A coordinated optimal scheduling method, device, medium and equipment for a distribution network - microgrid

Abstract

The present invention discloses a coordinated optimization scheduling method, device, medium and equipment in the technical field of smart grid, aiming to solve the problems of slow response speed, limited regulation ability and insufficient intelligence in the prior art. It includes obtaining an optimization scheduling model based on dynamic networking and price incentive constructed in advance; solving the optimization scheduling model based on dynamic networking and price incentive according to the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid and the price incentive strategy of the microgrid to generate an optimal scheduling scheme. By the organic combination of intelligent soft switches, dynamic networking and price incentive mechanisms, the present invention provides an efficient, intelligent and reliable coordinated optimization scheduling method for distribution network - microgrid, overcomes the deficiencies of the prior art in terms of response speed, regulation ability and intelligence level, and has remarkable technical innovation and practical application value.

Description

Technical Field

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

[0002] With the rapid development of smart grids and renewable energy, the structure of the distribution network has become increasingly complex, and its operating state has become more dynamic and uncertain. Traditional distribution networks mainly rely on fixed topological structures and static fault protection mechanisms, such as circuit breakers and protective relays. When dealing with complex and changing grid operating states, these traditional methods show defects such as slow response speed and limited adjustment ability, making it difficult to effectively prevent and control the spread of faults, resulting in serious impacts on the reliability and stability of the grid. Especially in medium - and low - voltage distribution networks, problems such as 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 by adjusting the topological structure of the distribution network in real - time, improving the operating efficiency and stability of the grid. However, existing dynamic networking methods are mostly based on heuristic algorithms or linear optimization models, which are difficult to handle the high non - linearity and complexity in grid operation, and have insufficient decision - making speed and accuracy in the face of sudden faults, unable to achieve rapid response and effective protection against 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 grids for high reliability and high security. Existing protection mechanisms mostly rely on preset protection strategies and parameters, lacking intelligence and adaptability, and unable to dynamically adjust to adapt to the real - time operating state of the grid, resulting in unsatisfactory protection effects in complex situations with multiple faults and multiple variables, and even possibly triggering large - scale power outages.

[0004] As an important part of distributed power sources, the microgrid has gradually become an important means to improve grid efficiency and sustainability by virtue of its advantages of autonomous operation and flexible scheduling. Existing research mostly focuses on the optimization scheduling and resource integration within the microgrid, but there is still a lack of systematic methods for coordinated optimization scheduling between the distribution network and the microgrid, especially in the combined application of dynamic networking and price incentive mechanisms, and the research is still insufficient. As a dynamic power flow control device, the intelligent soft switch (Soft Open Point, SOP) has advantages such as 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 mostly focuses on power flow optimization and fault recovery in the distribution network, and there is less research on its comprehensive application in the coordinated optimization scheduling of the distribution network - microgrid, and no systematic solution has been formed.

[0005] In summary, in the current distribution network's response to medium and low voltage over-limit and the disorderly scheduling of distributed resources, traditional methods have defects such as slow response speed, limited regulation ability, and insufficient intelligence. At the same time, there is still a lack of systematic research and effective solutions for the coordinated optimal scheduling 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 coordinated optimal scheduling method, device, medium and equipment for a distribution network - microgrid.

[0007] To solve the above technical problems, the present invention is implemented by the following technical solutions.

[0008] In the first aspect, the present invention discloses a coordinated optimal scheduling method for a distribution network - microgrid, including:

[0009] Obtain an optimal scheduling model based on dynamic network formation and price incentive constructed in advance according to the node and line information of the target distribution network and the microgrid information accessed to the target distribution network;

[0010] Obtain the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the price incentive strategy of the microgrid;

[0011] Solve the optimal scheduling model based on dynamic network formation and price incentive based on the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the price incentive strategy of the microgrid to generate an optimal scheduling plan;

[0012] The processing process of the optimal scheduling model based on dynamic network formation and price incentive includes:

[0013] Based on the price incentive strategy of the microgrid, the safe operation parameters of the target distribution network, and the safe operation parameters of the microgrid, use the covariance matrix adaptation evolution strategy algorithm to adjust the internal resource allocation with the goal of minimizing the microgrid operation cost, and feedback the adjusted internal resource allocation of the microgrid to the distribution network, so that the distribution network performs optimal power flow calculation for dynamic network formation in response to the internal resource allocation, obtains an optimized power flow distribution plan, and generates a dynamic network formation plan according to the optimized power flow distribution plan;

[0014] Generate the operation results of the distribution network and the microgrid according to the dynamic network formation plan, use the operation results of the distribution network and the microgrid as the new fitness evaluation basis of the covariance matrix adaptation evolution strategy algorithm, and perform microgrid price adjustment to conduct the next round of optimization iteration until the iteration is completed to generate a dynamic network formation plan under the optimal microgrid price.

[0015] 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 a distribution network pricing model, and the lower layer is a microgrid autonomous optimization model and a dynamic networking model.

[0016] Furthermore, the objective function expression of the distribution network pricing model is as follows:

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] Where: min represents minimization, C1 represents the objective function of the distribution network layer, C MG represents the transaction cost between the distribution network and the microgrid; C buy represents the power purchase cost of the distribution network from the superior power grid; C load represents the power selling revenue of the distribution network to ordinary loads; T represents the scheduling period; N represents the total number of microgrids; P sell,i,t 、 P buy,i,t represent the power selling and power purchase of the distribution network t at time period i to the ρ sell,t 、 ρ buy,t represent the power selling and power purchase of the distribution network t at time period ; P in,t represents the power purchase from the main grid by the distribution network t at time period ρ t represents t the time-of-use electricity price for the distribution network to purchase power from the main grid at time period P load,t represents the ordinary load power of the distribution network t at time period i ∈[1, N , t ∈[1, T ;

[0022] The constraint condition expression of the objective function of the distribution network pricing model is as follows:

[0023] ;

[0024] Wherein: ρ min,t represents the lower limit of the purchase and sale electricity price of the microgrid; ρ max,t represents the upper limit of the purchase and sale electricity price of the microgrid.

[0025] Furthermore, the objective function expression of the microgrid autonomous optimization model is:

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] Wherein: C MGs,i represents the i th objective function of the microgrid, C exc,i represents the electricity trading cost of the i th microgrid; C gas,i represents the gas turbine power generation cost of the i th microgrid; C st,i represents the energy storage depreciation cost of the i th microgrid; a , b represents the gas turbine cost coefficient; P gas,t represents the t period power generation power of the gas turbine; P ch,t , P dch,t represents the t period charging power and discharging power of the energy storage; ρ st represents the energy storage unit charge and discharge power depreciation cost;

[0031] The constraint conditions of the objective function of the microgrid autonomous optimization model include:

[0032] Energy storage constraint:

[0033] ;

[0034] Wherein: SOC max , SOCmin Indicate the upper and lower limits of the energy storage state of charge; SOC init 、 SOC t Indicate the initial state of charge and the current state of charge of the energy storage; SOC 1. SOC 25 Indicate the state of charge of the energy storage at the start and end times of scheduling, SOC t-1 Indicate t the state of charge of the distribution network at time - 1, Indicate the energy storage t charging power at time - 1, Indicate the energy storage t discharging power at time - 1, P max,st Indicate the energy storage t upper limit of charging and discharging power during a period; μ ch,t 、 μ dch,t Indicate the 0 - 1 variable of the energy storage charging and discharging state, μ ch,t being 1 indicates charging, μ dch,t being 1 indicates discharging, and the two cannot be 1 at the same time; η st Indicate the energy storage charging and discharging efficiency;

[0035] Constraints on the output of the gas turbine:

[0036] ;

[0037] In the formula: 、 Indicate the maximum output and minimum output of the gas turbine;

[0038] Constraints on the output of the photovoltaic:

[0039] ;

[0040] In the formula: P PV,t Indicate t the actual output of the photovoltaic during a period; Indicate t the maximum output of the photovoltaic during a period;

[0041] Constraints on the power interaction between the micro - grid and the distribution network:

[0042] ;

[0043] In the formula: Indicates the power upper limit of the tie line between the microgrid and the distribution network; Indicates t The actual power of the tie line between the microgrid and the distribution network during a time period;

[0044] Power balance constraint:

[0045] ;

[0046] In the formula: P load,t Indicates the load power, P gas,t 、 P PV,t 、 P dch,t 、 P ch,t 、 P MG,t respectively indicate the output of the gas turbine, the output of the photovoltaic, the discharge power of the energy storage, the charging power of the energy storage, and the actual power of the tie line between the microgrid and the distribution network.

[0047] Furthermore, the objective function expression of the dynamic networking model is:

[0048] ;

[0049] In the formula: C2 represents the objective function of the dynamic networking;

[0050] The constraint conditions of the objective function of the dynamic networking model include:

[0051] Power flow constraint:

[0052] ;

[0053] ;

[0054] ;

[0055] ;

[0056] In the formula: r i,j 、 x i,j represent the resistance and reactance of branch ij ; P i,j,t 、 Q i,j,t 、 I i,j,t represent t the active power, reactive power, and current of branch ij during a time period; 、 Indicates t Time period node j The inflow power and outflow power of the upper SOP; 、 、 V j,t Indicates t Time period node j The active load, reactive load, and voltage; Q i,k,t Indicates t Time period branch jk The reactive power; V i,t Indicates t Time period node i The voltage; P j,k,t Indicates t Time period branch jk The active power;

[0057] Node voltage constraint:

[0058] ;

[0059] In the formula: 、 Indicates the node i The upper and lower voltage limits;

[0060] Branch power constraint:

[0061] ;

[0062] In the formula: Indicates the branch ij The upper limit of the transmission power;

[0063] SOP constraint conditions:

[0064] ;

[0065] ;

[0066] ;

[0067] In the formula: 、 Indicates the inflow power and outflow power of the node on the SOP of the connecting branch ij on i ; 、 Indicates the inflow power and outflow power of the node on the SOP of the connecting branch ij on j ; μ S,ijRepresents a 0-1 state variable used to determine the power flow direction of the SOP; η ij Represents a branch ij The efficiency of the SOP on the branch, where SOP represents the intelligent soft switch; Represents a branch ij The maximum power of the SOP on the branch.

[0068] Furthermore, the processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes:

[0069] Set the voltage range of each node, the maximum capacity of each line, and the parameters of the SOP; Initialize the population center, step size, and covariance matrix of the CMA-ES algorithm, where the CMA-ES algorithm is the covariance matrix adaptation evolution strategy algorithm;

[0070] 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 this initial sample center point. The expression for generating the initial population is as follows:

[0071] ;

[0072] In the formula: x k Represents the generated sample point; m represents the current population center; σ Represents the step size of the CMA-ES algorithm; N (0,C) represents sampling from a multivariate normal distribution with a mean of 0; C represents the covariance matrix;

[0073] Then, determine the fitness of each group of electricity prices in the population according to the objective function including the lower-level problem, and select the optimal electricity price strategy to update the sample center, step size, and covariance matrix. The expressions are as follows:

[0074] ;

[0075] ;

[0076] ;

[0077] In the formula: m new Represents the new population center; ω i Represents the weight assigned according to the ranking, and the electricity price strategy with higher revenue has a higher weight; x i Represents the excellent individuals 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 represents the adjustment rate of the control step size; p σ represents the evolutionary path; represents the length of the desired normal distribution vector;

[0078] 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 corresponding equivalent loads or power sources according to the autonomous operation results of each microgrid;

[0079] Input the equivalent load or power source data of the microgrid into the dynamic networking model, so that the dynamic networking model uses the mixed-integer second-order cone programming method, combines the topological structure of the distribution network and the parameters of the SOP, and calculates the optimal power flow distribution scheme;

[0080] Determine the operating states and power flow directions of each SOP according to the optimal power flow distribution scheme, and generate a specific dynamic networking scheme;

[0081] Obtain the operation results of the distribution network and the microgrid according to the specific dynamic networking scheme, and the operation results of the distribution network and the microgrid include distribution network pricing, networking scheduling results, and microgrid autonomous operation results;

[0082] Feed back the operation results of the distribution network and the microgrid to the CMA-ES algorithm as a new fitness evaluation basis, calculate a new population according to the fitness of each individual, represent a new electricity price scheme, and perform the next round of optimization iteration until the iteration upper limit is reached, and determine the dynamic networking scheme under the optimal microgrid electricity price.

[0083] In a second aspect, the present invention also discloses a distribution network - microgrid coordinated optimization scheduling device, including:

[0084] An acquisition module, configured to acquire an optimization scheduling model based on dynamic networking and electricity price incentives constructed in advance according to the node and line information of the target distribution network and the microgrid information accessing the target distribution network; acquire 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;

[0085] A generation module, configured 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 scheme;

[0086] The processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes:

[0087] Based on the electricity price incentive strategy of the microgrid, the safe operation parameters of the target distribution network, and the safe operation parameters of the microgrid, the covariance matrix adaptation evolution strategy algorithm is used to adjust the internal resource allocation with the goal of minimizing the operation cost of the microgrid, and the adjusted internal resource allocation of the microgrid is fed back to the distribution network, so that the distribution network performs optimal power flow calculation for dynamic network formation in response to the internal resource allocation, obtains an optimized power flow distribution plan, and generates a dynamic network formation plan according to the optimized power flow distribution plan;

[0088] Generate the operation results of the distribution network and the microgrid according to the dynamic network formation plan, use the operation results of the distribution network and the microgrid as the new fitness evaluation basis for the covariance matrix adaptation evolution strategy algorithm, and perform microgrid electricity price adjustment to conduct the next round of optimization iteration until the iteration is completed to generate a dynamic network formation plan under the optimal microgrid electricity price.

[0089] Further, the optimization scheduling model based on dynamic network formation and electricity price incentive 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 network formation model.

[0090] In a third aspect, the present invention also discloses a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to execute the method of the first aspect.

[0091] In a fourth aspect, the present invention also discloses 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 the first aspect.

[0092] The beneficial effects achieved by the present invention:

[0093] Compared with the prior art, the present invention has significant advantages in the coordinated optimal scheduling of the distribution network - microgrid. First of all, the present invention combines dynamic network formation with an electricity price incentive mechanism to achieve intelligent coordinated scheduling between the distribution network and the microgrid. Through SOP, the distribution network can flexibly adjust the topological structure, optimize the distribution of power flow, and improve the operation efficiency and stability of the power grid. At the same time, the electricity price incentive mechanism effectively mobilizes the enthusiasm of the microgrid, promotes the efficient utilization of distributed resources, and reduces the overall operation cost of the system.

[0094] Secondly, the present invention adopts a two-layer optimization model, which combines the distribution network pricing model in the upper layer with the microgrid autonomous optimization model in the lower layer, ensuring the collaborative optimization between the distribution network and the microgrid. The distribution network operator optimizes the electricity price strategy to encourage the microgrid to adjust the internal resource allocation according to the electricity price signal, achieving the minimization of 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 between the distribution network and the microgrid, and enhancing the intelligent level and adaptive ability of the entire system.

[0095] In addition, in terms of voltage stability, the present invention optimizes the power flow distribution through dynamic network formation, effectively controlling the voltage over-limit problem in the distribution network and ensuring the power supply quality and stability of the grid. The simulation experiment results show that the proposed method can significantly improve the voltage distribution in different scenarios, avoid the problem that the voltages of multiple nodes drop below the lower limit, and enhance the anti-disturbance ability and fault recovery ability of the grid.

[0096] The electricity price incentive mechanism of the present invention promotes the coordinated dispatching of distributed resources, reduces the system's light curtailment rate, and enhances the consumption capacity of photovoltaic power generation. Through reasonable electricity price incentives, the microgrid can respond more actively to the requirements of the distribution network, optimize its own resource allocation, achieve the efficient utilization of electric energy, and maximize the economic benefits of the system.

[0097] In summary, the present invention provides an efficient, intelligent, and reliable coordinated optimization dispatching method for the distribution network - microgrid through the organic combination of intelligent soft switches, dynamic network formation, and electricity price incentive mechanisms, overcoming the deficiencies of the existing technologies in terms of response speed, regulation ability, and intelligent level, and having significant technological innovation and practical application value. Description of the Drawings

[0098] Figure 1 is the process schematic diagram of the present invention;

[0099] Figure 2 is the schematic diagram of the dispatching scheme framework of the present invention;

[0100] Figure 3 is the process schematic diagram of the present invention;

[0101] Figure 4 is the example topology diagram;

[0102] Figure 5 is the voltage schematic diagram of nodes in Scenarios 1 and 2;

[0103] Figure 6 is the optimal electricity price schematic diagram of Scenario 3;

[0104] Figure 7 is the schematic diagram of the power composition of each microgrid without electricity price incentives;

[0105] Figure 8 It is a schematic diagram of the power composition of each microgrid under electricity price incentives;

[0106] Figure 9 It is a schematic diagram of the SOP networking strategy and transmission power flow situation;

[0107] Figure 10 It is a schematic diagram of the distribution network loss under Scenarios 2 and 3. Specific implementation manner

[0108] The present invention will be further described below with reference to 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.

[0109] Embodiment 1, as Figure 1 shown, this embodiment provides a coordinated optimization dispatching method for a distribution network - microgrid, including:

[0110] Obtain an optimization dispatching model based on dynamic networking and electricity price incentives, which is pre - constructed according to the node and line information of the target distribution network and the microgrid information accessed to the target distribution network. The node and line information represents the topological structure and line parameters (resistance, reactance) of the distribution network;

[0111] 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;

[0112] Solve the optimization dispatching 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 dispatching plan;

[0113] The processing process of the optimization dispatching model based on dynamic networking and electricity price incentives includes:

[0114] Based on the electricity price incentive strategy of the microgrid, the safe operation parameters of the target distribution network, and the safe operation parameters of the microgrid, use the covariance matrix adaptation evolution strategy algorithm to adjust the internal resource allocation with the goal of minimizing the operation cost of the microgrid, and feedback the adjusted internal resource allocation of the microgrid to the distribution network, so that the distribution network performs optimal power flow calculation for dynamic networking in response to the internal resource allocation, obtains an optimized power flow distribution plan, and generates a dynamic networking plan according to the optimized power flow distribution plan;

[0115] Generate the operation results of the distribution network and the microgrid according to the dynamic networking plan, use the operation results of the distribution network and the microgrid as the new fitness evaluation basis of the covariance matrix adaptation evolution strategy algorithm, and perform electricity price adjustment of the microgrid to conduct the next round of optimization iteration until the iteration is completed to generate a dynamic networking plan under the optimal microgrid electricity price.

[0116] The optimization scheduling model based on dynamic networking and electricity price incentives adopts a two-layer optimization structure. The upper layer is the distribution network pricing model, and the lower layer is the microgrid autonomous optimization model and the dynamic networking model.

[0117] The expression of the objective function of the distribution network pricing model is:

[0118] ;

[0119] ;

[0120] ;

[0121] ;

[0122] In the formula: min represents minimization, C1 represents the objective function of the distribution network layer, C MG represents the transaction cost between the distribution network and the microgrid; C buy represents the power purchase cost of the distribution network from the superior power grid; C load represents the power selling revenue of the distribution network to ordinary loads; T represents the scheduling period; P sell,i,t , P buy,i,t represents the distribution network t during the i th time period to sell and purchase electric power from / to the r sell,t , r buy,t represents the distribution network t during the represents the scheduling step; P in,t represents the distribution network t during the ρ t represents t during the P load,t represents the distribution network t during the

[0123] The constraint condition expression of the objective function of the distribution network pricing model is as follows:

[0124] ;

[0125] In the formula: ρ min,t represents the lower limit of the microgrid power purchase and selling electricity price, ρmax,t Represents the upper limit of the purchase and sale electricity price of the microgrid.

[0126] The objective function expression of the microgrid autonomous optimization model is:

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] C MGs,i Represents the i th objective function of the microgrid, C exc,i Represents the i th power trading cost of the microgrid; C gas,i Represents the i th gas turbine power generation cost of the microgrid; C st,i Represents the i th energy storage depreciation cost of the microgrid; a , b Represents the gas turbine cost coefficient; P gas,t Represents the t time period power generation power of the gas turbine; P ch,t , P dch,t Represents the t time period charging power and discharging power of the energy storage; ρ st Represents the energy storage unit charge and discharge power depreciation cost;

[0132] The constraint conditions of the objective function of the microgrid autonomous optimization model include:

[0133] Energy storage constraint:

[0134] ;

[0135] In the formula: SOC max , SOC min Represents the upper and lower limits of the energy storage state of charge; SOC init , SOC t Represents the initial state of charge and the current state of charge of the energy storage; SOC 1, SOC25 Indicates the state of charge of the energy storage at the start and end times of the dispatch, SOC t-1 Indicates t the state of charge of the distribution network at time - 1, Indicates the energy storage t charging power at time - 1, Indicates the energy storage t discharging power at time - 1, P ch,t and P dch,t and P max,st Indicates the charging power, discharging power, and upper limits of charging - discharging power of the energy storage during the period; t μ ch,t and μ dch,t Indicates the 0 - 1 variable of the charging - discharging state of the energy storage, μ ch,t being 1 indicates charging, μ dch,t being 1 indicates discharging, and the two cannot be 1 simultaneously; η st Indicates the charging - discharging efficiency of the energy storage;

[0136] Output upper and lower limit constraints of the gas turbine:

[0137] ;

[0138] In the formula: and Indicate the maximum output and minimum output of the gas turbine;

[0139] Photovoltaic output constraint:

[0140] ;

[0141] In the formula: P PV,t Indicates t the actual photovoltaic output during the period; Indicates t the maximum output of the photovoltaic during the period;

[0142] Power constraint for the interaction between the micro - grid and the distribution network:

[0143] ;

[0144] In the formula: Indicates the upper limit of the power of the connection line between the micro - grid and the distribution network; Indicates t the actual power of the connection line between the micro - grid and the distribution network during the period; ​

[0145] Power balance constraint:

[0146] ;

[0147] In the formula: P load,t represents the load power, P gas,t , P PV,t , P dch,t , P ch,t , P MG,t respectively represent the output of the gas turbine, the output of the photovoltaic, the discharging power of the energy storage, the charging power of the energy storage, and the actual power of the connection line between the microgrid and the distribution network.

[0148] The objective function expression of the dynamic networking model is:

[0149] ;

[0150] In the formula: C2 represents the objective function of dynamic networking;

[0151] The constraint conditions of the objective function of the dynamic networking model include:

[0152] Power flow constraint:

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] In the formula: r i,j , x i,j , P i,j,t , Q i,j,t , I i,j,t represent the resistance, reactance, active power, reactive power and current of branch ij ; , represent t the inflow and outflow power of SOP at node j during period , , V j,t represent nodej Active load, reactive load and voltage; Q i,k,t Indicates a branch jk Reactive power; V i,t Indicates a node i Voltage;

[0158] Node voltage constraint:

[0159] ;

[0160] Where: , Indicates a node i Upper and lower voltage limits;

[0161] Branch power constraint:

[0162] ;

[0163] Where: Indicates the upper limit of the transmission power of the branch ij ;

[0164] SOP constraint conditions:

[0165] ;

[0166] ;

[0167] ;

[0168] Where: , , , Indicate the power flowing into and out of the node connecting ij SOP i And the power flowing into and out of the node j Power flowing into and out of the node; m R,ij Indicates a 0-1 state variable used to determine the power flow direction of the SOP; h ij Indicates ij The efficiency of the SOP on, where SOP represents an intelligent soft switch.

[0169] The specific process of the processing process of the optimization scheduling model based on dynamic networking and electricity price incentives includes:

[0170] Step 1: Set the voltage range of each node, the maximum capacity of each line, and the parameters of the SOP; Initialize the population center, step size, and covariance matrix of the CMA-ES algorithm. The CMA-ES algorithm is the covariance matrix adaptation evolution strategy algorithm;

[0171] Step 2: Randomly generate an electricity price as the initial sample center point, and then generate a group of electricity prices as the initial population based on this initial sample center point. The expression for generating the initial population is as follows:

[0172] ;

[0173] where: x k represents the generated sample point; m represents the current population center; σ represents the step size of the CMA-ES algorithm; N (0, C) represents sampling from a multivariate normal distribution with a mean of 0; C represents the covariance matrix;

[0174] Then, determine the fitness of each group of electricity prices in the population according to the objective function of the lower-level problem, and select the optimal electricity price strategy to update the sample center, step size, and covariance matrix. The expressions are as follows:

[0175] ;

[0176] ;

[0177] ;

[0178] where: m new represents the new population center; ω i represents the weight assigned according to the ranking, and the electricity price strategy with higher revenue has a higher weight; x i represents the excellent individuals 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 and d σ represent the adjustment rate of the control step size; p

[0179] σ represents the evolution path; represents the length of the expected normal distribution vector; N and i represent the total number of individuals and the individual number;

[0180] Solve the mixed-integer linear programming problem based on the objective function and constraint conditions of the microgrid autonomous optimization model to determine the autonomous operation results of each microgrid, and generate the corresponding equivalent load or power source according to the autonomous operation results of each microgrid;

[0181] Input the equivalent load or power source data of the microgrid into the dynamic network formation model, so that the dynamic network formation model uses the mixed-integer second-order cone programming method, combines the topological structure of the distribution network and the parameters of the SOP, and calculates the optimal power flow distribution scheme;

[0182] Determine the operating states and power flow directions of each SOP according to the optimal power flow distribution scheme, and generate a specific dynamic network formation scheme;

[0183] Obtain the operation results of the distribution network and the microgrid according to the specific dynamic network formation scheme, and the operation results of the distribution network and the microgrid include distribution network pricing, network formation scheduling results, and microgrid autonomous operation results;

[0184] Feed back the operation results of the distribution network and the microgrid to the CMA-ES algorithm as a new fitness evaluation basis, calculate a new population according to the fitness of each individual, represent a new electricity price scheme, and perform the next round of optimization iteration until the iteration upper limit is reached, and determine the dynamic network formation scheme under the optimal microgrid electricity price.

[0185] Embodiment 2, as Figure 2 shown, a distribution network - microgrid coordinated optimization scheduling method includes the following steps:

[0186] Step 1: Construct a scheduling scheme framework, specifically: The scheduling scheme framework of the present invention is as shown in the appendix Figure 2 shown, and a two-layer optimization structure is adopted. The upper layer is a distribution network pricing model, and the lower layer is a microgrid autonomous optimization model and a dynamic network formation model. The distribution network operator first sets the electricity purchase and sale prices of the microgrid, and 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 network formation according to the operation results of the microgrid, realizing 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, enabling the entire system to maintain an efficient and stable operating state in a dynamically changing operating environment.

[0187] Step 2: Data acquisition, specifically: Deploy intelligent sensors at each key node and line of the distribution network to collect operation parameters such as voltage, current, and power factor in real time. The types of sensors include voltage sensors, current sensors, power factor sensors, etc., to ensure comprehensive monitoring of the grid operating state. The data collected by the sensors is transmitted to the central data processing center through a high-speed communication network (such as optical fiber, wireless communication) to ensure the real-time and reliability of the data.

[0188] Step 3: Build a distribution network pricing model, specifically:

[0189] Step 31: On 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 as follows:

[0190] ;

[0191] ;

[0192] ;

[0193] ;

[0194] In the formula: min represents minimization, C1 represents the objective function of the distribution network layer, C MG represents the transaction cost between the distribution network and the microgrid; C buy represents the cost of purchasing electricity from the superior grid by the distribution network; C load represents the revenue from selling electricity to ordinary loads by the distribution network; T represents the scheduling period; P sell,i,t , P buy,i,t represent the distribution network t during the period to the i th microgrid for selling and purchasing electric power; ρ sell,t , ρ buy,t represent the distribution network t during the period for selling and purchasing electricity prices; represents the scheduling step; P in,t represents the distribution network t during the period for purchasing electric power from the main grid; ρ t represents t during the period for the time-of-use electricity price of the distribution network purchasing electricity from the main grid; P load,t represents the distribution network t during the period for the ordinary load power;

[0195] Step 32: The decision variable of the upper-layer distribution network is the purchase and sale electricity price, and the constraint condition is the upper and lower limit constraints of the electricity price. Its mathematical expression is as follows:

[0196] ;

[0197] In the formula: ρ min,t represents the lower limit of the microgrid purchase and sale electricity price, ρ max,t represents the upper limit of the microgrid purchase and sale electricity price.

[0198] Step 4: Build a microgrid autonomous operation model, specifically:

[0199] Step 41: The goal of the microgrid autonomous operation is to minimize its own operation cost, including the depreciation cost of energy storage operation, the power generation cost of gas turbines, and the electricity trading cost. The mathematical expression is as follows:

[0200] ;

[0201] ;

[0202] ;

[0203] ;

[0204] C MGs,i represents the objective function of the i th microgrid, C exc,i represents the electricity trading cost of the i th microgrid; C gas,i represents the power generation cost of the gas turbine of the i th microgrid; C st,i represents the depreciation cost of the energy storage of the i th microgrid; a , b represent the gas turbine cost coefficient; P gas,t represents the gas turbine t power generation power at the P ch,t , P dch,t represent the charging power and discharging power of the energy storage at the t time period; ρ st represents the depreciation cost per unit charge-discharge power of the energy storage.

[0205] Step 42: Constraint conditions;

[0206] Energy storage constraints: including upper and lower limits of energy storage capacity constraints, energy storage capacity cycle constraints, energy storage capacity update, charge and discharge power constraints;

[0207] ;

[0208] In the formula: SOC max , SOC min represent the upper and lower limits of the state of charge of the energy storage; SOC init ,SOC t Indicate the initial state of charge and the current state of charge of the energy storage; SOC 1. SOC 25 Indicate the state of charge of the energy storage at the start and end times of the dispatch, SOC t-1 Indicate t the state of charge of the distribution network at time -1, Indicate the energy storage t charging power at time -1, Indicate the energy storage t discharging power at time -1, P ch,t 、 P dch,t 、 P max,st Indicate the charging power, discharging power and upper limits of charging and discharging power of the energy storage during the t period; μ ch,t 、 μ dch,t Indicate the 0 - 1 variable of the charging and discharging state of the energy storage, μ ch,t being 1 indicates charging, μ dch,t being 1 indicates discharging, and the two cannot be 1 simultaneously; η st Indicate the charging and discharging efficiency of the energy storage.

[0209] Constraints on the upper and lower limits of the output of the gas turbine. Since the dispatch step is much longer than the ramp - up time of the gas turbine, the ramp - up process of the gas turbine is ignored;

[0210] ;

[0211] In the formula: 、 Indicate the maximum output and minimum output of the gas turbine.

[0212] Constraints on the output of the photovoltaic power:

[0213] ;

[0214] In the formula: P PV,t Indicate t the actual output of the photovoltaic power during the Indicate t the maximum output of the photovoltaic power during the

[0215] Constraints on the power interacting with the distribution network:

[0216] ;

[0217] In the formula: represents the power upper limit of the tie line between the microgrid and the distribution network; represents t the actual power of the tie line between the microgrid and the distribution network during the time period.

[0218] Power balance constraint:

[0219] ;

[0220] In the formula: P load,t represents the load power, P gas,t , P PV,t , P dch,t , P ch,t , P MG,t respectively represent the output of the gas turbine, the output of the photovoltaic, the discharging power of the energy storage, the charging power of the energy storage, and the actual power of the tie line between the microgrid and the distribution network.

[0221] Step 5: Build a dynamic networking model.

[0222] Step 51: Objective function. The purpose of the lower-layer two-stage dynamic networking is to obtain the optimal power flow to reduce the network loss cost and the SOP loss cost, which can be expressed by the power purchase cost from the superior power grid and the power selling income of the ordinary load in the upper-layer objective. Its mathematical expression is as follows:

[0223] ;

[0224] In the formula: C2 represents the objective function of the dynamic networking;

[0225] Step 52: Constraint conditions. Include power flow constraints, node voltage constraints, branch power constraints, and SOP constraints, etc.

[0226] Power flow constraints:

[0227] ;

[0228] ;

[0229] ;

[0230] ;

[0231] In the formula: r i,j , x i,j , Pi,j,t , Q i,j,t , I i,j,t represent the resistance, reactance, active power, reactive power, and current of the branch ij . , represent t the in-flow and out-flow power of SOP at the time-section node j . , , V j,t represent the active load, reactive load, and voltage of node j . Q i,k,t represent the reactive power of branch jk . V i,t represent the voltage of node i .

[0232] Node voltage constraint:

[0233] ;

[0234] In the formula: , represent the upper and lower limits of the voltage of node i .

[0235] Branch power constraint:

[0236] ;

[0237] In the formula: represents the upper limit of the transmission power of branch ij .

[0238] SOP constraint conditions:

[0239] ;

[0240] ;

[0241] ;

[0242] In the formula: , , , represent the in-flow and out-flow power of the node connecting ij SOP and i the in-flow and out-flow power of the node j . m R,ijRepresents a 0-1 state variable used to determine the power flow direction of the SOP; h ij Represents ij The efficiency of the upper SOP, where SOP represents the intelligent soft switch.

[0243] Step 6: Decision model solving algorithm. As shown in the appendix Figure 3 As shown, the present invention uses the covariance matrix adaptation evolution strategy (CMA-ES) algorithm to solve the pricing problem on the distribution network side, and combines the microgrid autonomous optimization model and the dynamic networking model to achieve two-layer optimal scheduling. The specific steps are as follows:

[0244] Step 61: System parameter initialization. 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 the SOP according to the specific situation 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 ability.

[0245] Step 62: Solve the distribution network pricing problem using the CMA-ES algorithm. Use the CMA-ES algorithm to optimize the distribution network pricing strategy. First, set initial parameters including voltage upper and lower limits, line capacity, etc., and then use CMA-ES to solve the distribution network layer pricing problem for electricity price optimization. CMA-ES can adaptively adjust parameters and has higher accuracy compared to ordinary heuristic algorithms. First, randomly generate an electricity price as the initial sample center point and initialize various data, and then generate a set of electricity prices as the initial population, and its expression is as follows:

[0246] ;

[0247] In the formula: x k Represents the generated sample point; m represents the current population center; σ Represents the step size of the CMA-ES algorithm; N (0, C) represents sampling from a multivariate normal distribution with a mean of 0; C represents the covariance matrix;

[0248] Then, determine the fitness of each group of electricity prices in the population according to the objective function including the lower-layer problem, and select the optimal electricity price strategy to update the sample center, step size, and covariance matrix. The expressions are as follows:

[0249] ;

[0250] ;

[0251] ;

[0252] In the formula: m newrepresents the new population center; w i represents the weight assigned according to the ranking, and the electricity price strategy with higher revenue has a higher weight; x i represents the excellent individuals in the evaluation; C new represents the new covariance matrix; c cov represents the learning rate of the covariance matrix, which controls the incorporation speed of new information; σ new represents the new step size of the CMA-ES algorithm; exp represents the exponential function; c σ 、 d σ represents the control

[0253] represents the adjustment rate of the control step size; p σ represents the evolution path, which reflects the direction of past changes; represents the length of the expected normal distribution vector, which is used to normalize the change of the step size; N and i represent the total number of individuals and the individual number.

[0254] Step 63: Microgrid autonomous optimization. Each microgrid optimizes the internal resource allocation based on the electricity price signal set by the upper-level distribution network, and solves the mixed-integer linear programming (MILP) problem.

[0255] Specifically, the microgrid optimizes the charge and discharge plan of the energy storage, the power generation plan of the gas turbine, and the output plan of the photovoltaic according to its own energy storage capacity, photovoltaic power generation, and gas turbine output and other parameters to minimize the operating cost. The optimized microgrid operation result is equivalent to the load or power source on the distribution network side for the distribution network to conduct further dynamic network formation optimization.

[0256] Step 64: Distribution network dynamic network formation optimization. After the microgrid is equivalent to the load or power source on the distribution network side, combined with the power control function of the intelligent soft switch (SOP), the dynamic network formation optimization of the distribution network is carried out. The specific steps include:

[0257] Step 641: Input equivalent load and power source data. The distribution network operator inputs the equivalent load or power source data of the microgrid into the dynamic network formation model;

[0258] Step 642: Optimize the power flow calculation. Using the mixed-integer second-order cone programming (MIQCP) method, as Figure 4 shown, combined with the topological structure of the distribution network and the parameters of the SOP, calculate the optimal power flow distribution scheme to ensure the optimized distribution of the electric energy flow, reduce the network loss cost and the SOP loss cost, Figure 4MG1, MG2, and MG3 in it correspond to three microgrids, serial numbers 1 to 33 respectively correspond to thirty-three nodes, and serial numbers 01 - 03 respectively correspond to three intelligent soft switches;

[0259] Generate a dynamic networking scheme: According to the optimization results, determine the operating states and power flow directions of each SOP, and generate a specific dynamic networking scheme;

[0260] Step 643: Feedback and iteration. Feed back the operation results of the distribution network and the microgrid to the CMA-ES algorithm as the basis for the new fitness evaluation. According to the feedback results, adjust the electricity price strategy and conduct the next round of optimization iteration. Through this iterative process, ensure that the coordinated optimal scheduling between the distribution network and the microgrid is continuously improved to minimize the total system cost.

[0261] Step 7: Simulation experiment and results:

[0262] The present invention connects three microgrids and three intelligent soft switches (SOPs) to the IEEE-33 node distribution network model, and conducts simulation and comparative analysis of three different scenarios. The computer hardware environment used for the simulation is Xeon Silver 4210R CPU @ 2.4 GHz * 2, 64GB of running memory, and the program is written in Python.

[0263] Simulation scenarios:

[0264] Scenario 1: Apply the normal time-of-use electricity price and feed-in tariff, do not adopt electricity price incentives, and do not adopt SOP dynamic networking.

[0265] Scenario 2: Apply the normal time-of-use electricity price and feed-in tariff, do not adopt electricity price incentives, and adopt SOP dynamic networking.

[0266] Scenario 3: Apply electricity price incentives, adopt the optimized electricity price, and adopt SOP dynamic networking.

[0267] The parameters of the three microgrids (Microgrid 1, Microgrid 2, and Microgrid 3) are shown in Table 1:

[0268] Table 1

[0269] 。

[0270] Analysis of simulation results:

[0271] Voltage distribution: The voltage distribution diagrams of each node in the distribution network under Scenario 1 and Scenario 2 (as Figure 5 ) show that when the dynamic networking strategy is not adopted, the voltages of multiple nodes drop below the lower limit, and the power supply quality is damaged. After introducing SOP and implementing dynamic networking, the voltage over-limit problem is effectively controlled, and the voltages of all nodes are restored to the normal range, significantly improving the stability and reliability of the power grid.

[0272] Electricity price illustration: Schematic diagrams of time-of-use electricity price, on-grid electricity price, and optimal purchase and sale electricity price under Scenario 3 (as Figure 6 ) show that the distribution network dynamically adjusts the electricity price according to the purchase and sale electricity behavior of the microgrid. During the electricity purchase period of the microgrid, the distribution network sets a higher electricity sale price to reduce operating costs; during the noon period with high photovoltaic power generation, the distribution network sets a lower electricity purchase price to reduce the electricity purchase cost and encourage the microgrid to sell power to the distribution network. This electricity price optimization strategy effectively promotes the energy exchange between the distribution network and the microgrid and maximizes the economic benefits.

[0273] Microgrid power composition: Schematic diagrams of the power composition of each microgrid without electricity price incentives (Scenarios 1 and 2) and with electricity price incentives (Scenario 3) (as Figure 7 , Figure 8 ) show that under the electricity price incentive mechanism, the energy storage utilization rate of the microgrid increases significantly. Under the electricity price incentive, the microgrid participates more actively in the load regulation and power flow optimization of the distribution network, assisting the distribution network in peak shaving and valley filling. Especially in Scenario 3 with electricity price incentives, Microgrid 3 actively increases the output of the gas turbine and outputs the excess power to the distribution network, realizing the coordinated dispatching and common benefit of distributed resources within the distribution network.

[0274] Comparison of curtailment rates: Table 2 shows that the curtailment rate of the microgrid under Scenario 3 decreases significantly, 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;

[0275] Table 2

[0276] .

[0277] Power flow optimization: The SOP networking strategy and transmitted power flow under Scenarios 2 and 3 (as Figure 9 ) show that SOP optimizes the power flow distribution through dynamic networking, reduces the main line power transmission value, optimizes the electric energy flow, maintains the voltage level, and reduces the network loss. Specifically, SOP1 transmits part of the power from Node 22 to Microgrid 3 during the period of high power consumption in Microgrid 3, reducing the power transmission on the main line and optimizing the overall power flow distribution; SOP2 and SOP3 also show similar power flow optimization effects, improving the overall operation efficiency and voltage quality of the power grid.

[0278] Distribution network loss: The distribution network loss distribution under Scenarios 2 and 3 (as Figure 10The results show that in Scenario 2 where the electricity price incentive mechanism is not adopted, due to the ineffective coordination of the distributed resource output in the microgrid, the network loss of the distribution network is relatively high. In Scenario 3 with the electricity price incentive mechanism, although the microgrid purchases electricity centrally during peak hours, resulting in an increase in instantaneous network loss, the overall network loss is lower than that in Scenario 2. This indicates that a reasonable electricity price incentive mechanism combined with the coordinated dispatching of distributed resources can effectively reduce the overall loss of the distribution network and improve the economy and efficiency of the power grid.

[0279] Benefits of each entity: Table 3 shows that the daily operating benefits of each microgrid and the distribution network in Scenario 3 have all increased. The benefits of Microgrid 1, 2, and 3 have increased by 12.3%, 11.7%, and 19.6% respectively, and the benefit of the distribution network has increased by 75.8%. The total benefit has increased by 21.9%. This result indicates that the electricity price incentive mechanism not only improves the economic benefits of the microgrid but also significantly enhances the operating benefits of the distribution network, achieving the common benefit of the distribution network and the microgrid.

[0280] Table 3

[0281] 。

[0282] Embodiment 3, based on the same inventive concept as Embodiment 1, this embodiment introduces a coordinated optimization dispatching device for a distribution network - microgrid, including:

[0283] An acquisition module, configured to acquire an optimization dispatching model based on dynamic network formation and electricity price incentives, which is pre - constructed according to the key node and line information of the target distribution network and the microgrid information accessing the target distribution network; acquire 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.

[0284] A generation module, configured to solve the optimization dispatching model based on dynamic network formation 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 dispatching plan.

[0285] The processing process of the optimization dispatching model based on dynamic network formation and electricity price incentives includes:

[0286] Based on the electricity price incentive strategy of the microgrid, the safe operation parameters of the target distribution network, and the safe operation parameters of the microgrid, using the covariance matrix adaptation evolution strategy algorithm, with the goal of minimizing the operating cost of the microgrid, adjust the internal resource allocation, and feedback the adjusted internal resource allocation of the microgrid to the distribution network, so that the distribution network performs an optimal power flow calculation for dynamic network formation in response to the internal resource allocation, obtains an optimized power flow distribution plan, and generates a dynamic network formation plan according to the optimized power flow distribution plan.

[0287] Generate the operation results of the distribution network and the microgrid according to the dynamic networking scheme, use the operation results of the distribution network and the microgrid as the new fitness evaluation basis for the covariance matrix adaptive evolution strategy algorithm, adjust the microgrid electricity price, and perform the next round of optimization iteration until the iteration is completed to generate a dynamic networking scheme under the optimal microgrid electricity price.

[0288] Example 4, based on the same inventive concept as other embodiments, this example introduces a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to execute the method of Example 1.

[0289] Example 5, based on the same inventive concept as other embodiments, this example introduces a computer device including one or more processors, a memory, and one or more programs, where 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.

[0290] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0291] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 a block or multiple blocks.

[0292] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions in Figure 1 one process or multiple processes and / or blocksFigure 1 The functions specified in one or more boxes.

[0293] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0294] The above is only the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present invention.

Claims

1. A coordinated optimization scheduling method for a distribution network - microgrid, characterized in that, Including: Obtain an optimal scheduling model based on dynamic networking and price incentives, which is pre-constructed 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; Obtain the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the price incentive strategy of the microgrid; Solve the optimal scheduling model based on dynamic networking and price incentives based on the safe operation parameters of the target distribution network, the safe operation parameters of the microgrid, and the price incentive strategy of the microgrid to generate an optimal scheduling plan; The processing process of the optimal scheduling model based on dynamic networking and price incentives includes: Based on the price incentive strategy of the microgrid, the safe operation parameters of the target distribution network, and the safe operation parameters of the microgrid, use the covariance matrix adaptation evolution strategy algorithm to adjust the internal resource allocation with the goal of minimizing the operation cost of the microgrid, and feedback the adjusted internal resource allocation of the microgrid to the distribution network, so that the distribution network performs optimal power flow calculation for dynamic networking in response to the internal resource allocation, obtains an optimized power flow distribution plan, and generates a dynamic networking plan according to the optimized power flow distribution plan; Generate the operation results of the distribution network and the microgrid according to the dynamic networking plan, use the operation results of the distribution network and the microgrid as the new fitness evaluation basis of the covariance matrix adaptation evolution strategy algorithm, and perform microgrid price adjustment to conduct the next round of optimization iteration until the iteration is completed to generate a dynamic networking plan under the optimal microgrid price.

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

3. The coordinated optimal scheduling method for the distribution network - microgrid according to claim 2, wherein The objective function expression of the distribution network pricing model is: ； ； ； ； where: min represents minimization, C1 represents the objective function of the distribution network layer, C MG represents the transaction cost between the distribution network and the microgrid; C buy represents the power purchase cost of the distribution network from the superior power grid; C load represents the revenue from selling electricity to ordinary loads by the distribution network; T represents the scheduling period; N represents the total number of microgrids; P sell,i,t 、 P buy,i,t represents the distribution network t during the period to the i th microgrid for selling and purchasing electric power; ρ sell,t 、 ρ buy,t represents the distribution network t during the period for selling and purchasing electricity prices; represents the scheduling step size; P in,t represents the distribution network t during the period for purchasing electric power from the main power grid; ρ t represents t during the period for the time-of-use electricity price of the distribution network purchasing electricity from the main power grid; P load,t represents the distribution network t during the period for the ordinary load power; i ∈[1, N , t ∈[1, T ; The constraint condition expression of the objective function of the distribution network pricing model is as follows: ； In the formula: ρ min,t represents the lower limit of the purchase and sale electricity price of the microgrid; ρ max,t represents the upper limit of the purchase and sale electricity price of the microgrid.

4. The coordinated optimal scheduling method for the distribution network-microgrid according to claim 3, characterized in that The objective function expression of the microgrid autonomous optimization model is: ； ； ； ； Wherein: C MGs,i represents the objective function of the i th microgrid; C exc,i represents the electricity trading cost of the i th microgrid; C gas,i represents the cost of gas turbine power generation of the i th microgrid; C st,i represents the storage depreciation cost of the i th microgrid; a and b represent the gas turbine cost coefficient; P gas,t represents the power generation power of the gas turbine at t time period; P ch,t and P dch,t represent the charging power and discharging power of the energy storage at t time period; ρ st represents the depreciation cost per unit charge-discharge power of the energy storage; The constraint conditions of the objective function of the microgrid autonomous optimization model include: Energy storage constraint: ； Wherein: SOC max and SOC min represent the upper and lower limits of the energy storage state of charge; SOC init and SOC t represent the initial state of charge and the current state of charge of the energy storage; SOC 1. SOC 25 represents the state of charge of the energy storage at the start and end times of scheduling, SOC t-1 represents t the state of charge of the distribution network at time represents the energy storage t charging power at time represents the energy storage t discharging power at time P max,st represents the upper limit of the charging and discharging power of the energy storage t during a period; μ ch,t and μ dch,t represent the 0-1 variable of the charging and discharging state of the energy storage, μ ch,t being 1 represents charging, μ dch,t being 1 represents discharging, and the two cannot be 1 at the same time; η st represents the charging and discharging efficiency of the energy storage; Upper and lower limits of gas turbine output constraint: ； Where: , represent the maximum output and minimum output of the gas turbine; Photovoltaic output constraint: ； In the formula: P PV,t represents t the actual PV output during a time period; represents t the maximum PV output during a time period; Power constraint for the interaction between the microgrid and the distribution network: ； Wherein: represents the power upper limit of the connection line between the microgrid and the distribution network; represents t the actual power of the connection line between the microgrid and the distribution network during the period; Power balance constraint: ； Wherein: P load,t represents the load power, P gas,t , P PV,t , P dch,t , P ch,t , P MG,t respectively represent the output of the gas turbine, the output of the photovoltaic power generation, the power of the energy storage discharging, the power of the energy storage charging, and the actual power of the connection line between the microgrid and the distribution network.

5. The coordinated optimal scheduling method for the distribution network-microgrid according to claim 4, characterized in that The objective function expression of the dynamic networking model is: ； In the formula: C2 represents the objective function of dynamic networking; The constraint conditions of the objective function of the dynamic networking model include: Power flow constraint: ； ； ； ； In the formula: r i,j and x i,j represent the resistance and reactance of branch ij . P i,j,t and Q i,j,t and I i,j,t represent t the active power, reactive power, and current of branch ij during a time period. and represent t the inflow power and outflow power of SOP at node j during a time period. and and V j,t represent t the active load, reactive load, and voltage of node j during a time period. Q i,k,t represents t the reactive power of branch jk during a time period. V i,t represents t the voltage of node i during a time period. P j,k,t represents t the active power of branch jk during a time period. Node voltage constraint: ； In the formula: and represent the upper and lower voltage limits of the node i . Branch power constraint: ； In the formula: represents the upper limit of the transmission power of the branch ij ; SOP constraint condition: ； ； ； Wherein: and represent the incoming power and outgoing power of the node of the SOP on the connection branch ij ; i and represent the incoming power and outgoing power of the node of the SOP on the connection branch ij ; j μ S,ij represents a 0-1 state variable for determining the power flow direction of the SOP; η ij represents the efficiency of the SOP on the branch ij ; the SOP represents an intelligent soft switch ij represents the maximum power of the SOP on the branch ij .

6. The coordinated optimal dispatching method for the distribution network - microgrid according to claim 5, characterized in that The processing process of the optimal scheduling model based on dynamic networking and 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, and the CMA-ES algorithm is the covariance matrix adaptation evolution strategy algorithm; 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 this initial sample center point. The expression for generating the initial population is as follows: ； where: x k represents the generated sample points; m represents the current population center; σ represents the step size of the CMA-ES algorithm; N (0, C) represents sampling from a multivariate normal distribution with a mean of 0; C represents the covariance matrix; Then, determine the fitness of each group of electricity prices in the population according to the objective function including the lower-level problem, and select the optimal electricity price strategy to update the sample center, step size, and covariance matrix. The expression is as follows: ； ； ； where: m new represents the new population center; ω i represents the weight assigned according to the ranking, and the electricity price strategy with higher revenue has a higher weight; x i represents the excellent individuals 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 represents the adjustment rate for controlling the step size; p σ represents the evolution path; represents the length of the desired normal distribution vector; Solve the mixed-integer linear programming problem based on the objective function and constraint conditions of the microgrid autonomous optimization model to determine the autonomous operation results of each microgrid, and generate corresponding equivalent loads or power sources according to the autonomous operation results of each microgrid; Input the equivalent load or power source data of the microgrid into the dynamic networking model, so that the dynamic networking model uses the mixed-integer second-order cone programming method, combines the topological structure of the distribution network and the parameters of the SOP, and calculates the optimal power flow distribution scheme; Determine the operating states and power flow directions of each SOP according to the optimal power flow distribution scheme, and generate a specific dynamic networking scheme; Obtain the operation results of the distribution network and the microgrid according to the specific dynamic networking scheme. The operation results of the distribution network and the microgrid include distribution network pricing, networking scheduling results, and microgrid autonomous operation results; Feed back the operation results of the distribution network and the microgrid to the CMA-ES algorithm as a new fitness evaluation basis. Calculate a new population according to the fitness of each individual, representing a new electricity price scheme, and perform the next round of optimization iteration until the iteration upper limit is reached, and determine the dynamic networking scheme under the optimal microgrid electricity price.

7. A coordinated optimization dispatching device for a distribution network - microgrid, characterized in that, Including: An acquisition module for acquiring an optimization scheduling model based on dynamic networking and electricity price incentives pre-constructed according to the node and line information of the target distribution network and the microgrid information 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 for 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, and generating an optimal scheduling scheme; 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, the safe operation parameters of the target distribution network, and the safe operation parameters of the microgrid, use the covariance matrix adaptation evolution strategy algorithm to adjust the internal resource allocation with the goal of minimizing the microgrid operation cost, and feedback the adjusted internal resource allocation of the microgrid to the distribution network, so that the distribution network performs optimal power flow calculation for dynamic networking in response to the internal resource allocation, obtains an optimized power flow distribution scheme, and generates a dynamic networking scheme according to the optimized power flow distribution scheme; Generate the operation results of the distribution network and the microgrid according to the dynamic networking scheme, use the operation results of the distribution network and the microgrid as a new fitness evaluation basis for the covariance matrix adaptation evolution strategy algorithm, and perform microgrid electricity price adjustment, and perform the next round of optimization iteration until the iteration is completed, and generate a dynamic networking scheme under the optimal microgrid electricity price.

8. The coordinated optimization scheduling device for a distribution network - microgrid according to claim 7, characterized in that, 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 a distribution network pricing model, and the lower layer of the two-layer optimization structure 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 that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1 to 6.

10. A computer device, characterized in that, 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 performing any of the methods of claims 1 to 6.

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