CN117293807A - Multi-time scale optimization method and system for information side model of power distribution network - Google Patents
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Abstract
The invention discloses a multi-time scale optimization method and a multi-time scale optimization system for an information side model of a power distribution network, wherein the information side model is established according to an information communication interaction process of the power distribution network and comprises an information node model, an information branch model and a controlled equipment model; presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimization actions; and according to different time scale optimization strategies and information side models, and combining model optimization requirements, the information side model optimization of the power distribution network is realized. Faults or errors caused by the information side model in the power distribution network modeling process are avoided.
Description
Technical Field
The invention relates to the technical field of power distribution network information side model multi-time-scale optimization, in particular to a power distribution network information side model multi-time-scale optimization method and system.
Background
Due to different development conditions such as population density, industry and commerce, different areas, different provinces, even between an urban power distribution network and a rural power distribution network, the difference of power consumption requirements, investment capacity and development level is obvious. It is difficult to consider the investment and construction requirements of different development level areas at the same time by adopting the exact same technical means and management means without considering the differences. Therefore, in the power distribution network construction process, power distribution network modeling is needed to be carried out in advance according to the regional requirements, and economic losses are reduced.
At present, technology is mature in terms of physical modeling of a power distribution network, but the information-physical interaction mechanism is still unclear, and the information factors are not fully considered by the existing power system calculation and analysis model. In the process of establishing a power distribution network model, the power distribution network information side model plays a vital role for the whole power distribution network model, most of the prior art directly models the information transmission process, if the model is wrong in the verification process, the whole model needs to be overturned to carry out model establishment again, projects cannot be completed on time, and early investment is wasted. Therefore, a method for optimizing the information side model of the power distribution network is needed.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a power distribution network information side model multi-time-scale optimization method and system, which can solve the problems in the background technology.
In order to solve the technical problems, the invention provides a multi-time scale optimization method for a power distribution network information side model, which comprises the following steps:
according to the information communication interaction process of the power distribution network, an information side model is established, wherein the information side model comprises an information node model, an information branch model and a controlled equipment model;
presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimizing actions;
and according to the different time scale optimization strategies and the information side model, and combining model optimization requirements, the information side model optimization of the power distribution network is realized.
As a preferable scheme of the power distribution network information side model multi-time scale optimization method, the invention comprises the following steps: the preset different time scale optimization strategies comprise an hour level optimization strategy, a minute level optimization strategy and a real-time optimization strategy;
the hour level optimizing strategy takes the network loss, the adjustable reactive power reserve of the inverter and the equipment adjusting cost as objective functions to act on the taps of the capacitor and the on-load transformer, and the hour level optimizing strategy optimizes the interval to be one hour;
the minute level optimizing strategy optimizes the output reactive power value of the inverter and transmits the output reactive power value to the inverter by taking the grid loss and the adjustable reactive power reserve of the inverter as objective functions, and the minute level optimizing strategy optimizes the interval of fifteen minutes;
and the inverter adjusts reactive power output according to the real-time voltage measurement value and the given control parameter, and the real-time optimization strategy is optimized at thirty seconds intervals.
As a preferable scheme of the power distribution network information side model multi-time scale optimization method, the invention comprises the following steps: the preset different time scale optimization strategy further comprises,
the tide model in the hour level optimization strategy and the minute level optimization strategy is as follows:
||[2P ij,t 2Q ij,t l ij,t -u i,t ] T || 2 ≤l ij,t +u i,t
u i,t ＝(V i,t ) 2
l ij,t ＝(I ij,t ) 2
wherein P is ij,t 、Q ij,t Respectively the active power and the reactive power flowing through the down branch ij at the moment t, wherein J (i) is a set of nodes connected with the node i; p (P) hi,t 、Q hi,t 、I hi The active, reactive and current squares, r, respectively, of the branch hi flowing at time t hi 、x hi H (i) is a set of nodes connected with the node i, which is the resistance and reactance of the branch hi; the active load value of the node i;capacity for line ij; u (u) i,t For voltage V at node i at time t i,t Is the square of (2); l (L) ij,t For the current I flowing in the lower branch ij at time t ij,t Square of (d).
As a preferable scheme of the power distribution network information side model multi-time scale optimization method, the invention comprises the following steps: the preset different time scale optimization strategy further comprises,
the on-load voltage regulating transformer model in the hour level optimization strategy and the minute level optimization strategy is as follows:
Tap k ∈{-10,-9,...,0,...,9,10},d k,t ∈{0,1}
wherein,tap is in gear Tap k The voltage value of the node connected with the transformer; u (u) 0,t Is the voltage value of the node connected with the transformer; d, d k,t For indicating where the tap is located.
As a preferable scheme of the power distribution network information side model multi-time scale optimization method, the invention comprises the following steps: the preset different time scale optimization strategy further comprises,
the capacitor bank model in the hour level optimization strategy and the minute level optimization strategy is as follows:
wherein,reactive output of the capacitor bank installed at node i at time t; />Is the gear of the capacitor bank; />Reactive capacity is adjusted for the minimum of the capacitor bank; />Is the maximum reactive output of the capacitor bank.
As a preferable scheme of the power distribution network information side model multi-time scale optimization method, the invention comprises the following steps: the preset different time scale optimization strategy further comprises,
the real-time optimization strategy comprises control logic of an inverter running state, wherein the inverter running state comprises a first running state, a second running state and a third running state;
when the reactive output of the inverter is constant as a reference value, the inverter is in a first running state, and the state is expressed as mathematical logic:
when the reactive power output of the inverter increases and is higher than the reference value, the inverter is in a second operation state, and the state is expressed as mathematical logic:
when the reactive power output of the inverter is reduced and is lower than the reference value, the inverter is in a third operation state, and the state is expressed as mathematical logic:
wherein delta i,t,1 、δ i,t,2 Delta i,t,3 For the introduced binary logic variable, for representing the inverter state, V i,t Represents the voltage amplitude of bus i at time t,represents the upper limit of the voltage amplitude reference value,Vrepresenting the lower limit of the voltage amplitude reference value.
As a preferable scheme of the power distribution network information side model multi-time scale optimization method, the invention comprises the following steps: the preset different time scale optimization strategy further comprises,
the voltage state and reactive output equation of the inverter in the real-time optimization strategy is as follows:
the change amount of the voltage amplitude and reactive output of the inverter in the real-time optimization strategy is as follows:
wherein,for in-situ regulation of the voltage amplitude of the rear bus i, deltat is the control interval of the in-situ controller, V i,t For the in-situ regulation of the voltage amplitude of the front busbar i +.>For the amount of decrease of the voltage amplitude of bus i, < >>For the rise of the voltage amplitude of bus i, < >>For the in-situ regulation of the reactive output value of the back inverter +.>Inverter reactive output reference value of kth dispatch period issued by master station to local controller,/for master station>For the reactive output rise of the inverter, < >>For the reactive output drop of the inverter, ω is the gain of the inverter, +.>Sensitivity coefficient for the voltage amplitude of busbar i to reactive injection of busbar i +.>
The utility model provides a distribution network information side model multiscale optimizing system which characterized in that: comprises a model building module, a strategy presetting module and an optimizing module,
the model building module is used for building an information side model according to the information communication interaction process of the power distribution network, and the information side model comprises an information node model, an information branch model and a controlled equipment model;
the strategy presetting module is used for presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimization actions;
and the optimization module is used for optimizing the information side model of the power distribution network according to the different time scale optimization strategies and the information side model and combining model optimization requirements.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method as described above when executing the computer program.
A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method as described above.
The invention has the beneficial effects that: the invention provides a multi-time scale optimization method and a multi-time scale optimization system for an information side model of a power distribution network, wherein the information side model comprises an information node model, an information branch model and a controlled equipment model; presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimizing actions; and according to the different time scale optimization strategies and the information side model, and combining model optimization requirements, the information side model optimization of the power distribution network is realized. Faults or errors caused by the information side model in the power distribution network modeling process are avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flowchart of a method and system for optimizing multiple time scales of a power distribution network information side model according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a multi-time-scale optimization framework of a multi-time-scale optimization method and a multi-time-scale optimization system for a power distribution network information side model according to an embodiment of the present invention;
fig. 3 is an internal structure diagram of a computer device of a power distribution network information side model multi-time scale optimization method and system according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1-3, a first embodiment of the present invention provides a method and a system for optimizing multiple time scales of a power distribution network information side model, where the method includes:
according to the information communication interaction process of the power distribution network, an information side model is established, wherein the information side model comprises an information node model, an information branch model and a controlled equipment model;
the modeling method of the information side model is as follows:
measuring node: the measurement node is used for completing projection of the operation state of the physical equipment from a physical space to an information space, and the mapping can be described by affine transformation:
σ i,t ＝K i,t χ i,t +β i,t +ε i,t
wherein i is the number of the measurement node,representing a state vector in a physical space,
measurement vector representing c measurement node, i.e., χ i In the information space the image is formed,
for measuring the deviation vector, ">Is a parameter matrix of affine mapping, +.>Is a parameter vector of the affine map.
Further, decision node: the decision node can store the input quantity of the decision node at each moment, and can realize the decision process from simple automatic control to complex optimization problem. The decision process of decision node j can be expressed as:
γ j,t ＝Φ j,t (ρ j,t )
ρ j,t ＝f j (δ j,t-Δt ,δ j,t-2·Δt ,....,δ j,t-h·Δt )
wherein delta j,t-h·Δt The input quantity f is the input quantity of the decision node at the time t-h.delta.t j In order to predict the state vector of the output of renewable energy sources, the load demand and the like of the system under the time t, ρ is j,t Predictive value vector phi required for reactive voltage optimization j,t And the voltage reactive power optimization problem is established for the decision node. Gamma ray j,t The element of the output vector is the output vector of the decision node, and the element is the output scalar of each device.
Further, the executing node: the execution node is used for caching the control instruction v l,t And as a control vector, control is carried out on the controlled object to drive the evolution of the state of the controlled object:
further, modeling of communication branches: the information branch is used for describing communication characteristics and information transfer characteristics among the information nodes in the information space. According to the communication topology, let θ= (u) 1 ,u 2 ,...,u m ) T For the vector of the sending end after signal reconstruction, m is the number of the sending end, ρ= (v) 1 ,v 2 ,...,v |ρ| ) T Constructing an information branch matrix pi= (r) for vectors formed by all receiving end signals ij ) |θ|×|ρ| The method is used for describing the communication relationship among the information nodes.
Further, pi corresponds to a static communication topology, being a constant matrix. Considering the situation that part of information links have communication faults in the actual communication process, the communication state matrix ψ= (y) with the same scale is introduced ij ) |θ|×|ρ| ：
At this time, the information association relationship between the transmitting end and the receiving end may be constructed by the following formula:
wherein ρ is - Is the signal vector received by the interactive receiving end of the previous round.
Further, the information-side model also includes a controlled device model, i.e., modeling of inverter hybrid logic dynamics.
Further, different time scale optimization strategies are preset, wherein the optimization strategies comprise preset corresponding time scale objective functions and optimization actions;
it should be noted that the preset different time scale optimization strategies include an hour level optimization strategy, a minute level optimization strategy and a real-time optimization strategy;
optionally, an hour level optimization strategy, wherein the optimization interval of the hour level optimization strategy is one hour, and the capacitor and the on-load transformer tap are acted by taking the grid loss, the adjustable reactive power reserve of the inverter and the equipment adjustment cost as objective functions;
optionally, optimizing the output reactive value of the inverter by taking the grid loss and the adjustable reactive reserve of the inverter as objective functions and delivering the output reactive value to the inverter, wherein the optimizing interval of the minute level optimizing strategy is fifteen minutes;
optionally, the real-time optimization strategy is implemented, and the inverter adjusts reactive power output according to the real-time voltage measurement value and the given control parameter, wherein the real-time optimization strategy is implemented at an optimization interval of thirty seconds.
In the embodiment of the application, the optimization interval time can be correspondingly adjusted according to actual requirements, the hour level optimization strategy optimization interval time can be adjusted to be up and down floating for thirty minutes by taking one hour as a fixed value, and the minute level optimization strategy optimization interval time can be adjusted to be up and down floating for five minutes by taking fifteen minutes as a fixed value, so that the application is not limited.
It should be noted that the preset different time scale optimization strategies also include,
the tide model in the hour level optimization strategy and the minute level optimization strategy is as follows:
||[2P ij,t 2Q ij,t l ij,t -u i,t ] T || 2 ≤l ij,t +u i,t
u i,t ＝(V i,t ) 2
l ij,t ＝(I ij,t ) 2
wherein P is ij,t 、Q ij,t Respectively the active power and the reactive power flowing through the down branch ij at the moment t, wherein J (i) is a set of nodes connected with the node i; p (P) hi,t 、Q hi,t 、I hi The active, reactive and current squares, r, respectively, of the branch hi flowing at time t hi 、x hi H (i) is a set of nodes connected with the node i, which is the resistance and reactance of the branch hi; the active load value of the node i;capacity for line ij; u (u) i,t For voltage V at node i at time t i,t Is the square of (2); l (L) ij,t For the current I flowing in the lower branch ij at time t ij,t Square of (d).
Further, the on-load tap changer model in the hour level optimization strategy and the minute level optimization strategy is as follows:
Tap k ∈{-10,-9,...,0,...,9,10},d k,t ∈{0,1}
wherein,tap is in gear Tap k The voltage value of the node connected with the transformer; u (u) 0,t Is the voltage value of the node connected with the transformer; d, d k,t For indicating where the tap is located.
It should be noted that if modeling needs to use a primary term of voltage and current, a linear model may be used:
V j,t ＝V i,t -(r ij P ij,t +x ij Q ij,t )/V 0
wherein i, J and H are the numbers of the buses, ij is a branch from the bus i to the bus J, J (i) is a sub-bus set of the bus i, H (i) is a parent line set of the bus i, and P ij,t And Q ij,t For active and reactive power on branch ij, r ij X is a group ij For the resistance and reactance of branch ij, P i,t 、And->For the injection active power of bus i, the load predicts the active power and the photovoltaic predicted active power, Q i,t 、/>And->Load predicted reactive power and output reactive power of inverter for the injected reactive power of node i,/>AndP 1 is the upper and lower limit of active power exchange between the distribution network and the upper power grid, and is ∈10->AndQ 1 the upper limit and the lower limit of reactive power exchange between the distribution network and the upper power grid are adopted. V (V) i,t For the voltage amplitude of busbar i +.>AndV i is the upper and lower voltage amplitude limits of bus i.Is the capacity of the branch ij.
Further, the preset different time scale optimization strategies also comprise,
the capacitor bank model in the hour level optimization strategy and the minute level optimization strategy is as follows:
wherein,reactive output of the capacitor bank installed at node i at time t; />Is the gear of the capacitor bank; />Reactive capacity is adjusted for the minimum of the capacitor bank; />Is the maximum reactive output of the capacitor bank.
Further, the preset different time scale optimization strategies also comprise,
the real-time optimization strategy comprises control logic of inverter running states, wherein the inverter running states comprise a first running state, a second running state and a third running state;
when the reactive output of the inverter is constant as a reference value, the inverter is in a first running state, and the state is expressed as mathematical logic:
when the reactive power output of the inverter increases and is higher than the reference value, the inverter is in a second operation state, and the state is expressed as mathematical logic:
when reactive power output of inverter is reducedAnd below the reference value, the inverter is in a third operating state, expressed mathematically as: [ V i,t ≤V]→[δ i,t,3 ＝1]；
Wherein delta i,t,1 、δ i,t,2 Delta i,t,3 For the introduced binary logic variable, for representing the inverter state, V i,t Represents the voltage amplitude of bus i at time t,represents the upper limit of the voltage amplitude reference value,Vrepresenting the lower limit of the voltage amplitude reference value.
The state in which the photovoltaic inverter is located is calculated from:
δ i,t,1 +δ i,t,2 +δ i,t,3 ＝1
wherein s is a negative number which is much smaller than V-V i,t 、/>V (V) i,t -VThe present application takes-1 p.u.alpha as a smaller positive number, and the present application takes 10 -3 p.u.
The preset different time scale optimization strategy further includes,
the voltage state and reactive output equation of the inverter in the real-time optimization strategy are as follows:
the change amount of the voltage amplitude and reactive output of the inverter in the real-time optimization strategy is as follows:
wherein,for in-situ regulation of the voltage amplitude of the rear bus i, deltat is the control interval of the in-situ controller, V i,t For the in-situ regulation of the voltage amplitude of the front busbar i +.>For the amount of decrease of the voltage amplitude of bus i, < >>For the rise of the voltage amplitude of bus i, < >>For the in-situ regulation of the reactive output value of the back inverter +.>Inverter reactive output reference value of kth dispatch period issued by master station to local controller,/for master station>For the reactive output rise of the inverter, < >>For the reactive output drop of the inverter, ω is the gain of the inverter, +.>Sensitivity coefficient for the voltage amplitude of busbar i to reactive injection of busbar i +.>
And according to different time scale optimization strategies and information side models, and combining model optimization requirements, the information side model optimization of the power distribution network is realized.
In summary, according to the embodiment, an information side model is first established according to the information communication interaction process of the power distribution network, where the information side model includes an information node model, an information branch model and a controlled device model; presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimizing actions; and optimizing the information side model of the power distribution network according to the different time scale optimization strategies and the information side model and combining model optimization requirements. Faults or errors caused by the information side model in the power distribution network modeling process are avoided.
In a preferred embodiment, the power distribution network information side model multi-time scale optimization system comprises a model building module, a strategy presetting module and an optimization module,
the model building module is used for building an information side model according to the information communication interaction process of the power distribution network, wherein the information side model comprises an information node model, an information branch model and a controlled equipment model;
the strategy presetting module is used for presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimization actions;
and the optimization module is used for optimizing the information side model of the power distribution network according to different time scale optimization strategies and the information side model and combining model optimization requirements.
The above unit modules may be embedded in hardware or independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above units.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program, when executed by the processor, implements a power distribution network information side model multi-time scale optimization method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
according to the information communication interaction process of the power distribution network, an information side model is established, wherein the information side model comprises an information node model, an information branch model and a controlled equipment model;
presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimization actions;
and according to different time scale optimization strategies and information side models, and combining model optimization requirements, the information side model optimization of the power distribution network is realized.
Example 2
Referring to fig. 2, for one embodiment of the present invention, a method and a system for optimizing multiple time scales of a power distribution network information side model are provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through a comparison experiment.
As shown in fig. 2, the multi-time scale framework comprises two parts, wherein the left half part is decision content, the right half part is a specific application scene, the decision content is preset different time scale optimization strategies, and the preset different time scale optimization strategies comprise an hour level optimization strategy, a minute level optimization strategy and a real-time optimization strategy;
when the strategy is optimized for the hour level, the whole decision takes the network loss, the adjustable reactive power reserve of the inverter and the equipment adjustment cost as objective functions to act on the capacitor and the on-load transformer tap, and the optimization interval of the optimization strategy for the hour level is one hour;
when the strategy is optimized for the minute level, the whole decision takes the grid loss and the adjustable reactive power reserve of the inverter as objective functions, optimizes the output reactive power value of the inverter and delivers the output reactive power value to the inverter, and the optimization interval of the minute level optimization strategy is fifteen minutes;
and when the real-time optimization strategy is adopted, the inverter adjusts reactive power output according to the real-time voltage measurement value and the given control parameter, and the real-time optimization strategy is adopted to optimize the interval to be thirty seconds.
The invention provides a multi-time scale optimization method and a multi-time scale optimization system for an information side model of a power distribution network, wherein the information side model comprises an information node model, an information branch model and a controlled equipment model; presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimizing actions; and according to the different time scale optimization strategies and the information side model, and combining model optimization requirements, the information side model optimization of the power distribution network is realized. Faults or errors caused by the information side model in the power distribution network modeling process are avoided.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. A power distribution network information side model multi-time scale optimization method is characterized by comprising the following steps of: comprising the steps of (a) a step of,
according to the information communication interaction process of the power distribution network, an information side model is established, wherein the information side model comprises an information node model, an information branch model and a controlled equipment model;
presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimizing actions;
and according to the different time scale optimization strategies and the information side model, and combining model optimization requirements, the information side model optimization of the power distribution network is realized.
2. The power distribution network information side model multi-time scale optimization method according to claim 1, wherein: the preset different time scale optimization strategies comprise an hour level optimization strategy, a minute level optimization strategy and a real-time optimization strategy;
the hour level optimizing strategy takes the network loss, the adjustable reactive power reserve of the inverter and the equipment adjusting cost as objective functions to act on the taps of the capacitor and the on-load transformer, and the hour level optimizing strategy optimizes the interval to be one hour;
the minute level optimizing strategy optimizes the output reactive power value of the inverter and transmits the output reactive power value to the inverter by taking the grid loss and the adjustable reactive power reserve of the inverter as objective functions, and the minute level optimizing strategy optimizes the interval of fifteen minutes;
and the inverter adjusts reactive power output according to the real-time voltage measurement value and the given control parameter, and the real-time optimization strategy is optimized at thirty seconds intervals.
3. The power distribution network information side model multi-time scale optimization method according to claim 2, wherein: the preset different time scale optimization strategy further comprises,
the tide model in the hour level optimization strategy and the minute level optimization strategy is as follows:
||[2P ij,t 2Q ij,t l ij,t -u i,t ] T || 2 ≤l ij,t +u i,t
u i,t ＝(V i,t ) 2
l ij,t ＝(I ij,t ) 2
wherein P is ij,t 、Q ij,t Respectively the active power and the reactive power flowing through the down branch ij at the moment t, wherein J (i) is a set of nodes connected with the node i; p (P) hi,t 、Q hi,t 、I hi The active, reactive and current squares, r, respectively, of the branch hi flowing at time t hi 、x hi H (i) is a set of nodes connected with the node i, which is the resistance and reactance of the branch hi; the active load value of the node i; />Capacity for line ij; u (u) i,t For voltage V at node i at time t i,t Is the square of (2); l (L) ij,t For the current I flowing in the lower branch ij at time t ij,t Square of (d).
4. A power distribution network information side model multi-time scale optimization method as claimed in claim 3, wherein: the preset different time scale optimization strategy further comprises,
the on-load voltage regulating transformer model in the hour level optimization strategy and the minute level optimization strategy is as follows:
Tap k ∈{-10,-9,...,0,...,9,10},d k,t ∈{0,1}
wherein,tap is in gear Tap k The voltage value of the node connected with the transformer; u (u) 0,t Is the voltage value of the node connected with the transformer; d, d k,t For indicating where the tap is located.
5. The power distribution network information side model multi-time scale optimization method according to claim 4, wherein: the preset different time scale optimization strategy further comprises,
the capacitor bank model in the hour level optimization strategy and the minute level optimization strategy is as follows:
wherein,reactive output of the capacitor bank installed at node i at time t; />Is the gear of the capacitor bank;reactive capacity is adjusted for the minimum of the capacitor bank; />Is the maximum reactive output of the capacitor bank.
6. The power distribution network information side model multi-time scale optimization method according to claim 5, wherein: the preset different time scale optimization strategy further comprises,
the real-time optimization strategy comprises control logic of an inverter running state, wherein the inverter running state comprises a first running state, a second running state and a third running state;
when the reactive output of the inverter is constant as a reference value, the inverter is in a first running state, and the state is expressed as mathematical logic:
when the reactive power output of the inverter increases and is higher than the reference value, the inverter is in a second operation state, and the state is expressed as mathematical logic:
when the reactive power output of the inverter is reduced and is lower than the reference value, the inverter is in a third operation state, and the state is expressed as mathematical logic: [ V i,t ≤V - ]→[δ i,t,3 ＝1]；
Wherein delta i,t,1 、δ i,t,2 Delta i,t,3 For the introduced binary logic variable, for representing the inverter state, V i,t Represents the voltage amplitude of bus i at time t,represents the upper limit of the voltage amplitude reference value,Vrepresenting the lower limit of the voltage amplitude reference value.
7. The power distribution network information side model multi-time scale optimization method according to claim 6, wherein: the preset different time scale optimization strategy further comprises,
the voltage state and reactive output equation of the inverter in the real-time optimization strategy is as follows:
the change amount of the voltage amplitude and reactive output of the inverter in the real-time optimization strategy is as follows:
wherein,for in-situ regulation of the voltage amplitude of the rear bus i, deltat is the control interval of the in-situ controller, V i,t For the in-situ regulation of the voltage amplitude of the front busbar i +.>For the amount of decrease of the voltage amplitude of bus i, < >>For the rise of the voltage amplitude of bus i, < >>For the in-situ regulation of the reactive output value of the back inverter +.>Inverter reactive output reference value of kth dispatch period issued by master station to local controller,/for master station>For the reactive output rise of the inverter, < >>For the reactive output drop of the inverter, ω is the gain of the inverter, +.>Sensitivity of the voltage amplitude of bus i to reactive injection of bus iSensitivity coefficient
8. The utility model provides a distribution network information side model multiscale optimizing system which characterized in that: comprises a model building module, a strategy presetting module and an optimizing module,
the model building module is used for building an information side model according to the information communication interaction process of the power distribution network, and the information side model comprises an information node model, an information branch model and a controlled equipment model;
the strategy presetting module is used for presetting different time scale optimization strategies, wherein the optimization strategies comprise presetting corresponding time scale objective functions and optimization actions;
and the optimization module is used for optimizing the information side model of the power distribution network according to the different time scale optimization strategies and the information side model and combining model optimization requirements.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106953359A (en) * | 2017-04-21 | 2017-07-14 | 中国农业大学 | A kind of active reactive coordinating and optimizing control method of power distribution network containing distributed photovoltaic |
| US20190067939A1 (en) * | 2016-05-18 | 2019-02-28 | China Electric Power Research Institute Company Limited | Multi-time-scale digital/analog hybrid simulation system and method for power distribution network and storage medium |
| CN113890017A (en) * | 2021-09-26 | 2022-01-04 | 天津大学 | Power distribution network voltage self-adaptive control method based on key measurement |
| CN114362196A (en) * | 2022-03-21 | 2022-04-15 | 南京邮电大学 | Multi-time-scale active power distribution network voltage control method |
| US20220405633A1 (en) * | 2021-06-17 | 2022-12-22 | Tsinghua University | Method for multi-time scale voltage quality control based on reinforcement learning in a power distribution network |
| CN115986750A (en) * | 2022-12-30 | 2023-04-18 | 南京邮电大学 | Voltage regulation method for layered multi-agent deep reinforcement learning power distribution network |
| CN116169731A (en) * | 2022-12-14 | 2023-05-26 | 国网浙江省电力有限公司宁波供电公司 | Two-time-scale voltage control method for active power distribution network |
-
2023
- 2023-09-25 CN CN202311250064.2A patent/CN117293807A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190067939A1 (en) * | 2016-05-18 | 2019-02-28 | China Electric Power Research Institute Company Limited | Multi-time-scale digital/analog hybrid simulation system and method for power distribution network and storage medium |
| CN106953359A (en) * | 2017-04-21 | 2017-07-14 | 中国农业大学 | A kind of active reactive coordinating and optimizing control method of power distribution network containing distributed photovoltaic |
| US20220405633A1 (en) * | 2021-06-17 | 2022-12-22 | Tsinghua University | Method for multi-time scale voltage quality control based on reinforcement learning in a power distribution network |
| CN113890017A (en) * | 2021-09-26 | 2022-01-04 | 天津大学 | Power distribution network voltage self-adaptive control method based on key measurement |
| CN114362196A (en) * | 2022-03-21 | 2022-04-15 | 南京邮电大学 | Multi-time-scale active power distribution network voltage control method |
| CN116169731A (en) * | 2022-12-14 | 2023-05-26 | 国网浙江省电力有限公司宁波供电公司 | Two-time-scale voltage control method for active power distribution network |
| CN115986750A (en) * | 2022-12-30 | 2023-04-18 | 南京邮电大学 | Voltage regulation method for layered multi-agent deep reinforcement learning power distribution network |
Non-Patent Citations (2)
| Title |
|---|
| 张雯程;徐艺铭;李征洲;王志强;牛强;李介夫;: "规模化光伏并网后配电网电压控制策略决策", 电测与仪表, no. 15, 10 August 2018 (2018-08-10), pages 80 - 85 * |
| 颜湘武;徐韵;李若瑾;金永盛;李铁;: "基于模型预测控制含可再生分布式电源参与调控的配电网多时间尺度无功动态优化", 电工技术学报, no. 10, 18 March 2019 (2019-03-18), pages 2022 - 2037 * |
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