Method and system for regulating voltage of low-voltage distribution network containing high-proportion household photovoltaic

Abstract

The invention provides a voltage regulation method and system for a low-voltage power distribution network containing high-proportion household photovoltaic. The method comprises the following steps: acquiring power operation data of the low-voltage power distribution network containing the high-proportion household photovoltaic; based on the power operation data, utilizing a pre-constructed intra-day optimization model to obtain an intra-day operation regulation and control scheme of the low-voltage power distribution network; based on the intra-day operation regulation and control scheme of the low-voltage power distribution network, obtaining a reactive power support of the low-voltage power distribution network by using a pre-constructed household photovoltaic self-adaptive dynamic control optimization model; according to the intra-day operation regulation and control scheme of the low-voltage distribution network and the reactive power support, performing voltage regulation on the low-voltage distribution network by using a voltage-reactive power control function, and outputting a voltage control strategy of the low-voltage distribution network; according to the method, centralized optimization control and self-adaptive control are combined, so that minute-level power fluctuation caused by household photovoltaic irradiation can be coped with under the condition that the overall performance of the system is ensured to be optimal.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation, and in particular to a voltage regulation method and system for a low-voltage distribution network containing a high proportion of household photovoltaics. Background Art

[0002] At present, building a new power system that adapts to the increasing proportion of new energy is an important way to achieve the low-carbon and clean transformation of energy. Photovoltaic, as a renewable energy, has gradually become one of the main power sources of the power system, mainly showing a development trend of centralized and distributed development, and the scale of construction has also shown an explosive growth trend. It has become an important pole to promote the development of the photovoltaic power generation industry and even the entire renewable energy industry.

[0003] The impedance ratio of low-voltage distribution network is large, the transmission line is long, and the high proportion of household photovoltaic power generation during the noon peak period cannot be consumed locally, resulting in reverse power flow, increased voltage at the terminal node, or even exceeding the upper limit. In addition, the uncertainty of household photovoltaic power generation caused by meteorological conditions can easily cause frequent and rapid voltage fluctuations, which affects the power quality of low-voltage distribution network. At present, the control methods of low-voltage distribution network are mainly divided into centralized optimization control and adaptive control. Among them, centralized optimization control obtains the grid topology data of the substation, household photovoltaic and load power forecast data, and gives the power adjustment instructions of the controllable equipment based on the optimal power flow; adaptive control only obtains the voltage measurement data of the grid connection point, and the controllable equipment automatically adjusts the output power according to the preset parameters. If centralized optimization control or adaptive control is used alone, there are the following disadvantages: centralized optimization control cannot cope with the minute-level power fluctuations caused by household photovoltaic radiation; adaptive control can quickly respond to minute-level power fluctuations, but does not consider the global optimality of the substation network loss and voltage, so that the network loss and voltage level of the entire substation may not reach the optimal state, resulting in overall performance degradation. Summary of the invention

[0004] In order to solve the problem that when only a centralized optimization control method is used for voltage control of a low-voltage distribution network, it will be impossible to cope with the minute-level power fluctuation caused by household photovoltaics being irradiated; if only an adaptive control method is used, the global optimality of the network loss and voltage in the area is not considered, so that the network loss and voltage level of the entire area may not reach the optimal state, resulting in a decrease in overall performance, the present invention proposes a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics, comprising:

[0005] Obtain power operation data of low-voltage distribution networks with a high proportion of household photovoltaics;

[0006] Based on the power operation data, using a pre-built intraday optimization model, an intraday operation control plan of the low-voltage distribution network is obtained;

[0007] Based on the intraday operation control scheme of the low-voltage distribution network, the reactive power support of the low-voltage distribution network is obtained by using a pre-built household photovoltaic adaptive dynamic control optimization model;

[0008] According to the intraday operation control scheme of the low-voltage distribution network and the reactive power support, the voltage of the low-voltage distribution network is regulated by using a voltage-reactive power control function, and a voltage control strategy of the low-voltage distribution network is output;

[0009] Among them, the intraday optimization model is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment of the low-voltage distribution network; the household photovoltaic adaptive dynamic control optimization model is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network.

[0010] Optionally, the intraday optimization model includes the following construction process:

[0011] Calculating the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the intra-day step information;

[0012] Calculating the voltage deviation of the low-voltage distribution network according to the target voltage of each node in the low-voltage distribution network and the day-ahead decision voltage;

[0013] Calculating the amount of household photovoltaic power abandonment in the low-voltage distribution network according to the household photovoltaic active power prediction value and the household photovoltaic active power dispatch value of the low-voltage distribution network;

[0014] The intra-day objective function is constructed with the goal of minimizing the sum of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network;

[0015] According to the intraday objective function, corresponding intraday constraints are formulated;

[0016] Based on the intraday objective function and the intraday constraints, construct an intraday optimization model;

[0017] The intraday constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, and active uncontrollable household photovoltaic operation constraints.

[0018] Optionally, the expression corresponding to the intraday objective function is as follows:

[0019]

[0020] Among them, F dip represents the intraday objective function value; ω Lrepresents the network loss weight of the low voltage distribution network; s L I represents the network loss normalization coefficient of the low-voltage distribution network; ij,t represents the current value between node i and node j at time t; t = 1…T dip ; T dip represents the total number of steps optimized within a day; ij∈L; L represents the set of all lines in the area; R ij Indicates the resistance value of the line between node i and node j; ΔT dip represents the step size of the intraday optimization model; ω vb represents the voltage offset weight; s vb represents the voltage offset normalization coefficient; B represents the set of all nodes in the substation; U i,t represents the target voltage of node i at time t; represents the day-ahead decision voltage of node i at time t; c pd Represents the penalty coefficient of the household photovoltaic active power control penalty item; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area.

[0021] Optionally, the household photovoltaic adaptive dynamic control optimization model includes the following construction process:

[0022] An adaptive objective function is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network;

[0023] According to the adaptive objective function, corresponding adaptive constraint conditions are formulated;

[0024] Based on the adaptive objective function and the adaptive constraint conditions, a household photovoltaic adaptive dynamic control optimization model is constructed;

[0025] The adaptive constraint conditions include one or more of the following: power compensation constraints and household photovoltaic operation constraints.

[0026] Optionally, the expression corresponding to the adaptive objective function is as follows:

[0027]

[0028] Among them, F rt represents the adaptive objective function value; represents the reduction in active power of the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario; Φ N Represents the set of household photovoltaic grid-connected nodes whose active power in the area is uncontrollable; Φc Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area; c pdr Represents the active power reduction penalty coefficient; It represents the reactive power support provided by the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario.

[0029] Optionally, the power operation data of the low-voltage distribution network includes one or more of the following: line impedance data, household photovoltaic installed capacity data, household photovoltaic adjustable capacity data, parallel capacitor data, on-load tap-changing transformer tap data and user load data.

[0030] Optionally, based on the power operation data, using a pre-built intraday optimization model to obtain the intraday operation control scheme of the low-voltage distribution network includes:

[0031] According to the power operation data, using a pre-built day-ahead optimization model, a day-ahead operation control plan of the low-voltage distribution network is obtained;

[0032] Obtain the predicted power of household photovoltaic power generation and user load in the substation area;

[0033] According to the day-ahead operation control plan, the power operation data, the predicted power of household photovoltaic power generation in the substation area and the predicted power of the user load, the intraday operation control plan of the low-voltage distribution network is obtained by using a pre-built intraday optimization model;

[0034] The day-ahead operation control scheme includes one or more of the following: the on-load voltage regulating tap position, the number of parallel capacitors switched, the day-ahead active power instruction of household photovoltaics, the day-ahead reactive power instruction of household photovoltaics, and the day-ahead operation voltage of the node in the preset day-ahead target time period;

[0035] The intraday operation control scheme includes one or more of the following: household photovoltaic active intraday power instructions, household photovoltaic intraday reactive power instructions and node intraday operation voltage in a preset intraday target time period.

[0036] Optionally, the day-ahead optimization model includes the following construction process:

[0037] Calculating the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the day-ahead step information;

[0038] Calculating the voltage fluctuation of the low-voltage distribution network according to the target voltage and the rated voltage of each node in the low-voltage distribution network;

[0039] Calculating the amount of household photovoltaic power abandonment in the low-voltage distribution network according to the household photovoltaic active power prediction value and the household photovoltaic active power dispatch value of the low-voltage distribution network;

[0040] A day-ahead objective function is constructed with the goal of minimizing the sum of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network;

[0041] According to the day-ahead objective function, formulate corresponding day-ahead constraint conditions;

[0042] Based on the day-ahead objective function and the day-ahead constraint condition, construct a day-ahead optimization model;

[0043] Among them, the day-ahead constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, active uncontrollable household photovoltaic operation constraints, distribution transformer constraints and parallel capacitor constraints.

[0044] Optionally, the voltage regulation of the low-voltage distribution network using a voltage-reactive power control function according to the intraday operation regulation scheme of the low-voltage distribution network and the reactive power support includes:

[0045] When the current voltage of the low-voltage distribution network is less than the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic daily reactive power instruction, the voltage of the low-voltage distribution network is regulated based on the initial voltage-reactive power control function value corresponding to the current voltage;

[0046] When the current voltage of the low-voltage distribution network is greater than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic intraday reactive power instruction, and is less than the intraday operating voltage of the node, the voltage of the low-voltage distribution network is regulated based on the household photovoltaic intraday reactive power instruction;

[0047] When the current voltage of the low-voltage distribution network is greater than or equal to the intraday operating voltage of the node and is less than the voltage of the node under the preset most serious operating mode of the low-voltage distribution network, the voltage of the low-voltage distribution network is regulated based on the intraday reactive power instruction of the household photovoltaic system and the intraday operating voltage of the node;

[0048] When the current voltage of the low-voltage distribution network is greater than the voltage of the node under the most serious operation mode of the low-voltage distribution network, and is less than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic intra-day reactive power command and the reactive power support, the voltage of the low-voltage distribution network is regulated based on the household photovoltaic intra-day reactive power command and the reactive power support;

[0049] When the current voltage of the low-voltage distribution network is greater than the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic daily reactive power command and the reactive power support, the low-voltage distribution network is voltage regulated based on the initial voltage-reactive power control function value corresponding to the current voltage.

[0050] Optionally, the expression corresponding to the voltage-reactive power control function is as follows:

[0051]

[0052] Among them, f IQU (U) represents the voltage-reactive power control function value corresponding to the current voltage of the low-voltage distribution network being U; f QU (U) represents the initial voltage-reactive power control function value when the voltage of the low-voltage distribution network is U; It represents the reactive power command of household photovoltaic in the day of node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to ; represents the daily operating voltage of node g; represents the voltage of node g under the most serious operation mode of the low-voltage distribution network; Indicates the voltage value of node g at the high point of the voltage control dead zone; represents the reactive power support at node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to .

[0053] Optionally, the step of regulating the voltage of the low-voltage distribution network by using a voltage-reactive power control function and outputting a voltage control strategy of the low-voltage distribution network further includes:

[0054] According to the voltage control strategy of the low-voltage distribution network, active power and reactive power output of household photovoltaic inverters in the low-voltage distribution network are adjusted.

[0055] Based on the same inventive concept, the present invention also provides a voltage regulation system for a low-voltage distribution network containing a high proportion of household photovoltaics, comprising:

[0056] A data acquisition module is used to obtain power operation data of a low-voltage distribution network containing a high proportion of household photovoltaics;

[0057] An intraday optimization module, used to obtain an intraday operation control plan of the low-voltage distribution network based on the power operation data and using a pre-built intraday optimization model;

[0058] An adaptive optimization module is used to obtain reactive power support of the low-voltage distribution network based on the intraday operation control scheme of the low-voltage distribution network and using a pre-built household photovoltaic adaptive dynamic control optimization model;

[0059] A voltage regulation module, used to regulate the voltage of the low-voltage distribution network using a voltage-reactive power control function according to the intraday operation control scheme of the low-voltage distribution network and the reactive power support, and output a voltage control strategy for the low-voltage distribution network;

[0060] Among them, the intraday optimization model is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment of the low-voltage distribution network; the household photovoltaic adaptive dynamic control optimization model is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network.

[0061] Optionally, the system further comprises: an intraday model building module, for:

[0062] Calculating the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the intra-day step information;

[0063] Calculating the voltage deviation of the low-voltage distribution network according to the target voltage of each node in the low-voltage distribution network and the day-ahead decision voltage;

[0064] Calculating the amount of household photovoltaic power abandonment in the low-voltage distribution network according to the household photovoltaic active power prediction value and the household photovoltaic active power dispatch value of the low-voltage distribution network;

[0065] The intra-day objective function is constructed with the goal of minimizing the sum of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network;

[0066] According to the intraday objective function, corresponding intraday constraints are formulated;

[0067] Based on the intraday objective function and the intraday constraints, construct an intraday optimization model;

[0068] The intraday constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, and active uncontrollable household photovoltaic operation constraints.

[0069] Optionally, the expression corresponding to the intraday objective function is as follows:

[0070]

[0071] Among them, F dip represents the intraday objective function value; ωL represents the network loss weight of the low voltage distribution network; s L I represents the network loss normalization coefficient of the low-voltage distribution network; ij,t represents the current value between node i and node j at time t; t = 1…T dip ; T dip represents the total number of steps optimized within a day; ij∈L; L represents the set of all lines in the area; R ij Indicates the resistance value of the line between node i and node j; ΔT dip represents the step size of the intraday optimization model; ω vb represents the voltage offset weight; s vb represents the voltage offset normalization coefficient; B represents the set of all nodes in the substation; U i,t represents the target voltage of node i at time t; represents the day-ahead decision voltage of node i at time t; c pd Represents the penalty coefficient of the household photovoltaic active power control penalty item; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area.

[0072] Optionally, the system further comprises: an adaptive model building module, configured to:

[0073] An adaptive objective function is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network;

[0074] According to the adaptive objective function, corresponding adaptive constraint conditions are formulated;

[0075] Based on the adaptive objective function and the adaptive constraint conditions, a household photovoltaic adaptive dynamic control optimization model is constructed;

[0076] The adaptive constraint conditions include one or more of the following: power compensation constraints and household photovoltaic operation constraints.

[0077] Optionally, the expression corresponding to the adaptive objective function is as follows:

[0078]

[0079] Among them, F rt represents the adaptive objective function value; represents the reduction in active power of the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario; Φ N Represents the set of household photovoltaic grid-connected nodes whose active power in the area is uncontrollable; Φc Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area; c pdr Represents the active power reduction penalty coefficient; It represents the reactive power support provided by the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario.

[0080] Optionally, the power operation data of the low-voltage distribution network includes one or more of the following: line impedance data, household photovoltaic installed capacity data, household photovoltaic adjustable capacity data, parallel capacitor data, on-load tap-changing transformer tap data and user load data.

[0081] Optionally, the intraday optimization module includes:

[0082] A day-ahead control submodule, configured to obtain a day-ahead operation control plan of the low-voltage distribution network according to the power operation data and using a pre-built day-ahead optimization model;

[0083] The power value acquisition submodule is used to obtain the predicted power of household photovoltaic power generation and user load in the substation area;

[0084] The intraday control submodule is used to obtain the intraday operation control plan of the low-voltage distribution network according to the day-ahead operation control plan, the power operation data, the predicted power of household photovoltaic power generation in the substation area and the predicted power of the user load using a pre-built intraday optimization model;

[0085] The day-ahead operation control scheme includes one or more of the following: the on-load voltage regulating tap position, the number of parallel capacitors switched, the day-ahead active power instruction of household photovoltaics, the day-ahead reactive power instruction of household photovoltaics, and the day-ahead operation voltage of the node in the preset day-ahead target time period;

[0086] The intraday operation control scheme includes one or more of the following: household photovoltaic active intraday power instructions, household photovoltaic intraday reactive power instructions and node intraday operation voltage in a preset intraday target time period.

[0087] Optionally, the intraday optimization module further includes: a day-ahead model building submodule, which is used to:

[0088] Calculating the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the day-ahead step information;

[0089] Calculating the voltage fluctuation of the low-voltage distribution network according to the target voltage and the rated voltage of each node in the low-voltage distribution network;

[0090] Calculating the amount of household photovoltaic power abandonment in the low-voltage distribution network according to the household photovoltaic active power prediction value and the household photovoltaic active power dispatch value of the low-voltage distribution network;

[0091] A day-ahead objective function is constructed with the goal of minimizing the sum of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network;

[0092] According to the day-ahead objective function, formulate corresponding day-ahead constraint conditions;

[0093] Based on the day-ahead objective function and the day-ahead constraint condition, construct a day-ahead optimization model;

[0094] Among them, the day-ahead constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, active uncontrollable household photovoltaic operation constraints, distribution transformer constraints and parallel capacitor constraints.

[0095] Optionally, the voltage regulation module includes:

[0096] A primary voltage regulation submodule, for regulating the voltage of the low-voltage distribution network based on the initial voltage-reactive power control function value corresponding to the current voltage when the current voltage of the low-voltage distribution network is less than the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic intraday reactive power instruction;

[0097] A secondary voltage regulation submodule, for regulating the voltage of the low-voltage distribution network based on the household photovoltaic intraday reactive power instruction when the current voltage of the low-voltage distribution network is greater than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic intraday reactive power instruction and is less than the intraday operating voltage of the node;

[0098] The third-level voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the household photovoltaic intra-day reactive power instruction and the intra-day operating voltage of the node when the current voltage of the low-voltage distribution network is greater than or equal to the intra-day operating voltage of the node and is less than the voltage of the node under the preset most serious operating mode of the low-voltage distribution network;

[0099] A four-level voltage regulation submodule, for regulating the voltage of the low-voltage distribution network based on the household photovoltaic intra-day reactive power command and the reactive power support when the current voltage of the low-voltage distribution network is greater than the voltage of the node under the most serious operation mode of the low-voltage distribution network and is less than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic intra-day reactive power command and the reactive power support;

[0100] The five-level voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the initial voltage-reactive power control function value corresponding to the current voltage when the current voltage of the low-voltage distribution network is greater than the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic intra-day reactive power instruction and the reactive power support.

[0101] Optionally, the expression corresponding to the voltage-reactive power control function is as follows:

[0102]

[0103] Among them, f IQU (U) represents the voltage-reactive power control function value corresponding to the current voltage of the low-voltage distribution network being U; f QU (U) represents the initial voltage-reactive power control function value when the voltage of the low-voltage distribution network is U; It represents the reactive power command of household photovoltaic in the day of node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to ; represents the daily operating voltage of node g; represents the voltage of node g under the most serious operation mode of the low-voltage distribution network; Indicates the voltage value of node g at the high point of the voltage control dead zone; represents the reactive power support at node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to .

[0104] Optionally, the system further includes: a power regulation module, configured to:

[0105] According to the voltage control strategy of the low-voltage distribution network, active power and reactive power output of household photovoltaic inverters in the low-voltage distribution network are adjusted.

[0106] In another aspect, the present invention further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0107] The memory is used to store one or more programs;

[0108] When the one or more programs are executed by the at least one processor, a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics as described above is implemented.

[0109] On the other hand, the present invention further provides a computer-readable storage medium having an execution program stored thereon, which, when executed, implements the voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics as described above.

[0110] Compared with the prior art, the present invention has the following beneficial effects:

[0111] The present invention provides a voltage regulation method and system for a low-voltage distribution network containing a high proportion of household photovoltaics, comprising: obtaining power operation data of a low-voltage distribution network containing a high proportion of household photovoltaics; based on the power operation data, using a pre-constructed intraday optimization model to obtain an intra-day operation regulation scheme of the low-voltage distribution network; based on the intra-day operation regulation scheme of the low-voltage distribution network, using a pre-constructed household photovoltaic adaptive dynamic control optimization model to obtain reactive power support of the low-voltage distribution network; according to the intra-day operation regulation scheme of the low-voltage distribution network and the reactive power support, using a voltage-reactive power control function to regulate the voltage of the low-voltage distribution network, outputting a voltage control strategy of the low-voltage distribution network, and controlling the voltage of the low-voltage distribution network by means of a household photovoltaic inverter. The device regulates the voltage of the low-voltage distribution network; wherein the intraday optimization model is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment of the low-voltage distribution network; the household photovoltaic adaptive dynamic control optimization model is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network; the application can consider the global information of the entire low-voltage distribution network through centralized optimization control, and consider the global optimality of the network loss and voltage in the substation area; the adaptive control can quickly respond to dynamic changes in the power grid; therefore, the present invention can not only ensure the optimal overall performance of the system by combining centralized optimization control and adaptive control, but also cope with minute-level power fluctuations caused by household photovoltaic irradiation. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 A schematic flow chart of a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics provided by the present invention;

[0113] Figure 2 A schematic diagram of a low-voltage distribution network topology for a method for regulating voltage in a low-voltage distribution network containing a high proportion of household photovoltaic power generation provided by a specific embodiment of the present invention;

[0114] Figure 3 A schematic diagram showing a comparison of the voltage of node 18 between a conventional centralized control strategy (SOC) and the control strategy (CSDC) of the present invention under a 5% prediction error in a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics provided by a specific embodiment of the present invention;

[0115] Figure 4A schematic diagram showing a comparison of the voltage of node 18 between a conventional centralized control strategy (SOC) and the control strategy (CSDC) of the present invention under a 10% prediction error in a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics provided by a specific embodiment of the present invention;

[0116] Figure 5 A schematic diagram of the voltage comparison of node 18 between a conventional adaptive control strategy (SLC) and the control strategy (CSDC) of the present invention under a 10% prediction error in a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics provided by a specific embodiment of the present invention;

[0117] Figure 6 A schematic diagram of voltage distribution of all nodes under the control strategy (CSDC) of the present invention with a 10% prediction error in a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics provided by a specific embodiment of the present invention;

[0118] Figure 7 A schematic diagram of the composition of a voltage regulation system for a low-voltage distribution network with a high proportion of household photovoltaics provided by the present invention;

[0119] Figure 8 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0120] The present invention provides a voltage regulation method, system, device and medium for a low-voltage distribution network containing a high proportion of household photovoltaics. The specific implementation methods of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0121] Embodiment 1:

[0122] The present invention provides a method for regulating the voltage of a low-voltage distribution network containing a high proportion of household photovoltaics. The flow chart is as follows: Figure 1 As shown, including:

[0123] Step 1: Obtain the power operation data of the low-voltage distribution network with a high proportion of household photovoltaics;

[0124] Step 2: Based on the power operation data, the pre-built intraday optimization model is used to obtain the intraday operation control plan of the low-voltage distribution network;

[0125] Step 3: Based on the intraday operation control scheme of the low-voltage distribution network, the reactive power support of the low-voltage distribution network is obtained by using the pre-built household photovoltaic adaptive dynamic control optimization model;

[0126] Step 4: According to the intraday operation control plan and reactive power support of the low-voltage distribution network, the voltage-reactive power control function is used to adjust the voltage of the low-voltage distribution network, and the voltage control strategy of the low-voltage distribution network is output;

[0127] Among them, the intraday optimization model is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment in the low-voltage distribution network; the household photovoltaic adaptive dynamic control optimization model is constructed with the goal of minimizing the difference between active power reduction and reactive power support in the low-voltage distribution network.

[0128] With the continuous advancement and popularization of renewable energy technology, the installation ratio of household photovoltaic systems in low-voltage distribution networks continues to rise, bringing significant green and economic benefits to the power system. However, this high proportion of household photovoltaic access has also brought unprecedented challenges to the operation management and optimization regulation of low-voltage distribution networks. In order to meet these challenges, it is particularly important to develop an efficient and intelligent voltage control strategy. First of all, in order to achieve effective management of low-voltage distribution networks with a high proportion of household photovoltaics, it is necessary to obtain its detailed power operation data. For example, these data may include but are not limited to line impedance data, household photovoltaic installed capacity data, household photovoltaic adjustable capacity data, parallel capacitor data, on-load tap changer (On-Load Tap Changer, OLTC) data and user load data and other key parameters. These data are not only the basis for subsequent optimization and regulation, but also an important basis for evaluating system performance and formulating regulation strategies. On this basis, by constructing an intraday optimization model, the model aims to minimize the network loss, voltage fluctuation and the sum of household photovoltaic power abandonment of the low-voltage distribution network, and comprehensively considers the randomness of photovoltaic power generation, the volatility of load demand and the constraints of the power grid. Through this model, the intraday operation control scheme of the low-voltage distribution network can be obtained, which aims to achieve economic operation of the power grid, voltage stability and efficient utilization of household photovoltaics. However, the intraday operation control scheme alone is not enough to deal with the reactive power problem caused by household photovoltaic access. Therefore, by further constructing a household photovoltaic adaptive dynamic control optimization model, the model aims to minimize the difference between the active power reduction and reactive power support of the low-voltage distribution network. It can adjust the reactive output of the household photovoltaic system in real time according to the intraday operation control scheme, provide the necessary reactive power support for the power grid, and thus ensure the stable operation of the power grid. Finally, in order to achieve voltage regulation of the low-voltage distribution network, the voltage-reactive control function is used, and a specific voltage control strategy is formulated according to the intraday operation control scheme and reactive power support. This strategy can comprehensively consider the actual situation and needs of the power grid and ensure the stability and safety of the voltage. Specifically:

[0129] In one implementation, the process of obtaining the intraday operation control scheme of the low-voltage distribution network based on the power operation data and using the pre-built intraday optimization model in step 2 may include:

[0130] According to the power operation data, the pre-built day-ahead optimization model is used to obtain the day-ahead operation control plan of the low-voltage distribution network;

[0131] The predicted power of household photovoltaic power generation in the area and the predicted power of user load are obtained. Preferably, the predicted power of household photovoltaic power generation in the area and the predicted power of user load can be set to the predicted power of household photovoltaic power generation in the area and the user load power in the next 24 hours required for the day-ahead optimization.

[0132] According to the day-ahead operation and control plan, power operation data, predicted power of household photovoltaic power generation in the substation area and predicted power of user load, the intraday operation and control plan of the low-voltage distribution network is obtained by using the pre-built intraday optimization model;

[0133] The day-ahead operation control scheme may include one or more of the following: the on-load voltage regulating tap position, the number of parallel capacitors switched, the day-ahead active power instruction of household photovoltaics, the day-ahead reactive power instruction of household photovoltaics, and the day-ahead operating voltage of the node in the preset day-ahead target time period;

[0134] The intraday operation control scheme may include one or more of the following: household photovoltaic active intraday power instructions, household photovoltaic intraday reactive power instructions and node intraday operation voltage in a preset intraday target time period;

[0135] In this implementation, by combining the day-ahead optimization model and the intraday optimization model, the power operation data, the predicted power of household photovoltaic power generation in the substation area and the predicted power of user load can be fully utilized to realize the accurate prediction and real-time regulation of the operation status of the low-voltage distribution network. This dual optimization method is conducive to improving the accuracy and response speed of regulation, so as to better adapt to the volatility of photovoltaic power generation and user load; by formulating the day-ahead and intraday operation and regulation plans, the key parameters such as the on-load voltage regulating tap position, the number of parallel capacitor switching, and the household photovoltaic active and reactive power instructions can be reasonably arranged, so as to effectively reduce the network loss of the low-voltage distribution network and improve the operation efficiency of the power grid; by accurately controlling the node operating voltage, it can ensure that the voltage of the low-voltage distribution network remains within a stable range when the photovoltaic power generation and user load fluctuate, which is conducive to improving the power supply quality and stability of the power grid and reducing the power grid failure caused by voltage fluctuations; by reasonably formulating the household photovoltaic active and reactive power instructions, it is possible to maximize the use of photovoltaic power generation resources, reduce the amount of abandoned electricity, and improve the utilization rate of renewable energy, which is of great significance for promoting the development of green energy and realizing the optimization of energy structure.

[0136] In this implementation, the above-mentioned day-ahead optimization model may include the following construction process:

[0137] Calculate the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the day-ahead step information;

[0138] Calculate the voltage fluctuation of the low-voltage distribution network according to the target voltage and rated voltage of each node in the low-voltage distribution network;

[0139] Calculate the amount of household photovoltaic power abandonment in the low-voltage distribution network based on the predicted value of household photovoltaic active power and the dispatch value of household photovoltaic active power in the low-voltage distribution network;

[0140] The day-ahead objective function is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment in the low-voltage distribution network.

[0141] According to the day-ahead objective function, formulate the corresponding day-ahead constraints;

[0142] Based on the day-ahead objective function and day-ahead constraints, a day-ahead optimization model is constructed. In this implementation, a comprehensive day-ahead objective function is constructed by comprehensively considering the three key indicators of network loss, voltage fluctuation and household photovoltaic power abandonment. This function aims to minimize the sum of these indicators, thereby achieving comprehensive optimization of the operating status of the low-voltage distribution network. This comprehensive optimization method is conducive to improving the overall performance of the power grid, including reducing losses, stabilizing voltage and improving the utilization rate of renewable energy. In addition, by utilizing the current information, line resistance information and day-ahead step information between each node in the low-voltage distribution network, the network loss can be calculated more accurately. At the same time, by combining the target voltage and rated voltage of each node, the voltage fluctuation can be more accurately evaluated. In addition, by comparing the predicted value and dispatched value of household photovoltaic active power, the abandoned power can be calculated more effectively. These accurate predictions and calculations provide strong support for the formulation of effective day-ahead operation and control plans.

[0143] For example, the expression corresponding to the above-mentioned day-ahead objective function can be as follows:

[0144]

[0145] Among them, F dap represents the day-ahead objective function value; ω L represents the network loss weight of the low voltage distribution network; s L I represents the network loss normalization coefficient of the low-voltage distribution network; ij,t represents the current value between node i and node j at time t; t = 1…T dap ; T dap represents the total number of steps for the day-ahead optimization, for example, 24h (i.e., the number of hours in a day); ij∈L; L represents the set of all lines in the area; R ij Indicates the resistance value of the line between node i and node j; ΔT dap represents the step size of the day-ahead optimization model. When T dap Take 24h, ΔT dap You can take 1h; ω vbrepresents the voltage offset weight; s vb represents the voltage offset normalization coefficient; B represents the set of all nodes in the substation; U i,t represents the target voltage of node i at time t; represents the rated voltage of node i at time t; c pd Represents the penalty coefficient of the household photovoltaic active power control penalty item; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; Φ c It represents the set of household photovoltaic grid-connected nodes with controllable active power in the substation area. In this expression, the first term after the equation corresponds to the network loss of the low-voltage distribution network, the second term corresponds to the voltage fluctuation of the low-voltage distribution network, and the third term corresponds to the household photovoltaic power abandonment of the low-voltage distribution network. When constructing the day-ahead objective function, the network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network are taken as the targets. By clearly quantifying the three key indicators of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network, the optimization process has a clear goal and direction, that is, minimizing the sum of these indicators. This quantification process is conducive to more accurate evaluation and optimization of the operation status of the power grid; in the process of constructing the day-ahead objective function, weight coefficients (such as network loss weight ω L , voltage offset weight ω vb ) and normalization coefficients (such as network loss normalization coefficient s L , voltage offset normalization coefficient s vb ), which can balance the priority and importance of different indicators, thereby improving the efficiency and accuracy of optimization. At the same time, these coefficients can also be adjusted according to actual needs to adapt to different optimization scenarios and conditions; in addition, the objective function comprehensively considers multiple factors such as current, resistance, voltage, household photovoltaic active power of the low-voltage distribution network, as well as their interactions and influences, which enables the optimization process to more comprehensively consider the actual situation and operating status of the power grid, thereby formulating a more reasonable optimization strategy.

[0146] By way of example, the above-mentioned day-ahead constraints may include one or more of the following: node power balance constraints, line flow constraints, grid safety constraints, active controllable household photovoltaic operation constraints, active uncontrollable household photovoltaic operation constraints, distribution transformer constraints and parallel capacitor constraints; in this example, the day-ahead optimization stage relies on the computing power of the distribution cloud master station or the power consumption information collection master station, adopts the master station centralized decision-making mode, establishes the day-ahead objective function, formulates the day-ahead constraints, thereby constructing the day-ahead optimization model, and finally outputs the optimization results (also the output results) through the model; by formulating the day-ahead constraints involved in this example, it can be ensured that the power grid meets various safety and technical requirements during the optimization process. These constraints jointly ensure the power balance, reasonable flow distribution and safe operation of equipment in the day-ahead dispatch plan of the power system, effectively prevents instability such as overload and voltage fluctuations, and improves the stability and safety of the entire power system.

[0147] For example, the expression of the above node power balance constraint can be as follows:

[0148]

[0149] Among them, P ij,t represents the active power value flowing through the line ij between node i and node j at time t; Q ij,t I represents the reactive power value flowing through the line ij between node i and node j at time t; ij,t represents the current value between node i and node j at time t; R ij,t represents the resistance value between node i and node j at time t; X ij,t represents the reactance value between lines ij at time t; P G,j,t represents the active power output of household photovoltaic power at node i at time t; Q G,j,t P represents the household photovoltaic reactive power output of node i at time t; L,j,t represents the active load of node i at time t; Q L,j,t P represents the reactive power load of node i at time t; jk,t represents the active power value flowing through line jk between node j and node k at time t; Q CB,j,t represents the reactive power output of the parallel capacitor at node j at time t; Q jk,trepresents the reactive power value flowing through the line jk between node j and node k at time t; O(j) represents the set of subordinate lines of node j; in this example, by accurately calculating the active power and reactive power of each node at a specific time, the system can maintain a stable operating state when facing external interference such as load changes and fluctuations in renewable energy output. By timely adjusting the power output and input of each node, the system can respond quickly and restore balance, which is conducive to preventing overload, voltage fluctuations and other problems, thereby improving the overall stability and ensuring the power balance of the entire power system.

[0150] For example, the expression corresponding to the above line power flow constraint can be as follows:

[0151]

[0152] Among them, U i,t represents the target voltage of node i at time t; U j,t represents the target voltage of node j at time t; R ij represents the resistance value of line ij between node i and node j; P ij,t represents the active power value flowing through the line ij between node i and node j at time t; X ij Indicates the reactance value between lines ij; Q ij,t Represents the reactive power value flowing through line ij at time t; I ij,t represents the current value between node i and node j at time t; Q ij Represents the reactive power value flowing through line ij; in this example, the expression corresponding to the line flow constraint describes in detail the power flow relationship between the lines of each node in the power system, and takes into account the influence of parameters such as voltage, resistance, and reactance. This constraint condition ensures that the line operates within the rated capacity by limiting the active power and reactive power flowing through the line, avoiding safety problems such as overload and overheating, which is beneficial to protecting line equipment, extending its service life, and reducing the risk of power outages caused by faults; and through this constraint condition, the flow can be distributed more reasonably, making the power flow more balanced and efficient, thereby reducing line losses, improving energy utilization efficiency, and reducing the operating cost of the power system.

[0153] For example, the expression corresponding to the above power grid security constraint is as follows:

[0154]

[0155] Among them, I ij,t represents the current value between node i and node j at time t; represents the lower limit of the transmission current between node i and node j at time t; represents the upper limit of the current transmitted between node i and node j at time t; U i,trepresents the target voltage of node i at time t; represents the safe operating lower limit of the voltage at node i at time t; Represents the safe operating upper limit of the voltage at node i at time t. In this example, the expression corresponding to the grid safety constraint describes in detail the safe operating range of current and voltage between nodes in the power system, which can prevent excessive current or excessively high / low voltage from damaging grid equipment. By limiting the fluctuation range of current and voltage, the system can transition to a new operating state more smoothly, thereby avoiding unstable phenomena such as voltage collapse and current overload.

[0156] For example, the expression corresponding to the above active controllable household photovoltaic operation constraint is as follows:

[0157]

[0158] in, It represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; S represents the reactive power dispatch value of household photovoltaic grid-connected node g at time t; G,g S represents the capacity of the household photovoltaic inverter connected to the grid at node g; G,g represents the capacity of the household photovoltaic inverter connected to the grid at node g; θ g Indicates the maximum power factor angle of household photovoltaic connected to the grid at node g; Φ c Represents a set of household photovoltaic grid-connected nodes with controllable active power in the substation area; in this example, the expression corresponding to the active controllable household photovoltaic operation constraint describes in detail the relationship between the active power and reactive power dispatching values ​​of the household photovoltaic grid-connected nodes in the power system and their predicted values, as well as the inverter capacity. By accurately controlling the active power and reactive power of the household photovoltaic grid-connected nodes, it is ensured that the household photovoltaics are dispatched according to the predetermined power when connected to the grid, which is conducive to maximizing the use of renewable energy and reducing energy waste. In addition, the constraint condition allows household photovoltaics to adjust power according to actual needs when connected to the grid, which enhances the flexibility of the power system, helps the system better adapt to load changes and fluctuations in renewable energy output, and improves the response speed and regulation capability of the system.

[0159] For example, the expression corresponding to the above active uncontrollable household photovoltaic operation constraint is as follows:

[0160]

[0161] Among them, Φ NRepresents the set of household photovoltaic grid-connected nodes with uncontrollable active power in the substation area. In this example, the uncontrollable active power household photovoltaic operation constraints allow household photovoltaics to adjust power according to actual needs when connected to the grid, which enhances the flexibility of the power system, helps the system better adapt to load changes and fluctuations in renewable energy output, and improves the system's response speed and regulation capabilities.

[0162] For example, the expression corresponding to the above distribution transformer constraint is as follows:

[0163]

[0164] Among them, U i,t represents the target voltage of node i at time t; Δδ i K represents the step length of the gear adjustment of the distribution transformer at node i; i,t Indicates the operating gear position of the distribution transformer at node i at time t; Indicates that the distribution transformer at node i is adjusted up or down to the maximum gear position; represents the rated voltage of node i at time t; in this example, the expression corresponding to the distribution transformer constraint describes in detail the various factors that need to be considered when the distribution transformer in the power system adjusts the gear, including the target voltage, gear adjustment step, current operating gear, maximum adjustment gear and rated voltage, etc. This constraint can ensure that the distribution transformer can accurately reach the required voltage level when adjusting the gear, which is conducive to optimizing the voltage distribution of the power system, improving the voltage quality, and reducing voltage fluctuations and deviations; as an important part of the power system, the stability of the operating state of the distribution transformer directly affects the stability of the entire system. By setting the operating gear and maximum adjustment gear of the distribution transformer, the constraint can limit the gear adjustment range of the transformer to prevent the system from being impacted by excessive adjustment, which is conducive to enhancing the stability of the system and improving the system's anti-disturbance capability.

[0165] For example, the expression corresponding to the above parallel capacitor constraint is as follows:

[0166]

[0167] Among them, Q CB,i,t represents the total reactive output power of the parallel capacitor at node i at time t; q CB,i,t represents the reactive output power of a single shunt capacitor at node i at time t; k CB,i represents the number of parallel capacitors connected to node i; represents the upper limit of the number of parallel capacitors connected to node i; Represents the lower limit of the number of parallel capacitors put into use at node i; In this example, the expression corresponding to the parallel capacitor constraint describes in detail the operating rules and restrictions of the parallel capacitors in the power system in terms of reactive power compensation. By setting the total reactive output power Q of the parallel capacitors CB,i,t Reactive output power q of a single shunt capacitor CB,i,t And the input quantity k CB,i This constraint can ensure that the parallel capacitor can accurately provide the required reactive power during reactive compensation, which is beneficial to optimize the reactive power distribution of the power system, reduce the flow of reactive power, and reduce line losses.

[0168] In this implementation, the day-ahead optimization model constructed based on the above-mentioned day-ahead objective function and day-ahead constraints is solved to obtain a day-ahead operation control plan. For example, the day-ahead operation control plan may include: the on-load voltage regulation tap position, the number of shunt capacitors switched, the day-ahead power instructions of household photovoltaic active / reactive power, and the day-ahead operation voltage of the node in each period of 24 hours the next day; based on the day-ahead operation control plan output by the day-ahead optimization model, an intraday optimization model of the low-voltage distribution network can be constructed, specifically:

[0169] In one implementation, the above intraday optimization model may include the following construction process:

[0170] Calculate the network loss of the low-voltage distribution network based on the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes, and the intra-day step information;

[0171] Calculate the voltage deviation of the low-voltage distribution network according to the target voltage of each node in the low-voltage distribution network and the day-ahead decision voltage;

[0172] Calculate the amount of household photovoltaic power abandonment in the low-voltage distribution network based on the predicted value of household photovoltaic active power and the dispatch value of household photovoltaic active power in the low-voltage distribution network;

[0173] The intraday objective function is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment in the low-voltage distribution network;

[0174] According to the intraday objective function, formulate corresponding intraday constraints;

[0175] Construct an intraday optimization model based on the intraday objective function and intraday constraints;

[0176] In this implementation, the power operation data of the low-voltage distribution network, the predicted power of household photovoltaic power generation in the substation (for example, the predicted power generation every 15 minutes within 1 hour after the current moment), the predicted power of user load (for example, the predicted load power every 15 minutes within 1 hour after the current moment), and the day-ahead operation control plan at the current moment are obtained through the day-ahead optimization model (at least including: the on-load voltage regulating tap position, the number of parallel capacitors switched, the day-ahead power instructions of household photovoltaic active / reactive power, and the day-ahead operation voltage of the node), and the intraday optimization model is constructed. The model construction process includes: intraday objective function, intraday constraints, and intraday optimization results (that is, the output results of the intraday optimization model). In the intraday optimization stage, relying on the edge computing capabilities of the substation fusion terminal, the terminal centralized decision-making mode is adopted. Preferably, in order to accurately perform intraday optimization, the time step can be 15 minutes. By taking the network loss, voltage deviation and household photovoltaic power abandonment of the low-voltage distribution network as the target, the intraday objective function is established, and the intraday constraints are formulated according to the intraday objective function to obtain the constructed intraday optimization model, and finally the intraday optimization results are output;

[0177] For example, the expression corresponding to the above intraday objective function can be as follows:

[0178]

[0179]

[0180] Among them, F dip represents the intraday objective function value; ω L represents the network loss weight of the low-voltage distribution network; s L Represents the network loss normalization coefficient of the low-voltage distribution network; I ij,t represents the current value between node i and node j at time t; t = 1…T dip ; T dip Indicates the total number of steps optimized within a day. When the time step ΔT dip When taking 15min, T dip Take 4; ij∈L; L represents the set of all lines in the area; R ij Indicates the resistance value of the line between node i and node j; ΔT dip Indicates the time step of the intraday optimization model. In order to accurately perform intraday optimization, the time step ΔT dip You can take 15min; vb represents the voltage offset weight; s vb represents the voltage offset normalization coefficient; B represents the set of all nodes in the substation; U i,t represents the target voltage of node i at time t; represents the day-ahead decision voltage of node i at time t; c pdRepresents the penalty coefficient of the household photovoltaic active power control penalty item; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; Φ c Represents a set of household photovoltaic grid-connected nodes with controllable active power in the substation area; in this example, the first term after the equation corresponds to the network loss of the low-voltage distribution network, the second term corresponds to the voltage deviation of the low-voltage distribution network, and the third term corresponds to the household photovoltaic power abandonment of the low-voltage distribution network; in this example, by comprehensively considering the three key indicators of network loss, voltage deviation and household photovoltaic power abandonment of the low-voltage distribution network, they are weighted and summed and minimized, thereby achieving a comprehensive optimization of the grid operation status. This comprehensive optimization method is conducive to balancing the economy, stability and renewable energy utilization of the grid; the time step ΔT is clearly stated in the expression dip It can be 15 minutes. The shorter time step enables the system to respond more quickly to changes in the power grid state, such as load fluctuations and changes in photovoltaic output, which is conducive to more accurately capturing changes in the power grid state and improving the real-time and accuracy of optimization. In addition, by introducing the network loss weight ω L , voltage offset weight ω vb and the penalty coefficient c of the penalty item of household photovoltaic active power control pd , allowing the priority of each optimization objective to be flexibly adjusted according to actual needs. This flexibility is conducive to achieving the optimal grid operation state under different operation scenarios.

[0181] For example, the above-mentioned intraday constraints may include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, and active uncontrollable household photovoltaic operation constraints.

[0182] For example, the expression of the above node power balance constraint can be as follows:

[0183]

[0184] Among them, P ij,t represents the active power value flowing through the line ij between node i and node j at time t; Q ij,t I represents the reactive power value flowing through the line ij between node i and node j at time t; ij,t represents the current value between node i and node j at time t; R ij,t represents the resistance value between node i and node j at time t; X ij,t Represents the reactance value between lines ij; P G,j,t represents the active power output of household photovoltaic power at node i at time t; Q G,j,t P represents the reactive power output of household photovoltaic power at node i at time t;L,j,t represents the active load of node i at time t; Q L,j,t P represents the reactive power load of node i at time t; jk,t represents the active power value flowing through line jk between node j and node k at time t; Q CB,j,t represents the reactive power output of the shunt capacitor at node j at time t; Q jk,t represents the reactive power value flowing through line jk between node j and node k at time t; O(j) represents the set of lower-level lines of node j;

[0185] For example, the expression corresponding to the above line power flow constraint can be as follows:

[0186]

[0187] Among them, U i,t represents the target voltage of node i at time t; U j,t represents the target voltage of node j at time t; R ij represents the resistance value of the line between node i and node j; P ij,t represents the active power value flowing through the line ij between node i and node j at time t; X ij Indicates the reactance value between lines ij; Q ij,t Represents the reactive power value flowing through line ij at time t; I ij,t represents the current value between node i and node j at time t; Q ij Indicates the reactive power value flowing through line ij;

[0188] For example, the expression corresponding to the above power grid security constraint is as follows:

[0189]

[0190] Among them, I ij,t represents the current value between node i and node j at time t; represents the lower limit of the transmission current between node i and node j at time t; represents the upper limit of the current transmitted between node i and node j at time t; U i,t represents the target voltage of node i at time t; represents the safe operating lower limit of the voltage at node i at time t; represents the safe operating upper limit of the voltage at node i at time t;

[0191] For example, the expression corresponding to the above active controllable household photovoltaic operation constraint is as follows:

[0192]

[0193] in, It represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; represents the household photovoltaic reactive power dispatch value of the grid-connected node g at time t; θ g S represents the maximum power factor angle of household photovoltaic connected to the grid at node g; G,g represents the capacity of the household photovoltaic inverter connected to the grid at node g; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area;

[0194] For example, the expression corresponding to the above active uncontrollable household photovoltaic operation constraint is as follows:

[0195]

[0196] Among them, Φ N Represents the set of household photovoltaic grid-connected nodes whose active power in the area is uncontrollable;

[0197] In this implementation, the intraday optimization model constructed based on the above-mentioned intraday objective function and intraday constraints is solved to obtain an intraday operation control plan. For example, the intraday operation control plan may include: household photovoltaic active / reactive intraday power instructions and node intraday operating voltage every 15 minutes within 1 hour after the current moment.

[0198] In one implementation, the household photovoltaic adaptive dynamic control optimization model in step 3 above may include the following construction process:

[0199] With the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network (it can also be expressed as taking the active power reduction compensation and reactive power support of household photovoltaic adaptive dynamic control as the goal, both of which have the same meaning), an adaptive objective function is constructed;

[0200] According to the adaptive objective function, formulate corresponding adaptive constraints;

[0201] Based on the adaptive objective function and adaptive constraints, a household photovoltaic adaptive dynamic control optimization model is constructed; in this implementation method, based on the household photovoltaic active / reactive intraday power instructions and node intraday operating voltage output by the intraday optimization model, the household photovoltaic adaptive dynamic control optimization model can be constructed. The purpose of constructing the household photovoltaic adaptive dynamic control optimization model is to obtain household photovoltaic active reduction and reactive support to eliminate the most serious voltage over-limit scenario in the low-voltage distribution network, and adjust the active and reactive output of household photovoltaics in real time, thereby effectively suppressing voltage fluctuations and improving the voltage stability of the power grid; in the model construction process, it includes adaptive objective functions, adaptive constraints and adaptive optimization results (that is, the output results of the household photovoltaic adaptive dynamic control optimization model); the household photovoltaic adaptive dynamic control optimization model constructed by the above method can automatically adjust the control strategy according to the actual operating status of the power grid and changes in the external environment to achieve adaptive control, which enhances the adaptability of the power grid to changes in the external environment and improves the robustness and reliability of the power grid.

[0202] For example, the expression corresponding to the above adaptive objective function can be as follows:

[0203]

[0204] Among them, F rt represents the adaptive objective function value; represents the reduction in active power of the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario; Φ N Represents the set of household photovoltaic grid-connected nodes whose active power in the area is uncontrollable; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area; c pdr Indicates the active power reduction penalty coefficient (used to ensure the minimum power abandonment); represents the reactive power support of the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario; in this example, by minimizing the adaptive objective function F rt , which can optimize the active power reduction of household photovoltaics in voltage-limited scenarios This is conducive to a more reasonable distribution of household photovoltaic power output, reducing unnecessary power loss and voltage fluctuations; the expression also takes into account the reactive power support of household photovoltaics to nodes in the voltage over-limit scenario. This is conducive to improving the voltage stability of the power grid. By increasing reactive power support, it can suppress voltage drops and keep the grid voltage within a reasonable range, thereby ensuring the normal operation of power equipment and the quality of electricity consumption for users. The expression can automatically adjust the power output of household photovoltaics according to the actual operating status of the power grid and changes in the external environment (such as light intensity, temperature, etc.). This adaptive capability is conducive to the power grid to cope with various complex situations and improve the robustness and reliability of the power grid.

[0205] For example, the above-mentioned adaptive constraint conditions may include one or more of the following: power compensation constraint and household photovoltaic operation constraint;

[0206] For example, the expression corresponding to the above power compensation constraint is as follows:

[0207]

[0208] in, It represents the maximum allowable operating voltage of the terminal node e of the low-voltage distribution network; It represents the voltage of the terminal node e under the most serious operation mode of the low-voltage distribution network (corresponding to the operation mode of all household photovoltaics at rated power generation); S P,ge represents the active power-voltage sensitivity of node g and node e; S Q,ge represents the reactive power-voltage sensitivity of node g and node e; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area; Φ N Represents the set of household photovoltaic grid-connected nodes whose active power in the area is uncontrollable; Indicates the reduction in active power of the household photovoltaic system connected to the node g to the node e in the voltage over-limit scenario; represents the reactive power support of the household photovoltaic connected to node g to node e in the voltage over-limit scenario; in this example, the power compensation constraint ensures that the grid voltage fluctuates within the allowable range by considering the relationship between the maximum allowable operating voltage and the actual operating voltage of the terminal node of the low-voltage distribution network, which is conducive to preventing damage to grid equipment and users caused by excessively high or low voltage; the constraint also considers the active-voltage sensitivity S between nodes P,ge and reactive-voltage sensitivity S Q,ge , thereby optimizing the reactive power support of household photovoltaics in voltage-over-limit scenarios. By adjusting the reactive power support, voltage fluctuations can be more effectively suppressed and the stability of the power grid can be improved.

[0209] For example, the expression corresponding to the above household photovoltaic operation constraints is as follows:

[0210]

[0211] in, represents the active power dispatch value of household photovoltaic connected to the grid at node g; θ g S represents the maximum power factor angle of household photovoltaic connected to the grid at node g; G,g Represents the capacity of the household photovoltaic inverter connected to the grid at node g; Represents the reactive power dispatch value of household photovoltaic connected to the grid at node g; In this example, the expression corresponding to the household photovoltaic operation constraint effectively restricts and optimizes the operation of household photovoltaic by setting a series of parameters and conditions. Within the inverter capacity range, the photovoltaic system can be prevented from overloading and the safe and stable operation of the system can be ensured. At the same time, by limiting the reactive power dispatch value The relationship with the maximum power factor angle can ensure that the photovoltaic system can reasonably absorb or emit reactive power while emitting active power to maintain the voltage stability of the power grid; the setting of household photovoltaic operation constraints enables the photovoltaic system to dispatch active and reactive power according to the needs of the power grid, thereby enhancing the flexibility of the power grid.

[0212] Solving the household photovoltaic adaptive dynamic control optimization model constructed based on the above adaptive objective function and adaptive constraints can obtain: the household photovoltaic reactive power support at node g (that is, the reactive power support of the low-voltage distribution network mentioned in the above steps). By obtaining the household photovoltaic active / reactive daily power instructions and node daily operating voltage output by the daily optimization model, the household photovoltaic reactive power support at node g output by the household photovoltaic adaptive dynamic control optimization model, the voltage control strategy of the low-voltage distribution network can be generated to achieve voltage regulation of the low-voltage distribution network. Specifically:

[0213] In one implementation, the process of regulating the voltage of the low-voltage distribution network using the voltage-reactive power control function according to the intraday operation control scheme and reactive power support of the low-voltage distribution network in step 4 may include:

[0214] When the current voltage of the low-voltage distribution network is less than the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic daily reactive power instruction, the voltage of the low-voltage distribution network is regulated based on the initial voltage-reactive power control function value corresponding to the current voltage;

[0215] When the current voltage of the low-voltage distribution network is greater than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic daily reactive power instruction, and is less than the node's daily operating voltage, the voltage of the low-voltage distribution network is regulated based on the household photovoltaic daily reactive power instruction;

[0216] When the current voltage of the low-voltage distribution network is greater than or equal to the node's daily operating voltage and is less than the node's voltage under the preset most severe operating mode of the low-voltage distribution network, the voltage of the low-voltage distribution network is regulated based on the household photovoltaic daily reactive power command and the node's daily operating voltage;

[0217] When the current voltage of the low-voltage distribution network is greater than the voltage of the node under the most serious operation mode of the low-voltage distribution network, and is less than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the reactive power command and reactive power support of the household photovoltaic day, the voltage of the low-voltage distribution network is regulated based on the reactive power command and reactive power support of the household photovoltaic day;

[0218] When the current voltage of the low-voltage distribution network is greater than the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the reactive power command and reactive power support of the household photovoltaic system during the day, the voltage of the low-voltage distribution network is regulated based on the initial voltage-reactive power control function value corresponding to the current voltage; in this implementation, the voltage regulation of the low-voltage distribution network is divided into multiple stages according to the voltage range of the current voltage of the low-voltage distribution network, and different voltage regulation strategies are adopted in each stage. This refined regulation method can more accurately control the grid voltage, keep it within a reasonable range, and improve the stability and safety of the grid; in addition, By considering the impact of household photovoltaic reactive power instructions and reactive power support on voltage regulation during the day, and reasonably allocating reactive power, it is beneficial to reduce reactive losses in low-voltage distribution networks and improve the energy efficiency of the power grid. At the same time, it can also better utilize the reactive support capacity of household photovoltaics and enhance the voltage regulation capability of the power grid. In addition, the scheme also considers the voltage regulation needs of the low-voltage distribution network under different operating scenarios. Whether it is normal operation, voltage over-limit or the most serious operating mode, it can be responded to through corresponding adjustment strategies. This adaptability enables the power grid to better cope with various complex situations, which is beneficial to improving the robustness and reliability of the power grid.

[0219] For example, the expression corresponding to the above voltage-reactive power control function can be as follows:

[0220]

[0221] Among them, f IQU (U) represents the voltage-reactive power control function value corresponding to the current voltage of the low-voltage distribution network is U; f QU (U) represents the initial voltage-reactive power control function value when the voltage of the low-voltage distribution network is U; It represents the reactive power command of household photovoltaic in the day of node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to ; represents the daily operating voltage of node g; It represents the voltage of node g under the most serious operation mode of low-voltage distribution network; Indicates the voltage value of node g at the high point of the voltage control dead zone; represents the reactive power support at node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to the time; In this example, by defining a complex voltage-reactive power control function f IQU (U), and adopt corresponding reactive power regulation strategies according to different voltage ranges. The voltage-reactive power control function can adapt to different grid operation scenarios and voltage requirements. Whether it is normal operation, voltage over-limit or the most serious operation mode, it can be dealt with through corresponding regulation strategies. This flexibility enables the grid to better adapt to various complex situations and improve the robustness and reliability of the grid. In addition, by defining the initial voltage-reactive power control function f QU and its inverse function It is able to accurately calculate the voltage response under different reactive power instructions, thereby achieving accurate management of the grid voltage, which is beneficial to protecting the safe operation of grid equipment and user electrical equipment and improving user satisfaction with electricity consumption. In addition, by optimizing reactive power distribution and reducing reactive power losses in this example, it is able to reduce the operating cost of the grid and improve the economy of the power system. By accurately controlling the voltage, it is also possible to reduce equipment damage and power outages caused by voltage fluctuations, further reducing the maintenance cost of the grid.

[0222] The above voltage-reactive power control function is obtained by improving the initial voltage-reactive power control function. The expression corresponding to the initial voltage-reactive power control function is as follows:

[0223]

[0224] Among them, f QU (U) represents the initial voltage-reactive power control function value when the voltage of the low-voltage distribution network is U; Indicates the voltage value of the high point of the typical curve voltage control dead zone; U indicates the current voltage; Indicates the maximum reactive capacity of the inverter of the household photovoltaic grid-connected node g; Indicates the overvoltage boundary point;

[0225] In this implementation, the household photovoltaic intraday reactive power instruction is introduced based on the initial voltage-reactive power control function. and reactive power support The improved voltage-reactive power control function f is obtained IQU (U), can make the voltage control of low-voltage distribution network more refined; especially in Within the interval, the use of linear interpolation method is conducive to making the control function more continuous and smooth, thereby improving the control accuracy. Different control strategies are used in different voltage intervals to better adapt to different operating conditions and improve the coordination relationship between voltage and reactive power, thereby improving the stability and reliability of the power system.

[0226] In one implementation, the voltage control strategy for outputting the voltage of the low-voltage distribution network by using the voltage-reactive power control function may further include:

[0227] According to the voltage control strategy of the low-voltage distribution network, the active power and reactive power output of the household photovoltaic inverter in the low-voltage distribution network are adjusted.

[0228] In this implementation, by writing the voltage control strategy obtained by the above voltage-reactive power control function into the household photovoltaic inverter at each time interval (for example, every 15 minutes), the household photovoltaic inverter adjusts the active power and reactive power output of the inverter with reference to the voltage control strategy, so that the household photovoltaic inverter can respond to the voltage demand of the power grid in real time to dynamically adjust the active power and reactive power. This rapid response capability is conducive to maintaining the stability of the power grid voltage and improving the dynamic performance of the power grid; and adjusting the output power of the household photovoltaic inverter through the voltage control strategy can effectively adjust the reactive power balance in the power grid, reduce voltage fluctuations and voltage flicker and other problems, and ensure that the photovoltaic system can maximize the use of solar energy resources while meeting the voltage demand of the power grid, so that the photovoltaic system can maximize the use of solar energy resources while meeting the voltage demand of the power grid, promote the consumption of renewable energy, and then promote the optimization and sustainable development of the energy structure.

[0229] In summary, the present invention aims at the existing problem that if only the centralized optimization control method is used for voltage control of the low-voltage distribution network, there are high data transmission delays and large calculation scales, which will lead to the inability to cope with the minute-level power fluctuations caused by household photovoltaics being irradiated; if only the adaptive control method is used, the global optimality of the network loss and voltage in the substation area is not considered, and the preset curve is easy to cause insufficient or wasteful power allocation of controllable devices distributed at the head and end of the line, so that the network loss and voltage level of the entire substation area may not reach the optimal state, resulting in the overall performance degradation. A voltage regulation method for a low-voltage distribution network with a high proportion of household photovoltaics is proposed, which combines centralized optimization control with adaptive control, makes full use of the existing communication conditions and hardware devices of the low-voltage distribution network, and uses centralized optimization control means to optimize the decision of adjustable resources in the day-ahead and intra-day stages, respectively, to reduce the transmission delay, and apply the optimization results to the real-time control stage, and construct a centralized-adaptive dynamic voltage control strategy for household photovoltaics that is adaptive to different control stages, so as to solve the voltage over-limit problem while taking into account the network loss and voltage distribution of the low-voltage distribution network, and make up for the regulation and operation requirements of each time scale, so as to provide a basis and basis for the subsequent safe operation of the terminal power grid supported by household photovoltaics.

[0230] Embodiment 2:

[0231] A specific embodiment is used to illustrate the voltage regulation method of a low-voltage distribution network with a high proportion of household photovoltaics provided by the present invention. For example, a 21-node low-voltage distribution network is used to illustrate the content of the present invention. The rated operating voltage of the low-voltage distribution network is 380V, the reference power is 1MW, the tap position of the on-load voltage-changing distribution transformer at the head end has 10 gears, the adjustable voltage range is set to 0.9375pu~1.0625pu, and the adjustment step is 0.0125pu. Household photovoltaics are installed at 9 nodes, namely nodes 4, 8, 10, 11, 12, 13, 16, 17, and 20, as shown in FIG. Figure 2 As shown in the figure, the red nodes are residential type I loads, the green nodes are residential type II loads, and the blue nodes are industrial and commercial loads. In addition, parallel capacitors are configured at the end nodes 10, 16, 18, and 21, and a single group of capacitors is set with 5 capacitors, and the power of a single capacitor is 5kVar.

[0232] Figure 3-Figure 4 The voltage conditions at node 18 using the traditional centralized control strategy (Centralized Optimization Control, COC) and the control strategy of the present invention (Centralized Self-adaptive Dynamic Control, CSDC) were compared when the errors between real-time fluctuation and intraday prediction were 5% and 10%, respectively. It can be seen from the figure that the real-time voltage during the noon period deviates significantly from the voltage after intraday rolling optimization, and the single control instruction of the COC mode cannot cope with the voltage limit exceeded caused by the photovoltaic power generation power prediction error. By comparison, it can be seen that the CSDC mode can quickly respond to node voltage changes and dynamically provide power support based on the real-time measurement results to effectively suppress the voltage limit exceeded on the basis of ensuring the target voltage fluctuation of the COC mode.

[0233] Figure 5 The voltage conditions at node 18 of the traditional adaptive strategy (Self-adaptive Local Control, SLC) and the control strategy CSDC of the present invention are compared when the error between real-time fluctuation and intraday prediction is 10%. As can be seen from the figure, since the SLC mode is a preset droop control curve, the control strategy lacks global optimization, and the regulation capacity of various controllable resources is not reasonably utilized, so the voltage deviation throughout the day is significantly higher than that of the CSDC mode. In addition, although the SLC mode can also respond quickly to node voltage changes, it is limited by the unified droop control curve, and some node household photovoltaics have insufficient regulation capabilities, resulting in the phenomenon of node voltage exceeding the limit. Figure 6The voltage distribution of all nodes in the low-voltage distribution network under a 10% prediction error throughout the day is shown. It can be seen that the CSDC strategy can not only quickly respond to the real-time changes in node voltage in the voltage regulation of the low-voltage distribution network with a high proportion of household photovoltaics, reduce the voltage deviation range of all nodes in the substation area, and suppress the voltage limit of the low-voltage distribution network, but also optimally allocate the regulation capacity of household photovoltaics in the real-time control stage, and reasonably utilize the regulation capacity of various controllable resources (such as household photovoltaic inverters, parallel capacitors, etc.), thereby achieving more accurate voltage control.

[0234] Embodiment 3:

[0235] Based on the same inventive concept, the present invention also provides a low-voltage distribution network voltage regulation system containing a high proportion of household photovoltaics, the structural composition diagram is shown in FIG. Figure 7 As shown, including:

[0236] A data acquisition module is used to obtain power operation data of a low-voltage distribution network containing a high proportion of household photovoltaics;

[0237] The intraday optimization module is used to obtain the intraday operation control plan of the low-voltage distribution network based on the power operation data and the pre-built intraday optimization model;

[0238] The adaptive optimization module is used for the intraday operation control scheme based on the low-voltage distribution network, using the pre-built household photovoltaic adaptive dynamic control optimization model to obtain the reactive power support of the low-voltage distribution network;

[0239] The voltage regulation module is used to regulate the voltage of the low-voltage distribution network according to the daily operation control plan and reactive power support of the low-voltage distribution network by using the voltage-reactive power control function, and output the voltage control strategy of the low-voltage distribution network;

[0240] Among them, the intraday optimization model is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment in the low-voltage distribution network; the household photovoltaic adaptive dynamic control optimization model is constructed with the goal of minimizing the difference between active power reduction and reactive power support in the low-voltage distribution network.

[0241] In one implementation, the above system may further include: an intraday model building module, specifically used for:

[0242] Calculate the network loss of the low-voltage distribution network based on the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes, and the intra-day step information;

[0243] Calculate the voltage deviation of the low-voltage distribution network according to the target voltage of each node in the low-voltage distribution network and the day-ahead decision voltage;

[0244] Calculate the amount of household photovoltaic power abandonment in the low-voltage distribution network based on the predicted value of household photovoltaic active power and the dispatch value of household photovoltaic active power in the low-voltage distribution network;

[0245] The intraday objective function is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment in the low-voltage distribution network;

[0246] According to the intraday objective function, formulate corresponding intraday constraints;

[0247] Construct an intraday optimization model based on the intraday objective function and intraday constraints;

[0248] Among them, the intraday constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints and active uncontrollable household photovoltaic operation constraints.

[0249] For example, the expression corresponding to the above intraday objective function can be as follows:

[0250]

[0251]

[0252] Among them, F dip represents the intraday objective function value; ω L represents the network loss weight of the low-voltage distribution network; s L Represents the network loss normalization coefficient of the low-voltage distribution network; I ij,t represents the current value between node i and node j at time t; t = 1…T dip ; T dip represents the total number of steps optimized within a day; ij∈L; L represents the set of all lines in the area; R ij Indicates the resistance value of the line between node i and node j; ΔT dip represents the step size of the intraday optimization model; ω vb represents the voltage offset weight; s vb represents the voltage offset normalization coefficient; B represents the set of all nodes in the substation; U i,t represents the target voltage of node i at time t; represents the day-ahead decision voltage of node i at time t; c pd Represents the penalty coefficient of the household photovoltaic active power control penalty item; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area.

[0253] In one implementation, the above system may further include: an adaptive model building module, configured to:

[0254] An adaptive objective function is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network.

[0255] According to the adaptive objective function, formulate corresponding adaptive constraints;

[0256] Based on the adaptive objective function and adaptive constraints, a household photovoltaic adaptive dynamic control optimization model is constructed;

[0257] The adaptive constraint conditions include one or more of the following: power compensation constraints and household photovoltaic operation constraints.

[0258] For example, the expression corresponding to the above adaptive objective function can be as follows:

[0259]

[0260] Among them, F rt represents the adaptive objective function value; represents the reduction in active power of the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario; Φ N Represents the set of household photovoltaic grid-connected nodes whose active power in the area is uncontrollable; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area; c pdr Represents the active power reduction penalty coefficient; It represents the reactive power support provided by the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario.

[0261] For example, the power operation data of the above-mentioned low-voltage distribution network may include one or more of the following: line impedance data, household photovoltaic installed capacity data, household photovoltaic adjustable capacity data, parallel capacitor data, on-load tap-changing transformer tap data and user load data.

[0262] In one implementation, the intraday optimization module may include:

[0263] The day-ahead control submodule is used to obtain the day-ahead operation control plan of the low-voltage distribution network based on the power operation data and the pre-built day-ahead optimization model;

[0264] The power value acquisition submodule is used to obtain the predicted power of household photovoltaic power generation and user load in the substation area;

[0265] The intraday control submodule is used to obtain the intraday operation control plan of the low-voltage distribution network based on the day-ahead operation control plan, power operation data, predicted power of household photovoltaic power generation in the substation area, and predicted power of user load using the pre-built intraday optimization model;

[0266] The day-ahead operation control scheme includes one or more of the following: the on-load voltage regulation tap position, the number of parallel capacitors switched, the day-ahead active power command of household photovoltaics, the day-ahead reactive power command of household photovoltaics, and the day-ahead operating voltage of the node in the preset day-ahead target time period;

[0267] The intraday operation control scheme includes one or more of the following: household photovoltaic active intraday power instructions, household photovoltaic intraday reactive power instructions and node intraday operating voltage in a preset intraday target time period.

[0268] In one implementation, the intraday optimization module may further include a day-ahead model building submodule, specifically configured to:

[0269] Calculate the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the day-ahead step information;

[0270] Calculate the voltage fluctuation of the low-voltage distribution network according to the target voltage and rated voltage of each node in the low-voltage distribution network;

[0271] Calculate the amount of household photovoltaic power abandonment in the low-voltage distribution network based on the predicted value of household photovoltaic active power and the dispatch value of household photovoltaic active power in the low-voltage distribution network;

[0272] The day-ahead objective function is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment in the low-voltage distribution network.

[0273] According to the day-ahead objective function, formulate the corresponding day-ahead constraints;

[0274] Based on the day-ahead objective function and day-ahead constraints, a day-ahead optimization model is constructed;

[0275] Among them, the day-ahead constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, active uncontrollable household photovoltaic operation constraints, distribution transformer constraints and parallel capacitor constraints.

[0276] In one implementation, the voltage regulation module may include:

[0277] The primary voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the initial voltage-reactive power control function value corresponding to the current voltage when the current voltage of the low-voltage distribution network is less than the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic daily reactive power instruction;

[0278] The secondary voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the household photovoltaic intraday reactive power instruction when the current voltage of the low-voltage distribution network is greater than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic intraday reactive power instruction and is less than the node intraday operating voltage;

[0279] The three-level voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the household photovoltaic intra-day reactive power instruction and the node intra-day operating voltage when the current voltage of the low-voltage distribution network is greater than or equal to the node intra-day operating voltage and is less than the node voltage under the preset most serious operation mode of the low-voltage distribution network;

[0280] The four-level voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the reactive power command and reactive power support of household photovoltaics during the day when the current voltage of the low-voltage distribution network is greater than the voltage of the node under the most serious operation mode of the low-voltage distribution network and is less than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the reactive power command and reactive power support of household photovoltaics during the day;

[0281] The five-level voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the initial voltage-reactive power control function value corresponding to the current voltage when the current voltage of the low-voltage distribution network is greater than the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic daily reactive power command and the reactive power support.

[0282] For example, the expression corresponding to the above voltage-reactive power control function can be as follows:

[0283]

[0284] Among them, f IOU (U) represents the voltage-reactive power control function value corresponding to the current voltage of the low-voltage distribution network is U; f QU (U) represents the initial voltage-reactive power control function value when the voltage of the low-voltage distribution network is U; It represents the reactive power command of household photovoltaic in the day of node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to ; represents the daily operating voltage of node g; It represents the voltage of node g under the most serious operation mode of low-voltage distribution network; Indicates the voltage value of node g at the high point of the voltage control dead zone; represents the reactive power support at node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to .

[0285] In one implementation, the above system may further include: a power regulation module, configured to:

[0286] According to the voltage control strategy of the low-voltage distribution network, the active power and reactive power output of the household photovoltaic inverter in the low-voltage distribution network are adjusted.

[0287] Embodiment 4:

[0288] like Figure 8 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0289] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics in the above-mentioned embodiment.

[0290] Embodiment 5:

[0291] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics in the above embodiment.

[0292] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0293] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0294] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0295] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0296] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics, characterized in that: include: Obtain power operation data of low-voltage distribution networks with a high proportion of household photovoltaics; Based on the power operation data, using a pre-built intraday optimization model, an intraday operation control plan of the low-voltage distribution network is obtained; Based on the intraday operation control scheme of the low-voltage distribution network, the reactive power support of the low-voltage distribution network is obtained by using a pre-built household photovoltaic adaptive dynamic control optimization model; According to the intraday operation control scheme of the low-voltage distribution network and the reactive power support, the voltage of the low-voltage distribution network is regulated by using a voltage-reactive power control function, and a voltage control strategy of the low-voltage distribution network is output; Among them, the intraday optimization model is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment of the low-voltage distribution network; the household photovoltaic adaptive dynamic control optimization model is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network.

2. The method according to claim 1, characterized in that The intraday optimization model includes the following construction process: Calculating the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the intra-day step information; Calculating the voltage deviation of the low-voltage distribution network according to the target voltage of each node in the low-voltage distribution network and the day-ahead decision voltage; Calculating the amount of household photovoltaic power abandonment in the low-voltage distribution network according to the household photovoltaic active power prediction value and the household photovoltaic active power dispatch value of the low-voltage distribution network; The intra-day objective function is constructed with the goal of minimizing the sum of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network; According to the intraday objective function, corresponding intraday constraints are formulated; Based on the intraday objective function and the intraday constraints, construct an intraday optimization model; The intraday constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, and active uncontrollable household photovoltaic operation constraints.

3. The method according to claim 2, characterized in that The expression corresponding to the intraday objective function is as follows: Among them, F dip represents the intraday objective function value; ω L represents the network loss weight of the low voltage distribution network; s L I represents the network loss normalization coefficient of the low-voltage distribution network; ij,t represents the current value between node i and node j at time t; t = 1…T dip ; T dip represents the total number of steps optimized within a day; ij∈L; L represents the set of all lines in the area; R ij Indicates the resistance value of the line between node i and node j; ΔT dip represents the step size of the intraday optimization model; ω vb represents the voltage offset weight; s vb represents the voltage offset normalization coefficient; B represents the set of all nodes in the substation; U i，t represents the target voltage of node i at time t; represents the day-ahead decision voltage of node i at time t; c pd Represents the penalty coefficient of the household photovoltaic active power control penalty item; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area.

4. The method according to claim 1, characterized in that The household photovoltaic adaptive dynamic control optimization model includes the following construction process: An adaptive objective function is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network; According to the adaptive objective function, corresponding adaptive constraint conditions are formulated; Based on the adaptive objective function and the adaptive constraint conditions, a household photovoltaic adaptive dynamic control optimization model is constructed; The adaptive constraint conditions include one or more of the following: power compensation constraints and household photovoltaic operation constraints.

5. The method according to claim 4, characterized in that The expression corresponding to the adaptive objective function is as follows: Among them, F rt represents the adaptive objective function value; represents the reduction in active power of the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario; Φ N Represents the set of household photovoltaic grid-connected nodes whose active power in the area is uncontrollable; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area; c pdr represents the active power reduction penalty coefficient; It represents the reactive power support provided by the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario.

6. The method according to any one of claims 1 to 5, characterized in that: The power operation data of the low-voltage distribution network includes one or more of the following: line impedance data, household photovoltaic installed capacity data, household photovoltaic adjustable capacity data, shunt capacitor data, on-load tap-changing transformer tap data and user load data.

7. The method according to claim 1 or 2, characterized in that: The method of obtaining the intraday operation control scheme of the low-voltage distribution network based on the power operation data and using a pre-built intraday optimization model includes: According to the power operation data, using a pre-built day-ahead optimization model, a day-ahead operation control plan of the low-voltage distribution network is obtained; Obtain the predicted power of household photovoltaic power generation and user load in the substation area; According to the day-ahead operation control plan, the power operation data, the predicted power of household photovoltaic power generation in the substation area and the predicted power of the user load, the intraday operation control plan of the low-voltage distribution network is obtained by using a pre-built intraday optimization model; The day-ahead operation control scheme includes one or more of the following: the on-load voltage regulating tap position, the number of parallel capacitors switched, the day-ahead active power instruction of household photovoltaics, the day-ahead reactive power instruction of household photovoltaics, and the day-ahead operation voltage of the node in the preset day-ahead target time period; The intraday operation control scheme includes one or more of the following: household photovoltaic active intraday power instructions, household photovoltaic intraday reactive power instructions and node intraday operation voltage in a preset intraday target time period.

8. The method according to claim 7, characterized in that The day-ahead optimization model includes the following construction process: Calculating the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the day-ahead step information; Calculating the voltage fluctuation of the low-voltage distribution network according to the target voltage and the rated voltage of each node in the low-voltage distribution network; Calculating the amount of household photovoltaic power abandonment in the low-voltage distribution network according to the household photovoltaic active power prediction value and the household photovoltaic active power dispatch value of the low-voltage distribution network; A day-ahead objective function is constructed with the goal of minimizing the sum of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network; According to the day-ahead objective function, formulate corresponding day-ahead constraint conditions; Based on the day-ahead objective function and the day-ahead constraint condition, construct a day-ahead optimization model; Among them, the day-ahead constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, active uncontrollable household photovoltaic operation constraints, distribution transformer constraints and parallel capacitor constraints.

9. The method according to claim 7, characterized in that The voltage regulation of the low-voltage distribution network using a voltage-reactive power control function according to the intraday operation control scheme of the low-voltage distribution network and the reactive power support includes: When the current voltage of the low-voltage distribution network is less than the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic daily reactive power instruction, the voltage of the low-voltage distribution network is regulated based on the initial voltage-reactive power control function value corresponding to the current voltage; When the current voltage of the low-voltage distribution network is greater than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic intraday reactive power instruction, and is less than the intraday operating voltage of the node, the voltage of the low-voltage distribution network is regulated based on the household photovoltaic intraday reactive power instruction; When the current voltage of the low-voltage distribution network is greater than or equal to the intraday operating voltage of the node and is less than the voltage of the node under the preset most serious operating mode of the low-voltage distribution network, the voltage of the low-voltage distribution network is regulated based on the intraday reactive power instruction of the household photovoltaic system and the intraday operating voltage of the node; When the current voltage of the low-voltage distribution network is greater than the voltage of the node under the most serious operation mode of the low-voltage distribution network, and is less than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic intra-day reactive power command and the reactive power support, the voltage of the low-voltage distribution network is regulated based on the household photovoltaic intra-day reactive power command and the reactive power support; When the current voltage of the low-voltage distribution network is greater than the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic daily reactive power command and the reactive power support, the voltage of the low-voltage distribution network is regulated based on the initial voltage-reactive power control function value corresponding to the current voltage.

10. The method according to claim 9, characterized in that The expression corresponding to the voltage-reactive power control function is as follows: Among them, f IQU (U) represents the voltage-reactive power control function value corresponding to the current voltage of the low-voltage distribution network being U; f QU (U) represents the initial voltage-reactive power control function value when the voltage of the low-voltage distribution network is U; It represents the reactive power command of household photovoltaic in the day of node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to ; represents the daily operating voltage of node g; represents the voltage of node g under the most serious operation mode of the low-voltage distribution network; Indicates the voltage value of node g at the high point of the voltage control dead zone; represents the reactive power support at node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to .

11. The method according to claim 1, characterized in that The method further comprises: regulating the voltage of the low-voltage distribution network by using the voltage-reactive power control function and outputting the voltage control strategy of the low-voltage distribution network; According to the voltage control strategy of the low-voltage distribution network, active power and reactive power output of household photovoltaic inverters in the low-voltage distribution network are adjusted.

12. A voltage regulation system for a low-voltage distribution network with a high proportion of household photovoltaics, characterized in that: include: A data acquisition module is used to obtain power operation data of a low-voltage distribution network containing a high proportion of household photovoltaics; An intraday optimization module, used to obtain an intraday operation control plan of the low-voltage distribution network based on the power operation data and using a pre-built intraday optimization model; An adaptive optimization module is used to obtain reactive power support of the low-voltage distribution network based on the intraday operation control scheme of the low-voltage distribution network and using a pre-built household photovoltaic adaptive dynamic control optimization model; A voltage regulation module, used to regulate the voltage of the low-voltage distribution network using a voltage-reactive power control function according to the intraday operation control scheme of the low-voltage distribution network and the reactive power support, and output a voltage control strategy for the low-voltage distribution network; Among them, the intraday optimization model is constructed with the goal of minimizing the sum of network losses, voltage fluctuations and household photovoltaic power abandonment of the low-voltage distribution network; the household photovoltaic adaptive dynamic control optimization model is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network.

13. The system of claim 12, wherein: Also includes: Intraday model building modules for: Calculating the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the intra-day step information; Calculating the voltage deviation of the low-voltage distribution network according to the target voltage of each node in the low-voltage distribution network and the day-ahead decision voltage; Calculating the amount of household photovoltaic power abandonment in the low-voltage distribution network according to the household photovoltaic active power prediction value and the household photovoltaic active power dispatch value of the low-voltage distribution network; The intra-day objective function is constructed with the goal of minimizing the sum of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network; According to the intraday objective function, corresponding intraday constraints are formulated; Based on the intraday objective function and the intraday constraints, construct an intraday optimization model; The intraday constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, and active uncontrollable household photovoltaic operation constraints.

14. The system of claim 13, wherein: The expression corresponding to the intraday objective function is as follows: Among them, F dip represents the intraday objective function value; ω L represents the network loss weight of the low voltage distribution network; s L I represents the network loss normalization coefficient of the low-voltage distribution network; ij，t represents the current value between node i and node j at time t; t = 1…T dip ; T dip represents the total number of steps optimized within a day; ij∈L; L represents the set of all lines in the area; R ij Indicates the resistance value of the line between node i and node j; ΔT dip represents the step size of the intraday optimization model; ω vb represents the voltage offset weight; s vb represents the voltage offset normalization coefficient; B represents the set of all nodes in the substation; U i，t represents the target voltage of node i at time t; represents the day-ahead decision voltage of node i at time t; c pd Represents the penalty coefficient of the household photovoltaic active power control penalty item; It represents the predicted value of household photovoltaic active power connected to the grid at node g at time t; represents the household photovoltaic active power dispatch value of the grid-connected node g at time t; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area.

15. The system of claim 12, wherein: Also includes: Adaptive model building blocks for: An adaptive objective function is constructed with the goal of minimizing the difference between active power reduction and reactive power support of the low-voltage distribution network; According to the adaptive objective function, corresponding adaptive constraint conditions are formulated; Based on the adaptive objective function and the adaptive constraint conditions, a household photovoltaic adaptive dynamic control optimization model is constructed; The adaptive constraint conditions include one or more of the following: power compensation constraints and household photovoltaic operation constraints.

16. The system of claim 15, wherein: The expression corresponding to the adaptive objective function is as follows: Among them, F rt represents the adaptive objective function value; represents the reduction in active power of the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario; Φ N Represents the set of household photovoltaic grid-connected nodes whose active power in the area is uncontrollable; Φ c Represents the set of household photovoltaic grid-connected nodes with controllable active power in the area; c pdr represents the active power reduction penalty coefficient; It represents the reactive power support provided by the household photovoltaic system connected to the grid at node g to node e in the voltage over-limit scenario.

17. The system according to any one of claims 12 to 16, characterized in that: The power operation data of the low-voltage distribution network includes one or more of the following: line impedance data, household photovoltaic installed capacity data, household photovoltaic adjustable capacity data, shunt capacitor data, on-load tap-changing transformer tap data and user load data.

18. The system according to claim 12 or 13, characterized in that The intraday optimization module includes: A day-ahead control submodule, configured to obtain a day-ahead operation control plan of the low-voltage distribution network according to the power operation data and using a pre-built day-ahead optimization model; The power value acquisition submodule is used to obtain the predicted power of household photovoltaic power generation and user load in the substation area; The intraday control submodule is used to obtain the intraday operation control plan of the low-voltage distribution network according to the day-ahead operation control plan, the power operation data, the predicted power of household photovoltaic power generation in the substation area and the predicted power of the user load using a pre-built intraday optimization model; The day-ahead operation control scheme includes one or more of the following: the on-load voltage regulating tap position, the number of parallel capacitors switched, the day-ahead active power instruction of household photovoltaics, the day-ahead reactive power instruction of household photovoltaics, and the day-ahead operation voltage of the node in the preset day-ahead target time period; The intraday operation control scheme includes one or more of the following: household photovoltaic active intraday power instructions, household photovoltaic intraday reactive power instructions and node intraday operation voltage in a preset intraday target time period.

19. The system of claim 18, wherein: The intraday optimization module further includes: a day-ahead model building submodule for: Calculating the network loss of the low-voltage distribution network according to the current information between the nodes in the low-voltage distribution network, the resistance information of the lines between the nodes and the day-ahead step information; Calculating the voltage fluctuation of the low-voltage distribution network according to the target voltage and the rated voltage of each node in the low-voltage distribution network; Calculating the amount of household photovoltaic power abandonment in the low-voltage distribution network according to the household photovoltaic active power prediction value and the household photovoltaic active power dispatch value of the low-voltage distribution network; A day-ahead objective function is constructed with the goal of minimizing the sum of network loss, voltage fluctuation and household photovoltaic power abandonment of the low-voltage distribution network; According to the day-ahead objective function, formulate corresponding day-ahead constraint conditions; Based on the day-ahead objective function and the day-ahead constraint condition, construct a day-ahead optimization model; Among them, the day-ahead constraints include one or more of the following: node power balance constraints, line flow constraints, grid security constraints, active controllable household photovoltaic operation constraints, active uncontrollable household photovoltaic operation constraints, distribution transformer constraints and parallel capacitor constraints.

20. The system of claim 18, wherein: The voltage regulation module comprises: A primary voltage regulation submodule, for regulating the voltage of the low-voltage distribution network based on the initial voltage-reactive power control function value corresponding to the current voltage when the current voltage of the low-voltage distribution network is less than the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic intraday reactive power instruction; A secondary voltage regulation submodule, for regulating the voltage of the low-voltage distribution network based on the household photovoltaic intraday reactive power instruction when the current voltage of the low-voltage distribution network is greater than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the household photovoltaic intraday reactive power instruction and is less than the intraday operating voltage of the node; The third-level voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the household photovoltaic intra-day reactive power instruction and the intra-day operating voltage of the node when the current voltage of the low-voltage distribution network is greater than or equal to the intra-day operating voltage of the node and is less than the voltage of the node under the preset most serious operating mode of the low-voltage distribution network; A four-level voltage regulation submodule, for regulating the voltage of the low-voltage distribution network based on the household photovoltaic intra-day reactive power command and the reactive power support when the current voltage of the low-voltage distribution network is greater than the voltage of the node under the most serious operation mode of the low-voltage distribution network and is less than or equal to the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic intra-day reactive power command and the reactive power support; The five-level voltage regulation submodule is used to regulate the voltage of the low-voltage distribution network based on the initial voltage-reactive power control function value corresponding to the current voltage when the current voltage of the low-voltage distribution network is greater than the inverse function value of the initial voltage-reactive power control function value corresponding to the sum of the household photovoltaic intra-day reactive power instruction and the reactive power support.

21. The system of claim 20, wherein: The expression corresponding to the voltage-reactive power control function is as follows: Among them, f IQU (U) represents the voltage-reactive power control function value corresponding to the current voltage of the low-voltage distribution network being U; f QU (U) represents the initial voltage-reactive power control function value when the voltage of the low-voltage distribution network is U; It represents the reactive power command of household photovoltaic in the day of node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to ; represents the daily operating voltage of node g; represents the voltage of node g under the most serious operation mode of the low-voltage distribution network; Indicates the voltage value of node g at the high point of the voltage control dead zone; represents the reactive power support at node g; The reactive power instruction of household photovoltaic power generation at node g during the day is The inverse function value of the initial voltage-reactive power control function value corresponding to .

22. The system of claim 12, wherein: Also includes: Power conditioning module for: According to the voltage control strategy of the low-voltage distribution network, active power and reactive power output of household photovoltaic inverters in the low-voltage distribution network are adjusted.

23. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics as described in any one of claims 1 to 11 is implemented.

24. A computing device readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a voltage regulation method for a low-voltage distribution network containing a high proportion of household photovoltaics as described in any one of claims 1 to 11 is implemented.

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