CN117060402B - Energy internet platform architecture method based on distributed smart grid - Google Patents
Energy internet platform architecture method based on distributed smart grid Download PDFInfo
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- 238000005265 energy consumption Methods 0.000 abstract description 2
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The application belongs to the technical field of electric digital data processing, and provides an energy internet platform architecture method based on a distributed intelligent power grid, which comprises the following steps: performing discrete processing on historical and current data of the micro-grid respectively to obtain a historical transition state and a current transition state, and obtaining a transition probability matrix based on the historical transition state; based on the transition probability matrix, obtaining an electric power expected value according to the current transition state; calculating basic attributes of network nodes and side paths in the distributed power grid, and constructing a distributed power grid energy network; constructing a power dispatching strategy for surplus power distribution and electric energy supplement based on a distributed power grid energy network; and adding the power dispatching strategy into the existing distributed energy management system to complete the energy internet platform architecture of the distributed intelligent power grid. The method provided by the invention reduces the power fluctuation of the main power grid, stabilizes the power supply and improves the energy utilization efficiency while completing the low energy consumption complementation among the nodes.
Description
Technical Field
The application relates to the technical field of electric digital data processing, in particular to an energy internet platform architecture method based on a distributed intelligent power grid.
Background
With the development of new energy technology, the power supply mode of the urban power grid is gradually generated by a power station, and the power consumption mode of each unit is changed into a source-network-load-storage distributed energy power grid mode. In the mode, the power system is composed of a main power grid and a micro power grid, the micro power grid utilizes a new energy power generation device to perform self-circulation power utilization in a 'nearby collection, nearby storage and nearby use' mode, and the redundant electric quantity is uploaded to the main power grid, so that the purposes of environmental protection and energy conservation are achieved.
In the new distributed power grid, a new energy power generation device is adopted to generate power, and the power supply of the new energy power generation device is unstable, so that the new energy power generation device becomes 'garbage power' if not scheduled, energy waste is caused, and the service life of equipment is reduced. The conventional solution is to install a storage battery in the micro grid system, store the surplus power nearby, upload the power grid when the surplus power cannot be stored, and still may cause unstable power supply of the main grid.
Disclosure of Invention
In order to solve the technical problems, the application provides an energy internet platform architecture method based on a distributed smart grid, which is used for stabilizing power supply.
The invention provides an energy internet platform architecture method based on a distributed intelligent power grid, which comprises the following steps:
acquiring historical data of a micro-grid and current data of the micro-grid through a power grid system and a local weather station;
performing discrete processing on the historical data of the micro-grid and the current data of the micro-grid respectively to obtain a historical transition state and a current transition state, and obtaining a transition probability matrix based on the historical transition state;
based on the transition probability matrix, obtaining an electric power expected value according to the current transition state;
calculating basic attributes of network nodes and side paths in the distributed power grid through the electric power expected value, and constructing a distributed power grid energy network;
constructing a power dispatching strategy for surplus power distribution and electric energy supplement based on the distributed power grid energy network;
and adding the power dispatching strategy into the existing distributed energy management system to complete the energy internet platform architecture of the distributed intelligent power grid.
In some embodiments of the present invention, performing discrete processing on the historical data of the micro-grid and the current data of the micro-grid to obtain a historical transition state and a current transition state, and obtaining a transition probability matrix based on the historical transition state includes:
classifying the historical data of the micro-grid according to whether the historical data is holidays or not and the average temperature of the same day;
performing discrete processing on each type of historical data of the micro-grid and the current data of the micro-grid by using a Markov chain;
according to discrete processing results, combining the historical data of the micro-grid with the current data of the micro-grid to obtain a historical transfer state and a current transfer state;
and adopting Monte Carlo simulation to count the historical transition state, and obtaining a transition probability matrix of a steady state.
In some embodiments of the present invention, performing discrete processing on the historical data of the micro-grid and the current data of the micro-grid includes:
intensity of illuminationThe dispersion is as follows: very weak light, general light, strong light, and super strong light, corresponding to；
Wind speedThe dispersion is as follows: low wind, stroke, strong wind, super strong wind, corresponding to +.>；
Electric powerThe intensity dispersion by power variation is: rapidly decline and slowly descendDescending, keeping steady, slowly ascending and rapidly ascending, corresponding to +.>。
In some embodiments of the invention, the power variation intensity is:
；
in the method, in the process of the invention,indicating the intensity of the power change, +.>Representing the last moment of power, +.>Indicating the current power.
In some embodiments of the invention, obtaining an electric power expectation value from the current transition state based on the transition probability matrix comprises:
based on the transition probability matrix, acquiring a transition probability vector of the current transition state according to the current transition stateSaid transition probability vector->Each element in the list corresponds to the transition state of the next moment;
the transition probability vectorExtracting the power change intensity in the corresponding transition state, and obtaining a power change intensity vector by keeping the order arrangement>；
The transition probability vectorExtracting the power variation intensity in the corresponding transition state, and obtaining power variation intensity vector by keeping the sequence>；
According to the transition probability vectorThe generation power variation intensity vector +.>And said power consumption variation intensity vector +.>An electric power desired value is obtained.
In some embodiments of the invention, the electric power expectation value is: the transition probability vectorElement->Power generation power variation intensity vector->Or the intensity vector of the change of the electric power>Element->Element of corresponding position, current generation power or power consumption +.>Multiplying and summing the products by traversing the elements in the corresponding vector to obtain the electric power expected value +.>。
In some embodiments of the invention, the basic attributes of the network nodes and edges in the distributed power network include:
power capacity of the microgrid nodes:
；
in the method, in the process of the invention,indicating the desired value of the electric power to be used,/->Representing the desired value of the generated power,/-, and>representing the power capacity of the microgrid node;
power capacity of the battery node:
；
in the method, in the process of the invention,representing the remaining capacity of the battery, +.>Indicating the used capacity of the battery, +.>Is the sampling time interval, +.>Indicating the rated charge-discharge power of the battery pack, +.>Representing the power capacity of the battery node;
power capacity of the power plant node:
will power the plantAs a special battery node, its power capacityCorresponding changes in the power regulation strategy;
side path attributes between non-battery nodes:
side path distance of micro-grid or power plant nodeEqual to the power loss rate of the corresponding transmission line +.>Capacity of side road->Equal to the rated transmission power of the corresponding transmission line +.>；
Side path attributes between battery nodes and other nodes:
side path distance between battery pack node and micro-grid node or power plant nodeThe method comprises the following steps:
；
in the method, in the process of the invention,indicating the power loss rate, ">Indicating charge-discharge efficiency of the battery pack,/-)>Representing the distance of the side road;
capacity of side roadEqual to the power capacity of the battery pack->。
In some embodiments of the present invention, constructing a power scheduling policy for surplus power distribution based on the distributed grid energy network includes:
setting all battery pack nodes as charging states, enabling the node power capacity of power plant nodes to be positive infinity, traversing nodes with negative power capacity, traversing all side ways of each negative capacity node, and obtaining that the power capacity of the other end of the side way is larger than 0 and the distance between the side waysThe smallest side road is +.>Ordering the nodes from big to small to obtain a residual electricity ordering table;
the head node of the residual electricity sequencing table is used as a transmitting endCalculating new power capacity of transmitting end>The method comprises the following steps: old power capacity of sender->Minimum value of the capacity of the side path and the old power capacity of the receiving endThe two are added to obtain the new power capacity of the transmitting end>；
The power capacity of the node is updated as follows:
A. if it isThe power capacity of the receiving end is updated as follows: reception ofOld power capacity of a terminalOld power capacity with sender->And (3) summing;
B. if it isThe power capacity of the receiving end is updated as follows: old power capacity of receiving endMinimum value of the capacity of the side channel and the old power capacity of the receiving end>The difference between the two is the new power capacity of the receiving terminal +.>；
Power capacity of transmitting endUpdated to->；
After the update is completed, if the power capacity of the transmitting endTraversing other side paths of the transmitting end node to obtain power capacity of the other end of the side path>A side road having a side road distance greater than 0 and the smallest side road distance +.>Reordering the nodes in a residual electricity ranking table;
the above steps are repeated until there are no elements in the residual electricity ordering table.
In some embodiments of the present invention, constructing a power scheduling policy for power replenishment based on the distributed grid energy network includes:
setting a battery pack node which does not execute a power receiving task in a discharging power regulating strategy as a discharging state, setting the power capacity of a power plant node as minus infinity, traversing all nodes with positive power capacities, and obtaining the power capacity of the other end of the side roadLess than 0 and side distance +.>The smallest side road is +.>Ordering the nodes from big to small to obtain a charging ordered list;
calculating new power capacity of the receiving end by taking the head node of the charging ordered list as the receiving endThe method comprises the following steps: old power capacity of receiver->The absolute value of the capacity of the side path and the old power capacity of the transmitting end takes the minimum valueThe two are subtracted to obtain new power capacity of the receiving end>；
The node capacity is updated as follows:
A. if it isThe power capacity of the update transmitting end is as follows: old power capacity of sender->Old power capacity with receiving end->And (3) summing;
B. if it isThe power capacity of the update transmitting end is as follows: old power capacity of sender->Minimum value of the capacity of the side channel and the old power capacity of the receiving end>The sum of the two is the new power capacity of the transmitting end +.>；
Power capacity of receiving end nodeUpdated to->；
After the update is completed, if the power capacity of the receiving endTraversing other lines of the receiving end node to obtain power capacity of the other end of the side line>A side road with a side road distance of less than 0 and the smallest side road distance +.>Reordering the nodes in a charge ordering table;
the above steps are repeated until there are no elements in the charge schedule.
In some embodiments of the present invention, the historical data of the micro-grid and the current data of the micro-grid include the generated power and the used power of the micro-grid, and the illumination intensity, the wind speed level and the air temperature of the area where the micro-grid is located.
As can be seen from the above embodiments, the energy internet platform architecture method based on the distributed smart grid provided by the embodiments of the present application has the following beneficial effects:
according to the embodiment of the invention, the historical data of the micro-grid and the current data of the micro-grid are obtained through a power grid system and a local weather station; performing discrete processing on the historical data of the micro-grid and the current data of the micro-grid respectively to obtain a historical transition state and a current transition state, and obtaining a transition probability matrix based on the historical transition state; based on the transition probability matrix, obtaining an electric power expected value according to the current transition state; calculating basic attributes of network nodes and side paths in the distributed power grid through the electric power expected value, and constructing a distributed power grid energy network; constructing a power dispatching strategy for surplus power distribution and electric energy supplement based on the distributed power grid energy network; and adding the power dispatching strategy into the existing distributed energy management system to complete the energy internet platform architecture of the distributed intelligent power grid. According to the embodiment of the invention, the expected value of the generated power and the expected value of the used power at the next moment of a single micro-grid are calculated through a Markov chain to obtain a predicted power gap, and the influence of unstable power of a distributed energy network on electric energy supply can be avoided through finishing regulation and control of the predicted value; the method comprises the steps that a micro-grid, a power plant and a storage battery pack are used as nodes, lines among node devices are abstracted to be connected, and related parameters of a predicted power gap, the lines and the devices are used as references, so that a node basic attribute power capacity is given, and a connecting line basic attribute side-path distance and side-path width are given to form a distributed grid energy network; and the power regulation strategy is further constructed through the distributed power grid energy network, so that the power fluctuation of the main power grid is reduced, the power supply is stabilized, and the energy utilization efficiency is improved while the low energy consumption complementation among all nodes is completed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a basic flow diagram of an energy internet platform architecture method based on a distributed smart grid according to an embodiment of the present application;
fig. 2 is a basic flow chart of a transition probability matrix acquisition method according to an embodiment of the present application.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution 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 only some embodiments of the present invention, not all embodiments. 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.
The following describes in detail an energy internet platform architecture method based on a distributed smart grid according to this embodiment with reference to the accompanying drawings.
Fig. 1 is a basic flow diagram of an energy internet platform architecture method based on a distributed smart grid, which is provided in an embodiment of the present application, and as shown in fig. 1, the method specifically includes the following steps:
s100: and acquiring historical data of the micro-grid and current data of the micro-grid through a grid system and a local weather station.
And acquiring historical data of the micro-grid and current data of the micro-grid through a grid system and a local weather station. The microgrid history data and the microgrid current data comprise: and acquiring five types of data, namely the power generation power of the micro-grid and the power consumption power of the micro-grid, namely the illumination intensity, the wind speed grade and the air temperature of the area where the micro-grid is located by taking the day as a unit. The micro-grid power generation power and micro-grid power consumption power are obtained through a grid control system; the illumination intensity, the wind speed grade and the air temperature are obtained through a local weather station. By a means ofThe sampling time interval with data isEmpirical value->Second. It is also necessary to obtain whether the day is holiday.
In addition, the electric energy loss rate during electric power transmission among the micro-grid, the power plant and the distributed storage battery packs is required to be obtained, and the charge and discharge efficiency of each distributed storage battery pack is required to be obtained.
S200: and respectively performing discrete processing on the historical data of the micro-grid and the current data of the micro-grid to obtain a historical transition state and a current transition state, and obtaining a transition probability matrix based on the historical transition state.
Fig. 2 is a basic flow chart of a transition probability matrix obtaining method provided in an embodiment of the present application, as shown in fig. 2, in which historical data of a micro-grid and current data of the micro-grid are separately processed in a discrete manner to obtain a historical transition state and a current transition state, and the transition probability matrix is obtained based on the historical transition state, which specifically includes the following steps:
s201: the microgrid history data are classified according to whether the microgrid history data are holidays or not and average temperatures of the holidays.
Because the data information is complex, in order to reduce the calculated amount of the transition probability matrix, the data is pre-classified as follows:
dividing whether the data is holidays into two types; the average temperature on the day is calculated, the temperature is high at 20 ℃ and above, the temperature is normal at 10 ℃ to 20 ℃, and the temperature below 10 ℃ is divided into three types. The data is divided into 6 classes, for example, one class of historical data is high-temperature holiday data, and when the probability matrix is calculated, a single probability matrix is calculated from the data, so that 6 probability matrices can be obtained. When the power prediction method is used, firstly, holiday information of the current day is obtained, the weather forecast predicts the average air temperature, and then the corresponding probability matrix is selected to realize the power prediction.
S202: and carrying out discrete processing on each type of historical data of the micro-grid and the current data of the micro-grid by using a Markov chain.
In step S201, the historical data of the micro-grid are classified into 6 categories, and then each category of historical data of the micro-grid and the current data of the micro-grid are analyzed by using markov chains alone, and first, the historical data of the micro-grid and the current data of the micro-grid are subjected to discrete processing. Performing discrete processing on historical data of the micro-grid and current data of the micro-grid, wherein the discrete processing comprises the following steps:
first, the illumination intensity is recorded asThe wind speed grade is marked as->The power generation is recorded as->The electric power is recorded as->。
Intensity of illuminationTaking the base 10 logarithm, then discretizing into 5 types of data, respectively: very weak light, general light, strong light, super strong light, corresponding to +.>。
Wind speedAccording to typhoon class classification, the typhoon is classified into 0 to 12, and according to typhoon service and service provision, the typhoon class classification is expanded into 13 to 17, so that the typhoon is divided into 4 classes, namely: low wind, apoplexy, strong wind, ultra-strong wind, corresponding to。
Electric powerThe power change intensity is discretized, and the power change intensity is calculated firstlyThe degree is as follows:
；
in the method, in the process of the invention,indicating the intensity of the power change, +.>Representing the last moment of power, +.>Indicating the current power. Intensity of power variation->Can represent the variation intensity of the generated or used electric power.
When the power is not changed,the method comprises the steps of carrying out a first treatment on the surface of the When the power is doubled, ">The method comprises the steps of carrying out a first treatment on the surface of the When the power is halved, +.>. Therefore, the power generation power or the power consumption power is +/according to the power variation intensity thereof>The discretization is 5 kinds, respectively: rapidly descending, slowly descending, keeping steady, slowly ascending, rapidly ascending, correspondingly +.>. The five discrete states being replaced by numbers, e.g. rapid decrease +.>Instead, the five kinds of states respectively correspond to。
S203: and according to the discrete processing result, combining the historical data of the micro-grid with the current data of the micro-grid to obtain a historical transition state and a current transition state.
The data collected at each sampling time point is processed according to the discrete method、/>、/>、/>The data is discretized and then combined to obtain a historical transition state and a current transition state. Such as: the data at a certain moment is calculated as extremely weak illumination, low wind, the generated power is rapidly reduced, and the power consumption is kept stable, so that the data at the certain moment belongs toThis state. Since discrete data is respectively 5 kinds, 4 kinds, 5 kinds and 5 kinds, it is obvious that there are 500 possible transition states of the data.
S204: and adopting Monte Carlo simulation to count historical transition states, and obtaining a transition probability matrix of a steady state.
The transition probability matrix of the steady state is obtained by adopting Monte Carlo to simulate and count the historical transition state, and the specific calculation process is a well-known technology in the field and is not repeated.
S300: based on the transition probability matrix, an electric power expected value is obtained according to the current transition state.
When electric power prediction is carried out, current data of the micro-grid is firstly obtained, and the current transition state is obtained through discrete calculation. The method for acquiring the current data and the current transfer state of the micro-grid is synchronous with the step S200, and the historical data and the historical transfer state of the micro-grid are acquired by colleagues in the step S200.
Based on the transition probability matrix, according to the current transitionAnd a state, obtaining an electric power expected value. Further may include: based on the transition probability matrix, acquiring a transition probability vector of the current transition state according to the current transition stateTransition probability vector->Each element in the list corresponds to the transition state of the next moment; transfer probability vector +.>Extracting the power change intensity in the corresponding transition state, and obtaining a power change intensity vector by keeping the order arrangement>The method comprises the steps of carrying out a first treatment on the surface of the Transfer probability vector +.>Extracting the power variation intensity in the corresponding transition state, and obtaining power variation intensity vector by keeping the sequence>The method comprises the steps of carrying out a first treatment on the surface of the According to the transition probability vector->Power generation power variation intensity vector->And the power variation intensity vector +.>An electric power desired value is obtained. The electric power expected value is: transition probability vector->Element->Power generation power variation intensity directionQuantity->Or the intensity vector of the change of the electric power>Element->Element of corresponding position, current generation power or power consumption +.>Multiplying and summing the products by traversing the elements in the corresponding vector to obtain the electric power expected value +.>. Namely:
；
in the method, in the process of the invention,representing transition probability vector +.>Represents the intensity vector of the change in the generated powerOr the intensity vector of the change of the electric power>Element->Element of corresponding position->Representing the current generated power or the used power; traversing the multiplication and summation of the elements in the corresponding vector to obtain the electric power expected value +.>Can be used as the predicted electric power at the next time.
The expected values of the generated power and the used power are respectively recorded asAnd->Meanwhile, the power scheduling strategy obtained by the method has priori property, the power scheduling scheme of the power grid can be changed in advance, and the power supply fluctuation is reduced.
S400: and calculating basic attributes of network nodes and side paths in the distributed power grid through the electric power expected value, and constructing the distributed power grid energy network.
The electric power expected value is obtained through step S300, and is further calculated as a distributed grid energy network to guide electric power dispatching according to the electric power expected value, specifically as follows:
according to experience, the devices can be classified into a micro-grid, a battery pack and a power plant in the distributed power grid, and a power transmission line exists among the devices, so that the micro-grid, the battery pack and the power plant are used as network nodes, and the power transmission line is used as a network side circuit, and the distributed power grid energy network is formed.
The basic attribute of a node is power capacityThe positive and negative of the node represent that the node is receiving or outputting electric quantity, and the absolute value of the node represents the power of the receiving or outputting electric quantity; taking a transmission line between nodes as an edge, wherein the basic attribute is the distance between the edges +.>Sum of side capacity->Wherein->Representing the power transmitting end, ">Representing the power receiving end, the side path distance represents the power transmission loss between the nodes, and the side path capacity represents the maximum transmission power which can be born by the power transmission line.
The calculation process of the basic attributes of the network nodes and the side paths in the distributed power grid comprises the following steps:
1. calculating the power capacity of the micro-grid node:
；
in the method, in the process of the invention,indicating the desired value of the electric power to be used,/->Representing the desired value of the generated power,/-, and>representing the power capacity of the microgrid nodes. When->Representing that the micro-grid may be out of power for the next second, when +.>Representing that the micro-grid reaches an electrical balance in the next second, when +.>Representing the possible remaining power of the micro-grid for the next second.
2. Calculating the power capacity of the battery nodes:
since the battery pack has a charging state and a discharging state, the power capacity is different in different states, and therefore, the power capacity calculation process is as follows:
；
in the method, in the process of the invention,representing the remaining capacity of the battery, +.>Indicating the used capacity of the battery, +.>Is that the empirical value of the sampling time interval is taken as 1 second, < + >>Indicating the rated charge-discharge power of the battery pack, +.>Representing the power capacity of the battery node.
Because the maximum power of the battery pack cannot be exceededThus pair->And capacitance time ratio takes the minimum value as +.>The method comprises the steps of carrying out a first treatment on the surface of the Since the charge state of the battery pack receives electric quantity, and the discharge state is the transmitted electric quantity, the power capacity is positive and negative correspondingly; to sum up to->。
3. Calculating the power capacity of the power plant node:
the power plant is used as a special battery pack node, and the power capacity of the power plant isAnd correspondingly changes in the power regulation strategy. The power plant is used as a special battery pack node, and in the surplus electricity distribution, the power capacity is positive infinity; in the power-shortage distribution, the power capacity is minus infinity. The power plant can be regarded as a special kind ofAnd a battery pack node. Thus, the power capacity of the power plant node +.>The acquisition method of (2) and the power capacity of the battery node +.>The acquisition method is the same.
4. Calculating the side path attribute between the non-battery nodes:
side path distance of micro-grid or power plant nodeEqual to the power loss rate of the corresponding transmission line +.>Capacity of side road->Equal to the rated transmission power of the corresponding transmission line +.>。
5. Computing the side path attribute between the battery pack node and other nodes:
side path distance between battery pack node and micro-grid node or power plant nodeIs of the formula:
；
in the method, in the process of the invention,indicating the power loss rate, ">Indicating charge-discharge efficiency of the battery pack,/-)>Representing the road distance.
Capacity of side roadEqual to the power capacity of the battery pack->。
And (3) calculating various basic attributes in the network to obtain the distributed power grid energy network.
S500: and constructing a power dispatching strategy for surplus power distribution and electric energy supplement based on the distributed power grid energy network.
In step S400, a distributed grid energy network is obtained. Analyzing a distributed power grid energy network, wherein a micro-grid node with negative power capacity possibly has redundant electric quantity in the next second, and a micro-grid node with positive power capacity possibly lacks electric quantity in the next second, so that power allocation can be completed among the nodes based on the power capacity, and the specific process is as follows:
on the premise of energy conservation, surplus electricity distribution is carried out as follows:
1. setting all battery nodes as charging states, setting the node power capacity of the power plant node as positive infinity, traversing the nodes with negative power capacity, traversing all side ways of each negative capacity node, and obtaining the power capacity of the other end of the side wayIs greater than 0 and the side path distance +.>The smallest side road is +.>And ordering the nodes from large to small to obtain the residual electricity ordering table.
2. The head node of the residual electricity sequencing table is used as a transmitting endMeter (D)Calculating new power capacity of transmitting end>The method comprises the following steps: old power capacity of sender->Minimum value of the capacity of the side channel and the old power capacity of the receiving end>The two are added to obtain the new power capacity of the transmitting end>The method comprises the following steps:
；
in the method, in the process of the invention,indicating old power capacity of sender, +.>Representing the minimum value of the capacity of the side path and the old power capacity of the receiving end; the two are subtracted to obtain +.>Is the new power capacity of the transmitting end. If->The transmitting end still has the remaining power if +.>Then the residual power of the sending end is eliminated.
3. The power capacity of the node is updated as follows:
A. if it isAt this time, the remaining power of the transmitting terminal is eliminated, and the power capacity of the transmitting terminal is +.>And setting 0. The power capacity of the receiving end is updated as follows: old power capacity of receiver->Old power capacity with senderThe sum is that:
；
in the method, in the process of the invention,representing the old power capacity of the receiving end, +.>Representing the old power capacity of the transmitting end, and adding the two to obtain +.>Is the new power capacity of the receiving end.
B. If it isAt this time, the transmitting segment still has surplus power, and the power capacity of the receiving end is possibly full, and the width of the side path is possibly too small, so that the power capacity of the receiving end is updated as follows: old power capacity of receiver->Minimum value of the capacity of the side channel and the old power capacity of the receiving end>The difference between the two is the new power capacity of the receiving terminal +.>The method comprises the following steps:
；
in the method, in the process of the invention,representing the old power capacity of the receiving end, +.>Representing the minimum value of the side path capacity and the old power capacity of the receiving end, and subtracting the two to obtain the new power capacity of the receiving end>。
At this time, the power capacity of the transmitting end is reducedUpdated to->。
4. After the update is completed, if the power capacity of the transmitting endRepresenting that the transmitting end still has residual electric quantity, traversing other side paths of the transmitting end node, and obtaining the power capacity of the other end of the side path>A side road having a side road distance greater than 0 and the smallest side road distance +.>The nodes are reordered in the residual electricity ordering table.
5. Repeating steps 2, 3 and 4 until there are no more elements in the residual electric ordering table.
The step sends the redundant electric quantity to the node with the lowest electric quantity loss in sequence, and the redundant electric quantity of the micro-grid is fully utilized. And recording the side road use condition in the step as a power regulation strategy, and using the side road use condition as a power regulation strategy of the next second to store the redundant electric quantity in situ and use the redundant electric quantity in situ, so that the adverse effect on a main power grid is reduced while energy is saved.
On the premise of energy conservation, the stored electric quantity is called to supplement the electric energy to the power failure node, and the process is as follows:
1. setting a battery node which does not execute a power receiving task in a discharging and power regulating strategy as a discharging state, setting the power capacity of a power plant node as minus infinity, traversing all nodes with positive power capacity, and obtaining the power capacity of the other end of the side roadLess than 0 and side distance +.>The smallest side road is +.>And ordering the nodes from large to small to obtain a charging ordered list.
2. Calculating new power capacity of the receiving end by taking the head node of the charging ordered list as the receiving endThe method comprises the following steps: old power capacity of receiver->The absolute value of the capacity of the side path and the old power capacity of the transmitting end takes the minimum valueThe two are subtracted to obtain new power capacity of the receiving end>The method comprises the following steps:
；
in the method, in the process of the invention,representing the old power capacity of the receiving end, +.>The absolute value representing the side path capacity and the old power capacity of the transmitting end takes the minimum value; the two are subtracted to obtain +.>Is the new power capacity of the receiving end. If->The receiving end is still lack of power, if +.>The receiving end electric quantity gap is satisfied.
3. The node capacity is updated as follows:
A. if it isThe electric quantity gap of the receiving end is satisfied, and the power capacity of the receiving end is obtained at the momentUpdated to 0. The power capacity of the update transmitting end is as follows: old power capacity of sender->Old power capacity with receiving end->The sum is that:
；
in the method, in the process of the invention,indicating old power capacity of sender, +.>Representing the old power capacity of the receiving end, and adding the two to obtain +.>Is the new power of the transmitting endCapacity. />
B. If it isAt this time, the receiving end still lacks power, which may be that the power capacity of the transmitting end is insufficient, and may be that the width of the side path is too small, so that the power capacity of the transmitting end is updated as follows: old power capacity of sender->Minimum value of the capacity of the side channel and the old power capacity of the receiving end>The sum of the two is the new power capacity of the transmitting end +.>Namely:
；
in the method, in the process of the invention,indicating old power capacity of sender, +.>Representing the minimum value of the side path capacity and the old power capacity of the receiving end, and adding the two to obtain the new power capacity of the transmitting end +.>。
At this point the power capacity of the receiving end node will beUpdated to->。
4. After the update is completed, if the power capacity of the receiving endThenRepresenting that the receiving end still lacks electric quantity, traversing other lines of the receiving end node to obtain the power capacity of the other end of the side line>A side road with a side road distance of less than 0 and the smallest side road distance +.>The nodes are reordered in a charge ordering table.
5. Repeating steps 2, 3, 4 until there are no elements in the charge schedule.
The above steps will require power from the lowest power loss node that lacks power. And the side road use condition in the steps is recorded as a power regulating strategy, and the side road use condition is used as a power regulating strategy of the next second, so that the adverse effect on a main power grid is reduced while energy is saved.
S600: and adding the power dispatching strategy into the existing distributed energy management system to complete the energy internet platform architecture of the distributed intelligent power grid.
The energy allocation method of the distributed intelligent power grid described in the foregoing embodiment is added into the existing distributed energy management system, the distributed power grid energy network of the next second is calculated according to the steps S200 to S400 through the historical data of the micro power grid and the current data of the micro power grid, and the power allocation strategy is obtained according to the step S500 by utilizing the network, so that the system is guided to allocate power, and the energy internet platform architecture of the distributed intelligent power grid is completed.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
It should be noted that unless otherwise specified and limited, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, the statement "comprises one … …" does not exclude that an additional identical element is present in an article or device that comprises the element. In addition, the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items, and the symbol/label is used herein for convenience of description only.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (9)
1. An energy internet platform architecture method based on a distributed smart grid, which is characterized by comprising the following steps:
acquiring historical data of a micro-grid and current data of the micro-grid through a power grid system and a local weather station;
performing discrete processing on the historical data of the micro-grid and the current data of the micro-grid respectively to obtain a historical transition state and a current transition state, and obtaining a transition probability matrix based on the historical transition state;
based on the transition probability matrix, obtaining an electric power expected value according to the current transition state;
calculating basic attributes of network nodes and side paths in the distributed power grid through the electric power expected value, and constructing a distributed power grid energy network;
constructing a power dispatching strategy for surplus power distribution and electric energy supplement based on the distributed power grid energy network;
adding the power dispatching strategy into the existing distributed energy management system to complete an energy internet platform architecture of a distributed intelligent power grid;
basic attributes of network nodes and edges in a distributed power grid include:
power capacity of the microgrid nodes:
in the method, in the process of the invention,indicating the desired value of the electric power to be used,/->Representing the desired value of the generated power,/-, and>representing the power capacity of the microgrid node;
power capacity of the battery node:
in the method, in the process of the invention,representing the remaining capacity of the battery, +.>Indicating the used capacity of the battery, +.>Is the sampling time interval, +.>Indicating the rated charge-discharge power of the battery pack, +.>Representing the power capacity of the battery node;
power capacity of the power plant node:
the power plant is used as a special battery pack node, and the power capacity of the power plant isCorresponding changes in the power regulation strategy;
side path attributes between non-battery nodes:
side path distance of micro-grid or power plant nodeEqual to the power loss rate of the corresponding transmission line +.>Capacity of side road->Equal to the rated transmission power of the corresponding transmission line +.>；
Side path attributes between battery nodes and other nodes:
side path distance between battery pack node and micro-grid node or power plant nodeThe method comprises the following steps:
in the method, in the process of the invention,representing electrical energyLoss rate->Indicating charge-discharge efficiency of the battery pack,/-)>Representing the distance of the side road;
capacity of side roadEqual to the power capacity of the battery pack->。
2. The energy internet platform architecture method based on the distributed smart grid according to claim 1, wherein the discrete processing is performed on the historical data of the micro grid and the current data of the micro grid to obtain a historical transition state and a current transition state, and the transition probability matrix is obtained based on the historical transition state, and the method comprises the steps of:
classifying the historical data of the micro-grid according to whether the historical data is holidays or not and the average temperature of the same day;
performing discrete processing on each type of historical data of the micro-grid and the current data of the micro-grid by using a Markov chain;
according to discrete processing results, combining the historical data of the micro-grid with the current data of the micro-grid to obtain a historical transfer state and a current transfer state;
and adopting Monte Carlo simulation to count the historical transition state, and obtaining a transition probability matrix of a steady state.
3. The energy internet platform architecture method based on a distributed smart grid according to claim 2, wherein performing discrete processing on the historical data of the micro grid and the current data of the micro grid comprises:
intensity of illuminationThe dispersion is as follows: very weak light, general light, strong light, and super strong light, corresponding to；
Wind speedThe dispersion is as follows: low wind, stroke, strong wind, super strong wind, corresponding to +.>；
Electric powerThe intensity dispersion by power variation is: rapidly descending, slowly descending, keeping steady, slowly ascending, rapidly ascending, corresponding +.>。
4. The energy internet platform architecture method based on the distributed smart grid according to claim 3, wherein the power variation intensity is:
in the method, in the process of the invention,indicating the intensity of the power change, +.>Representing the last moment of power, +.>Indicating the current power.
5. The distributed smart grid-based energy internet platform architecture method of claim 1, wherein obtaining an electric power expectation value from the current transition state based on the transition probability matrix comprises:
based on the transition probability matrix, acquiring a transition probability vector of the current transition state according to the current transition stateSaid transition probability vector->Each element in the list corresponds to the transition state of the next moment;
the transition probability vectorExtracting the power change intensity in the corresponding transition state, and obtaining a power change intensity vector by keeping the order arrangement>；
The transition probability vectorExtracting the power variation intensity in the corresponding transition state, and obtaining power variation intensity vector by keeping the sequence>；
According to the transition probability vectorThe generation power variation intensity vector +.>And saidIntensity vector of variation of electric power>An electric power desired value is obtained.
6. The distributed smart grid-based energy internet platform architecture method of claim 5, wherein the electric power expectations are: the transition probability vectorElement->Power generation power variation intensity vector->Or the intensity vector of the change of the electric power>Element->Element of corresponding position, current generation power or power consumption +.>Multiplying and summing the products by traversing the elements in the corresponding vector to obtain the electric power expected value +.>。
7. The distributed smart grid-based energy internet platform architecture method of claim 1, wherein constructing a power scheduling policy for surplus power distribution based on the distributed grid energy network comprises:
setting all battery pack nodes as charging states, setting the node power capacity of the power plant nodes as positive infinity, and traversing the power capacityTraversing all the side ways of each negative capacity node by nodes with negative quantity to obtain the power capacity of the other end of the side way which is more than 0 and the distance of the side wayThe smallest side road is +.>Ordering the nodes from big to small to obtain a residual electricity ordering table;
the head node of the residual electricity sequencing table is used as a transmitting endCalculating new power capacity of transmitting end>The method comprises the following steps: old power capacity of sender->Minimum value of the capacity of the side channel and the old power capacity of the receiving end>The two are added to obtain the new power capacity of the transmitting end>；
The power capacity of the node is updated as follows:
A. if it isThe power capacity of the receiving end is updated as follows: old power capacity of receiver->Old power capacity with sender->And (3) summing;
B. if it isThe power capacity of the receiving end is updated as follows: old power capacity of receiver->Minimum value of the capacity of the side channel and the old power capacity of the receiving end>The difference between the two is the new power capacity of the receiving terminal +.>；
Power capacity of transmitting endUpdated to->；
After the update is completed, if the power capacity of the transmitting endTraversing other side paths of the transmitting end node to obtain power capacity of the other end of the side path>A side road having a side road distance greater than 0 and the smallest side road distance +.>Reordering the nodes in a residual electricity ranking table;
the above steps are repeated until there are no elements in the residual electricity ordering table.
8. The distributed smart grid-based energy internet platform architecture method of claim 1, wherein constructing a power scheduling policy for power replenishment based on the distributed grid energy network comprises:
setting a battery pack node which does not execute a power receiving task in a discharging power regulating strategy as a discharging state, setting the power capacity of a power plant node as minus infinity, traversing all nodes with positive power capacities, and obtaining the power capacity of the other end of the side roadLess than 0 and side distance +.>The smallest side road is +.>Ordering the nodes from big to small to obtain a charging ordered list;
calculating new power capacity of the receiving end by taking the head node of the charging ordered list as the receiving endThe method comprises the following steps: old power capacity of receiver->The absolute value of the capacity of the side path and the old power capacity of the transmitting end takes the minimum valueThe two are subtracted to obtain new power capacity of the receiving end>；
The node capacity is updated as follows:
A. if it isThe power capacity of the update transmitting end is as follows: old power capacity of sender->Old power capacity with receiving end->And (3) summing;
B. if it isThe power capacity of the update transmitting end is as follows: old power capacity of sender->Minimum value of the capacity of the side channel and the old power capacity of the receiving end>The sum of the two is the new power capacity of the transmitting end +.>；
Power capacity of receiving end nodeUpdated to->；
After the update is completed, if the power capacity of the receiving endTraversing other lines of the receiving end node to obtain power capacity of the other end of the side line>A side road with a side road distance of less than 0 and the smallest side road distance +.>Reordering the nodes in a charge ordering table;
the above steps are repeated until there are no elements in the charge schedule.
9. The energy internet platform architecture method based on the distributed smart grid according to claim 1, wherein the historical data of the micro grid and the current data of the micro grid comprise the generated power and the used power of the micro grid, and the illumination intensity, the wind speed level and the air temperature of the area where the micro grid is located.
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