Power distribution network resource regulation method and device, equipment, storage medium and program product

Abstract

The application relates to a power distribution network resource regulation method, device, equipment, storage medium and program product. The method comprises the following steps: acquiring actual operation state parameters and cost parameters of each charging station in a power distribution network system at multiple time points; the actual operation state parameters and the cost parameters are used to determine the operation cost of the power distribution network system at the multiple time points; determining the total planned scheduling power of each charging station according to the minimum operation cost and the actual operation state parameters and the cost parameters corresponding to the minimum operation cost; determining the responsibility response power of each charging station according to the actual operation state parameters and the total planned scheduling power; the responsibility response power is used to determine the state deviation of each charging pile in each charging station; determining the target regulation power of each charging pile according to the minimum state deviation and the responsibility response power corresponding to the minimum state deviation; and regulating each charging pile according to the target regulation power of each charging pile. The operation cost and the state deviation improve the accuracy of the regulation of the power distribution network resources.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and in particular to a method, apparatus, equipment, storage medium, and program product for regulating power distribution network resources. Background Technology

[0002] With the increasing penetration of renewable energy in urban power distribution networks and the large-scale access of diversified loads, the operation of power distribution networks faces increasingly serious power surge risks.

[0003] Traditional power distribution network regulation methods use a fixed mode for adjustment. During the adjustment process, the volatility of distributed power sources such as photovoltaic and wind power, as well as the randomness of electric vehicle charging load, make it difficult for traditional power distribution network regulation methods to cope with rapid and frequent power fluctuations. As a result, the regulated values ​​do not match the actual power resources required at any given time, and this regulation method has the problem of being unable to accurately regulate. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, equipment, storage medium, and program product for controlling distribution network resources that can accurately regulate the response power of the distribution network in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for regulating distribution network resources, including:

[0006] The actual operating status parameters and cost parameters of each charging station in the power distribution network system at multiple time points are obtained; the actual operating status parameters and the cost parameters are used to determine the operating cost of the power distribution network system at multiple time points.

[0007] The planned total power of each charging station is determined based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters.

[0008] The responsibility response power of each charging station is determined based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each charging station.

[0009] The target control power of each charging pile is determined based on the minimum state deviation and the corresponding responsibility response power of the minimum state deviation.

[0010] The charging piles are regulated according to their target regulating power.

[0011] In one embodiment, determining the planned total power for each charging station based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters includes:

[0012] Based on the first preset constraint, the actual operating state parameters at multiple times are filtered to obtain the actual operating state parameters at multiple candidate times; the first preset constraint includes power balance constraint, line capacity constraint, load energy demand constraint and flexible load energy integrity constraint.

[0013] The operating costs for multiple candidate times are determined based on the actual operating status parameters and cost parameters at multiple candidate times.

[0014] The minimum operating cost is determined from the operating costs of multiple candidate times, and the target actual operating state parameter corresponding to the minimum operating cost is used as the planned total power of each charging station; the target actual operating state parameter includes the actual power of the charging station and the actual power of the flexible load.

[0015] In one embodiment, the actual operating status parameters include the actual total power of each of the charging stations, and the step of determining the responsibility response power of each of the charging stations based on the actual operating status parameters and the planned total power includes:

[0016] For each charging station, the difference between the actual total power and the planned total power is calculated to obtain the impact power of the charging station;

[0017] The current power of each charging pile in the charging station is obtained, and the total available adjustment capacity value of each charging station is determined based on the current power of each charging pile and the impact power of each charging station.

[0018] The adjustable capacity ratio of each charging station is calculated based on the total available adjustable capacity value of each charging station, and the responsibility response power of each charging station is determined based on the adjustable capacity ratio of each charging station and the impact power of each charging station.

[0019] In one embodiment, determining the responsibility response power of each charging station based on the adjustable capacity ratio of each charging station and the impact power of each charging station includes:

[0020] Obtain the current current value of each charging station, and divide the adjustable capacity ratio of each charging station by the sum of the current current values ​​of all charging stations to obtain the responsibility power allocation coefficient of each charging station.

[0021] The responsibility response power of each charging station is obtained by multiplying its responsibility power allocation coefficient with its impact power.

[0022] In one embodiment, determining the target control power of each charging pile based on the minimum state deviation and the responsibility response power corresponding to the minimum state deviation includes:

[0023] The operating status parameters of each charging pile in each of the aforementioned charging stations are obtained at multiple times; the operating status parameters include real-time power and remaining power.

[0024] The operating state parameters are filtered according to the second preset constraint to obtain the operating state parameters at candidate times; the second preset constraint includes power constraint and energy constraint.

[0025] The state deviation of each charging station is determined based on the operating state parameters at the candidate time and the responsibility response power;

[0026] The minimum state deviation is determined from the state deviations of each of the charging stations, and the real-time power of each of the charging piles corresponding to the minimum state deviation is determined as the target control power of each of the charging piles.

[0027] In one embodiment, regulating each charging pile according to the target regulating power of each charging pile includes:

[0028] For each charging station, at a preset time, the power of the charging station is set to the target control power of the charging station.

[0029] Secondly, this application also provides a control device for distribution network resources, comprising:

[0030] The acquisition module is used to acquire the actual operating status parameters and cost parameters of each charging station in the distribution network system at multiple times; the actual operating status parameters and the cost parameters are used to determine the operating cost of the distribution network system at multiple times.

[0031] The first determining module is used to determine the planned total power of each of the charging stations based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters.

[0032] The second determining module is used to determine the responsibility response power of each of the charging stations based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each of the charging stations.

[0033] The third determining module is used to determine the target control power of each of the charging piles based on the minimum state deviation and the responsibility response power corresponding to the minimum state deviation.

[0034] The control module is used to control each of the charging piles according to the target control power of each charging pile.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0036] The actual operating status parameters and cost parameters of each charging station in the power distribution network system at multiple time points are obtained; the actual operating status parameters and the cost parameters are used to determine the operating cost of the power distribution network system at multiple time points.

[0037] The planned total power of each charging station is determined based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters.

[0038] The responsibility response power of each charging station is determined based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each charging station.

[0039] The target control power of each charging pile is determined based on the minimum state deviation and the corresponding responsibility response power of the minimum state deviation.

[0040] The charging piles are regulated according to their target regulating power.

[0041] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0042] The actual operating status parameters and cost parameters of each charging station in the power distribution network system at multiple time points are obtained; the actual operating status parameters and the cost parameters are used to determine the operating cost of the power distribution network system at multiple time points.

[0043] The planned total power of each charging station is determined based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters.

[0044] The responsibility response power of each charging station is determined based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each charging station.

[0045] The target control power of each charging pile is determined based on the minimum state deviation and the corresponding responsibility response power of the minimum state deviation.

[0046] The charging piles are regulated according to their target regulating power.

[0047] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0048] The actual operating status parameters and cost parameters of each charging station in the power distribution network system at multiple time points are obtained; the actual operating status parameters and the cost parameters are used to determine the operating cost of the power distribution network system at multiple time points.

[0049] The planned total power of each charging station is determined based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters.

[0050] The responsibility response power of each charging station is determined based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each charging station.

[0051] The target control power of each charging pile is determined based on the minimum state deviation and the corresponding responsibility response power of the minimum state deviation.

[0052] The charging piles are regulated according to their target regulating power.

[0053] The aforementioned methods, devices, equipment, storage media, and program products for regulating distribution network resources acquire actual operating status parameters and cost parameters of each charging station in the distribution network system at multiple time points. Based on the minimum operating cost and the corresponding actual operating status parameters and cost parameters, the planned total power for each charging station is determined. The responsible response power for each charging station is determined based on the actual operating status parameters and the planned total power. The target regulation power for each charging pile is determined based on the minimum state deviation and the corresponding responsible response power. Regulation is then applied to each charging pile based on its target regulation power. The actual operating status parameters and cost parameters are used to determine the operating cost of the distribution network system at multiple time points; the responsible response power is used to determine the state deviation of each charging pile in each charging station. By calculating the operating cost of the distribution network system, multi-resource aggregation of the distribution network is achieved, and the responsible response power of each charging station under the minimum operating cost is calculated, which can meet the constraints of economic and safe operation of urban distribution networks. Simultaneously, by calculating the state deviation of each charging station and determining the target regulation power of each charging pile under the minimum state deviation, rapid response to impact power is achieved, improving the accuracy and economy of distribution network resource allocation. Compared to the traditional fixed-mode adjustment method, which is difficult to cope with rapid and frequent power fluctuations, this application calculates the final target control power based on operating costs and operating status deviations, and adjusts the charging pile through the target control power, thereby realizing real-time control of the charging station power and rapid response to impact power. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is an application environment diagram of a distribution network resource regulation method in one embodiment;

[0056] Figure 2 This is a flowchart illustrating a method for regulating power distribution network resources in one embodiment;

[0057] Figure 3 This is a schematic diagram of the process for adjusting the reactive power of a distributed photovoltaic system in one embodiment.

[0058] Figure 4 This is a flowchart illustrating the process of determining the planned total power of a charging station in one embodiment;

[0059] Figure 5 This is a flowchart illustrating the process of determining the responsible response power of a charging station in one embodiment;

[0060] Figure 6 This is a flowchart illustrating the process of determining the responsible response power of a charging station in another embodiment;

[0061] Figure 7 This is a flowchart illustrating a method for regulating power distribution network resources in another embodiment;

[0062] Figure 8 This is a structural block diagram of a distribution network resource control device in one embodiment;

[0063] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0065] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0066] With the increasing penetration of renewable energy and the large-scale integration of diversified loads in urban power distribution networks, the operation of these networks faces increasingly severe power surge risks. Traditional power distribution network regulation methods employ fixed modes. However, during regulation, the volatility of distributed power sources such as photovoltaics and wind power, as well as the randomness of electric vehicle charging loads, make it difficult for traditional regulation methods to cope with rapid and frequent power fluctuations. Consequently, the regulated values ​​do not match the actual power resources required at any given time, indicating a lack of precise regulation capabilities.

[0067] In view of the above-mentioned technical problems, the present application provides a method for regulating the power resources of a distribution network that can be precisely controlled. The following embodiments will specifically illustrate the method for regulating the power resources of the distribution network.

[0068] The method for regulating distribution network resources provided in this application embodiment can be applied to, for example... Figure 1 The application environment is illustrated. The power distribution network system includes multiple charging stations and flexible loads. Each charging station includes multiple charging piles. The processing device 102 is wiredly connected to the power distribution network system 104. The processing device 102 is used to adjust the power of each charging pile in the power distribution network system 104 to achieve a reasonable allocation of power resources in the power distribution network. In specific applications, sensors are installed in the power distribution network system 104 to collect real-time operating status parameters of the charging stations and charging piles, and transmit them to the processing device 102. The processing device 102 calculates the real-time operating status parameters of the charging stations and charging piles, and adjusts the power of the charging piles based on the calculation results. The processing device 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc.

[0069] In one exemplary embodiment, such as Figure 2As shown, a method for regulating power distribution network resources is provided. This embodiment illustrates the application of this method to processing equipment. In this embodiment, the method includes:

[0070] S201, obtain the actual operating status parameters and cost parameters of each charging station in the distribution network system at multiple times.

[0071] The actual operating status parameters and cost parameters are used to determine the operating costs of the distribution network system at multiple points in time. Actual operating status parameters may include, but are not limited to, total charging station power, average charging station power, flexible load power, node voltage, and reference voltage value. Charging stations can be electric vehicle charging stations. Cost parameters may include, but are not limited to, electric vehicle aggregation control costs, flexible load control costs, network loss costs, voltage deviation penalty costs, electric vehicle basic charging cost coefficient, power ramp-up cost coefficient, and preferred power cost coefficient. Multiple points in time can be the current moment and consecutive moments within a previously preset time period. The preset time period can be a week, a month, etc., or can be set according to the actual scenario; there are no restrictions here.

[0072] In the embodiments of this application, sensors can be installed on charging stations and flexible loads to directly collect real-time operational status information of charging stations and flexible loads in the distribution network system. Alternatively, sensors can be installed on each charging pile in a charging station, collecting operational status information from all charging piles and summing the operational status information to obtain the actual operational status information of the charging station. The processing device can obtain actual status parameters at multiple times from the data collected by the sensors as needed. The processing device can also retrieve a pre-stored list of power grid cost parameters from a database. This list includes cost parameters for various types of electrical equipment in the distribution network system, such as electric vehicle charging stations and conventional power loads (flexible loads), and extract the cost parameters for each charging station and flexible load in the current distribution network system from the power grid cost parameter list.

[0073] S202, determine the planned total power of each charging station based on the minimum operating cost and the actual operating status parameters and cost parameters corresponding to the minimum operating cost.

[0074] The planned total power can be defined as the power that each charging station can control under the minimum operating cost of the power distribution network system.

[0075] In the embodiments of this application, the processing device can calculate the total operating cost of the distribution network system at different times based on the actual operating status parameters and cost parameters of each charging station and each flexible load at multiple times. That is, it performs multiplication calculation on the actual status parameters and cost parameters, selects the minimum operating cost from the total operating cost, and extracts the charging station power and flexible load power under the minimum operating cost case to determine the planned total power.

[0076] Optionally, the processing equipment can also pre-construct an economic benefit objective function for the distribution network system. This economic benefit objective function aims to minimize the total operating cost, which includes the cost of flexible resource regulation, line loss, and voltage deviation. Then, the optimization time period of the economic benefit objective function is obtained. The actual operating status parameters and cost parameters of each charging station and each flexible load at multiple times are grouped by the optimization time period to obtain the actual operating status parameters and cost parameters of the electrical equipment in multiple time periods. The optimization time period can be 24 hours or can be set according to the actual scenario requirements, without any restrictions here. The actual operating status parameters and cost parameters of multiple time periods are simultaneously substituted into the economic benefit function for calculation to obtain the minimum operating cost of the distribution network system. The power of the charging station and the power of the flexible load under the minimum operating cost case are extracted to determine the total planned scheduling power. For example, the economic benefit function of the distribution network system can be expressed by the following relationship (1):

[0077] (1);

[0078] In the formula, T represents the total optimization time, which is typically 24 hours, and t represents the current time. , , , Let NEV, NFL, and N represent the electric vehicle aggregation control cost, flexible load control cost, network loss, and voltage deviation penalty cost at time t, respectively. NEV, NFL, and N represent the number of electric vehicles, flexible loads, and nodes, respectively. , , Let these represent the base charging cost coefficient, power ramp-up cost coefficient, and SOC equilibrium cost coefficient for the i-th electric vehicle, respectively. , , These represent the base adjustment cost coefficient, the cost coefficient for deviation from preferred power, and the energy integrity cost coefficient for the j-th flexible load, respectively. , , Let S represent the total power of the i-th electric vehicle charging station, the average SOC of the current electric vehicle charging station, and the expected SOC, respectively. , , Let represent the power of load j at time t, the preferred power of load j at time t, and the total energy required by load j, respectively. , Let Vi(t) and Vref represent the network loss cost coefficient and voltage deviation cost coefficient, respectively, and let Vi(t) and Vref represent the voltage and reference voltage values ​​of node i at time t, respectively.

[0079] S203, determine the responsible response power of each charging station based on the actual operating status parameters and the planned total power.

[0080] Among them, the responsibility response power is used to determine the state deviation of each charging pile in each charging station.

[0081] In the embodiments of this application, after calculating the planned total power of each charging station, the actual total power, power safety upper and lower limits, and ramping capability information at the current moment can be extracted from the actual operating status parameters of each charging station. Based on the actual total power, power safety upper and lower limits, ramping capability information, and planned total power, the responsible response power of each charging station is calculated.

[0082] S204. Determine the target control power of each charging pile based on the minimum state deviation and the corresponding responsibility response power.

[0083] The target adjustable power can be the power that the charging pile can be allocated at any subsequent moment.

[0084] In the embodiments of this application, for each charging station, the operating status information of each charging pile at multiple time points is collected by sensors. These multiple time points are consistent with the collection time of the charging station's operating status information. The target control power of the charging station is determined by the operating status information of each charging pile and the responsible response power of its respective charging station. Optionally, the operating status information of all charging piles of the charging station and the responsible response power of its respective charging station at a given time point are extracted, and the extracted data is used to calculate the difference with its corresponding expected data to obtain multiple deviation values. The multiple deviation values ​​are then weighted and summed to obtain the state deviation value of each charging station. The above calculation is performed on the operating status information of all charging piles of the charging station at all time points to obtain all state deviation values ​​of the charging station. The minimum state deviation value is determined from all state deviation values, and the actual power of each charging pile under the minimum state deviation value is used as the target control power of each charging pile.

[0085] Optionally, the processing device can also pre-construct a balanced control objective function for the charging station. This balanced control objective function aims to minimize the state deviation, which includes power deviation, energy deviation, and impact power response deviation. Then, the step size of the balanced control objective function is obtained, and the operating status information of the charging piles at multiple times is grouped by the step size to obtain the operating status information of all charging piles in the charging station for multiple time periods. The step size is set according to the actual scenario requirements and is not limited here. The operating status information of all charging piles in the charging station for multiple time periods and the responsibility response power of the charging station in each time period are simultaneously substituted into the balanced control objective function for calculation to obtain the minimum state deviation of the charging station, and the actual power of each charging pile under the minimum state deviation is extracted as the target control power of each charging pile. For example, the balanced control objective function of the charging station can be represented by the following relationship (2):

[0086] (2);

[0087] In the formula, H P p represents the step size of the predictive control model in the equilibrium control objective function. k (τ) represents the real-time power of the k-th charging pile at time τ, where K i This represents the total number of charging piles in the i-th charging station, where α, γ, and β represent the electric vehicle SOC equilibrium weighting coefficient, power deviation weighting coefficient, and impact power response weighting coefficient, respectively. Let be the responsible response power of the i-th charging station.

[0088] S205 adjusts the power of each charging pile according to the target power of each charging pile.

[0089] In the embodiments of this application, after obtaining the target controllable power, the processing device can adjust the power value of each charging pile to the target controllable power at every subsequent moment after the current moment. Optionally, the power value of the charging pile at the next moment after the current moment can also be adjusted to the target controllable power. Simultaneously, the target controllable power is used as the operating state parameter of the charging pile at the next moment, participating in the calculation of the operating cost of the power distribution system and the calculation of the state deviation of the charging station at subsequent moments, thereby calculating a new target controllable power to achieve real-time updating of the controllable power of the charging station.

[0090] The aforementioned method for regulating distribution network resources obtains the actual operating state parameters and cost parameters of each charging station in the distribution network system at multiple time points. These parameters are used to determine the operating cost of the distribution network system at these multiple time points. The planned total power for each charging station is determined based on the minimum operating cost and the corresponding actual operating state parameters and cost parameters. The responsible response power for each charging station is determined based on the actual operating state parameters and the planned total power. This responsible response power is used to determine the state deviation of each charging pile in each charging station. The target regulation power for each charging pile is determined based on the minimum state deviation and the corresponding responsible response power. The method then regulates each charging pile based on its target regulation power. By calculating the operating cost of the distribution network system, multi-resource aggregation of the distribution network is achieved, and the responsible response power of each charging station under the minimum operating cost is calculated, which meets the constraints of economic and safe operation of urban distribution networks. Furthermore, by calculating the state deviation of each charging station and determining the target regulation power of each charging pile under the minimum state deviation, rapid response to power surges is possible, improving the accuracy and economy of distribution network resource allocation. Compared to the traditional fixed-mode adjustment method, which is difficult to cope with rapid and frequent power fluctuations, this application calculates the final target control power based on operating costs and operating status deviations, and adjusts the charging pile through the target control power, thereby realizing real-time control of the charging station power and rapid response to impact power.

[0091] In one exemplary embodiment, such as Figure 3 As shown, the planned total power dispatch for each charging station is determined based on the minimum operating cost and the corresponding actual operating status parameters and cost parameters, including:

[0092] S301, based on the first preset constraint, the actual operating state parameters at multiple times are filtered to obtain the actual operating state parameters at multiple candidate times.

[0093] The first set of pre-defined constraints includes power balance constraints, line capacity constraints, load energy demand constraints, and flexible load energy integrity constraints.

[0094] In the embodiments of this application, before or during the calculation of the minimum operating cost of the distribution network system, it is necessary to determine whether the real-time operating state parameters meet the first preset constraint condition. Calculating the operating cost only for the actual operating state parameters that meet the first preset constraint condition can reduce the amount of data required for calculation and improve the accuracy of the operating cost calculation. Therefore, by substituting the actual operating state parameters at multiple times into the first preset constraint condition for filtering, multiple candidate actual operating state parameters are obtained.

[0095] Optionally, Distflow is used to construct linear power flow constraints for the radial distribution network, i.e., power balance constraints, including active power constraints, reactive power constraints, and node voltage constraints. For example, the active power constraints are expressed by the following equation (3):

[0096] (3);

[0097] In the formula, Let represent the active power of line ij at time t, and downstream(j) represent the set of downstream nodes of node j. This represents the load power at node j.

[0098] The reactive power balance constraint is expressed by the following relationship (4):

[0099] (4);

[0100] In the formula, Let represent the reactive power of line ij at time t. This represents the reactive power of the load at node j.

[0101] To avoid nonlinear terms in power flow constraints, a linearization method is used to simplify the calculation of node voltages. The node voltage constraint conditions are expressed by the following relationship (5):

[0102] (5);

[0103] In the formula, Vj(t), Vii(t), and V0 are the voltages at nodes j and i at time t, and the reference voltage, respectively. ij x ij These are the resistance and reactance of line ij, respectively.

[0104] Alternatively, the line capacity constraint can be expressed by the following relationship (6):

[0105] (6);

[0106] In the formula, Sij,max represents the upper limit of the apparent power capacity of line ij, and L represents the set of lines.

[0107] Optionally, since the total energy should remain constant within a specified time due to the charging needs of electric vehicle users, energy demand constraints are used to limit the power in real time. The load energy demand constraint is expressed by the following relationship (7):

[0108] (7);

[0109] In the formula, Δt represents the time interval. This represents the total energy demand of the i-th charging station.

[0110] Optionally, for flexible loads, it is necessary to ensure that the flexible load task is completed within a specified time. Therefore, a flexible load energy integrity constraint is set, which is expressed by the following relationship (8):

[0111] (8);

[0112] In the formula, Δt represents the time interval. , These represent the start time and end time of load regulation, respectively. This represents the energy required to complete the load j.

[0113] S302, determine the operating cost of multiple candidate times based on the actual operating status parameters and cost parameters of multiple candidate times.

[0114] In the embodiments of this application, after filtering the actual operating state parameters at multiple times through the first preset constraint, the actual operating state parameters at multiple candidate times are obtained, and the cost parameters at multiple candidate times are obtained at the same time. The actual operating state parameters and cost parameters at multiple candidate times are substituted into the above relationship (1) for calculation to obtain the operating cost of the distribution network system at multiple candidate times.

[0115] S303 determines the minimum operating cost from the operating costs of multiple candidate times, and uses the target actual operating state parameters corresponding to the minimum operating cost as the planned total power of each charging station.

[0116] The target actual operating status parameters include the actual power of the charging station and the actual power of the flexible load.

[0117] In the embodiments of this application, the operating costs of multiple candidate times are compared to determine the minimum operating cost. The actual power of each charging station corresponding to the minimum operating cost can be used as the planned total power of each charging station; alternatively, the sum of the actual power of each charging station and the actual power of the corresponding flexible load can be used as the planned total power of each charging station.

[0118] By constructing an economic benefit objective function that minimizes the cost of flexible resource regulation, line loss, and voltage deviation, and setting constraints on grid node power balance, network power flow, electric vehicle energy demand, and flexible load energy integrity, the system achieves aggregated regulation of micro-resources such as electric vehicle charging stations and flexible loads, thereby improving the reliability and economy of the distribution network system.

[0119] In one exemplary embodiment, such as Figure 4As shown, the actual operating status parameters include the actual total power of each charging station. Based on the actual operating status parameters and the planned total power, the responsible response power of each charging station is determined, including:

[0120] S401 calculates the difference between the actual total power and the planned total power for each charging station to obtain the impact power of the charging station.

[0121] In the embodiments of this application, the actual total power of each charging station at the current moment is extracted from the actual operating status parameters of each charging station at multiple times. For each charging station, the difference between the actual total power at the current moment and the planned total power is calculated to obtain the impact power of that charging station. Optionally, the impact power of the charging station is expressed by the following relationship (9):

[0122] (9);

[0123] In the formula, ΔPimpact(t) represents the magnitude of the impact power at time t, Pactual(t) represents the actual total power of the charging station at time t, and Pschedualed(t) represents the planned power of the charging station at time t.

[0124] S402, obtain the current power of each charging pile in the charging station, and determine the total available adjustment capacity value of each charging station based on the current power of each charging pile and the impact power of each charging station.

[0125] In the embodiments of this application, the actual power of the charging pile at the current moment is extracted from the operating status parameters of the charging pile at multiple times, i.e., the current power. For each charging station, the total available adjustable capacity value of each charging station is calculated by using the impact power of the charging station and the current power of the charging piles it contains. Optionally, the total available adjustable capacity value of the charging station can be expressed by the following relationship (10):

[0126] (10);

[0127] In the formula, p k,max p k,min These represent the maximum power and minimum charging power of the k-th charging pile, respectively. , These represent the charging station's maximum uphill and downhill climbing capabilities, respectively.

[0128] S403, calculate the adjustable capacity ratio of each charging station based on the total available adjustable capacity value of each charging station, and determine the responsible response power of each charging station based on the adjustable capacity ratio of each charging station and the impact power of each charging station.

[0129] In the embodiments of this application, after the processing device calculates the total available callable capacity value of the charging station, it first calculates the proportion of the total available callable capacity value of each charging station to the total available callable capacity value of all charging stations in the distribution network system, and obtains the current value of each charging station at the current moment from the real-time operating status parameters of each charging station at multiple moments. Then, it performs a division operation by the proportion of the total available callable capacity value of each charging station and the current value at the current moment to obtain the responsible response power of each charging station.

[0130] By constructing an impact power distribution mechanism, the total available adjustment capacity of the charging station can be accurately calculated, which is beneficial for calculating the regulation power of the charging pile and enables a rapid response to impact power.

[0131] In one exemplary embodiment, such as Figure 5 As shown, the responsible response power of each charging station is determined based on the adjustable capacity ratio and the impact power of each charging station, including:

[0132] S501: Obtain the current current value of each charging station, divide the adjustable capacity ratio of each charging station by the sum of the current current values ​​of all charging stations, and obtain the responsibility power allocation coefficient of each charging station.

[0133] In the embodiments of this application, the responsibility power allocation coefficient of the charging station can be expressed by the following relationship (11):

[0134] (11);

[0135] In the formula, Let represent the percentage of adjustable capacity for the i-th charging station. Let be the current value of the i-th charging station at time t. These are the weighting coefficients.

[0136] S502, multiply the responsibility power allocation coefficient of each charging station with the impact power of each charging station to obtain the responsibility response power of each charging station.

[0137] In the embodiments of this application, the responsibility response power of the charging station can be expressed by the following relationship (12):

[0138] (12);

[0139] In the formula, Let be the responsibility power allocation coefficient for the i-th charging station. Let be the impact power of the i-th charging station. Let be the responsible response power of the i-th charging station.

[0140] By calculating the responsibility response power, the adjustable power of the charging station can be accurately quantified, which is beneficial for the calculation of subsequent target control of charging piles.

[0141] In one exemplary embodiment, such as Figure 6 As shown, the target control power for each charging pile is determined based on the minimum state deviation and the corresponding responsibility response power, including:

[0142] S601, obtain the operating status parameters of each charging pile in each charging station at multiple times.

[0143] The operating status parameters include real-time power and remaining power.

[0144] In the embodiments of this application, sensors collect the operating status parameters of each charging pile in real time, and the processing device filters out the operating status parameters at multiple times from the operating status parameters collected by the sensors as needed.

[0145] S602, the running state parameters are filtered according to the second preset constraint conditions to obtain the running state parameters at candidate times.

[0146] The second preset constraint includes power constraint and energy constraint.

[0147] In the embodiments of this application, before or during the calculation of the minimum state deviation of the charging station, it is necessary to determine whether the operating state parameters of the charging pile meet the second preset constraint condition. Calculating the minimum state deviation only for the operating state parameters that meet the second preset constraint condition reduces the amount of data required for calculation and improves the accuracy of the state deviation calculation. Therefore, by substituting the operating state parameters at multiple times into the second preset constraint condition for filtering, candidate operating state parameters are obtained. These candidate times can be the same as or different from the candidate times in the actual operating state parameter filtering results of the charging station, depending on the actual calculation results.

[0148] Alternatively, the second preset constraint can be expressed by the following relation (13):

[0149] (13);

[0150] In the formula, η k,max E represents the charging efficiency of the k-th charging station. k SOC represents the rated capacity of the k-th charging pile. min SOC max These represent the lower and upper limits of the charging pile's State of Charge (SOC), respectively.

[0151] S603 determines the state deviation of each charging station based on the operating state parameters and responsibility response power at the candidate time.

[0152] In the embodiments of this application, after filtering the operating status parameters of charging piles at multiple times through the second preset constraint conditions, the operating status parameters of charging piles at multiple candidate times are obtained. At the same time, the responsibility response power of the charging station is obtained. For each charging station, the operating status parameters of the charging piles at multiple candidate times and the responsibility response power of the charging station to which they belong are substituted into the above relationship (2) for calculation to obtain the state deviation of the charging station at multiple candidate times.

[0153] S604 determines the minimum state deviation from the state deviations of each charging station, and determines the real-time power of each charging pile corresponding to the minimum state deviation as the target control power of each charging pile.

[0154] In the embodiments of this application, the state deviations of charging stations at multiple candidate times are compared to determine the minimum state deviation, and the real-time power of each charging pile under the minimum state deviation is used as the target control power of each charging pile.

[0155] By calculating the minimum state deviation to determine the target control power of the charging pile, the safety and reliability of the power distribution network system operation process are realized, which is conducive to the precise control of power distribution network resources.

[0156] In one exemplary embodiment, regulating each charging pile according to its target regulating power includes:

[0157] For each charging station, at the preset time, the power of the charging station is set to the target control power of the charging station.

[0158] The preset time can be the next time after the current time.

[0159] In the embodiments of this application, after calculating the target controllable power of the charging pile, the time of the power distribution network system is detected by a preset timer. When the preset time is reached, the power of the charging pile is set to the target controllable power. At the same time, the target controllable power is used as the actual power of the charging pile at the preset time, and the electricity consumption at the preset time is collected. The actual power and electricity consumption of the charging pile at the preset time are used to calculate the operating cost of the power distribution system and the state deviation of the charging station at the next preset time, thereby calculating a new target controllable power to achieve real-time updating of the controllable power of the charging pile.

[0160] In addition to the methods of all the above embodiments, a method for regulating distribution network resources is also provided, such as... Figure 7 As shown, the method includes:

[0161] S701, obtain the actual operating status parameters and cost parameters of each charging station in the distribution network system at multiple times; the actual operating status parameters and cost parameters are used to determine the operating cost of the distribution network system at multiple times;

[0162] S702, based on the first preset constraint, the actual operating state parameters at multiple times are filtered to obtain the actual operating state parameters at multiple candidate times; the first preset constraint includes power balance constraint, line capacity constraint, load energy demand constraint and flexible load energy integrity constraint.

[0163] S703, determine the operating cost of multiple candidate times based on the actual operating status parameters and cost parameters of multiple candidate times;

[0164] S704 determines the minimum operating cost from the operating costs of multiple candidate times, and uses the target actual operating state parameter corresponding to the minimum operating cost as the planned total power of each charging station; the target actual operating state parameter includes the actual power of the charging station and the actual power of the flexible load;

[0165] S705 calculates the difference between the actual total power and the planned total power for each charging station to obtain the impact power of the charging station.

[0166] S706, obtain the current power of each charging pile in the charging station, and determine the total available adjustment capacity value of each charging station based on the current power of each charging pile and the impact power of each charging station.

[0167] S707, calculates the proportion of adjustable capacity of each charging station based on the total available adjustable capacity value of each charging station;

[0168] S708: Obtain the current current value of each charging station, divide the adjustable capacity ratio of each charging station by the sum of the current current values ​​of all charging stations, and obtain the responsibility power allocation coefficient of each charging station.

[0169] S709, the responsibility power allocation coefficient of each charging station is multiplied by the impact power of each charging station to obtain the responsibility response power of each charging station; the responsibility response power is used to determine the state deviation of each charging pile in each charging station;

[0170] S710 acquires the operating status parameters of each charging pile in each charging station at multiple times; the operating status parameters include real-time power and remaining power.

[0171] S711, the operating state parameters are filtered according to the second preset constraint conditions to obtain the operating state parameters at candidate times; the second preset constraint conditions include power constraint conditions and energy constraint conditions.

[0172] S712 determines the state deviation of each charging station based on the operating state parameters and responsibility response power at the candidate time.

[0173] S713, determine the minimum state deviation from the state deviation of each charging station, and determine the real-time power of each charging pile corresponding to the minimum state deviation as the target control power of each charging pile.

[0174] S714 sets the power of each charging station to the target control power of the charging station when a preset time is reached.

[0175] Each of the above steps has been described in the foregoing embodiments. For details, please refer to the foregoing content. They will not be repeated here.

[0176] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0177] Based on the same inventive concept, this application also provides a distribution network resource control device for implementing the above-mentioned distribution network resource control method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more distribution network resource control device embodiments provided below can be found in the limitations of the distribution network resource control method described above, and will not be repeated here.

[0178] In one exemplary embodiment, such as Figure 8 As shown, a control device for distribution network resources is provided, comprising: an acquisition module 81, a first determination module 82, a second determination module 83, a third determination module 84, and a control module 85, wherein:

[0179] The acquisition module 81 is used to acquire the actual operating status parameters and cost parameters of each charging station in the distribution network system at multiple times; the actual operating status parameters and cost parameters are used to determine the operating cost of the distribution network system at multiple times.

[0180] The first determining module 82 is used to determine the planned total power of each charging station based on the minimum operating cost and the actual operating status parameters and cost parameters corresponding to the minimum operating cost.

[0181] The second determining module 83 is used to determine the responsibility response power of each charging station based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each charging station;

[0182] The third determining module 84 is used to determine the target control power of each charging pile based on the minimum state deviation and the responsibility response power corresponding to the minimum state deviation.

[0183] The control module 85 is used to control each charging pile according to the target control power of each charging pile.

[0184] In an exemplary embodiment, the first determining module 82 described above includes:

[0185] The first screening unit is used to screen the actual operating state parameters at multiple times according to the first preset constraints to obtain the actual operating state parameters at multiple candidate times; the first preset constraints include power balance constraints, line capacity constraints, load energy demand constraints and flexible load energy integrity constraints.

[0186] The first determining unit is used to determine the operating cost of multiple candidate times based on the actual operating state parameters and cost parameters of multiple candidate times;

[0187] The second determining unit is used to determine the minimum operating cost from the operating costs of multiple candidate times, and to use the target actual operating state parameters corresponding to the minimum operating cost as the planned total power of each charging station; the target actual operating state parameters include the actual power of the charging station and the actual power of the flexible load.

[0188] In an exemplary embodiment, the second determining module 83 described above includes:

[0189] The calculation unit is used to calculate the difference between the actual total power and the planned total power for each charging station to obtain the impact power of the charging station.

[0190] The first acquisition unit is used to acquire the current power of each charging pile in the charging station, and determine the total available adjustment capacity value of each charging station based on the current power of each charging pile and the impact power of each charging station.

[0191] The third determining unit is used to calculate the adjustable capacity ratio of each charging station based on the total available adjustable capacity value of each charging station, and to determine the responsible response power of each charging station based on the adjustable capacity ratio of each charging station and the impact power of each charging station.

[0192] In an exemplary embodiment, the third determining unit described above includes:

[0193] The acquisition subunit is used to acquire the current current value of each charging station, and divide the adjustable capacity ratio of each charging station by the sum of the current current values ​​of all charging stations to obtain the responsibility power allocation coefficient of each charging station.

[0194] The calculation subunit is used to multiply the responsibility power allocation coefficient of each charging station with the impact power of each charging station to obtain the responsibility response power of each charging station.

[0195] In an exemplary embodiment, the third determining module 84 described above includes:

[0196] The second acquisition unit is used to acquire the operating status parameters of each charging pile in each charging station at multiple times; the operating status parameters include real-time power and remaining power.

[0197] The second screening unit is used to screen the operating state parameters according to the second preset constraint conditions to obtain the operating state parameters at candidate times; the second preset constraint conditions include power constraint conditions and energy constraint conditions.

[0198] The fourth determining unit is used to determine the state deviation of each charging station based on the operating state parameters and responsibility response power at the candidate time.

[0199] The fifth determining subunit is used to determine the minimum state deviation from the state deviation of each charging station, and to determine the real-time power of each charging pile corresponding to the minimum state deviation as the target control power of each charging pile.

[0200] In an exemplary embodiment, the aforementioned control module 85 is specifically used for:

[0201] For each charging station, at the preset time, the power of the charging station is set to the target control power of the charging station.

[0202] Each module in the aforementioned power distribution network resource control device 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 computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0203] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database contains operational and cost information for charging stations and charging piles. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for regulating power distribution network resources.

[0204] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0205] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0206] Obtain the actual operating status parameters and cost parameters of each charging station in the distribution network system at multiple time points; the actual operating status parameters and cost parameters are used to determine the operating cost of the distribution network system at multiple time points;

[0207] The planned total power of each charging station is determined based on the minimum operating cost and the actual operating status parameters and cost parameters corresponding to the minimum operating cost.

[0208] The responsibility response power of each charging station is determined based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each charging station.

[0209] The target control power of each charging pile is determined based on the minimum state deviation and the corresponding responsibility response power.

[0210] The power of each charging station is adjusted according to its target power.

[0211] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0212] Based on the first preset constraint, the actual operating state parameters at multiple times are filtered to obtain the actual operating state parameters at multiple candidate times; the first preset constraint includes power balance constraint, line capacity constraint, load energy demand constraint and flexible load energy integrity constraint.

[0213] The operating costs for multiple candidate times are determined based on the actual operating status parameters and cost parameters at multiple candidate times.

[0214] The minimum operating cost is determined from the operating costs at multiple candidate times, and the target actual operating state parameters corresponding to the minimum operating cost are used as the planned total power of each charging station. The target actual operating state parameters include the actual power of the charging station and the actual power of the flexible load.

[0215] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0216] For each charging station, the difference between the actual total power and the planned total power is calculated to obtain the impact power of the charging station;

[0217] Obtain the current power of each charging pile in the charging station, and determine the total available adjustment capacity value of each charging station based on the current power of each charging pile and the impact power of each charging station.

[0218] The adjustable capacity ratio of each charging station is calculated based on the total available adjustable capacity value of each charging station, and the responsible response power of each charging station is determined based on the adjustable capacity ratio of each charging station and the impact power of each charging station.

[0219] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0220] Obtain the current current value of each charging station, and divide the adjustable capacity ratio of each charging station by the sum of the current current values ​​of all charging stations to obtain the responsibility power allocation coefficient of each charging station.

[0221] The responsibility response power of each charging station is obtained by multiplying its responsibility power allocation coefficient with its impact power.

[0222] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0223] The system acquires the operating status parameters of each charging pile at each charging station at multiple times; the operating status parameters include real-time power and remaining power.

[0224] The operating state parameters are filtered according to the second preset constraint to obtain the operating state parameters at candidate times; the second preset constraint includes power constraint and energy constraint.

[0225] The state deviation of each charging station is determined based on the operating status parameters and responsibility response power at the candidate time.

[0226] The minimum state deviation is determined from the state deviations of each charging station, and the real-time power of each charging pile corresponding to the minimum state deviation is determined as the target control power of each charging pile.

[0227] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0228] For each charging station, at the preset time, the power of the charging station is set to the target control power of the charging station.

[0229] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0230] Obtain the actual operating status parameters and cost parameters of each charging station in the distribution network system at multiple time points; the actual operating status parameters and cost parameters are used to determine the operating cost of the distribution network system at multiple time points;

[0231] The planned total power of each charging station is determined based on the minimum operating cost and the actual operating status parameters and cost parameters corresponding to the minimum operating cost.

[0232] The responsibility response power of each charging station is determined based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each charging station.

[0233] The target control power of each charging pile is determined based on the minimum state deviation and the corresponding responsibility response power.

[0234] The power of each charging station is adjusted according to its target power.

[0235] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0236] Based on the first preset constraint, the actual operating state parameters at multiple times are filtered to obtain the actual operating state parameters at multiple candidate times; the first preset constraint includes power balance constraint, line capacity constraint, load energy demand constraint and flexible load energy integrity constraint.

[0237] The operating costs for multiple candidate times are determined based on the actual operating status parameters and cost parameters at multiple candidate times.

[0238] The minimum operating cost is determined from the operating costs at multiple candidate times, and the target actual operating state parameters corresponding to the minimum operating cost are used as the planned total power of each charging station. The target actual operating state parameters include the actual power of the charging station and the actual power of the flexible load.

[0239] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0240] For each charging station, the difference between the actual total power and the planned total power is calculated to obtain the impact power of the charging station;

[0241] Obtain the current power of each charging pile in the charging station, and determine the total available adjustment capacity value of each charging station based on the current power of each charging pile and the impact power of each charging station.

[0242] The adjustable capacity ratio of each charging station is calculated based on the total available adjustable capacity value of each charging station, and the responsible response power of each charging station is determined based on the adjustable capacity ratio of each charging station and the impact power of each charging station.

[0243] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0244] Obtain the current current value of each charging station, and divide the adjustable capacity ratio of each charging station by the sum of the current current values ​​of all charging stations to obtain the responsibility power allocation coefficient of each charging station.

[0245] The responsibility response power of each charging station is obtained by multiplying its responsibility power allocation coefficient with its impact power.

[0246] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0247] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0248] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0249] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for regulating distribution network resources, characterized in that, The method includes: The actual operating status parameters and cost parameters of each charging station in the power distribution network system at multiple time points are obtained; the actual operating status parameters and the cost parameters are used to determine the operating cost of the power distribution network system at multiple time points. The planned total power of each charging station is determined based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters. The responsibility response power of each charging station is determined based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each charging station. The target control power of each charging pile is determined based on the minimum state deviation and the corresponding responsibility response power of the minimum state deviation. The charging piles are regulated according to their target regulating power.

2. The method according to claim 1, characterized in that, The step of determining the planned total power for each charging station based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters includes: Based on the first preset constraint, the actual operating state parameters at multiple times are filtered to obtain the actual operating state parameters at multiple candidate times; the first preset constraint includes power balance constraint, line capacity constraint, load energy demand constraint and flexible load energy integrity constraint. The operating costs for multiple candidate times are determined based on the actual operating status parameters and cost parameters at multiple candidate times. The minimum operating cost is determined from the operating costs of multiple candidate times, and the target actual operating state parameter corresponding to the minimum operating cost is used as the planned total power of each charging station; the target actual operating state parameter includes the actual power of the charging station and the actual power of the flexible load.

3. The method according to claim 1, characterized in that, The actual operating status parameters include the actual total power of each charging station. Determining the responsible response power of each charging station based on the actual operating status parameters and the planned total power includes: For each charging station, the difference between the actual total power and the planned total power is calculated to obtain the impact power of the charging station; The current power of each charging pile in the charging station is obtained, and the total available adjustment capacity value of each charging station is determined based on the current power of each charging pile and the impact power of each charging station. The adjustable capacity ratio of each charging station is calculated based on the total available adjustable capacity value of each charging station, and the responsibility response power of each charging station is determined based on the adjustable capacity ratio of each charging station and the impact power of each charging station.

4. The method according to claim 3, characterized in that, The step of determining the responsibility response power of each charging station based on the adjustable capacity ratio of each charging station and the impact power of each charging station includes: Obtain the current current value of each charging station, and divide the adjustable capacity ratio of each charging station by the sum of the current current values ​​of all charging stations to obtain the responsibility power allocation coefficient of each charging station. The responsibility response power of each charging station is obtained by multiplying its responsibility power allocation coefficient with its impact power.

5. The method according to claim 1, characterized in that, The step of determining the target control power of each charging pile based on the minimum state deviation and the corresponding responsibility response power includes: The operating status parameters of each charging pile in each of the aforementioned charging stations are obtained at multiple times; the operating status parameters include real-time power and remaining power. The operating state parameters are filtered according to the second preset constraint to obtain the operating state parameters at candidate times; the second preset constraint includes power constraint and energy constraint. The state deviation of each charging station is determined based on the operating state parameters at the candidate time and the responsibility response power; The minimum state deviation is determined from the state deviations of each of the charging stations, and the real-time power of each of the charging piles corresponding to the minimum state deviation is determined as the target control power of each of the charging piles.

6. The method according to any one of claims 1-5, characterized in that, The step of regulating each charging pile according to the target regulating power of each charging pile includes: For each charging station, at a preset time, the power of the charging station is set to the target control power of the charging station.

7. A control device for power distribution network resources, characterized in that, The device includes: The acquisition module is used to acquire the actual operating status parameters and cost parameters of each charging station in the distribution network system at multiple times; the actual operating status parameters and the cost parameters are used to determine the operating cost of the distribution network system at multiple times. The first determining module is used to determine the planned total power of each of the charging stations based on the minimum operating cost, the actual operating status parameters corresponding to the minimum operating cost, and the cost parameters. The second determining module is used to determine the responsibility response power of each of the charging stations based on the actual operating status parameters and the planned total power; the responsibility response power is used to determine the status deviation of each charging pile in each of the charging stations. The third determining module is used to determine the target control power of each of the charging piles based on the minimum state deviation and the responsibility response power corresponding to the minimum state deviation. The control module is used to control each of the charging piles according to the target control power of each charging pile.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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