CN116307562A - Optical storage planning configuration method and device for track traffic self-consistent energy system - Google Patents
Optical storage planning configuration method and device for track traffic self-consistent energy system Download PDFInfo
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Abstract
The invention discloses a method and a device for planning and configuring optical storage of a self-consistent energy system of rail transit, which relate to the technical field of rail transit planning, and comprise the following steps: constructing a target area typical scene set based on the historical illumination data, the historical traction load data and the historical fault data; constructing a power system response constraint function according to scene data in a typical scene of a target region, and further constructing a power system response model with the aim of minimum total load reduction cost of the power system; solving a power system response model to obtain the power supply load reduction amount of the target traction substation; and constructing a track traffic light energy storage source planning configuration model based on the reduction of the power supply load of the target traction substation, scene data in a typical scene of a target region and an energy storage system, and solving to obtain a track traffic light energy storage source configuration result. The invention can ensure the working stability and reliability of the self-consistent energy system of the rail transit.
Description
Technical Field
The invention relates to the technical field of track traffic planning, in particular to an optical storage planning configuration method and device of a track traffic self-consistent energy system.
Background
Along with the increase of the operation mileage of the electrified railway, the electricity demand of a railway system is increased sharply, and the electrified railway is required to realize the green, efficient and high-elasticity development of the energy consumption of the electrified railway while ensuring the safe and reliable power supply of the electrified railway. Under the background, the research and innovation traffic energy is derived from the technical of a consistent system, the green and intelligent level of traffic infrastructure is enhanced, and the problem of high efficiency and high elasticity planning and configuration technology of a track traffic self-consistent energy system is solved.
In particular, under extreme weather disasters, multiple disconnection faults of the power system can cause the topological structure of the system to change and influence the power transmission capacity of the system, but the influence of extreme weather on the electrified railway is not considered in the current planning configuration research of the electrified railway.
Disclosure of Invention
The invention aims to provide a method and a device for planning and configuring optical storage of a self-consistent energy system of rail transit, which are used for efficiently planning and configuring the optical storage of the rail transit based on illumination, traction load and faults so as to ensure the working stability and reliability of the self-consistent energy system of the rail transit.
In order to achieve the above object, the present invention provides the following solutions:
in a first aspect, the invention provides a method for planning and configuring optical storage of a self-consistent energy system of rail transit, wherein the self-consistent energy system of rail transit comprises a target traction substation, a power system and an energy storage system;
the method for planning and configuring the optical storage of the track traffic self-consistent energy system comprises the following steps:
acquiring historical illumination data, historical traction load data and historical fault data of the target traction substation;
constructing a target area typical scene set based on the historical illumination data, the historical traction load data and the historical fault data; the target area typical scene set comprises a plurality of target area typical scenes;
aiming at each target area typical scene, constructing a response constraint function of the power system according to scene data in the target area typical scene;
based on the power system response constraint function, establishing a power system response model with the aim of minimizing the total load reduction cost of the power system;
solving the power system response model to obtain the power supply load reduction amount of the target traction substation;
constructing a track traffic light energy storage source planning configuration model based on the reduction of the power supply load of the target traction substation, scene data in a typical scene of the target region and the energy storage system;
solving the track traffic light energy storage source planning configuration model to obtain a track traffic light energy storage source configuration result; the rail transit optical energy storage source configuration result is used for representing power configuration and capacity configuration of each component in the energy storage system.
Optionally, constructing a target area typical scene set based on the historical illumination data, the historical traction load data and the historical fault data specifically includes:
constructing a plurality of sunlight initial scenes based on the historical illumination data;
constructing a plurality of daily traction load initial scenes based on the historical traction load data;
constructing a plurality of transmission line fault typical scenes based on the historical fault data;
adopting a K-medoids clustering algorithm to respectively cut down a plurality of daily illumination initial scenes and a plurality of daily traction load initial scenes so as to obtain a daily illumination typical scene set and a daily traction load typical scene set;
and cross-combining the sunlight typical scene set, the daily traction load typical scene set and a plurality of transmission line fault typical scenes to obtain a target area typical scene set.
Optionally, a K-medoids clustering algorithm is adopted to cut down a plurality of sunlight initial scenes to obtain a sunlight typical scene set, and the method specifically comprises the following steps:
randomly dividing a plurality of sunlight initial scenes to obtain an initial clustering center scene set and a non-clustering center scene set;
according to a distance set corresponding to each non-clustering center scene, carrying out clustering division on the scenes in the non-clustering center scene set according to a distance nearest principle so as to obtain a first clustering result; the distance set corresponding to the non-clustering center scene comprises the distance between the non-clustering center scene and any initial clustering center scene;
based on a preset scene reduction objective function, calculating an objective function value corresponding to the first clustering result;
randomly selecting one non-clustering center scene from the non-clustering center scene set to replace any scene in the initial clustering center scene set so as to obtain an updated initial clustering center scene set and an updated non-clustering center scene set;
according to the distance nearest principle, clustering and dividing the scenes in the updated non-clustering center scene set according to the distance set corresponding to each updated non-clustering center scene to obtain a second clustering result;
based on a preset scene reduction objective function, calculating an objective function value corresponding to the second aggregation result;
determining a clustering state according to the objective function value corresponding to the first clustering result and the objective function value corresponding to the second clustering result; the cluster state comprises cluster update and stop cluster update;
when the clustering state is cluster updating, returning to the step of randomly selecting one non-cluster center scene from the non-cluster center scene set to replace any scene in the initial cluster center scene set;
and when the cluster state is that cluster updating is stopped, marking the updated initial cluster center scene set corresponding to the second cluster result as a sunlight typical scene set.
Optionally, the scene data in the target region typical scene includes:
the method comprises the steps of daily traction active load, daily traction reactive load, all other node sets in the power system, which do not comprise traction load nodes, active power source output of power transmission network nodes in the power system, reactive power source output of power transmission network nodes in the power system, active load of power transmission network nodes in the power system, reactive load of power transmission network nodes in the power system, active load reduction amount of power transmission network nodes in the power system, reactive load reduction amount of power transmission network nodes in the power system, voltage amplitude of power transmission network nodes in the power system, node phase angle difference of power transmission network nodes in the power system, node admittance matrix of power transmission network in the power system and power transmission line fault state in the power system.
Optionally, the energy storage system comprises a photovoltaic power station, a storage battery and a super capacitor;
the track traffic light energy storage source planning configuration model comprises an energy planning configuration objective function and an energy planning configuration constraint function; the energy planning configuration objective function is used for realizing that the running loss of the track traffic self-consistent energy system is minimum; the energy planning configuration constraint function comprises a power balance constraint, a hybrid energy storage constraint and a photovoltaic output constraint;
the power balance constraint is used for representing that the system discharge power and the system charge power in the track traffic self-consistent energy system reach balance; the system discharge power comprises power purchase power, photovoltaic power generation power, storage battery discharge power and super capacitor discharge power; the system charging power comprises a track traffic traction active load, storage battery charging power, super capacitor charging power and feed power;
the hybrid energy storage constraint comprises a storage battery energy storage constraint, a storage battery charging power constraint, a storage battery discharging power constraint, a super capacitor energy storage constraint, a super capacitor charging power constraint and a super capacitor discharging power constraint;
the photovoltaic output constraint includes a photovoltaic generated power constraint.
In a second aspect, the invention provides an optical storage planning configuration device of a track traffic self-consistent energy system, wherein the track traffic self-consistent energy system comprises a target traction substation, a power system and an energy storage system;
the optical storage planning configuration device of the track traffic self-consistent energy system comprises:
the data acquisition module is used for acquiring historical illumination data, historical traction load data and historical fault data of the target traction substation;
the scene construction module is used for constructing a typical scene set of a target area based on the historical illumination data, the historical traction load data and the historical fault data; the target area typical scene set comprises a plurality of target area typical scenes;
the constraint construction module is used for constructing a response constraint function of the power system according to the scene data in the target area typical scenes aiming at each target area typical scene;
the first model construction module is used for constructing a power system response model based on the power system response constraint function and with the aim of minimizing the total load reduction cost of the power system;
the first model solving module is used for solving the response model of the power system to obtain the power supply load reduction amount of the target traction substation;
the second model building module is used for building a track traffic light energy storage source planning configuration model based on the reduction of the power supply load of the target traction substation, the scene data in the typical scene of the target area and the energy storage system;
the second model solving module is used for solving the track traffic light energy storage source planning configuration model to obtain a track traffic light energy storage source configuration result; the rail transit optical energy storage source configuration result is used for representing power configuration and capacity configuration of each component in the energy storage system.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method and a device for planning and configuring optical storage of a track traffic self-consistent energy system, which are used for acquiring historical illumination data, historical traction load data and historical fault data corresponding to the weather influence of a target traction substation by considering extreme weather influence; and constructing a typical scene set of the target area based on the three types of historical data, thereby fully considering the condition that the system topology structure is changed and the power transmission capacity of the system is influenced due to multiple disconnection faults possibly occurring in the power system under extreme weather disasters, and further influencing the exchange power limit value of the self-consistent energy system and the power system of the rail transit. Aiming at each target area typical scene, constructing a power system response constraint function according to scene data in the target area typical scene, further constructing a power system response model by taking the minimum total load reduction cost of a power system as a target, solving the power system response model to obtain the power supply load reduction amount of a target traction substation, constructing a track traffic light energy storage source planning configuration model based on the power supply load reduction amount of the target traction substation, the scene data in the target area typical scene and an energy storage system, and solving the model to obtain a track traffic light energy storage source configuration result; and the configuration result of the rail transit light energy storage source is used for representing the power configuration and the capacity configuration of each component in the energy storage system. According to the invention, through the construction and solving of the two models, the planning of the configuration of the track traffic light energy storage source can be realized more efficiently; and the energy storage system in the actual rail transit is configured according to the configuration result of the rail transit light energy storage source, so that stable and reliable work of the energy storage system under extreme weather disasters is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in 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.
FIG. 1 is a flow chart of an optical storage planning configuration method of a track traffic self-consistent energy system of the invention;
FIG. 2 is a schematic diagram of a coupling structure of the rail transit self-consistent energy system of the present invention;
fig. 3 is a schematic structural diagram of an optical storage planning configuration device of the track traffic self-consistent energy system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a method and a device for planning and configuring optical storage of a track traffic self-consistent energy system, which are used for generating a typical daily operation scene according to historical illumination intensity, traction load and fault data, and configuring photovoltaic and energy storage around a traction substation so as to minimize the sum of the optical storage investment cost and the operation cost of the track traffic self-consistent energy system.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1, the present embodiment provides a method for planning and configuring optical storage of a self-consistent rail transit energy system, where the self-consistent rail transit energy system includes a target traction substation, an electric power system and an energy storage system; the method for planning and configuring the optical storage of the track traffic self-consistent energy system comprises the following steps:
and 100, acquiring historical illumination data, historical traction load data and historical fault data of the target traction substation. The method comprises the following steps: and in the region where the target traction substation is located, in a certain period of time, the illumination data, the traction load data and the fault data of each day are obtained.
Step 200 specifically includes:
1) And constructing a plurality of sunlight initial scenes based on the historical illumination data.
2) And constructing a plurality of daily traction load initial scenes based on the historical traction load data.
3) And constructing a plurality of transmission line fault typical scenes based on the historical fault data. Specifically, a typical scenario of a transmission line fault includes a transmission line fault state (0 indicates a fault, 1 indicates normal operation), a transmission line fault start time, and a transmission line fault duration.
4) And adopting a K-medoids clustering algorithm to respectively cut down a plurality of daily illumination initial scenes and a plurality of daily traction load initial scenes so as to obtain a daily illumination typical scene set and a daily traction load typical scene set.
The method comprises the steps of adopting a K-medoids clustering algorithm to cut down a plurality of sunlight initial scenes to obtain a sunlight typical scene set, and specifically comprises the following steps:
41 Randomly dividing a plurality of sunlight initial scenes to obtain an initial clustering center scene set and a non-clustering center scene set; specifically, r scenes are randomly selected from a plurality of initial daily lighting scenes to serve as initial clustering center scenes, so that the initial clustering center scenes are selected
A representation; and selecting the rest scenes to form a non-clustering center scene set.
42 According to the distance set corresponding to each non-clustering center scene, carrying out clustering division on the scenes in the non-clustering center scene set according to the distance nearest principle, namely distributing the scenes in the non-clustering center scene set to the corresponding positions of the initial clustering center scenesClustering to obtain a first clustering result; the distance set corresponding to the non-clustering center scene comprises the distance between the non-clustering center scene and any initial clustering center scene. Specifically, according to the formula
The distance is calculated.
43 Based on a preset scene reduction objective function, calculating an objective function value corresponding to the first clustering result.
44 Randomly selecting one non-clustering center scene from the non-clustering center scene set to replace any scene in the initial clustering center scene set so as to obtain an updated initial clustering center scene set and an updated non-clustering center scene set.
45 According to the distance nearest principle, clustering and dividing the scenes in the updated non-clustering center scene set according to the distance set corresponding to each updated non-clustering center scene to obtain a second clustering result; specifically, the distance set corresponding to the updated non-clustered central scene includes the distance between the updated non-clustered central scene and any updated initial clustered central scene, and the specific calculation formula is described in the above step 42).
46 Based on a preset scene reduction objective function, calculating an objective function value corresponding to the second aggregation result.
47 Determining a clustering state according to the objective function value corresponding to the first clustering result and the objective function value corresponding to the second clustering result; the cluster state includes a cluster update and a stop cluster update. Specifically, the objective function difference is calculated according to the formula Δw=w-W'; if the difference value delta W of the objective function is more than 0, the clustering state is cluster updating; if not, the clustering state is to stop the clustering update; further, if the objective function difference is 0, or the objective function value corresponding to the first clustering result is equal to the objective function value corresponding to the second clustering result, the clustering state is to stop the clustering update, and at this time, the objective function value is unchanged.
48 Returning to randomly selecting one from the non-cluster center scene set when the cluster state is cluster updateA step of replacing any scene in the initial cluster center scene set with a non-cluster center scene; when the clustering state is that the clustering update is stopped, marking the updated initial clustering center scene set corresponding to the second clustering result as a sunlight typical scene set, namely, R clustering centers { R ] obtained by final clustering 1 ,R 2 ,…，R r R typical scenes after scene cut; meanwhile, r scene probabilities pi (r) can be calculated, and the r scene probability pi (r) is the proportion of the number of initial scenes contained in the r-th scene to the total number of the initial scenes.
Further, the preset scene cut objective function is:
wherein W represents the value of a preset scene cut objective function, i.e., an objective function value, d (u) i ,u j ) Representing scene u i Scene u j Distance between p i Representing the probability of occurrence of a scene i, N2 represents a scene set formed by a plurality of initial daily illumination scenes, N1 represents an initial clustering center scene set, T d Is the duration of the daylight initial scene.
And similarly, cutting down a plurality of initial scenes of daily traction loads to obtain a typical scene set of daily traction loads.
5) And cross-combining the sunlight typical scene set, the daily traction load typical scene set and a plurality of transmission line fault typical scenes to obtain a target area typical scene set. Specifically, after the illumination intensity, the traction load and the transmission line fault typical scene are combined in a crossing way, N can be formed S =a.b.c typical scenarios, corresponding cross probability product pi s Is the probability of a typical scene S.
At this time, scene data in a typical scene of the target region includes: sunlight intensity beta s,t (t＝1,2,3,...N T ) Daily traction active load
Daily traction reactive load +.>
Transmission line fault start time, T in power system s Representing the duration of a transmission line fault in the power system and the status of the transmission line fault in the power system.
Under extreme weather disasters, multiple disconnection faults possibly occur in the power system, so that the topological structure of the system is changed, the power transmission capacity and safe operation of the system are influenced, and the running state of the unit and the level of the power-available load are required to be optimized and adjusted to ensure that the system is safe and stable.
Specifically, the scene data in the target region typical scene includes:
the method comprises the steps of daily traction active load, daily traction reactive load, all other node sets in the power system, which do not comprise traction load nodes, active power source output of power transmission network nodes in the power system, reactive power source output of power transmission network nodes in the power system, active load of power transmission network nodes in the power system, reactive load of power transmission network nodes in the power system, active load reduction amount of power transmission network nodes in the power system, reactive load reduction amount of power transmission network nodes in the power system, voltage amplitude of power transmission network nodes in the power system, node phase angle difference of power transmission network nodes in the power system, node admittance matrix of power transmission network in the power system and power transmission line fault state in the power system.
The power system response constraint function includes:
wherein P is s,Gi,t、 Q s,Gi,t Respectively representing the active power output and the reactive power output of a power transmission network node i in a power system at the moment t under a typical scene S of a target area;
respectively representing the m-level active load reduction amount and the reactive load reduction amount at the moment of the i node t in the typical scene S of the target area; p (P) Di,t 、Q Di,t Respectively representing the active load and the reactive load at the moment of the i node t; v (V) s,i,t 、θ s,ij,t Respectively representing the voltage amplitude value at the moment of the i node t and the phase angle difference of the ij node under the typical scene S of the target area; g s,ij 、B s,ij Respectively representing the real part and the imaginary part of the j column elements of the i rows of the node admittance matrix under the typical scene S of the target area; a is that s, ij The 01 variable indicates whether the line ij is broken or not in the typical scene S of the target region, 0 indicates broken lines, and 1 indicates unbroken lines.
To keep the power factor of the node load constant, it is assumed that when a certain number of active loads are cut down, the corresponding daily traction reactive load will also be cut down:
wherein S is N For all node sets of the power system, S N- The h node is a traction load node and is a set of all other nodes of the power system excluding the h node.
And 400, based on the power system response constraint function, establishing a power system response model with the aim of minimizing the total load reduction cost of the power system.
The method aims at minimizing the total load reduction cost of the power system and specifically comprises the following steps: according to the formula
Establishing a response objective function of the power system;
wherein t is s,0 Representing the fault starting time of a transmission line in a power system, T s Representing the duration of transmission line faults in a power system, M represents the daily traction load level, and in a specific practical application, M has a value of 3, the first-level load power supply reliability requirement is highest, and the cost coefficient alpha is reduced 1 The highest, the two-level and three-level load reduction cost coefficients are sequentially reduced; s is S N- Representing a set of all other nodes in the power system, not including traction load nodes, alpha m Cost reduction coefficient, alpha, representing a daily traction load class of m 1 A cut-down cost coefficient indicating a daily traction load level of 1,
representing the m-level load reduction amount of i node under the typical scene S of the target area, < >>
The power supply load reduction amount of the target traction substation is indicated, and the traction load belongs to the primary load.
And 500, solving the power system response model to obtain the power supply load reduction amount of the target traction substation. Inputting the scene data in the typical scene set of the target area obtained in the step 200 as a model, solving the model, and finally obtaining the power supply load reduction of the traction substation
And 600, constructing a track traffic light energy storage source planning configuration model based on the reduction of the power supply load of the target traction substation, the scene data in the typical scene of the target region and the energy storage system.
As shown in fig. 2, the energy storage system comprises a photovoltaic power station, a storage battery and a super capacitor, wherein the storage battery and the super capacitor form a hybrid energy storage. In addition, the traction substation in fig. 2 corresponds to the target traction substation in the text.
The track traffic light energy storage source planning configuration model comprises an energy planning configuration objective function and an energy planning configuration constraint function; the energy planning configuration objective function is used for realizing that the running loss of the track traffic self-consistent energy system is minimum; the energy planning configuration constraint function comprises a power balance constraint, a hybrid energy storage constraint and a photovoltaic output constraint.
The power balance constraint is used for representing that the system discharge power and the system charge power in the track traffic self-consistent energy system reach balance; the system discharge power comprises power purchase power, photovoltaic power generation power, storage battery discharge power and super capacitor discharge power; the system charging power comprises a track traffic traction active load, storage battery charging power, super capacitor charging power and feed power.
The hybrid energy storage constraints include a battery energy storage constraint, a battery charge power constraint, a battery discharge power constraint, a supercapacitor energy storage constraint, a supercapacitor charge power constraint, and a supercapacitor discharge power constraint.
The photovoltaic output constraint includes a photovoltaic generated power constraint.
Further, the energy planning configuration objective function is as follows:
Min C day ＝C I +C OM +C grid +C dem
wherein C is day Representing daily operation loss in track traffic self-consistent energy system, C I Representing the equivalent daily operation loss of the optical storage capacity in the energy storage system, C OM Representing the loss of light Chu Yunwei in an energy storage system, C grid Representing the exchange power loss of the power grid and the energy storage system, C dem Representing a demand loss;
wherein,,
representing the daily operating losses of a photovoltaic power plant, +.>
Indicating the daily operating loss of the battery, < >>
Representing the daily operational loss of the supercapacitor; r is the discount rate; y is PV For the service life of the photovoltaic power station, y Bat For the service life of the storage battery, y SC The service life of the super capacitor is prolonged;
The power loss coefficient is a unit photovoltaic power loss coefficient, a unit storage battery power loss coefficient, a unit super capacitor power loss coefficient, a unit storage battery capacity loss coefficient and a unit super capacitor capacity loss coefficient respectively;
Respectively preset rated photovoltaic power, rated storage battery power, rated super capacitor power, rated storage battery capacity and rated super capacitor capacity.
Wherein,,
loss coefficients of the photovoltaic, the storage battery and the super capacitor are respectively; Δt is an optimized time scale, specifically 1min; n (N) T Optimizing the time period number for each day, specifically 1440 time period;
and respectively obtaining the storage battery charging power, the storage battery discharging power, the super capacitor charging power and the super capacitor discharging power at the time t under the typical scene s of the target area.
Wherein,,
the power grid electricity purchasing loss coefficient at the moment t is the power grid electricity purchasing loss coefficient at the typical scene s of the target area;
The power grid feed loss coefficient at the moment t is the power grid feed loss coefficient at the typical scene s of the target area;
wherein c base The loss coefficient is the unit required quantity;
is the demand at time t under a typical scene s of a target area,
is the maximum demand under a typical scene s of the target area.
The energy planning configuration constraint function is used for determining a limit value of the exchange power of the track traffic self-consistent energy system and the power system, and specifically comprises the following steps:
power balance constraint:
wherein,,
the self-consistent energy system power purchasing power, the feed power, the photovoltaic power, the storage battery discharging power and charging power, the super capacitor discharging power and charging power and the track traffic traction active load at the t moment under the typical scene s of the target area are respectively represented;
Is a variable of 01;
Determined by the thermal stability limit of the wire.
Hybrid energy storage constraint:
wherein,,
respectively storing energy stored by a storage battery at the moment t and energy stored by a super capacitor under a typical scene s of a target area; epsilon Bat 、ε SC The self-discharge rate of the storage battery and the self-discharge rate of the super capacitor are respectively;
the storage battery discharging efficiency, the super capacitor discharging efficiency, the storage battery charging efficiency and the super capacitor charging efficiency are respectively;
The upper limit value of the charge state of the storage battery, the lower limit value of the charge state of the storage battery, the upper limit value of the super-capacity charge state and the lower limit value of the super-capacity charge state are respectively,
and the state of charge value at the initial moment of the storage battery and the state of charge value at the initial moment of the super capacitor are respectively represented.
Photovoltaic output constraint:
wherein beta is s,t 、β N Respectively the illumination intensity and the rated illumination intensity at the time t under a typical scene s of a target area;
for a configured nominal photovoltaic power. Step 600 performs optical storage configuration based on scene data in typical scenes of each target area, wherein the proportion of the typical scene quantity of each target area to the total scene is scene probability, and the objective function is planned under the condition of considering different data input of all scenes.
Example two
As shown in fig. 3, in order to execute a corresponding method of the foregoing embodiment to achieve corresponding functions and technical effects, this embodiment further provides an optical storage planning configuration device of a self-consistent rail traffic energy system, where the self-consistent rail traffic energy system includes a target traction substation, an electric power system and an energy storage system.
The optical storage planning configuration device of the track traffic self-consistent energy system comprises:
the data acquisition module 101 is configured to acquire historical illumination data, historical traction load data and historical fault data of the target traction substation.
A scene construction module 201, configured to construct a target area typical scene set based on the historical illumination data, the historical traction load data, and the historical fault data; the target region representative scene set includes a plurality of target region representative scenes.
The constraint construction module 301 is configured to construct, for each target region typical scenario, a power system response constraint function according to scenario data in the target region typical scenario.
The first model building module 401 is configured to build a power system response model based on the power system response constraint function, with the objective of minimizing the total load reduction cost of the power system.
And the first model solving module 501 is configured to solve the power system response model to obtain a power supply load reduction amount of the target traction substation.
The second model building module 601 is configured to build a track traffic light energy storage source planning configuration model based on the power supply load reduction amount of the target traction substation, the scene data in the typical scene of the target region, and the energy storage system.
The second model solving module 701 is configured to solve the track traffic light energy storage source planning configuration model to obtain a track traffic light energy storage source configuration result; the rail transit optical energy storage source configuration result is used for representing power configuration and capacity configuration of each component in the energy storage system.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (9)
1. The light storage planning configuration method of the track traffic self-consistent energy system is characterized in that the track traffic self-consistent energy system comprises a target traction substation, an electric power system and an energy storage system;
the method for planning and configuring the optical storage of the track traffic self-consistent energy system comprises the following steps:
acquiring historical illumination data, historical traction load data and historical fault data of the target traction substation;
constructing a target area typical scene set based on the historical illumination data, the historical traction load data and the historical fault data; the target area typical scene set comprises a plurality of target area typical scenes;
aiming at each target area typical scene, constructing a response constraint function of the power system according to scene data in the target area typical scene;
based on the power system response constraint function, establishing a power system response model with the aim of minimizing the total load reduction cost of the power system;
solving the power system response model to obtain the power supply load reduction amount of the target traction substation;
constructing a track traffic light energy storage source planning configuration model based on the reduction of the power supply load of the target traction substation, scene data in a typical scene of the target region and the energy storage system;
solving the track traffic light energy storage source planning configuration model to obtain a track traffic light energy storage source configuration result; the rail transit optical energy storage source configuration result is used for representing power configuration and capacity configuration of each component in the energy storage system.
2. The method for planning and configuring the optical storage of the rail transit self-consistent energy system according to claim 1, wherein the construction of the target area typical scene set based on the historical illumination data, the historical traction load data and the historical fault data specifically comprises:
constructing a plurality of sunlight initial scenes based on the historical illumination data;
constructing a plurality of daily traction load initial scenes based on the historical traction load data;
constructing a plurality of transmission line fault typical scenes based on the historical fault data;
adopting a K-medoids clustering algorithm to respectively cut down a plurality of daily illumination initial scenes and a plurality of daily traction load initial scenes so as to obtain a daily illumination typical scene set and a daily traction load typical scene set;
and cross-combining the sunlight typical scene set, the daily traction load typical scene set and a plurality of transmission line fault typical scenes to obtain a target area typical scene set.
3. The method for planning and configuring the light storage of the track traffic self-consistent energy system according to claim 2, wherein a K-means clustering algorithm is adopted to cut down a plurality of daily illumination initial scenes so as to obtain a daily illumination typical scene set, and the method specifically comprises the following steps:
randomly dividing a plurality of sunlight initial scenes to obtain an initial clustering center scene set and a non-clustering center scene set;
according to a distance set corresponding to each non-clustering center scene, carrying out clustering division on the scenes in the non-clustering center scene set according to a distance nearest principle so as to obtain a first clustering result; the distance set corresponding to the non-clustering center scene comprises the distance between the non-clustering center scene and any initial clustering center scene;
based on a preset scene reduction objective function, calculating an objective function value corresponding to the first clustering result;
randomly selecting one non-clustering center scene from the non-clustering center scene set to replace any scene in the initial clustering center scene set so as to obtain an updated initial clustering center scene set and an updated non-clustering center scene set;
according to the distance nearest principle, clustering and dividing the scenes in the updated non-clustering center scene set according to the distance set corresponding to each updated non-clustering center scene to obtain a second clustering result;
based on a preset scene reduction objective function, calculating an objective function value corresponding to the second aggregation result;
determining a clustering state according to the objective function value corresponding to the first clustering result and the objective function value corresponding to the second clustering result; the cluster state comprises cluster update and stop cluster update;
when the clustering state is cluster updating, returning to the step of randomly selecting one non-cluster center scene from the non-cluster center scene set to replace any scene in the initial cluster center scene set;
and when the cluster state is that cluster updating is stopped, marking the updated initial cluster center scene set corresponding to the second cluster result as a sunlight typical scene set.
4. A method for configuring an optical storage plan of a self-consistent energy system of rail transit as claimed in claim 3, wherein said preset scene cut objective function is:
wherein W represents the value of a preset scene cut objective function, i.e., an objective function value, d (u) i ,u j ) Representing scene u i Scene u j Distance between p i Representing the probability of occurrence of a scene i, N2 represents a scene set formed by a plurality of initial daily illumination scenes, N1 represents an initial clustering center scene set, T d Is the duration of the daylight initial scene.
5. The method for planning and configuring the optical storage of the rail transit self-consistent energy system according to claim 1, wherein the scene data in the typical scene of the target area comprises:
the method comprises the steps of daily traction active load, daily traction reactive load, all other node sets in the power system, which do not comprise traction load nodes, active power source output of power transmission network nodes in the power system, reactive power source output of power transmission network nodes in the power system, active load of power transmission network nodes in the power system, reactive load of power transmission network nodes in the power system, active load reduction amount of power transmission network nodes in the power system, reactive load reduction amount of power transmission network nodes in the power system, voltage amplitude of power transmission network nodes in the power system, node phase angle difference of power transmission network nodes in the power system, node admittance matrix of power transmission network in the power system and power transmission line fault state in the power system.
6. The method for planning and configuring the optical storage of the self-consistent energy system of the rail transit according to claim 1, wherein the objective of minimizing the total load reduction cost of the electric power system is specifically:
according to the formula
Establishing a response objective function of the power system;
wherein t is s,0 Representing the fault starting time of a transmission line in a power system, T s Representing the duration of a transmission line fault in an electrical power system, M representing the daily traction load level, S N- Representing a set of all other nodes in the power system, not including traction load nodes, alpha m Cost reduction coefficient, alpha, representing a daily traction load class of m 1 A cut-down cost coefficient indicating a daily traction load level of 1,
representing the m-level load reduction amount of i node under the typical scene S of the target area, < >>
The power supply load reduction amount of the target traction substation is indicated.
7. The method for planning and configuring the light storage of the rail transit self-consistent energy system according to claim 1, wherein the energy storage system comprises a photovoltaic power station, a storage battery and a super capacitor;
the track traffic light energy storage source planning configuration model comprises an energy planning configuration objective function and an energy planning configuration constraint function; the energy planning configuration objective function is used for realizing that the running loss of the track traffic self-consistent energy system is minimum; the energy planning configuration constraint function comprises a power balance constraint, a hybrid energy storage constraint and a photovoltaic output constraint;
the power balance constraint is used for representing that the system discharge power and the system charge power in the track traffic self-consistent energy system reach balance; the system discharge power comprises power purchase power, photovoltaic power generation power, storage battery discharge power and super capacitor discharge power; the system charging power comprises a track traffic traction active load, storage battery charging power, super capacitor charging power and feed power;
the hybrid energy storage constraint comprises a storage battery energy storage constraint, a storage battery charging power constraint, a storage battery discharging power constraint, a super capacitor energy storage constraint, a super capacitor charging power constraint and a super capacitor discharging power constraint;
the photovoltaic output constraint includes a photovoltaic generated power constraint.
8. The method for optical storage planning configuration of a rail transit self-consistent energy system according to claim 7, wherein the energy planning configuration objective function is:
Min C day ＝C I +C OM +C grid +C dem
wherein C is day Representing daily operation loss in track traffic self-consistent energy system, C I Representing the equivalent daily operation loss of the optical storage capacity in the energy storage system, C OM Representing an energy storage systemChu Yunwei loss of medium light, C grid Representing the exchange power loss of the power grid and the energy storage system, C dem Representing a demand loss;
wherein,,
representing the daily operating losses of a photovoltaic power plant, +.>
Indicating the daily operating loss of the battery, < >>
Representing the daily operational loss of the supercapacitor; r is the discount rate; y is PV For the service life of the photovoltaic power station, y Bat For the service life of the storage battery, y SC The service life of the super capacitor is prolonged;
Respectively the unit photovoltaic power lossCoefficient, unit storage battery power loss coefficient, unit super capacitor power loss coefficient, unit storage battery capacity loss coefficient and unit super capacitor capacity loss coefficient;
Respectively preset rated photovoltaic power, rated storage battery power, rated super capacitor power, rated storage battery capacity and rated super capacitor capacity;
wherein,,
loss coefficients of the photovoltaic, the storage battery and the super capacitor are respectively; Δt is the optimized time scale; n (N) T Optimizing the number of time periods for each day;
Respectively obtaining storage battery charging power, storage battery discharging power, super capacitor charging power and super capacitor discharging power at the moment t under a typical scene s of a target area;
wherein,,
the power grid electricity purchasing loss coefficient at the moment t is the power grid electricity purchasing loss coefficient at the typical scene s of the target area;
The power grid feed loss coefficient at the moment t is the power grid feed loss coefficient at the typical scene s of the target area;
wherein c base The loss coefficient is the unit required quantity;
is the demand at time t under the typical scene s of the target area, P s dem,max Is the maximum demand under a typical scene s of the target area.
9. The light storage planning configuration device of the track traffic self-consistent energy system is characterized in that the track traffic self-consistent energy system comprises a target traction substation, an electric power system and an energy storage system;
the optical storage planning configuration device of the track traffic self-consistent energy system comprises:
the data acquisition module is used for acquiring historical illumination data, historical traction load data and historical fault data of the target traction substation;
the scene construction module is used for constructing a typical scene set of a target area based on the historical illumination data, the historical traction load data and the historical fault data; the target area typical scene set comprises a plurality of target area typical scenes;
the constraint construction module is used for constructing a response constraint function of the power system according to the scene data in the target area typical scenes aiming at each target area typical scene;
the first model construction module is used for constructing a power system response model based on the power system response constraint function and with the aim of minimizing the total load reduction cost of the power system;
the first model solving module is used for solving the response model of the power system to obtain the power supply load reduction amount of the target traction substation;
the second model building module is used for building a track traffic light energy storage source planning configuration model based on the reduction of the power supply load of the target traction substation, the scene data in the typical scene of the target area and the energy storage system;
the second model solving module is used for solving the track traffic light energy storage source planning configuration model to obtain a track traffic light energy storage source configuration result; the rail transit optical energy storage source configuration result is used for representing power configuration and capacity configuration of each component in the energy storage system.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116914755A (en) * | 2023-07-13 | 2023-10-20 | 华北电力大学 | Light-storage joint planning method and system considering battery cycle life |
| CN118365051A (en) * | 2024-04-12 | 2024-07-19 | 华北电力大学 | Road traffic self-consistent energy system planning method based on scene adaptation |
| CN118643750A (en) * | 2024-08-15 | 2024-09-13 | 清华四川能源互联网研究院 | Load scene reduction method for traction power supply system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116914755A (en) * | 2023-07-13 | 2023-10-20 | 华北电力大学 | Light-storage joint planning method and system considering battery cycle life |
| CN116914755B (en) * | 2023-07-13 | 2024-05-28 | 华北电力大学 | Light-storage joint planning method and system considering battery cycle life |
| CN118365051A (en) * | 2024-04-12 | 2024-07-19 | 华北电力大学 | Road traffic self-consistent energy system planning method based on scene adaptation |
| CN118643750A (en) * | 2024-08-15 | 2024-09-13 | 清华四川能源互联网研究院 | Load scene reduction method for traction power supply system |
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