Power distribution network low-voltage adjusting method, adjusting device and electronic equipment

Abstract

The invention discloses a power distribution network low-voltage adjusting method, an adjusting device and electronic equipment. The method comprises the following steps: acquiring real-time load data of each load node and real-time energy storage data of each energy storage node based on a shared network; determining load demand prediction data of the load node according to the load demand prediction model and the real-time load data; energy storage sharing information between the energy storage nodes is obtained, and a cooperative adjustment coefficient is determined according to the energy storage sharing information; determining a power distribution strategy of each energy storage node according to the real-time load data, the load demand prediction data, the real-time energy storage data and the cooperative adjustment coefficient; and performing voltage regulation according to the power distribution strategy or the optimization strategy of the power distribution strategy. By designing a data sharing, load demand analysis, energy storage equipment multi-node cooperative adjustment and distributed energy storage management method, the load demand of each load node can be analyzed in real time, the output power of the energy storage equipment is dynamically adjusted, and the voltage of the power grid is effectively adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage regulation in a distribution network, and in particular to a method, a regulating device and an electronic device for regulating low voltage in a distribution network. Background Art

[0002] In mountain distribution networks, there are problems such as complex geographical environment and dispersed power demand. Traditional power supply methods often cannot meet the stable power supply needs of each node. During peak power consumption periods or in severe weather conditions, low voltage problems are prone to occur in mountain distribution networks, which seriously affect the normal lives of residents and the smooth progress of industrial production. In recent years, distributed small energy storage technology has been widely used in mountain distribution networks, which improves the reliability and stability of power grid operation through flexible adjustment of energy storage equipment.

[0003] When applying the existing distributed small energy storage technology to mountain distribution networks, some obvious shortcomings are exposed: First, the load demands of each node in the mountain distribution network vary greatly and change frequently. Traditional energy storage management methods often make it difficult to achieve real-time monitoring and accurate analysis of the load demands of each node, resulting in unreasonable output power distribution of energy storage equipment and inability to effectively regulate the grid voltage. Second, the nodes in the mountain distribution network are widely distributed, the information transmission distance is long, and they are easily affected by factors such as terrain and climate, resulting in great difficulties in information sharing and coordinated regulation between nodes. This not only reduces the coordinated regulation capability of the power grid, but also the delay and error in information transmission further aggravate the low voltage problem of the distribution network. Summary of the invention

[0004] The present invention provides a low-voltage regulation method, regulation device and electronic equipment for a power distribution network to solve the problem that the existing distributed small energy storage technology cannot respond to changes in load demand in a timely manner and there is communication delay, resulting in unreasonable output power distribution of energy storage equipment and inability to effectively regulate the grid voltage. The output power of the energy storage equipment is dynamically allocated to improve the stability of power distribution.

[0005] According to one aspect of the present invention, a method for low voltage regulation in a distribution network is provided, wherein the distribution network is provided with a distributed energy storage device, and the method comprises: obtaining real-time load data of each load node and real-time energy storage data of each energy storage node based on a shared network; determining the load demand prediction data of the load node according to a load demand prediction model and the real-time load data; wherein the load demand prediction model is established based on the historical load data of each load node in the network to be tested; obtaining energy storage sharing information between the energy storage nodes, and determining a coordinated regulation coefficient according to the energy storage sharing information; determining a power allocation strategy for each energy storage node according to the real-time load data, the load demand prediction data, the real-time energy storage data, and the coordinated regulation coefficient; and performing voltage regulation according to the power allocation strategy or an optimization strategy of the power allocation strategy.

[0006] Optionally, the power allocation strategy of each of the energy storage nodes is determined based on the real-time load data, the load demand forecast data, the real-time energy storage data, and the collaborative adjustment coefficient, including: establishing an energy storage coordination model between the energy storage nodes based on the load demand forecast data, the real-time energy storage data, and the collaborative adjustment coefficient; establishing a power output model of any of the energy storage nodes within a preset time window based on the real-time load data and the real-time energy storage data; and determining the power allocation strategy of each of the energy storage nodes based on the power output model and the energy storage coordination model.

[0007] Optionally, the energy storage coordination model between the energy storage nodes is established according to the load demand forecast data, the real-time energy storage data and the coordinated regulation coefficient, including: obtaining the coordinated regulation coefficient and energy storage status of any energy storage node at any sampling time; differentiating the energy storage status based on the energy storage load adjustment relationship to determine the energy storage change rate of the energy storage node; and establishing the energy storage coordination model according to the coordinated regulation coefficient, the energy storage change rate and the load demand forecast data of all the energy storage nodes.

[0008] Optionally, the power output model of any of the energy storage nodes within a preset time window is established based on the real-time load data and the real-time energy storage data, including: obtaining the real-time load voltage and real-time load demand of any of the load nodes at any sampling moment based on the real-time load data; obtaining the output limiting factor and time domain attenuation factor of the energy storage node at the sampling moment based on the real-time energy storage data; establishing the power demand at the sampling moment based on the real-time load voltage, the real-time load demand, the output limiting factor and the time domain attenuation factor; integrating the power demands at all the sampling moments within the preset time window to establish the power output model.

[0009] Optionally, determining the load demand forecast data of the load node based on the load demand forecast model and the real-time load data includes: analyzing the historical load data based on time series analysis, frequency domain analysis or machine learning technology to establish the load demand forecast model; performing data processing on the real-time load data and extracting key feature data from the processed data; analyzing the real-time load data based on time series analysis or frequency domain analysis technology to obtain real-time load demand; importing the real-time load demand into the load demand forecast model, and determining the output parameters of the load demand forecast model as the load demand forecast data.

[0010] Optionally, the acquiring of energy storage sharing information between the energy storage nodes and determining the collaborative adjustment coefficient based on the energy storage sharing information includes: acquiring the actual amount of information received by the energy storage nodes per unit time, and calculating the degree of information sharing based on the actual amount of information received and the standard amount of information; acquiring the actual response time of the energy storage nodes per unit time, and calculating the response speed coefficient based on the actual response time and the standard response time; acquiring the actual output power of the energy storage nodes per unit time, and calculating the load satisfaction rate based on the actual output power and the load demand power; and performing weighted summation of the information sharing degree, the response speed coefficient and the load satisfaction rate to calculate the collaborative adjustment coefficient.

[0011] Optionally, the voltage regulation according to the power allocation strategy or the optimization strategy of the power allocation strategy includes: determining whether a low voltage abnormality occurs according to the load demand prediction data and the energy storage sharing information, and optimizing the power allocation strategy when a low voltage abnormality occurs.

[0012] Optionally, the distribution network low voltage regulation method also includes: performing fault diagnosis and fault warning based on at least one of the real-time load data, the real-time energy storage data or the load demand forecast data; and / or obtaining image monitoring data of the distribution network, and performing fault diagnosis and fault warning based on the image monitoring data.

[0013] According to another aspect of the present invention, there is provided a low voltage regulation device for a distribution network, comprising: a data acquisition and sharing module, for acquiring and sharing real-time load data of each load node and real-time energy storage data of each energy storage node; a load demand analysis module, for determining the load demand prediction data of the load node according to a load demand prediction model and the real-time load data; wherein the load demand prediction model is established based on the historical load data of each load node in the network to be tested; a multi-node collaborative regulation module, for acquiring energy storage sharing information between the energy storage nodes, and determining a collaborative regulation coefficient according to the energy storage sharing information; an energy storage management module, for determining a power allocation strategy for each of the energy storage nodes according to the real-time load data, the load demand prediction data, the real-time energy storage data, and the collaborative regulation coefficient; and a voltage regulation module, for performing voltage regulation according to the power allocation strategy or an optimization strategy of the power allocation strategy.

[0014] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned distribution network low voltage regulation method.

[0015] The technical solution of the embodiment of the present invention collects the real-time load data of each load node and the real-time energy storage data of each energy storage node and shares the multi-node data; determines the load demand prediction data of the load node according to the load demand prediction model and the real-time load data, and determines the coordination adjustment coefficient according to the energy storage sharing information between the energy storage nodes, and then determines the power allocation strategy of each energy storage node according to the real-time load data, the load demand prediction data, the real-time energy storage data, and the coordination adjustment coefficient; and regulates the voltage according to the power allocation strategy or the optimization strategy of the power allocation strategy, thereby solving the problem that the existing distributed small energy storage technology cannot respond to changes in load demand in a timely manner and there is communication delay, resulting in unreasonable output power distribution of energy storage equipment and inability to effectively adjust the grid voltage, dynamically allocates the output power of the energy storage equipment, and improves the distribution stability.

[0016] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a method for regulating low voltage in a power distribution network provided by an embodiment of the present invention;

[0019] Figure 2 A flow chart of a load demand analysis method provided by an embodiment of the present invention;

[0020] Figure 3 A flowchart of a multi-node coordinated adjustment method for energy storage devices provided by an embodiment of the present invention;

[0021] Figure 4 A flow chart of distributed energy storage management provided by an embodiment of the present invention;

[0022] Figure 5 A flow chart of another method for regulating low voltage in a power distribution network provided by an embodiment of the present invention;

[0023] Figure 6 A schematic diagram of the structure of a low voltage regulating device for a power distribution network provided by an embodiment of the present invention;

[0024] Figure 7A schematic diagram of the structure of another low voltage regulating device for a power distribution network provided by an embodiment of the present invention;

[0025] Figure 8 It is a structural schematic diagram of an electronic device for implementing the low voltage regulation method for a power distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Figure 1 A flow chart of a method for regulating low voltage in a power distribution network provided by an embodiment of the present invention. This embodiment can be applied to the application scenario of regulating low voltage in a mountain power distribution network based on distributed small energy storage technology. The method can be executed by a low voltage regulating device for a power distribution network. The regulating device can be implemented in the form of hardware and / or software. The regulating device can be configured in a power grid dispatching platform or an independent electronic device. In this application, a distributed energy storage device is provided in the power distribution network. See Figure 1 As shown, the low voltage regulation method of the power distribution network of the present application specifically includes:

[0029] S1: Acquire the real-time load data of each load node and the real-time energy storage data of each energy storage node based on the shared network.

[0030] The shared network can be understood as a communication system based on wired or wireless transmission technology to achieve data interaction between monitoring devices.

[0031] A load node can be understood as a node connected to a load in a power distribution network. Real-time load data can be understood as data characterizing the power consumption status of a load node. Typically, real-time load data includes, but is not limited to: real-time voltage, real-time voltage fluctuation trend, real-time current, real-time power, and real-time load demand of a load node. In this embodiment, a voltage sensor or a smart meter can be set at the load node as a data acquisition device to collect real-time load data, and the collected real-time load data is sent to a shared network.

[0032] Energy storage nodes can be understood as nodes in the power distribution network that are connected to energy storage devices (such as battery modules). Real-time energy storage data can be understood as data that characterizes the energy storage power supply status of energy storage devices. Typically, real-time energy storage data includes, but is not limited to: energy storage device status (such as healthy operating status, charging and discharging status, and device temperature), remaining capacity, and output power. In this embodiment, an energy storage management system can be set at the energy storage node as a data acquisition device to collect real-time energy storage data, and the collected real-time energy storage data is sent to the shared network.

[0033] In this embodiment, a data acquisition device with communication function (such as a voltage sensor, a smart meter or an energy storage management system) can be set corresponding to the energy storage device or the load node, and the real-time load data of each load node and the real-time energy storage data of each energy storage node can be collected, sorted, forwarded and stored through a shared network, and the collected real-time load data and real-time energy storage data can be sent to the central control platform.

[0034] Specifically, the data acquisition equipment converts the voltage level and load demand obtained from real-time monitoring into a standard format for transmission and sharing in a shared network. In mountainous environments, wireless communication networks (such as Wi-Fi, LoRa, 4G / 5G) can be used to connect various energy storage nodes. Among them, low-power wide-area networks such as LoRa are suitable for long-distance transmission needs in mountainous areas, while 4G / 5G networks can be used in areas with signal coverage. Each node sends the collected data to the shared network through a communication module. During data transmission, a reasonable data publishing frequency is set, such as every 5 or 10 seconds, to ensure the timeliness of information from each node while avoiding network congestion. The data can be packaged into JSON or other lightweight formats, containing fields such as node ID, voltage level, load demand, timestamp, etc., for unified parsing.

[0035] S2: Determine the load demand forecast data of the load node according to the load demand forecast model and the real-time load data.

[0036] The load demand prediction model is established based on the historical load data of each load node in the network to be tested.

[0037] In this embodiment, the load demand forecasting data includes real-time load demand and forecasted load demand, and the real-time load demand includes the current real-time power consumption and the load demand change trend. The forecasted load demand includes the forecasted power consumption and the forecasted load demand change trend. The forecasted load demand is the load demand change trend and power consumption in a specific future period predicted based on the load demand forecasting model.

[0038] Specifically, load demand analysis can be performed based on time series analysis technology, frequency domain analysis technology or machine learning technology. By performing load demand analysis on real-time load data, the current real-time power consumption and load demand change trend are calculated; by modeling historical load data, the load demand change trend and power consumption in a specific period of time in the future are calculated.

[0039] S3: Obtain energy storage sharing information between energy storage nodes, and determine the coordination adjustment coefficient according to the energy storage sharing information.

[0040] The energy storage sharing information can be understood as data representing the transmission data and transmission status between energy storage nodes. The coordination adjustment coefficient can be understood as a parameter representing the information sharing degree and coordination efficiency between energy storage devices.

[0041] In this embodiment, the communication system between the energy storage devices (such as wireless communication, optical fiber network, etc.) can be used to monitor the collaboration between the devices. By regularly updating the real-time shared voltage, load, status and other data, the degree of information sharing and collaboration efficiency can be evaluated to ensure that the collaborative adjustment coefficient can reflect the actual collaboration between the energy storage devices.

[0042] S4: Determine the power allocation strategy of each energy storage node based on real-time load data, load demand forecast data, real-time energy storage data, and coordinated adjustment coefficient.

[0043] In this embodiment, the power allocation strategy uses a multi-node collaborative adjustment algorithm to calculate the output power allocation of each energy storage device based on the load voltage and load demand of each load node. The multi-node collaborative adjustment algorithm can consider the following factors: the voltage deviation of each load node, the load demand of each load node, the remaining capacity of the energy storage device, and the degree of information sharing and collaboration efficiency between energy storage devices. Based on the above data, and combined with the output power adjustment range of each node, a distributed output adjustment instruction is generated.

[0044] S5: Perform voltage regulation according to the power allocation strategy or an optimization strategy of the power allocation strategy.

[0045] Specifically, after obtaining the power allocation strategy of each energy storage node, an output power adjustment instruction is sent to each energy storage node through the shared network. After receiving the adjustment instruction, each pure node adjusts the output power of the energy storage device and feeds back the new voltage level and load conditions to the central control platform to form a closed-loop control.

[0046] The technical solution of the present application is based on the cooperation between load demand analysis, multi-node coordinated regulation of energy storage equipment and distributed energy storage management method. It can monitor and accurately analyze the load demand of each load node in the mountain distribution network in real time, and dynamically allocate the output power of the energy storage equipment based on the load demand analysis data. It can also effectively regulate the grid voltage and solve the low voltage problem in the mountain distribution network through information sharing and coordinated regulation among multiple energy storage nodes. This efficient coordinated regulation capability not only improves the stability and reliability of the power grid, but also optimizes energy utilization and reduces energy waste.

[0047] Figure 2 A flow chart of a load demand analysis method provided by an embodiment of the present invention. Figure 2 As shown, in the above step S2, the load demand forecast data of the load node is determined according to the load demand forecast model and the real-time load data, which specifically includes the following steps:

[0048] S201: Analyze historical load data based on time series analysis, frequency domain analysis or machine learning technology to establish a load demand prediction model.

[0049] S202: Process the real-time load data and extract key feature data from the processed data.

[0050] S203: Analyze the real-time load data based on time series analysis or frequency domain analysis technology to obtain real-time load demand.

[0051] In this embodiment, the real-time load demand can be intuitively displayed in the form of a table or a MAP diagram.

[0052] S204: Importing the real-time load demand into the load demand prediction model, and determining the output parameters of the load demand prediction model as the load demand prediction data.

[0053] For example, a load demand prediction model as shown in Formula 1 may be established:

[0054]

[0055] Among them, f p(t) represents the output value of the load demand prediction function at the future time t, which is a prediction model established in combination with the time series, and is used to predict the load demand at the future time. In this embodiment, the future load demand is predicted by a load demand prediction model (such as a machine learning model based on historical load data and seasonal patterns). The prediction model can be based on the historical data of the monitoring system and use statistical or machine learning algorithms to make predictions, which can reduce the response lag problem. Preferably, the load demand prediction model can be periodically updated (such as every hour, every day) to adjust according to the latest data.

[0056] λ·(P hist (t-τ))·e -μτ P represents the historical load demand hist (t-τ) multiplied by the attenuation coefficient e -μτ The weighted value after , the influence of historical data decays over time, and λ controls the overall weight of the influence;

[0057] λ represents the weight coefficient of historical load demand, which adjusts the influence of historical load demand on the prediction result at the current moment;

[0058] P hist (t-τ) is the historical load demand, which means the historical load demand at the time t-τ in the past;

[0059] e -μτ is an exponential decay function, which controls the weight of historical data in future load demand, and the parameter μ is used to adjust the decay rate;

[0060] τ is the historical time difference, which represents the time span from the current moment t forward, that is, t-τ is a certain time point in the past;

[0061] k·σ(t) means that the standard deviation σ(t) is used to measure the current load fluctuation, which is combined with the parameter k to smooth the forecast of future demand;

[0062] σ(t) represents the standard deviation of the load fluctuation at the current time t, which is used to measure the volatility of the load. When the fluctuation is large, the prediction function will tend to be more smooth.

[0063] δ i ·L i (t) represents the load demand L of the i-th node at the current moment i (t) multiplied by the prediction correction factor δ i , used to further correct future load requirements;

[0064] δ i It represents the load correction factor, which adjusts the load demand of the i-th load node in the prediction process;

[0065] Li (t) represents the load demand of the i-th load node at time t. This value will change over time and have a direct impact on the demand forecast at future moments;

[0066] η+θ i ·V i (t) is the voltage V i (t) and parameter θ i The combination of represents the regulating effect of voltage on load prediction;

[0067] θ i represents the voltage regulation coefficient of the i-th load node, which is used to correct the impact of voltage on load prediction;

[0068] V i (t) represents the voltage value of the i-th load node at time t, reflecting the dynamic regulation effect of the node voltage level on the load demand prediction;

[0069] η is used as a smoothing constant in the forecast to prevent the denominator from being zero and to control the relationship between load demand and voltage.

[0070] Figure 3 A flowchart of a multi-node coordinated adjustment method for energy storage devices provided by an embodiment of the present invention. Figure 3 As shown, in the above step S3, the energy storage sharing information between the energy storage nodes is obtained, and the coordinated adjustment coefficient is determined according to the energy storage sharing information, including:

[0071] S301: Obtain the actual amount of information received by the energy storage node per unit time, and calculate the degree of information sharing based on the actual amount of information received and the standard amount of information.

[0072] Specifically, the standard value of each energy storage device in unit time t is defined as I std , then the amount of information actually received by each device at time t is I j The ratio of (t) is: If all energy storage devices can reach or approach I std , indicating a high level of information sharing.

[0073] S302: Obtain the actual response time of the energy storage node in unit time, and calculate the response speed coefficient according to the actual response time and the standard response time.

[0074] Specifically, according to the actual response time and load balance of each energy storage device, its load demand satisfaction rate can be calculated. For example, if the response time of energy storage device j at time t is T j (t), can be compared with the standard response time T std Compare to get the response speed efficiency:

[0075] S303: Obtain the actual output power of the energy storage node in unit time, and calculate the load satisfaction rate according to the actual output power and the load demand power.

[0076] Specifically, the load satisfaction rate is equal to the ratio between the actual output power and the load demand power.

[0077] S304: Perform weighted summation on the information sharing degree, the response speed coefficient and the load satisfaction rate to calculate the collaborative adjustment coefficient.

[0078] Specifically, the information sharing degree, response speed coefficient and load satisfaction rate are weighted averaged to obtain the collaborative adjustment coefficient C j (t):

[0079] C j (t) = ω1·Information sharing degree+ω2·Response speed coefficient+ω3·Load satisfaction rate

[0080] Among them, ω1, ω2 and ω3 are weight coefficients, which can be adjusted according to actual needs to reflect the relative importance of information sharing, response speed and load satisfaction to collaborative work.

[0081] Figure 4 A flow chart of distributed energy storage management provided by an embodiment of the present invention. Figure 4 As shown, in the above step S4, the power allocation strategy of each energy storage node is determined according to the real-time load data, the load demand forecast data, the real-time energy storage data, and the coordinated adjustment coefficient, including:

[0082] S401: Establish an energy storage coordination model between energy storage nodes based on load demand forecast data, real-time energy storage data and coordinated regulation coefficients.

[0083] Optionally, an energy storage coordination model between energy storage nodes is established based on load demand forecast data, real-time energy storage data and coordinated regulation coefficients, including: obtaining the coordinated regulation coefficient and energy storage state of any energy storage node at any sampling time; differentiating the energy storage state based on the energy storage load adjustment relationship to determine the energy storage change rate of the energy storage node; and establishing an energy storage coordination model based on the coordinated regulation coefficients, energy storage change rates and load demand forecast data of all energy storage nodes.

[0084] The energy storage load adjustment relationship can be understood as the nonlinear adjustment relationship between the power output of the energy storage device and the load demand. For example, if the power output of the energy storage device is defined as L j (t), the energy storage load adjustment relationship can be set to L j (t)+γ. γ is a constant to prevent zero division. The energy storage state is differentiated based on the energy storage load adjustment relationship, and the established energy storage change rate is as follows: It represents the dynamic rate of change of the state of the energy storage device. Considering the influence of load demand, γ is a small constant to prevent zero division.

[0085] S402: Establish a power output model of any energy storage node within a preset time window according to real-time load data and real-time energy storage data.

[0086] Optionally, a power output model of any energy storage node within a preset time window is established based on real-time load data and real-time energy storage data, including: obtaining the real-time load voltage and real-time load demand of any load node at any sampling moment based on the real-time load data; obtaining the output limiting factor and time domain attenuation factor of the energy storage node at the sampling moment based on the real-time energy storage data; establishing the power demand at the sampling moment based on the real-time load voltage, real-time load demand, output limiting factor and time domain attenuation factor; integrating the power demands at all sampling moments within the preset time window to establish a power output model.

[0087] Among them, the output limiting factor can be understood as a parameter used to adjust the output power of the energy storage device to avoid excessive consumption of the energy storage device by the load node. If the storage state of the jth energy storage node at sampling time t is defined as S j (t), the output limiting factor can be expressed as S j (t)+β, where β is a small constant to prevent zero division. The time domain attenuation factor can be understood as a parameter that characterizes the degree of attenuation of the output power of the energy storage device over time.

[0088] S403: Determine the power allocation strategy of each energy storage node according to the power output model and the energy storage coordination model.

[0089] Exemplarily, the functional expression of the power allocation strategy is shown in Formula 2:

[0090]

[0091] Among them, P out (t) represents the output power of the energy storage device at sampling time t. In this embodiment, P out The value range of (t) depends on the maximum output power of the energy storage device and the requirements of each load node. If the maximum output power of the energy storage device is P max , then P out The range of (t) is: 0≤P out (t)≤P max .

[0092] Represents a power output model at a load node.

[0093] V i(t) represents the load voltage of the i-th load node at sampling time t. In this embodiment, a voltage sensor can be installed on the energy storage device to monitor the output voltage value in real time. The parameter V is read by the sensor acquisition device or data acquisition module. i (t). In order to ensure the accuracy of voltage regulation, the load voltage V i (t) High frequency collection should be adopted, such as once per second or more frequently.

[0094] L i (t) represents the real-time load demand of the i-th load node at sampling time t. In this embodiment, the real-time load demand can be obtained through a load monitoring device installed in the energy storage device or the distribution network. For large power systems, it is also possible to monitor the overall or distributed load demand in real time or periodically (such as every second) through connection with smart meters to reflect the dynamic changes of the current load demand.

[0095] S j (t) represents the storage state of the jth energy storage node at sampling time t; S j (t)+β is used to adjust the output power of the energy storage device to avoid excessive consumption of the energy storage device by the load node. j (t) usually reflects the current state of the energy storage device, such as the remaining charge (SOC) or health state (SOH) of the battery. It can be obtained through the energy storage device management system (BMS) or device status monitoring module built into the energy storage device. β is a fixed value or a constant adjusted according to system design requirements. It can be preset according to device performance and system requirements to avoid the situation where the denominator is zero.

[0096] e -αt Represents the time domain attenuation factor, which describes the attenuation of output power over time. V i (t)·L i (t)·e -αt Represents the interaction between the voltage, power demand and time dependence of the i-th load node. Wherein, α is a regulation parameter. In this embodiment, α is usually an empirical parameter in the device characteristics, which is used to control the exponential decay rate in the formula. It can be preset according to the characteristics of the energy storage device, or determined based on historical data and experimental analysis. Generally, real-time acquisition is not required, but it is determined by preset or during the device commissioning stage.

[0097] Indicates the coordinated regulation information of multiple energy storage nodes.

[0098] C j (t) represents the collaborative adjustment coefficient of the jth energy storage node at sampling time t, which reflects the degree of information sharing and collaborative work efficiency among energy storage devices.

[0099] Represents the dynamic rate of change of the energy storage device state, taking into account the impact of load demand, and γ is a small constant to prevent zero division. Among them, is the derivative of time t, which represents the rate of change of the storage state of the energy storage device or the load demand at the sampling time. j (t)+γ represents the nonlinear adjustment relationship between the power output of the energy storage device and the load demand. j (t) and L j (t) and calculate the rate of change between adjacent time points by differentiation. Preferably, it can be updated once per second by the data acquisition module, or automatically calculated by the differential calculation module.

[0100] Refer to the above formula 1, f p (t) is the function expression of the load demand forecasting model, which is a forecasting model established in combination with time series and is used to predict the load demand at future moments.

[0101] In combination with the above formula 2, the present application establishes a power output model by introducing an integral term, and establishes an energy storage coordination model between energy storage nodes by introducing a differential term. The integral term is used to accumulate the load impact and voltage change within a preset time window (T), and can describe the power output of the energy storage device over a period of time; the differential term helps the energy storage system respond to fluctuations in power demand in real time by capturing changes in the state of the energy storage device over time. The present application introduces a time attenuation factor α into the power output model to characterize the gradual decay of the system's "memory effect" of historical data, which can better fit the immediate variability of load demand, making the formula more flexible and suitable for processing the impact of load demand on current power within different time ranges, thereby improving the real-time and response speed of the system. By introducing the combination and allocation of various coefficients in the power output model (such as the capacity factor C j (t), load L i (t), voltage V i (t), etc.), combined with the mathematical principle of multivariable optimization to allocate output power between the states of multiple nodes and load demands, the formula achieves the effect of balancing loads and stabilizing voltage. The combination of the aforementioned mathematical and physical theories enables the power allocation strategy to flexibly respond to and adjust the output power of energy storage equipment, ensuring voltage stability and energy balance of the power system under load demand fluctuations.

[0102] Figure 5 A flowchart of another method for regulating low voltage in a power distribution network provided by an embodiment of the present invention. Figure 5 As shown, the low voltage regulation method of the power distribution network of the present application specifically includes:

[0103] S501: Acquire real-time load data of each load node and real-time energy storage data of each energy storage node based on a shared network.

[0104] S502: Determine load demand prediction data of the load node according to the load demand prediction model and real-time load data.

[0105] S503: Acquire energy storage sharing information between energy storage nodes, and determine a coordination adjustment coefficient according to the energy storage sharing information.

[0106] S504: Determine the power allocation strategy of each energy storage node according to the real-time load data, load demand forecast data, real-time energy storage data, and the coordinated adjustment coefficient.

[0107] S505: Perform voltage regulation according to the power allocation strategy or an optimization strategy of the power allocation strategy.

[0108] S506: Perform fault diagnosis and fault warning according to at least one of the real-time load data, the real-time energy storage data, or the load demand prediction data.

[0109] S507: Acquire image monitoring data of the power distribution network, and perform fault diagnosis and fault warning according to the image monitoring data.

[0110] In this embodiment, the early warning information can be pushed to the mobile terminal to promptly remind the operation and maintenance personnel to carry out fault repair. The working status of the on-site equipment can be identified through video monitoring to assist in fault diagnosis and maintenance. By adding fault diagnosis and image monitoring functions, system operation anomalies and potential problems can be discovered in a timely manner, assisting in fault diagnosis and maintenance, and improving system operation and maintenance efficiency.

[0111] Optionally, voltage regulation is performed according to a power allocation strategy or an optimization strategy of the power allocation strategy, including: determining whether a low voltage anomaly occurs according to load demand prediction data and energy storage sharing information, and optimizing the power allocation strategy when a low voltage anomaly occurs.

[0112] Among them, the low voltage abnormal condition is established by operator customization or in combination with experimental data calibration, and is not specifically limited.

[0113] For example, taking nodes A, B, and C as an example of information sharing and adjustment, in the data collection stage, node A monitors a voltage level of 220V and a load demand of 5kW; node B is 215V and requires 3kW; node C is 210V and requires 4kW. In the data sharing stage, nodes A, B, and C upload their own data to the shared network. In the power management stage: after analyzing the data, it is found that the voltage of node C is low, while the voltages of nodes A and B are higher. According to the collaborative adjustment algorithm, the instructions are distributed as follows: node A reduces the output power by 0.5kW; node B maintains the current output; node C increases the output power by 0.5kW. In the voltage adjustment stage, adjustment instructions are issued to each pure node to control nodes A, B, and C to perform corresponding power adjustments until there is no low voltage abnormality at each node voltage.

[0114] In some embodiments, blockchain technology can be used to achieve information sharing and collaboration between nodes, ensure the security and transparency of data transmission, and reduce information transmission delays.

[0115] In some embodiments, wireless charging technology can be used to replenish energy storage devices to achieve flexible deployment and use of equipment.

[0116] Based on the same inventive concept, an embodiment of the present invention also provides a low voltage regulation device for a distribution network, which is used to execute the low voltage regulation method for a distribution network provided in the above embodiment, and has functional modules and beneficial effects corresponding to the execution method.

[0117] Figure 6 A schematic diagram of the structure of a low voltage regulation device for a power distribution network provided by an embodiment of the present invention. Figure 6 As shown, the power distribution network low voltage regulation device of the present application includes: a data acquisition and sharing module 101, a load demand analysis module 102, a multi-node collaborative regulation module 103, an energy storage management module 104 and a voltage regulation module 105.

[0118] Among them, the data acquisition and sharing module 101 is used to collect and share the real-time load data of each load node and the real-time energy storage data of each energy storage node; the load demand analysis module 102 is used to determine the load demand prediction data of the load node according to the load demand prediction model and the real-time load data; wherein the load demand prediction model is established based on the historical load data of each load node in the network to be tested; the multi-node collaborative adjustment module 103 is used to obtain the energy storage sharing information between the energy storage nodes, and determine the collaborative adjustment coefficient according to the energy storage sharing information; the energy storage management module 104 is used to determine the power allocation strategy of each energy storage node according to the real-time load data, the load demand prediction data, the real-time energy storage data, and the collaborative adjustment coefficient; the voltage regulation module 105 is used to perform voltage regulation according to the power allocation strategy or the optimization strategy of the power allocation strategy.

[0119] In some embodiments, the load demand analysis module 102 and the energy storage management module 104 of the present application can be independently set up and connected to the central control platform. At this time, the load demand analysis module 102 and the energy storage management module 104 can exchange data through the central control platform.

[0120] In other embodiments, the load demand analysis module 102 and the energy storage management module 104 of the present application may also be integrated with a central control platform.

[0121] Optionally, the energy storage management module 104 is configured to: establish an energy storage coordination model between energy storage nodes based on load demand forecast data, real-time energy storage data and collaborative adjustment coefficients; establish a power output model of any energy storage node within a preset time window based on real-time load data and real-time energy storage data; and determine the power allocation strategy of each energy storage node based on the power output model and the energy storage coordination model.

[0122] Optionally, the energy storage management module 104 is also configured to: obtain the coordinated regulation coefficient and energy storage state of any energy storage node at any sampling time; differentiate the energy storage state based on the energy storage load adjustment relationship to determine the energy storage change rate of the energy storage node; and establish an energy storage coordination model based on the coordinated regulation coefficient, energy storage change rate and load demand forecast data of all energy storage nodes.

[0123] Optionally, the energy storage management module 104 is also configured to: obtain the real-time load voltage and real-time load demand of any load node at any sampling time based on real-time load data; obtain the output limiting factor and time domain attenuation factor of the energy storage node at the sampling time based on the real-time energy storage data; establish the power demand at the sampling time according to the real-time load voltage, real-time load demand, output limiting factor and time domain attenuation factor; integrate the power demands at all sampling times within the preset time window to establish a power output model.

[0124] Optionally, the load demand analysis module 102 is configured to: analyze historical load data based on time series analysis, frequency domain analysis or machine learning technology to establish a load demand prediction model; process real-time load data and extract key feature data from the processed data; analyze real-time load data based on time series analysis or frequency domain analysis technology to obtain real-time load demand; import the real-time load demand into the load demand prediction model, and determine the output parameters of the load demand prediction model as the load demand prediction data.

[0125] Optionally, the multi-node collaborative adjustment module 103 is configured to: obtain the actual amount of information received by the energy storage node per unit time, and calculate the degree of information sharing based on the actual amount of information received and the standard amount of information; obtain the actual response time of the energy storage node per unit time, and calculate the response speed coefficient based on the actual response time and the standard response time; obtain the actual output power of the energy storage node per unit time, and calculate the load satisfaction rate based on the actual output power and the load demand power; perform weighted summation of the information sharing degree, response speed coefficient and load satisfaction rate to calculate the collaborative adjustment coefficient.

[0126] Optionally, the voltage regulation module 105 is configured to determine whether a low voltage abnormality occurs according to the load demand prediction data and the energy storage sharing information, and optimize the power allocation strategy when a low voltage abnormality occurs.

[0127] Figure 7 A schematic diagram of the structure of another low voltage regulation device for a power distribution network provided by an embodiment of the present invention. Figure 7 As shown, the low voltage regulating device of the power distribution network of the present application also includes: a fault diagnosis module 106 and / or a monitoring module 107. The fault diagnosis module 106 and the monitoring module 107 can be connected to the central control platform 108, and data is exchanged with the data acquisition and sharing module 101 through the central control platform 108. The fault diagnosis module 106 is used to perform fault diagnosis and fault warning according to at least one of real-time load data, real-time energy storage data or load demand forecast data; the monitoring module 107 is used to obtain image monitoring data of the power distribution network, and perform fault diagnosis and fault warning according to the image monitoring data. In this application, the central control platform 108 can push the warning information to the mobile terminal to promptly remind the operation and maintenance personnel to perform fault repair. In this application, the monitoring module 107 has image recognition capabilities, can identify the working status of the on-site equipment through video monitoring, and assist in fault diagnosis and maintenance. By adding fault diagnosis and image monitoring functions, it is possible to timely discover system operation anomalies and potential problems, assist in fault diagnosis and maintenance, and improve system operation and maintenance efficiency.

[0128] Based on the inventive concept of the above-mentioned embodiment, an embodiment of the present invention further provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned distribution network low voltage regulation method.

[0129] Figure 8A schematic diagram of the structure of an electronic device for implementing the method for regulating the low voltage of a power distribution network according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0130] like Figure 8 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0131] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0132] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the above-mentioned distribution network low voltage regulation method.

[0133] In some embodiments, the above-mentioned power distribution network low voltage regulation method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power distribution network low voltage regulation method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the above-mentioned power distribution network low voltage regulation method by any other appropriate means (for example, by means of firmware).

[0134] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0136] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0137] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0138] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0139] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0140] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0141] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for regulating low voltage in a power distribution network, wherein the power distribution network is provided with a distributed energy storage device, characterized in that: The method comprises: Based on the real-time load data of each load node in the shared network and the real-time energy storage data of each energy storage node; Determining the load demand prediction data of the load node according to the load demand prediction model and the real-time load data; wherein the load demand prediction model is established based on the historical load data of each load node in the network to be tested; Acquire energy storage sharing information between the energy storage nodes, and determine a collaborative adjustment coefficient according to the energy storage sharing information; Determine a power allocation strategy for each of the energy storage nodes according to the real-time load data, the load demand forecast data, the real-time energy storage data, and the collaborative adjustment coefficient; Voltage regulation is performed according to the power allocation strategy or an optimization strategy of the power allocation strategy.

2. The method for regulating low voltage in a power distribution network according to claim 1, characterized in that: The determining of the power allocation strategy of each energy storage node according to the real-time load data, the load demand forecast data, the real-time energy storage data, and the coordinated adjustment coefficient includes: Establishing an energy storage coordination model between the energy storage nodes according to the load demand forecast data, the real-time energy storage data and the coordinated adjustment coefficient; Establishing a power output model of any of the energy storage nodes within a preset time window according to the real-time load data and the real-time energy storage data; The power allocation strategy of each energy storage node is determined according to the power output model and the energy storage coordination model.

3. The method for regulating low voltage in a power distribution network according to claim 2, characterized in that: The step of establishing an energy storage coordination model between the energy storage nodes according to the load demand forecast data, the real-time energy storage data and the coordinated adjustment coefficient includes: Obtaining the coordinated adjustment coefficient and energy storage state of any of the energy storage nodes at any sampling time; Differentiate the energy storage state based on the energy storage load adjustment relationship to determine the energy storage change rate of the energy storage node; The energy storage coordination model is established according to the coordinated adjustment coefficients of all the energy storage nodes, the energy storage change rates and the load demand forecast data.

4. The method for regulating low voltage in a power distribution network according to claim 2, characterized in that: The step of establishing a power output model of any of the energy storage nodes within a preset time window according to the real-time load data and the real-time energy storage data includes: Acquire the real-time load voltage and real-time load demand of any load node at any sampling time based on the real-time load data; Acquire the output limiting factor and the time domain attenuation factor of the energy storage node at the sampling time based on the real-time energy storage data; Establishing the power demand at the sampling moment according to the real-time load voltage, the real-time load demand, the output limiting factor and the time domain attenuation factor; The power demand at all the sampling moments within the preset time window is integrated to establish the power output model.

5. The method for regulating low voltage in a power distribution network according to claim 1, characterized in that: The step of determining the load demand prediction data of the load node according to the load demand prediction model and the real-time load data includes: Analyze the historical load data based on time series analysis, frequency domain analysis or machine learning technology to establish the load demand prediction model; Processing the real-time load data and extracting key feature data from the processed data; Analyze the real-time load data based on time series analysis or frequency domain analysis technology to obtain real-time load demand; The real-time load demand is imported into the load demand prediction model, and the output parameters of the load demand prediction model are determined as the load demand prediction data.

6. The method for regulating low voltage in a power distribution network according to claim 1, characterized in that: The acquiring the energy storage sharing information between the energy storage nodes and determining the coordination adjustment coefficient according to the energy storage sharing information includes: Acquire the actual amount of information received by the energy storage node per unit time, and calculate the degree of information sharing according to the actual amount of information received and the standard amount of information; Obtaining the actual response time of the energy storage node within a unit time, and calculating a response speed coefficient according to the actual response time and the standard response time; Obtaining the actual output power of the energy storage node per unit time, and calculating the load satisfaction rate according to the actual output power and the load demand power; The information sharing degree, the response speed coefficient and the load satisfaction rate are weightedly summed to calculate the collaborative adjustment coefficient.

7. The method for regulating low voltage in a power distribution network according to claim 1, characterized in that: The performing voltage regulation according to the power allocation strategy or the optimization strategy of the power allocation strategy includes: Determine whether a low voltage anomaly occurs according to the load demand forecast data and the energy storage sharing information, and optimize the power allocation strategy when a low voltage anomaly occurs.

8. The method for regulating low voltage in a power distribution network according to any one of claims 1 to 7, characterized in that: Also includes: Perform fault diagnosis and fault warning according to at least one of the real-time load data, the real-time energy storage data, or the load demand forecast data; and / or, Acquire image monitoring data of the power distribution network, and perform fault diagnosis and fault warning according to the image monitoring data.

9. A low voltage regulating device for a power distribution network, characterized in that: include: Data collection and sharing module, used to collect and share the real-time load data of each load node and the real-time energy storage data of each energy storage node; A load demand analysis module, used to determine the load demand forecast data of the load node according to the load demand forecast model and the real-time load data; wherein the load demand forecast model is established based on the historical load data of each load node in the network to be tested; A multi-node collaborative adjustment module, used to obtain energy storage sharing information between the energy storage nodes and determine a collaborative adjustment coefficient according to the energy storage sharing information; An energy storage management module, used to determine the power allocation strategy of each energy storage node according to the real-time load data, the load demand forecast data, the real-time energy storage data, and the collaborative adjustment coefficient; The voltage regulation module is used to perform voltage regulation according to the power allocation strategy or an optimization strategy of the power allocation strategy.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power distribution network low voltage regulation method according to any one of claims 1 to 8.

Images