodiments, the power balance constraints of the matching nodes of the grid-type energy storage system are constructed according to the following formula to clarify the relationship between the net energy storage output and the charging and discharging power, and to set minimum / maximum allowable net output boundaries to prevent energy storage devices from being damaged due to overload: ; in, This refers to the net outflow of active power during the black start period of a grid-type energy storage system. , These represent the charging and discharging active power of the grid-type energy storage system at time t, respectively. , These are the minimum and maximum allowable net output of a grid-type energy storage system, respectively.

[0019] The state of charge (SOC) constraints for grid-type energy storage systems are constructed based on the following formula. These SOC constraints incorporate parameters such as sampling time, charge / discharge efficiency, and rated power. By dynamically tracking changes in charge level, the system ensures that the energy storage operates within a safe charge range, preventing overcharging or over-discharging that could lead to lifespan degradation. ; in, The charge of a grid-type energy storage system at time t, The set state of charge sampling time, , They are respectively the corresponding , The charging and discharging efficiency ranges from 85% to 95%, with 95% taken as the efficiency for the first year of operation. The rated active power of the system. , These are the minimum and maximum charge capacities of a grid-type energy storage system, respectively.

[0020] Finally, determine whether the line capacitive reactance satisfies the following formula. If it does, then it is determined to meet the virtual impedance excitation characteristics: ; in, The electric angular velocity is 314. , The virtual impedance is set for the energy storage control system, and m is the operating node number of the thermal power unit in the grid-type energy storage start-up source-load islanding system. This refers to the access node number for the power supply to thermal power units in the source-load islanding system. It is the sum of the capacitive reactance of all lines from the energy storage terminal to the current operating node m. Let the charging capacitance of the k-th segment be . For the reactance of thermal power units in the source-load island system, The resistance of the thermal power unit in the source-load island system.

[0021] In summary, the preset tap positions of traditional transformers under steady-state operation cannot match the dynamic voltage fluctuations during the restart of a grid-based energy storage system. Adjustments are delayed, and decisions are prone to causing global imbalances. This invention, within a startup scheme for a grid-based energy storage access node and a source-load islanding system that conforms to virtual impedance excitation characteristics, constructs an adaptive algorithm model that performs rolling optimization and adaptive correction, and solves for the charging and discharging capacity of the grid-based energy storage system and the tap position of the step-up transformer under robust optimization. This model predicts and coordinates the tap position of the step-up transformer throughout the entire process, accurately matching dynamic operating conditions and improving the stability and efficiency of system restart.

[0022] Step S104: If the virtual impedance excitation characteristic verification is satisfactory, then determine the matching node and source-load islanding system startup scheme that meet the virtual impedance excitation characteristic, and calculate the charging and discharging capacity of the grid-type energy storage system and the tap position of the step-up transformer under the startup scheme. It should be noted that in this step, the tap position of the step-up transformer is obtained according to the following formula: ; in, This represents the maximum voltage amplitude of the k-th line segment during unloaded charging. This is the setpoint for the output voltage at the grid connection point of the grid-connected energy storage system. The equivalent virtual reactance of a grid-type energy storage system, Let K be the equivalent reactance of the k-th segment of the line. For the first The reactive power of the charging line segment The reactive power used for charging the load at the end of the k-th line segment. The net reactive power demand at node k+1 is the load net reactive power requirement. As the system reference apparent power, The equivalent reactance of node k+1 in a grid-type energy storage system. This refers to the final voltage of the power plant's auxiliary power bus. Let n-1 be the overvoltage value under load. This is the initial tap position before the step-up transformer is closed. This refers to the rated tap position of the transformer.

[0023] In summary, by establishing a functional relationship between the tap position of the step-up transformer and the line no-charge voltage, the setpoint of the energy storage output voltage, the equivalent virtual reactance, the line equivalent reactance, the charging reactive power, the net reactive power demand of the load, the final voltage of the power plant's auxiliary power bus, and the overvoltage value under load, dynamic optimization of the tap position is achieved. Compared with traditional solutions that fix the tap to the rated position or rely solely on experience for selection, this method can adaptively calculate the optimal tap position based on the voltage and reactive power demand at different charging stages, effectively suppressing the operating overvoltage during no-charge of the line, while ensuring that the bus voltage does not exceed the limit under load. In addition, the above formula also considers the influence of the equivalent virtual reactance of the grid-type energy storage, co-optimizing the energy storage control parameters with the transformer's physical tap position, thereby improving the utilization rate of the energy storage capacity under actual operating conditions.

[0024] Step S105: Output the configuration parameters of the grid-type energy storage system. The configuration parameters include the matching node that meets the verification of virtual impedance excitation characteristics, the charging and discharging capacity of the grid-type energy storage system, and the tap position of the step-up transformer.

[0025] In some embodiments, it is also necessary to verify whether the discharge capacity of the grid-type energy storage meets the time safety requirements of manual switching operations. If it does not meet the requirements, the additional reserve time is calculated and output as a configuration parameter. The additional reserve time is calculated using the following formula: ; in, For the rated capacity of a grid-type energy storage system, The discharge efficiency of a grid-type energy storage system. The total time spent on manual switching operations. To provide a safety margin, The total load power on the start-up path, Additional time is reserved to allow for the startup process if the grid-type energy storage system is insufficient. To meet the total load energy demand, This refers to the average charging power of a grid-type energy storage system.

[0026] In summary, based on ensuring the optimal configuration of grid-type energy storage under the source-storage collaborative system, the discharge time of the grid-type energy storage system is verified under the condition of meeting the safety margin of manual switching operation time. Its advantage is that it can reasonably assess the impact of manual switching operation on the optimal configuration of grid-type energy storage. By increasing the additional time that the grid-type energy storage system cannot support the entire startup process, the discharge process of the grid-type energy storage is made more reasonable and controllable.

[0027] Furthermore, it should be noted that to obtain the charging and discharging capacity, firstly, based on the source-load islanding system startup scheme verified through virtual impedance excitation characteristics, the active power demand curves of all loads requiring power supply along the startup path (including thermal power unit auxiliary power, line charging power, and transformer losses, etc.) in time sequence are determined, and the total energy required for the entire startup process is calculated. Then, using the established dynamic constraint equations of the state of charge, starting from the initial state of charge of the energy storage system (usually set to near full load) and ending with the state of charge not falling below the safety lower limit upon startup completion, the minimum rated power required to support the complete startup process is calculated in reverse. This rated power must be greater than or equal to the maximum instantaneous load power appearing along the startup path. After determining the rated power, the rated power is multiplied by the duration of the longest continuous discharge period during startup to obtain the basic energy capacity. In addition, if the final calculation results show that additional reserve time is required, the product of the average discharge power and the additional reserve time must be added to the basic energy capacity as an energy margin. The final output charge and discharge capacity consists of the rated power and rated energy capacity mentioned above, ensuring that the energy storage system has sufficient power output capability and energy reserves to complete the entire black start process while meeting the electromagnetic transient matching requirements.

[0028] In summary, based on the above-mentioned source-storage-load synergy-based grid-type energy storage optimization configuration method, by constructing a multi-dimensional node characteristic index system, introducing a node adaptability function screening mechanism, and coupling virtual impedance excitation characteristic verification with transformer tap dynamic setting, the comprehensiveness, reliability, and economy of grid-type energy storage in the location and capacity determination of complex power systems can be significantly improved. This effectively solves the configuration failure problem caused by the single index and lack of transient matching verification in traditional methods. Specifically: First, compared to existing technologies that only use a few indicators such as voltage deviation or sensitivity, this invention calculates eight dimensions of node characteristic indicators, including voltage deviation rate, voltage sensitivity, generator disturbance power, inertia support strength, new energy absorption margin, new energy fluctuation mitigation contribution, power flow transmission quantification indicators, and fault resistance capability. Based on specific pairwise combinations, it constructs a node fit function, which comprehensively evaluates the overall fit of grid-based energy storage deployment from multiple perspectives, such as static voltage support, transient inertia response, disturbance mitigation capability, and fault ride-through performance, avoiding insufficient support due to one-sided point selection based on a single indicator. Second, this invention not only constructs power balance and state of charge constraints but also further verifies the virtual impedance excitation characteristics of thermal power units in each charging stage of the grid-based energy storage start-up source-load islanding system. By judging the matching relationship between the sum of line capacitive reactance and virtual impedance and generator impedance, it can effectively avoid excitation during unloaded charging of the line. The invention addresses magnetic resonance and overvoltage issues, overcoming the shortcomings of existing technologies that only consider steady-state constraints while neglecting transient electromagnetic matching risks. Furthermore, by using the tap position of the step-up transformer as a collaborative optimization variable, the optimal tap position is dynamically calculated based on the line no-charge voltage, energy storage output voltage setpoint, equivalent virtual reactance, and load reactive power demand. This allows for adaptive matching of voltage regulation requirements at different charging stages, preventing voltage over-limits and improving energy storage capacity utilization. Additionally, the invention verifies whether the discharge capacity of grid-based energy storage meets the time safety requirements of manual switching operations, and calculates additional reserve time as an output parameter when it does not, thereby ensuring power supply continuity throughout the black start process. The final output configuration parameters (matching node, charging / discharging capacity, step-up transformer tap position, and additional reserve time) simultaneously meet multiple engineering requirements, including steady-state economic dispatch, transient electromagnetic compatibility, and dynamic operation safety, significantly improving the configuration effectiveness of grid-based energy storage in source-load synergy scenarios.

[0029] like Figure 4 As shown, one embodiment of the present invention proposes a grid-based energy storage optimization configuration system based on source-storage-load coordination, the system comprising: The node characteristic index acquisition module 10 is used to acquire the source-storage-load topology information of the target power grid system and calculate the node characteristic index based on the source-storage-load topology information. The node characteristic index includes voltage deviation rate, voltage sensitivity, generator disturbance power, inertia support strength, new energy absorption margin, new energy fluctuation smoothing contribution, power flow transmission quantitative index and fault resistance capability. The fitness function construction module 20 is used to combine the node feature indicators under the same node in pairs, and construct a node fitness function based on the combination result, so as to obtain the fitness of each node according to the node fitness function. The node selection module 30 is used to select matching nodes that meet the preset range one by one according to the adaptability, and construct a grid-type energy storage system connected to each matching node and grid connection and charge constraints, and verify the virtual impedance excitation characteristics of the thermal power unit in each charging stage of the grid-type energy storage system starting the source-load islanding system under the corresponding constraints. The connector location determination module 40 is used to determine the matching node and source-load islanding system startup scheme that meet the virtual impedance excitation characteristics if the virtual impedance excitation characteristics verification is met, and to calculate the charging and discharging capacity of the grid-type energy storage system and the tap position of the step-up transformer under the startup scheme. The configuration parameter output module 50 is used to output the configuration parameters of the grid-type energy storage system. The configuration parameters include the matching node that meets the verification of virtual impedance excitation characteristics, the charging and discharging capacity of the grid-type energy storage system, and the tap position of the step-up transformer.

[0030] In another aspect, the present invention also proposes a storage medium on which one or more programs are stored, which, when executed by a processor, implement the above-described method for optimizing the configuration of grid-type energy storage based on source-storage-load coordination.

[0031] In another aspect, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to realize the above-mentioned grid-type energy storage optimization configuration method based on source-storage-load coordination.

[0032] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain stored, communicated, propagated, or transmitted programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0033] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0034] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0035] While embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of the invention as set forth in the claims. Furthermore, the invention described herein may have other embodiments and can be implemented or carried out in various ways.

Claims

1. A method for optimizing the configuration of grid-type energy storage based on source-storage-load coordination, characterized in that, The method includes: The source-storage-load topology information of the target power grid system is obtained, and node characteristic indicators are calculated based on the source-storage-load topology information. The node characteristic indicators include voltage deviation rate, voltage sensitivity, generator disturbance power, inertia support strength, new energy absorption margin, new energy fluctuation mitigation contribution, power flow transmission quantitative indicators, and fault resistance capability. The node feature indicators under the same node are combined in pairs, and a node fitness function is constructed based on the combination result, so as to obtain the fitness of each node according to the node fitness function. Based on the suitability, matching nodes that meet the preset range are selected one by one, and a grid-type energy storage system connected to each matching node is constructed, along with grid connection and charge constraints. Under the corresponding constraints, the virtual impedance excitation characteristics of the thermal power unit in each charging stage of the grid-type energy storage system's start-up source-load islanding system are verified, specifically including: Determine whether the line capacitive reactance satisfies the following formula. If it does, then it is determined to meet the virtual impedance excitation characteristics: ； in, The electric angular velocity is 314. , The virtual impedance is set for the energy storage control system; m is the operating node number of the thermal power unit in the grid-type energy storage start-up source-load islanding system; and n is the access node number of the plant power supply for the thermal power unit in the source-load islanding system. It is the sum of the capacitive reactance of all lines from the energy storage terminal to the current operating node m. Let the charging capacitance of the k-th segment be . For the reactance of thermal power units in the source-load island system, The resistance of the thermal power unit in the source-load island system; If the virtual impedance excitation characteristic verification is successful, then the matching node and source-load islanding system startup scheme that meet the virtual impedance excitation characteristic are determined, and the charging and discharging capacity of the grid-type energy storage system and the tap position of the step-up transformer under the startup scheme are calculated, specifically including: The tap position of the step-up transformer can be determined using the following formula: ； in, This represents the maximum voltage amplitude of the k-th line segment during unloaded charging. This is the setpoint for the output voltage at the grid connection point of the grid-connected energy storage system. The equivalent virtual reactance of a grid-type energy storage system, Let K be the equivalent reactance of the k-th segment of the line. The charging reactive power of the k-th segment of the line. The reactive power used for charging the load at the end of the k-th line segment. The net reactive power demand at node k+1 is... As the system reference apparent power, The equivalent reactance of node k+1 in a grid-type energy storage system. This refers to the final voltage of the power plant's auxiliary power bus. Let n-1 be the overvoltage value under load. This is the initial tap position before the step-up transformer is closed. This is the rated tap position for the transformer; The configuration parameters of the grid-type energy storage system are output, including the matching nodes that meet the verification of virtual impedance excitation characteristics, the charging and discharging capacity of the grid-type energy storage system, and the tap position of the step-up transformer.

2. The method for optimizing the configuration of grid-type energy storage based on source-storage-load coordination according to claim 1, characterized in that, The steps of obtaining the source-storage-load topology information of the target power grid system and calculating node characteristic indicators based on the source-storage-load topology information include: Calculate the voltage deviation rate using the following formula: ； in, Let i be the average voltage deviation rate at node i. Let be the actual voltage of node i at time t. The voltage is the rated voltage, and T is the total number of time points counted. Calculate voltage sensitivity using the following formula: ； in, Let be the voltage sensitivity of node i. Let be the reactive power of node i at time t; Calculate the generator disturbance power using the following formula: ； in, Let be the disturbance power at node i. The sum of load regulation capabilities. Let i be the regulation capacity coefficient of the i-th generator in the system. The system frequency change is denoted by n, and n is the total number of generators connected to the target power grid system. The strength of the inertia brace is calculated using the following formula: ； in, For the inertia support strength of the target power grid system, Let be the inertial constant of the i-th unit. This refers to the rated capacity of a single unit. Let n be the total installed capacity of the units. The virtual inertia of the grid-connected energy storage system represents the maximum output power difference, where n is the total number of generators connected to the target power grid system. This represents the time variation value corresponding to the change in system frequency. The margin for renewable energy consumption is calculated using the following formula: ； in, The new energy consumption margin at node i. This represents the maximum power transmitted outward by the line where node i is located. For the local load of node i, Let i be the network loss of node i. Let i be the installed capacity of new energy sources at node i. The contribution of new energy sources to mitigating fluctuations is calculated using the following formula: ； in, Contribution to fluctuation mitigation , They are respectively time t in the target power grid system, The fluctuation in total active power caused by sudden changes in renewable energy output or load. , They are respectively system time t, Local load active power at any given time. Let t be the active power of the grid-type energy storage system in the target power grid system at time t; The quantitative index of power flow transmission is calculated using the following formula: ； in, To quantify the key influence of node i in power flow transmission in a power network, For unit power injected from node s, node When extracted, the power flow through node i is taken out, and N is the total number of nodes in the target power grid system; The fault tolerance capability is calculated using the following formula: ； in, To measure the fault tolerance of a power system in meeting safe operation criteria under preset fault scenarios, the value range is [0,1]. This refers to the number of scenarios in which the target power grid system does not experience overload, voltage exceedance, frequency instability, or load loss among all preset unit failure scenarios. This represents the total number of all components in the target power grid system that can be included in the verification.

3. The method for optimizing the configuration of grid-type energy storage based on source-storage-load coordination according to claim 2, characterized in that, The step of combining the node feature indicators under the same node in pairs and constructing a node fitness function based on the combination result, and obtaining the fitness of each node based on the node fitness function, includes: Define the total set ,in, to For the first to fifth basic combinations, Other physical quantities characterizing the strength of power grid nodes, which may be expanded as needed; From the total set, select at least two basic combinations to obtain , ,..., ; Construct the node adaptability function according to the following formula: ； in, The suitability of deploying a grid-type energy storage system at node i in a power system is given by x, where x is the number of combinations, ranging from 1 to 26. For the corresponding combination of standards, Let i be the comprehensive capability score of node i under the corresponding combination, where the comprehensive capability score is the product of the scores of each basic combination included under the corresponding combination; For the first to fourth basic combinations, assuming they contain node feature indices a and b, the basic combination score is calculated using the following formula: ； For the fifth basic combination, its basic combination score is... , This is the activation function.

4. The method for optimizing the configuration of grid-type energy storage based on source-storage-load coordination according to claim 3, characterized in that, The steps of selecting matching nodes that meet the preset range one by one according to the adaptability, and constructing a grid-type energy storage system connected to each matching node and the grid connection and charge constraint include: Filter out all matching nodes that meet the preset range, and select them sequentially from all matching nodes that meet the preset range in descending order; The power balance constraints for matching nodes in a grid-type energy storage system are constructed using the following formula: ； in, This refers to the net outflow of active power during the black start period of a grid-type energy storage system. , These represent the charging and discharging active power of the grid-type energy storage system at time t, respectively. , These are the minimum and maximum allowable net output of a grid-type energy storage system, respectively. The state-of-charge constraints for a grid-type energy storage system are constructed based on the following formula: ； in, For the grid-type energy storage system at time t, The set state of charge sampling time, , They are respectively the corresponding , The charging and discharging efficiency ranges from 85% to 95%, with 95% taken as the efficiency for the first year of operation. The rated active power of the system. , These are the minimum and maximum charge capacities of a grid-type energy storage system, respectively.

5. The method for optimizing the configuration of grid-type energy storage based on source-storage-load coordination according to claim 4, characterized in that, The method further includes: Verify whether the discharge capacity of the grid-type energy storage meets the time safety requirements of manual switching operations. If it does not meet the requirements, calculate the additional reserve time and output the additional reserve time as a configuration parameter. The additional reserve time is calculated using the following formula: ； in, For the rated capacity of a grid-type energy storage system, The discharge efficiency of a grid-type energy storage system. The total time spent on manual switching operations. To provide a safety margin, The total load power on the start-up path, Additional time is reserved to allow for the startup process if the grid-type energy storage system is insufficient. To meet the total load energy demand, This refers to the average charging power of a grid-type energy storage system.

6. A grid-type energy storage optimization configuration system based on source-storage-load coordination, used to implement the grid-type energy storage optimization configuration method based on source-storage-load coordination as described in any one of claims 1-5, characterized in that, The system includes: The node characteristic index acquisition module is used to acquire the source-storage-load topology information of the target power grid system and calculate the node characteristic index based on the source-storage-load topology information. The node characteristic index includes voltage deviation rate, voltage sensitivity, generator disturbance power, inertia support strength, new energy absorption margin, new energy fluctuation smoothing contribution, power flow transmission quantitative index and fault resistance capability. The fitness function construction module is used to combine the node feature indicators under the same node in pairs, and construct the node fitness function based on the combination result, so as to obtain the fitness of each node according to the node fitness function. The node selection module is used to select matching nodes that meet the preset range one by one according to the adaptability, and to construct a grid-type energy storage system connected to each matching node and grid connection and charge constraints, and to verify the virtual impedance excitation characteristics of the thermal power unit in each charging stage of the grid-type energy storage system starting the source-load islanding system under the corresponding constraints. The connector location determination module is used to determine the matching node and source-load islanding system startup scheme that meet the virtual impedance excitation characteristics if the virtual impedance excitation characteristics verification is met, and to calculate the charging and discharging capacity of the grid-type energy storage system and the tap position of the step-up transformer under the startup scheme. The configuration parameter output module is used to output the configuration parameters of the grid-type energy storage system. The configuration parameters include the matching node that meets the verification of virtual impedance excitation characteristics, the charging and discharging capacity of the grid-type energy storage system, and the tap position of the step-up transformer.

7. A storage medium, characterized in that, The storage medium stores one or more programs, which, when executed by a processor, implement the grid-based energy storage optimization configuration method based on source-storage-load coordination as described in any one of claims 1-5.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the grid-type energy storage optimization configuration method based on source-storage-load coordination as described in any one of claims 1-5.

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