A light storage planning and configuration method and device for a rail transit self-consistent energy system

Abstract

The application discloses a track traffic self-consistent energy system light storage planning configuration method and device, relates to the track traffic planning technical field, and the method comprises the following steps: constructing a target area typical scene set based on historical illumination data, historical traction load data and historical fault data; constructing a power system response constraint function according to scene data in the target area typical scene, and then establishing a power system response model with the minimum total load reduction cost of the power system as the target; solving the power system response model to obtain the power supply load reduction amount of the target traction substation; constructing a track traffic light storage energy planning configuration model based on the power supply load reduction amount of the target traction substation, scene data in the target area typical scene and an energy storage system, and solving to obtain a track traffic light storage energy configuration result. The application can guarantee the working stability and reliability of the track traffic self-consistent energy system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail transit planning, in particular to a light storage planning and configuration method and device for a rail transit self-consistent energy system. BACKGROUND

[0002] With the increase of electrified railway operation mileage, the demand for electricity of railway system increases sharply. In order to ensure safe and reliable power supply, electrified railways urgently need to realize green, efficient and flexible development of their own energy use. Under this background, it is of great significance to research and innovate the technology of self-consistent energy system of transportation, enhance the green and intelligent level of transportation infrastructure, and solve the problem of "efficient and flexible planning and configuration technology of rail transit self-consistent energy system".

[0003] Especially in extreme weather disasters, multiple line breakage failures of the power system may occur, resulting in changes in system topology and affecting the power transmission capacity of the system. However, the influence of extreme weather on electrified railways is not considered in the current research on the planning and configuration of electrified railway energy use. SUMMARY

[0004] The purpose of the present application is to provide a light storage planning and configuration method and device for a rail transit self-consistent energy system, which is based on light, traction load and fault, and plans and configures the light storage energy of rail transit efficiently to ensure the working stability and reliability of the rail transit self-consistent energy system.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In a first aspect, the present application provides a light storage planning and configuration method for a rail transit self-consistent energy system, which includes a target traction substation, a power system and an energy storage system.

[0007] The light storage planning and configuration method for the rail transit self-consistent energy system includes:

[0008] Obtaining historical light data, historical traction load data and historical fault data of the target traction substation;

[0009] Based on the historical light data, the historical traction load data and the historical fault data, a set of typical scenarios of the target area is constructed; the set of typical scenarios of the target area includes a plurality of typical scenarios of the target area;

[0010] For each typical scenario of the target area, a power system response constraint function is constructed according to the scenario data in the typical scenario of the target area;

[0011] Based on the power system response constraint function, a power system response model is established with the minimum total load reduction cost of the power system as the target;

[0012] solving the power system response model to obtain a power supply load reduction amount of the target traction substation;

[0013] constructing a rail transit light storage energy planning configuration model based on the power supply load reduction amount of the target traction substation, scene data in a target region typical scene, and the energy storage system;

[0014] solving the rail transit light storage energy planning configuration model to obtain a rail transit light storage energy configuration result; the rail transit light storage energy configuration result is used to represent power configuration and capacity configuration of each component in the energy storage system.

[0015] Optionally, based on the historical illumination data, the historical traction load data, and the historical fault data, a target region typical scene set is constructed, specifically including:

[0016] based on the historical illumination data, a plurality of daily illumination initial scenes are constructed;

[0017] based on the historical traction load data, a plurality of daily traction load initial scenes are constructed;

[0018] based on the historical fault data, a plurality of power transmission line fault typical scenes are constructed;

[0019] using a K-medoids clustering algorithm, the plurality of daily illumination initial scenes and the plurality of daily traction load initial scenes are reduced respectively to obtain a daily illumination typical scene set and a daily traction load typical scene set;

[0020] the daily illumination typical scene set, the daily traction load typical scene set, and the plurality of power transmission line fault typical scenes are cross combined to obtain the target region typical scene set.

[0021] Optionally, using a K-medoids clustering algorithm, the plurality of daily illumination initial scenes are reduced to obtain a daily illumination typical scene set, specifically including:

[0022] the plurality of daily illumination initial scenes are randomly divided to obtain an initial cluster center scene set and a non-cluster center scene set;

[0023] according to the distance nearest principle, the scenes in the non-cluster center scene set are clustered and divided according to the distance set corresponding to each non-cluster center scene, to obtain a first clustering result; the distance set corresponding to the non-cluster center scene includes the distance between the non-cluster center scene and any initial cluster center scene;

[0024] based on a preset scene reduction objective function, a target function value corresponding to the first clustering result is calculated;

[0025] 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 to obtain an updated initial cluster center scene set and an updated non-cluster center scene set;

[0026] According to the distance set corresponding to each updated non-cluster center scene, the scenes in the updated non-cluster center scene set are clustered and divided according to the nearest distance principle to obtain a second clustering result;

[0027] Based on a preset scene reduction objective function, the objective function value corresponding to the second clustering result is calculated;

[0028] According to the objective function value corresponding to the first clustering result and the objective function value corresponding to the second clustering result, a clustering state is determined; the clustering state includes cluster updating and stopping cluster updating;

[0029] When the clustering state is cluster updating, 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 is returned;

[0030] When the clustering state is stopping cluster updating, the updated initial cluster center scene set corresponding to the second clustering result is marked as a sunlight typical scene set.

[0031] Optionally, the scene data in the target region typical scene includes:

[0032] Daily traction active load, daily traction reactive load, all other node sets in the power system excluding 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 of power transmission network nodes in the power system, reactive load reduction 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 the power transmission network in the power system, and power transmission line fault state in the power system.

[0033] Optionally, the energy storage system includes a photovoltaic power station, a battery and a super capacitor;

[0034] The rail transit light storage energy planning and configuration model includes an energy planning and configuration objective function and an energy planning and configuration constraint function; the energy planning and configuration objective function is used to realize minimum rail transit self-consistent energy system operation loss; and the energy planning and configuration constraint function includes power balance constraint, hybrid energy storage constraint and photovoltaic output constraint;

[0035] The power balance constraint is used to represent that the system discharge power and the system charging power in the rail transit self-consistent energy system reach balance; the system discharge power includes purchased power, photovoltaic power generation power, battery discharge power and super capacitor discharge power; and the system charging power includes rail transit traction active load, battery charging power, super capacitor charging power and feeding power.

[0036] The hybrid energy storage constraint includes a battery energy storage constraint, a battery charging power constraint, a battery discharge power constraint, a super capacitor energy storage constraint, a super capacitor charging power constraint and a super capacitor discharge power constraint.

[0037] The photovoltaic output constraint includes a photovoltaic power generation power constraint.

[0038] In a second aspect, the present application provides a photovoltaic storage planning and configuration device of a rail transit self-consistent energy system, the rail transit self-consistent energy system including a target traction substation, a power system and an energy storage system.

[0039] The photovoltaic storage planning and configuration device of the rail transit self-consistent energy system includes:

[0040] A data acquisition module is configured to acquire historical illumination data, historical traction load data and historical fault data of the target traction substation.

[0041] A scene construction module is configured to construct a target region typical scene set based on the historical illumination data, the historical traction load data and the historical fault data; the target region typical scene set includes a plurality of target region typical scenes.

[0042] A constraint construction module is configured to, for each target region typical scene, construct a power system response constraint function according to scene data in the target region typical scene.

[0043] A first model construction module is configured to, based on the power system response constraint function, establish a power system response model with the minimum total load reduction cost of the power system as the target.

[0044] A first model solving module is configured to solve the power system response model to obtain a power supply load reduction amount of the target traction substation.

[0045] A second model construction module is configured to, based on the power supply load reduction amount of the target traction substation, scene data in the target region typical scene and the energy storage system, construct a rail transit photovoltaic storage energy planning and configuration model.

[0046] A second model solving module is configured to solve the rail transit light storage energy planning and configuration model to obtain a rail transit light storage energy configuration result, which is used to represent the power configuration and capacity configuration of each component in the energy storage system.

[0047] According to the embodiments of the present application, the following technical effects are achieved.

[0048] The present application discloses a rail transit self-consistent energy system light storage planning and configuration method and device, which considers the influence of extreme weather, obtains historical light data, historical traction load data and historical fault data corresponding to the target traction substation affected by weather; based on the above three kinds of historical data, a typical scene set of the target area is constructed, so as to fully consider the case that the multiple line-outage faults of the power system may occur under extreme meteorological disasters, which leads to the change of system topology structure and affects the system power transmission capacity, and further affects the exchange power limit value of the rail transit self-consistent energy system and the power system. For each typical scene of the target area, an electric power system response constraint function is constructed according to the scene data in the typical scene of the target area, and then a power system response model is established with the minimum total load reduction cost of the power system as the target, and the power supply load reduction of the target traction substation is obtained by solving the power system response model. Based on the power supply load reduction of the target traction substation, the scene data in the typical scene of the target area and the energy storage system, a rail transit light storage energy planning and configuration model is constructed, and the rail transit light storage energy configuration result is obtained by solving the model. The rail transit light storage energy configuration result is used to represent the power configuration and capacity configuration of each component in the energy storage system. Through the construction and solving of the two models, the planning of the rail transit light storage energy configuration can be more efficiently realized, and then the energy storage system in the actual rail transit is configured according to the rail transit light storage energy configuration result, so as to ensure the stable and reliable work of the energy storage system under extreme meteorological disasters. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative labor.

[0050] Figure 1 The flowchart of the rail transit self-consistent energy system light storage planning and configuration method of the present application;

[0051] Figure 2 The coupling structure diagram of the rail transit self-consistent energy system of the present application;

[0052] Figure 3This is a schematic diagram of the photovoltaic and energy storage planning and configuration device for the self-consistent energy system of rail transit according to the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] This invention provides a method and apparatus for planning and configuring photovoltaic and energy storage systems for self-sufficient rail transit. It generates typical daily operating scenarios based on historical light intensity, traction load, and fault data, and configures photovoltaic and energy storage systems around traction substations to minimize the sum of investment and operating costs of photovoltaic and energy storage systems for self-sufficient rail transit systems.

[0055] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Example 1

[0057] like Figure 1 As shown, this embodiment provides a method for planning and configuring photovoltaic and energy storage systems for a self-sufficient energy system in rail transit. The self-sufficient energy system includes a target traction substation, a power system, and an energy storage system. The method for planning and configuring photovoltaic and energy storage systems for a self-sufficient energy system in rail transit includes:

[0058] Step 100: Obtain historical sunshine data, historical traction load data, and historical fault data for the target traction substation. Specifically, this includes daily sunshine data, traction load data, and fault data for the area where the target traction substation is located within a certain time period.

[0059] Step 200: Based on the historical illumination data, the historical traction load data, and the historical fault data, construct a typical scenario set for the target area; the typical scenario set for the target area includes multiple typical scenarios for the target area.

[0060] Step 200 specifically includes:

[0061] 1) Based on the historical illumination data, construct multiple initial sunlight scenarios.

[0062] 2) Based on the historical traction load data, construct multiple initial scenarios for daily traction load.

[0063] 3) Based on the historical fault data, a plurality of power transmission line fault typical scenarios are constructed. Specifically, the power transmission line fault typical scenario includes power transmission line fault state (0 represents fault, 1 represents normal operation), power transmission line fault start time and power transmission line fault duration.

[0064] 4) The K-medoids clustering algorithm is used to reduce the plurality of sunlight initial scenarios and the plurality of day traction load initial scenarios respectively to obtain the sunlight typical scenario set and the day traction load typical scenario set.

[0065] Wherein, the K-medoids clustering algorithm is used to reduce the plurality of sunlight initial scenarios to obtain the sunlight typical scenario set, which specifically includes:

[0066] 41) The plurality of sunlight initial scenarios are randomly divided to obtain an initial cluster center scenario set and a non-cluster center scenario set; specifically, r scenes are randomly selected from the plurality of sunlight initial scenarios as initial cluster center scenarios, and the remaining scenes constitute the non-cluster center scenario set. Indicates; the remaining scenes constitute the non-cluster center scenario set.

[0067] 42) According to the distance nearest principle, the scenes in the non-cluster center scenario set are clustered and divided according to the distance set corresponding to each non-cluster center scenario, that is, the scenes in the non-cluster center scenario set are assigned to the clusters corresponding to each initial cluster center scenario to obtain the first clustering result; the distance set corresponding to the non-cluster center scenario includes the distance between the non-cluster center scenario and any initial cluster center scenario. Specifically, the distance is calculated according to the formula

[0068] 43) Based on the preset scene reduction objective function, the objective function value corresponding to the first clustering result is calculated.

[0069] 44) A non-cluster center scenario is randomly selected from the non-cluster center scenario set to replace any scene in the initial cluster center scenario set to obtain an updated initial cluster center scenario set and an updated non-cluster center scenario set.

[0070] 45) According to the distance nearest principle, the scenes in the updated non-cluster center scenario set are clustered and divided according to the distance set corresponding to each updated non-cluster center scenario to obtain the second clustering result; specifically, the distance set corresponding to the updated non-cluster center scenario includes the distance between the updated non-cluster center scenario and any updated initial cluster center scenario, and the specific calculation formula is as described in step 42) above.

[0071] 46) Based on the preset scene reduction objective function, the objective function value corresponding to the second clustering result is calculated. ​

[0072] 47) determining the clustering state according to the target function value corresponding to the first clustering result and the target function value corresponding to the second clustering result; the clustering state comprises clustering update and stopping clustering update. Specifically, the target function difference is calculated according to the formula ΔW = W - W'; if the target function difference ΔW > 0, the clustering state is clustering update; if not, the clustering state is stopping clustering update; further, if the target function difference is 0, or the target function value corresponding to the first clustering result is equal to the target function value corresponding to the second clustering result, the clustering state is stopping clustering update, at this time, the target function value is unchanged.

[0073] 48) when the clustering state is clustering update, returning to the step of replacing any scene in the initial clustering center scene set with a non-clustering center scene randomly selected from the non-clustering center scene set; when the clustering state is stopping clustering update, marking the updated initial clustering center scene set corresponding to the second clustering result as the sunlight typical scene set, that is, taking the r clustering centers {R1, R2, …, Rr} obtained by the final clustering as the r typical scenes after scene reduction; at the same time, the r scene probability π(r) can be calculated, which is the proportion of the number of initial scenes contained in the rth scene to the total number of initial scenes. r} as the r typical scenes after scene reduction; at the same time, the r scene probability π(r) can be calculated, which is the proportion of the number of initial scenes contained in the rth scene to the total number of initial scenes.

[0074] Further, the preset scene reduction target function is:

[0075]

[0076]

[0077] wherein W represents the value of the preset scene reduction target function, that is, the target function value, d(u i ,u j ) represents the distance between scene u i and scene u j , p i represents the probability of scene i appearing, N2 represents the scene set composed of multiple sunlight initial scenes, N1 represents the initial clustering center scene set, T d is the time length of the sunlight initial scene.

[0078] Similarly, the step of reducing the multiple daytime traction load initial scenes to obtain the daytime traction load typical scene set.

[0079] 5) cross-combining the sunlight typical scene set, the daytime traction load typical scene set and the multiple transmission line fault typical scenes to obtain the target region typical scene set. Specifically, after cross-combining the sunlight intensity, the traction load and the transmission line fault typical scene, N S= a·b·c typical scenarios, corresponding cross probability product π s is the probability of the typical scenario S.

[0080] At this time, the scenario data in the target region typical scenario includes: sunlight intensity β s,t (t = 1, 2, 3,..., N T ), daily traction active load daily traction reactive load power system transmission line fault start time, T s represent the power system transmission line fault duration and the power system transmission line fault state.

[0081] Step 300, for each target region typical scenario, constructing a power system response constraint function according to the scenario data in the target region typical scenario.

[0082] Under extreme weather disasters, multiple broken line faults may occur in the power system, causing changes in the system topology and affecting the system transmission capacity and safe operation, and the unit operation state and available power load level need to be optimized and adjusted to ensure the safe and stable operation of the system.

[0083] Specifically, the scenario data in the target region typical scenario includes:

[0084] Daily traction active load, daily traction reactive load, all node sets in the power system excluding traction load nodes, active power output of power transmission network nodes in the power system, reactive power 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 of power transmission network nodes in the power system, reactive load reduction 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.

[0085] The power system response constraint function includes:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] where P s,Gi,t、 Q s,Gi,t represent active power and reactive power output of power system transmission network node i at time t under target area typical scenario S, respectively; represent active power and reactive power load shedding of node i at time t under target area typical scenario S, respectively; P Di,t , Q Di,t represent active power and reactive power load of node i at time t, respectively; V s,i,t , θ s,ij,t represent voltage amplitude and phase angle difference of node i at time t under target area typical scenario S, respectively; G s,ij , B s,ij represent real part and imaginary part of node admittance matrix element i row j column under target area typical scenario S, respectively; A s, ij 01 variable represents whether line ij is broken under target area typical scenario S, 0 represents broken, and 1 represents not broken.

[0101] In order to keep the power factor of node load constant, it is assumed that when a certain amount of active load is reduced, the corresponding daily traction reactive load will also be reduced:

[0102]

[0103]

[0104] where S N is the set of all nodes of the power system, S N- is the set of all other nodes of the power system excluding node h, and node h is the traction load node.

[0105] Step 400, based on the power system response constraint function, the total load reduction cost of the power system is minimized to establish a power system response model.

[0106] The total load reduction cost of the power system is minimized, specifically including: according to the formula

[0107]

[0108] The power system response objective function is established;

[0109] Where, t s,0 represents the start time of the power transmission line fault in the power system, T s represents the duration of the power transmission line fault in the power system, M represents the daily traction load level, in a specific practical application, M is 3, and the first level load power supply reliability requirement is the highest, and the reduction cost coefficient α1 is the highest, and the reduction cost coefficient of the second and third level loads is reduced in turn; S N- represents the set of all nodes in the power system excluding the traction load nodes, α m represents the reduction cost coefficient of the daily traction load level m, α1 represents the reduction cost coefficient of the daily traction load level 1, represents the i node m level load reduction amount under the target area typical scene S, represents the power supply load reduction amount of the target traction substation, and the traction load belongs to the first level load.

[0110] Step 500, solving the power system response model to obtain the power supply load reduction amount of the target traction substation. The scene data in the target area typical scene set obtained in step 200 is taken as the model input, the model is solved, and finally the power supply load reduction amount of the traction substation is obtained

[0111] Step 600, based on the power supply load reduction amount of the target traction substation, the scene data in the target area typical scene and the energy storage system, an orbital traffic light storage energy planning and configuration model is constructed.

[0112] As Figure 2 shown, the energy storage system includes a photovoltaic power station, a battery and a super capacitor, wherein the battery and the super capacitor constitute a hybrid energy storage. In addition, Figure 2 The traction substation in the traction substation corresponds to the target traction substation in the traction substation.

[0113] The rail transit light storage energy 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 to realize minimum rail transit self-consistent energy system operation loss; and the energy planning configuration constraint function comprises a power balance constraint, a hybrid energy storage constraint and a photovoltaic output constraint.

[0114] The power balance constraint is used to represent that system discharge power and system charge power in the rail transit self-consistent energy system reach balance; the system discharge power comprises purchased power, photovoltaic power generation power, battery discharge power and super capacitor discharge power; and the system charge power comprises rail transit traction active load, battery charge power, super capacitor charge power and feeder power.

[0115] The hybrid energy storage constraint comprises a battery energy storage constraint, a battery charge power constraint, a battery discharge power constraint, a super capacitor energy storage constraint, a super capacitor charge power constraint and a super capacitor discharge power constraint.

[0116] The photovoltaic output constraint comprises a photovoltaic power generation power constraint.

[0117] Further, the energy planning configuration objective function is:

[0118] Min C day =C I +C OM +C grid +C dem

[0119] Wherein, C day represents daily operation loss in the rail transit self-consistent energy system, C I represents equivalent daily operation loss of light storage capacity in the energy storage system, C OM represents operation and maintenance loss of the energy storage system, C grid represents power exchange loss between the power grid and the energy storage system, and C dem represents demand loss.

[0120]

[0121]

[0122]

[0123]

[0124] Wherein, represents daily operation loss of the photovoltaic power station, represents daily operation loss of the battery, represents the daily operation loss of super capacitor; r is the discount rate; y PV is the service life of photovoltaic power station, y Bat is the service life of battery, y SC is the service life of super capacitor; are respectively the unit photovoltaic power loss coefficient, the unit battery power loss coefficient, the unit super capacitor power loss coefficient, the unit battery capacity loss coefficient, and the unit super capacitor capacity loss coefficient; are respectively the preset rated photovoltaic power, the rated battery power, the rated super capacitor power, the rated battery capacity, and the rated super capacitor capacity.

[0125]

[0126]

[0127]

[0128]

[0129] wherein, are respectively the loss coefficients of photovoltaic, battery, and super capacitor; Δt is the optimization time scale, specifically 1 min; N T is the number of daily optimization time periods, specifically 1440 periods; are respectively the battery charging power, the battery discharging power, the super capacitor charging power, and the super capacitor discharging power at time t under the typical scenario s of the target region.

[0130]

[0131] wherein, is the grid power purchase loss coefficient at time t under the typical scenario s of the target region; is the grid power feeding loss coefficient at time t under the typical scenario s of the target region;

[0132]

[0133]

[0134]

[0135] wherein, c base is the unit demand loss coefficient; is the demand at time t under the typical scenario s of the target region, is the maximum demand under the typical scenario s of the target region.

[0136] The energy planning configuration constraint function is used to determine the limit value of the power exchanged between the rail transit self-consistent energy system and the power system, and specifically comprises:

[0137] Power balance constraint:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] Wherein, respectively represent the rail transit self-consistent energy system power purchase power, power feeding power, photovoltaic power, battery discharge power and charging power, super capacitor discharge power and charging power, rail transit traction active load load at time t under target area typical scene s; is a 01 variable; determined by the thermal stability limit of the conductor.

[0145] Hybrid energy storage constraint:

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159] wherein, respectively represent the energy stored by the battery and the energy stored by the super capacitor at time t under the typical scene s of the target region; ε Bat , ε SC respectively represent the self-discharge rate of the battery and the self-discharge rate of the super capacitor; respectively represent the discharge efficiency of the battery, the discharge efficiency of the super capacitor, the charging efficiency of the battery and the charging efficiency of the super capacitor; respectively represent the upper limit value of the state of charge of the battery, the lower limit value of the state of charge of the battery, the upper limit value of the state of charge of the super capacitor and the lower limit value of the state of charge of the super capacitor, respectively represent the initial state of charge value of the battery and the initial state of charge value of the super capacitor.

[0160] Photovoltaic output constraint:

[0161]

[0162] wherein, β s,t , β N respectively represent the illumination intensity at time t and the rated illumination intensity under the typical scene s of the target region; is the configured rated photovoltaic power. Step 600 configures the light storage on the basis of the scene data in each typical scene of the target region, and the proportion of each typical scene of the target region in the total scenes is the scene probability. The objective function is planned under the consideration of different data inputs of all scenes.

[0163] Step 700, solving the rail transit light storage energy planning and configuration model to obtain a rail transit light storage energy configuration result; the rail transit light storage energy configuration result is used to represent the power configuration and capacity configuration of each component in the energy storage system; specifically, the rail transit light storage energy configuration result includes the configured rated photovoltaic power, the rated battery power, the rated super capacitor power, the battery capacity and the super capacitor capacity.

[0164] Embodiment two

[0165] As Figure 3 shown, in order to execute the method corresponding to the above-mentioned embodiment one to realize the corresponding functions and technical effects, the embodiment further provides a light storage planning and configuration device of a rail transit self-consistent energy system, which includes a target traction substation, a power system and an energy storage system.

[0166] The light storage planning and configuration device of the rail transit self-consistent energy system includes:

[0167] 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.

[0168] The scene construction module 201 is configured to construct a target region typical scene set based on the historical illumination data, the historical traction load data and the historical fault data; the target region typical scene set comprises a plurality of target region typical scenes.

[0169] The constraint construction module 301 is configured to construct, for each target region typical scene, a power system response constraint function according to scene data in the target region typical scene.

[0170] The first model construction module 401 is configured to construct a power system response model based on the power system response constraint function, with the minimum total load reduction cost of the power system as the target.

[0171] 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.

[0172] The second model construction module 601 is configured to construct a rail transit light storage energy planning configuration model based on the power supply load reduction amount of the target traction substation, scene data in the target region typical scene and the energy storage system.

[0173] The second model solving module 701 is configured to solve the rail transit light storage energy planning configuration model to obtain a rail transit light storage energy configuration result; the rail transit light storage energy configuration result is used to represent power configuration and capacity configuration of each component in the energy storage system.

[0174] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the embodiments can be referred to each other.

[0175] The principles and implementation manners of the present application are described by using specific examples in the present application. The above description of the embodiments is only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A light storage planning and configuration method for a rail transit self-consistent energy system, characterized in that, The rail transit self-consistent energy system comprises a target traction substation, a power system and an energy storage system; The light storage planning configuration method of the rail transit self-consistent energy system comprises the following steps: obtaining historical light data, historical traction load data and historical fault data of the target traction substation; based on the historical light data, the historical traction load data and the historical fault data, a set of typical scenarios of a target area is constructed; the set of typical scenarios of the target area comprises a plurality of typical scenarios of the target area; for each typical scenario of the target area, an electric power system response constraint function is constructed according to the scenario data in the typical scenario of the target area; based on the electric power system response constraint function, an electric power system response model is established with the minimum total load reduction cost of the electric power system as the target; solving the electric power system response model to obtain the power supply load reduction of the target traction substation; based on the power supply load reduction of the target traction substation, the scenario data in the typical scenario of the target area and the energy storage system, a rail transit light storage energy planning configuration model is constructed; solving the rail transit light storage energy planning configuration model to obtain a rail transit light storage energy configuration result; the rail transit light storage energy configuration result is used to represent the power configuration and capacity configuration of each component in the energy storage system.

2. The method of claim 1, wherein, based on the historical light data, the historical traction load data and the historical fault data, a set of typical scenarios of a target area is constructed, specifically comprising: based on the historical light data, a plurality of initial daily light scenarios are constructed; based on the historical traction load data, a plurality of initial daily traction load scenarios are constructed; based on the historical fault data, a plurality of typical transmission line fault scenarios are constructed; using a K-medoids clustering algorithm, the plurality of initial daily light scenarios and the plurality of initial daily traction load scenarios are reduced respectively to obtain a set of typical daily light scenarios and a set of typical daily traction load scenarios; the set of typical daily light scenarios, the set of typical daily traction load scenarios and the plurality of typical transmission line fault scenarios are cross combined to obtain the set of typical scenarios of the target area.

3. The method of claim 2, wherein, using a K-medoids clustering algorithm, the plurality of initial daily light scenarios are reduced to obtain a set of typical daily light scenarios, specifically comprising: the plurality of initial daily light scenarios are randomly divided to obtain an initial cluster center scenario set and a non-cluster center scenario set; according to the distance set corresponding to each non-cluster center scenario, the scenarios in the non-cluster center scenario set are clustered and divided according to the nearest distance principle to obtain a first clustering result; the distance set corresponding to the non-cluster center scenario comprises the distance between the non-cluster center scenario and any initial cluster center scenario; based on a preset scenario reduction objective function, the objective function value corresponding to the first clustering result is calculated; a non-cluster center scenario is randomly selected from the non-cluster center scenario set to replace any scenario in the initial cluster center scenario set to obtain an updated initial cluster center scenario set and an updated non-cluster center scenario set; According to the principle of the nearest distance, scenes in the set of updated non-cluster center scenes are clustered and divided according to the distance set corresponding to each updated non-cluster center scene, to obtain a second clustering result; Based on a preset scene reduction objective function, a target function value corresponding to the second clustering result is calculated; According to the target function value corresponding to the first clustering result and the target function value corresponding to the second clustering result, a clustering state is determined; the clustering state includes clustering update and stopping clustering update; When the clustering state is clustering update, the step of replacing any scene in the set of initial cluster center scenes with a non-cluster center scene randomly selected from the set of non-cluster center scenes is returned; When the clustering state is stopping clustering update, the set of updated initial cluster center scenes corresponding to the second clustering result is marked as a set of typical scenes of daylight illumination.

4. The method of claim 3, wherein, The preset scene reduction objective function is: Wherein, W represents the value of the preset scene reduction objective function, that is, the objective function value, d(u i , u j ) represents the distance between scene u i , scene u j , p i represents the probability of scene i appearing, N2 represents the scene set composed of multiple sunlight initial scenes, N1 represents the initial clustering center scene set, and T d is the duration of the sunlight initial scene.

5. The method of claim 1, wherein, Scene data in the target region typical scene includes: Daily traction active load, daily traction reactive load, all other node sets in the power system excluding 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 of power transmission network nodes in the power system, reactive load reduction 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 the power transmission network in the power system, and power transmission line fault state in the power system.

6. The method of claim 1, wherein, With the minimum total load reduction cost of the power system as the target, specifically including: According to the formula The power system response objective function is established; wherein, t s,0 represents the start time of the transmission line fault in the power system, T s represents the duration of the transmission line fault in the power system, M represents the daily traction load level, S N- represents all other node sets in the power system excluding the traction load nodes, α m represents the reduction cost coefficient of the daily traction load level m, α1 represents the reduction cost coefficient of the daily traction load level 1, represents the i node m level load reduction amount under the target area typical scene S, represents the power supply load reduction amount of the target traction substation.

7. The method of claim 1, wherein, The energy storage system includes a photovoltaic power station, a battery and a super capacitor; The track transportation light storage energy planning and configuration model includes an energy planning and configuration objective function and an energy planning and configuration constraint function; the energy planning and configuration objective function is used to achieve the minimum operating loss of the track transportation self-consistent energy system; and the energy planning and configuration constraint function includes a power balance constraint, a hybrid energy storage constraint and a photovoltaic output constraint. The power balance constraint is used to represent that the system discharge power and the system charge power of the track transportation self-consistent energy system reach balance; the system discharge power includes purchased power, photovoltaic power generation power, battery discharge power and super capacitor discharge power; and the system charge power includes track transportation traction active load, battery charge power, super capacitor charge power and feeder power. The hybrid energy storage constraint includes a battery energy storage constraint, a battery charge power constraint, a battery discharge power constraint, a super capacitor energy storage constraint, a super capacitor charge power constraint and a super capacitor discharge power constraint. The photovoltaic output constraint includes a photovoltaic power generation power constraint.

8. The method of claim 7, wherein, The energy planning and configuration objective function is: Min C day = C I + C OM + C grid + C dem C day represents the daily operation loss of the rail transit self-consistent energy system, C I represents the daily operation loss of the equivalent light storage capacity in the energy storage system, C OM represents the operation and maintenance loss of the energy storage system, C grid represents the power exchange loss between the power grid and the energy storage system, C dem represents the demand loss; wherein, represents daily operation loss of the photovoltaic power station, represents daily operation loss of the battery, represents daily operation loss of the super capacitor; r is the discount rate; y PV is the service life of the photovoltaic power station, y Bat is the service life of the battery, y SC is the service life of the super capacitor; are respectively unit photovoltaic power loss coefficient, unit battery power loss coefficient, unit super capacitor power loss coefficient, unit battery capacity loss coefficient, unit super capacitor capacity loss coefficient; are respectively preset rated photovoltaic power, rated battery power, rated super capacitor power, rated battery capacity, rated super capacitor capacity; wherein, are the loss coefficients of photovoltaic, battery, super capacitor, respectively; Δt is the optimization time scale; N T is the number of daily optimization time periods; are the battery charging power, battery discharging power, super capacitor charging power, super capacitor discharging power at time t under the typical scenario s of the target region, respectively. wherein, is the grid purchase loss coefficient at time t under the typical scenario s of the target region; is the grid feed-in loss coefficient at time t under the typical scenario s of the target region. wherein c base is the unit demand loss coefficient; is the demand of the target region in the typical scenario s at time t, P s dem,max is the maximum demand of the target region in the typical scenario s.

9. A light storage planning and configuration device for a rail transit self-consistent energy system, characterized in that, The track transportation self-consistent energy system includes a target traction substation, a power system and an energy storage system; The light storage planning and configuration device of the track transportation self-consistent energy system includes: The data acquisition module is configured to acquire historical illumination data, historical traction load data and historical fault data of the target traction substation. The scene construction module is configured to construct a target region typical scene set based on the historical illumination data, the historical traction load data and the historical fault data. The constraint construction module is configured to construct a power system response constraint function for each target region typical scene according to scene data in the target region typical scene. The first model construction module is configured to establish a power system response model based on the power system response constraint function, and to minimize the total load reduction cost of the power system. The first model solving module 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 construction module is configured to construct a rail transit light storage energy planning configuration model based on the power supply load reduction amount of the target traction substation, scene data in the target region typical scene and the energy storage system. The second model solving module is configured to solve the rail transit light storage energy planning configuration model to obtain a rail transit light storage energy configuration result, wherein the rail transit light storage energy configuration result is used to represent power configuration and capacity configuration of each component in the energy storage system.

Images