CN117200348A - Micro-grid group state prediction set calculation method considering wind-light disturbance - Google Patents
Micro-grid group state prediction set calculation method considering wind-light disturbance Download PDFInfo
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Abstract
The application discloses a method for calculating a state prediction set of a micro-grid group by considering wind-solar disturbance, which comprises the steps of firstly modularly dividing a distributed power supply, a network and loads in the micro-grid group, then establishing a small signal state space model of each module, then calculating an uncertain input item of the state space model of the micro-grid group, then carrying out set modeling on the change interval by utilizing a zirono polyhedron, next deriving a state space model of a single micro-grid, integrating the state space model of the single micro-grid to obtain the state space model of the micro-grid group, and finally obtaining a state prediction set of a system under the consideration of the disturbance. The application uses the aggregate form to describe the uncertain input quantity, can realize that all possible running tracks of the system state quantity after being disturbed are obtained by one-time simulation, improves the efficiency of simulation and analysis, and has good calculation efficiency and precision by using the modeling form of the zirono polyhedron.
Description
Technical Field
The application relates to the technical field of micro-grids, in particular to a micro-grid group state prediction set calculation method considering wind-light disturbance.
Background
The permeability of the distributed renewable power generation such as a wind power generation system, a photovoltaic power generation system, a fuel cell power generation system, a distributed energy storage system and the like in a modern power system is continuously increased, and great changes are made in the modes of promoting power generation, power transmission and distribution, electric energy use and the like. The distributed power generation technology can effectively reduce the loss of electric energy in the transmission process through on-site consumption, and enhance the stability of power supply of an electric power system. Thus, micro-grid systems are rapidly evolving by virtue of renewable energy generation and other forms of distributed generation and reliable integration of local loads. The renewable energy sources such as distributed photovoltaic, wind power and the like are accessed in a micro-grid on a large scale, and the flexible diversity of control strategies and the strong intermittence and weak support of power generation of the renewable energy sources can make the safe and stable operation and coordinated control of the micro-grid face serious challenges. Moreover, stability problems may then rapidly escalate when the micro-grids are interconnected to form a micro-grid cluster system. In the analysis of the micro-grid group, the stability analysis of the idealized renewable energy unit ignores the uncertainty of the output caused by renewable energy, and the control strategy made according to the uncertainty of the output uncertainty can not be enough to respond to uncertain disturbance, so that the safe and stable operation of the micro-grid group system is damaged.
Compared with the traditional power system, the new energy micro-grid group system running in the autonomous mode has the advantage that the low inertia characteristic makes the new energy micro-grid group system quite sensitive to disturbance. Therefore, it is important to consider the influence of primary energy disturbance such as solar irradiance and wind speed when analyzing the stability of the micro grid group. Because of uncertainty and diversity of wind-light disturbance, almost infinite scenes cannot be simulated one by one when the influence of the wind-light disturbance on a micro-grid group system is analyzed, most of the existing researches on uncertainty variables mainly pay attention to trend solutions of the system, and the dynamic response process of the system along with time under the uncertain disturbance is ignored, so that visual information of running state quantity changes such as frequency, voltage and the like in the dynamic process cannot be provided. In order to effectively analyze the influence of wind-light disturbance on the running characteristics of the micro-grid group system, the dynamic response of the system state along with time needs to be studied from the dynamic equation of the system. In the prior art, the Monte Carlo method is adopted to sample and simulate the uncertainty, so that the calculated amount is huge, the comprehensiveness of the simulation result cannot be ensured, and therefore, the most comprehensive operation data cannot be provided for the stability analysis of the micro-grid group.
Disclosure of Invention
The application provides a method for calculating a state prediction set of a micro-grid group by considering wind-solar disturbance, which overcomes the defects that the influence of primary energy disturbance such as solar irradiance, wind speed and the like is not considered, the uncertainty is sampled and simulated, the calculated amount is huge, and the most comprehensive operation data cannot be provided for stability analysis of the micro-grid group.
The primary purpose of the application is to solve the technical problems, and the technical scheme of the application is as follows:
the application provides a method for calculating a state prediction set of a micro-grid group by considering wind-light disturbance, which comprises the following steps:
s1: carrying out modularized division on distributed power supplies, networks and loads in the micro-grid group, wherein the distributed power supplies comprise photovoltaic power generation units, wind power generation units and energy storage units;
s2: according to the actual circuit of each divided module, obtaining a differential equation corresponding to each module based on kirchhoff theorem, and establishing a small signal state space model of each module according to the differential equation corresponding to each module;
s3: obtaining a relational expression which is related to solar irradiance and wind speed by differential equations corresponding to the photovoltaic and wind generating sets, solving a deviation of a variable which is in direct proportion to the solar irradiance and wind speed, and taking a result of solving the deviation as an uncertain input item of a micro-grid group state space model;
s4: adjusting a change interval of an uncertain input item according to fluctuation ranges of wind speed and solar irradiance, and performing set modeling on the change interval by utilizing a zirono polyhedron;
s5: adding a virtual resistor to classify the original input matrix and input variables into the state matrix according to the state space equation of S2, adding the uncertain input item deduced in S4, deducing a state space model of a single micro-grid, and finally integrating the state space model of the single micro-grid to obtain a state space model of a micro-grid group;
s6: and obtaining a state prediction set of the system under the consideration of wind and light disturbance by using a state matrix and an uncertain input matrix in the state space model of the micro-grid group and a set model of uncertain input items constructed according to the zirono polyhedron in S4.
Further, in step S2, the small signal state space model of the photovoltaic is:
wherein,is a state variable of photovoltaic, +.>For photovoltaic input variables, +.>For the output variable of photovoltaic, +.>Is a state matrix of photovoltaic->For a photovoltaic input matrix, < >>Is a photovoltaic output matrix;
the small signal state space equation of the wind generating set is as follows:
wherein,、/>、/>respectively a state variable, an input variable and an output variable of the wind generating set,、/>、/>the state matrix, the input matrix and the output matrix of the wind generating set are respectively;
the state space equation of energy storage is:
wherein,、/>、/>state variable, input variable and output variable of energy storage respectively,/->、、/>State matrix and input of energy storage respectivelyA matrix and an output matrix;
the state space equation of the network is:
wherein,、/>、/>the state variable, the input variable and the output variable of the network respectively,、/>、/>the state matrix, the input matrix and the output matrix of the network are respectively;
the state space equation of the load is:
wherein,、/>、/>state variable, input variable and output variable of the load, respectively,/->、/>、A state matrix, an input matrix and an output matrix of the load respectively.
Further, in step S3, in differential equations corresponding to the photovoltaic and wind turbine generator systems, there is a variable of solar irradianceWind speed->The relation is converted to a relation that is related to solar irradiance and wind speed.
Further, the relation associated with solar irradiance is:
wherein,the output current is the output current of the photovoltaic array; />A direct current capacitor voltage connected in parallel with the photovoltaic array; />、/>、The number of series batteries contained in each photovoltaic module, the number of series connection of the photovoltaic modules in the photovoltaic array and the number of series connection of the photovoltaic modules in the photovoltaic array are respectivelyA number of parallels; />Is the short-circuit current of each string of photovoltaic modules; />Is the reverse saturation current of the p-n junction; />Is the unit charge constant; />Is the boltzmann constant; />Is the p-n junction temperature; />Is an ideal factor; />Short-circuit current of a photovoltaic cell at irradiation level and reference temperature; />Is the temperature coefficient; />Is the photovoltaic reference temperature; />Is the actual temperature of the environment;is solar irradiance; taking the actual temperature of the environment to be equal to the reference temperature of the photovoltaic, and then carrying out photovoltaic short-circuit current>And solar irradiance->The proportional relation is as follows:
usingReflecting the change in solar irradiance; in the modeling process, in the pair +.>When the total differentiation of time is found, the +.>Consider the state quantity and do->Obtaining +.>As an uncertain input; here, theRepresenting the difference between the wind speed and the selected linearization point.
Further, the relation associated with wind speed is:
wherein,is a mechanical torque; />、/>Air density and blade radius, respectively; />The wind energy utilization coefficient of the wind turbine blade; />The mechanical angular velocity of the wind turbine rotation; />Is the wind speed; in the modeling process, in the right->When differentiating, will ∈ ->Consider the state quantity and do->To determine the deviation of time, will +.>Propose and linearize with +.>As an uncertain input; here->Representing the difference between the wind speed and the selected linearization point.
Further, in step S4, the variation range of solar irradiance and wind speed may be expressed as:
wherein,、/>respectively the current solar irradiance and the current wind speed; />、/>The lower limit of solar irradiance variation and the lower limit of wind speed variation are respectively; />、/>The upper limit of solar irradiance change and the upper limit of wind speed change are respectively;
the zino polyhedron is defined as:
wherein,is a zirono polyhedral center; />To generate a primitive; />To generate the number of elements; />Generating element change coefficients; the wind-light disturbance range is represented by a zirono polyhedron, namely the wind-light disturbance range is the variation range of solar irradiance and wind speed, wherein the center of the variation range of solar irradiance and wind speed can be used as the center of the zirono polyhedron, and the formula is represented as follows:
wherein,、/>respectively the center of the variation range of solar irradiance and wind speed; the generator can be expressed as:
wherein,、/>respectively the solar irradiance and wind speed.
Further, in step S5, the state space model of the single micro grid is:
wherein,is the firstiA single microgrid state matrix; />For uncertain input matrix +.>、/>The coefficient of the uncertainty after the deviation of the variable which is in direct proportion to the solar irradiance and the wind speed in S3 is respectively +.>And->Respectively the firstiIndividual single microgrid state variables and uncertain input variables; />、/>The change ranges of the photovoltaic short-circuit current and the wind speed are respectively;
finally integrating a plurality of single micro-grids to form a micro-grid group state space model;
the microgrid group state space model may be expressed as:
wherein,a state matrix which is a micro-grid group model; />Status change for micro-grid groupAmount of the components.
Further, in step S6, the calculation process of the state prediction set under the wind-light disturbance is considered as follows:
the first step: by the firstStatus prediction set of time of day->The first moment then calculates the +.>Status prediction set of time of day->The expression is:
wherein,incremental changes for uncertain input; />Is the time step; />Is natural logarithm; />For minkowski addition;
and a second step of: calculation ofTo->Status prediction set for this period +.>：
Wherein,represent the firstnA plurality of time periods; />To be from->To->The track curvature of this period gives the state prediction set an error; />Calculating for the set convex hull;
and a third step of: taking the union set of each time period to obtain a state prediction set in simulation timeThe method comprises the following steps:
wherein,is the simulation end time.
The second aspect of the application provides a micro-grid group state prediction set computing system considering wind-light disturbance, comprising: the micro-grid group state prediction set calculation method based on the wind-light disturbance comprises a memory and a processor, wherein the memory comprises a micro-grid group state prediction set calculation method program based on the wind-light disturbance, and the micro-grid group state prediction set calculation method based on the wind-light disturbance is implemented when executed by the processor:
s1: carrying out modularized division on distributed power supplies, networks and loads in the micro-grid group, wherein the distributed power supplies comprise photovoltaic power generation units, wind power generation units and energy storage units;
s2: according to the actual circuit of each divided module, obtaining a differential equation corresponding to each module based on kirchhoff theorem, and establishing a small signal state space model of each module according to the differential equation corresponding to each module;
s3: obtaining a relational expression which is related to solar irradiance and wind speed by differential equations corresponding to the photovoltaic and wind generating sets, solving a deviation of a variable which is in direct proportion to the solar irradiance and wind speed, and taking a result of solving the deviation as an uncertain input item of a micro-grid group state space model;
s4: adjusting a change interval of an uncertain input item according to fluctuation ranges of wind speed and solar irradiance, and performing set modeling on the change interval by utilizing a zirono polyhedron;
s5: adding a virtual resistor to classify the original input matrix and input variables into the state matrix according to the state space equation of S2, adding the uncertain input item deduced in S4, deducing a state space model of a single micro-grid, and finally integrating the state space model of the single micro-grid to obtain a state space model of a micro-grid group;
s6: and obtaining a state prediction set of the system under the consideration of wind and light disturbance by using a state matrix and an uncertain input matrix in the state space model of the micro-grid group and a set model of uncertain input items constructed according to the zirono polyhedron in S4.
The third aspect of the present application provides a computer readable storage medium, where the computer readable storage medium includes a micro-grid group state prediction set calculation method program considering wind-light disturbance, where the micro-grid group state prediction set calculation method program considering wind-light disturbance implements the steps of the micro-grid group state prediction set calculation method considering wind-light disturbance when the micro-grid group state prediction set calculation method program considering wind-light disturbance is executed by a processor.
Compared with the prior art, the technical scheme of the application has the beneficial effects that:
according to the method, the influence of primary energy fluctuation such as solar irradiance and wind speed on the system dynamics is considered, and the wind-light disturbance is modeled; the method comprises the steps of constructing an uncertain input item into a set form by utilizing the zirono polyhedron, wherein the set contains all conditions of wind speed and solar irradiance change, and introducing the conditions into reachability analysis, and calculating a state prediction set of the system under the conditions, namely obtaining all possible dynamic tracks of the system under the conditions, thereby improving simulation and analysis efficiency.
Drawings
Fig. 1 is a flowchart of a method for calculating a state prediction set of a micro-grid group in consideration of wind-solar disturbance according to an embodiment of the present application.
Fig. 2 is a topological diagram of a micro grid group system according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a two-dimensional zirono-polyhedral construction method according to an embodiment of the present application.
Fig. 4 is a composition diagram of a state prediction set for each time point according to an embodiment of the present application.
Fig. 5 is a composition diagram of a state prediction set for each period of time according to an embodiment of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
Example 1
As shown in fig. 1, the first aspect of the present application provides a method for calculating a state prediction set of a micro-grid cluster in consideration of wind-light disturbance, which includes the following steps:
s1: and carrying out modularized division on distributed power supplies, networks and loads in the micro-grid group, wherein the distributed power supplies comprise photovoltaic power generation units, wind power generation units and energy storage units.
As shown in fig. 2, the micro grid group system is composed of n sub micro grids. When modeling the micro-grid group system, the distributed power supply, the network and the load in the micro-grid group are divided in a modularization mode, wherein the distributed power supply comprises photovoltaic, a wind generating set and energy storage, and the network comprises a bus and a feeder.
S2: and according to the actual circuit of each divided module, obtaining the differential equation corresponding to each module based on the kirchhoff theorem, and establishing a small signal state space model of each module according to the differential equation corresponding to each module.
The small signal state space model of the photovoltaic is as follows:
wherein,is a state variable of photovoltaic, +.>For photovoltaic input variables, +.>For the output variable of photovoltaic, +.>Is a state matrix of photovoltaic->For a photovoltaic input matrix, < >>Is a photovoltaic output matrix.
The small signal state space equation of the wind generating set is as follows:
wherein,、/>、/>respectively a state variable, an input variable and an output variable of the wind generating set,、/>、/>the state matrix, the input matrix and the output matrix of the wind generating set are respectively adopted.
The state space equation of energy storage is:
wherein,、/>、/>state variable, input variable and output variable of energy storage respectively,/->、、/>Respectively a state matrix, an input matrix and an output matrix of energy storage.
The state space equation of the network is:
wherein,、/>、/>the state variable, the input variable and the output variable of the network respectively,、/>、/>a state matrix, an input matrix and an output matrix of the network, respectively.
The state space equation of the load is:
wherein,、/>、/>state variable, input variable and output variable of the load, respectively,/->、/>、A state matrix, an input matrix and an output matrix of the load respectively.
S3: and obtaining a relational expression which is related to solar irradiance and wind speed by differential equations corresponding to the photovoltaic and wind generating sets, solving a deviation of a variable which is in direct proportion to the solar irradiance and wind speed, and taking the result of solving the deviation as an uncertain input item of a micro-grid group state space model.
In a photovoltaic module, a photovoltaic short-circuit current proportional to solar irradiance is utilizedAs a state quantity reflecting the change of solar irradiance and regarding it as a state quantity, photovoltaic short-circuit current +_during modeling>And obtaining a bias derivative, and taking the bias derivative as an uncertain input quantity. In a wind park module, the mechanical torque can be directly +.>Find wind speed +.>And directly willAs a state quantity, it is biased in the modeling process, and is used as an uncertain input quantity. In the pair->、/>After the partial derivatives are obtained, they are respectively linearized to obtain +.>、/>And the coefficients of the corresponding terms are presented and expressed in the form of a matrix to form an output matrix +.>。
In differential equations corresponding to photovoltaic and wind generating sets, the existence variable is solar irradianceWind speedThe relation is converted to a relation that is related to solar irradiance and wind speed.
The relationship associated with solar irradiance is:
wherein,the output current is the output current of the photovoltaic array; />A direct current capacitor voltage connected in parallel with the photovoltaic array; />、/>、The number of series-connected batteries contained in each photovoltaic module, the series-connected number of the photovoltaic modules in the photovoltaic array,The number of parallel photovoltaic modules in the photovoltaic array; />Is the short-circuit current of each string of photovoltaic modules; />Is the reverse saturation current of the p-n junction; />Is the unit charge constant; />Is the boltzmann constant; />Is the p-n junction temperature; />Is an ideal factor; />Short-circuit current of a photovoltaic cell at irradiation level and reference temperature; />Is the temperature coefficient; />Is the photovoltaic reference temperature; />Is the actual temperature of the environment;is solar irradiance. Taking the actual temperature of the environment to be equal to the reference temperature of the photovoltaic, and then carrying out photovoltaic short-circuit current>And solar irradiance->The proportional relation is as follows:
thus, it is used hereinReflecting the change in solar irradiance. According to the above, can pass->Reflecting the change in solar irradiance. Then in the modeling process, in the case of +.>When the total differentiation of time is found, the +.>Consider the state quantity and do->Obtaining +.>As an uncertainty input. Here->Representing the difference between the wind speed and the selected linearization point.
The relation to wind speed is:
wherein,is a mechanical torque; />、/>Respectively air density andblade radius; />The wind energy utilization coefficient of the wind turbine blade; />The mechanical angular velocity of the wind turbine rotation; />Is the wind speed. In the modeling process, in the right->When differentiating, will ∈ ->Consider the state quantity and do->To determine the deviation of time, will +.>Propose and linearize with +.>As an uncertainty input. Here->Representing the difference between the wind speed and the selected linearization point.
S4: and adjusting a change interval of the uncertain input item according to fluctuation ranges of wind speed and solar irradiance, and performing collective modeling on the change interval by utilizing the zirono polyhedron.
The variation range of the uncertain input item is adjusted according to the fluctuation range of the wind speed and the solar irradiance to be studied, namely, the variation interval of the solar irradiance and the wind speed is determined, and according to the variation interval, the zirono polyhedron is utilized for carrying out set modeling, as shown in fig. 3, the variation interval is expressed in a polyhedron form, and the variation interval is conveniently brought into the calculation of a state prediction set later.
The range of variation of solar irradiance and wind speed can be expressed as:
wherein,、/>respectively the current solar irradiance and the current wind speed; />、/>The lower limit of solar irradiance variation and the lower limit of wind speed variation are respectively; />、/>The upper limit of solar irradiance change and the upper limit of wind speed change are respectively defined.
The zino polyhedron is defined as:
wherein,is a zirono polyhedral center; />To generate a primitive; />To generate the number of elements; />To generate meta-variation coefficients. The wind-light disturbance range is represented by a zirono polyhedron, namely the wind-light disturbance range is the variation range of solar irradiance and wind speed, wherein the center of the variation range of solar irradiance and wind speed can be used as the center of the zirono polyhedron, and the formula is represented as follows:
wherein,、/>respectively the center of the variation range of solar irradiance and wind speed; the generator can be expressed as:
wherein,、/>respectively the solar irradiance and wind speed. Thus, the uncertain modeling of wind and light fluctuation is completed in an aggregate mode.
S5: according to the state space equation of S2, adding a virtual resistor to classify the original input matrix and input variables into the state matrix, adding the uncertain input item deduced in S4, deducing a state space model of a single micro-grid, and finally integrating the state space model of the single micro-grid to obtain a state space model of a micro-grid group.
And carrying out linearization processing on the state equation and the output equation of each module to form a state space model form. Deducing a state space model of single micro electricity according to the input-output relation of the state space model, and finally integrating the state space models of a plurality of single micro power grids to form a micro power grid group state space model.
The deduced state space model of the single micro-grid is:
wherein,is the firstiA single microgrid state matrix; />For uncertain input matrix +.>、/>Respectively S3 is that the deviation of the variable which is in direct proportion to the solar irradiance and the wind speed is not obtainedDetermining the coefficient of the quantity->And->Respectively the firstiIndividual single microgrid state variables and uncertain input variables. />、/>The change ranges of the photovoltaic short-circuit current and the wind speed are respectively.
And finally integrating a plurality of single micro-grids to form a micro-grid group state space model.
The microgrid group state space model may be expressed as:
wherein,a state matrix which is a micro-grid group model; />Is a state variable of the micro-grid group.
S6: and obtaining a state prediction set of the system under the consideration of wind and light disturbance by using a state matrix and an uncertain input matrix in the state space model of the micro-grid group and a set model of uncertain input items constructed according to the zirono polyhedron in S4.
Using state matrices in state space models of microgrid clustersAnd input matrix->And according to the aggregate model of the uncertain input items constructed by the zirono polyhedron, calculating a state prediction set of the system under the condition of considering wind-light disturbance, wherein the state prediction set is formed as shown in fig. 4 and 5, and the obtained state prediction set comprises all possible dynamic running tracks of each state quantity of the system after being disturbed, so that the state prediction of the micro-grid group system after being disturbed by wind-light is completed.
The calculation process of the state prediction set under the consideration of wind-light disturbance is as follows:
the first step: by the firstStatus prediction set of time of day->(initial time then using pre-designed initial set) calculate +.>Status prediction set of time of day->The expression is:
wherein,incremental changes for uncertain input; />Is the time step; />Is natural logarithm; />Is minkowski addition.
Second step: calculation ofTo->Status prediction set for this period +.>：
Wherein,represent the firstnA plurality of time periods; />To be from->To->The track curvature of this period gives the state prediction set an error; />And (5) calculating for the set convex hull.
And a third step of: taking the union set of each time period to obtain a state prediction set in simulation timeThe method comprises the following steps:
wherein,is the simulation end time. The process of calculating the state prediction set is the accessibility analysis of the micro-grid group, thus completingAnd predicting the state of the micro-grid group system after being subjected to wind-light disturbance.
The second aspect of the application provides a micro-grid group state prediction set computing system considering wind-light disturbance, comprising: the micro-grid group state prediction set calculation method based on the wind-light disturbance comprises a memory and a processor, wherein the memory comprises a micro-grid group state prediction set calculation method program based on the wind-light disturbance, and the micro-grid group state prediction set calculation method based on the wind-light disturbance is implemented when executed by the processor:
s1: carrying out modularized division on distributed power supplies, networks and loads in the micro-grid group, wherein the distributed power supplies comprise photovoltaic power generation units, wind power generation units and energy storage units;
s2: according to the actual circuit of each divided module, obtaining a differential equation corresponding to each module based on kirchhoff theorem, and establishing a small signal state space model of each module according to the differential equation corresponding to each module;
s3: obtaining a relational expression which is related to solar irradiance and wind speed by differential equations corresponding to the photovoltaic and wind generating sets, solving a deviation of a variable which is in direct proportion to the solar irradiance and wind speed, and taking a result of solving the deviation as an uncertain input item of a micro-grid group state space model;
s4: adjusting a change interval of an uncertain input item according to fluctuation ranges of wind speed and solar irradiance, and performing set modeling on the change interval by utilizing a zirono polyhedron;
s5: adding a virtual resistor to classify the original input matrix and input variables into the state matrix according to the state space equation of S2, adding the uncertain input item deduced in S4, deducing a state space model of a single micro-grid, and finally integrating the state space model of the single micro-grid to obtain a state space model of a micro-grid group;
s6: and obtaining a state prediction set of the system under the consideration of wind and light disturbance by using a state matrix and an uncertain input matrix in the state space model of the micro-grid group and a set model of uncertain input items constructed according to the zirono polyhedron in S4.
The third aspect of the present application provides a computer readable storage medium, where the computer readable storage medium includes a micro-grid group state prediction set calculation method program considering wind-light disturbance, where the micro-grid group state prediction set calculation method program considering wind-light disturbance implements the steps of the micro-grid group state prediction set calculation method considering wind-light disturbance when the micro-grid group state prediction set calculation method program considering wind-light disturbance is executed by a processor.
It is to be understood that the above examples of the present application are provided by way of illustration only and not by way of limitation of the embodiments of the present application. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are desired to be protected by the following claims.
Claims (10)
1. A method for calculating a state prediction set of a micro-grid group by considering wind-light disturbance is characterized by comprising the following steps:
s1: carrying out modularized division on distributed power supplies, networks and loads in the micro-grid group, wherein the distributed power supplies comprise photovoltaic power generation units, wind power generation units and energy storage units;
s2: according to the actual circuit of each divided module, obtaining a differential equation corresponding to each module based on kirchhoff theorem, and establishing a small signal state space model of each module according to the differential equation corresponding to each module;
s3: obtaining a relational expression which is related to solar irradiance and wind speed by differential equations corresponding to the photovoltaic and wind generating sets, solving a deviation of a variable which is in direct proportion to the solar irradiance and wind speed, and taking a result of solving the deviation as an uncertain input item of a micro-grid group state space model;
s4: adjusting a change interval of an uncertain input item according to fluctuation ranges of wind speed and solar irradiance, and performing set modeling on the change interval by utilizing a zirono polyhedron;
s5: adding a virtual resistor to classify the original input matrix and input variables into the state matrix according to the state space equation of S2, adding the uncertain input item deduced in S4, deducing a state space model of a single micro-grid, and finally integrating the state space model of the single micro-grid to obtain a state space model of a micro-grid group;
s6: and obtaining a state prediction set of the system under the consideration of wind and light disturbance by using a state matrix and an uncertain input matrix in the state space model of the micro-grid group and a set model of uncertain input items constructed according to the zirono polyhedron in S4.
2. The method for calculating a state prediction set of a micro grid group considering wind-solar disturbance according to claim 1, wherein in step S2, a small signal state space model of the photovoltaic is:
wherein,is a state variable of photovoltaic, +.>For photovoltaic input variables, +.>For the output variable of photovoltaic, +.>Is a state matrix of photovoltaic->For a photovoltaic input matrix, < >>Is a photovoltaic output matrix;
the small signal state space equation of the wind generating set is as follows:
wherein,、/>、/>the state variable, the input variable and the output variable of the wind generating set are respectively>、、/>The state matrix, the input matrix and the output matrix of the wind generating set are respectively;
the state space equation of energy storage is:
wherein,、/>、/>state variable, input variable and output variable of energy storage respectively,/->、、/>Respectively a state matrix, an input matrix and an output matrix of energy storage;
the state space equation of the network is:
wherein,、/>、/>state variable, input variable and output variable of the network, respectively,/->、/>、/>The state matrix, the input matrix and the output matrix of the network are respectively;
the state space equation of the load is:
wherein,、/>、/>state variable, input variable and output variable of the load, respectively,/->、/>、/>A state matrix, an input matrix and an output matrix of the load respectively.
3. The method for calculating a state prediction set of a micro grid group considering wind-solar disturbance according to claim 1, wherein in step S3, in differential equations corresponding to photovoltaic and wind generating sets, there is a variable of solar irradianceWind speed->The relation is converted to a relation that is related to solar irradiance and wind speed.
4. A method for calculating a state prediction set of a micro grid group in consideration of wind-solar disturbance according to claim 3, wherein the relation associated with solar irradiance is:
wherein,the output current is the output current of the photovoltaic array; />A direct current capacitor voltage connected in parallel with the photovoltaic array; />、/>、/>The number of series batteries contained in each photovoltaic module, the number of series connection of the photovoltaic modules in the photovoltaic array and the number of parallel connection of the photovoltaic modules in the photovoltaic array are respectively; />Is the short-circuit current of each string of photovoltaic modules; />Is the reverse saturation current of the p-n junction; />Is the unit charge constant; />Is the boltzmann constant; />Is the p-n junction temperature; />Is an ideal factor; />Short-circuit current of a photovoltaic cell at irradiation level and reference temperature; />Is the temperature coefficient; />Is the photovoltaic reference temperature; />Is the actual temperature of the environment; />Is solar irradiance; taking the actual temperature of the environment to be equal to the reference temperature of the photovoltaic, and then carrying out photovoltaic short-circuit current>And solar irradiance->The proportional relation is as follows:
usingReflecting the change in solar irradiance; in the modeling process, in the pair +.>When the total differentiation of time is obtained, the methodConsider the state quantity and do->Obtaining +.>As an uncertain input; here->Representing the difference between the wind speed and the selected linearization point.
5. A method for computing a state prediction set for a micro grid cluster in consideration of wind-light disturbance according to claim 3, wherein the relation associated with wind speed is:
wherein,is a mechanical torque; />、/>Air density and blade radius, respectively; />The wind energy utilization coefficient of the wind turbine blade; />The mechanical angular velocity of the wind turbine rotation; />Is the wind speed; in the modeling process, in the right->When differentiating, the method willConsider the state quantity and do->To determine the deviation of time, will +.>Propose and linearize with +.>As an uncertain input; here->Representing the difference between the wind speed and the selected linearization point.
6. The method for calculating a state prediction set of a micro grid cluster in consideration of wind-solar disturbance according to claim 1, wherein in step S4, the variation ranges of solar irradiance and wind speed are expressed as:
wherein,、/>respectively the current solar irradiance and the current wind speed; />、/>The lower limit of solar irradiance variation and the lower limit of wind speed variation are respectively; />、/>The upper limit of solar irradiance change and the upper limit of wind speed change are respectively;
the zino polyhedron is defined as:
wherein,is a zirono polyhedral center; />To generate a primitive; />To generate the number of elements; />Generating element change coefficients; the wind-light disturbance range is represented by a zirono polyhedron, namely the wind-light disturbance range is the variation range of solar irradiance and wind speed, wherein the center of the variation range of solar irradiance and wind speed can be used as the center of the zirono polyhedron, and the formula is represented as follows:
wherein,、/>respectively the center of the variation range of solar irradiance and wind speed; the generator can be expressed as:
wherein,、/>respectively the solar irradiance and wind speed.
7. The method for calculating a state prediction set of a micro-grid cluster considering wind-solar disturbance according to claim 1, wherein in step S5, the state space model of the single micro-grid is:
wherein,is the firstiA single microgrid state matrix; />For uncertain input matrix +.>、/>The coefficient of the uncertainty after the deviation of the variable which is in direct proportion to the solar irradiance and the wind speed in S3 is respectively +.>And->Respectively the firstiIndividual single microgrid state variables and uncertain input variables; />、/>The change ranges of the photovoltaic short-circuit current and the wind speed are respectively;
finally integrating a plurality of single micro-grids to form a micro-grid group state space model;
the microgrid group state space model may be expressed as:
wherein,a state matrix which is a micro-grid group model; />Is a state variable of the micro-grid group.
8. The method for calculating a state prediction set of a micro grid group considering wind-light disturbance according to claim 1, wherein in step S6, the calculation process of the state prediction set considering wind-light disturbance is as follows:
the first step: by the firstStatus prediction set of time of day->The first moment then calculates the +.>Status prediction set of time of day->The expression is:
wherein,incremental changes for uncertain input; />Is the time step; />Is natural logarithm; />For minkowski addition;
and a second step of: calculation ofTo->Status prediction set for this period +.>：
Wherein,represent the firstnA plurality of time periods; />To be from->To->The track curvature of this period gives the state prediction set an error; />Calculating for the set convex hull;
and a third step of: taking the union of each time period to obtainState prediction set during simulation timeThe method comprises the following steps:
wherein,is the simulation end time.
9. A micro-grid cluster state prediction set computing system taking into account wind-solar disturbances, the system comprising: the micro-grid group state prediction set calculation method based on the wind-light disturbance comprises a memory and a processor, wherein the memory comprises a micro-grid group state prediction set calculation method program based on the wind-light disturbance, and the micro-grid group state prediction set calculation method based on the wind-light disturbance is implemented when executed by the processor:
s1: carrying out modularized division on distributed power supplies, networks and loads in the micro-grid group, wherein the distributed power supplies comprise photovoltaic power generation units, wind power generation units and energy storage units;
s2: according to the actual circuit of each divided module, obtaining a differential equation corresponding to each module based on kirchhoff theorem, and establishing a small signal state space model of each module according to the differential equation corresponding to each module;
s3: obtaining a relational expression which is related to solar irradiance and wind speed by differential equations corresponding to the photovoltaic and wind generating sets, solving a deviation of a variable which is in direct proportion to the solar irradiance and wind speed, and taking a result of solving the deviation as an uncertain input item of a micro-grid group state space model;
s4: adjusting a change interval of an uncertain input item according to fluctuation ranges of wind speed and solar irradiance, and performing set modeling on the change interval by utilizing a zirono polyhedron;
s5: adding a virtual resistor to classify the original input matrix and input variables into the state matrix according to the state space equation of S2, adding the uncertain input item deduced in S4, deducing a state space model of a single micro-grid, and finally integrating the state space model of the single micro-grid to obtain a state space model of a micro-grid group;
s6: and obtaining a state prediction set of the system under the consideration of wind and light disturbance by using a state matrix and an uncertain input matrix in the state space model of the micro-grid group and a set model of uncertain input items constructed according to the zirono polyhedron in S4.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a micro grid group state prediction set calculation method program taking into account wind-light disturbance, which when executed by a processor, implements the steps of a micro grid group state prediction set calculation method taking into account wind-light disturbance according to any one of claims 1 to 8.
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