An intelligent energy management system for urban rail transit

Abstract

The present invention discloses an intelligent management system for urban rail transit energy, including an intelligent monitoring subsystem, an intelligent management subsystem and an intelligent control subsystem; the intelligent monitoring subsystem is used to obtain monitoring information of on-site data acquisition equipment and perform monitoring, energy consumption statistical analysis and visual display, and send the monitoring information and processing information to the intelligent management subsystem and the intelligent control subsystem; the intelligent management subsystem is used to import the received information into a pre-stored energy consumption index system and energy consumption diagnosis model, output energy consumption index analysis results and energy consumption diagnosis results and send them to the intelligent control subsystem; the intelligent control subsystem is used to import the received information into a pre-stored energy-saving control model, and output control instructions to on-site execution equipment. The present invention can realize energy consumption monitoring, analysis, diagnosis and energy-saving control of rail transit.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and more specifically, to an intelligent energy management system for urban rail transit. Background Art

[0002] Urban rail transit has the characteristics of long lines, large scale, many facilities, and scattered layout of energy-consuming equipment, which brings certain difficulties to energy data collection and management. At present, some cities have set up energy management systems during construction. The hardware of these systems is basically composed of electricity meters set in high, medium and low voltage distribution cabinets, station-level communication management machines, central-level energy management centers and communication channels. The central-level energy management center is mainly composed of data servers, energy management workstations, network equipment, etc. The system mainly realizes the classification and sub-item collection, sorting, statistics, analysis and storage of rail transit electricity. Some cities have reserved access conditions for water, gas and fuel data in the energy management system. The system is mainly to replace traditional manual meter reading, while increasing the amount of data collection and reducing the workload of personnel. However, it is not yet possible to achieve dynamic energy-saving management of specific equipment / systems. Summary of the invention

[0003] In response to at least one defect or improvement need in the prior art, the present invention provides an intelligent energy management system for urban rail transit, which can realize energy consumption monitoring, analysis, diagnosis and energy-saving control of rail transit.

[0004] To achieve the above-mentioned purpose, the present invention provides an urban rail transit energy intelligent management system, including an intelligent monitoring subsystem, an intelligent management subsystem and an intelligent control subsystem;

[0005] The intelligent monitoring subsystem is used to obtain monitoring information of the on-site data acquisition equipment and perform monitoring, energy consumption statistical analysis and visual display, and send the monitoring information and processing information to the intelligent management subsystem and the intelligent control subsystem;

[0006] The intelligent management subsystem is used to import the received information into the pre-stored energy consumption index system and energy consumption diagnosis model, output the energy consumption index analysis results and energy consumption diagnosis results and send them to the intelligent control subsystem;

[0007] The intelligent control subsystem is used to import the received information into a pre-stored energy-saving control model and output control instructions to the on-site execution equipment.

[0008] Furthermore, the intelligent monitoring subsystem, the intelligent management subsystem and the intelligent control subsystem are all implemented based on monitoring workstations, operating workstations, data servers and application servers arranged at the station level and the central level.

[0009] Furthermore, the intelligent management subsystem includes an energy consumption index analysis submodule, and the energy consumption index analysis submodule includes:

[0010] The line-level energy consumption analysis submodule is used to realize energy consumption analysis based on passenger turnover, energy consumption analysis based on operating mileage, energy consumption analysis based on station area, energy consumption analysis based on main line kilometer indicators, energy consumption analysis based on line total electricity consumption indicators, and energy consumption analysis based on line-level equipment energy efficiency.

[0011] 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 consumption indicators, and energy consumption analysis based on station-level equipment energy efficiency;

[0012] The traction-level energy consumption analysis submodule is used for energy consumption analysis based on passenger turnover, energy consumption analysis based on operating mileage, energy consumption analysis based on total train electricity consumption indicators, and energy consumption analysis based on traction-level equipment energy efficiency.

[0013] Furthermore, the intelligent control subsystem includes a traction feeding intelligent control subsystem, a ventilation and air conditioning intelligent control subsystem and a lighting intelligent control subsystem.

[0014] Furthermore, the traction feed intelligent control subsystem includes an automatic closed-loop regulation module, which is used to coordinate the control device to retrieve the operating status of the traction substation and its adjacent traction substation devices when it is determined that the DC traction network voltage value of a traction substation where one or several traction substation devices are located exceeds the set collaborative absorption voltage threshold. If the retrieved device has remaining capacity available, the starting voltage threshold of the retrieved device is controlled to be lowered until its starting voltage threshold reaches the lower limit or the DC traction network voltage of the traction substation drops to within the collaborative absorption voltage threshold.

[0015] Furthermore, the traction feeding intelligent control subsystem includes an analysis control module driven by an operation diagram.

[0016] Furthermore, the ventilation and air-conditioning intelligent control subsystem includes a cooling load prediction model, which is used to determine the expression form of the cooling load function with the number of people in each station as variables and the cooling load function with outdoor temperature and humidity as variables by obtaining the number of people in each station at different times, the outdoor temperature of each station and the actual value of the total cooling load corresponding to each station at each sampling time point, thereby realizing the total cooling load prediction value based on the number of people in each station and the outdoor temperature of each station, and providing cooling load to each station according to the total cooling load prediction value.

[0017] Furthermore, the on-site data collection equipment includes hierarchically arranged electric energy meters, distributedly arranged sensors, and monitoring units provided by energy-consuming equipment.

[0018] In general, compared with the prior art, the above technical scheme conceived by the present invention can achieve the following beneficial effects: through the mutual collaboration 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, visualization display, energy consumption index analysis, energy consumption diagnosis and intelligent energy-saving control can be realized, thereby improving energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 Schematic diagram of three major subsystems of the energy intelligent management system according to an embodiment of the present invention;

[0021] Figure 2 This is a functional diagram of the three subsystems of the energy intelligent management system according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the central level structure of the energy intelligent management system according to an embodiment of the present invention;

[0023] Figure 4 It is a schematic diagram of station-level and on-site equipment of an energy intelligent management system according to an embodiment of the present invention;

[0024] Figure 5 It is a schematic diagram of a line energy feedback coordination control system of an energy intelligent management system according to an embodiment of the present invention;

[0025] Figure 6 It is a control logic diagram of coordinated control of multiple inverter devices of the energy intelligent management system according to an embodiment of the present invention;

[0026] Figure 7 is a simplified schematic diagram of the operation diagram of the energy intelligent management system according to an embodiment of the present invention;

[0027] Figure 8 It is a schematic diagram of the division of the train operation process of the energy intelligent management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0029] The terms "first", "second", "third", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0030] In addition, in order to avoid obscuring the understanding of the present invention by the skilled person, well-known or widely used techniques, elements, structures and processes may not be described or shown in detail. Although the drawings represent exemplary embodiments of the present invention, the drawings are not necessarily drawn to scale, and certain features may be enlarged or omitted to better illustrate and explain the present invention.

[0031] like Figure 1 and Figure 2 As shown, an urban rail transit energy intelligent management system according to an embodiment of the present invention includes an intelligent monitoring subsystem, an intelligent management subsystem and an intelligent control subsystem.

[0032] The intelligent monitoring subsystem is used to obtain the monitoring information of the on-site data acquisition equipment and perform monitoring, energy consumption statistical analysis and visual display, and send the monitoring information and processing information to the intelligent management subsystem and the intelligent control subsystem.

[0033] The intelligent management subsystem is used to import the received information into the pre-stored energy consumption index system and energy consumption diagnosis model, output the energy consumption index analysis results and energy consumption diagnosis results and send them to the intelligent control subsystem.

[0034] The intelligent control subsystem is used to import the received information into the pre-stored energy-saving control model and output control instructions to the on-site execution equipment.

[0035] Furthermore, the urban rail transit intelligent energy management system hardware platform is composed of central level, station level, on-site equipment and a communication channel connecting the three.

[0036] The central level and station level include monitoring workstations, operator workstations, data servers, application servers, and LAN networking equipment, etc. The functions of the intelligent monitoring subsystem, intelligent management subsystem, and intelligent control subsystem are realized based on the monitoring workstations, operator workstations, data servers, and application servers arranged at the station level and central level.

[0037] like Figure 3As shown in the figure, the central level of the urban rail transit intelligent energy management system is composed of monitoring workstations, operator workstations, data servers, application servers, and LAN equipment networking, and exchanges data with the station-level energy management system through the subway's communication backbone network.

[0038] like Figure 4 As shown in the figure, the station level of the urban rail transit intelligent energy management system is composed of monitoring workstations, operator workstations, data servers, application servers and LAN equipment networking, and exchanges data with the central energy management system through the subway communication backbone network; exchanges data with energy-consuming equipment and various sensors through the local area network or field bus network distributed in the station; and obtains information about the operation of subway station equipment, environment and personnel through the communication interface with BAS, AFC and other system interfaces.

[0039] Field equipment includes field data acquisition system and field execution equipment.

[0040] The on-site data acquisition system includes hierarchically arranged electricity meters, distributed sensors (collecting pressure / temperature / humidity / illuminance / human flow / gas, etc.), energy-consuming equipment’s own monitoring units, communication networks, etc.

[0041] On-site execution equipment includes various controllers and communication networks installed near the energy-consuming equipment.

[0042] Furthermore, the intelligent management subsystem includes an energy consumption index analysis submodule, which includes three modules: a line-level energy consumption analysis submodule, a station-level energy consumption analysis submodule, and a traction-level energy consumption analysis submodule, which implement energy consumption management at three levels respectively.

[0043] The line-level energy consumption analysis submodule is used to realize energy consumption analysis based on passenger turnover, energy consumption analysis based on operating mileage, energy consumption analysis based on station area, energy consumption analysis based on main line kilometer indicators, energy consumption analysis based on line total electricity consumption indicators, and energy consumption analysis based on line-level equipment energy efficiency.

[0044] 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 consumption indicators, and energy consumption analysis based on station-level equipment energy efficiency.

[0045] The traction-level energy consumption analysis submodule is used for energy consumption analysis based on passenger turnover, energy consumption analysis based on operating mileage, energy consumption analysis based on total train electricity consumption indicators, and energy consumption analysis based on traction-level equipment energy efficiency.

[0046] Furthermore, the intelligent control subsystem includes a traction feeding intelligent control subsystem, a ventilation and air conditioning intelligent control subsystem and a lighting intelligent control subsystem.

[0047] Furthermore, the traction feeder intelligent control subsystem can realize functions such as automatic closed-loop regulation, open-loop regulation, multi-machine coordinated reactive power compensation, and analysis and control driven by operation diagrams. The above functions can be arbitrarily combined, deleted, or added as needed.

[0048] The control principles of the automatic closed-loop regulation and analytical control driven by the operation diagram are explained in detail below.

[0049] The traction feeder intelligent control subsystem includes an automatic closed-loop regulation module. By collecting the working status and real-time electrical quantities of the inverter feedback devices or bidirectional converter devices of each traction substation on the entire line, and according to the operating status, remaining capacity, DC grid voltage of the traction substation, AC grid power factor and other information of each device, the operating status and operating parameters of each traction substation are dynamically adjusted to achieve optimal control of the DC traction grid voltage. It has the following functions:

[0050] like Figure 5 As shown, the automatic closed-loop regulation module includes the coordinated control function of multiple inverter devices, communicates with all inverter devices in the entire line through high-speed channels, collects the voltage value of the DC traction network where each traction device is located, the AC bus voltage value, and the operating status, starting threshold and output power value of the inverter device, and at the same time sends the starting voltage threshold of each traction device.

[0051] When the voltage value of the DC traction network at the traction substation where one or several traction substation devices are located exceeds the set collaborative absorption voltage threshold, the coordination control device retrieves the operating status of the traction substation and its adjacent traction substations (the number of retrievals can be set, generally four adjacent substations). If the retrieved device has remaining capacity available (not in the inverter absorption state or the absorbed power does not reach the rated power), the coordination controller lowers the starting voltage threshold of the retrieved device until its starting voltage threshold reaches the lower limit, or the voltage of the DC traction network at the traction substation drops to within the collaborative absorption voltage threshold. When the voltage of the DC traction network where the traction substation is located returns to the collaborative absorption voltage threshold, the line coordination control device can automatically exit the coordination control and restore the value of the inverter device with the lowered threshold to the initial set threshold.

[0052] The logic diagram of the coordinated control algorithm of multiple inverters is as follows: Figure 6 shown.

[0053] The traction feed intelligent control subsystem also includes an analysis and control module driven by an operation diagram.

[0054] The preferred implementation method of the analysis and control module driven by the operation diagram is described in detail below.

[0055] (1) Build an operation graph model

[0056] The operation diagram is a diagram showing the train operation in the section and the arrival, departure and stop times at the station. According to the train timetable provided by the operation department, the operation diagram based on the operation plan can be drawn. According to the actual arrival and departure times of the train, the actual operation diagram in the operation stage can be obtained. The simplified model of the operation diagram is as follows Figure 7 shown. Figure 7 In the above equation, k is the interval number; D i_k1 is the time when train i arrives at station node k1; F i_k1 is the time when train i leaves station node k1; T i_k is the planned running time of train i in section k; b i_k1 Flag indicating whether train i stops at k1 (b i_k1 =1 means stop, b i_k1 =0 means no stop); S i_k1 is the stop time of train i at station node k1.

[0057] The simplified model of the operation diagram can be used i_k and S i_k1 Described as:

[0058] T i_k =D i_k2 -F i_k1

[0059] S i_k1 =F i_k1 -D i_k1

[0060] Where i∈M, k∈L i ,k1,k2∈N i , where M is the set of all trains running on the entire line; L i is the set of arcs corresponding to train i running in each section; N i is the set of station nodes that train i passes through.

[0061] (2) Constructing a train timing energy-saving control model considering the coasting control coefficient

[0062] (2.1) Train operation process division

[0063] Research shows that using coasting before train braking is an effective energy-saving operation, and finding the optimal coasting point is the key to energy-saving control. Taking the operation of train i in section k as an example, in order to find the optimal coasting point, the target speed v is introduced. cm_i_k (m is the level number of the target speed) and the coasting control coefficient τ i_k, the train operation process is divided into three stages in sequence: stage I is the train start process, stage II is the intermediate speed regulation process, and stage III is the braking and deceleration process. Figure 8 As shown, in the figure, P represents traction, C represents coasting, and B represents braking.

[0064] In the stage division process, stage I is when the train speed accelerates from 0 to v cm_i_k The startup process, Phase III is based on the speed curve and considers τ i_k The subsequent traction back-calculation curve intersection is determined, such as Figure 8 As shown in the figure, the train traction reverse calculation speed curve intersects with the line speed limit at point b. During the actual operation of the train, the speed curve intersects with the traction reverse calculation curve at point b′. The intersection position is x i_k_b′ , introducing τ i_k After that, the train speed curve intersects with the traction inverse calculation curve at point b″, and the intersection position is x i_k (τ i_k , v cm_i_k ), then τ i_k for

[0065]

[0066] To ensure safe operation, according to engineering experience i_k The value range is 0≤τ i_k ≤0.25.

[0067] Consider v cm_i_k and τ i_k The train operation intervals are divided, with the goal of ensuring punctual and energy-saving operation of trains in the intervals. The operation levels of each train in the operation diagram are used as the main constraints, and a scheduled energy-saving operation control model for each train is established.

[0068] (2.2) Objective function

[0069] The goal of the timing layer is to train i in the kth interval with τ i_k 、v cm_i_k The actual running time T i_k (τ i_k , v cm_i_k ) and T i_k The difference δ i_k (τ i_k , v cm_i_k ) is the smallest, that is

[0070] f Ti_k =minδ i_k (τ i_k , v cm_i_k )=min|T i_k (τ i_k , vcm_i_k )-T i_k |

[0071] Train i is in interval k with τ i_k 、v cm_i_k The traction power P at time t during operation i_k (τ i_k , v cm_i_k , t) can be expressed as

[0072]

[0073] in:

[0074] A=μ i_k (τ i_k ,v cm_i_k ,t)

[0075] B=f maxi_k (v i_k (τ i_k ,v cm_i_k ,t))

[0076] C=v i_k (τ i_k ,v cm_i_k ,t)

[0077] In the formula, μ i_k is the traction force utilization coefficient, 0≤μ i_k (t)≤1;η i is the electromechanical efficiency; v i_k is the speed; f maxi_k (v i_k ) is when the speed is v i_k Maximum traction at .

[0078] Train i is in interval k with τ i_k 、v cmi_k Traction energy consumption during operation E i_k (τ i_k , v cmi_k )for

[0079]

[0080] Select multiple groups of combined variables (τ i_k , v cm_i_k ) i_k k The smallest set of variables is taken as the final optimization result, and the final goal of train section operation is

[0081] f Ei_k =minE i_k (τ i_k,v cm_i_k )

[0082] (2.3) Constraints

[0083] In order to solve the above objective function, the boundary conditions such as the arrival and departure time, starting and stopping speed, and position of each train at each station in the operation diagram must be met; in order to ensure safe operation, the line speed limit and passenger comfort index constraints must be met.

[0084] (1) Boundary constraints

[0085] ① Arrival and departure time constraints

[0086] |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

[0087] In the formula, δ max Indicates T i_k (τ i_k , v cm_i_k ) and T i_k The maximum error allowed between .

[0088] ②Starting and stopping speed and position constraints

[0089] Train i passes b at the starting point k1 and the end point k2 of section k. i_k1 and b i_k2 The value of determines whether to stop.

[0090] If b i_k1 =1 and b i_k2 =1, then the boundary constraint can be expressed as

[0091]

[0092] If b i_k1 = 0 and b i_k2 =1, then the boundary constraint can be expressed as

[0093]

[0094] If b i_k1 =1 and b i_k2 = 0, then the boundary constraint can be expressed as

[0095]

[0096] If b i_k1 = 0 and b i_k2 = 0, then the boundary constraint can be expressed as

[0097]

[0098] In the formula, x i_k1 、x i_k2 is the starting and ending position of interval k, v lim (x i_k1 ) represents the speed limit at node k1.

[0099] (2) Line speed limit constraints

[0100] 0≤v i_k (x)≤v lim (x)

[0101] In the formula, v lim (x) is the speed limit at x.

[0102] (3) Passenger comfort index constraints

[0103] During train operation, large acceleration changes or frequent switching of operating conditions will affect passenger comfort. This paper uses the acceleration change rate as an indicator to evaluate passenger comfort.

[0104]

[0105] In the formula, Δa i_′ (t), Δa i_k (t) are the acceleration change rate and change amount of train i in interval k at time t; a i_k (t), a i_k (t-1) are the accelerations of train i at time t and time (t-1) in section k respectively; Δt is the simulation interval.

[0106] (3) Constructing a DC traction power supply system model

[0107] The DC traction power supply system is mainly composed of traction network, energy storage device, train, rectifier unit, etc. Considering the solution efficiency and calculation accuracy of the power flow algorithm, the traction network adopts a three-layer ground network model, the rectifier unit adopts a constant voltage source-internal resistance model, the train adopts a power source model, and the regenerative braking energy utilization device is modeled based on external characteristics.

[0108] Restore the running trajectories of multiple trains to obtain the position x of each train at time t i_k (τ i_k , v cm_i_k , t), power P i_k (τ i_k , v cm_i_k , t), etc. According to the equivalent model of DC traction power supply system, if the number of nodes in the whole line at time t is n, the node voltage equation at time t constructed on the DC side is:

[0109]

[0110] In the formula, G fg represents 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 injected current of node f at time t. Depending on the node type, I f (t) can be expressed as:

[0111]

[0112] Where U i_k (t) is the voltage of train i at time t; U s (t), R s (t) represents the output voltage and equivalent resistance of the traction unit at time t.

[0113] (4) Model solution and power flow calculation

[0114] Considering the coasting control coefficient, τ i_k 、v cm_i_k The train operation process is divided, and the on-time energy-saving operation of each train in each section is completed based on the fixed step target speed search algorithm, and the speed v i_k (t) Position x i_k (t), locomotive output F i_k (t), locomotive flow I i_k (t) and other electrical information, and is used for load process simulation calculation of urban rail power supply system. The specific steps are as follows.

[0115] Step 1: Load train, line and operation diagram data;

[0116] Step 2: Initialize simulation conditions: let i = 1, k = 1, τ i_k =0,m=0,v cm_i_k =v maxi_k ;

[0117] Step 3: Using v cm_i_k and the speed limit information on the line, re-divide the ramp, and according to τ i_k 、v cm_i_k Divide the train interval operation process and complete the speed connection of train i in interval k;

[0118] Step 4: Calculate T i_k (τ i_k , v cm_i_k ), if T i_k (τ i_k , v cm_i_k )>Ti_k , then output the running record of train i in interval k, k++, and return to Step 2; otherwise, let v cm_i_k =v averi_k , execute Step3 and Step5;

[0119] Step 5: If T i_k (τ i_k , v cm_i_k ) is not equal to T i_k , then use the fixed ladder search algorithm and modify v cm_i_k , until T i_k (τ i_k , v cm_i_k )=T i_k , the specific search steps are as follows:

[0120] ① Initialize 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-level search times d m =1;

[0121] ②Calculate when the target speed is v cm_i_k +d m λ m Time T i_k (τ i_k , v cm_i_k +d m λ m ), if T i_k (τ i_k ,v cm_i_k +d m λ m )=T i_k , output v cm_i_k =v cm_i_k +d m λ m , go to ③; if T i_k (τ i_k , v cm_i_k +d m λ m )>T i_k >T i_k (τ i_k , v cm_i_k ), then T i_k (τ i_k , v cm_i_k )=T i_k (τ i_k , vcm_i_k +d m λ m ), m++, d m =1, transfer 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 m λ m ), transfer to ②; if d m >σ m , then m--, d m =1, transfer to ②;

[0122] ③Save train section operation records R i_k (τ i_k , v cm_i_k );

[0123] Step 6: Let τ i_k =τ i_k +Δτ, if τ i_k ≤0.25, then let v cm_i_k =v maxi_k Return to Step 3. Otherwise, select the R with the smallest traction energy consumption value. i_k (τ i_k 、v cm_i_k ) is the final optimization result of train i in interval k;

[0124] Step 7: Let k++, if k <L i , then let τ i k =0,m=0,v cm_i_k =v maxi_k , go to Step3; otherwise go to Step8;

[0125] Step8: Set i++. If i>M, go to Step9; otherwise, set k=1. If T i_k =T z_k (z≤i-1), then R i_k =R z_k (τ z_k , v cm_z_k ), and go to Step 7, otherwise let τ i_k =0,m=0,v cm_i_k =v maxi_k , go to Step 3;

[0126] Step 9: Based on the optimized operation records of each train, restore the operation tracks of multiple trains with electrical information.

[0127] Step 10: Initialize the iteration count r, voltage convergence accuracy ε, substation state W, simulation start and end times, and simulation duration T, and the node admittance matrix G of the DC side power supply system;

[0128] Step 11: Read the position and power information of each train at the current time t;

[0129] Step 12: Perform a power flow calculation for the DC traction power supply system at time t, and update the node voltages U and node currents I;

[0130] Step 13: Determine whether |U r - U r-1 | < ε. If not satisfied, set r = r + 1, return to Step 12, otherwise go to Step 14;

[0131] Step 14: Determine whether the traction substation state W needs to be adjusted. If W is unreasonable, adjust it and then return to Step 13. Otherwise, set t = t + 1 and go to Step 15;

[0132] Step 15: If t < T, return to Step 11, otherwise output the simulation results.

[0133] Where λ m is the step value corresponding to the mth level, σ m is the maximum search times of the mth level. m, λ, and σ can be specifically set as needed according to the line conditions and vehicle conditions; Δτ is the simulation step size of the coasting control coefficient.

[0134] Furthermore, the intelligent control subsystem of the ventilation and air conditioning can implement the control of the basic control strategy and the in-depth energy-saving optimization control. Among them, the in-depth energy-saving optimization control also includes various strategies. The above functions can be arbitrarily combined, deleted, and added according to needs.

[0135] Next, specifically explain the control principle of the cooling load prediction model.

[0136] The intelligent control subsystem of the ventilation and air conditioning includes a cooling load prediction model. The cooling load prediction model, as the basis of the system's cold source energy consumption model, is one of the basic conditions for realizing the optimal energy efficiency control. It provides an initial prediction value for the operation of the cold source system. And during the operation process, the cooling load prediction model can use the intelligent fuzzy control method to continuously correct the cooling load value according to the energy consumption value and the cooling capacity, providing a basic condition for finding the optimal state point of the system's cold source energy efficiency model. The cooling load prediction model is established through the operation condition data of the ventilation and air conditioning system, provides a cooling load prediction value for the initial operation of the system, and continuously corrects it according to the relationship between the energy consumption and the cooling capacity during the operation process.

[0137] Excluding fixed parameter information such as electrical and lighting, the total load forecast value is mainly a function of personnel and outdoor temperature and humidity as variables, which can be simplified as shown below:

[0138] Q 总 =f(p)+f(t,d)+Qa

[0139] In the formula, Q 总 is the total cooling load prediction value, kW; f(p) is the cooling load function with the number of people in the station as the variable; f(t, d) is the cooling load function with the outdoor temperature and humidity as the variable; Q a It is the fixed load value of electrical, lighting, etc.

[0140] By collecting the number of people in each station at different times, the outdoor temperature of each station, and the actual value of the total cooling load corresponding to each station at each sampling time point, the expression form of the functions f(p) and f(t, d) is determined by using methods such as a neural network model based on deep learning, thereby realizing the total cooling load prediction value based on the number of people in each station and the outdoor temperature of each station, and providing cooling load to each station according to the total cooling load prediction value.

[0141] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An urban rail transit energy intelligent management system, characterized in that: Including intelligent monitoring subsystem, intelligent management subsystem and intelligent control subsystem; The intelligent monitoring subsystem is used to obtain monitoring information of the on-site data acquisition equipment and perform monitoring, energy consumption statistical analysis and visual display, and send the monitoring information and processing information to the intelligent management subsystem and the intelligent control subsystem; The intelligent management subsystem is used to import the received information into the pre-stored energy consumption index system and energy consumption diagnosis model, output the energy consumption index analysis results and energy consumption diagnosis results and send them to the intelligent control subsystem; The intelligent control subsystem is used to import the received information into a pre-stored energy-saving control model and output control instructions to the on-site execution equipment; The energy-saving control model introduces the target speed v cm_i_k and coasting control coefficient τ i_k The train operation process is divided into three stages: stage I is the train start process, stage II is the intermediate speed adjustment process, and stage III is the braking and deceleration process; among them, stage I is the train speed accelerating from 0 to v cm_i_k The startup process, phase III is based on the speed curve and considerations τ i_k The subsequent traction back-calculation curve intersection point is determined; the train traction back-calculation speed curve intersects with the line speed limit at point b During the actual operation of the train, the speed curve and the traction back-calculation curve intersect at point b ′, the intersection point is x i_k _ b′ , introduce τ i_k After that, the train speed curve intersects with the traction inverse calculation curve at point b ″, the intersection point is x i_k ( τ i_k , v cm_i_k ),but τ i_k for: in, τ i_k The value range is 0≤ τ i_k ≤0.25; consider v cm_i_k and τ i_k The train operation intervals are divided, with the goal of punctual and energy-saving operation of trains in the intervals, and the operation levels of each train as the main constraints, and a timing energy-saving control model for each train is established.

2. The urban rail transit energy intelligent management system according to claim 1, characterized in that: 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 the station level and the central level.

3. The urban rail transit energy intelligent management system according to claim 1, characterized in that: The intelligent management subsystem includes an energy consumption index analysis submodule, and the energy consumption index analysis submodule includes: The line-level energy consumption analysis submodule is used to realize energy consumption analysis based on passenger turnover, energy consumption analysis based on operating mileage, energy consumption analysis based on station area, energy consumption analysis based on main line kilometer indicators, energy consumption analysis based on line total power consumption indicators, 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 consumption indicators, and energy consumption analysis based on station-level equipment energy efficiency; The traction-level energy consumption analysis submodule is used for energy consumption analysis based on passenger turnover, energy consumption analysis based on operating mileage, energy consumption analysis based on total train electricity consumption indicators, and energy consumption analysis based on traction-level equipment energy efficiency.

4. The urban rail transit energy intelligent management system according to claim 1, characterized in that: The intelligent control subsystem includes a traction power feeding intelligent control subsystem, a ventilation and air conditioning intelligent control subsystem and a lighting intelligent control subsystem.

5. The urban rail transit energy intelligent management system according to claim 4, characterized in that: The traction feed intelligent control subsystem includes an automatic closed-loop regulation module, which is used to coordinate the control device to retrieve the operating status of the traction substation and its adjacent traction substation devices when it is determined that the voltage value of the DC traction network at the traction substation where one or several traction substation devices are located exceeds the set collaborative absorption voltage threshold. If the retrieved device has residual capacity available, the starting voltage threshold of the retrieved device is controlled to be lowered until its starting voltage threshold reaches the lower limit or the DC traction network voltage at the traction substation drops to within the collaborative absorption voltage threshold.

6. The urban rail transit energy intelligent management system according to claim 4, characterized in that: The traction feed intelligent control subsystem includes an analysis control module driven by an operation diagram.

7. An urban rail transit energy intelligent management system as claimed in claim 4, characterized in that: The ventilation and air-conditioning intelligent control subsystem includes a cooling load prediction model, which is used to determine the cooling load function with the number of people in each station as a variable and the expression form of the cooling load function with the outdoor temperature and humidity as variables by obtaining the number of people in each station at different times, the outdoor temperature of each station and the actual value of the total cooling load corresponding to each station at each sampling time point, so as to realize the total cooling load prediction value based on the number of people in each station and the outdoor temperature of each station, and provide cooling load to each station according to the total cooling load prediction value.

8. The urban rail transit energy intelligent management system according to claim 1, characterized in that: The on-site data collection equipment includes hierarchically arranged electric energy meters, distributed sensors, and monitoring units provided by energy-consuming equipment.

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