CN117748622A - Micro-grid polymorphic coordination control method and system - Google Patents
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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
Technical Field
The invention relates to the technical field of micro-grids, in particular to a micro-grid polymorphic coordination control method and system.
Background
With the development and progress of social economy, the demand of people for electric power is continuously increasing, and higher standards are put forward for power supply quality and reliability. The distributed power generation technology based on renewable energy sources has the advantages of low investment cost, flexible power generation mode, environment compatibility and the like, and is rapid in development, and mainly comprises solar photovoltaic power generation and wind power generation, fuel cell power generation, micro-fuel engine power generation, diesel generator power generation and the like. Distributed generation, although having outstanding advantages, is often limited to the widespread use of distributed generation due to the random, fluctuating, intermittent nature of photovoltaic and wind power generation, and the problems caused by its incorporation into the grid. In order to solve the conflict between the large-scale power grid and the distributed power supply, the value and benefits brought by the distributed power generation to the power grid and users are fully exerted, and the micro-power grid is generated. The micro-grid can be in grid-connected operation with the main grid, and can be disconnected with the main grid to perform island operation under the condition that the main grid fails or other conditions. Micro-grids contain a large number of distributed energy sources, energy storage devices, and communication systems, combining renewable resources with one or more conventional power sources to produce clean, sustainable, stable, reliable power have become an important component in power systems.
Because the micro-grid system comprises uncontrollable renewable energy sources such as wind power generation, photovoltaic power generation and the like, the system scheduling and control mechanism is more complex, and therefore, an innovative AC/DC grid transient state, dynamic and steady state micro-grid control method is needed to solve the problem that various power sources in the AC/DC hybrid micro-grid multi-type power source combination affect the scheduling of the power grid, improve the capacity of large-scale power source power grid scheduling and meet the power consumption requirement of micro-grid load users.
Disclosure of Invention
Aiming at the defects of the existing method and the requirements of practical application, in order to solve the problem that various power supplies in the multi-type power supply combination of the AC/DC hybrid micro-grid affect the power grid to be scheduled, the capacity of large-scale power supply power grid scheduling is improved, and the power consumption requirement of micro-grid load users is met, on one hand, the invention provides a micro-grid multi-state coordination control method, which 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, 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. The invention solves the problem that various power supplies in the AC/DC hybrid micro-grid multi-type power supply combination affect the dispatching of the power grid, improves the dispatching capacity of the large-scale power supply grid, and meets the power consumption requirement of micro-grid load users.
Optionally, the establishing the micro-grid multi-objective optimization scheduling model includes: 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. The invention comprehensively considers the power balance constraint during the running of the micro-grid, each distributed power constraint and the tie line power constraint of the micro-grid and the main network, and builds the micro-grid optimal scheduling model when meeting the constraint conditions, so that the model prediction result is more accurate and objective.
Optionally, the micro-grid multi-state control coordination method further includes: 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. According to the invention, the influence factors of the user electricity consumption are further determined by collecting the historical electricity consumption of the user, a regression equation is established according to the influence factors and the corresponding weight coefficients, the predicted value of the user electricity consumption is output through the regression equation, and the optimal scheduling strategy is adjusted according to the outputted predicted value of the user electricity consumption, so that the electricity consumption requirement of the user is ensured to be met.
Optionally, the mathematical model of solar photovoltaic power generation, 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. According to the invention, the influence factors of photovoltaic power generation are analyzed, the mathematical model of solar photovoltaic power generation is established, the output power of the photovoltaic cell is calculated, the optimal scheduling model of the distributed power supply of the micro-grid is further established, and the multi-power-supply optimal scheduling capability of the micro-grid is improved.
Optionally, the wind power generation mathematical model is that the output power of the 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. According to the invention, the influence factors of wind power generation are analyzed, a wind power generation mathematical model is established, the output power of the wind power generator is calculated, and the optimization adjustment of the micro-grid distributed power supply is facilitated to be further establishedAnd the degree model improves the capacity of the optimal scheduling of the multiple power supplies of the micro-grid.
Optionally, 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. According to the invention, the mathematical model of the micro gas turbine is established by analyzing the influence factors of the micro gas turbine in power generation, and the output power of the micro gas turbine is calculated, so that the optimal scheduling model of the distributed power supply of the micro power grid is further established, and the multi-power-supply optimal scheduling capability of the micro power grid is improved.
Optionally, 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. According to the invention, the mathematical model of the fuel cell is established by analyzing the influence factors of the output power of the fuel cell, so that the optimal scheduling model of the distributed power supply of the micro-grid is further established, and the multi-power-supply optimal scheduling capability of the micro-grid is improved.
Optionally, 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, ->Is time ofWeight coefficient of factor->Is a time factor. According to the method, the influence factors of the electricity consumption of the user are analyzed, a regression equation for predicting the electricity consumption of the user is established according to the weather factors, the seasonal factors and the time factors and the corresponding weight coefficients, the electricity consumption predicted value is calculated through the regression equation, the multi-power-supply optimizing strategy of the micro-grid is further optimized according to the predicted value, and the utilization rate of each distributed power supply in the micro-grid is improved.
Optionally, the constraint satisfies the following formula:
，
wherein,for the total load of the microgrid, < >>For distributed power supply->Power (I)>For the link power of the microgrid and the main network,and->Respectively +.>Minimum and maximum value of output power of individual distributed power supply, +.>And->Respectively isMinimum and maximum values of link power of the microgrid to the main network. The invention ensures safe and stable operation of the micro-grid under the constraint conditions by setting the power balance constraint, the power constraint of each distributed power supply and the tie line power constraint of the micro-grid and the main network.
In a second aspect, in order to efficiently execute the micro-grid polymorphism coordination control method provided by the present invention, the present invention further provides a micro-grid polymorphism coordination control system, where the system includes a processor, an input device, an output device, and a memory, and the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to invoke the program instructions to execute the micro-grid polymorphism coordination control method according to the first aspect of the present invention. The micro-grid multi-state coordination control system has compact structure and stable performance, and can stably execute the micro-grid multi-state coordination control method provided by the invention, thereby improving the overall applicability and practical application capability of the invention.
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FIG. 1 is a flowchart of a method for coordinated control of a micro-grid polymorphism according to an embodiment of the present invention;
fig. 2 is a structural diagram of a micro-grid multi-state coordination control system provided by an embodiment of the invention.
Detailed Description
Specific embodiments of the invention will be described in detail below, it being noted that the embodiments described herein are for illustration only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been described in detail in order not to obscure the invention.
Throughout the specification, references to "one embodiment," "an embodiment," "one example," or "an example" mean: a particular feature, structure, or characteristic described in connection with the embodiment or example is included within at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "one example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale.
In an alternative embodiment, please refer to fig. 1, fig. 1 is a flowchart of a method for coordinated control of micro-grid polymorphisms according to an embodiment of the present invention. As shown in fig. 1, the micro-grid multi-state coordination control method comprises the following steps:
s1, collecting real-time data of the micro-grid, and determining the structure and the running state of the micro-grid.
In this embodiment, real-time data of each node in the micro-grid is collected by installing a data collection device in the micro-grid, the collected data include voltage, current, power and load electric quantity of the micro-grid, the data collection device includes a voltage sensor, a current sensor and a power sensor, and the voltage, current and power of each node in the micro-grid are collected by the data collection device, and the running state of the micro-grid at the moment is obtained through analysis.
In particular, the operating states of the microgrid include transient, dynamic and steady states. Transient refers to transient processes in which the power system is greatly fluctuating. In the micro-grid, a transient may occur in the case of a system failure, abrupt switching in or off of a heavy load, abrupt change of energy resources, and the like. During transients, power parameters such as voltage, current, power, etc. may fluctuate and change instantaneously, requiring time for the system to reestablish steady state operation. Dynamic refers to the process of the power system changing over time. In micro-grids, dynamic processes typically involve changes in energy resources, load changes, battery charging and discharging, etc. In the dynamic process, the system can make corresponding adjustment and response according to the external input and the internal control signal so as to keep stable operation. Steady state refers to the situation where the power system is running in a relatively stable operating state. In a micro grid, various parameters of the power system (such as voltage, current, power, etc.) in a steady state will maintain stable values, and the energy generation and consumption in the system will be substantially balanced. According to the invention, by combining with an artificial intelligent learning technology, the transient state, dynamic state and stable state of the AC/DC micro-grid are calculated and analyzed, and intelligent control is formed. At present, the domestic system mainly uses KW level power generation system, which lacks MW level large-scale and multi-type distributed power projects, and the micro-grid polymorphic coordination control method provided by the invention is stable and reliable, and solves the problem that various power sources in the AC/DC hybrid micro-grid multi-type power combination affect the power grid to schedule.
The invention provides a transient, dynamic and steady state control method based on MW-level artificial intelligence AC/DC micro-grid, which further comprises the step of carrying out equivalent treatment on the micro-grid. The equivalence process of the micro-grid refers to that when the micro-grid is connected with the main network, the micro-grid can keep the original characteristics and functions after being connected with the main network through arrangement. The equivalence process of the micro-grid comprises the following steps:
establishing an electrical connection: and electrically connecting the micro-grid with a 35kV main grid frame. This process may be accomplished through cables, switching devices, and connectors.
Calculating short circuit current: for the equivalent distributed generator, the short-circuit current of the equivalent distributed generator under the support of the main net rack needs to be calculated, so that the equivalent distributed generator is ensured to be identical to the original network. The process can obtain the result by calculating the power generation capacity and the electrical parameters of each power supply unit in the micro-grid and then applying a short-circuit current calculation method.
Control voltage and power: in order to keep the micro-grid and the backbone network running synchronously, the voltage and power of the micro-grid need to be controlled in the equivalence process. Voltage and power controllers may be used to achieve regulation of the voltage and power of the microgrid to keep it synchronized with the backbone network.
Monitoring and protecting: in the equivalence process, the communication between the micro-grid and the backbone network needs to be monitored. This process can be accomplished by installing sensors and monitoring equipment and using a monitoring and protection system. The monitoring system can monitor parameters such as current, voltage, power and the like between the micro-grid and the backbone network in real time.
Simulation and verification: after the equivalence process is finished, simulation and verification are carried out, and stability and reliability of connection of the micro-grid and the backbone network are ensured. And (5) performing electrical characteristic simulation and performance verification on the equivalent system through simulation software or experimental equipment.
Through the steps, the micro-grid can keep 35kV main grid frame, important direct current lines and alternating current connection among the main grid frame and the important direct current lines in the equivalence process, the short circuit current of the equivalent distributed generator on the main grid frame is guaranteed to be identical to that of the original network, electromagnetic looped networks between 35kV, 10kV and 04KV voltage levels do not need to be untied in the equivalence process, the system after the equivalence can be used for calculating interaction characteristics among large-scale alternating current and direct current systems in an electromagnetic transient control platform, the dynamic equivalence method well keeps dynamic characteristics of the large-scale alternating current and direct current systems, and further, the micro-grid and the main grid can be effectively connected and jointly operated, so that reliable and efficient power supply is provided for users.
S2, 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.
In the embodiment, the matlab model simulation design is adopted to realize the multi-power supply multi-state simulation function, compared with the traditional split power supply simulation design, the model simulation design is suitable for multi-type power supplies, the operation efficiency is remarkably improved, and the model simulation design method can be further applied to the field of multi-type energy characteristic research of unmanned islands.
Further, the matlab simulation based multi-power supply multi-state simulation function comprises the following steps:
determining the basic structure of a simulation system: a plurality of power supplies and other related components in the system are determined and a connection relationship is established therebetween. The power sources involved include photovoltaic cells, wind generators, micro gas turbines, and fuel cells.
Establishing a power supply model: a corresponding mathematical model is designed for each power supply to represent its electrical characteristics.
Specifically, in this embodiment, in the mathematical model of solar photovoltaic power generation, the output power of the photovoltaic cell is mainly related to factors such as temperature, illumination intensity, and the like, and 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.
Specifically, in the present embodiment, in the wind power generation mathematical model, the output power of the 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.
Specifically, in the present embodiment, 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.
Specifically, in the present embodiment, 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.
Designing a polymorphic control strategy: multi-power systems require a reasonable control strategy to manage switching and power distribution between different power sources. And the power supply is dynamically selected under different conditions through a control algorithm, so that the power distribution is optimized.
Building a simulation model: a simulation model is built in matlab, a circuit and system model is built by using a Simulink toolbox, and a power supply model, a control strategy and other relevant components required by integration are integrated. And constructing a complete multi-power-supply multi-state simulation model by connecting all modules and setting simulation parameters.
Further, the built simulation model is operated, the state, power output, adjustment of control strategy and the like of the power supply can be analyzed through output signals and simulation results, and optimization and improvement are carried out according to requirements.
And S3, establishing a micro-grid multi-objective optimization scheduling model according to the type of the distributed power supply in the micro-grid.
Specifically, in this embodiment, according to requirements and optimization objectives of the micro-grid, in combination with different types of distributed power sources included in the micro-grid, an objective function is determined, and further, a micro-grid multi-objective optimization scheduling model is established, where the establishing the micro-grid multi-objective optimization scheduling model includes:
s31, 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 of the micro-grid, the environmental pollution emission cost and the additional benefit are respectively obtained.
The invention aims at minimizing the running cost of the micro-grid, minimizing the environmental pollution emission cost and maximizing the additional benefit, and gives different weight coefficients to the running cost, the environmental pollution emission cost and the additional benefit of the micro-grid according to the influence degree of the running cost, the environmental pollution emission cost and the additional benefit of the micro-grid on the running comprehensive cost of the micro-grid, thereby optimizing the running comprehensive cost of the micro-grid.
S32, constraint conditions during grid-connected operation of the micro-grid are analyzed, 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 the main network.
Further, in the present embodiment, 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 microgrid and the main network,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.
The invention comprehensively considers the power balance condition, the limiting condition of the output power of each distributed power supply and the limiting condition of the interconnection line power of the micro power grid and the main network when the micro power grid operates, and ensures the safe and stable operation of the micro power grid under the constraint condition by setting the power balance constraint, the power constraint of each distributed power supply and the interconnection line power constraint of the micro power grid and the main network.
And S33, under the condition that the constraint condition is met, establishing the micro-grid multi-objective optimization scheduling model.
Specifically, selection weights of different distributed power supplies in the micro-grid are determined according to the micro-grid state and the user requirements. And giving weight values corresponding to different distributed power supplies according to factors such as the type, cost, reliability, environmental conditions and the like of the distributed power supplies, and taking the weighted summation result as an evaluation index of contribution degree of the weighted summation result in the micro-grid. It should be understood that the influence factors of the distributed power weight values in the micro-grid can be adjusted according to actual situations.
Further, dynamic power selection rules are designed to select distributed power sources in the microgrid based on the microgrid state and weight values of the distributed power sources. The specific rules are as follows:
calculating available electric quantity of each distributed power supply according to output power of the distributed power supply contained in the micro-grid, multiplying a weight value of the distributed power supply by the available electric quantity to obtain an upper limit of output of the distributed power supply in the running process of the micro-grid, selecting other distributed power supplies to replace if the upper limit cannot meet the electric quantity demand of a current micro-grid load user, and running in a mode of combining multiple distributed power supplies to meet the electric quantity demand of the micro-grid load user if all the distributed power supplies cannot meet the electric quantity demand of the current micro-grid load user independently.
Further, during operation of the micro-grid, the selection of the distributed power sources is updated in time according to the change of the micro-grid state, and the selection of the distributed power sources can be re-evaluated and updated periodically or according to event triggering.
And S4, solving the micro-grid optimization scheduling model to obtain an optimization scheduling strategy in grid-connected operation of the micro-grid.
Specifically, in this embodiment, according to the established optimal scheduling model of the micro grid, an optimal scheduling policy during grid-connected operation of the micro grid and the main grid is obtained. According to the strategy, reasonable distribution of various distributed power supplies in the micro-grid is realized, reasonable utilization of energy is realized, the running cost of the micro-grid is reduced, pollution to the environment is reduced, extra income of running of the micro-grid is increased, and the safety and reliability of the micro-grid are improved. Furthermore, the control strategy is continuously adjusted according to the actual running condition of the micro-grid. And monitoring the power fluctuation of each distributed power supply in the micro-grid and the change condition of the power consumption requirement of the load user of the micro-grid, and reasonably distributing each distributed power supply in the micro-grid according to the feedback information to ensure the stable operation of the micro-grid.
In still another or some alternative embodiments, in order to adjust the optimal scheduling policy, meet the power consumption requirement of the load user of the micro-grid, and improve the operation efficiency and reliability of the micro-grid, as shown in fig. 1, the micro-grid polymorphic coordination control method further includes the following steps:
s5, collecting historical electricity consumption of the user, and determining influence factors of the electricity consumption of the user.
Specifically, in this embodiment, in order to accurately measure the electricity consumption of the user, an electric energy meter or a smart meter needs to be installed to record actual electricity consumption data. The electric energy meter and the intelligent electric meter can provide accurate electricity utilization data of each period of the micro-grid load user. The reading of the electric energy meter or the intelligent electric meter is recorded periodically, which can be carried out once a day, a week or a month, and the recording period can be set according to actual conditions. And monitoring the user environment information through a sensor network, analyzing the environment information by utilizing an information processing platform, and determining the influence factors of the user electricity consumption.
S6, determining weight coefficients of influences of different influence factors on the electricity consumption of the user.
Specifically, in this embodiment, different weight coefficients are given to the user power consumption according to the influence degrees of different influence factors. The weight coefficient can analyze and process the input historical power consumption data through a machine learning algorithm, a neural network model and the like in combination with the weather, season and time factors corresponding to the historical power consumption data, further obtain the influence degree of the weather, season and time factors on the power consumption of the user, and output the corresponding weight coefficient.
And S7, establishing a regression equation for predicting the electricity consumption of the user according to the weight coefficient and the influence factors.
Specifically, in this embodiment, a regression equation for predicting the electricity consumption of the user is established according to weather factors, seasonal factors, time factors, and their corresponding weight coefficients, where 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.
S8, obtaining a predicted value of the power consumption of the user through the regression equation.
Specifically, in this embodiment, a predicted value of the electricity consumption of the user is calculated through the regression equation, so that future electricity consumption of the micro-grid load user can be predicted more accurately, the regression equation can be used for checking the fitting effect of the regression equation through residual analysis, a gap between an actual measured value and the predicted value is obtained, and further, according to a feedback value of the residual analysis, a weight coefficient of an influence factor in the regression equation is adjusted, so that the regression equation has a better fitting effect, and a more accurate predicted value of the electricity consumption is output.
Compared with the original prediction method of the user electricity consumption data, the method provided by the invention has the advantages that the user electricity consumption is creatively collected, various factors influencing the user electricity consumption are comprehensively considered, a regression equation is fitted according to the influence factors of the user electricity consumption, a more accurate predicted value is further output, and the method can be applied to the field of intelligent online monitoring of the micro-grid electricity consumption.
And S9, adjusting the optimal scheduling strategy according to the predicted value of the power consumption of the user.
Specifically, in this embodiment, the optimal scheduling policy is further adjusted according to the predicted value, and each distributed power supply in the micro-grid is reasonably allocated according to the output upper limit thereof, so that the utilization rate of each distributed power supply in the micro-grid is improved, the power consumption requirement of the load user of the micro-grid is met, and the running efficiency and reliability of the micro-grid are improved.
Referring to fig. 2, in an alternative embodiment, to be able to efficiently execute the method for controlling the polymorphism of the micro grid provided by the present invention, the present invention further provides a system for controlling the polymorphism of the micro grid, where the system includes a processor, an input device, an output device, and a memory, and the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to invoke the program instructions to execute the specific steps of the related embodiments of the method for controlling the polymorphism of the micro grid provided by the present invention. The micro-grid multi-state coordination control system has complete, objective and stable structure, can efficiently execute the micro-grid multi-state coordination control method, and improves the overall applicability and practical application capability of the micro-grid multi-state coordination control system.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
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.
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