Method and System for Collaborative Optimal Scheduling of Source-Network-Load-Storage in Microgrid Clusters Considering Reliability

Abstract

The present disclosure provides a method and system for collaborative optimal scheduling of source-network-load-storage in a microgrid cluster, which acquires the operation parameter data of the microgrid cluster; and obtains the scheduling control strategy of the microgrid cluster according to the acquired operation parameter data and a preset scheduling model. The objective function of the preset scheduling model is a multi-objective optimization objective function for minimizing the operation cost of the microgrid cluster, the environmental pollution treatment cost, and the user power outage loss. The present disclosure considers the impacts of the microgrid operation cost, the environmental pollution treatment cost, and the user power outage loss, and the obtained scheduling strategy of the microgrid cluster can maximize the stability of the power system.

Description

Technical Field

[0001] The present disclosure relates to the technical field of microgrid group scheduling, and particularly to a method and system for collaborative optimal scheduling of power sources, grids, loads, and energy storage in a microgrid group considering reliability. Background Art

[0002] The statements in this part merely provide background art related to the present disclosure and do not necessarily constitute prior art.

[0003] The internal structure and functions of the microgrid group are becoming more and more complex, and its role in the power system is also becoming more and more important. With the continuous development of the microgrid group, the balanced development of its economy and reliability is extremely important.

[0004] The power supply reliability of the microgrid group is a characterization of the ability to continuously supply electrical energy to customers under certain conditions. From the perspective of the load, the power supply reliability of the microgrid group means the continuous supply of electrical energy by the microgrid group. However, in actual situations, due to reasons such as network structure design, component equipment failures, and planned maintenance, the microgrid group cannot achieve uninterrupted power supply to all loads.

[0005] The inventors found that most of the existing collaborative optimal scheduling strategies for "power source - grid - load - energy storage" in the microgrid group do not consider the impact of environmental pollution treatment costs and user power outage losses, resulting in the final scheduling strategy being unable to achieve stable and reliable power supply for the microgrid group. Summary of the Invention

[0006] To solve the deficiencies of the existing technology, the present disclosure provides a method and system for collaborative optimal scheduling of power sources, grids, loads, and energy storage in a microgrid group considering reliability, which takes into account the impact of microgrid operating costs, environmental pollution treatment costs, and user power outage losses, and the obtained microgrid group scheduling strategy can ensure the stability of the power system to the greatest extent.

[0007] To achieve the above object, the present disclosure adopts the following technical solutions:

[0008] The first aspect of the present disclosure provides a method for collaborative optimal scheduling of power sources, grids, loads, and energy storage in a microgrid group considering reliability.

[0009] A method for collaborative optimal scheduling of power sources, grids, loads, and energy storage in a microgrid group considering reliability includes the following processes:

[0010] Obtain the operation parameter data of the microgrid group;

[0011] According to the obtained operation parameter data and a preset scheduling model, obtain the scheduling control strategy of the microgrid group;

[0012] Among them, the objective function of the preset scheduling model is a multi - objective optimization objective function with the minimum microgrid group operation cost, the minimum environmental pollution treatment cost, and the minimum user power outage loss.

[0013] The second aspect of the present disclosure provides a reliability - considered collaborative optimal scheduling system for a micro - grid group source - network - load - storage.

[0014] A reliability - considered collaborative optimal scheduling system for a micro - grid group source - network - load - storage includes:

[0015] A data acquisition module, configured to: acquire the operation parameter data of the micro - grid group;

[0016] An optimal scheduling module, configured to: obtain the scheduling control strategy of the micro - grid group according to the acquired operation parameter data and a preset scheduling model;

[0017] Among them, the objective function of the preset scheduling model is a multi - objective optimization objective function with the minimum operation cost of the micro - grid group, the minimum environmental pollution treatment cost, and the minimum user power outage loss.

[0018] The third aspect of the present disclosure provides a computer - readable storage medium, on which a program is stored. When the program is executed by a processor, it implements the steps in the reliability - considered collaborative optimal scheduling method for a micro - grid group source - network - load - storage as described in the first aspect of the present disclosure.

[0019] The fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the reliability - considered collaborative optimal scheduling method for a micro - grid group source - network - load - storage as described in the first aspect of the present disclosure.

[0020] Compared with the prior art, the beneficial effects of the present disclosure are:

[0021] 1. The method, system, medium, or electronic device described in the present disclosure considers the impacts of the micro - grid operation cost, environmental pollution treatment cost, and user power outage loss. The obtained scheduling strategy for the micro - grid group can ensure the stability of the power system to the greatest extent.

[0022] 2. The method, system, medium, or electronic device described in the present disclosure further improves the stability of the power system by considering the electric power balance constraint, thermal power balance constraint, cooling power balance constraint, output constraints of each micro - source, and the transmission power constraint between the system and the large power grid.

[0023] 3. The method, system, medium, or electronic device described in the present disclosure uses a multi - objective particle swarm algorithm to solve the multi - objective optimization function, which greatly improves the optimization accuracy of the micro - grid group scheduling strategy. Description of the Drawings

[0024] The accompanying drawings forming a part of this disclosure are used to provide a further understanding of the disclosure. The schematic embodiments and descriptions thereof of the disclosure are used to explain the disclosure and do not constitute an improper limitation of the disclosure.

[0025] Figure 1 It is a schematic flow diagram of a method for coordinated optimal scheduling of source-network-load-storage in a microgrid group considering reliability provided in Embodiment 1 of the present disclosure.

[0026] Figure 2 It is the power outage losses of various users provided in Embodiment 1 of the present disclosure.

[0027] Figure 3 It is a schematic diagram of the Pareto optimal frontier provided in Embodiment 1 of the present disclosure.

[0028] Figure 4 It is a cooling, heating and power load curve provided in Embodiment 1 of the present disclosure.

[0029] Figure 5 It is a schematic diagram of the simulation results provided in Embodiment 1 of the present disclosure. Detailed implementation manners

[0030] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanations of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present disclosure belongs.

[0032] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary implementation manners according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0033] Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0034] Embodiment 1:

[0035] As Figure 1 shown, Embodiment 1 of the present disclosure provides a method for coordinated optimal scheduling of source-network-load-storage in a microgrid group considering reliability, including the following processes:

[0036] Obtain the operation parameter data of the microgrid group;

[0037] According to the obtained operating parameter data and the preset scheduling model, a scheduling control strategy for the microgrid cluster is obtained;

[0038] Among them, the objective function of the preset scheduling model is a multi-objective optimization objective function with the minimum operating cost of the microgrid cluster, the minimum environmental pollution treatment cost, and the minimum user power outage loss.

[0039] Specifically, it includes:

[0040] S1: Power supply reliability of the microgrid cluster

[0041] In this embodiment, the reliability of the system is mainly measured by the power outage loss of users. The power outage loss is the economic loss caused to power users or power enterprises when the system power supply is insufficient or stopped. The method of fault enumeration is adopted to estimate the power outage loss of users according to the length of power outage time caused by micro-source faults. The specific modeling process is as follows:

[0042] During the time period t, the expected power outage loss C oc (t) can be expressed as the product of E NS (t) and f IEAR , where E NS (t) represents the expected power supply shortage of the microgrid cluster, and f IEAR represents the evaluation rate of the system's lack of power supply loss, which is expressed by the formula:

[0043] C oc (t) = f IEAR E NS (t)

[0044] In the formula, f IEAR is jointly affected by various factors such as user types, the duration of the system's lack of power supply, and the frequency of the system's lack of power supply. Through investigation, it can be found that f IEAR will change with the change of t, and the power outage losses of different types of users are different, as Figure 2 shown.

[0045] During the time period Δt, E NS (t) can be expressed by the following formula:

[0046]

[0047] Under the condition of state k, P K represents the loss of the load power of the microgrid cluster system; p K is the probability that the microgrid cluster system is in this state within the time period t; N represents the set of unit states, and in the case of fewer micro-sources, it can be obtained by a simple enumeration method.

[0048] P K can also be expressed by the following formula:

[0049]

[0050] Wherein, S represents the set of unavailable units, and U represents the set of available units; the output power of unit j in the t time period is represented by P j (t); R i (t) represents the standby power generation unit; W represents the set of micro-sources input into the microgrid group at this moment.

[0051] The probability p of the microgrid group system being in a certain state k at time t k can be expressed as:

[0052]

[0053] Wherein, p i (t) represents the probability of micro-source i being out of service at time t; p j (t) represents the probability of micro-source j being out of service at time t. Describing the transient outage rate of components with the instantaneous state probability of components based on the homogeneous Markov process, it can be expressed as:

[0054]

[0055] Wherein, λ i represents the failure rate of power generation micro-source i, and μ i represents the repair rate of power generation micro-source i. When power generation unit i is available, P DN (0) = 1, p UP (0) = 0; otherwise, P DN (0) = 0, p UP (0) = 1.

[0056] S2: Multi-objective function

[0057] S2.1: The operation cost of the microgrid group is minimized. The operation cost of the microgrid group includes the operation fuel cost of the microgrid group, the equipment maintenance cost, and the cost of exchanging electric energy with the large power grid.

[0058]

[0059] Wherein, F 1 is the operation cost of the microgrid group; N is the total number of micro-sources; C FUi is the fuel consumption cost of the i-th micro-source; K OMi is the operation and maintenance coefficient of the i-th micro-power supply; P i (t) is the power generated by the i-th micro-source in the t time period; P Grid (t) is the power exchanged between the system and the large power grid in the t time period; is the time-of-use electricity price adopted.

[0060] S2.2: Environmental pollution treatment cost:

[0061]

[0062] In the formula, F 2 is the environmental pollution treatment cost generated during the operation of the microgrid cluster; ρ j is the treatment unit price of the jth pollutant (considering four types of pollution, namely NO x , CO 2 , CO, SO 2 ); β ij is the emission coefficient of the jth type of emission of the ith micro-source of the micro-power source.

[0063] S2.3: Minimize the power outage loss of users. The power outage loss of users is:

[0064] F 3 = f IEAR E NS (t)

[0065] In the formula, F 3 is the power outage loss cost of users; E NS (t) is the expected function; f IEAR represents the power outage loss evaluation rate.

[0066] S3: Constraint conditions

[0067] S3.1: Power balance constraint

[0068] The power supply of the combined cooling, heat and power system (including the sum of the power supplies of each micro-source and the power sold within the system) is the same as the electrical load demand within the system.

[0069] P Load (t) = P MT (t) + P FC (t) + P WT (t) + P PV (t) + P Batt (t) + P Grid (t) + P air (t)

[0070] In the formula, P Load (t) is the electrical load demand at time t; P MT (t), P FC (t), P WT (t), P PV (t), P Batt (t) are the active power outputs of the gas turbine, fuel cell, wind turbine generator, photovoltaic generator, and battery at time t, respectively; P Grid (t) is the exchanged electrical power between the microgrid cluster and the large power grid at time t;air (t) is the power consumption of the air conditioner during the t period.

[0071] S3.2: Thermal power balance constraint

[0072] The sum of the powers of all heating units in the system is always equal to the heat load demand, that is:

[0073] Q heat，L (t) = Q heat，air (t) + Q heat，MT (t)

[0074] In the formula, Q heat，L (t) is the heat load demand of the system during the t period; Q heat，air (t), Q heat，MT (t) are the heat provided by the air conditioner and the heat exchanger during the t period respectively.

[0075] S3.3: Cooling power balance constraint

[0076] The sum of the powers of all cooling units in the system is always equal to the cooling load demand, that is:

[0077] Q cool，L (t) = Q cool，air (t) + Q cool，MT (t)

[0078] In the formula, Q cool，L (t) is the cooling load demand of the system during the t period; Qc ool，air (t), Q cool，MT (t) are the cooling capacities of the air conditioner and the lithium bromide absorption chiller during the t period respectively.

[0079] S3.4: Output constraints of each micro-source in the system

[0080] To ensure the safe and stable operation of the entire system, the output of each micro-source should be restricted.

[0081]

[0082] In the formula, P i (t) is the output of each micro-source; P i，min (t) and P i，max (t) represent the minimum output and the maximum output of the i-th micro-source respectively.

[0083] S3.5: Transmission power constraint between the system and the large power grid

[0084] The combined cooling, heat and power microgrid group is connected to the external large power grid, and the transmission power satisfies:

[0085] -P Grid，max (t) ≤ P Grid (t) ≤ PGrid，max

[0086] In the formula, P Grid (t) is the exchanged electric power between the microgrid cluster and the main grid during the time period t; P Grid，max (t) is the upper limit of the electric energy transmission power between the main grid and the system.

[0087] S4: Model solution

[0088] In solving theoretical research problems and practical engineering applications, multi-objective problems are always encountered. The single-objective optimization method can only solve one objective function and can only find one optimal solution, and it is impossible to solve multi-objective problems. Multi-objective optimization problems often need to solve two or more objective functions. Its main goal is to be able to take into account multiple objectives simultaneously and find a balanced solution set, rather than one optimal solution. Generally, the obtained objective solution is called a non-dominated solution, and the solution set of the objective solutions obtained after optimization is called a non-dominated solution set. The multi-objective optimization problem can be defined in the following form:

[0089] min f(x) = (f 1 (x), f 2 (x), …, f m (x))

[0090]

[0091] In the formula, x = (x 1 , x 2 ,......, x n ) is an n-dimensional decision variable; f = (f 1 , f 2 ,....., f m ) is the objective function, which may include multiple objective functions; g i (x) is the inequality constraint condition; h j (x) is the equality constraint condition.

[0092] When conducting multi-objective optimization problems, when one of the objectives reaches the optimal, the other objective quantities may not reach the optimal. The dimensions, change trends, etc. of several objectives may be different, and it is difficult to obtain an optimal solution that takes into account multiple objectives simultaneously. Pareto theory is precisely to solve this difficult problem. Pareto theory points out that there is more than one optimal solution to this type of problem, but an optimal solution set. When substituting one of the solutions into a single objective, the obtained conclusion is meaningless. Therefore, it is essential to obtain the solution set of such problems, that is, the Pareto optimal solution set. These solutions draw an approximate inverse proportional function curve in the two-dimensional plane, which is called the Pareto optimal front, as Figure 3 shown.

[0093] There are already various methods to solve multi-objective optimization problems. For example, the indirect solution method that converts multi-objectives into a single objective represented by linear weighted summation and the direct solution method of intelligent algorithms represented by the Multi-Objective Particle Swarm Optimization (MOPSO). The former is relatively simple and fast, but has low accuracy. Especially when dealing with special multi-objective problems, when the dimensions and orders of magnitude of several objective functions are different, it will cause great errors, affecting the summary of practical problems and the derivation of final conclusions. The latter has characteristics such as flexibility and high efficiency, and has become a reliable method for dealing with multi-objective problems in recent years.

[0094] When using the multi-objective particle swarm algorithm for optimization and solution, the solution ideas of the multi-objective particle swarm and the conventional particle swarm are the same. Both update the particle position and velocity information during each iteration process to search for the individual optimal P i and all the global optimal P g . The difference is that the multi-objective particle swarm algorithm obtains an optimal solution set rather than a single optimal solution through multiple iterations. Therefore, when applying the multi-objective particle swarm, the concepts of internal and external memory banks must be added. When the current iteration process ends, compare the function values of each objective corresponding to the current x i with the function values of x j corresponding in the internal and external memory banks, and make different next iteration operations according to different comparison results: when the corresponding value of x i is better than the corresponding value of x j , replace the x j in the internal and external banks with xi; when the corresponding value of x i is worse than the corresponding value of x j , the values in the internal and external banks remain unchanged; when it is impossible to judge the superiority or inferiority of the two values, the general processing method is to also store xi in the internal and external memory banks. However, it is also possible that the internal and external memory banks are full. At this time, other discriminant bases are used to determine which element should be retained in the bank, such as convergence and convergence speed.

[0095] The particle position update formula is:

[0096]

[0097] The particle velocity update formula is:

[0098]

[0099]

[0100] Among them, ω is the inertia weight, used to maintain the velocity in the previous iteration process; c 1is the individual extreme value acceleration coefficient, which is used to maintain the learning of the particle itself; c 2 is the global extreme value acceleration coefficient, which is used to maintain the learning of all particles; c 3 is the time-varying acceleration coefficient; I k is the optimal solution that has appeared in the k-th iteration; u, η, and λ are random numbers within [0, 1].

[0101] The update of the acceleration coefficient includes:

[0102]

[0103] Among them, the minimum value of the acceleration coefficient c 1min = c 2min = 0.5; the maximum value of the acceleration coefficient c 1max = c 2max = 2, k max is the maximum number of iterations.

[0104] The solution process of the multi-objective particle swarm algorithm:

[0105] S4.1: Initialize the multi-objective particle swarm. That is, set the initial position, initial velocity, population size, number of iterations of the particles, as well as the sizes of the internal and external memory banks and the optimal objective extreme values contained in the internal and external memory banks.

[0106] S4.2: Solve the objective function value in the instance. Solve the corresponding objective function value at this time and compare it with the function values in the internal and external memory banks. The three situations described in the above multi-objective particle swarm can occur, that is, when the obtained objective function value is better than the extreme value in the library, it replaces it and becomes the new extreme value in the library; when the obtained objective function value is not better than the extreme value in the library, the extreme value in the library remains unchanged and is still the original extreme value; when it is impossible to judge whether the obtained objective function value at this time is better or worse than the extreme value in the library, and the library capacity is not full, the solution obtained at this time is also added to the internal and external memory banks. When the library capacity is full, other discrimination criteria are used to select and discard the solution obtained.

[0107] S4.3: After the iteration is completed, increment the number of iterations by 1. At the same time, determine whether the set number of iterations has been reached. If the set number of iterations at initialization has not been reached, start a new iteration, update the position and velocity of the particles, etc., and then repeat the process of S4.2. If the set number of iterations at initialization has been reached, find the optimal Pareto solution set and end the optimization solution process.

[0108] S4.4: Organize and obtain the non-dominated solution set sought, that is, the optimal objective function solution.

[0109] S4.5: After obtaining the Pareto optimal solution set of the problem, use the fuzzy mathematics method to calculate the satisfaction degree of each non-dominated solution in the solution set, and select the solution with the maximum satisfaction degree as the compromise solution of the problem.

[0110] The satisfaction degrees of the objective functions corresponding to each non-inferior solution can be expressed by the following formula:

[0111]

[0112] In the formula, f ik is the k-th objective function value of the i-th non-inferior solution; f k,min and f k,max are the minimum and maximum values of the k-th objective function respectively. Then, the satisfaction degrees of each non-inferior solution can be expressed as:

[0113]

[0114] In the formula, K is the number of objective functions; N is the number of non-inferior solutions in the Pareto solution set. Select the non-inferior solution with the maximum satisfaction degree as the compromise solution of this problem.

[0115] S5: Model simulation

[0116] In this embodiment, a small microgrid cluster is taken as an example. Based on the load data of this microgrid cluster, with economy, environmental protection, and reliability as the objective functions, a multi-objective particle swarm optimization algorithm is used for simulation and solution. The power generation power of the gas turbine in the system is 200 kW; the rated power of the fuel cell is 100 kW; the rated power of the photovoltaic power generation unit is 30 kW; the rated power of the wind power generation unit is 60 kW; the rated power of the storage battery is 50 kW, and the cooling, heating, and power load curves are as Figure 4 shown.

[0117] The multi-objective particle swarm optimization algorithm is used to simulate and solve the example. The calculation period is 24 h, and each hour is a calculation period. Set the number of particles N = 100, the number of iterations k max is 500, the internal memory bank N 1 = 10, the external memory bank N 2 = 100, c 1 = c 2 = 1.492, r 1 and r 2 are random numbers between 0 and 1. The simulation results are as Figure 5 shown.

[0118] It can be seen that if optimization is carried out with a single objective, it may affect other objectives. The single-objective optimization model may reduce the total multi-objective cost, but it cannot coordinate the relationship between various objectives well. Using the multi-objective optimization algorithm can well balance the economic, environmental protection, and reliability indicators, obtain relatively satisfactory results, and better reflect the actual operation of the microgrid cluster, having obvious advantages compared with the single-objective optimal scheduling model.

[0119] Embodiment 2:

[0120] Embodiment 2 of the present disclosure provides a reliability - considered coordinated optimization scheduling system for a micro - grid group source - grid - load - storage, including:

[0121] A data acquisition module, configured to: acquire operation parameter data of the micro - grid group;

[0122] An optimization scheduling module, configured to: obtain a scheduling control strategy for the micro - grid group according to the acquired operation parameter data and a preset scheduling model;

[0123] Among them, the objective function of the preset scheduling model is a multi - objective optimization objective function with the minimum operation cost of the micro - grid group, the minimum environmental pollution treatment cost, and the minimum user power outage loss.

[0124] The working method of the system is the same as the reliability - considered coordinated optimization scheduling method for the micro - grid group source - grid - load - storage provided in Embodiment 1, and will not be elaborated here.

[0125] Embodiment 3:

[0126] Embodiment 3 of the present disclosure provides a computer - readable storage medium, on which a program is stored. When the program is executed by a processor, it implements the steps in the reliability - considered coordinated optimization scheduling method for the micro - grid group source - grid - load - storage as described in the first aspect of the present disclosure.

[0127] Embodiment 4:

[0128] Embodiment 4 of the present disclosure provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the reliability - considered coordinated optimization scheduling method for the micro - grid group source - grid - load - storage as described in Embodiment 1 of the present disclosure.

[0129] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, a system, or a computer program product. Therefore, the present disclosure can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer - usable program code.

[0130] This disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can 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, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0131] 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, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such 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 steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0133] Those of ordinary skill in the art can understand that to implement all or part of the processes in the above-described embodiment methods, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0134] The above are only the preferred embodiments of this disclosure and are not used to limit this disclosure. For those skilled in the art, this disclosure can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for collaborative optimal scheduling of source-network-load-storage in a microgrid cluster considering reliability, Characterized in that: It includes the following processes: Obtain the operation parameter data of the microgrid cluster; According to the obtained operation parameter data and the preset scheduling model, obtain the scheduling control strategy of the microgrid cluster; Among them, the objective function of the preset scheduling model is a multi-objective optimization objective function with the minimum operation cost of the microgrid cluster, the minimum environmental pollution treatment cost, and the minimum user power outage loss; The operation cost of the microgrid cluster is the sum of the operation fuel cost, equipment maintenance cost, and the cost of exchanging electric energy with the large power grid of the microgrid cluster; The operation cost of the microgrid cluster is: Where, F 1 is the operating cost of the microgrid group; N is the total number of micro-sources; C FUi is the fuel consumption cost of the i-th micro-source; K OMi is the operation and maintenance coefficient of the i-th micro-power supply; P i (t) is the power generated by the i-th micro-source during the t-th period; P Grid (t) is the power exchanged between the system and the main grid during the t-th period; is the time-of-use electricity price adopted; The environmental pollution treatment cost is: where F2 is the cost of treating environmental pollution generated during the operation of the microgrid group; ρ j is the unit price for treating the j-th type of pollutant; β ij is the emission factor of the j-th type of emissions from the i-th micro-source of the micro-power source; The user power outage loss is: F 3 = f IEAR E NS (t) where, F 3 is the cost of power outage loss for users; f IEAR represents the power outage loss evaluation rate, which is jointly affected by various factors such as user types, system power supply shortage duration, and system power supply shortage frequency; E NS (t) is the expected function. During the time period of Δt, E NS (t) is expressed as: Under the condition of state k, P K represents the loss of the load power of the microgrid group system; p K is the probability that the microgrid group system is in this state within time t; N represents the set of unit states; The preset scheduling model includes at least the electric power balance constraint, and the electric power balance constraint is: The power supply of the combined cooling, heating and power system is the same as the electric load demand in the system, that is: P Load P(t) = P MT P(t) + P FC P(t) + P WT P(t) + P PV P(t) + P Batt P(t) + P Grid P(t) + P air(t) where P Load (t) is the electrical load demand during time period t; P MT (t), P FC (t), P WT (t), P PV (t), P Batt (t) are the active power outputs of the gas turbine, fuel cell, wind turbine, photovoltaic generator, and battery during time period t, respectively; P Grid (t) is the exchanged electric power between the microgrid cluster and the main grid during time period t; P air (t) is the power consumption of the air conditioner during time period t: The preset scheduling model includes at least the heat power balance constraint, and the heat power balance constraint is: The sum of the powers of each heating unit in the system is always equal to the heat load demand, that is: Q heat，L Q(t) = Q heat，air Q(t) + Q heat，MT (t) where Q heat,L (t) is the system heat load demand during the time period t; Q heat,air (t), Q heat,MT (t) are the heat provided by the air conditioner and the heat exchanger respectively during the time period t; The preset scheduling model includes at least the cooling power balance constraint, and the cooling power balance constraint is: The sum of the powers of each cooling unit in the system is always equal to the cooling load demand, that is: Q cool，L Q(t) = Q cool，air + Q(t) cool，MT (t) where, Q cool,L (t) is the system's cooling load demand during time period t; Q cool,air (t), Q cool,MT (t) are the refrigerating capacities of the air conditioner and the lithium bromide absorption chiller during time period t, respectively; The preset scheduling model includes at least the output constraints of each micro-source in the system, and the output of each micro-source is within a preset range, that is: where P i (t) is the output of each micro-source; P i , min (t) and P i , max (t) respectively represent the minimum output and the maximum output of the i-th micro-source; The preset scheduling model includes at least the transmission power constraint between the system and the large power grid, and the transmission power is within a preset range, that is: The combined cooling, heating and power type microgrid cluster is connected to the external large power grid, and the transmission power satisfies: -P Grid，max (t) ≤ P Grid (t) ≤ P Grid，max Where, P Grid (t) is the exchanged electric power between the microgrid cluster and the main grid during the time period t; P Grid , max (t) is the upper limit of the electric energy transmission power between the main grid and the system.

2. A collaborative optimal scheduling system for source-network-load-storage in a microgrid cluster considering reliability, Characterized in that: It includes: A data acquisition module configured to: Obtain the operation parameter data of the microgrid cluster; An optimal scheduling module configured to: According to the obtained operation parameter data and the preset scheduling model, obtain the scheduling control strategy of the microgrid cluster; Among them, the objective function of the preset scheduling model is a multi-objective optimization objective function with the minimum operation cost of the microgrid cluster, the minimum environmental pollution treatment cost, and the minimum user power outage loss; The operation cost of the microgrid cluster is the sum of the operation fuel cost, equipment maintenance cost, and the cost of exchanging electric energy with the large power grid of the microgrid cluster; The operation cost of the microgrid cluster is: Where, F 1 is the operating cost of the microgrid cluster; N is the total number of micro-sources; C FUi is the fuel consumption cost of the i-th micro-source; K OMi is the operation and maintenance coefficient of the i-th micro-power supply; P i (t) is the power generated by the i-th micro-source during the t-th period; P Grid (t) is the power exchanged between the system and the main grid during the t-th period; is the time-of-use electricity price adopted; The environmental pollution treatment cost is: In the formula, F 2 is the environmental pollution treatment cost generated during the operation of the microgrid cluster; ρ j is the treatment unit price of the j-th pollutant; β ij is the emission coefficient of the j-th type of emissions from the i-th micro-source of the micro-power source; The user power outage loss is: F 3 = f IEAR E NS (t) where, F 3 is the cost of power outage loss for users; f IEAR represents the power outage loss evaluation rate, which is jointly affected by various factors such as user types, system power supply interruption duration, and system power supply interruption frequency; E NS (t) is the expected function. During the time period of Δt, E NS (t) is expressed as: Under the condition of state k, P K represents the loss of the load power of the microgrid group system; p K is the probability that the microgrid group system is in this state within time t; N represents the set of unit states; The preset scheduling model includes at least the electric power balance constraint, and the electric power balance constraint is: The power supply of the combined cooling, heating and power system is the same as the electric load demand in the system, that is: P Load P(t) = P MT P(t) + P FC P(t) + P WT P(t) + P PV P(t) + P Batt P(t) + P Grid P(t) + P air P(t) where, P Load (t) is the electrical load demand in period t; P MT (t), P FC (t), P WT (t), P PV (t), P Batt (t) are the active power outputs of the gas turbine, fuel cell, wind turbine, photovoltaic generator, and battery in period t, respectively; P Grid (t) is the exchanged electric power between the microgrid cluster and the main grid in period t; P air (t) is the power consumption of the air conditioner in period t; The preset scheduling model includes at least the heat power balance constraint, and the heat power balance constraint is: The sum of the powers of each heating unit in the system is always equal to the heat load demand, that is: Q heat，L Q(t) = Q heat，air + Q(t) heat，MT (t) Where, Q heat,L (t) is the system heat load demand during the time period t; Q heat,air (t), Q heat,MT (t) are the heat provided by the air conditioner and the heat exchanger respectively during the time period t; The preset scheduling model includes at least the cooling power balance constraint, and the cooling power balance constraint is: The sum of the powers of each cooling unit in the system is always equal to the cooling load demand, that is: Q cool，L Q(t) = Q cool，air + Q(t) cool，MT (t) where Q cool,L (t) is the system's cooling load demand during time period t; Q cool,air (t), Q cool,MT (t) are the refrigerating capacities of the air conditioner and the lithium bromide absorption chiller during time period t, respectively; The preset scheduling model includes at least the output constraints of each micro-source in the system, and the output of each micro-source is within a preset range, that is: where P i (t) is the output of each micro-source; P i,min (t) and P i,max (t) respectively represent the minimum output and the maximum output of the i-th micro-source: The preset scheduling model at least includes the transmission power constraint between the system and the large power grid. The transmission power is within a preset range, that is, the combined cooling, heat and power microgrid group is connected to the external large power grid, and the transmission power satisfies: -P Grid，max ψ(t) ≤ P Grid φ(t) ≤ P Grid，max where P Grid (t) is the exchanged active power between the microgrid cluster and the main grid during the time period t; P Grid,max (t) is the upper limit of the power transmission between the main grid and the system.

3. A computer-readable storage medium, on which a program is stored, characterized in that, when the program is executed by a processor, it implements the steps in the reliability-considered coordinated optimal scheduling method for the microgrid group source-network-load-storage as described in claim 1.

4. An electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the steps in the reliability-considered coordinated optimal scheduling method for the microgrid group source-network-load-storage as described in claim 1.

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