A smart grid dispatching method

Abstract

The application discloses a kind of smart grid scheduling methods, the method includes the following steps: S1, according to the actual situation and operation demand of power grid, generates expected scheduling target;S2, acquires electric power parameter information, realizes the comprehensive evaluation of electric power system by monitoring and analyzing the set electric power parameter in power grid;S3, according to the comprehensive evaluation result of electric power system, analysis expected scheduling target, and generates the scheduling scheme of electric power system according to the analysis result;S4, construct optimization algorithm, analyze the feasibility of scheduling scheme, and optimize scheduling scheme;S5, monitor the operating state of power grid, and adjust the operating parameter of power grid according to the actual operating state of power grid.The application combines flow migration operator with the directional search operator of standard particle swarm algorithm, obtains improved particle swarm optimization algorithm, and sets different particles to replace different scheduling schemes, to obtain the optimal scheduling scheme.

Description

Technical Field

[0001] The present invention relates to the field of power grid dispatching, and in particular to a smart grid dispatching method. Background Art

[0002] Rapid economic development has further intensified resource consumption, placing significant pressure on the ecology and environment. Gradually implementing energy conservation and emission reduction policies requires further improvements in resource utilization. Currently, international competition in the energy market is intensifying, and countries are further intensifying their energy competition to meet their own development needs.

[0003] Electricity is the primary source of energy in my country and the main source of energy consumption in modern agriculture and industry. The solution to this problem is to increase the supply of resources and, on the other hand, to rationally utilize resources and improve the effective utilization rate of electricity.

[0004] The power system is a systemic concept encompassing the generation, transportation, and distribution of electricity, ultimately delivering it to consumers. The core mission of the power system is to provide consumers with quality electricity. To ensure this quality, the reliability and safety of the power system must be ensured. Numerous factors influence the reliability and safety of the power system, and each link directly impacts the quality of electricity. Among these, optimal scheduling of the power system is a crucial component of safe grid operation.

[0005] Grid dispatching refers to the process of ensuring safe, stable, economical and efficient operation of the power grid through the coordinated utilization, operation control, dispatch arrangement and information feedback of various energy resources in the power system. In grid dispatching, in order to meet electricity demand and ensure the safe and stable operation of the power system.

[0006] In recent years, there are many goals for dynamic dispatch of power systems, mainly aiming at maximizing system operating efficiency, as well as dispatching with the goal of power supply stability. The expected dispatching goals are too strict, resulting in excessive power dispatching and a reduction in the service life of power equipment. Therefore, it is necessary to design a technical solution for a smart grid dispatching method to solve the above technical problems. Summary of the Invention

[0007] In response to the problems in the related art, the present invention proposes a smart grid scheduling method to overcome the above technical problems existing in the existing related art.

[0008] To this end, the specific technical solutions adopted in the present invention are as follows:

[0009] A smart grid dispatching method, the smart grid dispatching method comprising the following steps:

[0010] S1. Generate the expected dispatch target based on the actual situation and operation requirements of the power grid;

[0011] S2. Collect power parameter information and achieve a comprehensive assessment of the power system by monitoring and analyzing the set power parameters in the power grid;

[0012] S3. Analyze the expected dispatching targets based on the comprehensive assessment results of the power system and generate a dispatching plan for the power system based on the analysis results;

[0013] S4. Build an optimization algorithm, analyze the feasibility of the scheduling plan, and optimize the scheduling plan;

[0014] S5. Monitor the operating status of the power grid and adjust the operating parameters of the power grid according to the actual operating status of the power grid to ensure the stable operation of the power grid.

[0015] Furthermore, generating a dispatch target according to the actual situation and operation requirements of the power grid includes the following steps:

[0016] S11. Rationally adjust the output of each generator set in the power grid and the expected load distribution of the transmission lines based on the actual load demand of the power grid;

[0017] S12. Generate an expected optimization strategy by evaluating energy utilization and transmission loss in the power grid;

[0018] S13, evaluating the power generation cost of each power generation unit in the power grid to obtain an expected power generation cost;

[0019] S14. Generate an expected dispatch target based on the expected load distribution, the expected optimization strategy, and the expected power generation cost.

[0020] Furthermore, the collecting of power parameter information and achieving a comprehensive evaluation of the power system by monitoring and analyzing the set power parameters in the power grid includes the following steps:

[0021] S21, collecting power parameter information of node parameters, line parameters, transformer parameters and load parameters in the power system;

[0022] S22, pre-processing the power parameter information, organizing the data into a power system data file and storing it;

[0023] S23, using the power flow calculation model and the load forecasting model to calculate and forecast the power flow and load of the power system, and obtain the load demand and power parameter status of the power system;

[0024] S24. Based on the analysis results of the load demand in the power system and the real-time data of the status of power parameters, a comprehensive evaluation of the power system is achieved.

[0025] Furthermore, the method of calculating and predicting the power flow and load of the power grid using the power flow calculation model and the load forecasting model to obtain the load demand and power parameter status of the power grid includes the following steps:

[0026] S231. Collect information on power grid topology, equipment parameters, historical load data, and weather forecast data;

[0027] S232. Perform power flow calculation on each node of the power grid using a power flow calculation model to obtain voltage and power parameters of the node;

[0028] S233. Using the load forecasting model, forecast the load in the future period to obtain a load forecast result for each time period;

[0029] S234, distributing the load to the power grid according to the nodes to obtain the load size of each node;

[0030] S235. Calculate the load size of each line and the power flow and power flow direction of each node based on the load distribution and power flow calculation results.

[0031] Furthermore, the method of performing power flow calculation on each node of the power grid using the power flow calculation model to obtain the voltage and power parameters of the node includes the following steps:

[0032] S2321. Draw the topology diagram of the power grid according to the actual situation and mark all parameter information of each node and line;

[0033] S2322. Use the Newton-Raphson method to calculate the power flow and establish the rectangular coordinate equation for power.

[0034] S2323. Solve the rectangular coordinate equation using the modified equation to calculate the voltage vector of each node;

[0035] Wherein, the rectangular coordinate equation is:

[0036]

[0037] Where i is the node;

[0038] s is the composite parameter of active power and reactive power;

[0039] e i is the real part of the voltage at node i;

[0040] a i is the voltage phase angle of node i;

[0041] f i is the real part of the branch power flow in node i;

[0042] b i is the imaginary part of the branch flow in the i-th node;

[0043] ΔP i is the correction of the active power of the i-th node;

[0044] ΔQ i is the correction of the reactive power of the i-th node;

[0045] P is is the active power of the i-th node complex parameter;

[0046] Q is is the reactive power of the i-th node complex parameter;

[0047] The correction equation is:

[0048]

[0049] wherein H is a Jacobian matrix, representing the first order derivative of the node injection power with respect to the node voltage, the matrix elements are the admittance and conductance between nodes;

[0050] N is a correction matrix, representing the difference between the node injection power and the expected injection power, the matrix elements are the difference between the active power and the reactive power of the node and the expected value;

[0051] J is a node admittance matrix, representing the first order derivative of the node voltage with respect to the node injection power, the matrix elements are the admittance and conductance between nodes;

[0052] L is a correction matrix, representing the difference between the node voltage and the expected voltage, the matrix elements are the difference between the node voltage and the expected value;

[0053] Δe, Δf are correction quantities;

[0054] ΔU is a vector of node voltage correction;

[0055] ΔP is the correction of the active power;

[0056] ΔQ is the correction of the reactive power.

[0057] Further, the step of generating a dispatch scheme of the power system according to the analysis result of the comprehensive evaluation result of the power system includes the following steps:

[0058] S31, analyzing the data information of the expected load distribution, the expected optimization strategy and the expected generation cost in the expected dispatch target;

[0059] S32, comparing the data information with the analysis results of the load demand in the power system and the real-time data of the status of the power parameters to obtain a comparison result of the load demand and the power parameters in the power system;

[0060] S33. Generate a scheduling plan for load demand and power parameters based on the difference in the comparison results.

[0061] Furthermore, the construction of the optimization algorithm, analysis of the feasibility of the scheduling scheme, and optimization of the scheduling scheme include the following steps:

[0062] S41. Obtain historical data of power parameters in the power system;

[0063] S42. Generate evaluation indicators based on historical data, optimize the output distribution of each unit, and generate a mathematical model for scheduling in the power system;

[0064] S43. Build an improved particle swarm optimization algorithm, set different particles to replace different scheduling schemes, and obtain the optimal scheduling scheme by continuously adjusting power parameters.

[0065] Furthermore, generating evaluation indicators and converting historical data of power parameters into a mathematical model for scheduling includes the following steps:

[0066] S421. Specify evaluation indicators based on power load, power demand, and unit constraints;

[0067] S422. Optimize the output distribution of each unit to minimize the total operating cost of the power system;

[0068] S423. Set constraints on the power system and generate an objective function for scheduling;

[0069] The objective function is constructed as follows:

[0070]

[0071] Where K is the total cost of power generation, n is the number of units, and p c is the output power of unit c, k c (p c ) is the power generation cost of unit c, and α, β, γ, and φ are all cost coefficients of unit c.

[0072] Furthermore, the constraints include the scheduling objective function, upper and lower limit constraints of unit output, unit ramp rate constraints, unit working dead zone constraints and line capacity constraints.

[0073] Furthermore, the improved particle swarm optimization algorithm is constructed, different particles are set to replace different scheduling schemes, and the optimal scheduling scheme is obtained by continuously adjusting the power parameters, including the following steps:

[0074] S41. Construct the objective function and power constraints and define each particle;

[0075] S42, selecting the maximum value of the initial solution and the minimum value of the initial solution in the particle cost;

[0076] S43. According to the actual power system dispatching, in order to ensure the power demand of users, a symmetric penalty function is adopted for the power balance constraint, and a fitness function is constructed;

[0077] S44. Iterate the function value of the fitness function and select the optimal fitness value.

[0078] The beneficial effects of the present invention are:

[0079] 1. The present invention introduces the migration concept into the particle swarm optimization algorithm, combines the flow migration operator with the directional search operator of the standard particle swarm optimization algorithm, and obtains an improved particle swarm optimization algorithm. Different particles are set to replace different scheduling schemes, and the optimal scheduling scheme is obtained by continuously adjusting the power parameters.

[0080] 2. The present invention determines the stability and reliability of the power grid by monitoring the voltage, frequency, power factor and other power parameters in the power grid, and generates corresponding scheduling measures for the problems found. By monitoring the voltage, current, power quality and other parameters in the power grid, it determines whether the power quality of the transmission line meets the standards and makes corresponding adjustments and optimizations. By monitoring the power parameters of each generator set in the power grid, it determines whether its operating status is normal and promptly discovers and solves faults. By monitoring the load power parameters in the power grid, it analyzes the load characteristics and load change trends, etc., and provides a reference basis for the scheduling and planning of the power grid. By analyzing the power parameters in the power grid, it evaluates the energy utilization efficiency and generates corresponding energy management and scheduling measures to improve the energy utilization efficiency. By monitoring various power parameters in the power grid, it evaluates the safety status of the power grid, promptly discovers potential safety risks, and generates corresponding safety measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0082] Figure 1 This is a flow chart of a smart grid scheduling method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0083] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0084] According to an embodiment of the present invention, a smart grid scheduling method is provided.

[0085] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the smart grid scheduling method of an embodiment of the present invention, the smart grid scheduling method includes the following steps:

[0086] S1. Generate the expected dispatch target based on the actual situation and operation requirements of the power grid;

[0087] In one embodiment, generating a dispatch target according to the actual situation and operation requirements of the power grid includes the following steps:

[0088] S11. Obtaining actual load demand information of the power grid, and allocating output information of each generator set in the power grid and expected load distribution information of the transmission lines;

[0089] S12. generating an expected optimization strategy based on energy utilization information and transmission loss information in the power grid;

[0090] S13, obtaining power generation cost information of each power generation unit in the power grid and generating expected power generation cost information;

[0091] S14. Obtain expected dispatch target information by combining expected load distribution information, expected optimization strategy, and expected power generation cost information.

[0092] Specifically, in actual application, the generated dispatching objectives may include balancing grid supply and demand, optimizing grid energy efficiency, reducing power generation costs, improving grid stability, and saving energy and reducing emissions.

[0093] The balance of power supply and demand can be achieved by reasonably allocating the output of each generator set and the load distribution of the power transmission line according to the actual load demand of the power grid, so as to ensure the balance of power supply and demand and voltage stability of the power grid; the power grid energy efficiency can be optimized by analyzing and evaluating the energy utilization efficiency and power transmission loss in the power grid, and generating optimization strategies and measures to improve the energy efficiency of the power grid and reduce energy consumption and cost; the power generation cost can be reduced by evaluating the power generation cost of each generator set in the power grid and generating a reasonable output distribution scheme to minimize the power generation cost; the stability of the power grid can be improved by monitoring and analyzing the operating state of each device in the power grid and generating a reasonable scheduling scheme to ensure the stability and reliability of the power grid; energy saving and emission reduction can be achieved by optimizing the supply and demand structure of the power grid and increasing the proportion of clean energy to reduce the consumption of fossil energy and reduce environmental pollution. In addition, the generation of scheduling targets also needs to consider the actual situation of the power grid, including the size of the power supply area, the power supply structure, the load characteristics, etc., to ensure the feasibility and effectiveness of the scheduling targets.

[0094] S2, collecting power parameter information, by monitoring and analyzing the set power parameters in the power grid, realizing the comprehensive evaluation of the power system;

[0095] In one embodiment, the collecting power parameter information, by monitoring and analyzing the set power parameters in the power grid, realizing the comprehensive evaluation of the power system includes the following steps:

[0096] S21, collecting the power parameter information of the node parameters, line parameters, transformer parameters and load parameters in the power system;

[0097] S22, preprocessing the power parameter information, arranging the data into power system data files and storing them;

[0098] S23, using the power flow calculation model and the load prediction model to calculate and predict the power flow and load of the power system, and obtaining the load demand and the state of the power parameters of the power system;

[0099] S24, according to the analysis results of the load demand and the real-time data of the state of the power parameters in the power system, realizing the comprehensive evaluation of the power system.

[0100] In one embodiment, the calculating and predicting the power flow and load of the power grid by using the power flow calculation model and the load prediction model, and obtaining the load demand and the state of the power parameters of the power grid includes the following steps:

[0101] S231, collecting the information of the power grid topology structure, device parameters, historical load data and weather forecast data;

[0102] S232. Perform power flow calculation on each node of the power grid using a power flow calculation model to obtain voltage and power parameters of the node;

[0103] S233. Using the load forecasting model, forecast the load in the future period to obtain a load forecast result for each time period;

[0104] S234, distributing the load to the power grid according to the nodes to obtain the load size of each node;

[0105] S235. Calculate the load size of each line and the power flow and power flow direction of each node based on the load distribution and power flow calculation results.

[0106] In one embodiment, performing power flow calculation on each node of the power grid using a power flow calculation model to obtain voltage and power parameters of the node includes the following steps:

[0107] S2321. Draw the topology diagram of the power grid according to the actual situation and mark all parameter information of each node and line;

[0108] S2322. Use the Newton-Raphson method to calculate the power flow and establish the rectangular coordinate equation for power.

[0109] S2323. Solve the rectangular coordinate equation using the modified equation to calculate the voltage vector of each node;

[0110] Wherein, the rectangular coordinate equation is:

[0111]

[0112] Where i is the node; s is the composite parameter of active power and reactive power; e i is the real part of the voltage at node i; a i is the voltage phase angle of node i; f i is the real part of the branch power flow in node i; b i is the imaginary part of the branch power flow in node i; ΔP i is the correction value of active power of node i; ΔQ i is the correction value of reactive power of node i; P is is the active power of the composite parameter of node i; Q is is the reactive power of the composite parameter of node i;

[0113] The correction equation is:

[0114]

[0115] Where H is the Jacobian matrix, which represents the first-order derivative of the node injection power with respect to the node voltage, and the matrix elements are the admittance and conductance between the nodes; N is the correction matrix, which represents the difference between the node injection power and the expected injection power, and the matrix elements are the differences between the active power and reactive power of the node and the expected values; J is the node admittance matrix, which represents the first-order derivative of the node voltage with respect to the node injection power, and the matrix elements are the admittance and conductance between the nodes; L is the correction matrix, which represents the difference between the node voltage and the expected voltage, and the matrix elements are the differences between the node voltage and the expected values; Δe and Δf are corrections; ΔU is the vector of node voltage corrections; ΔP is the correction for active power; and ΔQ is the correction for reactive power.

[0116] Specifically, for a load node, the P and Q of the node are determined by the load demand and are generally uncontrollable. The characteristic of this node is that if P and Q are given, then the U of the node is to be determined, so it is also called a P and Q node. The contact node can also be regarded as a P and Q given node, and its P and Q values ​​are both zero.

[0117] For generator nodes, since generator excitation regulation maintains the voltage amplitude at that node within a certain range and active power is determined by the generator output power, P and U at that node are given, while θ and Q remain to be determined. These nodes are also called PV nodes. Furthermore, two principles should be adhered to when selecting PV nodes: In real systems, power plants are relatively few relative to the load, and the majority of nodes are P and Q nodes, with a smaller minority being PV nodes. Therefore, a large number of PV nodes is not necessary across the entire network, but rather they should be evenly distributed across the network by region. This is because PV nodes absorb unbalanced reactive power, which cannot be transmitted remotely, otherwise it would cause excessive voltage drop and network losses.

[0118] In a power system plant, it is best to select only one high-voltage bus as a PV node and avoid setting up multiple PV nodes on another bus. This is because the impedance between adjacent buses is extremely small. If the PV node voltage is not set reasonably, it will cause a huge reactive power flow between the two nodes.

[0119] S3. Analyze the expected dispatching targets based on the comprehensive assessment results of the power system and generate a dispatching plan for the power system based on the analysis results;

[0120] In one embodiment, analyzing the expected dispatching target based on the comprehensive evaluation results of the power system and generating a dispatching plan for the power system based on the analysis results includes the following steps:

[0121] S31. Analyze data information on expected load distribution, expected optimization strategy, and expected power generation cost in the expected dispatch target;

[0122] S32, comparing the data information with the analysis results of the load demand in the power system and the real-time data of the status of the power parameters to obtain a comparison result of the load demand and the power parameters in the power system;

[0123] S33. Generate a scheduling plan for load demand and power parameters based on the difference in the comparison results.

[0124] S4. Build an optimization algorithm, analyze the feasibility of the scheduling plan, and optimize the scheduling plan;

[0125] In one embodiment, constructing an optimization algorithm, analyzing the feasibility of a scheduling solution, and optimizing the scheduling solution include the following steps:

[0126] S41. Obtain historical data of power parameters in the power system;

[0127] S42. Generate evaluation indicators based on historical data, optimize the output distribution of each unit, and generate a mathematical model for scheduling in the power system;

[0128] S43. Build an improved particle swarm optimization algorithm, set different particles to replace different scheduling schemes, and obtain the optimal scheduling scheme by continuously adjusting power parameters.

[0129] In one embodiment, generating evaluation indicators and converting historical data of power parameters into a scheduling mathematical model includes the following steps:

[0130] S421. Specify evaluation indicators based on power load, power demand, and unit constraints;

[0131] S422. Optimize the output distribution of each unit to minimize the total operating cost of the power system;

[0132] S423. Set constraints on the power system and generate an objective function for scheduling;

[0133] The objective function is constructed as follows:

[0134]

[0135] Where K is the total cost of power generation, n is the number of units, P c is the output power of unit c, k c (p c ) is the power generation cost of unit c, and α, β, γ, and φ are all cost coefficients of unit c.

[0136] In one embodiment, the constraints include the scheduling objective function, upper and lower limit constraints of unit output, unit ramp rate constraints, unit working dead zone constraints and line capacity constraints.

[0137] In one embodiment, the improved particle swarm optimization algorithm is constructed, different particles are set to replace different scheduling schemes, and the optimal scheduling scheme is obtained by continuously adjusting power parameters, including the following steps:

[0138] S41. Construct the objective function and power constraints and define each particle;

[0139] S42, selecting the maximum value of the initial solution and the minimum value of the initial solution in the particle cost;

[0140] S43. According to the actual power system dispatching, in order to ensure the power demand of users, a symmetric penalty function is adopted for the power balance constraint, and a fitness function is constructed;

[0141] S44. Iterate the function value of the fitness function and select the optimal fitness value.

[0142] S5. Monitor the operating status of the power grid and adjust the operating parameters of the power grid according to the actual operating status of the power grid to ensure the stable operation of the power grid.

[0143] Specifically, various parameters of the power grid are monitored in real time, including voltage, current, power, frequency, and other parameters, as well as the operating status of various devices, such as transformers and switches. Various monitoring devices, such as sensors and telemetry and remote control devices, can be used to transmit data to the data acquisition system for analysis and processing.

[0144] In addition, by analyzing the monitored data, we can understand the actual operating status of the power grid, including load conditions, voltage stability, power balance, etc. Various analysis tools can be used, such as data mining, machine

[0145] In summary, with the help of the above technical solutions of the present invention, the present invention introduces the migration idea into the particle swarm optimization algorithm, combines the flow migration operator with the directional search operator of the standard particle swarm algorithm, and obtains an improved particle swarm optimization algorithm. Different particles are set to replace different scheduling schemes, and the optimal scheduling scheme is obtained by continuously adjusting the power parameters. The present invention judges the stability and reliability of the power grid by monitoring the power parameters such as voltage, frequency, and power factor in the power grid, and generates corresponding scheduling measures for the problems found. By monitoring the parameters such as voltage, current, and power quality in the power grid, it is judged whether the power quality of the transmission line meets the standard and makes corresponding adjustments and optimizations. By monitoring the power parameters of each generator set in the power grid, it is judged whether its operating status is normal and faults are discovered and resolved in a timely manner. By monitoring the load power parameters in the power grid, the load characteristics and load change trends are analyzed to provide a reference basis for the scheduling and planning of the power grid. By analyzing the power parameters in the power grid, the energy utilization efficiency is evaluated and corresponding energy management and scheduling measures are generated to improve energy utilization efficiency. By monitoring various power parameters in the power grid, the security status of the power grid is evaluated, potential security risks are discovered in a timely manner, and corresponding security measures are generated.

[0146] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A smart grid dispatching method, characterized in that: The smart grid dispatching method comprises the following steps: S1. Generate the expected dispatch target based on the actual situation and operation requirements of the power grid; S2. Collect power parameter information and achieve a comprehensive assessment of the power system by monitoring and analyzing the set power parameters in the power grid; S3. Analyze the expected dispatching targets based on the comprehensive assessment results of the power system and generate a dispatching plan for the power system based on the analysis results; S4. Build an optimization algorithm, analyze the feasibility of the scheduling plan, and optimize the scheduling plan; S5. Monitor the operating status of the power grid and adjust the operating parameters of the power grid according to the actual operating status of the power grid to ensure the stable operation of the power grid; Wherein, the S2 includes: The power flow and load of the power system are calculated and predicted using a power flow calculation model and a load forecasting model to obtain the load demand and power parameter status of the power system; the steps include: collecting information on the power grid topology, equipment parameters, historical load data, and weather forecast data; using the power flow calculation model to perform power flow calculations on each node of the power grid to obtain the voltage and power parameters of the node; using the load forecasting model to forecast the load for a period of time in the future to obtain the load forecast results for each time period; distributing the load to the power grid according to the nodes to obtain the load size of each node; and calculating the load size of each line and the power flow and power flow direction of each node based on the load distribution and power flow calculation results. Based on the analysis results of load demand in the power system and the real-time data of the status of power parameters, a comprehensive assessment of the power system can be achieved; The S4 comprises the following steps: Obtain historical data of power parameters in the power system; Generate evaluation indicators based on historical data, optimize the output distribution of each unit and generate a mathematical model for power system scheduling; including the following steps: specify evaluation indicators based on power load, power demand and unit constraints; optimize the output distribution of each unit to minimize the total operating cost of the power system; set the power system constraints and generate a scheduling objective function; Build an improved particle swarm optimization algorithm, set different particles to replace different scheduling schemes, and obtain the optimal scheduling scheme by continuously adjusting power parameters; The objective function is constructed as follows: Where K is the total cost of power generation; n is the number of units; p c is the output power of unit c; k c (p c ) is the electricity generation cost of unit c; α, β, γ, and φ are all cost coefficients of the unit.

2. A smart grid dispatching method according to claim 1, characterized in that: Generating a dispatch target according to the actual situation and operation requirements of the power grid includes the following steps: S11. Rationally adjust the output of each generator set in the power grid and the expected load distribution of the transmission lines based on the actual load demand of the power grid; S12. Generate an expected optimization strategy by evaluating energy utilization and transmission loss in the power grid; S13, evaluating the power generation cost of each power generation unit in the power grid to obtain an expected power generation cost; S14. Generate an expected dispatch target based on the expected load distribution, the expected optimization strategy, and the expected power generation cost.

3. A smart grid dispatching method according to claim 1, characterized in that: The method of calculating and predicting the power system's power flow and load using the power flow calculation model and the load forecasting model to obtain the power system's load demand and power parameter status includes the following steps: Collect power parameter information such as node parameters, line parameters, transformer parameters and load parameters in the power system; Preprocess the power parameter information, organize the data into power system data files and store them.

4. A smart grid dispatching method according to claim 3, characterized in that: The method of performing power flow calculation on each node of the power grid using the power flow calculation model to obtain the voltage and power parameters of the node includes the following steps: According to the actual situation, draw the topological structure diagram of the power grid and mark all the parameter information of each node and line; Establish the rectangular coordinate equation of power using the Newton-Raphson method for power flow calculation; The voltage vector of each node is calculated by solving the rectangular coordinate equation using the modified equation; Wherein, the rectangular coordinate equation is: Where i is the node; s is the composite parameter of active power and reactive power; e i is the real part of the voltage at node i; a i is the voltage phase angle of node i; f i is the real part of the branch power flow in node i; b i is the imaginary part of the branch power flow in node i; ΔP i is the correction value of active power of node i; ΔQ i is the correction value of reactive power of node i; P is is the active power of the composite parameter of node i; Q is is the reactive power of the composite parameter of node i; The correction equation is: Where H is the Jacobian matrix, which represents the first-order derivative of the node injection power with respect to the node voltage, and the matrix elements are the admittance and conductance between nodes; N is the correction matrix, which represents the difference between the node injection power and the expected injection power. The matrix elements are the differences between the node's active power and reactive power and the expected values. J is the node admittance matrix, which represents the first-order derivative of the node voltage with respect to the node injection power. The matrix elements are the admittance and conductance between nodes. L is the correction matrix, which represents the difference between the node voltage and the expected voltage. The matrix elements are the differences between the node voltage and the expected value. Δe and Δf are correction values; ΔU is the vector of node voltage correction; ΔP is the correction value of active power; ΔQ is the correction value of reactive power.

5. A smart grid dispatching method according to claim 1, characterized in that: The method of analyzing the expected dispatching target based on the comprehensive evaluation results of the power system and generating the dispatching plan of the power system based on the analysis results includes the following steps: S31. Analyze data information on expected load distribution, expected optimization strategy, and expected power generation cost in the expected dispatch target; S32, comparing the data information with the analysis results of the load demand in the power system and the real-time data of the status of the power parameters to obtain a comparison result of the load demand and the power parameters in the power system; S33. Generate a scheduling plan for load demand and power parameters based on the difference in the comparison results.

6. A smart grid dispatching method according to claim 5, characterized in that: The constraints include the scheduling objective function, upper and lower limit constraints of unit output, unit ramp rate constraints, unit working dead zone constraints and line capacity constraints.

7. A smart grid dispatching method according to claim 6, characterized in that: The improved particle swarm optimization algorithm is constructed, different particles are set to replace different scheduling schemes, and the optimal scheduling scheme is obtained by continuously adjusting power parameters. The following steps are included: S41. Construct the objective function and power constraints and define each particle; S42, selecting the maximum value of the initial solution and the minimum value of the initial solution in the particle cost; S43. According to the actual power system dispatching, in order to ensure the power demand of users, a symmetric penalty function is adopted for the power balance constraint, and a fitness function is constructed; S44. Iterate the function value of the fitness function and select the optimal fitness value.

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