Power distribution network dynamic reconfiguration method, device, equipment, storage medium and program product

Abstract

Embodiments of the present application provide a power distribution network dynamic reconstruction method, device, equipment, storage medium and program product. The method comprises: acquiring a predicted load of a power distribution network and a first predicted output of photovoltaic; determining an output order of the photovoltaic, a corresponding energy storage system of the photovoltaic and a plurality of distributed power sources according to load distribution information of the power distribution network; constructing a first objective function between the predicted output of the photovoltaic, the corresponding energy storage system of the photovoltaic and the plurality of distributed power sources and the predicted load of the power distribution network according to the output order, and a second objective function corresponding to the plurality of distributed power sources; solving the first objective function and the second objective function to determine output distribution information representing the output distribution of the photovoltaic, the corresponding energy storage system of the photovoltaic and the plurality of distributed power sources, and access information representing the access of the plurality of distributed power sources to the power distribution network. The method is used to reasonably configure the electric energy generated by the access of the photovoltaic and the distributed power source to the power grid, and to reduce the line loss of the power distribution network.

Description

Technical Field

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

[0002] With the integration of photovoltaic (PV) and distributed power sources (including wind, hydro, and biomass) into the distribution network, line losses have increased. Some of these renewable energy sources are intermittent (e.g., PV power generation varies with solar irradiance, wind power output varies with wind speed, and hydropower output varies seasonally) and volatile (e.g., cloud cover causes minute-level drops in PV power, wind turbulence causes second-level oscillations in wind turbine output, and torrential rains and floods cause short-term abrupt changes in hydropower output), leading to variable power flow directions in the distribution network. Furthermore, excessively large distributed power sources or inappropriate site selection within the distribution network (e.g., far from loads) can also cause reversed power flow directions. These changes in power flow direction reduce the stability of current in the lines, further increasing line losses.

[0003] Related technologies configure electrical energy based on static scenario assumptions. In static scenario assumptions, the power supply of the distribution network is concentrated in large power plants, and the power supply is connected to the entrance of the distribution network (such as substation) through high-voltage transmission lines. The electrical energy of the distribution network flows unidirectionally from the high-voltage substation to the user, without closed loop.

[0004] Based on the above analysis, it can be concluded that because some renewable energy sources and distributed generation sources can cause changes in the power flow direction of the distribution network, the use of related technologies cannot reasonably allocate the electrical energy generated by photovoltaic and distributed generation sources, thereby increasing the line losses of the distribution network. Therefore, how to reasonably allocate the electrical energy generated by these power sources connected to the grid, thereby reducing the line losses of the distribution network, has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, storage medium, and program product for dynamic reconfiguration of power distribution networks, which are used to rationally configure the electrical energy generated by renewable energy access to the power grid, thereby reducing line losses in the power distribution network.

[0006] In a first aspect, embodiments of this application provide a method for dynamic reconfiguration of a distribution network, including:

[0007] In response to the fulfillment of preset update conditions, the predicted load of the distribution network and the first predicted output of photovoltaic power are obtained;

[0008] Based on the load distribution information of the power distribution network, determine the output order of photovoltaic power, the corresponding energy storage system, and multiple distributed power sources.

[0009] Based on the output order, a first objective function is constructed between the predicted output of the photovoltaic system, the energy storage system corresponding to the photovoltaic system, and the multiple distributed power sources and the predicted load of the distribution network, and a second objective function is constructed for the multiple distributed power sources. The second objective function includes the line loss function of the distribution network caused by the distributed power sources. The line loss function is related to the output of the distributed power sources connected to each grid node.

[0010] Solving the first objective function and the second objective function determines the power output distribution information and the access information; wherein, the power output distribution information represents the power output distribution of the photovoltaic, the energy storage system corresponding to the photovoltaic, and the multiple distributed power sources; the access information represents the access of the multiple distributed power sources to the distribution network; the power output distribution information and the access information are used to dynamically reconstruct the topology of the distribution network.

[0011] In one possible implementation, determining the output order of the photovoltaic system, the corresponding energy storage system, and multiple distributed power sources based on the load distribution information of the power distribution network includes:

[0012] If the power distribution network is in a peak load period, the output order of the multiple distributed power sources and the energy storage system corresponding to the photovoltaic is determined to take precedence over the output order of the photovoltaic.

[0013] If the power distribution network is in a period of flat load, the output order of the photovoltaic and the multiple distributed power sources shall be prioritized over the output order of the energy storage system corresponding to the photovoltaic.

[0014] If the power distribution network is in a low-load period, the output order of the multiple distributed power sources and the energy storage system corresponding to the photovoltaic is determined to take precedence over the output order of the photovoltaic.

[0015] In one possible implementation, the first objective function and the second objective function include:

[0016] Extreme value constraints and reactive power constraints of distributed generation output;

[0017] Capacity extreme constraints of energy storage systems;

[0018] Extreme voltage constraints at power grid nodes;

[0019] The constraint between the sum of the power provided by distributed generation and the power purchased by the distribution network and the load of the distribution network;

[0020] The constraint between the total output of distributed power sources within the distribution network area and the peak load of the distribution network area.

[0021] In one possible implementation, the second objective function further includes at least one of the following: a first minimization function, a second minimization function, and a third minimization function; wherein,

[0022] The first minimization function characterizes the voltage deviation minimization function of the grid node, and the first minimization function is related to the output of the distributed power source connected to each grid node;

[0023] The second minimization function represents the minimization function of electricity purchase cost, environmental impact value, and investment cost of distributed power generation;

[0024] The third minimization function characterizes the function that minimizes the impact of distributed generation output changes on the line loss of the distribution network.

[0025] In one possible implementation, solving the second objective function includes:

[0026] The second objective function is solved according to the quantum particle swarm optimization algorithm, wherein, when solving the second objective function according to the quantum particle swarm optimization algorithm, the local optimum is escaped according to a preset quantum tunneling probability.

[0027] In one possible implementation, the method further includes:

[0028] In response to photovoltaic fluctuations occurring in the distribution network within a preset time period, the actual active power of the photovoltaic system and the predicted active power of the photovoltaic system within the preset time period are obtained.

[0029] The output of the energy storage system corresponding to the photovoltaic and the output of the multiple distributed energy sources are adjusted based on the difference between the actual active power of the photovoltaic and the predicted active power of the photovoltaic.

[0030] Secondly, embodiments of this application provide a power distribution network dynamic reconfiguration device, comprising:

[0031] The acquisition module is used to acquire the predicted load of the distribution network and the first predicted output of photovoltaic power in response to the satisfaction of preset update conditions;

[0032] The first determining module is used to determine the output order of photovoltaic, the corresponding energy storage system and multiple distributed power sources based on the load distribution information of the power distribution network.

[0033] A construction module is used to construct, according to the output order, a first objective function relating the predicted output of the photovoltaic system, the energy storage system corresponding to the photovoltaic system, and the multiple distributed power sources to the predicted load of the distribution network, and a second objective function corresponding to the multiple distributed power sources. The second objective function includes a line loss function of the distribution network caused by the distributed power sources; the line loss function is related to the output of the distributed power sources connected to each grid node.

[0034] The second determining module is used to solve the first objective function and the second objective function to determine the power output distribution information and the access information; wherein, the power output distribution information represents the power output distribution of the photovoltaic, the energy storage system corresponding to the photovoltaic, and the multiple distributed power sources; the access information represents the access of the multiple distributed power sources to the distribution network; the power output distribution information and the access information are used to dynamically reconstruct the topology of the distribution network.

[0035] Thirdly, embodiments of this application provide a power distribution network dynamic reconfiguration device, including: a memory and a processor;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0038] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0039] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0040] The distribution network dynamic reconfiguration method, apparatus, equipment, storage medium, and program product provided in this application, when preset update conditions are met, determine the output order of photovoltaic, photovoltaic corresponding energy storage system, and distributed power source according to the load distribution information of the distribution network; the output order determined according to the load distribution information of the distribution network can enable the power output of the power source in the distribution network to meet the load demand of the distribution network. Based on the output order, a first objective function is constructed, which includes the predicted output of photovoltaic (PV), energy storage systems, and distributed generation (DG) and the predicted load of the distribution network. A second objective function for DG is constructed, which includes the line loss function of the distribution network caused by DG. By solving the first and second objective functions, the output distribution information of the energy storage system and DG, as well as the access information of DG, is obtained. Since the output of PV, the corresponding energy storage system, and multiple DG obtained by solving the first objective function satisfies the objective of the first objective function, the output of PV, the corresponding energy storage system, and DG can be rationally configured to reduce line losses caused by changes in network power flow direction. Since the second objective function includes the line loss function of the distribution network caused by DG, which is related to the output of DG connected to each grid node, the access of DG can be rationally configured to further reduce line losses caused by changes in network power flow direction. Thus, the technical effect of rationally configuring the electricity generated by PV and DG connected to the grid and reducing line losses in the distribution network is achieved. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] Figure 1 A flowchart illustrating the dynamic reconfiguration method for power distribution networks provided in this application embodiment;

[0043] Figure 2 A schematic diagram of the deep fusion framework composed of the first objective function and the second objective function provided in the embodiments of this application;

[0044] Figure 3 This is a schematic diagram of the structure of the power distribution network dynamic reconfiguration device provided in the embodiments of this application;

[0045] Figure 4 This is a schematic diagram of the structure of the power distribution network dynamic reconfiguration device provided in the embodiments of this application.

[0046] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0048] With the integration of photovoltaic and distributed power sources (including wind, hydro, and biomass energy) into the distribution network, some of these renewable energy sources exhibit intermittent and fluctuating characteristics, leading to diverse power flow directions in the distribution network. When the penetration rate of fluctuating power sources such as photovoltaics and distributed power sources in the grid exceeds 15%, the grid system faces multiple challenges, including bidirectional power flow impacts and random fluctuations in node voltages.

[0049] Since the power grid system was originally designed so that electrical energy flows unidirectionally from the power source (substation) to the load, the cross-sectional area of ​​the power grid line conductors decreases step by step from the substation to the user end. If the capacity of the distributed power source is too large or the distributed power source is not properly located in the distribution network (such as being far away from the load), high power will flow through small cross-sectional conductors.

[0050] The above analysis shows that as photovoltaic and distributed power sources are connected to the distribution network, the distribution network faces the impact of bidirectional network power flow, which reduces the stability of current and voltage in the lines and leads to increased line losses in the distribution network.

[0051] In related technologies, power allocation is based on a static scenario assumption. This static scenario assumes that the power supply of the distribution network is concentrated in large power plants, and the power source is connected to the distribution network entrance (such as a substation) through high-voltage transmission lines. The power in the distribution network flows unidirectionally from the high-voltage substation to the user, without a closed-loop circuit. When a distributed power source is connected to the power grid system, the distributed power source is usually installed at a fixed location on the power grid line (such as at 2 / 3 of the power grid line), and the capacity of the distributed power source is fixed at 2 / 3 of the capacity of the upstream power source.

[0052] Based on the above analysis, it can be concluded that the use of related technologies cannot reasonably configure the electrical energy generated by photovoltaic and distributed power sources, and the flexibility in the location and capacity of distributed power sources is not high, which makes the power grid system unable to reasonably cope with the impact of bidirectional power flow and increases the line losses of the distribution network.

[0053] The distribution network dynamic reconfiguration method, apparatus, equipment, storage medium, and program products provided in this application are used to solve the above-mentioned technical problems.

[0054] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0055] Figure 1 This is a flowchart illustrating the dynamic reconfiguration method for distribution networks provided in this embodiment, as shown below. Figure 1 As shown, the method includes:

[0056] S101. In response to meeting the preset update conditions, obtain the predicted load of the distribution network and the first predicted output of the photovoltaic system.

[0057] To achieve real-time dynamic reconfiguration of the power grid topology, the aforementioned preset update condition can be to set a time window, and reconfigure the distribution network topology at fixed intervals according to the set time window.

[0058] The power sources of the distribution network include photovoltaic (PV) systems, corresponding energy storage systems, and multiple distributed power sources. By reconstructing the topology of the distribution network, the predicted load and the first predicted output of PV systems can be obtained. Power can be provided based on PV systems, corresponding energy storage systems, and multiple distributed power sources. According to the predicted load and the first predicted output, the output of the corresponding energy storage systems and the output of multiple distributed power sources can be rationally configured.

[0059] The predicted load of a distribution network can be the predicted value of the power demand of the distribution network over a future period based on a set time window.

[0060] For example, based on the analysis of historical data, regression analysis models and machine learning models can be used to predict the power demand of the distribution network in the future, according to the current environmental conditions.

[0061] The first predicted output of photovoltaic power can be the predicted value of photovoltaic power output over a period of time in the future.

[0062] For example, historical data analysis, real-time meteorological data observation, and the current energy distribution strategy of the power grid can be combined to predict the forecast value of photovoltaic power output for a period of time in the future.

[0063] S102. Based on the load distribution information of the distribution network, determine the output order of photovoltaic, the corresponding energy storage system and multiple distributed power sources.

[0064] It should be noted that the power sources of the distribution network include photovoltaics, the corresponding energy storage systems, and multiple distributed power sources. By rationally configuring the output sequence of photovoltaics, the corresponding energy storage systems, and multiple distributed power sources, the optimal coordinated operation of photovoltaics, energy storage, and distributed power sources can be achieved.

[0065] For example, the output order of photovoltaic power, the corresponding energy storage system, and multiple distributed power sources can be determined based on the load distribution information of the power distribution network.

[0066] For example, the output order of photovoltaic (PV) systems, their corresponding energy storage systems, and multiple distributed power sources can be determined based on the load conditions of the distribution network, thereby maximizing the energy utilization of these systems. For instance, if the load conditions of the distribution network indicate a need for additional power from energy storage systems, then the output order of the energy storage system should be prioritized over that of the PV systems and their corresponding energy storage systems to improve the overall energy efficiency of the PV systems and distributed power sources.

[0067] S103. Based on the output order, construct a first objective function relating the predicted output of photovoltaic power, the energy storage system corresponding to photovoltaic power, and multiple distributed power sources to the predicted load of the distribution network, as well as a second objective function corresponding to multiple distributed power sources. The second objective function includes the line loss function of the distribution network caused by the distributed power sources; the line loss function is related to the output of the distributed power sources connected to each grid node.

[0068] For example, based on the output order determined in S102, a first objective function can be constructed between the predicted output of photovoltaic power, the energy storage system corresponding to photovoltaic power, and multiple distributed power sources and the predicted load of the distribution network, as well as a second objective function corresponding to multiple distributed power sources. The second objective function includes the line loss function of the distribution network caused by the distributed power sources; the line loss function is related to the output of the distributed power sources connected to each grid node.

[0069] For example, based on the predicted output and output order of photovoltaics, the corresponding energy storage system, and multiple distributed power sources, and considering that the sum of the predicted outputs of photovoltaics, the corresponding energy storage system, and multiple distributed power sources meets the predicted load demand of the distribution network, a first objective function can be constructed. Specifically, the first objective function can be expressed by the following formula (1):

[0070] (1);

[0071] In the formula, This represents the real-time output of the distributed power source at node i in the power grid at time t. This indicates whether a distributed power source is connected to grid node i. A value of 1 indicates that a distributed power source is connected to grid node i, and a value of 0 indicates that a distributed power source is not connected to grid node i. This represents the real-time output of the photovoltaic system at time t; This represents the load of the distribution network at time t; This represents the active power loss of the distribution network at time t; This represents the power output state of the energy storage system corresponding to the photovoltaic system at time t; It can be expressed by the following formula (2):

[0072] (2);

[0073] In the formula, Indicates the efficiency of the photovoltaic module; A represents the area of ​​the photovoltaic array; Indicates solar irradiance; Indicates the temperature compensation coefficient; Indicates the temperature of the photovoltaic module; Indicates reference temperature; It can be expressed by the following formula (3):

[0074] (3);

[0075] In the formula, This indicates the discharge power of the energy storage system corresponding to photovoltaics; This indicates the charging power of the energy storage system corresponding to photovoltaics; and The relationship between them can be represented as follows (4):

[0076] (4);

[0077] In the formula, This represents the state of charge of the energy storage system corresponding to the photovoltaic system at time t+1; This represents the state of charge of the energy storage system corresponding to the photovoltaic system at time t; This indicates the charging efficiency of the energy storage system corresponding to photovoltaics; This indicates the discharge efficiency of the energy storage system corresponding to photovoltaics; This indicates the capacity of the energy storage system corresponding to photovoltaic power. This indicates the time difference.

[0078] Based on the output order and the predicted output of photovoltaic, photovoltaic energy storage systems and multiple distributed power sources, the reduction of distribution network line losses due to the output of multiple distributed power sources over a historical period can be analyzed. Based on this analysis, a second objective function can be constructed. The second objective function includes the distribution network line loss function caused by distributed power sources. Specifically, the line loss function included in the second objective function can be expressed by the following formula (5):

[0079] (5);

[0080] In the formula, This represents the line loss function value; L represents the total number of nodes in the distribution network. This represents the line resistance at node k; This represents the active power of node k; This represents the reactive power of node k; The set of nodes representing candidate distributed power sources; This represents the voltage magnitude at node k; This represents the time-weighted factor, used to dynamically adjust the weights of historical states, so that recent data has a greater impact on the objective function; It can be expressed by the following formula (6):

[0081] (6);

[0082] In the formula, The time weighting factor is represented by T; the historical time period is represented by e; and the natural constant is represented by e, which is approximately equal to 2.71828. This represents the time decay coefficient, where the weight of historical data decays over time.

[0083] The second objective function, which includes formula (5), can minimize the line losses (including active and reactive power) of the distribution network, taking into account the location and real-time output of distributed power sources.

[0084] S104. Solve the first objective function and the second objective function to determine the power output distribution information and the access information; wherein, the power output distribution information represents the power output distribution of photovoltaic, photovoltaic corresponding energy storage system and multiple distributed power sources; the access information represents the access of multiple distributed power sources to the distribution network; the power output distribution information and the access information are used to dynamically reconstruct the topology of the distribution network.

[0085] For example, after constructing the first objective function and the second objective function, the first objective function and the second objective function can be solved to determine the power output distribution information and the access information; wherein, the power output distribution information represents the power output distribution of photovoltaic, photovoltaic corresponding energy storage system and multiple distributed power sources; the access information represents the access of multiple distributed power sources to the distribution network; the power output distribution information and the access information are used to dynamically reconstruct the topology of the distribution network.

[0086] For example, a pre-defined multi-objective solution algorithm can be used to jointly solve the first objective function and the second objective function. Based on the first objective function, the first objective output of the photovoltaic (PV) system, the first objective output of the corresponding energy storage system, and the third objective output of multiple distributed power sources can be determined. The location and capacity information of each distributed power source can be determined based on the second objective function. For instance, a non-dominated sorting genetic algorithm can be used to jointly solve the first and second objective functions. This application does not specify the specific algorithm type for the multi-objective solution algorithm. The first, second, and third objective outputs are the optimal predicted output distribution data for the photovoltaic system, the corresponding energy storage system, and the combined output of multiple distributed power sources. The power switch status in the photovoltaic system can be adjusted based on the first objective output; the power switch status in the corresponding energy storage system can be adjusted based on the second objective output; the access address of each distributed power source in the distribution network can be determined based on the location information of each distributed power source; and the maximum and minimum capacity values ​​of each distributed power source can be configured based on its capacity information.

[0087] By solving the first objective function, the output of photovoltaic power, the corresponding energy storage system, and multiple distributed power sources can be rationally configured, reducing line losses caused by changes in network power flow direction. By solving the second objective function, the location and capacity information of multiple distributed power sources can be determined, reducing distribution network line losses caused by changes in network power flow direction due to unreasonable location and capacity configuration of distributed power sources.

[0088] The power distribution network dynamic reconfiguration method provided in this application determines the output order of photovoltaic, photovoltaic corresponding energy storage system and distributed power source according to the load distribution information of the power distribution network when the preset update conditions are met. The output order determined according to the load distribution information of the power distribution network can enable the power output of the power source in the power distribution network to meet the load demand of the power distribution network. Based on the output order, a first objective function is constructed, which includes the output of photovoltaic (PV), energy storage system, and distributed generation (DG) systems relative to the predicted load of the distribution network. A second objective function for DG systems is constructed, which includes the line loss function of the distribution network caused by DG systems. By solving the first and second objective functions, the output distribution information of the energy storage system and DG systems, as well as the connection information of DG systems, is obtained. Since the output of PV, the corresponding energy storage system, and multiple DG systems obtained from solving the first objective function satisfies the objective of the first objective function, solving the first objective function allows for the rational configuration of the output of PV, the corresponding energy storage system, and DG systems, reducing line losses caused by changes in network power flow direction. Since the second objective function includes the line loss function of the distribution network caused by DG systems, and the line loss function is related to the output of DG systems connected to each grid node, solving the second objective function allows for the rational configuration of DG connection, further reducing line losses caused by changes in network power flow direction. This achieves the technical effect of rationally configuring the electricity generated by PV and DG systems connected to the grid and reducing line losses in the distribution network.

[0089] The following embodiments provide a more detailed explanation of the power output sequence. In this embodiment, S102 includes:

[0090] First, if the distribution network is in a peak load period, the output order of multiple distributed power sources and the corresponding energy storage systems should be prioritized over the output order of photovoltaics.

[0091] Second, if the distribution network is in a period of flat load, the output order of photovoltaic and multiple distributed power sources should be prioritized over the output order of the energy storage system corresponding to the photovoltaic.

[0092] Third, if the distribution network is in a low-load period, the output order of the energy storage system corresponding to multiple distributed power sources and photovoltaics should be prioritized over the output order of photovoltaics.

[0093] For example, when determining the load distribution information of the distribution network, if it is determined that the distribution network is in a peak load period, the output order of the energy storage system corresponding to multiple distributed power sources and photovoltaics is prioritized over the output order of photovoltaics; if it is determined that the distribution network is in a flat load period, the output order of photovoltaics and multiple distributed power sources is prioritized over the output order of the energy storage system corresponding to photovoltaics; if it is determined that the distribution network is in a low load period, the output order of the energy storage system corresponding to multiple distributed power sources and photovoltaics is prioritized over the output order of photovoltaics.

[0094] For example, when determining the load distribution information of a distribution network, if it is determined that the distribution network is in a peak load period, the output of photovoltaic power is affected by weather (such as cloud cover and solar radiation intensity), which is volatile and uncontrollable. During peak load periods, the power grid needs a stable and controllable power source to balance supply and demand and avoid voltage fluctuations or frequency instability. Therefore, determining the output order of multiple distributed power sources and the corresponding energy storage systems of photovoltaic power is prioritized over the output order of photovoltaic power.

[0095] If the distribution network is determined to be in a period of low load, given the low cost of photovoltaic (PV) and distributed power sources, and the charging and discharging losses of energy storage systems, prioritizing PV and distributed power generation can avoid the additional losses from energy storage system charging and discharging, making it more economical. Therefore, the output order of PV and multiple distributed power sources is determined to be higher than the output order of the corresponding energy storage system.

[0096] If the distribution network is determined to be in a low-load period, since these periods typically occur at night or in the early morning when there is no sunlight, photovoltaic (PV) systems cannot directly contribute to power generation. Therefore, the output order of multiple distributed power sources and their corresponding energy storage systems should be prioritized over the output order of PV systems.

[0097] Specifically, when prioritizing the combined power supply of distributed power sources and the corresponding energy storage systems for photovoltaics, the configuration strategy represented by the following formula (7) must be met:

[0098] (7);

[0099] In the formula, min represents the minimum value function; This indicates the output state of the photovoltaic energy storage system at time t, including the charging state with negative output and the discharging state with positive output. This represents the minimum output power of the energy storage system corresponding to the photovoltaic system during its discharge state. This indicates the discharge rate of the energy storage system corresponding to photovoltaics; Indicates the time difference; This indicates the peak load of the distribution network.

[0100] In some specific embodiments, S102 further includes: if the power fluctuation of the distribution network is large (such as changes in sunlight conditions affecting photovoltaic power supply, or changes in wind conditions affecting wind power generation in distributed power sources), then the fast response characteristics of the energy storage system corresponding to the photovoltaic are used to compensate for the power output fluctuations of the photovoltaic and multiple distributed power sources.

[0101] For example, when the combined output rate of photovoltaic and multiple distributed power sources exceeds the maximum power change rate, the energy storage system corresponding to the photovoltaic needs to respond quickly to charging or discharging. The power change rate of the energy storage system corresponding to the photovoltaic is sufficient to offset the power fluctuation, thereby smoothing out the fluctuation. This can be expressed by the following formula (8):

[0102] (8);

[0103] In the formula, This represents the real-time output of the photovoltaic system at time t; Indicates the maximum permissible rate of power change; This indicates the power output status of the energy storage system corresponding to the photovoltaic system; This indicates the power fluctuation threshold that needs to be compensated by the corresponding energy storage system for photovoltaics.

[0104] The following examples further illustrate the first objective function and the second objective function.

[0105] The first objective function achieves power balance between photovoltaics, the corresponding energy storage system, and multiple distributed power sources according to formula (1), while also satisfying the constraints of formula (9) below:

[0106] (9);

[0107] In the formula, , representing the charge and discharge state control variable of the energy storage system corresponding to photovoltaics; This indicates the maximum charging power of the energy storage system corresponding to the photovoltaic system; This represents the maximum discharge power of the energy storage system corresponding to the photovoltaic system. This represents the minimum state of charge of the energy storage system corresponding to photovoltaics. This represents the maximum state of charge of the energy storage system corresponding to the photovoltaic system. and This is used to represent the capacity extremum constraint of the energy storage system in the first objective function.

[0108] The second objective function also includes at least one of the following: a first minimization function, a second minimization function, and a third minimization function.

[0109] The first minimization function characterizes the voltage deviation minimization function of the grid node, and the first minimization function is related to the output of the distributed power sources connected to each grid node.

[0110] The voltage deviation can be expressed by the following formula (10):

[0111] (10);

[0112] In the formula, This represents the voltage deviation rate of a power grid node (the voltage deviation rate reflects the voltage deviation); max represents the maximum voltage deviation value of each node in the power grid. This represents the actual voltage at node i; This represents the rated voltage of the i-th node; The voltage change caused by the connection of distributed power sources can be expressed by the following formula (11):

[0113] (11);

[0114] In the formula, Indicates the line resistance at node i; Indicates the line reactance of node i; This represents the real-time reactive power of the distributed power source at node i in the power grid at time t. This represents the voltage amplitude at node i.

[0115] The voltage deviation function can be used to evaluate the impact of distributed generation access on the voltage of distribution network nodes. By minimizing the maximum voltage deviation rate, the voltage stability of the grid nodes can be improved.

[0116] The second minimization function represents the minimization function of electricity purchase cost, environmental impact value, and investment cost of distributed power generation.

[0117] The investment cost can be expressed by the following formula (12):

[0118] (12);

[0119] In the formula, Indicates the investment cost value; This represents the real-time electricity price at time t; This indicates the amount of electricity purchased from the power grid; Indicates the carbon emission factor; This represents the total carbon emissions of photovoltaic (PV) and its corresponding energy storage system (reflecting environmental impact). This represents the unit capacity investment cost of distributed power sources.

[0120] Formula (12) divides the investment cost into three parts: the cost of purchasing electricity from the power grid, the cost of carbon emissions, and the investment cost of distributed power sources. By comprehensively considering the economic efficiency of the distribution network, the cost of carbon emissions, and the investment cost of distributed power sources, the optimal economic and environmental effect can be achieved.

[0121] The third minimization function characterizes the function that minimizes the impact of distributed generation output changes on the line losses of the distribution network.

[0122] The impact of line loss can be expressed by the following formula (13):

[0123] (13);

[0124] In the formula, It indicates the sensitivity of distributed generation output changes to the active power loss of the distribution network and can be used to reflect the impact of line losses. This represents the total active power loss of the distribution network lines; This represents the real-time output of the distributed power source at grid node i.

[0125] Formula (13) optimizes the location and output of distributed power sources by quantifying the local impact of the access location and output of distributed power sources on the active power loss of the distribution network.

[0126] The second objective function includes the following constraints: extreme value constraints on the output of distributed generation and reactive power constraints, extreme value constraints on grid node voltages, constraints between the sum of the power provided by distributed generation and the power purchased by the distribution network and the load of the distribution network, and constraints between the total output of distributed generation within the distribution network area and the peak load of the distribution network area. Specifically, it can be expressed by the following formulas (14)-(20):

[0127] (14);

[0128] In the formula, This represents the real-time active power (output) of the distributed power source at time t. This represents the minimum real-time active power of the distributed power source. This represents the maximum real-time active power of the distributed power source;

[0129] (15);

[0130] In the formula, This represents the real-time reactive power of the distributed power source at time t; This represents the minimum real-time reactive power of the distributed power source. The formula (14)-(15) represents the maximum value of the real-time reactive power of the distributed power source; the formula (14)-(15) represents the extreme value constraint and reactive power constraint of the output of the distributed power source.

[0131] (16);

[0132] In the formula, This represents the voltage amplitude at node i at time t; This represents the minimum voltage amplitude at a power grid node. The maximum voltage amplitude of a power grid node is represented by formula (16); Formula (16) represents the voltage extreme value constraint of a power grid node.

[0133] (17);

[0134] In the formula, This represents the real-time output of the distributed power source at node i in the power grid at time t. This indicates the amount of electricity purchased from the power grid; The load of the distribution network at time t is represented; Formula (17) represents the constraint between the sum of the power provided by the distributed generation and the power purchased by the distribution network and the load of the distribution network.

[0135] (18);

[0136] In the formula, This indicates the maximum number of distributed power sources that can be connected.

[0137] (19);

[0138] (20);

[0139] In the formula, This represents the set of power grid nodes in region j; This represents the upper limit of distributed power penetration in region j; The peak load of region j is represented by formula (20); formula (20) represents the constraint between the total output of distributed power sources in the distribution network area and the peak load of the distribution network area.

[0140] Combining the first objective function and the second objective function, this embodiment realizes the real-time power output configuration of the distribution network, and simultaneously optimizes the site selection planning and capacity configuration of distributed power sources, thus obtaining a joint power supply model of photovoltaic, photovoltaic corresponding energy storage system and distributed power sources, which can be expressed by the following formula (21):

[0141] (twenty one);

[0142] In the formula, min represents the minimization function; express Weighting coefficients; express Weighting coefficients; express Weighting coefficients; express The weighting coefficients.

[0143] The following examples illustrate the coordinated operation stability of a combined power supply model of photovoltaics, photovoltaic energy storage systems, and distributed power sources.

[0144] Construct the dynamic equation based on formula (22):

[0145] (twenty two);

[0146] In the formula, This represents the state of charge of the energy storage system corresponding to the photovoltaic system at time t; Indicates the voltage amplitude at critical nodes; This represents a dynamic equation used to describe the nonlinear relationship between the state of charge (SOC) and the voltage amplitude V at key nodes of a photovoltaic energy storage system as a function of time. This represents the active power output of a distributed power source. This indicates the active power output of photovoltaic power generation.

[0147] The Lyapunov function is defined according to formula (23):

[0148] (twenty three);

[0149] In the formula, Represents the Lyapunov function that varies with time t; This indicates that the penalty voltage deviates from the reference value; This represents the optimal state of charge (SOC) of the energy storage system corresponding to the photovoltaic system.

[0150] Under the control strategy defined by formula (24):

[0151] (twenty four);

[0152] In the formula, This indicates the output of the energy storage system corresponding to the photovoltaic system; This indicates the proportional term, used for rapid response to voltage deviations; The integral term is used to eliminate steady-state error; if the gain matrix... and The following formula (25) must be satisfied:

[0153] (25);

[0154] In the formula, Indicates the system at the equilibrium point All eigenvalues ​​of the Jacobian matrix If the real part of the equilibrium is negative, then the equilibrium point is locally asymptotically stable.

[0155] Differentiate formula (23) with respect to time t:

[0156] (26);

[0157] In the formula, , , They represent , , Their respective derivatives; substituting them into the above dynamic equations, we can obtain the LaSalle invariant set principle. .

[0158] The following example illustrates the process of solving the second objective function. Solving the second objective function includes:

[0159] The second objective function is solved using the quantum particle swarm optimization algorithm, wherein, when solving the second objective function using the quantum particle swarm optimization algorithm, the local optimum is escaped according to a preset quantum tunneling probability.

[0160] Specifically, the binary variable x is encoded using qubits. i Mapped to the Bloch sphere, the entire candidate solution is explored synchronously using the quantum accumulation state property; in continuous variables Introducing dynamic inertia weights into space m represents the current iteration number, and M represents the total number of iterations. This represents the weight at the m-th iteration. This indicates a high initial weight. This indicates a smaller weight in the later stages, achieving a balance between wide-area search in the early stages and fine-tuning in the later stages; simultaneously, a Pareto solution set selection mechanism based on non-dominated sorting is designed, combining crowding degree calculation to retain diverse optimal solutions; and quantum tunneling probability is introduced. , Let exp represent the bounce probability, and let exp represent the natural exponential function. This represents the fitness difference between the current solution and its neighboring solutions. The temperature parameter represents the probability of the particle swarm getting stuck in a local optimum. Exit the current area, temperature parameters The algorithm adaptively decays with the number of iterations, ensuring that it converges to the global optimum in the later stages.

[0161] Regarding constraint output, a hierarchical relaxation strategy is proposed: linear constraints are directly encoded using hard constraints; nonlinear constraints are transformed into additional terms of the objective function through a penalty function, and the constraint strength is dynamically adjusted through an adaptive penalty factor, thereby avoiding over-constraint or under-constraint problems caused by a fixed penalty factor; for regional penetration rate limitations, a decomposition and coordination algorithm based on Lagrange duality is used to decompose the global optimization problem into multiple sub-regions for parallel solution, and then global coordination is achieved through consistency constraints.

[0162] The existence and uniqueness of the solution to the first objective function are analyzed below.

[0163] The first objective function is a multi-objective optimization problem, and the feasible solution set is a non-empty compact convex set. Specifically:

[0164] First, the first objective function includes... It is continuous in a mixed integer space, and the constraints form a closed set.

[0165] Second, objective function about It is a convex function (positive semi-definite second derivative), with voltage constraint. By introducing slack variables, it can be transformed into a convex constraint. According to Boyd's convex optimization theory, the feasible region is a convex set.

[0166] Third, at least one trivial solution exists. All constraints are satisfied.

[0167] Therefore, within a multi-objective optimization framework, if the second objective function includes... If all of them are semi-continuous and have compact feasible regions, then according to the Deb multi-objective optimization existence theorem, the Pareto preamble is non-empty.

[0168] The convergence of the quantum particle swarm optimization algorithm is analyzed below.

[0169] Map the decision variables to the Hibert space:

[0170] (27);

[0171] In the formula, H represents the value of the decision variable mapped to the Hibert space; Representing the space of qubits (encoding) (superposition state) Represent the space of square-integrable functions (describing continuous variables) (probability distribution) This indicates the minimum capacity of distributed power sources connected to a power grid node; This indicates the maximum capacity of distributed power sources connected to the grid node.

[0172] The global convergence objective is to prove the algorithm's iterative sequence { }, where m is the number of iterations, converging with probability 1 to the global Pareto optimal solution set. :

[0173] (28);

[0174] According to the quantum tunneling effect, the tunneling probability satisfy:

[0175] (29);

[0176] This ensures that the algorithm can escape local optima.

[0177] Let the dynamic inertia weight sequence be... satisfy:

[0178] (30);

[0179] The algorithm then satisfies the traversal condition of random search.

[0180] Since the non-dominated sorting and crowding calculation form a Markov chain, its steady-state distribution is concentrated in... .

[0181] Figure 2 This is a schematic diagram of the deep fusion framework composed of the first objective function and the second objective function provided in the embodiment. Figure 2 As shown, the main power grid is stepped down to 35 kV via a transformer through a 220 kV bus, and then connected to a 10 kV feeder network via a distribution transformer, forming the main energy transmission backbone. At the critical nodes (No. 6 and No. 24) of feeders L1 and L2, distributed power source 1 (850 kW) is connected to the grid through access point 6; distributed power source 2 (620 kW) is connected to the grid through access point 24. Its location and capacity parameters are controlled in real time by a dynamic multi-objective optimization module (an optimization module composed of the first objective function and the second objective function), and the distributed power source location flag is output synchronously through the quantum particle swarm optimization algorithm. Capacity limits The photovoltaic power output plan forms a closed-loop control system for the physical equipment. The photovoltaic array (5 MW peak) and the energy storage system (2 MW peak) are connected to the end of the feeder via sectionalizing switches and interconnecting switches, respectively. The power output status of the corresponding energy storage system is monitored. It is generated by a model predictive control algorithm (a lightweight model control algorithm controller embedded in photovoltaic inverters and energy storage converters. The core algorithm compresses a micro code package with a 50-millisecond calculation cycle through model reduction technology to ensure that the end-to-end control latency under 5G network is less than 10 milliseconds) and realizes millisecond-level command interaction through a 5G communication link.

[0182] The dynamic multi-objective optimization module uses a 15-minute time window to solve for the output schemes of the corresponding energy storage system and distributed power sources based on the first predicted output and load forecast values ​​of photovoltaics.

[0183] To address ultra-short-term (second-level) photovoltaic fluctuations, a real-time correction strategy based on model predictive control is designed. In response to photovoltaic fluctuations occurring in the distribution network within a preset time period (5 seconds), the actual active power and predicted active power of the photovoltaic system within the preset time period are obtained. The output of the energy storage system and multiple distributed energy sources are adjusted based on the difference between the actual and predicted active power of the photovoltaic system. The adjustment value of the power output state of the energy storage system corresponding to the photovoltaic system can be expressed by the following formula (31):

[0184] (31);

[0185] In the formula, This indicates the adjustment value representing the power output state of the energy storage system corresponding to the photovoltaic system; Indicates the predicted active power of photovoltaic power; Indicates the actual active power of photovoltaic power; Indicates proportional gain; This represents the integral gain.

[0186] The output of distributed power sources can be expressed by the following formula (32):

[0187] (32);

[0188] In the formula, This indicates that the active power is adjusted in conjunction with the distributed power supply. This represents the response coefficient of distributed generation, which is dynamically calculated based on the ramp rate of distributed generation and the energy storage regulation capability.

[0189] Figure 3 This is a schematic diagram of the distribution network dynamic reconfiguration device provided in this embodiment, as shown below. Figure 3 As shown, the power distribution network dynamic reconfiguration device 30 provided in this embodiment includes:

[0190] The acquisition module 301 is used to acquire the predicted load of the distribution network and the first predicted output of photovoltaic power in response to the satisfaction of preset update conditions;

[0191] The first determining module 302 is used to determine the output order of photovoltaic, the corresponding energy storage system and multiple distributed power sources based on the load distribution information of the distribution network.

[0192] Module 303 is used to construct, according to the output order, a first objective function between the predicted output of photovoltaic, the energy storage system corresponding to photovoltaic, and multiple distributed power sources and the predicted load of the distribution network, and a second objective function corresponding to multiple distributed power sources. The second objective function includes the line loss function of the distribution network caused by the distributed power sources; the line loss function is related to the output of the distributed power sources connected to each grid node.

[0193] The second determining module 304 is used to solve the first objective function and the second objective function to determine the power output distribution information and the access information. The power output distribution information represents the power output distribution of photovoltaics, the corresponding energy storage system of photovoltaics, and multiple distributed power sources. The access information represents the access of multiple distributed power sources to the distribution network. The power output distribution information and the access information are used to dynamically reconstruct the topology of the distribution network.

[0194] In one possible implementation, the first determining module 302 is further configured to determine the output order of multiple distributed power sources and the corresponding energy storage system of photovoltaics as taking priority over the output order of photovoltaics if the distribution network is in a peak load period.

[0195] If the distribution network is in a period of flat load, the output order of photovoltaic and multiple distributed power sources should be prioritized over the output order of the energy storage system corresponding to the photovoltaic.

[0196] If the power distribution network is in a low-load period, the output order of the energy storage system corresponding to multiple distributed power sources and photovoltaics should be prioritized over the output order of photovoltaics.

[0197] In one possible implementation, the first objective function and the second objective function include:

[0198] Extreme value constraints and reactive power constraints of distributed generation output;

[0199] Capacity extreme constraints of energy storage systems;

[0200] Extreme voltage constraints at power grid nodes;

[0201] The constraint between the sum of the power provided by distributed generation and the power purchased by the distribution network and the load of the distribution network;

[0202] The constraint between the total output of distributed power sources within the distribution network area and the peak load of the distribution network area.

[0203] In one possible implementation, the second objective function further includes at least one of the following: a first minimization function, a second minimization function, and a third minimization function; wherein,

[0204] The first minimization function characterizes the voltage deviation minimization function of the grid node, and the first minimization function is related to the output of the distributed power source connected to each grid node;

[0205] The second minimization function represents the minimization function of electricity purchase cost, environmental impact value, and investment cost of distributed power generation;

[0206] The third minimization function characterizes the function that minimizes the impact of distributed generation output changes on the line losses of the distribution network.

[0207] In one possible implementation, the solver module 304 is also used for:

[0208] The second objective function is solved using the quantum particle swarm optimization algorithm, wherein, when solving the second objective function using the quantum particle swarm optimization algorithm, the local optimum is escaped according to a preset quantum tunneling probability.

[0209] In one possible implementation, the distribution network dynamic reconfiguration device 30 includes an adjustment module for responding to photovoltaic fluctuations in the distribution network within a preset time period, and obtaining the actual active power of the photovoltaic and the predicted active power of the photovoltaic within the preset time period.

[0210] The output of the corresponding energy storage system and the output of multiple distributed energy sources are adjusted based on the difference between the actual active power of the photovoltaic system and the predicted active power of the photovoltaic system.

[0211] The power distribution network dynamic reconfiguration device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0212] Figure 4 This is a schematic diagram of the distribution network dynamic reconfiguration device provided in this embodiment. Figure 4 As shown, the power distribution network dynamic reconfiguration device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the power distribution network dynamic reconfiguration device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus.

[0213] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0214] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0215] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0216] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0217] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0218] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0219] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0220] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0221] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0222] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0223] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0224] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0225] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0226] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0227] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for dynamic reconfiguration of a distribution network, characterized in that, include: In response to the fulfillment of preset update conditions, the predicted load of the distribution network and the first predicted output of photovoltaic power are obtained; Based on the load distribution information of the power distribution network, determine the output order of photovoltaic power, the corresponding energy storage system, and multiple distributed power sources. Based on the output order, a first objective function is constructed between the predicted output of the photovoltaic system, the energy storage system corresponding to the photovoltaic system, and the multiple distributed power sources and the predicted load of the distribution network, and a second objective function is constructed for the multiple distributed power sources. The second objective function includes the line loss function of the distribution network caused by the distributed power sources. The line loss function is related to the output of the distributed power sources connected to each grid node. Solving the first objective function and the second objective function determines the power output distribution information and the access information; wherein, the power output distribution information represents the power output distribution of the photovoltaic, the energy storage system corresponding to the photovoltaic, and the multiple distributed power sources; the access information represents the access of the multiple distributed power sources to the distribution network; the power output distribution information and the access information are used to dynamically reconstruct the topology of the distribution network.

2. The method according to claim 1, characterized in that, The step of determining the output order of photovoltaic power, the corresponding energy storage system, and multiple distributed power sources based on the load distribution information of the power distribution network includes: If the power distribution network is in a peak load period, the output order of the multiple distributed power sources and the energy storage system corresponding to the photovoltaic is determined to take precedence over the output order of the photovoltaic. If the power distribution network is in a period of flat load, the output order of the photovoltaic and the multiple distributed power sources shall be prioritized over the output order of the energy storage system corresponding to the photovoltaic. If the power distribution network is in a low-load period, the output order of the multiple distributed power sources and the energy storage system corresponding to the photovoltaic is determined to take precedence over the output order of the photovoltaic.

3. The method according to claim 1, characterized in that, The first objective function and the second objective function include: Extreme value constraints and reactive power constraints of distributed generation output; Capacity extreme constraints of energy storage systems; Extreme voltage constraints at power grid nodes; The constraint between the sum of the power provided by distributed generation and the power purchased by the distribution network and the load of the distribution network; The constraint between the total output of distributed power sources within the distribution network area and the peak load of the distribution network area.

4. The method according to claim 1, characterized in that, The second objective function further includes at least one of the following: a first minimization function, a second minimization function, and a third minimization function; wherein, The first minimization function characterizes the voltage deviation minimization function of the grid node, and the first minimization function is related to the output of the distributed power source connected to each grid node; The second minimization function represents the minimization function of electricity purchase cost, environmental impact value, and investment cost of distributed power generation; The third minimization function characterizes the function that minimizes the impact of distributed generation output changes on the line loss of the distribution network.

5. The method according to any one of claims 1-4, characterized in that, Solving the second objective function includes: The second objective function is solved according to the quantum particle swarm optimization algorithm, wherein, when solving the second objective function according to the quantum particle swarm optimization algorithm, the local optimum is escaped according to a preset quantum tunneling probability.

6. The method according to any one of claims 1-4, characterized in that, The method further includes: In response to photovoltaic fluctuations occurring in the distribution network within a preset time period, the actual active power of the photovoltaic system and the predicted active power of the photovoltaic system within the preset time period are obtained. The output of the energy storage system corresponding to the photovoltaic and the output of the multiple distributed energy sources are adjusted based on the difference between the actual active power of the photovoltaic and the predicted active power of the photovoltaic.

7. A dynamic reconfiguration device for a distribution network, characterized in that, include: The acquisition module is used to acquire the predicted load of the distribution network and the first predicted output of photovoltaic power in response to the satisfaction of preset update conditions; The first determining module is used to determine the output order of photovoltaic, the corresponding energy storage system and multiple distributed power sources based on the load distribution information of the power distribution network. A construction module is used to construct, according to the output order, a first objective function relating the predicted output of the photovoltaic system, the energy storage system corresponding to the photovoltaic system, and the multiple distributed power sources to the predicted load of the distribution network, and a second objective function corresponding to the multiple distributed power sources. The second objective function includes a line loss function of the distribution network caused by the distributed power sources; the line loss function is related to the output of the distributed power sources connected to each grid node. The second determining module is used to solve the first objective function and the second objective function to determine the power output distribution information and the access information; wherein, the power output distribution information represents the power output distribution of the photovoltaic, the energy storage system corresponding to the photovoltaic, and the multiple distributed power sources; the access information represents the access of the multiple distributed power sources to the distribution network; the power output distribution information and the access information are used to dynamically reconstruct the topology of the distribution network.

8. A dynamic reconfiguration device for a power distribution network, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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