Hierarchical aggregation medium and low voltage distributed new energy active power regulation and control method and system

Abstract

The invention discloses a hierarchical aggregation medium and low voltage distributed new energy active power regulation and control method and system, and the method comprises the steps: carrying out the hierarchical modeling and aggregation management of medium and low voltage distributed new energy, and obtaining an aggregation power model of each layer; receiving an active power regulation and control instruction and monitoring a load rate out-of-limit condition of a feeder line or a transformer area in real time, so as to adaptively select a power distribution strategy, and decomposing system-level target power or out-of-limit total regulation power to each level step by step in combination with an aggregation power model of each level, so as to obtain target power of a control unit of each level and generate a control instruction; security check is carried out before the control instruction is issued, an executable instruction sequence is generated after the security check is passed, and the executable instruction is issued to each level of control unit and is fed back and corrected. According to the invention, distributed new energy global regulation and control across middle and low voltage levels can be realized, the observable and controllable capability of new energy is improved, and safe and stable operation of a power distribution network is ensured.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatching and control technology, and relates to a hierarchical aggregation method and system for active power regulation of medium and low voltage distributed renewable energy sources. Background Technology

[0002] With the rapid development of new energy sources globally and the advancement of carbon peaking and carbon neutrality goals, the proportion of distributed energy sources such as distributed photovoltaic, wind power, and energy storage connected to distribution networks continues to rise. However, the randomness, intermittency, and uncontrollability of distributed new energy sources bring new challenges to the safe operation of distribution networks: The distribution network experiences large load fluctuations and complex constraints: the high proportion of distributed renewable energy access leads to increased voltage fluctuations in the distribution area, feeder overload and abnormal transformer capacity utilization, and conventional single-point control and experience-based regulation are difficult to meet the requirements for safe operation.

[0003] Lack of systematic and hierarchical control methods: Existing distributed energy control methods are mostly concentrated on a single voltage level or a single resource unit, lacking hierarchical modeling and hierarchical aggregation strategies from medium-voltage power stations and distribution areas to low-voltage photovoltaic units, making it difficult to achieve coordinated and optimized control in multi-level and multi-unit environments.

[0004] The power allocation strategy is too simplistic and lacks comprehensive control capabilities: Traditional power allocation methods rely heavily on capacity or proportional allocation, ignoring the combined effect of real-time adjustable power of units and control priorities, resulting in some waste of resources or excessive reduction, making it difficult to balance safety, economy and maximizing the capacity of new energy access.

[0005] Lack of over-limit protection and closed-loop safety verification mechanism: When the feeder or transformer area is overloaded, the existing methods usually lack dynamic over-limit protection logic and command verification mechanism, which may lead to the failure of power control commands to be executed or the introduction of new risks.

[0006] Therefore, there is an urgent need for a medium- and low-voltage distributed renewable energy active power control scheme that can achieve hierarchical aggregation, step-by-step allocation, over-limit protection, and closed-loop verification, so as to improve the safety, reliability, and renewable energy access capabilities of the distribution network, and provide technical support for source-grid-load-storage coordinated control and intelligent distribution dispatch. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a hierarchical aggregation method and system for active power regulation of distributed renewable energy in medium and low voltage ranges. This system enables full-domain regulation of distributed renewable energy across medium and low voltage levels, enhancing the observability and controllability of renewable energy and ensuring the safe and stable operation of the power distribution network.

[0008] The present invention adopts the following technical solution.

[0009] The first aspect of this invention proposes a hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources, comprising: Hierarchical modeling and aggregation management of medium- and low-voltage distributed renewable energy sources are performed to obtain aggregation power models for each layer. It receives active power control commands and monitors the over-limit situation of feeder or transformer area load rate in real time. It adaptively selects power allocation strategies and combines the aggregated power models of each level to decompose the system-level target power or over-limit total regulation power to each level, obtain the target power of each level control unit and generate control commands. Before issuing control commands, a safety check is performed. Once the safety check is passed, an executable command sequence is generated, and the executable commands are issued to control units at each level and feedback is provided for correction.

[0010] Preferably, the hierarchical modeling and aggregation management of medium- and low-voltage distributed renewable energy sources to obtain aggregation power models for each layer includes: The medium-voltage power station, distribution area, and low-voltage photovoltaic unit are modeled in layers. The medium-voltage power station is modeled as a power station aggregation node control unit, forming the medium-voltage layer; the distribution area is modeled as a low-voltage distributed power aggregation node control unit, forming the distribution area layer; and the low-voltage photovoltaic unit is modeled as an adjustable basic control unit, forming the low-voltage layer. Based on the results of hierarchical modeling, a distributed new energy aggregation management system is established, which aggregates multiple control units at the same level into a single control object and establishes a hierarchical mapping relationship between different levels to form an aggregated power model for each level.

[0011] Preferably, the step of receiving active power regulation commands and monitoring in real time the load rate of feeders or transformer areas exceeding limits to adaptively select power allocation strategies includes: The system receives and parses active power control instructions. If it is a routine control, it selects a strategy to allocate the system-level target power based on the proportion of installed capacity or the proportion of real-time adjustable power. If it is an emergency control, it selects a strategy to allocate the system-level target power based on the control priority or a hybrid allocation strategy. If the load rate of a feeder or transformer area exceeds the limit, a priority allocation or hybrid allocation strategy is selected to decompose the total regulation power exceeding the limit.

[0012] Preferably, after the adaptive power allocation strategy is selected, the system-level target power or over-limit total regulation power is decomposed to each level in combination with the aggregated power model of each level to obtain the target power of each level, and then allocated within the level to obtain the target power of each control unit.

[0013] Preferably, the target power of the allocation level If the adaptive power allocation strategy is selected as a hybrid allocation strategy, then the target power for each control unit is:

[0014] in, The rated installed capacity of the device for control unit i; For the real-time adjustable power of control unit i; The control priority of control unit i; N is the total number of control units in this level; , , These are the weighting coefficients for the rated installed capacity of the equipment, the weighting coefficient for the real-time adjustable power considering the predictive drive gain factor, and the weighting coefficient for the control priority, respectively.

[0015] Preferably, The calculation formula is:

[0016] in: The response delay of control unit i; For the device health status of control unit i; The historical execution accuracy of control unit i; , , These are the weighting coefficients.

[0017] Preferably, The calculation formula is:

[0018]

[0019] in, This represents the real-time adjustable power base weighting coefficient of control unit i; To predict the driving gain factor; To predict power deviation; Rated power of the equipment; This is the adjustment coefficient.

[0020] Preferably, the formula for calculating the total over-limit regulating power is:

[0021] in, Total regulating power exceeding the limit; This is a quantitative value representing the impact of active power on equipment current. For power flow sensitivity matrix; A collection of devices that exceed limits; This represents the current heavy overload depth. For equipment thermal risk factors; The current overload has lasted for [duration]. This refers to the allowable time for thermal margin. , For adjustment coefficients; This refers to the risk weighting coefficient. For equipment i The current; For equipment i Maximum allowable current; The variables that need to be optimized; This is the system power reference value.

[0022] Preferably, an adaptive adjustment coefficient is used to adjust the total over-limit adjustment power. Adaptive adjustment is performed, and the mechanism for adjusting the adaptive adjustment coefficient is as follows:

[0023] in, , For the current time and the next time, the adaptive adjustment coefficients are used. To adjust the step size, To take system state into account The monotonic adjustment function, and For future load change rates and photovoltaic availability margin, This represents the severity index of exceeding the limit.

[0024] Preferably, the security verification includes instruction legality verification, instruction security verification, and instruction executability verification; The instruction validity check includes: determining whether the target power of the control unit exceeds the unit's rated capacity; if so, automatically correcting it to the rated range limit or failing the instruction validity check. Command safety verification includes: predicting the load rate of the feeder and transformer area after the control command is executed. If the predicted load rate exceeds the safety threshold, the command safety verification fails. The executability verification of instructions includes: checking the control response capability and communication delay of each control unit to ensure that the instructions can be accurately executed within the actual control cycle; for unexecutable instructions, the control unit corresponding to the instruction is included in the uncontrollable sequence and will not be called in the next round of adjustment until the restriction is manually lifted; and the unexecuted control instructions are transferred to other controllable control units with adjustment margin to ensure the reliable operation of the hierarchical aggregated control closed loop.

[0025] Preferably, the feedback correction includes: continuously monitoring the execution effect of the command and correcting it through a feedback loop: if the control unit does not execute according to the target, then recalculate and issue an adjustment command; if the global power deviation exceeds the allowable range, then trigger the next round of power allocation and over-limit protection.

[0026] A second aspect of this invention proposes a hierarchical aggregation-based medium- and low-voltage distributed renewable energy active power control system, comprising: The hierarchical aggregation module is used to perform hierarchical modeling and aggregation management of medium and low voltage distributed renewable energy sources, and obtain the aggregation power model of each layer; The power allocation module is used to receive active power control commands and monitor the over-limit situation of feeder or transformer area load rate in real time. It adaptively selects the power allocation strategy, combines the aggregated power model of each layer, decomposes the system-level target power or over-limit total regulation power to each level, obtains the target power of each level control unit, and generates control commands. The safety verification module is used to perform safety verification before the control commands are issued. After the safety verification is passed, an executable command sequence is generated, and the executable commands are issued to the control units at each level and feedback is provided for correction.

[0027] A third aspect of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0028] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0029] Compared with the prior art, the beneficial effects of the present invention include at least the following: This invention, through hierarchical aggregation, step-by-step allocation, over-limit protection, and closed-loop verification, can meet the needs of the distribution network for optimized regulation, coordinated allocation, and flexible response under the premise of ensuring safe operation in the context of high proportion of distributed renewable energy access, and provides support for intelligent dispatching and source-grid-load-storage coordinated control of new power systems.

[0030] The hybrid allocation strategy proposed in this invention uses a weighted allocation based on comprehensive capacity, adjustable power, and priority. It introduces a mechanism for evaluating and controlling priority by integrating operation and maintenance characteristics with response characteristics, as well as an adaptive weighting mechanism based on prediction-driven gain, thereby achieving intelligence and foresight in the control process.

[0031] When performing over-limit control, this invention determines the total over-limit regulation power based on the equipment thermal risk factor, power flow sensitivity, and risk weight. This enables the dynamic elimination of heavy overloads in equipment such as feeders and transformers and the restoration of safe operating margins. Furthermore, the adaptive regulation coefficient no longer relies on manual experience to set, but instead makes a predictive response to system trends and risk levels, forming an adaptive control closed loop. Attached Figure Description

[0032] Figure 1 This is a flowchart of the hierarchical aggregation method for active power regulation of medium and low voltage distributed renewable energy sources according to the present invention. Figure 2This is a flowchart illustrating the implementation of the hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to the present invention. Figure 3 This is a schematic diagram of control execution and closed-loop feedback in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0034] This invention provides a hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources. It relates to the active power regulation of distributed renewable energy sources (including photovoltaic, wind power, energy storage, and other renewable energy sources) integrated into a distribution network. Addressing the operational risks and regulation challenges arising from the increasing proportion of distributed renewable energy access in existing distribution networks, such as insufficient single-point control capabilities, lack of hierarchical coordination, a single power allocation strategy, and a lack of over-limit protection and command security verification mechanisms, this method constructs a multi-level architecture comprising a dispatching end, a control end, and an execution end. It performs hierarchical modeling and aggregation management of medium- and low-voltage distributed renewable energy sources. Based on the regulation objectives, a hierarchical aggregation algorithm is used to decompose and allocate active power. When the load on a feeder, distribution area, or transformer exceeds a threshold, over-limit control logic is triggered to reduce the output of low-voltage distributed photovoltaic power. Before issuing commands, legality, security, and executability are verified. This method enables hierarchical modeling, aggregation management, power decomposition, and step-by-step allocation of medium-voltage substations, distribution areas, and low-voltage photovoltaic units. Combined with load constraints, regulation priorities, and over-limit protection logic, it achieves safe, accurate, fast, and closed-loop regulation of renewable energy active power. Specifically, as follows... Figures 1-3 As shown, the method includes: Step 1: Perform hierarchical modeling and aggregation management of medium and low voltage distributed renewable energy sources to obtain the aggregation power model of each layer; Step 1.1: Perform layered modeling for medium-voltage power stations, distribution areas, and low-voltage photovoltaic units respectively; More preferably, the hierarchical modeling step is as follows: hierarchical modeling is performed on the medium-voltage power station, distribution area, and low-voltage photovoltaic unit respectively, wherein the medium-voltage power station is modeled as a power station aggregation node, the distribution area is modeled as a low-voltage distributed power generation aggregation node, and the low-voltage photovoltaic unit is modeled as an adjustable basic unit; specifically: First, a hierarchical model is constructed for distributed new energy resources connected to the distribution network.

[0035] In the medium-voltage layer, it mainly includes medium-voltage photovoltaic power stations, wind farms, or station units containing energy storage, denoted as... ; At the distribution area level, it mainly includes the collection of distributed power sources within each distribution area, denoted as... ; In the low-voltage layer, it mainly includes distributed photovoltaic units on the low-voltage residential side or industrial and commercial user side, denoted as... ; Through the above hierarchical modeling, a hierarchical structure of one medium-voltage power station area and one low-voltage unit is formed, and a top-down aggregateable power relationship is established:

[0036] in, It provides adjustable active power for the entire network.

[0037] This hierarchical structure supports both hierarchical information aggregation and hierarchical instruction issuance, providing a unified logical framework for subsequent regulation and control.

[0038] Step 1.2: Based on the hierarchical modeling results, establish a distributed renewable energy aggregation management system, aggregate multiple control units at the same level into a single control object, and establish a hierarchical mapping relationship between levels to form an aggregated power model for each level. ; Step 2: Receive active power control commands and monitor the overload rate of feeders or transformer areas in real time. Adaptively select power allocation strategies and combine the aggregated power models of each level to decompose the system-level target power or the total overload regulation power to each level, obtain the target power of each level control unit and generate control commands. More preferably, receiving active power control commands and monitoring in real time whether the load rate of the feeder or transformer area exceeds the limit, in order to adaptively select a power allocation strategy, including: The system receives and parses active power control instructions. If it is a routine control, it selects a strategy to allocate the system-level target power based on the proportion of installed capacity or the proportion of real-time adjustable power. If it is an emergency control, it selects a strategy to allocate the system-level target power based on the control priority or a hybrid allocation strategy. If the load rate of a feeder or transformer area exceeds the limit, a priority allocation or hybrid allocation strategy is selected to decompose the total regulation power exceeding the limit.

[0039] After adaptively selecting the power allocation strategy, the system-level target power or over-limit total regulation power is decomposed into each level by combining the aggregated power model of each level to obtain the target power of each level. Then, the power is allocated within the level to obtain the target power of each control unit.

[0040] The details are as follows: The power decomposition and allocation steps are as follows: Based on the control target, combined with the installed capacity, real-time output, operating status and control priority of each unit, the active power target is decomposed step by step using the allocation strategy until it is decomposed to the low-voltage photovoltaic unit. The power allocation strategy includes at least one of the following, and is calculated in combination with unit capacity, real-time adjustable power, and control priority to allocate target power at each level. For example, the various power allocation strategies are introduced as follows: (1) If the adaptive power allocation strategy is to allocate power according to the proportion of installed capacity, then: The target power at this level Based on the rated installed capacity of each unit The power is allocated proportionally, with a target power of:

[0041] in, N represents the rated installed capacity of control unit i; N represents the number of control units in this level. This strategy relies on capacity information from hierarchical modeling, which ensures that larger capacity units can undertake more control tasks while maintaining safe operation.

[0042] (2) If the adaptive power allocation strategy is to allocate power according to the real-time adjustable power ratio, then: The power is allocated based on the current adjustable power percentage of each unit. The target power for each control unit is:

[0043] in, For the real-time adjustable power of control unit i; This strategy combines the execution steps of progressively decomposing active power to adapt to real-time fluctuations in photovoltaic power and achieve dynamic regulation.

[0044] (3) If the adaptive power allocation strategy is to allocate power according to the control priority, then: Set control priorities for units Alternatively, power allocation can be achieved through weighted distribution based on electricity price compensation levels, with the target power for each control unit being:

[0045] in, The control priority of control unit i.

[0046] Prioritizing the output of high-priority units and reducing the output of low-priority units when necessary, combined with over-limit control logic, can optimize economy while ensuring system safety.

[0047] (4) If the adaptive power allocation strategy is a hybrid allocation strategy, then the overall capacity, adjustable power, and priority are weighted and allocated: This invention introduces adaptive weighting, integrated evaluation of operation and maintenance characteristics and response characteristics, and a predictive driving gain mechanism to achieve intelligence and foresight in the control process and allocate target power at different levels. If the adaptive power allocation strategy is selected as a hybrid allocation strategy, then the target power for each control unit is:

[0048] The equipment weighting coefficient can be adaptively calculated using the following formula:

[0049] in, The rated installed capacity of the device for control unit i; The adjustable margin (real-time adjustable power) of control unit i. The control priority of control unit i; This is a sensitivity factor, and its value can be selected according to actual needs, such as 0.8, 1.5, and 2.0 respectively; Furthermore, the hybrid allocation strategy is improved, resulting in the following improved target power allocation:

[0050] Priority assignment is no longer fixed, but rather dynamically adjusted in real time:

[0051] in: The response delay of control unit i reflects its dynamic response speed from receiving the command to actual execution. It can be obtained by statistically analyzing the average response time of the last 10 adjustment actions, with a typical range of 0.5 to 5 seconds. To determine the health status of control unit i, factors such as equipment aging, failure rate, and maintenance cycle are taken into account. The value can be a decimal between 0 and 1, with 1 indicating the best health status. The historical execution accuracy of control unit i reflects the ratio of instruction completion rate to target deviation, which can be obtained through control error accumulation analysis. , , These are weighting coefficients, ranging from 0 to 1, and satisfying... + + = 1, adjusted according to actual operational priorities; A prediction-driven gain factor is also introduced:

[0052]

[0053] in, This represents the real-time adjustable power base weighting coefficient of control unit i, reflecting its current proportional control capability. This is a prediction-driven gain factor used to dynamically adjust weights based on prediction errors. To predict power deviation (interpolation between predicted power and actual output). Rated power of the equipment; This is an adjustment coefficient, ranging from 0.1 to 0.5, which can be adjusted according to the system sensitivity or the accuracy of the prediction model.

[0054] When the predicted output deviation If the value is too large (e.g., the predicted value is less than the actual value), the system will automatically increase it. ,promote That is, a higher adjustment ratio is preferentially allocated to units with larger prediction deviations to achieve dynamic error compensation; conversely, when the prediction accuracy is high... If the value approaches 1, maintain the baseline weight.

[0055] Weighting coefficient , , It can be dynamically adjusted based on real-time assessment of the system's operating status: For systems with relatively balanced installed capacity of various new energy sources, the proportion of installed capacity α can be increased to ensure fair adjustment of each new energy device. For systems that have been in operation for a long time, the actual output of new energy sources will decrease due to the degradation of device performance, resulting in a large difference from the installed capacity. In this case, the coefficient β of the real-time adjustable power can be increased. When the system needs to be adjusted urgently, some new energy sources may respond quickly. The priority weight coefficient γ can be increased to adjust the system as quickly as possible.

[0056] The adaptive adjustment method of the strategy is as follows: Based on different adjustment scenarios, the above strategies are adaptively selected or switched. In daily regulation, priority should be given to allocation based on capacity or real-time adjustable power ratio; In cases of overload or emergency control, the system can switch to priority allocation or hybrid allocation strategies to achieve flexible and efficient active power allocation.

[0057] In practice, emergency control measures have a high priority, and even if the limits are exceeded, emergency control measures are still used.

[0058] The combination of hierarchical aggregation and step-by-step decomposition is as follows: Each strategy is based on a hierarchical aggregation model, which decomposes power step-by-step to medium-voltage power plants, distribution areas, and low-voltage photovoltaic units. This ensures that the control process conforms to the hierarchical topology and power constraints, while facilitating the triggering and execution of over-limit control logic. Specifically, after adaptively selecting the power allocation strategy, it combines the aggregated power models of each layer to allocate the system-level target power. Alternatively, the total regulating power exceeding the limit can be decomposed stepwise to each level to obtain the target power for each level. Then, the power is allocated within the hierarchy to obtain the target power for each control unit. .

[0059]

[0060]

[0061] in, , and These are the total installed capacity, total real-time adjustable power, and control priority for each level; , and These are the rated installed capacity, real-time adjustable power, and control priority of each unit i, respectively. For the target power of unit i, The target power for each level. and Both are power allocation strategy functions. In daily control, priority is given to allocation based on capacity or real-time adjustable power ratio. In case of overload or emergency control, priority allocation or hybrid allocation strategy can be switched to achieve flexible and efficient active power allocation.

[0062] More preferably, the over-limit control strategy is as follows: when the load rate of a feeder or transformer substation exceeds a preset threshold, the over-limit control logic is triggered to prioritize reducing the photovoltaic output in the over-limit area, and the reduction ratio is dynamically adjusted according to the degree of load rate exceeding the limit; the over-limit control logic is used to prioritize reducing the photovoltaic output in the relevant area when the load rate of a feeder or transformer substation exceeds a set threshold, so as to ensure the safe operation of the distribution network, and its specific implementation includes the following steps: Load rate monitoring: Real-time load rate for each feeder and transformer area. Monitoring:

[0063] in, This represents the actual load power of the feeder or transformer substation. This refers to the rated capacity of the feeder or distribution area.

[0064] Threshold determination: when ≤ When (without exceeding limits), the allocation instruction is executed normally; when > When an out-of-limit event occurs, the out-of-limit protection logic is triggered.

[0065] Over-limit reduction strategy: Employ a selected priority allocation or hybrid allocation strategy to decompose the over-limit reduction amount layer by layer. Combine this with a hierarchical aggregation model to implement regulation, gradually reducing photovoltaic output according to a set reduction step size (prioritizing the reduction of low-priority units or units with lower electricity price compensation in over-limit areas) until the load factor is reached. Restore to The following measures will also ensure that the overall power regulation target of the system is met as much as possible.

[0066] More preferably, during emergency regulation, the system-level target power is decomposed, while when the feeder or transformer area load rate exceeds the limit, the total regulation power exceeding the limit is decomposed, wherein the calculation method for the total regulation power exceeding the limit is as follows: The calculation of the total over-limit regulation power is based on the joint determination of the equipment thermal stability model, power flow sensitivity analysis and risk prospective assessment, so as to realize the dynamic elimination of heavy overload of equipment such as feeders and transformers and the restoration of safe operation margin.

[0067] First, the set of over-limit devices. Construct a thermal stability zone assessment model. Calculate the current heavy overload depth:

[0068] Mapping current over-limit to active power regulation demand, using a power flow sensitivity matrix. To quantify the impact of active power on equipment current:

[0069] Among them, the deeper the limit and the stronger the sensitivity of the equipment, the higher the weight, to ensure that the adjustment direction and amplitude have a clear electrical contribution; P is the active power of the control unit participating in the adjustment.

[0070] Secondly, a thermal risk factor for the equipment is constructed to reflect the difference between the equipment's thermal accumulation trend and its short-term overload tolerance:

[0071] in, The current overload has lasted for [duration]. This refers to the allowable time for thermal margin. , For adjustment coefficients; For equipment i The current; For equipment i Maximum allowable current; If the equipment has been overloaded for a long time, its safety boundary is approaching, requiring more aggressive control.

[0072] Based on the above two types of factors, a joint objective function is constructed:

[0073] The higher the risk, the more proactive the adjustment should be to mitigate the potential overload in advance.

[0074] in, This is the risk weighting coefficient, used to balance the weights between the adjustment power deviation and thermal risk in the objective function; This is a reference value for system power, which can be taken as 1% to 5% of the system's rated total capacity or a typical power regulation unit (such as 1MW). Let be the power regulation amount, be the variable to be optimized, and the final result be... ; This invention determines the total over-limit regulation power based on the equipment thermal stability model, power flow sensitivity analysis, and risk prospective assessment. It achieves dynamic elimination of heavy overloads in equipment such as feeders and transformers and restoration of safe operating margins. Moreover, the adaptive regulation coefficient no longer relies on manual experience setting, but makes a predictive response to system trends and risk levels, forming an adaptive control closed loop.

[0075] More preferably, the present invention proposes an adaptive adjustment coefficient, which adjusts the total over-limit adjustment power based on the load trend in the future period, the availability margin of distributed photovoltaic power, and the severity of over-limit. Perform adaptive adjustments and adopt the following constraint logic: 1) Construct the load change rate for future periods and photovoltaic availability margin ; like >0 indicates that the load is expected to increase; like <0 indicates that photovoltaic output is expected to decrease; 2) Calculate the severity index of exceeding limits :

[0076] in, The load rate threshold; Real-time load power, representing the actual total active power of the monitored feeder or transformer area at the current moment; Rated capacity indicates the maximum active power capacity that the feeder or distribution area (transformer) is allowed to operate safely for a long period of time. 3) An adaptive adjustment coefficient update strategy driven by three types of state factors: With increasing load and decreasing photovoltaic output, To avoid insufficient regulation, the adaptive adjustment coefficient is increased to ; With load reduction and ample photovoltaic output, In smaller, normal control scenarios, the adaptive adjustment coefficient is reduced to ; When the predicted trend and the actual over-limit state alternate, a sliding range is used for the adaptive adjustment coefficient. Make smooth adjustments to avoid oscillations caused by excessive regulation; in and These represent the lower and upper limits of the safe range.

[0077] Based on the above 1)-3), the specific adjustment rules are as follows: Table 1

[0078] in , This is the segmentation threshold.

[0079] 4) To enhance the stability of regulation, the update of the adaptive adjustment coefficient adopts a feedback damping mechanism:

[0080] in To adjust the step size, It is a monotonic adjustment function;

[0081] in: The sign function determines the adjustment direction based on the combined trend of load and photovoltaic changes; These are weighting coefficients used to adjust the relative importance of load and photovoltaic changes in decision-making; The gain coefficient represents the severity of exceeding the limit. and (Saturation function) is used to restrict functions The maximum output range is set to prevent excessive adjustments in a single operation. G `max` represents the upper limit of the adjustment amount in a single instance. Saturation function The definition of is: Its function is to address the severity of exceeding the limit. Exceeding the critical value Then, provide a stable and limited stimulus to avoid overreaction.

[0082] This function ensures output. With system status The changing trend remains consistent, satisfying the monotonicity requirement, thus achieving stable and predictable adaptive adjustment of the adjustment coefficient. This function can clearly transform the qualitative judgment in Table 1 into quantitative calculation: When the load increases and the photovoltaic power decreases (i.e.) If the first term of the function is positive, it drives the adaptive adjustment coefficient to increase; conversely, it drives it to decrease. Furthermore, the more it is limited, the more severe the effect becomes. The larger the value of the second term, the greater its contribution, which together ensures the proactive and adaptive nature of the regulation.

[0083] In practice, the status is monitored in real time, and in accordance with the adjustment rules in Table 1, when it is necessary to increase the adaptive adjustment coefficient, Output a positive number. The adaptive adjustment coefficient increases steadily; when it is necessary to decrease the adaptive adjustment coefficient... Output a negative number. The adaptive adjustment coefficient decreases steadily; when dynamic, gradual adjustment is required... Output a number close to zero. The adaptive adjustment coefficient remains almost constant, achieving smoothness.

[0084] Updated It can be used to calculate the final over-limit regulation power:

[0085] The above method enables the adaptive adjustment coefficient to no longer rely on manual experience for setting, but to make a predictive response to the system trend and risk level, forming an adaptive control closed loop.

[0086] Then, the regulating power of each control unit under the feeder or transformer area can be calculated: according to the priority allocation method described above, units with fast response rates and large capacities are regulated first to eliminate over-limit situations as quickly as possible and maintain grid safety. If allocation is selected according to regulation priority, the regulating output of the control unit is:

[0087] in, Adjust the output power for each control unit.

[0088] When multiple feeders exceed their limits simultaneously, the order of load shedding in each region can be adjusted according to preset priority weights to ensure the load safety of critical areas and avoid local power shortages caused by concentrated load shedding.

[0089] Step 3: Perform a safety check before issuing control commands. After the safety check is passed, generate an executable command sequence, issue the executable commands to each level of control unit, and provide feedback for correction.

[0090] More preferably, the security verification strategy is as follows: before issuing control commands, the legality, security, and executability of the commands are verified. Legality includes whether the command scope exceeds the device's capabilities; security includes whether execution will create new risks of exceeding limits; and executability includes verification of the communication link and terminal availability. This is combined with steps 1-2 as follows: Hierarchical execution and feedback: When limits are exceeded, the reduction amount can be progressively distributed to medium-voltage substations, distribution areas, and low-voltage photovoltaic units after safety verification, and control can be implemented in conjunction with a hierarchical aggregation model. Real-time monitoring of load factor. If the load factor recovers to below the threshold, stop reducing or gradually restore photovoltaic output to ensure a balance between grid security and the efficiency of new energy utilization.

[0091] Dynamic optimization and adaptation: The over-limit control logic works in conjunction with the hierarchical aggregation active power regulation strategy to adaptively adjust the reduction ratio and sequence based on load fluctuations, photovoltaic output forecasts, and energy storage status. The over-limit control logic is closely linked to hierarchical modeling, hierarchical aggregation, and power allocation strategies, achieving closed-loop control from scheduling target decomposition to each unit. Combined with the aforementioned power allocation strategy, an economically optimal photovoltaic reduction scheme can be achieved while ensuring system safety.

[0092] The security check is used to ensure the legality, security, and executability of the issued control commands, and its specific details are as follows: (1) Verification of the legality of the instruction: the active power control instruction to be issued With the rated capacity of each unit Perform verification: For photovoltaics, the target power regulation should meet the following requirements:

[0093] For energy storage, its target power regulation should meet the following requirements:

[0094] If the instruction exceeds the unit's rated capacity, it will be automatically corrected to adjust the upper and lower limits of the range or rejected.

[0095] (2) Command security check: Considering the load rate of the feeder and transformer area to which the control unit belongs, a security check is performed on the issued power command:

[0096] in To predict the load, ensure that each region does not exceed the safety threshold after the command is executed.

[0097] in, To set a threshold; The predicted load rate after the control unit command is executed in the feeder or transformer area; The predicted load after the control unit command is executed for the feeder or transformer area; This represents the current total load of the feeder or transformer area.

[0098] (3) Command Executability Verification: Check the control response capability and communication delay of each unit to ensure that the command can be accurately executed within the actual control cycle. For commands that are not executable, the control unit corresponding to the command is included in the uncontrollable sequence and will not be called in the next round of adjustment unless the restriction is manually lifted. The unexecuted control commands are transferred to other controllable control units with adjustment margin to ensure the reliable operation of the hierarchical aggregated control closed loop.

[0099] Combined with hierarchical aggregation control, the safety verification module works in tandem with hierarchical modeling, over-limit control logic, and power allocation strategies to achieve a safe closed-loop control from scheduling targets to terminal units. Through iterative verification and simulation prediction, power allocation and over-limit reduction strategies can be optimized, improving system stability and renewable energy utilization efficiency.

[0100] The closed-loop control strategy is as follows: after the command is issued, the system continuously monitors the execution effect and makes corrections through feedback loops: if the unit does not execute according to the target, the adjustment command is recalculated and issued; if the global power deviation exceeds the allowable range, the next round of power allocation and over-limit protection is triggered. Closed-loop control ensures that the control process is dynamically traceable and the results are consistent with the expected goals.

[0101] Embodiment 2 of this invention provides a hierarchical aggregation medium- and low-voltage distributed renewable energy active power regulation system. Based on hierarchical modeling, power allocation, limit-crossing control, and safety verification methods, it achieves closed-loop active power regulation from the scheduling target to the terminal unit. It mainly consists of multiple functional modules, which interact and coordinate their operation through communication links. Its overall structure includes, but is not limited to, the following modules: The hierarchical aggregation module is used for hierarchical modeling and aggregation management of medium-voltage power stations, distribution areas and low-voltage photovoltaic units. It establishes aggregation power models for each layer, calculates the total adjustable power and power constraints for each layer, and is used for the decomposition calculation and legality verification of target power at each level, providing a basis for power decomposition and over-limit control. More preferably, the hierarchical aggregation module establishes a hierarchical model of medium-voltage power stations, distribution substations, and low-voltage photovoltaic units, aggregating operating parameters level by level to form a hierarchical structure that can be aggregated and managed, thereby realizing the aggregated management of active power of multi-level new energy units. This module provides topology support and data aggregation capabilities for subsequent power decomposition and allocation. Specifically, the hierarchical aggregation module includes: Hierarchical modeling unit: Based on the actual structure of the distribution network, medium-voltage substations, distribution areas and low-voltage photovoltaic units are modeled in layers to form a three-level hierarchical relationship.

[0102] Information aggregation unit: Collects parameters such as rated capacity, real-time output, adjustable power, and priority of each level of unit, and aggregates them to the upper-level node level by level.

[0103] Topology identification unit: Supports dynamic identification of the operating topology of the distribution network. When the line is switched or the equipment is under maintenance, the hierarchical relationship is automatically updated to ensure the accuracy of the model.

[0104] Regional decoupling unit: Allows for independent aggregation management of different regions, enabling flexible switching between regional and global control.

[0105] Through this module, the system can realize multi-level aggregation and unified management of distributed new energy sources, effectively supporting the coordinated regulation of large-scale distributed power sources.

[0106] The power allocation module is used to receive active power control commands and monitor the over-limit situation of feeder or transformer area load rate in real time. It adaptively selects the power allocation strategy, combines the aggregated power model of each layer, decomposes the system-level target power or over-limit total regulation power to each level, obtains the target power of each level control unit, and generates control commands. The dispatching unit is used to receive system-level active power control targets from the superior dispatching center or distribution dispatching system. It generates hierarchical control tasks, transmits power targets to the power decomposition and allocation module, and considers real-time load data, the upper and lower limits of the adjustable power range of distributed renewable energy, and control priorities. ; More preferably, the instruction receiving unit (scheduling unit) acquires the active power control target issued by the superior dispatch center, distribution master station, or regional control system, and completes instruction parsing, legality checking, and priority ranking, converting it into a standardized control task that the system can recognize and execute. It is the entry point for the entire system control process. Specifically, the instruction receiving module includes: Target receiving unit: used to collect external control commands, including parameters such as total target power (PTAR), control period, and control type (e.g., peak shaving, valley filling, power curtailment, economic optimization); Command parsing unit: Parses the received commands and identifies the scope of the control target (such as the entire grid, medium-voltage substation, distribution area or low-voltage photovoltaic unit), priority and constraints (such as maximum adjustable range, execution delay); Priority sorting unit: Based on the importance and urgency of the control tasks, multiple tasks are sorted to ensure that high-priority tasks are executed first when resources are limited or tasks conflict. Redundant channel unit: When the main communication link fails, the continuity and reliability of command reception are ensured through backup links (such as fiber optic, 5G or wireless private network). Instruction caching and verification unit: When the system load is high or execution is temporarily impossible, the received instructions are cached and the integrity and format validity of the data are verified to prevent abnormal instructions from entering the subsequent control process.

[0107] Through the above design, the instruction receiving unit can ensure timely access, correct parsing, and secure transmission of external control targets, providing reliable input for subsequent hierarchical aggregation, power decomposition, and execution control.

[0108] The power decomposition and allocation unit is used to allocate system-level power targets according to the hierarchical aggregation structure. (Over-limit total regulating power) The power allocation is decomposed level by level and combined with the selected power allocation strategy, including allocation by capacity, allocation by proportion, allocation by priority, and hybrid allocation strategies, based on the capacity of each unit. Real-time adjustable power and regulatory priorities Calculate target output It meets the following conditions:

[0109] More preferably, the power decomposition and allocation module can decompose the global active power target transmitted by the command receiving unit level by level, and perform weighted allocation based on parameters such as capacity, real-time adjustable power, and priority to generate target power for each unit and allocate it to each new energy unit, realizing the top-down target transmission. Specifically, the power decomposition and allocation unit includes: Allocation strategy unit: Supports multiple strategies such as capacity allocation, proportional allocation, priority allocation, and mixed allocation; Hierarchical decomposition unit: Based on the hierarchical structure, the power target is calculated level by level from top to bottom; Weighted calculation unit: Calculation is performed by combining unit capacity, real-time adjustable power, and priority weight; Result generation unit: generates the final target power for each unit and transmits it to the execution stage.

[0110] This unit ensures the reasonable decomposition and efficient allocation of target power at each level, improving the precision and fairness of regulation.

[0111] Over-limit control unit, used to control the load rate of feeders or transformer substations. Exceeding the set threshold When this occurs, the active power output protection / over-limit reduction logic is triggered to promptly reduce distributed photovoltaic output, prioritizing the reduction of output from low-priority units or units with low electricity price compensation in areas with high load rates, until the load rate recovers below the threshold. Simultaneously, the overall system power regulation target is met as much as possible to prevent overload operation. Specifically, the over-limit control unit includes: Load monitoring unit: Real-time monitoring of the operating power of feeders and transformer substations, and calculation of load rate; Threshold determination unit: Triggers over-limit control logic when the load rate of a feeder or transformer area exceeds a set threshold; Reduction Strategy Unit: Prioritize reducing the output of low-priority or low-compensation photovoltaic units, and adopt a phased reduction approach to avoid power quality fluctuations; Recovery control unit: When the operating state returns to a safe range, gradually release the reduced unit output.

[0112] This unit can ensure the safe and stable operation of the distribution network under the condition of high proportion of new energy access.

[0113] The safety verification module is used to perform safety verification before the control commands are issued. After the safety verification is passed, an executable command sequence is generated, and the executable commands are issued to the control units at each level and feedback is provided for correction.

[0114] More preferably, before issuing the instruction, the safety verification module performs a comprehensive verification of its legality, security, and executability, including verifying the target power of each unit. Check whether the installed capacity is exceeded, whether the power change rate is within the adjustable range, and whether the instruction issuance sequence and time interval comply with system constraints, and generate an executable instruction sequence Cm. This ensures that instruction execution will not cause operational risks and prevents unreasonable instructions from entering the execution phase. Specifically, the security verification module includes: Legality verification unit: Determines whether the target power is within the unit's adjustable range; Safety verification unit: Predicts whether the grid voltage, current, and frequency meet the operating specifications after execution; Executability verification unit: Checks the current operating status and communication status of the unit to ensure that it has the conditions for execution.

[0115] This module enables the system to complete comprehensive security verification before control and regulation are implemented, effectively reducing operational risks.

[0116] The control execution interface unit is used to send control commands that have passed safety verification to medium-voltage substations, distribution areas, and low-voltage photovoltaic units to achieve real-time closed-loop control. It also updates the hierarchical aggregation module and power decomposition unit based on the feedback measurement values ​​from each unit to complete dynamic correction and optimization.

[0117] More preferably, the control execution unit sends the verified target power command to each target unit and performs closed-loop control based on feedback information to ensure that the actual execution result is consistent with the expectation, specifically including: Command issuing unit: Sends the target power allocation results to medium-voltage substations, distribution areas, or photovoltaic units; Feedback acquisition unit: collects the actual execution status and power output of each unit in real time; Closed-loop correction unit: When the actual execution deviates from the target, the control command is recalculated and corrected.

[0118] This unit ensures the closed-loop nature and consistency of regulation, thereby improving execution effectiveness.

[0119] Through the collaborative work of the above modules / units, the system, with its modular design, can be flexibly deployed in the dispatching master station, distribution automation system, or transformer area control unit, forming a top-down, hierarchical aggregation control closed loop. This enables hierarchical aggregation of active power optimization and control, over-limit protection, and safe and reliable execution of medium and low voltage distributed renewable energy under different load and operating conditions, while also taking into account the accurate achievement of capacity utilization, control priority, and overall system power targets.

[0120] The system workflow mainly includes the following steps: the instruction receiving unit receives external control targets; the hierarchical aggregation unit establishes multi-level models and summarizes information; the power decomposition unit allocates target power level by level; the over-limit control unit monitors the operating status in real time and triggers protection logic; the safety verification unit performs comprehensive verification; the control execution unit completes instruction issuance and feedback correction; thus forming a complete closed-loop control link.

[0121] This process ensures that the system has complete functions: task reception, hierarchical modeling, power allocation, over-limit protection, security verification, and closed-loop execution.

[0122] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0123] Processor: Used to execute computer programs stored in memory to implement the above method steps, including instruction reception, hierarchical aggregation, power decomposition, limit control, security verification, and control execution; Memory: Used to store the operating system, applications and related data, including hierarchical model data, real-time monitoring data, weighting parameters and thresholds. Communication interface: Used for bidirectional communication with the superior dispatch center, power distribution station and various distributed new energy units, supporting multiple communication methods including wired and wireless; Input / output unit: Used to provide local configuration, debugging, and status display.

[0124] The terminal device can be a standalone controller, server, or integrated into the master station / edge node of the power distribution automation system, supporting both centralized and distributed deployment.

[0125] Compared with the prior art, the beneficial effects of the present invention include at least the following: This invention, through hierarchical aggregation, step-by-step allocation, over-limit protection, and closed-loop verification, can meet the needs of the distribution network for optimized regulation, coordinated allocation, and flexible response under the premise of ensuring safe operation in the context of high proportion of distributed renewable energy access, and provides support for intelligent dispatching and source-grid-load-storage coordinated control of new power systems.

[0126] The hybrid allocation strategy proposed in this invention uses a weighted allocation based on comprehensive capacity, adjustable power, and priority. It introduces a mechanism for evaluating and controlling priority by integrating operation and maintenance characteristics with response characteristics, as well as an adaptive weighting mechanism based on prediction-driven gain, thereby achieving intelligence and foresight in the control process.

[0127] When performing over-limit control, this invention determines the total over-limit regulation power based on the equipment thermal risk factor, power flow sensitivity, and risk weight. This enables the dynamic elimination of heavy overloads in equipment such as feeders and transformers and the restoration of safe operating margins. Furthermore, the adaptive regulation coefficient no longer relies on manual experience to set, but instead makes a predictive response to system trends and risk levels, forming an adaptive control closed loop.

[0128] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0129] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0130] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0131] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources, characterized in that, include: Hierarchical modeling and aggregation management of medium- and low-voltage distributed renewable energy sources are performed to obtain aggregation power models for each layer. It receives active power control commands and monitors the over-limit situation of feeder or transformer area load rate in real time. It adaptively selects power allocation strategies and combines the aggregated power models of each level to decompose the system-level target power or over-limit total regulation power to each level, obtain the target power of each level control unit and generate control commands. Before issuing control commands, a safety check is performed. Once the safety check is passed, an executable command sequence is generated, and the executable commands are issued to control units at each level and feedback is provided for correction.

2. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 1, characterized in that: The hierarchical modeling and aggregation management of medium- and low-voltage distributed renewable energy sources yields aggregation power models for each layer, including: The medium-voltage power station, distribution area, and low-voltage photovoltaic unit are modeled in layers. The medium-voltage power station is modeled as a power station aggregation node control unit, forming the medium-voltage layer; the distribution area is modeled as a low-voltage distributed power aggregation node control unit, forming the distribution area layer; and the low-voltage photovoltaic unit is modeled as an adjustable basic control unit, forming the low-voltage layer. Based on the results of hierarchical modeling, a distributed new energy aggregation management system is established, which aggregates multiple control units at the same level into a single control object and establishes a hierarchical mapping relationship between different levels to form an aggregated power model for each level.

3. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 1, characterized in that: The process of receiving active power regulation commands and monitoring feeder or transformer area load rate exceeding limits in real time to adaptively select power allocation strategies includes: The system receives and parses active power control instructions. If it is a routine control, it selects a strategy to allocate the system-level target power based on the proportion of installed capacity or the proportion of real-time adjustable power. If it is an emergency control, it selects a strategy to allocate the system-level target power based on the control priority or a hybrid allocation strategy. If the load rate of a feeder or transformer area exceeds the limit, the total regulation power exceeding the limit will be decomposed by selecting a priority allocation or hybrid allocation strategy.

4. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 1, characterized in that: After the adaptive power allocation strategy is selected, the system-level target power or over-limit total regulation power is decomposed into each level in combination with the aggregated power model of each level to obtain the target power of each level. Then, the power is allocated within the level to obtain the target power of each control unit.

5. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 1, characterized in that: Target power of allocation level If the adaptive power allocation strategy is selected as a hybrid allocation strategy, then the target power for each control unit is: in, The rated installed capacity of the device for control unit i; For the real-time adjustable power of control unit i; The control priority of control unit i; N is the total number of control units in this level; , , These are the weighting coefficients for the rated installed capacity of the equipment, the weighting coefficient for the real-time adjustable power considering the predictive drive gain factor, and the weighting coefficient for the control priority, respectively.

6. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 5, characterized in that: The calculation formula is: in: For the response delay of control unit i; For the device health status of control unit i; The historical execution accuracy of control unit i; , , These are the weighting coefficients.

7. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 5, characterized in that: The calculation formula is: in, This represents the real-time adjustable power base weighting coefficient of control unit i; To predict the driving gain factor; To predict power deviation; Rated power of the equipment; This is the adjustment coefficient.

8. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 1, characterized in that: The formula for calculating the total over-limit regulation power is as follows: in, Total regulating power exceeding the limit; This is a quantitative value representing the impact of active power on equipment current. For power flow sensitivity matrix; For a collection of devices that exceed limits; This represents the current heavy overload depth. For equipment thermal risk factors; The current overload has lasted for [duration]. This refers to the allowable time for thermal margin. , For adjustment coefficients; This refers to the risk weighting coefficient. For equipment i The current; For equipment i Maximum allowable current; The variables that need to be optimized; This is the system power reference value.

9. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 8, characterized in that: Adaptive adjustment coefficients are used to adjust the total regulating power beyond the limit. Adaptive adjustment is performed, and the mechanism for adjusting the adaptive adjustment coefficient is as follows: in, , For the current time and the next time, the adaptive adjustment coefficients are used. To adjust the step size, To take system state into account The monotonic adjustment function, and For future load change rates and photovoltaic availability margin, This represents the severity index of exceeding the limit.

10. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 1, characterized in that: The security verification includes instruction legality verification, instruction security verification, and instruction executability verification; The instruction validity check includes: determining whether the target power of the control unit exceeds the unit's rated capacity; if so, automatically correcting it to the rated range limit or failing the instruction validity check. Command safety verification includes: predicting the load rate of the feeder and distribution area after the control command is executed. If the predicted load rate exceeds the safety threshold, the command safety verification fails. The executability verification of instructions includes: checking the control response capability and communication delay of each control unit to ensure that the instructions can be accurately executed within the actual control cycle; for unexecutable instructions, the control unit corresponding to the instruction is included in the uncontrollable sequence and will not be called in the next round of adjustment until the restriction is manually lifted; and the unexecuted control instructions are transferred to other controllable control units with adjustment margin to ensure the reliable operation of the hierarchical aggregated control closed loop.

11. The hierarchical aggregation method for active power regulation of medium- and low-voltage distributed renewable energy sources according to claim 1, characterized in that: The feedback correction includes: continuously monitoring the execution effect of the command and correcting it through the feedback loop; if the control unit does not execute according to the target, the adjustment command is recalculated and issued; if the global power deviation exceeds the allowable range, the next round of power allocation and over-limit protection is triggered.

12. A hierarchical aggregation medium- and low-voltage distributed renewable energy active power control system, operating the method described in any one of claims 1-11, characterized in that, The system includes: The hierarchical aggregation module is used to perform hierarchical modeling and aggregation management of medium and low voltage distributed renewable energy sources, and obtain the aggregation power model of each layer; The power allocation module is used to receive active power control commands and monitor the over-limit situation of feeder or transformer area load rate in real time. It adaptively selects the power allocation strategy, combines the aggregated power model of each layer, decomposes the system-level target power or over-limit total regulation power to each level, obtains the target power of each level control unit, and generates control commands. The safety verification module is used to perform safety verification before the control commands are issued. After the safety verification is passed, an executable command sequence is generated, and the executable commands are issued to the control units at each level and feedback is provided for correction.

13. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-11.

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