Method and System for Synchronous Operation of Increment of Microgrid Load and Liquid Cooling System

Abstract

The present invention provides a method and system for synchronously operating a microgrid's load increment and liquid cooling system. The implementation scheme comprises: performing a boost simulation on the liquid-cooled boost branch corresponding to each load mutation node in the microgrid, and performing a buck simulation on the liquid-cooled buck branch corresponding to each load stabilization node, to obtain a target rising hydraulic pressure for each liquid-cooled boost branch and a target falling hydraulic pressure for each liquid-cooled buck branch; based on the predicted load mutation probability of each load stabilization node within a first timeframe, and the target falling hydraulic pressure of each corresponding liquid-cooled buck branch, the risk of insufficient cooling for each liquid-cooled buck branch in dissipating heat for the corresponding load stabilization node is predicted, and the target falling hydraulic pressure of each liquid-cooled buck branch is adjusted, thereby operating the liquid cooling system within a first timeframe to dissipate heat from the microgrid. The present invention can improve the heat dissipation efficiency of the microgrid.

Description

Technical Field

[0001] The present invention relates to the field of electromechanical control technology, and in particular to a method and system for synchronous operation of a microgrid load increment and a liquid cooling system. Background Art

[0002] A microgrid is a small-scale power generation and distribution system consisting of distributed power sources, energy storage devices, energy conversion devices, and monitoring and protection devices. These devices generate heat when generating, transferring, or carrying loads of electrical energy. In some technologies, microgrids incorporate liquid cooling systems as heat dissipation devices, reducing the temperature of microgrid equipment while also recycling heat.

[0003] Typically, a liquid cooling system consists of multiple parallel U-shaped pipes, each of which can be controlled by valves to control information such as the flow rate and flow rate. During microgrid operation, controlling the flow distribution among the parallel branches in the liquid cooling system to improve the overall heat dissipation of the microgrid is a technical problem to be solved in this field. Summary of the Invention

[0004] The present invention provides a method and system for synchronous operation of microgrid load increment and liquid cooling system, which can solve at least one of the above technical problems.

[0005] According to one aspect of the present invention, a method for synchronous operation of a microgrid load increment and a liquid cooling system is provided, comprising:

[0006] Determining a load mutation node and a load stability node based on a current load change rate of each load node in the microgrid;

[0007] Performing a pressure-increasing simulation on the liquid-cooled pressure-increasing branch corresponding to each of the sudden load nodes and performing a pressure-reducing simulation on the liquid-cooled pressure-reducing branch corresponding to each of the stable load nodes, to obtain a target rising hydraulic pressure of each of the liquid-cooled pressure-increasing branches and a target falling hydraulic pressure of each of the liquid-cooled pressure-reducing branches;

[0008] Based on the predicted load mutation probability of each load stabilizing node within the first time and the target hydraulic pressure drop of each corresponding liquid-cooled pressure-reducing branch, the risk of insufficient cooling of each liquid-cooled pressure-reducing branch for dissipating heat for the corresponding load stabilizing node is predicted;

[0009] adjusting the target hydraulic pressure drop of each of the liquid-cooled pressure-reducing branches based on the risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches;

[0010] Based on the target rising hydraulic pressure of each of the liquid-cooled boost branches and the adjusted target falling hydraulic pressure of each of the liquid-cooled depressurization branches, the liquid cooling system is operated within the first time to dissipate heat from the microgrid.

[0011] According to another aspect of the present invention, there is provided a device for synchronous operation of a microgrid load increment and a liquid cooling system, comprising:

[0012] A load node determination module is used to determine a load mutation node and a load stability node based on the current load change rate of each load node in the microgrid;

[0013] a boost and buck simulation module, configured to perform a boost simulation on the liquid-cooled boost branch corresponding to each of the sudden load nodes and a buck simulation on the liquid-cooled buck branch corresponding to each of the stable load nodes, to obtain a target rising hydraulic pressure for each of the liquid-cooled boost branches and a target falling hydraulic pressure for each of the liquid-cooled buck branches;

[0014] a risk prediction module, configured to predict a risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches for dissipating heat for the corresponding load-stabilizing nodes based on a predicted load mutation probability of each of the load-stabilizing nodes within a first period of time and a target hydraulic pressure drop of each of the corresponding liquid-cooled pressure-reducing branches;

[0015] a hydraulic pressure adjustment module, configured to adjust a target hydraulic pressure drop of each of the liquid-cooled pressure-reducing branches based on a risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches;

[0016] The liquid cooling system operation module is used to operate the liquid cooling system within the first time based on the target rising hydraulic pressure of each of the liquid cooling boost branches and the adjusted target falling hydraulic pressure of each of the liquid cooling step-down branches to dissipate heat for the microgrid.

[0017] According to another aspect of the present invention, a system for synchronous operation of a microgrid load increment and a liquid cooling system is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for synchronous operation of a microgrid load increment and a liquid cooling system as described in any one of the embodiments of the present invention.

[0018] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the method for synchronous operation of microgrid load increment and liquid cooling system as described in any embodiment of the present invention.

[0019] Using the technical solution of the present invention, based on the current load change rate of each load node in the microgrid, load mutation nodes and load stabilization nodes are determined; a boost simulation is performed on the liquid-cooled boost branch corresponding to each load mutation node, and a buck simulation is performed on the liquid-cooled buck branch corresponding to each load stabilization node, to obtain the target rising hydraulic pressure of each liquid-cooled boost branch and the target falling hydraulic pressure of each liquid-cooled buck branch. In this way, accurate target rising hydraulic pressures for the liquid-cooled boost branch and target falling hydraulic pressures for the liquid-cooled buck branch are obtained through the boost and buck simulations. Based on the predicted load mutation probability of each load stabilization node within the first time period and the target falling hydraulic pressure of each corresponding liquid-cooled buck branch, the risk of insufficient cooling for each liquid-cooled buck branch to dissipate heat for the corresponding load stabilization node is predicted; based on the insufficient cooling risk of each liquid-cooled buck branch, the target falling hydraulic pressure of each liquid-cooled buck branch is adjusted; this can prevent the liquid-cooled boost branch from dropping too much, resulting in insufficient heat dissipation. Therefore, based on the target rising hydraulic pressure of each liquid-cooled boost branch and the adjusted target falling hydraulic pressure of each liquid-cooled step-down branch, the liquid cooling system is operated in the first time to achieve balanced heat dissipation of the microgrid, which can not only improve the heat dissipation efficiency of nodes with sudden load changes, but also avoid insufficient heat dissipation at nodes with stable loads.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are provided for a better understanding of the present invention and do not constitute a limitation of the present invention.

[0022] Figure 1 is a flow chart of a method for synchronous operation of a microgrid load increment and a liquid cooling system according to an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of the structure and distribution of a microgrid according to an embodiment of the present invention;

[0024] Figure 3 This is a structural block diagram of a device for synchronous operation of a microgrid load increment and a liquid cooling system according to an embodiment of the present invention;

[0025] Figure 4 is a block diagram of an electronic device for implementing the method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following description of exemplary embodiments of the present invention is made in conjunction with the accompanying drawings, and various details of the embodiments of the present invention are included to facilitate understanding. These details should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0027] Figure 1 This is a flow chart of a method for synchronous operation of a microgrid load increment and a liquid cooling system according to an embodiment of the present invention.

[0028] like Figure 1 As shown, the method for synchronous operation of the microgrid load increment and the liquid cooling system may include:

[0029] S110, determining a load mutation node and a load stability node based on a current load change rate of each load node in the microgrid;

[0030] S120, performing a pressure-increasing simulation on the liquid-cooled pressure-increasing branch corresponding to each sudden load node and a pressure-reducing simulation on the liquid-cooled pressure-reducing branch corresponding to each stable load node, to obtain a target rising hydraulic pressure for each liquid-cooled pressure-increasing branch and a target falling hydraulic pressure for each liquid-cooled pressure-reducing branch;

[0031] S130, based on the predicted load mutation probability of each load stabilizing node within the first time period and the target hydraulic pressure drop of each corresponding liquid cooling pressure reduction branch, predicting the risk of insufficient cooling of each liquid cooling pressure reduction branch for dissipating heat for each corresponding load stabilizing node;

[0032] S140, adjusting the target hydraulic pressure drop of each liquid-cooled pressure-reducing branch based on the risk of insufficient cooling of each liquid-cooled pressure-reducing branch;

[0033] S150 , based on the target rising hydraulic pressure of each liquid-cooled boost branch and the adjusted target falling hydraulic pressure of each liquid-cooled depressurization branch, operating the liquid cooling system within the first time to dissipate heat from the microgrid.

[0034] For example, the microgrid may include multiple distributed load nodes and power generation nodes. The distribution of the load nodes may be as follows: Figure 2 The distance between these load nodes is not far, usually 1 meter or 2 meters, and of course it can be within 10 meters.

[0035] For example, the liquid cooling system may include multiple liquid cooling branches, one for each load node, to dissipate heat from the equipment at that load node. Each liquid cooling branch may include interconnected liquid cooling elbows or U-shaped pipes. Each liquid cooling branch inlet may be equipped with an on / off valve to control the inlet opening and the hydraulic pressure of that branch. By controlling the hydraulic pressure of each branch, the liquid flow rate in each liquid cooling branch can be adjusted, thereby adjusting the heat exchange efficiency.

[0036] Exemplarily, if the current load change rate of a load node in a microgrid is greater than a preset load change rate threshold, the load node is determined to be a load mutation node; if the current load change rate of a load node in a microgrid is less than a preset load change rate threshold, the load node is determined to be a load stable node.

[0037] Exemplarily, the load change rate threshold may be an average value or a median value of the current load change rates of each load node in the microgrid.

[0038] It can be understood that a load mutation node may be a node where the load suddenly increases or decreases. A load stability node may be a node where the load change rate is relatively small, which means that the load remains unchanged or the load change is relatively small.

[0039] For example, for the first time period from the current moment to the next moment, a pressure-boosting simulation can be performed on the liquid-cooled boost branch corresponding to each sudden load change node, and a pressure-reducing simulation can be performed on the liquid-cooled pressure-reducing branch corresponding to each stable load node, to obtain a target rising hydraulic pressure for each liquid-cooled boost branch and a target falling hydraulic pressure for each liquid-cooled pressure-reducing branch. The pressure-boosting and pressure-reducing simulations can be performed simultaneously, or the pressure-boosting simulation can be performed first and then the pressure-reducing simulation.

[0040] For example, the current load change rate and current load value of each sudden load node can be used to predict the predicted load change amplitude of the liquid-cooled boost branch corresponding to each sudden load node within the first time period. Based on this predicted load change amplitude, a boost simulation is performed on the liquid-cooled boost branch corresponding to each sudden load node, and a buck simulation is performed on the liquid-cooled buck branch corresponding to each stable load node, to obtain the target rising hydraulic pressure of each liquid-cooled boost branch and the target falling hydraulic pressure of each liquid-cooled buck branch.

[0041] It can be understood that the target rising hydraulic pressure is used to control the hydraulic pressure of the liquid-cooled boost branch to rise and reach the target rising hydraulic pressure value, and the target falling hydraulic pressure is used to control the hydraulic pressure of the liquid-cooled depressurization branch to fall and reach the target falling hydraulic pressure value.

[0042] For example, a mapping relationship between the hydraulic pressure drop and the historical impact of insufficient cooling is constructed based on historical hydraulic pressure drop amplitudes and the corresponding historical impact of insufficient cooling. An estimated hydraulic pressure drop amplitude is determined based on the difference between the current hydraulic pressure of the load-stabilizing node and the target hydraulic pressure drop. Based on the estimated hydraulic pressure drop amplitude, the corresponding impact of insufficient cooling is found in the aforementioned mapping relationship. The risk of insufficient cooling for the corresponding liquid-cooled pressure-reducing branch within the first timeframe is determined based on the product of the found impact of insufficient cooling and the predicted load mutation probability for the load-stabilizing node within the first timeframe.

[0043] For example, if the risk of insufficient cooling of the liquid-cooled pressure-reducing branch is too high, the target hydraulic pressure drop value of the liquid-cooled pressure-reducing branch is increased to reduce the hydraulic pressure drop amplitude. If the risk of insufficient cooling of the liquid-cooled pressure-reducing branch is low, the target hydraulic pressure drop value of the liquid-cooled pressure-reducing branch remains unchanged.

[0044] Exemplarily, based on the target rising hydraulic pressure of each liquid-cooled boost branch and the adjusted target falling hydraulic pressure of each liquid-cooled step-down branch, the hydraulic pressure of the hydraulic valve at the entrance of each corresponding branch is adjusted to operate the liquid cooling system as soon as possible to achieve balanced heat dissipation of the power equipment at each load node in the microgrid.

[0045] According to the above embodiment, based on the current load change rate of each load node in the microgrid, load mutation nodes and load stabilization nodes are determined; a boost simulation is performed on the liquid-cooled boost branch corresponding to each load mutation node, and a buck simulation is performed on the liquid-cooled buck branch corresponding to each load stabilization node, to obtain target rising hydraulic pressures for each liquid-cooled boost branch and target falling hydraulic pressures for each liquid-cooled buck branch. In this way, accurate target rising hydraulic pressures for the liquid-cooled boost branch and target falling hydraulic pressures for the liquid-cooled buck branch are obtained through the boost and buck simulations. Based on the predicted load mutation probability of each load stabilization node within the first timeframe and the target falling hydraulic pressures of each corresponding liquid-cooled buck branch, the risk of insufficient cooling for each liquid-cooled buck branch in dissipating heat for the corresponding load stabilization node is predicted; based on the risk of insufficient cooling for each liquid-cooled buck branch, the target falling hydraulic pressure for each liquid-cooled buck branch is adjusted. This prevents the liquid-cooled boost branch from excessively dropping its pressure, resulting in insufficient heat dissipation. Therefore, based on the target rising hydraulic pressure of each liquid-cooled boost branch and the adjusted target falling hydraulic pressure of each liquid-cooled step-down branch, the liquid cooling system is operated in the first time to achieve balanced heat dissipation of the microgrid, which can not only improve the heat dissipation efficiency of nodes with sudden load changes, but also avoid insufficient heat dissipation at nodes with stable loads.

[0046] In one embodiment, a boost simulation is performed on the liquid-cooled boost branch corresponding to each load mutation node, and a buck simulation is performed on the liquid-cooled buck branch corresponding to each load stabilization node, to obtain a target rising hydraulic pressure of each liquid-cooled boost branch and a target falling hydraulic pressure of each liquid-cooled buck branch, including: determining the liquid flow resistance coefficient from the liquid-cooled buck branch corresponding to each load stabilization node to the liquid-cooled boost branch corresponding to the load mutation node based on the distance between the load mutation node and each load stabilization node; performing a boost simulation on the liquid-cooled boost branch based on the liquid flow resistance coefficient from the liquid-cooled buck branch corresponding to each load stabilization node to the liquid-cooled boost branch corresponding to the load mutation node, and the predicted load change amplitude of the load mutation node within the first time, to obtain the target rising hydraulic pressure of the liquid-cooled boost branch; determining the target falling hydraulic pressure of each liquid-cooled buck branch based on the target rising hydraulic pressure of each liquid-cooled boost branch.

[0047] For example, a preset flow resistance coefficient calculation function can be used. The distance between the load mutation node and the load stability node can be input into the flow resistance coefficient calculation function to obtain the output flow resistance coefficient. The function can be a linear function. The greater the distance between the load mutation node and the load stability node, the greater the flow resistance coefficient between the corresponding branches.

[0048] Exemplarily, while keeping the liquid-cooled pressure-reducing branch unchanged, a pressure-increasing simulation is performed on the liquid-cooled pressure-increasing branch to obtain a corresponding target rising hydraulic pressure.

[0049] Exemplarily, after obtaining the target rising hydraulic pressure for each liquid-cooled boost branch, the target falling hydraulic pressure for each liquid-cooled depressurization branch is determined based on the target rising hydraulic pressure for each liquid-cooled boost branch. For example, the amplitudes of the target rising hydraulic pressure for each liquid-cooled boost branch relative to its current hydraulic pressure are summed to obtain the total hydraulic pressure rising amplitude. A genetic algorithm is then employed to minimize the standard deviation of the amplitudes of the target falling hydraulic pressure for each liquid-cooled depressurization branch relative to its current hydraulic pressure, ensuring that the sum of the amplitudes of the target falling hydraulic pressure for each liquid-cooled depressurization branch relative to its current hydraulic pressure equals the total hydraulic pressure rising amplitude. In this manner, the target falling hydraulic pressure for each liquid-cooled depressurization branch can be obtained.

[0050] According to the above embodiment, the distance between the load-sudden change node and each load-stabilizing node is used to determine the flow resistance coefficient between the liquid-cooled pressure-reducing branch corresponding to each load-stabilizing node and the liquid-cooled pressure-boosting branch corresponding to the load-sudden change node. Using these flow resistance coefficients, a pressure-boost simulation can be performed on the corresponding liquid-cooled pressure-boosting branch based on the predicted load change amplitude at each load-sudden change node within the first timeframe, improving simulation accuracy. This allows accurate target rising hydraulic pressures for the liquid-cooled pressure-boosting branch to be determined. Furthermore, based on the target rising hydraulic pressures of each liquid-cooled pressure-reducing branch, the target falling hydraulic pressures of each liquid-cooled pressure-reducing branch can also be accurately determined.

[0051] In one embodiment, based on the liquid flow resistance coefficient between the liquid-cooled pressure-reducing branch corresponding to each load stable node and the liquid-cooled pressure-reducing branch corresponding to the load mutation node, and the predicted load change amplitude of the load mutation node within the first time, a pressure-reducing simulation is performed on the liquid-cooled pressure-reducing branch to obtain a target rising hydraulic pressure of the liquid-cooled pressure-reducing branch, including: determining a first predicted temperature change curve of the load mutation node within the first time based on the current hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load mutation node and the predicted load change amplitude of the load mutation node within the first time; for each candidate rising hydraulic pressure, controlling the liquid-cooled pressure-reducing branch to operate under the candidate rising hydraulic pressure, and controlling the load mutation node to change the load according to the predicted load change amplitude within the time length corresponding to the first time, so as to simulate the flow change of the liquid flow of each liquid-cooled pressure-reducing branch to the liquid-cooled pressure-reducing branch corresponding to the load mutation node under the respective corresponding liquid flow resistance coefficients, and obtaining a simulated temperature change curve of the load mutation node within the time length corresponding to the first time; based on the difference between the first predicted temperature change curve and the simulated temperature change curve corresponding to each candidate rising hydraulic pressure, determining the target rising hydraulic pressure of the liquid-cooled pressure-reducing branch in each candidate rising hydraulic pressure.

[0052] It can be understood that the target boosting hydraulic pressure of each liquid-cooled boosting branch can be determined according to the above example.

[0053] It can be understood that the first predicted temperature change curve is used to describe the temperature change of the load mutation node within the first time when the current hydraulic pressure remains unchanged.

[0054] For example, the degree of deviation between the first predicted temperature change curve and the simulated temperature change curve corresponding to the candidate rising hydraulic pressure is determined based on the deviation values at each time point between the first predicted temperature change curve and the simulated temperature change curve corresponding to the candidate rising hydraulic pressure. Consequently, the candidate rising hydraulic pressure with the smallest degree of deviation is selected from the candidate rising hydraulic pressures using the degree of deviation between the first predicted temperature change curve and the simulated temperature change curve corresponding to each candidate rising hydraulic pressure as the target rising hydraulic pressure for the liquid-cooled boost branch.

[0055] According to the above embodiment, based on the liquid flow resistance coefficient between the liquid-cooled pressure reduction branch corresponding to each load stabilization node and the liquid-cooled pressure boost branch corresponding to the load mutation node, and the predicted load change amplitude of the load mutation node in the first time, the liquid-cooled pressure boost branch is simulated to accurately obtain the target rising hydraulic pressure of the liquid-cooled pressure boost branch.

[0056] In one embodiment, the target descending hydraulic pressure of each liquid-cooled pressure-reducing branch is determined based on the target rising hydraulic pressure of each liquid-cooled pressure-reducing branch, including: determining the total rising hydraulic pressure based on the target rising hydraulic pressure of each liquid-cooled pressure-reducing branch; determining the target descending hydraulic pressure of each liquid-cooled pressure-reducing branch based on the total rising hydraulic pressure.

[0057] It can be understood that the total hydraulic pressure increase refers to the total hydraulic pressure increase amplitude. The total hydraulic pressure increase amplitude of each liquid-cooled boost branch should be equal to or greater than the total hydraulic pressure decrease amplitude of each liquid-cooled depressurization branch. For example, in addition to providing liquid cooling branches for each load node, the liquid cooling system can also be equipped with a backup liquid cooling tank to achieve the pressurization effect even if one or more liquid cooling branches are pressurized while other branches remain unchanged.

[0058] For example, after obtaining the target rising hydraulic pressure for each liquid-cooled boost branch, the target falling hydraulic pressure for each liquid-cooled depressurization branch is determined based on the target rising hydraulic pressure for each liquid-cooled boost branch. For example, the amplitude of the target rising hydraulic pressure for each liquid-cooled boost branch relative to its current hydraulic pressure is summed to obtain the total hydraulic pressure increase amplitude.

[0059] Exemplarily, a genetic algorithm is used to solve a problem in which the standard deviation of the amplitudes of the target lowering hydraulic pressure of each liquid-cooled pressure-reducing branch relative to its current hydraulic pressure is minimized, and the sum of the amplitudes of the target lowering hydraulic pressure of each liquid-cooled pressure-reducing branch relative to its current hydraulic pressure is equal to the total hydraulic pressure increase amplitude. In this way, the target lowering hydraulic pressure of each liquid-cooled pressure-reducing branch can be obtained.

[0060] According to the above embodiment, based on the target rising hydraulic pressure of each liquid-cooled boost branch, the total rising hydraulic pressure amplitude is obtained, so that the total falling hydraulic pressure amplitude can be determined. The total falling hydraulic pressure amplitude is used to determine the target falling hydraulic pressure of each liquid-cooled pressure-reducing branch, thereby accurately obtaining the target falling hydraulic pressure of each liquid-cooled pressure-reducing branch.

[0061] In one embodiment, based on the predicted load mutation probability of each load stabilizing node within the first time and the target drop hydraulic pressure of each corresponding liquid-cooled pressure-reducing branch, the risk of insufficient cooling of each liquid-cooled pressure-reducing branch for dissipating heat for the corresponding load stabilizing node is predicted, including: based on the predicted load mutation probability of the load stabilizing node within the first time and the current hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilizing node, predicting a second predicted temperature change curve of the liquid-cooled pressure-reducing branch within the first time; based on the predicted load mutation probability of the load stabilizing node within the first time and the target drop hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilizing node, predicting a third predicted temperature change curve of the liquid-cooled pressure-reducing branch within the first time; based on the difference between the second predicted temperature change curve and the third predicted temperature change curve, determining the risk of insufficient cooling of the liquid-cooled pressure-reducing branch for dissipating heat for the load stabilizing node.

[0062] For example, based on the predicted load mutation probability of the load stabilizing node within the first time period, the predicted load mutation probability of the load stabilizing node at each moment within the first time period is determined. Thus, combined with the current hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilizing node, a second predicted temperature change curve for the liquid-cooled pressure-reducing branch within the first time period can be predicted. For example, by inputting the current hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilizing node and the predicted load mutation probability of the load stabilizing node at each moment within the first time period into the simulation platform, the simulation platform can output the second predicted temperature change curve for the liquid-cooled pressure-reducing branch within the first time period.

[0063] For example, based on the predicted load mutation probability of the load stabilization node within the first time period, the predicted load mutation probability of the load stabilization node at each moment within the first time period is determined. Thus, combined with the target drop hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilization node, a third predicted temperature change curve for the liquid-cooled pressure-reducing branch within the first time period can be predicted. For example, by inputting the target drop hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilization node and the predicted load mutation probability of the load stabilization node at each moment within the first time period into the simulation platform, the simulation platform can output the third predicted temperature change curve for the liquid-cooled pressure-reducing branch within the first time period.

[0064] For example, based on the temperature at each time point in the second predicted temperature change curve and the temperature at the corresponding time point in the third predicted temperature change curve, the degree of temperature deviation between the two curves is determined. The degree of temperature deviation between the two curves is used to determine the risk of insufficient cooling. The higher the temperature deviation, the greater the risk of insufficient cooling.

[0065] According to the above embodiment, the risk of insufficient cooling of the liquid-cooled pressure-reducing branch for dissipating heat for the corresponding load-stabilizing node can be accurately predicted.

[0066] In one embodiment, based on the insufficient cooling risk of each liquid-cooled pressure-reducing branch, the target descent hydraulic pressure of each liquid-cooled pressure-reducing branch is adjusted, including: when the insufficient cooling risk of the liquid-cooled pressure-reducing branch is greater than a preset first risk threshold, multiplying the difference between the insufficient cooling risk of the liquid-cooled pressure-reducing branch and the first risk threshold and the preset hydraulic standard amplitude to obtain a first value; based on the first value, increasing the target descent hydraulic pressure of the liquid-cooled pressure-reducing branch to obtain an adjusted target descent hydraulic pressure of the liquid-cooled pressure-reducing branch.

[0067] Exemplarily, the first value is added to the target lowering hydraulic pressure of the liquid-cooled pressure-reducing branch to obtain the adjusted target lowering hydraulic pressure of the liquid-cooled pressure-reducing branch.

[0068] According to the above embodiment, based on the risk of insufficient cooling of each liquid-cooled pressure-reducing branch, the target drop hydraulic pressure of each liquid-cooled pressure-reducing branch is adjusted, which can avoid insufficient heat dissipation of the corresponding load stabilization node of the subsequent liquid-cooled pressure-reducing branch.

[0069] Figure 3 This is a structural block diagram of a device for synchronous operation of a microgrid load increment and a liquid cooling system according to an embodiment of the present invention.

[0070] like Figure 3 As shown, the device for synchronous operation of microgrid load increment and liquid cooling system may include:

[0071] A load node determination module 310 is configured to determine a load mutation node and a load stability node based on a current load change rate of each load node in the microgrid;

[0072] The boost and depressurization simulation module 320 is configured to perform a boost simulation on the liquid-cooled boost branch corresponding to each of the sudden load nodes and a depressurization simulation on the liquid-cooled depressurization branch corresponding to each of the stable load nodes, thereby obtaining a target rising hydraulic pressure for each of the liquid-cooled boost branches and a target falling hydraulic pressure for each of the liquid-cooled depressurization branches.

[0073] The risk prediction module 330 is configured to predict the risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches for dissipating heat for the corresponding load-stabilizing nodes based on the predicted load mutation probability of each of the load-stabilizing nodes within the first time period and the target hydraulic pressure drop of each of the corresponding liquid-cooled pressure-reducing branches;

[0074] a hydraulic pressure adjustment module 340 for adjusting a target hydraulic pressure drop of each of the liquid-cooled pressure-reducing branches based on a risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches;

[0075] The liquid cooling system operation module 350 is used to operate the liquid cooling system within the first time based on the target rising hydraulic pressure of each of the liquid cooling boost branches and the adjusted target falling hydraulic pressure of each of the liquid cooling step-down branches to dissipate heat for the microgrid.

[0076] In one embodiment, the voltage boost and voltage drop simulation module 320 includes:

[0077] a resistance coefficient determining unit, configured to determine, based on a distance between the load mutation node and each of the load stabilization nodes, a liquid flow resistance coefficient between the liquid cooling pressure reduction branch corresponding to each of the load stabilization nodes and the liquid cooling pressure boost branch corresponding to the load mutation node;

[0078] a boost simulation unit, configured to perform a boost simulation on the liquid-cooled boost branch based on a liquid flow resistance coefficient between the liquid-cooled pressure-reducing branch corresponding to each of the load-stable nodes and the liquid-cooled pressure-reducing branch corresponding to the load-sudden change node, and a predicted load change amplitude at the load-sudden change node within the first time, to obtain a target boost pressure of the liquid-cooled pressure-reducing branch;

[0079] The descending hydraulic pressure determination unit is configured to determine a target descending hydraulic pressure of each of the liquid-cooled pressure-reducing branches based on a target ascending hydraulic pressure of each of the liquid-cooled pressure-reducing branches.

[0080] In one embodiment, the boost simulation unit is specifically configured to:

[0081] Determining a first predicted temperature change curve of the load mutation node within the first time based on the current hydraulic pressure of the liquid-cooled boost branch corresponding to the load mutation node and the predicted load change amplitude of the load mutation node within the first time;

[0082] For each candidate rising hydraulic pressure, the liquid-cooled boost branch is controlled to operate at the candidate rising hydraulic pressure, and the load mutation node is controlled to perform a load change within a time length corresponding to the first time according to the predicted load change amplitude, so as to simulate the flow change of the liquid in each liquid-cooled pressure-reducing branch toward the liquid-cooled boost branch corresponding to the load mutation node under the corresponding liquid flow resistance coefficient, and obtain a simulated temperature change curve of the load mutation node within the time length corresponding to the first time;

[0083] Based on the difference between the first predicted temperature variation curve and the simulated temperature variation curve corresponding to each of the candidate rising hydraulic pressures, a target rising hydraulic pressure of the liquid-cooling boost branch is determined from each of the candidate rising hydraulic pressures.

[0084] In one embodiment, the descending hydraulic pressure determination unit is specifically configured to:

[0085] determining a total rising hydraulic pressure based on target rising hydraulic pressures of the respective liquid-cooled boost branches;

[0086] Based on the total rising hydraulic pressure, the target falling hydraulic pressure of each of the liquid-cooling pressure-reducing branches is determined.

[0087] In one embodiment, the risk prediction module 330 includes:

[0088] a first curve determining unit, configured to predict a second predicted temperature change curve of the liquid-cooled pressure-reducing branch within the first time based on the predicted load mutation probability of the load stabilizing node within the first time and the current hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilizing node;

[0089] a second curve determining unit, configured to predict a third predicted temperature change curve of the liquid-cooled pressure-reducing branch within the first time based on the predicted load mutation probability of the load stabilizing node within the first time and the target drop hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilizing node;

[0090] The risk prediction unit is configured to determine a risk of insufficient cooling of the load stabilizing node by the liquid-cooled pressure-reducing branch based on a difference between the second predicted temperature change curve and the third predicted temperature change curve.

[0091] In one embodiment, the hydraulic adjustment module 340 includes:

[0092] a first value determining unit configured to, when the risk of insufficient cooling of the liquid-cooled pressure-reducing branch is greater than a preset first risk threshold, multiply a difference between the risk of insufficient cooling of the liquid-cooled pressure-reducing branch and the first risk threshold by a preset hydraulic standard amplitude to obtain a first value;

[0093] The hydraulic value adjustment unit is used to increase the target descending hydraulic pressure of the liquid-cooled pressure-reducing branch based on the first value to obtain the adjusted liquid-cooled pressure-reducing branch.

[0094] For the description of specific functions and examples of each module and submodule of the system in the embodiment of the present invention, please refer to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0095] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0096] According to an embodiment of the present invention, the present invention further provides a system and a readable storage medium.

[0097] Figure 4A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0098] like Figure 4 As shown, electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. Computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to bus 804.

[0099] Multiple components in electronic device 800 are connected to I / O interface 805, including: an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0100] The computing unit 801 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for synchronizing microgrid load increments with liquid cooling systems. For example, in some embodiments, the method for synchronizing microgrid load increments with liquid cooling systems can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method for synchronizing microgrid load increments with liquid cooling systems described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured in any other appropriate manner (for example, by means of firmware) to execute the method for synchronous operation of the microgrid load increment and the liquid cooling system.

[0101] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0103] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0105] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0106] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0107] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. This is not limited herein.

[0108] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for synchronous operation of microgrid load increment and liquid cooling system, characterized in that: include: Based on the current load change rate of each load node in the microgrid, determine the load mutation node and load stability node; Performing a pressure-increasing simulation on the liquid-cooled pressure-increasing branch corresponding to each of the sudden load nodes and performing a pressure-reducing simulation on the liquid-cooled pressure-reducing branch corresponding to each of the stable load nodes, to obtain a target rising hydraulic pressure of each of the liquid-cooled pressure-increasing branches and a target falling hydraulic pressure of each of the liquid-cooled pressure-reducing branches; Based on the predicted load mutation probability of each load stabilizing node within the first time and the target hydraulic pressure drop of each corresponding liquid-cooled pressure-reducing branch, the risk of insufficient cooling of each liquid-cooled pressure-reducing branch for dissipating heat for the corresponding load stabilizing node is predicted; adjusting the target hydraulic pressure drop of each of the liquid-cooled pressure-reducing branches based on the risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches; Based on the target rising hydraulic pressure of each of the liquid-cooled boost branches and the adjusted target falling hydraulic pressure of each of the liquid-cooled depressurization branches, the liquid cooling system is operated within the first time to dissipate heat from the microgrid.

2. The method according to claim 1, characterized in that The step of performing a pressure boost simulation on the liquid-cooled pressure boost branch corresponding to each of the load mutation nodes and performing a pressure drop simulation on the liquid-cooled pressure drop branch corresponding to each of the load stable nodes to obtain a target rising hydraulic pressure of each of the liquid-cooled pressure boost branches and a target falling hydraulic pressure of each of the liquid-cooled pressure drop branches includes: Based on the distance between the load mutation node and each of the load stabilization nodes, respectively determining the liquid flow resistance coefficient between the liquid cooling pressure reduction branch corresponding to each of the load stabilization nodes and the liquid cooling pressure boost branch corresponding to the load mutation node; Based on the liquid flow resistance coefficient between the liquid-cooled pressure-reducing branch corresponding to each of the load-stable nodes and the liquid-cooled pressure-boosting branch corresponding to the load-sudden change node, and the predicted load change amplitude of the load-sudden change node within the first time, a pressure boost simulation is performed on the liquid-cooled pressure-reducing branch to obtain a target rising hydraulic pressure of the liquid-cooled pressure-reducing branch; The target lowering hydraulic pressure of each of the liquid-cooled pressure-reducing branches is determined based on the target rising hydraulic pressure of each of the liquid-cooled pressure-reducing branches.

3. The method according to claim 2, characterized in that The step of performing a pressure boost simulation on the liquid-cooled pressure-reducing branch based on the liquid flow resistance coefficient between the liquid-cooled pressure-reducing branch corresponding to each load-stable node and the liquid-cooled pressure-reducing branch corresponding to the load-sudden change node, and the predicted load change amplitude of the load-sudden change node within the first time, to obtain a target rising hydraulic pressure of the liquid-cooled pressure-reducing branch, includes: Determining a first predicted temperature change curve of the load mutation node within the first time based on the current hydraulic pressure of the liquid-cooled boost branch corresponding to the load mutation node and the predicted load change amplitude of the load mutation node within the first time; For each candidate rising hydraulic pressure, the liquid-cooled boost branch is controlled to operate at the candidate rising hydraulic pressure, and the load mutation node is controlled to perform a load change within a time length corresponding to the first time according to the predicted load change amplitude, so as to simulate the flow change of the liquid in each liquid-cooled pressure-reducing branch toward the liquid-cooled boost branch corresponding to the load mutation node under the corresponding liquid flow resistance coefficient, and obtain a simulated temperature change curve of the load mutation node within the time length corresponding to the first time; Based on the difference between the first predicted temperature variation curve and the simulated temperature variation curve corresponding to each of the candidate rising hydraulic pressures, a target rising hydraulic pressure of the liquid-cooling boost branch is determined from each of the candidate rising hydraulic pressures.

4. The method according to claim 2, characterized in that The determining of the target falling hydraulic pressure of each of the liquid-cooled pressure-reducing branches based on the target rising hydraulic pressure of each of the liquid-cooled pressure-reducing branches includes: determining a total rising hydraulic pressure based on target rising hydraulic pressures of the respective liquid-cooled boost branches; Based on the total rising hydraulic pressure, the target falling hydraulic pressure of each of the liquid-cooling pressure-reducing branches is determined.

5. The method according to claim 1, wherein The predicting, based on the predicted load mutation probability of each load stabilizing node within the first time and the target hydraulic pressure drop of each corresponding liquid-cooled pressure-reducing branch, the risk of insufficient cooling of each liquid-cooled pressure-reducing branch for dissipating heat for the corresponding load stabilizing node includes: Predicting a second predicted temperature change curve of the liquid-cooled pressure-reducing branch within the first time based on the predicted load mutation probability of the load stabilizing node within the first time and the current hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilizing node; Based on the predicted load mutation probability of the load stabilization node within the first time and the target drop hydraulic pressure of the liquid-cooled pressure-reducing branch corresponding to the load stabilization node, predicting a third predicted temperature change curve of the liquid-cooled pressure-reducing branch within the first time; Based on the difference between the second predicted temperature change curve and the third predicted temperature change curve, a risk of insufficient cooling of the load stabilizing node by the liquid-cooled pressure-reducing branch is determined.

6. The method according to claim 1, characterized in that The adjusting of the target hydraulic pressure drop of each of the liquid-cooled pressure-reducing branches based on the risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches includes: When the risk of insufficient cooling of the liquid-cooled pressure-reducing branch is greater than a preset first risk threshold, multiplying the difference between the risk of insufficient cooling of the liquid-cooled pressure-reducing branch and the first risk threshold by a preset hydraulic standard amplitude to obtain a first value; Based on the first value, the target descending hydraulic pressure of the liquid-cooled pressure-reducing branch is increased to obtain the adjusted liquid-cooled pressure-reducing branch.

7. A device for synchronous operation of microgrid load increment and liquid cooling system, characterized in that: include: A load node determination module is used to determine load mutation nodes and load stability nodes based on the current load change rate of each load node in the microgrid; a boost and buck simulation module, configured to perform a boost simulation on the liquid-cooled boost branch corresponding to each of the sudden load nodes and a buck simulation on the liquid-cooled buck branch corresponding to each of the stable load nodes, to obtain a target rising hydraulic pressure for each of the liquid-cooled boost branches and a target falling hydraulic pressure for each of the liquid-cooled buck branches; a risk prediction module, configured to predict a risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches for dissipating heat for the corresponding load-stabilizing nodes based on a predicted load mutation probability of each of the load-stabilizing nodes within a first period of time and a target hydraulic pressure drop of each of the corresponding liquid-cooled pressure-reducing branches; a hydraulic pressure adjustment module, configured to adjust a target hydraulic pressure drop of each of the liquid-cooled pressure-reducing branches based on a risk of insufficient cooling of each of the liquid-cooled pressure-reducing branches; The liquid cooling system operation module is used to operate the liquid cooling system within the first time based on the target rising hydraulic pressure of each of the liquid cooling boost branches and the adjusted target falling hydraulic pressure of each of the liquid cooling step-down branches to dissipate heat for the microgrid.

8. The device according to claim 7, characterized in that The voltage step-up and voltage step-down simulation module includes: a resistance coefficient determining unit, configured to determine, based on a distance between the load mutation node and each of the load stabilization nodes, a liquid flow resistance coefficient between the liquid cooling pressure reduction branch corresponding to each of the load stabilization nodes and the liquid cooling pressure boost branch corresponding to the load mutation node; a boost simulation unit, configured to perform a boost simulation on the liquid-cooled boost branch based on a liquid flow resistance coefficient between the liquid-cooled pressure-reducing branch corresponding to each of the load-stable nodes and the liquid-cooled pressure-reducing branch corresponding to the load-sudden change node, and a predicted load change amplitude at the load-sudden change node within the first time, to obtain a target boost pressure of the liquid-cooled pressure-reducing branch; The descending hydraulic pressure determination unit is configured to determine a target descending hydraulic pressure of each of the liquid-cooled pressure-reducing branches based on a target ascending hydraulic pressure of each of the liquid-cooled pressure-reducing branches.

9. A system for synchronous operation of microgrid load increment and liquid cooling system, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

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