Photovoltaic power generation method and system integrated with prefabricated cabin type liquid cooling energy storage

Abstract

The application provides a photovoltaic power generation method and system integrated with prefabricated cabin type liquid cooling energy storage, wherein the method comprises the following steps: collecting direct current through a photovoltaic assembly, and converting the collected direct current into alternating current synchronized with a power grid through a photovoltaic inverter; transmitting the alternating current to a local load; if there is excess power, charging the excess power into a prefabricated cabin type liquid cooling energy storage device integrated with the photovoltaic assembly; if the power is insufficient, discharging the prefabricated cabin type liquid cooling energy storage device to the local load; and when the prefabricated cabin type liquid cooling energy storage device is working, the cabin parameters are pre-adjusted and controlled according to the expected charging and discharging situation. The photovoltaic power generation method and system integrated with the prefabricated cabin type liquid cooling energy storage device realize efficient energy conversion and storage through photovoltaic power generation and the prefabricated cabin type liquid cooling energy storage device. The expected charging and discharging situation is introduced to realize systematic and collaborative pre-adjustment and control of the cabin parameters, and the safety and cycle life of the battery are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of solar photovoltaic technology, and in particular to a photovoltaic power generation method and system with integrated prefabricated liquid-cooled energy storage. Background Technology

[0002] Currently, the penetration rate of renewable energy sources, represented by photovoltaic (PV) power generation, in the power system continues to increase. However, PV power generation is characterized by significant intermittency, volatility, and unschedulable nature. Its output is affected by factors such as solar radiation intensity, weather changes, and day-night cycles, making it difficult to match with electricity load in real time. To improve the local absorption capacity and power supply reliability of PV systems, integrated PV-energy storage technology has become an important development direction.

[0003] In existing technologies, air-cooled energy storage cabinets or simple battery racks are often used in conjunction with air conditioning for thermal management. However, such solutions suffer from problems such as low heat dissipation efficiency, large temperature differences between battery modules, short cycle life, and high safety risks. Especially under high-rate charging and discharging or high-temperature environments, batteries are prone to localized overheating, accelerating capacity decay and even inducing thermal runaway, which seriously restricts the long-term stable operation of energy storage systems.

[0004] Furthermore, the thermal management of energy storage systems often operates independently, without deep coordination with battery charging and discharging plans, environmental conditions, and equipment status. This results in high cooling energy consumption, slow response, and an inability to proactively optimize the cabin thermal environment before critical operating conditions. Although some solutions have attempted to introduce predictive control, a systematic and coordinated control of the expected charging and discharging scenarios and the prefabricated cabin thermal, electrical, and control parameters has not yet been achieved.

[0005] In view of this, there is an urgent need for a photovoltaic power generation method and system that integrates prefabricated liquid-cooled energy storage to at least address the above-mentioned shortcomings. Summary of the Invention

[0006] One objective of this invention is to provide a photovoltaic power generation method and system integrating prefabricated liquid-cooled energy storage, which transmits AC power to local loads through photovoltaic power generation. When there is excess power, it is charged into the prefabricated liquid-cooled energy storage device; if the power is insufficient, it is discharged to the local load through the prefabricated liquid-cooled energy storage device, achieving efficient energy conversion and storage. By introducing anticipated charge and discharge scenarios and performing systematic and coordinated pre-regulation of in-cabin parameters, battery safety and cycle life are significantly improved.

[0007] This invention provides a photovoltaic power generation method integrating prefabricated liquid-cooled energy storage, comprising:

[0008] Direct current (DC) is collected by photovoltaic (PV) modules and then input into a PV inverter to convert it into alternating current (AC) that is synchronized with the power grid.

[0009] Transmitting alternating current to local loads;

[0010] If there is excess power, the excess power will be charged into the prefabricated liquid-cooled energy storage device integrated with the photovoltaic modules.

[0011] If the power is insufficient, it will be discharged to the local load through a prefabricated liquid-cooled energy storage device.

[0012] When the prefabricated liquid-cooled energy storage device is in operation, the parameters inside the chamber are pre-regulated in a coordinated manner according to the expected charging and discharging scenarios.

[0013] Preferably, cluster management is implemented for the prefabricated liquid-cooled energy storage device integrated with the photovoltaic module.

[0014] Preferably, the steps for determining the expected charge / discharge scenario are as follows:

[0015] Get the weather forecast for a future time.

[0016] Predict local load conditions at future times;

[0017] Based on weather forecasts and local load conditions, the charging and discharging status of the prefabricated liquid-cooled energy storage device is predicted for future moments;

[0018] By collecting the charging and discharging conditions corresponding to each future moment, the expected charging and discharging scenario can be obtained.

[0019] Preferably, the cabin parameters include: pump speed, flow rate, valves, air conditioning power, and heat exchanger start / stop.

[0020] Preferably, the in-cabin parameters are pre-regulated in a coordinated manner according to the expected charging and discharging scenario, including:

[0021] Based on the design and structural information of the prefabricated liquid-cooled energy storage device, construct a three-dimensional model of the target.

[0022] Based on the expected charging and discharging scenarios, determine the dynamic thermodynamic information of each battery cluster module in the target 3D model;

[0023] Based on dynamic thermal information, determine the required cooling control data for the cooling plate module corresponding to each battery cluster module;

[0024] Based on the required cooling control data, obtain the cooling control data corresponding to the battery cluster module;

[0025] The cooling control data corresponding to all battery cluster modules are fused in the time domain to obtain fused cooling control data;

[0026] Based on the cooling control fusion data, determine the heat exchanger start / stop control data and the air conditioning power control data;

[0027] The cooling control fusion data, heat exchanger start / stop control data, and air conditioning power control data are used as cabin parameters and controlled accordingly.

[0028] Preferably, based on dynamic thermal information, the required cooling control data for the cooling plate module corresponding to each battery cluster module is determined, including:

[0029] The average heat value of each battery cell module in the battery cluster module is calculated based on dynamic thermal information.

[0030] Multiple pre-selected cooling control data are determined based on the average heat value and the preset heat value redundancy range;

[0031] The cooling effect score is determined based on pre-selected cooling control data and dynamic thermodynamic information;

[0032] The pre-selected refrigeration control data with the highest refrigeration effect score is used as the required refrigeration control data.

[0033] Preferably, based on the required cooling control data, the cooling control data corresponding to the battery cluster module is obtained, including:

[0034] Determine the cooling path of the cooling module corresponding to the required refrigeration control data;

[0035] A first cooling path loss model is constructed based on the path parameters of each cooling path.

[0036] The cooling capacity parameters at the starting point of the cooling path are derived from the required cooling control data and the first cooling path loss model, and used as the cooling control data for the corresponding battery cluster.

[0037] Preferably, based on the required cooling control data, the cooling control data corresponding to the battery cluster module is obtained, and the method further includes:

[0038] Before constructing the first cooling path loss model based on the path parameters of each cooling path, the connection time span is determined according to the connection relationship between the cooling paths.

[0039] If the connection time span is less than the standard time span, the corresponding connected cooling path will be used as a combined path.

[0040] A second cooling path loss model is constructed based on the path parameters of the combined path.

[0041] The cooling capacity parameters at the starting point of the combined path are inverted based on the required cooling control dataset corresponding to the combined path and the loss model of the second cooling path.

[0042] If the inversion is successful, the cooling control data of the corresponding battery cluster will be determined based on the cooling capacity parameters at the starting point of the combined pathway.

[0043] If the inversion fails or the cooling path is not a combined path, continue with the subsequent steps of constructing the first cooling path loss model based on the path parameters of each cooling path.

[0044] Preferably, based on the cooling control fusion data, the heat exchanger start / stop control data and air conditioning power control data are determined, including:

[0045] Based on the fusion data of cooling control, multiple coolant recirculation scenarios were determined;

[0046] Determine the heat exchange target based on the coolant reflux situation;

[0047] The start-up and shutdown control data for the heat exchanger and the power control data for the air conditioning are determined based on the heat exchange target.

[0048] Preferably, the heat exchange target is determined based on the coolant reflux situation, including:

[0049] Based on the coolant return flow pattern, obtain the return flow conflict event;

[0050] If there is no backflow conflict event, the heat exchange task is determined based on each coolant backflow situation;

[0051] If a backflow conflict occurs, the conflicting backflow paths are scheduled in a timely manner or the heat load is merged to generate a unified merged heat exchange task.

[0052] The heat exchange task and the fusion heat exchange task are taken as the heat exchange objectives.

[0053] This invention provides a photovoltaic power generation system with integrated prefabricated liquid-cooled energy storage, comprising:

[0054] The current conversion module is used to collect direct current through photovoltaic modules and input the collected direct current into a photovoltaic inverter to convert it into alternating current synchronized with the grid.

[0055] Current transmission module, used to transmit AC power to local loads;

[0056] Energy storage module, used to charge excess electricity into a prefabricated liquid-cooled energy storage device integrated with photovoltaic modules if there is excess electricity.

[0057] The power supply module is used to discharge to the local load through a prefabricated liquid-cooled energy storage device if the power is insufficient.

[0058] The in-cabin parameter coordinated pre-regulation module is used to coordinate and pre-regulate the in-cabin parameters according to the expected charging and discharging scenarios when the prefabricated liquid-cooled energy storage device is working.

[0059] The cabin parameter coordinated pre-regulation module performs the following operations:

[0060] Based on the design and structural information of the prefabricated liquid-cooled energy storage device, construct a three-dimensional model of the target.

[0061] Based on the expected charging and discharging scenarios, determine the dynamic thermodynamic information of each battery cluster module in the target 3D model;

[0062] Based on dynamic thermal information, determine the required cooling control data for the cooling plate module corresponding to each battery cluster module;

[0063] Based on the required cooling control data, obtain the cooling control data corresponding to the battery cluster module;

[0064] The cooling control data corresponding to all battery cluster modules are fused in the time domain to obtain fused cooling control data;

[0065] Based on the cooling control fusion data, determine the heat exchanger start / stop control data and the air conditioning power control data;

[0066] The cooling control fusion data, heat exchanger start / stop control data, and air conditioning power control data are used as cabin parameters and corresponding controls are implemented.

[0067] Specifically, based on the required cooling control data, the cooling control data corresponding to the battery cluster module is obtained, including:

[0068] Determine the cooling path of the cooling module corresponding to the required refrigeration control data;

[0069] A first cooling path loss model is constructed based on the path parameters of each cooling path.

[0070] Based on the required cooling control data and the first cooling path loss model, the cooling capacity parameters at the starting point of the cooling path are inverted and used as the cooling control data for the corresponding battery cluster.

[0071] This includes obtaining the cooling control data corresponding to the battery cluster module based on the required cooling control data, and also includes:

[0072] Before constructing the first cooling path loss model based on the path parameters of each cooling path, the connection time span is determined according to the connection relationship between the cooling paths.

[0073] If the connection time span is less than the standard time span, the corresponding connected cooling path will be used as a combined path.

[0074] A second cooling path loss model is constructed based on the path parameters of the combined path.

[0075] The cooling capacity parameters at the starting point of the combined path are inverted based on the required cooling control dataset corresponding to the combined path and the loss model of the second cooling path.

[0076] If the inversion is successful, the cooling control data of the corresponding battery cluster will be determined based on the cooling capacity parameters at the starting point of the combined pathway.

[0077] If the inversion fails or the cooling path is not a combined path, continue with the subsequent steps of constructing the first cooling path loss model based on the path parameters of each cooling path.

[0078] The beneficial effects of this invention are as follows:

[0079] This invention transmits alternating current to local loads via photovoltaic power generation. When there is excess power, it is charged into a prefabricated liquid-cooled energy storage device; if the power is insufficient, it is discharged to the local load through the prefabricated liquid-cooled energy storage device, achieving efficient energy conversion and storage. By introducing anticipated charge and discharge scenarios and systematically coordinating the pre-regulation of in-cabin parameters, battery safety and cycle life are significantly improved.

[0080] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0081] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0082] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0083] Figure 1 This is a schematic diagram of a photovoltaic power generation method with integrated prefabricated liquid-cooled energy storage in an embodiment of the present invention;

[0084] Figure 2 This is a schematic diagram of a photovoltaic power generation system with integrated prefabricated liquid-cooled energy storage in an embodiment of the present invention. Detailed Implementation

[0085] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0086] This invention provides a photovoltaic power generation method integrating prefabricated liquid-cooled energy storage, such as... Figure 1 As shown, it includes:

[0087] Step 1: Collect DC power through photovoltaic modules and input the collected DC power into a photovoltaic inverter to convert it into AC power synchronized with the power grid.

[0088] In this embodiment, the photovoltaic module is a power generation unit composed of multiple solar cells encapsulated together, used to directly convert sunlight into direct current (DC). The photovoltaic inverter is a power electronic device used to convert DC into alternating current (AC). Synchronization with the grid means that the AC output of the inverter is consistent with the public power grid in terms of voltage amplitude (e.g., 220V / 380V), frequency (e.g., 50Hz), and phase angle.

[0089] Step 2: Transfer AC power to the local load.

[0090] In this embodiment, local load refers to electrical equipment that uses photovoltaic power generation devices with integrated prefabricated liquid-cooled energy storage, such as factory motors, office building lighting, air conditioners, and data center servers.

[0091] Step 3: If there is excess power, charge the excess power into the prefabricated liquid-cooled energy storage device integrated with the photovoltaic modules.

[0092] In this embodiment, the prefabricated liquid-cooled energy storage device refers to an energy storage device in which subsystems such as batteries, cooling, fire protection, and monitoring have been assembled and debugged in the factory, and the corresponding equipment is installed in a standard container. Cluster management is implemented for the prefabricated liquid-cooled energy storage device integrated with photovoltaic modules: Each battery cluster is independently managed within the prefabricated liquid-cooled energy storage device integrated with photovoltaic modules, including status awareness, charge / discharge control, thermal management coordination, fault isolation, and power scheduling.

[0093] Step 4: If the power is insufficient, discharge to the local load through the prefabricated liquid-cooled energy storage device.

[0094] Step 5: When the prefabricated liquid-cooled energy storage device is working, the parameters inside the chamber are pre-regulated in a coordinated manner according to the expected charging and discharging scenarios.

[0095] In this embodiment, the expected charging and discharging scenario is the charging and discharging situation of the prefabricated liquid-cooled energy storage device based on the weather forecast and the predicted local load at the corresponding future time. For example, if the photovoltaic output is predicted to be greater than the predicted local load based on the weather forecast at the corresponding future time, the charging of the prefabricated liquid-cooled energy storage device at the corresponding future time can be further inferred. The prediction method for the predicted local load at the corresponding future time is: periodically summarizing the historical load data of the local load, and determining the predicted local load at the corresponding future time based on the summarization results of the period nodes at the corresponding future time. The charging and discharging scenario includes charging data (charging power at the corresponding future time) and discharging data (discharging power at the corresponding future time). The in-cabin parameters include pump speed, flow rate, valves, air conditioning power, and heat exchanger start / stop. Coordinated pre-control refers to the coordinated adjustment of the above in-cabin parameters in advance. For example, if it is known that the device will be charged at 1C for the next 10 minutes, the pump will be started, the flow rate will be increased, and the coolant will be pre-cooled 2-3 minutes in advance.

[0096] The working principle and beneficial effects of the above technical solution are as follows:

[0097] This invention transmits alternating current to local loads via photovoltaic power generation. When there is excess power, it is charged into a prefabricated liquid-cooled energy storage device; if the power is insufficient, it is discharged to the local load through the prefabricated liquid-cooled energy storage device, achieving efficient energy conversion and storage. By introducing anticipated charge and discharge scenarios and systematically coordinating the pre-regulation of in-cabin parameters, battery safety and cycle life are significantly improved.

[0098] In one embodiment, coordinated pre-regulation of in-cabin parameters based on the expected charging and discharging scenario includes:

[0099] Step 51: Construct a target 3D model based on the design structure information of the prefabricated liquid-cooled energy storage device.

[0100] In this embodiment, the design structural information includes CAD / BIM data such as battery layout, module dimensions, cooling plate location, piping routing, cabin insulation layer, and air conditioning and heat exchanger installation locations. The target 3D model is a digital twin model constructed based on the above design structural information.

[0101] Step 52: Determine the dynamic thermal information of each battery cluster module in the target 3D model based on the expected charging and discharging scenario.

[0102] In this embodiment, the battery cluster module is a locally model in the target 3D model that can be controlled independently. The dynamic thermal information is the heat source distribution data of each battery cluster module determined according to the expected charge and discharge scenario.

[0103] Step 53: Based on the dynamic thermal information, determine the required cooling control data for the cooling plate module corresponding to each battery cluster module.

[0104] In this embodiment, the cooling plate module corresponds to a local model in the target 3D model as the liquid-cooled plate below each battery cluster. The required cooling control data is to maintain the battery cluster operating within a safe temperature range, corresponding to the cooling capacity indicators that the cooling plate needs to achieve, such as coolant flow rate, inlet temperature, and pressure drop requirements. Specifically, step 53 includes:

[0105] Step 531: Calculate the average heat value of the individual battery modules in the battery cluster module based on the dynamic thermal information.

[0106] In this embodiment, the battery cell module is the smallest energy storage unit (e.g., a single cell) in the battery cluster, corresponding to a local model in the target 3D model. The average heat value is the arithmetic mean of the heat generated by all battery cells in the battery cluster at a future time.

[0107] Step 532: Determine multiple pre-selected cooling control data based on the average heat value and the preset heat value redundancy range.

[0108] In this embodiment, the preset heat value redundancy range refers to an allowable heat deviation range set to cope with fluctuations in the heat load area of ​​the battery cluster. For example, it could fluctuate downwards to the heat generation of the second-to-last battery cell and upwards to the heat generation of the second-to-last battery cell. When determining the pre-selected cooling control data, the closer the data is to the average heat value, the more densely the sampling of the pre-selected cooling control data. During sampling, a simulation is performed based on the target three-dimensional model, and the cooling capacity index required to maintain the corresponding sampled heat value within a safe range obtained from the simulation is used as the pre-selected cooling control data based on the corresponding sampled heat value.

[0109] Step 533: Determine the cooling effect score based on the pre-selected cooling control data and dynamic thermodynamic information.

[0110] In this embodiment, the cooling effect score is based on the dynamic thermodynamic information after the pre-selected cooling control data is controlled and the preset scoring criteria. For example, a deduction system is implemented: for each battery cell that is 1 degree Celsius above the battery's suitable operating temperature range, 2 points are deducted; for each battery cell that is 1 degree Celsius below the battery's suitable operating temperature range, 1 point is deducted; the deductions for each battery cell are summed to obtain the cooling effect score.

[0111] Step 534: Select the pre-selected cooling control data with the highest cooling effect score as the required cooling control data.

[0112] Step 54: Obtain the cooling control data corresponding to the battery cluster module based on the required cooling control data. Specifically, Step 54 includes:

[0113] Step 541: Determine the cooling path of the cooling module corresponding to the required cooling control data.

[0114] In this embodiment, the cooling path refers to the fluid path of the coolant from the pump, cooling plate to the heat exchanger in sequence.

[0115] Step 542: Construct the first cooling path loss model based on the path parameters of each cooling path.

[0116] In this embodiment, the first cooling path loss model is a heat loss model constructed based on the path parameters (pipe diameter, length, bends, valves, etc.) of the cooling path, which can deduce the heat loss of the refrigerant flowing through the cooling path under different refrigeration control data.

[0117] Step 543: Based on the required cooling control data and the first cooling path loss model, invert the cooling capacity parameters of the starting point of the cooling path and use them as the cooling control data for the corresponding battery cluster.

[0118] In this embodiment, the cooling capacity parameters refer to the actual control variables that need to be set at the starting point of the cooling path, including: inlet pressure, flow rate setpoint, coolant temperature, and valve opening command. These parameters compensate for path losses so that the cooling plate can achieve the required cooling control data.

[0119] Step 55: Perform time-domain fusion of the cooling control data corresponding to all battery cluster modules to obtain fused cooling control data.

[0120] In this embodiment, time-domain fusion refers to sorting the cooling control data according to the time sequence of the corresponding expected future control time.

[0121] Step 56: Determine the heat exchanger start / stop control data and air conditioning power control data based on the cooling control fusion data.

[0122] In this embodiment, when determining the heat exchanger start-stop control data and air conditioning power control data based on the cooling control fusion data, the future heat exchange tasks of the heat exchanger are first determined (e.g., when and how much capacity and what temperature of coolant needs to be processed in the future), and the heat exchanger start-stop control data and air conditioning power control data are determined based on the heat exchange tasks.

[0123] Step 57: Use the cooling control fusion data, heat exchanger start / stop control data, and air conditioning power control data as cabin parameters and perform corresponding controls.

[0124] The working principle and beneficial effects of the above technical solution are as follows:

[0125] Existing technologies for liquid cooling control of energy storage batteries typically involve triggering a high-temperature alarm after the battery temperature rises before initiating cooling. This passive response exhibits a certain degree of lag, and cooling is usually performed on the entire battery pack, resulting in ineffective cooling. This invention introduces the design structure information of a prefabricated liquid-cooled energy storage device to construct a target 3D model. Based on the expected charge and discharge scenarios, the dynamic thermodynamic information of each battery cluster module in the target 3D model is determined. The required cooling control data is determined by analyzing the dynamic thermodynamic information. Simultaneously, considering the cooling capacity loss of the refrigerant during circulation in the pipeline, a first cooling path loss model is constructed for each cooling path based on its path parameters. The required cooling control data and the first cooling path loss model are used to inversely derive the cooling capacity parameters at the starting point of the cooling path as the cooling control data for the corresponding battery cluster. This allows for precise cooling control for each battery cluster, resulting in higher cooling efficiency.

[0126] Simultaneously, time-domain fusion of cooling control data is performed to accurately predict the cooling task of the coolant subsequently collected in the heat exchanger. Based on the cooling task, heat exchanger start-up and shutdown control data, as well as air conditioning power control data, are determined, significantly improving heat exchange efficiency and synergistic efficiency.

[0127] In one embodiment, the coordinated pre-regulation of in-cabin parameters based on the expected charging and discharging scenario further includes:

[0128] Step 541a: Before constructing the first cooling path loss model based on the path parameters of each cooling path, determine the connection time span according to the connection relationship between the cooling paths.

[0129] In this embodiment, the connection time span refers to the refrigerant flow time of the active channel after the cooling channels are connected by a dynamically controlled active channel, calculated based on the historical average refrigerant flow rate.

[0130] Step 541b: If the connection time span is less than the standard time span, then the corresponding connected cooling path is used as a combined path.

[0131] In this embodiment, the standard time span refers to the control time interval of the corresponding cooling control data required for the connected cooling path.

[0132] Step 541c: Construct a second cooling path loss model based on the path parameters of the combined path.

[0133] In this embodiment, the construction principle of the second cooling path loss model is the same as that of the first cooling path loss model.

[0134] Step 541d: Invert the cooling capacity parameters of the starting point of the combined path based on the required cooling control dataset corresponding to the combined path and the loss model of the second cooling path.

[0135] In this embodiment, the inversion principle is the same as the inversion principle in step 543.

[0136] Step 541e: If the inversion is successful, determine the cooling control data of the corresponding battery cluster based on the cooling capacity parameters at the starting point of the combined pathway.

[0137] Step 541f: If the inversion fails or the cooling path is not a combined path, continue with the subsequent steps of constructing the first cooling path loss model based on the path parameters of each cooling path.

[0138] The working principle and beneficial effects of the above technical solution are as follows:

[0139] While individual cooling control for each battery cluster module offers greater precision, it presents several drawbacks: the temperature rise time distribution across the entire battery pack is uneven. For example, after battery cluster A has cooled down, the coolant's cooling capacity may still be sufficient to cool battery cluster B. Preparing separate coolant for each cooling path of a battery cluster requires significant coolant redundancy in the design of the prefabricated liquid-cooled energy storage device, increasing its size and causing inconvenience in subsequent use. Therefore, before constructing the first cooling path loss model based on the path parameters of each cooling path, this invention determines whether the connection time span is less than the standard time span. If the connection time span is less than the control time interval of the corresponding cooling control data required for the connected cooling path, it indicates that the coolant used earlier is available for the battery clusters cooled later in time, and the corresponding connected cooling paths are considered as a combined path. A second cooling path loss model is then constructed based on the path parameters of the combined path. The cooling capacity parameters at the starting point of the combined path are inverted based on the required cooling control dataset corresponding to the combined path and the second cooling path loss model. During the inversion, the cooling capacity of different cooling paths flowing through the combined path is verified. That is, when the cooling capacity parameters at the starting point of the combined path are lost through the second cooling path loss model, the cooling control data at the corresponding positions of different cooling paths in the combined path meet the requirements of the required cooling control dataset corresponding to the combined path. If the inversion is successful, the verification is successful, and the coolant can be reused. If the inversion fails or the cooling path is not a combined path, the process returns to the step of constructing the first cooling path loss model based on the path parameters of each cooling path to independently determine the cooling control data. This invention achieves the synergy of cooling control for individual channels and cooling control for combined channels. Under the premise of precise cooling control, it greatly reduces the original demand for coolant and improves the convenience of subsequent use of the prefabricated liquid-cooled energy storage device.

[0140] In one embodiment, heat exchanger start / stop control data and air conditioning power control data are determined based on cooling control fusion data, including:

[0141] Step 561: Determine multiple coolant recirculation scenarios based on the cooling control fusion data.

[0142] In this embodiment, the coolant reflux refers to the state in which the coolant in each liquid-cooled branch flows out from the battery cooling plate, undergoes heat exchange in the heat exchanger, and returns to the main circuit. Specifically, this includes the time window, flow rate, and temperature of the coolant passing through the heat exchanger.

[0143] Step 562: Determine the heat exchange target based on the coolant reflux situation. Specifically, step 562 includes:

[0144] Step 5621: Obtain the backflow conflict event based on the coolant backflow situation.

[0145] In this embodiment, a backflow conflict event refers to an unfavorable overlap of multiple coolant backflow paths in terms of time, temperature, or flow rate. For example, high and low temperature backflow mixing causes distortion of the heat exchanger inlet temperature; the total backflow flow rate instantaneously exceeds the limit, causing pump or pipeline overload; or the heat exchanger load changes suddenly, preventing the air conditioner from responding in time.

[0146] Step 5622: If there is no backflow conflict event, determine the heat exchange task based on each coolant backflow situation.

[0147] In this embodiment, the heat exchange task refers to the specific heat exchange target that the heat exchanger needs to achieve to meet the heat dissipation requirements of one or more return flows without recirculation conflict. Examples include: required heat dissipation (kW), coolant outlet temperature, and heat exchange duration. To determine whether a recirculation conflict exists, the time overlap of the several return flows, the recirculation temperature difference, and the superimposed flow rate are used. For example, a recirculation conflict is determined to exist if the time overlap is greater than 60% and the superimposed flow rate is greater than the design limit of the pipe or pump.

[0148] Step 5623: If a backflow conflict event exists, perform time-series scheduling or heat load merging on the conflicting backflow paths to generate a unified merging heat exchange task.

[0149] In this embodiment, timing scheduling causes conflicting backflows to be staggered in time, thereby eliminating conflicts. Heat load merging equates conflicting multiple backflows to a single virtual backflow. The merged heat exchange task is the heat exchange target after the aforementioned conflict adjustment.

[0150] Step 5624: Combine the heat exchange task and the fusion heat exchange task as the heat exchange target.

[0151] Step 563: Determine the heat exchanger start / stop control data and air conditioning power control data based on the heat exchange target.

[0152] In this embodiment, the heat exchanger start / stop control data (instructions to control the fan of the air-cooled heat exchanger or the valve of the liquid-liquid heat exchanger) and the air conditioning power control data (instructions to control the refrigeration unit (such as an inverter air conditioner)) are executable equipment control signals determined based on the heat exchange target.

[0153] The working principle and beneficial effects of the above technical solution are as follows:

[0154] When determining the coordinated control command for the heat exchanger, this invention performs a recirculation conflict assessment for multiple liquid-cooled branches. If no recirculation conflict exists, the heat exchange task is directly determined based on the recirculation situation; otherwise, the conflicting recirculation paths are time-sequentially scheduled or their heat loads are merged to generate a unified merged heat exchange task. This combines the heat exchange task and the merged heat exchange task as the heat exchange objective, avoiding inaccurate heat exchange caused by inaccurate temperature detection at the heat exchanger inlet when recirculation conflicts exist. It also reduces pipeline pressure caused by superimposed flow rates. Furthermore, it avoids frequent start-ups and shutdowns of the heat exchange device caused by determining the task based solely on a single recirculation flow. This achieves seamless and precise heat exchange when multiple liquid-cooled branches recirculate, improving the rationality of the heat exchange process.

[0155] This invention provides a photovoltaic power generation system integrating prefabricated liquid-cooled energy storage, such as... Figure 2 As shown, it includes:

[0156] Current conversion module 1 is used to collect DC power through photovoltaic modules and input the collected DC power into a photovoltaic inverter to convert it into AC power synchronized with the grid.

[0157] Current transmission module 2 is used to transmit AC power to the local load;

[0158] Energy storage module 3 is used to charge the excess power into a prefabricated liquid-cooled energy storage device integrated with the photovoltaic module if there is excess power.

[0159] Power supply module 4 is used to discharge to the local load through a prefabricated liquid-cooled energy storage device if the power is insufficient.

[0160] The in-cabin parameter coordinated pre-regulation module 5 is used to coordinate and pre-regulate the in-cabin parameters according to the expected charging and discharging scenarios when the prefabricated liquid-cooled energy storage device is working.

[0161] Among them, the cabin parameter coordinated pre-regulation module 5 performs the following operations:

[0162] Based on the design and structural information of the prefabricated liquid-cooled energy storage device, construct a three-dimensional model of the target.

[0163] Based on the expected charging and discharging scenarios, determine the dynamic thermodynamic information of each battery cluster module in the target 3D model;

[0164] Based on dynamic thermal information, determine the required cooling control data for the cooling plate module corresponding to each battery cluster module;

[0165] Based on the required cooling control data, obtain the cooling control data corresponding to the battery cluster module;

[0166] The cooling control data corresponding to all battery cluster modules are fused in the time domain to obtain fused cooling control data;

[0167] Based on the cooling control fusion data, determine the heat exchanger start / stop control data and the air conditioning power control data;

[0168] The cooling control fusion data, heat exchanger start / stop control data, and air conditioning power control data are used as cabin parameters and corresponding controls are implemented.

[0169] Specifically, based on the required cooling control data, the cooling control data corresponding to the battery cluster module is obtained, including:

[0170] Determine the cooling path of the cooling module corresponding to the required refrigeration control data;

[0171] A first cooling path loss model is constructed based on the path parameters of each cooling path.

[0172] Based on the required cooling control data and the first cooling path loss model, the cooling capacity parameters at the starting point of the cooling path are inverted and used as the cooling control data for the corresponding battery cluster.

[0173] This includes obtaining the cooling control data corresponding to the battery cluster module based on the required cooling control data, and also includes:

[0174] Before constructing the first cooling path loss model based on the path parameters of each cooling path, the connection time span is determined according to the connection relationship between the cooling paths.

[0175] If the connection time span is less than the standard time span, the corresponding connected cooling path will be used as a combined path.

[0176] A second cooling path loss model is constructed based on the path parameters of the combined path.

[0177] The cooling capacity parameters at the starting point of the combined path are inverted based on the required cooling control dataset corresponding to the combined path and the loss model of the second cooling path.

[0178] If the inversion is successful, the cooling control data of the corresponding battery cluster will be determined based on the cooling capacity parameters at the starting point of the combined pathway.

[0179] If the inversion fails or the cooling path is not a combined path, continue with the subsequent steps of constructing the first cooling path loss model based on the path parameters of each cooling path.

[0180] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A photovoltaic power generation method integrating prefabricated compartment-type liquid-cooled energy storage, characterized in that, include: Direct current (DC) is collected by photovoltaic (PV) modules and then input into a PV inverter to convert it into alternating current (AC) that is synchronized with the power grid. Transmitting alternating current to local loads; If there is excess power, the excess power will be charged into the prefabricated liquid-cooled energy storage device integrated with the photovoltaic modules. If the power is insufficient, it will be discharged to the local load through a prefabricated liquid-cooled energy storage device. When the prefabricated liquid-cooled energy storage device is in operation, a target three-dimensional model is constructed based on the design structural information of the prefabricated liquid-cooled energy storage device. Based on the expected charging and discharging scenarios, determine the dynamic thermodynamic information of each battery cluster module in the target 3D model; Based on dynamic thermal information, determine the required cooling control data for the cooling plate module corresponding to each battery cluster module; Based on the required cooling control data, obtain the cooling control data corresponding to the battery cluster module; The cooling control data corresponding to all battery cluster modules are fused in the time domain to obtain fused cooling control data; Based on the cooling control fusion data, determine the heat exchanger start / stop control data and the air conditioning power control data; The cooling control fusion data, heat exchanger start / stop control data, and air conditioning power control data are used as cabin parameters and corresponding controls are implemented. Before constructing the first cooling path loss model based on the path parameters of each cooling path, the connection time span is determined according to the connection relationship between the cooling paths. If the connection time span is less than the standard time span, the corresponding connected cooling path will be used as a combined path. A second cooling path loss model is constructed based on the path parameters of the combined path. The cooling capacity parameters at the starting point of the combined path are inverted based on the required cooling control dataset corresponding to the combined path and the loss model of the second cooling path. If the inversion is successful, the cooling control data of the corresponding battery cluster will be determined based on the cooling capacity parameters at the starting point of the combined pathway. If the inversion fails or the cooling path is not a combined path, continue with the subsequent steps of constructing the first cooling path loss model based on the path parameters of each cooling path. Based on the required cooling control data, obtain the cooling control data corresponding to the battery cluster module, including: Determine the cooling path of the cooling module corresponding to the required refrigeration control data; A first cooling path loss model is constructed based on the path parameters of each cooling path. The cooling capacity parameters at the starting point of the cooling path are derived from the required cooling control data and the first cooling path loss model, and used as the cooling control data for the corresponding battery cluster.

2. The photovoltaic power generation method with integrated prefabricated liquid-cooled energy storage as described in claim 1, characterized in that, Cluster management of prefabricated liquid-cooled energy storage devices integrated with photovoltaic modules.

3. A photovoltaic power generation method with integrated prefabricated liquid-cooled energy storage as described in claim 1, characterized in that, The steps for determining the expected charge / discharge scenario are as follows: Get the weather forecast for a future time. Predict local load conditions at future times; Based on weather forecasts and local load conditions, the charging and discharging status of the prefabricated liquid-cooled energy storage device is predicted for future moments; By collecting the charging and discharging conditions corresponding to each future moment, the expected charging and discharging scenario can be obtained.

4. A photovoltaic power generation method with integrated prefabricated liquid-cooled energy storage as described in claim 1, characterized in that, The parameters inside the cabin include: pump speed, flow rate, valves, air conditioning power, and heat exchanger start / stop.

5. A photovoltaic power generation method with integrated prefabricated liquid-cooled energy storage as described in claim 1, characterized in that, Based on the cooling control fusion data, the heat exchanger start / stop control data and air conditioning power control data are determined, including: Based on the fusion data of cooling control, multiple coolant recirculation scenarios were determined; Determine the heat exchange target based on the coolant reflux situation; The start-up and shutdown control data for the heat exchanger and the power control data for the air conditioning are determined based on the heat exchange target.

6. A photovoltaic power generation method with integrated prefabricated liquid-cooled energy storage as described in claim 5, characterized in that, The heat exchange target is determined based on the coolant reflux pattern, including: Based on the coolant return flow pattern, obtain the return flow conflict event; If there is no backflow conflict event, the heat exchange task is determined based on each coolant backflow situation; If a backflow conflict occurs, the conflicting backflow paths are scheduled in a timely manner or the heat load is merged to generate a unified merged heat exchange task. The heat exchange task and the fusion heat exchange task are taken as the heat exchange objectives.

7. A photovoltaic power generation system integrating prefabricated containerized liquid-cooled energy storage, characterized in that, include: The current conversion module is used to collect direct current through photovoltaic modules and input the collected direct current into a photovoltaic inverter to convert it into alternating current synchronized with the grid. Current transmission module, used to transmit AC power to local loads; Energy storage module, used to charge excess electricity into a prefabricated liquid-cooled energy storage device integrated with photovoltaic modules if there is excess electricity. The power supply module is used to discharge to the local load through a prefabricated liquid-cooled energy storage device if the power is insufficient. The in-cabin parameter collaborative pre-regulation module is used to construct a target three-dimensional model based on the design structure information of the prefabricated liquid-cooled energy storage device when it is in operation. Based on the expected charging and discharging scenarios, determine the dynamic thermodynamic information of each battery cluster module in the target 3D model; Based on dynamic thermal information, determine the required cooling control data for the cooling plate module corresponding to each battery cluster module; Based on the required cooling control data, obtain the cooling control data corresponding to the battery cluster module; The cooling control data corresponding to all battery cluster modules are fused in the time domain to obtain fused cooling control data; Based on the cooling control fusion data, determine the heat exchanger start / stop control data and the air conditioning power control data; The cooling control fusion data, heat exchanger start / stop control data, and air conditioning power control data are used as cabin parameters and corresponding controls are implemented. Before constructing the first cooling path loss model based on the path parameters of each cooling path, the connection time span is determined according to the connection relationship between the cooling paths. If the connection time span is less than the standard time span, the corresponding connected cooling path will be used as a combined path. A second cooling path loss model is constructed based on the path parameters of the combined path. The cooling capacity parameters at the starting point of the combined path are inverted based on the required cooling control dataset corresponding to the combined path and the loss model of the second cooling path. If the inversion is successful, the cooling control data of the corresponding battery cluster will be determined based on the cooling capacity parameters at the starting point of the combined pathway. If the inversion fails or the cooling path is not a combined path, continue with the subsequent steps of constructing the first cooling path loss model based on the path parameters of each cooling path. Based on the required cooling control data, obtain the cooling control data corresponding to the battery cluster module, including: Determine the cooling path of the cooling module corresponding to the required refrigeration control data; A first cooling path loss model is constructed based on the path parameters of each cooling path. The cooling capacity parameters at the starting point of the cooling path are derived from the required cooling control data and the first cooling path loss model, and used as the cooling control data for the corresponding battery cluster.

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