Virtual power plant demand response optimization system and method based on source-load aggregation

Abstract

The present invention belongs to the field of power control technology and relates to a virtual power plant demand response optimization system and method based on source-load aggregation. The system receives external load demand instructions and performs safety boundary verification and instantaneous adjustment margin matching in parallel. When the safety boundary verification and instantaneous adjustment margin matching are both passed, a commitment response signal is returned to the demander and power ramp-up preparation is started. In the power ramp-up preparation stage, the system synchronously performs current surplus power analysis of the dispatchable power source, predicted charging amount analysis within the continuous response window, and predicted loss analysis of charging and discharging, generates a net deliverable power evaluation result, and performs hierarchical feedback based on the response deviation between the load demand instruction and the net deliverable power evaluation result. This not only breaks through the inherent bottleneck of the existing technology in response timeliness, but also accurately traces the supplier's deliverable power resource capabilities, provides corresponding feedback measures, and greatly improves the reliability of power supply response.

Description

Technical Field

[0001] The present invention belongs to the field of power control technology, and in particular relates to a virtual power plant demand response optimization system and method based on source-load aggregation. Background Art

[0002] With the widespread access to distributed energy, large-scale energy storage and flexible loads, virtual power plants, as the core carrier for aggregating distributed resources to participate in the electricity market, their demand response capabilities directly affect the stability of the power grid and market operation efficiency.

[0003] In the actual operation of the power market, demanders often make short-term power adjustment demands due to sudden working conditions, requiring suppliers to complete power ramp-up or ramp-down responses within a time window of seconds. In the scenario where supplier resources respond to demander requirements, a variety of application solutions have emerged in the existing technical field, but there are still limitations. Specifically: 1. The response processing mode of the supplier to the demander in the existing technology is rigid and lacks a parallel coordination mechanism. It often adopts a serial full-quantity verification mode, that is, it needs to complete multiple links such as full-quantity safety verification and power evaluation in sequence before it can feedback the response commitment signal to the demander.

[0004] On the one hand, the demander needs to wait for the complete results, and the lengthy decision-making chain results in a poor experience and fails to meet the mandatory requirement of second-level response in the power dispatching regulations.

[0005] On the other hand, some existing technologies will feedback commitment signals in advance based on simplified analysis to avoid exceeding the delay standard, but will no longer optimize the feasibility analysis of power delivery, which may easily cause a disconnect between the promised response capacity and the actual deliverable capacity.

[0006] 2. Existing technologies are not comprehensive and accurate enough in tracing the power supply resources of suppliers. They only rely on the rated output parameters of the power supply units for static capacity aggregation, and fail to quantify the actual power supply capabilities of different types of power supply units based on their inherent characteristics. In addition, the transient losses during the charging and discharging process are not accurately quantified, which can easily lead to significant deviations between the predicted value of net deliverable power and the actual value. Summary of the Invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a virtual power plant demand response optimization system and method based on source-load aggregation is proposed.

[0008] The technical solution adopted by the present invention to solve its technical problems is: First, the present invention provides a virtual power plant demand response optimization system based on source-load aggregation, including: a response activation module, a resource prediction module and an execution feedback module.

[0009] The response activation module is connected to the resource prediction module, and the resource prediction module is connected to the execution feedback module.

[0010] The response activation module receives an external load demand instruction, executes safety boundary verification and instantaneous regulation margin matching in parallel, and returns a commitment response signal to the demander and starts power ramp-up preparation when both the safety boundary verification and the instantaneous regulation margin matching are passed.

[0011] The resource prediction module, during the power ramp preparation phase, simultaneously performs analysis on the current surplus power of the dispatchable power source, analysis on the predicted charge capacity within the continuous response window, and analysis on the predicted loss during charging and discharging, to generate a net deliverable power assessment result.

[0012] The execution feedback module performs hierarchical feedback based on the response deviation between the load demand instruction and the net deliverable power evaluation result.

[0013] In the second aspect, the present invention provides a virtual power plant demand response optimization method based on source-load aggregation, including: receiving external load demand instructions, executing safety boundary verification and instantaneous regulation margin matching in parallel, and when the safety boundary verification and instantaneous regulation margin matching are both passed, returning a commitment response signal to the demander and starting power ramp-up preparation.

[0014] During the power ramp preparation phase, the current surplus power analysis of the dispatchable power source, the predicted charge capacity analysis within the continuous response window, and the predicted loss analysis of charging and discharging are simultaneously performed to generate a net deliverable power assessment result.

[0015] Hierarchical feedback is performed based on the response deviation between the load demand command and the net deliverable power evaluation result.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention performs safety boundary verification and instantaneous adjustment margin matching in parallel, and returns the commitment response signal to the demander on the premise of ensuring that the supplier has safety response capability and instantaneous scheduling capability, breaking through the inherent bottleneck of the prior art in response timeliness. It can not only significantly reduce the time cost and opportunity cost of the demander due to scheduling waiting, but also simultaneously ensure the margin reserve capacity in the instantaneous interaction process, and realize the coordinated optimization of safety constraints, response speed and scheduling flexibility.

[0017] (2) The present invention simultaneously performs accurate tracing of the real-time surplus, continuous charging amount and dynamic loss amount of the dispatchable power supply during the power ramp preparation stage. It not only distinguishes different types of power supply units to conduct different power supply resource analyses, but also analyzes similar loss situations in the charging and discharging process in combination with historical sample data, thereby greatly improving the adaptability of resource evaluation.

[0018] (3) The present invention implements hierarchical feedback for response deviations, transforming passive response into forward-looking protection, significantly reducing the impact of deviations on power supply reliability, and thus reducing the plan adjustment costs caused by sudden deviations on the demand side. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0020] Figure 1 This is a module connection block diagram of a virtual power plant demand response optimization system based on source-load aggregation provided by the first embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the analysis logic of the discharge execution prediction loss amount in the resource prediction module provided by the first embodiment of the present invention.

[0022] Figure 3 This is a flowchart of the implementation of the virtual power plant demand response optimization method based on source-load aggregation provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] Example 1

[0025] See also Figure 1 As shown, in the first embodiment of the present invention, a virtual power plant demand response optimization system based on source-load aggregation is provided, including: a response activation module, a resource prediction module and an execution feedback module.

[0026] The response activation module is connected to the resource prediction module, and the resource prediction module is connected to the execution feedback module.

[0027] The response activation module receives an external load demand instruction, executes safety boundary verification and instantaneous regulation margin matching in parallel, and returns a commitment response signal to the demander and starts power ramp-up preparation when both the safety boundary verification and the instantaneous regulation margin matching are passed.

[0028] It should be noted that the above-mentioned external load demand instructions include the power change direction, power adjustment amount, power change rate and continuous response window duration, among which the product of the power adjustment amount and the continuous response duration can represent the total amount of electricity actually required by the demander, and the power adjustment amount is a composite state parameter including the starting power value, the target power value and the adjustment range between the two.

[0029] In a preferred embodiment of the present invention, the safety boundary verification execution process includes the following progressive steps: a) correlating the real-time locking status of each section circuit breaker in the electrical main wiring diagram with the disconnector position signal to verify whether the current network topology meets the isolation redundancy requirements under the preset fault scenario.

[0030] It should be noted that the verification process of whether the above-mentioned current network topology meets the isolation redundancy requirements under the preset fault scenario includes: calling the typical single-point fault model in the preset fault scenario library, including main transformer fault, outgoing line loop fault and bus segment fault, and for any fault scenario: searching whether there is an effective electrical isolation path in the non-fault area through topological connectivity analysis.

[0031] The mechanical locking code of the isolation switch is compared with the preset isolation path operation sequence at the instruction level to check whether there is a conflict between the isolation switch locking state and the preset isolation path.

[0032] Check the action priority identifiers of the protection devices on the fault side and the non-fault side, and verify the inclusion relationship between the fault side action time window and the non-fault side lockout delay window, to determine whether the action timing of the protection devices on both sides of the fault point meets the preset coordination requirements. The preset coordination requirements specifically refer to the action priority identifier of the fault side protection device being higher than that of the non-fault side, and the fault side protection action time window being completely included in the non-fault side lockout delay window.

[0033] When there are effective isolation paths for all fault scenarios, the protection action timing meets the preset coordination requirements, and there is no conflict in the blocking signals, it is determined that the isolation redundancy requirements are met.

[0034] b) Predict the extreme value of the current at the property boundary point after executing the load demand instruction, check whether the extreme value exceeds the overcurrent action threshold preset by the grid connection point relay protection device, and confirm that the current direction matches the polarity of the relay protection action zone.

[0035] It should be noted that the above-mentioned tidal extreme value specifically refers to the maximum steady-state power reached at the property boundary point of the interconnection line after the load demand instruction is executed. It mainly dynamically couples the power change trajectory of the load demand instruction with the measured step response characteristics of the dispatchable power source. The dynamic coupling is specifically a time domain convolution integral to generate the power time domain envelope of the property boundary point, and extract the positive / negative vertex value of the power time domain envelope as the tidal extreme value prediction result.

[0036] c) Compare the power change rate of the load demand instruction with the inherent mechanical delay characteristics of the grid-connected point circuit breaker operating mechanism to confirm whether the switching device action sequence can cover the entire instruction execution process.

[0037] It should be noted that the above-mentioned inherent mechanical time delay characteristic specifically refers to the shortest time required for the circuit breaker operating mechanism to receive the opening and closing command and the contacts to complete the action. It is determined by the mechanical structure of the equipment and can be directly referred to the technical guide specification value provided by the manufacturer of the grid-connected circuit breaker operating mechanism.

[0038] d) If and only if none of steps a) to c) triggers the preset locking condition, it is determined that the safety boundary check has passed.

[0039] It should be noted that the preset locking conditions refer to the technical rules for forced interruption response when any of the following situations is triggered: the current network topology does not meet the isolation redundancy requirements under the preset fault scenario, the extreme flow value exceeds the preset overcurrent action threshold or direction polarity mismatch of the grid connection point relay protection device, and the switching device action sequence does not cover the entire instruction execution process.

[0040] In a preferred embodiment of the present invention, the instantaneous regulation margin matching execution process includes: performing the following operations on the set of dispatchable power supply units of this party according to the power change direction, power adjustment amount and change rate requirements of the load demand instruction: Ⅰ. Eliminating power supply units that do not match the regulation capability of the instructed power change direction, including production capacity power supply units that are in a non-increased state under a power increase instruction or energy storage power supply units that are in a non-reduced output state under a power reduction instruction.

[0041] II. Eliminate the power supply units of the capacity type whose minimum power ramp rate is lower than the required command power change rate.

[0042] III. Exclude energy storage power supply units whose power regulation dead zone covers the command power adjustment range under the charge and discharge mode switching condition.

[0043] IV. Eliminate the power supply units whose maximum sustainable response time does not reach the minimum cycle threshold of the instruction continuous response window.

[0044] The rated output upper limits of the remaining power unit subsets are aggregated. If the aggregated instantaneous adjustable capacity covers the extreme value of the command power change and leaves a preset adjustment margin, it is determined that the instantaneous adjustment margin matching has passed.

[0045] It should be noted that the above-mentioned rated output upper limit refers to the absolute upper limit of power output permanently calibrated by the power supply unit equipment manufacturer in the technical specification book, the command power variation extreme value refers to the target power value ultimately required by the load demand instruction, and the preset adjustment margin refers to the safe operation buffer power reserved after the aggregated rated output upper limit is deducted from the command power variation extreme value. Its size is determined according to the overload tolerance capability of the equipment and can be manually calibrated before system development based on the most recently updated overload test data of the power supply unit.

[0046] The embodiment of the present invention executes safety boundary verification and instantaneous adjustment margin matching in parallel, and returns a committed response signal to the demander on the premise of ensuring that the supplier has safety response capability and instantaneous scheduling capability, thereby breaking through the inherent bottleneck of the existing technology in response timeliness. It can not only significantly reduce the time cost and opportunity cost of the demander due to scheduling waiting, but also simultaneously ensure the margin reserve capacity in the instantaneous interaction process, and realize the coordinated optimization of safety constraints, response speed and scheduling flexibility.

[0047] The resource prediction module, during the power ramp preparation phase, simultaneously performs analysis on the current surplus power of the dispatchable power source, analysis on the predicted charge capacity within the continuous response window, and analysis on the predicted loss during charging and discharging, to generate a net deliverable power assessment result.

[0048] In a preferred embodiment of the present invention, the current surplus power analysis process of the dispatchable power source includes: based on the real-time locking signal and operation mode identification of the battery management terminal of the local party, screening each energy storage power supply unit that is in a grid-connected operation state and has not triggered the charging and discharging hard constraints.

[0049] It should be explained that the above-mentioned grid-connected operating state refers to an operating state in which the power supply unit establishes an electrical connection with the external power grid through a power electronic interface and has the ability to exchange power in accordance with the grid dispatch instructions or strategies.

[0050] Hard charge and discharge constraints refer to the insurmountable limits imposed on energy storage power units during the charging and discharging process, determined by the device's physical characteristics or safety specifications. Examples include the maximum current limit to prevent overcurrent damage to the battery during charging, the minimum voltage threshold to prevent battery life degradation due to overdischarge during discharging, and the maximum duration of a single charge or discharge cycle. These constraints are set by the device manufacturer during the design phase and serve as rigid boundaries to ensure the safe and stable operation of the energy storage unit. Once triggered, they forcibly interrupt the current charging or discharging process or limit power output.

[0051] Based on the measured state of charge boundary value and the preset efficiency compensation coefficient stated on the power conversion device nameplate, the net releasable power threshold of each energy storage power supply unit under the current operating conditions is determined.

[0052] It should be noted that the process of determining the net releasable power threshold of each of the above-mentioned energy storage power supply units under the current operating conditions is: collecting the measured state of charge value, measured state of charge boundary value and rated capacity of each energy storage power supply unit under the current operating conditions, the measured state of charge value is the ratio of the remaining battery power monitored in real time to the rated capacity, and the measured state of charge boundary value refers to the highest state of charge that can be safely discharged at present.

[0053] The absolute difference between the measured state of charge value and the measured state of charge boundary value is calculated, and the absolute difference is further accumulated with the rated capacity of the energy storage power supply unit and the preset efficiency compensation coefficient stated on the nameplate of the power conversion device to obtain the net releasable power threshold.

[0054] The locked power that has been promised to be supplied to the load side is deducted from the net releasable power threshold to obtain the current surplus power of each energy storage power supply unit, which is integrated to generate the current surplus power of the dispatchable power supply.

[0055] In a preferred embodiment of the present invention, the predicted charge capacity analysis process within the continuous response window includes: screening each capacity-type power supply unit that is in a grid-connected operation state and has not triggered a hard output constraint.

[0056] It should be noted that the above-mentioned hard output constraints refer to the hard conditions under which the production capacity power supply unit cannot adjust its output due to the activation of the equipment's own safety protection mechanism or the forced dispatch restrictions of the power grid.

[0057] Check whether each power unit of each capacity type can maintain the minimum stable output power within the continuous response window, eliminate the units that cannot meet the minimum stable output continuity, and mark the remaining units as effective capacity units.

[0058] It should be noted that the verification process for whether the above-mentioned capacity power supply units can maintain the minimum stable output power within the continuous response window is: retrieve the minimum stable output power value and the corresponding maximum sustainable operation time parameter stated in the technical manual of the capacity power supply unit equipment, and compare the coverage relationship between the continuous response window duration in the load demand instruction and the maximum sustainable operation time parameter. If the maximum sustainable operation time parameter is greater than or equal to the continuous response window duration, it means that the minimum stable output power can be maintained, otherwise it means that it cannot.

[0059] Obtain the upper limit of the additional power of each effective production unit under its current operating conditions, combine it with the duration of the continuous response window, and generate the theoretical charging capacity of each effective production unit, so as to calculate the predicted charging capacity within the continuous response window.

[0060] In a preferred embodiment of the present invention, the charging execution prediction loss analysis process includes: selecting an effective production capacity unit as a target unit, and retrieving historical charging record points with the same operating condition characteristics based on the target unit's current increaseable power upper limit level and operating condition parameter distribution.

[0061] The complete operation records of the matching historical charging record points within the subsequent continuous response window are extracted as samples, and a dynamic process sample set is constructed based on the change trajectory of the operating condition parameters and power output of each sample.

[0062] The actual charging loss performance of each sample in the sample set is analyzed, the inevitable loss component and the fluctuating loss component are deconstructed, and based on the loss component analysis, the loss characteristic envelope interval of the target unit in the current prediction scenario is generated.

[0063] It should be noted that the process of the above-mentioned inevitable loss component and fluctuating loss component is: grouping the operation records of the same increaseable power upper limit level in the sample set, extracting the actual loss measured value of each group in the stable operating condition section, fitting the power-loss baseline to characterize the inevitable loss component, and the stable operating condition section is represented by the fluctuation standard deviation of all indicators in the operating condition parameters being lower than the preset stability standard deviation threshold. The operating condition parameters include external environmental parameters and power grid quality parameters, wherein the external environmental parameters include at least one of ambient temperature, humidity and atmospheric pressure, which are collected in real time by sensors deployed on the power supply unit body, and the power grid quality parameters include at least one of harmonic distortion rate, voltage imbalance and frequency deviation, which are obtained by the grid connection point power quality monitoring device.

[0064] By calculating the deviation of the actual loss value of each sample relative to the baseline of the same power level, combining the cluster analysis of external environmental parameters and grid quality parameters, the mapping relationship between the fluctuating loss component and the operating condition parameter variables is deconstructed. Based on the current real-time monitored external variable value and the mapping relationship, the range of the fluctuating loss component is determined, and the inevitable loss component is coupled to generate the loss characteristic envelope interval.

[0065] The characteristic reference value in the loss characteristic envelope interval that coincides with the high-frequency distribution area of ​​the historical samples is selected as the charging prediction loss of the target unit.

[0066] Traverse all effective production capacity units to count the predicted charging loss of each unit and obtain the predicted charging execution loss.

[0067] See also Figure 2 As shown, in a preferred embodiment of the present invention, the discharge execution prediction loss analysis process includes: extracting the starting power value, target power value and power change rate of the load demand instruction to form a discharge condition characteristic matrix.

[0068] Determine whether there is a historical interaction record between this party and the demander: (i) If so, retrieve the monitoring data of each historical interactive climbing stage to construct a loss sample set, deconstruct the loss sample set to generate the associated loss amount corresponding to the climbing requirement from the starting power value to the target power value and the power change rate requirement, and accumulate them to obtain the discharge execution predicted loss amount.

[0069] (ii) If none exists, then filter out historical interactors with similar grid impedance characteristics to the demander, and correct the discharge loss values ​​of the historical interactors based on the rated ramp loss coefficient of the equipment to generate an equivalent loss envelope interval, taking the conservative upper limit of the interval as the predicted discharge execution loss amount.

[0070] In a preferred embodiment of the present invention, the net deliverable power evaluation process is: accumulating the current surplus power of the dispatchable power source and the predicted charging capacity within the continuous response window to obtain the initial available power.

[0071] The initial available power is calibrated for loss based on the predicted loss amount during charge and discharge, and the calibrated power is output as a net deliverable power evaluation result.

[0072] The embodiment of the present invention simultaneously performs accurate tracing of the real-time surplus, continuous charging amount and dynamic loss amount of the dispatchable power supply during the power ramp preparation stage. It not only distinguishes different power supply unit types to carry out different power supply resource analyses, but also analyzes similar loss situations in the charging and discharging process in combination with historical sample data, thereby greatly improving the adaptability of resource evaluation.

[0073] The execution feedback module performs hierarchical feedback based on a response deviation between the load demand instruction and the net deliverable power evaluation result.

[0074] In a preferred embodiment of the present invention, the hierarchical feedback execution process includes: based on the continuous response window duration and power adjustment amount of the load demand instruction, quantifying the total power actually required by the demander and superimposing the safety power, and comparing the superposition result with the net deliverable power.

[0075] If the comparison relationship is less than or equal to, the discharge operation is maintained until the sustained response window ends.

[0076] If the comparison relationship is greater than, a power shortage alarm and an external resource coordination request will be triggered.

[0077] The embodiment of the present invention performs hierarchical feedback for response deviations, transforming passive response into forward-looking protection, significantly reducing the impact of deviations on power supply reliability, and thereby reducing the plan adjustment costs of the demander caused by sudden deviations.

[0078] Example 2

[0079] like Figure 3 As shown, in the second embodiment of the present invention, a virtual power plant demand response optimization method based on source-load aggregation is provided, including: receiving external load demand instructions, executing safety boundary verification and instantaneous regulation margin matching in parallel, and when the safety boundary verification and instantaneous regulation margin matching are both passed, returning a commitment response signal to the demander and starting power ramp-up preparation.

[0080] During the power ramp preparation phase, the current surplus power analysis of the dispatchable power source, the predicted charge capacity analysis within the continuous response window, and the predicted loss analysis of charging and discharging are simultaneously performed to generate a net deliverable power assessment result.

[0081] Hierarchical feedback is performed based on the response deviation between the load demand command and the net deliverable power evaluation result.

[0082] The virtual power plant demand response optimization method based on source-load aggregation provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned system embodiment. For the sake of brief description, for matters not mentioned in the method embodiment, please refer to the corresponding content in the aforementioned system embodiment.

[0083] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A virtual power plant demand response optimization system based on source-load aggregation, characterized by: include: a response activation module that receives an external load demand instruction, performs safety margin verification and instantaneous regulation margin matching in parallel, and returns a commitment response signal to the demander and initiates power ramp-up preparation when both the safety margin verification and the instantaneous regulation margin matching are passed; The resource prediction module, during the power ramp preparation phase, simultaneously analyzes the current surplus power of dispatchable power sources, the predicted charge capacity within the sustained response window, and the predicted loss during charge and discharge, generating a net deliverable power assessment result. An execution feedback module performs hierarchical feedback based on a response deviation between a load demand instruction and a net deliverable power evaluation result; The security boundary check execution process includes the following progressive steps: a) Correlate the real-time blocking status of each section circuit breaker in the main electrical wiring diagram with the disconnector position signal to verify whether the current network topology meets the isolation and redundancy requirements under the preset fault scenario; b) Predict the extreme value of the power flow at the property boundary point after the load demand instruction is executed, check whether the extreme value exceeds the preset overcurrent action threshold of the grid connection point relay protection device, and confirm that the power flow direction matches the polarity of the relay protection action zone; c) comparing the power change rate of the load demand instruction with the inherent mechanical delay characteristics of the grid-connected circuit breaker operating mechanism to confirm whether the switching device action sequence can cover the entire instruction execution process; d) if and only if none of steps a) to c) triggers the preset locking condition, the safety boundary check is determined to have passed; The instantaneous adjustment margin matching execution process includes: According to the power change direction, power adjustment amount and change rate requirements of the load demand instruction, the following operations are performed on the dispatchable power unit set of the local party: Ⅰ. Exclude power supply units that do not match the power change direction adjustment capability of the command, including power generation units that cannot increase output under power increase command or energy storage units that cannot reduce output under power decrease command; II. Eliminate power supply units of the capacity type whose minimum power ramp rate is lower than the required power change rate; III. Exclude energy storage power supply units whose power regulation dead zone covers the command power adjustment range under charge and discharge mode switching conditions; IV. Eliminate power supply units whose maximum sustainable response time does not reach the minimum cycle threshold of the instruction continuous response window; The rated output upper limits of the remaining power unit subsets are aggregated. If the aggregated instantaneous adjustable capacity covers the extreme value of the command power change and leaves a preset adjustment margin, it is determined that the instantaneous adjustment margin matching has passed.

2. The virtual power plant demand response optimization system based on source-load aggregation according to claim 1 is characterized by: The current surplus power analysis process of the dispatchable power source includes: Based on the real-time blocking signal and operation mode identification of the battery management terminal, the energy storage power supply units that are in grid-connected operation and have not triggered the hard constraints of charge and discharge are screened; Determine the net releasable power threshold of each energy storage power supply unit under the current operating conditions based on the measured state of charge boundary value and the preset efficiency compensation coefficient stated on the power conversion device nameplate; The locked power that has been promised to be supplied to the load side is deducted from the net releasable power threshold to obtain the current surplus power of each energy storage power supply unit, which is then integrated to generate the current surplus power of the dispatchable power supply.

3. The virtual power plant demand response optimization system based on source-load aggregation according to claim 1 is characterized by: The predicted charge capacity analysis process within the continuous response window includes: Screen power units of various production capacity categories that are in grid-connected operation and have not triggered output hard constraints; Check whether each power unit of each capacity type can maintain the minimum stable output power within the continuous response window, eliminate the units that cannot meet the minimum stable output continuity, and mark the remaining units as effective capacity units; Obtain the upper limit of the additional power of each effective production unit under its current operating conditions, combine it with the duration of the continuous response window, and generate the theoretical charging capacity of each effective production unit, so as to calculate the predicted charging capacity within the continuous response window.

4. The virtual power plant demand response optimization system based on source-load aggregation according to claim 3 is characterized by: The charging execution prediction loss analysis process includes: Select an effective production capacity unit as the target unit, and based on the target unit's current upper limit of power that can be increased and the distribution of operating condition parameters, retrieve historical charging record points with the same operating condition characteristics; Extract the complete operation records of the matching historical charging record points within the subsequent continuous response window as samples, and construct a dynamic process sample set based on the change trajectory of the operating condition parameters and power output of each sample; Analyze the actual charging loss performance of each sample in the sample set, deconstruct the inevitable loss component and the fluctuating loss component, and generate the loss characteristic envelope interval of the target unit in the current prediction scenario based on the loss component analysis; Select the characteristic reference value in the loss characteristic envelope interval that coincides with the high-frequency distribution area of ​​the historical sample as the charging prediction loss of the target unit; Traverse all effective production capacity units to count the predicted charging loss of each unit and obtain the predicted charging execution loss.

5. The virtual power plant demand response optimization system based on source-load aggregation according to claim 1 is characterized by: The discharge execution prediction loss analysis process includes: Extract the starting power value, target power value and power change rate of the load demand instruction to form a discharge condition characteristic matrix; Determine whether there is a historical interaction record between this party and the demander: (i) If it exists, retrieve the monitoring data of each interactive ramp-up phase in history to construct a loss sample set, deconstruct the loss sample set to generate associated loss amounts corresponding to the ramp-up requirement from the starting power value to the target power value and the power change rate requirement, and accumulate the associated loss amounts to obtain the predicted discharge execution loss amount; (ii) If none exists, then filter out historical interactors with similar grid impedance characteristics to the demander, and correct the discharge loss values ​​of the historical interactors based on the rated ramp loss coefficient of the equipment to generate an equivalent loss envelope interval, taking the conservative upper limit of the interval as the predicted discharge execution loss amount.

6. The virtual power plant demand response optimization system based on source-load aggregation according to claim 1 is characterized by: The net deliverable electricity evaluation process is as follows: The current surplus power of the dispatchable power source is accumulated with the predicted charging capacity within the continuous response window to obtain the initial available power; The initial available power is calibrated for loss based on the predicted loss amount during charge and discharge, and the calibrated power is output as a net deliverable power evaluation result.

7. The virtual power plant demand response optimization system based on source-load aggregation according to claim 1 is characterized by: The hierarchical feedback execution process includes: Based on the duration of the continuous response window of the load demand instruction and the power adjustment amount, the actual total power required by the demander is quantified and the safety power is added, and the added result is compared with the net deliverable power; If the comparison relationship is less than or equal to, the discharge operation is maintained until the continuous response window ends; If the comparison relationship is greater than, a power shortage alarm and an external resource coordination request will be triggered.

8. A virtual power plant demand response optimization method based on source-load aggregation is characterized by: include: Receive an external load demand instruction, perform safety margin verification and instantaneous regulation margin matching in parallel, and when both the safety margin verification and instantaneous regulation margin matching are passed, return a commitment response signal to the demander and start power ramp-up preparation; During the power ramp preparation phase, the system simultaneously analyzes the current surplus power of dispatchable power sources, the predicted charge capacity within the continuous response window, and the predicted loss during charge and discharge, generating a net deliverable power assessment result. Hierarchical feedback is performed based on the response deviation between the load demand command and the net deliverable power evaluation result.

Images