Power distribution network energy management method, device, equipment, storage medium and program product

Abstract

The invention relates to a power distribution network energy management method and device, equipment, a storage medium and a program product. The method comprises the following steps: obtaining an objective function of the operation cost of all electric equipment in a power distribution network, and solving the objective function of the operation cost through a preset optimization index to obtain a load required by each electric equipment under the minimum operation cost; the preset optimization indexes comprise the operation cost, the voltage stability, the average voltage deviation and the line load rate; determining the power of power supply equipment in the power distribution network and the voltage amplitude of a substation bus according to the load required by each piece of electric equipment; the power comprises active power and reactive power; and adjusting the energy of the power distribution network according to the power of the power supply equipment in the power distribution network and the voltage amplitude of the substation bus so as to manage the energy of the power distribution network. Through the effective fusion of the demand response of the power utilization equipment of the power distribution network, the distributed power supply equipment and the flexible power supply equipment, the operation state of the power distribution network is comprehensively and finely adjusted.

Description

Technical Field

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

[0002] With the rapid integration of distributed energy sources such as photovoltaics and wind power into the power distribution network system and the rapid growth in the number of electric vehicles, the actual operation of the power distribution network system faces problems such as frequent power fluctuations, widening load peak-valley differences, increased voltage fluctuations, and uneven power flow distribution, which in turn affect the final power supply effect.

[0003] Traditional power distribution network output energy management methods typically involve supplying a fixed amount of power to different electrical devices to meet user electricity demands. For example, the power supply received by an electric vehicle charging station is always fixed. However, due to the highly random nature of electric vehicle charging behavior, the final electricity consumption of an electric vehicle may be less than the power supply provided by the charging station, leading to energy waste and making it difficult to achieve the optimal balance between ensuring the safe operation of the power grid and maintaining economic efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a distribution network energy management method, device, equipment, storage medium, and program product that can accurately adjust the operation status of the distribution network in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a power distribution network energy management method, including:

[0006] An objective function for the operating cost of all electrical equipment in the distribution network is obtained. The objective function for the operating cost is then solved using preset optimization indicators to obtain the load required for each electrical equipment at the minimum operating cost. The preset optimization indicators include operating cost, voltage stability, average voltage deviation, and line load rate.

[0007] The power of the power supply equipment and the voltage amplitude of the substation busbar in the power distribution network are determined based on the load required by each of the aforementioned electrical devices; the power includes active power and reactive power.

[0008] The energy of the distribution network is regulated according to the power of the power supply equipment and the voltage amplitude of the substation bus in order to achieve energy management of the distribution network.

[0009] In one embodiment, solving the objective function of the operating cost using preset optimization indicators to obtain the load required for each of the electrical devices at the minimum operating cost includes:

[0010] Based on the operating cost, voltage stability, average voltage deviation, and line load rate, constraints are constructed; these constraints are used to ensure that the optimal solution of the objective function is the minimum operating cost.

[0011] By solving the objective function using the constraints, the load required for each of the electrical devices under the minimum operating cost can be obtained;

[0012] The constraints include the following:

[0013] The ratio of the difference between the real-time measured value of the preset optimization indicator and the upper or lower limit threshold of the preset optimization indicator to the difference between the upper and lower limit thresholds, the minimum constraint value, and the maximum constraint value.

[0014] In one embodiment, solving the objective function using the constraints to obtain the load required for each of the electrical devices at the minimum operating cost includes:

[0015] The constraint values ​​are determined using the aforementioned constraints.

[0016] The minimum operating cost is calculated based on the Euclidean distance between the constraint value and the ideal value of the operating cost.

[0017] Substituting the minimum operating cost into the objective function and solving it, the load required for each of the electrical devices under the minimum operating cost is obtained.

[0018] In one embodiment, determining the power of the power supply equipment in the distribution network and the substation bus voltage amplitude based on the load required by each of the electrical devices includes:

[0019] The load of each of the electrical devices is substituted into the power flow equation of the distribution network for calculation, and multiple sets of candidate active power, candidate reactive power and candidate bus voltage values ​​are obtained.

[0020] A population is constructed based on multiple sets of candidate active power, candidate reactive power, and candidate bus voltage values; the population includes multiple particles, each of which represents the candidate active power, candidate reactive power, and candidate bus voltage value.

[0021] The fitness of each particle is determined, and the optimal value is obtained by using the particle swarm optimization algorithm based on the fitness of each particle. The optimal value is then used as the power of the power supply equipment and the voltage amplitude of the substation bus in the power distribution network.

[0022] In one embodiment, determining the fitness of each of the particles includes:

[0023] For each particle, a first difference value is determined between the active power represented by the particle and the corresponding actual measured active power; a second difference value is determined between the reactive power represented by the particle and the corresponding actual measured reactive power; and a third difference value is determined between the bus voltage amplitude represented by the particle and the corresponding actual measured bus voltage amplitude.

[0024] The fitness of the particle is determined based on the first difference value, the second difference value, and the third difference value.

[0025] In one embodiment, the step of adjusting the energy of the distribution network based on the power of the power supply equipment and the voltage amplitude of the substation bus in the distribution network to achieve energy management of the distribution network includes:

[0026] Obtain real-time operating status parameters of the power distribution network; the real-time operating status parameters include the current power of the power supply equipment and the current substation bus voltage amplitude;

[0027] The current power of the power supply equipment is compared with the power of the power supply equipment, the current voltage amplitude of the substation bus is compared with the voltage amplitude of the substation bus, and the power distribution network energy is adjusted according to the comparison results.

[0028] Secondly, this application also provides a power distribution network energy management device, comprising:

[0029] The acquisition module is used to acquire the objective function of the operating cost of all electrical equipment in the distribution network, and solve the objective function of the operating cost through preset optimization indicators to obtain the load required for each electrical equipment under the minimum operating cost; the preset optimization indicators include operating cost, voltage stability, average voltage deviation and line load rate;

[0030] The determination module is used to determine the power of the power supply equipment in the distribution network and the voltage amplitude of the substation busbar based on the load required by each of the electrical devices; the power includes active power and reactive power.

[0031] The regulating module is used to regulate the energy of the distribution network according to the power of the power supply equipment and the voltage amplitude of the substation bus in order to manage the energy of the distribution network.

[0032] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0033] An objective function for the operating cost of all electrical equipment in the distribution network is obtained. The objective function for the operating cost is then solved using preset optimization indicators to obtain the load required for each electrical equipment at the minimum operating cost. The preset optimization indicators include operating cost, voltage stability, average voltage deviation, and line load rate.

[0034] The power of the power supply equipment and the voltage amplitude of the substation busbar in the power distribution network are determined based on the load required by each of the aforementioned electrical devices; the power includes active power and reactive power.

[0035] The energy of the distribution network is regulated according to the power of the power supply equipment and the voltage amplitude of the substation bus in order to achieve energy management of the distribution network.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0037] An objective function for the operating cost of all electrical equipment in the distribution network is obtained. The objective function for the operating cost is then solved using preset optimization indicators to obtain the load required for each electrical equipment at the minimum operating cost. The preset optimization indicators include operating cost, voltage stability, average voltage deviation, and line load rate.

[0038] The power of the power supply equipment and the voltage amplitude of the substation busbar in the power distribution network are determined based on the load required by each of the aforementioned electrical devices; the power includes active power and reactive power.

[0039] The energy of the distribution network is regulated according to the power of the power supply equipment and the voltage amplitude of the substation bus in order to achieve energy management of the distribution network.

[0040] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0041] An objective function for the operating cost of all electrical equipment in the distribution network is obtained. The objective function for the operating cost is then solved using preset optimization indicators to obtain the load required for each electrical equipment at the minimum operating cost. The preset optimization indicators include operating cost, voltage stability, average voltage deviation, and line load rate.

[0042] The power of the power supply equipment and the voltage amplitude of the substation busbar in the power distribution network are determined based on the load required by each of the aforementioned electrical devices; the power includes active power and reactive power.

[0043] The energy of the distribution network is regulated according to the power of the power supply equipment and the voltage amplitude of the substation bus in order to achieve energy management of the distribution network.

[0044] The aforementioned distribution network energy management methods, devices, equipment, storage media, and program products obtain the objective function of the operating cost of all electrical equipment in the distribution network. By solving the objective function of the operating cost through preset optimization indicators, the required load for each electrical device under the minimum operating cost is obtained. The preset optimization indicators include operating cost, voltage stability, average voltage deviation, and line load rate. Based on the required load of each electrical device, the power of the power supply equipment in the distribution network and the voltage amplitude of the substation bus are determined. Power includes active power and reactive power. The energy of the distribution network is adjusted according to the power of the power supply equipment and the voltage amplitude of the substation bus to achieve energy management of the distribution network. By obtaining the objective function of operating cost, the demand response of electrical equipment in the distribution network is effectively integrated with distributed power supply equipment and flexible power supply equipment, fully leveraging the flexibility of demand response in adjusting user-side load curves and peak shaving and valley filling. Simultaneously, by calculating the power of the power supply equipment and the voltage amplitude of the substation, comprehensive and refined adjustment of the distribution network's operating status is achieved. Compared to the energy waste caused by traditional energy management methods that set a fixed power supply for the distribution network, this approach employs a phased hybrid optimization method that separates demand response optimization from the adjustment of operating parameters for flexible power electronic devices. The first phase uses linear programming to quickly solve the demand response problem to obtain the optimal load regulation scheme. The second phase utilizes particle swarm optimization combined with distribution network power flow calculations to globally optimize the active and reactive power injection of soft switches and smart transformers, as well as the voltage amplitude of substations. Throughout this process, the optimization results between phases achieve seamless integration of data and constraints, reducing overall computational complexity and ensuring the consistency and feasibility of the optimization results across each phase. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is an application environment diagram of a power distribution network energy management method in one embodiment;

[0047] Figure 2 This is a flowchart illustrating a power distribution network energy management method in one embodiment;

[0048] Figure 3This is a schematic diagram of a process for calculating the load required for each electrical device at the minimum cost in one embodiment;

[0049] Figure 4 This is a flowchart illustrating the process of calculating the load required for each electrical device at the minimum cost in another embodiment;

[0050] Figure 5 This is a flowchart illustrating the process of determining the power of the power supply equipment and the voltage amplitude of the substation bus in one embodiment.

[0051] Figure 6 This is a schematic diagram of the process for determining the fitness of a particle in one embodiment;

[0052] Figure 7 This is a schematic diagram of the process for regulating the energy of a power distribution network in one embodiment;

[0053] Figure 8 This is a flowchart illustrating a power distribution network energy management method in another embodiment;

[0054] Figure 9 This is a structural block diagram of a power distribution network energy management device in one embodiment;

[0055] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0058] With the rapid integration of distributed energy sources such as photovoltaics and wind power into the power distribution network system and the rapid growth in the number of electric vehicles, the actual operation of the power distribution network system faces problems such as frequent power fluctuations, widening load peak-valley differences, increased voltage fluctuations, and uneven power flow distribution, which in turn affect the final power supply effect.

[0059] Traditional power distribution network output energy management methods typically involve supplying a fixed amount of power to different electrical devices to meet user demand. For example, the power supply received by an electric vehicle charging station is always fixed. However, due to the highly random nature of electric vehicle charging behavior, the final electricity consumption of an electric vehicle may be less than the power supply of the charging station, resulting in energy waste and making it difficult to achieve the optimal balance between ensuring the safe operation of the power grid and operational economy.

[0060] In view of the above-mentioned technical problems, this application provides a distribution network energy management method that can improve the regulation efficiency of the power supply at the nodes of the distribution network. The following embodiments will specifically illustrate the distribution network energy management method.

[0061] The power distribution network energy management method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the processing device 102 is wired to the distribution network 104. The distribution network 104 is equipped with multiple data acquisition devices to collect data such as the load power of the electrical equipment in the distribution network 104 and the active and reactive power, voltage amplitude, and current amplitude of the power supply equipment. The collected data is transmitted to the processing device 102. The processing device 102 performs optimization processing on the collected data to obtain power and voltage data with the lowest operating cost, enabling real-time adjustment of the energy in the distribution network 104. The processing device 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle equipment, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses.

[0062] In one exemplary embodiment, such as Figure 2 As shown, a power distribution network energy management method is provided. This embodiment illustrates the application of this method to processing equipment. In this embodiment, the method includes:

[0063] S201: Obtain the objective function of the operating cost of all electrical equipment in the distribution network. Solve the objective function of the operating cost by using preset optimization indicators to obtain the load required for each electrical equipment under the minimum operating cost.

[0064] The electrical equipment in the distribution network can be conventional power loads, residential electric vehicle charging stations, public electric vehicle charging stations, or other types of electrical equipment; there are no restrictions here. Preset optimization indicators include operating cost, voltage stability, average voltage deviation, and line load rate. The load required for each electrical device at the minimum operating cost includes the active power of the conventional load, the residential electric vehicle charging load, and the public electric vehicle charging load for the corresponding time period.

[0065] In the embodiments of this application, the distribution network includes power supply equipment and power consumption equipment. Power supply equipment includes, but is not limited to, photovoltaic power generation systems, wind power generation systems, and soft switches; power consumption equipment includes, but is not limited to, conventional power loads, residential electric vehicle charging stations, and public electric vehicle charging stations. Some conventional loads, residential and public electric vehicle charging stations can participate in demand response, providing flexible adjustment capabilities for the distribution network operation. The distribution network is connected to the upstream power grid through smart transformers, and soft switches are configured at key network nodes. The smart transformers and soft switches together provide active and reactive power regulation capabilities for the distribution network operation. When the processing equipment needs to manage the energy of the distribution network, it obtains the current market time-of-use electricity price and demand response compensation price, and constructs an objective function for operating costs based on the obtained data. Constraints are constructed based on operating costs, voltage stability, average voltage deviation, and line load rate; these constraints are used to ensure that the optimal solution of the objective function is the minimum operating cost; the objective function is solved using the constraints to obtain the load required for each power consumption device under the minimum operating cost.

[0066] Alternatively, the objective function can be expressed by the following relation (1):

[0067] (1);

[0068] In the formula, For operating costs, The cost of purchasing electricity from the upper-level power grid, This indicates the cost of implementing demand response; For the scheduling time period, The set of all scheduling periods under consideration; The electricity purchase price for that period. Power purchased from the upper-level power grid, Demand-response pricing, and These represent the normal load power before and after the demand response is implemented. and These represent the residential electric vehicle charging load power before and after the implementation of the demand response, respectively. and These represent the public electric vehicle charging load power before and after the implementation of the demand response, respectively. This refers to the collection of busbars in the distribution network.

[0069] S202, determine the power of the power supply equipment in the distribution network and the voltage amplitude of the substation bus based on the load required by each electrical device.

[0070] The power supply equipment can be a soft switch, a photovoltaic power generation system, a wind power generation system, or other types of power supply equipment; there are no restrictions here. Power includes active power and reactive power; the active power of the power supply equipment is the active power injected by the soft switch at the beginning of the bus, and the reactive power of the power supply equipment includes the reactive power of the soft switch at the beginning of the bus and the reactive power at the end of the bus.

[0071] In the embodiments of this application, the load of each electrical device is substituted into the power flow equation of the distribution network for calculation to obtain multiple sets of candidate active power, candidate reactive power and candidate bus voltage values. Then, a population is constructed using the multiple sets of candidate active power, candidate reactive power and candidate bus voltage values. The population includes multiple particles, each particle representing a candidate active power, candidate reactive power and candidate bus voltage value. The fitness of each particle is determined, and optimization is performed based on the fitness of each particle according to the particle swarm algorithm to obtain the optimal value. The optimal value is used as the target power of the power supply equipment and the target substation bus voltage amplitude in the distribution network.

[0072] S203 regulates the energy of the distribution network based on the power of the power supply equipment and the voltage amplitude of the substation bus in order to manage the energy of the distribution network.

[0073] In the embodiments of this application, after obtaining the power of the power supply equipment in the distribution network and the voltage amplitude of the substation bus, the energy of the distribution network is regulated. Optionally, the first method is to directly set the power of the soft switch to the power supply equipment and the voltage amplitude of the substation to the voltage amplitude of the substation bus when the upper-level power grid is not outputting power to the distribution network, so as to realize the management of the energy of the distribution network. Optionally, the second method is to monitor the operating power of the soft switch and the operating voltage amplitude of the transformer in the distribution network in real time when the upper-level power grid has output power to the distribution network. The operating power of the soft switch is compared with the power supply equipment, and the operating voltage amplitude of the transformer is compared with the voltage amplitude of the substation bus. When the comparison result meets the energy regulation conditions, the energy of the distribution network is regulated according to the power supply equipment and the voltage amplitude of the substation bus. When the comparison result does not meet the energy regulation conditions, the operating power of the soft switch and the operating voltage amplitude of the transformer in the distribution network are maintained.

[0074] The aforementioned distribution network energy management method obtains the objective function of the operating cost of all electrical equipment in the distribution network. It solves this objective function using preset optimization indicators to obtain the load required by each electrical device at the minimum operating cost. These preset optimization indicators include operating cost, voltage stability, average voltage deviation, and line load rate. Based on the load required by each electrical device, it determines the power of the power supply equipment in the distribution network and the voltage amplitude of the substation bus. Power includes active power and reactive power. The method then adjusts the energy of the distribution network based on the power of the power supply equipment and the voltage amplitude of the substation bus to achieve energy management. By obtaining the objective function of operating cost, it effectively integrates demand response with distributed power supply equipment and flexible power supply equipment, fully leveraging the flexibility of demand response in adjusting user-side load curves and peak shaving / valley filling. Simultaneously, by calculating the power of the power supply equipment and the voltage amplitude of the substation, it achieves comprehensive and refined adjustment of the distribution network's operating status. Compared to the energy waste inherent in traditional fixed-supply energy management, this method employs a phased hybrid optimization approach that separates demand response optimization from the adjustment of operating parameters for flexible power electronic devices. The first phase uses linear programming to quickly solve the demand response problem and obtain the optimal load regulation scheme. The second phase utilizes particle swarm optimization combined with power flow calculations in the distribution network to globally optimize the active and reactive power injection of soft switches and smart transformers, as well as the substation voltage amplitude. Throughout this process, the optimization results between phases achieve seamless integration of data and constraints, reducing overall computational complexity and ensuring the consistency and feasibility of the optimization results across each phase.

[0075] In one exemplary embodiment, such as Figure 3 As shown, by solving the objective function of operating cost through preset optimization indicators, the required load for each electrical device under the minimum operating cost is obtained, including:

[0076] S301, based on operating costs, voltage stability, average voltage deviation and line load rate, construct constraints.

[0077] The constraints are used to ensure that the optimal solution of the objective function is the minimum operating cost. The constraints include the following: the ratio of the difference between the real-time measured value of the preset optimization index and the upper or lower threshold of the preset optimization index to the difference between the upper and lower thresholds; the minimum constraint value; and the maximum constraint value. Each preset optimization index corresponds to one constraint, which can be a fuzzy membership function.

[0078] In this embodiment, when constructing the constraints for each preset optimization index, the real-time measured value of each preset optimization index and the ratio of the difference between the upper limit threshold or the lower limit threshold of the preset optimization index and the difference between the upper and lower limit thresholds, the minimum constraint value, and the maximum constraint value are first obtained. Then, the constraints are constructed based on the obtained data. Optionally, when the preset optimization index is the operating cost, the constraints are expressed by the following relationship (2):

[0079] (2);

[0080] In the formula, and These are the upper and lower limits of operating costs, respectively. For real-time operating costs, The fuzzy membership degree of operating costs.

[0081] When the preset optimization target is voltage stability, the voltage stability index (VSI) is calculated based on the radial structure of the distribution network, assuming the bus... and The lines are the beginning and end points, and their resistance and reactance are respectively... and The voltage stability index (VSI) of the bus can be expressed by the following formula (3):

[0082] (3);

[0083] in, and They represent the passage through the busbars. Active and reactive power, busbar voltage amplitude, busbar The voltage amplitude.

[0084] Minimum operating cost requires maximizing the minimum voltage stability. The minimum formula for maximizing voltage stability is expressed by the following relationship (4):

[0085] (4);

[0086] In the formula, This represents the set of all buses. The objective function of the operating cost is to improve the voltage stability index value. That is, the higher the voltage stability index value, the higher the fuzzy membership degree of voltage stability.

[0087] Alternatively, the voltage stability constraint can be expressed by the following relationship (5):

[0088] (5);

[0089] In the formula, and They are respectively Upper and lower thresholds For real-time voltage stability, The fuzzy membership degree represents the voltage stability.

[0090] When the preset optimization index is the average voltage deviation, the average voltage deviation (AVD) is expressed by the following relationship (6):

[0091] (6);

[0092] In the formula, 1.0 represents the rated voltage amplitude. () represents the bus voltage amplitude. This represents the average voltage deviation.

[0093] The objective function for operating cost is to minimize the average voltage deviation, and the constraint on the average voltage deviation is expressed by the following relationship (7):

[0094] (7);

[0095] in, and They are respectively Upper and lower thresholds This represents the average voltage deviation. The fuzzy membership degree is the average voltage deviation.

[0096] When the preset optimization index is the line load rate, the line load rate is expressed by the following relationship (8):

[0097] (8);

[0098] In the formula, This represents the actual current amplitude of the line. The rated current amplitude of the line. This represents the set of all busbars.

[0099] The objective function for operating costs aims to minimize the line load rate, and the constraint on the line load rate can be expressed by the following relationship (9):

[0100] (9);

[0101] in, and They are respectively Upper and lower thresholds The fuzzy membership degree represents the line load rate.

[0102] S302, using the constraints, solve the objective function to obtain the load required for each electrical device under the minimum operating cost.

[0103] In the embodiments of this application, the real-time values ​​of preset optimization indicators in the distribution network are substituted into the above constraints to obtain the fuzzy membership degree values ​​of the preset optimization indicators under different constraints. Based on the Euclidean distance between the fuzzy membership degree values ​​and the ideal value of the operating cost under different constraints, the minimum operating cost is calculated. The minimum operating cost is then substituted into the objective function of the operating cost for solution, yielding the load required for each electrical device under the minimum operating cost. , , ; Indicates time period The active power of the normal load, This indicates the residential electric vehicle charging load. This indicates the charging load of public electric vehicles.

[0104] The above method integrates multiple objectives such as operating cost, voltage stability index, average voltage deviation and line load rate into the normalization framework of fuzzy membership function. Through multi-objective fuzzy matching method, it transforms indicators with different dimensions and optimization directions into comparable dimensionless objective values. Using the Euclidean distance of the ideal point as the comprehensive optimization criterion, it achieves a dynamic balance between technical performance and economy.

[0105] In one exemplary embodiment, such as Figure 4 As shown, by solving the objective function using constraints, the required load for each electrical device under the minimum operating cost is obtained, including:

[0106] S401, Use constraints to determine constraint values.

[0107] In the embodiments of this application, the real-time values ​​of the preset optimization index in the distribution network are substituted into the above constraints for calculation to obtain the fuzzy membership values ​​of the preset optimization index under different constraints, i.e., the constraint values.

[0108] S402 calculates the minimum operating cost based on the Euclidean distance between the constraint value and the ideal value of the operating cost.

[0109] In the embodiments of this application, in order to simultaneously satisfy the objectives of multiple preset optimization indicators, the fuzzy membership degrees of each objective are combined, and a compromise optimization is achieved by minimizing the Euclidean distance between each fuzzy membership degree and the ideal value (1.0) of the operating cost. The Euclidean distance is expressed by the following relationship (10):

[0110] (10);

[0111] In the formula, For fuzzy membership degrees under different constraints, To minimize operating costs.

[0112] S403, substitute the minimum operating cost into the objective function to solve for the load required for each electrical device under the minimum operating cost.

[0113] In the embodiments of this application, after calculating the minimum operating cost, the minimum operating cost is substituted into the objective function of operating cost (Equation 1) for calculation to obtain the load required for each electrical device under the minimum operating cost, i.e. , , ; Indicates time period The active power of the normal load, This indicates the residential electric vehicle charging load. This indicates the charging load of public electric vehicles.

[0114] The above method, by setting constraints, can achieve a balance between economy and voltage quality while ensuring the various operating indicators of the distribution network system.

[0115] In one exemplary embodiment, such as Figure 5 As shown, the power of the power supply equipment and the voltage amplitude of the substation busbar in the distribution network are determined according to the load required by each electrical device, including:

[0116] S501 substitutes the load of each electrical device into the power flow equation of the distribution network for calculation, and obtains multiple sets of candidate active power, candidate reactive power and candidate bus voltage values.

[0117] The power flow equations of the distribution network include the active power balance equation of the distribution network system, the reactive power balance equation of the distribution network system, the active power balance equation of the substation bus, the reactive power balance equation of the substation bus, the line current constraint equation, the bus voltage constraint equation, the apparent power equation of the low-voltage side converter of the smart transformer, the energy conservation equation of various adjustable response loads in the dispatch cycle, the load transfer amplitude constraint equation, the power balance equation of the soft switch at each port, and the apparent power equation of the soft switch.

[0118] In the embodiments of this application, the loads (adjustable response loads) of all electrical equipment are substituted into the power flow equation of the distribution network for calculation. It is determined whether the loads of the electrical equipment meet the inequality constraints of the power flow equation. If they do, the active power, reactive power and bus voltage values ​​of the equations in the power flow equation are calculated to obtain multiple sets of candidate active power, candidate reactive power and candidate bus voltage values.

[0119] Optionally, during the optimization process, various physical and operational constraints in the distribution network operation are strictly considered to ensure the optimization results are engineering feasible and implementable. The distribution network system must satisfy the active power balance and reactive power balance conditions of the busbars during any scheduling period. Specifically, photovoltaic power generation units at the busbars... active power Active power of wind power generation unit Soft-start point injection of active power Compared with normal load Residential electric vehicle charging load Public electric vehicle charging load The difference between them should be based on the bus voltage amplitude. Phase angle and node admittance matrix elements and phase angle The active power components on the right-hand side of the calculated power flow equations are equal, and the active power balance equation of the distribution network system is expressed by the following relationship (11):

[0120] (11);

[0121] The reactive power balance equation of the distribution network system is expressed by the following relationship (12):

[0122] (12);

[0123] The balance equation for active power on the substation busbar is expressed by the following relationship (13):

[0124] (13);

[0125] The balance equation for reactive power on the substation busbar is expressed by the following relationship (14):

[0126] (14);

[0127] In the formula, For photovoltaic power generation units on the bus reactive power, This refers to the reactive power of the wind power generation unit. Inject reactive power into the soft switching point. For normal load, For residential electric vehicle charging load, For public electric vehicle charging load. and These are the magnitude and phase elements of the nodal admittance matrix, respectively. Indicates busbar The voltage phase angle; and These refer to the active and reactive power injected into the distribution network by the load-side converter of the intelligent transformer.

[0128] The line current constraint equation is expressed by the following relation (15):

[0129] (15);

[0130] The bus voltage constraint equation is expressed by the following relationship (16):

[0131] (16);

[0132] The apparent power equation of the low-voltage side converter of the intelligent transformer is expressed by the following relationship (17):

[0133] (17);

[0134] The energy conservation equations for various adjustable response loads during the scheduling cycle are expressed by the following relationship (18):

[0135]

[0136]

[0137] (18);

[0138] The load transfer amplitude constraint equation is expressed by the following relation (19):

[0139]

[0140] (19);

[0141]

[0142] The power balance equations at each port of the soft-start point are expressed by the following relationship (20):

[0143] (20);

[0144] The apparent power equation at the soft-start point is expressed by the following relation (21):

[0145]

[0146] (twenty one);

[0147] In the formula, For soft contact connection to busbar The rated capacity of the voltage source converter The soft-start loss coefficient, This refers to the set of soft switches in the distribution network.

[0148] S502 constructs a population based on multiple sets of candidate active power, candidate reactive power, and candidate bus voltage values.

[0149] The population consists of multiple particles, each representing a candidate active power, a candidate reactive power, and a candidate bus voltage value.

[0150] In the embodiments of this application, after obtaining multiple sets of candidate active power, candidate reactive power, and candidate bus voltage values, it is determined whether the number of sets is less than a preset number. When the number of sets is less than the preset number, each set of candidate active power, candidate reactive power, and candidate bus voltage value is sequentially treated as a particle, and all particles are determined to constitute a population. When the number of sets is not less than the preset number, the multiple sets of candidate active power, candidate reactive power, and candidate bus voltage values ​​are truncated according to the preset number, resulting in a preset number of multiple sets of candidate active power, candidate reactive power, and candidate bus voltage values. Each set of candidate active power, candidate reactive power, and candidate bus voltage value is sequentially treated as a particle, and all particles are determined to constitute a population.

[0151] S503: Determine the fitness of each particle, and use the particle swarm optimization algorithm to find the optimal value based on the fitness of each particle. The optimal value is then used as the power of the power supply equipment and the voltage amplitude of the substation bus in the distribution network.

[0152] The target power of the power supply equipment includes active power and reactive power. The active power is the active power injected by the soft switch at the beginning of the bus. The reactive power of the power supply equipment includes the reactive power at the beginning of the bus and the reactive power at the end of the bus.

[0153] In the embodiments of this application, for each particle in the population, a first difference value is determined between the active power represented by the particle and the corresponding actual measured active power; a second difference value is determined between the reactive power represented by the particle and the corresponding actual measured reactive power; and a third difference value is determined between the bus voltage amplitude represented by the particle and the corresponding actual measured bus voltage amplitude. Based on the first, second, and third difference values, the fitness of the particle is determined, and optimization is performed based on the fitness of each particle using the particle swarm optimization algorithm to obtain the optimal value. The optimal value is then used as the power of the power supply equipment in the distribution network and the bus voltage amplitude of the substation. That is, the initial fitness values ​​of all particles are compared, and the optimal (minimum or maximum, depending on the problem) particle is found, and its position is set as the global historical optimal position of the entire population. The algorithm begins a loop until a termination condition is met (such as reaching the maximum number of iterations or the quality of the solution is good enough). For each iteration, every particle in the population is traversed, the particle velocity is updated, the particle position is updated, the fitness of the new position is evaluated, the individual optimal position is updated, and the global optimal position is updated. When the maximum number of iterations is reached, or the improvement of the global historical optimal position is less than a preset threshold in several consecutive iterations, the algorithm terminates and outputs the final global optimal position, which is the power of the power supply equipment and the voltage amplitude of the substation bus in the distribution network.

[0154] The above method calculates various equality constraints using the power flow equations of the distribution network, and fully considers the impact of system network losses at this stage. If a control variable reaches its upper or lower limit constraint value, the variable is fixed at that boundary value. For inequality constraints, the penalty function method is used to ensure that the constraints are strictly satisfied during the optimization process, which improves the accuracy of the calculation results of the optimal energy value of the distribution network and is conducive to the effective management of energy under the operation state of the distribution network in subsequent processes.

[0155] In one exemplary embodiment, such as Figure 6 As shown, the fitness of each particle is determined, including:

[0156] S601, for each particle, determine a first difference value between the active power represented by the particle and the corresponding actual measured active power, determine a second difference value between the reactive power represented by the particle and the corresponding actual measured reactive power, and determine a third difference value between the bus voltage amplitude represented by the particle and the corresponding actual measured bus voltage amplitude.

[0157] In the embodiments of this application, for each particle, the active power represented by the particle is subtracted from the corresponding actual measured active power to obtain a first difference value; the reactive power represented by the particle is subtracted from the corresponding actual measured reactive power to obtain a second difference value; and the bus voltage amplitude represented by the particle is subtracted from the corresponding actual measured bus voltage amplitude to obtain a third difference value.

[0158] S602, determine the fitness of the particle based on the first difference value, the second difference value and the third difference value.

[0159] In the embodiments of this application, the square values ​​of the first difference value, the second difference value, and the third difference value are calculated respectively, and the obtained square values ​​are summed. The square root of the summation result is calculated, and the reciprocal of the square root result is determined as the fitness of the particle.

[0160] In one exemplary embodiment, such as Figure 7 As shown, the energy of the distribution network is regulated according to the power of the power supply equipment and the voltage amplitude of the substation bus in order to achieve energy management of the distribution network, including:

[0161] S701, obtains real-time operating status parameters of the power distribution network.

[0162] The real-time operating status parameters include the current power of the power supply equipment and the current voltage amplitude of the substation bus; the current power includes the active power and reactive power of the soft-start bus.

[0163] In the embodiments of this application, the processing device can obtain the real-time operating status parameters of the power distribution network by receiving data from the power distribution network's data acquisition device.

[0164] S702 compares the current power of the power supply equipment with the power of the power supply equipment, compares the current voltage amplitude of the substation bus with the voltage amplitude of the substation bus, and adjusts the energy of the distribution network based on the comparison results.

[0165] In the embodiments of this application, after obtaining the power of the power supply equipment in the distribution network and the voltage amplitude of the substation bus, the energy of the distribution network is regulated. Optionally, the first method is to directly set the power of the soft switch to the power supply equipment and the voltage amplitude of the substation to the voltage amplitude of the substation bus when the upper-level power grid is not outputting power to the distribution network, so as to realize the management of the energy of the distribution network. Optionally, the second method is to monitor the operating power of the soft switch and the operating voltage amplitude of the transformer in the distribution network in real time when the upper-level power grid has output power to the distribution network. The operating power of the soft switch is compared with the power supply equipment, and the operating voltage amplitude of the transformer is compared with the voltage amplitude of the substation bus. When the comparison result meets the energy regulation conditions, the energy of the distribution network is regulated according to the power supply equipment and the voltage amplitude of the substation bus. When the comparison result does not meet the energy regulation conditions, the operating power of the soft switch and the operating voltage amplitude of the transformer in the distribution network are maintained. That is, the energy regulation condition can be whether the difference between the operating power of the soft switch and the power of the power supply equipment is within a first preset range and / or whether the difference between the operating voltage amplitude of the transformer and the voltage amplitude of the substation bus is within a second preset range. If the difference between the operating power of the soft switch and the power of the power supply equipment is not within the first preset range and / or the difference between the operating voltage amplitude of the transformer and the voltage amplitude of the substation bus is not within the second preset range, it indicates that the comparison result meets the energy regulation condition, and the current operating power of the soft switch is adjusted to the power of the power supply equipment and / or the current operating voltage amplitude of the transformer is adjusted to the voltage amplitude of the substation bus. If the difference between the operating power of the soft switch and the power of the power supply equipment is within the first preset range, and the difference between the operating voltage amplitude of the transformer and the voltage amplitude of the substation bus is within the second preset range, it indicates that the current operating state of the distribution network does not need to be adjusted, the comparison result does not meet the energy regulation condition, and the operating power of the soft switch and the operating voltage amplitude of the transformer in the distribution network are maintained.

[0166] In addition to the methods of all the above embodiments, a power distribution network energy management method is also provided, such as... Figure 8 As shown, the method includes:

[0167] S801, the objective function for obtaining the operating costs of all electrical equipment in the power distribution network;

[0168] S802, based on operating cost, voltage stability, average voltage deviation and line load rate, construct constraints; the constraints are used to ensure that the optimal solution of the objective function is the minimum operating cost;

[0169] S803, determine constraint values ​​using constraint conditions;

[0170] S804 calculates the minimum operating cost based on the Euclidean distance between the constraint value and the ideal value of the operating cost;

[0171] S805, substitute the minimum operating cost into the objective function to solve for the load required by each electrical device under the minimum operating cost;

[0172] S806 substitutes the load of each electrical device into the power flow equation of the distribution network for calculation, and obtains multiple sets of candidate active power, candidate reactive power and candidate bus voltage values;

[0173] S807 constructs a population based on multiple sets of candidate active power, candidate reactive power, and candidate bus voltage values; the population includes multiple particles, each particle representing a candidate active power, candidate reactive power, and candidate bus voltage value;

[0174] S808, for each particle, determine the first difference value between the active power represented by the particle and the corresponding actual measured active power, determine the second difference value between the reactive power represented by the particle and the corresponding actual measured reactive power, and determine the third difference value between the bus voltage amplitude represented by the particle and the corresponding actual measured bus voltage amplitude.

[0175] S809, determine the fitness of the particle based on the first difference value, the second difference value and the third difference value;

[0176] S810, based on the fitness of each particle, the particle swarm optimization algorithm is used to find the optimal value, and the optimal value is used as the power of the power supply equipment and the voltage amplitude of the substation bus in the distribution network.

[0177] S811, obtain the real-time operating status parameters of the distribution network; the real-time operating status parameters include the current power of the power supply equipment and the current substation bus voltage amplitude;

[0178] S812 compares the current power of the power supply equipment with the power of the power supply equipment, compares the current voltage amplitude of the substation bus with the voltage amplitude of the substation bus, and adjusts the energy of the distribution network based on the comparison results.

[0179] Each of the above steps has been described in the foregoing embodiments. For details, please refer to the foregoing content. They will not be repeated here.

[0180] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0181] Based on the same inventive concept, this application also provides a distribution network energy management device for implementing the aforementioned distribution network energy management method. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more distribution network energy management device embodiments provided below can be found in the limitations of the distribution network energy management method described above, and will not be repeated here.

[0182] In one exemplary embodiment, such as Figure 9 As shown, a power distribution network energy management device is provided, comprising: an acquisition module 91, a determination module 92, and an adjustment module 93, wherein:

[0183] The acquisition module 91 is used to acquire the objective function of the operating cost of all electrical equipment in the distribution network. The objective function of the operating cost is solved by preset optimization indicators to obtain the load required for each electrical equipment under the minimum operating cost. The preset optimization indicators include operating cost, voltage stability, average voltage deviation and line load rate.

[0184] Module 92 is used to determine the power of the power supply equipment in the distribution network and the voltage amplitude of the substation bus based on the load required by each electrical device; the power includes active power and reactive power;

[0185] The regulating module 93 is used to regulate the energy of the distribution network according to the power of the power supply equipment and the voltage amplitude of the substation bus in order to realize the management of the energy of the distribution network.

[0186] In an exemplary embodiment, the acquisition module 91 includes:

[0187] The first building unit is used to construct constraints based on operating cost, voltage stability, average voltage deviation, and line load rate. The constraints are used to ensure that the optimal solution of the objective function is the minimum operating cost. The constraints include the following items: the ratio of the difference between the real-time measured value of the preset optimization index and the upper limit threshold or the lower limit threshold of the preset optimization index to the difference between the upper and lower limit thresholds, the minimum constraint value, and the maximum constraint value.

[0188] The solver unit is used to solve the objective function using constraints to obtain the load required for each electrical device under the minimum operating cost;

[0189] In an exemplary embodiment, the solution unit is specifically used to: determine constraint values ​​using constraint conditions; calculate the minimum operating cost based on the Euclidean distance between the constraint values ​​and the ideal value of the operating cost; and substitute the minimum operating cost into the objective function to solve for the load required by each electrical device under the minimum operating cost.

[0190] In an exemplary embodiment, the determining module 92 includes:

[0191] The calculation unit is used to substitute the load of each electrical device into the power flow equation of the distribution network for calculation, and obtain multiple sets of candidate active power, candidate reactive power and candidate bus voltage values;

[0192] The second building unit is used to build a population based on multiple sets of candidate active power, candidate reactive power and candidate bus voltage values; the population includes multiple particles, each particle representing candidate active power, candidate reactive power and candidate bus voltage value;

[0193] The determination unit is used to determine the fitness of each particle, and to perform optimization based on the fitness of each particle according to the particle swarm algorithm to obtain the optimal value. The optimal value is used as the power of the power supply equipment in the distribution network and the voltage amplitude of the substation bus.

[0194] In an exemplary embodiment, the determining unit is specifically configured to: for each particle, determine a first difference value between the active power represented by the particle and the corresponding actual measured active power; determine a second difference value between the reactive power represented by the particle and the corresponding actual measured reactive power; determine a third difference value between the bus voltage amplitude represented by the particle and the corresponding actual measured bus voltage amplitude; and determine the fitness of the particle based on the first difference value, the second difference value, and the third difference value.

[0195] In an exemplary embodiment, the adjustment module 93 includes:

[0196] The acquisition unit is used to acquire real-time operating status parameters of the power distribution network; the real-time operating status parameters include the current power of the power supply equipment and the current substation bus voltage amplitude.

[0197] The regulating unit is used to compare the current power of the power supply equipment with the power of the power supply equipment, compare the current voltage amplitude of the substation bus with the voltage amplitude of the substation bus, and regulate the energy of the distribution network based on the comparison results.

[0198] Each module in the aforementioned power distribution network energy management device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0199] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database is used for demand response prices of electrical equipment in the power distribution network, power supply equipment power, and voltage amplitude. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a power distribution network energy management method.

[0200] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0201] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0202] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0203] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0204] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0205] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0206] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power distribution grid energy management method, characterized by, The method comprises: obtaining a target function of operating costs of all power-consuming devices in a power distribution network, solving the target function of operating costs by a preset optimization index to obtain the required load of each power-consuming device under the minimum operating cost; the preset optimization index comprises operating cost, voltage stability, average voltage deviation and line load rate; determining the power of power supply devices and the bus voltage amplitude of substations in the power distribution network according to the required load of each power-consuming device; the power comprises active power and reactive power; adjusting the energy of the power distribution network according to the power of power supply devices and the bus voltage amplitude of substations to realize the management of the energy of the power distribution network.

2. The method of claim 1, wherein, The solving of the target function of operating costs by a preset optimization index to obtain the required load of each power-consuming device under the minimum operating cost comprises: constructing a constraint condition according to the operating cost, voltage stability, average voltage deviation and line load rate; the constraint condition is used to constrain the optimal solution of the target function to be the minimum operating cost; solving the target function by using the constraint condition to obtain the required load of each power-consuming device under the minimum operating cost; wherein the constraint condition comprises the following items: a ratio of a difference between a real-time measurement value of the preset optimization index and an upper threshold value or a lower threshold value of the preset optimization index and a difference between the upper and lower threshold values, a minimum constraint value and a maximum constraint value.

3. The method of claim 2, wherein, The solving of the target function by using the constraint condition to obtain the required load of each power-consuming device under the minimum operating cost comprises: determining a constraint value by using the constraint condition; calculating the minimum operating cost according to the Euclidean distance between the constraint value and an ideal value of the operating cost; substituting the minimum operating cost into the target function to solve and obtain the required load of each power-consuming device under the minimum operating cost.

4. The method of claim 1, wherein, The determination of the power of power supply devices and the bus voltage amplitude of substations according to the required load of each power-consuming device comprises: substituting the load of each power-consuming device into a power flow equation of the power distribution network to calculate a plurality of groups of candidate active power, candidate reactive power and candidate bus voltage values; constructing a population according to the plurality of groups of candidate active power, candidate reactive power and candidate bus voltage values; the population comprises a plurality of particles, and each particle represents the candidate active power, candidate reactive power and candidate bus voltage value; determining the fitness of each particle and optimizing based on the fitness of each particle according to a particle swarm algorithm to obtain an optimal value, and taking the optimal value as the power of power supply devices and the bus voltage amplitude of substations in the power distribution network.

5. The method of claim 4, wherein, The determination of the fitness of each particle comprises: for each particle, determining a first difference value between the active power represented by the particle and the corresponding actual measured active power, determining a second difference value between the reactive power represented by the particle and the corresponding actual measured reactive power, and determining a third difference value between the bus voltage amplitude represented by the particle and the corresponding actual measured bus voltage amplitude. According to the first difference value, the second difference value and the third difference value, fitness of the particle is determined.

6. The method of claim 1, wherein, The power of the power supply device in the power distribution network and the voltage amplitude of the transformer bus are used to adjust the energy of the power distribution network to achieve management of the energy of the power distribution network, including: Obtaining real-time operating state parameters of the power distribution network; the real-time operating state parameters include current power of the power supply device and current voltage amplitude of the transformer bus; Comparing the current power of the power supply device with the power of the power supply device, comparing the current voltage amplitude of the transformer bus with the voltage amplitude of the transformer bus, and adjusting the energy of the power distribution network according to the comparison result.

7. A power distribution grid energy management device, characterized by, The device comprises: An acquisition module is configured to acquire a target function of operating cost of all power consumption devices in a power distribution network, and solve the target function of operating cost by using a preset optimization index to obtain required load of each power consumption device under minimum operating cost; the preset optimization index includes operating cost, voltage stability, average voltage deviation and line load rate; A determination module is configured to determine power of a power supply device and voltage amplitude of a transformer bus in the power distribution network according to the required load of each power consumption device; the power includes active power and reactive power; An adjustment module is configured to adjust energy of the power distribution network according to the power of the power supply device and the voltage amplitude of the transformer bus in the power distribution network to achieve management of the energy of the power distribution network.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the method in any one of claims 1 to 6.

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

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

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