A method for estimating the energy consumption of tunnel vehicle lighting and the length of the optimal control section

Abstract

The invention relates to the technical field of tunnel lighting, and is a method for estimating the energy consumption of on-board lighting in a tunnel and the length of an optimal control section. The energy consumption estimation method comprises: obtaining geometric parameters of the tunnel, positions of lamps in the tunnel and historical traffic volume data; obtaining vehicle trajectory data based on the historical traffic volume data; establishing a one-dimensional coordinate system of the tunnel; and calculating the energy consumption of the tunnel within one day based on the one-dimensional coordinate system of the tunnel and the vehicle trajectory data. The energy consumption estimation method of the invention takes into account the distribution of historical traffic flow over time, so that the simulation scene is closer to reality and the energy consumption estimation result is more accurate. The energy consumption estimation method proposed by the invention can also evaluate the energy-saving effect under different control section lengths, thereby determining the optimal control section length, providing a more scientific basis for the intelligent lighting construction of the tunnel and maximizing the energy utilization.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel lighting, and is a method for estimating tunnel vehicle lighting energy consumption and optimal control section length. Background Art

[0002] As the problem of tunnel lighting energy consumption becomes increasingly prominent, how to accurately estimate the energy consumption of tunnel lighting has become an important technical challenge in the construction of intelligent transportation systems and smart lighting systems. At present, the common control methods of tunnel lighting mainly include timing control and dimming control based on external brightness. Although these methods can adjust the lighting intensity to a certain extent, these methods are often accompanied by a large amount of energy waste, which does not meet the dual carbon goals and cannot meet the needs of efficient energy consumption management. Therefore, the control strategy of on-demand lighting with vehicles came into being, that is, the tunnel is divided into multiple control sections. When a vehicle enters a control section, the lights in the control section are on, and when the vehicle leaves the control section, the lights in the control section are dimmed. When the tunnel is renovated or constructed, the evaluation of tunnel lighting energy consumption is often determined only based on the peak hour traffic volume. In addition, when designing the length of the control section, there is a lack of estimation of the energy-saving effect of on-vehicle lighting, which leads to a lack of more scientific basis for construction decisions and construction parameters, resulting in low energy utilization and investment return rate. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention proposes a method for estimating the energy consumption of tunnel vehicle lighting and the optimal control section length. The method for estimating the energy consumption of tunnel vehicle lighting estimates the tunnel lighting energy consumption based on a microscopic traffic flow model, taking into account the distribution of traffic flow over time, which is closer to the actual scene. Based on the energy consumption estimation method, the energy-saving effect under different control section lengths can also be evaluated, thereby determining the optimal control section length, providing a more scientific basis for the construction of intelligent lighting in tunnels and maximizing energy utilization.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] The present invention proposes a method for estimating the energy consumption of on-board lighting in a tunnel, which is characterized by comprising the following steps:

[0006] S1. Obtain tunnel geometry parameters, lamp positions in the tunnel, and historical traffic volume data;

[0007] S2, obtaining vehicle trajectory data based on historical traffic volume data;

[0008] S3. Establish a one-dimensional coordinate system. Based on the geometric parameters of the tunnel and the positions of the lamps in the tunnel, mark the starting point of the tunnel, the end point of the tunnel, the starting coordinates of the four lighting sections of the tunnel, and the coordinates of the lamps in the tunnel in the coordinate system. The four lighting sections are the entrance section, the transition section, the middle section, and the exit section from one end of the tunnel.

[0009] S4. In the coordinate system, mark the sub-lighting sections within each lighting section, and mark the starting coordinates of all control sections within each sub-lighting section. When a vehicle arrives at a control section, turn on the lights in the control section. When the vehicle leaves the control section, dim the lights in the control section. Based on the vehicle trajectory data obtained in S2, obtain the change of vehicle position over time and calculate the energy consumption of the tunnel in one day.

[0010] Specifically, S2 includes the following sub-steps:

[0011] S201, calculating the annual average daily traffic volume of the most recent year based on historical traffic volume data, performing Gaussian fitting on the annual average daily traffic volume, and obtaining a time distribution function f(x) of the daily traffic flow;

[0012] S202, generating a vehicle based on f(x);

[0013] S203: Simulate the driving trajectory of the vehicle in the tunnel through a car-following model to obtain vehicle trajectory data.

[0014] Specifically, in S202, the process of generating a vehicle is as follows:

[0015] Sample f(x) to determine each fine time granularity t u The traffic volume Q(tu) in the u and Q(tu) determine the time step tstep, ensuring that the average traffic volume in each tstep is 0.5 vehicles, t step The calculation formula is:

[0016]

[0017] in:

[0018] t step : time step of microscopic traffic simulation;

[0019] t u : Fine time granularity;

[0020] Q(tu): Each fine time granularity t u Traffic volume within

[0021] For each time step, a (0, 1) uniformly distributed random number is generated and compared with 0.5. If the random number is less than 0.5, a vehicle is generated within the time step; if the random number is greater than 0.5, no vehicle is generated.

[0022] Specifically, in S203, the car-following model is a Krauss model.

[0023] Specifically, in S203, during the simulation of the car-following model, the vehicle acceleration is calculated first, and then the vehicle position is calculated, and the collection of all vehicle positions constitutes the vehicle trajectory data; wherein, the vehicle acceleration is determined as follows: within each time step, the first vehicle determines its acceleration according to the tunnel speed limit and vehicle dynamics parameters, and the vehicle dynamics parameters include the maximum acceleration of the vehicle; starting from the second vehicle, if the distance between the current vehicle and the vehicle in front is greater than or equal to the maximum following distance, the current vehicle acceleration is determined in the same manner as the first vehicle, and if the distance between the current vehicle and the vehicle in front is less than the maximum following distance, the current vehicle acceleration is determined according to the speed and position of the vehicle in front, the speed of the current vehicle, and the distance between the current vehicle and the vehicle in front.

[0024] Specifically, starting from the second vehicle, when the distance between the i-th vehicle and the vehicle in front is less than the maximum following distance, the acceleration a of the i-th vehicle is i The calculation formula is:

[0025]

[0026] in:

[0027] a max is the maximum acceleration of the vehicle;

[0028] v i is the speed of the i-th car;

[0029] T is the expected following time interval;

[0030] d i is the distance between the i-th vehicle and the preceding vehicle;

[0031] D max is the maximum following distance.

[0032] Specifically, in S3, the starting point of the tunnel is marked at 200m to 400m on the X-axis of the coordinate system.

[0033] Specifically, in S4, the lighting of the lamps in each control section is controlled simultaneously by one controller.

[0034] Specifically, in S4, the energy consumption calculation formula of the tunnel in one day is:

[0035]

[0036] in:

[0037] l k : Control section length;

[0038] Etotal(lk): Control segment length is l k Total lighting energy consumption of the tunnel in one day;

[0039] i: tunnel lighting section number, 1, 2, 3, 4 represent the entrance section, transition section, middle section, and exit section respectively;

[0040] j: the serial number of the sub-lighting section in each lighting section;

[0041] n i : The number of sub-lighting sections in the i-th lighting section;

[0042] L ij : The length of the jth sub-lighting section in the i-th lighting section;

[0043] m: the serial number of the control section in each sub-lighting section;

[0044] q: the serial number of the lamps in each control section;

[0045] N ijm : The number of lamps contained in the mth control section of the jth sub-lighting section in the i-th lighting section;

[0046] g: lighting power grade number of the lamp;

[0047] g n : Number of lighting power levels of lamps;

[0048] In the i-th lighting section, the q-th lamp in the m-th control section of the j-th sub-lighting section is at the g-th level of power;

[0049] The time that the qth lamp in the mth control section of the jth sub-lighting section in the ith lighting section is at the gth power level within 24 hours.

[0050] The present invention also proposes a method for estimating the optimal control section length of tunnel vehicle-mounted lighting, comprising: presetting the control section length, obtaining the investment energy saving ratio of tunnel construction under the preset control section length based on the above energy consumption estimation method, determining the optimal control section length, and calculating the investment / energy saving ratio of tunnel construction by the following formula:

[0051]

[0052] in:

[0053] L k : Control the preset length of the section;

[0054] I(L k ): The length of the control section is L k The corresponding investment amount at that time;

[0055] ratio1 : Investment / energy saving ratio;

[0056] base: baseline energy consumption;

[0057] E(L k ): The length of the control section is L k The total energy consumption of the tunnel lighting in one day.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The energy consumption estimation method proposed in the present invention integrates historical traffic flow data, simulates vehicle trajectories with the help of microscopic traffic flow simulation, and estimates the energy consumption of tunnel on-board lighting based on the distribution of historical traffic flow over time. The estimation scenario is closer to reality, and the estimation result is scientific and accurate. The present invention also presets the control section length, estimates the energy consumption under different control section lengths, and determines the optimal control section length of on-board lighting by comparing the investment / energy saving ratio when setting different control section lengths, thereby providing reliable data support for the construction of on-board lighting control system, thereby achieving a balance between energy saving and cost-effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of the method for estimating the energy consumption of tunnel vehicle lighting of the present invention;

[0061] Figure 2 It is a flow chart of Gaussian fitting of the present invention; DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example 1

[0064] refer to Figure 1 In this embodiment, a method for estimating the energy consumption of tunnel vehicle lighting includes the following steps:

[0065] S1. Obtain the geometric parameters of the tunnel, the location of the lamps in the tunnel and the traffic volume data of the tunnel in the past year;

[0066] S2, obtaining vehicle trajectory data based on historical traffic volume data; specifically including the following sub-steps:

[0067] S201, Reference Figure 2, the annual average daily traffic volume of the most recent year is calculated based on the historical traffic volume data, and the annual average daily traffic volume is Gaussian fitted to obtain the time distribution function f(x) of the daily traffic volume; the function f(x) can not only reveal the peak and trough of traffic flow in a day, but also reflect the periodic changes of traffic flow and adapt to the traffic conditions of the tunnel in different time periods;

[0068] S202, sample f(x) to determine each fine time granularity t u The traffic volume Q(tu) in the u and Q(tu) determine the time step tstep, ensuring that the average traffic volume in each tstep is 0.5 vehicles, t step The calculation formula is:

[0069]

[0070] in:

[0071] t step : time step of microscopic traffic simulation;

[0072] t u : Fine time granularity;

[0073] Q(tu): Each fine time granularity t u Traffic volume within

[0074] For each time step, a (0, 1) uniformly distributed random number is generated and compared with 0.5. If the random number is less than 0.5, a vehicle is generated in the time step; if the random number is greater than 0.5, no vehicle is generated. In this way, the arrival process of the vehicle can be simulated according to a smaller time unit and the initial input is provided for the subsequent car-following model. In this embodiment, the generation process of the vehicle follows the law of traffic flow changes, that is, when Q(t u ) is larger, the time step t step The smaller it is, the denser the intervals between generated vehicles will be, which can simulate heavy traffic.

[0075] S203, simulating the driving trajectory of the vehicle in the tunnel by means of a car-following model. During the simulation of the car-following model, the vehicle acceleration is first calculated, and then the vehicle position is calculated. The set of all vehicle positions constitutes the vehicle trajectory data. The method for determining the vehicle acceleration is as follows: within each time step, the first vehicle determines its acceleration according to the tunnel speed limit and the vehicle dynamics parameters, and the vehicle dynamics parameters include the maximum acceleration of the vehicle. Starting from the second vehicle, if the distance between the current vehicle and the vehicle in front is greater than or equal to the maximum following distance, the method for determining the acceleration of the current vehicle is the same as that of the first vehicle. If the distance between the current vehicle and the vehicle in front is less than the maximum following distance, the acceleration of the current vehicle is determined according to the speed and position of the vehicle in front, the speed of the current vehicle, and the distance between the current vehicle and the vehicle in front. Specifically, starting from the second vehicle, when the distance between the i-th vehicle and the vehicle in front is less than the maximum following distance, the acceleration a of the i-th vehicle i The calculation formula is:

[0076]

[0077] in:

[0078] a max is the maximum acceleration of the vehicle;

[0079] v i is the speed of the i-th car;

[0080] T is the expected following time interval;

[0081] d i is the distance between the i-th vehicle and the vehicle in front;

[0082] D max is the maximum following distance;

[0083] S3. Establish a one-dimensional coordinate system. Based on the geometric parameters of the tunnel and the positions of the lamps in the tunnel, mark the starting point of the tunnel, the end point of the tunnel, the starting coordinates of the four lighting sections of the tunnel, and the coordinates of the lamps in the tunnel in the coordinate system. The four lighting sections are the entrance section, the transition section, the middle section, and the exit section from one end of the tunnel.

[0084] S4. In the coordinate system, mark the sub-lighting sections in each lighting section, mark the starting coordinates of all control sections in each sub-lighting section, and control the brightness of the lamps in each control section by a controller. When the vehicle arrives at a control section, the lamps in the control section are turned on, and when the vehicle leaves the control section, the lamps in the control section are dimmed. Based on the vehicle trajectory data obtained in S2, the change of the vehicle position over time in each time step is obtained, and the energy consumption of the tunnel lamps in one day is calculated using the following formula:

[0085]

[0086] in:

[0087] l k : Control section length;

[0088] Etotal(lk): Control segment length is l k Total lighting energy consumption of the tunnel in one day;

[0089] i: tunnel lighting section number, 1, 2, 3, 4 represent the entrance section, transition section, middle section, and exit section respectively;

[0090] j: the serial number of the sub-lighting section in each lighting section;

[0091] n i : The number of sub-lighting sections in the i-th lighting section;

[0092] L ij : The length of the jth sub-lighting section in the i-th lighting section;

[0093] m: the serial number of the control section in each sub-lighting section;

[0094] q: the serial number of the lamps in each control section;

[0095] N ijm : The number of lamps contained in the mth control section of the jth sub-lighting section in the i-th lighting section;

[0096] g: lighting power grade number of the lamp;

[0097] g n : Number of lighting power levels of lamps;

[0098] The power of the qth luminaire in the mth control section of the jth sub-lighting section in the ith lighting section at the gth level;

[0099] The time that the qth lamp in the mth control section of the jth sub-lighting section in the ith lighting section is at the gth power level within 24 hours.

[0100] In this embodiment, the starting point of the tunnel is preferably marked at 200m to 400m on the X-axis of the coordinate system; the lamps in each control section are controlled by a controller at the same time. When the vehicle reaches a position 1.154Ds before a control section, the lamps in the control section are turned on. When the vehicle leaves the end of the section at a certain distance, the lamps in the control section are dimmed, where Ds is the stopping sight distance determined according to the speed limit of the tunnel. When the lamps are dimmed, the distance between the vehicle and the end of the control section is an empirical value, and 30m is preferably selected in this embodiment.

[0101] This embodiment simulates the real scene of tunnel traffic flow based on historical traffic flow data, and the estimated energy consumption is more scientific and accurate.

[0102] Example 2

[0103] This embodiment proposes a method for estimating the optimal control section length of tunnel vehicle-mounted lighting, including: presetting the control section length, using the method for estimating the energy consumption of tunnel vehicle-mounted lighting proposed in Example 1 to calculate the energy consumption of tunnel lamps under different control section lengths, and comparing the investment energy saving ratios under different control section lengths to determine the optimal control section length. The control section length corresponding to the minimum investment energy saving ratio is the optimal control section length, and the calculation formula for the investment energy saving ratio is:

[0104]

[0105] in:

[0106] L k : Control the preset length of the section;

[0107] I(L k ): The length of the control section is L k The amount of investment at the time;

[0108] ratio 1 : Investment / energy saving ratio;

[0109] base: baseline energy consumption;

[0110] E(L k ): The length of the control section is L k The total energy consumption of the tunnel lighting in one day.

[0111] In this embodiment, the lower limit of the preset length value range is the minimum lighting spacing in the tunnel, and the upper limit is 1.154Ds, where Ds is the parking sight distance determined according to the tunnel speed limit. The preset length of the control section starts from the lower limit and takes the minimum lighting spacing in the tunnel as the interval until it is greater than or equal to the upper limit. It should be noted here that the actual control section length calculated according to the preset length may be different for each lighting section. For example, the preset length of the control section is 25 meters, but the entrance section is only 40 meters. Then the number of control sections in the entrance section is 1, which is an integer of 40 / 25. Therefore, the control section length of the entrance section is 40 meters. This is also the reason why the upper limit of the summation sign in the energy consumption formula is rounded down;

[0112] For renovated tunnels, the benchmark energy consumption is the average daily energy consumption of the tunnel in the past year under the tunnel lighting control scheme before the renovation; for newly built tunnels, the benchmark energy consumption is the estimated energy consumption within one day under the stepless dimming control strategy.

[0113] Since the on-board lighting system is controlled according to the control section, the light or dimming of the lamps in each control section is controlled by a controller at the same time. Since the tunnel length is fixed, the selection of the control section length will affect the number of controllers. A long control section requires a small number of controllers, which can save investment, but is not conducive to energy saving. A short control section requires a large number of controllers, but a short section can more accurately control the range of lighting required, which is more conducive to energy saving. Therefore, this embodiment selects a series of control section lengths to estimate the energy consumption respectively, and then by estimating and comparing the investment / energy saving ratio when setting different control section lengths, the optimal control section length can be determined, thereby achieving a balance between energy saving and cost-effectiveness.

[0114] The specific embodiments of the present invention enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention.

[0115] It should be understood that the present invention is not limited to what has been described above and that various modifications and changes may be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for estimating the energy consumption of tunnel vehicle lighting, characterized in that: The following steps are involved: S1. Obtain tunnel geometry parameters, lamp positions in the tunnel, and historical traffic volume data; S2, obtaining vehicle trajectory data based on historical traffic volume data; S3. Establish a one-dimensional coordinate system. Based on the geometric parameters of the tunnel and the positions of the lamps in the tunnel, mark the starting point of the tunnel, the end point of the tunnel, the starting coordinates of the four lighting sections of the tunnel, and the coordinates of the lamps in the tunnel in the coordinate system. The four lighting sections are the entrance section, the transition section, the middle section, and the exit section from one end of the tunnel. S4. In the coordinate system, mark the sub-lighting sections in each lighting section, and mark the starting coordinates of all control sections in each sub-lighting section. When a vehicle arrives at a certain control section, turn on the lights in the control section. When the vehicle leaves the control section, dim the lights in the control section. Based on the vehicle trajectory data obtained by S2, the change of vehicle position over time is obtained, and the energy consumption of the tunnel in one day is calculated.

2. A method for estimating the energy consumption of tunnel vehicle lighting according to claim 1, characterized in that: S2 includes the following sub-steps: S201, calculating the annual average daily traffic volume of the most recent year based on historical traffic volume data, performing Gaussian fitting on the annual average daily traffic volume, and obtaining a time distribution function f(x) of the daily traffic flow; S202, generating a vehicle based on f(x); S203: Simulate the driving trajectory of the vehicle in the tunnel through a car-following model to obtain vehicle trajectory data.

3. A method for estimating the energy consumption of tunnel vehicle lighting according to claim 2, characterized in that: In S202, the process of generating a vehicle is as follows: Sample f(x) to determine each fine time granularity t u Traffic volume Q(t u ), according to t u and Q(t u ) Determine the time step t step , ensuring that on average each t step The traffic volume inside is 0.5 vehicles, t step The calculation formula is: in: t step : time step of microscopic traffic simulation; t u : Fine time granularity; Q(t u ): Each fine time granularity t u Traffic volume within For each time step, a (0, 1) uniformly distributed random number is generated and compared with 0.

5. If the random number is less than 0.5, a vehicle is generated within the time step; if the random number is greater than 0.5, no vehicle is generated.

4. The method for estimating the energy consumption of tunnel vehicle lighting according to claim 2, characterized in that: In S203, the car-following model is a Krauss model.

5. The method for estimating the energy consumption of tunnel vehicle lighting according to claim 2, characterized in that: In S203, during the simulation of the car-following model, the vehicle acceleration is calculated first, and then the vehicle position is calculated, and the collection of all vehicle positions constitutes the vehicle trajectory data; wherein, the vehicle acceleration is determined in the following manner: within each time step, the first vehicle determines its acceleration according to the tunnel speed limit and vehicle dynamics parameters, and the vehicle dynamics parameters include the maximum acceleration of the vehicle; starting from the second vehicle, if the distance between the current vehicle and the preceding vehicle is greater than or equal to the maximum following distance, the current vehicle acceleration is determined in the same manner as the first vehicle, and if the distance between the current vehicle and the preceding vehicle is less than the maximum following distance, the current vehicle acceleration is determined according to the speed and position of the preceding vehicle, the speed of the current vehicle, and the distance between the current vehicle and the preceding vehicle.

6. A method for estimating the energy consumption of on-board lighting in a tunnel according to claim 5, characterized in that: Starting from the second car, when the distance between the i-th car and the front car is less than the maximum following distance, the acceleration a of the i-th car i The calculation formula is: in: a max is the maximum acceleration of the vehicle; v i is the speed of the i-th car; T is the expected following time interval; d i is the distance between the i-th vehicle and the vehicle in front; D max is the maximum following distance.

7. The method for estimating the energy consumption of on-board lighting in a tunnel according to claim 1, characterized in that: In S3, the starting point of the tunnel is marked at 200m to 400m on the X-axis of the coordinate system.

8. The method for estimating the energy consumption of on-board lighting in a tunnel according to claim 1, characterized in that: In S4, the lighting of the lamps in each control section is controlled by one controller at the same time.

9. The method for estimating the energy consumption of on-board lighting in a tunnel according to claim 1, characterized in that: In S4, the energy consumption of the tunnel in one day is calculated as: in: l k : Control section length; E total (l k ): The length of the control section is l k Total energy consumption of tunnel lighting in one day; i: tunnel lighting section number, 1, 2, 3, 4 represent the entrance section, transition section, middle section, and exit section respectively; j: the serial number of the sub-lighting section in each lighting section; n i : The number of sub-lighting sections in the i-th lighting section; L ij : The length of the jth sub-lighting section in the i-th lighting section; m: the serial number of the control section in each sub-lighting section; q: the serial number of the lamps in each control section; N ijm : The number of lamps contained in the mth control section of the jth sub-lighting section in the i-th lighting section; g: lighting power grade number of the lamp; g n : Number of lighting power levels of lamps; The power of the qth luminaire in the mth control section of the jth sub-lighting section in the ith lighting section at the gth level; The time that the qth lamp in the mth control section of the jth sub-lighting section in the ith lighting section is at the gth power level within 24 hours.

10. A method for estimating the length of the optimal control section of tunnel vehicle lighting, characterized in that: include: The control section length is preset, and the energy consumption estimation method according to claim 1 is used to obtain the investment energy saving ratio of the tunnel construction under the preset control section length, and the optimal control section length is determined. The investment / energy saving ratio of the tunnel construction is calculated by the following formula: in: L k : Control the preset length of the section; I(L k ): The length of the control section is L k The corresponding investment amount at that time; ratio1: investment / energy saving ratio; base: baseline energy consumption; E(L k ): The length of the control section is L k The total energy consumption of the tunnel lighting in one day.

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