CN117748622A - A microgrid multi-state coordinated control method and system - Google Patents

Abstract

The invention relates to the technical field of micro-grids, in particular to a micro-grid polymorphic coordination control method and system. The method comprises the following steps: collecting real-time data of a micro-grid, and determining the structure and the running state of the micro-grid; establishing a micro-grid distributed power source mathematical model; establishing a micro-grid multi-objective optimization scheduling model according to the type of the distributed power supply in the micro-grid; and solving the micro-grid optimization scheduling model to obtain an optimization scheduling strategy in the grid-connected operation of the micro-grid. According to the invention, the real-time data of the micro-grid is collected, the mathematical model of the distributed power supply of the micro-grid is established, the optimal scheduling model is established according to the type of the distributed power supply, and the optimal scheduling strategy in the grid-connected operation of the micro-grid is further obtained.

Description

A microgrid multi-state coordinated control method and system

Technical field

The invention relates to the technical field of microgrid, specifically a microgrid multi-state coordinated control method and system.

Background technique

With the development and progress of social economy, people's demand for electricity continues to grow, setting higher standards for power supply quality and reliability. Distributed power generation technology based on renewable energy has the advantages of low investment cost, flexible power generation method, and environmental compatibility. It is developing rapidly. It mainly includes solar photovoltaic power generation and wind power generation, as well as fuel cell power generation, micro-gas turbine power generation and diesel power generation. Mechanical power generation, etc. Although distributed power generation has outstanding advantages, due to the randomness, volatility, and intermittent characteristics of photovoltaic power generation and wind power generation, problems caused by their integration into the power grid often limit the widespread use of distributed power generation. In order to solve the conflict between large power grids and distributed power sources and give full play to the value and benefits that distributed power generation brings to the power grid and users, microgrids were born. The microgrid can operate in parallel with the main grid, or it can be disconnected from the main grid for island operation when the main grid fails or under other circumstances. Microgrid contains a large number of distributed energy resources, energy storage equipment and communication systems. It combines renewable resources with one or more conventional power sources to generate clean, sustainable, stable and reliable power. It has become an important part of the power system. important part.

Since the microgrid system contains uncontrollable renewable energy sources such as wind power and photovoltaic power generation, its system scheduling and control mechanism is more complex. Therefore, an innovative AC and DC power grid transient, dynamic and stable microgrid control is required. This method is used to solve the scheduling problem of the impact of various power sources on the power grid in the multi-type power supply combination of AC and DC hybrid microgrids, improve the dispatching capabilities of large-scale power grids, and meet the power consumption needs of microgrid load users.

Contents of the invention

In view of the shortcomings of existing methods and the needs of practical applications, in order to solve the scheduling problem of the impact of various power sources on the power grid in the multi-type power supply combination of AC and DC hybrid microgrids, improve the dispatching capabilities of large-scale power grids, and meet the needs of microgrid load users. Power demand, on the one hand, the present invention provides a microgrid multi-state coordinated control method. The method includes the following steps: collecting real-time data of the microgrid, determining the structure and operating status of the microgrid; establishing a mathematical model of microgrid distributed power supply, The distributed power mathematical model includes a solar photovoltaic power generation mathematical model, a wind power generation mathematical model, a micro gas turbine mathematical model, and a fuel cell mathematical model; a microgrid multi-objective optimization dispatch model is established according to the type of distributed power supply in the microgrid; and the solution is The microgrid optimal dispatching model obtains the optimal dispatching strategy when the microgrid is connected to the grid. The invention solves the problem of the impact of various power sources on the power grid in the AC-DC hybrid microgrid multi-type power supply combination, improves the dispatching capability of the large-scale power grid, and meets the power consumption needs of microgrid load users.

Optionally, establishing a microgrid multi-objective optimization dispatch model includes establishing an objective function that minimizes microgrid operating costs, minimizes environmental pollution emission costs, and maximizes additional benefits. The objective function satisfies the following formula:

,

in, is the comprehensive cost,/> is the microgrid operating cost,/> For the cost of environmental pollution emissions,/> For additional benefits,/> ,/> ,/> are the weight coefficients corresponding to the microgrid operating costs, environmental pollution emission costs, and additional benefits respectively; analyze the constraints when the microgrid is connected to the grid. The constraints include power balance constraints, power constraints of each distributed power supply, and the relationship between the microgrid and the main grid. The tie line power constraints of the microgrid are met; under the condition that the constraints are met, a multi-objective optimal dispatch model of the microgrid is established. This invention comprehensively considers the power balance constraints during microgrid operation, the power constraints of various distributed power sources, and the power constraints of the tie line between the microgrid and the main grid. When the above constraints are met, a microgrid optimal dispatch model is constructed, making the model prediction results more accurate. Be accurate and objective.

Optionally, the microgrid multi-state control coordination method also includes: collecting the user's historical power consumption, determining the influencing factors of the user's power consumption; determining the weight coefficient of the influence of different influencing factors on the user's power consumption; according to the weight The coefficients and influencing factors establish a regression equation for predicting the user's power consumption; the user's power consumption prediction value is obtained through the regression equation; and the optimized dispatching strategy is adjusted according to the user's power consumption prediction value. The present invention further determines the influencing factors of the user's electricity consumption by collecting the user's historical electricity consumption, establishes a regression equation based on the influencing factors and the corresponding weight coefficients, and outputs the predicted value of the user's electricity consumption through the regression equation. The user's power consumption forecast value is adjusted to optimize the dispatching strategy to ensure that the user's power demand is met.

Optionally, according to the solar photovoltaic power generation mathematical model, the output power of the photovoltaic cell is expressed as:

,

in, is the output power of the photovoltaic cell, k is the temperature compensation coefficient, S is the solar radiation intensity,/> is the inclination angle of the panel, A is the area of the panel, and eta is the conversion efficiency of the panel. By analyzing the influencing factors of photovoltaic power generation, the present invention establishes a mathematical model of solar photovoltaic power generation and calculates the output power of photovoltaic cells, which is conducive to further establishing an optimal dispatching model of microgrid distributed power sources and improving the ability of microgrid multi-power optimal dispatching.

Optionally, in the mathematical model of wind power generation, the output power of the wind turbine is related to the wind speed, and the output power of the wind turbine satisfies the following formula:

,

in, is the output power of the wind turbine,/> is the spoiler factor compensation coefficient,/> is the fan impeller radius,/> is the wind speed,/> is the air density,/> is the wind energy conversion coefficient. By analyzing the influencing factors of wind power generation, the present invention establishes a mathematical model of wind power generation and calculates the output power of the wind turbine, which is conducive to further establishing an optimal dispatching model of microgrid distributed power sources and improving the ability of microgrid multi-power optimal dispatching.

Optionally, the micro gas turbine mathematical model satisfies the following formula:

,

in, is the output power of the micro gas turbine,/> is the flow rate of gas,/> is the gas pressure,/> is the humidity of gas,/> is the sulfur content of the gas, exp is the exponential function of the base e of the natural logarithm,/> ,/> ,/> is the experience coefficient. By analyzing the influencing factors of micro-combustion gas turbine power generation, the present invention establishes a mathematical model of the micro-gas turbine and calculates the output power of the micro-gas turbine, which is conducive to further establishing an optimal dispatch model of microgrid distributed power sources and improving the optimal dispatch of multiple power sources in microgrids. Ability.

Optionally, the fuel cell mathematical model satisfies the following formula:

,

in, is the output power of the fuel cell,/> is the temperature correction factor of the fuel cell, U is the output voltage of the fuel cell, I is the current through the fuel cell,/> for fuel cell efficiency. The present invention establishes a mathematical model of the fuel cell by analyzing factors affecting the output power of the fuel cell, which is conducive to further establishing an optimal dispatch model of microgrid distributed power sources and improves the ability of the microgrid to optimize multi-power dispatch.

Optionally, the regression equation satisfies the following formula:

,

in, is the predicted value of electricity consumption,/> is the intercept,/> is the weight coefficient of weather factors,/> For weather factors,/> is the weight coefficient of seasonal factors,/> For seasonal factors,/> is the weight coefficient of the time factor,/> for the time factor. This invention analyzes the influencing factors of user's electricity consumption, establishes a regression equation for predicting user's electricity consumption based on weather factors, seasonal factors and time factors and their corresponding weight coefficients, and calculates the predicted value of electricity consumption through the regression equation. Based on the predicted value, the multi-power optimization strategy of the microgrid is further optimized to improve the utilization rate of various distributed power sources in the microgrid.

Optionally, the constraints satisfy the following formula:

,

in, is the total load of the microgrid,/> For distributed power supply/> Power,/> is the power of the tie line between the microgrid and the main grid, and/> Respectively:/> The minimum and maximum output power of a distributed power supply,/> and/> are respectively the minimum value and the maximum value of the tie line power between the microgrid and the main grid. The present invention ensures safe and stable operation of the microgrid under the constraint conditions by setting power balance constraints, power constraints of each distributed power supply, and power constraints of the tie line between the microgrid and the main grid.

In the second aspect, in order to efficiently execute a microgrid multi-state coordinated control method provided by the present invention, the present invention also provides a microgrid multi-state coordinated control system. The system includes a processor, an input device, an output device and a a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to invoke the program instructions, The microgrid multi-state coordinated control method described in the first aspect of the present invention is executed. The microgrid multi-state coordination control system of the present invention has a compact structure and stable performance, can stably execute the microgrid multi-state coordination control method provided by the present invention, and improves the overall applicability and practical application capabilities of the present invention.

Description of drawings

Figure 1 is a flow chart of a multi-state coordinated control method for microgrids provided by an embodiment of the present invention;

Figure 2 is a structural diagram of a microgrid multi-state coordination control system provided by an embodiment of the present invention.

Detailed ways

Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described here are only for illustration and are not intended to limit the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that these specific details need not be employed in order to practice the invention. In other instances, well-known circuits, software or methods have not been described in detail in order to avoid obscuring the present invention.

Throughout this specification, reference to "one embodiment," "an embodiment," "an example," or "an example" means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in the invention. In at least one embodiment. Thus, appearances of the phrases "in one embodiment," "in an embodiment," "one example," or "example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and/or subcombination. Furthermore, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and that the drawings are not necessarily drawn to scale.

In an optional embodiment, please refer to FIG. 1 , which is a flow chart of a microgrid multi-state coordinated control method provided by an embodiment of the present invention. As shown in Figure 1, the microgrid multi-state coordination control method includes the following steps:

S1. Collect real-time data of the microgrid and determine the structure and operating status of the microgrid.

In this embodiment, real-time data of each node in the microgrid is collected by installing a data collection device in the microgrid. The collected data includes voltage, current, power and load capacity of the microgrid. The data collection device includes a voltage sensor, The current sensor and power sensor collect the voltage, current and power of each node in the microgrid through the data acquisition device, and analyze the operating status of the microgrid at this time.

Specifically, the operating states of microgrids include transient, dynamic and steady states. Transient refers to the instantaneous process with large changes in the power system. In microgrids, transient states may occur when a system fails, a large load is suddenly connected or disconnected, or energy resources suddenly change. During the transient process, power parameters such as voltage, current, and power may fluctuate and change instantaneously, and the system needs time to re-establish steady-state operation. Dynamics refers to the process of changes in the power system over time. In microgrids, dynamic processes usually involve changes in energy resources, load changes, battery charging and discharging, etc. During the dynamic process, the system will make corresponding adjustments and responses based on external inputs and internal control signals to maintain stable operation. Steady state refers to a situation in which a power system operates in a relatively stable operating state. In a microgrid, various parameters of the power system (such as voltage, current, power, etc.) will maintain stable values in the steady state, and the energy generation and consumption in the system are basically balanced. This invention combines artificial intelligence learning technology to calculate and analyze the transient, dynamic and stable states of AC and DC microgrids and form intelligent control. At present, domestic power generation systems are mainly based on KW-level power generation systems, and there is a lack of MW-level large-scale and multiple types of distributed power projects. The multi-state coordinated control method of microgrids provided by the present invention is stable and reliable, and solves the problem of AC and DC hybrid microgrids. The impact of various power sources on the power grid in multi-type power combinations is a dispatch problem.

The present invention provides a transient, dynamic and steady-state control method for AC and DC microgrids based on MW-level artificial intelligence, which also includes equivalent processing of the microgrids. The equivalent process of microgrid refers to setting up the microgrid so that it can maintain its original characteristics and functions after being connected to the main network when connecting the microgrid to the main network. The equivalent process of microgrid includes the following steps:

Establish electrical connection: Electrically connect the microgrid to the 35kV main grid. This process can be achieved using cables, switching devices and connectors.

Calculate the short-circuit current: For equivalent distributed generators, it is necessary to calculate the short-circuit current supported by the main grid to ensure that it is the same as the original network. This process can be achieved by calculating the power generation capacity and electrical parameters of each power supply unit in the microgrid and then applying the short-circuit current calculation method to obtain the results.

Control voltage and power: In order to keep the microgrid operating synchronously with the main network, the voltage and power of the microgrid need to be controlled during the equalization process. Voltage and power controllers can be used to regulate microgrid voltage and power to keep it in sync with the backbone.

Monitoring and protection: During the equalization process, the communication connection between the microgrid and the backbone network needs to be monitored. This process can be achieved by installing sensors and monitoring equipment and using monitoring and protection systems. The monitoring system can monitor the current, voltage, power and other parameters between the microgrid and the backbone network in real time.

Simulation and verification: After the equivalence process is completed, simulation and verification are performed to ensure the stability and reliability of the connection between the microgrid and the backbone network. Use simulation software or experimental equipment to simulate the electrical characteristics and verify the performance of the equivalent system.

Through the above steps, the microgrid can retain the 35kV backbone grid, important DC lines and the communication connections between them during the equivalent process, and ensure that the short-circuit current supported by the equivalent distributed generators on the backbone grid is the same as the original network. And there is no need to unravel the electromagnetic ring network between the 35kV, 10kV, and 04KV voltage levels during the equivalent process. The equivalent system can be used to calculate the interaction characteristics between large-scale AC and DC systems in the electromagnetic transient control platform. , this dynamic equivalent method well retains the dynamic characteristics of large-scale AC and DC systems. Furthermore, the microgrid and the backbone network can be effectively connected and run together to provide users with reliable and efficient power supply.

S2. Establish a mathematical model of microgrid distributed power supply. The distributed power mathematical model includes a mathematical model of solar photovoltaic power generation, a mathematical model of wind power generation, a mathematical model of micro gas turbine, and a mathematical model of fuel cell.

In this embodiment, matlab model simulation design is used to realize multi-power supply multi-state simulation function. Compared with the traditional sub-item power supply simulation design, this model simulation design is suitable for multiple types of power supplies, and the operating efficiency is significantly improved. This model simulation design The method can also be applied to the research field of multi-type energy characteristics of uninhabited islands.

Furthermore, implementing multi-power multi-state simulation function based on matlab simulation includes the following steps:

Determine the basic structure of the simulation system: identify multiple power supplies and other related components in the system, and establish the connection relationships between them. Power sources involved include photovoltaic cells, wind turbines, micro gas turbines and fuel cells.

Build a power supply model: Design a corresponding mathematical model for each power supply to represent its electrical characteristics.

Specifically, in this embodiment, according to the mathematical model of solar photovoltaic power generation, the output power of photovoltaic cells is mainly related to factors such as temperature, light intensity, etc. The output power of photovoltaic cells is expressed as:

,

in, is the output power of the photovoltaic cell, k is the temperature compensation coefficient, S is the solar radiation intensity,/> is the inclination angle of the panel, A is the area of the panel, and eta is the conversion efficiency of the panel.

Specifically, in this embodiment, according to the mathematical model of wind power generation, the output power of the wind turbine is related to the wind speed, and the output power of the wind turbine satisfies the following formula:

,

in, is the output power of the wind turbine,/> is the spoiler factor compensation coefficient,/> is the fan impeller radius,/> is the wind speed,/> is the air density,/> is the wind energy conversion coefficient.

Specifically, in this embodiment, the micro gas turbine mathematical model satisfies the following formula:

,

in, is the output power of the micro gas turbine,/> is the flow rate of gas,/> is the gas pressure,/> is the humidity of gas,/> is the sulfur content of the gas, exp is the exponential function of the base e of the natural logarithm,/> ,/> ,/> is the experience coefficient.

Specifically, in this embodiment, the fuel cell mathematical model satisfies the following formula:

,

in, is the output power of the fuel cell,/> is the temperature correction factor of the fuel cell, U is the output voltage of the fuel cell, I is the current through the fuel cell,/> for fuel cell efficiency.

Design multi-state control strategies: Multi-power systems require reasonable control strategies to manage switching and power distribution between different power supplies. Through the control algorithm, the power supply is dynamically selected under different situations to achieve the optimization of power distribution.

Build a simulation model: Build a simulation model in MATLAB, use the Simulink toolbox to build circuit and system models, and integrate the required power supply model, control strategy and other related components. By connecting each module and setting simulation parameters, a complete multi-power multi-state simulation model is constructed.

Furthermore, by running the built simulation model, you can analyze the status of the power supply, power output, and adjust the control strategy through the output signals and simulation results, and optimize and improve as needed.

S3. Establish a microgrid multi-objective optimization dispatch model according to the types of distributed power sources in the microgrid.

Specifically, in this embodiment, according to the needs and optimization goals of the microgrid, combined with different types of distributed power sources included in the microgrid, the objective function is determined, and further, a multi-objective optimization dispatch model of the microgrid is established. The microgrid multi-objective optimization dispatch model includes:

S31. Establish an objective function that minimizes the operating cost of the microgrid, minimizes the cost of environmental pollution emissions, and maximizes additional benefits. The objective function satisfies the following formula:

,

in, is the comprehensive cost,/> is the microgrid operating cost,/> is the cost of environmental pollution emissions,/> For additional benefits,/> ,/> ,/> They are the weight coefficients corresponding to the microgrid operating costs, environmental pollution emission costs, and additional benefits respectively.

The present invention aims at minimizing the operating cost of the microgrid, minimizing the cost of environmental pollution emissions, and maximizing additional benefits, and assigns different operating costs, environmental pollution emission costs, and additional benefits to the microgrid based on their impact on the comprehensive cost of operating the microgrid. The weight coefficient achieves the optimization of the comprehensive cost of microgrid operation.

S32. Analyze the constraints when the microgrid is connected to the grid. The constraints include power balance constraints, power constraints of each distributed power supply, and tie line power constraints between the microgrid and the main grid.

Further, in this embodiment, the constraints satisfy the following formula:

,

in, is the total load of the microgrid,/> For distributed power supply/> Power,/> is the power of the tie line between the microgrid and the main grid, and/> Respectively:/> The minimum and maximum output power of a distributed power supply,/> and/> are respectively the minimum value and the maximum value of the tie line power between the microgrid and the main grid.

This invention comprehensively considers the power balance conditions during the operation of the microgrid, the limiting conditions of the output power of each distributed power source, and the limiting conditions of the power of the tie line between the microgrid and the main grid. By setting the power balance constraints, the power constraints of each distributed power source and the microgrid, The power constraints of the tie line between the power grid and the main grid ensure the safe and stable operation of the microgrid under the constraints.

S33. Under the condition that the constraints are met, establish the microgrid multi-objective optimization dispatch model.

Specifically, the selection weights of different distributed power sources in the microgrid are determined according to the microgrid status and user needs. According to factors such as the type, cost, reliability, and environmental conditions of distributed power sources, different distributed power sources are assigned corresponding weight values, and the weighted summation result is used as an evaluation index for their contribution to the microgrid. It should be understood that the influencing factors of the weight value of distributed power sources in the microgrid can be adjusted according to the actual situation.

Further, a dynamic power source selection rule is designed to select distributed power sources in the microgrid based on the microgrid status and the weight value of the distributed power sources. The specific rules are as follows:

According to the output power of the distributed power sources included in the microgrid, the available power of each distributed power source is calculated, and the weight value of the distributed power source is multiplied by the available power to obtain the upper limit of the output of the distributed power source during the operation of the microgrid. If the upper limit cannot meet the power demand of current microgrid load users, other distributed power sources will be selected as replacements. If several distributed power sources cannot alone meet the power demand of current microgrid load users, multiple distributed power sources will be used. The distributed power supply combination operates to meet the power consumption needs of microgrid load users.

Furthermore, during the operation of the microgrid, the selection of distributed power sources can be updated in a timely manner according to changes in the status of the microgrid. The selection of distributed power sources can be re-evaluated and updated periodically or based on event triggers.

S4. Solve the microgrid optimal dispatch model to obtain the optimal dispatch strategy when the microgrid is connected to the grid.

Specifically, in this embodiment, according to the established microgrid optimal dispatching model, an optimized dispatching strategy is obtained when the microgrid and the main grid are connected to the grid. According to this strategy, the rational distribution of various distributed power sources in the microgrid can be achieved, the rational utilization of energy can be achieved, the operating costs of the microgrid can be reduced, the pollution caused to the environment can be increased, the additional income of the microgrid operation can be increased, and the microgrid can be improved. safety and reliability. Furthermore, the control strategy is continuously adjusted based on the actual operation of the microgrid. Monitor the power fluctuations of various distributed power sources in the microgrid and changes in power demand of microgrid load users, and rationally allocate various distributed power sources in the microgrid based on feedback information to ensure the stable operation of the microgrid.

In another or some optional embodiments, in order to adjust the optimal dispatching strategy to meet the power consumption needs of the microgrid load users and improve the operating efficiency and reliability of the microgrid, as shown in Figure 1 , the microgrid multi-state coordinated control method also includes the following steps:

S5. Collect the user's historical electricity consumption and determine the factors affecting the user's electricity consumption.

Specifically, in this embodiment, in order to accurately measure the user's electricity consumption, an electric energy meter or a smart electric meter needs to be installed to record actual electricity consumption data. Electric energy meters and smart meters can provide microgrid load users with accurate power consumption data in various periods. Record the readings of electric energy meters or smart meters regularly, which can be done daily, weekly or monthly. The recording period can be set according to the actual situation. The user's environmental information is monitored through the sensor network, and the information processing platform is used to analyze the environmental information to determine the influencing factors of the user's electricity consumption.

S6. Determine the weight coefficients of different influencing factors on the user's electricity consumption.

Specifically, in this embodiment, different weighting coefficients are assigned to different influencing factors according to their degree of impact on the user's power consumption. This weight coefficient can be analyzed and processed through machine learning algorithms, neural network models, etc., by combining the input historical electricity consumption data with its corresponding weather, season, and time factors. Further, it can be concluded that the impact of weather, season, and time factors on user electricity consumption The degree of influence of the quantity is output, and the corresponding weight coefficient is output.

S7. Establish a regression equation for predicting the user's electricity consumption based on the weight coefficient and influencing factors.

Specifically, in this embodiment, a regression equation for predicting user electricity consumption is established based on weather factors, seasonal factors, and time factors and their corresponding weight coefficients. The regression equation satisfies the following formula:

,

in, is the predicted value of electricity consumption,/> is the intercept,/> is the weight coefficient of weather factors,/> For weather factors,/> is the weight coefficient of seasonal factors,/> For seasonal factors,/> is the weight coefficient of the time factor,/> for the time factor.

S8. Obtain the predicted value of the user's power consumption through the regression equation.

Specifically, in this embodiment, the user's power consumption prediction value is calculated through the regression equation to more accurately predict the future power consumption of the microgrid load user. The regression equation can be used to test the regression through residual analysis. The fitting effect of the equation can be used to obtain the difference between the actual measured value and the predicted value. Furthermore, according to the feedback value of the residual analysis, the weight coefficients of the influencing factors in the regression equation are adjusted so that the regression equation has a better fit. The combined effect is to output more accurate power consumption prediction values.

Compared with the original user power consumption data prediction method, the present invention innovatively collects user power consumption, comprehensively considers multiple factors that affect user power consumption, and fits a regression equation based on the influencing factors of user power consumption. , further outputting more accurate prediction values, this method can also be applied in the field of smart power online monitoring of microgrids.

S9: Adjust the optimized scheduling strategy according to the predicted value of user power consumption.

Specifically, in this embodiment, the optimized dispatching strategy is further adjusted based on the predicted value, and each distributed power source in the microgrid is reasonably allocated according to its output upper limit, so as to improve the utilization rate of each distributed power source in the microgrid. , to meet the power consumption needs of the microgrid load users and improve the operating efficiency and reliability of the microgrid.

Please refer to Figure 2. In an optional embodiment, in order to efficiently execute a microgrid multi-state coordinated control method provided by the present invention, the present invention also provides a microgrid multi-state coordinated control system. The system includes a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, the The processor is configured to call the program instructions to execute specific steps of relevant embodiments of the microgrid multi-state coordinated control method provided by the present invention. The microgrid multi-state coordination control system of the present invention has a complete structure, is objectively stable, can efficiently execute the microgrid multi-state coordination control method of the present invention, and improves the overall applicability and practical application capabilities of the present invention.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, but not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features can be equivalently replaced; and these modifications or substitutions do not deviate from the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention. scope, they should be covered by the claims and the scope of the description of the present invention.

Claims (10)

1. A micro-grid multi-state coordination control method is characterized by comprising the following steps:

collecting real-time data of a micro-grid, and determining the structure and the running state of the micro-grid;

establishing a micro-grid distributed power source mathematical model, wherein the distributed power source mathematical model comprises a solar photovoltaic power generation mathematical model, a wind power generation mathematical model, a micro gas turbine mathematical model and a fuel cell mathematical model;

establishing a micro-grid multi-objective optimization scheduling model according to the type of the distributed power supply in the micro-grid;

and solving the micro-grid optimization scheduling model to obtain an optimization scheduling strategy in the grid-connected operation of the micro-grid.

2. The method for coordinated control of multiple states of a micro-grid according to claim 1, wherein the establishing a multi-objective optimal scheduling model of the micro-grid comprises:

establishing an objective function with minimum running cost, minimum environmental pollution emission cost and maximum additional benefit of the micro-grid, wherein the objective function meets the following formula:

，

wherein,for the comprehensive cost->For the running cost of the micro-grid, < >>For environmental pollution emission cost, < >>For additional benefit(s)>、/>、The weight coefficients corresponding to the running cost, the environmental pollution emission cost and the additional benefit of the micro-grid are respectively obtained;

analyzing constraint conditions of the micro-grid during grid-connected operation, wherein the constraint conditions comprise power balance constraint, power constraint of each distributed power supply and tie line power constraint of the micro-grid and a main network;

and under the condition that the constraint condition is met, establishing the micro-grid multi-objective optimization scheduling model.

3. The micro-grid multi-state coordination control method according to claim 1, characterized in that the micro-grid multi-state coordination control method further comprises:

collecting historical electricity consumption of a user, and determining influence factors of the electricity consumption of the user;

determining weight coefficients of influences of different influencing factors on the electricity consumption of the user;

establishing a regression equation for predicting the electricity consumption of the user according to the weight coefficient and the influence factors;

obtaining a predicted value of the electricity consumption of the user through the regression equation;

and adjusting the optimal scheduling strategy according to the predicted value of the electricity consumption of the user.

4. The micro-grid multi-state coordination control method according to claim 1, wherein the output power of the photovoltaic cell is expressed as:

，

wherein,for the output power of the photovoltaic cell, k is the temperature compensation coefficient, S is the solar radiation intensity, +.>The inclination angle of the battery plate is that A is the area of the battery plate, and eta is the conversion efficiency of the battery plate.

5. The micro grid multi-state coordination control method according to claim 1, wherein the wind power generation mathematical model is that the output power of a wind power generator is related to the wind speed, and the output power of the wind power generator satisfies the following formula:

，

wherein,for the output power of the wind power generator, < > for>Compensating the coefficient for the turbulence factor->Is the radius of the fan impeller>For wind speed>For air density->Is the wind energy conversion coefficient.

6. The micro grid multi-state coordination control method according to claim 1, wherein the mathematical model of the micro gas turbine satisfies the following formula:

，

wherein,for the output of the micro gas turbine, +.>For the flow of gas, ">For the pressure of the fuel gas, ">Humidity of fuel gas, ">For the sulfur content of the gas, exp is an exponential function of the base e of natural logarithm, ++>、/>、/>Is an empirical coefficient.

7. The micro grid multi-state coordination control method according to claim 1, wherein the mathematical model of the fuel cell satisfies the following formula:

，

wherein,for the output power of the fuel cell, +.>U is the output voltage of the fuel cell, I is the current through the fuel cell, which is the temperature correction factor of the fuel cell, +.>Is the efficiency of the fuel cell.

8. The micro grid multi-state coordination control method according to claim 3, wherein the regression equation satisfies the following formula:

，

wherein,for the predicted value of electricity consumption, < >>For the intercept->Weight coefficient for weather factor, +.>For weather factors, ->Weight coefficient for seasonal factors, +.>For seasonal factors, ->Weight coefficient for time factor, +.>Is a time factor.

9. The micro grid multi-state coordination control method according to claim 2, wherein the constraint condition satisfies the following formula:

，

wherein,for the total load of the microgrid, < >>For distributed power supply->Power (I)>For the link power of the micro-grid and the main grid, < >>And->Respectively +.>Minimum and maximum value of output power of individual distributed power supply, +.>And->The minimum and maximum values of the link power of the micro-grid and the main network are respectively.

10. A micro grid polymorphism coordination control system, characterized in that the system comprises a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is for storing a computer program comprising program instructions, the processor being configured to invoke the program instructions for executing the micro grid polymorphism coordination control method as claimed in any one of claims 1-9.