CN113988578A - Microgrid group source network load storage cooperative optimization scheduling method and system considering reliability - Google Patents
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Abstract
The utility model provides a micro-grid group source network load storage cooperative optimization scheduling method and system considering reliability, which obtains the operation parameter data of the micro-grid group; obtaining a scheduling control strategy of the microgrid group according to the obtained operation parameter data and a preset scheduling model; the target function of the preset scheduling model is a multi-target optimization target function with the minimum operation cost of the micro-grid group, the minimum environmental pollution treatment cost and the minimum user power failure loss; according to the method, the influence of the operation cost of the micro-grid, the environmental pollution treatment cost and the power failure loss of users is considered, and the stability of the power system can be guaranteed to the greatest extent by the obtained micro-grid group scheduling strategy.
Description
Technical Field
The disclosure relates to the technical field of microgrid cluster scheduling, in particular to a microgrid cluster source grid load-storage cooperative optimization scheduling method and system considering reliability.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The internal structure and function of the micro-grid group are more and more complex, the role in the power system is more and more important, and with the continuous development of the micro-grid group, the balance development of the economy and the reliability is extremely important.
The reliability of power supply of a microgrid group is a representation of capability of uninterruptedly providing electric energy to customers under a certain condition, and from the load perspective, the reliability of power supply of the microgrid group means continuous supply of electric energy to the microgrid group, but in practical terms, the reliability of power supply of the microgrid group cannot achieve uninterrupted power supply to all loads due to reasons of network structure design or element equipment failure, planned maintenance and the like.
The inventor finds that most of the existing 'source-network-load-storage' cooperative optimization scheduling strategies of the microgrid group do not consider the influence of environmental pollution processing cost and user power failure loss, so that the final scheduling strategy cannot realize stable and reliable power supply of the microgrid group.
Disclosure of Invention
In order to solve the defects of the prior art, the method and the system for the load storage collaborative optimization scheduling of the microgrid group source grid considering reliability are provided, the influences of the operation cost of the microgrid, the environmental pollution treatment cost and the power failure loss of users are considered, and the stability of the power system can be guaranteed to the greatest extent by the obtained microgrid group scheduling strategy.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
the first aspect of the disclosure provides a microgrid cluster source grid load-storage cooperative optimization scheduling method considering reliability.
A microgrid cluster source network load-storage cooperative optimization scheduling method considering reliability comprises the following processes:
acquiring operation parameter data of the microgrid group;
obtaining a scheduling control strategy of the microgrid group according to the obtained operation parameter data and a preset scheduling model;
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 power failure loss of users.
The second aspect of the disclosure provides a microgrid group source grid load storage cooperative optimization scheduling system considering reliability.
A microgrid group source grid load-storage collaborative optimization scheduling system considering reliability comprises:
a data acquisition module configured to: acquiring operation parameter data of the microgrid group;
an optimization scheduling module configured to: obtaining a scheduling control strategy of the microgrid group according to the obtained operation parameter data and a preset scheduling model;
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 power failure loss of users.
A third aspect of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, implements the steps in the microgrid source grid load storage cooperative optimization scheduling method considering reliability according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor implements the steps in the microgrid group source grid load-storage cooperative optimization scheduling method considering reliability according to the first aspect of the present disclosure when executing the program.
Compared with the prior art, the beneficial effect of this disclosure is:
1. according to the method, the system, the medium or the electronic equipment, the influence of the operation cost of the micro-grid, the environmental pollution treatment cost and the power failure loss of a user is considered, and the stability of the power system can be guaranteed to the greatest extent by the obtained micro-grid group scheduling strategy.
2. The method, system, medium or electronic device of the present disclosure further improves the stability of the power system by considering the electrical power balance constraint, the thermal power balance constraint, the cold power balance constraint, the output constraints of each micro-source, and the transmission power constraints of the system and the large power grid.
3. According to the method, the system, the medium or the electronic equipment, the multi-target particle swarm algorithm is adopted to solve the multi-target optimization function, and the optimization precision of the micro-grid group scheduling strategy is greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a schematic flowchart of a microgrid group source grid load-storage cooperative optimization scheduling method considering reliability according to embodiment 1 of the present disclosure.
Fig. 2 shows various types of power loss of the user according to embodiment 1 of the present disclosure.
Fig. 3 is a schematic diagram of a pareto optimal leading edge provided in embodiment 1 of the present disclosure.
Fig. 4 is a cooling, heating and power load graph provided in embodiment 1 of the present disclosure.
Fig. 5 is a schematic diagram of a simulation result provided in embodiment 1 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
as shown in fig. 1, an embodiment 1 of the present disclosure provides a microgrid cluster source grid load-storage cooperative optimization scheduling method considering reliability, including the following processes:
acquiring operation parameter data of the microgrid group;
obtaining a scheduling control strategy of the microgrid group according to the obtained operation parameter data and a preset scheduling model;
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 power failure loss of users.
Specifically, the method comprises the following steps:
s1: micro-grid group power supply reliability
In this embodiment, the reliability of the system is mainly measured by the power outage loss of a user, the power outage loss is economic loss caused to a power consumer or a power enterprise when the power supply of the system is insufficient or stopped, the power outage loss of the user is estimated by adopting a fault enumeration method according to the length of the power outage time caused by a micro-source fault, and the specific modeling process is as follows:
in the time period t, the expected C of the system power failure lossoc(t) may be represented by ENS(t) and fIEARProduct of (a), wherein ENS(t) microgrid group brownout expectation, fIEARAnd (3) expressing the evaluation rate of the power loss of the system, which is expressed by the formula:
Coc(t)＝fIEARENS(t)
in the formula (f)IEARThe type of the user, the duration of the power supply lack of the system and the system lackPower supply frequency, and the like. It can be found through investigation that fIEARThe power loss varies with the variation of t, and different types of users have different power loss, as shown in fig. 2.
During time Δ t, ENS(t) may be represented by the following formula:
under the condition of state k, PKRepresenting the loss of the load power of the microgrid group system; p is a radical ofKThe probability that the microgrid group system is in the state within t time is shown; n represents a set of unit states, and can be obtained by a simple enumeration method under the condition of less micro-sources.
PKIt can also be represented by the following formula:
in the formula, S represents an unavailable unit set, and U represents an available unit set; p for output power of unit j in t time periodj(t) represents; ri(t) represents a backup power generation unit; w represents the set of micro sources that the microgrid group is invested in at this time.
Probability p of micro-grid group system being in a certain state k at time tkCan be expressed as:
in the formula, pi(t) represents the probability that the micro-source i is out of service at time t; p is a radical ofj(t) represents the probability that the micro source j stops at the time t, and the transient stop rate of the component is described by the component transient state probability based on the homogeneous Markov process, which can be represented as:
in the formula, λiRepresents the failure rate, mu, of the micro-source i for power generationiRepresents the repair rate of the micro-source of electricity generation i, and P is the repair rate of the micro-source of electricity generation when the unit of electricity generation i is availableDN(0)＝1，pUP(0) 0; otherwise, PDN(0)＝0，pUP(0)＝1。
S2: multiple objective function
S2.1: the micro-grid group operation cost is the minimum, and comprises micro-grid group operation fuel cost, equipment maintenance cost and electric energy exchange cost with a large power grid.
In the formula, F1The operating cost of the micro-grid group; n is the total number of the micro sources; cFUiFuel consumption cost for the ith micro-source; kOMiOperating a maintenance coefficient for the ith micro-power source; pi(t) is the power emitted by i micro sources in the t time period; pGrid(t) the power exchanged by the system and the large power grid in the period of t;
the time-of-use electricity price is adopted.
S2.2: environmental pollution treatment cost:
in the formula, F2The cost for treating the environmental pollution generated in the operation of the micro-grid group; rhojUnit price for treating jth pollutant (four types of pollution are considered, namely NO)x、CO2、CO、SO2)；βijAnd the emission coefficient of j-th type emission of the ith micro-power source.
S2.3: the user power failure loss is minimum, and the user power failure loss is:
F3＝fIEARENS(t)
in the formula, F3Loss of cost for the user in power outage; eNS(t) is a desired function; f. ofIEARThe power failure loss evaluation rate is shown.
S3: constraint conditions
S3.1: electric power balance constraint
The power supply power (including the sum of each micro-source power supply and power selling in the system) of the combined cooling heating and power system is the same as the requirement of the electrical load in the system.
PLoad(t)＝PMT(t)+PFC(t)+PWT(t)+PPV(t)+PBatt(t)+PGrid(t)+Pair(t)
In the formula, PLoad(t) electrical load demand for time period t; pMT(t)、PFC(t)、PWT(t)、PPV(t)、PBatt(t) the active outputs of the gas turbine, the fuel cell, the wind driven generator, the photovoltaic generator and the storage battery in the period of t are respectively obtained; pGrid(t) the exchange electric power of the micro-grid group and the large power grid within a time period t; pairAnd (t) is the power consumption of the air conditioner in the t period.
S3.2: thermal power balance constraint
The sum of the power of each heating unit in the system is always equal to the heat load demand, namely:
Qheat，L(t)＝Qheat，air(t)+Qheat，MT(t)
in the formula, Qheat，L(t) system thermal load demand over a period of t; qheat，air(t)、Qheat，MTAnd (t) the heat provided by the air conditioner and the heat exchanger in the period of t respectively.
S3.3: cold power balance constraint
The sum of the power of each cooling unit in the system is always equal to the cooling load demand, namely:
Qcool，L(t)＝Qcool，air(t)+Qcool，MT(t)
in the formula, Qcool，L(t) the system cooling load demand in the time period t; qcool，air(t)、Qcool，MTAnd (t) the refrigerating capacities of the air conditioner and the lithium bromide absorption refrigerating unit in the time period t respectively.
S3.4: micro source output constraint in system
For the safe and stable operation of the whole system, the output of each micro source is limited.
In the formula, Pi(t) the output of each micro source; pi，min(t) and Pi，max(t) represents the minimum and maximum contribution of the ith micro-source, respectively.
S3.5: system and large power grid transmission power constraint
The cold and hot electric confession type microgrid crowd links to each other with the big electric wire netting of outside, and transmission power satisfies:
-PGrid，max(t)≤PGrid(t)≤PGrid，max
in the formula, PGrid(t) the exchange electric power of the micro-grid group and the large power grid within a time period t; pGrid，maxAnd (t) is the upper limit of electric energy transmission power between a large power grid and a system.
S4: model solution
In solving theoretical research problems and practical engineering applications, multi-objective problems are always encountered. The optimization method of the single target can only solve one objective function, can only find one optimal solution, and cannot realize the solution of the multi-objective problem. The multi-objective optimization problem usually needs to solve two or more objective functions, and the most important objective is to consider multiple objectives simultaneously and find a balanced solution set instead of an optimal solution. Generally, a found target solution is called a non-dominated solution, a solution set of the target solution obtained after optimization is called a non-dominated solution set, and the optimization problem of multiple targets can be defined as the following form:
min f(x)＝(f1(x)，f2(x)，…，fm(x))
wherein x is (x)1，x2，......，xn) Is an n-dimensional decision variable; f ═ f1，f2，.....，fm) The target function may comprise a plurality of target functions; gi(x) Is an inequality constraint condition; h isj(x) Is an equality constraint.
In the multi-objective optimization problem, when a certain target is optimal, the quantities of other targets may not be optimal, the dimensions, the variation trends and the like of several targets may be different, and it is difficult to obtain an optimal solution that considers multiple targets simultaneously. The Pareto theory aims to solve the problem, indicates that the optimal solution of the problem is not only one but also an optimal solution set, and the conclusion obtained when one solution is brought into a single target is meaningless. Therefore, finding a solution set for such problems is essential, i.e. a pareto optimal solution set. These solutions draw a curve of an approximately inverse proportional function in a two-dimensional plane, called the pareto optimal leading edge, as shown in fig. 3.
There are many methods for solving the Multi-Objective Optimization problem, such as an indirect solution for converting multiple objectives into a single Objective represented by linear weighted summation and a direct solution for an intelligent algorithm represented by a Multi-Objective Particle Swarm Optimization (MOPSO). The former is simple and quick, but has low accuracy, and especially when the dimension and the order of magnitude of several objective functions are different in the process of processing special multi-objective problems, the great error is caused, and the conclusion of actual problems and the drawing of final conclusions are influenced. The latter has the characteristics of flexibility, high efficiency and the like, and becomes a reliable method for processing multi-target problems in recent years.
The optimization solution is carried out by adopting a multi-target particle swarm algorithm, the solution idea of the multi-target particle swarm is the same as that of the conventional particle swarm, and the position and the speed information of the particles are updated in the process of one iteration, so that the individual optimal P of the particles is searchediAnd all global optimum Pg. The method is different from the method that the multi-target particle swarm algorithm obtains an optimal solution set instead of a single optimal solution through multiple iterations. Therefore, the temperature of the molten metal is controlled,when using multi-target particle groups, the concept of internal and external memory banks needs to be added. When the iteration process of this time is finished, x of this time is carried outiFunction value of each corresponding target and x in internal and external memory banksjAnd comparing the corresponding function values, and performing different next iterative operation according to different comparison results: when x isiThe corresponding value outperforms xjCorresponding to the value, x in the internal and external libraries is divided by xijReplacing; when x isiCorresponding value inferior to xjIf the corresponding value is not changed, the values in the inner and outer banks are not changed; when the two values cannot be judged to be good or bad, the general processing method is to store xi in the internal and external memory banks, but the internal and external memory banks may be full, and at this time, other judgment bases are adopted to judge which element should be reserved in the bank, such as convergence, convergence speed, and the like.
The particle position update formula is:
the particle velocity update formula is:
wherein, ω is an inertial weight used to maintain the velocity in the previous iteration process; c. C1The acceleration coefficient is an individual extreme value and is used for maintaining the learning of the particles; c. C2The acceleration coefficient is a global extreme value acceleration coefficient and is used for maintaining the learning of all particles; c. C3Is a time-varying acceleration coefficient; i iskThe optimal solution appeared in the k iteration; u, eta and lambda are random numbers in [0,1 ]]Within.
Updating of the acceleration factor, comprising:
wherein the minimum value c of the acceleration coefficient1min＝c2min0.5; maximum value of acceleration coefficient c1max＝c2max＝2，kmaxIs the maximum number of iterations.
Solving process of multi-target particle swarm algorithm:
s4.1: initializing the multi-target particle swarm. Namely setting the initial position, the initial speed, the population size and the iteration number of the particles, the sizes of the internal memory base and the external memory base and the optimal target extreme value contained in the internal memory base and the external memory base.
S4.2: the objective function values in the example are solved. Solving the corresponding objective function value at the moment, and comparing the objective function value with the function values in the internal and external memory libraries, three conditions in the multi-objective particle swarm brief can appear, namely when the objective function value is better than the extreme value in the library, the objective function value is replaced by the extreme value in the new library; when the objective function value is not better than the extreme value in the library, the extreme value in the library is unchanged and still is the original extreme value; and when the objective function value and the extreme value in the library which are obtained at the moment cannot be judged to be superior or inferior and the library capacity is not full, adding the obtained objective function value and the extreme value in the library into the internal and external memory libraries, and when the library capacity is full, adopting other judgment bases to obtain the objective function value and the extreme value in the library.
S4.3: and after the iteration is finished, adding 1 to the iteration number, judging whether the set iteration number is reached, if the iteration number specified in the initialization is not reached, starting new iteration, updating the position speed and the like of the particles, repeating the process of S4.2, and if the iteration number set in the initialization is reached, searching an optimal pareto solution set and finishing the optimization solution process.
S4.4: and (5) sorting to obtain the solved non-inferior solution set, namely the optimal objective function solution.
S4.5: after the pareto optimal solution set of the problem is obtained, the satisfaction degree of each non-inferior solution in the solution set is calculated by adopting a fuzzy mathematical method, and the solution with the maximum satisfaction degree is selected as a compromise solution of the problem.
Wherein the satisfaction of each objective function corresponding to each non-inferior solution can be represented by the following formula:
in the formula (f)ikA kth objective function value for the ith non-inferior solution; f. ofk,min、fk,maxThe minimum and maximum of the kth objective function, respectively, then the satisfaction of each non-inferior solution can be expressed as:
in the formula, K is the number of objective functions; n is the number of the non-inferior solutions in the pareto solution set, and the non-inferior solution with the maximum satisfaction degree is selected as the compromise solution of the problem.
S5: simulation of a model
In this embodiment, a small microgrid group is taken as an example, and according to load data of the microgrid group, an economic function, an environmental protection function and a reliability function are taken as objective functions, and a multi-objective particle swarm algorithm is adopted to perform simulation 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 generator set is 30 kW; the rated power of the wind generating set is 60 kW; the rated power of the storage battery is 50kW, and a cold-heat-electricity load curve is shown in FIG. 4.
And (3) performing simulation solution on the calculation examples by adopting a multi-target particle swarm algorithm, wherein the calculation cycle is 24h, and each hour is a calculation time interval. The number of particles N is set to 100, and the iteration number k max500, internal memory bank N 110, external memory bank N2＝100，c1＝c2＝1.492，r1、r2The simulation results are shown in fig. 5 for random numbers between 0 and 1.
It can be seen that if a single target is optimized, other targets may be affected, and the single target optimization model may reduce the total cost compared to multiple targets, but may not coordinate the relationships between the targets. The multi-objective optimization algorithm can well give consideration to economic, environmental protection and reliability indexes, obtain a satisfactory result, better reflect the actual operation condition of the microgrid group, and has obvious advantages compared with a single-objective optimization scheduling model.
Example 2:
the embodiment 2 of the present disclosure provides a microgrid group source grid load storage cooperative optimization scheduling system considering reliability, including:
a data acquisition module configured to: acquiring operation parameter data of the microgrid group;
an optimization scheduling module configured to: obtaining a scheduling control strategy of the microgrid group according to the obtained operation parameter data and a preset scheduling model;
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 power failure loss of users.
The working method of the system is the same as the reliability-considered cooperative optimization scheduling method for load storage of the microgrid cluster source grid provided in embodiment 1, and details are not repeated here.
Example 3:
an embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the steps in the microgrid group source grid load storage collaborative optimization scheduling method considering reliability according to the first aspect of the present disclosure.
Example 4:
an embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and capable of running on the processor, where the processor implements the steps in the microgrid group source grid load-storage cooperative optimization scheduling method considering reliability according to embodiment 1 of the present disclosure when executing the program.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A microgrid cluster source network load-storage cooperative optimization scheduling method considering reliability is characterized by comprising the following steps: the method comprises the following steps:
acquiring operation parameter data of the microgrid group;
obtaining a scheduling control strategy of the microgrid group according to the obtained operation parameter data and a preset scheduling model;
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 power failure loss of users.
2. The microgrid group-source grid load-storage cooperative optimization scheduling method considering reliability as claimed in claim 1, characterized in that:
the micro-grid group operation cost is the sum of the micro-grid group operation fuel cost, the equipment maintenance cost and the cost of exchanging electric energy with the large power grid.
3. The microgrid group-source grid load-storage cooperative optimization scheduling method considering reliability as claimed in claim 1, characterized in that:
presetting a scheduling model, at least comprising electric power balance constraints, wherein the electric power balance constraints are as follows: the power supply power of the combined cooling heating and power system is the same as the demand of the electrical load in the system.
4. The microgrid group-source grid load-storage cooperative optimization scheduling method considering reliability as claimed in claim 1, characterized in that:
the method comprises the steps of presetting a scheduling model, wherein the scheduling model at least comprises a thermal power balance constraint, and the thermal power balance constraint is as follows: the sum of the power of each heating unit in the system is always equal to the heat load demand.
5. The microgrid group-source grid load-storage cooperative optimization scheduling method considering reliability as claimed in claim 1, characterized in that:
presetting a scheduling model, at least comprising a cold power balance constraint, wherein the cold power balance constraint is as follows: the sum of the power of each cooling unit in the system is always equal to the cooling load demand.
6. The microgrid group-source grid load-storage cooperative optimization scheduling method considering reliability as claimed in claim 1, characterized in that:
and presetting a scheduling model, wherein the scheduling model at least comprises output constraints of all micro sources in the system, and the output of each micro source is in a preset range.
7. The microgrid group-source grid load-storage cooperative optimization scheduling method considering reliability as claimed in claim 1, characterized in that:
and the preset scheduling model at least comprises transmission power constraints of the system and the large power grid, and the transmission power is within a preset range.
8. The utility model provides a consider little electric wire netting crowd source net load storage collaborative optimization dispatch system of reliability which characterized in that: the method comprises the following steps:
a data acquisition module configured to: acquiring operation parameter data of the microgrid group;
an optimization scheduling module configured to: obtaining a scheduling control strategy of the microgrid group according to the obtained operation parameter data and a preset scheduling model;
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 power failure loss of users.
9. A computer readable storage medium, on which a program is stored, which when executed by a processor implements the steps in the microgrid source grid load storage cooperative optimization scheduling method considering reliability according to any of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the microgrid group source grid load storage cooperative optimization scheduling method considering reliability according to any one of claims 1 to 7 when executing the program.
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