Multi-mode coordinated control method for energy storage device in transformer area

Abstract

The application discloses a multi-mode cooperative control method for a transformer area energy storage device, real-time operation data of a transformer area is collected to identify the operation mode of the current transformer area in real time. Then, a multi-objective optimization function is created, and the minimum network loss, the minimum voltage deviation, the lowest power consumption cost and the optimal energy storage life loss of the transformer area are taken as the optimization targets of the multi-objective optimization function, and then the weight coefficients of the multi-objective optimization function are generated. Then, the fuzzy decision algorithm is used based on the obtained weight coefficients to dynamically select the combination of the leading control mode and the auxiliary control mode, and the multi-mode cooperative control instruction is generated based on the combination of the leading control mode and the auxiliary control mode, and the charging and discharging power of the energy storage device and the reactive power output of the converter are distributed. The control of the energy storage device is more adaptive.

Description

Technical Field

[0001] This application relates to the field of energy storage equipment control technology, and in particular to a multi-mode collaborative control method for energy storage equipment in distribution areas. Background Technology

[0002] In recent years, the demand for power distribution in power grids has been continuously increasing. To ensure the power supply quality of transformer substations, energy storage devices are connected to the power supply lines of these substations to improve the overall power quality. Specifically, by setting target voltage parameters, the energy storage devices can convert these parameters into their operating power, thereby controlling the charging and discharging state of the devices and adjusting the voltage at the energy storage connection points on the power supply lines, thus improving the overall power quality of the substation.

[0003] Currently, the control methods for energy storage devices or units in the distribution area are relatively simple, mostly using capacity-based proportional adjustment, and can only adapt to a single application scenario. When multiple scenarios coexist, such as peak shaving and voltage support, it is impossible to balance the conflicts when multiple scenarios coexist. Summary of the Invention

[0004] The summary section of this application is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0005] In order to overcome the shortcomings of the prior art, this application provides a multi-mode collaborative control method for energy storage equipment in distribution areas.

[0006] To achieve the above objectives, this application adopts the following technical solution, including:

[0007] Real-time collection of transformer area operation data, including load power, photovoltaic output, grid voltage, electricity price signal and energy storage SOC status;

[0008] Based on real-time collection of transformer area operation data, the current operation mode requirements of the transformer area are dynamically identified. The operation modes include: peak shaving and valley filling mode, voltage support mode, new energy consumption mode, and emergency backup mode.

[0009] A multi-objective optimization function is established, with the optimization objectives being the minimum network loss in the transformer area, the minimum voltage deviation, the minimum electricity cost, and the optimal energy storage lifespan loss, and weighting coefficients are generated accordingly.

[0010] Based on weighting coefficients, a fuzzy decision-making algorithm is used to dynamically select the combination of dominant and auxiliary control modes.

[0011] Multi-mode collaborative control commands are generated based on a combination of dominant and auxiliary control modes to allocate the charging and discharging power of energy storage devices and the reactive power output of converters.

[0012] Furthermore, based on real-time collection of transformer area operation data, the current operation mode requirements of the transformer area are dynamically identified. These operation modes include: peak shaving and valley filling mode, voltage support mode, renewable energy consumption mode, and emergency backup mode.

[0013] When the load power in the transformer area operation data continuously exceeds the threshold and is during the peak power period, the peak shaving and valley filling mode is activated.

[0014] When the voltage at a monitoring point in the transformer area exceeds the limit, the voltage support mode is activated.

[0015] When the photovoltaic output fluctuation rate in the transformer area operation data exceeds the set value and the local consumption is insufficient, the new energy consumption mode is activated.

[0016] When a power grid fault signal is detected or a backup capacity requirement is needed, the emergency backup mode is activated.

[0017] Furthermore, the multi-objective optimization function is established with the optimization objectives of minimizing grid loss, minimizing voltage deviation, minimizing electricity cost, and maximizing energy storage lifespan loss, and weighting coefficients are generated, including:

[0018] Create the objective optimization function, as shown in Formula 1;

[0019] Formula 1;

[0020] in, , , , These are dynamic weighting coefficients; This represents the total active power loss of the transformer substation. This represents the average voltage deviation of the transformer substation area. For electricity costs; This is the energy storage lifetime loss factor.

[0021] Furthermore, the fuzzy decision algorithm includes:

[0022] The input variables of the fuzzy decision algorithm include voltage limit exceedance severity, load peak-valley difference rate, photovoltaic volatility, SOC balance degree, and electricity price difference.

[0023] The output of the fuzzy decision algorithm is the activation priority of each mode; and a mode switching hysteresis interval is set to avoid frequent switching.

[0024] Furthermore, the generation of multi-mode cooperative control commands based on the combination of the dominant control mode and the auxiliary control mode includes:

[0025] When the dominant mode is peak shaving and valley filling, the charging and discharging power is constrained with the goal of load smoothing.

[0026] When the dominant mode is voltage support mode, the reactive power output of the energy storage converter is adjusted first, and the SOC is limited to the safe range.

[0027] Under the new energy consumption model, the energy storage response rate is dynamically adjusted with photovoltaic volatility smoothing as the control objective.

[0028] Furthermore, the multi-mode collaborative control method for the energy storage equipment in the distribution area also includes:

[0029] When multiple modes of demand are activated simultaneously, arbitration control commands are issued according to the priority of voltage safety over equipment protection, and equipment protection over economy.

[0030] Model predictive control (MPC) is used to continuously optimize the control sequence for the next 15 minutes.

[0031] Furthermore, the multi-mode collaborative control method for the energy storage equipment in the distribution area also includes:

[0032] The upper-level controller coordinates the mode allocation of each energy storage unit within the distribution area;

[0033] The lower-level controller uses a consensus algorithm to achieve SOC balancing and precise power allocation.

[0034] Furthermore, the lower-level controller implements SOC equalization and precise power allocation based on a consensus algorithm, including:

[0035] Create the formula for calculating the output power of each energy storage unit, as shown in Formula 2;

[0036] Formula 2;

[0037] in, For the serial number The output power of the energy storage unit; For the serial number The equivalent impedance from the energy storage unit to the heavy-load area of ​​the transformer substation; For the serial number The rated capacity of the energy storage unit; For the serial number The rated capacity of the energy storage unit; For the serial number The state of charge of the energy storage unit; For the serial number The state of charge of the energy storage unit; This represents the overall power demand.

[0038] Furthermore, the multi-mode collaborative control method for the energy storage equipment in the distribution area also includes:

[0039] Record the number of times the historical mode was switched and the target achievement rate;

[0040] If the optimization objective is not improved after N consecutive switching attempts, the fuzzy decision rule parameters are automatically adjusted, where N is a positive integer.

[0041] Furthermore, it interacts with the regional energy management system (EMS) based on the OPC-UA protocol;

[0042] The control cycle is set to real-time response at the second level, and the data reporting cycle is set to 5 minutes.

[0043] The advantages of this application are: it provides a multi-mode collaborative control method for energy storage devices in a distribution area, which identifies the current operating mode of the area in real time by collecting the area's operating data. Next, a multi-objective optimization function is created, with the optimization objectives being minimum grid loss, minimum voltage deviation, minimum electricity cost, and optimal energy storage lifespan loss. Weight coefficients for the multi-objective optimization function are then generated. Based on these weight coefficients, a fuzzy decision algorithm is used to dynamically select the combination of dominant and auxiliary control modes, and multi-mode collaborative control commands are generated based on the combination of these modes to allocate the charging and discharging power of the energy storage device and the reactive power output of the converter. This makes the control of the energy storage device more adaptive. Attached Figure Description

[0044] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application.

[0045] In the attached diagram:

[0046] Figure 1 This is a flowchart illustrating the process of this application. Detailed Implementation

[0047] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0048] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0049] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0050] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0051] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0052] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0053] like Figure 1 As shown, in one embodiment of this application, the multi-mode collaborative control method for the energy storage equipment in the distribution area includes the following steps S100 to S500:

[0054] The S100 collects real-time operating data of the distribution area, including load power, photovoltaic output, grid voltage, electricity price signal, and energy storage SOC status.

[0055] Specifically, photovoltaic output refers to the ratio of the actual electrical energy output of a photovoltaic system under specific lighting conditions to its theoretical maximum output electrical energy. It is an indicator for measuring the power generation capacity of a photovoltaic system.

[0056] State of Charge (SOC) refers to the current remaining charge level of a battery, usually expressed as a percentage, reflecting the battery's charging level.

[0057] S200 dynamically identifies the current operating mode requirements of the transformer area based on real-time collection of transformer area operation data. The operating modes include: peak shaving and valley filling mode, voltage support mode, new energy consumption mode, and emergency backup mode.

[0058] S300 establishes a multi-objective optimization function with the optimization objectives of minimizing network loss in the distribution area, minimizing voltage deviation, minimizing electricity cost, and optimizing energy storage lifespan loss, and generates weight coefficients.

[0059] The S400 uses a fuzzy decision-making algorithm based on weighting coefficients to dynamically select the combination of dominant and auxiliary control modes.

[0060] The S500 generates multi-mode collaborative control commands based on a combination of dominant and auxiliary control modes, allocating the charging and discharging power of energy storage devices and the reactive power output of converters.

[0061] In this embodiment, real-time acquisition of transformer area operation data allows for real-time identification of the current operating mode of the transformer area. Next, a multi-objective optimization function is created, with the optimization objectives being minimum transformer area network loss, minimum voltage deviation, minimum electricity cost, and optimal energy storage lifespan loss. Weight coefficients for the multi-objective optimization function are then generated. Based on these weight coefficients, a fuzzy decision algorithm is used to dynamically select a combination of dominant and auxiliary control modes. Multi-mode coordinated control commands are then generated based on the combination of dominant and auxiliary control modes to allocate the charging and discharging power of the energy storage device and the reactive power output of the converter. This makes the control of the energy storage device more adaptive.

[0062] In one embodiment of this application, the step of dynamically identifying the current operating mode requirements of the transformer substation based on real-time collected substation operation data, wherein the operating modes include: peak shaving and valley filling mode, voltage support mode, renewable energy consumption mode, and emergency backup mode, includes the following steps S201 to S204:

[0063] S201, when the load power in the transformer area operation data continuously exceeds the threshold and is in the peak power period, the peak shaving and valley filling mode is activated.

[0064] S202, when the voltage of the monitoring point in the transformer area is detected to be out of limit, the voltage support mode is activated.

[0065] S203: When the photovoltaic output fluctuation rate in the transformer area operation data exceeds the set value and the local consumption is insufficient, the new energy consumption mode is activated.

[0066] S204, when a power grid fault signal is detected or a backup capacity requirement is needed, the emergency backup mode is activated.

[0067] In this embodiment, the peak shaving and valley filling mode can refer to a load of 190kW at 6 pm, with a threshold of 175kW, where the load of 190kW is greater than the threshold of 175kW.

[0068] Voltage support mode can refer to the voltage at the end of the transformer area dropping to 210V, while the limit is 214V. At this time, the voltage at the end of the transformer area is less than the limit of 214V.

[0069] The renewable energy consumption mode can refer to a situation where the photovoltaic output fluctuates by 35kW within 1 minute, while the set value is 30kW, at which point the renewable energy consumption mode is met.

[0070] Emergency backup mode refers to the mode of operation when the power grid fails, such as when there is no power supply.

[0071] In one embodiment of this application, the establishment of a multi-objective optimization function, with the optimization objectives of minimizing grid loss, minimizing voltage deviation, minimizing electricity cost, and maximizing energy storage lifespan loss, and the generation of weighting coefficients, includes the following S301:

[0072] S301, Create the objective optimization function, as shown in Formula 1;

[0073] Formula 1;

[0074] in, , , , These are dynamic weighting coefficients; This represents the total active power loss of the transformer substation. This represents the average voltage deviation of the transformer substation area. For electricity costs; This is the energy storage lifetime loss factor.

[0075] Specifically,

[0076] in, The total active power loss in the transformer area; Let be the effective value of the current in the k-th branch; is the resistance value of the kth branch; m is the total number of branches in the transformer area.

[0077]

[0078] in, The effective voltage value (unit: V) at the i-th monitoring point is obtained through a voltage sensor; The rated voltage of the transformer substation (usually 220V or 380V); This represents the number of voltage monitoring points.

[0079]

[0080] in, The active power absorbed by the distribution area from the power grid during time period t (unit: kW), with positive values ​​indicating electricity purchases; The real-time electricity price for time period t (unit: yuan / kWh); The time interval (unit: h) is typically 15 minutes (0.25 h) or 1 hour; To optimize the total number of time periods within the cycle.

[0081]

[0082] in, Battery charging and discharging current (unit: A), with discharging being positive and charging being negative; The change in SOC per unit time (unit: %), such as the rate of change per minute; , This is the aging factor, which is determined by the battery type (e.g., for lithium iron phosphate batteries, k1=0.002, k2=0.05).

[0083] In this embodiment, , , , The relative importance of each optimization objective is used to reflect the relative importance of the various objectives, and then adjustments are made based on the real-time status of the transformer area. This can be calculated using the analytic hierarchy process (AHP).

[0084] In one embodiment of this application, the fuzzy decision algorithm includes the following steps S401 to S402:

[0085] S401, the input variables of the fuzzy decision algorithm include voltage over-limit severity, load peak-valley difference rate, photovoltaic volatility, SOC balance degree, and electricity price difference.

[0086] S402, the output of the fuzzy decision algorithm is the activation priority of each mode; and a mode switching hysteresis interval is set to avoid frequent switching.

[0087] In this embodiment, the inputs for fuzzy decision-making are voltage over-limit severity = 0.8, load peak-valley difference rate = 0.6, and photovoltaic volatility = 0.7.

[0088] The fuzzy rules for fuzzy decision-making are as follows: if the voltage exceedance is greater than 0.6 and the peak-to-valley difference is greater than 0.5, then the peak shaving weight is 0.3 and the voltage support weight is 0.7.

[0089] The hysteresis range is when the voltage recovers to 216V before exiting the voltage support mode (to avoid oscillation at the 215V critical point).

[0090] In one embodiment of this application, the generation of multi-mode cooperative control commands based on a combination of dominant control mode and auxiliary control mode includes steps S501 to S503:

[0091] S501, when the dominant mode is peak shaving and valley filling mode, the charging and discharging power is constrained with the goal of load smoothing.

[0092] Specifically, when the dominant mode is peak shaving and valley filling, the charging and discharging power is constrained within ±50kW, and the load is smoothed down to 160kW.

[0093] S502, when the dominant mode is voltage support mode, prioritizes adjusting the reactive power output of the energy storage converter and limits the SOC to the safe range.

[0094] Specifically, when the dominant mode is voltage-supported mode, the SOC is limited to 30%-80%, and reactive power is output first (maximum 50kVar).

[0095] S503, under the new energy consumption mode, uses the smoothing of photovoltaic volatility as the control objective and dynamically adjusts the energy storage response rate.

[0096] Specifically, under the new energy consumption model, a moving average filter is used to reduce the photovoltaic volatility from 30% to 15%.

[0097] In one embodiment of this application, the multi-mode collaborative control method for the energy storage equipment in the distribution area further includes:

[0098] When multiple modes of demand are activated simultaneously, arbitration control commands are issued according to the priority of voltage safety over equipment protection, and equipment protection over economy.

[0099] Model predictive control (MPC) is used to continuously optimize the control sequence for the next 15 minutes.

[0100] In this embodiment, when the voltage support mode (requiring energy storage discharge) conflicts with the peak shaving mode (requiring charging), the voltage is prioritized, that is, the energy storage device is instructed to discharge 20kW to boost the voltage; MPC rolling optimization, that is, predicting the load drop in the next 15 minutes, delays charging to the next cycle.

[0101] In one embodiment of this application, the multi-mode collaborative control method for the energy storage equipment in the distribution area further includes:

[0102] The upper-level controller coordinates the mode allocation of each energy storage unit within the distribution area;

[0103] The lower-level controller uses a consensus algorithm to achieve SOC balancing and precise power allocation.

[0104] In this embodiment, the upper layer: the EMS of the distribution area determines that the dominant mode is renewable energy consumption and allocates total power commands. (Charge).

[0105] Lower layer: Three energy storage units (capacity 100 / 150 / 250kWh, SOC=70% / 50% / 60%), allocated through a consensus algorithm. , , .

[0106] In one embodiment of this application, the lower-level controller implements SOC equalization and precise power allocation based on a consensus algorithm, including:

[0107] Create the formula for calculating the output power of each energy storage unit, as shown in Formula 2;

[0108] Formula 2;

[0109] in, For the serial number The output power of the energy storage unit; For the serial number The equivalent impedance from the energy storage unit to the heavy-load area of ​​the transformer substation; For the serial number The rated capacity of the energy storage unit; For the serial number The rated capacity of the energy storage unit; For the serial number The state of charge of the energy storage unit; For the serial number The state of charge of the energy storage unit; This represents the overall power demand.

[0110] Specifically, This refers to the serial number. The remaining adjustable space of the energy storage unit.

[0111] When the energy storage unit needs to be charged: The larger the size, the more remaining space there is, and thus more charging power can be allocated.

[0112] When the energy storage unit needs to discharge: it needs to be replaced with (high (Priority discharge).

[0113]

[0114] in, Let be the equivalent resistance from energy storage node i to the target region (T); This is the equivalent reactance.

[0115] The smaller the value, the closer the electrical distance, the lower the power transmission loss, and the higher the power distribution.

[0116] In Formula 2, the denominator is the sum of the "weighted adjustable capacity" of all energy storage units, used to convert the numerator into a proportionality coefficient. This improves overall fairness and avoids localized overload.

[0117] In this embodiment, compared to the traditional allocation based on capacity ratio, this embodiment reduces network losses and extends the service life of energy storage units through the coordinated optimization of capacity, SOC, and electrical distance.

[0118] In one embodiment of this application, the multi-mode collaborative control method for the energy storage equipment in the distribution area further includes:

[0119] Record the number of times the historical mode was switched and the target achievement rate;

[0120] If the optimization objective is not improved after N consecutive switching attempts, the fuzzy decision rule parameters are automatically adjusted, where N is a positive integer.

[0121] In this embodiment, if three consecutive mode switches (from renewable energy consumption to peak shaving) do not reduce grid losses (grid loss of 3.2kW before switching and 3.5kW after switching), the "photovoltaic volatility threshold" in the fuzzy rule will be increased from 0.6 to 0.7 to reduce false switching.

[0122] In one embodiment of this application, the multi-mode collaborative control method for the energy storage equipment in the distribution area further includes:

[0123] Interacting with the regional energy management system (EMS) based on the OPC-UA protocol;

[0124] The control cycle is set to real-time response at the second level, and the data reporting cycle is set to 5 minutes.

[0125] Specifically, the OPC-UA protocol refers to a communication protocol used in industrial automation, designed to enable data exchange and interoperability between different devices and platforms. Developed by the OPC Foundation, as the successor to OPC Classic, OPC UA not only supports cross-platform operation but also provides enhanced security and more powerful data processing capabilities.

[0126] In this embodiment, the electricity price signal is obtained from the EMS via OPC UA, and the MQTT sends P / Q commands to the energy storage converter. The SOC, network loss, and voltage qualification rate are uploaded to the cloud platform every 5 minutes.

[0127] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A multi-mode collaborative control method for energy storage equipment in a distribution area, characterized in that: The multi-mode cooperative control method of the transformer area energy storage device comprises the following steps: Real-time acquisition of transformer area operation data, including load power, photovoltaic output, grid voltage, electricity price signal and energy storage SOC state; Based on the real-time acquisition of transformer area operation data, the current operation mode demand of the transformer area is dynamically identified, and the operation mode includes: peak load shifting mode, voltage support mode, new energy consumption mode and emergency standby mode; A multi-objective optimization function is established, taking the minimum transformer area network loss, the minimum voltage deviation, the lowest electricity cost and the optimal energy storage life loss as the optimization objectives, and weight coefficients are generated; The multi-objective optimization function is established, taking the minimum transformer area network loss, the minimum voltage deviation, the lowest electricity cost and the optimal energy storage life loss as the optimization objectives, and weight coefficients are generated, which comprises the following steps: The target optimization function is created, as shown in formula 1; Formula 1 ; wherein, , , , is a dynamic weight coefficient; is the total active power loss of the transformer area; is the average voltage deviation of the transformer area; E is the electricity cost; and D is the energy storage life loss factor. Based on the weight coefficients, a fuzzy decision algorithm is used to dynamically select the dominant control mode and the auxiliary control mode combination; The fuzzy decision algorithm comprises the following steps: The input variables of the fuzzy decision algorithm include voltage overrun severity, load peak-valley difference rate, photovoltaic fluctuation rate, SOC balance degree and electricity price difference; The output of the fuzzy decision algorithm is the activation priority of each mode; Based on the dominant control mode and the auxiliary control mode combination, a multi-mode cooperative control instruction is generated, and the charging and discharging power of the energy storage device and the reactive power output of the converter are allocated; The multi-mode cooperative control instruction is generated based on the dominant control mode and the auxiliary control mode combination, which comprises the following steps: When the dominant mode is the peak load shifting mode, the charging and discharging power is constrained by the target of load suppression; When the dominant mode is the voltage support mode, the reactive power output of the energy storage converter is preferentially adjusted, and the SOC is limited in the safe interval; In the new energy consumption mode, the photovoltaic fluctuation rate is smoothed as the control target, and the energy storage response rate is dynamically adjusted.

2. The multi-mode collaborative control method for the transformer energy storage device according to claim 1, characterized in that: Based on the real-time acquisition of transformer area operation data, the current operation mode demand of the transformer area is dynamically identified, and the operation mode includes: peak load shifting mode, voltage support mode, new energy consumption mode and emergency standby mode, which comprises the following steps: When it is detected that the load power in the transformer area operation data continuously exceeds the threshold value and is in the peak electricity period, the peak load shifting mode is activated; When it is detected that the voltage of the monitoring point of the transformer area is out of limit, the voltage support mode is activated; When it is detected that the photovoltaic output fluctuation rate in the transformer area operation data exceeds the set value and the local consumption is insufficient, the new energy consumption mode is activated; When the grid fault signal trigger or standby capacity demand is detected, the emergency standby mode is activated.

3. The multi-mode collaborative control method for the transformer energy storage device according to claim 1, characterized in that: The multi-mode cooperative control method of the transformer area energy storage device further comprises the following steps: When the multi-mode demand is activated at the same time, the voltage safety is given priority to the device protection, and the device protection is given priority to the economy in the priority arbitration control instruction; The model predictive control (MPC) is used to rollingly optimize the control sequence of the future 15 minutes.

4. The multi-mode collaborative control method for a transformer energy storage device according to claim 1, characterized in that: The multi-mode cooperative control method of the transformer area energy storage device further comprises the following steps: The upper controller coordinates the mode distribution of each energy storage unit in the transformer area; The lower controller realizes the SOC balance and power accurate distribution based on the consensus algorithm.

5. The multi-mode collaborative control method of the transformer area energy storage device according to claim 4, characterized in that: The lower controller realizes the SOC balance and power accurate distribution based on the consensus algorithm, which comprises the following steps: The output power calculation formula of each energy storage unit is created, as shown in formula 2; Formula 2; wherein, is the output power of the energy storage unit with serial number is the equivalent impedance of the energy storage unit with serial number to the heavy load area of the transformer area; is the rated capacity of the energy storage unit with serial number is the rated capacity of the energy storage unit with serial number is the state of charge of the energy storage unit with serial number is the state of charge of the energy storage unit with serial number is the total power demand.​​​​​ 6. The multi-mode collaborative control method for a transformer energy storage device according to claim 1, characterized in that: The multi-mode cooperative control method of the transformer area energy storage device further includes: Record the number of historical mode switching and the target achievement rate; When the switching does not improve the optimization target for N times in succession, the fuzzy decision rule parameter is automatically adjusted, wherein N is a positive integer.

7. The multi-mode collaborative control method for a transformer energy storage device according to claim 1, characterized in that: The multi-mode cooperative control method of the transformer area energy storage device further includes: Interact with the transformer area energy management system based on the OPC-UA protocol; The control period is a real-time response of a second level, and the data reporting period is 5 minutes.

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