CN115498702B - Path optimization method and device for data transmission requirements of distribution network under operation or fault state - Google Patents

Path optimization method and device for data transmission requirements of distribution network under operation or fault state Download PDF

Info

Publication number
CN115498702B
CN115498702B CN202211180017.0A CN202211180017A CN115498702B CN 115498702 B CN115498702 B CN 115498702B CN 202211180017 A CN202211180017 A CN 202211180017A CN 115498702 B CN115498702 B CN 115498702B
Authority
CN
China
Prior art keywords
data
representing
distribution network
path
node
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202211180017.0A
Other languages
Chinese (zh)
Other versions
CN115498702A (en
Inventor
岳东
窦春霞
张博
张占强
张智俊
严婷
徐雷
李厚俊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202211180017.0A priority Critical patent/CN115498702B/en
Publication of CN115498702A publication Critical patent/CN115498702A/en
Application granted granted Critical
Publication of CN115498702B publication Critical patent/CN115498702B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • 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
    • 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
    • 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
    • H02J3/381—Dispersed generators
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

The invention discloses a path optimization method and a device for data transmission requirements of a distribution network under operation or fault conditions, wherein the method comprises the following steps: judging the physical system running state and the communication network reliable state of the power distribution network; based on the physical system running state of the power distribution network and the communication network reliable state obtained by discrimination, evaluating the importance degree of the data and constructing an objective function; and solving the objective function by adopting a particle swarm optimization algorithm combined with random variation to obtain an optimal path scheme. Designing a data security transmission strategy based on matching of the data importance degree and the security degree in the running state; and a data emergency transmission strategy based on matching of the importance degree of the data and the transmission speed in the fault state. By means of route scheduling or path reconstruction, data with high importance are guaranteed to be transmitted to a destination end preferentially through paths with superior performance, and communication reliability of an active power distribution network under information physical fusion is improved.

Description

Path optimization method and device for data transmission requirements of distribution network under operation or fault state
Technical Field
The invention belongs to the technical field of active power distribution network communication, and relates to a path optimization method and device for data transmission requirements of a distribution network under operation or fault conditions.
Background
The active distribution network is an advanced distribution network with the capability of controlling various distributed energy sources (distributed power sources, controllable loads, energy storage, demand side management and the like) in a combined way, generally combines an advanced regulation and control technology with an advanced communication technology, and can play a certain role in supporting power of the system on the basis of meeting supervision and access criteria. The construction goal of the active power distribution network is to improve the power generation ratio of renewable distributed power sources such as photovoltaic power and fans in the power distribution network, improve the capacity of the power distribution network for absorbing renewable energy sources, and on the basis of ensuring safe and high-quality power supply, the active power distribution network is also an important development direction of the future power distribution network.
In the active power distribution network, as the power generation duty ratio of the distributed renewable energy source is gradually increased and the information physical fusion degree is deepened, the problem of output randomness, the problem of output fluctuation and the problem of communication are easily overlapped to cause the operation of the active power distribution network to be transferred from a normal state to an alert state, namely the occurrence of disturbance causes the system voltage, the frequency, the supply and demand balance state and the like to be threatened by safety. In addition to advanced communication techniques, if advanced regulation techniques are not employed, once small disturbances accumulate into large disturbances, the system will shift from an alert state to a fault state or even collapse, i.e., the system is at risk of disconnection. Therefore, advanced communication and regulation technology is required to be cooperatively designed, so that the safe operation of the system in the alert state is ensured, the risks of voltage, frequency and output out-of-limit are reduced, and the problems are avoided.
New power systems are increasingly dependent on communication as they require advanced communication technologies to support advanced control technologies. But the power system communication environment often has uncertainty for the following reasons: firstly, the transmission bandwidth of the channel is limited, inherent time lag, packet loss and noise exist, and the safety of the channel is required to be improved; secondly, the active distribution network is easily affected by external emergencies, and especially injection attack, external DoS attack and the like exist at the distributed power access points, which further causes limited communication bandwidth, reduced data availability and even channel transmission interruption. The unsafe data transmission will affect the normal execution of the decision control, and if the processing is not timely, the system is even paralyzed. Considering the safe transmission problem under the running state-fault state-recovery state, how to design a path optimization scheduling strategy to ensure available data at a transmission destination end needs further research.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a path optimization method and a path optimization device for the data transmission requirement of a distribution network under the operation or fault state, which are based on route scheduling or reconstructing paths after the requirement of a decision control target on the data transmission speed and the accuracy in the power grid is considered, so as to ensure that the processes of data uploading and instruction issuing can be normally carried out and support the smooth execution of power business. The data transmission can be recovered through routing scheduling or reconstructing paths so as to support the requirements of the power grid for the data transmission in different states. The method and the system provide a data safety transmission strategy design based on matching of the data importance degree and the safety degree in the running state and a data emergency transmission and path reconstruction strategy design based on matching of the data importance degree and the transmission speed in the fault state, so that the communication reliability of the intelligent power grid in executing power business in different running states is improved.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
In a first aspect, a path optimization method considering a data transmission requirement of a distribution network in an operation or fault state is provided, including:
Judging the physical system running state and the communication network reliable state of the power distribution network;
Based on the physical system running state of the power distribution network and the communication network reliable state obtained by discrimination, evaluating the importance degree of the data and constructing an objective function;
and solving the objective function by adopting a particle swarm optimization algorithm combined with random variation to obtain an optimal path scheme.
Wherein constructing the objective function comprises:
a data security transmission strategy based on matching of the importance degree and the security degree of the data in the running state;
And a data emergency transmission strategy based on matching of the importance degree of the data and the transmission speed in the fault state.
In some embodiments, determining the physical system operational state of the power distribution network includes:
(A) When the supply is short, there are cases where:
case 1: the supply and demand balance can be realized only by means of photovoltaic, wind power and energy storage; in this case, the following sub-events should be considered:
event 1-1: the generated energy of photovoltaic and wind power exceeds the load demand:
Wherein P PVv (t) and P WTy (t) respectively represent the generated energy of the v-th photovoltaic and the generated energy of the y-th fan; n PV,NWT and N Load are photovoltaic, fan and load numbers, respectively; p Loadz (t) represents the demand of the z-th load; in this event, the photovoltaic will operate in a maximum power point tracking mode; the fan will operate in a maximum power point tracking mode or a constant power mode; the stored energy will operate in a charging or shutdown mode;
Event 1-2: the generated energy of the photovoltaic and wind generation set is insufficient to meet the load demand, but when some energy storage is operated in a discharging mode, the problem can be solved, and the related expression is as follows:
Wherein P ESUw (t) represents the power generation amount of the w-th energy storage; n ESU represents the amount of stored energy;
Case 2: in addition to the photovoltaic, wind power and energy storage involved in the energy supply, diesel DG will also be involved in the electrical energy supply:
Wherein P DGn (t) represents the power generation amount of the nth diesel engine DG; n DG represents the number of diesel engines DG;
Case 3: when overload is serious, the load end requirement cannot be met only by adjusting the source end, redundant load is cut off from the system, and the system is operated in a fault state;
(B) When the supply is greater than the demand, there are cases where:
Event 1: power generation reduction in some distributed power sources, and Wherein,Representing the output regulating quantity of the mth interruptible distributed power supply; n IDER represents the number of interruptible distributed power supplies;
event 2: part of the power supply will be cut off from the distribution network system, i.e
The system is in a fault state at this time.
In some embodiments, determining a communication network reliability status of a power distribution network includes:
When the physical operation state is determined, and the route scheduling or the route reconstruction is to be selected to form defenses, the availability of the router needs to be judged firstly, namely whether a route can transmit data from the router to other routers; setting n routers in the system, wherein the availability discrimination conditions are as follows;
the input and output channels of each router must be simultaneously communicated except the initial end and the destination end, and for the xth router, there is Wherein x', x "represent the head end router index of the input channel and the tail end router index of the output channel of the xth router, respectively; and is a logical AND operator, a xx'、ax"x represents the connection relation from the xth router to the xth router, and the definition of similar symbols is similar; k represents the adjacent router set of the x-th router.
In some embodiments, evaluating the importance of the data includes:
the importance degree of the data is calculated through the partial derivative of the decision control target on the data; wherein the decision control target expression is modeled as f (x 1,…,u1, …), where x 1,x2, … represent state variables; u 1,u2, … represent a control amount;
when the system is in an operation state, the sensitivity calculation expression of the data change to the decision control effect is as follows:
for x 1: For x 2:
For u 1: For u 2:
Wherein the method comprises the steps of AndRepresenting the sensitivity of x 1,x2,u1 and u 2, respectively, in the operating state; the greater the sensitivity, the greater the degree of importance of the data;
when the system is in a fault state, the sensitivity calculation expression of the data change to time is as follows:
for x 1: For x 2:
For u 1: For u 2:
Wherein the method comprises the steps of AndRepresenting the sensitivity of x 1,x2,u1 and u 2 in the fault state, respectively; the greater the sensitivity, the greater the degree of importance of the data;
According to the sensitivity calculation expression, the importance degree sequence of the data can be obtained, so that communication paths of different data corresponding to different attributes can be arranged conveniently, and the influence of the unreliable communication problem on the execution effect of the decision control strategy of the power distribution network is reduced.
In some embodiments, constructing the objective function Z based on a data security transmission policy that matches a data importance level with a security level in an operational state includes:
the operation state is as follows:
because the system has longer operation time scale in the operation state and has less strict requirement on the transmission speed, the priority is considered to ensure the data transmission precision; optimizing the transmission paths of the data according to the order of the importance degree of the data from large to small, and classifying the corresponding optimization targets into the following categories:
If all the attributes of the channels are to be superimposed, there is:
Wherein N represents the number of regions; min represents a minimum algorithm; sign is a sign function; a ij represents a connection relationship from the i-th node to the j-th node, and if there is a connection, a ij =1; otherwise, a ij＝0; Attij.k represents the value of the kth attribute in the channel from the ith node to the jth node, wherein the attribute includes delay, packet loss rate, signal-to-noise ratio;
if only the channel with the most prominent attribute in the path is required to meet the constraint, then there are:
Z＝min{max[Attij.k·sign(aij)]},j∈Ni (8)
Dynamic adjustment path:
After the path from the starting position to the destination position is determined, whether the path meets the related constraint condition or not is checked; if these conditions have been met, path planning is complete; otherwise, the above process should be repeated; in channel optimization, the selected constraints are as follows:
6) There is only one output channel at the start position:
wherein a startj represents a connection relationship from the start position to the jth node; a jstart represents a connection relationship from the j-th node to the start position; n start represents the number of adjacent relay routes at the initial position;
7) There is only one input channel at the destination:
Wherein a endj represents a connection relationship from the destination position to the jth node; a jend represents a connection relationship from the j-th node to the destination position; n end represents the number of adjacent relay routes of the destination position;
8) Relay routing has both input and output channels:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; representing the number of adjacent relay routes of the first relay route;
9) The number of input channels and the number of output channels of the relay route do not exceed an allowable upper limit:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; An upper limit representing the number of input channels that allow the first relay route; Representing the upper limit of the number of output channels allowed by the first relay route;
10 The attributes of the planned path meet the set requirements:
Wherein the method comprises the steps of Representing the definition of the kth attribute value.
In some embodiments, constructing the objective function Z based on the data emergency transmission policy that the data importance level matches the transmission speed in the fault state includes:
Under the fault state:
Considering that data should be recovered for transmission at the fastest speed, and ensuring that data with high importance and high time sensitivity is recovered for transmission preferentially; wherein, the data importance degree and the sensitivity of the data to time need to be calculated in a superposition way, and the method is concretely as follows:
1. de-unitizing: And Wherein the method comprises the steps ofThe sensitivity of the decision control target to the ith data after the de-unitization; the sensitivity of the ith data after de-unitization to time;
3. and (3) weighting: when the two types of sensitivity are comprehensively considered, the comprehensive sensitivity is obtained Where λ i1 and λ i2 are weight coefficients, and λ i1+λi2 =1;
In the optimization, the method should be according to Optimizing the transmission path of each data from big to small, and the corresponding optimization targets are as follows:
Wherein Delay ij represents a time lag in the channel from the ith node to the jth node; constraint conditions in addition to (9) to (15),
6) There is only one output channel at the start position:
wherein a startj represents a connection relationship from the start position to the jth node; a jstart represents a connection relationship from the j-th node to the start position; n start represents the number of adjacent relay routes at the initial position;
7) There is only one input channel at the destination:
Wherein a endj represents a connection relationship from the destination position to the jth node; a jend represents a connection relationship from the j-th node to the destination position; n end represents the number of adjacent relay routes of the destination position;
8) Relay routing has both input and output channels:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; representing the number of adjacent relay routes of the first relay route;
9) The number of input channels and the number of output channels of the relay route do not exceed an allowable upper limit:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; An upper limit representing the number of input channels that allow the first relay route; Representing the upper limit of the number of output channels allowed by the first relay route;
10 The attributes of the planned path meet the set requirements:
Wherein the method comprises the steps of A constraint representing a kth attribute value;
it should also be ensured that the accuracy of the data transmission meets the requirements, i.e. the optimization of the path requires additional satisfaction of constraints Wherein risk ij represents the risk probability in the channel from the ith node to the jth node; condition risk is a set risk constraint index.
In some embodiments, solving the objective function using a particle swarm optimization algorithm that incorporates random variation includes:
Step 4-1: selecting an initial population:
Selecting initial population quantity: sizepop, variable dimension: spacedim; maximum number of iterations: a ger; position limitation; a speed limit; inertial weight: c_1; individual learning factors: c_2; group learning factor: c_3;
step 4-2: judging whether the individuals meet constraint conditions or not and selecting optimal individuals:
by substituting the individual into the formulas (9) to (15), whether or not the satisfaction of the condition is judged
Furthermore, the optimal selection, argmax (Z), is made among all individuals satisfying the constraint condition;
Step 4-3: updating the population by adopting a random variation mode, and for the corresponding individuals in the current iteration, carrying out position variation according to pop_x (:j) =pop_x (random (1, dim), j) +ζ to expand the number attribute of the population so as to facilitate jumping out of a local optimal solution, wherein pop_x (:j) represents the j-th position in the individuals in the current iteration; dim represents the number of individuals in the population; random (1, dim) represents a random number from 1 to dim; ζ ε [ -0.2,0.2];
Step 4-4: determining whether the updated population individuals meet the constraint conditions and selecting the optimal individuals, and substituting the individuals into formulas (9) - (15) to determine whether the updated population individuals meet the constraint conditions Furthermore, the optimal selection, argmax (Z), is made among all individuals satisfying the constraint condition;
Step 4-5: repeating the steps 4-3 and 4-4 until the set number of iterations is reached.
In a second aspect, the present invention provides a path optimization device that considers the data transmission requirements of a distribution network in an operational or failure state, including a processor and a storage medium;
the storage medium is used for storing instructions;
The processor is configured to operate in accordance with the instructions to perform the steps of the method according to the first aspect.
In a third aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
The beneficial effects are that: the path optimization method and the path optimization device for the data transmission requirement of the distribution network under the consideration of operation or fault state have the following advantages:
1. Under the running state, the matching of the data communication path and the data importance degree is realized, the data with high importance degree is ensured to be transmitted to the destination end preferentially through the path with superior performance, the optimized rationality is improved, and the requirement of the power business on the data transmission is met;
2. Under the fault state, the sensitivity of the data change to time is considered, the matching of the data communication path and the data importance degree is realized, the data with high importance degree and high time sensitivity of the data change is ensured to be transmitted to the destination end preferentially through the path with superior performance, the rapid support is ensured to complete the electric power basic service, and the system is timely restored to the running state;
3. in the optimization process, a random variation particle swarm optimization solving algorithm is designed, so that the solving speed and generalization capability of the algorithm are improved, and a reasonable path reconstruction scheme is facilitated to be obtained.
Drawings
FIG. 1 is a schematic diagram of policy enforcement according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of scenario 1 in an operational state of an embodiment of the present invention.
Fig. 3 is a schematic diagram of scenario 2 in an operational state of an embodiment of the present invention.
Fig. 4 is a schematic diagram of scenario 3 in an operational state of an embodiment of the present invention.
Fig. 5 is a schematic diagram of scenario 4 in an operational state of an embodiment of the present invention.
Fig. 6 is a schematic diagram of scenario 1 in a failure state according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of scenario 2 in a failure state according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
In the description of the present invention, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number is understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, the descriptions of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1
A path optimization method considering the data transmission requirement of a distribution network under an operation or fault state comprises the following steps:
Judging the physical system running state and the communication network reliable state of the power distribution network;
Based on the physical system running state of the power distribution network and the communication network reliable state obtained by discrimination, evaluating the importance degree of the data and constructing an objective function;
and solving the objective function by adopting a particle swarm optimization algorithm combined with random variation to obtain an optimal path scheme.
In some embodiments, based on the physical system operation state of the power distribution network and the communication network reliability state obtained by the discrimination, evaluating the importance degree of the data, constructing an objective function includes:
a data security transmission strategy based on matching of the importance degree and the security degree of the data in the running state;
And a data emergency transmission strategy based on matching of the importance degree of the data and the transmission speed in the fault state.
In some embodiments, determining the physical system operational state of the power distribution network includes:
(A) When the supply is short, there are cases where:
case 1: the supply and demand balance can be realized only by means of photovoltaic, wind power and energy storage; in this case, the following sub-events should be considered:
event 1-1: the generated energy of photovoltaic and wind power exceeds the load demand: Wherein P PVv (t) and P WTy (t) respectively represent the generated energy of the v-th photovoltaic and the generated energy of the y-th fan; n PV,NWT and N Load are photovoltaic, fan and load numbers, respectively; p Loadz (t) represents the demand of the z-th load; in this event, the photovoltaic will operate in a maximum power point tracking mode; the fan will operate in a maximum power point tracking mode or a constant power mode; the stored energy will operate in a charging or shutdown mode;
Event 1-2: the generated energy of the photovoltaic and wind generation set is insufficient to meet the load demand, but when some energy storage is operated in a discharging mode, the problem can be solved, and the related expression is as follows:
Wherein P ESUw (t) represents the power generation amount of the w-th energy storage; n ESU represents the amount of stored energy;
Case 2: in addition to the photovoltaic, wind power and energy storage involved in the energy supply, diesel DG will also be involved in the electrical energy supply:
Wherein P DGn (t) represents the power generation amount of the nth diesel engine DG; n DG represents the number of diesel engines DG;
Case 3: when overload is serious, the load end requirement cannot be met only by adjusting the source end, redundant load is cut off from the system, and the system is operated in a fault state;
note that: the method does not consider the power supply of the main network, and only performs strategy research on the active power distribution network.
(B) When the supply is greater than the demand, there are cases where:
Event 1: power generation reduction in some distributed power sources, and Wherein,Representing the output regulating quantity of the mth interruptible distributed power supply; n IDER represents the number of interruptible distributed power supplies;
event 2: part of the power supply will be cut off from the distribution network system, i.e
The system is in a fault state at this time.
In some embodiments, determining a communication network reliability status of a power distribution network includes:
When the physical operation state is determined, and the route scheduling or the route reconstruction is to be selected to form defenses, the availability of the router needs to be judged firstly, namely whether a route can transmit data from the router to other routers; setting n routers in the system, wherein the availability discrimination conditions are as follows;
the input and output channels of each router must be simultaneously communicated except the initial end and the destination end, and for the xth router, there is Wherein x', x "represent the head end router index of the input channel and the tail end router index of the output channel of the xth router, respectively; and is a logical AND operator, a xx'、ax"x represents the connection relation from the xth router to the xth router, and the definition of similar symbols is similar; k represents the adjacent router set of the x-th router.
Further, the design of the data security transmission strategy based on matching of the importance degree and the security degree of the data in the running state is considered in the following cases:
scene 1: the communication network transmission is not attacked in the running state, and a plurality of selectable paths are provided for data transmission
Aiming at the safety transmission problem in the running state, a multipath planning strategy is researched, and the method concretely comprises the following steps: 1) Based on sensitivity analysis of the influence of the speed and the precision of the acquired state data on the multi-element decision control service, evaluating the importance degree of the terminal data; 2) Based on the principle that the importance degree of the data is matched with the path safety measure of the data, constructing a single-target optimization model by taking the transmission rapidity as a constraint condition; 3) Based on the data importance degree sequencing, sequentially solving an optimization model to obtain a path optimization strategy, and transmitting data by selecting a path with optimal safety performance to ensure that the affected data are sequentially transmitted to a destination terminal according to the importance degree, so as to support the power business to execute;
scene 2: under the running state, the communication network transmission suffers from malicious attack, so that the problems of slight congestion of channel transmission and the like are caused, but the influence of data transmission caused by congestion does not exceed the tolerance upper limit of decision control tasks;
1) Similar to scenario 1, based on sensitivity analysis, the importance of the data is studied first; 2) Based on the principle that the importance degree of the data is matched with the path safety measure of the data, the service is ensured to be effective, and the path optimization strategy is researched. Firstly judging whether an existing communication network has a standby path or not, and if so, performing routing scheduling according to the principle that the importance degree of data is matched with the path safety degree of the existing communication network; otherwise, determining the head-tail node, and carrying out path reconstruction according to the head-tail node and with the safety, rapidness or optimal transmission length as the target to obtain an optimal path scheme.
Scene 3: under the running state, the communication network transmission is subjected to malicious attack, so that the channel transmission congestion is serious and even interrupted, but the importance degree of the affected data is low, and the safe running of the novel power system is not affected. Examples: cost information in energy management, such as time-of-use electricity prices, etc., the system can still operate safely even if interrupted.
Wherein the execution strategy is similar to scenario 2.
Scene 4: communication equipment such as routing or channels in the running state is failed, so that transmission is interrupted, but the importance degree of affected data is low, and the safe running of the novel power system is not affected.
Wherein the execution strategy is similar to scenario 2.
Furthermore, the design of the data emergency transmission strategy based on the matching of the importance degree of the data and the transmission speed in the fault state is considered in the following cases:
Scene 1: the communication network transmission is subjected to malicious attacks, so that channel transmission congestion is serious, even interruption and the like, and the importance degree of the affected data is high, so that the safe operation of the novel power system is affected.
Aiming at the problem of data incompleteness or unavailability in a fault state, a path optimization strategy is designed by taking critical recovery important data as a target, and the steps are as follows: 1) Based on the speed and the precision of the acquired state data, analyzing the information-physical sensitivity of the data change on the safety operation of the power system and the information-time sensitivity of the data change on time, and comprehensively evaluating the importance degree of the data; 2) Based on the rapidity evaluation of the transmission path, the principle of matching the importance degree of the data with the transmission speed of the path is adopted, the rapid transmission of important data is ensured as a main target, the transmission precision is taken as a constraint condition, an optimization model is constructed, an optimal transmission path strategy is obtained, and meanwhile, the safety of data transmission is improved.
Scene 2: communication equipment such as routing or channels and the like is broken down in an operation state, transmission is interrupted, the importance degree of affected data is high, and the safe operation of the novel power system is affected. In this case, the execution strategy is similar to scenario 1.
In some embodiments, evaluating the importance of the data includes:
the importance degree of the data is calculated through the partial derivative of the decision control target on the data; wherein the decision control target expression is modeled as f (x 1,…,u1, …), where x 1,x2, … represent state variables; u 1,u2, … represent a control amount;
when the system is in an operation state, the sensitivity calculation expression of the data change to the decision control effect is as follows:
for x 1: For x 2:
For u 1: For u 2:
Wherein the method comprises the steps of AndRepresenting the sensitivity of x 1,x2,u1 and u 2, respectively, in the operating state; the greater the sensitivity, the greater the degree of importance of the data;
when the system is in a fault state, the sensitivity calculation expression of the data change to time is as follows:
for x 1: For x 2:
For u 1: For u 2:
Wherein the method comprises the steps of AndRepresenting the sensitivity of x 1,x2,u1 and u 2 in the fault state, respectively; the greater the sensitivity, the greater the degree of importance of the data;
According to the sensitivity calculation expression, the importance degree sequence of the data can be obtained, so that communication paths of different data corresponding to different attributes can be arranged conveniently, and the influence of the unreliable communication problem on the execution effect of the decision control strategy of the power distribution network is reduced.
In some embodiments, constructing the objective function Z includes:
the operation state is as follows:
because the system has longer operation time scale in the operation state and has less strict requirement on the transmission speed, the priority is considered to ensure the data transmission precision; optimizing the transmission paths of the data according to the order of the importance degree of the data from large to small, and classifying the corresponding optimization targets into the following categories:
If all the attributes of the channels are to be superimposed, there is:
Wherein N represents the number of regions; min represents a minimum algorithm; sign is a sign function; a ij represents a connection relationship from the i-th node to the j-th node, and if there is a connection, a ij =1; otherwise, a ij＝0; Attij.k represents the value of the kth attribute in the channel from the ith node to the jth node, wherein the attribute includes delay, packet loss rate, signal-to-noise ratio;
if only the channel with the most prominent attribute in the path is required to meet the constraint, then there are:
Z＝min{max[Attij.k·sign(aij)]},j∈Ni (8)
Dynamic adjustment path:
After the path from the starting position to the destination position is determined, whether the path meets the related constraint condition or not is checked; if these conditions have been met, path planning is complete; otherwise, the above process should be repeated; in channel optimization, the selected constraints are as follows:
11 At the start position there is only one output channel:
wherein a startj represents a connection relationship from the start position to the jth node; a jstart represents a connection relationship from the j-th node to the start position; n start represents the number of adjacent relay routes at the initial position;
12 At the destination there is only one input channel):
Wherein a endj represents a connection relationship from the destination position to the jth node; a jend represents a connection relationship from the j-th node to the destination position; n end represents the number of adjacent relay routes of the destination position;
13 Relay routing with both input and output channels:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; representing the number of adjacent relay routes of the first relay route;
14 The number of input channels and the number of output channels of the relay route do not exceed an allowable upper limit:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; An upper limit representing the number of input channels that allow the first relay route; Representing the upper limit of the number of output channels allowed by the first relay route;
15 The attributes of the planned path meet the set requirements:
Wherein the method comprises the steps of Representing the definition of the kth attribute value.
In some embodiments, constructing the objective function Z includes:
Under the fault state:
Considering that data should be recovered for transmission at the fastest speed, and ensuring that data with high importance and high time sensitivity is recovered for transmission preferentially; wherein, the data importance degree and the sensitivity of the data to time need to be calculated in a superposition way, and the method is concretely as follows:
1. de-unitizing: And Wherein the method comprises the steps ofThe sensitivity of the decision control target to the ith data after the de-unitization; the sensitivity of the ith data after de-unitization to time;
4. and (3) weighting: when the two types of sensitivity are comprehensively considered, the comprehensive sensitivity is obtained Where λ i1 and λ i2 are weight coefficients, and λ i1+λi2 =1;
In the optimization, the method should be according to Optimizing the transmission path of each data from big to small, and the corresponding optimization targets are as follows:
Wherein Delay ij represents a time lag in the channel from the ith node to the jth node; constraint conditions in addition to (9) to (15),
11 At the start position there is only one output channel:
wherein a startj represents a connection relationship from the start position to the jth node; a jstart represents a connection relationship from the j-th node to the start position; n start represents the number of adjacent relay routes at the initial position;
12 At the destination there is only one input channel):
Wherein a endj represents a connection relationship from the destination position to the jth node; a jend represents a connection relationship from the j-th node to the destination position; n end represents the number of adjacent relay routes of the destination position;
13 Relay routing with both input and output channels:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; representing the number of adjacent relay routes of the first relay route;
14 The number of input channels and the number of output channels of the relay route do not exceed an allowable upper limit:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; An upper limit representing the number of input channels that allow the first relay route; Representing the upper limit of the number of output channels allowed by the first relay route;
15 The attributes of the planned path meet the set requirements:
Wherein the method comprises the steps of A constraint representing a kth attribute value;
it should also be ensured that the accuracy of the data transmission meets the requirements, i.e. the optimization of the path requires additional satisfaction of constraints Wherein risk ij represents the risk probability in the channel from the ith node to the jth node; condition risk is a set risk constraint index.
In some embodiments, solving the objective function using a particle swarm optimization algorithm that incorporates random variation includes:
Step 4-1: selecting an initial population:
Selecting initial population quantity: sizepop, variable dimension: spacedim; maximum number of iterations: a ger; position limitation; a speed limit; inertial weight: c_1; individual learning factors: c_2; group learning factor: c_3;
step 4-2: judging whether the individuals meet constraint conditions or not and selecting optimal individuals:
by substituting the individual into the formulas (9) to (15), whether or not the satisfaction of the condition is judged
Furthermore, the optimal selection, argmax (Z), is made among all individuals satisfying the constraint condition;
Step 4-3: updating the population by adopting a random variation mode, and for the corresponding individuals in the current iteration, carrying out position variation according to pop_x (:j) =pop_x (random (1, dim), j) +ζ to expand the number attribute of the population so as to facilitate jumping out of a local optimal solution, wherein pop_x (:j) represents the j-th position in the individuals in the current iteration; dim represents the number of individuals in the population; random (1, dim) represents a random number from 1 to dim; ζ ε [ -0.2,0.2];
Step 4-4: determining whether the updated population individuals meet the constraint conditions and selecting the optimal individuals, and substituting the individuals into formulas (9) - (15) to determine whether the updated population individuals meet the constraint conditions
Furthermore, the optimal selection, argmax (Z), is made among all individuals satisfying the constraint condition;
Step 4-5: repeating the steps 4-3 and 4-4 until the set number of iterations is reached.
Example 2
In a second aspect, the present embodiment provides a path optimization apparatus that considers a data transmission requirement of a distribution network in an operation or failure state, including a processor and a storage medium;
the storage medium is used for storing instructions;
The processor is operative according to the instructions to perform the steps of the method according to embodiment 1.
Example 3
In a third aspect, the present embodiment provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described in embodiment 1.
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 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.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.

Claims (8)

1. A path optimization method for data transmission requirements of a distribution network under an operation or fault state is considered, and is characterized by comprising the following steps:
Judging the physical system running state and the communication network reliable state of the power distribution network;
based on the physical system running state of the power distribution network and the communication network reliable state obtained by discrimination, evaluating the importance degree of the data and constructing an objective function; wherein constructing the objective function Z comprises:
Under the fault state:
Considering that data should be recovered for transmission at the fastest speed, and ensuring that data with high importance and high time sensitivity is recovered for transmission preferentially; wherein, the data importance degree and the sensitivity of the data to time need to be calculated in a superposition way, and the method is concretely as follows:
1. de-unitizing: And Wherein the method comprises the steps ofThe sensitivity of the decision control target to the ith data after the de-unitization; the sensitivity of the ith data after de-unitization to time;
2. and (3) weighting: when the two types of sensitivity are comprehensively considered, the comprehensive sensitivity is obtained Where λ i1 and λ i2 are weight coefficients, and λ i1+λi2 =1;
In the optimization, the method should be according to Optimizing the transmission path of each data from big to small, and the corresponding optimization targets are as follows:
Wherein Delay ij represents a time lag in the channel from the ith node to the jth node; constraint conditions in addition to (9) to (15),
1) There is only one output channel at the start position:
wherein a startj represents a connection relationship from the start position to the jth node; a jstart represents a connection relationship from the j-th node to the start position; n start represents the number of adjacent relay routes at the initial position;
2) There is only one input channel at the destination:
Wherein a endj represents a connection relationship from the destination position to the jth node; a jend represents a connection relationship from the j-th node to the destination position; n end represents the number of adjacent relay routes of the destination position;
3) Relay routing has both input and output channels:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; representing the number of adjacent relay routes of the first relay route;
4) The number of input channels and the number of output channels of the relay route do not exceed an allowable upper limit:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; An upper limit representing the number of input channels that allow the first relay route; Representing the upper limit of the number of output channels allowed by the first relay route;
5) The attribute of the planned path meets the set requirement:
Wherein the method comprises the steps of A constraint representing a kth attribute value;
it should also be ensured that the accuracy of the data transmission meets the requirements, i.e. the optimization of the path requires additional satisfaction of constraints
Wherein risk ij represents the risk probability in the channel from the ith node to the jth node; condition risk is a set risk constraint index;
and solving the objective function by adopting a particle swarm optimization algorithm combined with random variation to obtain an optimal path scheme.
2. The path optimization method for data transmission requirements of a distribution network in consideration of operation or failure states according to claim 1, wherein the step of judging the operation state of the physical system of the distribution network comprises the steps of:
(A) When the supply is short, there are cases where:
case 1: the supply and demand balance can be realized only by means of photovoltaic, wind power and energy storage; in this case, the following sub-events should be considered:
event 1-1: the generated energy of photovoltaic and wind power exceeds the load demand: Wherein P PVv (t) and P WTy (t) respectively represent the generated energy of the v-th photovoltaic and the generated energy of the y-th fan; n PV,NWT and N Load are photovoltaic, fan and load numbers, respectively; p Loadz (t) represents the demand of the z-th load; in this event, the photovoltaic will operate in a maximum power point tracking mode; the fan will operate in a maximum power point tracking mode or a constant power mode; the stored energy will operate in a charging or shutdown mode;
Event 1-2: the generated energy of the photovoltaic and wind generation set is insufficient to meet the load demand, but when some energy storage is operated in a discharging mode, the problem can be solved, and the related expression is as follows:
Wherein P ESUw (t) represents the power generation amount of the w-th energy storage; n ESU represents the amount of stored energy;
Case 2: in addition to the photovoltaic, wind power and energy storage involved in the energy supply, diesel DG will also be involved in the electrical energy supply:
Wherein P DGn (t) represents the power generation amount of the nth diesel engine DG; n DG represents the number of diesel engines DG;
Case 3: when overload is serious, the load end requirement cannot be met only by adjusting the source end, redundant load is cut off from the system, and the system is operated in a fault state;
(B) When the supply is greater than the demand, there are cases where:
Event 1: power generation reduction in some distributed power sources, and Wherein,Representing the output regulating quantity of the mth interruptible distributed power supply; n IDER represents the number of interruptible distributed power supplies;
event 2: part of the power supply will be cut off from the distribution network system, i.e
The system is in a fault state at this time.
3. The path optimization method considering the data transmission requirements of the distribution network in the operation or fault state according to claim 1, wherein the determining the reliable state of the communication network of the distribution network comprises:
When the physical operation state is determined, and the route scheduling or the route reconstruction is to be selected to form defenses, the availability of the router needs to be judged firstly, namely whether a route can transmit data from the router to other routers; setting n routers in the system, wherein the availability discrimination conditions are as follows;
The input and output channels of each router must communicate simultaneously except for the initial and destination, and for the x-th router there is Wherein x', x "represent the head end router index of the input channel and the tail end router index of the output channel of the xth router, respectively; and is a logical AND operator, a xx'、ax"x represents the connection relation from the xth router to the xth router, and the definition of similar symbols is similar; k represents the adjacent router set of the x-th router.
4. The path optimization method considering the demand of a distribution network for data transmission in an operation or failure state according to claim 1, wherein evaluating the importance of the data comprises:
the importance degree of the data is calculated through the partial derivative of the decision control target on the data; wherein the decision control target expression is modeled as f (x 1,…,u1, …), where x 1,x2, … represent state variables; u 1,u2, … represent a control amount;
when the system is in an operation state, the sensitivity calculation expression of the data change to the decision control effect is as follows:
Wherein the method comprises the steps of AndRepresenting the sensitivity of x 1,x2,u1 and u 2, respectively, in the operating state; the greater the sensitivity, the greater the degree of importance of the data;
when the system is in a fault state, the sensitivity calculation expression of the data change to time is as follows:
Wherein the method comprises the steps of AndRepresenting the sensitivity of x 1,x2,u1 and u 2 in the fault state, respectively; the greater the sensitivity, the greater the degree of importance of the data;
According to the sensitivity calculation expression, the importance degree sequence of the data can be obtained, so that communication paths of different data corresponding to different attributes can be arranged conveniently, and the influence of the unreliable communication problem on the execution effect of the decision control strategy of the power distribution network is reduced.
5. The path optimization method considering the data transmission requirements of the distribution network in the operation or fault state according to claim 1, wherein constructing the objective function Z comprises:
the operation state is as follows:
because the system has longer operation time scale in the operation state and has less strict requirement on the transmission speed, the priority is considered to ensure the data transmission precision; optimizing the transmission paths of the data according to the order of the importance degree of the data from large to small, and classifying the corresponding optimization targets into the following categories:
If all the attributes of the channels are to be superimposed, there is:
Wherein N represents the number of regions; min represents a minimum algorithm; sign is a sign function; a ij represents a connection relationship from the i-th node to the j-th node, and if there is a connection, a ij =1; otherwise, a ij＝0;Attij.k represents the value of the kth attribute in the channel from the ith node to the jth node, wherein the attribute includes delay, packet loss rate, signal-to-noise ratio;
if only the channel with the most prominent attribute in the path is required to meet the constraint, then there are:
Z＝min{max[Attij.k·sign(aij)]},j∈Ni (8)
Dynamic adjustment path:
After the path from the starting position to the destination position is determined, whether the path meets the related constraint condition or not is checked; if these conditions have been met, path planning is complete; otherwise, the above process should be repeated; in channel optimization, the selected constraints are as follows:
1) There is only one output channel at the start position:
wherein a startj represents a connection relationship from the start position to the jth node; a jstart represents a connection relationship from the j-th node to the start position; n start represents the number of adjacent relay routes at the initial position;
2) There is only one input channel at the destination:
Wherein a endj represents a connection relationship from the destination position to the jth node; a jend represents a connection relationship from the j-th node to the destination position; n end represents the number of adjacent relay routes of the destination position;
3) Relay routing has both input and output channels:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; representing the number of adjacent relay routes of the first relay route;
4) The number of input channels and the number of output channels of the relay route do not exceed an allowable upper limit:
Wherein the method comprises the steps of Representing a connection relationship from the ith relay route to the jth node; Representing a connection relationship from the jth node to the jth relay route; An upper limit representing the number of input channels that allow the first relay route; Representing the upper limit of the number of output channels allowed by the first relay route;
5) The attribute of the planned path meets the set requirement:
Wherein the method comprises the steps of Representing the definition of the kth attribute value.
6. The method for optimizing a path of a distribution network for data transmission requirements in consideration of operation or failure states according to claim 1 or 5, wherein solving the objective function by using a particle swarm optimization algorithm combined with random variation comprises:
Step 4-1: selecting an initial population:
Selecting initial population quantity: sizepop, variable dimension: spacedim; maximum number of iterations: a ger; position limitation; a speed limit; inertial weight: c_1; individual learning factors: c_2; group learning factor: c_3;
step 4-2: judging whether the individuals meet constraint conditions or not and selecting optimal individuals:
by substituting the individual into the formulas (9) to (15), whether or not the satisfaction of the condition is judged
Furthermore, the optimal selection, argmax (Z), is made among all individuals satisfying the constraint condition;
Step 4-3: updating the population by adopting a random variation mode, and for the corresponding individuals in the current iteration, carrying out position variation according to pop_x (:j) =pop_x (random (1, dim), j) +ζ to expand the number attribute of the population so as to facilitate jumping out of a local optimal solution, wherein pop_x (:j) represents the j-th position in the individuals in the current iteration; dim represents the number of individuals in the population; random (1, dim) represents a random number from 1 to dim; ζ ε [ -0.2,0.2];
Step 4-4: determining whether the updated population individuals meet the constraint conditions and selecting the optimal individuals, and substituting the individuals into formulas (9) - (15) to determine whether the updated population individuals meet the constraint conditions Furthermore, the optimal selection, argmax (Z), is made among all individuals satisfying the constraint condition;
Step 4-5: repeating the steps 4-3 and 4-4 until the set number of iterations is reached.
7. A path optimization device considering the data transmission requirement of a distribution network under an operation or fault state is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1 to 6.
8. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method according to any of claims 1 to 6.
CN202211180017.0A 2022-09-27 2022-09-27 Path optimization method and device for data transmission requirements of distribution network under operation or fault state Active CN115498702B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211180017.0A CN115498702B (en) 2022-09-27 2022-09-27 Path optimization method and device for data transmission requirements of distribution network under operation or fault state

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211180017.0A CN115498702B (en) 2022-09-27 2022-09-27 Path optimization method and device for data transmission requirements of distribution network under operation or fault state

Publications (2)

Publication Number Publication Date
CN115498702A CN115498702A (en) 2022-12-20
CN115498702B true CN115498702B (en) 2024-07-05

Family

ID=84473314

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211180017.0A Active CN115498702B (en) 2022-09-27 2022-09-27 Path optimization method and device for data transmission requirements of distribution network under operation or fault state

Country Status (1)

Country Link
CN (1) CN115498702B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106786543A (en) * 2017-01-05 2017-05-31 国网江苏省电力公司电力科学研究院 A kind of distribution network optimization drop for considering net capability constraint damages reconstructing method
CN109447344A (en) * 2018-10-26 2019-03-08 国网天津市电力公司 Based on the repairing stationary point of Distribution Network Failure big data and method for optimizing route and system

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109617065B (en) * 2019-01-02 2020-09-15 清华大学 Power transmission network line planning method of power system considering mass operation scenes
CN112994017A (en) * 2021-03-19 2021-06-18 国网江苏省电力有限公司南通供电分公司 Distributed photovoltaic power supply site selection optimization method based on power distribution network probability load flow calculation
CN113393172B (en) * 2021-07-15 2023-10-10 华北电力大学 Source network storage planning method considering multi-equipment time sequence operation of power distribution network

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106786543A (en) * 2017-01-05 2017-05-31 国网江苏省电力公司电力科学研究院 A kind of distribution network optimization drop for considering net capability constraint damages reconstructing method
CN109447344A (en) * 2018-10-26 2019-03-08 国网天津市电力公司 Based on the repairing stationary point of Distribution Network Failure big data and method for optimizing route and system

Also Published As

Publication number Publication date
CN115498702A (en) 2022-12-20

Similar Documents

Publication Publication Date Title
CN109818865B (en) SDN enhanced path boxing device and method
Firouz et al. Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system
Parandehgheibi et al. Mitigating cascading failures in interdependent power grids and communication networks
Su et al. Deep belief network enabled surrogate modeling for fast preventive control of power system transient stability
Moussa et al. Critical links identification for selective outages in interdependent power-communication networks
CN111967738B (en) Risk early warning method, system and medium of power grid information energy fusion system
CN108650131B (en) Processing system for multi-controller deployment in SDN network
CN106355308B (en) A method of wind power integration system core equipment is recognized based on decision tree
CN110994673B (en) Prediction method for micro-grid self-adaptive anti-islanding disturbance load impedance value
CN113361054B (en) Route optimization method and system for power information physical system
CN110350510A (en) A kind of power distribution network service restoration method considering failure disturbance degree
US20190258970A1 (en) Practical Optimization-Free Economic Load Dispatcher Based on Slicing Fuel-Cost Curves of Electric Generating Machines
Gholizadeh et al. A comparative study of reinforcement learning algorithms for distribution network reconfiguration with deep Q-learning-based action sampling
Naderi et al. A machine learning-based framework for fast prediction of wide-area remedial control actions in interconnected power systems
CN105356466A (en) Layered cooperative control and dynamic decision-making method for large-scale power transmission network frame restoration
van der Sar et al. Multi-Agent Reinforcement Learning for Power Grid Topology Optimization
Matavalam et al. Curriculum based reinforcement learning of grid topology controllers to prevent thermal cascading
CN110797918A (en) Source network load system load recovery method and system based on closed-loop control
CN115498702B (en) Path optimization method and device for data transmission requirements of distribution network under operation or fault state
CN112415959B (en) Edge cloud cooperative industrial information physical system active safety response method and framework
Hasanat et al. An ant colony optimization algorithm for load shedding minimization in smart grids
Deng et al. Preventive control strategy of cascading fault considering safety and economy
Ali et al. Hybrid architecture for autonomous load balancing in distributed systems based on smooth fuzzy function
Bertsekas et al. Adaptive multi-platform scheduling in a risky environment
Samanta et al. Multi-objective availability allocation using hesitated attraction based particle swarm optimization

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB03 Change of inventor or designer information
CB03 Change of inventor or designer information

Inventor after: Yue Dong

Inventor after: Dou Chunxia

Inventor after: Zhang Bo

Inventor after: Zhang Zhanqiang

Inventor after: Zhang Zhijun

Inventor after: Yan Ting

Inventor after: Xu Lei

Inventor after: Li Houjun

Inventor before: Zhang Bo

Inventor before: Dou Chunxia

Inventor before: Yue Dong

Inventor before: Zhang Zhanqiang

Inventor before: Zhang Zhijun

Inventor before: Yan Ting

Inventor before: Xu Lei

Inventor before: Li Houjun

GR01 Patent grant
GR01 Patent grant