A power smoothing method for a water surface energy storage super-dispersion system based on light storage coordination and related device

Abstract

The application provides a water surface energy storage super-dispersion system power smoothing method based on light storage cooperation and related devices, and belongs to the technical field of energy storage super-dispersion system data processing. The application obtains a short-time power change rate based on first data, and obtains a next-time photovoltaic power prediction value based on the short-time power change rate. The application obtains a next-time energy storage SOC value based on the next-time photovoltaic power prediction value and second data. It is judged whether the next-time energy storage SOC value exceeds a preset safety interval. If the next-time energy storage SOC value exceeds the preset safety interval, the second data is corrected to obtain a corrected smoothed reference power. Each light storage cooperation node of the water surface energy storage super-dispersion system performs light storage power distribution based on the corrected smoothed reference power to obtain a light storage power distribution result. The application solves the problem of low reliability of the water surface energy storage super-dispersion system.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology for ultra-distributed energy storage systems, specifically relating to a power smoothing method and related apparatus for a surface energy storage ultra-distributed system based on photovoltaic-energy storage synergy. Background Technology

[0002] Each photovoltaic column in the ultra-distributed energy storage system is equipped with an independent energy storage unit, and each unit has independent control, realizing a more refined and distributed architecture design. This allows each node of the ultra-distributed energy storage system to autonomously manage and control energy during operation.

[0003] With the advancement of the "dual carbon" goals, the proportion of photovoltaic power generation in the energy structure continues to increase. However, photovoltaic power output is characterized by significant intermittency, volatility, and randomness, posing a significant challenge to the grid's absorption capacity and stable operation, leading to increasingly prominent curtailment issues. Energy storage technology, with its advantages of fast response and precise power regulation, has become a key means to address the fluctuations in new energy power generation and improve grid friendliness.

[0004] Currently, as an important way to improve land utilization, floating photovoltaic power plants mostly adopt the centralized energy storage architecture of onshore photovoltaic power plants. However, this type of architecture reveals several limitations in floating applications: First, centralized energy storage requires a large area of ​​water surface to build battery compartments, which often poses space and safety hazards.

[0005] Secondly, traditional floating photovoltaic energy storage systems mostly adopt distributed AC coupling technology. The DC power generated by the photovoltaic system needs to be inverted and combined with AC power before being converted back to DC power by the energy storage converter for storage. During discharge, it is inverted back to AC power again. There are multiple DC (Direct Current) - AC (Alternating Current) - DC conversion losses, which seriously restrict the overall energy efficiency of ultra-distributed energy storage systems.

[0006] Moreover, most existing research on distributed energy storage is limited to the battery cluster level. This larger energy storage unit has high requirements for battery consistency, and when some battery clusters experience performance degradation due to aging or failure, the energy storage system is usually forced to operate at the power of the lowest-performing cluster, making it difficult to fully utilize the regulation capabilities of the healthy cells.

[0007] In addition, under complex conditions of frequent changes in wind speed, water flow, water temperature and incident angle of light on the water surface, a large number of "pseudo-fluctuations" (such as instantaneous shading caused by the swaying of the floating body) will be generated, which will cause the control of the ultra-distributed energy storage system on the water surface to malfunction and reduce the regulation accuracy. Summary of the Invention

[0008] The purpose of this invention is to provide a power smoothing method and related apparatus for a hydroelectric-storage synergistic ultra-distributed surface energy storage system, which solves the problem of low reliability in existing hydroelectric-storage ultra-distributed surface energy storage systems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a power smoothing method for a distributed surface energy storage system based on photovoltaic-energy storage synergy, comprising the following steps: Acquire the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the ultra-distributed water surface energy storage system, and record them as the first data and the second data respectively; Based on the first data, the short-time power change rate is obtained, and based on the short-time power change rate, the photovoltaic power prediction value for the next moment is obtained; Based on the photovoltaic power prediction value and the second data at the next moment, the energy storage SOC (State of Charge) value at the next moment is obtained; Determine whether the energy storage SOC value at the next moment exceeds the preset safe range. If the energy storage SOC value at the next moment exceeds the preset safe range, then correct the second data to obtain the corrected smoothed reference power. In the ultra-distributed solar energy storage system, each solar-storage collaborative node performs solar-storage power allocation based on the corrected smoothed reference power, and obtains the solar-storage power allocation result.

[0010] A further improvement of the present invention is that the photovoltaic-storage co-location node adopts a low-voltage DC coupling architecture, and the photovoltaic side and the energy storage side are directly connected through a DC / DC converter.

[0011] A further improvement of this invention is that the formula for calculating the photovoltaic power prediction value at the next moment is:

[0012] in, This is the predicted photovoltaic power value for the next moment. The photovoltaic power at the current moment, The number of steps in the sliding window. The sampling period is Photovoltaic power at a historical reference point.

[0013] A further improvement of this invention is that the calculation method for the next energy storage SOC value is as follows:

[0014] in, The SOC value for energy storage at the next moment. The SOC value at the current moment. Current smoothed reference power, This is the predicted photovoltaic power value for the next moment. The sampling period is For the current charge and discharge efficiency, To predict the capacity factor, This refers to the nominal capacity of the energy storage unit.

[0015] A further improvement of the present invention is that the step of correcting the second data to obtain the corrected smoothed reference power specifically involves: If the energy storage SOC value is greater than the preset upper limit value at the next moment, the second data will be reduced and the discharge power will be increased. If the energy storage SOC value is less than the preset lower limit in the next moment, the second data will be increased and the charging power will be increased. Furthermore, the corrected smoothed reference power is limited to the allowable fluctuation bandwidth centered on the current photovoltaic output.

[0016] A further improvement of this invention is that, after obtaining the power allocation result of the photovoltaic-storage system, when any photovoltaic-storage collaborative node is identified as a derating unit, the neighboring photovoltaic-storage collaborative nodes increase the charging and discharging compensation power based on the received operating status information, under the premise of their own SOC safety, and collaboratively maintain the smoothness of the total output power of the ultra-distributed surface energy storage system.

[0017] A further improvement of the present invention is that the identification method of the derating unit is as follows: Obtain the SOC change rate, charge / discharge efficiency, and historical capacity decay curves of each photovoltaic-storage collaborative node in the ultra-distributed surface energy storage system; Based on the SOC change rate, charge and discharge efficiency, and historical capacity decay curves of each photovoltaic-storage co-working node in the ultra-distributed photovoltaic energy storage system, the predicted capacity coefficient of each photovoltaic-storage co-working node is determined. Based on the predicted capacity coefficient of each optical-storage collaborative node, the current available capacity of each optical-storage collaborative node is obtained; Determine whether the current available capacity of each optical-storage collaborative node is lower than the preset capacity coefficient threshold. If the current available capacity of any optical-storage collaborative node is lower than the preset capacity coefficient threshold, it is determined that there is a derated unit in the optical-storage collaborative node.

[0018] Secondly, the present invention provides a power smoothing system for a hyper-distributed surface energy storage system based on photovoltaic-energy storage synergy, comprising: The data acquisition module is used to acquire the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the water surface energy storage ultra-distributed system, which are respectively denoted as the first data and the second data. The photovoltaic power prediction value determination module is used to obtain the short-time power change rate based on the first data, and to obtain the photovoltaic power prediction value at the next moment based on the short-time power change rate. The energy storage SOC value determination module is used to obtain the energy storage SOC value at the next moment based on the photovoltaic power prediction value and the second data. The judgment module is used to determine whether the energy storage SOC value at the next moment exceeds the preset safety range. If the energy storage SOC value at the next moment exceeds the preset safety range, the second data is corrected to obtain the corrected smooth reference power. The power allocation module is used to allocate photovoltaic and energy storage power to each photovoltaic-energy storage collaborative node in the ultra-distributed photovoltaic energy storage system based on the corrected smooth reference power, and obtain the photovoltaic-energy storage power allocation result.

[0019] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the power smoothing method for the ultra-distributed surface energy storage system based on photovoltaic-storage synergy described above.

[0020] Fourthly, the present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the power smoothing method for a hyper-distributed surface energy storage system based on photoelectric storage synergy described above.

[0021] Compared with the prior art, the present invention has the following beneficial effects: The proposed power smoothing method for a surface-based ultra-distributed energy storage system based on photovoltaic-energy storage synergy has two aspects. First, it obtains the short-term power change rate based on the first data, and then obtains the predicted photovoltaic power value for the next moment based on the short-term power change rate. It can be seen that the present invention can predict the power trend in advance, avoid pseudo-power fluctuations caused by floating body swaying and changes in illumination, prevent the surface-based ultra-distributed energy storage system from making incorrect adjustment actions, and improve the reliability of the surface-based ultra-distributed energy storage system. Second, if the energy storage SOC value exceeds the preset safety range in the next moment, the second data is corrected to obtain the corrected smoothed reference power. This operation can reserve SOC margin for the next moment, thereby avoiding the battery from frequently reaching the limit range, significantly slowing down capacity decay, and extending cycle life. Attached Figure Description

[0022] Figure 1 This is a flowchart of the power smoothing method for a distributed surface energy storage system based on photovoltaic-storage synergy, as described in this invention. Figure 2 This is a schematic diagram of the power smoothing system of the ultra-distributed water surface energy storage system based on photovoltaic-storage synergy of the present invention; Figure 3 This is a schematic diagram of the ultra-distributed water surface energy storage system in Embodiment 4 of the present invention; Figure 4 This is a schematic diagram of the structure of the electronic device of the present invention. Detailed Implementation

[0023] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0024] Example 1: The flowchart of the power smoothing method for the ultra-distributed surface energy storage system based on photovoltaic-storage synergy of the present invention is as follows: Figure 1 As shown, the power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-storage synergy of the present invention includes the following steps: S1. Obtain the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the water surface energy storage ultra-distributed system, and record them as the first data and the second data respectively; S2. Based on the first data, obtain the short-time power change rate, and based on the short-time power change rate, obtain the predicted photovoltaic power value for the next moment; S3. Based on the predicted photovoltaic power value and the second data at the next moment, obtain the energy storage SOC value at the next moment; S4. Determine whether the energy storage SOC value at the next moment exceeds the preset safe range. If the energy storage SOC value at the next moment exceeds the preset safe range, then correct the second data to obtain the corrected smoothed reference power. S5. The photovoltaic-storage collaborative nodes of the ultra-distributed surface energy storage system allocate photovoltaic and storage power based on the corrected smooth reference power to obtain the photovoltaic-storage power allocation result.

[0025] Example 2: A schematic diagram of the power smoothing system of the ultra-distributed water surface energy storage system based on photovoltaic-energy storage synergy of the present invention is shown below. Figure 2 As shown, the power smoothing system of the ultra-distributed surface energy storage system based on photovoltaic-energy storage synergy of the present invention includes: The data acquisition module is used to acquire the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the water surface energy storage ultra-distributed system, which are respectively denoted as the first data and the second data. The photovoltaic power prediction value determination module is used to obtain the short-time power change rate based on the first data, and to obtain the photovoltaic power prediction value at the next moment based on the short-time power change rate. The energy storage SOC value determination module is used to obtain the energy storage SOC value at the next moment based on the photovoltaic power prediction value and the second data. The judgment module is used to determine whether the energy storage SOC value at the next moment exceeds the preset safety range. If the energy storage SOC value at the next moment exceeds the preset safety range, the second data is corrected to obtain the corrected smooth reference power. The power allocation module is used to allocate photovoltaic and energy storage power to each photovoltaic-energy storage collaborative node in the ultra-distributed photovoltaic energy storage system based on the corrected smooth reference power, and obtain the photovoltaic-energy storage power allocation result.

[0026] Example 3: The present invention provides a power smoothing method for a distributed surface energy storage system based on photovoltaic-energy storage synergy, comprising the following steps: S1. Obtain the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the water surface energy storage ultra-distributed system, and record them as the first data and the second data respectively.

[0027] In this step, the photovoltaic-storage co-location node adopts a low-voltage DC coupling architecture, with the photovoltaic side and the energy storage side directly connected through a DC / DC converter.

[0028] S2. Based on the first data, obtain the short-time power change rate, and based on the short-time power change rate, obtain the predicted photovoltaic power value for the next moment.

[0029] The formula for calculating the predicted photovoltaic power value at the next moment in this step is:

[0030] in, This is the predicted photovoltaic power value for the next moment. The photovoltaic power at the current moment, The number of steps in the sliding window. The sampling period is Photovoltaic power at a historical reference point.

[0031] S3. Based on the photovoltaic power prediction value and the second data at the next time step, obtain the energy storage SOC value at the next time step.

[0032] The calculation method for the next energy storage SOC value in this step is as follows:

[0033] in, The SOC value for energy storage at the next moment. The SOC value at the current moment. Current smoothed reference power, This is the predicted photovoltaic power value for the next moment. The sampling period is For the current charge and discharge efficiency, To predict the capacity factor, This refers to the nominal capacity of the energy storage unit.

[0034] S4. Determine whether the energy storage SOC value at the next moment exceeds the preset safety range. If the energy storage SOC value at the next moment exceeds the preset safety range, then correct the second data to obtain the corrected smooth reference power.

[0035] In this step, the second data is corrected to obtain the corrected smoothed reference power, specifically as follows: If the energy storage SOC value is greater than the preset upper limit value at the next moment, the second data will be reduced and the discharge power will be increased. If the energy storage SOC value is less than the preset lower limit in the next moment, the second data will be increased and the charging power will be increased. Furthermore, the corrected smoothed reference power is limited to the allowable fluctuation bandwidth centered on the current photovoltaic output.

[0036] S5. The photovoltaic-storage collaborative nodes of the ultra-distributed surface energy storage system allocate photovoltaic and storage power based on the corrected smooth reference power to obtain the photovoltaic-storage power allocation result.

[0037] After obtaining the power allocation results of the photovoltaic and energy storage system in this step, when any photovoltaic-energy storage collaborative node is identified as a derating unit, the neighboring photovoltaic-energy storage collaborative nodes increase the charging and discharging compensation power based on the received operating status information, under the premise of their own SOC safety, and collaboratively maintain the smooth total output power of the ultra-distributed surface energy storage system.

[0038] The method for identifying the devaluation unit in this step is as follows: Obtain the SOC change rate, charge / discharge efficiency, and historical capacity decay curves of each photovoltaic-storage collaborative node in the ultra-distributed surface energy storage system; Based on the SOC change rate, charge and discharge efficiency, and historical capacity decay curves of each photovoltaic-storage co-working node in the ultra-distributed photovoltaic energy storage system, the predicted capacity coefficient of each photovoltaic-storage co-working node is determined. Based on the predicted capacity coefficient of each optical-storage collaborative node, the current available capacity of each optical-storage collaborative node is obtained; Determine whether the current available capacity of each optical-storage collaborative node is lower than the preset capacity coefficient threshold. If the current available capacity of any optical-storage collaborative node is lower than the preset capacity coefficient threshold, it is determined that there is a derated unit in the optical-storage collaborative node.

[0039] Example 4: The method of the present invention will be described in detail below. The power smoothing method of the ultra-distributed water surface energy storage system based on photovoltaic-storage synergy of the present invention includes the following steps: First, the structure of the photovoltaic-storage synergistic surface energy storage ultra-distributed system is described. The energy storage units of the floating photovoltaic system are integrated into each floating photovoltaic support column in a cell-level manner, forming multiple independent photovoltaic-storage collaborative nodes, each node being equipped with a local edge controller.

[0040] S1. Obtain the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the water surface energy storage ultra-distributed system, and record them as the first data and the second data respectively.

[0041] S2. Based on the first data, obtain the short-time power change rate, and based on the short-time power change rate, obtain the predicted photovoltaic power value for the next moment.

[0042] S3. Based on the photovoltaic power prediction value and the second data at the next time step, obtain the energy storage SOC value at the next time step.

[0043] S4. Determine whether the energy storage SOC value at the next moment exceeds the preset safe range. If the energy storage SOC value at the next moment exceeds the preset safe range, then correct the second data based on the one-step advance correction strategy to obtain the corrected smooth reference power.

[0044] S5. The photovoltaic-storage collaborative nodes of the ultra-distributed surface energy storage system allocate photovoltaic and storage power based on the corrected smooth reference power to obtain the photovoltaic-storage power allocation result.

[0045] This invention relates to a floating photovoltaic (PV) power station. The power station consists of several PV support columns, each integrating a PV module, a cell-level energy storage unit, a low-voltage DC coupling interface, and a local edge controller. A PV-storage co-operation node is a minimal independent controllable unit, typically composed of a single (or a group of parallel) PV module combined with a cell-level (or single-pack) energy storage unit, a bidirectional DC / DC converter, and an edge controller. Multiple such nodes are connected in parallel to the DC bus, forming the DC side of the entire floating PV power station. Each PV-storage co-operation node is equipped with an edge controller for local data acquisition, algorithm execution, control output, and distributed communication. The edge controller's hardware structure typically includes a microcontroller unit (MCU) or digital signal processor (DSP), a sampling circuit, a communication module, a storage module, and a power management module.

[0046] A decentralized distributed communication network is formed among the various optical storage collaborative nodes, and only the SOC value, smoothing reference power, and derating flag are exchanged.

[0047] First, in step S1, each optical-storage collaborative node is equipped with an edge controller, which is used to measure the sampling period. Typically, a sampling period of 10 seconds is set to collect the photovoltaic output (power) of this node. A lightweight recursive model is used to predict the photovoltaic power at the next moment.

[0048] Then, in step S3, the edge controller adjusts the current smoothing reference power. Calculate the SOC value of the energy storage at the next moment based on the photovoltaic power prediction value at the next moment.

[0049] Next, in step S4, a safe SOC range is preset, and the SOC value of the energy storage at the next moment is judged within the preset safe range. If the value exceeds the preset safe range, an overcharge risk is predicted, and the current smoothing reference power is reduced. Increase the discharge power in advance; if the energy storage SOC value is less than the preset safe range at the next moment, the risk of over-discharge is predicted, and the current smoothing reference power needs to be increased. It can increase the charging power in advance. If it is within the preset safe range, no correction is required. In addition, the corrected smooth reference power is still limited to an acceptable fluctuation bandwidth centered on the current photovoltaic output to ensure smooth grid-connected power.

[0050] Finally, in step S5, each node is adjusted according to the corrected... The system performs power allocation between photovoltaic and energy storage, enabling photovoltaic power to be processed at its limit and supplementing the energy storage deficit. It should be noted that the power allocation is completed entirely on the DC side, without the need for multi-stage DC-AC-DC conversion. At the same time, the nodes can broadcast information through a distributed communication network, including the current SOC value, whether it is in derating mode, and the current smoothing reference power.

[0051] Short-time power prediction uses a lightweight recursive model, and the calculation method is as follows:

[0052] in, For the photovoltaic power at the next moment, The photovoltaic power at the current moment, The number of steps in the sliding window. The sampling period is Photovoltaic power at a historical reference point.

[0053] This embodiment uses a lightweight recursive model for short-term power prediction. This prediction method does not require a centralized server or deep learning model, making it suitable for edge operation. It is completed independently by the edge controller of each optical-storage collaborative node locally.

[0054] The predicted photovoltaic power value at the next moment is calculated according to the formula. The formula simplifies to This formula calculates the average rate of change of photovoltaic power over a past period and linearly extrapolates it to the next sampling time to achieve short-term power trend prediction. Reflecting the recent changes in sunlight, and To smooth out short-term random fluctuations and predict the power trend at the next moment, it achieves good response sensitivity to the unique cloud shadow movement, floating body swaying, and water surface reflection changes of water surface photovoltaic.

[0055] The calculation method for the energy storage SOC value at the next moment is as follows:

[0056] in, The SOC value for energy storage at the next moment. The SOC value at the current moment. The current smoothed reference power at this node. This is the predicted photovoltaic power value for the next moment. The sampling period is For the current charge and discharge efficiency, To predict the capacity factor, This refers to the nominal capacity of the energy storage unit.

[0057] This embodiment uses formulas To perform the calculation, where, This indicates the amount of electricity that the energy storage system needs to absorb or release in the next time period under the current smoothing strategy. This represents the actual usable effective capacity of the energy storage unit under current environmental conditions and aging state. Moreover, this formula allows us to know in advance whether the State of Charge (SOC) might exceed the limit at the next moment, before actual charging and discharging occurs.

[0058] Taking a photovoltaic pillar node on a water surface as an example, currently Current smoothed reference power The photovoltaic power is 1.2kW, and the predicted power at the next moment is... It is 2.9kW, and the sampling period is... The charge / discharge efficiency is 10 seconds. It is 0.92, nominal capacity It is 10 kWh; calculated as follows With a value of 0.7795, the edge controller determines that the SOC is still within the safe range at the next moment, and there is no need to correct the smoothing reference power.

[0059] The one-step advance correction strategy specifically includes: If the energy storage SOC value is greater than the preset upper limit value at the next moment, the current smoothing reference power of this node will be reduced and the discharge power will be increased. If the energy storage SOC value is less than the preset lower limit in the next moment, the current smoothing reference power of this node will be increased, and the charging power will be increased. Furthermore, the corrected smoothed reference power is limited to the allowable fluctuation bandwidth centered on the current photovoltaic output.

[0060] Specifically, when > Then reduce Increase discharge power in advance. < Then improve Increase charging power in advance and use future risks to correct current actions.

[0061] in, This is a preset upper limit value. The lower limit is preset, and the safe operating range of the energy storage SOC is pre-defined for each edge controller. ∈ , where the value =20%, =90%.

[0062] Edge controller based on the next moment The following judgment is executed to predict overcharging scenarios: like > If the current smoothed reference power continues to operate, the energy storage will face process risks in the next moment, and a correction will be performed: reduce the current smoothed reference power at this node. The equivalent pre-increase in discharge power causes the energy storage to shift from a charging trend to a discharging trend; if an over-discharge scenario is anticipated, if < If it is determined that if operation continues at the current smoothed reference power, the energy storage will face the risk of over-discharge at the next moment. In this case, the corrector is executed to increase the current smoothed reference power at this node. This is equivalent to increasing the electrical power, causing the energy storage to shift from a discharge trend to a charging trend.

[0063] In addition, to prevent the correction process from disrupting the grid-connected power smoothing effect, the corrected smoothed reference power must meet the fluctuation bandwidth limit: ≤ ,and For the corrected smoothed reference power, The photovoltaic power at the current moment, This represents the maximum allowable power deviation.

[0064] The method of the present invention also includes: S6. When any optical-storage collaborative node is identified as a derating unit, the neighboring optical-storage collaborative nodes increase the charging and discharging compensation power based on the received operating status information, under the premise of their own SOC safety, and collaboratively maintain the smoothness of the total output power of the system.

[0065] For nodes where high humidity, salt spray corrosion, or long-term cyclic aging on the water surface lead to a significant decrease in energy storage capacity, a distributed collaborative mechanism is used to avoid the impact of single-point performance degradation on the overall power smoothing effect of the system. Specifically, in water-surface photovoltaic scenarios, due to the long-term high humidity and salt spray environment, some energy storage units on the pillars will show significant changes, commonly including increased cell internal resistance, decreased usable capacity, and reduced charge and discharge efficiency.

[0066] The method for identifying the depreciation unit is as follows: The edge controller calculates and determines the predicted capacity coefficient based on the node's SOC change rate, charging and discharging efficiency, and historical capacity decay curve. It then uses the predicted capacity coefficient to determine whether the current available capacity is lower than the preset capacity coefficient threshold. If it is lower than the preset capacity coefficient threshold, the node automatically enters the derating operation mode, reduces the node's charging and discharging bandwidth, and transmits the derating flag to the neighboring optical-storage collaborative node to execute the collaborative compensation strategy.

[0067] The method of this invention quantifies the health of nodes by predicting capacity coefficients, thereby achieving accurate identification of derating units. The edge controller identifies the node as a derating unit based on the aforementioned predicted capacity coefficients, determining that its capacity decay is caused by high humidity, salt spray, and aging. The edge controller periodically calculates the predicted capacity coefficients by subtracting the current SOC value from the historical SOC value, and compares the predicted capacity coefficients with the set capacity coefficient threshold to determine whether it is a derating unit or a healthy unit. Based on the identified derating unit, the node automatically enters the derating operation mode.

[0068] The formula for calculating the predicted capacity factor is:

[0069] in, The nominal capacity of the energy storage unit The change in SOC per unit time. This represents the theoretical charge / discharge capacity for the same period. The values ​​are obtained by looking up a table of charging / discharging current and ambient temperature. This is the predicted exponential decay value obtained by fitting historical capacity test data.

[0070] If the predicted capacity coefficient < Then the node is identified as a derated unit.

[0071] Otherwise, the node is considered a photonics-storage collaborative node; where, This is the preset capacity coefficient threshold.

[0072] The method of this invention uses a predicted capacity factor to represent the ratio of the actual usable capacity of the current energy storage unit to its nominal capacity. The specific calculation formula is as follows: By calculating the predicted capacity coefficient, the change in capacity decay can be quantified, and This represents the actual change in SOC caused by a unit of theoretical charge / discharge. This indicates a historical decline trend.

[0073] nominal capacity of energy storage unit The value is determined by the factory parameters of the energy storage unit and is set to 10 kWh. The formula for calculating the change in SOC per unit time is:

[0074] in, This indicates the current SOC value. For backwards The SOC value for each sampling period is taken as follows. The value is 3, corresponding to a 30-second time window; the theoretical charge / discharge capacity during the same period. , For the edge controller at time Given the smoothed reference power, sampling period Usually taken .

[0075] Based on current charge and discharge efficiency The value is obtained by the edge controller based on a table lookup of the charging / discharging current and ambient temperature. Furthermore, in high humidity and salt spray environments, this value decreases due to increased electrochemical impedance. The calculation is based on monthly standard capacity tests and fitted using an exponential decay model. Weighting coefficients are used to suppress misjudgments caused by short-term fluctuations. Used to balance real-time responsiveness and long-term stability; sets a capacity coefficient threshold. If the value is 0.85, the edge controller performs a judgment; if... < If it is determined to be a derated unit, it is determined to be a photoelectric storage collaborative node. Substitute it into the formula. ,and < The node was identified as a derating unit, and its capacity was determined to have decreased due to high humidity or salt spray corrosion.

[0076] The following is a detailed introduction to the collaborative compensation strategy, which is as follows: After receiving the derating flag, if the state of charge (SOC) of the photovoltaic-storage collaborative node is within the safe range, it will increase the charging and discharging power according to a preset ratio, without exceeding the maximum allowable power of the node, to compensate for the smoothing space given by the derating unit, so that the fluctuation of the total output power of the system meets the grid connection requirements.

[0077] In a distributed surface energy storage system, a collaborative compensation strategy eliminates system-level power fluctuations caused by single-node capacity decay. When any photovoltaic-storage co-location node is identified as a derating unit, its edge controller generates a derating flag and broadcasts the following information to neighboring nodes within one or two hops via a distributed communication network: the current State of Charge (SOC) value and the current smoothed reference power. Reduction coefficient This ensures that after receiving the above information, the neighboring optical-storage collaborative node will automatically trigger the collaborative compensation process.

[0078] In addition, the compensating derating unit optical-storage collaborative node calculates the compensation power that this node should bear based on the power space vacated by the derating unit: .

[0079] Among them, the compensation allocation coefficient The compensation is dynamically determined based on the node's remaining SOC margin, predicted capacity coefficient, communication distance between the node and the derating unit, and the node's current available power. The compensation process is executed by the optical-storage collaborative node in the next control cycle, adding compensation power to the original smoothed reference power. The system adjusts the charge and discharge commands of this node, and at the same time, the optical-storage collaborative node continuously monitors its own SOC changes: if the SOC is close to the safety boundary, the compensation power is gradually reduced; if the derating unit returns to normal, the compensation is automatically stopped. The whole process does not require manual intervention and is completed autonomously by the edge controller.

[0080] A schematic diagram of the structure of the surface energy storage ultra-distributed system is shown below. Figure 3 As shown, the photovoltaic-storage co-location node adopts a low-voltage DC coupling architecture, with the photovoltaic side and the energy storage side directly connected through a DC / DC converter. All power smoothing and correction processes are completed on the DC side, without the need for multi-stage DC-AC-DC conversion.

[0081] The ultra-distributed surface-mount energy storage system achieves high-efficiency, low-loss power smoothing and correction through a low-voltage DC-coupled architecture. The photovoltaic (PV) side and the energy storage side are directly connected via a DC / DC converter, without any DC-AC inverter stage. Moreover, all power smoothing-related operations are completed in a closed loop within the DC domain. When the PV output exceeds the smoothing reference power, the excess energy is transferred to the energy storage battery via the DC / DC converter. When the PV output is less than the smoothing reference power, and the energy storage battery discharges, the energy is replenished to the DC bus via the DC / DC converter. The DC bus then sends the corrected power information to the grid.

[0082] The edge controller calculates the corrected smoothed reference power. Then, the duty cycle command is directly sent to the DC / DC converter to adjust the energy storage charging and discharging current, with no AC link involved in the entire process; even when performing environmental adaptive adjustment or domain coordination, the increase or decrease in power only changes the inductor current in the DC / DC converter, and the response speed can reach the millisecond level.

[0083] The method of the present invention also includes environmental adaptive processing, specifically: The edge controller combines changes in surface wind speed, water temperature, or light incidence angle to dynamically adjust the SOC safety range boundary and smoothing bandwidth in the one-step advance correction strategy, suppressing pseudo-ripples caused by buoy swaying or surface reflection.

[0084] This invention provides an environmental adaptive step to enhance the robustness and anti-interference capability of power smoothing control in ultra-distributed water storage systems operating in complex and variable water environments. Because water-based photovoltaic power stations operate in unique open water environments, unlike onshore power stations, they frequently face two typical "pseudo-fluctuation" interference sources: floating body swaying and water surface reflection. By dynamically adjusting the sensitivity of the control strategy based on the environment, unnecessary charge-discharge cycles are reduced, thus minimizing battery lifespan and avoiding excessive limitation of power regulation bandwidth, which reduces grid support capacity. The edge controller dynamically corrects the two core control boundaries in the one-step advance correction strategy based on changes in water surface wind speed and the light incidence angle measured by the water thermometer. Specifically, this includes: A. Under standard operating conditions, the SOC safe range is a fixed value. In environmental adaptive mode, the range narrows as the environment deteriorates to reduce risk.

[0085]

[0086] Among them, when When the threshold is exceeded, Increase and reserve more SOC buffer in advance to cope with power surges caused by appendage swaying. When When the temperature is too low, battery activity decreases. Increased to prevent low-temperature, high-current charging and discharging.

[0087] B. By dynamically adjusting the smoothing bandwidth This determines the permissible deviation of power output from photovoltaic output. In harsh environments, the bandwidth can be appropriately widened to tolerate certain fluctuations, thereby avoiding over-adjustment. The calculation formula for this adjustment method is as follows:

[0088] When the incident angle of light changes drastically (causing large swaying of the floating body), the smoothing bandwidth is widened, allowing the ultra-distributed surface energy storage system to temporarily sacrifice some smoothness in exchange for battery life and equipment safety.

[0089] Compared with the prior art, the present invention has the following advantages: A. In this invention, under a hyper-distributed architecture, a local edge controller is configured for each photovoltaic-storage node to achieve local autonomous control. Through short-time power prediction, the system calculates the next-momentum storage SOC value based on the predicted photovoltaic power and the current smoothed reference power of the node, achieving one-step advance SOC prediction. Local correction is then performed through a preset safety range, enabling each node to achieve high-precision power smoothing without the involvement of a central controller. Furthermore, compared to traditional centralized control, this eliminates the risk of single-point failures and communication delay bottlenecks, ensuring that even if some nodes are offline, the remaining nodes can still autonomously maintain stable grid-connected power, significantly improving the overall reliability of the hyper-distributed surface energy storage system.

[0090] B. Traditional smoothing methods only make hysteretic adjustments based on the current power, which can easily lead to the energy storage being in a fully charged or deeply discharged state for a long time. By calculating the SOC value at the next moment, the smoothing reference power is corrected in advance within the current control cycle, reserving SOC margin for the next moment, thereby avoiding the battery from frequently reaching the limit range, significantly slowing down capacity decay, and extending cycle life.

[0091] C. By introducing a predicted capacity factor and based on the SOC change rate, charge and discharge efficiency, and historical capacity decay curves, the health status of nodes is quantitatively assessed, accurately identifying units with capacity decay caused by high humidity, salt spray, or aging. Through a collaborative compensation mechanism between adjacent photovoltaic and energy storage nodes, healthy units are automatically filled, avoiding the "barrel effect" of traditional systems where the overall operation is reduced due to individual weak units, and fully releasing the overall regulation capability of the ultra-distributed surface energy storage system.

[0092] Example 5: Please see Figure 4 As shown, the present invention also provides an electronic device 100 for a power smoothing method for a super-distributed surface energy storage system based on photovoltaic-storage synergy; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0093] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-storage synergy described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0094] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0095] The memory 101 in the electronic device 100 stores multiple instructions to implement a power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-storage synergy, and the processor 102 can execute the multiple instructions to achieve the following: Acquire the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the ultra-distributed water surface energy storage system, and record them as the first data and the second data respectively; Based on the first data, the short-time power change rate is obtained, and based on the short-time power change rate, the photovoltaic power prediction value for the next moment is obtained; Based on the photovoltaic power prediction value and the second data, the energy storage SOC value at the next time moment is obtained; Determine whether the energy storage SOC value at the next moment exceeds the preset safe range. If the energy storage SOC value at the next moment exceeds the preset safe range, then correct the second data to obtain the corrected smoothed reference power. In the ultra-distributed solar energy storage system, each solar-storage collaborative node performs solar-storage power allocation based on the corrected smoothed reference power, and obtains the solar-storage power allocation result.

[0096] Example 6: If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-energy storage synergy, characterized in that, Includes the following steps: Acquire the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the ultra-distributed water surface energy storage system, and record them as the first data and the second data respectively; Based on the first data, the short-time power change rate is obtained, and based on the short-time power change rate, the predicted photovoltaic power value for the next moment is obtained; Based on the photovoltaic power prediction value and the second data, the energy storage SOC value at the next time moment is obtained; Determine whether the energy storage SOC value at the next moment exceeds the preset safe range. If the energy storage SOC value at the next moment exceeds the preset safe range, then correct the second data to obtain the corrected smoothed reference power. In the ultra-distributed solar energy storage system, each solar-storage collaborative node performs solar-storage power allocation based on the corrected smoothed reference power, and obtains the solar-storage power allocation result.

2. The power smoothing method for a distributed surface energy storage system based on photovoltaic-energy storage synergy as described in claim 1, wherein the photovoltaic-energy storage synergy node adopts a low-voltage DC coupling architecture, and the photovoltaic side and the energy storage side are directly connected through a DC / DC converter.

3. The power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-storage synergy according to claim 1, characterized in that, The formula for calculating the predicted photovoltaic power value at the next moment is: in, This is the predicted photovoltaic power value for the next moment. The photovoltaic power at the current moment, The number of steps in the sliding window. The sampling period is Photovoltaic power at a historical reference point.

4. The power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-storage synergy as described in claim 1, characterized in that, The calculation method for the next moment's energy storage SOC value is as follows: in, The SOC value for energy storage at the next moment. The SOC value at the current moment. Current smoothed reference power, This is the predicted photovoltaic power value for the next moment. The sampling period is For the current charge and discharge efficiency, To predict the capacity factor, This refers to the nominal capacity of the energy storage unit.

5. The power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-storage synergy according to claim 1, characterized in that, The step of correcting the second data to obtain the corrected smoothed reference power is specifically as follows: If the energy storage SOC value is greater than the preset upper limit value at the next moment, the second data will be reduced and the discharge power will be increased. If the energy storage SOC value is less than the preset lower limit in the next moment, the second data will be increased and the charging power will be increased. Furthermore, the corrected smoothed reference power is limited to the allowable fluctuation bandwidth centered on the current photovoltaic output.

6. The power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-storage synergy according to claim 1, characterized in that, After obtaining the power allocation results of the photovoltaic and energy storage system, when any photovoltaic-energy storage collaborative node is identified as a derating unit, the neighboring photovoltaic-energy storage collaborative nodes increase the charging and discharging compensation power based on the received operating status information, under the premise of their own SOC safety, and collaboratively maintain the smooth total output power of the ultra-distributed surface energy storage system.

7. The power smoothing method for a hyper-distributed surface energy storage system based on photovoltaic-storage synergy according to claim 6, characterized in that, The method for identifying the devaluation unit is as follows: Obtain the SOC change rate, charge / discharge efficiency, and historical capacity decay curves of each photovoltaic-storage collaborative node in the ultra-distributed surface energy storage system; Based on the SOC change rate, charge and discharge efficiency, and historical capacity decay curves of each photovoltaic-storage co-working node in the ultra-distributed photovoltaic energy storage system, the predicted capacity coefficient of each photovoltaic-storage co-working node is determined. Based on the predicted capacity coefficient of each optical-storage collaborative node, the current available capacity of each optical-storage collaborative node is obtained; Determine whether the current available capacity of each optical-storage collaborative node is lower than the preset capacity coefficient threshold. If the current available capacity of any optical-storage collaborative node is lower than the preset capacity coefficient threshold, it is determined that there is a derated unit in the optical-storage collaborative node.

8. A power smoothing system for a distributed surface energy storage system based on photovoltaic-energy storage synergy, characterized in that, include: The data acquisition module is used to acquire the photovoltaic output data and current smoothed reference power of each photovoltaic-storage collaborative node in the water surface energy storage ultra-distributed system, which are respectively denoted as the first data and the second data. The photovoltaic power prediction value determination module is used to obtain the short-time power change rate based on the first data, and to obtain the photovoltaic power prediction value at the next moment based on the short-time power change rate. The energy storage SOC value determination module is used to obtain the energy storage SOC value at the next moment based on the photovoltaic power prediction value and the second data. The judgment module is used to determine whether the energy storage SOC value at the next moment exceeds the preset safety range. If the energy storage SOC value at the next moment exceeds the preset safety range, the second data is corrected to obtain the corrected smooth reference power. The power allocation module is used to allocate photovoltaic and energy storage power to each photovoltaic-energy storage collaborative node in the ultra-distributed photovoltaic energy storage system based on the corrected smooth reference power, and obtain the photovoltaic-energy storage power allocation result.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power smoothing method for a super-distributed surface energy storage system based on photovoltaic-storage synergy, as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power smoothing method for a super-distributed surface energy storage system based on photovoltaic-storage synergy, as described in any one of claims 1 to 7.

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