Multi-mode cooperative control method for transformer area energy storage equipment

Abstract

The invention discloses a multi-mode cooperative control method for transformer area energy storage equipment, and the method comprises the steps: collecting the operation data of a transformer area in real time, so as to recognize the operation mode of the current transformer area in real time. Thirdly, a multi-objective optimization function is created, the minimum transformer area network loss, the minimum voltage deviation, the minimum power consumption cost and the optimal energy storage life loss serve as optimization objectives of the multi-objective optimization function, and then a weight coefficient of the multi-objective optimization function is generated; and then, dynamically selecting a combination of a dominant control mode and an auxiliary control mode by using a fuzzy decision algorithm based on the obtained weight coefficient, generating a multi-mode cooperative control instruction based on the combination of the dominant control mode and the auxiliary control mode, and distributing the charging and discharging power of the energy storage equipment and the reactive output quantity of the converter. Therefore, the control of the energy storage equipment is more adaptive.

Description

Technical Field

[0001] The present application relates to the field of energy storage device control technology, and in particular to a multi-mode collaborative control method for substation energy storage devices. Background Art

[0002] In recent years, the demand for power distribution on the power grid has continued to increase. To ensure the power supply quality of substation transformers, energy storage devices are being connected to the power supply lines of substation transformers to improve the overall power quality of the substation transformers. Specifically, energy storage devices can control the charge and discharge state of the energy storage devices by setting target voltage parameters and converting them into operating power. This adjusts the voltage at the energy storage device's access point on the power supply line, thereby improving the overall power quality of the substation.

[0003] Currently, the control method for energy storage devices or units in substations is 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 content of this application is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this application is not intended to identify key features or essential features of the technical solution for which protection is sought, nor is it intended to limit the scope of the technical solution for which protection is sought.

[0005] In order to overcome the deficiencies of the prior art, the present application provides a multi-mode collaborative control method for substation energy storage equipment.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions: including: Real-time collection of substation operation data, including load power, photovoltaic output, grid voltage, electricity price signals and energy storage SOC status; Based on real-time collection of substation operation data, dynamically identify the current operation mode requirements of the substation, including peak shaving and valley filling mode, voltage support mode, new energy consumption mode and emergency standby mode; Establish a multi-objective optimization function, with the optimization objectives of minimizing grid loss, minimizing voltage deviation, minimizing electricity cost, and optimizing energy storage life loss, and generate weight coefficients; Based on the weight coefficient, the fuzzy decision algorithm is used to dynamically select the combination of the dominant control mode and the auxiliary control mode; Based on the combination of the dominant control mode and the auxiliary control mode, multi-mode collaborative control instructions are generated to allocate the charging and discharging power of the energy storage equipment and the reactive output of the converter.

[0007] Furthermore, based on the real-time collection of substation operation data, the current operation mode requirements of the substation are dynamically identified. The operation modes include: peak shaving and valley filling mode, voltage support mode, new energy consumption mode and emergency standby mode, including: When it is detected that the load power in the substation operation data exceeds the threshold continuously and is in the peak power period, the peak shaving and valley filling mode is activated; When the voltage at the monitoring point in the substation is detected to be out of limit, the voltage support mode is activated; When it is detected that the PV output fluctuation rate in the substation operation data exceeds the set value and the local absorption is insufficient, the new energy absorption mode is activated; When a grid fault signal trigger or backup capacity demand is detected, the emergency backup mode is activated.

[0008] Furthermore, the multi-objective optimization function is established, with the optimization objectives of minimizing the grid loss, minimizing the voltage deviation, minimizing the electricity cost, and optimizing the energy storage life loss, and generating weight coefficients, including: Create the target optimization function as shown in Formula 1; Formula 1; in, 、 、 、 is the dynamic weight coefficient; is the total active power loss in the substation area; is the mean voltage deviation of the substation area; For electricity costs; is the energy storage life loss factor.

[0009] Furthermore, the fuzzy decision algorithm includes: The input variables of the fuzzy decision algorithm include voltage over-limit severity, load peak-to-valley difference rate, photovoltaic fluctuation rate, SOC balance and electricity price difference; 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.

[0010] Furthermore, the generation of multi-mode collaborative control instructions based on the combination of the dominant control mode and the auxiliary control mode includes: When the dominant mode is peak shaving and valley filling, the charging and discharging power is constrained with the goal of load balancing; When the dominant mode is the voltage support mode, the reactive output of the energy storage converter is adjusted first and the SOC is limited to a safe range; Under the new energy consumption mode, the smoothing of photovoltaic fluctuations is used as the control target, and the energy storage response rate is dynamically adjusted.

[0011] Furthermore, the multi-mode coordinated control method for substation energy storage equipment further includes: When multiple mode demands are activated simultaneously, the control instructions are arbitrated based on the priority of voltage safety over equipment protection, and equipment protection over economy; Model predictive control (MPC) is used to optimize the control sequence for the next 15 minutes.

[0012] Furthermore, the multi-mode coordinated control method for substation energy storage equipment further includes: The upper controller coordinates the mode allocation of each energy storage unit in the substation; The lower-level controller implements SOC balancing and precise power distribution based on the consistency algorithm.

[0013] Furthermore, the lower-layer controller implements SOC balancing and precise power distribution based on a consistency algorithm, including:

[0014] Create a calculation formula for the output power of each energy storage unit, as shown in Formula 2; Formula 2; in, For serial number The output power of the energy storage unit; For serial number The equivalent impedance from the energy storage unit to the heavy-load area of ​​the substation; For serial number Rated capacity of energy storage unit; For serial number The rated capacity of the energy storage unit; For serial number The state of charge of the energy storage unit; For serial number The state of charge of the energy storage unit; For the overall power demand.

[0015] Furthermore, the multi-mode coordinated control method for substation energy storage equipment further includes: Record the number of historical mode switches and target achievement rate;

[0016] When the optimization objective is not improved after N consecutive switching, the fuzzy decision rule parameters are automatically adjusted, where N is a positive integer.

[0017] Furthermore, it interacts with the district energy management system (EMS) based on the OPC-UA protocol; The control cycle is set to real-time response in seconds, and the data reporting cycle is set to 5 minutes.

[0018] The benefit of this application is that it provides a multi-mode collaborative control method for substation energy storage equipment, which collects the operating data of the substation in real time to identify the operating mode of the current substation in real time. Then, a multi-objective optimization function is created, and the minimum substation network loss, minimum voltage deviation, lowest electricity cost and optimal energy storage life loss are used as the optimization objectives of the multi-objective optimization function, and then the weight coefficient of the multi-objective optimization function is generated. Then, based on the obtained weight coefficient, a fuzzy decision algorithm is used to dynamically select the combination of the dominant control mode and the auxiliary control mode, and a multi-mode collaborative control instruction is generated based on the combination of the dominant control mode and the auxiliary control mode to allocate the charging and discharging power of the energy storage device and the reactive output of the converter. So that the control of the energy storage device is more adaptive. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings constituting a part of this application are used to provide a further understanding of this application and make other features, purposes and advantages of this application more apparent. The drawings and descriptions of the exemplary embodiments of this application are used to explain this application and do not constitute an improper limitation on this application.

[0020] In the attached figure: Figure 1 This is a flowchart of the application. DETAILED DESCRIPTION

[0021] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0022] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0023] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0024] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0025] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0026] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0027] like Figure 1 As shown, in one embodiment of the present application, the multi-mode coordinated control method of the substation energy storage equipment includes the following S100 to S500:

[0028] S100 collects substation operation data in real time, including load power, photovoltaic output, grid voltage, electricity price signals and energy storage SOC status.

[0029] 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 to measure the power generation capacity of a photovoltaic system.

[0030] The SOC state refers to the current remaining power of the battery, usually expressed as a percentage, reflecting the battery's charge level.

[0031] S200, based on the real-time collection of substation operation data, dynamically identify the current operation mode requirements of the substation, the operation modes include: peak shaving and valley filling mode, voltage support mode, new energy consumption mode and emergency standby mode.

[0032] S300: Establish a multi-objective optimization function, take the minimum grid loss, minimum voltage deviation, lowest electricity cost and optimal energy storage life loss as the optimization objectives, and generate weight coefficients.

[0033] S400, based on the weight coefficient, adopts fuzzy decision algorithm to dynamically select the dominant control mode and auxiliary control mode combination.

[0034] S500: Generate a multi-mode coordinated control instruction based on the combination of the dominant control mode and the auxiliary control mode to distribute the charging and discharging power of the energy storage device and the reactive output of the converter.

[0035] In this embodiment, the operation data of the substation is collected in real time to identify the operation mode of the current substation in real time. Next, a multi-objective optimization function is created, and the minimum network loss, minimum voltage deviation, lowest electricity cost and optimal energy storage life loss of the substation are used as the optimization objectives of the multi-objective optimization function, and then the weight coefficient of the multi-objective optimization function is generated. Then, based on the obtained weight coefficient, a fuzzy decision algorithm is used to dynamically select the combination of the dominant control mode and the auxiliary control mode, and a multi-mode collaborative control instruction is generated based on the combination of the dominant control mode and the auxiliary control mode to allocate the charging and discharging power of the energy storage device and the reactive output of the converter. This makes the control of the energy storage device more adaptable.

[0036] In one embodiment of the present application, the current operation mode requirement of the substation is dynamically identified based on the real-time collection of substation operation data. The operation modes include: peak shaving and valley filling mode, voltage support mode, new energy consumption mode and emergency standby mode, including the following S201 to S204:

[0037] S201: When it is detected that the load power in the substation operation data continuously exceeds the threshold and is in the peak power period, the peak shaving and valley filling mode is activated.

[0038] S202: When it is detected that the voltage at the monitoring point in the substation exceeds the limit, the voltage support mode is activated.

[0039] S203: When it is detected that the photovoltaic output fluctuation rate in the substation operation data exceeds the set value and the local absorption is insufficient, the new energy absorption mode is activated.

[0040] S204: When a grid fault signal trigger or a backup capacity requirement is detected, the emergency backup mode is activated.

[0041] In this embodiment, the peak shaving and valley filling mode may mean that the load reaches 190 kW at 6 pm, and the threshold is 175 kW, and the load of 190 kW is greater than the threshold of 175 kW.

[0042] The voltage support mode may refer to the voltage at the end of the substation dropping to 210V, while the limit is 214V. At this time, the voltage at the end of the substation is less than the limit of 214V.

[0043] The new energy consumption mode may refer to the photovoltaic output fluctuating by 35kW within 1 minute, while the set value is 30kW, which meets the new energy consumption mode.

[0044] Emergency backup mode refers to when a power grid failure occurs, such as failure to supply power.

[0045] In one embodiment of the present application, the multi-objective optimization function is established, with the optimization objectives of minimizing the grid loss, minimizing the voltage deviation, minimizing the electricity cost, and optimizing the energy storage life loss, and generating weight coefficients, including the following S301:

[0046] S301, creating a target optimization function, as shown in Formula 1; Formula 1; in, 、 、 、 is the dynamic weight coefficient; is the total active power loss in the substation area; is the mean voltage deviation of the substation area; For electricity costs; is the energy storage life loss factor.

[0047] Specifically,

[0048] in, is the total active network loss in the substation area; is the effective value of the current in the kth branch; is the resistance value of the kth branch; m is the total number of branches in the substation.

[0049]

[0050] in, is the effective value of the voltage at the i-th monitoring point (unit: V), obtained by the voltage sensor; The rated voltage of the substation (usually 220V or 380V); is the number of voltage monitoring points.

[0051]

[0052] in, is the active power absorbed by the substation from the grid during period t (unit: kW), a positive value indicates purchased power; is the real-time electricity price during period t (unit: yuan / kWh); is the time interval (unit: h), usually 15 minutes (0.25h) or 1 hour; is the total number of time periods in the optimization cycle.

[0053]

[0054] in, is the battery charge and discharge current (unit: A), discharge is positive and charge is negative; The SOC change per unit time (unit: %), such as the rate of change per minute; 、 is the aging coefficient, which is determined by the battery type (for example: lithium iron phosphate battery k1=0.002, k2=0.05).

[0055] In this embodiment, 、 、 、 It is used to reflect the relative importance of various optimization objectives and then make adjustments based on the real-time status of the substation. It can be calculated using the hierarchical analysis method.

[0056] In one embodiment of the present application, the fuzzy decision algorithm includes the following steps S401 to S402:

[0057] S401, the input variables of the fuzzy decision algorithm include voltage over-limit severity, load peak-to-valley difference rate, photovoltaic fluctuation rate, SOC balance and electricity price difference.

[0058] 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.

[0059] In this embodiment, the inputs of the fuzzy decision are voltage over-limit severity = 0.8, load peak-to-valley difference rate = 0.6, and photovoltaic fluctuation rate = 0.7.

[0060] The fuzzy rule of fuzzy decision making is: if the voltage over-limit is greater than 0.6 and the peak-to-valley difference is greater than 0.5, then the peak shaving weight = 0.3 and the voltage support weight = 0.7.

[0061] The hysteresis range is when the voltage recovers to 216V before exiting the voltage support mode (to avoid 215V critical point oscillation).

[0062] In one embodiment of the present application, the generation of the multi-mode coordinated control instruction based on the combination of the dominant control mode and the auxiliary control mode includes S501 to S503:

[0063] S501 , when the dominant mode is the peak shaving and valley filling mode, constraining the charging and discharging power with the goal of load balancing.

[0064] Specifically, when the dominant mode is the peak shaving and valley filling mode, the charging and discharging power is constrained within ±50kW, and the load is smoothed to 160kW.

[0065] S502: When the dominant mode is the voltage support mode, the reactive output of the energy storage converter is adjusted first, and the SOC is limited to a safe range.

[0066] Specifically, when the dominant mode is voltage support mode, the SOC is limited to 30%-80%, and reactive power is output first (maximum 50kVar).

[0067] S503, in the new energy consumption mode, takes the smoothing of photovoltaic fluctuation rate as the control target and dynamically adjusts the energy storage response rate.

[0068] Specifically, under the new energy consumption mode, sliding average filtering is used to reduce the photovoltaic volatility from 30% to 15%.

[0069] In one embodiment of the present application, the multi-mode coordinated control method for substation energy storage equipment further includes: When multiple mode demands are activated simultaneously, the control instructions are arbitrated based on the priority of voltage safety over equipment protection, and equipment protection over economy; Model predictive control (MPC) is used to optimize the control sequence for the next 15 minutes.

[0070] In this embodiment, when the voltage support mode (requiring energy storage discharge) conflicts with the peak shaving mode (requiring charging), priority is given to ensuring voltage, i.e., instructing the energy storage device to discharge 20kW to increase the voltage; MPC rolling optimization, i.e., predicting a load drop in the next 15 minutes, delays charging until the next cycle.

[0071] In one embodiment of the present application, the multi-mode coordinated control method for substation energy storage equipment further includes: The upper controller coordinates the mode allocation of each energy storage unit in the substation; The lower-level controller implements SOC balancing and precise power distribution based on the consistency algorithm.

[0072] In this embodiment, the upper layer: the EMS of the substation determines that the dominant mode is new energy consumption, and allocates the total power instruction (Charge).

[0073] Lower layer: three energy storage units (capacity 100 / 150 / 250kWh, SOC = 70% / 50% / 60%), allocated through the consensus algorithm 、 、 .

[0074] In one embodiment of the present application, the lower-layer controller implements SOC balancing and precise power distribution based on a consistency algorithm, including:

[0075] Create a calculation formula for the output power of each energy storage unit, as shown in Formula 2; Formula 2; in, For serial number The output power of the energy storage unit; For serial number The equivalent impedance from the energy storage unit to the heavy-load area of ​​the substation; For serial number Rated capacity of energy storage unit; For serial number The rated capacity of the energy storage unit; For serial number The state of charge of the energy storage unit; For serial number The state of charge of the energy storage unit; For the overall power demand.

[0076] Specifically, Refers to the serial number The remaining adjustable space of the energy storage unit.

[0077] When the energy storage unit needs to be charged: The larger it is, the more remaining space there is and the more charging power can be allocated.

[0078] When the energy storage unit needs to be discharged: it needs to be replaced with (high Priority discharge).

[0079]

[0080] in, is the equivalent resistance from the energy storage node i to the target area (T); is the equivalent reactance.

[0081] The smaller it is, the shorter the electrical distance is, the lower the power transmission loss is, and the higher the distributed power is.

[0082] The denominator in Formula 2 is the sum of the "weighted adjustable capacities" of all energy storage units, which is used to convert the numerator into a proportionality coefficient. This improves overall fairness and avoids local overloads.

[0083] In this embodiment, compared with the traditional capacity-proportional allocation, this embodiment reduces network losses and extends the service life of the energy storage unit through the coordinated optimization of capacity, SOC and electrical distance.

[0084] In one embodiment of the present application, the multi-mode coordinated control method for substation energy storage equipment further includes:

[0085] Record the number of historical mode switches and target achievement rate; When the optimization objective is not improved after N consecutive switching, the fuzzy decision rule parameters are automatically adjusted, where N is a positive integer.

[0086] In this embodiment, if three consecutive mode switches (from new energy consumption to peak shaving) do not reduce the grid loss (grid loss is 3.2kW before switching and 3.5kW after switching), the "PV volatility threshold" in the fuzzy rule is increased from 0.6 to 0.7 to reduce erroneous switching.

[0087] In one embodiment of the present application, the multi-mode coordinated control method for substation energy storage equipment further includes: Interact with the district energy management system (EMS) based on the OPC-UA protocol; The control cycle is set to real-time response in seconds, and the data reporting cycle is set to 5 minutes.

[0088] Specifically, OPC-UA is a communication protocol for industrial automation that enables 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 offers enhanced security and more powerful data processing capabilities.

[0089] In this embodiment, electricity price signals are obtained from the EMS through OPC UA, MQTT sends P / Q instructions to the energy storage converter, and the SOC, network loss, and voltage qualification rate are uploaded to the cloud platform every 5 minutes.

[0090] The above descriptions are merely some preferred embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A multi-mode coordinated control method for energy storage equipment in a substation, characterized by: The multi-mode coordinated control method for substation energy storage equipment includes: Real-time collection of substation operation data, including load power, photovoltaic output, grid voltage, electricity price signals and energy storage SOC status; Based on real-time collection of substation operation data, dynamically identify the current operation mode requirements of the substation, including peak shaving and valley filling mode, voltage support mode, new energy consumption mode and emergency standby mode; Establish a multi-objective optimization function, with the optimization objectives of minimizing grid loss, minimizing voltage deviation, minimizing electricity cost, and optimizing energy storage life loss, and generate weight coefficients; Based on the weight coefficient, the fuzzy decision algorithm is used to dynamically select the combination of the dominant control mode and the auxiliary control mode; Based on the combination of the dominant control mode and the auxiliary control mode, multi-mode collaborative control instructions are generated to allocate the charging and discharging power of the energy storage equipment and the reactive output of the converter.

2. The multi-mode coordinated control method for energy storage equipment in a substation according to claim 1, characterized in that: The system dynamically identifies the current operation mode requirements of the substation based on real-time collection of substation operation data. The operation modes include: peak shaving and valley filling mode, voltage support mode, new energy consumption mode and emergency standby mode, including: When it is detected that the load power in the substation operation data exceeds the threshold continuously and is in the peak power period, the peak shaving and valley filling mode is activated; When the voltage at the monitoring point in the substation is detected to be out of limit, the voltage support mode is activated; When it is detected that the PV output fluctuation rate in the substation operation data exceeds the set value and the local absorption is insufficient, the new energy absorption mode is activated; When a grid fault signal trigger or backup capacity demand is detected, the emergency backup mode is activated.

3. The multi-mode coordinated control method for substation energy storage equipment according to claim 1, characterized in that: The multi-objective optimization function is established with the optimization objectives of minimizing the grid loss, minimizing the voltage deviation, minimizing the electricity cost, and optimizing the energy storage life loss, and generates weight coefficients, including: Create the target optimization function as shown in Formula 1; Formula 1: in, 、 、 、 is the dynamic weight coefficient; is the total active power loss in the substation area; is the mean voltage deviation of the substation area; For electricity costs; is the energy storage life loss factor.

4. The multi-mode coordinated control method for substation energy storage equipment according to claim 1, characterized in that: The fuzzy decision algorithm includes: The input variables of the fuzzy decision algorithm include voltage over-limit severity, load peak-to-valley difference rate, photovoltaic fluctuation rate, SOC balance and electricity price difference; The output of the fuzzy decision algorithm is the activation priority of each mode.

5. The multi-mode coordinated control method for substation energy storage equipment according to claim 1, characterized in that: The generating of the multi-mode coordinated control instruction based on the combination of the dominant control mode and the auxiliary control mode includes: When the dominant mode is peak shaving and valley filling, the charging and discharging power is constrained with the goal of load balancing; When the dominant mode is the voltage support mode, the reactive output of the energy storage converter is adjusted first and the SOC is limited to a safe range; Under the new energy consumption mode, the smoothing of photovoltaic fluctuations is used as the control target, and the energy storage response rate is dynamically adjusted.

6. The multi-mode coordinated control method for substation energy storage equipment according to claim 1, characterized in that: The multi-mode coordinated control method for energy storage equipment in the substation area further includes: When multiple mode demands are activated simultaneously, the control instructions are arbitrated based on the priority of voltage safety over equipment protection, and equipment protection over economy; Model predictive control (MPC) is used to optimize the control sequence for the next 15 minutes.

7. The multi-mode coordinated control method for substation energy storage equipment according to claim 1, characterized in that: The multi-mode coordinated control method for substation energy storage equipment further includes: The upper controller coordinates the mode allocation of each energy storage unit in the substation; The lower-level controller implements SOC balancing and precise power distribution based on the consistency algorithm.

8. The multi-mode coordinated control method for substation energy storage equipment according to claim 7, characterized in that: The lower-layer controller implements SOC balancing and precise power distribution based on a consistency algorithm, including: Create a calculation formula for the output power of each energy storage unit, as shown in Formula 2; Formula 2: in, For serial number The output power of the energy storage unit; For serial number The equivalent impedance from the energy storage unit to the heavy-load area of ​​the substation; For serial number Rated capacity of energy storage unit; For serial number The rated capacity of the energy storage unit; For serial number The state of charge of the energy storage unit; For serial number The state of charge of the energy storage unit; For the overall power demand.

9. The multi-mode coordinated control method for substation energy storage equipment according to claim 1, characterized in that: The multi-mode coordinated control method for substation energy storage equipment further includes: Record the number of historical mode switches and target achievement rate; When the optimization objective is not improved after N consecutive switching, the fuzzy decision rule parameters are automatically adjusted, where N is a positive integer.

10. The multi-mode coordinated control method for substation energy storage equipment according to claim 1, characterized in that: The multi-mode coordinated control method for substation energy storage equipment further includes: Interact with the substation energy management system based on the OPC-UA protocol; The control cycle is set to real-time response in seconds, and the data reporting cycle is set to 5 minutes.

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