Integrated Source-Grid-Load-Storage Coordinated Scheduling: Supply and Demand Optimization Dynamic Response Method and System

Abstract

This invention discloses an integrated source-grid-load-storage coordinated dispatching supply and demand optimization dynamic response method and system, belonging to the field of power dispatching technology. The integrated source-grid-load-storage coordinated dispatching supply and demand optimization dynamic response method includes: constructing a structured resource set; establishing a disturbance sensitivity model to calculate the comprehensive disturbance risk value of each resource under the current dispatch power; constructing a multi-period optimization model and solving it under multiple constraints to obtain multi-period dispatching paths; wherein the multi-period optimization model includes economic cost terms, disturbance risk penalty terms, and fluctuation suppression terms; performing feasibility scoring and verification on the dispatching paths; and generating discrete control commands executable by the equipment based on the verification results. This invention achieves high-precision, low-risk, and implementable source-grid-load-storage coordinated dispatching in complex operating environments with multiple parallel sources and high disturbance sensitivity.

Description

Technical Field

[0001] This invention belongs to the field of power dispatching technology, specifically relating to a dynamic response method and system for supply and demand optimization in integrated source-grid-load-storage coordinated dispatching. Background Technology

[0002] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art.

[0003] With the large-scale integration of new energy sources and the continuous growth of diversified loads on the user side, the operational boundaries of the power system are constantly expanding. The traditional dispatching model relying on centralized power sources can no longer effectively cope with the high coupling and strong uncertainty characteristics among power sources, grids, loads, and storage. In the new power system, the output of renewable energy sources such as photovoltaics and wind power fluctuates frequently, and there are behavioral differences in the response of industrial, commercial, and residential loads. Although energy storage devices have rapid adjustment capabilities, their lifespan is limited, and the short-circuit capacity of some grid nodes is low and easily amplified by disturbances. These factors combined require the dispatching system to make multi-dimensional trade-offs between supply and demand balance, power quality assurance, equipment lifespan maintenance, and user acceptance.

[0004] However, the inventors discovered that existing source-grid-load-storage coordinated scheduling methods often remain at the static or single-period optimization level, lacking the ability to dynamically model based on real-time status and disturbance risks. This makes it difficult to maintain the stability and executability of scheduling paths during continuous operation across multiple time periods. Especially in scenarios such as large-scale charging of electric vehicles, centralized grid connection of renewable energy, and multi-source parallel power supply, traditional methods cannot effectively utilize the capacity boundaries of all adjustable resources, nor can they predict and suppress potential power quality disturbances in advance. This leads to the risk of scheduling results lagging or even failing under extreme operating conditions. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an integrated source-grid-load-storage coordinated scheduling method and system for supply and demand optimization dynamic response. This system can achieve high-precision, low-risk, and practical source-grid-load-storage coordinated scheduling in complex operating environments with multiple parallel sources and high disturbance sensitivity.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0007] On the one hand, the technical solution of the present invention provides an integrated source-grid-load-storage coordinated scheduling method for dynamic response to supply and demand optimization, including:

[0008] Collect operational status data and user behavior data of various controllable resources in the system, calculate the maximum callable power and unit call cost of each resource in the current scheduling cycle, and construct a structured resource set;

[0009] Establish a disturbance sensitivity model for each resource and calculate the comprehensive disturbance risk value of each resource under the current call power;

[0010] A multi-period optimization model is constructed and solved under multiple constraints to obtain scheduling paths for multiple periods. The multi-period optimization model includes an economic cost term, a disturbance risk penalty term, and a fluctuation suppression term.

[0011] The feasibility of the scheduling path is scored and verified, and discrete control commands that can be executed by the equipment are generated based on the verification results.

[0012] In at least one embodiment, each resource item in the structured resource set includes the current maximum adjustable power, unit call cost, and operating boundary.

[0013] In at least one embodiment, the calculation of the maximum callable power takes into account both the device's rated power parameters and the current available energy state; the unit call cost is determined based on the device loss cost or the reciprocal of the user response probability.

[0014] In at least one embodiment, the overall disturbance risk value of the resource under the current call power is specifically expressed as follows:

[0015]

[0016] In the formula, Represents resource item At the current scheduling power The overall disturbance risk value is as follows; Indicates the current maximum adjustable power. ; Indicates the nonlinear amplification index of the disturbance; Indicates the amplification factor for weak power grids; Indicates the power change penalty coefficient; This indicates the power of the resource accessed in the previous scheduling cycle.

[0017] In at least one embodiment, the objective function of the multi-time-period optimization model is specifically expressed as:

[0018]

[0019] In the formula, This represents the number of discrete time intervals within the optimized scrolling window; It is a resource item At any moment The power of the call; Indicates the cost per unit of data collection; Represents resource item At any moment The overall disturbance risk value; It is the perturbation risk weight; It is the fluctuation suppression regularity coefficient; It is the weak grid amplification factor, reflecting the sensitivity of resource location to disturbance amplification.

[0020] In at least one embodiment, the constraints include supply and demand balance constraints, resource capacity constraints, disturbance restriction constraints, and state boundary constraints.

[0021] In at least one embodiment, the execution feasibility of the scheduling path is scored, specifically as follows:

[0022]

[0023] In the formula, Indicates time period resource items Feasibility score for implementation; For a moment resource items State variables; and These represent the planned power for two adjacent time periods; It is a minimal constant used to avoid the denominator being zero; For the set of scheduling paths The reference step scale is obtained by adaptive calculation based on internal resources.

[0024] In at least one embodiment, discrete control instructions executable by the device are generated based on the verification results, specifically including:

[0025] If the feasibility score is not lower than the preset threshold, the power plan in the scheduling path is directly used as the control command, and the corresponding standard cycle command sequence is issued; if the feasibility score is lower than the preset threshold, the scheduling period with the score lower than the preset threshold is corrected and then issued according to the sub-cycle time calibration.

[0026] The discrete control commands that the equipment can execute include: the active power setting sequence for energy storage generation; the active / reactive power setting for grid-connected inverter generation; and the power limit or duty cycle allocation sequence for user flexible load generation.

[0027] In at least one embodiment, if the feasibility score is lower than a preset threshold, a two-step correction is performed, including power smoothing and sub-cycle subdivision.

[0028] Power smoothing, specifically, involves adjusting the target power for that time period according to... Scaling is performed, and adjacent time periods are finely adjusted in the opposite direction by the same amount to maintain energy conservation throughout the cycle; among which, Indicates time period The planned power; Indicates time period resource items Feasibility score for implementation; Indicates a preset threshold;

[0029] Sub-period subdivision, specifically: dividing each scheduling period into... Each sub-cycle is used to generate a sub-cycle sequence by performing an arithmetic transition between the target power of the current period and the previous period, so that the change between adjacent sub-cycles is a constant increment and the mean is equal to the target power of the current period.

[0030] On the other hand, the technical solution of the present invention also provides an integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response system, including:

[0031] The data acquisition module is configured to: collect the operating status data and user behavior data of various controllable resources in the system, calculate the maximum callable power and unit call cost of each resource in the current scheduling cycle, and construct a structured resource set;

[0032] The disturbance modeling module is configured to: build a disturbance sensitivity model for each resource and calculate the comprehensive disturbance risk value of each resource under the current call power;

[0033] The scheduling optimization module is configured to: construct a multi-period optimization model, solve it under multiple constraints, and obtain scheduling paths for multiple periods; wherein, the multi-period optimization model includes an economic cost term, a disturbance risk penalty term, and a fluctuation suppression term;

[0034] The instruction generation module is configured to: score the feasibility of the scheduling path and perform feasibility verification, and generate discrete control instructions that the device can execute based on the verification results.

[0035] The beneficial effects of the above-described technical solution of the present invention are as follows:

[0036] This invention presents an integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method. Based on resource capacity modeling, it accurately identifies the maximum callable power and unit call cost of various controllable resources in the current cycle, and constructs a power quality disturbance sensitivity model, establishing a nonlinear mapping relationship between resource call behavior and disturbance effects such as voltage fluctuations, harmonic variations, and frequency shifts. Building upon this, a multi-period optimization model considering energy storage state evolution, user flexible recovery characteristics, and disturbance smoothing regularization is introduced to generate a scheduling path that achieves comprehensive optimality among supply and demand balance, economic efficiency, power quality, and equipment lifespan. Finally, the optimization results are verified through an execution feasibility score based on state margin and power step amplitude, and power scaling and sub-cycle smoothing are performed when necessary to generate directly executable equipment control commands. This invention integrates resource capacity identification, disturbance prediction, state evolution optimization, and execution verification into a closed-loop system, enabling high-precision, low-risk, and implementable source-grid-load-storage coordinated scheduling in complex operating environments with multiple parallel sources and high disturbance sensitivity. Attached Figure Description

[0037] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0038] Figure 1 This is a schematic diagram of the integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method disclosed in Embodiment 1 of the present invention. Detailed Implementation

[0039] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0040] As described in the background section, the purpose of this invention is to overcome the shortcomings of the prior art and provide an integrated source-grid-load-storage coordinated scheduling method and system for supply and demand optimization and dynamic response. This system can achieve high-precision, low-risk, and practical source-grid-load-storage coordinated scheduling in complex operating environments with multiple parallel sources and high disturbance sensitivity.

[0041] Example 1

[0042] In a typical embodiment of the present invention, such as Figure 1 As shown in the figure, this embodiment discloses a dynamic response method for supply and demand optimization based on integrated source-grid-load-storage coordinated scheduling, which specifically includes the following steps:

[0043] S1. Collect the operating status data and user behavior data of various controllable resources in the system, calculate the maximum callable power and unit call cost of each resource in the current scheduling cycle, and construct a structured resource set;

[0044] S2. Establish a disturbance sensitivity model for each resource and calculate the comprehensive disturbance risk value of each resource under the current call power;

[0045] S3. Construct a multi-period optimization model and solve it under multiple constraints to obtain scheduling paths for multiple periods; the multi-period optimization model includes an economic cost term, a disturbance risk penalty term, and a volatility suppression term;

[0046] S4. Analyze the feasibility of the scheduling path and perform a feasibility check. Based on the check results, generate discrete control commands that the equipment can execute.

[0047] The following detailed description of the above-mentioned integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method is provided in conjunction with specific implementation methods.

[0048] S1. Collect the operating status data and user behavior data of various controllable resources in the system, calculate the maximum callable power and unit call cost of each resource in the current scheduling cycle, and construct a structured resource set.

[0049] The core objective of this step is to uniformly model all resources with regulatory capabilities in the current system—including energy storage devices, renewable energy output terminals, and user behavior data—to calculate their maximum available capacity at the current moment, quantify their call costs, and organize them into a resource set in a unified format. This will provide the input basis for the next step of power quality disturbance analysis and subsequent scheduling optimization.

[0050] S11. Collect operational status data and user behavior data of various controllable resources in the system.

[0051] In this step, the first step is to collect real, observable data from the system. This data includes both equipment operating status data (such as SOC, rated power, etc.) and user behavior data (such as load response habits, participation history, etc.). All data collection is completed through the existing platform data links, without adding any redundant collection equipment. For example, the real-time SOC, battery temperature, and rated power parameters of the energy storage device are reported in real time by the Battery Management System (BMS) to the local Energy Management Controller (EMC) via MODBUS or CAN protocol. The platform pulls the data every 5 minutes to form a floating-point time series. Each energy storage unit's device file also records its minimum allowable SOC, maximum continuous discharge power, and other boundary data, which come from the static parameter configuration table at the time of platform deployment.

[0052] S12. Calculate the maximum callable power and unit call cost of each resource in the current scheduling cycle.

[0053] Taking a typical electrochemical energy storage unit as an example, with a current SOC of 65%, a battery capacity of 2kWh, a minimum allowable SOC of 20%, and a current dispatch cycle of 30 minutes, its dischargeable energy is 90kWh, and its average power output is 180kW. If the device's rated power is 150kW, then its maximum dispatchable power will be the smaller of the two, i.e., 150kW. That is, the maximum dispatchable power can be specifically expressed as:

[0054]

[0055] In the formula, Indicates the first The maximum available power of an energy storage device during the current scheduling cycle; This is the rated maximum discharge power of the equipment, which is read from the platform equipment file; It is the current energy storage capacity, which is derived from the product of SOC and battery capacity, and is uploaded in real time by BMS; This is the minimum allowed energy storage threshold, set by the configuration table; This is the current scheduling cycle length, typically 30 minutes. If there are inconsistencies in the numerical units, the platform will internally normalize the values ​​to ensure consistency in the result dimensions.

[0056] For user behavior data, this step uses statistical modeling instead of the traditional direct load forecasting method. Specifically, based on the individual load control terminals (such as smart circuit breakers, electric water heater controllers, air conditioning gateways, etc.) already deployed on the platform, the system automatically records whether users respond, the duration of the response, and whether the recovery is completed within the expected time window each time the platform initiates a load adjustment request. The system establishes a load response record table by user group (such as "residential air conditioning group" or "office lighting group"), and uses a sliding window with a set duration (such as three days) to statistically analyze the success rate of the response. For example, if the "residential air conditioning group" received 50 adjustment requests from the platform in the past three days, and successfully responded to 38 of them, then its response probability for the current period is:

[0057]

[0058] In the formula, Indicates the first The probability of adjustment response for the current user group during the current time period; This represents the number of successful responses from this user group over the past three days. This represents the total number of requests initiated by the platform. It is a decimal constant introduced to avoid the denominator being zero, and its value is generally 1.

[0059] In this step, the response probability is automatically updated hourly by the platform and used to construct the user scheduling cost function. During the optimization phase, the response probability is used... The reciprocal of the response probability reflects the scheduling cost. A low response probability indicates poor user controllability and a high scheduling cost. A higher response probability signifies a higher success rate and stronger reliability of the load in historical control, thus resulting in a higher scheduling priority and a higher call weight in subsequent scheduling models. Based on the user's response probability, the schedulable load groups are classified and labeled: when... When, mark the load group as "highly dispatchable load"; when When this happens, the load group is marked as "medium-high dispatchable load"; when... At that time, the load group is marked as "low dispatchable load".

[0060] Taking an industrial park in a certain city as an example, its platform manages a group of 8 multi-split air conditioning systems. The platform conducts peak-shaving control tests on these systems three times a day. If the system successfully responds 7 out of 9 tests over the past three days, then... Based on this, the platform marks the group as "medium-high schedulable load" and assigns it a higher call priority in the subsequent scheduling model.

[0061] S13. Construct a structured resource set.

[0062] In this step, all resources are uniformly abstracted into resource items in a standard format. Each resource item Include:

[0063] (1) Current maximum adjustable power It is calculated by formula (1);

[0064] (2) Unit call cost It includes an energy storage component and a user component. The energy storage component is estimated based on its cycle life curve, with approximately 0.3 cycles lost for every 1 kWh discharged, which is converted into unit cost based on the cell price; the user component is... The calculated weights are used as cost weights in the optimization process.

[0065] (3) Operating boundary Settings such as minimum / maximum start / stop time and adjustment granularity are configured during the initial deployment of the platform.

[0066] S2. Establish a disturbance sensitivity model for each resource in the structured resource set and calculate the comprehensive disturbance risk value of each resource under the current call power.

[0067] S21. Establish a disturbance sensitivity model for each resource.

[0068] In this step, the first step is to establish a baseline coefficient for the disturbance sensitivity of each resource. This coefficient was obtained through offline data analysis. The analysis process involved the platform using historical scheduling records and PQ monitoring data to calculate the disturbance response intensity under different dispatch ratios, and then determining the baseline coefficient through weighted regression. Taking energy storage equipment as an example, the maximum voltage change rate corresponding to 30 historical discharge actions at different power levels was taken. After removing extreme outliers, the slope of the disturbance curve was fitted with the dispatch ratio as the independent variable and the voltage change rate as the dependent variable, serving as the baseline coefficient. This step is performed only once during the system deployment phase, and is loaded into the scheduling controller as a static parameter after deployment.

[0069] S22. Calculate the overall disturbance risk value of each resource under the current power of call.

[0070] To combine disturbance sensitivity with current call capabilities, this step introduces two special correction terms into the traditional disturbance function: one is a regional weak grid amplification term. First, it amplifies the risk of resource disturbances at nodes with low short-circuit capacity; second, it includes a regularization term for scheduling action mutations. This regularization term is used to penalize the accumulation of disturbances caused by rapid power changes between adjacent scheduling cycles. This regularization design is particularly important in power quality-sensitive scenarios, because even if the magnitude of a single call is small, frequent switching can cause fluctuations to accumulate.

[0071] Based on this, this step combines the short-circuit capacity characteristics of power grid nodes and the rate of power change to calculate the comprehensive disturbance risk value of each resource under the current power consumption, specifically expressed as follows:

[0072]

[0073] In the formula, Represents resource item At the current scheduling power The overall disturbance risk value is as follows; Indicates the current maximum adjustable power. It is calculated by formula (1); It is the perturbation nonlinear amplification index, usually taken as 1.5 to reflect the increased sensitivity to high-proportion calls; This represents the amplification factor of the weak network, which is calculated from the short-circuit capacity of the system topology. The value is relatively large for weak network nodes (e.g., 0.2~0.5). The value approaches 0; This represents the penalty factor for power variation, such as when motor loads frequently start and stop. Larger; It is the power of the resource accessed in the previous scheduling cycle, which can be directly read from the scheduling history. In formula (3), the first part The second part measures the risk of steady-state disturbances. This refers to the dynamic disturbance regularity for the rate of power change. The two parts are added together to form the comprehensive disturbance risk value. It is generally considered that a comprehensive disturbance risk value less than 0.3 is low risk, between 0.3 and 0.6 is medium risk, and greater than 0.6 is high risk.

[0074] Specifically, in actual operation, let's assume an energy storage unit , , (Due to connection to a weak power grid). , Current call power Power called in the previous cycle According to formula (3), the following can be calculated:

[0075] The steady-state disturbance part is ;

[0076] The dynamic perturbation regularization part is ;

[0077] Therefore, the comprehensive disturbance risk value This indicates that the overall risk is at a moderate level, but the dynamic disturbance accounts for about 90%, indicating that the scheduling is mainly affected by the power change rate, and should be smoothed or restricted in subsequent optimizations.

[0078] This step calculates the comprehensive disturbance risk value, making each All are related to step S1 , , These parameters are directly correlated, and correction terms related to system topology location and call rate are introduced to ensure that disturbance risk measurement reflects both device characteristics and network structure impact.

[0079] S3. Construct a multi-period optimization model and solve it under multiple constraints to obtain scheduling paths for multiple periods; the multi-period optimization model includes an economic cost term, a disturbance risk penalty term, and a fluctuation suppression term.

[0080] S31. Construct a multi-time period optimization model.

[0081] Because a high proportion of renewable energy integration leads to frequent power output fluctuations, and while energy storage and user load have high response capabilities, they are constrained by lifespan and flexibility, and weak nodes in the grid are easily affected by amplified disturbances. Therefore, this step constructs a multi-period optimization model, which simultaneously introduces resource state evolution, disturbance constraints, and a "fluctuation suppression regularization term" designed for renewable energy scenarios in multi-period rolling optimization. This ensures that the optimization result is not only economically optimal but also proactively smooths out the disturbance risks brought about by renewable energy fluctuations.

[0082] Specifically, the resource state evolution is based on the boundary parameters in step S1. and the current maximum available power This describes the changes in resource status (such as energy storage SOC, user adjustable capacity ratio) over multiple time periods, and can be specifically represented as:

[0083]

[0084] In the formula, It is a moment resource items The state variables are tracked and stored by the platform in real time; The proportion of power consumed by the call to the state is determined through device testing during deployment; This represents the recovery amount caused by external factors, such as the load recovery rate and the natural increase in wind and solar power output. This resource state evolution equation ensures that the scheduling results will not result in resource depletion or loss of flexibility in the future.

[0085] In this step, the objective function of the multi-period optimization model adds two scenario-specific disturbance control terms to the traditional supply-demand balance and economic cost terms: a disturbance constraint term and a fluctuation suppression term. The disturbance constraint term refers to the weighted penalty based on the comprehensive disturbance risk value from step S22, i.e., the disturbance risk penalty term, ensuring that dispatched power allocation is proactively reduced near weak grid nodes or disturbance-sensitive equipment. The fluctuation suppression term is an innovative fluctuation suppression regularization term proposed specifically for the characteristics of new energy fluctuations, specifically expressed as... ,in, The weak grid amplification factor from step S2 is used to suppress the amplification effect of large power changes on disturbance accumulation during the period of new energy fluctuations.

[0086] Based on this, the objective function of the multi-time period optimization model can be specifically expressed as:

[0087]

[0088] In the formula, This represents the number of discrete time intervals within the optimized scrolling window; It is a resource item At any moment The power of the call; This indicates the unit call cost, derived from step S13; Represents resource item At any moment The comprehensive disturbance risk value is calculated using formula (3); It is the perturbation risk weight; It is the fluctuation suppression regularity coefficient; This is the weak grid amplification factor, reflecting the sensitivity of resource location to disturbance amplification (larger at weak nodes, smaller at strong nodes). In the objective function of the multi-time period optimization model, the first term... To ensure economic efficiency, the second item Controlling disturbance risks, the third item It is an innovative fluctuation suppression regularization term proposed specifically for the fluctuation characteristics of new energy sources, which can significantly reduce grid stress during periods of rapid changes in wind and solar power.

[0089] S32. Solve the objective function of the multi-period optimization model under multiple constraints to obtain the scheduling path for the multi-period.

[0090] In this step, multiple constraints, including supply and demand balance constraints, resource capacity constraints, disturbance restriction constraints, and state boundary constraints, can be obtained by directly referencing the output and prediction data from steps S1 and S2.

[0091] Specifically, the supply and demand balance constraint can be expressed as:

[0092]

[0093] In the formula, Source: Platform load forecasting module.

[0094] Resource capacity constraints can be expressed as:

[0095]

[0096] In the formula, This represents the current maximum adjustable power, calculated using formula (1) in step S13.

[0097] The disturbance constraint can be expressed as:

[0098]

[0099] In the formula, The security threshold set for the platform.

[0100] State boundary constraints are It must run at the boundary in step S13. Within the specified range.

[0101] Under the conditions of satisfying the above-mentioned supply and demand balance constraints, resource capacity constraints, disturbance restriction constraints, and state boundary constraints, the objective function of the multi-period optimization model is solved to obtain the multi-period scheduling path and state trajectory. Specifically, the multi-period scheduling path is expressed as follows:

[0102]

[0103] The state trajectory is specifically represented as follows:

[0104]

[0105] The multi-time period scheduling paths and status trajectories are passed to the next step to generate actual control commands.

[0106] S4. Analyze the feasibility of the scheduling path and perform a feasibility check. Based on the check results, generate discrete control commands that the equipment can execute.

[0107] This step involves optimizing the multi-time-period collaborative scheduling and then outputting the set of scheduling paths. and set of state trajectories Perform engineering feasibility verification before execution and generate discrete control instructions that the equipment can execute.

[0108] S41. Perform an execution feasibility score on the scheduling path.

[0109] Specifically, for each resource item Each time period Calculate the feasibility score for implementation. Specifically, it can be expressed as:

[0110]

[0111] In the formula, Indicates time period resource items Feasibility score for implementation; For a moment resource items The state variable is the normalized state value output in step S3; and These represent the planned power for two adjacent time periods; It is a minimal constant used to avoid the denominator being zero; For the set of scheduling paths The reference step scale is obtained through resource-adaptive calculation. The power step amplitude in the set of scheduling paths The maximum amplitude of the nearest neighbor step is obtained by internal adaptive normalization. If there are no step jumps throughout the process, a minimum constant is added to the denominator. Avoid dividing by zero.

[0112] S42. Perform feasibility verification and generate discrete control commands that the equipment can execute based on the verification results.

[0113] In this step, a threshold is first preset. Compare the execution feasibility scores of the scheduling paths. With preset threshold To conduct a feasibility check of the project before implementation.

[0114] If a feasibility assessment is performed Not lower than the preset threshold The power plan in the original scheduling path is directly used as the control command, and the corresponding standard cycle command sequence is issued; if a feasibility score is performed... Below the preset threshold If the score is lower than the preset threshold, the scheduling period will be corrected and then issued according to the sub-cycle time calibration.

[0115] In this step, when the feasibility score is... Below the preset threshold At that time, a two-step correction is performed, including power smoothing and sub-cycle subdivision, as follows:

[0116] (1) Power smoothing: The target power for this period is processed according to... Scaling is performed, and adjacent time periods are finely adjusted in the opposite direction by the same amount to maintain energy conservation throughout the cycle; among which, Indicates time period The planned power; Indicates time period resource items Feasibility score for implementation; This indicates a preset threshold.

[0117] (2) Sub-cycle subdivision: Dividing each scheduling period into sub-cycles Each sub-cycle (configuration constant), the target power for that time period. Compared with the target power of the previous period Perform an arithmetic progression to generate a sub-period sequence. This ensures that the changes between adjacent sub-cycles are constant increments, and that the average value equals the target power for that period. .

[0118] Specifically, in this embodiment:

[0119] Energy storage: , , , ,but Sub-periodic sequence .

[0120] Charging group: , , , ,but Sub-periodic sequence .

[0121] The generated sub-period power sequence is mapped to discrete instructions on the device side:

[0122] (1) Energy storage generates active power setting sequence;

[0123] (2) The grid-connected inverter generates active / reactive power settings;

[0124] (3) User flexible load generation power limit or duty cycle allocation sequence.

[0125] All instructions are issued according to sub-cycle time markers and confirmed through a platform-device handshake.

[0126] Example 2

[0127] In a typical embodiment of the present invention, this embodiment discloses an integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response system, comprising:

[0128] The data acquisition module is configured to: collect the operating status data and user behavior data of various controllable resources in the system, calculate the maximum callable power and unit call cost of each resource in the current scheduling cycle, and construct a structured resource set;

[0129] The disturbance modeling module is configured to: build a disturbance sensitivity model for each resource and calculate the comprehensive disturbance risk value of each resource under the current call power;

[0130] The scheduling optimization module is configured to: construct a multi-period optimization model, solve it under multiple constraints, and obtain scheduling paths for multiple periods; wherein, the multi-period optimization model includes an economic cost term, a disturbance risk penalty term, and a fluctuation suppression term;

[0131] The instruction generation module is configured to: score the feasibility of the scheduling path and perform feasibility verification, and generate discrete control instructions that the device can execute based on the verification results.

[0132] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An integrated source-grid-load-storage coordinated scheduling method for supply and demand optimization and dynamic response, characterized in that, include: Collect operational status data and user behavior data of various controllable resources in the system, calculate the maximum callable power and unit call cost of each resource in the current scheduling cycle, and construct a structured resource set; Establish a disturbance sensitivity model for each resource and calculate the comprehensive disturbance risk value of each resource under the current call power; A multi-period optimization model is constructed and solved under multiple constraints to obtain scheduling paths for multiple periods. The multi-period optimization model includes an economic cost term, a disturbance risk penalty term, and a fluctuation suppression term. The feasibility of the scheduling path is scored and verified, and discrete control commands that can be executed by the equipment are generated based on the verification results. The overall disturbance risk value of resources under the current power of access is specifically expressed as follows: In the formula, Represents resource item At the current scheduling power The overall disturbance risk value is as follows; Indicates the current maximum adjustable power. ; Indicates the nonlinear amplification index of the disturbance; Indicates the amplification factor for weak power grids; Indicates the power change penalty coefficient; This indicates the power of the resource accessed in the previous scheduling cycle; The objective function of the multi-time-period optimization model is specifically expressed as: In the formula, This represents the number of discrete time intervals within the optimized scrolling window; It is a resource item At any moment The power of the call; Indicates the cost per unit of data collection; Represents resource item At any moment The overall disturbance risk value; It is the perturbation risk weight; It is the fluctuation suppression regularity coefficient; It is the weak grid amplification factor, reflecting the sensitivity of resource location to disturbance amplification.

2. The integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method as described in claim 1, characterized in that, Each resource item in the structured resource set contains the current maximum adjustable power, unit call cost, and operating boundary.

3. The integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method as described in claim 1, characterized in that, The calculation of the maximum callable power takes into account both the device's rated power parameters and the current available energy status; the unit call cost is determined based on the device's loss cost or the reciprocal of the user's response probability.

4. The integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method as described in claim 1, characterized in that, The constraints include supply and demand balance constraints, resource capacity constraints, disturbance restriction constraints, and state boundary constraints.

5. The integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method as described in claim 1, characterized in that, The feasibility of the scheduling path is scored, specifically as follows: In the formula, Indicates time period resource items Feasibility score for implementation; For a moment resource items State variables; and These represent the planned power for two adjacent time periods; It is a minimal constant used to avoid the denominator being zero; For the set of scheduling paths The reference step scale is obtained by adaptive calculation based on internal resources.

6. The integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method as described in claim 1, characterized in that, Based on the verification results, discrete control instructions executable by the device are generated, specifically including: If the feasibility score is not lower than the preset threshold, the power plan in the original scheduling path is directly used as the control command, and the corresponding standard cycle command sequence is issued; if the feasibility score is lower than the preset threshold, the scheduling period with the score lower than the preset threshold is corrected and then issued according to the sub-cycle time calibration. The discrete control commands that the equipment can execute include: the active power setting sequence for energy storage generation; the active / reactive power setting for grid-connected inverter generation; and the power limit or duty cycle allocation sequence for user flexible load generation.

7. The integrated source-grid-load-storage coordinated scheduling supply and demand optimization dynamic response method as described in claim 6, characterized in that, If the feasibility score is lower than the preset threshold, a two-step correction is performed, including power smoothing and sub-cycle subdivision. Power smoothing, specifically, involves adjusting the target power for that time period according to... Scaling is performed, and adjacent time periods are finely adjusted in the opposite direction by the same amount to maintain energy conservation throughout the cycle; among which, Indicates time period The planned power; Indicates time period resource items Feasibility score for implementation; Indicates a preset threshold; Sub-period subdivision, specifically: dividing each scheduling period into... Each sub-cycle is used to generate a sub-cycle sequence by performing an arithmetic transition between the target power of the current period and the previous period, so that the change between adjacent sub-cycles is a constant increment and the mean is equal to the target power of the current period.

8. An integrated dynamic response system for supply and demand optimization through coordinated scheduling of power generation, grid, load, and storage, characterized in that: include: The data acquisition module is configured to: collect the operating status data and user behavior data of various controllable resources in the system, calculate the maximum callable power and unit call cost of each resource in the current scheduling cycle, and construct a structured resource set; The disturbance modeling module is configured to: build a disturbance sensitivity model for each resource and calculate the comprehensive disturbance risk value of each resource under the current call power; The scheduling optimization module is configured to: construct a multi-period optimization model, solve it under multiple constraints, and obtain scheduling paths for multiple periods; wherein, the multi-period optimization model includes an economic cost term, a disturbance risk penalty term, and a fluctuation suppression term; The instruction generation module is configured to: score the feasibility of the scheduling path and perform a feasibility check, and generate discrete control instructions that the device can execute based on the check results; The overall disturbance risk value of resources under the current power of access is specifically expressed as follows: In the formula, Represents resource item At the current scheduling power The overall disturbance risk value is as follows; Indicates the current maximum adjustable power. ; Indicates the nonlinear amplification index of the disturbance; Indicates the amplification factor for weak power grids; Indicates the power change penalty coefficient; This indicates the power of the resource accessed in the previous scheduling cycle; The objective function of the multi-time-period optimization model is specifically expressed as: In the formula, This represents the number of discrete time intervals within the optimized scrolling window; It is a resource item At any moment The power of the call; Indicates the cost per unit of data collection; Represents resource item At any moment The overall disturbance risk value; It is the perturbation risk weight; It is the fluctuation suppression regularity coefficient; It is the weak grid amplification factor, reflecting the sensitivity of resource location to disturbance amplification.

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