Distributed energy storage cluster aggregation decomposition regulation method and device, equipment and medium
By constructing a power aggregation model and a decomposition control method, the information asymmetry problem between distributed energy storage clusters and distribution network operators is solved, improving resource utilization and data privacy, and achieving accurate scheduling and real-time adjustment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
- Filing Date
- 2026-04-22
- Publication Date
- 2026-07-24
Smart Images
Figure CN122456573A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power grid planning technology, and in particular to a method, device, equipment and medium for the aggregation and decomposition control of distributed energy storage clusters. Background Technology
[0002] Currently, with the gradual advancement of the global clean energy transition and the development of renewable energy power generation technologies, the penetration rate of distributed energy resources in power distribution systems is increasing year by year. The rapid growth of distributed energy resources has brought greater uncertainty and complexity to power distribution systems, driving the evolution of power systems on the distribution side. Against this backdrop, the concept of distributed energy storage clusters has emerged, which can effectively aggregate distributed energy storage resources.
[0003] At the theoretical level, existing literature outlines methods and interaction mechanisms for the coordinated operation of distributed energy resources within distributed energy storage clusters, enabling the aggregation of various distributed energy resources to provide flexible power curves. To accurately quantify the operating range of distributed energy storage clusters, existing literature proposes methods for estimating the feasible regions of these systems. In current technologies, the rapid growth of distributed energy storage resources has brought higher uncertainties and complexities to power distribution systems. Distributed energy storage clusters lack access to detailed network topology information, making it difficult for power distribution operators to fully utilize the flexibility of distributed energy resources to ensure efficient operation. Effective coordinated operation between the day-ahead and real-time phases is impossible, leading to information asymmetry between distributed energy storage clusters and power distribution operators, low utilization of distributed energy resources, poor data privacy, and a lack of real-time control capabilities. Summary of the Invention
[0004] This application provides a method, apparatus, equipment, and medium for the aggregation and decomposition control of distributed energy storage clusters, aiming to solve the information barrier problem between distributed energy storage clusters and distribution network operators, and improve resource utilization and data privacy in the control process of distributed energy storage clusters.
[0005] To achieve the above objectives, embodiments of this application provide a distributed energy storage cluster aggregation and decomposition control method, including: The active power feasible region of the common connection point and the aggregation cost function of the distributed energy storage cluster are sent to the distribution network operator so that the distribution network operator can generate a target power curve based on the active power feasible region of the common connection point and the aggregation cost function of the distributed energy storage cluster. The active power feasible region of the common connection point and the aggregation cost function of the distributed energy storage cluster are calculated based on a power aggregation model. The power aggregation model is constructed based on representative data from the distribution network operator and the operating power range of each distributed energy resource in the distributed energy storage cluster. The representative data includes: predicted values and sensitivity coefficients of network variables. The target power curve is received, and the target power curve is decomposed into each distributed energy resource in a cost-minimizing manner to obtain a day-ahead scheduling plan; wherein, the day-ahead scheduling plan includes the active power output instructions of each distributed energy resource; Control each distributed energy resource to operate according to the day-ahead scheduling plan.
[0006] Compared to existing technologies where information asymmetry exists between distributed energy storage clusters and distribution network operators, leading to low utilization of distributed energy resources and poor data privacy, the distributed energy storage cluster aggregation decomposition control method provided in this application addresses this issue. This method involves the distribution network operator providing representative data to the distributed energy storage cluster instead of complete network information. The distributed energy storage cluster then calculates the feasible active power region and aggregation cost function based on this data and feeds it back to the distribution network operator. Finally, the distributed energy storage cluster performs optimal decomposition and execution of the target power curve. This approach reduces the reliance on direct interaction of sensitive underlying information between the two parties, improves data privacy, and enables the distribution network operator to implement more accurate scheduling based on cluster-level adjustability. Ultimately, this improves the utilization rate of distributed energy resources and the collaborative control effect between the distributed energy storage cluster and the distribution network operator.
[0007] In a distributed energy storage cluster aggregation and decomposition control method provided in this application embodiment, after controlling each distributed energy resource to operate according to the day-ahead scheduling plan, the method further includes: Summarize the adjustable power of each distributed energy resource during real-time operation; Based on the adjustable power, the adjustable power data corresponding to the distributed energy storage cluster is calculated, and the adjustable power data is sent to the distribution network operator so that the distribution network operator can generate the target adjustable power of the distributed energy storage cluster based on the adjustable power data; the adjustable power data includes: adjustable power range and adjustment cost function. Receive the target regulation power sent by the distribution network operator, optimize the day-ahead scheduling plan based on the target regulation power and the aggregation cost function, and output real-time scheduling instructions for each distributed energy resource; Control each distributed energy resource according to the real-time scheduling instructions.
[0008] Compared to existing technologies where distributed energy storage clusters struggle to provide timely feedback on their adjustable capabilities to distribution network operators during real-time operation, resulting in delayed real-time power fluctuation response and insufficient adjustment accuracy, the distributed energy storage cluster aggregation and decomposition control method provided in this application further aggregates the real-time adjustable power of each distributed energy resource based on day-ahead scheduling. This forms adjustable power data including an upper limit, a lower limit, and a corresponding adjustment cost function. Based on this data, the day-ahead scheduling plan is optimized after receiving the target adjustable power from the distribution network operator. This enables the distribution network operator to implement more accurate dynamic scheduling based on the real-time adjustment capabilities of the distributed energy storage cluster, while simultaneously decomposing the target adjustable power to each distributed energy resource in a more cost-effective manner, thereby improving real-time power regulation capabilities.
[0009] In the distributed energy storage cluster aggregation and decomposition control method provided in this application embodiment, the step of constructing a power aggregation model based on representative data provided by the distribution network operator and the operating power range of each distributed energy resource in the distributed energy storage cluster includes: Read the operating parameters of each distributed energy resource to determine the operating power range of each distributed energy resource; Based on the linear power flow method, an affine relationship is established between the aggregated power at the common junction point and the active power injection and reactive power injection of each node. Based on the access relationship of each distributed energy resource at each node, a mapping relationship is established between the power injected by each node and the output power of each distributed energy resource. Based on the affine and mapping relationships, a power aggregation model is generated between the aggregated power at the common connection point and the output power of each distributed energy resource.
[0010] Compared to existing technologies that rely on complete network topology information and a large amount of underlying operational data to build aggregation models, resulting in large information exchange volumes and poor data privacy, the distributed energy storage cluster aggregation decomposition control method provided in this application obtains the predicted values of network variables and sensitivity coefficients sent by the distribution network operator, and constructs a power aggregation model by combining the operational constraints of each distributed energy resource. This method can establish a mapping relationship between the aggregated power of the common connection point and the output power of each distributed energy resource without directly disclosing detailed network information, thereby improving both the accuracy of power aggregation modeling and the effectiveness of data privacy protection.
[0011] In a distributed energy storage cluster aggregation decomposition control method provided in this application embodiment, the step of calculating the active power feasible region corresponding to each distributed energy resource and the aggregation cost function corresponding to each distributed energy resource according to the power aggregation model includes: Based on the power aggregation model and in combination with the operating power range of each distributed energy resource, the upper and lower limits of the aggregated active power at the common junction point are solved to determine the feasible area of active power. Obtain the basic cost function for each distributed energy resource, wherein the basic cost function is expressed as a quadratic function; The equivalent contribution power of each distributed energy resource at the common junction point, as well as the correction coefficients for network loss and reactive power coupling, obtained by solving the power aggregation model, are used to correct the basic cost function of each distributed energy resource, thus obtaining the corrected cost function of each distributed energy resource. The corrected cost functions of each distributed energy resource are aggregated to obtain the aggregated cost function.
[0012] Compared to existing technologies that struggle to accurately assess the feasible output range and cost characteristics of distributed energy storage clusters while considering network impacts, leading to insufficient resource utilization, the distributed energy storage cluster aggregation decomposition control method provided in this application solves for the upper and lower limits of aggregated active power at the point of common coupling (PCC) based on a power aggregation model and the operating power range of each distributed energy resource. This determines the feasible active power region. Furthermore, it uses the equivalent contribution power of PCC, network loss, and reactive power coupling correction coefficients to correct and aggregate the basic cost function of each distributed energy resource. This method accurately describes the adjustability and cost characteristics of distributed energy storage clusters, thereby improving the accuracy of dispatch decisions by distribution network operators and the utilization rate of distributed energy resources.
[0013] In the distributed energy storage cluster aggregation decomposition control method provided in this application embodiment, the aggregation of the correction cost functions of each distributed energy resource to obtain the aggregated cost function includes: Based on the principle of equal marginal cost, the distributed energy resources are ranked. Based on the output status of each distributed energy resource in different power ranges, the distributed energy resources are divided into minimum output group, maximum output group and variable output group; For different power ranges at common coupling points, determine the corresponding variable output group combinations; Based on the variable output group combination, the correction cost function of each distributed energy resource is aggregated in segments to construct a segmented secondary aggregated cost function corresponding to different common connection point power ranges.
[0014] Compared to existing technologies that simply sum or roughly equate the costs of multiple distributed energy resources, leading to inaccurate control results, the distributed energy storage cluster aggregation decomposition control method provided in this application coordinates each distributed energy resource according to the principle of equal marginal cost, and divides it into minimum output group, maximum output group, and variable output group according to its output status in different power ranges. Furthermore, it constructs a piecewise quadratic aggregation cost function for different common connection point power ranges, which can more accurately characterize the cost characteristics of distributed energy storage clusters at different output levels, thereby improving the accuracy of target power decomposition and scheduling optimization.
[0015] In a distributed energy storage cluster aggregation and decomposition control method provided in this application embodiment, the step of receiving the target power curve and decomposing the target power curve into each distributed energy resource in a cost-minimizing manner to obtain a day-ahead scheduling plan includes: The target power curve is used as the target power constraint at the common junction point; The operating power range of each distributed energy resource is used as a constraint in the decomposition process. Minimize the aggregation cost function as the optimization objective; Solve for the active power output of each distributed energy resource during each scheduling period; Based on the active power output values of each distributed energy resource obtained from the solution, the day-ahead scheduling plan is generated, and active power output instructions are issued to the corresponding distributed energy resources.
[0016] Compared to existing technologies where the target power issued by distribution network operators is difficult to accurately allocate to each distributed energy resource, the distributed energy storage cluster aggregation decomposition control method provided in this application uses the target power curve as the target power constraint at the common connection point, uses the operating power range of each distributed energy resource as the decomposition constraint, and solves the active power output value of each distributed energy resource in each scheduling period based on the aggregation cost function to generate and issue the day-ahead scheduling plan. This method can effectively transform the target power curve at the cluster level into control commands that can be executed by each distributed energy resource, thereby improving the accuracy of the output allocation of distributed energy resources in the day-ahead scheduling plan.
[0017] In a distributed energy storage cluster aggregation and decomposition control method provided in this application embodiment, the step of calculating the adjustable power data corresponding to the distributed energy storage cluster based on the adjustable power, and receiving the target adjustable power and outputting the real-time adjustment command corresponding to each distributed energy resource includes: Based on the real-time operating status of each distributed energy resource, the day-ahead scheduling plan, and the real-time representative data provided by the distribution network operator, the upper limit of the upward adjustable power and the lower limit of the downward adjustable power of the distributed energy storage cluster at the point of common connection are solved respectively. Based on the aforementioned upper adjustable power limit and lower adjustable power limit, an adjustable power range for the distributed energy storage cluster is formed. By utilizing the equivalent contribution power of the common connection point corresponding to the real-time stage, as well as the correction coefficients for network loss and reactive power coupling, the real-time adjustment cost of each distributed energy resource is corrected, and an adjustment cost function is generated. The adjustable power range and adjustment cost function are sent to the distribution network operator. After receiving the target regulation power sent by the distribution network operator, the target regulation power is used as the real-time regulation target at the point of common coupling, and the target regulation power is decomposed with the goal of minimizing the regulation cost function. Based on the decomposition results, the real-time adjustment power corresponding to each distributed energy resource is determined, and the real-time adjustment command is output.
[0018] Compared to existing technologies that struggle to decompose the regulation demands issued by distribution network operators to various distributed energy resources during real-time operation, resulting in poor real-time collaborative control, the distributed energy storage cluster aggregation decomposition regulation method provided in this application solves for the adjustable power range of the distributed energy storage cluster at the point of common coupling based on the real-time operating status of each distributed energy resource, the day-ahead scheduling plan, and real-time representative data provided by the distribution network operator. It then generates a corresponding regulation cost function, decomposes the target regulation power as a real-time regulation target after receiving it, obtains the real-time regulation power corresponding to each distributed energy resource, and outputs real-time regulation commands. This improves the accuracy of target regulation power decomposition, enhances the response capability of the distributed energy storage cluster to real-time power fluctuations, and improves the collaborative regulation effect among various distributed energy resources.
[0019] Based on the above embodiments, another embodiment of this application provides a distributed energy storage cluster aggregation and decomposition control device, including: an aggregation module, a day-ahead scheduling module, a day-ahead control module, a real-time aggregation module, a real-time calculation module, a real-time optimization module, and a real-time control module.
[0020] The aggregation module is used to send the aggregation cost function of the active power feasible area of the common connection point and the distributed energy storage cluster to the distribution network operator. Furthermore, the aggregation module includes: an aggregation model construction unit, a feasible region calculation unit, and an aggregation cost function calculation unit; The aggregation model construction unit is used to construct a power aggregation model based on representative data provided by the distribution network operator and the operating power range of each distributed energy resource in the distributed energy storage cluster. The feasible region calculation unit is used to determine the active power feasible region based on the power aggregation model and in combination with the operating power range of each distributed energy resource. The aggregate cost function calculation unit is used to correct the cost function of each distributed energy resource according to the aggregate model, and to aggregate the corrected cost functions of each distributed energy resource to obtain the aggregate cost function. The day-ahead scheduling module is used to receive the target power curve, decompose the target power curve into each distributed energy resource in a cost-minimizing manner, and output the day-ahead scheduling plan. Furthermore, the day-ahead scheduling module includes: an active power output calculation unit and a scheduling plan generation unit; The active power output calculation unit is used to calculate the active power output value of each distributed energy resource in each scheduling period. The scheduling plan generation unit is used to generate the day-ahead scheduling plan based on the active power output values of each distributed energy resource obtained by solving. The day-ahead control module is used to control each distributed energy resource to operate according to the day-ahead scheduling plan; The real-time aggregation module is used to aggregate the adjustable power of each distributed energy resource during real-time operation. The real-time computing module is used to calculate the adjustable power data corresponding to the distributed energy storage cluster and send the adjustable power data to the distribution network operator. The real-time optimization module is used to receive the target regulation power sent by the distribution network operator, optimize the day-ahead scheduling plan according to the target regulation power and the aggregation cost function, and output real-time scheduling instructions for each distributed energy resource. Furthermore, the real-time optimization module includes: an adjustable power calculation unit, an adjustment cost function calculation unit, a data transmission unit, a target power decomposition unit, and an instruction generation unit; The adjustable power calculation unit calculates the upper limit of the adjustable power and the lower limit of the adjustable power at the common connection point of the distributed energy storage cluster based on the real-time operating status of each distributed energy resource, the day-ahead scheduling plan, and the real-time representative data provided by the distribution network operator. Based on the upper limit of the adjustable power and the lower limit of the adjustable power, the adjustable power range of the distributed energy storage cluster is formed. The adjustment cost function calculation unit is used to correct the real-time adjustment cost of each distributed energy resource by using the equivalent contribution power of the common connection point corresponding to the real-time stage and the correction coefficients of network loss and reactive power coupling, and to generate the adjustment cost function. The data transmission unit is used to send the adjustable power range and adjustment cost function to the distribution network operator; The target power decomposition unit is used to receive the target regulation power sent by the distribution network operator and decompose the target regulation power. The instruction generation unit is used to determine the real-time adjustment power corresponding to each distributed energy resource based on the decomposition results, and output the real-time adjustment instruction. The real-time control module is used to control each distributed energy resource according to the real-time scheduling instructions.
[0021] Based on the above embodiments, another embodiment of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the distributed energy storage cluster aggregation and decomposition control method described in the above-mentioned application embodiments.
[0022] Based on the above embodiments, another embodiment of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the distributed energy storage cluster aggregation decomposition control method described in the above-mentioned embodiments. Attached Figure Description
[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. The drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0024] Figure 1 This is a schematic flowchart of a distributed energy storage cluster aggregation and decomposition control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the real-time stage control process after the execution of the day-ahead scheduling plan in a distributed energy storage cluster aggregation and decomposition control method according to an embodiment of this application; Figure 3 This application provides a framework for the coordinated operation between distributed energy storage clusters and distribution network operators, as exemplified in one embodiment. Figure 4 This is a line graph illustrating the regulation capability of a distributed energy storage cluster according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a distributed energy storage cluster aggregation and decomposition control device provided in one embodiment of this application. Detailed Implementation
[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0026] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.
[0027] To address the issues of information asymmetry between distributed energy storage clusters and distribution network operators, low utilization rate of distributed energy resources, poor data privacy, and lack of day-ahead and real-time coordination and control capabilities in existing technologies, such as... Figure 1 As shown in the figure, this application provides a schematic diagram of the specific process of a distributed energy storage cluster aggregation and decomposition control method. The distributed energy storage cluster aggregation and decomposition control method of this embodiment includes steps S101 to S103, which are described in detail below: This embodiment uses a modified IEEE 33-node distribution system as an example. In this embodiment, the distribution network operator and the distributed energy storage cluster exchange information and coordinate power through a common junction point. The distributed energy storage cluster is used to aggregate the adjustable power of multiple distributed energy resources and to decompose and control each distributed energy resource according to the scheduling targets issued by the distribution network operator.
[0028] In this embodiment, the distributed energy resources include a distributed energy storage system, distributed photovoltaics, and a micro gas turbine. The distributed energy storage system is connected to nodes 14, 23, and 29 of the power distribution system; the distributed photovoltaics are connected to nodes 5, 11, and 16; and the micro gas turbine is connected to nodes 6, 10, and 19.
[0029] The parameters of the distributed energy resources are set as follows: the rated power of the distributed energy storage system is 200kW, the rated capacity is 500kWh, the initial state of charge (SOC) is set to 50%, the charge and discharge efficiency is set to 95%, and the SOC constraint range is 0% to 100%; the rated power of the distributed photovoltaic system is 300kW to 400kW, the power factor is 0.85 to 0.95, the maximum curtailment rate is set to 20%, and its output can be adjusted between 80% and 100% of the predicted value; the rated power of the micro gas turbine is 500kW to 1000kW, the power factor is 0.85 to 0.95, the ramp rate is set to 50% of the rated power, and its output is limited to the upper and lower limits of the rated power.
[0030] The power distribution system simulation environment uses MATLAB, with a scheduling cycle of 24 hours, a time resolution of 15 minutes, and is divided into 96 scheduling periods. The system's fixed load data comes from the Open Power System dataset.
[0031] Step S101: Read representative data of the power distribution system and the operating power range of each distributed energy resource in the distributed energy storage cluster, wherein the representative data includes: predicted values and sensitivity coefficients of network variables; Furthermore, based on representative data provided by distribution network operators and the operating power range of each distributed energy resource in the distributed energy storage cluster, a power aggregation model is constructed. Specific construction methods include: Read the operating parameters of each distributed energy resource, determine the operating power range of each distributed energy resource, and calculate the formula as follows: In the formula, and They represent the first An energy storage system at any time The discharge power and charging power; Indicates the first The maximum discharge power and charging power of each energy storage system; Indicates the first An energy storage system at any time The reactive power output; Indicates the first The power factor of an energy storage system; Indicates the first An energy storage system at any time SOC; and They represent the first The lower and upper limits of the State of Charge (SOC) of an energy storage system; Indicates the first The discharge and charging efficiency of an energy storage system; Indicates the first The installed capacity of the energy storage system.
[0032] Establish an affine relationship between the aggregated power at the common junction point and the active and reactive power injections at each node. The calculation formula is as follows: In the formula, and These represent the time intervals of the distributed energy storage cluster. Active and reactive power output at the point of common coupling; and These represent vectors composed of all active power injections and reactive power injections at each node, respectively; the remaining parameters are the defined and derived linear power flow parameters. The mapping relationship between the injected power of each node and the output power of each distributed energy resource is established, and the calculation formula is as follows: index Indicates the first Is the distributed energy resource connected to the node? .
[0033] Based on the affine and mapping relationships calculated above, a power aggregation model is constructed between the aggregated power at the common connection point and the output power of each distributed energy resource. The calculation formula is as follows: A power aggregation model is obtained between the aggregated power at the common junction point and the output power of each distributed energy resource.
[0034] Furthermore, based on the power aggregation model, the feasible region of active power at the common junction is calculated.
[0035] Based on the power aggregation model and considering the operating power range of each distributed energy resource, the upper and lower limits of the aggregated active power at the common junction are calculated to determine the feasible active power region. In the formula, and These represent the time intervals of the distributed energy storage cluster. The upper and lower limits of active power; , , and For decision variables; and These represent the distributed energy storage cluster operating at maximum active power output, respectively. Distributed energy resources at any time The active and reactive power output; and These represent the distributed energy storage cluster operating under minimum active power output conditions, specifically the [number]th [unit]. Distributed energy resources at any time The active and reactive power output.
[0036] Furthermore, based on the power aggregation model, the aggregation cost function corresponding to each distributed energy resource is calculated, including: Obtain the basic cost function for each distributed energy resource, wherein the basic cost function is expressed as a quadratic function, specifically in the form of: In the formula, For the first Taiwan Energy Storage Net output at any moment This is the battery degradation cost coefficient; This refers to the cost coefficient for the charge / discharge price difference. Fixed maintenance cost per unit of time.
[0037] Using the equivalent contribution power of each distributed energy resource at the point of common coupling, as well as the correction coefficients for network loss and reactive power coupling, obtained from the power aggregation model, the basic cost function of each distributed energy resource is corrected to obtain the corrected cost function of each distributed energy resource. The specific form of the corrected cost function is as follows: In the formula, This contributes equivalent power to the distributed energy storage cluster. This is the amortized correction factor for network loss and reactive power coupling.
[0038] Based on the corrected cost function of each distributed energy resource, the distributed energy resources are ranked according to the principle of equal marginal cost.
[0039] Based on the output status of each distributed energy resource in different power ranges, the distributed energy resources are divided into minimum output group, maximum output group, and variable output group.
[0040] For different power ranges at common connection points, determine the corresponding variable output combinations.
[0041] Based on the variable output group combination, the correction cost function of each distributed energy resource is aggregated in segments to construct a segmented secondary aggregated cost function corresponding to different common connection point power ranges.
[0042] Furthermore, based on the obtained feasible active power region of the common connection point and the aggregation cost function of the distributed energy storage cluster, a target power curve is generated.
[0043] Step S102: Receive the target power curve and decompose it into each distributed energy resource in a cost-minimizing manner to obtain a day-ahead scheduling plan; wherein, the day-ahead scheduling plan includes active power output instructions for each distributed energy resource, controlling each distributed energy resource to operate according to the day-ahead scheduling plan, including: The target power curve is used as the target power constraint at the common junction point; The operating power range of each distributed energy resource is used as a constraint in the decomposition process. Minimize the aggregation cost function as the optimization objective; Solve for the active power output of each distributed energy resource during each scheduling period; Based on the active power output values of each distributed energy resource obtained from the solution, the day-ahead scheduling plan is generated, and active power output instructions are issued to the corresponding distributed energy resources.
[0044] Step S103: According to the active power output command, control each distributed energy resource to operate in accordance with the day-ahead scheduling plan.
[0045] Implementing the embodiments of this application addresses the issue of information asymmetry between distributed energy storage clusters and distribution network operators in existing technologies, which leads to low utilization of distributed energy resources and poor data privacy. The distributed energy storage cluster aggregation and decomposition control method provided in this application addresses this by having the distribution network operator provide representative data to the distributed energy storage cluster instead of complete network information. The distributed energy storage cluster then calculates the feasible active power region and aggregation cost function based on this data and feeds it back to the distribution network operator. Finally, the distributed energy storage cluster performs optimal decomposition and execution of the target power curve. This reduces the reliance on direct interaction of sensitive underlying information between the two parties, improves data privacy, and enables the distribution network operator to implement more accurate scheduling based on cluster-level adjustability. Ultimately, this improves the utilization rate of distributed energy resources and the collaborative control effect between the distributed energy storage cluster and the distribution network operator.
[0046] Furthermore, based on the above embodiments controlling each distributed energy resource to operate according to the day-ahead dispatch plan, in order to enable distribution network operators to implement more accurate dynamic dispatching based on the real-time adjustment capabilities of distributed energy storage clusters and improve real-time power regulation capabilities, such as... Figure 2 As shown, this embodiment also includes steps S201 to S204, which are detailed below: Step S201: Summarize the adjustable power of each distributed energy resource during real-time operation.
[0047] Step S202: Calculate the adjustable power data corresponding to the distributed energy storage cluster based on the adjustable power, and send the adjustable power data to the distribution network operator so that the distribution network operator can generate the target adjustable power of the distributed energy storage cluster based on the adjustable power data; the adjustable power data includes: adjustable power range and adjustment cost function.
[0048] The formula for calculating the adjustable power range is as follows: In the formula, Indicates the distributed energy storage cluster at time The power can be flexibly adjusted; the lower and upper limits of the power that can be flexibly adjusted in a distributed energy storage cluster are respectively determined by... and This means that the objective function is determined by minimizing and maximizing it. and They represent the first Distributed energy resources at any time The active and reactive power regulation power; and They represent the first Distributed energy resources at any time The day-ahead active and reactive power; and They represent the first The active and reactive power of a distributed energy resource are increased by its flexible power adjustment.
[0049] Step S203: Receive the target regulation power sent by the distribution network operator, optimize the day-ahead scheduling plan based on the target regulation power and the aggregation cost function, and output real-time scheduling instructions for each distributed energy resource.
[0050] Methods for solving real-time scheduling instructions for various distributed energy resources include: Based on the real-time operating status of each distributed energy resource, the day-ahead scheduling plan, and the real-time representative data provided by the distribution network operator, the upper limit of the upward adjustable power and the lower limit of the downward adjustable power of the distributed energy storage cluster at the point of common connection are solved respectively.
[0051] Based on the aforementioned upper adjustable power limit and lower adjustable power limit, the adjustable power range of the distributed energy storage cluster is formed, and the calculation formula is as follows: In the formula, Indicates the distributed energy storage cluster at time The power can be flexibly adjusted; the lower and upper limits of the power that can be flexibly adjusted in a distributed energy storage cluster are respectively determined by... and This means that the objective function is determined by minimizing and maximizing it. and They represent the first Distributed energy resources at any time The active and reactive power regulation power; and They represent the first Distributed energy resources at any time The day-ahead active and reactive power; and They represent the first The active and reactive power of a distributed energy resource are increased by its flexible power adjustment.
[0052] By utilizing the equivalent contribution power of the common connection point corresponding to the real-time stage, as well as the correction coefficients for network loss and reactive power coupling, the real-time adjustment cost of each distributed energy resource is corrected, and an adjustment cost function is generated. The adjustable power range and adjustment cost function are sent to the distribution network operator. After receiving the target regulation power sent by the distribution network operator, the target regulation power is used as the real-time regulation target at the point of common coupling. The target regulation power is then decomposed with the goal of minimizing the regulation cost function. The calculation formula is as follows: In the formula, This represents the balanced power purchased by the distribution network operator from the real-time market; This indicates the real-time market price of electricity; This indicates the real-time deviation of the power exchanged between the distribution network operator and the upstream power grid at the point of common coupling.
[0053] Based on the decomposition results, the real-time adjustment power corresponding to each distributed energy resource is determined, and the real-time adjustment command is output. The calculation formula is as follows: In the formula, This indicates the target power that the distribution network operator needs to flexibly adjust.
[0054] Step S204: Control each distributed energy resource according to the real-time scheduling instruction.
[0055] For a better illustration of this embodiment, see [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram illustrating the two-stage coordinated operation process between the distributed energy storage cluster and the distribution network operator in this embodiment. Figure 3As shown, the coordinated operation process includes a day-ahead phase and a real-time phase. In the day-ahead phase, the distribution network operator provides representative data to the distributed energy storage cluster. Based on this data, the distributed energy storage cluster aggregates distributed energy resources to obtain feasible active power regions, corrects the cost function of the distributed energy resources to form an aggregated cost function, and then reports the feasible active power regions and aggregated cost function to the distribution network operator. The distribution network operator implements day-ahead optimized scheduling based on the feasible active power regions and aggregated cost function, and publishes the day-ahead target power curve to the distributed energy storage cluster. Subsequently, the distributed energy storage cluster breaks down the target power into various distributed energy resources to form the base power. Furthermore, in the real-time phase, the distributed energy storage cluster aggregates the adjustable power of each distributed energy resource based on the base power formed in the day-ahead phase, and corrects the cost function of the distributed energy resources in real time. The adjustable power and cost function are then reported to the distribution network operator. The distribution network operator performs real-time optimized scheduling based on the adjustable power and cost function to balance power deviations. Subsequently, the distributed energy storage cluster decouples the adjustable power required by the distribution network operator based on the known cost function, and the distributed energy resources execute the corresponding scheduling plan, thereby achieving coordinated operation between the distributed energy storage cluster and the distribution network operator.
[0056] Implementing the embodiments of this application has the following beneficial effects: The distributed energy storage cluster aggregation and decomposition control method provided in this application involves the distribution network operator providing representative data to the distributed energy storage cluster to replace complete network information. The distributed energy storage cluster then calculates the feasible active power region and aggregation cost function based on this data and feeds it back to the distribution network operator. Finally, the distributed energy storage cluster performs optimal decomposition and execution on the target power curve. This reduces the reliance on direct interaction of sensitive underlying information between the two parties, improves data privacy, and enables the distribution network operator to implement more accurate scheduling based on cluster-level adjustability. In this way, it improves the utilization rate of distributed energy resources and the collaborative control effect between the distributed energy storage cluster and the distribution network operator.
[0057] In all simulation test scenarios of this embodiment, Figure 4 This is a line graph showing the regulation capability of a distributed energy storage cluster. The graph illustrates the changes in the cluster's regulation capability over a daily scheduling cycle, explaining the relationship between the upward regulation power, downward regulation power, and target power at different times, and verifying that the distributed energy storage cluster possesses the regulation capability to meet scheduling requirements. Figure 4As shown, the upward and downward adjustment power of the distributed energy storage cluster remains above zero in all scenarios, indicating that the distributed energy storage cluster has sufficient adjustment capability and can effectively balance the power deviation generated during the charging and discharging of distributed energy storage. Therefore, the distributed energy storage cluster aggregation and decomposition control method provided in this embodiment can accurately assess the aggregation flexibility and cost characteristics of the distributed energy storage cluster based on representative data provided by the distribution network operator and the operational constraints of each distributed energy storage resource without disclosing complete network topology information. Through day-ahead and real-time two-stage coordinated operation, it achieves optimized decomposition control of each distributed energy storage resource, thereby improving the utilization rate of distributed energy storage resources, scheduling accuracy, and data privacy protection.
[0058] Based on the above method embodiments, this application provides corresponding apparatus embodiments.
[0059] like Figure 5 As shown, one embodiment of this application provides a distributed energy storage cluster aggregation and decomposition control method apparatus, including: aggregation module 100, day-ahead scheduling module 200, day-ahead control module 300, real-time aggregation module 400, real-time calculation module 500, real-time optimization module 600 and real-time control module 700.
[0060] The aggregation module 100 is used to send the aggregation cost function of the active power feasible area of the common connection point and the distributed energy storage cluster to the distribution network operator. Furthermore, the aggregation module 100 includes: an aggregation model construction unit, a feasible region calculation unit, and an aggregation cost function calculation unit; The aggregation model construction unit is used to construct a power aggregation model based on representative data provided by the distribution network operator and the operating power range of each distributed energy resource in the distributed energy storage cluster. The feasible region calculation unit is used to determine the active power feasible region based on the power aggregation model and in combination with the operating power range of each distributed energy resource. The aggregate cost function calculation unit is used to correct the cost function of each distributed energy resource according to the aggregate model, and to aggregate the corrected cost functions of each distributed energy resource to obtain the aggregate cost function. The day-ahead scheduling module 200 is used to receive the target power curve, decompose the target power curve into each distributed energy resource in a cost-minimizing manner, and output the day-ahead scheduling plan. Furthermore, the day-ahead scheduling module 200 includes: an active power output calculation unit and a scheduling plan generation unit; The active power output calculation unit is used to calculate the active power output value of each distributed energy resource in each scheduling period. The scheduling plan generation unit is used to generate the day-ahead scheduling plan based on the active power output values of each distributed energy resource obtained by solving. The day-ahead control module 300 is used to control each distributed energy resource to operate according to the day-ahead scheduling plan; The real-time aggregation module 400 is used to aggregate the adjustable power of each distributed energy resource during real-time operation. The real-time computing module 500 is used to calculate the adjustable power data corresponding to the distributed energy storage cluster and send the adjustable power data to the distribution network operator. The real-time optimization module 600 is used to receive the target regulation power sent by the distribution network operator, optimize the day-ahead scheduling plan according to the target regulation power and the aggregation cost function, and output real-time scheduling instructions for each distributed energy resource. Furthermore, the real-time optimization module 600 includes: an adjustable power calculation unit, an adjustment cost function calculation unit, a data transmission unit, a target power decomposition unit, and an instruction generation unit; The adjustable power calculation unit calculates the upper limit of the adjustable power and the lower limit of the adjustable power at the common connection point of the distributed energy storage cluster based on the real-time operating status of each distributed energy resource, the day-ahead scheduling plan, and the real-time representative data provided by the distribution network operator. Based on the upper limit of the adjustable power and the lower limit of the adjustable power, the adjustable power range of the distributed energy storage cluster is formed. The adjustment cost function calculation unit is used to correct the real-time adjustment cost of each distributed energy resource by using the equivalent contribution power of the common connection point corresponding to the real-time stage and the correction coefficients of network loss and reactive power coupling, and to generate the adjustment cost function. The data transmission unit is used to send the adjustable power range and adjustment cost function to the distribution network operator; The target power decomposition unit is used to receive the target regulation power sent by the distribution network operator and decompose the target regulation power. The instruction generation unit is used to determine the real-time adjustment power corresponding to each distributed energy resource based on the decomposition results, and output the real-time adjustment instruction. The real-time control module 700 is used to control each distributed energy resource according to the real-time scheduling instructions.
[0061] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can implement any of the above-described method embodiments of this application to provide a distributed energy storage cluster aggregation decomposition control method.
[0062] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0063] Based on the above-described embodiment of the distributed energy storage cluster aggregation and decomposition control method, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a distributed energy storage cluster aggregation and decomposition control method according to any embodiment of this application.
[0064] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.
[0065] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.
[0066] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.
[0067] Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute a distributed energy storage cluster aggregation and decomposition control method as described in any of the above-described method embodiments of this application.
[0068] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.
[0069] Based on the above-described method embodiments, another embodiment of this application provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a distributed energy storage cluster aggregation, decomposition, and control method.
[0070] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A distributed energy storage cluster aggregation and decomposition control method, characterized in that, include: The active power feasible region of the common connection point and the aggregation cost function of the distributed energy storage cluster are sent to the distribution network operator so that the distribution network operator can generate a target power curve based on the active power feasible region of the common connection point and the aggregation cost function of the distributed energy storage cluster. The active power feasible region of the common connection point and the aggregation cost function of the distributed energy storage cluster are calculated based on a power aggregation model. The power aggregation model is constructed based on representative data from the distribution network operator and the operating power range of each distributed energy resource in the distributed energy storage cluster. The representative data includes: predicted values and sensitivity coefficients of network variables. The target power curve is received, and the target power curve is decomposed into each distributed energy resource in a cost-minimizing manner to obtain a day-ahead scheduling plan; wherein, the day-ahead scheduling plan includes the active power output instructions of each distributed energy resource; Control each distributed energy resource to operate according to the day-ahead scheduling plan.
2. The distributed energy storage cluster aggregation and decomposition control method according to claim 1, characterized in that, After controlling each distributed energy resource to operate according to the day-ahead scheduling plan, the process also includes: Summarize the adjustable power of each distributed energy resource during real-time operation; Based on the adjustable power, the adjustable power data corresponding to the distributed energy storage cluster is calculated, and the adjustable power data is sent to the distribution network operator so that the distribution network operator can generate the target adjustable power of the distributed energy storage cluster based on the adjustable power data; the adjustable power data includes: adjustable power range and adjustment cost function. Receive the target regulation power sent by the distribution network operator, optimize the day-ahead scheduling plan based on the target regulation power and the aggregation cost function, and output real-time scheduling instructions for each distributed energy resource; Control each distributed energy resource according to the real-time scheduling instructions.
3. The distributed energy storage cluster aggregation and decomposition control method according to claim 1, characterized in that, The power aggregation model is constructed based on representative data provided by the distribution network operator and the operating power range of each distributed energy resource in the distributed energy storage cluster, including: Read the operating parameters of each distributed energy resource to determine the operating power range of each distributed energy resource; Based on the linear power flow method, an affine relationship is established between the aggregated power at the common junction point and the active power injection and reactive power injection of each node. Based on the access relationship of each distributed energy resource at each node, a mapping relationship is established between the power injected by each node and the output power of each distributed energy resource. Based on the affine and mapping relationships, a power aggregation model is generated between the aggregated power at the common connection point and the output power of each distributed energy resource.
4. The distributed energy storage cluster aggregation and decomposition control method according to claim 1, characterized in that, The step of calculating the feasible active power region corresponding to each distributed energy resource and the aggregation cost function corresponding to each distributed energy resource according to the power aggregation model includes: Based on the power aggregation model and in combination with the operating power range of each distributed energy resource, the upper and lower limits of the aggregated active power at the common junction point are solved to determine the feasible area of active power. Obtain the basic cost function for each distributed energy resource, wherein the basic cost function is expressed as a quadratic function; The equivalent contribution power of each distributed energy resource at the common junction point, as well as the correction coefficients for network loss and reactive power coupling, obtained by solving the power aggregation model, are used to correct the basic cost function of each distributed energy resource, thus obtaining the corrected cost function of each distributed energy resource. The corrected cost functions of each distributed energy resource are aggregated to obtain the aggregated cost function.
5. The distributed energy storage cluster aggregation and decomposition control method according to claim 4, characterized in that, The aggregation of the correction cost functions of each distributed energy resource yields an aggregated cost function, which includes: Based on the principle of equal marginal cost, the distributed energy resources are ranked. Based on the output status of each distributed energy resource in different power ranges, the distributed energy resources are divided into minimum output group, maximum output group and variable output group; For different power ranges at common coupling points, determine the corresponding variable output group combinations; Based on the variable output group combination, the correction cost function of each distributed energy resource is aggregated in segments to construct a segmented secondary aggregated cost function corresponding to different common connection point power ranges.
6. The distributed energy storage cluster aggregation and decomposition control method according to claim 1, characterized in that, The process of receiving the target power curve and decomposing it into each distributed energy resource in a cost-minimizing manner to obtain a day-ahead scheduling plan includes: The target power curve is used as the target power constraint at the common junction point; The operating power range of each distributed energy resource is used as a constraint in the decomposition process. Minimize the aggregation cost function as the optimization objective; Solve for the active power output of each distributed energy resource during each scheduling period; Based on the active power output values of each distributed energy resource obtained from the solution, the day-ahead scheduling plan is generated, and active power output instructions are issued to the corresponding distributed energy resources.
7. The distributed energy storage cluster aggregation and decomposition control method according to claim 2, characterized in that, The step of calculating the adjustable power data corresponding to the distributed energy storage cluster based on the adjustable power, and outputting real-time adjustment instructions corresponding to each distributed energy resource after receiving the target adjustable power, includes: Based on the real-time operating status of each distributed energy resource, the day-ahead scheduling plan, and the real-time representative data provided by the distribution network operator, the upper limit of the upward adjustable power and the lower limit of the downward adjustable power of the distributed energy storage cluster at the point of common connection are solved respectively. Based on the aforementioned upper adjustable power limit and lower adjustable power limit, an adjustable power range for the distributed energy storage cluster is formed. By utilizing the equivalent contribution power of the common connection point corresponding to the real-time stage, as well as the correction coefficients for network loss and reactive power coupling, the real-time adjustment cost of each distributed energy resource is corrected, and an adjustment cost function is generated. The adjustable power range and adjustment cost function are sent to the distribution network operator. After receiving the target regulation power sent by the distribution network operator, the target regulation power is used as the real-time regulation target at the point of common coupling, and the target regulation power is decomposed with the goal of minimizing the regulation cost function. Based on the decomposition results, the real-time adjustment power corresponding to each distributed energy resource is determined, and the real-time adjustment command is output.
8. A distributed energy storage cluster aggregation and decomposition control device, characterized in that, include: The module includes an aggregation module, a day-ahead scheduling module, a day-ahead control module, a real-time aggregation module, a real-time calculation module, a real-time optimization module, and a real-time control module. The aggregation module is used to send the aggregation cost function of the active power feasible area of the common connection point and the distributed energy storage cluster to the distribution network operator. The day-ahead scheduling module is used to receive the target power curve, decompose the target power curve into each distributed energy resource in a cost-minimizing manner, and output the day-ahead scheduling plan. The day-ahead control module is used to control each distributed energy resource to operate according to the day-ahead scheduling plan; The real-time aggregation module is used to aggregate the adjustable power of each distributed energy resource during real-time operation. The real-time computing module is used to calculate the adjustable power data corresponding to the distributed energy storage cluster and send the adjustable power data to the distribution network operator. The real-time optimization module is used to receive the target regulation power sent by the distribution network operator, optimize the day-ahead scheduling plan according to the target regulation power and the aggregation cost function, and output real-time scheduling instructions for each distributed energy resource. The real-time control module is used to control each distributed energy resource according to the real-time scheduling instructions.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the distributed energy storage cluster aggregation decomposition control method as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the distributed energy storage cluster aggregation decomposition control method as described in any one of claims 1 to 7.