Power distribution network-microgrid coordinated optimization scheduling method and device, medium and equipment

Abstract

The invention discloses a power distribution network-micro-grid coordinated optimization scheduling method and device, a medium and equipment in the technical field of intelligent power grids, and aims to solve the problems of low response speed, limited adjustment capability and insufficient intelligence in the prior art. The method comprises the following steps: acquiring a pre-constructed optimal scheduling model based on dynamic networking and electricity price excitation; and solving the optimal scheduling model based on dynamic networking and electricity price excitation based on the safe operation parameters of the target power distribution network, the safe operation parameters of the micro-grid and an electricity price excitation strategy of the micro-grid, and generating an optimal scheduling scheme. Through organic combination of intelligent soft switching, dynamic networking and an electricity price incentive mechanism, the efficient, intelligent and reliable power distribution network-microgrid coordinated optimization scheduling method is provided, the defects of the prior art in the aspects of response speed, adjusting capacity and intelligent level are overcome, and the method has remarkable technical innovation and practical application value.

Description

Technical Field

[0001] The present invention relates to a distribution network-microgrid coordinated optimization dispatching method, device, medium and equipment, belonging to the technical field of smart grid. Background Art

[0002] With the rapid development of smart grids and renewable energy, the structure of distribution networks is becoming increasingly complex, and the operating status is becoming more dynamic and uncertain. Traditional distribution networks mainly rely on fixed topological structures and static fault protection mechanisms, such as circuit breakers and protection relays. These traditional methods have the defects of slow response speed and limited regulation ability when dealing with complex and changeable grid operating conditions. It is difficult to effectively prevent and control the spread of faults, resulting in serious impact on the reliability and stability of the grid. Especially in medium and low voltage distribution networks, the problems of voltage over-limit and disordered scheduling of distributed resources are more prominent, and traditional scheduling and control methods are difficult to cope with these challenges.

[0003] In the prior art, dynamic networking technology optimizes the distribution of power flow and improves the operating efficiency and stability of the power grid by adjusting the topological structure of the distribution network in real time. However, the existing dynamic networking methods are mostly based on heuristic algorithms or linear optimization models, which are difficult to handle the high nonlinearity and complexity of power grid operation. In addition, when faced with sudden faults, the decision-making speed and accuracy are insufficient, and it is impossible to achieve rapid response and effective protection to faults. In addition, with the expansion of the scale and increase in complexity of the distribution network, traditional fault protection strategies are difficult to meet the requirements of modern power grids for high reliability and high security. The existing protection mechanisms mostly rely on preset protection strategies and parameters, lack intelligence and adaptive capabilities, and cannot be dynamically adjusted to adapt to the real-time operating status of the power grid, resulting in unsatisfactory protection effects in complex situations with multiple faults and multiple variables, and may even cause large-scale power outages.

[0004] As an important component of distributed power sources, microgrids have gradually become an important means to improve the efficiency and sustainability of power grids by virtue of their autonomous operation and flexible scheduling advantages. Existing research has mostly focused on the optimization scheduling and resource integration within microgrids, but there is still a lack of systematic methods in the coordinated optimization scheduling between distribution networks and microgrids, especially in the combined application of dynamic networking and electricity price incentive mechanisms. The research is still insufficient. As a dynamic power flow control device, the smart soft switch (Soft Open Point, SOP) has the advantages of fast action speed, accurate power flow control, and low cost, and is widely used in voltage and power flow regulation. However, in existing research, the application of SOP is mostly focused on the power flow optimization and fault recovery of distribution networks, and there is little research on its comprehensive application in the coordinated optimization scheduling of distribution networks and microgrids, and no systematic solution has yet been formed.

[0005] In summary, the current distribution network has traditional methods for dealing with medium and low voltage over-limit and disorderly dispatch of distributed resources, with defects such as slow response speed, limited regulation capability, and insufficient intelligence. At the same time, there is still a lack of systematic research and effective solutions for the coordinated optimization dispatch between microgrids and distribution networks. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a distribution network-microgrid coordinated optimization scheduling method, device, medium and equipment.

[0007] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0008] In a first aspect, the present invention discloses a distribution network-microgrid coordinated optimization scheduling method, comprising: Obtaining an optimization dispatching model based on dynamic networking and electricity price incentives, which is pre-constructed according to information of each node and line of the target distribution network and information of a microgrid connected to the target distribution network; Obtain the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the electricity price incentive strategy of the microgrid; Solving the optimization scheduling model based on dynamic networking and electricity price incentives based on the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the electricity price incentive strategy of the microgrid to generate an optimal scheduling plan; The processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Based on the electricity price incentive strategy of the microgrid and the safe operation parameters of the target distribution network and the safe operation parameters of the microgrid, the internal resource configuration is adjusted by using the covariance matrix adaptive evolutionary strategy algorithm with the goal of minimizing the operation cost of the microgrid, and the adjusted internal resource configuration of the microgrid is fed back to the distribution network, so that the distribution network performs the optimized power flow calculation of dynamic networking in response to the internal resource configuration, and obtains the optimized power flow allocation plan, and generates the dynamic networking plan according to the optimized power flow allocation plan; The operating results of the distribution network and the microgrid are generated according to the dynamic networking plan, and the operating results of the distribution network and the microgrid are used as the new fitness evaluation basis of the covariance matrix adaptive evolutionary strategy algorithm. The microgrid electricity price is adjusted and the next round of optimization iteration is carried out until the iteration is completed to generate a dynamic networking plan under the optimal microgrid electricity price.

[0009] Furthermore, the optimization scheduling model based on dynamic networking and electricity price incentives adopts a two-layer optimization structure, the upper layer of the two-layer optimization structure is the distribution network pricing model, and the lower layer of the two-layer optimization structure is the microgrid autonomous optimization model and the dynamic networking model.

[0010] Furthermore, the objective function expression of the distribution network pricing model is: ; ; ; ; Where: min means minimization, C 1 represents the objective function of the distribution network layer, C MG represents the transaction costs of distribution network and microgrid; C buy It represents the cost of the distribution network purchasing electricity from the upper power grid; C load It represents the revenue from the distribution network selling electricity to ordinary loads; T Indicates the scheduling period; N represents the total number of microgrids; P sell,i,t , P buy,i,t Represents the distribution network t Time period to i The power sales and purchase of each microgrid; ρ sell,t , ρ buy,t Represents the distribution network t The electricity sales and purchase prices during the time period; represents the scheduling step length; P in,t Represents the distribution network t Purchase power from the main grid during the period; ρ t express t The time-of-use electricity price that the distribution network purchases from the main grid; P load,t Represents the distribution network t Normal load power during the period; i ∈[1, N ], t ∈[1, T ]; The constraint expression of the objective function of the distribution network pricing model is as follows: ; Where: ρ min,t , ρ max,t Indicates the upper and lower limits of the microgrid's electricity purchase and sales prices.

[0011] Furthermore, the objective function expression of the microgrid autonomous optimization model is: ; ; ; ; Where: C MGs,i Indicates i The objective function of a microgrid is: C exc,i Indicates i The electricity transaction cost of each microgrid; C gas,i Indicates i Cost of power generation from a gas turbine in a microgrid; C st,i Indicates i Depreciation cost of energy storage in a microgrid; a , b represents the gas turbine cost factor; P gas,t Gas Turbine t The power generated during the period; P ch,t , P dch,t Indicates energy storage t Charging power and discharging power in each time period; ρ st Represents the depreciation cost of energy storage per unit charge and discharge power; The constraints of the objective function of the microgrid autonomous optimization model include: Energy storage constraints: ; Where: SOC max , SOC min Indicates the upper and lower limits of the energy storage charge state; SOC init , SOC t Indicates the initial state of charge and current state of charge of the energy storage; SOC 1 , SOC 25 Indicates the energy storage charge state at the start and end of the dispatch. SOC t-1 express t -1 distribution network charge status at time, Indicates energy storage t -1 moment charging power, Indicates energy storage t -1 moment discharge power, P max,st Indicates energy storaget The upper limit of charging and discharging power in the time period; μ ch,t , μ dch,t A 0-1 variable representing the energy storage charge and discharge status, μ ch,t 1 means charging, μ dch,t 1 means discharge, and both cannot be 1 at the same time; η st Indicates the energy storage charging and discharging efficiency; Gas turbine output upper and lower limit constraints: ; Where: , Indicates the maximum and minimum output of the gas turbine; Photovoltaic output constraints: ; Where: P PV,t express t Actual photovoltaic output during the period; express t The maximum output of photovoltaic power during the period; Power constraints for interaction between microgrid and distribution network: ; Where: Indicates the upper limit of the power of the tie line between the microgrid and the distribution network; express t The actual power of the interconnection line between the microgrid and the distribution network during the time period; Power balance constraints: ; Where: P load,t Indicates the load power, P gas,t , P PV,t , P dis,t , P ch,t , P MG,t They respectively represent the gas turbine output, photovoltaic output, energy storage discharge power, energy storage charging power, and the actual power of the interconnection line between the microgrid and the distribution network.

[0012] Furthermore, the objective function expression of the dynamic networking model is: ; Where: C 2Represents the objective function of dynamic networking; The constraints of the objective function of the dynamic networking model include: Power flow constraints: ; ; ; ; Where: r i,j , x i,j Indicates branch ij Resistance and reactance; P i,j,t , Q i,j,t , I i,j,t express t Time period branch ij Active power, reactive power and current; , express t Time period node j The inflow power and outflow power of the upper SOP; , , V j,t express t Time period node j Active load, reactive load, and voltage; Q i,k,t express t Time period branch jk Reactive power; V i,t express t Time period node i Voltage; P j,k,t express t Time period branch jk Active power of Node voltage constraints: ; Where: , Representation Node i The voltage upper and lower limits; Branch power constraints: ; Where: Indicates branch ij The upper limit of transmission power; SOP constraints: ; ; ; Where: , Indicates the connecting branch ij On SOP i Node inflow power and outflow power; , Indicates the connecting branch ij On SOP j Node inflow power and outflow power; μ S,ij Represents a 0-1 state variable, which is used to determine the power flow direction of the SOP; η ij Indicates branch ij The efficiency of SOP, SOP stands for intelligent soft switch; Indicates branch ij Maximum power on SOP.

[0013] Furthermore, the processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Set the voltage range of each node, the maximum capacity of each line and the parameters of SOP; initialize the population center, step size and covariance matrix of the CMA-ES algorithm, which is a covariance matrix adaptive evolutionary strategy algorithm; A random electricity price is generated as the initial sample center point, and then a set of electricity prices is generated based on the initial sample center point as the initial population. The expression for generating the initial population is as follows: ; Where: x k represents the generated sample point; m represents the current population center; σ Indicates the step size of the CMA-ES algorithm; N (0,C) means sampling from a multivariate normal distribution with a mean of 0; C represents the covariance matrix; Then, the fitness of each group of electricity prices in the population is determined according to the objective function containing the lower-level problem, and the optimal electricity price strategy is selected to update the sample center, step size and covariance matrix. The expression is as follows: ; ; ; Where: m new indicates the new population center; ω iIndicates that according to the weights assigned by the ranking, the electricity price strategy with higher benefits has a higher weight; x i Indicates an excellent individual in the evaluation; C new represents the new covariance matrix; c cov represents the learning rate of the covariance matrix; σ new represents the new step size of the CMA-ES algorithm; exp represents the exponential function; c σ , d σ Indicates the adjustment rate of the control step size; p σ represents the evolutionary path; represents the length of the expected normal distribution vector; Solve the mixed integer linear programming problem based on the objective function and constraint conditions of the microgrid autonomous optimization model, determine the autonomous operation results of each microgrid, and generate the corresponding equivalent load or power supply according to the autonomous operation results of each microgrid; The equivalent load or power data of the microgrid is input into the dynamic networking model, so that the dynamic networking model uses the mixed integer second-order cone programming method, combined with the topological structure of the distribution network and the parameters of the SOP, to calculate the optimal power flow allocation scheme; According to the optimal power flow allocation plan, determine the operating status and power flow direction of each SOP and generate a specific dynamic networking plan; Obtaining the operation results of the distribution network and the microgrid according to the specific dynamic networking scheme, wherein the operation results of the distribution network and the microgrid include distribution network pricing, networking scheduling results and microgrid autonomous operation results; The operating results of the distribution network and the microgrid are fed back to the CMA-ES algorithm as a new fitness evaluation basis. According to the fitness of each individual, a new population is calculated to represent the new electricity price scheme, and the next round of optimization iteration is carried out until the iteration limit is reached to determine the dynamic networking scheme under the optimal microgrid electricity price.

[0014] In a second aspect, the present invention further discloses a distribution network-microgrid coordinated optimization dispatching device, comprising: An acquisition module is used to acquire an optimization scheduling model based on dynamic networking and electricity price incentives, which is pre-built according to the information of each node and line of the target distribution network and the information of the microgrid connected to the target distribution network; acquire the safe operation parameters of the target distribution network and the safe operation parameters of the microgrid and the electricity price incentive strategy of the microgrid; A generation module, used to solve the optimization scheduling model based on dynamic networking and electricity price incentives based on the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid and the electricity price incentive strategy of the microgrid, and generate an optimal scheduling plan; The processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Based on the electricity price incentive strategy of the microgrid and the safe operation parameters of the target distribution network and the safe operation parameters of the microgrid, the internal resource configuration is adjusted by using the covariance matrix adaptive evolutionary strategy algorithm with the goal of minimizing the operation cost of the microgrid, and the adjusted internal resource configuration of the microgrid is fed back to the distribution network, so that the distribution network performs the optimized power flow calculation of dynamic networking in response to the internal resource configuration, and obtains the optimized power flow allocation plan, and generates the dynamic networking plan according to the optimized power flow allocation plan; The operating results of the distribution network and the microgrid are generated according to the dynamic networking plan, and the operating results of the distribution network and the microgrid are used as the new fitness evaluation basis of the covariance matrix adaptive evolutionary strategy algorithm. The microgrid electricity price is adjusted and the next round of optimization iteration is carried out until the iteration is completed to generate a dynamic networking plan under the optimal microgrid electricity price.

[0015] Furthermore, the optimization dispatching model based on dynamic networking and electricity price incentives adopts a two-layer optimization structure, the upper layer of which is a distribution network pricing model, and the lower layer is a microgrid autonomous optimization model and a dynamic networking model.

[0016] In a third aspect, the present invention further discloses a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the method of the first aspect.

[0017] In a fourth aspect, the present invention further discloses a computer device, comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method of the first aspect.

[0018] The beneficial effects achieved by the present invention are: Compared with the prior art, the present invention has significant advantages in the coordinated optimization and dispatching of distribution network-microgrid. First, the present invention combines dynamic networking with the electricity price incentive mechanism to realize the intelligent coordinated dispatching between the distribution network and the microgrid. Through the SOP distribution network, the topology structure can be flexibly adjusted, the distribution of electric energy flow can be optimized, and the operation efficiency and stability of the power grid can be improved. At the same time, the electricity price incentive mechanism effectively mobilizes the enthusiasm of the microgrid, promotes the efficient use of distributed resources, and reduces the overall operation cost of the system.

[0019] Secondly, the present invention adopts a two-layer optimization model, combining the upper distribution network pricing model with the lower microgrid autonomous optimization model, ensuring the coordinated optimization between the distribution network and the microgrid. The distribution network operator optimizes the electricity price strategy to encourage the microgrid to adjust its internal resource configuration according to the electricity price signal to minimize the operating cost. While optimizing its own operation, the microgrid feeds back the optimization results to the distribution network, realizing the mutual coordination and optimization of the distribution network and the microgrid, and improving the intelligence level and adaptive ability of the entire system.

[0020] In addition, in terms of voltage stability, the present invention optimizes the distribution of power flow through dynamic networking, effectively controls the voltage over-limit problem in the distribution network, and ensures the power supply quality and stability of the power grid. The simulation experiment results show that the proposed method can significantly improve the voltage distribution in different scenarios, avoid the problem of multiple node voltages falling below the lower limit, and improve the anti-disturbance ability and fault recovery ability of the power grid.

[0021] The electricity price incentive mechanism of the present invention promotes the coordinated dispatch of distributed resources, reduces the system's abandoned light rate, and improves the absorption capacity of photovoltaic power generation. Through reasonable electricity price incentives, microgrids can more actively respond to the needs of the distribution network, optimize their own resource allocation, achieve efficient use of electricity and maximize the economic benefits of the system.

[0022] In summary, the present invention provides an efficient, intelligent and reliable distribution network-microgrid coordinated optimization scheduling method through the organic combination of intelligent soft switches, dynamic networking and electricity price incentive mechanism, which overcomes the shortcomings of the existing technology in response speed, regulation capability and intelligence level, and has significant technical innovation and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram of the scheduling scheme framework of the present invention; Figure 3 It is a schematic diagram of the process of the present invention; Figure 4 is the topological diagram of the example; Figure 5 This is a schematic diagram of node voltages in scenarios one and two; Figure 6 This is a schematic diagram of the optimal electricity price for scenario three; Figure 7 It is a schematic diagram of the power composition of each microgrid without electricity price incentive; Figure 8 This is a schematic diagram of the power composition of each microgrid under the incentive of electricity price; Fig. 9 It is a schematic diagram of SOP networking strategy and transmission flow; Fig.10 This is a schematic diagram of distribution network losses under scenarios two and three. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0025] Embodiment 1, as Figure 1 As shown, this embodiment provides a distribution network-microgrid coordinated optimization scheduling method, including: Obtaining an optimization dispatching model based on dynamic networking and electricity price incentives, which is pre-constructed according to information of each node and line of the target distribution network and information of a microgrid connected to the target distribution network, wherein the information of each node and line represents the topological structure and line parameters (resistance, reactance) of the distribution network; Obtain the safe operating parameters of the target distribution network and the safe operating parameters of the microgrid and the electricity price incentive strategy of the microgrid; Solving the optimization scheduling model based on dynamic networking and electricity price incentives based on the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the electricity price incentive strategy of the microgrid to generate an optimal scheduling plan; The processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Based on the electricity price incentive strategy of the microgrid and the safe operation parameters of the target distribution network and the safe operation parameters of the microgrid, the internal resource configuration is adjusted by using the covariance matrix adaptive evolutionary strategy algorithm with the goal of minimizing the operation cost of the microgrid, and the adjusted internal resource configuration of the microgrid is fed back to the distribution network, so that the distribution network performs the optimized power flow calculation of dynamic networking in response to the internal resource configuration, and obtains the optimized power flow allocation plan, and generates the dynamic networking plan according to the optimized power flow allocation plan; The operating results of the distribution network and the microgrid are generated according to the dynamic networking plan, and the operating results of the distribution network and the microgrid are used as the new fitness evaluation basis of the covariance matrix adaptive evolutionary strategy algorithm. The microgrid electricity price is adjusted and the next round of optimization iteration is carried out until the iteration is completed to generate a dynamic networking plan under the optimal microgrid electricity price.

[0026] The optimization dispatching model based on dynamic networking and electricity price incentives adopts a two-layer optimization structure, the upper layer of which is a distribution network pricing model, and the lower layer is a microgrid autonomous optimization model and a dynamic networking model.

[0027] The objective function expression of the distribution network pricing model is: ; ; ; ; Where: min means minimization, C 1 represents the objective function of the distribution network layer, C MG represents the transaction costs of distribution network and microgrid; C buy It represents the cost of the distribution network purchasing electricity from the upper power grid; C load It represents the revenue from the distribution network selling electricity to ordinary loads; T Indicates the scheduling period; P sell,i,t , P buy,i,t Represents the distribution network t Time period to i The power sales and purchase of each microgrid; r sell,t , r buy,t Represents the distribution network t The electricity sales and purchase prices during the time period; represents the scheduling step length; P in,t Represents the distribution network t Purchase power from the main grid during the period; ρ t express t The time-of-use electricity price that the distribution network purchases from the main grid; P load,t Represents the distribution network t Normal load power during the period; The constraint expression of the objective function of the distribution network pricing model is as follows: ; Where: r min,t , r max,t Indicates the upper and lower limits of the microgrid's electricity purchase and sales prices.

[0028] The objective function expression of the microgrid autonomous optimization model is: ; ; ; ; C MGs,i Indicates i The objective function of a microgrid is: C exc,i Indicates iThe electricity transaction cost of each microgrid; C gas,i Indicates i The cost of generating electricity from a microgrid gas turbine; C st,i Indicates i Depreciation cost of energy storage in a microgrid; a , b represents the gas turbine cost factor; P gas,t Gas Turbine t The power generated during the period; P ch,t , P dch,t Indicates energy storage t Charging power and discharging power during the time period; ρ st Represents the depreciation cost of energy storage per unit charge and discharge power; The constraints of the objective function of the microgrid autonomous optimization model include: Energy storage constraints: ; Where: SOC max , SOC min Indicates the upper and lower limits of the energy storage charge state; SOC init , SOC t Indicates the initial state of charge and current state of charge of the energy storage; SOC 1 , SOC 25 Indicates the energy storage charge state at the start and end of scheduling, SOC t-1 express t -1 distribution network charge status at time, Indicates energy storage t -1 moment charging power, Indicates energy storage t -1 moment discharge power, P ch,t , P dch,t , P max,st Indicates energy storage t Charging power, discharging power and upper limits of charging and discharging power in the time period; μ ch,t , μ dch,t A 0-1 variable representing the energy storage charge and discharge status, μ ch,t 1 means charging, μdch,t 1 means discharge, and both cannot be 1 at the same time; η st Indicates the energy storage charging and discharging efficiency; Gas turbine output upper and lower limit constraints: ; Where: , Indicates the maximum and minimum output of the gas turbine; Photovoltaic output constraints: ; Where: P PV,t express t Actual photovoltaic output during the period; express t The maximum output of photovoltaic power during the period; Power constraints for interaction between microgrid and distribution network: ; Where: Indicates the upper limit of the power of the tie line between the microgrid and the distribution network; express t The actual power of the interconnection line between the microgrid and the distribution network during the time period; Power balance constraints: ; Where: P load,t Indicates the load power, P gas,t , P PV,t , P dis,t , P ch,t , P MG,t They respectively represent the gas turbine output, photovoltaic output, energy storage discharge power, energy storage charging power, and the actual power of the interconnection line between the microgrid and the distribution network.

[0029] The objective function expression of the dynamic networking model is: ; Where: C 2 Represents the objective function of dynamic networking; The constraints of the objective function of the dynamic networking model include: Power flow constraints: ; ; ; ; Where: r i,j , x i,j , P i,j,t , Q i,j,t , I i,j,t Indicates branch ij resistance, reactance, active power, reactive power and current; , express t Time period node j The inflow and outflow power of the upper SOP; , , V j,t Representation Node j Active load, reactive load and voltage; Q i,k,t Indicates branch jk Reactive power; V i,t Representation Node i Voltage; Node voltage constraints: ; Where: , Representation Node i The voltage upper and lower limits; Branch power constraints: ; Where: Indicates branch ij The upper limit of transmission power; SOP constraints: ; ; ; Where: , , , Indicates connection ij SOP i Node inflow and outflow power and j Node inflow and outflow power; m R,ij Represents a 0-1 state variable, which is used to determine the power flow direction of the SOP; h ij express ij The efficiency of SOP is measured by the power supply, SOP stands for intelligent soft switching.

[0030] The specific process of the processing of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Step 1: Set the voltage range of each node, the maximum capacity of each line and the parameters of SOP; initialize the population center, step size and covariance matrix of the CMA-ES algorithm. The CMA-ES algorithm is a covariance matrix adaptive evolution strategy algorithm; Step 2: Randomly generate an electricity price as the initial sample center point, and then generate a set of electricity prices as the initial population based on the initial sample center point. The expression for generating the initial population is as follows: ; Where: x k represents the generated sample point; m represents the current population center; σ Indicates the step size of the CMA-ES algorithm; N (0,C) means sampling from a multivariate normal distribution with a mean of 0; C represents the covariance matrix; Then, the fitness of each group of electricity prices in the population is determined according to the objective function containing the lower-level problem, and the optimal electricity price strategy is selected to update the sample center, step size and covariance matrix. The expression is as follows: ; ; ; Where: m new indicates the new population center; ω i Indicates that according to the weights assigned by the ranking, the electricity price strategy with higher benefits has a higher weight; x i Indicates an excellent individual in the evaluation; C new represents the new covariance matrix; c cov represents the learning rate of the covariance matrix; σ new represents the new step size of the CMA-ES algorithm; exp represents the exponential function; c σ ,d σ Indicates control The adjustment rate of the control step length; p σ represents the evolutionary path; represents the length of the expected normal distribution vector; N and i Indicates the total number of individuals and individual numbers; Solve the mixed integer linear programming problem based on the objective function and constraint conditions of the microgrid autonomous optimization model, determine the autonomous operation results of each microgrid, and generate the corresponding equivalent load or power supply according to the autonomous operation results of each microgrid; The equivalent load or power data of the microgrid is input into the dynamic networking model, so that the dynamic networking model uses the mixed integer second-order cone programming method, combined with the topological structure of the distribution network and the parameters of the SOP, to calculate the optimal power flow allocation scheme; According to the optimal power flow allocation plan, determine the operating status and power flow direction of each SOP and generate a specific dynamic networking plan; Obtaining the operation results of the distribution network and the microgrid according to the specific dynamic networking scheme, wherein the operation results of the distribution network and the microgrid include distribution network pricing, networking scheduling results and microgrid autonomous operation results; The operating results of the distribution network and the microgrid are fed back to the CMA-ES algorithm as a new fitness evaluation basis. According to the fitness of each individual, a new population is calculated to represent the new electricity price scheme, and the next round of optimization iteration is carried out until the iteration limit is reached to determine the dynamic networking scheme under the optimal microgrid electricity price.

[0031] Embodiment 2, as Figure 2 As shown, a distribution network-microgrid coordinated optimization scheduling method includes the following steps: Step 1: Construct a scheduling scheme framework, specifically: the scheduling scheme framework of the present invention is as shown in the attached Figure 2 As shown in the figure, a two-layer optimization structure is adopted. The upper layer is the distribution network pricing model, and the lower layer is the microgrid autonomous optimization model and dynamic networking model. The distribution network operator first sets the purchase and sale electricity prices of the microgrid. The microgrid optimizes the operation of internal resources based on this electricity price signal to minimize costs. Subsequently, the distribution network operator optimizes the power flow through dynamic networking according to the operation results of the microgrid, and realizes the coordinated optimization scheduling of the distribution network and the microgrid. This two-layer optimization structure ensures the information interaction and collaborative optimization between the distribution network and the microgrid, so that the entire system can maintain efficient and stable operation in a dynamically changing operating environment.

[0032] Step 2: Data collection, specifically: deploy smart sensors at key nodes and lines of the distribution network to collect operating parameters such as voltage, current, and power factor in real time. Sensor types include voltage sensors, current sensors, power factor sensors, etc., to ensure comprehensive monitoring of the grid operation status. The data collected by the sensors is transmitted to the central data processing center through high-speed communication networks (such as optical fiber and wireless communication) to ensure the real-time and reliability of the data.

[0033] Step 3: Build a distribution network pricing model, specifically: Step 31: Under the premise of ensuring safe and stable operation, the distribution network operator aims to minimize its own operating costs. The specific expression of the objective function is: ; ; ; ; Where: min means minimization, C 1 represents the objective function of the distribution network layer, C MG represents the transaction costs of distribution network and microgrid; C buy It represents the cost of the distribution network purchasing electricity from the upper power grid; C load It represents the revenue from the distribution network selling electricity to ordinary loads; T Indicates the scheduling period; P sell,i,t , P buy,i,t Represents the distribution network t Time period to i The power sales and purchase of each microgrid; ρ sell,t , ρ buy,t Represents the distribution network t The electricity sales and purchase prices during the time period; represents the scheduling step length; P in,t Represents the distribution network t Purchase power from the main grid during the period; ρ t express t The time-of-use electricity price that the distribution network purchases from the main grid; P load,t Represents the distribution network t Normal load power during the period; Step 32: The decision variable of the upper distribution network is the purchase and sale price of electricity, and the constraint condition is the upper and lower limit constraints of the electricity price. Its mathematical expression is as follows: ; Where: ρ min,t , ρ max,t Indicates the upper and lower limits of the microgrid's electricity purchase and sales prices.

[0034] Step 4: Build a microgrid autonomous operation model, specifically: Step 41: The goal of the microgrid’s autonomous operation is to minimize its own operating costs, including the energy storage operation depreciation cost, gas turbine power generation cost, and power transaction cost. The mathematical expression is as follows: ; ; ; ; C MGs,i Indicates i The objective function of a microgrid is: C exc,i Indicates i The electricity transaction cost of each microgrid; C gas,i Indicates i The cost of generating electricity from a microgrid gas turbine; C st,i Indicates i Depreciation cost of energy storage in a microgrid; a , b represents the gas turbine cost factor; P gas,t Gas Turbine t The power generated during the period; P ch,t , P dch,t Indicates energy storage t Charging power and discharging power during the time period; ρ st Represents the depreciation cost of energy storage per unit charging and discharging power.

[0035] Step 42: Constraints; Energy storage constraints: including upper and lower limits of energy storage capacity, energy storage capacity cycle constraints, energy storage capacity update, and charge and discharge power constraints; ; Where: SOC max , SOC min Indicates the upper and lower limits of the energy storage charge state; SOC init , SOC t Indicates the initial state of charge and current state of charge of the energy storage; SOC 1 , SOC 25 Indicates the energy storage charge state at the start and end of scheduling, SOC t-1 express t -1 distribution network charge status at time, Indicates energy storage t -1 moment charging power, Indicates energy storaget -1 moment discharge power, P ch,t , P dch,t , P max,st Indicates energy storage t Charging power, discharging power and upper limits of charging and discharging power in the time period; μ ch,t , μ dch,t A 0-1 variable representing the energy storage charge and discharge status, μ ch,t 1 means charging, μ dch,t 1 means discharge, and both cannot be 1 at the same time; η st Indicates the energy storage charging and discharging efficiency.

[0036] The upper and lower limits of gas turbine output are constrained. Since the scheduling step length is much longer than the ramp-up time of the gas turbine, the ramp-up process of the gas turbine is ignored; ; Where: , Indicates the maximum and minimum output of the gas turbine.

[0037] Photovoltaic output constraints: ; Where: P PV,t express t Actual photovoltaic output during the period; express t The maximum photovoltaic output during a period.

[0038] Power constraints interacting with the distribution network: ; Where: Indicates the upper limit of the power of the tie line between the microgrid and the distribution network; express t The actual power of the interconnection line between the microgrid and the distribution network during this period.

[0039] Power balance constraints: ; Where: P load,t Indicates the load power, P gas,t , P PV,t , P dis,t , Pch,t , P MG,t They respectively represent the gas turbine output, photovoltaic output, energy storage discharge power, energy storage charging power, and the actual power of the interconnection line between the microgrid and the distribution network.

[0040] Step 5: Build a dynamic networking model.

[0041] Step 51: Objective function. The purpose of the lower two-stage dynamic networking is to obtain the optimal power flow to reduce the network loss cost and SOP loss cost. It can be expressed by the upper power grid purchase cost and ordinary load power sales income in the upper target. The mathematical expression is as follows: ; Where: C 2 Represents the objective function of dynamic networking; Step 52: Constraints, including power flow constraints, node voltage constraints, branch power constraints, and SOP constraints.

[0042] Power flow constraints: ; ; ; ; Where: r i,j , x i,j , P i,j,t , Q i,j,t , I i,j,t Indicates branch ij resistance, reactance, active power, reactive power and current; , express t Time period node j The inflow and outflow power of the upper SOP; , , V j,t Representation Node j Active load, reactive load and voltage; Q i,k,t Indicates branch jk Reactive power; V i,t Representation Node i voltage.

[0043] Node voltage constraints: ; Where: , Representation Node i The voltage upper and lower limits.

[0044] Branch power constraints: ; Where: Indicates branch ij The upper limit of the transmission power.

[0045] SOP constraints: ; ; ; Where: , , , Indicates connection ij SOP i Node inflow and outflow power and j Node inflow and outflow power; m R,ij Represents a 0-1 state variable, which is used to determine the power flow direction of the SOP; h ij express ij The efficiency of SOP is measured by the power supply, SOP stands for intelligent soft switching.

[0046] Step 6: Decision model solution algorithm. Figure 3 As shown, the present invention adopts the covariance matrix adaptive evolution strategy (CMA-ES) algorithm to solve the pricing problem on the distribution network side, and combines the microgrid autonomous optimization model with the dynamic networking model to achieve double-layer optimization scheduling. The specific steps are as follows: Step 61: Initialize system parameters. First, set initial parameters including voltage upper and lower limits, line capacity, SOP parameters, etc. The distribution network operator defines the voltage range of each node, the maximum capacity of each line, and the relevant parameters of SOP according to the specific conditions of the distribution network. Then, initialize the population center, step size, and covariance matrix of the CMA-ES algorithm to ensure that the algorithm has good initial search capabilities.

[0047] Step 62: CMA-ES algorithm solves the distribution network pricing problem. The CMA-ES algorithm is used to optimize the distribution network pricing strategy. First, the initial parameters including the upper and lower limits of voltage and line capacity are set, and then CMA-ES is used to solve the distribution network layer pricing problem and optimize the electricity price. CMA-ES can adjust parameters adaptively and has higher accuracy than ordinary heuristic algorithms. First, a random electricity price is generated as the initial sample center point, and various data are initialized. Then a set of electricity prices is generated as the initial population, and its expression is as follows: ; Where: x k represents the generated sample point; m represents the current population center; σ Indicates the step size of the CMA-ES algorithm; N (0,C) means sampling from a multivariate normal distribution with a mean of 0; C represents the covariance matrix; Then, the fitness of each group of electricity prices in the population is determined according to the objective function containing the lower-level problem, and the optimal electricity price strategy is selected to update the sample center, step size and covariance matrix. The expression is as follows: ; ; ; Where: m new represents the new population center; w i Indicates that according to the weights assigned by the ranking, the electricity price strategy with higher benefits has a higher weight; x i Indicates an excellent individual in the evaluation; C new represents the new covariance matrix; c cov Represents the learning rate of the covariance matrix, which controls the speed of incorporating new information; σ new represents the new step size of the CMA-ES algorithm; exp represents the exponential function; c σ , d σ Indicates control The adjustment rate of the control step length; p σ It represents the evolutionary path, reflecting the direction of past changes; Represents the length of the expected normal distribution vector, used to normalize the change in step size; N and i Indicates the total number of individuals and individual numbers.

[0048] Step 63: Microgrid autonomous optimization. Each microgrid optimizes internal resource allocation based on the electricity price signal set by the upper distribution network and solves the mixed integer linear programming (MILP) problem.

[0049] Specifically, the microgrid optimizes the energy storage charging and discharging plan, gas turbine power generation plan and photovoltaic output plan based on its own energy storage capacity, photovoltaic power generation and gas turbine output parameters to minimize operating costs. The optimized microgrid operation results are equivalent to the load or power supply on the distribution network side, which is used for further dynamic networking optimization of the distribution network.

[0050] Step 64: Dynamic network optimization of the distribution network. After the microgrid is equivalent to the load or power supply on the distribution network side, the power control function of the smart soft switch (SOP) is combined to perform dynamic network optimization of the distribution network. The specific steps include: Step 641: Input equivalent load and power supply data. The distribution network operator inputs the equivalent load or power supply data of the microgrid into the dynamic networking model; Step 642: Optimize the power flow calculation. Use the mixed integer second-order cone programming (MIQCP) method, such as Figure 4 As shown, the optimal power flow distribution scheme is calculated by combining the topological structure of the distribution network and the parameters of the SOP to ensure the optimal distribution of the power flow and reduce the network loss cost and the SOP loss cost. Figure 4 MG1, MG2 and MG3 correspond to three microgrids, serial numbers 1 to 33 correspond to thirty-three nodes, and serial numbers 01 to 03 correspond to three intelligent soft switches; Generate dynamic networking plan: According to the optimization results, determine the operating status and power flow of each SOP and generate a specific dynamic networking plan; Step 643: Feedback and iteration. Feedback the operation results of the distribution network and the microgrid to the CMA-ES algorithm as a new fitness evaluation basis. According to the feedback results, adjust the electricity price strategy and carry out the next round of optimization iteration. Through this iterative process, ensure that the coordinated optimization and scheduling between the distribution network and the microgrid is continuously improved to minimize the total system cost.

[0051] Step 7: Simulation experiment and results: The present invention connects three microgrids and three smart soft switches (SOPs) to the IEEE-33 node distribution network model, and conducts simulation comparative analysis of three different scenarios. The computer hardware environment used for the simulation is Xeon Silver 4210RCPU@2.4 GHz*2, 64GB running memory, and the program is written in Python.

[0052] Simulation scenario: Scenario 1: Apply normal time-of-use electricity prices and grid-connected electricity prices, do not use electricity price incentives, and do not use SOP dynamic networking.

[0053] Scenario 2: Apply normal time-of-use electricity prices and grid-connected electricity prices, do not use electricity price incentives, and use SOP dynamic networking.

[0054] Scenario 3: Apply electricity price incentives, adopt the optimized electricity price, and use SOP dynamic networking.

[0055] The parameters of the three microgrids (microgrid 1, microgrid 2 and microgrid 3) are shown in Table 1: Table 1 .

[0056] Simulation results analysis: Voltage distribution: Voltage distribution diagram of each node in the distribution network under scenario 1 and scenario 2 (such as Figure 5 ) shows that when the dynamic networking strategy is not adopted, the voltage of multiple nodes falls below the lower limit and the power supply quality is impaired. After the SOP is introduced and dynamic networking is implemented, the voltage limit problem is effectively controlled, and the voltage of all nodes is restored to the normal range, which significantly improves the stability and reliability of the power grid.

[0057] Electricity price diagram: Schematic diagram of time-of-use electricity price, grid-connected electricity price and optimal purchase and sale electricity price under scenario three (such as Figure 6 ) shows that the distribution network dynamically adjusts the electricity price according to the microgrid's electricity purchase and sales behavior. The distribution network sets a higher electricity sales price during the microgrid's electricity purchase period to reduce operating costs; during the noon period when photovoltaic power generation is high, the distribution network sets a lower electricity purchase price to reduce electricity purchase costs and encourage microgrids to sell power to the distribution network. This electricity price optimization strategy effectively promotes energy exchange between the distribution network and the microgrid and maximizes economic benefits.

[0058] Microgrid power composition: Schematic diagram of the power composition of each microgrid without electricity price incentives (scenario 1 and 2) and with electricity price incentives (scenario 3) (see Figure 2). Figure 7 , Figure 8 ) shows that under the electricity price incentive mechanism, the energy storage utilization rate of microgrids has increased significantly. Under the electricity price incentive, microgrids are more actively involved in the load regulation and flow optimization of the distribution network, assisting the distribution network in peak load reduction and valley filling. Especially in scenario three with electricity price incentives, microgrid three actively increased the output of gas turbines and output excess power to the distribution network, realizing the coordinated scheduling and common benefit of distributed resources in the distribution network.

[0059] Comparison of abandoned power rate: Table 2 shows that the abandoned power rate of the microgrid in scenario 3 is significantly reduced, from 7.0% in scenario 2 to 4.5% in scenario 3, indicating that the electricity price incentive mechanism effectively promotes the consumption of photovoltaic power generation and improves the utilization efficiency of renewable energy; Table 2 .

[0060] Power flow optimization: SOP networking strategy and transmission power flow in scenarios 2 and 3 (such as Fig. 9) shows that SOP optimizes power flow distribution through dynamic networking, reduces the power transmission value of the trunk line, optimizes the power flow, maintains the voltage level and reduces network loss. Specifically, SOP1 transmits part of the power from node 22 to microgrid 3 during the period of high power consumption of microgrid 3, reduces the power transmission on the trunk line, and optimizes the overall power flow distribution; SOP2 and SOP3 also show similar power flow optimization effects, which improves the overall operation efficiency and voltage quality of the power grid.

[0061] Distribution network loss: Distribution network loss distribution in scenarios 2 and 3 (e.g. Fig.10 ) shows that in scenario 2 where the electricity price incentive mechanism is not adopted, the network loss of the distribution network is relatively high due to the failure to effectively coordinate the output of distributed resources within the microgrid. In scenario 3 under the electricity price incentive mechanism, although the microgrid centralized power purchase during peak hours leads to increased instantaneous network losses, the overall network loss is reduced compared to scenario 2. This shows that a reasonable electricity price incentive mechanism combined with the coordinated dispatch of distributed resources can effectively reduce the overall loss of the distribution network and improve the economy and efficiency of the power grid.

[0062] Revenue of each entity: Table 3 shows that the daily operating revenue of each microgrid and distribution network under scenario 3 has increased. The revenue of microgrids 1, 2 and 3 increased by 12.3%, 11.7% and 19.6% respectively, and the revenue of the distribution network increased by 75.8%, with a total revenue increase of 21.9%. This result shows that the electricity price incentive mechanism not only improves the economic benefits of microgrids, but also greatly improves the operating revenue of distribution networks, achieving common benefits for distribution networks and microgrids; Table 3 .

[0063] Embodiment 3 is based on the same inventive concept as Embodiment 1. This embodiment introduces a distribution network-microgrid coordinated optimization scheduling device, including: An acquisition module is used to acquire an optimization scheduling model based on dynamic networking and electricity price incentives, which is pre-built according to the key nodes and line information of the target distribution network and the information of the microgrid connected to the target distribution network; acquire the safe operation parameters of the target distribution network and the safe operation parameters of the microgrid and the electricity price incentive strategy of the microgrid; A generation module, used to solve the optimization scheduling model based on dynamic networking and electricity price incentives based on the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid and the electricity price incentive strategy of the microgrid, and generate an optimal scheduling plan; The processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Based on the electricity price incentive strategy of the microgrid and the safe operation parameters of the target distribution network and the safe operation parameters of the microgrid, the internal resource configuration is adjusted by using the covariance matrix adaptive evolutionary strategy algorithm with the goal of minimizing the operation cost of the microgrid, and the adjusted internal resource configuration of the microgrid is fed back to the distribution network, so that the distribution network performs the optimized power flow calculation of dynamic networking in response to the internal resource configuration, and obtains the optimized power flow allocation plan, and generates the dynamic networking plan according to the optimized power flow allocation plan; The operating results of the distribution network and the microgrid are generated according to the dynamic networking plan, and the operating results of the distribution network and the microgrid are used as the new fitness evaluation basis of the covariance matrix adaptive evolutionary strategy algorithm. The microgrid electricity price is adjusted and the next round of optimization iteration is carried out until the iteration is completed to generate a dynamic networking plan under the optimal microgrid electricity price.

[0064] Embodiment 4 is based on the same inventive concept as other embodiments. This embodiment introduces a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the method of Embodiment 1.

[0065] Example 5 is based on the same inventive concept as other examples. This example introduces a computer device, including one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method of Example 1.

[0066] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0070] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A distribution network-microgrid coordinated optimization scheduling method, characterized in that: include: Obtaining an optimization dispatching model based on dynamic networking and electricity price incentives, which is pre-constructed according to information of each node and line of the target distribution network and information of a microgrid connected to the target distribution network; Obtain the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the electricity price incentive strategy of the microgrid; Solving the optimization scheduling model based on dynamic networking and electricity price incentives based on the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the electricity price incentive strategy of the microgrid to generate an optimal scheduling plan; The processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Based on the electricity price incentive strategy of the microgrid and the safe operation parameters of the target distribution network and the safe operation parameters of the microgrid, the internal resource configuration is adjusted by using the covariance matrix adaptive evolutionary strategy algorithm with the goal of minimizing the operation cost of the microgrid, and the adjusted internal resource configuration of the microgrid is fed back to the distribution network, so that the distribution network performs the optimized power flow calculation of dynamic networking in response to the internal resource configuration, and obtains the optimized power flow allocation plan, and generates the dynamic networking plan according to the optimized power flow allocation plan; The operating results of the distribution network and the microgrid are generated according to the dynamic networking plan, and the operating results of the distribution network and the microgrid are used as the new fitness evaluation basis of the covariance matrix adaptive evolutionary strategy algorithm. The microgrid electricity price is adjusted and the next round of optimization iteration is carried out until the iteration is completed to generate a dynamic networking plan under the optimal microgrid electricity price.

2. The distribution network-microgrid coordinated optimization scheduling method according to claim 1 is characterized in that: The optimization scheduling model based on dynamic networking and electricity price incentives adopts a double-layer optimization structure, the upper layer of which is a distribution network pricing model, and the lower layer of which is a microgrid autonomous optimization model and a dynamic networking model.

3. The distribution network-microgrid coordinated optimization scheduling method according to claim 2 is characterized in that: The objective function expression of the distribution network pricing model is: ； ； ； ； Where: min means minimization, C1 represents the distribution network layer objective function, C MG represents the transaction costs of distribution network and microgrid; C buy It represents the cost of the distribution network purchasing electricity from the upper power grid; C load It represents the revenue from the distribution network selling electricity to ordinary loads; T Indicates the scheduling period; N represents the total number of microgrids; P sell,i,t , P buy,i,t Represents the distribution network t Time period to i The power sales and purchase of each microgrid; ρ sell,t , ρ buy,t Represents the distribution network t The electricity sales and purchase prices during the time period; represents the scheduling step length; P in,t Represents the distribution network t Purchase power from the main grid during the period; ρ t express t The time-of-use electricity price that the distribution network purchases from the main grid; P load,t Represents the distribution network t Normal load power during the period; i ∈[1, N ], t ∈[1, T ]; The constraint expression of the objective function of the distribution network pricing model is as follows: ； Where: ρ min,t , ρ max,t Indicates the upper and lower limits of the microgrid's electricity purchase and sales prices.

4. The distribution network-microgrid coordinated optimization scheduling method according to claim 3 is characterized in that: The objective function expression of the microgrid autonomous optimization model is: ； ； ； ； Where: C MGs,i Indicates i The objective function of a microgrid is: C exc,i Indicates i The electricity transaction cost of each microgrid; C gas,i Indicates i Cost of power generation from a gas turbine in a microgrid; C st,i Indicates i Depreciation cost of energy storage in a microgrid; a , b represents the gas turbine cost factor; P gas,t Gas Turbine t The power generated during the period; P ch,t , P dch,t Indicates energy storage t Charging power and discharging power in each time period; ρ st Represents the depreciation cost of energy storage per unit charge and discharge power; The constraints of the objective function of the microgrid autonomous optimization model include: Energy storage constraints: ； Where: SOC max , SOC min Indicates the upper and lower limits of the energy storage charge state; SOC init , SOC t Indicates the initial state of charge and current state of charge of the energy storage; SOC 1. SOC 25 Indicates the energy storage charge state at the start and end of the dispatch. SOC t-1 express t -1 distribution network charge status at time, Indicates energy storage t -1 moment charging power, Indicates energy storage t -1 moment discharge power, P max,st Indicates energy storage t The upper limit of charging and discharging power in the time period; μ ch,t , μ dch,t A 0-1 variable representing the energy storage charge and discharge status, μ ch,t 1 means charging, μ dch,t 1 means discharge, and both cannot be 1 at the same time; η st Indicates the energy storage charging and discharging efficiency; Gas turbine output upper and lower limit constraints: ； Where: , Indicates the maximum and minimum output of the gas turbine; Photovoltaic output constraints: ； Where: P PV,t express t Actual photovoltaic output during the period; express t The maximum output of photovoltaic power during the period; Power constraints for interaction between microgrid and distribution network: ； Where: Indicates the upper limit of the power of the tie line between the microgrid and the distribution network; express t The actual power of the interconnection line between the microgrid and the distribution network during the time period; Power balance constraints: ； Where: P load,t Indicates the load power, P gas,t , P PV,t , P dis,t , P ch,t , P MG,t They respectively represent the gas turbine output, photovoltaic output, energy storage discharge power, energy storage charging power, and the actual power of the interconnection line between the microgrid and the distribution network.

5. The distribution network-microgrid coordinated optimization scheduling method according to claim 4 is characterized in that: The objective function expression of the dynamic networking model is: ； Where: C2 represents the objective function of dynamic networking; The constraints of the objective function of the dynamic networking model include: Power flow constraints: ； ； ； ； Where: r i,j , x i,j Indicates branch ij Resistance and reactance; P i,j,t , Q i,j,t , I i,j,t express t Time period branch ij Active power, reactive power and current; , express t Time period node j The inflow power and outflow power of the upper SOP; , , V j,t express t Time period node j Active load, reactive load, and voltage; Q i,k,t express t Time period branch jk Reactive power; V i,t express t Time period node i Voltage; P j,k,t express t Time period branch jk Active power of Node voltage constraints: ； Where: , Representation Node i The voltage upper and lower limits; Branch power constraints: ； Where: Indicates branch ij The upper limit of transmission power; SOP constraints: ； ； ； Where: , Indicates the connecting branch ij On SOP i Node inflow power and outflow power; , Indicates the connecting branch ij On SOP j Node inflow power and outflow power; μ S,ij Represents a 0-1 state variable, which is used to determine the power flow direction of the SOP; η ij Indicates branch ij The efficiency of SOP, SOP stands for intelligent soft switch; Indicates branch ij Maximum power on SOP.

6. The distribution network-microgrid coordinated optimization scheduling method according to claim 5 is characterized in that: The processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Set the voltage range of each node, the maximum capacity of each line and the parameters of SOP; initialize the population center, step size and covariance matrix of the CMA-ES algorithm, which is a covariance matrix adaptive evolutionary strategy algorithm; A random electricity price is generated as the initial sample center point, and then a set of electricity prices is generated based on the initial sample center point as the initial population. The expression for generating the initial population is as follows: ； Where: x k represents the generated sample point; m represents the current population center; σ Indicates the step size of the CMA-ES algorithm; N (0,C) means sampling from a multivariate normal distribution with a mean of 0; C represents the covariance matrix; Then, the fitness of each group of electricity prices in the population is determined according to the objective function containing the lower-level problem, and the optimal electricity price strategy is selected to update the sample center, step size and covariance matrix. The expression is as follows: ； ； ； Where: m new indicates the new population center; ω i Indicates that according to the weights assigned by the ranking, the electricity price strategy with higher benefits has a higher weight; x i Indicates an excellent individual in the evaluation; C new represents the new covariance matrix; c cov represents the learning rate of the covariance matrix; σ new represents the new step size of the CMA-ES algorithm; exp represents the exponential function; c σ , d σ Indicates the adjustment rate of the control step size; p σ represents the evolutionary path; represents the length of the expected normal distribution vector; Solve the mixed integer linear programming problem based on the objective function and constraint conditions of the microgrid autonomous optimization model, determine the autonomous operation results of each microgrid, and generate the corresponding equivalent load or power supply according to the autonomous operation results of each microgrid; The equivalent load or power data of the microgrid is input into the dynamic networking model, so that the dynamic networking model uses the mixed integer second-order cone programming method, combined with the topological structure of the distribution network and the parameters of the SOP, to calculate the optimal power flow allocation scheme; According to the optimal power flow allocation plan, determine the operating status and power flow direction of each SOP and generate a specific dynamic networking plan; Obtaining the operation results of the distribution network and the microgrid according to the specific dynamic networking scheme, wherein the operation results of the distribution network and the microgrid include distribution network pricing, networking scheduling results, and microgrid autonomous operation results; The operating results of the distribution network and the microgrid are fed back to the CMA-ES algorithm as a new fitness evaluation basis. According to the fitness of each individual, a new population is calculated to represent the new electricity price scheme, and the next round of optimization iteration is carried out until the iteration limit is reached to determine the dynamic networking scheme under the optimal microgrid electricity price.

7. A distribution network-microgrid coordinated optimization dispatching device, characterized in that: include: An acquisition module, used to acquire an optimization scheduling model based on dynamic networking and electricity price incentives, which is pre-constructed according to information of each node and line of the target distribution network and information of a microgrid connected to the target distribution network; Obtain the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the electricity price incentive strategy of the microgrid; A generation module, used to solve the optimization scheduling model based on dynamic networking and electricity price incentives based on the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the electricity price incentive strategy of the microgrid, and generate an optimal scheduling plan; The processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes: Based on the electricity price incentive strategy of the microgrid and the safe operation parameters of the target distribution network and the safe operation parameters of the microgrid, the internal resource configuration is adjusted by using the covariance matrix adaptive evolutionary strategy algorithm with the goal of minimizing the operation cost of the microgrid, and the adjusted internal resource configuration of the microgrid is fed back to the distribution network, so that the distribution network performs the optimized power flow calculation of dynamic networking in response to the internal resource configuration, and obtains the optimized power flow allocation plan, and generates the dynamic networking plan according to the optimized power flow allocation plan; The operating results of the distribution network and the microgrid are generated according to the dynamic networking plan, and the operating results of the distribution network and the microgrid are used as the new fitness evaluation basis of the covariance matrix adaptive evolutionary strategy algorithm. The microgrid electricity price is adjusted and the next round of optimization iteration is carried out until the iteration is completed to generate a dynamic networking plan under the optimal microgrid electricity price.

8. The distribution network-microgrid coordinated optimization dispatching device according to claim 7, characterized in that: The optimization scheduling model based on dynamic networking and electricity price incentives adopts a double-layer optimization structure, the upper layer of which is a distribution network pricing model, and the lower layer of which is a microgrid autonomous optimization model and a dynamic networking model.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 6.

10. A computer device, characterized in that: include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in claims 1 to 6.

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