Distributed source-load-storage aggregation adjustable capability correction method and system
By constructing a distributed source-load-storage aggregation adjustable capability correction method, the conservative coefficient is dynamically adjusted to correct the actual feasible domain, which solves the problem of scheduling instruction execution deviation in the existing technology and achieves higher scheduling executability and system security.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LISHUI POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER
- Filing Date
- 2026-04-16
- Publication Date
- 2026-05-15
AI Technical Summary
Existing methods for calculating aggregate feasible regions fail to accurately reflect the actual adjustment capabilities of distributed resources, leading to deviations in scheduling instruction execution and issues with security and stability.
A method for adjusting the adjustable capacity of distributed source-load-storage aggregation is constructed. By determining the power-energy feasible region, the theoretical feasible region is narrowed using conservative coefficients, and the conservative coefficients are updated based on the decomposition error to form a closed-loop adjustment system. The actual feasible region is dynamically adjusted to match the real adjustment potential of resources.
It improves the executability of scheduling instructions and the security of system operation, enabling it to quickly adapt to changes in resource status and environment, and providing accurate and reliable aggregation capabilities.
Smart Images
Figure CN122052200A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power distribution network technology, and in particular to a method and system for correcting the adjustable capacity of distributed source-load-storage aggregation. Background Technology
[0002] With the widespread integration of distributed photovoltaic, energy storage systems, and adjustable loads, the distribution network exhibits significant flexibility. Aggregating these dispersed resources and participating in grid dispatch as virtual power plants is key to improving grid absorption capacity and operational economy. Aggregation capacity assessment is the core component of virtual power plant participation in grid dispatch, with its core objective being to accurately describe the range of power that the aggregate can adjust upwards or downwards over a future period.
[0003] Currently, existing methods for calculating the aggregated feasible region typically employ a "first independent modeling, then arithmetic superposition" approach. This involves first establishing adjustable capacity models for each dispersed resource separately, and then obtaining the theoretical adjustable boundary of the aggregate through simple arithmetic summation. While this method is simple in principle, it has significant drawbacks in practical applications: Firstly, the adjustable boundary obtained through arithmetic superposition is overly idealized, failing to consider the complex relationships between different resources, such as temporal coupling and power constraint coupling. Secondly, this method ignores the model uncertainties caused by the output characteristics of the source, load, and storage resources themselves, as well as changes in the external environment, leading to an estimated aggregated adjustable capacity that is generally higher than the actual level. When dispatching agencies issue adjustment commands based on this overestimated aggregated capacity, problems often arise such as the command failing to be effectively decomposed to each independent resource, and decomposition errors exceeding the allowable range. This not only affects the achievement of dispatching objectives but also threatens the safe and stable operation of the distribution network.
[0004] Therefore, how to construct an online, dynamic aggregation adjustability correction mechanism so that the evaluation results truly reflect the actual response capability of the aggregate has become an urgent technical problem to be solved. Summary of the Invention
[0005] To address the problems existing in the prior art, the present invention provides a method and system for correcting the adjustable capability of distributed source-load-storage aggregation.
[0006] In a first aspect, embodiments of the present invention provide a method for correcting the adjustable capability of distributed source-load-storage aggregation, comprising: Determine the power-energy feasible domain of each distributed source-load-storage terminal in the future scheduling time domain, and superimpose the power-energy feasible domain of each terminal to obtain the theoretical aggregate feasible domain; The theoretical aggregated feasible region is shrunk based on a predefined conservative coefficient to obtain the actual aggregated feasible region applicable to the current scheduling cycle. The target power curve sampled from the actual aggregated feasible region is decomposed into each terminal, and the power deviation obtained after decomposition is used as the decomposition error of the current scheduling period. The conservative coefficients are updated based on the decomposition error, and the updated conservative coefficients are applied to the next scheduling time domain to perform rolling correction on the aggregation adjustability of the distributed source-load-storage system.
[0007] Preferably, the step of determining the power-energy feasible region of each distributed source-load-storage terminal in the future scheduling time domain within the distribution network, and superimposing the power-energy feasible regions of each terminal to obtain a theoretical aggregate feasible region, includes: Obtain the status information of each terminal of the distributed source-load-storage system in the distribution network at the current moment, and determine the power boundary and energy boundary of the corresponding terminal based on the status information of each terminal; Based on the power boundary and energy boundary of each terminal, the power-energy feasible region of the corresponding terminal is determined; The theoretical aggregate feasible region is obtained by arithmetically summing the boundary parameters of each power-energy feasible region.
[0008] Preferably, the shrinking of the theoretical aggregate feasible region based on a predefined conservative coefficient to obtain the actual aggregate feasible region applicable to the current scheduling cycle includes: Based on a predefined conservative coefficient, the upper and lower boundaries of the theoretical aggregate feasible region are proportionally shrunk towards a predetermined aggregate baseline value to obtain the actual aggregate feasible region applicable to the current scheduling cycle.
[0009] Preferably, the step of shrinking the upper and lower boundaries of the theoretical aggregate feasible region proportionally to a predetermined aggregate baseline value based on a predefined conservatism coefficient to obtain the actual aggregate feasible region applicable to the current scheduling cycle includes: The upper and lower power boundary parameters of the theoretical feasible region are respectively differentially calculated with the predetermined aggregated power baseline value to obtain two difference results. The two difference results are weighted based on the conservative coefficient, and the weighted result and the aggregated power baseline value are summed to obtain the upper and lower power boundary parameters of the actual feasible region. The upper and lower energy boundary parameters of the theoretical feasible region are obtained by performing a difference operation with the predetermined aggregated energy baseline value. The two difference results are then weighted based on the conservative coefficient, and the weighted results are summed with the aggregated energy baseline value to obtain the upper and lower energy boundary parameters of the actual feasible region.
[0010] Preferably, the aggregated power baseline value is determined by superimposing the predicted output cumulative value of each photovoltaic power generation terminal and the baseline power consumption cumulative value of each adjustable load terminal, and the aggregated energy baseline value is obtained by accumulating the aggregated power baseline value from the initial time to the current time according to the scheduling step size.
[0011] Preferably, the step of decomposing the target power curve sampled from the actual aggregate feasible region to each of the terminals includes: A power decomposition model is constructed with the goal of minimizing the power deviation. The power deviation after decomposing the target power curve is obtained by solving the power decomposition model based on the target power curve sampled from the actual aggregation feasible region, according to the aggregation power balance constraint and the power-energy feasible region of each terminal.
[0012] Preferably, the aggregation power balance constraint condition includes the power decomposition accumulation value of each terminal achieving balance with the target power by subtracting the positive adjustment power and superimposing the negative adjustment power.
[0013] Preferably, updating the conservative coefficients based on the decomposition error includes: When the decomposition error exceeds a preset threshold, the conservative coefficient is reduced proportionally.
[0014] Preferably, when the decomposition error is greater than a preset threshold, the update process of the conservative coefficient includes: the updated conservative coefficient is the larger of the difference between the current conservative coefficient and the weighted decomposition error and zero.
[0015] Secondly, embodiments of the present invention provide a distributed source-load-storage aggregation adjustable capability correction system, comprising: The theoretical feasible region determination module is used to determine the power-energy feasible region of each terminal of the distributed source-load-storage system in the future scheduling time domain, and to superimpose the power-energy feasible region of each terminal to obtain the theoretical aggregate feasible region. The actual feasible region determination module is used to shrink the theoretical aggregated feasible region based on a predefined conservative coefficient to obtain the actual aggregated feasible region applied to the current scheduling cycle; The decomposition error determination module is used to decompose the target power curve sampled from the actual aggregated feasible region to each terminal, and use the power deviation obtained after decomposition as the decomposition error of the current scheduling period. The rolling correction module is updated to update the conservative coefficients based on the decomposition error and apply the updated conservative coefficients to the next scheduling time domain to perform rolling correction on the aggregation adjustability of the distributed source-load-storage system.
[0016] Compared with existing technologies, the distributed source-load-storage aggregation adjustable capability correction method and system of this invention has the following advantages: This invention constructs a closed-loop correction system of "feasible domain aggregation - conservative contraction - error quantification - coefficient update", which breaks through the limitations of traditional static evaluation. It dynamically adjusts the conservative coefficient based on the decomposition error as the core feedback basis, so that the actual aggregation feasible domain continuously matches the real adjustment potential of source-load-storage resources. It effectively solves the problem of scheduling command execution deviation caused by the overestimation of the theoretical feasible domain. It can quickly adapt to the dynamic changes of resource status and operating environment, provide accurate and reliable aggregation capability support for power grid dispatch, and significantly improve the executability of scheduling commands and the safety of system operation. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating a method for correcting the adjustable capability of distributed source-load-storage aggregation according to an embodiment of the present invention. Figure 2 This is a schematic diagram showing the SOC changes of two energy storage units in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the change of the conservatism coefficient over time in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a distributed source-load-storage aggregation adjustable capability correction system according to an embodiment of the present invention; Figure label: 01. Theoretical feasible region determination module; 02. Actual feasible region determination module; 03. Decomposition error determination module; 04. Update rolling correction module. Detailed Implementation
[0018] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.
[0019] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by those skilled in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.
[0020] like Figure 1 The diagram shown is a flowchart illustrating a method for correcting the adjustable capability of distributed source-load-storage aggregation according to an embodiment of the present invention. (Refer to...) Figure 1 An embodiment of the present invention provides a method for correcting the adjustable capability of distributed source-load-storage aggregation, comprising the following steps: S1. Determine the power-energy feasible region of each distributed source-load-storage terminal in the future scheduling time domain, and superimpose the power-energy feasible region of each terminal to obtain the theoretical aggregate feasible region. Specifically, step S1 includes: 11) Obtain the status information of each terminal of the distributed source-load-storage system in the distribution network at the current moment, and determine the power boundary and energy boundary of the corresponding terminal based on each status information; A unified power-energy feasible domain model is established for distributed resources such as energy storage, photovoltaics, and adjustable loads in the distribution network to accurately describe their physical constraints in the future scheduling time domain.
[0021] Specifically, in this embodiment, at the beginning of each rolling moment, i.e., the current moment, the feasible domain of each resource for the next 4 hours is modeled: This embodiment includes two energy storage units (ESS), with the following parameters: ESS-1: Maximum charge / discharge power =150kW, rated capacity =500kWh, charge / discharge efficiency =0.95, initial state of charge =250kWh.
[0022] ESS-2: Maximum charge / discharge power =150kW, rated capacity =500kWh, charge / discharge efficiency =0.95, initial state of charge =300kWh.
[0023] For any energy storage unit, the power at future times t=1,...,T and accumulated energy Must meet: Power boundary: in, This represents the power of the energy storage unit at time t. This indicates the maximum charging and discharging power of the energy storage unit.
[0024] Energy boundary: Must be by the current and capacity Within the determined dynamic range. For example, the accumulated energy for upward adjustment (discharge) cannot exceed the current... The accumulated energy during downward adjustment (charging) cannot make the battery... Exceeding its capacity In other words, the energy boundary is specifically manifested as a constraint on the state of charge at any time t.
[0025] In this embodiment, the power generation of the photovoltaic unit is affected by ultra-short-term forecasts, and its output limit for the next 4 hours is... The lower limit of output is input from an external prediction system. Typically, the value is 0, meaning no power is generated. To unify the model, we define the power injected into the grid as negative, then its power boundary is: in, This represents the actual output of the photovoltaic unit at time t. , These represent the predicted minimum and maximum output of the photovoltaic unit at time t, respectively.
[0026] The energy boundary of a photovoltaic unit is the cumulative integral constraint of its actual output over time in the future scheduling time domain, that is, the cumulative injected energy at any time t must be within the range of the cumulative energy of the predicted minimum output and the predicted maximum output.
[0027] Baseline power consumption of adjustable load units Input from the load forecasting system. In this embodiment, the load is allowed to adjust within 10% above and below its baseline, therefore its power boundary is: in, This represents the actual power consumption of the adjustable load unit at time t. This represents the baseline power consumption of the adjustable load unit at time t.
[0028] The energy boundary of an adjustable load unit is the cumulative integral constraint of its actual power consumption over time in the future scheduling time domain. That is, the cumulative power consumption at any time t must match the power adjustment range to ensure that the total power consumption of the adjustable load in the scheduling time domain meets the energy constraint corresponding to its power adjustment range, forming a logical closed loop with the power boundary.
[0029] 12) Based on the power boundary and energy boundary of each terminal, determine the power-energy feasible region of the corresponding terminal; By integrating the power and energy boundaries of each terminal, its power-energy feasible region is defined.
[0030] 13) Perform arithmetic summation on the boundary parameters of each power-energy feasible region to obtain the theoretical aggregate feasible region.
[0031] The theoretical aggregate feasible region is obtained by arithmetically summing the upper and lower boundary parameters of the power and energy for each power-energy feasible region. and Specifically, the theoretically aggregated feasible region is characterized by the following formula: in, , These represent the lower bound and upper bound of the theoretical polymerization power, respectively. , These represent the lower bound and the upper bound of the theoretical fusion energy, respectively. , , These represent the total number of energy storage units, photovoltaic units, and adjustable load units in the polymer, respectively. , These represent the lower and upper power bounds of the i-th energy storage unit, respectively. , Let these represent the lower and upper power bounds of the j-th photovoltaic unit, respectively. , These represent the lower and upper power bounds of the k-th adjustable load unit, respectively. , These represent the lower and upper bounds of the energy for the i-th energy storage unit, respectively. , Let these represent the lower and upper bounds of the energy of the j-th photovoltaic unit, respectively. , These represent the lower and upper bounds of the energy for the k-th adjustable load unit, respectively.
[0032] S2. Based on predefined conservative coefficients, the theoretical aggregated feasible region is shrunk to obtain the actual aggregated feasible region applicable to the current scheduling cycle; Based on a predefined conservative coefficient, the upper and lower boundaries of the theoretical aggregate feasible region are proportionally shrunk towards a predetermined aggregate baseline value to obtain the actual aggregate feasible region applicable to the current scheduling cycle.
[0033] Specifically, the upper and lower power boundary parameters of the theoretical feasible region are differentially calculated with the predetermined aggregated power baseline value to obtain two difference results. The two difference results are then weighted based on a conservative coefficient, and the weighted result is summed with the aggregated power baseline value to obtain the upper and lower power boundary parameters of the actual feasible region.
[0034] This embodiment uses the following formula to calculate the upper and lower boundary parameters of the actual aggregation feasible region: in, , Let these represent the lower and upper power boundary parameters of the actual feasible region for aggregation, respectively. Indicates the baseline value of polymerization power. This represents the conservative coefficient, which can be initialized to [value] at the first time step k=0. =1.
[0035] The aggregated power baseline value is determined by superimposing the cumulative predicted power output of each photovoltaic power generation terminal and the cumulative baseline power consumption of each adjustable load terminal. Specifically, the aggregated power baseline value is calculated using the following formula: in, Let represent the predicted power output of the j-th photovoltaic unit at time t. This predicted power output value can be obtained through many existing and mature ultra-short-term prediction technologies, and no specific method is specified here. This represents the baseline power consumption of the k-th adjustable load unit at time t.
[0036] Furthermore, the upper and lower boundary parameters of the energy of the theoretical feasible region are differentially calculated with the predetermined baseline value of the aggregation energy to obtain two difference results. The two difference results are then weighted based on the conservative coefficient, and the weighted results are summed with the baseline value of the aggregation energy to obtain the upper and lower boundary parameters of the energy of the actual feasible region.
[0037] In this embodiment, the energy upper and lower boundary parameters of the actual feasible aggregation region are calculated using the following formula: in, , These represent the lower and upper energy boundary parameters of the actual feasible region of aggregation, respectively. This represents the baseline value of the polymerization energy.
[0038] The pooled energy baseline value is obtained by accumulating the pooled power baseline value from the initial time to the current time according to the scheduling step size. Specifically, the pooled energy baseline value is calculated using the following formula: in, Indicates the initial time. Indicates the current moment. Indicates the scheduling step size.
[0039] S3. Decompose the target power curve sampled from the actual aggregated feasible region to each terminal, and use the power deviation obtained after decomposition as the decomposition error of the current scheduling cycle. A power decomposition model is constructed with the goal of minimizing the power deviation. Based on the target power curve sampled from the actual aggregation feasible region, the power decomposition model is solved according to the aggregation power balance constraint and the power-energy feasible region of each terminal to obtain the power deviation after the target power curve is decomposed.
[0040] To decompose the target power curve sampled from the actual aggregate feasible region to each terminal without bias, a power decomposition model needs to be constructed and solved. Specifically, the target power curve can be decomposed to the power-energy feasible region of each terminal by solving the objective function of the power decomposition model. The objective function minimizes the power deviation of the decomposition and is characterized by the following formula: in, This indicates the total number of time steps in the current scheduling cycle. This indicates positive adjustment power or positive power deviation. This indicates negative adjustment power or negative power deviation.
[0041] The power balance constraint for aggregation includes achieving a balance between the cumulative power decomposition value of each terminal and the target power by subtracting the positive regulation power and superimposing the negative regulation power. Specifically, the power balance constraint is characterized by the following formula: in, Indicates total polymerization power. Indicates the target aggregate power.
[0042] Furthermore, the power decomposition model is solved using an optimization solver based on the aggregated power balance constraints and the power-energy feasible region of each terminal. The obtained objective function value is the absolute error. The absolute error is then processed to obtain the relative error, which is represented as the decomposition error for normalization and evaluation of the degree of bias. Specifically, the relative error is calculated using the following formula: in, This indicates relative error or decomposition error. This indicates the absolute error.
[0043] S4. Update the conservative coefficients based on the decomposition error, and apply the updated conservative coefficients to the next scheduling time domain to perform rolling correction on the aggregation adjustability of distributed source-load-storage.
[0044] When the decomposition error exceeds a preset threshold, the conservative coefficient is reduced proportionally. Specifically, the updated conservative coefficient is the larger of the difference between the current conservative coefficient and the weighted decomposition error, and zero. This embodiment uses the following formula to characterize the updating principle of the conservative coefficient: in, This indicates adjusting the weighting coefficients; if the decomposition error... If the value is too large, the conservative coefficient will be reduced proportionally, meaning that the current aggregation capability assessment is too optimistic and a more conservative contraction is needed in the next period. In this embodiment, the preset threshold is 0.0001 and the set adjustment weight coefficient is 1. The larger the coefficient, the faster the convergence speed, but it may also cause risks. Based on the above factors, the decomposition error is used as the change in the conservative coefficient, which ensures both the convergence speed and the system's safety.
[0045] After updating the conservative coefficients, the first step of the power commands obtained from solving the power decomposition model (such as the charging and discharging power of each energy storage unit, the photovoltaic output adjustment value, and the power consumption of adjustable loads) is first sent to the corresponding terminals for execution. Then, the resource status is updated based on the actual power data executed by each terminal. Among them, the state of charge of the energy storage unit needs to be updated by combining the initial state of charge of the current rolling cycle, the cumulative amount of the executed power with respect to the scheduling step size, and the segmented charging and discharging efficiency.
[0046] Specifically, the state of charge of the energy storage unit at time t. It can be represented as: in, This indicates the initial state of charge in the current rolling cycle. This represents the first step of the power command obtained from the optimization decomposition in the current scheduling cycle. The charge / discharge efficiency is typically expressed as a piecewise function as follows: in, This indicates the charge / discharge efficiency.
[0047] Finally, the scheduling time window is rolled forward by one step, and the next round of assessment, decomposition and correction of aggregate adjustable capabilities is started with the updated conservative coefficient and resource status, forming a closed-loop rolling optimization mechanism.
[0048] To verify the effectiveness of the distributed source-load-storage aggregation adjustable capability correction method according to an embodiment of the present invention, please refer to... Figures 2 to 3 .in, Figure 2 This is a schematic diagram showing the SOC changes of two energy storage units in an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the change of the conservative coefficient over time in an embodiment of the present invention.
[0049] Specifically, refer to Figure 2 The horizontal axis represents the time step, and the vertical axis represents the State of Charge (SOC) of the energy storage unit. Throughout the process, the SOC operates safely within its upper and lower capacity limits, verifying the physical feasibility of the decomposition results of the distributed source-load-storage aggregation adjustable capacity correction method in this embodiment; refer to Figure 3 The horizontal axis represents the time step, and the vertical axis represents the conservative coefficient. As the aggregation scheduling system runs, this coefficient continuously self-corrects based on decomposition error feedback, and eventually stabilizes at a dynamic equilibrium level that truly reflects the system's aggregation capability. This proves the effectiveness of the online learning and adaptive correction of the distributed source-load-storage aggregation adjustable capability correction method in this embodiment.
[0050] This invention provides a method for correcting the adjustable capacity of distributed source-load-storage aggregation. It constructs a closed-loop correction system of "feasible domain aggregation - conservative contraction - error quantification - coefficient update," overcoming the limitations of traditional static assessment. By dynamically adjusting conservative coefficients based on error decomposition as the core feedback basis, the actual aggregated feasible domain continuously aligns with the true adjustment potential of source-load-storage resources. This effectively solves the problem of scheduling command execution deviation caused by overestimation of the theoretical feasible domain. It can quickly adapt to dynamic changes in resource status and operating environment, providing accurate and reliable aggregation capability support for power grid dispatch, and significantly improving the executability of scheduling commands and the security of system operation.
[0051] like Figure 4 As shown, this is a schematic diagram of a distributed source-load-storage aggregation adjustable capability correction system according to an embodiment of the present invention. (Refer to...) Figure 4 An embodiment of the present invention provides a distributed source-load-storage aggregation adjustable capability correction system, comprising: Theoretical feasible region determination module 01 is used to determine the power-energy feasible region of each terminal of distributed source-load-storage in the future scheduling time domain, and to superimpose the power-energy feasible region of each terminal to obtain the theoretical aggregate feasible region. The actual feasible region determination module 02 is used to shrink the theoretical aggregate feasible region based on a predefined conservative coefficient to obtain the actual aggregate feasible region applied to the current scheduling cycle; The decomposition error determination module 03 is used to decompose the target power curve sampled from the actual aggregated feasible region to each terminal, and use the power deviation obtained after decomposition as the decomposition error of the current scheduling cycle. The rolling correction module 04 is updated to update the conservative coefficients based on the decomposition error and apply the updated conservative coefficients to the next scheduling time domain to perform rolling correction on the aggregation adjustability of distributed source-load-storage.
[0052] It should be noted that each module in the aforementioned distributed source-load-storage aggregation adjustable capacity correction system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module. For specific limitations regarding the distributed source-load-storage aggregation adjustable capacity correction system, please refer to the limitations of the distributed source-load-storage aggregation adjustable capacity correction method described above; both have the same function and role, and will not be repeated here.
[0053] In summary, the present invention provides a method and system for correcting the adjustable capability of distributed source-load-storage aggregation. It constructs a closed-loop correction system of "feasible domain aggregation - conservative contraction - error quantification - coefficient update," overcoming the limitations of traditional static evaluation. By dynamically adjusting conservative coefficients based on error decomposition as the core feedback basis, it ensures that the actual aggregated feasible domain continuously matches the true adjustment potential of source-load-storage resources. This effectively solves the problem of scheduling command execution deviation caused by overestimation of the theoretical feasible domain. It can quickly adapt to dynamic changes in resource status and operating environment, providing accurate and reliable aggregation capability support for power grid dispatch, and significantly improving the executability of scheduling commands and the security of system operation.
[0054] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.
[0055] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.
Claims
1. A method for correcting the adjustable capability of distributed source-load-storage aggregation, characterized in that, include: Determine the power-energy feasible domain of each distributed source-load-storage terminal in the future scheduling time domain, and superimpose the power-energy feasible domain of each terminal to obtain the theoretical aggregate feasible domain; The theoretical aggregated feasible region is shrunk based on a predefined conservative coefficient to obtain the actual aggregated feasible region applicable to the current scheduling cycle. The target power curve sampled from the actual aggregated feasible region is decomposed into each terminal, and the power deviation obtained after decomposition is used as the decomposition error of the current scheduling period. The conservative coefficients are updated based on the decomposition error, and the updated conservative coefficients are applied to the next scheduling time domain to perform rolling correction on the aggregation adjustability of the distributed source-load-storage system.
2. The method for adjusting the adjustable capability of distributed source-load-storage aggregation according to claim 1, characterized in that, The process of determining the power-energy feasible domain of each distributed source-load-storage terminal in the future scheduling time domain within the distribution network, and superimposing the power-energy feasible domain of each terminal to obtain the theoretical aggregate feasible domain, includes: Obtain the status information of each terminal of the distributed source-load-storage system in the distribution network at the current moment, and determine the power boundary and energy boundary of the corresponding terminal based on the status information of each terminal; Based on the power boundary and energy boundary of each terminal, the power-energy feasible region of the corresponding terminal is determined; The theoretical aggregate feasible region is obtained by arithmetically summing the boundary parameters of each power-energy feasible region.
3. The method for adjusting the adjustable capability of distributed source-load-storage aggregation according to claim 1, characterized in that, The shrinking of the theoretical aggregated feasible region based on a predefined conservative coefficient to obtain the actual aggregated feasible region applicable to the current scheduling cycle includes: Based on a predefined conservative coefficient, the upper and lower boundaries of the theoretical aggregate feasible region are proportionally shrunk towards a predetermined aggregate baseline value to obtain the actual aggregate feasible region applicable to the current scheduling cycle.
4. The method for adjusting the adjustable capability of distributed source-load-storage aggregation according to claim 3, characterized in that, The step of shrinking the upper and lower boundaries of the theoretical aggregate feasible region proportionally to a predetermined aggregate baseline value based on a predefined conservative coefficient to obtain the actual aggregate feasible region applicable to the current scheduling cycle includes: The upper and lower power boundary parameters of the theoretical feasible region are respectively differentially calculated with the predetermined aggregated power baseline value to obtain two difference results. The two difference results are weighted based on the conservative coefficient, and the weighted result and the aggregated power baseline value are summed to obtain the upper and lower power boundary parameters of the actual feasible region. The upper and lower energy boundary parameters of the theoretical feasible region are obtained by performing a difference operation with the predetermined aggregated energy baseline value. The two difference results are then weighted based on the conservative coefficient, and the weighted results are summed with the aggregated energy baseline value to obtain the upper and lower energy boundary parameters of the actual feasible region.
5. The method for adjusting the adjustable capability of distributed source-load-storage aggregation according to claim 4, characterized in that, The aggregated power baseline value is determined by superimposing the predicted output cumulative value of each photovoltaic power generation terminal and the baseline power consumption cumulative value of each adjustable load terminal. The aggregated energy baseline value is obtained by accumulating the aggregated power baseline value from the initial time to the current time according to the scheduling step size.
6. The method for adjusting the adjustable capability of distributed source-load-storage aggregation according to claim 1, characterized in that, The step of decomposing the target power curve sampled from the actual aggregate feasible region to each terminal includes: A power decomposition model is constructed with the goal of minimizing the power deviation. The power deviation after decomposing the target power curve is obtained by solving the power decomposition model based on the target power curve sampled from the actual aggregation feasible region, according to the aggregation power balance constraint and the power-energy feasible region of each terminal.
7. The method for adjusting the adjustable capability of distributed source-load-storage aggregation according to claim 6, characterized in that, The aggregated power balance constraint condition includes the power decomposition accumulation value of each terminal achieving balance with the target power by subtracting the positive adjustment power and superimposing the negative adjustment power.
8. The method for adjusting the adjustable capability of distributed source-load-storage aggregation according to claim 1, characterized in that, The step of updating the conservative coefficients based on the decomposition error includes: When the decomposition error exceeds a preset threshold, the conservative coefficient is reduced proportionally.
9. The method for adjusting the adjustable capability of distributed source-load-storage aggregation according to claim 8, characterized in that, When the decomposition error is greater than a preset threshold, the update process of the conservative coefficient includes: the updated conservative coefficient is the larger of the difference between the current conservative coefficient and the weighted decomposition error and zero.
10. A distributed source-load-storage aggregation adjustable capability correction system, characterized in that, include: The theoretical feasible region determination module is used to determine the power-energy feasible region of each terminal of the distributed source-load-storage system in the future scheduling time domain, and to superimpose the power-energy feasible region of each terminal to obtain the theoretical aggregate feasible region. The actual feasible region determination module is used to shrink the theoretical aggregated feasible region based on a predefined conservative coefficient to obtain the actual aggregated feasible region applied to the current scheduling cycle; The decomposition error determination module is used to decompose the target power curve sampled from the actual aggregated feasible region to each terminal, and use the power deviation obtained after decomposition as the decomposition error of the current scheduling period. The rolling correction module is updated to update the conservative coefficients based on the decomposition error and apply the updated conservative coefficients to the next scheduling time domain to perform rolling correction on the aggregation adjustability of the distributed source-load-storage system.