Power distribution network voltage optimization control method, device, equipment and program product

Abstract

The invention relates to the technical field of power distribution network voltage control and optimization, and discloses a power distribution network voltage optimization control method, device, equipment and program product. A complex power distribution network scene of high-proportion distributed photovoltaic and multiple types of flexible resources can be accurately locked, and control strategy failure caused by scene generalization is avoided; a relational expression is constructed through a moment difference analysis theory, and moment difference sensitivity calculation is combined, so that the problem of insufficient reliability caused by a complex model of traditional voltage sensitivity is avoided, calculation is simplified, and real-time performance is guaranteed; according to the invention, sequential control is carried out on the equipment according to a preset principle of economy first and effectiveness later, low-cost regulation and control resources are preferentially used, and the problem of overhigh cost caused by the fact that economy is not considered in a traditional method is solved. Meanwhile, through closed-loop control until the real-time moment difference reaches the standard, voltage out-of-limit is thoroughly eliminated, economy and safety are both considered, and economic consumption of distributed photovoltaic is achieved.

Description

Technical Field

[0001] This invention relates to the field of distribution network voltage control and optimization technology, specifically to a distribution network voltage optimization control method, device, equipment, and program product. Background Technology

[0002] With the increasing penetration rate of distributed photovoltaic (PV) power, distribution networks face problems such as power flow backflow and voltage limit exceedance, seriously threatening their safe and stable operation. The increasing number of flexible resources connected to the network and the growing complexity of voltage sensitivity calculations make it difficult for traditional voltage optimization control methods to meet reliability requirements.

[0003] To better achieve voltage control and distributed photovoltaic (PV) integration, it is advisable to calculate the sensitivity of the moment difference to each flexible resource and distributed PV based on moment difference analysis theory. By calculating the moment difference sensitivity of each flexible resource and distributed PV, the active and reactive power of different flexible resources at different locations can be determined to improve the overall voltage control capability of the distribution network. Since the moment difference sensitivity is only related to line parameters, the reliability of real-time control can be improved.

[0004] In distribution network voltage control, due to the different adjustment costs of photovoltaic and various flexible resources, the economic efficiency of using different adjustment resources to achieve the same voltage regulation effect varies greatly. When node voltage exceeds the limit, the most economical low-cost reactive power control should be used first. After all low-cost reactive power control methods are exhausted, the medium-cost active power charging control of energy storage converters should be used. After all medium-cost reactive power control methods are exhausted, the high-cost active power curtailment control of photovoltaic converters should be used last to improve the economic efficiency of distribution network voltage control.

[0005] Currently, voltage control methods for power distribution networks involve complex algorithms, fail to consider computational complexity in engineering practice, and lack economic efficiency in voltage control. Summary of the Invention

[0006] In view of this, the present invention provides a distribution network voltage optimization control method, device, equipment and program product to solve the problems that distribution network voltage control methods involve relatively complex algorithms, do not consider the computational complexity in engineering practice, and the economics of voltage control, which leads to the difficulty in meeting the reliability requirements of traditional voltage optimization control.

[0007] In a first aspect, the present invention provides a method for optimizing voltage control in a power distribution network, the method comprising:

[0008] A basic operation dataset of the distribution network to be controlled is obtained on multiple typical days. The distribution network to be controlled includes a high proportion of distributed photovoltaic, energy storage, reactive power compensation equipment, and various loads. Based on the basic operation dataset, the moment difference analysis formula of the distribution network to be controlled is determined using moment difference analysis theory. According to the moment difference analysis formula, the real-time moment difference value of the distribution network to be controlled and multiple moment difference sensitivities of different devices in the distribution network to be controlled are calculated. Multiple moment difference sensitivities are constants. Based on the basic operation dataset, the standard moment difference of the distribution network to be controlled is determined. When the real-time moment difference value is greater than the standard moment difference value of the distribution network to be controlled, based on multiple moment difference sensitivities and the control costs of different devices, the voltage of the distribution network to be controlled is sequentially optimized according to a preset principle until the real-time moment difference value is less than the standard moment difference value. The voltage optimization control result of the distribution network to be controlled is obtained. The preset principle is used to characterize the priority order from economy to effectiveness. The effectiveness is determined according to the moment difference sensitivity of the device.

[0009] The distribution network voltage optimization control method provided by this invention, by acquiring basic operational datasets of the distribution network under control on multiple typical days, can accurately pinpoint complex distribution network scenarios of "high proportion of distributed photovoltaic power generation + multiple flexible resources," solving the problem of insufficient adaptability of traditional methods to this scenario and avoiding control strategy failure due to scenario generalization. Furthermore, by constructing a relational formula through moment difference analysis theory and combining it with moment difference sensitivity (related only to line parameters and a constant), the reliability problem of insufficient traditional voltage sensitivity due to complex models is avoided, simplifying calculations and ensuring real-time performance. Simultaneously, by calculating real-time moment difference values, the risk of voltage exceeding limits can be intuitively reflected, solving the cumbersome problem of traditional voltage monitoring requiring node-by-node judgment. Furthermore, by using a preset principle of prioritizing economy over effectiveness for sequential control of equipment, prioritizing the use of low-cost control resources, the problem of excessively high costs caused by traditional methods not considering economic factors is solved. At the same time, through closed-loop control until the real-time moment difference reaches the target, voltage exceeding limits are completely eliminated, balancing economy and safety, and achieving economic absorption of distributed photovoltaic power generation.

[0010] In one alternative implementation, a basic operational dataset of the distribution network to be controlled over multiple typical days is obtained, including:

[0011] Obtain historical photovoltaic output data and historical load data of the distribution network feeders containing multiple distributed photovoltaics within the distribution network to be controlled; use the K-means clustering algorithm to cluster the historical photovoltaic output data and historical load data and determine multiple typical days of the distribution network to be controlled; obtain the basic operation data of the distribution network to be controlled on multiple typical days.

[0012] The distribution network voltage optimization control method provided by this invention uses K-means clustering to cluster historical photovoltaic output and load data, and extracts typical daily scenarios. This avoids computational redundancy caused by directly using massive amounts of historical data, reducing the engineering implementation difficulty of subsequent analysis. Furthermore, by obtaining basic operating datasets from multiple typical days, it can represent the normal operating characteristics of photovoltaics and loads, providing realistic basic data for subsequent moment difference analysis. This ensures that the moment difference relationship and sensitivity calculation are more consistent with the actual operating conditions of the distribution network, enhancing the practicality of the control strategy.

[0013] In one optional implementation, based on the basic operational dataset, the initial moment difference analysis formula for the distribution network to be controlled is determined using moment difference analysis theory, including:

[0014] Based on the basic operation dataset, the photovoltaic moment relationship and the initial load moment relationship of the distribution network to be controlled are determined using moment difference analysis theory. Based on the basic operation dataset, the energy storage moment relationship and the reactive power compensation moment relationship of the distribution network to be controlled are determined using moment difference analysis theory. Based on the energy storage moment relationship, the reactive power compensation moment relationship and the initial load moment relationship, the target load moment relationship is determined. Based on the photovoltaic moment relationship and the target load moment relationship, the moment difference analysis relationship of the distribution network to be controlled is determined.

[0015] The power distribution network voltage optimization control method provided by this invention decomposes the moment difference into photovoltaic moment, load moment, energy storage moment, and reactive power compensation moment, which can clearly quantify the impact of various devices on voltage and solve the problem of insufficient consideration of multi-resource coordination in traditional methods. Simultaneously, by incorporating the voltage regulation effects of energy storage and reactive power compensation into the moment difference analysis, multi-resource collaborative modeling is achieved, overcoming the problem of insufficient consideration of flexible resources such as energy storage in traditional methods. Furthermore, through a method of derivation and reintegration of each item, the logical rigor of the moment difference analysis formula is ensured, providing a clear mathematical foundation for subsequent sensitivity calculations and voltage control, avoiding the inaccuracy problems caused by oversimplification or complex modeling in traditional sensitivity models.

[0016] In one optional implementation, based on the basic operational dataset, the photovoltaic moment relationship and the initial load moment relationship of the distribution network to be controlled are determined using moment difference analysis theory, including:

[0017] Based on the basic operational dataset, multiple photovoltaic active power, multiple photovoltaic reactive power, and multiple first resistance values ​​and multiple first reactance values ​​from photovoltaic nodes to the root node in the distribution network to be controlled are obtained. Based on the basic operational dataset, multiple load active power, multiple load reactive power, and multiple second resistance values ​​and multiple second reactance values ​​from load nodes to the root node in the distribution network to be controlled are obtained. Based on the multiple photovoltaic active power, multiple photovoltaic reactive power, multiple first resistance values, and multiple first reactance values, the photovoltaic moment relationship is determined. Based on the multiple load active power, multiple load reactive power, multiple second resistance values, and multiple second reactance values, the initial load moment relationship is determined.

[0018] The distribution network voltage optimization control method provided by this invention addresses the shortcomings of traditional methods that fail to consider the impact of distributed photovoltaic (PV) systems at different locations on voltage regulation capabilities by clearly defining the influence of resistance and reactance from PV / load nodes to the root node on the torque value. Furthermore, by combining active power, resistance, reactive power, and reactance, the coupling relationship between power flow and line impedance on voltage can be intuitively reflected, making the torque difference analysis more consistent with the physical laws of the power system and improving the accuracy of subsequent sensitivity calculations.

[0019] In one alternative implementation, the standard moment deviation of the distribution network to be controlled is determined based on the basic operational dataset, including:

[0020] Based on the basic operational dataset, the voltage values ​​of multiple nodes in the distribution network to be controlled are obtained through power flow calculation methods. When the highest node voltage value of the distribution network to be controlled meets the preset voltage requirements, multiple voltage change values ​​are determined based on the multiple node voltage values. The voltage change values ​​are used to reflect the voltage change of adjacent nodes. Based on the multiple node voltage values ​​and the multiple voltage change values, the standard moment difference of the distribution network to be controlled is determined.

[0021] The distribution network voltage optimization control method provided by this invention can obtain accurate node voltage distribution through power flow calculation, avoiding critical threshold deviations caused by inaccurate voltage data. Furthermore, by ensuring that the highest node voltage meets preset voltage requirements, it ensures that voltage changes reflect power flow characteristics under safe conditions, providing a reasonable calculation basis for the standard moment difference and avoiding setting the critical threshold too high or too low. Moreover, by transforming the voltage safety state into a quantifiable moment difference threshold, the standard moment difference can reflect both voltage upper limit constraints and line parameters, providing a stable and comparable safety benchmark for real-time control. This ensures that voltage optimization has a clear convergence target, avoids blindness in the control process, and guarantees the effectiveness of voltage exceedance management and the stability of closed-loop control.

[0022] In a second aspect, the present invention provides a power distribution network voltage optimization control device, the device comprising:

[0023] The system comprises the following modules: an acquisition module for acquiring basic operational datasets of the distribution network to be controlled over multiple typical days; a first determination module for determining the moment difference analysis formula of the distribution network to be controlled based on the basic operational dataset using moment difference analysis theory; a calculation module for calculating the real-time moment difference value of the distribution network to be controlled and multiple moment difference sensitivities of different devices within the distribution network to be controlled, based on the moment difference analysis formula, where multiple moment difference sensitivities are constants; a second determination module for determining the standard moment difference of the distribution network to be controlled based on the basic operational dataset; and a control module for sequentially optimizing the voltage of the distribution network to be controlled according to preset principles when the real-time moment difference value is greater than the standard moment difference value, based on multiple moment difference sensitivities and the control costs of different devices, until the real-time moment difference value is less than the standard moment difference value, thus obtaining the voltage optimization control result of the distribution network to be controlled. The preset principles characterize the priority order from economy to effectiveness, and effectiveness is determined based on the moment difference sensitivity of the devices.

[0024] In one alternative implementation, the acquisition module includes:

[0025] The first acquisition submodule is used to acquire the historical photovoltaic power output dataset and historical load dataset of the distribution network feeder containing multiple distributed photovoltaics within the distribution network to be controlled; the clustering submodule is used to cluster the historical photovoltaic power output dataset and historical load dataset using the K-means clustering algorithm and determine multiple typical days of the distribution network to be controlled; the second acquisition submodule is used to acquire the basic operation dataset of the distribution network to be controlled on multiple typical days.

[0026] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the power distribution network voltage optimization control method of the first aspect or any corresponding embodiment described above.

[0027] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the power distribution network voltage optimization control method of the first aspect or any corresponding embodiment described above.

[0028] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the power distribution network voltage optimization control method described in the first aspect or any corresponding embodiment. Attached Figure Description

[0029] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating the power distribution network voltage optimization control method according to an embodiment of the present invention;

[0031] Figure 2 This is a schematic flowchart of another power distribution network voltage optimization control method according to an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of power flow distribution in a power distribution network according to an embodiment of the present invention;

[0033] Figure 4 This is a flowchart illustrating another power distribution network voltage optimization control method according to an embodiment of the present invention;

[0034] Figure 5 This is a structural block diagram of a power distribution network voltage optimization control device according to an embodiment of the present invention;

[0035] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Currently, there is a great deal of research on voltage sensitivity and voltage control:

[0038] (1) A method for coordinated control of active and reactive power of distributed photovoltaic power generation based on voltage sensitivity matrix was established. The model utilizes the reactive power output capability of distributed photovoltaic power generation and makes all distributed photovoltaic power generation on the same feeder equally share the active power reduction and jointly bear the power generation loss caused by participating in voltage regulation. However, the model does not consider the influence of different locations of distributed photovoltaic power generation on voltage regulation capability.

[0039] (2) A multi-sensitivity coefficient matrix is ​​derived based on the active and reactive power joint optimization method of active distribution network based on multiple sensitivity, which simplifies the traditional power flow model. This model addresses the problem that multiple sensitivities are difficult to obtain in real time through power flow physical model. It establishes a real-time sensing model of multiple sensitivities based on social network search algorithm and radial basis neural network to improve the control accuracy of distribution network. However, the model is relatively complex and does not adequately consider practical engineering applications.

[0040] (3) In the data-driven distribution network voltage sensitivity sensing method, the model is based on historical data and fits the mapping relationship between injected power and voltage through a generalized regression neural network. The smoothing factor value is optimized by the pattern search method, and the voltage sensitivity matrix under this state is obtained by fitting the least squares method. However, this method uses deep learning algorithms, and the processing speed is slow when the amount of data is large.

[0041] (4) A method for calculating voltage-active power sensitivity of a power system node. In the system, the admittance matrix of the nonlinear node is inverted to obtain the impedance matrix of the nonlinear node in the power system; the reactance matrix of the nonlinear node in the power system is determined based on the impedance matrix of the nonlinear node, and is used as the first reactance matrix; an approximate reactance matrix of the nonlinear node in the power system is constructed, and is used as the second reactance matrix; the relationship between the active power injected by each nonlinear node in the power system and the voltage of any nonlinear node is determined based on the first reactance matrix and the second reactance matrix, so as to determine the voltage sensitivity of each nonlinear node. However, this model does not adequately consider the steady-state optimization of the distribution network.

[0042] (5) In the real-time voltage control method of distributed photovoltaic power distribution network driven by historical control strategy set, a traditional voltage control strategy based on approximate sensitivity is formulated; a historical control strategy set based on data driving and improved BP neural network is constructed; the historical control strategy set is used to assist decision-making and correction of traditional voltage control strategy; the output of each photovoltaic power is corrected according to the historical strategy library-assisted decision-making control method to complete the voltage over-limit governance of the distribution network. However, the construction of historical control strategy library of this model is complicated.

[0043] (6) A distributed photovoltaic voltage regulation method based on reactive power voltage sensitivity is used to perform local linearization processing on the steady-state operating point of the distribution network, and to find the active power regulation node and reactive power regulation node that have the most significant impact on the voltage level of the distribution network. The objective function is established based on maximizing the output of distributed photovoltaic in the distribution network and then optimized. However, this method does not adequately consider the coordination of other resources in the distribution network.

[0044] The aforementioned studies involve complex algorithms, do not consider computational complexity in engineering practice, and the economics of voltage control. Currently, no research has pointed out a voltage control method that considers the economics of voltage regulation based on moment difference analysis, by calculating moment difference sensitivity to determine the global voltage regulation capability of massive flexible resources.

[0045] According to an embodiment of the present invention, a method for optimizing voltage control of a power distribution network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0046] This embodiment provides a power distribution network voltage optimization control method, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 1 This is a flowchart of a power distribution network voltage optimization control method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0047] Step S101: Obtain the basic operation dataset of the distribution network to be controlled on multiple typical days.

[0048] The distribution network to be controlled includes a high proportion of distributed photovoltaic, energy storage, reactive power compensation equipment and various loads.

[0049] Furthermore, a typical day represents a day that can represent a typical scenario of photovoltaic and load output within a year, and reflects the characteristic patterns of photovoltaic power generation and load electricity consumption in the distribution network at different times (such as different seasons and different weather conditions).

[0050] Furthermore, the basic operation dataset represents a collection of data related to the operation of a distribution network that is to be controlled and contains a high proportion of distributed photovoltaic, energy storage, reactive power compensation equipment and various loads, collected over multiple typical days. It may include: the topology of the distribution network, line parameters (such as line resistance, reactance, etc.), the access location and capacity of distributed photovoltaic, the access location and capacity of energy storage, and the diameter, material, and maximum current that can flow through the conductors connecting two adjacent network nodes or the root node of a transformer area and the first network node.

[0051] The topology can be configured to connect one root node to multiple network nodes, and these network nodes can be connected in series.

[0052] Step S102: Based on the basic operation dataset, determine the moment difference analysis relationship of the distribution network to be controlled using moment difference analysis theory.

[0053] Among them, moment difference analysis theory is based on the relationship between power flow and voltage change in the distribution network. It defines photovoltaic moment and load moment, and uses the difference between the two (moment difference) to quantify the trend of voltage deviation from the normal range in the distribution network. Its core is to transform the impact of photovoltaic, load, energy storage, reactive power compensation equipment, etc. on voltage into the calculation of "moments" related to line parameters (resistance, reactance), and use moment difference to reflect the risk of voltage exceeding limits.

[0054] Furthermore, the moment difference analysis relationship is a mathematical expression based on the moment difference analysis theory, describing the relationship between the moment difference of the distribution network and the power and line parameters of photovoltaic, load, energy storage, reactive power compensation equipment, etc., as shown in the following relationship (1):

[0055]

[0056] In the formula: M represents the moment difference; M PV M represents the photovoltaic moment of the distribution network; LD This represents the load moment of the distribution network; This represents the active power output of the i-th photovoltaic power generation unit; This represents the reactive power output of the i-th photovoltaic power generation unit; This represents the sum of the line resistances from the i-th photovoltaic power generation unit access node to the root node; It represents the sum of the line reactance from the i-th photovoltaic power generation unit access node to the root node; This represents the active power of the j-th load; This represents the reactive power of the j-th load; This represents the sum of the line resistances from the j-th load node to the root node; This represents the sum of the line reactance from the j-th load node to the root node; This represents the active power of the k-th energy storage unit during charging. This represents the reactive power of the charging of the k-th energy storage unit; This represents the sum of the line resistances from the k-th energy storage unit access node to the root node; This represents the sum of the line reactance from the k-th energy storage unit access node to the root node; This represents the reactive power of the l-th reactive power compensation device; represents the sum of line reactance from the l-th reactive power compensation device access node to the root node; m represents the total number of photovoltaic power generation units; n represents the total number of loads; K represents the total number of energy storage units; L represents the total number of reactive power compensation devices.

[0057] Specifically, based on the line parameters and topology of the centralized distribution network in the basic operation data, and targeting distribution networks with a high proportion of distributed photovoltaic, energy storage, reactive power compensation equipment and various loads, the moment difference analysis theory is used to establish the moment difference analysis relationship of the distribution network to be controlled.

[0058] Furthermore, the moment difference analysis formula can link the impact of photovoltaics, loads, energy storage, reactive power compensation equipment, etc. on voltage, forming a quantifiable relationship between moment difference and various power and line parameters, providing a theoretical formula basis for subsequent calculation of moment difference sensitivity and implementation of voltage optimization control.

[0059] Step S103: Calculate the real-time moment difference value of the distribution network to be controlled and the moment difference sensitivity of multiple devices in the distribution network to be controlled, based on the moment difference analysis formula.

[0060] Among them, the moment difference sensitivity represents the sensitivity of moment difference to changes in the power (active or reactive) of various equipment (photovoltaic power generation units, loads, energy storage units, reactive power compensation equipment, etc.) in the distribution network. Its value is equal to the sum of the line resistance or reactance from the access node of the equipment to the root node (related only to line parameters and is a constant). It reflects the magnitude of the impact of unit power change on the moment difference of the distribution network and is the core indicator for measuring the effectiveness of equipment voltage regulation.

[0061] Specifically, based on the real-time data of the distribution network to be controlled, the photovoltaic moment M of the distribution network is calculated. PV and load moment M LD M is calculated as the moment difference.

[0062] Furthermore, the sum of the moment difference for the line resistance from the access node of the i-th photovoltaic power generation unit to the root node is calculated separately. The sum of line reactance Then, the moment difference sensitivity of each distributed photovoltaic system is calculated.

[0063] Furthermore, the sum of the line resistances from the i-th load node to the root node is calculated separately. The sum of line reactance Then, the moment difference sensitivity of each load is calculated.

[0064] Furthermore, the moment difference is calculated as the sum of the line resistances from the i-th energy storage unit access node to the root node. and the sum of the resistance of the power line Then, the sensitivity of energy storage at different locations is calculated.

[0065] Furthermore, the sum of the line reactance of the root node at the moment difference of the i-th reactive power compensation device is calculated. Then, the torque difference sensitivity of different reactive power compensation devices is calculated.

[0066] Step S104: Determine the standard moment error of the distribution network to be controlled based on the basic operation dataset.

[0067] The standard moment difference represents the moment difference in the active power flow feedback section of a given distribution network. Given a fixed topology and line parameters, as photovoltaic output gradually increases, when the voltage at the highest node of the distribution network reaches the specified voltage limit, the moment difference is approximately a constant. Furthermore, this value is only related to the distribution network's topology, line parameters, and voltage limit, and is independent of photovoltaic and load distribution. It serves as a critical reference value for determining whether the distribution network voltage may exceed the limit.

[0068] Specifically, based on the acquired basic operation dataset, the torque difference constant reflecting the critical state of the distribution network when the voltage reaches the upper limit is analyzed, and the standard torque difference M0 is calculated.

[0069] Step S105: When the real-time torque difference value is greater than the standard torque difference value of the distribution network to be controlled, based on multiple torque difference sensitivities and the control costs of different equipment, the voltage of the distribution network to be controlled is sequentially optimized according to the preset principle until the real-time torque difference value is less than the standard torque difference value, and the voltage optimization control result of the distribution network to be controlled is obtained.

[0070] Among them, the preset principle is used to characterize the priority order from economy to effectiveness, and further, effectiveness is determined based on the moment difference sensitivity of the equipment.

[0071] Specifically, if the real-time torque difference value M is greater than the standard torque difference value M0 of the distribution network to be controlled, it indicates that there is a risk of voltage exceeding the upper limit in the distribution network to be controlled. In accordance with the preset principle of "first economy (cost from low to high) and then effectiveness (sensitivity from high to low)," voltage sequence optimization control should be implemented using various equipment until the real-time torque difference value is less than the standard torque difference value.

[0072] First, the control resources within the distribution network to be controlled can be divided into three categories according to the order of control costs from low to high:

[0073] The first category is low-cost reactive power regulation, which can include reactive power compensation equipment, energy storage converters, photovoltaic converters, and reactive power regulation of loads, reducing voltage by absorbing reactive power.

[0074] The second category is medium-cost active power regulation, which can include active power charging control of energy storage converters and active power increase control of loads, thereby reducing voltage by consuming active power.

[0075] The third category is high-cost active power regulation, which can include active power curtailment control of photovoltaic converters, reducing voltage by cutting off photovoltaic active power output.

[0076] Secondly, for each type of resource, sort them from largest to smallest according to their moment difference sensitivity (the greater the sensitivity, the more significant the control effect):

[0077] The sensitivity ranking for the first category (reactive power regulation) is as follows: Furthermore, the following steps are taken in sequence: first, activate the reactive power compensation equipment to absorb reactive power, then activate the energy storage converter and photovoltaic converter, and finally adjust the load to absorb more reactive power.

[0078] The sensitivity ranking for the second category (reactive power control) is as follows: Furthermore, following this sequence, the following steps are executed sequentially: first, control the energy storage converter to charge (increasing active power consumption), and then increase the active power of the load.

[0079] The sensitivity of the third type (active power curtailment) is the sum of the resistances from the photovoltaic access node to the root node. Multiple photovoltaic units are sorted from largest to smallest, and their active power output (curtailment) is reduced accordingly.

[0080] Then, the control measures are implemented sequentially and the moment difference is monitored.

[0081] Specifically, the first type of resources are prioritized and adjusted gradually from high to low sensitivity until the resource reaches its adjustment limit (such as the reactive power compensation equipment at full capacity or the reactive power regulation of the photovoltaic converter reaching its limit).

[0082] Furthermore, if the real-time moment difference is still greater than the standard moment difference after the first type of resources are exhausted, the second type of resources are activated and adjusted sequentially according to sensitivity ranking until the upper limit of this type of resource is reached.

[0083] Furthermore, if the real-time moment difference still does not meet the requirements after the first two types of resources are exhausted, the third type of resource (photovoltaic curtailment) is activated, and the active power of photovoltaics is reduced in order of sensitivity until the real-time moment difference value is less than the standard moment difference value.

[0084] Finally, when the real-time torque difference value drops below the standard torque difference value, the control is stopped, and the final adjustment quantities of various equipment (such as the reactive power absorption of reactive power compensation equipment, energy storage charging power, photovoltaic curtailment, etc.) are recorded, which is the voltage optimization control result of the distribution network.

[0085] Furthermore, through the above-mentioned regulation process, the regulation cost is reduced to the minimum while ensuring voltage safety, thus achieving a balance between economy and effectiveness.

[0086] The distribution network voltage optimization control method provided in this embodiment can accurately pinpoint complex distribution network scenarios of "high proportion of distributed photovoltaic + multiple flexible resources" by acquiring basic operation datasets of the distribution network under control on multiple typical days. This solves the problem of insufficient adaptability of traditional methods to this scenario and avoids control strategy failure due to scenario generalization. Furthermore, by constructing a relational formula through moment difference analysis theory and combining it with moment difference sensitivity (which is only related to line parameters and is a constant), the reliability problem of insufficient traditional voltage sensitivity due to complex models is avoided, simplifying the calculation and ensuring real-time performance. At the same time, by calculating the real-time moment difference value, the risk of voltage exceeding the limit can be intuitively reflected, solving the cumbersome problem of traditional voltage monitoring requiring node-by-node judgment. Furthermore, by using the preset principle of prioritizing economy over effectiveness to sequentially control equipment and giving priority to the use of low-cost control resources, the problem of excessive cost caused by traditional methods not considering economy is solved. At the same time, through closed-loop control until the real-time moment difference reaches the target, the voltage exceeding the limit is completely eliminated, taking into account both economy and safety, and realizing the economic absorption of distributed photovoltaic.

[0087] This embodiment provides a power distribution network voltage optimization control method, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 2 This is a flowchart of a power distribution network voltage optimization control method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0088] Step S201: Obtain the basic operation dataset of the distribution network to be controlled on multiple typical days.

[0089] Specifically, step S201 includes:

[0090] Step S2011: Obtain the historical photovoltaic output dataset and historical load dataset of the distribution network feeders containing multiple distributed photovoltaics within the distribution network to be controlled.

[0091] Among them, a distribution network feeder containing multiple distributed photovoltaics refers to a specific power supply line (feeder) in the distribution network to be controlled. This line is connected to multiple distributed photovoltaic power sources and also includes energy storage units, reactive power compensation equipment, and various loads.

[0092] Furthermore, the historical photovoltaic power output dataset can include active power output and reactive power output data of each distributed photovoltaic unit at different times (such as every hour or every 15 minutes).

[0093] Furthermore, the historical load dataset can include data such as active power and reactive power of various loads (such as residential loads, industrial and commercial loads, etc.) in the distribution network to be controlled at the corresponding time.

[0094] Specifically, for distribution network feeders containing multiple distributed photovoltaic (PV) systems in the distribution network to be controlled, historical PV output data and historical load data are collected over a period of time (usually one year).

[0095] In some alternative implementations, the collected data can also be cleaned to remove outliers (such as jump data caused by equipment failure) and missing values ​​to ensure data integrity and accuracy.

[0096] Step S2012: Use the K-means clustering algorithm to cluster the historical photovoltaic power output dataset and the historical load dataset and determine multiple typical days of the distribution network to be controlled.

[0097] Among them, the K-means clustering algorithm represents an unsupervised machine learning algorithm. Its core is to divide the dataset into K different clusters (K is a preset value), so that each data point belongs to the cluster center with the most similar cluster. By iteratively optimizing the cluster centers, the algorithm finally achieves the effect of high similarity between data points of the same class and low similarity between data points of different classes.

[0098] Specifically, a reasonable number of clusters K can be preset based on the climate characteristics, seasonal changes, and load patterns of the distribution network area.

[0099] Furthermore, the historical photovoltaic power output dataset and the historical load dataset are merged into comprehensive feature data (e.g., using the daily photovoltaic power output curve and load curve as a sample), and the data is standardized (e.g., normalized to the range of [0, 1]) to eliminate the influence of different dimensions.

[0100] Further, perform K-means clustering:

[0101] (1) Randomly initialize K cluster centers, each center representing a potential typical daily feature.

[0102] (2) Calculate the distance (e.g., Euclidean distance) between each sample (daily photovoltaic-load curve) and each cluster center, and assign the sample to the nearest cluster.

[0103] (3) Recalculate the center of each cluster (take the average of all samples in the cluster) and update the cluster centers.

[0104] (4) Repeat steps (2) and (3) until the cluster centers no longer change significantly (convergence) or the preset number of iterations is reached.

[0105] Furthermore, after clustering is completed, the photovoltaic-load curve corresponding to the center of each cluster represents a typical day, reflecting the common characteristics of all samples in that cluster, such as high photovoltaic output days and low load days.

[0106] Step S2013: Obtain the basic operation dataset of the distribution network to be controlled on multiple typical days.

[0107] Specifically, for distribution networks to be controlled that contain a high proportion of distributed photovoltaic, energy storage, reactive power compensation equipment and various loads, data related to the operation of the distribution network to be controlled are collected during several typical days to obtain the corresponding basic operation dataset.

[0108] Step S202: Based on the basic operation dataset, determine the moment difference analysis relationship of the distribution network to be controlled using moment difference analysis theory.

[0109] Specifically, step S202 includes:

[0110] Step S2021: Based on the basic operation dataset, use moment difference analysis theory to determine the photovoltaic moment relationship and the initial load moment relationship of the distribution network to be controlled.

[0111] In some optional implementations, step S2022 above includes:

[0112] Step a1: Based on the basic operation dataset, obtain multiple photovoltaic active power, multiple photovoltaic reactive power, and multiple first resistance values ​​and multiple first reactance values ​​from photovoltaic nodes to the root node in the distribution network to be controlled.

[0113] Step a2: Based on the basic operation dataset, obtain the active power of multiple loads, the reactive power of multiple loads, and multiple second resistance values ​​and multiple second reactance values ​​from the load nodes to the root node in the distribution network to be controlled.

[0114] Step a3: Determine the photovoltaic moment relationship based on multiple photovoltaic active power, multiple photovoltaic reactive power, multiple first resistance values, and multiple first reactance values.

[0115] Step a4: Determine the initial load moment relationship based on multiple load active power, multiple load reactive power, multiple second resistance values, and multiple second reactance values.

[0116] Specifically, to reflect the real-world scenario where power flow reversal causes node voltage to exceed its limit, we assume line power flow reversal and initially ignore power losses on the line. The photovoltaic and load power distributions are as follows: Figure 3 As shown.

[0117] Furthermore, assume that the photovoltaic power injected into node i and the load power flowing out are respectively and

[0118] Furthermore, define U iLet be the voltage amplitude at node i; the line between node i-1 and node i is the i-th line segment. Also, define R... i and X i These are the resistance and reactance of the i-th line segment, respectively; S i =P i +jQ i It is the return power on the i-th line segment, ΔU i This represents the voltage rise on the i-th line segment.

[0119] Furthermore, according to such Figure 3 The probability distribution shown can be used to obtain the following relationship (2) or (3):

[0120]

[0121] Furthermore, the voltage rise ΔU of the i-th line segment i The following relation (4) is shown:

[0122]

[0123] Furthermore, the following relation (5) can be obtained:

[0124] P i R i +Q i X i =U i ·ΔU i (5)

[0125] Furthermore, substituting the above relations (2) and (3) into the above relation (5), we can obtain:

[0126]

[0127] Furthermore, the above relation (6) represents a nested form, which holds true for each line segment.

[0128] Furthermore, we introduce the definitions of photovoltaic moment and load moment:

[0129] (1) Definition of photovoltaic moment: The photovoltaic moment of each photovoltaic power generation unit is equal to the apparent power of that unit. Vector and the line impedance vector from the photovoltaic node to the root node The inner product of two vectors. According to the definition of the inner product of two vectors, we have:

[0130]

[0131] In the formula: Let represent the photovoltaic moment of the i-th photovoltaic power generation unit.

[0132] (2) Define load moment: The load moment of each load is equal to the apparent power of that load. Vector and the line impedance from the load node to the root node The dot product of vectors, i.e.:

[0133]

[0134] In the formula: This represents the load moment of the i-th load node.

[0135] In a uniform distribution network, the resistance and reactance per unit length of each line segment are equal. Therefore, the above equations (7) and (8) are both the product of photovoltaic output / load size and line length, similar to the concept of "torque" in mechanics. Hence, they are named "photovoltaic torque" and "load torque".

[0136] Furthermore, based on the above relationship (7), the final photovoltaic moment relationship can be determined as shown in the following relationship (9):

[0137]

[0138] Furthermore, based on the above relationship (8), the initial photovoltaic moment relationship can be determined as shown in the following relationship (10):

[0139]

[0140] By clarifying the impact of resistance and reactance from photovoltaic / load nodes to the root node on the moment value, this method overcomes the deficiency in traditional methods that did not consider the influence of different locations of distributed photovoltaics on voltage regulation capabilities. Furthermore, by combining active power, resistance, reactive power, and reactance, the coupling relationship between power flow and line impedance on voltage can be intuitively reflected, making moment difference analysis more consistent with the physical laws of power systems and improving the accuracy of subsequent sensitivity calculations.

[0141] Step S2022: Based on the basic operation dataset, use the moment difference analysis theory to determine the energy storage moment relationship and reactive power compensation moment relationship of the distribution network to be controlled.

[0142] Specifically, the process of determining the photovoltaic moment relationship and the reactive power compensation moment relationship in step S2022 above can be referred to to determine the corresponding energy storage moment relationship and reactive power compensation moment relationship, as shown in the following relationships (11) and (12):

[0143]

[0144] Where: M ES M represents the storage torque; VC This represents the reactive power compensation torque.

[0145] Step S2023: Based on the energy storage moment relationship, the reactive power compensation moment relationship, and the initial load moment relationship, determine the target load moment relationship.

[0146] Specifically, the moments of energy storage and reactive power compensation are combined with the initial load moment to form the generalized load moment, i.e., the target load moment relationship, as shown in the following relationship (13):

[0147]

[0148] Furthermore, by incorporating the voltage regulation effects of energy storage and reactive power compensation into moment difference analysis, multi-resource collaborative modeling was achieved, overcoming the problem of insufficient consideration of flexible resources such as energy storage in traditional methods.

[0149] Step S2024: Based on the photovoltaic moment relationship and the target load moment relationship, determine the moment difference analysis relationship of the distribution network to be controlled.

[0150] Specifically, by combining the photovoltaic moment relationship shown in equation (9) and the target load moment relationship shown in equation (13), the moment difference analysis relationship shown in equation (1) can be obtained.

[0151] Step S203: Based on the moment difference analysis formula, calculate the real-time moment difference value of the distribution network to be controlled and the moment difference sensitivities of multiple devices within the distribution network to be controlled. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0152] Step S204: Determine the standard moment error of the distribution network to be controlled based on the basic operational dataset. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0153] Step S205: When the real-time torque difference value is greater than the standard torque difference value of the distribution network to be controlled, based on multiple torque difference sensitivities and the control costs of different equipment, the voltage of the distribution network to be controlled is sequentially optimized according to preset principles until the real-time torque difference value is less than the standard torque difference value, thus obtaining the voltage optimization control result of the distribution network to be controlled. For details, please refer to... Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0154] The distribution network voltage optimization control method provided in this embodiment uses K-means clustering to cluster historical photovoltaic output and load data, and extracts typical daily scenarios, avoiding computational redundancy caused by directly using massive historical data and reducing the engineering implementation difficulty of subsequent analysis. Furthermore, by obtaining basic operating datasets from multiple typical days, it can represent the normal operating characteristics of photovoltaics and loads, providing realistic basic data for subsequent moment difference analysis, ensuring that the moment difference relationship and sensitivity calculation are more consistent with the actual operating conditions of the distribution network, and enhancing the practicality of the control strategy. Furthermore, by decomposing the moment difference into photovoltaic moment, load moment, energy storage moment, and reactive power compensation moment, the impact of various devices on voltage can be clearly quantified, solving the problem of insufficient consideration of multi-resource coordination in traditional methods. Simultaneously, by incorporating the voltage regulation effects of energy storage and reactive power compensation into the moment difference analysis, multi-resource collaborative modeling is achieved, overcoming the problem of insufficient consideration of flexible resources such as energy storage in traditional methods. Furthermore, by deriving and integrating each item separately, the relationship between moment difference analysis is made logically rigorous, providing a clear mathematical foundation for subsequent sensitivity calculation and voltage control, and avoiding the problem of insufficient accuracy caused by excessive simplification or complex modeling in traditional sensitivity models.

[0155] This embodiment provides a power distribution network voltage optimization control method, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 4 This is a flowchart of a power distribution network voltage optimization control method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0156] Step S401: Obtain the basic operational dataset of the distribution network to be controlled over multiple typical days. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0157] Step S402: Based on the basic operational dataset, determine the moment difference analysis formula for the distribution network to be controlled using moment difference analysis theory. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0158] Step S403: Based on the moment difference analysis formula, calculate the real-time moment difference value of the distribution network to be controlled and the moment difference sensitivities of multiple devices within the distribution network to be controlled. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0159] Step S404: Determine the standard moment error of the distribution network to be controlled based on the basic operation dataset.

[0160] Specifically, step S404 includes:

[0161] Step S4041: Based on the basic operational dataset, the voltage values ​​of multiple nodes in the distribution network to be controlled are obtained through power flow calculation methods.

[0162] Among them, power flow calculation methods can include the Newton-Raphson method, the forward-backward substitution method, etc.

[0163] Specifically, based on the basic operational dataset, iterative calculations using power flow calculation methods can solve for the voltage amplitude of each node in the distribution network to be controlled, i.e., the voltage values ​​of multiple nodes.

[0164] Step S4042: When the highest node voltage value of the distribution network to be controlled meets the preset voltage requirement, determine multiple voltage change values ​​based on multiple node voltage values.

[0165] The voltage change value is used to reflect the voltage change of adjacent nodes.

[0166] Specifically, when the highest node voltage value of the distribution network to be controlled reaches the specified upper voltage limit, thus meeting the preset voltage requirement, the voltage change value of adjacent nodes is calculated based on the obtained multiple node voltage values.

[0167] Step S4043: Determine the standard moment difference of the distribution network to be controlled based on multiple node voltage values ​​and multiple voltage change values.

[0168] Specifically, in conjunction with the above relation (5), the above relation (1) can also be transformed into the following relation (14):

[0169]

[0170] In the formula: p represents the number of nodes excluding the root node of the distribution network; L B This represents the cumulative voltage reference value, which changes with variations in photovoltaic and load distribution.

[0171] Furthermore, the focus is on examining the moment difference changes in active power flow back-feed sections within the distribution network. With a high proportion of distributed photovoltaic (PV) power generation, the volatility and intermittency of its generation significantly impact the power grid. When PV output exceeds the grid's load absorption capacity, active power flow back-feeding occurs, leading to voltage increases. When multiple active power flow back-feeding sections occur consecutively, the voltage rise effect is superimposed, potentially causing the voltage to exceed the safe upper limit and threatening grid stability. In the distribution network, the load power factor is typically 0.9 or higher, while the PV power factor is mostly 1.0 or at least 0.9, resulting in PV active power output being significantly greater than reactive power output. In medium- and low-voltage distribution networks containing a high proportion of distributed PV, the effects of resistance and reactance are balanced. The consecutive occurrence of active power flow back-feeding sections can cause voltage increases on multiple distribution lines, potentially leading to the voltage at the end node of the back-feeding section reaching its maximum value or even exceeding the upper limit.

[0172] It is worth noting that the distance between the downstream photovoltaic (PV) and loads of the active power flow return section and the end of the return section has no direct impact on the section division and power flow distribution; that is, it does not substantially change the power flow and voltage distribution within the section. Therefore, these PV and loads can be equated to the end of the return section, simplifying the calculation and analysis of the real-time moment difference in the active power flow return section of the grid. Subsequent moment difference analysis will focus on the region from the root node of the distribution network to the end of the active power flow return section, studying the voltage fluctuations and moment difference changes in this section. This simplified calculation method can effectively assess the grid voltage changes caused by PV integration, providing a theoretical basis for grid regulation strategies, helping to accurately assess the impact of PV integration on the distribution network, and providing reliable data support for voltage control and grid optimization.

[0173] Further analysis revealed that for a given distribution network, once the topology and line parameters are determined, as the photovoltaic output gradually increases, when the voltage at the highest node reaches the specified upper voltage limit, the moment difference in the aforementioned section is approximately equal to a constant, which can be called the standard moment difference. This value is only related to the distribution network's topology, line parameters, and upper voltage limit, and is independent of the photovoltaic distribution and load distribution, as shown in the following relationship (15):

[0174]

[0175] Where: M PV+ M LD+ , These represent the photovoltaic moment, load moment, and cumulative voltage reference value when the highest node voltage reaches the voltage upper limit, respectively. Δδ represents the arithmetic mean of the voltage magnitudes at all nodes from the root node to the end of the active power flow return section. + This indicates the specified upper limit of voltage. For example, the upper limit of voltage in a 10kV distribution network is +7%, which is a constant.

[0176] Furthermore, the obtained multiple node voltage values ​​U i and multiple voltage change values ​​ΔU i Substituting into the above relationship (15), the standard moment difference M0 of the corresponding distribution network to be controlled can be calculated.

[0177] Step S405: When the real-time torque difference value is greater than the standard torque difference value of the distribution network to be controlled, based on multiple torque difference sensitivities and the control costs of different equipment, the voltage of the distribution network to be controlled is sequentially optimized according to preset principles until the real-time torque difference value is less than the standard torque difference value, thus obtaining the voltage optimization control result of the distribution network to be controlled. For details, please refer to... Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0178] The distribution network voltage optimization control method provided in this embodiment can obtain accurate node voltage distribution through power flow calculation, avoiding critical threshold deviations caused by inaccurate voltage data. Furthermore, by ensuring that the highest node voltage meets preset voltage requirements, it ensures that voltage changes reflect power flow characteristics under safe conditions, providing a reasonable calculation basis for the standard moment difference and avoiding setting the critical threshold too high or too low. Moreover, by transforming the voltage safety state into a quantifiable moment difference threshold, the standard moment difference can reflect both voltage upper limit constraints and line parameters, providing a stable and comparable safety benchmark for real-time control. This ensures that voltage optimization has a clear convergence target, avoids blindness in the control process, and guarantees the effectiveness of voltage exceedance management and the stability of closed-loop control.

[0179] This embodiment also provides a power distribution network voltage optimization control device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0180] This embodiment provides a power distribution network voltage optimization control device, such as... Figure 5 As shown, the device includes:

[0181] The acquisition module 501 is used to acquire the basic operation dataset of the distribution network to be controlled on multiple typical days.

[0182] The first determining module 502 is used to determine the moment difference analysis relationship of the distribution network to be controlled based on the basic operation dataset and the moment difference analysis theory.

[0183] The calculation module 503 is used to calculate the real-time moment difference value of the distribution network to be controlled and the moment difference sensitivity of multiple devices in the distribution network to be controlled, based on the moment difference analysis relationship.

[0184] The second determining module 504 is used to determine the standard moment difference of the distribution network to be controlled based on the basic operating dataset.

[0185] The control module 505 is used to perform sequential optimization control of the voltage of the distribution network to be controlled according to preset principles when the real-time torque difference value is greater than the standard torque difference value of the distribution network to be controlled. This optimization control is based on multiple torque difference sensitivities and the control costs of different devices, until the real-time torque difference value is less than the standard torque difference value, thus obtaining the voltage optimization control result of the distribution network to be controlled.

[0186] In some optional implementations, the acquisition module 501 includes:

[0187] The first acquisition submodule is used to acquire historical photovoltaic power output data and historical load data of the distribution network feeders containing multiple distributed photovoltaics within the distribution network to be controlled.

[0188] The clustering submodule is used to cluster historical photovoltaic power output datasets and historical load datasets using the K-means clustering algorithm and to determine multiple typical days of the distribution network to be controlled.

[0189] The second acquisition submodule is used to acquire the basic operation dataset of the distribution network to be controlled on multiple typical days.

[0190] In some alternative implementations, the first determining module 502 includes:

[0191] The first determination submodule is used to determine the photovoltaic moment relationship and the initial load moment relationship of the distribution network to be controlled based on the basic operation dataset and the moment difference analysis theory.

[0192] The second determination submodule is used to determine the energy storage torque relationship and reactive power compensation torque relationship of the distribution network to be controlled based on the basic operation dataset and the torque difference analysis theory.

[0193] The third determination submodule is used to determine the target load moment relationship based on the energy storage moment relationship, the reactive power compensation moment relationship, and the initial load moment relationship.

[0194] The fourth determination submodule is used to determine the moment difference analysis relationship of the distribution network to be controlled based on the photovoltaic moment relationship and the target load moment relationship.

[0195] In some alternative implementations, the first determining submodule includes:

[0196] The first acquisition unit is used to acquire multiple photovoltaic active power, multiple photovoltaic reactive power, and multiple first resistance values ​​and multiple first reactance values ​​from photovoltaic nodes to the root node in the distribution network to be controlled, based on the basic operation dataset.

[0197] The second acquisition unit is used to acquire, based on the basic operation dataset, multiple load active power, multiple load reactive power, and multiple second resistance values ​​and multiple second reactance values ​​from the load nodes to the root node in the distribution network to be controlled.

[0198] The first determining unit is used to determine the photovoltaic moment relationship based on multiple photovoltaic active power, multiple photovoltaic reactive power, multiple first resistance values ​​and multiple first reactance values.

[0199] The second determining unit is used to determine the initial load moment relationship based on multiple load active power, multiple load reactive power, multiple second resistance values ​​and multiple second reactance values.

[0200] In some alternative implementations, the second determining module 504 includes:

[0201] The calculation submodule is used to obtain the voltage values ​​of multiple nodes in the distribution network to be controlled based on the basic operational dataset and through power flow calculation methods.

[0202] The fifth determination submodule is used to determine multiple voltage change values ​​based on multiple node voltage values ​​when the highest node voltage value of the distribution network to be controlled meets the preset voltage requirements. The voltage change values ​​are used to reflect the voltage change of adjacent nodes.

[0203] The sixth determination submodule is used to determine the standard torque difference of the distribution network to be controlled based on multiple node voltage values ​​and multiple voltage change values.

[0204] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0205] In this embodiment, the power distribution network voltage optimization control device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0206] This invention also provides a computer device having the above-described features. Figure 5 The power distribution network voltage optimization control device shown is shown.

[0207] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0208] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0209] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0210] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0211] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0212] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0213] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0214] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0215] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for optimizing voltage control in a power distribution network, characterized in that, The method includes: Obtain basic operation datasets of the distribution network to be controlled on multiple typical days, wherein the distribution network to be controlled includes a high proportion of distributed photovoltaic, energy storage, reactive power compensation equipment and various loads; Based on the aforementioned basic operational dataset, the moment difference analysis relationship of the distribution network to be controlled is determined using moment difference analysis theory. Based on the moment difference analysis formula, the real-time moment difference value of the distribution network to be controlled and multiple moment difference sensitivities of different devices in the distribution network to be controlled are calculated, and the multiple moment difference sensitivities are all constants; Based on the aforementioned basic operational dataset, the standard moment error of the distribution network to be controlled is determined; When the real-time torque difference value is greater than the standard torque difference value of the distribution network to be controlled, based on the multiple torque difference sensitivities and the control costs of the different devices, the voltage of the distribution network to be controlled is sequentially optimized according to a preset principle until the real-time torque difference value is less than the standard torque difference value, and the voltage optimization control result of the distribution network to be controlled is obtained. The preset principle is used to characterize the priority order from economy to effectiveness, and the effectiveness is determined according to the torque difference sensitivity of the device.

2. The method according to claim 1, characterized in that, Obtain the basic operational dataset of the distribution network to be controlled on multiple typical days, including: Obtain historical photovoltaic output data and historical load data of the distribution network feeders containing multiple distributed photovoltaics within the distribution network to be controlled; The historical photovoltaic power output dataset and historical load dataset are clustered using the K-means clustering algorithm to determine multiple typical days of the distribution network to be controlled; Obtain the basic operation dataset of the distribution network to be controlled on multiple typical days.

3. The method according to claim 1, characterized in that, Based on the aforementioned basic operational dataset, the moment difference analysis formulas for the distribution network under control are determined using moment difference analysis theory, including: Based on the aforementioned basic operational dataset, the photovoltaic moment relationship and the initial load moment relationship of the distribution network to be controlled are determined using the moment difference analysis theory. Based on the aforementioned basic operational dataset, the energy storage moment relationship and reactive power compensation moment relationship of the distribution network to be controlled are determined using the aforementioned moment difference analysis theory. Based on the energy storage moment relationship, the reactive power compensation moment relationship, and the initial load moment relationship, the target load moment relationship is determined. Based on the photovoltaic moment relationship and the target load moment relationship, the moment difference analysis relationship of the distribution network to be controlled is determined.

4. The method according to claim 3, characterized in that, Based on the aforementioned basic operational dataset, and utilizing the aforementioned moment difference analysis theory, the photovoltaic moment relationship and initial load moment relationship of the distribution network to be controlled are determined, including: Based on the basic operational dataset, multiple photovoltaic active power, multiple photovoltaic reactive power, and multiple first resistance values ​​and multiple first reactance values ​​from photovoltaic nodes to the root node in the distribution network to be controlled are obtained. Based on the basic operation dataset, obtain multiple load active power, multiple load reactive power, and multiple second resistance values ​​and multiple second reactance values ​​from the load node to the root node in the distribution network to be controlled. The photovoltaic moment relationship is determined based on the plurality of photovoltaic active power, the plurality of photovoltaic reactive power, the plurality of first resistance values, and the plurality of first reactance values; The initial load moment relationship is determined based on the multiple load active power, the multiple load reactive power, the multiple second resistance values, and the multiple second reactance values.

5. The method according to claim 1, characterized in that, Based on the aforementioned basic operational dataset, the standard moment deviation of the distribution network to be controlled is determined, including: Based on the aforementioned basic operational dataset, the voltage values ​​of multiple nodes in the distribution network to be controlled are obtained through power flow calculation methods. When the highest node voltage value of the distribution network to be controlled meets the preset voltage requirement, multiple voltage change values ​​are determined based on the multiple node voltage values. The voltage change values ​​are used to reflect the voltage change of adjacent nodes. The standard torque difference of the distribution network to be controlled is determined based on the multiple node voltage values ​​and the multiple voltage change values.

6. A power distribution network voltage optimization control device, characterized in that, The device includes: The acquisition module is used to acquire the basic operational dataset of the distribution network to be controlled over multiple typical days; The first determining module is used to determine the moment difference analysis relationship of the distribution network to be controlled based on the basic operation dataset and using moment difference analysis theory; The calculation module is used to calculate the real-time moment difference value of the distribution network to be controlled and the moment difference sensitivity of multiple devices in the distribution network to be controlled, based on the moment difference analysis relationship. The second determining module is used to determine the standard moment difference of the distribution network to be controlled based on the basic operation dataset. The control module is used to perform sequential optimization control on the voltage of the distribution network to be controlled according to a preset principle when the real-time torque difference value is greater than the standard torque difference value of the distribution network to be controlled, based on the multiple torque difference sensitivities and the control costs of the different devices, until the real-time torque difference value is less than the standard torque difference value, thereby obtaining the voltage optimization control result of the distribution network to be controlled.

7. The apparatus according to claim 6, characterized in that, The acquisition module includes: The first acquisition submodule is used to acquire the historical photovoltaic power output dataset and historical load dataset of the distribution network feeder containing multiple distributed photovoltaics within the distribution network to be controlled; The clustering submodule is used to cluster the historical photovoltaic output dataset and historical load dataset using the K-means clustering algorithm and to determine multiple typical days of the distribution network to be controlled. The second acquisition submodule is used to acquire the basic operation dataset of the distribution network to be controlled on the multiple typical days.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the power distribution network voltage optimization control method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the power distribution network voltage optimization control method according to any one of claims 1 to 5.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the power distribution network voltage optimization control method according to any one of claims 1 to 5.

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