CN114781799A

CN114781799A - Urban rail transit energy intelligent management system

Info

Publication number: CN114781799A
Application number: CN202210278240.2A
Authority: CN
Inventors: 王开康; 张华志; 何俊文; 温建民; 王德发; 李强; 车轮飞; 刘俊; 何斌; 王爱军
Original assignee: China Railway Siyuan Survey and Design Group Co Ltd
Current assignee: China Railway Siyuan Survey and Design Group Co Ltd
Priority date: 2022-03-21
Filing date: 2022-03-21
Publication date: 2022-07-22

Abstract

The invention discloses an intelligent management system for urban rail transit energy, which comprises an intelligent monitoring subsystem, an intelligent management subsystem and an intelligent control subsystem; the intelligent monitoring subsystem is used for acquiring monitoring information of the field data acquisition equipment, carrying out monitoring, energy consumption statistical analysis and visual display, and sending the monitoring information and processing information to the intelligent management subsystem and the intelligent control subsystem; the intelligent management subsystem is used for importing the received information into a pre-stored energy consumption index system and an energy consumption diagnosis model, outputting an energy consumption index analysis result and an energy consumption diagnosis result and sending the energy consumption index analysis result and the energy consumption diagnosis result to the intelligent control subsystem; and the intelligent control subsystem is used for importing the received information into a pre-stored energy-saving control model and outputting a control instruction to the field execution equipment. The invention can realize energy consumption monitoring, analysis, diagnosis and energy-saving control of rail transit.

Description

Urban rail transit energy intelligent management system

Technical Field

The invention relates to the technical field of rail transit, in particular to an intelligent energy management system for urban rail transit.

Background

The urban rail transit has the characteristics of long line, large scale, multiple facilities, scattered energy utilization equipment layout and the like, and brings certain difficulty to the acquisition and management of energy data. At present, energy management systems are arranged in part of cities during construction, and basically hardware of the systems comprises electric energy meters arranged in high-voltage, medium-voltage and low-voltage power distribution cabinets, station-level communication managers, a central-level energy management center and communication channels, wherein the central-level energy management center mainly comprises a data server, energy management work stations, network equipment and the like, the system mainly realizes the functions of rail transit electric energy classification, subentry collection, arrangement, statistics, analysis, storage and the like, access conditions of water, gas and fuel oil data are reserved in the energy management systems of part of cities, the system mainly aims to replace the traditional manual meter reading, and the workload of personnel is reduced while the data acquisition amount is increased. But dynamic energy-saving management of specific devices/systems has not been achieved.

Disclosure of Invention

Aiming at least one defect or improvement requirement in the prior art, the invention provides an intelligent management system for urban rail transit energy, which can realize energy consumption monitoring, analysis, diagnosis and energy-saving control on rail transit.

In order to achieve the aim, the invention provides an intelligent management system for urban rail transit energy, which comprises an intelligent monitoring subsystem, an intelligent management subsystem and an intelligent control subsystem;

the intelligent monitoring subsystem is used for acquiring monitoring information of the field data acquisition equipment, carrying out monitoring, energy consumption statistical analysis and visual display, and sending the monitoring information and processing information to the intelligent management subsystem and the intelligent control subsystem;

the intelligent management subsystem is used for importing the received information into a pre-stored energy consumption index system and an energy consumption diagnosis model, outputting an energy consumption index analysis result and an energy consumption diagnosis result and sending the energy consumption index analysis result and the energy consumption diagnosis result to the intelligent control subsystem;

and the intelligent control subsystem is used for importing the received information into a pre-stored energy-saving control model and outputting a control instruction to the field execution equipment.

Furthermore, the intelligent monitoring subsystem, the intelligent management subsystem and the intelligent control subsystem are all realized based on a monitoring workstation, an operating workstation, a data server and an application server which are arranged at a station level and a central level.

Further, the intelligent management subsystem includes an energy consumption index analysis submodule, and the energy consumption index analysis submodule includes:

and the line-level energy consumption analysis submodule is used for realizing energy consumption analysis based on passenger transportation turnover, energy consumption analysis based on operation mileage, energy consumption analysis based on station area, energy consumption analysis based on a positive line kilometer index, energy consumption analysis based on a line electricity total amount index and energy consumption analysis based on line-level equipment energy efficiency.

The station level energy consumption analysis submodule is used for energy consumption analysis based on station area, energy consumption analysis based on passenger flow, energy consumption analysis based on station electricity utilization total amount indexes and energy consumption analysis based on station level equipment energy efficiency;

and the traction level energy consumption analysis submodule is used for analyzing the energy consumption based on the passenger transportation turnover number, analyzing the energy consumption based on the operation mileage, analyzing the energy consumption based on the total power consumption index of the train and analyzing the energy consumption based on the energy efficiency of the traction level equipment.

Furthermore, the intelligent control subsystem comprises a traction feeding intelligent control subsystem, a ventilation and air conditioning intelligent control subsystem and an illumination intelligent control subsystem.

Further, the traction feed intelligent control subsystem comprises an automatic closed-loop adjusting module, wherein the automatic closed-loop adjusting module is used for searching the running states of the traction substation and the adjacent traction substation devices by a coordination control device when the voltage value of a direct current traction network at the traction substation where one or more traction substation devices are located is judged to exceed a set cooperative absorption voltage threshold value, and controlling to adjust down the starting voltage threshold value of the searched device until the starting voltage threshold value reaches a lower limit or the voltage of the direct current traction network at the traction substation falls within the cooperative absorption voltage threshold value if the searched device has available residual capacity.

Further, the traction feeding intelligent control subsystem comprises an analysis control module driven by an operation diagram.

Furthermore, the ventilation air-conditioning intelligent control subsystem comprises a cold load prediction model, wherein the cold load prediction model is used for determining a cold load function taking personnel in the stations as variables and an expression form of the cold load function taking outdoor temperature and humidity as variables by acquiring the number of the personnel in each station at different times, the outdoor temperature of each station and a total cold load actual value corresponding to each station at each sampling time point, so that a total cold load prediction value based on the number of the personnel in each station and the outdoor temperature of each station is realized, and cold loads are provided for each station according to the total cold load prediction value.

Furthermore, the field data acquisition equipment comprises electric energy meter meters arranged in a grading manner, distributed sensors and self-carried monitoring units of energy utilization equipment.

In general, compared with the prior art, the above technical solution conceived by the present invention can achieve the following beneficial effects: through mutual cooperation of the three functional modules of the intelligent monitoring subsystem, the intelligent management subsystem and the intelligent control subsystem, energy consumption monitoring, energy consumption statistical analysis, visual display, energy consumption index analysis, energy consumption diagnosis and intelligent energy-saving control can be realized, and the energy utilization rate can be improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a schematic diagram of three major subsystems of an energy intelligent management system according to an embodiment of the invention;

FIG. 2 is a functional diagram of three subsystems of an energy intelligent management system according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the central level of the energy intelligent management system according to the embodiment of the invention;

FIG. 4 is a schematic diagram of station level and field devices of the energy intelligent management system of the embodiment of the invention;

FIG. 5 is a schematic diagram of a line energy feed coordination control system of the energy intelligent management system according to an embodiment of the present invention;

fig. 6 is a control logic diagram of coordination control of a plurality of inverter devices in the energy intelligent management system according to the embodiment of the present invention;

FIG. 7 is a simplified schematic diagram of the operation of an energy intelligence management system according to an embodiment of the present invention;

fig. 8 is a schematic diagram of train operation process division of the energy intelligent management system according to the embodiment of the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.

The terms "first," "second," "third," and the like in the description and in the claims, and in the above-described drawings, are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

In other instances, well-known or widely used techniques, elements, structures and processes may not be described or shown in detail in order to avoid obscuring the understanding of the invention by the skilled artisan. Although the drawings represent exemplary embodiments of the present invention, the drawings are not necessarily to scale and certain features may be exaggerated or omitted in order to better illustrate and explain the present invention.

As shown in fig. 1 and fig. 2, an intelligent management system for energy of urban rail transit according to an embodiment of the present invention includes an intelligent monitoring subsystem, an intelligent management subsystem, and an intelligent control subsystem.

The intelligent monitoring subsystem is used for acquiring monitoring information of the field data acquisition equipment, carrying out monitoring, energy consumption statistical analysis and visual display, and sending the monitoring information and processing information to the intelligent management subsystem and the intelligent control subsystem.

And the intelligent management subsystem is used for importing the received information into a pre-stored energy consumption index system and an energy consumption diagnosis model, outputting an energy consumption index analysis result and an energy consumption diagnosis result and sending the energy consumption index analysis result and the energy consumption diagnosis result to the intelligent control subsystem.

The intelligent control subsystem is used for leading the received information into a pre-stored energy-saving control model and outputting a control instruction to the field execution equipment.

Furthermore, the hardware platform of the intelligent energy management system for the urban rail transit is composed of a central level, a station level, field equipment and a communication channel for connecting the central level, the station level and the field equipment.

The central level and the station level respectively comprise a monitoring workstation, an operator workstation, a data server, an application server, a local area network networking device and the like. The functions of the intelligent monitoring subsystem, the intelligent management subsystem and the intelligent control subsystem are realized on the basis of a monitoring work station, an operation work station, a data server and an application server which are arranged at a station level and a central level.

As shown in fig. 3, the central level of the intelligent energy management system for urban rail transit is formed by networking a monitoring workstation, an operator workstation, a data server, an application server, a local area network device, and the like, and exchanges data with the station level energy management system through a communication backbone network of a subway.

As shown in fig. 4, the station level of the intelligent energy management system for urban rail transit is composed of a monitoring workstation, an operator workstation, a data server, an application server, a local area network device network, and the like, and exchanges data with the central level energy management system through a communication backbone network of a subway; data exchange is carried out between the energy utilization equipment and various sensors through a local area network or a field bus network distributed in the station; and acquiring related information of the operation, environment and personnel of subway station equipment through the communication interface and system interfaces such as BAS and AFC.

The field device comprises a field data acquisition system and a field execution device.

The field data acquisition system comprises electric energy meters arranged in a grading way, distributed sensors (for acquiring pressure, temperature, humidity, illumination, people flow, gas and the like), a self-contained energy utilization device monitoring unit, a communication network and the like.

The field-enforcement devices include various controllers, communication networks, etc., installed in close proximity with the devices.

Furthermore, the intelligent management subsystem comprises an energy consumption index analysis submodule, and the energy consumption index analysis submodule comprises three modules: the system comprises a line-level energy consumption analysis submodule, a station-level energy consumption analysis submodule and a traction-level energy consumption analysis submodule, and energy consumption management is realized from three levels respectively.

And the station level energy consumption analysis submodule is used for analyzing the energy consumption based on the area of a station, analyzing the energy consumption based on passenger flow, analyzing the energy consumption based on the total station electricity utilization index and analyzing the energy consumption based on the station level equipment energy efficiency.

And the traction level energy consumption analysis submodule is used for analyzing the energy consumption based on the passenger transport turnover amount, analyzing the energy consumption based on the operation mileage, analyzing the energy consumption based on the total power consumption index of the train and analyzing the energy consumption based on the energy efficiency of the traction level equipment.

Furthermore, the intelligent control subsystem comprises a traction feeding intelligent control subsystem, a ventilation air-conditioning intelligent control subsystem and an illumination intelligent control subsystem.

Furthermore, the traction feed intelligent control subsystem can realize functions such as analysis control based on automatic closed-loop regulation, open-loop regulation, multi-machine cooperative reactive compensation and operation diagram driving. The above functions can be arbitrarily combined, deleted and added as required.

The control principle of the automatic closed-loop control and the analysis control driven by the operation diagram is specifically explained below.

The traction feed intelligent control subsystem comprises an automatic closed-loop adjusting module. The method comprises the following steps of dynamically adjusting the running state and the running parameters of each traction substation device according to the running state, the residual capacity, the direct current network voltage of the traction substation, the alternating current network power factor and other information of each device by collecting the working state and the real-time electric quantity of each inversion feedback device or each bidirectional converter device of the whole traction substation, so as to realize the optimal control of the direct current traction network voltage, and has the following functions:

as shown in fig. 5, the automatic closed-loop adjusting module includes a coordination control function of multiple inverters, and communicates with all inverters in the whole line through a high-speed channel, collects a voltage value of a dc traction network in which each traction station device is located, an ac bus voltage value, and an operating state, a start threshold and an output power value of the inverter, and issues a start voltage threshold of each traction station device at the same time.

When the voltage value of a direct current traction network at a traction substation where one or more traction substation devices are located exceeds a set cooperative absorption voltage threshold value, the cooperative control device searches the running states of the traction substation and the devices of the adjacent traction substations (the search number can be set, and generally four adjacent substations) of the traction substation, and if the searched devices have available residual capacity (are not in an inversion absorption state or the absorption power does not reach the rated power), the cooperative controller down-regulates the starting voltage threshold value of the searched devices until the starting voltage threshold value reaches the lower limit or the voltage of the direct current traction network at the traction substation falls within the cooperative absorption voltage threshold value. When the direct-current traction network voltage of the traction substation is recovered to the cooperative absorption voltage threshold value, the line coordination control device can automatically quit coordination control, and the value of the inversion device for reducing the threshold value is recovered to be the initial set threshold value.

A logic diagram of a coordinated control algorithm of a plurality of inverter devices is shown in fig. 6.

The traction feeding intelligent control subsystem also comprises an analysis control module which is driven by the operation diagram.

The following describes a preferred implementation of the analysis control module driven by the operating diagram.

(1) Building a runtime graph model

The operation diagram is a diagram showing that a train is operated in a section and arrives at a train station to a starting and stopping time. According to a train schedule provided by a running organization of an operation department, a running chart based on a running plan can be drawn, and according to the actual arrival and departure time of the train, an actual running chart of an operation stage can be obtained. The simplified model of the operational diagram is shown in fig. 7. In fig. 7, k is a section number; d_{i_k1}The time when the train i arrives at the station node k 1; f_{i_k1}The time when the train i departs from the station node k 1; t is a unit of_{i_k}The planned running time of the section k for the train i; b_{i_k1}Marking whether the train i stops at k1 (b)_{i_k1}1 stands for stop, b_{i_k1}0 means no stop); s_{i_k1}The stop time of the train i at the station node k 1.

Available T of simplified model of operation diagram_{i_k}And S_{i_k1}The description is as follows:

T_{i_k}＝D_{i_k2}-F_{i_k1}

S_{i_k1}＝F_{i_k1}-D_{i_k1}

wherein i belongs to M, k belongs to L_i，k1,k2∈N_iWherein M is the set of all trains running on the whole line; l is_iCorresponding arc line sets are set when the train i runs in each interval; n is a radical of_iIs a station node set passed by the train i.

(2) Train timing energy-saving control model considering coasting control coefficient

(2.1) division of train operation Process

Researches show that the adoption of the coasting working condition before the braking and the deceleration of the train is effective energy-saving operation, and the solution of the optimal coasting point is the key of energy-saving control. Taking the running process of the train i in the section k as an example, in order to solve the optimal coasting point, a target speed v is introduced_{cm_i_k}(m is the number of stages at which the target speed is located) and the coasting control factor τ_{i_k}The train running process is divided into 3 stages again, wherein the stage I is the train starting process, the stage II is the intermediate speed regulating process, and the stage III is the braking speed reduction process, as shown in fig. 8, wherein P represents traction, C represents coasting, and B represents braking.

In the stage division process, the stage I is that the speed of the train is accelerated from 0 to v_{cm_i_k}Phase III according to the speed profile and taking into account τ_{i_k}Then, determining the intersection point of the traction back-calculation curve, as shown in fig. 8, the train traction back-calculation speed curve intersects with the line speed limit at a point b, the speed curve intersects with the traction back-calculation curve at a point b' in the actual running process of the train, and the intersection point position is x_{i_k_b′}Introduction of tau_{i_k}Then, the train speed curve and the traction inverse calculation curve intersect at a point b', and the intersection position is x_{i_k}(τ_{i_k}，v_{cm_i_k}) Then τ is_{i_k}Is composed of

To ensure safe operation, according to engineering experience tau_{i_k}Is a value range of 0 to tau_{i_k}≤0.25。

Consider v_{cm_i_k}And τ_{i_k}And dividing train operation intervals, aiming at the energy-saving operation of the trains at interval quasi points, and establishing a timing energy-saving operation control model of each train by taking the operation of each train in an operation diagram as a main constraint condition.

(2.2) objective function

The timing horizon is aimed at the k interval of train i by tau_{i_k}、v_{cm_i_k}Actual runtime T of a runtime_{i_k}(τ_{i_k}，v_{cm_i_k}) And T_{i_k}Difference value delta of_{i_k}(τ_{i_k}，v_{cm_i_k}) At a minimum, i.e.

f_{Ti_k}＝minδ_{i_k}(τ_{i_k}，v_{cm_i_k})＝min|T_{i_k}(τ_{i_k}，v_{cm_i_k})-T_{i_k}|

The train i is in the section k with tau_{i_k}、v_{cm_i_k}Traction power P at time t of operation_{i_k}(τ_{i_k}，v_{cm_i_k}And t) can be represented as

Wherein:

A＝μ_{i_k}(τ_{i_k},v_{cm_i_k},t)

B＝f_{maxi_k}(v_{i_k}(τ_{i_k},v_{cm_i_k},t))

C＝v_{i_k}(τ_{i_k},v_{cm_i_k},t)

in the formula, mu_{i_k}The coefficient of traction is 0-mu_{i_k}(t)≤1；η_iElectromechanical efficiency; v. of_{i_k}Is the speed; f. of_{maxi_k}(v_{i_k}) When the velocity is v_{i_k}Maximum traction force.

The train i is in the section k with tau_{i_k}、v_{cmi_k}Energy consumption for traction during operation E_{i_k}(τ_{i_k}，v_{cmi_k}) Is composed of

Selecting sets of combination variables (tau) that meet the timing objective of equation (3.1.2-4)_{i_k}，v_{cm_i_k}) Lower make E_{i_k} ^kThe minimum set of variables is used as the final optimization result, and the final target of the train interval operation is

f_{Ei_k}＝minE_{i_k}(τ_{i_k},v_{cm_i_k})

(2.3) constraint Condition

In order to solve the objective function, boundary condition constraints such as arrival time, starting and stopping speed and position of each train at each station in the operation diagram need to be met; in order to ensure safe operation, the line speed limit and passenger comfort level index constraints need to be met.

(1) Boundary constraint

(ii) arrival time constraints

|T_{i_k}(τ_{i_k}，v_{cm_i_k})-T_{i_k}|＝δ_{i_k}(τ_{i_k}，v_{cm_i_k})≤δ_max

In the formula, delta_maxRepresents T_{i_k}(τ_{i_k}，v_{cm_i_k}) And T_{i_k}With the maximum error allowed.

② speed and position restraint of start-stop

The train i passes through the section k at the starting point k1 and the ending point k2_{i_k1}And b_{i_k2}The value of (d) determines whether to stop the station.

If b is_{i_k1}1 and b_{i_k2}If 1, then the boundary constraint can be expressed as

If b is_{i_k1}0 and b _{i_k2}1, then the boundary constraint can be tabulatedShown as

If b is_{i_k1}1 and b_{i_k2}If 0, then the boundary constraint can be expressed as

If b is_{i_k1}Is equal to 0 and b_{i_k2}If 0, then the boundary constraint can be expressed as

In the formula, x_{i_k1}、x_{i_k2}Is the starting and ending positions of the interval k, v_lim(x_{i_k1}) Indicating the speed limit at node k 1.

(2) Line speed limit restraint

0≤v_{i_k}(x)≤v_lim(x)

In the formula, v_lim(x) Is the rate limit at x.

(3) Passenger comfort index constraints

In the running process of the train, the comfort degree of passengers can be influenced by overlarge acceleration change or too frequent working condition switching. The acceleration change rate is used as an index for evaluating the comfort of the passenger.

In the formula,. DELTA.a_{i_′}(t)、Δa_{i_k}(t) the acceleration rate and the change of the train i at the time t of the section k are respectively; a is_{i_k}(t)、a_{i_k}(t-1) acceleration of the train i at the time t and the time (t-1) of the section k respectively; Δ t is the simulation interval.

(3) Building direct current traction power supply system model

The direct current traction power supply system mainly comprises a traction network, an energy storage device, a train, a rectifier unit and the like. Considering the solving efficiency and the calculation precision of the power flow algorithm, a three-layer earth network model is adopted for the traction network, a constant voltage source-internal resistance model is adopted for the rectifier unit, a power source model is adopted for the train, and the regenerative braking energy utilization device is modeled based on external characteristics.

The running tracks of a plurality of trains are restored to obtain the position x of each train at the moment t_{i_k}(τ_{i_k}，v_{cm_i_k}T), power P_{i_k}(τ_{i_k}， v_{cm_i_k}And t) and the like, if the number of all-line nodes at the moment t is n, constructing a node voltage equation at the moment t on the direct current side according to the equivalent model of the direct current traction power supply system, wherein the node voltage equation at the moment t is as follows:

in the formula, G_fgDenotes the mutual admittance (f ≠ g) or self-admittance (f ═ g) of nodes f and g, U_f(t)、I_f(t) represents the voltage and injection current at node f at time t, I_f(t) can be expressed as:

in the formula of U_{i_k}(t) is the voltage of train i at time t; u shape_s(t)、R_s(t) represents the output voltage and equivalent resistance of the traction device s at time tstrap.

(4) Model solution and load flow calculation

Taking into account the coasting control factor by tau_{i_k}、v_{cm_i_k}Dividing the train running process, and finishing the quasi-point energy-saving running of each train in each interval based on a fixed step target speed search algorithm to obtain a speed v_{i_k}(t) position x_{i_k}(t) locomotive output F_{i_k}(t) locomotive taking stream I_{i_k}And (t) and other actual running tracks of electrical information are used for simulating and calculating the load process of the urban rail power supply system, and the specific steps are as follows.

Step 1: loading data of a train, a line and a running chart;

step 2: initializing simulation conditions: let i equal 1, k equal 1, τ_{i_k}＝0，m＝0，v_{cm_i_k}＝v_{maxi_k}；

Step 3: using v_{cm_i_k}And speed limit information in the line is summed, ramp division is carried out again, and according to tau_{i_k}、v_{cm_i_k}Dividing the running process of the train section to finish the speed connection of the train i in the section k;

step 4: calculating T_{i_k}(τ_{i_k}，v_{cm_i_k}) If T is_{i_k}(τ_{i_k}，v_{cm_i_k})>T_{i_k}If so, outputting the running record of the train i in the section k, k + +, and returning to Step 2; otherwise, let v_{cm_i_k}＝v_{averi_k}Executing Step3 and Step 5;

step 5: if T_{i_k}(τ_{i_k}，v_{cm_i_k}) Is not equal to T_{i_k}Then, a fixed step level search algorithm is adopted to modify v_{cm_i_k}Up to T_{i_k}(τ_{i_k}，v_{cm_i_k})＝T_{i_k}The specific searching steps are as follows:

(r) initializing v_{cm_i_k}＝v_{averi_k}，T_{i_k}(τ_{i_k}，v_{cm_i_k})＝T_{i_k}(τ_{i_k}，v_{aver_i_k}) M number of search orders d_m＝1；

When the target speed is v_{cm_i_k}+d_mλ_mTime T of_{i_k}(τ_{i_k}，v_{cm_i_k}+d_mλ_m) If T is_{i_k}(τ_{i_k},v_{cm_i_k}+d_mλ_m)＝T_{i_k}Output v_{cm_i_k}＝v_{cm_i_k}+d_mλ_mGo to ③; if T is_{i_k}(τ_{i_k}，v_{cm_i_k} +d_mλ_m)>T_{i_k}>T_{i_k}(τ_{i_k}，v_{cm_i_k}) Then T is_{i_k}(τ_{i_k}，v_{cm_i_k})＝T_{i_k}(τ_{i_k}，v_{cm_i_k}+d_mλ_m)，m++， d_mWhen the value is 1, go to ②, otherwise d_m++，T_{i_k}(τ_{i_k}，v_{cm_i_k})＝T_{i_k}(τ_{i_k}，v_{cm_i_k}+d_mλ_m) Then, go to step two; if d is_m>σ_mThen m- -, d_mWhen the value is 1, the operation is switched to ②;

saving running record R of train section_{i_k}(τ_{i_k}，v_{cm_i_k})；

Step 6: let tau_{i_k}＝τ_{i_k}+ Δ τ, if τ_{i_k}When v is less than or equal to 0.25, make v_{cm_i_k}＝v_{maxi_k}Returning to Step3, otherwise, selecting R with the minimum traction energy consumption value_{i_k}(τ_{i_k}、v_{cm_i_k}) The final optimization result of the train i in the section k is obtained;

step 7: let k + +, if k<L_iThen let τ be_i ^k＝0，m＝0，v_{cm_i_k}＝v_{maxi_k}Turning to Step 3; otherwise, turning to Step 8;

step 8: let i + +, if i>M, then go to Step 9; otherwise let k equal to 1, if T_{i_k}＝T_{z_k}(z is not more than i-1), then R_{i_k}＝R_{z_k}(τ_{z_k}，v_{cm_z_k}) And go to Step7, otherwise let τ_{i_k}＝0，m＝0，v_{cm_i_k}＝v_{maxi_k}Go to Step 3;

step 9: and restoring the running tracks of the multiple trains with the electrical information according to the optimized running records of the trains.

Step 10: initializing iteration times r, voltage convergence accuracy epsilon, substation state W, simulation start-stop time and simulation duration T, and a direct-current side power supply system node admittance matrix G;

step 11: reading the position and power information of each train at the current moment t;

step 12: carrying out load flow calculation on the direct current traction power supply system at the time t, and updating the voltage U and the current I of each node;

step 13: judging whether | U is satisfied_r-U_r-1|<If not, making r equal to r +1, returning to Step12, otherwise, shifting to Step 14;

step 14: judging whether the state W of the traction needs to be adjusted, if the state W is not reasonable, returning to Step13, otherwise, changing t to t +1, and switching to Step 15;

step 15: if T < T, returning to Step11, otherwise, outputting the simulation result.

Wherein λ_mFor m levels corresponding to step values, σ_mM, lambda and sigma can be set according to the line condition and the vehicle condition as required specifically for the m-level maximum search times; and delta tau is the simulation step length of the idle control coefficient.

Furthermore, the ventilation air-conditioning intelligent control subsystem can realize the control of a basic control strategy and deep energy-saving optimization control. Wherein, the deep energy-saving optimization control also comprises a plurality of strategies. The above functions can be arbitrarily combined, deleted and added as required.

The control principle of the cold load prediction model is specifically described below.

The intelligent control subsystem of the ventilation air conditioner comprises a cold load prediction model. The cold load prediction model is used as the basis of a cold source energy consumption model of the system, is one of basic conditions for realizing energy efficiency optimization control, provides an initial predicted value for the operation of a cold source system, and can continuously correct the cold load value by using an intelligent fuzzy control method according to the energy consumption value and the cold quantity in the operation process so as to provide basic conditions for searching an optimal state point for the system cold source energy efficiency model. A cold load prediction model is established through the operation condition data of the ventilation air-conditioning system, a cold load prediction value is provided for the initial operation of the system, and the system is continuously corrected according to the relation between energy consumption and refrigerating capacity in the operation process.

The predicted value of the total load is mainly a function relation formula taking personnel and outdoor temperature and humidity as variables by removing fixed parameter information such as electricity, illumination and the like, and can be simplified as shown in the following formula:

Q_{general assembly}＝f(p)+f(t,d)+Qa

In the formula, Q_{General assembly}The total cold load predicted value is kW; f (p) is a cooling load function taking personnel in the station as variables; f (t, d) is a cold load function taking outdoor temperature and humidity as variables; q_aThe load value is fixed for electric and lighting.

The method comprises the steps of collecting the number of people in each station at different time, the outdoor temperature of each station and the actual total cooling load value corresponding to each station at each sampling time point, and determining the expression forms of the functions f (p) and f (t, d) by methods such as a neural network model based on deep learning, so that the total cooling load predicted value based on the number of people in each station and the outdoor temperature of each station is realized, and the cooling load is provided for each station according to the total cooling load predicted value.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

It will be understood by those skilled in the art that the foregoing is only an exemplary embodiment of the present invention, and is not intended to limit the invention to the particular forms disclosed, since various modifications, substitutions and improvements within the spirit and scope of the invention are possible and within the scope of the appended claims.

Claims

1. An intelligent management system for urban rail transit energy is characterized by comprising an intelligent monitoring subsystem, an intelligent management subsystem and an intelligent control subsystem;

2. The intelligent energy management system for urban rail transit according to claim 1, wherein the intelligent monitoring subsystem, the intelligent management subsystem and the intelligent control subsystem are implemented based on monitoring workstations, operation workstations, data servers and application servers arranged at station level and central level.

3. The intelligent management system for urban rail transit energy sources according to claim 1, wherein the intelligent management subsystem comprises an energy consumption index analysis submodule, and the energy consumption index analysis submodule comprises:

4. The intelligent management system for urban rail transit energy sources according to claim 1, wherein the intelligent control subsystem comprises a traction feed intelligent control subsystem, a ventilation air-conditioning intelligent control subsystem and a lighting intelligent control subsystem.

5. The system according to claim 4, wherein the traction feed intelligent control subsystem comprises an automatic closed-loop adjusting module, the automatic closed-loop adjusting module is configured to, when it is determined that the voltage value of the direct current traction network at the traction substation where one or more traction substation devices are located exceeds a set cooperative absorption voltage threshold, the cooperative control device retrieves the operating states of the traction substation and its neighboring traction substation devices, and if the retrieved devices have available remaining capacity, controls to down-regulate the start voltage threshold of the retrieved device until the start voltage threshold reaches a lower limit or the voltage of the direct current traction network at the traction substation falls within the cooperative absorption voltage threshold.

6. The intelligent management system for urban rail transit energy sources according to claim 4, wherein the traction feed intelligent control subsystem comprises an analysis control module driven by a running chart.

7. The system as claimed in claim 4, wherein the intelligent control subsystem of ventilation and air conditioning comprises a cold load prediction model, and the cold load prediction model is used for determining a cold load function using the staff in the stations as variables and an expression form of the cold load function using the outdoor temperature and humidity as variables by obtaining the number of staff in each station, the outdoor temperature of each station and the actual value of the total cold load corresponding to each station at each sampling time point at different times, so as to realize a predicted value of the total cold load based on the number of staff in each station and the outdoor temperature of each station, and provide the cold load for each station according to the predicted value of the total cold load.

8. The intelligent energy management system for urban rail transit according to claim 1, wherein the field data acquisition equipment comprises electric energy meter meters arranged in a hierarchical manner, sensors arranged in a distributed manner, and self-monitoring units of energy utilization equipment.