Scheduling method and system for collaborative optimization of power distribution grid and micro-grid

Abstract

The invention provides a dispatching method and system for collaborative optimization of a power distribution grid and a micro-grid. The method comprises the following steps: constructing a micro-grid target function by taking micro-grid distributed energy bearing capacity maximization and micro-grid total adjustment cost minimization as targets, and solving the micro-grid target function based on micro-grid constraint conditions to obtain maximum adjustable capacity and response cost; constructing a power distribution network objective function by taking power distribution network distributed energy bearing capacity maximization and total adjustment cost minimization of a power distribution network and a micro-grid as targets, solving the power distribution network objective function based on power distribution network constraint conditions, maximum adjustable capability and response cost, generating a response power instruction, and calculating the power distribution network according to the response power instruction. The micro-grid serving as the power distribution network access node executes internal optimization scheduling according to the response power instruction, and the target scheduling power and the target operation cost of the micro-grid are obtained. According to the embodiment of the invention, efficient linkage scheduling and distributed energy high-proportion consumption between the power distribution grids and the micro-grids can be realized.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a dispatching method and system for coordinated optimization of distribution microgrids. Background Technology

[0002] With the large-scale integration of distributed renewable energy, microgrids, as important nodes for carrying and regulating distributed energy, play a crucial role in system flexibility and local regulation capabilities. Existing research largely focuses on the economic operation of microgrids, that is, improving the utilization efficiency of local resources through cost minimization strategies. However, these optimization methods generally lack explicit modeling and constraints on the carrying capacity of distributed energy, and cannot effectively reflect its limitations on the system's local absorption capacity, resulting in a still low local consumption rate under the background of high proportion of renewable energy integration.

[0003] Furthermore, the centralized optimization or single-layer control structures commonly used in current dispatching systems lack dynamic coordination mechanisms with the upper-level distribution network. This structure limits the flexible allocation of distributed resources and the scope of dispatching responses, making it difficult to meet the actual needs for coordinated and efficient operation under multi-source and multi-entity participation. Summary of the Invention

[0004] This invention provides a scheduling method and system for coordinated optimization of distribution microgrids, which can solve at least one of the above-mentioned technical problems.

[0005] In a first aspect, embodiments of the present invention provide a scheduling method for coordinated optimization of distribution microgrids, comprising:

[0006] With the objectives of maximizing the distributed energy carrying capacity of the microgrid and minimizing the total regulation cost of the microgrid, a microgrid objective function is constructed. Based on the first constraint condition of the microgrid, the minimum value of the microgrid objective function is solved to obtain a first solution result, wherein the first solution result includes the scheduling plan of the microgrid for each day-ahead period.

[0007] The distribution network power flow calculation is performed on the dispatch plan of the microgrid for each time period before the day, and the distribution network regulation response command of the microgrid for each time period before the day is generated. The microgrid updates its first constraint condition according to the distribution network regulation in the distribution network regulation response command, and obtains the second constraint condition of the microgrid.

[0008] Based on the second constraint of the microgrid, the objective function of the microgrid is minimized to obtain a second solution result, wherein the second solution result includes the maximum adjustable capacity and response cost of the microgrid in each day-ahead period;

[0009] With the objectives of maximizing the distributed energy carrying capacity of the distribution network and minimizing the total regulation cost of the distribution network and microgrids, a distribution network objective function is constructed. Based on the constraints of the distribution network and the maximum adjustable capacity and response cost of the microgrid in each day-ahead period, the minimum value of the distribution network objective function is solved to generate the response power command of the microgrid in each day-ahead period. This enables the microgrid, as a node connected to the distribution network, to perform internal optimization scheduling according to the response power command, thereby obtaining the target scheduling power and target operating cost of the microgrid in each day-ahead period. The target scheduling power of the microgrid in each day-ahead period includes the target output power of the gas turbine, the target charging and discharging power of the energy storage device, and the target regulation power of the adjustable load.

[0010] Secondly, embodiments of the present invention provide a dispatching device for coordinated optimization of distribution microgrids, comprising:

[0011] The microgrid objective function construction module is used to construct a microgrid objective function with the objectives of maximizing the distributed energy carrying capacity of the microgrid and minimizing the total regulation cost of the microgrid, and to solve for the minimum value of the microgrid objective function based on the first constraint condition of the microgrid to obtain a first solution result, wherein the first solution result includes the scheduling plan of the microgrid for each day-ahead period;

[0012] The update module is used to perform distribution network power flow calculation on the dispatch plan of the microgrid for each time period before the day, and generate the distribution network regulation response command of the microgrid for each time period before the day, so that the microgrid updates the first constraint condition of the microgrid according to the distribution network regulation in the distribution network regulation response command, and obtains the second constraint condition of the microgrid.

[0013] The first solution module is used to solve for the minimum value of the objective function of the microgrid based on the second constraint condition of the microgrid, and obtain a second solution result, wherein the second solution result includes the maximum adjustable capacity and response cost of the microgrid in each day-ahead period;

[0014] The second solution module is used to construct a distribution network objective function with the goal of maximizing the distributed energy carrying capacity of the distribution network and minimizing the total regulation cost of the distribution network and microgrids. Based on the constraints of the distribution network and the maximum adjustable capacity and response cost of the microgrid in each day-ahead period, the module solves for the minimum value of the distribution network objective function and generates the response power command of the microgrid in each day-ahead period. This enables the microgrid, as a node connected to the distribution network, to perform internal optimization scheduling according to the response power command, thereby obtaining the target scheduling power and target operating cost of the microgrid in each day-ahead period. The target scheduling power of the microgrid in each day-ahead period includes the target output power of the gas turbine, the target charging and discharging power of the energy storage device, and the target regulation power of the adjustable load.

[0015] Thirdly, embodiments of the present invention also provide a scheduling system for coordinated optimization of distribution microgrids, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described in any one of the embodiments of the present invention.

[0016] Fourthly, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the method described in any one of the embodiments of the present invention.

[0017] This invention employs a method that aims to maximize the distributed energy carrying capacity of a microgrid and minimize its total regulation cost. A microgrid objective function is constructed, and based on the microgrid's first constraint, this objective function is optimized to obtain the microgrid's scheduling plan for each day-ahead period. Since this scheduling does not consider power exchange matching with the distribution network, it may lead to power flow inconsistencies at the system level. Therefore, based on the microgrid's scheduling plan for each day-ahead period, distribution network power flow calculations are performed to generate distribution network regulation response commands for each period. The first constraint of the microgrid is updated according to the distribution network regulation included in these response commands, resulting in a revised second constraint. Based on this, the microgrid objective function is solved again according to the second constraint to obtain the maximum adjustable capacity and corresponding response cost of the microgrid for each day-ahead period. Subsequently, with the goal of maximizing the distributed energy carrying capacity of the distribution network and minimizing the total regulation cost of the distribution network and microgrid, a distribution network objective function is constructed. Combining the distribution network constraints and the microgrid's maximum adjustable capacity and response cost, the distribution network objective function is solved to generate the microgrid's response power commands for each day-ahead period. The microgrid responds to the power command by executing internal scheduling, thereby obtaining the target scheduling power and target operating cost for each day-ahead period. This achieves efficient coordinated scheduling between distribution microgrids and a high proportion of distributed energy consumption.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of the invention. Wherein:

[0020] Figure 1 This is a flowchart of a scheduling method for coordinated optimization of distribution microgrids according to an embodiment of the present invention;

[0021] Figure 2 This is a structural block diagram of a dispatching device for coordinated optimization of distribution microgrids according to an embodiment of the present invention;

[0022] Figure 3 This is a block diagram of an electronic device used to implement the methods of embodiments of the present invention. Detailed Implementation

[0023] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0024] Figure 1 This is a flowchart of a scheduling method for coordinated optimization of distribution microgrids according to an embodiment of the present invention.

[0025] like Figure 1 As shown, the scheduling method for coordinated optimization of the microgrid can include:

[0026] S110, with the objectives of maximizing the distributed energy carrying capacity of the microgrid and minimizing the total regulation cost of the microgrid, a microgrid objective function is constructed, and based on the first constraint condition of the microgrid, the minimum value of the microgrid objective function is solved to obtain the first solution result, wherein the first solution result includes the microgrid's scheduling plan for each day-ahead period;

[0027] S120: Perform distribution network power flow calculation on the dispatch plan of the microgrid for each day-ahead period, and generate distribution network regulation response instructions for the microgrid for each day-ahead period, so that the microgrid can update the first constraint condition of the microgrid according to the distribution network regulation in the distribution network regulation response instructions, and obtain the second constraint condition of the microgrid.

[0028] S130, based on the second constraint of the microgrid, the objective function of the microgrid is minimized to obtain the second solution result, which includes the maximum adjustable capacity and response cost of the microgrid in each day-ahead period;

[0029] S140 aims to maximize the distributed energy carrying capacity of the distribution network and minimize the total regulation cost of the distribution network and microgrids. A distribution network objective function is constructed. Based on the constraints of the distribution network and the maximum adjustable capacity and response cost of the microgrid in each day-ahead period, the minimum value of the distribution network objective function is obtained. This generates response power commands for the microgrid in each day-ahead period, enabling the microgrid, as a node connected to the distribution network, to perform internal optimization scheduling based on these commands. This yields the target scheduling power and target operating cost of the microgrid in each day-ahead period. The target scheduling power of the microgrid in each day-ahead period includes the target output power of the gas turbine, the target charging and discharging power of the energy storage device, and the target regulation power of the adjustable load.

[0030] In this embodiment of the invention, a microgrid objective function is constructed with the objectives of maximizing the distributed energy carrying capacity of the microgrid and minimizing the total regulation cost of the microgrid. Based on the first constraint condition of the microgrid, the objective function is minimized to obtain a first solution result including the microgrid's scheduling plan for each time period of the day-ahead. Based on the microgrid's scheduling plan for each time period of the day-ahead, distribution network power flow calculations are performed to generate distribution network regulation response commands for each time period. According to the distribution network regulation included in the response command, the first constraint condition of the microgrid is updated to obtain a revised second constraint condition. Based on this, the microgrid objective function is minimized again according to the second constraint condition to obtain a second solution result including the maximum adjustable capacity of the microgrid and the corresponding response cost for each time period of the day-ahead. Subsequently, with the objectives of maximizing the distributed energy carrying capacity of the distribution network and minimizing the total regulation cost of the distribution network and the microgrid, a distribution network objective function is constructed. Combining the distribution network constraint condition, the maximum adjustable capacity of the microgrid, and the response cost, the distribution network objective function is solved to generate the microgrid's response power command for each time period of the day-ahead. The microgrid responds to the power command by executing internal scheduling, thereby obtaining the target scheduling power and target operating cost for the microgrid in each day-ahead period. Thus, the two-layer collaborative optimization scheduling method based on the distribution network and microgrid enables efficient coordinated scheduling between the distribution network and microgrid. Simultaneously, it enhances the coordinated utilization capability of distributed energy resources in various regions of the distribution network and microgrid, thereby increasing their high-proportion absorption rate in the power grid.

[0031] It should be noted that the objective function of the microgrid is minimized based on the constraints of the microgrid (including the first and second constraints). The resulting solution (including the first and second solutions) includes the microgrid's scheduling plan, maximum adjustable capacity, and corresponding response cost for each day-ahead period. Specifically, in step S120, the scheduling plan is used to update the first constraint, while in step S140, the maximum adjustable capacity and corresponding response cost are used for further processing.

[0032] For example, the objective function of a distribution network can be expressed as a function expression:

[0033]

[0034] f1=λ gen ,

[0035] f3 = C m =C gen +C ES +C load ,

[0036] In the formula, F represents the objective function of the distribution network; λ gen Indicates the distributed energy consumption rate; C represents the maximum value of distributed energy consumption rate; DN This indicates the regulation cost of the distribution network; P represents the maximum regulation cost of the distribution network; ex This represents the power exchange cost of the distribution network; Indicates the network loss cost of the distribution network; C m This represents the total regulation cost of the microgrid; C represents the maximum total regulation cost of a microgrid; gen Indicates the cost of gas turbine power generation; C ES Indicates the cost of energy storage operation and maintenance; C load f1 represents the load regulation cost; f2 represents the objective function of the distribution network's renewable energy absorption rate; f3 represents the objective function of the distribution network's total regulation cost; f4 represents the objective function of the microgrid's total regulation cost; f5 represents the objective function of the distribution network's total regulation cost. ' 1, f ' 2 and f ' 3 represents the normalized results of the renewable energy absorption rate, the total regulation cost of the distribution network, and the total regulation cost of the microgrid, respectively, with the aim of eliminating the impact of differences in dimensions between different objective functions; ω1, ω2, and ω3 are the weights of f1, f2, and f3, respectively, satisfying ω1+ω2+ω3=1, and ω1, ω2, and ω3 are all positive numbers; S1, S2, and S3 are the baseline values ​​of each objective function f1, f2, and f3, respectively, with the aim of normalizing the values ​​of each objective function.

[0037] For example, the distributed energy absorption rate of the distribution network serves as an evaluation indicator of the distributed carrying capacity of the distribution network, thus incorporating distributed carrying capacity into the framework of the optimization function model. First, through a calculable absorption rate indicator, the degree to which the distribution network accepts distributed energy can be accurately characterized, avoiding subjective judgments about the access capacity of distributed energy. Second, by using this indicator as one of the optimization objectives, the scheduling model composed of the objective function can be guided to prioritize improving the actual carrying capacity of the distribution network, thereby promoting the local absorption of more distributed energy.

[0038] For example, the constraints of the distribution network are power flow constraints and system security constraints.

[0039] In this example, power flow constraints and system safety constraints can be represented by functional expressions as follows:

[0040]

[0041] |P ij,t |≤P ij,max ;

[0042] In the formula, and These represent the active power and reactive power injected into the distribution network by the new energy generation at point i, respectively. and These represent the active power and reactive power generated by the energy storage element at point i in the distribution network, respectively. and These represent the active power and reactive power absorbed by the energy storage element at point i in the distribution network, respectively. and These represent the reactive load and active load requirements, respectively. and P represents the active power injection and reactive power injection of the distribution network i to which the microgrid is connected, respectively; exchange,i (t) represents the limitation of power exchange between the distribution network and the microgrid at time t; This represents the minimum limit for power exchange between the distribution network and the microgrid at time t; U represents the maximum limit of power exchange between the distribution network and the microgrid at time t; i,t This represents the voltage amplitude of distribution network i at time t; This indicates the lower limit of the voltage at point i in the distribution network; I represents the upper limit of the voltage at point i in the distribution network; ij,t This represents the current flowing through the branch with the first node being distribution network i and the last node being distribution network j at time t; P represents the upper limit of the current in branch ij; ij,t P represents the power transmitted between distribution network i and distribution network j; ij,max This represents the maximum transmission power of line ij.

[0043] For example, the objective function of a microgrid can be expressed as a functional expression:

[0044]

[0045] In the formula, F1 represents the microgrid objective function; f ' 3 represents the normalized result of the microgrid renewable energy absorption rate; f ' 4 represents the normalized result of the total regulation cost of the microgrid; ω4 and ω5 are f ' 3 and f ' Weight of 3; C m This represents the total regulation cost of the microgrid; This represents the maximum total regulation cost of the microgrid; This indicates the average renewable energy absorption rate of the microgrid during the assessment period; This represents the maximum value of the distributed energy absorption rate.

[0046] In one embodiment, the distributed energy carrying capacity of a microgrid includes: if the actual electricity load is greater than or equal to the total output of distributed energy, then a preset first renewable energy absorption rate is used as the distributed energy carrying capacity of the microgrid; if the actual electricity load is less than the total output of distributed energy, then the difference between the total output of distributed energy and the amount of distributed energy reduction at each time before the current day is calculated to obtain the renewable energy absorption of the microgrid at each time before the current day, and based on the ratio of the renewable energy absorption of the microgrid at each time before the current day to the total output of distributed energy, the renewable energy absorption rate of the microgrid at each time before the current day is determined, and the renewable energy absorption rate of the microgrid at each time before the current day is used as the distributed energy carrying capacity of the microgrid.

[0047] For example, the first new energy absorption rate is a preset value. When the actual electricity load is greater than or equal to the total output of distributed energy, this preset value is used as the distributed energy carrying capacity of the microgrid.

[0048] In this example, when the actual electrical load Greater than or equal to the total output of distributed energy At that time, the first new energy consumption rate was taken as 100%, that is This indicates that distributed energy can be fully absorbed at this point.

[0049] The distributed energy carrying capacity of a microgrid can be expressed as a function:

[0050]

[0051] In the formula, This represents the renewable energy absorption rate of the microgrid at time t; Indicates the amount of distributed energy reduction; Indicates the actual electrical load; This represents the total output of distributed energy; 100% is the preset first renewable energy consumption rate. Both represent the average renewable energy absorption rate of the microgrid during the assessment period; f4 represents the renewable energy absorption rate of the microgrid.

[0052] For example, the renewable energy absorption rate reflects the microgrid's ability to absorb and regulate fluctuating renewable energy sources (such as wind and solar), and can intuitively assess the microgrid's adaptability and acceptance capacity for distributed energy. Therefore, using the renewable energy absorption rate as an evaluation indicator of the microgrid's distributed energy carrying capacity, or measuring carrying capacity with the renewable energy absorption rate, helps to focus more on the local utilization efficiency of renewable energy during the optimized dispatch process, thereby increasing the proportion of green energy used in the system.

[0053] According to the above implementation method, if the actual electricity load of the microgrid is greater than or equal to the total output of distributed energy, a preset first renewable energy absorption rate is directly adopted as the maximum carrying capacity of the microgrid for distributed energy; if the actual electricity load is less than the total output of distributed energy, the carrying capacity of the microgrid for distributed energy is calculated. This carrying capacity assessment method can accurately reflect the renewable energy absorption capacity of the microgrid under different load levels, taking into account both changes in electricity load and fluctuations in distributed energy output, achieving dynamic access assessment and optimized control of distributed energy, which helps improve the efficiency of renewable energy utilization and ensures the stability and economy of microgrid operation.

[0054] In one implementation, the first constraints of the microgrid include: gas turbine energy power exchange constraints, energy storage charging and discharging constraints, and distribution microgrid power constraints.

[0055] For example, the energy-power exchange constraint of a gas turbine can be expressed by a functional expression as follows:

[0056]

[0057] In the formula, P gen，i (t) represents the output power of gas turbine energy i at time t. This indicates the upper limit of the power output of the gas turbine generator; This indicates the lower limit of the output power of a gas turbine generator.

[0058] For example, the energy storage charge and discharge constraints can be expressed as a function:

[0059]

[0060] SOC ESS，i (t0)=SOC ESS，i (t n );

[0061]

[0062] In the formula, SOC ESS，i (t) represents the value of the State of Charge (SOC) at time t; and These represent the minimum and maximum allowable State of Charge (SOC) values ​​for energy storage, respectively, set to 20% and 80%. A 24-hour day is considered a charge / discharge cycle for the energy storage device, and it should be ensured that the initial state of the SOC is the same in each cycle. ESS，i (t0) represents the value of SOC at the beginning of the day; SOC ESS，i (t n P represents the State of Charge (SOC) value at the end of the day; charge(t) represents the value of the energy storage charging power at time t; and P represents the minimum and maximum values ​​of the energy storage charging power, respectively. discharge (t) represents the value of the stored energy discharge power at time t; and These represent the minimum and maximum values ​​of the energy storage discharge power, respectively. and It is a binary 0-1 variable.

[0063] For example, the power constraint of a distribution microgrid can be expressed by a functional expression as follows:

[0064]

[0065] In the formula, ΔP l，t This indicates the adjustment amount of the adjustable load; This indicates the adjustable lower limit of the adjustable load; This indicates the adjustable upper limit of the adjustable load; This represents the active power output of the gas turbine at point i in the microgrid; This represents the active power injected into the microgrid by the new energy generation at point i; This represents the active power generated by the energy storage element at point i in the microgrid; This represents the active power absorbed by the energy storage element at point i in the microgrid; This represents the active power output of the gas turbine at point i in the microgrid; This represents the reactive power output of the gas turbine at point i in the microgrid; This represents the reactive power injected into the microgrid by the new energy power generation at point i; This represents the reactive power generated by the energy storage element at point i in the microgrid; This represents the reactive power absorbed by the energy storage element at point i in the microgrid; U represents the reactive power output of the gas turbine at point i in the microgrid; i,t U represents the voltage amplitude of microgrid i at time t; j,t B represents the voltage amplitude of microgrid j at time t; ig G represents the susceptance between microgrid i and microgrid j; ig B represents the conductance between microgrid i and microgrid g; ig sinθ ig,t G represents the phase modulation term; ig cosθ ig,t G represents the loss term; ig sinθ ig,t B represents the phase-driving term; ig cosθ ig,t This indicates the dominant voltage regulation term.

[0066] According to the above implementation method, the gas turbine energy power exchange constraint is used to limit the output power of the gas turbine to not exceeding its rated capacity, thus preventing equipment overload operation. The energy storage device charge / discharge constraint is used to ensure that the energy storage device operates within the allowable charge / discharge power range, avoiding adverse effects on the cycle life of the energy storage system. The distribution microgrid power exchange constraint is used to balance the power exchange relationship between the distribution network and the microgrid, preventing backflow or load surges exceeding safe limits. By setting these constraints, the physical boundaries and operational limitations of various resources (gas turbines, energy storage devices, etc.) can be fully considered during the optimized scheduling process, improving the executability of the scheduling solution and the safety of system operation, laying the foundation for the efficient utilization of distributed energy resources and the stable operation of the microgrid.

[0067] In one implementation, the microgrid updates its first constraint condition based on the distribution network regulation quantity in the distribution network regulation response command to obtain the microgrid's second constraint condition. This includes: using the distribution network regulation quantity in the distribution network regulation response command as a relaxation variable and summing it with each constraint condition in the microgrid's first constraint condition to obtain the microgrid's second constraint condition.

[0068] For example, the relaxation of the distribution microgrid power constraint in the first constraint condition will be used as an example. Assume that in a certain scheduling cycle, the original distribution network power exchange constraint (i.e., the distribution microgrid power constraint in the first constraint condition) of a microgrid is: -0.5MW≤ΔP l，t ≤0.5MW, meaning it can output a maximum of 0.5 megawatts or receive a maximum of 0.5 megawatts (MW) from external sources.

[0069] If the distribution network regulation response command issues a regulation amount of ΔP = +0.2MW (indicating a desire for the microgrid to increase its external power supply by 0.2MW), then the microgrid adds this regulation amount as a slack variable to the existing microgrid power constraints, updating the second constraint condition to: -0.5MW ≤ ΔP. l，t +0.2≤0.5MW means that, based on the original power boundary conditions, additional adjustment space is relaxed to achieve more efficient coordinated control between the distribution network and the microgrid.

[0070] It should be noted that the second constraint condition of the microgrid is simply a relaxation of the first constraint condition, that is, only the values ​​of each constraint condition are changed, while the types of constraints (gas turbine energy power exchange constraint, energy storage charging and discharging constraint, and distribution microgrid power constraint) remain unchanged.

[0071] According to the above implementation method, the present invention achieves the integration of external regulation demand and internal constraint capability without breaking the local operation logic of the microgrid, effectively improving the joint regulation elasticity of the microgrid group, and thus enhancing the overall capacity of the distribution microgrid system to bear and absorb the fluctuations in new energy output.

[0072] In one embodiment, the distributed energy carrying capacity of the distribution network includes: if the actual electricity load is greater than or equal to the total output of distributed power sources, then a preset second renewable energy absorption rate is used as the distributed energy carrying capacity of the distribution network; if the actual electricity load is less than the total output of distributed energy, then the difference between the total output of distributed power sources and the amount of distributed energy reduction at each time before the current day is calculated to obtain the renewable energy absorption of the distribution network at each time before the current day, and based on the ratio of the renewable energy absorption of the distribution network at each time before the current day to the total output of distributed power sources, the renewable energy absorption rate of the distribution network at each time before the current day is determined, and the renewable energy absorption rate of the distribution network at each time before the current day is used as the distributed energy carrying capacity of the distribution network.

[0073] For example, the preset second new energy absorption rate is a preset value. When the actual power load is greater than or equal to the total output of the distributed power source, the value of the distributed energy carrying capacity of the distribution network is fixed, where the value can be 100%.

[0074] In this example, when the actual electrical load P L (t) is greater than or equal to the total output P of the distributed power source. gen,0 When (t), λ gen (t) = 100%, indicating that distributed energy can be fully absorbed at this time.

[0075] For example, the carrying capacity of distributed energy resources in a distribution network can be expressed as a function:

[0076]

[0077] In the formula, λ gen (t) represents the distributed renewable energy absorption rate of the distribution network at time t; P re (t) represents the reduction in distributed energy resources; P gen,0 (t) represents the total output of the distributed power source; P L (t) represents the actual electrical load; λ gen This represents the average renewable energy consumption rate during the assessment period; T represents the total assessment period.

[0078] According to the above implementation method, if the actual electricity load is greater than or equal to the total output of distributed power sources, the preset second renewable energy absorption rate is directly used as the distributed energy carrying capacity of the distribution network; if the actual electricity load is less than the total output of distributed power sources, the renewable energy absorption rate of the distribution network needs to be calculated at the corresponding time, and this is used as the distributed energy carrying capacity of the distribution network at that time. This method can dynamically reflect the absorption capacity of the distribution network for distributed power sources under different load levels. It is not only suitable for the reasonable reduction and evaluation of renewable energy surplus periods, but also for quickly assessing the system carrying capacity limit when the load is sufficient. By combining preset values ​​with actual measurement calculations, both calculation simplicity and scheduling accuracy are ensured, which helps to improve the flexibility of renewable energy access and enhance the friendliness and dispatchability of the distribution network to distributed energy sources.

[0079] In one implementation, the total regulation cost of the distribution network and microgrid includes: load regulation cost, energy storage operation and maintenance cost, gas turbine power generation cost, distribution network loss cost, and distribution network power exchange cost.

[0080] For example, load adjustment costs can be expressed as a function:

[0081]

[0082] In the formula, C load Indicates load adjustment cost; C a ΔP represents the unit cost of load regulation; l,t This represents the amount of load to be adjusted at time t; T represents the total time of the scheduling cycle.

[0083] For example, the operation and maintenance cost of energy storage can be expressed as a function:

[0084]

[0085] In the formula, C ES Indicates energy storage operation and maintenance costs; μ i P represents the energy storage operation and maintenance cost coefficient. ES,i,t N represents the energy storage charging and discharging power of energy storage i at time t; ES This indicates the number of energy storage devices; T represents the total time of the scheduling cycle.

[0086] For example, the cost of gas turbine power generation can be expressed as a function:

[0087]

[0088] In the formula, C gen N represents the cost of generating electricity using a gas turbine. gen Indicates the number of gas turbines; P gen,i,tC represents the power generation of the i-th gas turbine at time t; gen,i,t Let represent the unit power generation cost of the i-th gas turbine; T represents the total time of the dispatch cycle.

[0089] For example, the network loss cost of a distribution network can be expressed as a function:

[0090]

[0091] In the formula, This represents the network loss cost of the distribution network; P represents the time-of-use electricity price at time t; loss,t This represents the distribution network loss at time t; T represents the total time of the dispatching cycle.

[0092] For example, the power exchange cost of a distribution network can be expressed as a function:

[0093]

[0094] In the formula, P ex P represents the power exchange cost of the distribution network; exchange,i (t) represents the power exchanged between the distribution network and the microgrid at time t; λ exchange,i N represents the cost per unit switching power of microgrid i; mic The number of microgrids is represented by T; T represents the total time of the system scheduling cycle.

[0095] According to the above implementation method, the regulation behavior of distribution networks and microgrids is uniformly incorporated into the cost framework, which can comprehensively quantify the economic costs of various resources participating in regulation, and construct a distribution network optimization model with the goal of "minimum total cost", which helps to achieve coordinated resource allocation and improve the accuracy of regulation decisions.

[0096] In one implementation, the microgrid performs internal optimization scheduling based on the response power command to obtain the target scheduling power and target operating cost of the microgrid for each time period before the day, including: responding to the response power command, wherein the response power command includes the response power for each time period before the day; using the response power for each time period before the day as a relaxation variable to update the second constraint condition of the microgrid to obtain the third constraint condition of the microgrid; and based on the third constraint condition, solving for the minimum value of the objective function of the microgrid through mixed integer linear programming to obtain the target scheduling power and target operating cost of the microgrid for each time period before the day.

[0097] For example, an industrial park has a microgrid system interconnected with the main power distribution network, which is equipped with: a 100kW gas turbine, a 200kWh lithium battery energy storage system, and a group of adjustable load devices (such as smart air conditioning groups) with power adjustable within a range of 50kW.

[0098] The distribution network operator recently issued a response power instruction, requiring the park's microgrids to provide the following responses during the following time periods, as shown in Table 1:

[0099] Table 1

[0100]

[0101] In the table, "+" indicates an upward adjustment, and "-" indicates a downward adjustment.

[0102] The system reads the response power commands for each time period during the day and uses them as external inputs for optimized scheduling. That is, the response power commands in Table 1 are introduced into the microgrid scheduling model (abstractly represented as the microgrid objective function) as "soft constraints" or "relaxation variables" to extend the original scheduling constraints (the second constraint) and form the third constraint. Since only the second constraint is relaxed, the microgrid's constraints remain the gas turbine energy power exchange constraint, energy storage charging and discharging constraint, and distribution microgrid power constraint.

[0103] For example, if an additional 20kW power is required during the 08:00-10:00 period, the microgrid dispatch model needs to perform internal optimization scheduling during this period to rationally adjust the output of the gas turbine, the power of the energy storage system, and the power of the load equipment. This can be achieved by minimizing the objective function of the microgrid using mixed-integer linear programming to obtain the final dispatch plan. Solution methods include using the CPLEX optimizer (IBMILOG CPLEX Optimization Studio) or the CBC (Coin-or Branch and Cut) solver.

[0104] For example, the final scheduling plan is shown in Table 2:

[0105] Table 2

[0106]

[0107] According to the above implementation method, the response power of each day-ahead period is introduced as a relaxation variable into the microgrid dispatch model, and a mixed-integer linear programming model is constructed based on the updated third constraint condition for solution. On the one hand, it realizes the joint optimization dispatch of multiple types of energy resources, and can coordinate and control heterogeneous resources such as gas turbines, energy storage devices and adjustable loads, effectively improving resource allocation efficiency; on the other hand, it optimizes the operating economy of the microgrid, minimizes operating costs while meeting regulation requirements, and improves the economic benefits of the dispatch plan.

[0108] Figure 2This is a structural block diagram of a dispatching device for coordinated optimization of microgrids according to an embodiment of the present invention.

[0109] like Figure 2 As shown, the dispatching device for coordinated optimization of the microgrid may include:

[0110] The microgrid objective function construction module 510 is used to construct a microgrid objective function with the objectives of maximizing the distributed energy carrying capacity of the microgrid and minimizing the total regulation cost of the microgrid, and to solve for the minimum value of the microgrid objective function based on the first constraint condition of the microgrid to obtain a first solution result, wherein the first solution result includes the scheduling plan of the microgrid in each daytime period;

[0111] The update module 520 is used to perform distribution network power flow calculation on the dispatch plan of the microgrid for each time period before the day, and generate the distribution network regulation response command of the microgrid for each time period before the day, so that the microgrid updates the first constraint condition of the microgrid according to the distribution network regulation in the distribution network regulation response command, and obtains the second constraint condition of the microgrid.

[0112] The first solution module 530 is used to solve for the minimum value of the objective function of the microgrid based on the second constraint condition of the microgrid, and obtain a second solution result, wherein the second solution result includes the maximum adjustable capacity and response cost of the microgrid in each day-ahead period;

[0113] The second solution module 540 is used to construct a distribution network objective function with the goal of maximizing the distributed energy carrying capacity of the distribution network and minimizing the total regulation cost of the distribution network and microgrid. Based on the constraints of the distribution network and the maximum adjustable capacity and response cost of the microgrid in each day-ahead period, the module solves for the minimum value of the distribution network objective function and generates the response power command of the microgrid in each day-ahead period. This enables the microgrid, as a node connected to the distribution network, to perform internal optimization scheduling according to the response power command, thereby obtaining the target scheduling power and target operating cost of the microgrid in each day-ahead period. The target scheduling power of the microgrid in each day-ahead period includes the target output power of the gas turbine, the target charging and discharging power of the energy storage device, and the target regulation power of the adjustable load.

[0114] In one implementation, the distributed energy carrying capacity of the microgrid is specifically used for:

[0115] If the actual power load is greater than or equal to the total output of distributed energy, then the preset first new energy absorption rate will be used as the distributed energy carrying capacity of the microgrid.

[0116] If the actual electricity load is less than the total output of distributed energy, the difference between the total output of distributed energy and the amount of distributed energy reduction at each time point on the previous day is calculated to obtain the microgrid renewable energy absorption capacity at each time point on the previous day. Based on the ratio of the microgrid renewable energy absorption capacity to the total output of distributed energy at each time point on the previous day, the microgrid renewable energy absorption rate at each time point on the previous day is determined, and the microgrid renewable energy absorption rate at each time point on the previous day is used as the distributed energy carrying capacity of the microgrid.

[0117] In one implementation, the first constraint condition of the microgrid is specifically used for: gas turbine energy power exchange constraint, energy storage charging and discharging constraint, and distribution microgrid power constraint.

[0118] In one implementation, the microgrid updates its first constraint condition based on the distribution network regulation quantity in the distribution network regulation response command to obtain a second constraint condition for the microgrid, specifically used for:

[0119] The distribution network regulation quantity in the distribution network regulation quantity response command is used as a relaxation variable and summed with each constraint condition in the first constraint condition of the microgrid to obtain the second constraint condition of the microgrid.

[0120] In one implementation, the distributed energy carrying capacity of the distribution network is specifically used for:

[0121] If the actual power load is greater than or equal to the total output of the distributed power source, then the preset second new energy absorption rate will be used as the distributed energy carrying capacity of the distribution network.

[0122] If the actual electricity load is less than the total output of distributed energy, the difference between the total output of distributed power sources and the amount of distributed energy reduction at each time point on the previous day is calculated to obtain the renewable energy absorption capacity of the distribution network at each time point on the previous day. Based on the ratio of the renewable energy absorption capacity of the distribution network to the total output of distributed power sources at each time point on the previous day, the renewable energy absorption rate of the distribution network at each time point on the previous day is determined, and the renewable energy absorption rate of the distribution network at each time point on the previous day is used as the distributed energy carrying capacity of the distribution network.

[0123] In one implementation, the total regulation cost of the distribution network and microgrid is specifically used for: load regulation cost, energy storage operation and maintenance cost, gas turbine power generation cost, distribution network loss cost, and distribution network power exchange cost.

[0124] In one implementation, the microgrid performs internal optimized scheduling based on the response power command to obtain the target scheduling power and target operating cost of the microgrid for each day-ahead period, specifically for:

[0125] In response to the response power command, wherein the response power command includes the response power for each time period of the day;

[0126] The response power of each time period before the date is used as a relaxation variable to update the second constraint condition of the microgrid, thereby obtaining the third constraint condition of the microgrid.

[0127] Based on the third constraint, the objective function of the microgrid is minimized by mixed integer linear programming to obtain the target dispatch power and target operating cost of the microgrid in each day-ahead period.

[0128] The specific functions and examples of each module and submodule of the system in this embodiment of the invention can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0129] The acquisition, storage, and application of user personal information involved in the technical solution of this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0130] This invention also provides a dispatching system for coordinated optimization of distribution microgrids, comprising:

[0131] At least one processor; and a memory communicatively connected to said at least one processor;

[0132] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in any one of the embodiments of the present invention.

[0133] The beneficial effects of the distribution microgrid collaborative optimization scheduling system in this embodiment of the invention are equivalent to the beneficial effects of the above-described distribution microgrid collaborative optimization scheduling method, and will not be repeated here.

[0134] This invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the method described in any one of the embodiments of this invention.

[0135] The beneficial effects of the storage medium of the present invention are equivalent to the beneficial effects of the above-described scheduling method for coordinated optimization of microgrids, and will not be repeated here.

[0136] Figure 3A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present invention is shown. Electronic device 800 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 800 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0137] like Figure 3 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the electronic device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0138] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of displays, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows electronic device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0139] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the scheduling method for coordinated optimization of distribution microgrids. For example, in some embodiments, the scheduling method for coordinated optimization of distribution microgrids can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the scheduling method for coordinated optimization of distribution microgrids described above can be performed. Alternatively, in other embodiments, computing unit 801 may be configured by any other suitable means (e.g., by means of firmware) to perform a scheduling method for microgrid collaborative optimization.

[0140] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0142] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0143] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0144] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0145] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0146] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0147] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the scope of protection of this invention.

Claims

1. A scheduling method for coordinated optimization of distribution microgrids, characterized in that, include: With the objectives of maximizing the distributed energy carrying capacity of the microgrid and minimizing the total regulation cost of the microgrid, a microgrid objective function is constructed. Based on the first constraint condition of the microgrid, the minimum value of the microgrid objective function is solved to obtain a first solution result, wherein the first solution result includes the scheduling plan of the microgrid for each day-ahead period. The distribution network power flow calculation is performed on the dispatch plan of the microgrid for each time period before the day, and the distribution network regulation response command of the microgrid for each time period before the day is generated. The microgrid updates its first constraint condition according to the distribution network regulation in the distribution network regulation response command, and obtains the second constraint condition of the microgrid. Based on the second constraint of the microgrid, the objective function of the microgrid is minimized to obtain a second solution result, wherein the second solution result includes the maximum adjustable capacity and response cost of the microgrid in each day-ahead period; With the objectives of maximizing the distributed energy carrying capacity of the distribution network and minimizing the total regulation cost of the distribution network and microgrids, a distribution network objective function is constructed. Based on the constraints of the distribution network and the maximum adjustable capacity and response cost of the microgrid in each day-ahead period, the minimum value of the distribution network objective function is solved to generate the response power command of the microgrid in each day-ahead period. This enables the microgrid, as a node connected to the distribution network, to perform internal optimization scheduling according to the response power command, thereby obtaining the target scheduling power and target operating cost of the microgrid in each day-ahead period. The target scheduling power of the microgrid in each day-ahead period includes the target output power of the gas turbine, the target charging and discharging power of the energy storage device, and the target regulation power of the adjustable load.

2. The method according to claim 1, characterized in that, The distributed energy carrying capacity of the microgrid includes: If the actual power load is greater than or equal to the total output of distributed energy, then the preset first new energy absorption rate will be used as the distributed energy carrying capacity of the microgrid. If the actual electricity load is less than the total output of distributed energy, the difference between the total output of distributed energy and the amount of distributed energy reduction at each time point on the previous day is calculated to obtain the microgrid renewable energy absorption capacity at each time point on the previous day. Based on the ratio of the microgrid renewable energy absorption capacity to the total output of distributed energy at each time point on the previous day, the microgrid renewable energy absorption rate at each time point on the previous day is determined, and the microgrid renewable energy absorption rate at each time point on the previous day is used as the distributed energy carrying capacity of the microgrid.

3. The method according to claim 1, characterized in that, The first constraints of the microgrid include: gas turbine energy power exchange constraints, energy storage charging and discharging constraints, and distribution microgrid power constraints.

4. The method according to claim 1, characterized in that, The microgrid updates its first constraint condition based on the distribution network regulation quantity in the distribution network regulation quantity response command, resulting in a second constraint condition for the microgrid, including: The distribution network regulation quantity in the distribution network regulation quantity response command is used as a relaxation variable and summed with each constraint condition in the first constraint condition of the microgrid to obtain the second constraint condition of the microgrid.

5. The method according to claim 1, characterized in that, The distributed energy carrying capacity of the distribution network includes: If the actual power load is greater than or equal to the total output of the distributed power source, then the preset second new energy absorption rate will be used as the distributed energy carrying capacity of the distribution network. If the actual electricity load is less than the total output of distributed energy, the difference between the total output of distributed power sources and the amount of distributed energy reduction at each time point on the previous day is calculated to obtain the renewable energy absorption capacity of the distribution network at each time point on the previous day. Based on the ratio of the renewable energy absorption capacity of the distribution network to the total output of distributed power sources at each time point on the previous day, the renewable energy absorption rate of the distribution network at each time point on the previous day is determined, and the renewable energy absorption rate of the distribution network at each time point on the previous day is used as the distributed energy carrying capacity of the distribution network.

6. The method according to claim 1, characterized in that, The total regulation cost of the distribution network and microgrid includes: load regulation cost, energy storage operation and maintenance cost, gas turbine power generation cost, distribution network loss cost, and distribution network power exchange cost.

7. The method according to claim 1, characterized in that, The microgrid performs internal optimized scheduling based on the response power command to obtain the target scheduling power and target operating cost of the microgrid for each day-ahead period, including: In response to the response power command, wherein the response power command includes the response power for each time period of the day; The response power of each time period before the date is used as a relaxation variable to update the second constraint condition of the microgrid, thereby obtaining the third constraint condition of the microgrid. Based on the third constraint, the objective function of the microgrid is minimized by mixed integer linear programming to obtain the target dispatch power and target operating cost of the microgrid in each day-ahead period.

8. A dispatching device for coordinated optimization of distribution microgrids, characterized in that, include: The microgrid objective function construction module is used to construct a microgrid objective function with the objectives of maximizing the distributed energy carrying capacity of the microgrid and minimizing the total regulation cost of the microgrid, and to solve for the minimum value of the microgrid objective function based on the first constraint condition of the microgrid to obtain a first solution result, wherein the first solution result includes the scheduling plan of the microgrid for each day-ahead period; The update module is used to perform distribution network power flow calculation on the dispatch plan of the microgrid for each time period before the day, and generate the distribution network regulation response command of the microgrid for each time period before the day, so that the microgrid updates the first constraint condition of the microgrid according to the distribution network regulation in the distribution network regulation response command, and obtains the second constraint condition of the microgrid. The first solution module is used to solve for the minimum value of the objective function of the microgrid based on the second constraint condition of the microgrid, and obtain a second solution result, wherein the second solution result includes the maximum adjustable capacity and response cost of the microgrid in each day-ahead period; The second solution module is used to construct a distribution network objective function with the goal of maximizing the distributed energy carrying capacity of the distribution network and minimizing the total regulation cost of the distribution network and microgrids. Based on the constraints of the distribution network and the maximum adjustable capacity and response cost of the microgrid in each day-ahead period, the module solves for the minimum value of the distribution network objective function and generates the response power command of the microgrid in each day-ahead period. This enables the microgrid, as a node connected to the distribution network, to perform internal optimization scheduling according to the response power command, thereby obtaining the target scheduling power and target operating cost of the microgrid in each day-ahead period. The target scheduling power of the microgrid in each day-ahead period includes the target output power of the gas turbine, the target charging and discharging power of the energy storage device, and the target regulation power of the adjustable load.

9. A dispatching system for coordinated optimization of distribution microgrids, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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