Hybrid energy storage collaborative planning method and device for power distribution network and computer equipment

Abstract

This application relates to a method, apparatus, and computer equipment for hybrid energy storage collaborative planning in power distribution networks. The method includes: constructing a two-layer planning model; optimizing energy storage capacity configuration with economic indicators as the objective, and optimizing operating control parameters with power distribution network frequency security indicators as the objective in the upper layer verification layer; inputting multiple capacity configuration schemes output by the upper layer planning layer into the lower layer verification layer, performing transient frequency response simulation and parameter optimization under various power distribution network operating modes to obtain the optimized control parameters and frequency security indicators corresponding to each of the multiple capacity configuration schemes; feeding back the frequency security indicators to the upper layer planning layer, causing it to adjust the multiple capacity configuration schemes and re-input them into the lower layer verification layer for iterative optimization until the iteration termination condition is met, thus obtaining the collaborative planning scheme. This method can simultaneously address energy storage capacity configuration and control parameter optimization under multiple operating modes, improving the accuracy of hybrid energy storage collaborative planning.

Description

Technical Field

[0001] This application relates to the field of power system energy storage planning technology, and in particular to a hybrid energy storage collaborative planning method, device and computer equipment for distribution networks. Background Technology

[0002] With the large-scale integration of distributed photovoltaic, wind power, and other new energy sources into distribution networks, the operating characteristics of distribution networks have undergone profound changes. The traditional passive network operation mode of distribution networks is shifting towards an active distribution network. The integration of numerous power electronic interfaces has significantly reduced the inertia level of distribution networks, posing severe challenges to system frequency and voltage stability. Simultaneously, as a crucial link connecting the transmission network and users, the reliability of power supply directly impacts user experience. When a distribution network loses grid connection due to faults, maintenance, or extreme weather, it needs to be able to quickly switch to islanded operation mode to ensure continuous power supply to critical loads. However, existing distribution networks often lack sufficient voltage and frequency regulation capabilities after being disconnected from the main grid, making it difficult to maintain stable operation in islanded mode. To address these challenges, configuring energy storage systems to provide rapid power support, frequency regulation, and voltage control services has become an industry consensus.

[0003] In related technologies, energy storage planning for distribution networks focuses on capacity configuration optimization under grid-connected mode, while insufficient consideration is given to energy storage support capabilities under islanded operation mode. Furthermore, frequency control parameters (such as virtual inertia time constant and primary frequency regulation coefficient) are often set to fixed values, which cannot adapt to the time-varying characteristics of different operating modes of the distribution network. This results in insufficient redundancy or safety margin in energy storage configuration, making it difficult to achieve economic optimization while ensuring power supply security. Summary of the Invention

[0004] Based on this, it is necessary to provide a hybrid energy storage collaborative planning method, device, computer equipment, computer-readable storage medium, and computer program product for distribution networks that can take into account the optimization of energy storage capacity configuration and control parameters under multiple operating modes and improve the accuracy of hybrid energy storage collaborative planning.

[0005] Firstly, this application provides a hybrid energy storage collaborative planning method for distribution networks, including:

[0006] A two-layer planning model for hybrid energy storage systems is constructed. The two-layer planning model includes an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the target, and the lower verification layer optimizes the operation control parameters of the hybrid energy storage system with the distribution network frequency security indicator as the target.

[0007] The multiple capacity configuration schemes output by the upper planning layer are input into the lower verification layer, and transient frequency response simulation and parameter optimization are performed under various distribution network operation modes to obtain the optimized control parameters and frequency safety indicators corresponding to each of the multiple capacity configuration schemes; the frequency safety indicators are determined based on the frequency stability indicators corresponding to each of the multiple distribution network operation modes.

[0008] The frequency security index is fed back to the upper planning layer, which then adjusts multiple sets of capacity configuration schemes and re-inputs them to the lower verification layer for iterative optimization until the iteration termination condition is met, thus obtaining the collaborative planning scheme of the hybrid energy storage system. The collaborative planning scheme includes a target capacity configuration scheme and target optimization control parameters.

[0009] Secondly, this application also provides a hybrid energy storage collaborative planning device for power distribution networks, comprising:

[0010] The model building module is used to build a two-layer planning model for hybrid energy storage systems. The two-layer planning model includes an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the target, and the lower verification layer optimizes the operation control parameters of the hybrid energy storage system with the distribution network frequency security indicators as the target.

[0011] The processing module is used to input multiple capacity configuration schemes output by the upper planning layer into the lower verification layer, and perform transient frequency response simulation and parameter optimization under various distribution network operation modes to obtain the optimized control parameters and frequency safety indicators corresponding to each of the multiple capacity configuration schemes; the frequency safety indicators are determined based on the frequency stability indicators corresponding to each of the various distribution network operation modes.

[0012] The output module is used to feed back the frequency security index to the upper planning layer, so that the upper planning layer adjusts multiple sets of the capacity configuration schemes and re-inputs them to the lower verification layer for iterative optimization until the iteration termination condition is met, thereby obtaining the collaborative planning scheme of the hybrid energy storage system. The collaborative planning scheme includes the target capacity configuration scheme and the target optimization control parameters.

[0013] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0014] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0015] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0016] The aforementioned hybrid energy storage collaborative planning method, device, computer equipment, computer-readable storage medium, and computer program product for distribution networks construct a two-layer planning model comprising an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the objective. The lower verification layer performs transient frequency response simulation and operational control parameter optimization of the capacity configuration scheme under various distribution network operation modes, outputting frequency safety indicators. The upper planning layer then adjusts the capacity configuration scheme based on the frequency safety indicators and performs iterative optimization. This decouples and closes the loop between energy storage capacity configuration optimization and operational control parameter optimization, enabling the upper layer to achieve... The multiple capacity configuration schemes output by the planning layer are simulated and optimized for transient frequency response under various distribution network operation modes, such as grid-connected and islanded, in the lower verification layer. The frequency security index is fed back to the upper layer to drive the iterative adjustment of the capacity configuration, which improves the frequency support capability of the collaborative planning scheme of the hybrid energy storage system for multiple operation modes. In the above process, the operational stability and economy of the hybrid energy storage system under various operation modes of the distribution network are taken into account, avoiding the problems of energy storage configuration redundancy or insufficient safety margin. Thus, the economic optimization of energy storage configuration is achieved under the premise of ensuring the frequency security of the distribution network, and the accuracy of the collaborative planning of hybrid energy storage is improved. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a hybrid energy storage system configuration that includes a distributed new energy distribution network in one embodiment;

[0019] Figure 2 This is an application environment diagram of a hybrid energy storage collaborative planning method for power distribution networks in one embodiment;

[0020] Figure 3 This is a flowchart illustrating a hybrid energy storage collaborative planning method for a power distribution network in one embodiment.

[0021] Figure 4 This is a schematic diagram of the processing flow of the lower-level verification layer in one embodiment;

[0022] Figure 5This is a schematic diagram illustrating the application process of the hybrid energy storage collaborative planning method in one embodiment;

[0023] Figure 6 This is a structural block diagram of a hybrid energy storage collaborative planning device for a power distribution network in one embodiment;

[0024] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0026] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0027] For easier understanding, please refer to Figure 1 , Figure 1 A schematic diagram of a hybrid energy storage system configuration including a distributed renewable energy distribution network, a hybrid energy storage system, and loads is shown.

[0028] Distributed renewable energy distribution networks refer to networks that connect distributed renewable energy sources (such as...) to the AC power grid. Figure 1 The distribution network (shown in the figure) includes photovoltaic and wind power.

[0029] exist Figure 1 In this context, hybrid energy storage systems can include grid-type electrochemical energy storage systems and hydrogen energy storage systems. Grid-type electrochemical energy storage systems refer to electrochemical energy storage devices that operate using a grid-type control method; hydrogen energy storage systems refer to energy storage systems that use a "water electrolysis to produce hydrogen - hydrogen storage - fuel cell power generation" energy conversion path, typically including an electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell.

[0030] Depend on Figure 1As can be seen, distributed wind power and distributed photovoltaic power are connected to the AC bus of the distribution network through their respective grid-connected converters (not shown in the figure); the AC grid is connected to the AC bus through a tie switch (not shown in the figure), and the grid-type electrochemical energy storage system and hydrogen energy storage system are connected to the AC bus through bidirectional power conversion devices (not shown in the figure) to realize bidirectional flow of electrical energy between the energy storage system and the AC bus; among them, the bidirectional arrows represent that power can flow bidirectionally, that is, the energy storage system can absorb electrical energy when the output of new energy is excessive, and can inject electrical energy into the AC bus when the output of new energy is insufficient, and the load draws power from the AC bus.

[0031] When the tie switch is closed, the distribution network is in grid-connected operation mode, with the AC grid, distributed wind power, and distributed photovoltaic power supplying power to the loads. In this mode, the hybrid energy storage system primarily participates in power regulation. When the tie switch is open, the distribution network is in islanded operation mode, where grid-type electrochemical energy storage and hydrogen energy storage systems work together to support the voltage and frequency stability of the AC bus. Specifically, the grid-type electrochemical energy storage system provides rapid inertia support and primary frequency regulation, while the hydrogen energy storage system provides long-term energy support. By implementing the method provided in this application, the configuration planning scheme among various energy storage systems in the hybrid energy storage system can be determined, thereby ensuring economic efficiency while accommodating stable operation under multiple operating modes.

[0032] The hybrid energy storage collaborative planning method for distribution networks provided in this application embodiment can be applied to, for example... Figure 2 The application environment shown depicts a scenario where terminal 201 communicates with server 202 via a network. A data storage system can store the data that server 202 needs to process. This data storage system can be integrated onto server 202 or hosted on a cloud or other network server.

[0033] Planners initiate a planning task for the hybrid energy storage system via terminal 201. Server 202 then responds to the task, constructing a two-layer planning model for the hybrid energy storage system. This model comprises an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system based on economic indicators, while the lower verification layer optimizes the operating control parameters of the hybrid energy storage system based on distribution network frequency security indicators. Multiple capacity configuration schemes output from the upper planning layer are input into the lower verification layer, where transient frequency response simulations and parameter optimizations are performed under various distribution network operating modes, yielding multiple sets of... Each capacity configuration scheme has its own corresponding optimized control parameters and frequency safety indicators. The frequency safety indicators are determined based on the frequency stability indicators corresponding to various distribution network operation modes. The frequency safety indicators are fed back to the upper planning layer, which adjusts multiple sets of capacity configuration schemes and re-inputs them to the lower verification layer for iterative optimization until the iteration termination condition is met, resulting in a collaborative planning scheme for the hybrid energy storage system. The collaborative planning scheme includes the target capacity configuration scheme and the target optimized control parameters. Finally, the server 202 sends the generated collaborative planning scheme to the terminal 201, which then presents the scheme to the planners.

[0034] The terminal 201 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. The server 202 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0035] In one exemplary embodiment, such as Figure 3 As shown, a hybrid energy storage collaborative planning method for distribution networks is provided, which is applied to... Figure 2 Taking the server in the example, the explanation includes the following steps 301 to 303. Wherein:

[0036] Step 301: Establish a two-layer planning model for the hybrid energy storage system. The two-layer planning model includes an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the target, and the lower verification layer optimizes the operation control parameters of the hybrid energy storage system with the distribution network frequency security indicators as the target.

[0037] Among them, the bi-level programming model refers to a mathematical model composed of two optimization problems with different levels and different optimization objectives but which are coupled with each other.

[0038] In this embodiment, the upper planning layer refers to the level in the two-layer planning model that undertakes capacity configuration decisions, optimizing the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the objective. Here, energy storage capacity configuration is a set of parameters describing the construction scale of each energy storage system in the hybrid energy storage system, and economic indicators are indicators used to measure the economic efficiency of the capacity configuration scheme. For example, economic indicators can be total construction investment, average daily operating cost, or a weighted sum of both.

[0039] It should be noted that, since different energy storage systems have different energy storage paths, the configuration items included in the energy storage capacity configuration of different energy storage systems in a hybrid energy storage system are different. For example, for a grid-type electrochemical energy storage system, the energy storage capacity configuration includes rated power and rated capacity; for a hydrogen energy storage system, the energy storage capacity configuration includes the rated power of the electrolyzer, the rated capacity of the hydrogen storage tank, and the rated power of the hydrogen fuel cell.

[0040] The lower-level verification layer refers to the level in the two-level programming model that undertakes operational-level verification and optimization. It optimizes the operational control parameters of the hybrid energy storage system with the distribution network frequency safety index as the objective. The distribution network frequency safety index is used to measure the degree to which the distribution network frequency deviates from the rated frequency during transient processes.

[0041] Among them, the operating control parameters are the control parameters that determine the output power characteristics of the energy storage system during transient processes, given that the capacity configuration of the energy storage system has been determined. For example, the operating control parameters may include virtual inertia time constant and primary frequency regulation coefficient, etc.

[0042] Step 302: Input the multiple capacity configuration schemes output by the upper planning layer into the lower verification layer, and perform transient frequency response simulation and parameter optimization under various distribution network operation modes to obtain the optimized control parameters and frequency safety indicators corresponding to each of the multiple capacity configuration schemes; the frequency safety indicators are determined based on the frequency stability indicators corresponding to each of the various distribution network operation modes.

[0043] Among them, multiple distribution network operation modes refer to the different working methods corresponding to different connection states between the distribution network and the external main grid.

[0044] In some embodiments, the multiple distribution network operation modes include at least grid-connected operation mode and islanded operation mode; further, in grid-connected operation mode, the frequency of the distribution network is supported by the main grid, and the fluctuations in renewable energy power and load within the distribution network mainly cause changes in tie line power; in islanded operation mode, the frequency of the distribution network is fully supported by the internal renewable energy power source and energy storage system, and in this operation mode, the frequency stability of the distribution network is extremely sensitive to the power support capability of the energy storage system.

[0045] Transient frequency response simulation refers to the process of obtaining the frequency change process of the distribution network over a short period of time (e.g., tens of seconds) after a disturbance (such as loss of mains connection, load surge, etc.) occurs, under a given capacity configuration scheme and operating control parameters, and then extracting frequency characteristic quantities from the process.

[0046] In some embodiments, transient frequency response simulation can be implemented using electromechanical transient simulation, thereby simultaneously reflecting the electromechanical transient process and the control dynamics of each energy storage power conversion device in the simulation.

[0047] It is understandable that different distribution network operation modes have different sensitivities and response characteristics to various disturbances. Therefore, it is necessary to perform transient frequency response simulations for each distribution network operation mode in order to accurately assess the frequency stability level under that operation mode.

[0048] Frequency stability index refers to the index obtained by extracting the characteristic quantities from the frequency curve obtained by transient frequency response simulation under a distribution network operation mode.

[0049] In some embodiments, frequency stability indicators under a power distribution network operation mode may include the maximum rate of frequency change and the maximum instantaneous frequency deviation.

[0050] Among them, the maximum rate of change of frequency (RoCoF) refers to the maximum absolute value of the rate of change of frequency with respect to time during a transient process, which characterizes the drasticness of frequency change, and is measured in Hz / s; the maximum instantaneous frequency deviation refers to the maximum absolute value of the deviation of the instantaneous frequency from the rated frequency during a transient process, which characterizes the depth of frequency drop, and is measured in Hz.

[0051] In some embodiments, considering that the distribution network needs to operate under multiple distribution network operation modes, the frequency security index can be an index obtained by comprehensively considering the frequency stability indices corresponding to each of the multiple distribution network operation modes, thereby uniformly measuring the frequency security level of the distribution network under multiple operation modes.

[0052] Among them, parameter optimization refers to the process of iteratively adjusting the operating control parameters of the energy storage system under a given capacity configuration scheme and distribution network operation mode, with the goal of optimizing the distribution network frequency security index, until the optimal combination of operating control parameters that optimizes the distribution network frequency security index is found.

[0053] It is understandable that even with a fixed capacity configuration and distribution network operation mode, different optimized control parameters can still lead to different frequency response characteristics. For example, if the control parameters are set too low, the energy storage power support will be insufficient, resulting in a deep frequency drop and slow recovery. If the control parameters are set too high, it may cause frequency overshoot or power oscillation, which is also detrimental to system stability. Therefore, it is necessary to find the optimal operating control parameters under a given capacity configuration and determine the frequency safety index under the optimal control parameters to ensure that the frequency safety index can truly reflect the frequency support capability of the capacity configuration under the best control strategy.

[0054] Step 303: Feed the frequency security index back to the upper planning layer, so that the upper planning layer adjusts multiple capacity configuration schemes and re-inputs them to the lower verification layer for iterative optimization until the iteration termination condition is met, and obtains the collaborative planning scheme of the hybrid energy storage system. The collaborative planning scheme includes the target capacity configuration scheme and the target optimization control parameters.

[0055] Iterative optimization is the process of repeatedly interacting between the upper planning layer and the lower verification layer to continuously adjust the capacity configuration scheme and re-perform transient frequency response simulation and parameter optimization in order to gradually approach the optimal collaborative planning scheme that simultaneously satisfies economic objectives and frequency security constraints. Furthermore, the iteration termination condition refers to the pre-set criteria used to determine whether the iterative optimization has converged and can be stopped.

[0056] In some embodiments, the iteration termination condition may be that the number of iterations reaches an iteration threshold.

[0057] In other embodiments, the iteration termination condition may also be that the change between the optimal capacity configuration schemes in adjacent iteration rounds is less than a preset change threshold, and the corresponding frequency security index meets the preset security constraints.

[0058] It is understandable that the target capacity configuration scheme can be used to guide the construction scale of the hybrid energy storage system, and the target optimized control parameters can be used to guide the tuning of the controller parameters of the hybrid energy storage system. Therefore, by combining the target capacity configuration scheme and the target optimized control parameters to generate a collaborative planning scheme, a complete decision-making basis can be provided for the planning, construction and operation control of the hybrid energy storage system, thereby realizing the joint optimization of energy storage capacity configuration and operation control parameters, and taking into account the economy and frequency security of the distribution network under various operating modes.

[0059] The aforementioned hybrid energy storage collaborative planning method for distribution networks constructs a two-layer planning model comprising an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the objective. The lower verification layer performs transient frequency response simulation and operational control parameter optimization under various distribution network operation modes, outputting frequency safety indicators. The upper planning layer then adjusts the capacity configuration scheme based on the frequency safety indicators and performs iterative optimization. This decouples and closes the loop between energy storage capacity configuration optimization and operational control parameter optimization, enabling the upper planning layer to output multiple sets of capacity configuration schemes. The scheme performs transient frequency response simulation and parameter optimization in the lower verification layer under various distribution network operation modes, such as grid-connected and islanded, and feeds back the frequency security index to the upper layer to drive the iterative adjustment of capacity configuration. This improves the frequency support capability of the hybrid energy storage system's collaborative planning scheme for multiple operation modes. In the above process, the stability and economy of the hybrid energy storage system under various distribution network operation modes are taken into account, avoiding the problems of energy storage configuration redundancy or insufficient safety margin. Thus, under the premise of ensuring the frequency security of the distribution network, the economic optimization of energy storage configuration is achieved, and the accuracy of hybrid energy storage collaborative planning is improved.

[0060] In one exemplary embodiment, such as Figure 4 As shown, Figure 4 A schematic diagram of the processing flow of the lower-level verification layer is shown. Step 302 includes steps 401 to 403. Wherein:

[0061] Step 401: Obtain the simulation parameters corresponding to each of the various power distribution network operation modes.

[0062] In some embodiments, multiple distribution network operation modes include grid-connected operation mode and islanded operation mode; simulation parameters may include input data used in transient frequency response simulation to describe the initial operating state of the distribution network, network topology and parameters, power output characteristics, load characteristics, and disturbance conditions.

[0063] In some embodiments, simulation parameters can be determined based on historical operating data of the distribution network dispatch automation system, forecast data output by the new energy power forecasting system, load forecast data, distribution network ledger data, and typical daily operating data.

[0064] For example, for grid-connected operation mode, the corresponding simulation parameters may include the network parameters of the distribution network (such as line impedance parameters, transformer parameters, etc.), the equivalent parameters of the main grid, the hourly output curves of distributed photovoltaic and distributed wind power, the hourly power curves of the load, and the preset disturbance parameters in grid-connected operation mode.

[0065] For islanded operation mode, the corresponding simulation parameters may include the network parameters of the distribution network, the hourly output curves of distributed photovoltaic and distributed wind power, the hourly power curves of the load, the disconnection time of the tie switch, and the preset disturbance parameters in islanded operation mode.

[0066] Step 402: For each capacity configuration scheme, perform electromechanical transient simulation based on the initial control parameters and the simulation parameters corresponding to each of the various distribution network operation modes to determine the frequency stability index corresponding to each capacity configuration scheme under the various distribution network operation modes; the frequency stability index includes the maximum frequency change rate and the maximum instantaneous frequency deviation.

[0067] Electromechanical transient simulation is a simulation method that uses millisecond-level time steps to numerically solve power systems. It can be implemented using power system analysis software and can reflect the dynamic changes in system frequency, voltage, and power within tens of seconds after a disturbance occurs.

[0068] In some embodiments, the frequency stability metric may be a combination of the maximum rate of frequency change and the maximum instantaneous frequency deviation.

[0069] In other embodiments, the frequency stability index can also be a comprehensive index obtained by post-processing the maximum frequency change rate and the maximum instantaneous frequency deviation. For example, the maximum frequency change rate and the maximum instantaneous frequency deviation can be processed by per-unit values ​​and then weighted and summed to obtain the frequency stability index.

[0070] Step 403: Using the weighted sum of frequency stability indices corresponding to various distribution network operation modes as the optimization objective, the initial control parameters of the capacity configuration scheme are iteratively optimized until the iteration termination condition is met, thereby obtaining the optimized control parameters and frequency security indices corresponding to the capacity configuration scheme.

[0071] For example, taking multiple distribution network operation modes, including grid-connected operation mode and islanded operation mode, the initial control parameters of the capacity configuration scheme can be iteratively optimized using the following objective function:

[0072] .

[0073] in, The weighting coefficient for the grid-connected operation mode. This represents the weighting coefficient for the isolated operation mode. The maximum allowable frequency change rate safety threshold (unit: Hz / s) for the distribution network is usually set to 0.5~1.0Hz / s (e.g., 0.80Hz / s). and These are the absolute values ​​of the maximum frequency change rate (unit: Hz / s) calculated by electromechanical transient simulation under given parameters in grid-connected and islanded operation modes. and These are the absolute values ​​of the maximum instantaneous frequency deviation during the transient process in grid-connected operation mode and islanded operation mode, respectively (i.e., the difference between the lowest point of frequency drop and the rated frequency of 50Hz, in Hz). The maximum allowable instantaneous frequency deviation safety threshold (unit: Hz) for the distribution network is usually set to 0.5Hz (i.e., the frequency must not be lower than 49.5Hz).

[0074] In some embodiments, since islanding loss of mains power is a serious fault with catastrophic consequences for critical loads, islanding weights are used in engineering settings. Much greater than the grid connection weight .

[0075] In some embodiments, the hybrid energy storage system includes a grid-type electrochemical energy storage system and a hydrogen energy storage system. Based on this, the optimizable control parameters include the primary frequency regulation coefficient and the virtual inertia time constant.

[0076] For example, an optimizable set of control parameters ,in, and These are the primary frequency regulation coefficients for electrochemical energy storage and hydrogen energy storage systems, respectively. and Let be the virtual inertia time constant of the electrochemical energy storage and hydrogen energy storage systems; further, the range of values ​​for the control variables is as follows: , , , .

[0077] In some embodiments, optimization constraints may be introduced during the parameter optimization process, such as state of charge constraints of the energy storage system, charging and discharging power constraints of the energy storage system, and power balance constraints.

[0078] In some embodiments, the frequency security index of the capacity configuration scheme may be the maximum frequency change rate with the largest absolute value selected from the maximum frequency change rates corresponding to each of the multiple operating modes, and the maximum instantaneous frequency deviation with the largest absolute value selected from the maximum instantaneous frequency deviations corresponding to each of the multiple operating modes.

[0079] In this embodiment, by using the weighted sum of the frequency stability indicators corresponding to the grid-connected operation mode and the islanded operation mode as the optimization target, the lower verification layer can automatically search for the operation control parameters with the optimal frequency safety margin under a given capacity configuration scheme, thereby ensuring the frequency stability of the distribution network under severe fault scenarios such as loss of mains power.

[0080] In some embodiments, the capacity configuration scheme includes the rated power and rated capacity of each energy storage system in the hybrid energy storage system, and the multiple distribution network operation modes include at least grid-connected operation mode and islanded operation mode. The hybrid energy storage collaborative planning method for the distribution network includes:

[0081] For each capacity configuration scheme, the curtailment penalty cost is determined based on the amount of curtailment and the cost of curtailment under the capacity configuration scheme; the outage penalty cost is determined based on the islanding operation data, load gap, and unit power shortage cost under the capacity configuration scheme; for each energy storage system under the capacity configuration scheme, the operating cost of the energy storage system is determined based on the unit cost parameters, rated power, and rated capacity of the energy storage system; and the economic indicators of the capacity configuration scheme are determined based on the curtailment penalty cost, the outage cost, and the operating cost of each energy storage system. Among these, the curtailment amount, islanding operation data, and load gap under the capacity configuration scheme are determined based on operational simulations of the capacity configuration scheme.

[0082] Among them, operation simulation refers to the process of calculating the output of each power source, the charging and discharging power of energy storage, and the power of unsupplied loads in each time period, with a given capacity configuration scheme as a constraint, hourly new energy output curves and load curves as inputs, and power balance of the distribution network as a constraint.

[0083] In some embodiments, the simulation can be implemented using a time-series production simulation method.

[0084] Among them, the abandoned power refers to the power that cannot be connected to the grid under a given capacity configuration scheme because the theoretical power generation of new energy exceeds the absorption capacity of the distribution network. For example, the hourly wind power and photovoltaic theoretical power generation curves and hourly load curves throughout the year are input into the operation simulation model. Under the condition of energy storage charging and discharging power constraints and energy constraints, the hourly actual grid-connected power of new energy is calculated. The difference between the theoretical power generation and the actual grid-connected power is determined as the abandoned power for that hour. Then, the abandoned power is obtained by summing it over the whole year.

[0085] The islanding operation period data is the time and duration when the distribution network loses its grid connection (i.e., islanding operation mode) determined during the operation simulation. Furthermore, the load gap is the unmet load power when the maximum discharge power that the hybrid energy storage system can provide during the corresponding period is insufficient to meet the load demand in the islanding operation mode.

[0086] For example, under a given capacity configuration scheme, the hourly load demand power during the islanding period is subtracted from the maximum discharge power that the hybrid energy storage system can provide during that period. If the difference is positive, the difference is determined as the load gap for that period; otherwise, it is zero.

[0087] Operating costs refer to the construction investment costs of each energy storage system allocated to each day during its operation.

[0088] In some embodiments, the product of the abandoned power and the abandoned power cost can be determined as the abandoned power penalty cost; the product of the duration of the islanded operation period data, the load gap, and the unit power shortage cost can be determined as the power outage penalty cost; for each energy storage system, the product of its unit power cost and rated power, and the product of its unit capacity cost and rated capacity can be summed, and the summation result can be converted into a daily average value according to the operating cycle and discount rate, which is determined as the operating cost of the energy storage system.

[0089] In some embodiments, the sum of curtailment penalty costs, outage penalty costs, and the operating costs of each energy storage system can be determined as the economic indicator of the capacity configuration scheme.

[0090] In other embodiments, different weights may be assigned to different costs, thereby determining the weighted result of the curtailment penalty cost, the power outage penalty cost, and the operating costs of each energy storage system as the economic indicator of the capacity configuration scheme.

[0091] It should be noted that a hydrogen energy storage system includes an electrolyzer, a hydrogen storage tank, and a fuel cell. The rated power of the system includes the rated power of the electrolyzer and the rated power of the hydrogen fuel cell.

[0092] Understandably, the upper planning layer, based on the frequency security indicators fed back by the lower verification layer, aims to minimize economic indicators. After adjusting the economic indicators according to the feedback frequency security indicators, it makes comparisons to guide the capacity configuration scheme to evolve in a direction that balances economy and frequency security.

[0093] In the above embodiments, by determining the amount of power curtailment, islanded operating periods, and load gaps based on operational simulations, and calculating the curtailment penalty cost and power outage penalty cost accordingly, while also considering the operating costs of each energy storage system, the economics of the capacity configuration scheme can be comprehensively quantified throughout the annual operating cycle. Furthermore, by iteratively adjusting the upper planning layer based on the frequency security indicators fed back from the lower layer with the goal of minimizing economic indicators, the synergistic optimization of economics and frequency security is achieved, which can effectively reduce the total planning and operating costs of the hybrid energy storage system while ensuring the frequency security of the distribution network.

[0094] In some embodiments, frequency security indicators are fed back to the upper planning layer, enabling the upper planning layer to adjust the capacity configuration scheme and re-input it to the lower verification layer for iterative optimization until the iteration termination condition is met, resulting in a collaborative planning scheme for the hybrid energy storage system, including:

[0095] For each capacity configuration scheme, the fitness of the capacity configuration scheme is determined based on the corresponding economic and frequency security indicators. Based on the fitness of multiple capacity configuration schemes, the best scheme is selected to participate in the next iteration. For the multiple capacity configuration schemes participating in the next iteration, cross-processing and mutation processing are performed sequentially to generate new capacity configuration schemes, which are then re-input into the lower verification layer for iterative optimization until the iteration termination condition is met, thus obtaining the collaborative planning scheme of the hybrid energy storage system.

[0096] Among them, adaptability represents the quality of the capacity configuration scheme.

[0097] In some embodiments, the economic indicators and frequency security indicators corresponding to the capacity configuration scheme can be weighted and summed to obtain the fitness of the capacity configuration scheme.

[0098] In other embodiments, a penalty term corresponding to the frequency security index can be determined based on a preset security threshold. The penalty term is then weighted and summed with the economic index to obtain the fitness of the capacity configuration scheme. Specifically, when the frequency security index exceeds the preset security threshold, the penalty term is positive; otherwise, it is zero, thus obtaining the fitness of the capacity configuration scheme.

[0099] In some embodiments, multiple capacity configuration schemes can be sorted in descending order of fitness, and a preset number of capacity configuration schemes ranked first can be selected to determine the multiple capacity configuration schemes participating in the next iteration.

[0100] In other embodiments, an elite retention strategy combined with roulette wheel selection can be used to directly retain the most fitness capacity configuration schemes in the current iteration round to the next round, while the remaining capacity configuration schemes are selected by roulette wheel selection based on their fitness ratio, thereby determining the multiple capacity configuration schemes to participate in the next iteration.

[0101] Cross-processing refers to selecting two capacity configuration schemes from the multiple capacity configuration schemes participating in the next round of iteration as parent schemes, and exchanging or combining the decision variables in the two capacity configuration schemes according to the preset cross-processing probability to generate two new capacity configuration schemes.

[0102] Mutation processing refers to randomly perturbing the decision variables in the capacity allocation scheme generated by cross-processing according to a preset mutation probability to generate a new capacity allocation scheme.

[0103] It is understandable that by performing cross-processing and mutation processing on multiple capacity configuration schemes with better performance in the current iteration round, the generated new capacity configuration scheme can inherit the excellent features of the parent scheme, while introducing new directions for exploring the solution space, avoiding the iteration process from getting stuck in local optima, and thus continuously approaching the economically optimal capacity configuration scheme while ensuring frequency security.

[0104] In the above embodiments, by determining the fitness based on economic and frequency security indicators, and performing selection, crossover and mutation processing based on the fitness, the population of capacity configuration schemes can continuously evolve during the iteration process and eventually converge to a collaborative planning scheme that takes into account both economy and frequency security. This can effectively improve the solution efficiency and global optimization capability of collaborative planning for hybrid energy storage systems.

[0105] In some embodiments, frequency security indicators include the target maximum frequency change rate and the target maximum instantaneous frequency deviation, and the fitness of the capacity configuration scheme can be calculated using the following formula:

[0106] Based on preset safety thresholds and frequency safety indicators, penalty parameters are determined; preset safety thresholds include frequency change rate thresholds and instantaneous frequency deviation thresholds; the penalty parameters and frequency safety indicators are summed to obtain the corrected frequency indicator; the economic indicators and corrected frequency indicator are weighted to obtain the suitability of the capacity configuration scheme.

[0107] For example, the adaptability of capacity configuration schemes It can be calculated using the following formula:

[0108] .

[0109] in, Economic indicators representing capacity configuration schemes Represents the frequency cost coefficient. The quantitative value representing the frequency security index of the capacity configuration scheme can be determined based on the target maximum frequency change rate and the target maximum instantaneous frequency deviation. This represents the penalty parameter, which can also be understood as the frequency over-limit penalty function.

[0110] Furthermore, ;

[0111] ;

[0112] in, The maximum permissible rate of frequency change safety threshold for the distribution network is typically set to 0.5~1.0 Hz / s; The absolute value of the target maximum frequency change rate is the mean or the maximum value of the absolute value of the maximum frequency change rate corresponding to each of the various operating modes calculated by electromechanical transient simulation under given parameters. The target maximum instantaneous frequency deviation is the average or the maximum value of the absolute value of the maximum instantaneous frequency deviation in the transient process corresponding to each of the various operating modes (i.e., the difference between the lowest point of frequency drop and the rated frequency of 50Hz). The maximum allowable instantaneous frequency deviation safety threshold for the distribution network is typically set to 0.5Hz.

[0113] In the above embodiments, the corrected frequency index is obtained by summing the penalty parameter and the frequency security index, and then the corrected frequency index is weighted by the frequency cost coefficient. This can simultaneously reflect the extent and overall level of frequency security exceeding the limit, so that when selecting the best option, the capacity configuration scheme with better economic indicators, better overall frequency performance and lower risk of frequency exceeding the limit is given priority.

[0114] In some embodiments, the hybrid energy storage collaborative planning method for distribution networks further includes:

[0115] The first capacity configuration scheme with the highest fitness among the multiple capacity configuration schemes in the previous iteration round, and the second capacity configuration scheme with the highest fitness among the multiple capacity configuration schemes in the current iteration round are determined. Based on the configuration difference between the first and second capacity configuration schemes, the configuration change amount is determined. If the configuration change amount is lower than the preset change threshold and the frequency security indicators of the second capacity configuration scheme all meet the preset security constraints, the iteration termination condition is determined. The second capacity configuration scheme is determined as the target capacity configuration scheme, and the optimization control parameters corresponding to the second capacity configuration scheme are determined as the target optimization control parameters.

[0116] The configuration change is used to measure the degree of difference between the optimal capacity configuration schemes in the two iterations. Obviously, if the configuration change is small, it means that the optimal capacity configuration schemes obtained in the two iterations have become consistent and the algorithm is converging. If the configuration change is large, it means that the optimal capacity configuration schemes obtained in the two iterations are significantly different and the algorithm has not yet converged. It is still necessary to continue iterating to search for a better capacity configuration scheme.

[0117] In some embodiments, the configuration change amount It can be determined by the following formula:

[0118] .

[0119] in, The value of the k-th decision variable in the optimal capacity configuration scheme of the current iteration (i.e., the second capacity configuration scheme); Let K be the value of the k-th decision variable in the optimal capacity configuration scheme of the previous iteration (i.e., the first capacity configuration scheme), where K is the number of decision variables. It is a preset small positive number to avoid the denominator being zero.

[0120] Among them, the preset safety constraints can be the maximum allowable frequency change rate safety threshold and the maximum instantaneous frequency deviation safety threshold of the distribution network.

[0121] It is understandable that if the configuration change is lower than the preset change threshold and the frequency security indicators of the second capacity configuration scheme meet the preset security constraints, it means that the current optimal capacity configuration scheme has converged and meets the frequency security requirements, so there is no need to continue iterating. Therefore, the second capacity configuration scheme can be determined as the target capacity configuration scheme, and the optimization control parameters corresponding to the second capacity configuration scheme can be determined as the target optimization control parameters.

[0122] In the above embodiments, by using the configuration change between the optimal capacity configuration schemes in two rounds of iteration as the convergence criterion, and combining it with whether the frequency security index meets the preset security constraints for joint judgment, it is possible to accurately judge the iteration convergence under the premise of ensuring frequency security, avoid invalid iteration, and improve the solution efficiency and result reliability of the collaborative planning of hybrid energy storage systems.

[0123] For easier understanding, please refer to Figure 5 , Figure 5 This application illustrates a schematic diagram of the application process for a hybrid energy storage collaborative planning method for distribution networks, according to an embodiment of this application. The process includes:

[0124] First, set the economic parameters and control initialization parameters, and initialize the population, which contains multiple capacity configuration schemes, and set the iteration number i=0. Then, simulate the grid being disturbed and operating in an isolated manner under different distribution network operation modes, and perform electromechanical transient simulation to obtain frequency security index results. Next, calculate the fitness of each capacity configuration scheme and determine whether it meets the convergence accuracy. If it does not meet the convergence accuracy, set the iteration number i=i+1, perform gene recombination and mutation to generate a new population, and return to the step of simulating the grid being disturbed and operating in an isolated manner to continue iterating. If the convergence accuracy is met, output the configuration result of the hybrid energy storage system.

[0125] The following will describe the solution provided in this application with specific embodiments, taking a 10kV distribution network in an industrial park in a city as an application scenario for specific implementation verification. This distribution network is a typical active distribution network, including distributed photovoltaic, distributed wind power, and various loads, and needs to meet the requirements of islanded operation. The power supply range of the industrial park distribution network covers three 10kV feeders with a total load capacity of 12MW, of which the capacity of important loads (such as emergency lighting, key production equipment, etc.) is 4.5MW, and the capacity of ordinary loads is 7.5MW; the capacity of distributed photovoltaic connected to the distribution network is 8MW, and the capacity of distributed wind power connected is 2MW, with an average annual power generation hours of 1200h and 1800h respectively; the operation cycle of the distribution network is 15 years, and the discount rate is 5%.

[0126] Based on this, the cost calculation parameters for the economic indicators are determined, as shown in Table 1 below:

[0127] Table 1

[0128] parameter symbol numerical values Unit power cost of grid-type electrochemical energy storage <![CDATA[c b,p ]]> 1800 yuan / kW Unit capacity cost of grid-type electrochemical energy storage <![CDATA[c b,e ]]> 1200 yuan / kWh Unit power cost of electrolytic cells <![CDATA[c el ]]> 5200 yuan / kW Unit capacity cost of hydrogen storage tank <![CDATA[c tk ]]> 130 yuan / kWh Unit power cost of hydrogen fuel cells <![CDATA[c fc ]]> 7800 yuan / kW Unit wind curtailment penalty cost <![CDATA[c curt,w ]]> 320 yuan / MWh Unit curtailment penalty cost <![CDATA[c curt,s ]]> 350 yuan / MWh Unit power shortage penalty cost <![CDATA[c loss ]]> 2000 yuan / MWh

[0129] Based on the cost calculation parameters shown in Table 1, the average daily total investment and operating cost for each capacity configuration scheme is calculated. It was identified as an economic indicator.

[0130] Specifically, economic indicators ;in, This refers to the operating cost of a grid-type electrochemical energy storage system, i.e., the average daily investment cost of the grid-type electrochemical energy storage system. This refers to the operating cost of a hydrogen energy storage system, i.e., the average daily investment cost of the hydrogen energy storage system. The cost of curtailment is the penalty for curtailing electricity, which in this embodiment is also the penalty for curtailing wind and solar power. The cost of penalties for power outages.

[0131] Furthermore, the average daily investment cost of grid-type electrochemical energy storage systems Where r is the discount rate, typically set to 5%, and T is the preset operating period. The rated power of electrochemical energy storage, This refers to the rated capacity of electrochemical energy storage.

[0132] Average daily investment cost of hydrogen energy storage systems ,in, The rated power of the electrolyzer in the hydrogen energy storage system. For the rated capacity of the hydrogen storage tank, This refers to the rated power of the hydrogen fuel cell.

[0133] Cost of power curtailment ;in, The amount of wind power curtailed in hour t. Let t represent the abandoned power of photovoltaic power in hour t, where t is the hour number. The upper limit of the summation is 8760, which means that the year is calculated as 8760 hours.

[0134] Power outage penalty costs ;in, This represents the load power that was not satisfied during the t-th time period when the grid power was lost and the grid was islanded. For isolated runtime segments, This is the time step for running the simulation.

[0135] Subsequently, the upper-level planning layer minimizes economic indicators (i.e. Optimize the energy storage capacity configuration of hybrid energy storage systems to achieve the desired results. ,in, The rated power of electrochemical energy storage, The rated capacity of electrochemical energy storage, The rated power of the electrolyzer in the hydrogen energy storage system. For the rated capacity of the hydrogen storage tank, This refers to the rated power of the hydrogen fuel cell.

[0136] The initial control parameters of each energy storage system during transient frequency response simulation are shown in Table 2 below:

[0137] Table 2

[0138] Control parameter name symbol Initial parameter values Range of values Electrochemical energy storage virtual inertia time constant 4 The virtual inertia time constant of hydrogen energy storage 2.5 Electrochemical energy storage primary frequency regulation coefficient 20 Hydrogen energy storage primary frequency regulation coefficient 12.5

[0139] The lower-level verification layer iteratively optimizes the initial control parameters of the capacity configuration scheme using an objective function, which is the same as the one described in the previous embodiment. .

[0140] Finally, the iterative parameters for iterative optimization are shown in Table 3 below:

[0141] Table 3

[0142] Iteration parameters numerical values Genetic Algorithm Population Size 60 Maximum number of iterations 120 Crossover probability 0.75 Mutation probability 0.08 Frequency cost coefficient 1×106 yuan / Hz

[0143] The bilevel programming model is used for iterative optimization based on the iterative parameters in Table 3, wherein the fitness is calculated using the method described in the aforementioned embodiment. The optimization is terminated when the iteration termination condition is met, that is, when the capacity configuration change in two consecutive iterations is less than the threshold and the frequency safety index is not violated, thus obtaining the collaborative planning scheme of the hybrid energy storage system.

[0144] In this embodiment, the optimal energy storage capacity configuration of the hybrid energy storage system is shown in Table 4 below:

[0145] Table 4

[0146] parameter symbol numerical values Electrochemical energy storage rated power, 3.25MW Electrochemical energy storage rated capacity 8.75MWh Rated power of electrolyzer in hydrogen energy storage system 2.15MW Hydrogen storage tank rated capacity 85.6MWh Hydrogen fuel cell rated power 2.85MW

[0147] The target optimization control parameters for the hybrid energy storage system are shown in Table 5 below:

[0148] Table 5

[0149] parameter symbol Target parameters Electrochemical energy storage virtual inertia time constant 4.2 The virtual inertia time constant of hydrogen energy storage 3.1 Electrochemical energy storage primary frequency regulation coefficient 24.5 Hydrogen energy storage primary frequency regulation coefficient 15.8

[0150] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0151] Based on the same inventive concept, this application also provides a hybrid energy storage collaborative planning device for a distribution network to implement the above-mentioned hybrid energy storage collaborative planning method for a distribution network. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the hybrid energy storage collaborative planning device for a distribution network provided below can be found in the limitations of the hybrid energy storage collaborative planning method for a distribution network described above, and will not be repeated here.

[0152] In one exemplary embodiment, such as Figure 6 As shown, a hybrid energy storage collaborative planning device 600 for power distribution networks is provided, comprising:

[0153] The model building module 601 is used to build a two-layer planning model for hybrid energy storage systems. The two-layer planning model includes an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the target, and the lower verification layer optimizes the operation control parameters of the hybrid energy storage system with the distribution network frequency security indicators as the target.

[0154] The processing module 602 is used to input multiple capacity configuration schemes output by the upper planning layer into the lower verification layer, and perform transient frequency response simulation and parameter optimization under various distribution network operation modes to obtain the optimized control parameters and frequency safety indicators corresponding to each of the multiple capacity configuration schemes; the frequency safety indicators are determined based on the frequency stability indicators corresponding to each of the various distribution network operation modes.

[0155] The output module 603 is used to feed back the frequency security index to the upper planning layer, so that the upper planning layer can adjust multiple sets of capacity configuration schemes and re-input them to the lower verification layer for iterative optimization until the iteration termination condition is met, and obtain the collaborative planning scheme of the hybrid energy storage system. The collaborative planning scheme includes the target capacity configuration scheme and the target optimization control parameters.

[0156] In some embodiments, the processing module 602 is used to obtain simulation parameters corresponding to various distribution network operation modes; for each capacity configuration scheme, electromechanical transient simulation is performed based on the initial control parameters and the simulation parameters corresponding to various distribution network operation modes to determine the frequency stability index corresponding to each capacity configuration scheme under various distribution network operation modes; the frequency stability index includes the maximum frequency change rate and the maximum instantaneous frequency deviation; the initial control parameters of the capacity configuration scheme are iteratively optimized using the weighted sum of the frequency stability indexes corresponding to various distribution network operation modes as the optimization objective until the iteration termination condition is met, so as to obtain the optimized control parameters and frequency safety index corresponding to the capacity configuration scheme.

[0157] In some embodiments, the capacity configuration scheme includes the rated power and rated capacity of each energy storage system in the hybrid energy storage system, and the multiple distribution network operation modes include at least grid-connected operation mode and islanded operation mode. The processing module 602 is further configured to, for each group of capacity configuration schemes, determine the curtailment penalty cost based on the curtailment amount and curtailment cost under the capacity configuration scheme; determine the outage penalty cost based on the islanded operation segment data, load gap, and unit power shortage cost under the capacity configuration scheme; determine the operating cost of each energy storage system under the capacity configuration scheme based on the unit cost parameters, rated power, and rated capacity of the energy storage system; and determine the economic indicators of the capacity configuration scheme based on the curtailment penalty cost, outage penalty cost, and operating cost of each energy storage system; wherein, the curtailment amount, islanded operation segment data, and load gap under the capacity configuration scheme are determined based on the operation simulation of the capacity configuration scheme.

[0158] In some embodiments, the output module 603 is used to determine the fitness of each capacity configuration scheme based on the economic indicators and frequency security indicators corresponding to the capacity configuration scheme; select the best option based on the fitness of multiple capacity configuration schemes to determine multiple capacity configuration schemes to participate in the next iteration; perform cross-processing and mutation processing on the multiple capacity configuration schemes participating in the next iteration in sequence to generate new capacity configuration schemes and re-input them to the lower verification layer for iterative optimization until the iteration termination condition is met, and obtain the collaborative planning scheme of the hybrid energy storage system.

[0159] In some embodiments, the frequency security index includes the maximum frequency change rate and the maximum instantaneous frequency deviation; the output module 603 is used to determine the penalty parameter based on the preset security threshold and the frequency security index; the preset security threshold includes the frequency change rate threshold and the instantaneous frequency deviation threshold; the penalty parameter and the frequency security index are summed to obtain the corrected frequency index; the economic index and the corrected frequency index are weighted to obtain the fitness of the capacity configuration scheme.

[0160] In some embodiments, the output module 603 is further configured to: determine the first capacity configuration scheme with the highest fitness among the multiple capacity configuration schemes in the previous iteration round, and the second capacity configuration scheme with the highest fitness among the multiple capacity configuration schemes in the current iteration round; determine the configuration change amount based on the configuration difference between the first capacity configuration scheme and the second capacity configuration scheme; determine that the iteration termination condition is met when the configuration change amount is lower than a preset change threshold and the frequency security indicators of the second capacity configuration scheme all meet the preset security constraints; determine the second capacity configuration scheme as the target capacity configuration scheme, and determine the optimization control parameters corresponding to the second capacity configuration scheme as the target optimization control parameters.

[0161] The modules in the aforementioned hybrid energy storage collaborative planning device for power distribution networks can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0162] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data related to hybrid energy storage planning. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a hybrid energy storage collaborative planning method for power distribution networks.

[0163] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0164] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0165] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0166] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0167] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0168] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0169] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0170] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A hybrid energy storage collaborative planning method for power distribution networks, characterized in that, The method includes: A two-layer planning model for hybrid energy storage systems is constructed. The two-layer planning model includes an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the target, and the lower verification layer optimizes the operation control parameters of the hybrid energy storage system with the distribution network frequency security indicator as the target. The multiple capacity configuration schemes output by the upper planning layer are input into the lower verification layer. Transient frequency response simulation and parameter optimization are performed under various distribution network operation modes to obtain the optimized control parameters and frequency safety indicators corresponding to each of the multiple capacity configuration schemes. The frequency safety indicators are determined based on the frequency stability indicators corresponding to each of the multiple distribution network operation modes. The frequency security index is fed back to the upper planning layer, which then adjusts multiple sets of capacity configuration schemes and re-inputs them to the lower verification layer for iterative optimization until the iteration termination condition is met, thus obtaining the collaborative planning scheme of the hybrid energy storage system. The collaborative planning scheme includes a target capacity configuration scheme and target optimization control parameters.

2. The method according to claim 1, characterized in that, The process involves inputting multiple capacity configuration schemes output from the upper planning layer into the lower verification layer, performing transient frequency response simulations and parameter optimizations under various distribution network operation modes, and obtaining optimized control parameters and frequency safety indicators corresponding to each of the multiple capacity configuration schemes, including: Obtain the simulation parameters corresponding to each of the various power distribution network operation modes; For each of the aforementioned capacity configuration schemes, electromechanical transient simulations are performed based on initial control parameters and simulation parameters corresponding to various distribution network operation modes to determine the frequency stability indicators corresponding to each capacity configuration scheme under various distribution network operation modes; the frequency stability indicators include the maximum rate of frequency change and the maximum instantaneous frequency deviation; Using the weighted sum of frequency stability indices corresponding to various distribution network operation modes as the optimization objective, the initial control parameters of the capacity configuration scheme are iteratively optimized until the iteration termination condition is met, thereby obtaining the optimized control parameters and frequency security indices corresponding to the capacity configuration scheme.

3. The method according to claim 1, characterized in that, The capacity configuration scheme includes the rated power and rated capacity of each energy storage system in the hybrid energy storage system, and the various distribution network operation modes include at least grid-connected operation mode and islanded operation mode. The method further includes: For each capacity configuration scheme, the power curtailment penalty cost is determined based on the amount of power curtailment and the cost of power curtailment under the capacity configuration scheme. Based on the islanded operation segment data, load gap, and unit power shortage cost under the aforementioned capacity configuration scheme, determine the power outage penalty cost; For each of the energy storage systems under the aforementioned capacity configuration scheme, the operating cost of the energy storage system is determined based on the unit cost parameters, rated power, and rated capacity of the energy storage system. Based on the curtailment penalty cost, the power outage penalty cost, and the operating cost of each of the energy storage systems, the economic indicators of the capacity configuration scheme are determined. The abandoned power, islanded operation time data, and load gap under the capacity configuration scheme are determined based on the operation simulation of the capacity configuration scheme.

4. The method according to claim 1, characterized in that, The step of feeding back the frequency security index to the upper planning layer, causing the upper planning layer to adjust the capacity configuration scheme and re-input it to the lower verification layer for iterative optimization until the iteration termination condition is met, thereby obtaining the collaborative planning scheme of the hybrid energy storage system, includes: For each capacity configuration scheme, the suitability of the capacity configuration scheme is determined based on the economic indicators and frequency security indicators corresponding to the capacity configuration scheme. Based on the fitness of multiple sets of capacity configuration schemes, the optimal schemes are selected to participate in the next round of iteration. For the multiple capacity configuration schemes participating in the next iteration, cross-processing and mutation processing are performed sequentially to generate new capacity configuration schemes, which are then re-input into the lower verification layer for iterative optimization until the iteration termination condition is met, thus obtaining the collaborative planning scheme of the hybrid energy storage system.

5. The method according to claim 4, characterized in that, The frequency safety indicators include the target maximum frequency change rate and the target maximum instantaneous frequency deviation; The process of determining the suitability of the capacity configuration scheme based on the economic and frequency security indicators corresponding to the capacity configuration scheme includes: The penalty parameters are determined based on the preset safety threshold and the frequency safety index; the preset safety threshold includes the frequency change rate threshold and the instantaneous frequency deviation threshold. The corrected frequency index is obtained by summing the penalty parameter and the frequency security index. The fitness of the capacity configuration scheme is obtained by weighting the economic indicators and the correction frequency indicators.

6. The method according to claim 5, characterized in that, The method further includes: Determine the first capacity configuration scheme with the highest fitness among the multiple capacity configuration schemes in the previous iteration round, and the second capacity configuration scheme with the highest fitness among the multiple capacity configuration schemes in the current iteration round; Based on the configuration differences between the first capacity configuration scheme and the second capacity configuration scheme, determine the amount of configuration change; If the configuration change is less than a preset change threshold and the frequency security indicators of the second capacity configuration scheme all meet the preset security constraints, then the iteration termination condition is determined to be met. The second capacity configuration scheme is determined as the target capacity configuration scheme, and the optimization control parameters corresponding to the second capacity configuration scheme are determined as the target optimization control parameters.

7. A hybrid energy storage collaborative planning device for power distribution networks, characterized in that, The device includes: The model building module is used to build a two-layer planning model for hybrid energy storage systems. The two-layer planning model includes an upper planning layer and a lower verification layer. The upper planning layer optimizes the energy storage capacity configuration of the hybrid energy storage system with economic indicators as the target, and the lower verification layer optimizes the operation control parameters of the hybrid energy storage system with the distribution network frequency security indicators as the target. The processing module is used to input multiple capacity configuration schemes output by the upper planning layer into the lower verification layer, and perform transient frequency response simulation and parameter optimization under various distribution network operation modes to obtain the optimized control parameters and frequency safety indicators corresponding to each of the multiple capacity configuration schemes; the frequency safety indicators are determined based on the frequency stability indicators corresponding to each of the various distribution network operation modes. The output module is used to feed back the frequency security index to the upper planning layer, so that the upper planning layer adjusts multiple sets of the capacity configuration schemes and re-inputs them to the lower verification layer for iterative optimization until the iteration termination condition is met, thereby obtaining the collaborative planning scheme of the hybrid energy storage system. The collaborative planning scheme includes the target capacity configuration scheme and the target optimization control parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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