CN117811051A - Micro-grid elasticity control method based on demand side response - Google Patents
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Abstract
The invention provides a micro-grid elasticity control method based on demand side response, which comprises the following steps: constructing a micro-grid model containing a new energy automobile; constructing a hierarchical micro-grid energy management framework based on a demand side response; based on the micro-grid model containing the new energy automobile, the constraint condition is constructed, based on the constructed micro-grid model containing the new energy automobile and the constraint condition, the Takagi-Sugeno fuzzy neural network optimized by the differential evolution algorithm is applied to a hierarchical micro-grid energy management architecture to control energy dispatching of the micro-grid, and meanwhile, the mobility and energy storage and adjustment functions of the new energy electric automobile are utilized to enhance the elasticity of the micro-grid. The invention can fully utilize the new energy automobile to improve the elasticity of the micro-grid while reducing the running cost of the micro-grid, and obviously improve the running stability of the micro-grid.
Description
Technical Field
The invention relates to the technical field of power grid data processing, in particular to a micro-grid elasticity control method based on demand side response.
Background
Traditional fossil energy is not renewable and is severely polluting the environment, so research and development personnel are looking for new energy sources that are renewable, clean and low polluting. The growing popularity of renewable energy sources and intelligent devices has raised attention in industry and other fields to micro-grids, which are electric power distribution systems composed of distributed power sources, loads, energy storage systems, energy conversion devices, and the like.
However, the existing new energy sources such as photovoltaic power generation have the defects of dispersibility, uncertainty of output and the like, and meanwhile, the load side of the power grid also has great fluctuation, so that the safety operation of the traditional power distribution system is greatly challenged. Meanwhile, as a large number of new energy electric vehicles are connected into the micro-grid, the volatility of the micro-grid is increased, and a greater challenge is brought to the safe and reliable operation of the micro-grid, so that the technical problem that the technical requirements of the technicians in the field are met is that how to improve the operation stability of the micro-grid while reducing the operation cost of the micro-grid.
Disclosure of Invention
The invention aims to provide a micro-grid elasticity control method based on demand side response, which is used for fully utilizing a new energy automobile to improve the elasticity of a micro-grid and remarkably improving the running stability of the micro-grid while reducing the running cost of the micro-grid.
A micro-grid elasticity control method based on demand side response comprises the following steps:
step S1, constructing a micro-grid model containing a new energy automobile;
step S2, constructing a hierarchical micro-grid energy management framework based on a demand side response;
step S3, constructing constraint conditions based on a micro-grid model containing the new energy automobile, wherein the constraint conditions comprise operation cost minimization constraint, power value constraint of micro-grid and operator exchange, charge and discharge power value constraint of a battery energy storage system and charge and discharge power value constraint of the new energy automobile in a micro-grid coverage area;
and S4, based on the constructed micro-grid model containing the new energy automobile and constraint conditions, applying the Takagi-Sugeno fuzzy neural network optimized by the differential evolution algorithm to a hierarchical micro-grid energy management architecture to control energy scheduling of the micro-grid, and simultaneously, enhancing the elasticity of the micro-grid by utilizing the mobility and energy storage and regulation functions of the new energy electric automobile.
According to the micro-grid elasticity control method based on the demand side response, provided by the invention, the micro-grid elasticity control method has the following beneficial effects:
(1) The invention establishes a hierarchical micro-grid energy management framework based on a demand side response to solve the problem of micro-grid energy scheduling of a new energy automobile;
(2) The invention considers the constraint of the running cost of the micro-grid, the charging and discharging power of the energy storage system, the charging and discharging power of the new energy electric automobile and the like, and ensures the efficient and stable running of the micro-grid;
(3) According to the invention, the differential evolution optimized Takagi-Sugeno fuzzy neural network is applied to the hierarchical micro-grid energy management architecture, so that the energy scheduling of the micro-grid can be effectively controlled;
(4) The invention utilizes the mobility and the energy storage and regulation functions of the new energy electric automobile, and can enhance the elasticity of the micro-grid.
Drawings
FIG. 1 is a schematic flow chart of a micro-grid elasticity control method based on demand side response according to the present invention;
fig. 2 is a schematic structural diagram of a hierarchical micro-grid energy management architecture based on demand side response.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the method for controlling elasticity of a micro-grid based on a demand side response according to the embodiment of the present invention includes steps S1 to S4.
And S1, constructing a micro-grid model containing the new energy automobile.
The micro-grid model comprises a new energy automobile, wherein the micro-grid model specifically comprises a two-layer grid structure, a first-layer grid is a main grid, and a second-layer grid is a plurality of micro-grids;
the micro-grids specifically comprise a photovoltaic energy system, a battery energy storage system, resident loads, industrial and commercial loads and new energy automobiles.
Step S2, constructing a hierarchical micro-grid energy management framework based on the response of the demand side.
Referring to fig. 2, a hierarchical micro-grid energy management architecture based on demand side response includes two control levels, respectively: the system comprises a main power grid level controller based on a demand side response and a micro power grid level controller based on the demand side response, wherein the main power grid level controller based on the demand side response is used as a monitoring controller, and the micro power grid level controller based on the demand side response is responsible for managing energy resources of a corresponding micro power grid;
the workflow of the hierarchical micro-grid energy management architecture based on the demand side response is as follows:
firstly, the main power grid level controller based on the demand side response sends a power reference signal and a demand transfer reference signal to each micro power grid level controller based on the demand side response according to the running condition of a power grid in an area;
then, the micro-grid level controller based on the demand side response calculates according to the received power reference signal and the demand transfer reference signal, and sends a power control signal and a demand transfer signal to the managed micro-grid according to a calculation result;
and finally, uploading the local operation data to the micro-grid level controller based on the demand side response by each micro-grid, and uploading the local operation data to the main grid level controller based on the demand side response by the micro-grid level controller based on the demand side response, thereby providing basis for decision making of the main grid level controller based on the demand side response and the micro-grid level controller based on the demand side response.
And step S3, constructing constraint conditions based on a micro-grid model containing the new energy automobile, wherein the constraint conditions comprise operation cost minimization constraint, power value constraint of micro-grid and operator exchange, charge and discharge power value constraint of a battery energy storage system and charge and discharge power value constraint of the new energy automobile in a micro-grid coverage area.
In step S3, the expression of the running cost minimization constraint is:
；
wherein,kthe time of day is indicated as such,jrepresenting the predicted time step size of the time,Nrepresenting the prediction time domain,ia variable representing the sequence number is indicated,Mrepresenting the number of micro-grids,T S for a predetermined sampling time period to be set,S(k+j) The external grid operator representing the prediction time horizon gives the energy costs,representing the translation factor over the predicted time range,L 0 for weight parameter, ++>Representing the power value exchanged by the microgrid with the operator, < >>Energy values exchanged by each micro-grid within a predicted time rangeDecision (S)>The expression of (2) is:
；
xenergy values exchanged for each micro-grid within a predicted time frameTranslation factor in the prediction time range +.>Is a collection of (1);
in step S3, the microgrid exchanges power values with the operatorThe constraints of (2) are:
；
wherein,representing the minimum power value exchanged by the microgrid with the operator,/->Representing a maximum power value exchanged by the micro grid with the operator;
in step S3, the charge/discharge power value of the battery energy storage systemThe constraints of (2) are:
；
；
；
wherein,for the lower limit value of charge and discharge power of the battery energy storage system, < + >>The upper limit value of charge and discharge power of the battery energy storage system is +.>For the energy state of the battery energy storage system, +.>Is the maximum instantaneous power of the battery energy storage system, < >>、/>A heuristically defined scaling parameter for the battery energy storage system;
in step S3, the charging/discharging power value of the new energy automobile in the micro-grid coverage areaThe constraints of (2) are:
；
；
；
wherein,is the lower limit value of charge and discharge power of the new energy automobile, < ->Charging and discharging power upper limit value of new energy automobile, < ->Is the energy state of the new energy automobile, < +.>Maximum instantaneous power of the battery of the new energy vehicle,/->、/>Heuristic defined proportion parameter of new energy automobile.
And S4, based on the constructed micro-grid model containing the new energy automobile and constraint conditions, applying the Takagi-Sugeno fuzzy neural network optimized by the differential evolution algorithm to a hierarchical micro-grid energy management architecture to control energy scheduling of the micro-grid, and simultaneously, enhancing the elasticity of the micro-grid by utilizing the mobility and energy storage and regulation functions of the new energy electric automobile.
In step S4, based on the constructed micro-grid model including the new energy automobile and the constraint condition, the Takagi-Sugeno fuzzy neural network optimized by the differential evolution algorithm is applied to the hierarchical micro-grid energy management architecture to control the energy scheduling of the micro-grid, and specifically includes:
firstly, extracting a network topological structure and collecting historical operation data of each micro power grid according to a constructed micro power grid model containing a new energy automobile, then cleaning the extracted historical operation data of the power grid, deleting error data and repeated data, and complementing vacant data by a Lagrange interpolation method;
then, a corresponding training sample set is manufactured according to the extracted historical operation data of each micro-grid, and front piece parameters and back piece parameters of a Takagi-Sugeno fuzzy neural network are optimized through a differential evolution algorithm, wherein each fuzzy rule is composed of the front piece parameters and the back piece parameters, the front piece parameters are used for matching fuzzy variables, and the back piece parameters are used for generating fuzzy rules;
and finally, optimizing the Takagi-Sugeno fuzzy neural network according to a differential evolution algorithm to predict the running condition of the micro-grid, and performing corresponding micro-grid energy scheduling control to coordinate energy balance between the main grid and a plurality of micro-grids and between the power generation side and the energy storage side.
In step S4, the mobility and the energy storage and adjustment function of the new energy electric vehicle are utilized to enhance the elasticity of the micro-grid, which specifically includes:
monitoring the running condition of the micro-grid in real time, when the generated energy cannot meet the load demand, guiding the new energy electric automobile in the area, reversely transmitting power to the micro-grid by utilizing the battery of the new energy electric automobile, and sharing the load pressure of the micro-grid;
monitoring the running condition of the micro-grid in real time, and timely supplying power to key loads in the micro-grid by utilizing the movable characteristics of the new energy electric automobile under the conditions of electricity consumption peaks and grid faults;
when the generator in the micro-grid trips, the battery of the new energy electric automobile is utilized to transmit power to the generator in the micro-grid, the black start of the generator in the micro-grid is assisted, and the self-repairing of the fault of the generator in the micro-grid is completed.
In summary, the micro-grid elasticity control method based on the demand side response provided by the invention has the following beneficial effects:
(1) The invention establishes a hierarchical micro-grid energy management framework based on a demand side response to solve the problem of micro-grid energy scheduling of a new energy automobile;
(2) The invention considers the constraint of the running cost of the micro-grid, the charging and discharging power of the energy storage system, the charging and discharging power of the new energy electric automobile and the like, and ensures the efficient and stable running of the micro-grid;
(3) According to the invention, the differential evolution optimized Takagi-Sugeno fuzzy neural network is applied to the hierarchical micro-grid energy management architecture, so that the energy scheduling of the micro-grid can be effectively controlled;
(4) The invention utilizes the mobility and the energy storage and regulation functions of the new energy electric automobile, and can enhance the elasticity of the micro-grid.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (6)
1. The utility model provides a micro-grid elasticity control method based on demand side response, which is characterized by comprising the following steps:
step S1, constructing a micro-grid model containing a new energy automobile;
step S2, constructing a hierarchical micro-grid energy management framework based on a demand side response;
step S3, constructing constraint conditions based on a micro-grid model containing the new energy automobile, wherein the constraint conditions comprise operation cost minimization constraint, power value constraint of micro-grid and operator exchange, charge and discharge power value constraint of a battery energy storage system and charge and discharge power value constraint of the new energy automobile in a micro-grid coverage area;
and S4, based on the constructed micro-grid model containing the new energy automobile and constraint conditions, applying the Takagi-Sugeno fuzzy neural network optimized by the differential evolution algorithm to a hierarchical micro-grid energy management architecture to control energy scheduling of the micro-grid, and simultaneously, enhancing the elasticity of the micro-grid by utilizing the mobility and energy storage and regulation functions of the new energy electric automobile.
2. The micro-grid elasticity control method based on demand side response according to claim 1, wherein in step S1, the micro-grid model including the new energy automobile specifically includes a two-layer grid structure, the first layer grid is a main grid, and the second layer grid is a plurality of micro-grids;
the micro-grids specifically comprise a photovoltaic energy system, a battery energy storage system, resident loads, industrial and commercial loads and new energy automobiles.
3. The method for controlling the elasticity of a micro-grid based on a demand side response according to claim 2, wherein in step S2, the hierarchical micro-grid energy management architecture based on a demand side response comprises two control levels, respectively: the system comprises a main power grid level controller based on a demand side response and a micro power grid level controller based on the demand side response, wherein the main power grid level controller based on the demand side response is used as a monitoring controller, and the micro power grid level controller based on the demand side response is responsible for managing energy resources of a corresponding micro power grid;
the workflow of the hierarchical micro-grid energy management architecture based on the demand side response is as follows:
firstly, the main power grid level controller based on the demand side response sends a power reference signal and a demand transfer reference signal to each micro power grid level controller based on the demand side response according to the running condition of a power grid in an area;
then, the micro-grid level controller based on the demand side response calculates according to the received power reference signal and the demand transfer reference signal, and sends a power control signal and a demand transfer signal to the managed micro-grid according to a calculation result;
and finally, uploading the local operation data to the micro-grid level controller based on the demand side response by each micro-grid, and uploading the local operation data to the main grid level controller based on the demand side response by the micro-grid level controller based on the demand side response, thereby providing basis for decision making of the main grid level controller based on the demand side response and the micro-grid level controller based on the demand side response.
4. The demand side response-based micro grid elasticity control method according to claim 3, wherein in step S3, the expression of the running cost minimization constraint is:
；
wherein,kthe time of day is indicated as such,jrepresenting the predicted time step size of the time,Nrepresenting the prediction time domain,ia variable representing the sequence number is indicated,Mrepresenting the number of micro-grids,T S for a predetermined sampling time period to be set,S(k+j) The external grid operator representing the prediction time horizon gives the energy costs,representing the translation factor over the predicted time range,L 0 for weight parameter, ++>Representing the power value exchanged by the microgrid with the operator, < >>Energy values exchanged by each micro-grid within a predicted time rangeDecision (S)>The expression of (2) is:
；
xenergy values exchanged for each micro-grid within a predicted time frameTranslation factor in the prediction time range +.>Is a collection of (1);
in step S3, the microgrid exchanges power values with the operatorThe constraints of (2) are:
；
wherein,representing the minimum power value exchanged by the microgrid with the operator,/->Representing a maximum power value exchanged by the micro grid with the operator;
in step S3, the charge/discharge power value of the battery energy storage systemThe constraints of (2) are:
；
；
；
wherein,for the lower limit value of charge and discharge power of the battery energy storage system, < + >>The upper limit value of charge and discharge power of the battery energy storage system is +.>For the energy state of the battery energy storage system, +.>Is the maximum instantaneous power of the battery energy storage system, < >>、/>A heuristically defined scaling parameter for the battery energy storage system;
in step S3, the charging/discharging power value of the new energy automobile in the micro-grid coverage areaThe constraints of (2) are:
；
；
；
wherein,is the lower limit value of charge and discharge power of the new energy automobile, < ->Charging and discharging power upper limit value of new energy automobile, < ->Is the energy state of the new energy automobile, < +.>Maximum instantaneous power of the battery of the new energy vehicle,/->、/>Heuristic defined proportion parameter of new energy automobile.
5. The method for controlling elasticity of a micro-grid based on demand side response according to claim 4, wherein in step S4, based on the constructed micro-grid model including the new energy automobile and constraint conditions, the Takagi-Sugeno fuzzy neural network optimized by the differential evolution algorithm is applied to a hierarchical micro-grid energy management architecture to control energy scheduling of the micro-grid, and specifically comprises:
firstly, extracting a network topological structure and collecting historical operation data of each micro power grid according to a constructed micro power grid model containing a new energy automobile, then cleaning the extracted historical operation data of the power grid, deleting error data and repeated data, and complementing vacant data by a Lagrange interpolation method;
then, a corresponding training sample set is manufactured according to the extracted historical operation data of each micro-grid, and front piece parameters and back piece parameters of a Takagi-Sugeno fuzzy neural network are optimized through a differential evolution algorithm, wherein each fuzzy rule is composed of the front piece parameters and the back piece parameters, the front piece parameters are used for matching fuzzy variables, and the back piece parameters are used for generating fuzzy rules;
and finally, optimizing the Takagi-Sugeno fuzzy neural network according to a differential evolution algorithm to predict the running condition of the micro-grid, and performing corresponding micro-grid energy scheduling control to coordinate energy balance between the main grid and a plurality of micro-grids and between the power generation side and the energy storage side.
6. The method for controlling elasticity of a micro-grid based on demand side response according to claim 1, wherein in step S4, the mobility and energy storage and adjustment functions of the new energy electric vehicle are utilized to enhance elasticity of the micro-grid, and specifically comprising:
monitoring the running condition of the micro-grid in real time, when the generated energy cannot meet the load demand, guiding the new energy electric automobile in the area, reversely transmitting power to the micro-grid by utilizing the battery of the new energy electric automobile, and sharing the load pressure of the micro-grid;
monitoring the running condition of the micro-grid in real time, and timely supplying power to key loads in the micro-grid by utilizing the movable characteristics of the new energy electric automobile under the conditions of electricity consumption peaks and grid faults;
when the generator in the micro-grid trips, the battery of the new energy electric automobile is utilized to transmit power to the generator in the micro-grid, the black start of the generator in the micro-grid is assisted, and the self-repairing of the fault of the generator in the micro-grid is completed.
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