Optimized scheduling method for photovoltaic-storage-containing micro-grid

Abstract

The invention, which relates to the technical field of micro-grid optimization scheduling, discloses an optical storage-containing micro-grid optimization scheduling method comprising the following steps of: describing operation characteristics of each component by using a mathematical model, and constructing a dynamic simulation model in combination with weather data, load prediction and electricity price information; determining optimization targets, including maximizing the use of renewable energy sources, minimizing the operation cost, and balancing the relationship between the load and power generation; a group of initial solutions are generated randomly, the fitness value of each solution is calculated according to a set optimization target, and the higher the fitness value is, the better the solution is. According to the method, innovative algorithms such as Kent mapping are used, so that the global search capability of the algorithm is improved, the trouble of a local optimal solution is avoided, and the optimization effect is more remarkable.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimization scheduling, and in particular to a method for optimizing scheduling of a microgrid containing photovoltaic storage. Background Art

[0002] As an emerging energy structure, microgrid integrates distributed energy, energy storage system and load equipment, aiming to achieve efficient use of renewable energy (such as wind and solar energy) and ensure that the power load demand can be stably met. However, microgrid faces many challenges during operation, including the volatility and uncertainty of renewable energy output, which leads to the need for reasonable scheduling strategies to optimize power output and achieve smooth operation.

[0003] At present, there have been many achievements in the research of optimal dispatching of microgrids, involving various algorithms and models. For example, some studies have used improved optimization algorithms, such as the white shark optimization algorithm, the particle swarm optimization algorithm, and the salp algorithm, to construct and solve dispatching models for the economy and environmental protection of microgrids. Although these studies have made some progress in operating efficiency, they often only focus on operating costs and fail to fully consider multi-dimensional factors such as system operating efficiency and dispatch flexibility, thus affecting the overall dispatching effect of photovoltaic storage microgrids. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above-mentioned problems existing in the existing scheduling method of a microgrid containing photovoltaic storage, the present invention is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A method for optimizing scheduling of a microgrid with photovoltaic storage, the method comprising the following steps:

[0008] Step 1: Identify the components in the microgrid, including photovoltaic power generation units, energy storage systems, load equipment, and grid connection points, use mathematical models to describe the operating characteristics of each component, and build a dynamic simulation model based on weather data, load forecasts, and electricity price information;

[0009] Step 2: Clarify the optimization objectives, including: maximizing the use of renewable energy, minimizing operating costs, and balancing the relationship between load and power generation;

[0010] Step 3: Randomly generate a set of initial solutions, each of which represents a potential scheduling strategy, including power generation scheduling, energy storage operation and load distribution. The fitness value of each solution is calculated according to the set optimization goal. The higher the fitness value, the better the solution.

[0011] As a preferred solution of the method for optimizing scheduling of a microgrid with photovoltaic storage described in the present invention, the step three also includes constructing a search and update mechanism, which includes: globally exploring the solution through Kent mapping to avoid the algorithm from falling into local optimality; selecting solutions with high fitness and using their characteristics to generate new solutions to improve population diversity; setting a stop condition, and when the fitness improvement reaches a certain threshold, judging whether the algorithm should end.

[0012] As a preferred solution of the method for optimizing scheduling of a microgrid with photovoltaic storage described in the present invention, wherein:

[0013] The expressions for constructing dynamic simulation models include:

[0014] Net power output model: P net (t) = P PV (t)+P discharge -L(t);

[0015] Among them, P net (t) represents the net power output at time t, P PV (t) represents photovoltaic power generation, P discharge (t) represents the discharge power of the energy storage system, and L(t) represents the load demand;

[0016] Energy storage state model:

[0017] Where, E(t) represents the energy storage state at time t, E(t-1) represents the energy storage state at the previous moment, and η chaege represents the charging efficiency, η discharge Indicates the discharge efficiency, P charge (t) represents the charging power;

[0018] Electricity price model: P price (t) = P base +P dynamic (t);

[0019] Among them, P price (t) represents the electricity price at time t, P base represents the basic electricity price, P dynamic (t) represents the dynamic electricity price.

[0020] As a preferred solution of the method for optimizing scheduling of a microgrid with photovoltaic storage described in the present invention, wherein:

[0021] In step 2, when the optimization goal is to maximize the use of renewable energy, the optimization objective function expression is:

[0022]

[0023] The constraint condition of this function is to ensure that the photovoltaic power generation power is within its maximum capacity and satisfies 0≤P PV (t)≤P PV,max ;

[0024] Minimizing operating costs means reducing the economic costs of electricity generation and storage while ensuring load demand. The optimization objective function expression is:

[0025]

[0026] Among them, C charge ,C discharge are the unit costs of charging and discharging, respectively. The constraints of this function are: ensuring that the charging and discharging of the energy storage system is within its maximum and minimum capacity range, and ensuring that the load demand is met, that is, P net (t) ≥ L(t);

[0027] Balance the relationship between load and power generation, that is, ensure that the power generation capacity matches the load demand to achieve stable operation of the system. The optimization objective function expression is:

[0028]

[0029] Among them, Z3 represents the difference between load and power generation. The goal is to minimize this difference. The constraint is to ensure that at each moment, the net power output can meet the load demand, that is, P net (t)≥L(t).

[0030] As a preferred solution of the method for optimizing scheduling of a microgrid with photovoltaic storage described in the present invention, wherein:

[0031] In the process of step 3, each solution can be represented by a vector, by randomly generating the scheduling strategies of power generation, energy storage and load within the set range:

[0032] P PV (t) = rand(0, P PV,max )

[0033] P charge (t) = rand(0, P charge,max )

[0034] P discharge (t) = rand(0, P discharge,max )

[0035] Repeat the above process to generate N different initial solutions X, each of which is a scheduling strategy vector.

[0036] As a preferred solution of the method for optimizing scheduling of a microgrid with photovoltaic storage described in the present invention, the fitness value of each solution should reflect the pros and cons of the solution in meeting the optimization goal. The fitness value is calculated in combination with the above three objective functions, that is, maximizing the goal Z1 and minimizing the goals Z2 and Z3. The expression is: F(i) = λ1Z1-λ2Z2-λ3Z3, wherein F(i) represents the fitness value of the i-th solution, and λ1, λ2, and λ3 are weight coefficients of each objective function, which are used to adjust the importance of each goal.

[0037] As a preferred solution of the method for optimizing scheduling of a microgrid with photovoltaic storage described in the present invention, a solution X is randomly selected. i , apply Kent mapping to transform the selected solution and obtain a new solution X new1 :

[0038] X new1 =X i +Δ·sin(k·X i )

[0039] Among them, Δ is a random perturbation factor, and k is a tuning parameter used to control the amplitude of the mapping;

[0040] From the new solution new1 Find the high fitness solution X i The features of the new solution X new2 , that is, from the k solutions with the highest fitness new1 Extract features from the solution and generate a new solution X by combining random perturbations new2 , the specific steps are:

[0041] S301: Feature extraction: record the generation scheduling, energy storage operation and load distribution of the high fitness solution;

[0042] S302: Combination generation: Generate a new solution X by linear combination of features new2 :

[0043]

[0044] S303: Add small random perturbations to the new solution to maintain diversity:

[0045] X new3 =X new2 +∈·rand(0,1)

[0046] where ∈ is the perturbation amplitude.

[0047] In summary, the present invention constructs an effective search and update mechanism. This mechanism combines Kent mapping for global exploration, uses high fitness solutions to generate new solutions to improve population diversity, and sets reasonable stopping conditions to ensure that the algorithm can efficiently find the optimal solution to the microgrid optimization scheduling problem. In this way, the algorithm can not only effectively avoid falling into local optimality, but also enhance the comprehensiveness and flexibility of the search.

[0048] A dispatching system including a photovoltaic storage microgrid optimization dispatching method as described above, the system comprises:

[0049] The data acquisition module is responsible for collecting real-time data, including photovoltaic power generation, energy storage status, load demand, meteorological data, and electricity price information; the model building module builds a dynamic simulation model based on the collected data to describe the operating characteristics of each component in the microgrid to ensure the accuracy and real-time performance of the model;

[0050] The optimization goal setting module specifies the goals of system optimization, including maximizing the use of renewable energy, minimizing operating costs, balancing the relationship between load and power generation, and providing an objective function for the optimization algorithm, as well as the scheduling strategy generation module, which uses an improved sparrow search algorithm to generate a set of initial solutions representing different scheduling strategies and calculates the fitness value according to the set optimization goals;

[0051] The search and update mechanism module implements the algorithm's search and update mechanism, including global exploration, selection of high-fitness solutions and generation of new solutions, as well as judgment of fitness improvement, to ensure that the search process is efficient and effective. The scheduling decision module generates the final scheduling strategy based on the optimization results, covering power generation scheduling, energy storage operation and load distribution, to ensure the smooth operation of the microgrid.

[0052] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned method for optimizing scheduling of a microgrid containing photovoltaic energy storage are implemented.

[0053] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned method for optimizing scheduling of a microgrid containing photovoltaic energy storage are implemented.

[0054] Beneficial effects of the present invention:

[0055] 1. Comprehensive optimization scheduling: By introducing an improved sparrow search algorithm, that is, seeking various optimization solutions, it is possible to simultaneously achieve the maximum use of renewable energy, the minimization of operating costs, and the balance between load and power generation in multi-objective optimization, effectively improving the scheduling flexibility of the microgrid.

[0056] 2. Improve operational efficiency: Building a dynamic simulation model can more accurately reflect the operating status of the microgrid, thereby improving the accuracy and reliability of scheduling.

[0057] 3. Enhanced search capability: By using innovative algorithms such as Kent mapping, the global search capability of the algorithm is improved, avoiding the problem of local optimal solutions, making the optimization effect more significant. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0059] Figure 1 This is a schematic diagram of the overall process of an optimized scheduling method for a microgrid with photovoltaic storage proposed in the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] Reference Figure 1 , is an embodiment of the present invention, and provides a method for optimizing scheduling of a microgrid with photovoltaic storage, the method comprising the following steps:

[0064] Step 1: Identify the components in the microgrid, including photovoltaic power generation units, energy storage systems, load equipment, and grid connection points, use mathematical models to describe the operating characteristics of each component, and build a dynamic simulation model based on weather data, load forecasts, and electricity price information;

[0065] Specifically, the expressions for constructing the dynamic simulation model include:

[0066] Net power output model: P net (t) = P PV (t)+P discharge -L(t);

[0067] Among them, P net (t) represents the net power output at time t, P PV (t) represents photovoltaic power generation, P discharge (t) represents the discharge power of the energy storage system, and L(t) represents the load demand;

[0068] Energy storage state model:

[0069] Where, E(t) represents the energy storage state at time t, E(t-1) represents the energy storage state at the previous moment, and η chaege represents the charging efficiency, η discharge Indicates the discharge efficiency, P charge (t) represents the charging power;

[0070] Electricity price model: P price (t) = P base +P dynamic (t);

[0071] Among them, P price (t) represents the electricity price at time t, P base represents the basic electricity price, P dynamic (t) represents the dynamic electricity price.

[0072] In summary, the dynamic simulation model of the microgrid can comprehensively reflect the impact of photovoltaic power generation, energy storage management and electricity price changes on the operation of the microgrid, and provide a data basis for optimizing scheduling.

[0073] Step 2: Clarify the optimization objectives, including: maximizing the use of renewable energy, minimizing operating costs, and balancing the relationship between load and power generation.

[0074] Specifically, when the optimization goal is to maximize the use of renewable energy, the optimization objective function expression is:

[0075]

[0076] The constraint condition of this function is to ensure that the photovoltaic power generation power is within its maximum capacity and satisfies 0≤P PV (t)≤P PV,max ;

[0077] Minimizing operating costs means reducing the economic costs of electricity generation and storage while ensuring load demand. The optimization objective function expression is:

[0078]

[0079] Among them, C charge ,C discharge are the unit costs of charging and discharging, respectively. The constraints of this function are: ensuring that the charging and discharging of the energy storage system is within its maximum and minimum capacity range, and ensuring that the load demand is met, that is, P net (t) ≥ L(t);

[0080] Balance the relationship between load and power generation, that is, ensure that the power generation capacity matches the load demand to achieve stable operation of the system. The optimization objective function expression is:

[0081]

[0082] Among them, Z3 represents the difference between load and power generation. The goal is to minimize this difference. The constraint is to ensure that at each moment, the net power output can meet the load demand, that is, P net (t)≥L(t).

[0083] In summary, the optimization scheduling objectives of the microgrid can be effectively achieved, ensuring that while maximizing the utilization of renewable energy, the operating costs are reduced and the balance between load and power generation is achieved.

[0084] Step 3: Randomly generate a set of initial solutions, each of which represents a potential scheduling strategy, including power generation scheduling, energy storage operation and load distribution. The fitness value of each solution is calculated according to the set optimization goal. The higher the fitness value, the better the solution.

[0085] Each solution can be represented by a vector by randomly generating the dispatch strategies of power generation, energy storage and load within the set range:

[0086] P PV (t) = rand(0, P PV,max )

[0087] P charge (t) = rand(0, P charge,max )

[0088] P discharge (t) = rand(0, P discharge,max )

[0089] Repeat the above process to generate N different initial solutions X, each of which is a scheduling strategy vector.

[0090] The fitness value of each solution should reflect the quality of the solution in meeting the optimization goal. Combined with the above three objective functions, the fitness value is calculated, that is, maximizing the goal Z1 and minimizing the goals Z2 and Z3. The expression is: F(i) = λ1Z1-λ2Z2-λ3Z3, where F(i) represents the fitness value of the i-th solution, λ1, λ2, and λ3 are the weight coefficients of each objective function, which are used to adjust the importance of each goal.

[0091] This step also includes building a search update mechanism, which includes: global exploration of solutions through Kent mapping to prevent the algorithm from falling into local optimality; selecting solutions with high fitness and using their characteristics to generate new solutions to improve population diversity; setting stopping conditions to determine whether the algorithm should end when the fitness improvement reaches a certain threshold.

[0092] Randomly select a solution X i , apply Kent mapping to transform the selected solution and obtain a new solution X new1 :

[0093] X new1 =X i +Δ·sin(k·X i )

[0094] Among them, Δ is a random perturbation factor, and k is a tuning parameter used to control the amplitude of the mapping;

[0095] From the new solution new1 Find the high fitness solution X i The features of the new solution X new2 , that is, from the k solutions with the highest fitness new1 Extract features from the solution and generate a new solution X by combining random perturbations new2 , the specific steps are:

[0096] S301: Feature extraction: record the generation scheduling, energy storage operation and load distribution of the high fitness solution;

[0097] S302: Combination generation: Generate a new solution X by linear combination of features new2 :

[0098]

[0099] S303: Add small random perturbations to the new solution to maintain diversity:

[0100] X new3 =X new2 +∈·rand(0,1)

[0101] where ∈ is the perturbation amplitude.

[0102] When the fitness value F of the best solution in the population best When the preset threshold T is reached, the algorithm ends.

[0103] After each iteration, the newly generated solution is merged with the current population, and the solution with the highest fitness is selected as the next generation population: New Population = {X best , X new}, In this way, the algorithm continuously optimizes the population structure and improves the global search capability.

[0104] In summary, the present invention can simultaneously achieve the maximum use of renewable energy, the minimization of operating costs and the balance between load and power generation in multi-objective optimization by introducing an improved sparrow search algorithm, effectively improving the scheduling flexibility of the microgrid; by constructing a dynamic simulation model, it can more accurately reflect the operating status of the microgrid, thereby improving the accuracy and reliability of scheduling. By using innovative algorithms such as Kent mapping, the global search capability of the algorithm is improved, avoiding the trouble of local optimal solutions, and making the optimization effect more significant.

[0105] A dispatching system including a photovoltaic storage microgrid optimization dispatching method as described above, the system comprises:

[0106] The data acquisition module is responsible for collecting real-time data, including photovoltaic power generation, energy storage status, load demand, meteorological data, and electricity price information; the model building module builds a dynamic simulation model based on the collected data to describe the operating characteristics of each component in the microgrid to ensure the accuracy and real-time performance of the model;

[0107] The optimization target setting module specifies the system optimization targets, including maximizing the use of renewable energy, minimizing operating costs, balancing the relationship between load and power generation, and providing the objective function for the optimization algorithm, as well as the scheduling strategy generation module, which uses the sparrow search algorithm to generate a set of initial solutions (the sparrow search algorithm randomly generates scheduling strategies for power generation, energy storage, and load within a set range) representing different scheduling strategies and calculates the fitness value according to the set optimization target;

[0108] The search and update mechanism module implements the algorithm's search and update mechanism, including global exploration, selection of high-fitness solutions and generation of new solutions, as well as judgment of fitness improvement, to ensure that the search process is efficient and effective. The scheduling decision module generates the final scheduling strategy based on the optimization results, covering power generation scheduling, energy storage operation and load distribution, to ensure the smooth operation of the microgrid.

[0109] This embodiment also provides a computer device, which is applicable to a method for optimizing scheduling of a microgrid with photovoltaic storage, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for optimizing scheduling of a microgrid with photovoltaic storage as proposed in the above embodiment.

[0110] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0111] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for optimizing scheduling of a microgrid containing photovoltaic storage as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory, referred to as SRAM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), an erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), a programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), a read-only memory (Read-Only Memory, referred to as ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the scheduling of a microgrid with photovoltaic storage, characterized in that: The method comprises the following steps: Step 1: Identify the components in the microgrid, including photovoltaic power generation units, energy storage systems, load equipment, and grid connection points, use mathematical models to describe the operating characteristics of each component, and build a dynamic simulation model based on weather data, load forecasts, and electricity price information; Step 2: Clarify the optimization objectives, including: maximizing the use of renewable energy, minimizing operating costs, and balancing the relationship between load and power generation; Step 3: Randomly generate a set of initial solutions, each of which represents a potential scheduling strategy, including power generation scheduling, energy storage operation and load distribution. The fitness value of each solution is calculated according to the set optimization goal. The higher the fitness value, the better the solution.

2. The method for optimizing the scheduling of a microgrid with photovoltaic energy storage according to claim 1, characterized in that: The step three also includes constructing a search update mechanism, which includes: globally exploring the solution through Kent mapping to prevent the algorithm from falling into the local optimum; selecting solutions with high fitness and using their characteristics to generate new solutions to improve population diversity; setting a stop condition, and when the fitness improvement reaches a certain threshold, judging whether the algorithm should end.

3. The method for optimizing the scheduling of a microgrid with photovoltaic storage according to claim 2, characterized in that: The expressions for constructing dynamic simulation models include: Net power output model: P net (t) = P PV (t)+P discharge (t)-L(t); Among them, P net (t) represents the net power output at time t, P PV (t) represents photovoltaic power generation, P discharge (t) represents the discharge power of the energy storage system, and L(t) represents the load demand; Energy storage state model: Where, E(t) represents the energy storage state at time t, E(t-1) represents the energy storage state at the previous moment, and η chaege represents the charging efficiency, η discharge Indicates the discharge efficiency, P charge (t) represents the charging power; Electricity price model: P price (t) = P base +P dynamic (t); Among them, P price (t) represents the electricity price at time t, P base represents the basic electricity price, P dynamic (t) represents the dynamic electricity price.

4. The method for optimizing the scheduling of a microgrid with photovoltaic storage according to claim 3 is characterized in that: In step 2, when the optimization goal is to maximize the use of renewable energy, the optimization objective function expression is: The constraint condition of this function is to ensure that the photovoltaic power generation power is within its maximum capacity and satisfies 0≤P PV (t)≤P PV,max ; Minimizing operating costs means reducing the economic costs of electricity generation and storage while ensuring load demand. The optimization objective function expression is: Among them, C charge ,C discharge are the unit costs of charging and discharging, respectively. The constraints of this function are: ensuring that the charging and discharging of the energy storage system is within its maximum and minimum capacity range, and ensuring that the load demand is met, that is, P net (t) ≥ L(t); The relationship between load and power generation is balanced, that is, the power generation capacity is matched with the load demand to achieve stable operation of the system. The optimization objective function expression is: Among them, Z3 represents the difference between load and power generation. The goal is to minimize this difference. The constraint is to ensure that at each moment, the net power output can meet the load demand, that is, P net (t)≥L(t).

5. The method for optimizing scheduling of a microgrid with photovoltaic storage according to claim 4 is characterized in that: In the process of step 3, each solution is represented by a vector, and the scheduling strategies of power generation, energy storage and load are randomly generated within the set range: P PV (t)=rand(0,P PV,max ) P charge (t)=rand(0,P charge,max ) P discharge (t)=rand(0,P discharge,max ) Repeat the above process to generate N different initial solutions X, each of which is a scheduling strategy vector.

6. The method for optimizing scheduling of a microgrid with photovoltaic storage according to claim 5, characterized in that: The fitness value of each solution should reflect the quality of the solution in meeting the optimization goal. Combined with the above three objective functions, the fitness value is calculated, that is, maximizing the goal Z1 and minimizing the goals Z2 and Z3. The expression is: F(i) = λ1Z1-λ2Z2-λ3Z3, where F(i) represents the fitness value of the i-th solution, λ1, λ2, and λ3 are the weight coefficients of each objective function, which are used to adjust the importance of each goal.

7. The method for optimizing the scheduling of a microgrid with photovoltaic power generation and storage according to claim 6, characterized in that: Randomly select a solution X i , apply Kent mapping to transform the selected solution and obtain a new solution X new1 : X new1 =X i +Δ sin(k X i ) Among them, Δ is a random perturbation factor, and k is a tuning parameter used to control the amplitude of the mapping; From the new solution new1 Find the high fitness solution X i The features of the new solution X new2 , that is, from the k solutions with the highest fitness new1 Extract features from the solution and combine them with random perturbations to generate new solutions X new2 , the specific steps are: S301: Feature extraction: record the generation scheduling, energy storage operation and load distribution of the high fitness solution; S302: Combination generation: Generate a new solution X by linear combination of features new2 : S303: Add small random perturbations to the new solution to maintain diversity: X new3 =X new2 +∈·rand(0,1) where ∈ is the perturbation amplitude.

8. A dispatching system including a photovoltaic and energy storage microgrid optimization dispatching method according to claim 7, characterized in that: The system includes: The data acquisition module is responsible for collecting real-time data, including photovoltaic power generation, energy storage status, load demand, meteorological data, and electricity price information; the model building module builds a dynamic simulation model based on the collected data to describe the operating characteristics of each component in the microgrid to ensure the accuracy and real-time performance of the model; The optimization goal setting module specifies the goals of system optimization, including maximizing the use of renewable energy, minimizing operating costs, balancing the relationship between load and power generation, and providing an objective function for the optimization algorithm, as well as the scheduling strategy generation module, which uses the sparrow search algorithm to generate a set of initial solutions representing different scheduling strategies and calculates the fitness value according to the set optimization goals; The search and update mechanism module implements the algorithm's search and update mechanism, including global exploration, selection of high-fitness solutions and generation of new solutions, as well as judgment of fitness improvement, to ensure that the search process is efficient and effective. The scheduling decision module generates the final scheduling strategy based on the optimization results, covering power generation scheduling, energy storage operation and load distribution, to ensure the smooth operation of the microgrid.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for optimizing scheduling of a microgrid containing photovoltaic energy storage are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for optimizing scheduling of a microgrid containing photovoltaic energy storage are implemented as described in any one of claims 1 to 7.

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