Smart regulation methods, systems, equipment, and media for transformer substation energy storage based on weather forecasting

Abstract

This invention provides a method, system, device, and medium for intelligent control of energy storage in power distribution areas based on weather forecasts, belonging to the fields of power system energy storage control and new energy consumption. The method includes: acquiring next day's weather forecast data and setting the target state of charge (SOC) of the energy storage system according to the weather type; collecting real-time operating data of the distribution area; pre-setting energy storage charging and discharging periods and constructing charging and discharging trigger logic; after triggering charging or discharging, calculating and adjusting the charging and discharging current based on the real-time power feedback or extraction power, tracking SOC changes in real-time, and executing shutdown control when the SOC reaches a preset upper limit threshold or drops to the target SOC. By integrating refined next day's weather data with the real-time operating status of the distribution area, the method achieves predictive dynamic setting of the target SOC of the energy storage system and composite logic triggering of charging and discharging periods, improving the local photovoltaic consumption rate and load peak shaving and valley filling effect.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage regulation and new energy consumption technology, specifically to a method, system, equipment and medium for intelligent regulation of transformer area energy storage based on weather forecasting. Background Technology

[0002] In the context of the construction of new power systems, a large number of distributed photovoltaic power stations are connected to diverse loads such as electric vehicle charging piles and agricultural processing loads. As the core equipment for the coordinated regulation of power generation, grid, load and storage in power distribution areas, the charging and discharging regulation effect of energy storage systems directly affects the renewable energy absorption rate, load peak and valley regulation capacity and power supply reliability of power distribution areas.

[0003] Traditional energy storage regulation in distribution substations often employs a crude charging and discharging mode with fixed time periods and currents. It passively regulates based solely on real-time substation operating conditions, lacking predictability of photovoltaic (PV) output. This leads to untimely charging of energy storage during peak PV generation at midday, resulting in significant PV power being fed back into the grid and forced curtailment. Insufficient energy storage discharge during evening peak load periods fails to effectively alleviate transformer load pressure, resulting in poor peak shaving and valley filling effects. Furthermore, traditional energy storage regulation algorithms simply incorporate meteorological data without adapting to the climate response characteristics of substation PV output. This results in irrational SOC control of energy storage. On rainy days with insufficient PV output, energy storage capacity is too low to provide emergency energy support for the substation; on sunny days with sufficient PV output, energy storage capacity is too high, leaving insufficient charging space and hindering local PV consumption.

[0004] Furthermore, traditional control systems and power quality management equipment in distribution areas operate independently. Energy storage only performs a single energy regulation function, failing to fully utilize its active / reactive power support capabilities. This makes it unable to provide precise auxiliary management for issues such as three-phase imbalance, voltage fluctuations, and voltage dips in distribution areas. Moreover, fault protection mechanisms only target the battery itself, lacking end-to-end protection for the charging / discharging circuit and converter, thus requiring improvement in the safety and accuracy of energy storage charging and discharging processes. Simultaneously, traditional data acquisition systems lack abnormal data preprocessing and data assurance mechanisms, which can easily lead to calculation errors in the control algorithm, further reducing the control effectiveness. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, equipment, and medium for intelligent regulation of energy storage in distribution substations based on weather forecasting, ensuring the stability of data transmission and regulation execution, and ultimately significantly improving the local photovoltaic absorption rate and load peak shaving and valley filling effect, providing stable energy support for emergency power supply in distribution substations, so as to meet the actual needs of coordinated regulation of source, grid, load, and storage in distribution substations under the new power system.

[0006] To achieve the above objectives, embodiments of the present invention provide a method for intelligent regulation and control of energy storage in transformer substations based on weather forecasting, comprising: Obtain the weather forecast data for the next day, and set the target state of charge (SOC) of the energy storage system based on the weather type in the weather forecast data; Real-time acquisition of transformer substation operation data, wherein the transformer substation operation data includes at least the bidirectional power flow status of the transformer secondary side and the current SOC of the energy storage; Preset the energy storage charging and discharging time periods, and construct charging and discharging trigger logic based on time period, power flow direction and SOC; After triggering charging or discharging, the charging and discharging current is calculated and adjusted according to the real-time reverse power supply or power extraction power, while the SOC change is tracked in real time. When the SOC reaches the preset upper limit threshold or drops to the target state of charge SOC, shutdown control is executed. During the charge and discharge regulation process, the regulation effect is quantitatively evaluated based on real-time feedback operating data, and the regulation parameters are adaptively adjusted to form a closed-loop optimization.

[0007] Optionally, preset energy storage charging and discharging periods are provided, and charging and discharging triggering logic based on period, power flow direction, and SOC is constructed, including: Charging is triggered when the charging period is in progress, power backflow to the grid is detected, and the current SOC has not reached the upper limit threshold. Discharge is triggered when power is drawn from the grid during a discharge period and the current state of charge (SOC) is higher than the target SOC.

[0008] Optionally, after triggering charging or discharging, the charging / discharging current is calculated and adjusted based on the real-time reverse power supply or power draw, including: calculating the charging / discharging current according to the following formula:

[0009]

[0010] In the formula, Indicates the charging current. This indicates the scenario-based adjustment coefficient for the reverse power transmission. Indicates the rated voltage of the DC bus. This represents the absolute value of the active power fed back from the secondary side of the transformer. Indicates the discharge current. This represents the scenario-based power consumption adjustment coefficient. This refers to the active power drawn from the secondary load of the transformer.

[0011] Optionally, the system tracks SOC changes in real time, and when the SOC reaches a preset upper limit threshold or drops to the target SOC, it performs shutdown control, including: When the SOC reaches the preset upper limit threshold, the charging current limiting shutdown logic is triggered, and the charging current is gradually reduced to 0 and charging stops. When the State of Charge (SOC) drops to the target SOC, the discharge current limiting shutdown logic is triggered, and the discharge current gradually decreases to 0 and the discharge stops.

[0012] Optionally, the weather forecast-based intelligent control method for transformer substation energy storage further includes: During the charging and discharging process, the operating status of the energy storage battery body, charging and discharging circuit and converter is monitored in real time. When any fault is detected in the energy storage battery body, charging and discharging circuit, and inverter across all dimensions of operation, the DC / DC module is immediately triggered to soft shutdown, and the charging and discharging circuit is disconnected in stages.

[0013] Optionally, the weather forecast-based intelligent control method for transformer substation energy storage further includes: During the energy storage charging and discharging control process, a three-phase current independent distribution strategy is adopted. The charging and discharging current of each phase of the energy storage is adjusted in real time according to the three-phase imbalance of the transformer area, so as to provide reactive power compensation and three-phase power balance support for the transformer area. When a voltage dip in the transformer area is detected, the output current is supported through a fast response control algorithm.

[0014] Optionally, when a voltage dip in the transformer area is detected, a fast response control algorithm is used to output current support, including: Real-time acquisition of three-phase voltage data on the secondary side of the transformer and performance of feature calculations are performed to identify voltage sag events and confirm their triggering. Identify the sag type based on voltage drop depth and phase characteristics, calculate the amplitude, phase, and duration of the supporting current required to raise the voltage to the target value, and determine the reactive power compensation component as needed. After confirming the voltage dip, the energy storage system is switched to voltage support mode. A current reference value is generated based on the DC bus voltage, and current tracking is achieved through a fast response controller to ensure that the response time meets the requirements. Based on the sag type, a three-phase current independent allocation strategy is adopted to determine the required support current for each phase, and the converter dynamically outputs the corresponding current and sets the current upper limit. During the support process, the voltage is continuously monitored. Once the voltage recovers to the target value and stabilizes, a smooth exit strategy is adopted to gradually reduce the support current to zero and restore the original charging and discharging mode.

[0015] Secondly, the present invention also provides a smart control system for transformer substation energy storage based on weather forecasting, comprising: The data acquisition unit is used to acquire the next day's weather forecast data and set the target state of charge (SOC) of the energy storage system according to the weather type in the weather forecast data; and to collect the transformer substation operation data in real time, wherein the transformer substation operation data includes at least the bidirectional power flow status of the transformer secondary side and the current SOC of the energy storage. The logic triggering unit is used to preset the energy storage charging and discharging period and construct charging and discharging triggering logic based on the period, power flow direction and SOC. The control unit is used to calculate and adjust the charging and discharging current based on the real-time power feedback or power extraction after charging or discharging is triggered, and to track the SOC change in real time. When the SOC reaches the preset upper limit threshold or drops to the target state of charge SOC, the unit executes shutdown control. In the process of charge and discharge regulation, the regulation effect is quantitatively evaluated based on real-time feedback operation data, and the regulation parameters are adaptively adjusted to form a closed-loop optimization.

[0016] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described intelligent control method for transformer area energy storage based on weather forecasting.

[0017] Fourthly, the present invention also provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described intelligent control method for transformer area energy storage based on weather forecasting.

[0018] Through the above technical solutions Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a smart regulation method for energy storage in transformer substations based on weather forecasting, provided by an embodiment of the present invention. Figure 2 This is a detailed flowchart of a smart control method for transformer substation energy storage based on weather forecasting provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of a smart control system for energy storage in a transformer substation based on weather forecasting, provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] Various embodiments of this disclosure will be described more fully in the following detailed description. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0021] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions or operations and do not limit the addition of one or more functions or operations. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing, or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing.

[0022] In various embodiments of this disclosure, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This method is applied to a 200kW / 200kWh energy storage system in a power quality optimization integrated device for a distribution area. Using a DC 750V bus as the energy interaction carrier, and combining next day's weather forecast data, the photovoltaic output characteristics of the distribution area, load consumption patterns, and grid operating status, a smart energy storage control system is constructed. (See reference...) Figure 1 The diagram shows a flowchart of a smart regulation method for transformer substation energy storage based on weather forecasting in a specific embodiment, including the following execution steps: Step 100: Obtain the weather forecast data for the next day, and set the target state of charge (SOC) of the energy storage system according to the weather type in the weather forecast data.

[0025] Specifically, the system connects to the meteorological platform via a 5G communication module to obtain refined meteorological forecast data for the next day at a frequency of once per day, specifying the weather type as sunny, rainy, or cloudy / partly cloudy. It also simultaneously obtains the trends of meteorological factors related to photovoltaic output and supports real-time supplementary collection, updating, and completion of meteorological factor trends to ensure data accuracy.

[0026] For example, the specific rules for setting the target state of charge (SOC) of an energy storage system are as follows: Rainy day the following day: Photovoltaic output decreases significantly, and the energy storage target SOC is set to 100% to maintain full charge and provide stable energy support for the power supply to the transformer area and emergency power supply; Sunny day the following day: Photovoltaic output is sufficient, and the energy storage target SOC is set to 20% to maximize the release of electricity, reserve sufficient charging space for the midday photovoltaic power generation, and improve the local photovoltaic consumption rate; Cloudy / partly cloudy day: Photovoltaic output is unstable, and the energy storage target SOC is set to 80% to take into account the dual needs of photovoltaic consumption and load power supply; Extreme weather protection: If it is a rainy day or there are several consecutive days of rainfall, and the photovoltaic output is continuously insufficient, the extreme weather energy storage power protection mechanism will be triggered, and the energy storage target SOC will be forcibly set to 100% to ensure the stability of the energy supply in the transformer area.

[0027] Step 101: Collect real-time operating data of the transformer area.

[0028] The operating data of the transformer substation includes at least the bidirectional power flow status of the transformer secondary side and the current SOC of the energy storage.

[0029] Specifically, a dual-layer heterogeneous control and control data acquisition system is constructed, consisting of local real-time high-frequency acquisition and remote precise data docking. Through high-precision intelligent sensors, power meters, photovoltaic combiner boxes, and a full-parameter monitoring BMS battery management system within the distribution area, real-time data on the operation of the distribution area is collected at a frequency of 50ms / time. This includes: active power P, reactive power Q, and three-phase voltage / current on the secondary side of the distribution transformer; real-time output power of distributed photovoltaics; active / reactive power consumption of the load in the distribution area; and full-dimensional operating status of the energy storage battery, such as SOC, charging and discharging current, battery temperature, single-cell voltage, and insulation status.

[0030] The collected transformer operation data undergoes a three-stage preprocessing process: filtering, noise reduction, and anomaly removal. Abnormal data and interference signals are eliminated to ensure the accuracy of subsequent control algorithm calculations. At the same time, the acquisition system has a dual data protection mechanism of local caching and remote synchronization to avoid control interruptions caused by data loss.

[0031] Step 102: Preset the energy storage charging and discharging period and construct the charging and discharging trigger logic based on the period, power flow direction and SOC.

[0032] Specifically, when executing step 102, the following steps can be performed: S1020: When charging is in progress and power is detected being fed back to the grid, and the current SOC has not reached the upper limit threshold, charging is triggered.

[0033] Specifically, the charging triggering conditions are: during the charging period, the active power P on the secondary side of the transformer is less than 0 (photovoltaic output is greater than load power consumption, and the system feeds back power to the grid) and the energy storage SOC is less than 90%.

[0034] S1021: When in a discharge period and power is detected being drawn from the grid and the current SOC is higher than the target SOC, discharge is triggered.

[0035] Specifically, the discharge triggering conditions are: during the discharge period, the active power P on the secondary side of the transformer is greater than 0 (the load power consumption is greater than the photovoltaic output, and the load draws power from the grid) and the energy storage SOC is higher than the target SOC for the corresponding weather type.

[0036] For example, the charging period is set to 8:00 by default (the start of the photovoltaic power generation period), and the discharging period is set to 18:00 by default (the peak period of residential load). The charging and discharging periods can be finely customized through the local / back-end monitoring module to adapt to the load and photovoltaic characteristics of different distribution areas and realize the configurable timing.

[0037] Step 103: After triggering charging or discharging, calculate and adjust the charging and discharging current based on the real-time reverse power supply or power extraction power, and simultaneously track the SOC change in real time. When the SOC reaches the preset upper limit threshold or drops to the target state of charge SOC, execute shutdown control.

[0038] Specifically, after triggering charging or discharging, the charging and discharging current is calculated and adjusted based on the real-time power feedback or extraction, including: calculating the charging and discharging current according to the following formula:

[0039]

[0040] In the formula, Indicates the charging current. This represents the scenario-based adjustment coefficient for reverse power transmission, ranging from 0.8 to 1.2. This indicates the rated voltage of the DC bus, which is fixed at 750V. This represents the absolute value of the active power fed back from the secondary side of the transformer. Indicates the discharge current. This represents the scenario-based power consumption adjustment coefficient, ranging from 0.8 to 1.2. The active power drawn from the secondary side load of the transformer is set to a charging current upper limit of 50A. When the calculated value is greater than 50A, Ic is set to 50A. The upper limit of the discharge current is set to 50A. When the calculated value is greater than 50A, Id is set to 50A.

[0041] Specifically, in step 103, the SOC change is tracked in real time. When the SOC reaches a preset upper limit threshold or drops to the target state of charge (SOC), shutdown control is executed, including the following steps: S1030: When the SOC reaches the preset upper limit threshold, the charging current limiting shutdown logic is triggered, and the charging current is gradually reduced to 0 and charging stops.

[0042] Preferably, the preset upper limit threshold is 90%. It should be noted that the preset upper limit threshold can be set according to the specific application scenario, and there is no restriction here.

[0043] S1031: When the SOC drops to the target state of charge SOC, the discharge current limiting shutdown logic is triggered, and the discharge current gradually decreases to 0 and the discharge stops.

[0044] In one specific implementation, when the energy storage SOC reaches 90%, the charging current limiting shutdown logic is triggered, the charging current is gradually reduced to 0A and charging is stopped to avoid overcharging of the battery; when the energy storage SOC drops to the target SOC for the corresponding weather type (20% for sunny days, 80% for cloudy / partly cloudy days, and 100% for rainy days), the discharging current limiting shutdown logic is triggered, the discharging current is gradually reduced to 0A and discharging is stopped to avoid over-discharging of the battery.

[0045] Step 104: During the charge and discharge regulation process, the regulation effect is quantitatively evaluated based on the real-time feedback operation data, and the regulation parameters are adaptively adjusted to form a closed-loop optimization.

[0046] In one specific embodiment, the weather forecast-based intelligent control method for energy storage in the transformer substation further includes: during the charging and discharging process, real-time monitoring of the full-dimensional operating status of the energy storage battery body, the charging and discharging circuit, and the converter; when any fault is detected in the full-dimensional operating status of the energy storage battery body, the charging and discharging circuit, and the converter, the DC / DC module is immediately triggered to soft shutdown, and the charging and discharging circuit is disconnected in stages.

[0047] In one specific embodiment, the weather forecast-based smart regulation method for transformer substation energy storage further includes performing the following steps: SA: During the energy storage charging and discharging control process, a three-phase current independent distribution strategy is adopted. The charging and discharging current of each phase of the energy storage is adjusted in real time according to the three-phase imbalance of the distribution area, so as to provide reactive power compensation and three-phase power balance support for the distribution area.

[0048] SB: When a voltage dip in the transformer area is detected, the output current is supported through a fast response control algorithm.

[0049] Specifically, when executing step SB, the following steps can be performed: SB0: Real-time acquisition of three-phase voltage data on the secondary side of the transformer and performance of feature calculations, identification of voltage sag events and confirmation of triggering.

[0050] Specifically, the instantaneous values ​​of the three-phase voltage on the secondary side of the transformer are collected in real time at a frequency of 50ms / time. The voltage amplitude and phase of each phase are calculated using the dq transformation method. When the voltage amplitude of any phase drops below 90% of the rated voltage and lasts for more than 5ms, it is determined to be a voltage sag event. An anti-jitter mechanism is introduced to confirm the trigger if the conditions are met in three consecutive samplings.

[0051] In one specific implementation, the formula for calculating the three-phase voltage dq transformation is as follows:

[0052] In the formula, , , Here, θ represents the instantaneous value of the three-phase voltage, and θ is the phase angle of the grid voltage output by the phase-locked loop. , The voltage component is in a rotating coordinate system.

[0053] The formula for calculating voltage amplitude is as follows:

[0054] The criteria for temporary descent are: This triggers a temporary landing response, where... This is the rated voltage of the transformer substation. , , This represents the voltage amplitude of each phase.

[0055] SB1: Identify the sag type based on voltage drop depth and phase characteristics, calculate the amplitude, phase, and duration of the supporting current required to raise the voltage to the target value, and determine the reactive power compensation component as needed.

[0056] Specifically, the type of sag is identified based on the three-phase voltage drop depth and phase jump, including single-phase, two-phase, or three-phase asymmetrical sags. The amplitude, phase, and duration of the required supporting current are calculated, with the goal of raising the sag point voltage to more than 95% of the rated value. If the sag is accompanied by reactive power fluctuations, the reactive current component that needs to be compensated is also calculated.

[0057] In one specific implementation, the formula for calculating the voltage drop depth of each phase is as follows:

[0058] The formulas for calculating the required active / reactive support current for each phase are as follows: The required injection current to boost the voltage to 95% of the rated value is determined by the equivalent impedance of the transformer area:

[0059]

[0060] In the formula, , They are respectively The active and reactive power support current components of the phase, Taiwan District Equivalent impedance, for The phase difference between phase voltage and current, , This is the strength coefficient for the support.

[0061] The formula for the synthesis of total support current is as follows:

[0062] SB2: After confirming the sag, switch the energy storage system to voltage support mode, generate a current reference value based on the DC bus voltage, and achieve current tracking through a fast response controller to ensure that the response time meets the requirements.

[0063] Specifically, upon confirmation of a temporary drop, the current charging and discharging task is immediately interrupted, the energy storage system is switched to voltage support mode, voltage-oriented control based on instantaneous power theory is adopted, a current reference value is generated with the DC bus voltage of 750V as a reference, and the fundamental current is tracked without steady-state error through the PR controller, with the response time controlled within 5ms.

[0064] SB3: Based on the sag type, a three-phase current independent distribution strategy is adopted to determine the required support current for each phase, and the converter dynamically outputs the corresponding current and sets the current upper limit.

[0065] Specifically, based on the identified sag type, a three-phase current independent allocation strategy is adopted: if it is a single-phase sag, only the faulty phase is injected with supporting current; if it is a two-phase or three-phase sag, the required current for each phase is calculated separately to ensure that the three-phase voltage tends to be balanced after injection. The DC / DC converter outputs the corresponding current according to the command, and the current amplitude is dynamically adjusted in real time to track the voltage drop depth. The upper limit of the supporting current is set to 50A.

[0066] In one specific implementation, the transfer function of the current inner loop PR controller is as follows:

[0067] In the formula, This is the proportionality coefficient. The resonance coefficient, The cutoff frequency, ω is the fundamental angular frequency.

[0068] The formula for generating the current reference value is as follows:

[0069] DC / DC converter output current command: In the formula, for Instantaneous value of phase grid voltage for Phase current reference value, η is the DC bus voltage, and η is the converter efficiency.

[0070] In one specific implementation, for a single-phase sag, taking phase A as an example, the current distribution is as follows:

[0071] For a two-phase sag, taking phases AB as an example, the current distribution is as follows:

[0072] For a three-phase unbalanced sag, the current distribution is as follows:

[0073] The formula for calculating the expected voltage after injection is as follows: .

[0074] SB4: During the support process, the voltage is continuously monitored. When the voltage recovers to the target value and stabilizes, a smooth exit strategy is adopted to gradually reduce the support current to zero and restore the original charging and discharging mode.

[0075] Specifically, during the support process, the three-phase voltage is continuously monitored. When the voltage recovers to more than 95% of the rated value and remains stable for 100ms, the sag is determined to be over. A soft exit strategy is adopted to gradually reduce the support current to zero with a linear slope. After exiting, the energy storage system automatically returns to the charging and discharging mode before the sag or is rescheduled according to the current SOC and the target SOC.

[0076] In one specific implementation, the exit slope control formula is as follows:

[0077] In the formula, The supporting current at the time of exit. This is the end time of the temporary landing. This refers to the time to exit the slope.

[0078] In one specific implementation, after executing step SB4, the following steps are also performed: support effect evaluation and parameter self-optimization: recording data such as response time, voltage rise effect, and current output accuracy for each sag response; linking with the monitoring and early warning module to generate a support effect evaluation report; if the sag response effect fails to meet expectations for multiple consecutive times, automatically adjusting control parameters (such as PR coefficient and anti-jitter threshold) to achieve algorithm self-optimization.

[0079] Specifically, the evaluation indicators for support effectiveness are determined according to the following formula: The following formula is used for the response time metric: ,Require .

[0080] The voltage rise rate index is expressed by the following formula: ,Require .

[0081] The following formula is used for the current output accuracy specification: .

[0082] In one specific implementation, a full-link hierarchical fault protection and manual / automatic seamless switching control mode are set up to ensure the safety of the entire energy storage control process. A full-link hierarchical energy storage fault protection mechanism is set up: The transformer area operation monitoring module collects the full-dimensional operating status (battery temperature, single cell voltage, insulation status, circuit current, converter operating status, etc.) of the energy storage battery body, charging and discharging circuit, and converter in real time. When any fault, over-temperature, over-voltage / under-voltage, over-current or other abnormal conditions are detected, the control algorithm module immediately triggers the command to control the energy storage DC / DC module to soft shutdown, cut off the energy storage charging and discharging circuit in stages, and send a fault signal to the monitoring and early warning module at the same time to realize full-link protection from the battery body to the circuit.

[0083] The system retains the control mode that allows seamless switching between manual and automatic operation to meet the needs of on-site manual operation: When the intelligent charging and discharging switch is off and the system operation mode is switched to "manual", the charging current reference value can be set by the local monitoring module. The energy storage execution module will achieve constant current charging according to the preset value. In manual mode, the priority execution right of the fault protection mechanism is retained. When a fault is detected, manual operation will be interrupted immediately and the protection mechanism will be triggered to ensure operational safety.

[0084] In this embodiment, 1. This invention innovatively integrates the refined meteorological forecast data of the next day with the trend of photovoltaic output meteorological factors to construct a multi-scenario adaptive energy storage charging and discharging control algorithm, and introduces a forced guarantee mechanism for energy storage power in extreme weather. This achieves precise predictive control of energy storage charging and discharging, solves the drawbacks of traditional passive control, reserves sufficient charging space for photovoltaic power generation on sunny days, and maintains full charge status to ensure emergency supply on rainy days. This significantly improves the local photovoltaic consumption rate in the distribution area (from 60%-70% to over 95%), and is adaptable to distribution areas with different climate characteristics.

[0085] 2. This invention constructs a real-time power matching dynamic adjustment algorithm for charging and discharging current. It adopts a scenario-adaptive adjustment coefficient and linearly adjusts the current according to the real-time magnitude of the power fed back from the grid / power taken from the load, avoiding energy regulation mismatch caused by a fixed current. At the same time, it sets a SOC threshold for graded control and a current upper limit, realizing precise and refined control of energy storage charging and discharging. It effectively reduces the transformer load rate by more than 25% during peak load periods, with significant peak shaving and valley filling effects, and effectively avoids battery overcharging and over-discharging problems.

[0086] 3. This invention designs a full-link layered fault protection mechanism to achieve full-dimensional protection from the battery body, charging and discharging circuit to the converter, and adopts dual protection of DC / DC module soft shutdown and intelligent DC circuit breaker hard protection; at the same time, it sets a control mode that can be seamlessly switched between manual and automatic. In manual mode, the priority execution right of the fault protection mechanism is retained, which not only realizes the full-process safety protection of energy storage charging and discharging process, but also adapts to the needs of on-site manual operation, greatly improving the practicality, reliability and safety of the system.

[0087] 4. This invention establishes a deep collaborative control logic for energy storage and power quality management in distribution areas. It adopts a three-phase current independent allocation strategy to provide precise active / reactive support for three-phase imbalance correction and reactive power compensation in distribution areas. At the same time, it achieves short-term voltage support for power fluctuation management in distribution areas through a millisecond-level fast response control algorithm, comprehensively improving the power quality of distribution areas, reducing the three-phase imbalance to below 5%, increasing the power factor to above 0.95, reducing line losses by more than 18%, and eliminating load outages caused by power fluctuations.

[0088] 5. The system of this invention adopts a two-layer architecture of local control + remote monitoring, which has the dual capabilities of local independent operation and remote collaborative regulation. It is seamlessly integrated with the power quality optimization device of the distribution area. The communication module has built-in general industrial protocol and has an open communication interface. It has strong hardware adaptability and good communication compatibility. It can realize integrated collaborative regulation with the distribution area network and energy mutual assistance between distribution areas. It perfectly adapts to the needs of flexible regulation of distribution area source-grid-load-storage under the new power system. Moreover, it is easy to install and maintain, and has good engineering practicality and large-scale promotion value.

[0089] 6. This invention establishes a dual-layer heterogeneous data acquisition system to achieve high-frequency acquisition and three-level preprocessing of transformer substation operation data, as well as accurate acquisition and supplementation of meteorological data. At the same time, it constructs a self-optimizing closed-loop control system of "prediction-control-execution-feedback-optimization". Combined with the full-parameter visualization, multi-level linkage early warning and historical data traceability functions of the monitoring and early warning module, it improves the automation and intelligence level of transformer substation energy storage operation and maintenance, and significantly reduces the cost and difficulty of manual operation and maintenance.

[0090] 7. The energy storage execution module of this invention achieves high-precision voltage regulation control of the DC750V DC bus, with a voltage regulation accuracy of ≤±1%. It can still ensure the stability of the DC bus voltage when the transformer area is off-grid, providing emergency power supply support for important loads in the transformer area and further improving the reliability and stability of the transformer area power supply.

[0091] In one embodiment, Figure 2 This embodiment provides a detailed flowchart of a weather-forecast-based smart regulation method for transformer substation energy storage, based on an embodiment of the present invention. This embodiment further optimizes and expands upon the aforementioned embodiments. The method is applied to a rural distribution substation with high distributed photovoltaic penetration. This substation is equipped with a 200kW distributed photovoltaic system, a 200kW / 200kWh energy storage system, and a comprehensive power quality optimization device. The substation load is mainly composed of residential electricity consumption and agricultural processing loads, and it faces problems such as difficulties in photovoltaic absorption, transformer overload during peak load periods, poor power supply stability during rainy days, high three-phase imbalance, and power fluctuations causing agricultural processing load outages.

[0092] Operating parameter settings: Charging period: 8:00-14:00 (peak photovoltaic power generation period), Discharging period: 18:00-22:00 (peak load period); Maximum charging / discharging current: 50A; Maximum charging SOC: 90%; Current scenario adaptation adjustment coefficient: k1=1.0, k2=1.0; Voltage sag support time: 2s; DC bus voltage regulation accuracy: ≤±1%.

[0093] Running result: 1. Sunny Day Operation: The next day is sunny. The target SOC of the energy storage is set at 20%. During the charging period, the photovoltaic power generation is high, and the system feeds back electricity. The energy storage starts charging, and the current is dynamically adjusted in real time according to the fed-back electricity. The SOC rises steadily from 20% to 90%, and the local photovoltaic consumption rate reaches 96%. There is no phenomenon of curtailment of photovoltaic power fed back. During the discharge period, the load is at its peak, and the energy storage starts discharging. The SOC drops steadily from 90% to 20%, and the transformer load rate decreases by 28%. The peak shaving and valley filling effect is significant, and the transformer does not experience overload.

[0094] 2. Rainy Day Operation: If the next day is rainy, the target SOC of the energy storage is set to 100%, and the energy storage remains fully charged. When the photovoltaic output is insufficient, the energy storage discharges smoothly to supply power to the load in the distribution area. The emergency supply time is more than 8 hours, ensuring a continuous and stable supply of electricity for agricultural processing loads and residential electricity consumption in the distribution area, without any power outages.

[0095] 3. Cloudy / Partly Cloudy Condition: The next day is cloudy. The target SOC of energy storage is set to 80%. The energy storage charging and discharging current is dynamically adjusted according to the real-time fluctuations of photovoltaic output and load power consumption, perfectly adapting to the instability of photovoltaic output, taking into account both photovoltaic absorption and load power supply needs. The voltage fluctuation rate of the distribution area is controlled within ±2%, and the power supply stability is greatly improved.

[0096] 4. Deep Collaborative Governance of Power Quality: During the energy storage regulation process, precise reactive power compensation and three-phase power balance support are provided to the distribution area. By adopting an independent three-phase current distribution strategy, the three-phase imbalance of the distribution area is reduced from 22% to 4%, the power factor is improved from 0.82 to 0.96, and the line loss is reduced by 18%. When the distribution area experiences power fluctuations, the energy storage provides short-term voltage support through rapid current output within 5ms, eliminating the outage of agricultural processing loads due to power fluctuations and significantly improving the continuity of power supply.

[0097] 5. Full-link fault protection: When an abnormal battery cell voltage is detected, the system immediately triggers a soft shutdown of the DC / DC module, cuts off the charging and discharging circuits in stages, and sends local audible and visual warnings and remote APP message pushes. There was no overcharging, over-discharging, or fault expansion, ensuring the safe operation of the equipment and system.

[0098] 6. Seamless switching between manual and automatic modes: During on-site maintenance, switch to manual mode, customize the charging current to achieve constant current charging. When an overcurrent is detected in the circuit, the fault protection mechanism is triggered first, immediately interrupting manual charging and triggering protection, thus fully ensuring operational safety.

[0099] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0100] like Figure 3 As shown, the following are embodiments of the intelligent control system for transformer area energy storage based on weather forecasting provided in this disclosure. These embodiments belong to the same inventive concept as the intelligent control methods for transformer area energy storage based on weather forecasting described above. For details not described in detail in the embodiments of the intelligent control system for transformer area energy storage based on weather forecasting, please refer to the embodiments of the intelligent control methods for transformer area energy storage based on weather forecasting described above.

[0101] A smart control system for transformer substation energy storage based on weather forecasting includes: The data acquisition unit is used to acquire the next day's weather forecast data and set the target state of charge (SOC) of the energy storage system according to the weather type in the weather forecast data; and to collect the transformer substation operation data in real time, wherein the transformer substation operation data includes at least the bidirectional power flow status of the transformer secondary side and the current SOC of the energy storage. The logic triggering unit is used to preset the energy storage charging and discharging period and construct charging and discharging triggering logic based on the period, power flow direction and SOC. The control unit is used to calculate and adjust the charging and discharging current based on the real-time power feedback or power extraction after charging or discharging is triggered, and to track the SOC change in real time. When the SOC reaches the preset upper limit threshold or drops to the target state of charge SOC, the unit executes shutdown control. In the process of charge and discharge regulation, the regulation effect is quantitatively evaluated based on real-time feedback operation data, and the regulation parameters are adaptively adjusted to form a closed-loop optimization.

[0102] Figure 4 This is a schematic diagram of the hardware structure of an electronic device that implements various embodiments of the present invention.

[0103] The weather forecast-based smart regulation method for transformer area energy storage provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0104] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0105] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0106] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0107] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0108] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0109] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0110] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0111] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0112] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0113] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0114] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0115] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0116] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0117] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0118] The storage medium provided in this application stores a program product capable of implementing a smart control method for energy storage in transformer substations based on weather forecasts.

[0119] The weather forecast-based intelligent control method for energy storage in transformer substations includes: acquiring the next day's weather forecast data and setting the target state of charge (SOC) of the energy storage system according to the weather type in the forecast data; collecting real-time operating data of the transformer substation, including at least the bidirectional power flow status of the transformer secondary side and the current SOC of the energy storage; pre-setting energy storage charging and discharging periods and constructing charging and discharging trigger logic based on the period, power flow direction, and SOC; after triggering charging or discharging, calculating and adjusting the charging and discharging current according to the real-time reverse power supply or power extraction, while simultaneously tracking SOC changes in real time, and executing shutdown control when the SOC reaches the preset upper limit threshold or drops to the target SOC; during the charging and discharging control process, quantitatively evaluating the control effect based on real-time feedback operating data and adaptively adjusting the control parameters to form a closed-loop optimization.

[0120] In some possible implementations, the subject matter of this disclosure, namely, "Smart Regulation Method and System for Substation Energy Storage Based on Weather Forecast," can be implemented as a program product comprising program code. When the program product is run on a terminal device, the program code causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0121] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0122] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart regulation method for transformer substation energy storage based on weather forecasting, characterized in that, include: Obtain the weather forecast data for the next day, and set the target state of charge (SOC) of the energy storage system based on the weather type in the weather forecast data; Real-time acquisition of transformer substation operation data, wherein the transformer substation operation data includes at least the bidirectional power flow status of the transformer secondary side and the current SOC of the energy storage; Preset the energy storage charging and discharging time periods, and construct charging and discharging trigger logic based on time period, power flow direction and SOC; After triggering charging or discharging, the charging and discharging current is calculated and adjusted according to the real-time reverse power supply or power extraction power, while the SOC change is tracked in real time. When the SOC reaches the preset upper limit threshold or drops to the target state of charge SOC, shutdown control is executed. During the charge and discharge regulation process, the regulation effect is quantitatively evaluated based on real-time feedback operating data, and the regulation parameters are adaptively adjusted to form a closed-loop optimization.

2. The intelligent control method for transformer substation energy storage based on weather forecasting according to claim 1, characterized in that, The system presets energy storage charging and discharging periods and constructs charging and discharging triggering logic based on time period, power flow direction, and SOC, including: Charging is triggered when the charging period is in progress, power backflow to the grid is detected, and the current SOC has not reached the upper limit threshold. Discharge is triggered when power is drawn from the grid during a discharge period and the current state of charge (SOC) is higher than the target SOC.

3. The intelligent control method for transformer substation energy storage based on weather forecasting according to claim 1, characterized in that, After triggering charging or discharging, the charging and discharging current is calculated and adjusted based on the real-time reverse power supply or power extraction power, including: calculating the charging and discharging current according to the following formula: In the formula, Indicates the charging current. This indicates the scenario-based adjustment coefficient for the reverse power transmission. Indicates the rated voltage of the DC bus. This represents the absolute value of the active power fed back from the secondary side of the transformer. Indicates the discharge current. This represents the scenario-based power consumption adjustment coefficient. This refers to the active power drawn from the secondary load of the transformer.

4. The intelligent control method for transformer substation energy storage based on weather forecasting according to claim 1, characterized in that, Real-time tracking of SOC changes; when SOC reaches a preset upper limit threshold or drops to the target SOC, shutdown control is executed, including: When the SOC reaches the preset upper limit threshold, the charging current limiting shutdown logic is triggered, and the charging current is gradually reduced to 0 and charging stops. When the State of Charge (SOC) drops to the target SOC, the discharge current limiting shutdown logic is triggered, and the discharge current gradually decreases to 0 and the discharge stops.

5. The intelligent control method for transformer substation energy storage based on weather forecasting according to claim 1, characterized in that, The intelligent control method for energy storage in transformer substations based on weather forecasting also includes: During the charging and discharging process, the operating status of the energy storage battery body, charging and discharging circuit and converter is monitored in real time. When any fault is detected in the energy storage battery body, charging and discharging circuit, and inverter across all dimensions of operation, the DC / DC module is immediately triggered to soft shutdown, and the charging and discharging circuit is disconnected in stages.

6. The intelligent control method for transformer substation energy storage based on weather forecasting according to claim 1, characterized in that, The intelligent control method for energy storage in transformer substations based on weather forecasting also includes: During the energy storage charging and discharging control process, a three-phase current independent distribution strategy is adopted. The charging and discharging current of each phase of the energy storage is adjusted in real time according to the three-phase imbalance of the transformer area, so as to provide reactive power compensation and three-phase power balance support for the transformer area. When a voltage dip in the transformer area is detected, the output current is supported through a fast response control algorithm.

7. The intelligent control method for transformer substation energy storage based on weather forecasting according to claim 6, characterized in that, When a voltage dip in the transformer area is detected, a fast response control algorithm outputs current support, including: Real-time acquisition of three-phase voltage data on the secondary side of the transformer and performance of feature calculations are performed to identify voltage sag events and confirm their triggering. Identify the sag type based on voltage drop depth and phase characteristics, calculate the amplitude, phase, and duration of the supporting current required to raise the voltage to the target value, and determine the reactive power compensation component as needed. After confirming the voltage dip, the energy storage system is switched to voltage support mode. A current reference value is generated based on the DC bus voltage, and current tracking is achieved through a fast response controller to ensure that the response time meets the requirements. Based on the sag type, a three-phase current independent allocation strategy is adopted to determine the required support current for each phase, and the converter dynamically outputs the corresponding current and sets the current upper limit. During the support process, the voltage is continuously monitored. Once the voltage recovers to the target value and stabilizes, a smooth exit strategy is adopted to gradually reduce the support current to zero and restore the original charging and discharging mode.

8. A smart control system for transformer substation energy storage based on weather forecasting, characterized in that, include: The data acquisition unit is used to acquire the weather forecast data for the next day and set the target state of charge (SOC) of the energy storage system according to the weather type in the weather forecast data. And real-time collection of transformer substation operation data, wherein the transformer substation operation data includes at least the bidirectional power flow status of the transformer secondary side and the current SOC of the energy storage; The logic triggering unit is used to preset the energy storage charging and discharging period and construct charging and discharging triggering logic based on the period, power flow direction and SOC. The control unit is used to calculate and adjust the charging and discharging current based on the real-time power feedback or power extraction after charging or discharging is triggered, and to track the SOC change in real time. When the SOC reaches the preset upper limit threshold or drops to the target state of charge SOC, the unit executes shutdown control. In the process of charge and discharge regulation, the regulation effect is quantitatively evaluated based on real-time feedback operation data, and the regulation parameters are adaptively adjusted to form a closed-loop optimization.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the intelligent control method for transformer area energy storage based on weather forecast 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 intelligent control method for transformer area energy storage based on weather forecast as described in any one of claims 1 to 7.

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