Power distribution area uninterrupted operation based on distributed energy storage aggregation and virtual power plant collaborative regulation method

Abstract

The method for the coordinated regulation of the uninterrupted operation of a distribution area based on distributed energy storage aggregation and a virtual power plant comprises the following steps: collecting source network load storage operation data of the distribution area; determining the uninterrupted power supply capability of the distribution area and the SOC safety state of the energy storage according to the collected operation data; executing a local safety control strategy of the distribution area under the safety constraint of the distribution area; aggregating the distributed energy storage in multiple distribution areas and evaluating the adjustable capacity of the virtual power plant; constructing an economic operation optimization model of the virtual power plant with the adjustable capacity of the virtual power plant as the constraint to determine the charging and discharging plan of the energy storage of each distribution area; and executing a rolling update to realize the dynamic coordination of the uninterrupted operation of the distribution area and the economic dispatching of the virtual power plant. The application can improve the distributed photovoltaic consumption capacity, the power supply reliability of the distribution area and the comprehensive income of the marketized regulation of the distributed energy storage aggregation on the basis of ensuring the uninterrupted operation of the distribution area and the local safety control.

Description

Technical Field

[0001] This invention relates to the fields of distribution transformer area operation control, distributed energy storage aggregation regulation and virtual power plant technology, and in particular to a method for uninterrupted operation of distribution transformer areas and coordinated regulation of virtual power plants based on distributed energy storage aggregation. It is applicable to distribution transformer area operation scenarios that include distributed photovoltaic, transformer area energy storage, low-voltage user loads and cloud management and control platforms, and can be used for emergency power supply during transformer area faults or maintenance, photovoltaic reverse overload suppression, transformer area voltage stability control, peak shaving and valley filling, and virtual power plant aggregation to participate in power market regulation. Background Technology

[0002] With the continuous advancement of new power system construction, the scale of distributed photovoltaic, distributed energy storage, electric vehicle charging facilities, and various adjustable loads connected to low-voltage distribution substations is constantly increasing. Traditional distribution substations are gradually transforming from passive distribution networks with unidirectional power supply to active distribution systems with multi-dimensional interaction among power sources, grids, loads, and energy storage. Against this backdrop, distribution substations not only need to undertake conventional power supply and distribution tasks, but also need to possess the capabilities for distributed renewable energy consumption, power quality management, load fluctuation mitigation, emergency power supply, and coordinated interaction with the upper-level power grid. How to effectively improve the power supply reliability, operational flexibility, and comprehensive carrying capacity of distribution substations without large-scale modifications to distribution network equipment has become a key issue in distribution network operation and management.

[0003] Low-voltage distribution substations typically feature long lines, numerous branches, dispersed users, and voltage sensitivity at the terminals, making them susceptible to power outages due to severe weather conditions such as strong winds, thunderstorms, rain, and snow. Simultaneously, with the growth of residential electricity load and the integration of high-power equipment, some substations are prone to transformer overload and low voltage at the terminals during peak load periods. Conversely, during periods of high distributed photovoltaic power generation, issues such as increased reverse power flow, transformer reverse overload, and overvoltage on the user side are likely to arise. Relying solely on traditional distribution network expansion, line upgrades, or manual maintenance methods not only results in high investment costs and long construction periods but also makes it difficult to adapt to the rapid integration of distributed resources and frequent load fluctuations.

[0004] Distributed energy storage features flexible charging and discharging, fast response speed, flexible deployment, and bidirectional adjustment of active power, enabling it to play multiple roles in distribution transformer areas. On one hand, in scenarios such as transformer area failures, maintenance, or severe weather, distributed energy storage can serve as an emergency power source, providing temporary power support for critical loads and improving the uninterrupted operation capability of the transformer area. On the other hand, when photovoltaic output is high or the reverse power of the transformer area exceeds its limit, distributed energy storage can absorb excess photovoltaic power through charging, reducing the reverse load rate of the transformer and alleviating overvoltage problems for users. During peak load periods, distributed energy storage can also reduce peak loads through discharging, mitigating the risk of transformer overload and achieving peak shaving and valley filling, as well as dynamic capacity expansion. Therefore, connecting distributed energy storage to distribution transformer areas is an important technical approach to improving the reliability of power supply, power quality, and the capacity for renewable energy absorption in the transformer area.

[0005] Existing energy storage applications in transformer substations mostly adopt local control or independent operation of individual energy storage units, typically using simple charge and discharge control based on substation voltage, load factor, or time-of-use pricing signals. While this approach can alleviate issues such as local voltage exceedances, solar backfeeding, and peak-valley load differences to some extent, it still has significant shortcomings: 1) Firstly, the energy storage capacity of a single transformer substation is limited. If control is only implemented from the perspective of a single device, it is difficult to achieve large-scale regulation capabilities and cannot fully participate in the upper-level grid's peak shaving, backup support, demand response, or market transactions. 2) Secondly, existing methods often handle emergency supply, voltage regulation, solar power consumption, and economic operation separately, lacking a unified operational priority and coordinated control logic. This can easily lead to premature depletion of energy storage for economic arbitrage, resulting in insufficient power supply during severe weather or substation failures. 3) Thirdly, some energy storage control strategies only focus on local operational safety and fail to integrate with virtual power plant cloud-based aggregation and dispatch, resulting in a lack of effective coordination between substation-side safety constraints and upper-level platform dispatch instructions.

[0006] Virtual power plants, as an important form of distributed resource aggregation and coordinated control, can unify the management of distributed power sources, energy storage systems, and controllable loads through technologies such as smart metering, communication transmission, centralized monitoring, and optimized scheduling. This transforms resources that were originally dispersed in capacity, location, and response characteristics into a tangible, measurable, and controllable overall regulation capability. For distribution substations, aggregating distributed energy storage from multiple substations into a virtual power plant through a cloud platform not only enhances the local power supply security of the substation but also enables dispersed energy storage resources to participate in electricity market transactions, demand response, peak shaving and valley filling, and ancillary services, while meeting the substation's safe operation constraints, thereby unlocking the value of large-scale energy storage utilization.

[0007] However, existing research on virtual power plant dispatching often focuses on optimizing economic benefits, carbon trading, green certificate trading, power purchase and sale revenue, or renewable energy consumption targets. It tends to treat distributed energy storage as a flexible resource that can be uniformly dispatched, with less consideration given to the operational constraints of low-voltage distribution substations and their uninterrupted power supply needs. In practical engineering, substation energy storage is not only an adjustable resource for virtual power plants but also a crucial supporting device for local power supply reliability, voltage stability, and photovoltaic consumption. If virtual power plants dispatch energy storage solely based on market prices or profit maximization targets, without fully considering substation voltage, transformer load factor, reverse power flow, guaranteed power supply, and critical load demands, the energy storage dispatching results may conflict with the substation's safe operation requirements, reducing operational reliability.

[0008] Furthermore, during the aggregated operation of energy storage in distribution substations, there are still issues with imperfect data acquisition, communication transmission, and rolling correction mechanisms. Data such as transformer operating conditions, substation bus voltage, photovoltaic output, user load, energy storage status of charge, and dispatch instructions exhibit significant time-varying characteristics. Without real-time interaction between the integrated control terminal and the cloud platform, energy storage control strategies struggle to respond promptly to photovoltaic fluctuations, load surges, and fault-based supply demands. Simultaneously, the control objectives of energy storage in distribution substations differ under various operating scenarios: prioritizing high charge status to meet emergency power needs before severe weather; prioritizing the absorption of surplus photovoltaic power during peak photovoltaic generation to limit reverse load rates and voltage exceedances; implementing economical operation strategies when peak-valley electricity price differences are large; and providing aggregated regulation capabilities without compromising substation safety when the upper-level grid issues peak-shaving or demand response instructions. Existing methods lack a hierarchical collaborative control mechanism that can uniformly address these multiple scenarios. Summary of the Invention

[0009] The technical problem to be solved by this invention is to provide a method for uninterrupted operation of distribution transformer substations and coordinated control of virtual power plants based on distributed energy storage aggregation. This method can achieve coordinated unity among uninterrupted power supply of distribution transformer substations, local safety control, local photovoltaic consumption, energy storage aggregation scheduling, and economic operation of virtual power plants, thereby improving the power supply reliability, operational safety, resource utilization efficiency, and market operation value of distribution transformer substations.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation includes the following steps: Step 1: Collect power grid-load-storage operation data for distribution transformer areas; Step 2: Based on the operational data collected in Step 1, determine whether the energy storage in the distribution area has the capability to ensure uninterrupted power supply, and determine the current capacity range of the energy storage in the distribution area that can participate in dispatch. Step 3: Under the security constraints of the distribution area, implement the local security control policy for the distribution area; Step 4: Aggregate distributed energy storage in multiple distribution substations and conduct a virtual power plant adjustable capacity assessment; Step 5: Using the adjustable capacity of the virtual power plant obtained in Step 4 as a constraint, construct an economic operation optimization model for the virtual power plant and determine the charging and discharging plan for energy storage in each distribution area; after execution, update Steps 1 to 5 on a rolling basis according to the real-time status of each distribution area and changes in market signals, so as to achieve dynamic coordination between uninterrupted operation of distribution areas and economic dispatch of the virtual power plant.

[0011] In step 1, the operating data should include at least the bus voltage of the distribution area, the active power of the distribution transformer, the forward load rate of the distribution transformer, the reverse load rate of the distribution transformer, the photovoltaic output, the user load power, the energy storage status of charge, the rated capacity of energy storage, the energy storage charging and discharging power, the available capacity of energy storage, the time-of-use electricity price, the upper-level peak shaving instructions, the demand response instructions, the severe weather warning signals, and the supply requirements of important loads. Through information interaction between the energy storage cabinet's main data acquisition unit, the distribution transformer's voltage and current acquisition device, the photovoltaic inverter's communication module, the integrated control terminal, and the cloud energy storage integrated management and control platform, the data foundation required for local control of the distribution area and cloud-based scheduling of the virtual power plant is formed.

[0012] Step 2 involves determining whether the energy storage in the distribution area has uninterrupted power supply capabilities and identifying the current capacity range that the energy storage can participate in dispatching. Specifically, this includes: 2.1: Determine the operating scenario of the distribution radio area; 2.2: Determine the SOC update relationship for energy storage; 2.3: Set safety constraints for energy storage SOC; 2.4: Determine the minimum power supply capacity; based on the power of critical loads and the expected duration of power supply guarantee, the first... The minimum energy storage capacity that needs to be reserved for each distribution area is expressed as follows: ; In the formula: Indicates the first The minimum energy storage capacity that each distribution area needs to reserve for supply guarantee; Indicates the first The power of key loads in each transformer substation area; Indicates the first The estimated duration of supply guarantee for each distribution area; This indicates the energy storage discharge efficiency.

[0013] 2.5: Determine the energy storage capacity eligible for discharge dispatch; after deducting the minimum guaranteed supply capacity and the minimum SOC corresponding to the electricity, the... Individual power storage The capacity available for regular discharge scheduling at any given time is expressed as: ; In the formula: Indicates the first Individual power storage Capacity that can participate in regular discharge scheduling at any time; Indicates the first The current stored electricity in each distribution area; This indicates the reserve capacity corresponding to the lowest SOC; Indicates the minimum guaranteed supply capacity; 2.6: Determine the energy storage capacity that can participate in charging dispatch; Individual power storage The capacity that can absorb electricity at any given time is expressed as: ; In the formula: Indicates the first Individual power storage Capacity available for charging scheduling at all times; Indicates the maximum permissible state of charge for energy storage; Indicates the current state of charge of the energy storage; Indicates the rated capacity of energy storage; 2.7: Determining Energy Storage Operation Priorities: In scenarios involving severe weather, faults, or maintenance, the priority for energy storage operation is uninterrupted power supply. Energy storage should maintain a high charge state as much as possible and retain reserve capacity to meet the power supply needs of important loads. In scenarios involving photovoltaic reverse overload and voltage exceeding limits, the priority for energy storage operation is local safety management. Energy storage should prioritize implementing control strategies to absorb excess photovoltaic power and reduce voltage in the distribution area. In scenarios involving heavy overload during peak loads, the priority for energy storage operation is peak shaving support. Energy storage should reduce the load rate of distribution transformers by discharging. In normal economic operation scenarios, energy storage can participate in peak-valley arbitrage and virtual power plant dispatch, provided that the minimum supply capacity is met.

[0014] 2.8: Output area supply status and energy storage adjustable status.

[0015] Step 3 involves implementing the local security control policy for the distribution area, specifically including: 3.1: Establish reverse load rate criteria for transformer substations; 3.2: Set reverse overload constraints; 3.3: Establish positive load factor criteria for transformer substations; 3.4: Set positive heavy overload constraints; 3.5: Set voltage safety constraints for transformer substations; 3.6: Implement supply control strategies during severe weather; 3.7: Implement photovoltaic reverse overload control strategy; 3.8: Implement the voltage stabilization control strategy for the transformer substation; 3.9: Implement peak load shaving control strategy; 3.10: Implement normal economic operation control strategies; 3.11: Output local control results.

[0016] Step 4 involves assessing the adjustable capacity of distributed energy storage aggregation and virtual power plants across multiple distribution substations, specifically including: 4.1: Determine the energy storage set of the distribution area participating in the aggregation: The cloud energy storage integrated management and control platform receives the energy storage status, distribution area operation scenario and local constraint information uploaded by the integrated control terminal of each distribution area, and includes the energy storage that is in normal operation and has adjustable capabilities into the aggregated resource pool of the current scheduling cycle; removes the energy storage that is in a state of fault, maintenance, communication abnormality or insufficient supply capacity from the adjustable resource pool, or only retains its emergency backup attributes; 4.2: Calculate the rechargeable power of a single energy storage unit in a given area; 4.3: Calculate the discharge power of a single energy storage unit; 4.4: Forming a virtual power plant to aggregate rechargeable power; 4.5: Forming a virtual power plant to aggregate dischargeable power; 4.6: Forming virtual power plant aggregated adjustable capacity; 4.7: Forming a virtual power plant to aggregate emergency reserve capacity: Virtual power plants The aggregated emergency reserve capacity that needs to be maintained at all times is expressed as follows: ; In the formula: Indicates virtual power plants in Aggregate emergency backup capacity that must be maintained at all times; Indicates the first The minimum energy storage capacity that each distribution area needs to reserve for supply guarantee; 4.8: Output Aggregated Evaluation Results: Through step 4, the rechargeable power, dischargeable power, adjustable capacity, emergency reserve capacity, and available capacity for market trading at the virtual power plant level are obtained. These aggregated evaluation results serve as the basic input for constructing the virtual power plant economic operation optimization model in step 5.

[0017] Step 5 involves cloud-based collaborative scheduling and economic operation optimization of the virtual power plant, specifically including: 5.1: Construct a distributed energy storage operation cost model; 5.2: Constructing a distributed energy storage revenue model; 5.3: Constructing a renewable energy quota model; 5.4: Construct a model for green certificate excess completion considering cross-day carryover; after unifying the wind power output item in the original model into a renewable energy output item, the green certificate excess completion amount is expressed as: ; In the formula: Indicates the first Daily green certificate over-quota completion; Indicates the number of renewable energy units participating in the green certificate accounting; Indicates the first Each renewable energy unit in Efforts during a specific time period; This indicates the maximum coefficient that the Green Certificate Exchange allows for carryover; This indicates the excess amount of green certificates that can be carried over from the previous day; This indicates the daily quota for renewable energy.

[0018] 5.5: Constructing a carbon emission model; In the extended scenario where the virtual power plant includes a gas turbine or other controllable carbon emission power source, the carbon emissions of the virtual power plant during the dispatch cycle are expressed as follows: ; In the formula: This indicates the carbon emissions of the virtual power plant during the dispatch cycle; Indicates the number of gas turbines or other controlled carbon emission power sources; Indicates the first A carbon emission power source in The start / stop status variable for a time period has a value of 1 indicating that the device is on and a value of 0 indicating that the device is off. , , They represent the first The carbon emission coefficient of a carbon-emitting power source; Indicates the first A carbon emission power source in Efforts during a specific time period; 5.6: Constructing a risk cost model for carbon-green certificate trading. The risk costs of the carbon market and green certificate market, considering conditional value of risk, are expressed as follows: ; ; In the formula: This represents the carbon trading costs that take risk into account; This represents the transaction costs of green certificates, taking into account the risks involved. This represents the carbon market risk appetite coefficient; This represents the risk appetite coefficient in the green certificate market; Indicates the first Risks in the carbon trading market; Indicates the first Risks associated with individual green certificate trading markets; 5.7: Constructing the objective function for the net revenue of the virtual power plant The net revenue of a virtual power plant is expressed as follows: ; In the formula: This indicates the net revenue of the virtual power plant; Represents revenue from electricity sales; Indicates the economic benefits of distributed energy storage systems; This refers to the benefits or costs of green certificate transactions that take into account risks. This refers to the carbon trading benefits or costs that take into account risks. Indicates that the gas turbine or other controllable power source is in The cost of generating electricity during a given period; express Total operating and maintenance costs of renewable energy units during specific time periods; This represents the total operating cost of the energy storage system; Indicates the cost of purchasing electricity; This represents the total investment depreciation cost; 5.8: Constructing investment return evaluation indicators; 5.9: Setting Cloud Dispatch Constraints: The cloud dispatch of the virtual power plant must meet the following constraints: distribution area safety constraints, energy storage SOC constraints, energy storage adjustable capacity boundary, minimum power supply guarantee capacity constraint, charging and discharging power boundary, photovoltaic consumption constraints, and upper-level dispatch command constraints. If the cloud-based economic optimization results conflict with the local safety constraints of the distribution area, the local safety constraints and uninterrupted power supply guarantee constraints shall take priority, and the energy storage charging and discharging plan shall be revised accordingly. 5.10: Solve for the collaborative scheduling scheme of virtual power plants; 5.11: Perform rolling updates.

[0019] This invention provides a method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation, which has the following technical advantages: 1) Improve the voltage stability of the distribution area and suppress voltage exceedance issues caused by distributed photovoltaic (PV) power. This invention introduces a local energy storage control strategy based on reverse load rate constraints. During periods of high PV output, it prioritizes absorbing excess power from the distribution area, effectively reducing the reverse load level. The results of the embodiment show that when the reverse load rate gradually decreases from 80% to 20%, the bus voltage of the distribution area drops from 408.512V to 404.081V. This indicates that energy storage charging regulation can effectively suppress voltage rise caused by distributed PV, improving the voltage qualification rate and operational stability of the distribution area.

[0020] 2) Reduce reverse power surges in distribution transformer areas and improve the safety margin of distribution transformer operation. This invention dynamically adjusts the energy storage charging power under conditions of high photovoltaic power generation by real-time monitoring and hierarchical control of reverse power and load rate, so that surplus renewable energy can be effectively absorbed, thereby reducing the reverse power surge of distribution transformers, reducing equipment operating pressure, and improving the safety margin of distribution transformer operation.

[0021] 3) Enhance the energy storage aggregation capability of multiple transformer substations and increase the adjustable resource scale of the virtual power plant. This invention introduces a cloud-based energy storage aggregation mechanism on the basis of local control of transformer substations. It performs unified assessment and optimized scheduling of the charge and discharge capabilities of energy storage in multiple transformer substations, achieving resource integration while meeting the minimum guaranteed supply capacity constraints of each substation. Compared to the independent operation mode of a single transformer substation, this invention can significantly improve the adjustable capacity and emergency backup capability at the virtual power plant level, enhancing the overall flexibility of the system.

[0022] 4) Reduce system operating costs and improve overall economic benefits. By introducing carbon emission trading mechanisms, green certificate trading mechanisms, and electricity market pricing mechanisms, this invention constructs an optimized scheduling model that considers multi-market benefits. In comparisons across different operating scenarios, the method of this invention reduces system operating costs, lowers carbon emission levels, and increases net profit, demonstrating that this invention can improve overall economic benefits while ensuring the safe operation of distribution areas and meeting minimum supply capacity constraints.

[0023] 5) Improved investment recovery and engineering economics. This invention comprehensively evaluates the investment payback period, return on investment, and cost-benefit ratio, reflecting the engineering economics of distributed energy storage aggregated virtual power plants in terms of investment recovery, profitability, and input-output relationship. The results of the examples show that after adopting a carbon emission trading mechanism and a green certificate trading mechanism in conjunction with the distributed energy storage system, the investment payback period index decreases, while the return on investment index and cost-benefit ratio index increase, indicating that this invention has good investment recovery capabilities and engineering promotion value. Attached Figure Description

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the present invention.

[0025] Figure 2 This is a diagram showing the relationship between reverse load rate and transformer bus voltage in an embodiment of the present invention.

[0026] Figure 3 This is a normalized comparison chart of carbon emissions, operating costs, and net profit under different operating scenarios in this invention.

[0027] Figure 4 This is a comparison chart of return on investment indicators for different operating scenarios in embodiments of the present invention. Detailed Implementation

[0028] like Figure 1As shown, this invention provides a method for uninterrupted operation of distribution substations and collaborative control of virtual power plants based on distributed energy storage aggregation. This method follows a process of "data acquisition—status determination—local control—energy storage aggregation—cloud scheduling—rolling update." Specifically: First, the integrated control terminal collects operational data from the distribution transformer area, including voltage, current, power, frequency, load factor, photovoltaic output, user load, and energy storage SOC. Then, it determines whether the transformer area is experiencing severe weather, fault repair, critical load supply, photovoltaic reverse overload, voltage exceeding limits, or forward overload; based on this, it calculates the minimum energy storage supply capacity and the capacity available for dispatch. Subsequently, it determines the energy storage charging, discharging, standby, or economical operation control mode based on the transformer area's operating status. Finally, the cloud-based energy storage integrated management and control platform aggregates energy storage from multiple transformer areas and performs virtual power plant collaborative dispatch, ensuring compliance with transformer area safety constraints and minimum supply capacity constraints. After each dispatch cycle, the system re-collects transformer area status data and updates the energy storage SOC, entering the next round of rolling control.

[0029] In this embodiment, an integrated control terminal is set up in the distribution transformer area. The integrated control terminal is connected to the voltage and current acquisition device, distributed photovoltaic inverter, distributed energy storage cabinet and user load metering device on the distribution transformer side, and communicates with the cloud energy storage integrated management and control platform through 4G, 5G or dispatch data network.

[0030] The integrated control terminal is used to collect local operating data of the distribution area, execute local security control logic, and receive cloud scheduling instructions.

[0031] The cloud-based energy storage integrated management and control platform is used to receive data uploaded from multiple power distribution areas, calculate and aggregate adjustable capacity, and form an economic dispatch plan at the virtual power plant level.

[0032] As shown in Table 2, in this embodiment, the primary measurement data of the distribution substation collected by the integrated control terminal includes primary measurements of phase A current, phase B current, and phase C current; primary measurements of phase A voltage, phase B voltage, and phase C voltage; primary measurements of inter-phase voltage (AB, BC, and CA); frequency; synchronous frequency; primary active power; primary reactive power; power factor; and load factor. This primary measurement data reflects the current operating status of the distribution substation and serves as the foundational input for subsequent judgments regarding voltage exceeding limits, load factor exceeding limits, power backfeeding, energy storage charging and discharging control modes, and the virtual power plant's aggregated dispatch capabilities.

[0033]

[0034] Specifically, when the integrated control terminal detects a negative primary active power value for a distribution transformer area, it indicates that the distributed photovoltaic (PV) output may exceed local load demand, resulting in power being fed back to the upper-level grid. A negative load factor indicates that the transformer is in a reverse load state. In this case, the integrated control terminal further considers PV output, user load, energy storage SOC, and rechargeable energy storage capacity to determine whether it is necessary to control the energy storage to enter a charging state to absorb excess PV power and reduce reverse power flow in the distribution transformer area. If the primary voltage or phase-to-phase voltage of the distribution transformer area approaches or exceeds the preset voltage limit, the integrated control terminal prioritizes voltage management control strategies. If the distribution transformer area is under fault maintenance or severe weather warning, the integrated control terminal prioritizes uninterrupted power supply control strategies.

[0035] For the j-th distribution substation, let its photovoltaic output at time t be... User load power is Energy storage charging power is The energy storage discharge power is The rated capacity of the distribution transformer is The bus voltage is The rated energy storage capacity is The energy storage state of charge is Where j represents the distribution area number and t represents the scheduling time.

[0036] In step 1, the integrated control terminal collects the operating data of the distribution area according to a preset sampling period and performs unified time stamping and validity judgment on the collected data. If there are short-term anomalies in the collected voltage, current, power, or load rate data, they are corrected by limiting, moving average processing, or substitution with the previous valid value; if there are short-term missing photovoltaic output or load data, they are supplemented by data from adjacent time points; if there is an abnormality in energy storage communication or an energy storage fault alarm, the energy storage of that distribution area is marked as not eligible to participate in regular aggregated scheduling. The preprocessed distribution area operating data is used for both local control judgment and cloud-based aggregated scheduling.

[0037] In step 2, the integrated control terminal determines whether the energy storage has the capability to participate in local control and virtual power plant dispatch based on the operating scenario of the distribution area and the SOC status of the energy storage. For the j-th distribution area energy storage, its SOC update relationship is expressed as: ; In the formula: Indicates energy storage charging efficiency. Indicates the energy storage discharge efficiency. This represents the scheduling time interval. This formula is used to describe the change in SOC (State of Charge) during the charging and discharging processes of energy storage.

[0038] Energy storage should meet SOC safety constraints during operation: ; In the formula: This represents the minimum permissible state of charge for the j-th energy storage area. This represents the maximum allowable state of charge for the j-th energy storage area. Below If a fault alarm is detected in the energy storage, the energy storage will not participate in the regular discharge scheduling.

[0039] When there are severe weather warnings, fault repairs, or critical load supply needs in a transformer substation, the energy storage in the j-th substation needs to reserve a minimum supply capacity, which is expressed as: ; In the formula: This represents the minimum energy storage capacity that needs to be reserved for the j-th distribution area. This represents the power of the important load in the j-th transformer area. This indicates the expected duration of power supply guarantee. The minimum power supply capacity is prioritized for supplying power to critical loads during transformer area faults, maintenance, or severe weather scenarios, and is not included in ordinary economic discharge dispatch.

[0040] After deducting the minimum SOC-corresponding electricity and the minimum guaranteed supply capacity, the capacity of the j-th distribution area's energy storage that can participate in regular discharge scheduling at time t is expressed as: ; The capacity of the j-th energy storage area that can participate in charging scheduling at time t is expressed as: ; The two capacities mentioned above are used to determine the amount of electricity that energy storage can release and absorb at the current moment. Only when the minimum guaranteed supply capacity and the SOC safety boundary are met can the remaining energy storage capacity participate in peak shaving and valley filling, photovoltaic power consumption, and virtual power plant economic dispatch.

[0041] In step 3, the integrated control terminal executes local energy storage control under the safety constraints of the distribution area. This local control includes photovoltaic reverse overload control, distribution area voltage stabilization control, peak load shaving control, severe weather supply guarantee control, and normal economic operation control.

[0042] In photovoltaic reverse overload control, the reverse power of the j-th transformer area at time t is expressed as: ; This gives the reverse load rate of the j-th transformer area at time t: ; To avoid reverse overload of the distribution transformer, the reverse load rate should meet the following requirements: ; In the formula: This represents the maximum allowed reverse load rate for the j-th transformer area. At that time, the integrated control terminal prioritizes controlling the energy storage to enter the charging state, by increasing... Absorbing excess photovoltaic power, making The load factor will decrease and bring the reverse load rate back to within acceptable limits.

[0043] like Figure 2 As shown, in one embodiment, when the reverse load rate of the distribution transformer decreases from 80% to 20%, the bus voltage of the distribution transformer decreases from 408.512V to 404.081V. This result indicates that after the reverse load rate of the distribution transformer decreases, the bus voltage of the distribution transformer also decreases, demonstrating that absorbing excess photovoltaic power through energy storage charging can effectively suppress the voltage rise problem caused by large-scale distributed photovoltaic power generation. Therefore, this invention can not only reduce the reverse power flow of the distribution transformer in scenarios with large-scale photovoltaic power generation, but also improve the voltage operating status of the distribution transformer.

[0044] In the voltage stabilization control of transformer substations, the bus voltage of the j-th transformer substation should satisfy: ; In the formula: This indicates the lower limit of the allowable voltage for the transformer area. This indicates the maximum allowable voltage for the transformer area. When... When necessary, the integrated control terminal prioritizes controlling the energy storage charging to absorb active power, reducing the photovoltaic backfeed power; if necessary, it utilizes the reactive power regulation capability of the energy storage grid-connected converter to restore the transformer area bus voltage to within the allowable range. Furthermore, when the energy storage has a discharge capacity, the integrated control terminal can control the energy storage to discharge appropriately, providing support for the voltage at the end of the distribution area.

[0045] In peak load shaving control, the positive power of the j-th transformer area at time t is expressed as: ; Forward load factor is expressed as: ; To avoid forward overload of the distribution transformer, the forward load factor should meet the following requirements: ; In the formula: This represents the maximum allowed positive load rate for the j-th transformer area. When... Furthermore, when the energy storage has a discharge capacity, the integrated control terminal controls the energy storage discharge, thereby improving... This reduces the power absorbed by the distribution area from the upper-level power grid, thereby mitigating the risk of heavy overload of the distribution transformer.

[0046] During severe weather power supply control, when the integrated control terminal receives severe weather warnings, transformer area maintenance plans, fault risk signals, or power supply demands from critical loads, the energy storage exits the normal economic discharge mode and prioritizes entering the power supply preparation state. Based on photovoltaic output, load levels, and electricity price information, the integrated control terminal controls energy storage charging during periods of high photovoltaic power generation, light load, or low electricity prices, ensuring that the energy storage's State of Charge (SOC) is as close as possible to the preset power supply preparation SOC. Once the energy storage reaches the power supply preparation state, it will not execute normal economic discharge commands except for emergency power supply, voltage over-limit mitigation, or reverse overload mitigation needs, to ensure that critical loads have sufficient reserve power.

[0047] Under normal economic operation control, when there are no reverse overloads, forward overloads, voltage exceedances, or supply guarantee risks in the distribution area, the integrated control terminal executes economic operation strategies based on future load forecasts, photovoltaic output forecasts, time-of-use pricing, and cloud dispatch instructions. Energy storage can be charged during off-peak electricity price periods or peak photovoltaic power generation periods, and discharged during peak electricity price periods, provided that the capacity does not fall below the minimum guaranteed supply capacity, thus achieving peak shaving and valley filling, and peak-valley arbitrage. If cloud dispatch instructions conflict with local safety constraints in the distribution area, the local safety constraints and uninterrupted power supply constraints take priority, and the restricted status is uploaded to the cloud energy storage integrated management and control platform.

[0048] In step 4, the cloud-based energy storage integrated management and control platform receives data from multiple distribution area integrated control terminals, including energy storage SOC, rechargeable capacity, dischargeable capacity, minimum guaranteed supply capacity, local operating mode, and local constraint status, to determine the energy storage set that can participate in aggregation within the current scheduling cycle. Energy storage that is experiencing communication abnormalities, faults, maintenance, or insufficient guaranteed supply capacity is not included in the regular adjustable resource pool; energy storage with normal SOC, normal communication, and adjustable capacity is included in the virtual power plant aggregation resource pool.

[0049] The power that the energy storage in the j-th distribution area can participate in charging regulation at time t is expressed as: ; The power of the j-th energy storage area that can participate in discharge regulation at time t is expressed as: ; In the formula: This represents the maximum charging power of the energy storage in the j-th transformer area. This represents the maximum discharge power of the energy storage in the j-th transformer area.

[0050] like Let be a state variable representing whether the energy storage of the j-th distribution area participates in cloud aggregation at time t. A value of 1 indicates participation in aggregation, and a value of 0 indicates non-participation. Then, the aggregated chargeable power and aggregated dischargeable power of the virtual power plant at time t are expressed as follows: ; ; The aggregate discharge capacity and aggregate charge capacity of the virtual power plant at time t are expressed as follows: ; ; To ensure uninterrupted power supply to all transformer substations, the aggregated emergency reserve capacity that the virtual power plant needs to maintain is represented as follows: ; The above aggregation results are used to form the callable power boundary and capacity boundary when virtual power plants participate in peak shaving, demand response, ancillary services and market transactions.

[0051] In step 5, the cloud-based energy storage integrated management and control platform constructs a virtual power plant economic operation optimization model, under the premise of meeting local safety constraints of the distribution area, energy storage SOC constraints, minimum supply capacity constraints, aggregated adjustable capacity boundaries, and superior dispatch instructions. This model comprehensively considers electricity sales revenue, electricity purchase cost, energy storage operating cost, energy storage peak-shaving and valley-filling revenue, carbon trading revenue, green certificate trading revenue, and investment depreciation costs to determine the charging power, discharging power, reserve capacity, and market participation capacity of energy storage in multiple distribution areas at each dispatch time.

[0052] In this embodiment, the total operating cost of the energy storage system is expressed as: ; In the formula: This represents the total operating cost of the energy storage system. Indicates unit operating cost, This represents the output of the j-th energy storage area at time t. This represents the cost of energy storage lifespan degradation.

[0053] The economic benefits of energy storage systems are expressed as follows: ; In the formula: This refers to the revenue gained from energy storage discharge or participation in peak shaving. This refers to the revenue generated from energy storage charging by absorbing low-priced electricity or surplus photovoltaic electricity.

[0054] The objective function for the net revenue of the virtual power plant is expressed as: ; In the formula: This represents the net revenue of the virtual power plant. Revenue from electricity sales This indicates the benefits or costs of green certificate transactions that take into account risks. This indicates the carbon trading benefits or costs that take into account risks. This indicates the operating cost of a conventional power supply. This indicates the operating and maintenance costs of renewable energy. Indicates the cost of purchasing electricity. This indicates the cost of investment depreciation.

[0055] To evaluate the economic performance of distributed energy storage aggregation virtual power plants, the investment payback period, return on investment, and cost-benefit ratio can also be calculated, expressed as follows: ; ; ; In the formula: Indicates the investment return period. Indicates return on investment. This represents the benefit-cost ratio. The above indicators are used to evaluate the economic performance of virtual power plants in terms of investment recovery capacity, profitability, and input-output relationship.

[0056] like Figure 3 As shown, in one embodiment, the operating scenario of the CET-GCT collaborative mechanism without a distributed energy storage system is used as the baseline scenario, and carbon emissions, operating costs, and net profit are normalized. The results show that after introducing a distributed energy storage system and cooperating with the carbon-green certificate trading mechanism, the carbon emission index and operating cost index decrease, while the net profit index increases. This result indicates that distributed energy storage aggregation and control can not only serve local safety control in power distribution areas but also reduce operating costs, decrease carbon emissions, and increase overall returns at the virtual power plant level.

[0057] like Figure 4 As shown, in the comparison of investment return indicators under different operating scenarios, after adopting the CET-GCT collaborative mechanism and connecting to the distributed energy storage system, the investment return cycle index decreased, while the investment return rate index and the benefit-cost ratio index increased. The results indicate that this invention, while meeting the constraints of uninterrupted power supply, voltage stability, and load factor safety in the distribution substation, can further improve the economic efficiency and engineering feasibility of virtual power plant operation.

[0058] In a specific operation, if a midday surge in solar power generation causes the reverse load rate of a transformer area to exceed the preset limit, the integrated control terminal will prioritize the solar reverse overload control strategy, controlling the energy storage to charge within the rechargeable power range. If the cloud platform issues a market-based discharge command at this time, the local integrated control terminal will reject the discharge command and upload a restricted status of "reverse overload management priority" to the cloud platform. If the evening load peak causes the transformer's forward load rate to exceed the preset limit, and the energy storage SOC is higher than the minimum guaranteed supply SOC, the integrated control terminal will control the energy storage to discharge and reduce peak loads. If the discharge may encroach on the minimum guaranteed supply capacity, the discharge power will be limited or normal discharge will be stopped. If the nighttime electricity price is low and there is no risk of voltage or load rate exceeding limits in the transformer area, the energy storage will charge according to the economic operation plan, reserving capacity space for solar power consumption and emergency supply the next day.

[0059] At the end of each dispatch cycle, the cloud-based energy storage integrated management and control platform re-receives data uploaded by the integrated control terminals of each distribution area, including distribution bus voltage, transformer load rate, photovoltaic output, user load, energy storage SOC, energy storage fault status, local control execution results, and market operation data. It then re-executes the data acquisition, status determination, local control, energy storage aggregation, and cloud-based dispatch processes. Through rolling updates, the system can dynamically adjust subsequent dispatch plans based on changes in distribution area operating status, photovoltaic output, load variations, remaining energy storage capacity, and market prices, ensuring consistency between local safety control in distribution areas and cloud-based economic dispatch of the virtual power plant.

[0060] In summary, this embodiment uses... Figure 1 The specific execution process is shown in Table 2, along with the single measurement operation data for the transformer substation. Figure 3 The relationship between reverse load rate and transformer bus voltage is shown. Figure 3 The results show the normalized comparison of carbon emissions, operating costs, and net profit under different operating scenarios. Figure 4 The comparison of investment return indicators shows that this invention can further enhance the aggregation and dispatch capabilities of distributed energy storage in multiple transformer substations and the economic operation of virtual power plants, while achieving uninterrupted power supply, photovoltaic reverse overload mitigation, voltage stability control, and peak load reduction. Compared with virtual power plant dispatch methods that rely on independent control of a single energy storage unit or solely pursue maximum economic benefits, this invention can improve the comprehensive value of distributed energy storage in participating in the electricity market, demand response, ancillary services, and carbon-green certificate trading, while ensuring local safety and power supply to important loads in the transformer substation.

Claims

1. A method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation, characterized in that: Includes the following steps: Step 1: Collect power grid-load-storage operation data for distribution transformer areas; Step 2: Based on the operational data collected in Step 1, determine whether the energy storage in the distribution area has the capability to ensure uninterrupted power supply, and determine the current capacity range of the energy storage in the distribution area that can participate in dispatch. Step 3: Under the security constraints of the distribution area, implement the local security control policy for the distribution area; Step 4: Aggregate distributed energy storage in multiple distribution substations and conduct a virtual power plant adjustable capacity assessment; Step 5: Using the adjustable capacity of the virtual power plant obtained in Step 4 as a constraint, construct an economic operation optimization model for the virtual power plant and determine the charging and discharging plan for energy storage in each distribution area; after execution, update Steps 1 to 5 on a rolling basis according to the real-time status of each distribution area and changes in market signals, so as to achieve dynamic coordination between uninterrupted operation of distribution areas and economic dispatch of the virtual power plant.

2. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 1, characterized in that: Step 1 involves collecting operational data from the power grid, distribution network, load, and storage systems in the distribution area. This specifically includes: 1.1: Collecting distribution transformer operation status data in distribution substation areas: By configuring voltage and current acquisition devices on the distribution transformer side, the active power, reactive power, forward load rate, reverse load rate, and operating voltage of the distribution transformer can be obtained in real time. The distribution transformer operating status data is used to determine whether there is a risk of forward heavy overload, reverse heavy overload, and voltage exceeding the limit in the distribution transformer area. 1.2: Collecting Distributed Photovoltaic Operation Data: Real-time power output data of distributed photovoltaics in the distribution substation is obtained through the photovoltaic inverter communication interface or carrier communication module, and after being normalized or aggregated, a photovoltaic power output curve of the substation is formed. The distributed photovoltaic power output data is used to determine whether there are operational problems in the distribution substation such as excessive photovoltaic power generation, increased reverse power flow, and insufficient local photovoltaic consumption. 1.3: Collect distributed energy storage operation status data: The energy storage cabinet's main control unit collects information such as energy storage status of charge, rated energy storage power, rated energy storage capacity, current charging and discharging power, rechargeable status, discharging status, operating temperature, fault alarms, and available capacity. Distributed energy storage operation status data is used to determine whether the energy storage has the ability to participate in local control, emergency supply, and cloud-based aggregation and scheduling. 1.4: Collect user load and critical load demand data: User load power, load forecast data, and important load list are obtained through smart metering devices or integrated control terminals in distribution substations; when a substation experiences a fault, maintenance, or severe weather risk, the important load demand data is used to determine the minimum energy storage capacity and emergency discharge power. 1.5: Collect cloud-based scheduling and market operation data: The system receives peak-shaving instructions, demand response instructions, emergency power support instructions, time-of-use electricity prices, carbon trading prices, green certificate trading prices, and operating strategy parameters from the cloud energy storage integrated management and control platform, the dispatch master station, or the multi-party collaborative management and control platform via 4G, 5G, or dispatch data network; cloud dispatch and market operation data are used for cloud collaborative dispatch of the virtual power plant in step 5. 1.6: Establish a local-cloud collaborative communication link: The integrated control terminal is deployed near the transformer in the distribution area. On the one hand, it communicates locally with the energy storage cabinet, distribution transformer data acquisition device and photovoltaic communication module. On the other hand, it connects to the cloud energy storage integrated management and control platform through 4G, 5G or dispatch data network. This forms an information transmission structure of "distribution area equipment layer - integrated control terminal layer - cloud management and control platform layer", which provides data support for subsequent local safety control and virtual power plant aggregation dispatch.

3. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 1, characterized in that: In step 2, based on the energy storage state of charge, rated capacity, important load power, expected supply duration, severe weather warning status, fault or maintenance status and minimum SOC constraint collected in step 1, it is determined whether the energy storage in the distribution area has the ability to provide uninterrupted power supply and the capacity range that the energy storage can currently participate in dispatch is determined. When the distribution area is under severe weather warning, fault maintenance or important load supply guarantee, priority should be given to maintaining the energy storage high-charge state and reserving the minimum supply guarantee capacity. When the distribution area is in normal operation, energy storage is allowed to participate in photovoltaic consumption, peak shaving and valley filling, and virtual power plant economic dispatch, provided that the minimum supply capacity and SOC safety boundary are met.

4. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 3, characterized in that: Step 2 involves determining whether the energy storage in the distribution area has uninterrupted power supply capabilities and identifying the current capacity range that the energy storage can participate in dispatching. Specifically, this includes: 2.1: Determine the operating scenario of the distribution radio area: Based on severe weather warning signals, maintenance plans, fault alarms, important load supply requirements, transformer voltage status, transformer forward and reverse load rates, and superior dispatch instructions, the operating scenarios of distribution transformer areas are divided into severe weather supply scenarios, fault or maintenance supply scenarios, photovoltaic reverse overload scenarios, voltage over-limit scenarios, peak load heavy overload scenarios, and normal economic operation scenarios. 2.2: Determine the SOC update relationship for energy storage: For the Energy storage in individual distribution stations, in The state of charge at time t is represented as: ； In the formula: and They represent the first Individual power storage Time and The state of charge at any given moment; Indicates the first Individual power storage The charging power at any given moment; Indicates the first Individual power storage Discharge power at any given moment; Indicates energy storage charging efficiency; Indicates the energy storage discharge efficiency; Indicates the scheduling time interval; Indicates the first The rated capacity of energy storage in each distribution area; 2.3: Setting Safety Constraints for Energy Storage SOC: Energy storage must satisfy upper and lower limits of state of charge during operation, and its expression is as follows: ； In the formula: Indicates the first The minimum state of charge allowed for energy storage in each distribution area; Indicates the first The maximum state of charge allowed for energy storage in each distribution area; Indicates the first Individual power storage The state of charge at any given moment; 2.4: Determine the minimum supply capacity: Based on the power of critical loads and the expected duration of power supply, the first The minimum energy storage capacity that needs to be reserved for each distribution area is expressed as follows: ； In the formula: Indicates the first The minimum energy storage capacity that each distribution area needs to reserve for supply guarantee; Indicates the first The power of key loads in each transformer substation area; Indicates the first The estimated duration of supply guarantee for each distribution area; Indicates the energy storage discharge efficiency; 2.5: Determine the energy storage capacity that can participate in discharge scheduling: After deducting the minimum guaranteed supply capacity and the power corresponding to the minimum SOC, the first Individual power storage The capacity available for regular discharge scheduling at any given time is expressed as: ； In the formula: Indicates the first Individual power storage Capacity that can participate in regular discharge scheduling at any time; Indicates the first The current stored electricity in each distribution area; This indicates the reserve capacity corresponding to the lowest SOC; Indicates the minimum guaranteed supply capacity; 2.6: Determine the energy storage capacity that can participate in charging dispatch: No. Individual power storage The capacity that can absorb electricity at any given time is expressed as: ； In the formula: Indicates the first Individual power storage Capacity available for charging scheduling at all times; Indicates the maximum permissible state of charge for energy storage; Indicates the current state of charge of the energy storage; Indicates the rated capacity of energy storage; 2.7: Determine the operational priority of energy storage: In scenarios involving severe weather, faults, or maintenance, the priority for energy storage operation is uninterrupted power supply. Energy storage should be kept at a high charge level and reserve capacity to meet the power supply needs of important loads. In photovoltaic reverse overload and voltage limit exceedance scenarios, the priority of energy storage operation is local safety management. Energy storage should prioritize the control strategies of charging to absorb excess photovoltaic power and reducing the voltage of the transformer area. In peak load and heavy overload scenarios, the priority of energy storage operation is to support peak shaving, and energy storage should reduce the load rate of distribution transformers by discharging. Under normal economic operating conditions, energy storage can participate in peak-valley arbitrage and virtual power plant dispatch, provided that the minimum supply capacity is met. 2.8: Output area supply status and energy storage adjustable status.

5. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 1, characterized in that: In step 3, based on the distribution area bus voltage, transformer forward load rate, transformer reverse load rate, photovoltaic output, and user load changes, the local safety control strategy for the distribution area is executed: When the photovoltaic power generation causes the reverse load rate of the distribution transformer to exceed the preset upper limit, the energy storage is controlled to enter the charging state to absorb the excess photovoltaic power. When the voltage of the distribution transformer area bus exceeds the overvoltage action threshold, the voltage of the transformer area is reduced by adjusting the energy storage charging power or reactive power regulation capability. When peak load causes distribution transformers to be positively overloaded, control the energy storage discharge to reduce the peak load of the distribution area; When there is a fault or maintenance outage risk in the distribution area, the energy storage is switched to emergency power support mode to provide temporary power supply for important loads.

6. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 5, characterized in that: Step 3 involves implementing the local security control policy for the distribution area, specifically including: 3.1: Establish the reverse load rate criterion for transformer substations: No. Each district The reverse power caused by photovoltaic power generation at any given moment is expressed as: ； In the formula: Indicates the first Each district Reverse power at time; Indicates the first Each district Solar power output at all times; Indicates the first Individual distribution area energy storage discharge power; Indicates the first Load power of users in each distribution area; Indicates the first Individual energy storage charging power; From the previous equation, we get the first... Each district The reverse load rate at any given time is expressed as: ； In the formula: Indicates the first Each district Reverse load rate at any given time; Indicates the first Rated capacity of distribution transformers in each area; 3.2: Set reverse overload constraints: To avoid exceeding the reverse load rate limit of the distribution transformer, the reverse load rate should meet the following requirements: ； In the formula: Indicates the first The maximum allowed reverse load rate for each transformer area; when At the same time, the integrated control terminal prioritizes the charging of energy storage, and reduces the reverse power flow by absorbing excess photovoltaic power, so that the reverse load rate falls back to the allowable range. 3.3: Establish the positive load factor criterion for the transformer area: No. Each district The positive power caused by peak user load at any given time is expressed as: ； In the formula: Indicates the first Each district The positive power at any given moment; From the previous equation, we get the first... Each district The positive load factor at any given time is expressed as: ； In the formula: Indicates the first Each district Positive load rate at any given time; 3.4: Setting positive overload constraints: To avoid exceeding the limit for the forward load rate of the distribution transformer, the forward load rate should meet the following requirements: ； In the formula: Indicates the first The maximum allowable positive load rate for each distribution area; when At the same time, the integrated control terminal controls the energy storage discharge to reduce the power absorbed by the distribution area from the upper-level power grid and reduce the risk of heavy overload of the distribution transformer; 3.5: Set voltage safety constraints for transformer substations: No. The bus voltage of each transformer substation area should meet the following requirements: ； In the formula: Indicates the first Each district Bus voltage at time; This indicates the lower limit of the permissible voltage for the transformer substation; This indicates the maximum allowable voltage limit for the transformer substation. when At the same time, the integrated control terminal restores the voltage in the distribution area to the allowable range by increasing the charging power of the energy storage, absorbing the surplus photovoltaic output, or calling the reactive power regulation capability of the energy storage converter. 3.6: Implement supply control strategies during severe weather: When severe weather or power outage risks are imminent in the distribution area, the integrated control terminal receives the power supply guarantee instruction from the cloud platform and charges the energy storage during periods of high photovoltaic power generation, low electricity prices, or light load, based on photovoltaic output, load conditions, and electricity price information, so that the energy storage reaches a high-charge state. After the energy storage reaches the preset power supply guarantee state, it will no longer perform regular economic discharge except for emergency power supply or safety control needs. 3.7: Implement photovoltaic reverse overload control strategy: When the photovoltaic grid-connected power is large, causing the reverse load rate of the distribution transformer in the area to exceed the preset upper limit of the reverse load rate, the integrated control terminal controls the energy storage to enter the charging state. By absorbing the excess photovoltaic power, the reverse power flow of the distribution transformer is reduced, and the reverse load rate is brought back to the target range. 3.8: Implement the voltage stabilization control strategy for the transformer substation: When the voltage of the transformer area bus exceeds the overvoltage action threshold or enters the overvoltage action dead zone, the integrated control terminal reduces the reverse power level of the transformer area by adjusting the energy storage charging power; when necessary, it can also combine the reactive power regulation capability of the energy storage converter to perform auxiliary adjustment of the transformer area voltage and power factor. 3.9: Implement peak load shaving control strategy: When the load of the distribution area is at its peak and the positive load rate of the distribution transformer is close to or exceeds the preset upper limit, the integrated control terminal controls the energy storage to discharge, thereby reducing the power absorbed by the distribution area from the upper-level power grid. 3.10: Implement normal economic operation control strategies: When there is no voltage over-limit, reverse overload, forward overload or supply guarantee risk in the transformer area, the energy storage will formulate a charging and discharging plan based on historical load data, the load forecast results for the next 24 hours and the time-of-use electricity price; the energy storage will charge during off-peak electricity price periods and on the premise of meeting the load power supply demand, and discharge during peak electricity price periods and on the premise of not lowering the minimum supply guarantee capacity. 3.11: Output local control results.

7. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 1, characterized in that: In step 4, the distributed energy storage in multiple distribution substations is uniformly aggregated through the cloud energy storage integrated management and control platform to form the rechargeable power, dischargeable power, rechargeable capacity, dischargeable capacity, emergency backup capacity and market regulation capacity at the virtual power plant level. The aggregation process, while satisfying the local safety constraints, SOC constraints, and minimum power supply requirements of each distribution substation, calculates the adjustable capacity of energy storage in each distribution substation and determines the power and capacity boundaries that the virtual power plant can call upon within the current dispatch cycle based on the energy storage status, substation operation requirements, and superior dispatch instructions.

8. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 7, characterized in that: Step 4 involves assessing the adjustable capacity of distributed energy storage aggregation and virtual power plants across multiple distribution substations, specifically including: 4.1: Determine the energy storage clusters participating in the aggregation: The cloud-based energy storage integrated management and control platform receives energy storage status, operation scenarios, and local constraint information uploaded by the integrated control terminals of each distribution area. It includes energy storage that is in normal operation and has adjustable capabilities into the aggregated resource pool of the current scheduling cycle; it removes energy storage that is in a fault, under maintenance, communication abnormality, or insufficient supply capacity from the adjustable resource pool, or only retains its emergency backup attributes. 4.2: Calculate the rechargeable power of a single energy storage unit in a given area: No. Individual power storage The power that is constantly involved in charging regulation is expressed as: ； In the formula: Indicates the first Individual power storage The power that is constantly involved in charging regulation; Indicates the first The maximum allowable charging power for energy storage in each distribution area; Indicates the first Individual power storage The capacity to absorb electrical energy at any given time; Indicates charging efficiency; Indicates the scheduling time interval; 4.3: Calculate the discharge power of a single energy storage unit: No. Individual power storage The power available for discharge regulation at any given time is expressed as: ； In the formula: Indicates the first Individual power storage The power that can participate in discharge regulation at any time; Indicates the first The maximum allowable discharge power of energy storage in each distribution area; Indicates the first Individual power storage Capacity that can participate in regular discharge scheduling at any time; Indicates discharge efficiency; 4.4: Forming a virtual power plant to aggregate rechargeable power: If there are a total of [number] in the current scheduling period If energy storage in each distribution area participates in aggregation, then the virtual power plant will be able to... The aggregate rechargeable power at any given time is expressed as: ； In the formula: Indicates virtual power plants in Aggregated rechargeable power at any given moment; Indicates the number of stations participating in the aggregation; Indicates the first Individual power storage The state variable indicating whether or not to participate in aggregation at any given time has a value of 1 indicating participation in aggregation and a value of 0 indicating non-participation in aggregation. Indicates the first The power that can be used for charging regulation by the energy storage of each distribution area; 4.5: Forming a virtual power plant to aggregate dischargeable power: Virtual power plants The aggregate discharge power at time t is expressed as: ； In the formula: Indicates virtual power plants in The aggregate dischargeable power at any given moment; Indicates the first The power that can be used for discharge regulation by the energy storage in each distribution area; 4.6: Forming virtual power plant aggregated adjustable capacity: Virtual power plants The aggregate discharge capacity and aggregate recharge capacity at time t are expressed as follows: ； ； In the formula: Indicates virtual power plants in Aggregate discharge capacity at any given time; Indicates virtual power plants in Aggregate rechargeable capacity at any given moment; Indicates the first The capacity of energy storage in each distribution area that can participate in regular discharge dispatch; Indicates the first The capacity of energy storage in each distribution area that can participate in charging dispatch; 4.7: Forming a virtual power plant to aggregate emergency reserve capacity: Virtual power plants The aggregated emergency reserve capacity that needs to be maintained at all times is expressed as follows: ； In the formula: Indicates virtual power plants in Aggregate emergency backup capacity that must be maintained at all times; Indicates the first The minimum energy storage capacity that each distribution area needs to reserve for supply guarantee; 4.8: Output the aggregated evaluation results.

9. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 1, characterized in that: In step 5, based on the adjustable capacity boundary of the virtual power plant obtained in step 4, and combined with time-of-use electricity price, electricity sales revenue, electricity purchase cost, energy storage operation cost, energy storage peak shaving and valley filling revenue, carbon-green certificate trading revenue and investment depreciation cost, an economic operation optimization model for the virtual power plant is constructed. Under the premise of meeting the safety constraints of the distribution area, energy storage operation constraints, minimum supply capacity constraints and upper-level dispatch instructions, the charging and discharging plan of energy storage in each distribution area is determined. After the dispatch is executed, steps 1 to 5 are updated on a rolling basis according to the real-time operating status of the distribution area, the remaining capacity of energy storage, changes in photovoltaic output, changes in load, and changes in market prices, so as to realize the dynamic coordination between uninterrupted operation of the distribution area and economic dispatch of the virtual power plant.

10. The method for uninterrupted operation of distribution substations and coordinated control of virtual power plants based on distributed energy storage aggregation according to claim 9, characterized in that: Step 5 involves cloud-based collaborative scheduling and economic operation optimization of the virtual power plant, specifically including: 5.1: Constructing a distributed energy storage operating cost model: The total operating cost of an energy storage system is expressed as follows: ； In the formula: This represents the total operating cost of the energy storage system; Indicates the total number of scheduling cycles; This indicates the number of energy storage units participating in the aggregation; This represents the unit operating cost of a single energy storage unit; Indicates the first Individual power storage Efforts made at all times; Indicates that the battery energy storage system is in The cost of lifespan decay at any given moment; 5.2: Constructing a distributed energy storage revenue model: The benefits of a distributed energy storage system are expressed as follows: ； In the formula: This indicates the economic benefits of distributed energy storage systems; This indicates the benefits gained from energy storage participating in peak shaving or discharge. This refers to the revenue generated by energy storage participating in valley filling or charging to absorb low-priced electricity and surplus photovoltaic power; 5.3: Constructing a renewable energy quota model: The daily renewable energy quota is expressed as follows: ； In the formula: Indicates the daily quota for renewable energy; Indicates the renewable energy quota coefficient; Indicates the scheduling period The total amount of electricity demand at any given moment; Indicates the scheduling period; 5.4: Constructing a model for excess green certificate completion considering cross-day carryover: After unifying the wind power output item in the original model into the renewable energy output item, the excess green certificate completion amount is expressed as: ； In the formula: Indicates the first Daily green certificate over-quota completion; Indicates the number of renewable energy units participating in the green certificate accounting; Indicates the first Each renewable energy unit in Efforts during a specific time period; This indicates the maximum coefficient that the Green Certificate Exchange allows for carryover; This indicates the excess amount of green certificates that can be carried over from the previous day; Indicates the daily quota for renewable energy; 5.5: Constructing a carbon emission model: In the extended scenario where the virtual power plant includes a gas turbine or other controllable carbon emission power source, the carbon emissions of the virtual power plant during the dispatch cycle are expressed as follows: ； In the formula: This indicates the carbon emissions of the virtual power plant during the dispatch cycle; Indicates the number of gas turbines or other controlled carbon emission power sources; Indicates the first A carbon emission power source in The start / stop status variable for a time period has a value of 1 indicating that the device is on and a value of 0 indicating that the device is off. , , They represent the first The carbon emission coefficient of a carbon-emitting power source; Indicates the first A carbon emission power source in Efforts during a specific time period; 5.6: Constructing a risk and cost model for carbon-green certificate trading: The risk costs of carbon markets and green certificate markets, taking into account conditional value of risk, are expressed as follows: ； ； In the formula: This represents the carbon trading costs that take risk into account; This represents the transaction costs of green certificates, taking into account the risks involved. This represents the carbon market risk appetite coefficient; This represents the risk appetite coefficient in the green certificate market; Indicates the first Risks in the carbon trading market; Indicates the first Risks associated with individual green certificate trading markets; 5.7: Constructing the objective function for the net revenue of the virtual power plant: The net revenue of a virtual power plant is expressed as follows: ； In the formula: This indicates the net revenue of the virtual power plant; Represents revenue from electricity sales; Indicates the economic benefits of distributed energy storage systems; This refers to the benefits or costs of green certificate transactions that take into account risks. This refers to the carbon trading benefits or costs that take into account risks. Indicates that the gas turbine or other controllable power source is in The cost of generating electricity during a given period; express Total operating and maintenance costs of renewable energy units during specific time periods; This represents the total operating cost of the energy storage system; Indicates the cost of purchasing electricity; This represents the total investment depreciation cost; 5.8: Constructing investment return evaluation indicators: The investment payback period, rate of return on investment, and cost-benefit ratio are expressed as follows: ； ； ； In the formula: Indicates the investment return period; Indicates the rate of return on investment; Indicates the benefit-cost ratio; This represents the total investment depreciation cost; This indicates the net revenue of the virtual power plant; 5.9: Setting Cloud Scheduling Constraints: The virtual power plant cloud dispatch must meet the following constraints: distribution area safety constraints, energy storage SOC constraints, energy storage adjustable capacity boundary, minimum power supply guarantee capacity constraint, charging and discharging power boundary, photovoltaic consumption constraints, and upper-level dispatch command constraints. If the cloud economic optimization results conflict with the local safety constraints of the distribution area, the local safety constraints of the distribution area and the uninterrupted power supply guarantee constraints shall take priority, and the energy storage charging and discharging plan shall be revised accordingly. 5.10: Solving for the virtual power plant collaborative scheduling scheme: Based on the revenue model, cost model, risk model and constraints constructed in steps 5.1 to 5.9, the charging power, discharging power, reserve capacity and market participation capacity of energy storage in each distribution area during different scheduling periods are solved to form a cloud-based collaborative scheduling scheme for virtual power plants. 5.11: Perform rolling updates: After the dispatch plan is executed, the cloud energy storage integrated management and control platform will receive the operating status uploaded by the integrated control terminals of each distribution area, including distribution area voltage, distribution transformer load rate, photovoltaic output, user load, energy storage SOC, energy storage remaining capacity and local control execution results, and re-execute steps 1 to 5 according to the latest status. Through rolling updates, the energy storage regulation strategy can continuously adapt to changes in distribution area operating status, photovoltaic output fluctuations, load changes and market price changes.

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