Monte carlo simulation system and method for electric vehicle tunnel lighting load impact

Abstract

The application discloses a kind of Monte Carlo simulation system and method of electric vehicle tunnel lighting load influence, belong to traffic electrification and tunnel lighting control technical field.S1, generate the total number of electric vehicles of the day;S2, generate the electric vehicle trip volume of each hour added in the day;S3, calculate natural light intensity factor;S4, the tunnel to be evaluated is divided into three sections, and the lighting control dynamic threshold of each section is calculated;S5, light level decision;S6, synthesis load curve;S7, repeat S1-S6, realize the probabilistic evaluation of electric vehicle to tunnel lighting load influence by Monte Carlo simulation.The application is simulated repeatedly, collects daily power consumption, daily peak load, peak-valley difference and other samples;Calculate expected value, standard deviation and confidence interval, quantify the fluctuation range of tunnel lighting load under the change of electric vehicle penetration rate, provide data support for energy-saving reconstruction and intelligent control strategy of tunnel lighting system.

Description

Technical Field

[0001] This invention relates to the field of traffic electrification and tunnel lighting energy-saving control technology, specifically a Monte Carlo simulation system and method for the impact of electric vehicle tunnel lighting load. Background Technology

[0002] With the rapid growth of electric vehicles, electric vehicle travel has a significant impact on the power load of transportation infrastructure. Tunnel lighting systems are the main energy-consuming equipment in highway tunnels, with power reaching tens or even hundreds of kilowatts.

[0003] Traditional tunnel lighting control mainly relies on fixed timing or simple traffic flow detection, failing to consider the dynamic impact of fluctuations in the number of electric vehicles and the distribution of travel times. The specific problems are as follows: (1) Due to factors such as policies, subsidies, and technological updates, electric vehicles are experiencing explosive growth, and the actual number of vehicles passing through each day fluctuates greatly. Traditional lighting control strategies based on fixed traffic flow prediction are difficult to adapt to this high randomness, which can easily lead to insufficient lighting or energy waste.

[0004] (2) Electric vehicles return home to charge after the evening rush hour, and their charging load may overlap with the tunnel's basic nighttime lighting load in time, causing double pressure on the regional power grid.

[0005] (3) To take advantage of off-peak electricity prices at night or to meet commuting needs, drivers' travel time is more concentrated during morning and evening rush hours. This concentration effect leads to a rapid increase in tunnel traffic in a short period of time, which in turn triggers the lighting more frequently and for longer periods, thereby increasing the instantaneous peak load.

[0006] Furthermore, existing technologies lack comprehensive modeling of multiple factors such as the seasonal variations of electric vehicles and the intensity of natural light, resulting in low accuracy in tunnel lighting load prediction and difficulty in supporting power grid dispatching and tunnel energy-saving retrofits. Therefore, there is an urgent need for a simulation method that can scientifically assess the impact of electric vehicles on tunnel lighting load, providing a basis for decision-making in intelligent tunnel lighting control. Summary of the Invention

[0007] To address the problems existing in the prior art, the present invention provides a Monte Carlo simulation system and method for the impact of tunnel lighting load on electric vehicles.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: providing a solution including the following steps: S1. Set the annual average daily number of electric vehicles in the tunnel to be evaluated and the seasonal adjustment factor for each month. Calculate the average daily number of electric vehicles in any month and use the Poisson distribution to generate the total number of electric vehicles on that day. S2. Establish a 24-hour travel time probability mass function, and use the total number of electric vehicles generated in S1 as the total number of trials, and the probability mass function corresponding to each hour as the probability vector, and use a multinomial distribution to generate the number of new electric vehicle trips per hour on that day. S3. Calculate the normalized natural light intensity outside the tunnel, and calculate the natural light intensity factor based on the normalized natural light intensity outside the tunnel; S4. Divide the tunnel to be evaluated into three sections: the entrance section, the transition section, and the exit section. Set the baseline threshold for the minimum traffic flow required to turn on the enhanced lighting at night and the coefficient for each section. Calculate the dynamic threshold for lighting control in each section. S5. Set the base traffic flow when there are no electric vehicles in the tunnel, add the total number of electric vehicles obtained in S2, and calculate the total traffic flow in the tunnel. Use the ratio of the total traffic flow to the dynamic threshold for lighting control obtained in S4 as the traffic flow ratio, and make a dimming level decision based on the traffic flow ratio. S6. Set the total power of basic tunnel lighting and calculate the power of enhanced lighting in each section. Add the power of enhanced lighting in each section to the total power of basic lighting to obtain the total power of tunnel lighting. Combine the total power of tunnel lighting for each hour to form a load curve and calculate the daily electricity consumption, daily peak load and peak-valley difference. S7. Repeat steps S1-S6, and use Monte Carlo simulation to collect and statistically analyze the daily electricity consumption, daily peak load and peak-valley difference samples of the tunnel. Calculate the sample mean, standard deviation and 95% confidence interval to achieve a probabilistic assessment of the impact of electric vehicles on tunnel lighting load.

[0009] Preferably, the specific process of S1 is as follows: setting the average daily number of electric vehicle bases for the tunnel to be evaluated. The unit is vehicles per day, and seasonal adjustment factors are set for each month. , ; Calculate the average number of electric vehicles per day in any given month : The total number of electric vehicles for the day is generated using a Poisson distribution. : .

[0010] Preferably, the specific process of S2 is as follows: establishing a 24-hour travel time probability mass function. The formula is: In the formula, A time index for the day. ; For unnormalized weights, higher weights are assigned to the morning rush hour (7-9 am) and the evening rush hour (17-19 pm). This is the sum of weights; This is the normalized probability mass function value.

[0011] The following constraints must be satisfied: Generated by S1 The total number of trials, with Using a probability vector, the number of new electric vehicle trips per hour on a given day is generated using a multinomial distribution. The formula is: In the formula, The unit is vehicles per hour, indicating the number of vehicles per hour. The number of times the hour appears.

[0012] Preferably, in S2, The values ​​for 7-9 hours are 0.3, 0.4, and 0.3 respectively, the values ​​for 17-19 hours are 0.25, 0.5, and 0.25 respectively, and the value for the rest of the time is 0.

[0013] Preferably, the specific process of S3 is as follows: Calculate Normalized natural light intensity outside the tunnel at any time : Only It generates a positive value during certain periods and a value of 0 during other periods.

[0014] Based on the calculation of normalized natural light intensity outside the tunnel, the natural light intensity factor is used. : To reflect the random attenuation effect of cloud cover on natural light, a random perturbation term is introduced. The final natural light intensity factor for: In the formula, and Here are the truncation functions, which take the minimum and maximum values, respectively. It is an independent and identically distributed normal random variable used to simulate the random modulation of natural light intensity caused by weather changes, including sunny, cloudy, and overcast days.

[0015] Preferably, the specific process of S4 is as follows: the tunnel to be evaluated is divided into three sections: the entrance section, the transition section, and the exit section. The entrance section is the section at the tunnel entrance that adapts to changes in external brightness; the transition section is the section connecting to the entrance section and whose brightness gradually decreases; and the exit section is the section at the tunnel exit that adapts to external brightness. The specific division method is based on distance or based on the proportion of tunnel length. (1) Based on distance: the inlet section is 50-100m inward from the entrance end; the transition section connects to the inlet section and is 100-200m in length; the outlet section is 50-100m inward from the outlet end into the tunnel. (2) Based on the proportion of tunnel length: the entrance section accounts for 10%~15% of the total tunnel length; the transition section accounts for 20%~30% of the total tunnel length; the exit section accounts for 10%~15% of the total tunnel length, and the rest is the basic lighting section.

[0016] Set a baseline threshold for the minimum traffic volume required to activate enhanced nighttime lighting. The unit is pcu / h; set the section coefficient for each section. , The introduction section, transition section, and exit section are respectively represented as... , and : Each section in The dynamic threshold for lighting control at any given time is The calculation formula is: The unit is This indicates the traffic flow threshold that triggers enhanced lighting for each hour and each section. , and The corresponding calculated dynamic thresholds for lighting control are as follows: , and ; for or , The value range is 0.4-1. The value is 1 at night and less than 1 during the day, with a minimum value of 0.4. When only reflecting diurnal patterns (lower during the day, higher at night), for When it is necessary to reflect the random attenuation of natural light due to sunny / cloudy / overcast weather, for .

[0017] Preferably, the specific process of S5 is as follows: Assume the hourly basic traffic flow in the tunnel when there are no electric vehicles is... If the unit is pcu / h, then Total traffic volume at any time for: In the formula, The number of new electric vehicle trips per hour is calculated for S2.

[0018] Calculate the traffic flow ratio The formula is: , and The corresponding traffic flow ratios are as follows: , and ; according to Make dimming level decisions: Dimming level 0 indicates that the enhanced lighting is off, dimming level 0.5 indicates that the enhanced lighting is operating at 50% of its rated power, and dimming level 1 indicates that the enhanced lighting is fully on at 100% of its rated power.

[0019] pass , and Calculated They are respectively , and .

[0020] Preferably, the specific process of S6 is as follows: setting the total power of basic lighting. The unit is kW; the rated power of the enhanced lighting in each section is set. , These correspond to the inlet section, transition section, and outlet section, respectively, with units of kW. The total power of the tunnel lighting at all times is: The average charging power of a single electric vehicle is set as follows: Establish a 24-hour charging time probability distribution, with units in kW. ,satisfy Simulates the behavior pattern of electric vehicles charging at home after the evening rush hour: Total charging load at any given time for: In the formula, This represents the total number of electric vehicles on that day.

[0021] Adding the total charging load to the lighting load yields... Total power of the time system for: In hours The x-axis is the calculated value. Using the vertical axis as the ordinate, connect sequentially The daily tunnel lighting load curve is obtained by taking discrete points of the total tunnel lighting power at each time point. Based on the load curve, the following three characteristic quantities are calculated: Calculate daily electricity consumption The formula is: Calculate daily peak load The formula is: Calculate peak-valley difference The formula is: Electricity consumption of the day The unit is kWh; daily peak load Peak-valley difference The units are all kW; for or If it is necessary to assess the cumulative impact of electric vehicle charging on the regional power grid, then the sum of lighting and charging should be taken, i.e. for ,otherwise for .

[0022] Preferably, the specific process of S7 is as follows: repeat S1-S6. Second-rate, ,collect Daily electricity consumption obtained from the simulation Daily peak load Peak-valley difference For the sample, calculate the following statistics: Calculate the sample mean The formula is: In the formula, For simulating round index; For the first The index values ​​obtained from the simulation; Calculate the standard deviation The formula is: The formula for calculating the 95% confidence interval is: Three statistics were used to conduct a probabilistic assessment of the impact of electric vehicles on tunnel lighting load.

[0023] The present invention also includes a Monte Carlo simulation system for the impact of electric vehicle tunnel lighting load on implementing the above method, comprising an electric vehicle number generation module, a travel time allocation module, a natural light calculation module, a lighting threshold calculation module, a dimming level decision module, and a load synthesis and statistics module; The electric vehicle quantity generation module is used to execute step S1, the travel time allocation module is used to execute step S2, the natural light calculation module is used to execute step S3, the lighting threshold calculation module is used to execute step S4, the dimming level decision module is used to execute step S5, and the load synthesis and statistics module is used to execute steps S6 and S7.

[0024] This invention provides a Monte Carlo simulation system and method for the impact of tunnel lighting load on electric vehicles, which has the following beneficial effects: (1) This invention collects samples such as daily electricity consumption, daily peak load, and peak-valley difference through a large number of repeated simulations, and calculates the expected value, standard deviation and confidence interval. It can quantify the fluctuation range of tunnel lighting load under the change of electric vehicle penetration rate, help power grid operators to reasonably reserve capacity and avoid transformer overload, and provide data support for the energy-saving transformation and intelligent control strategy formulation of tunnel lighting system.

[0025] (2) The present invention uses Poisson distribution and multinomial distribution to simulate the daily fluctuation of the number of electric vehicles and the concentrated characteristics of morning and evening peak travel, respectively. It overcomes the shortcomings of traditional deterministic models that cannot handle random fluctuations and can quantitatively evaluate the impact of electric vehicle uncertainty on the fluctuation range of tunnel lighting load and extreme working conditions.

[0026] (3) Based on the comparison between the traffic flow ratio and the preset threshold, the present invention divides the enhanced lighting into three levels: off, half power on, and full power on. This avoids equipment damage caused by frequent switching and can accurately match the lighting power according to the actual traffic flow demand, thereby improving the operating economy of the lighting system.

[0027] (4) The present invention establishes a normalized natural light intensity model and a natural light intensity factor, and uses this to dynamically adjust the lighting control threshold of each section, so that the tunnel lighting can automatically adjust the threshold for turning on enhanced lighting according to the changes in natural light during the day and night, and achieve energy saving under the premise of ensuring driving safety. Attached Figure Description

[0028] Figure 1 This is a flowchart of the Monte Carlo simulation method for the impact of electric vehicles on tunnel lighting load according to the present invention.

[0029] Figure 2 This is the curve showing the relationship between the natural light intensity factor and time in Embodiment 1 of the present invention.

[0030] Figure 3 This is a comparison chart showing the impact of different numbers of electric vehicles on the daily load curve of the tunnel, generated in Embodiment 1 of the present invention. Detailed Implementation

[0031] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings, but the scope of protection of the present invention is not limited to the content described.

[0032] This invention provides a Monte Carlo simulation method for the impact of electric vehicles on tunnel lighting load. It can comprehensively consider the travel time distribution of electric vehicles, changes in natural light, and tunnel segmented lighting control strategies to quantitatively evaluate the daily tunnel lighting load curve, peak-valley difference, and electricity consumption changes under different electric vehicle penetration rates.

[0033] The Monte Carlo simulation method for the impact of electric vehicles on tunnel lighting load of the present invention is as follows: Figure 1 As shown, it includes the following steps: S1, Daily Electric Vehicle Count Generation Set the average daily number of electric vehicle bases for the tunnel to be evaluated. (Unit: vehicles / day), and setting seasonal adjustment factors for each month. ( (Dimensionless). Calculate the average number of electric vehicles per day for the current month (select the month based on the actual situation). : Then, the total number of electric vehicles for the day is generated using a Poisson distribution. : The Poisson distribution was used to simulate the actual, fluctuating daily electric vehicle traffic flow.

[0034] S2, Travel Time Allocation Establish a 24-hour travel time probability quality function The formula is: In the formula, A time index for the day; ; For unnormalized weights, higher weights are assigned during the morning peak (7-9 am) and evening peak (17-19 pm) periods, indicating that at certain times... The relative probability of a travel incident occurring, preferably between 7:00 and 9:00 AM. The values ​​are 0.3, 0.4, and 0.3 respectively, and the values ​​are between 17:00 and 19:00. The values ​​are 0.25, 0.5, and 0.25 respectively, and 0 for the remaining time periods; This is the sum of weights; This is the normalized probability mass function value.

[0035] The following constraints must be satisfied: Generated by S1 The total number of trials, with Using a probability vector, the number of new electric vehicle trips per hour on a given day is generated using a multinomial distribution. The formula is: In the formula, the (Unit: vehicles / hour), indicating the number of vehicles / hour. The number of times each hour occurs. This process uses a "lottery" method (multiple distribution) to distribute the daily number of electric vehicle trips calculated by S1 to the 24 hours of the day according to the probability of morning and evening peak hours.

[0036] S3, Calculation of Natural Light Intensity Factor Normalized natural light intensity outside the tunnel at any time : Only It generates a positive value during certain periods and a value of 0 during other periods, simulating the characteristics of having light during the day and no light at night.

[0037] Based on the calculation of normalized natural light intensity outside the tunnel, the natural light intensity factor is used. : .

[0038] S4. Calculation of Lighting Control Thresholds The tunnel to be evaluated is divided into three sections along the direction of traffic: the entrance section, the transition section, and the exit section. The entrance section is the section at the tunnel entrance that adapts to changes in external brightness; the transition section is the section connecting to the entrance section and whose brightness gradually decreases; and the exit section is the section at the tunnel exit that adapts to changes in external brightness. The specific division methods are based on distance or proportion to the tunnel length. (1) Based on distance: the inlet section is 50-100m inward from the entrance end; the transition section connects to the inlet section and is 100-200m in length; the outlet section is 50-100m inward from the outlet end into the tunnel. (2) Based on the proportion of tunnel length: the entrance section accounts for 10%~15% of the total tunnel length; the transition section accounts for 20%~30% of the total tunnel length; the exit section accounts for 10%~15% of the total tunnel length, and the rest is the basic lighting section. The basic lighting section does not participate in the dynamic dimming of enhanced lighting.

[0039] Set a baseline threshold The unit is pcu / h, which represents the standard vehicle equivalents per hour. This value represents the minimum traffic volume required to activate enhanced nighttime lighting. Set the section coefficient for each zone. , The entry section, transition section, and exit section are respectively , and : Each section in The dynamic threshold for lighting control at any given time is The calculation formula is: The unit is The dynamic threshold represents the traffic flow threshold that triggers enhanced lighting for each hour and each section. , and The corresponding calculated dynamic thresholds for lighting control are as follows: , and ; for , The value range is 0.4-1. The value is 1 at night and less than 1 during the day, with a minimum value of 0.4.

[0040] S5, Dimming Level Decision Set basic traffic volume The unit is pcu / h, representing the background traffic flow in the tunnel each hour when there are no electric vehicles. This is set based on historical statistical data. Total traffic volume at any time for: In the formula, The number of new electric vehicle trips per hour is calculated for S2.

[0041] Calculate the traffic flow ratio The formula is: , and The corresponding traffic flow ratios are as follows: , and .

[0042] according to Make dimming level decisions: Dimming level 0 indicates that the enhanced lighting is off, dimming level 0.5 indicates that the enhanced lighting fixtures operate at 50% of their rated power (e.g., through pulse width modulation dimming or grouped intermittent lighting), and dimming level 1 indicates that the enhanced lighting fixtures are fully on at 100% of their rated power.

[0043] pass , and Calculated They are respectively , and The values ​​of 0.7 and 1.2 in this step, and in S4... The value is an empirical parameter set according to the tunnel lighting design specifications and typical operating data.

[0044] S6, Load Curve Synthesis Set the total power of basic lighting The unit is kW, and this power is maintained at all times. Set the rated power of the enhanced lighting for each zone. , These correspond to the inlet section, transition section, and outlet section, respectively, with units of kW. The total power of the tunnel lighting at all times is: In hours The x-axis is the calculated value. Using the vertical axis as the ordinate, connect sequentially The daily tunnel lighting load curve is obtained by taking discrete points of the total tunnel lighting power at each time point. Based on the load curve, the following three characteristic quantities are calculated: Calculate daily electricity consumption The formula is: Calculate daily peak load The formula is: Calculate peak-valley difference The formula is: Electricity consumption of the day The unit is kWh, daily electricity consumption. This represents the area enclosed by the load curve and the time axis. Daily peak load. Peak-valley difference The units are all kW. Daily peak load The maximum value of the load curve, the peak-to-valley difference Reflects the magnitude of load fluctuations. for .

[0045] This step transforms the "control decision" into the "energy consumption result". S4 determines whether to enhance lighting, and S5 determines the dimming level (which determines how to turn on the lights). This step calculates how much electricity will be consumed and what the load curve looks like.

[0046] S7, Monte Carlo Simulation and Statistics Repeat S1-S6 for a total of Second-rate( Each time, random numbers are generated independently (including random selection of the month, generation, etc.). and (etc.). Collection Daily electricity consumption obtained from the simulation Daily peak load Peak-valley difference Sample. Calculate the following statistics: Calculate the expected value (sample mean). The formula is: In the formula, For simulating round index; For the first The index values ​​obtained from the simulation; Calculate the standard deviation The formula is: The formula for calculating the 95% confidence interval is: These statistical results, serving as the final output for assessing the impact of electric vehicles on tunnel lighting load, provide a probabilistic basis for intelligent tunnel lighting control and power grid planning. The calculation of the above three statistics represents an improvement from "single simulation" to "reliable conclusions." Through the law of large numbers and statistical methods, scientifically credible assessment results are obtained, showing how much more electricity will be used on average each day, the maximum possible surge, and that the reliability of the conclusions is 95%.

[0047] The present invention also provides a Monte Carlo simulation system for the impact of electric vehicles on tunnel lighting load to implement the above method, comprising: an electric vehicle quantity generation module (for executing step S1), a travel time allocation module (for executing step S2), a natural light calculation module (for executing step S3), a lighting threshold calculation module (for executing step S4), a dimming level decision module (for executing step S5), and a load synthesis and statistics module (for executing steps S6 and S7). Based on the above technical solution, the present invention will be further illustrated by the following embodiments: Example 1 This embodiment takes a two-way four-lane highway tunnel as the object and uses the method of the present invention to simulate and evaluate the impact of electric vehicles on the tunnel lighting load.

[0048] S1. Parameter initialization and generation of the total number of electric vehicles for the day. Set the average daily number of electric vehicles per year Vehicles / day; Seasonal moderating factor The various values - The values ​​are 0.8, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.3, 1.2, 1.1, 0.9, and 0.8 respectively. The current simulated month is July (i.e., Monte Carlo simulation times .

[0049] calculate: Vehicles / day.

[0050] Generation: Call the Poisson random number generator. The result of one simulation is... Vehicles.

[0051] S2, Travel Time Allocation Normalized probability mass function ( ） for: The cumulative probability is 0.4 between 7-9 AM and 17-7 PM, and 0 for the rest; these values ​​have been normalized and conform to the weighting ratio of morning and evening peak hours.

[0052] by For the total number of times, The probability vector is used to generate the number of new electric vehicles per hour using a multinomial distribution. The result of one generation is: ,the remaining Verify the summation: This equals the total number of electric vehicles on that day. .

[0053] S3, Calculation of Natural Light Intensity Factor Using formula , .

[0054] Example of hourly calculation: : , .

[0055] : , .

[0056] : , .

[0057] Taking the introduction segment t=8 as an example: First : , .

[0058] In this embodiment, the relationship curve between the natural light intensity factor and time is as follows: Figure 2 As shown, Figure 2 The horizontal axis represents hours. (0~23), the vertical axis represents the basic natural light intensity factor. The value ranges from 0.4 to 1. The curve remains at 1 at night (6 PM to 6 AM the next day), gradually decreases from 6 AM, reaches its lowest point of 0.4 at 12 PM, and then gradually rises back to 1 at 6 PM. This curve reflects the dynamic adjustment of the tunnel lighting control threshold with the intensity of natural light: the threshold decreases during the day (more intense lighting makes it easier to trigger), and the threshold increases at night (more intense lighting makes it harder to trigger), thus achieving energy saving.

[0059] S4. Calculation of Lighting Control Thresholds set up: pcu / h, .

[0060] but pcu / h.

[0061] S5, Dimming Level Decision Set basic traffic volume (Typical daily data): Calculate the total traffic flow at t=8: pcu / h.

[0062] Traffic flow ratio: .

[0063] determination: Therefore .

[0064] The same calculation is performed according to the above process. , , S6, Load Curve Synthesis set up: kW, kW, kW, kW.

[0065] but: Repeat the calculation for all t=0-23 periods to obtain the complete daily load curve. Calculate daily indicators: Daily electricity consumption (Example calculated value is 1865kWh).

[0066] Daily peak load (Example appears at t=18, 117.1kW).

[0067] Peak-valley difference (Minimum power in the early morning was 32.8kW, resulting in 84.3kW).

[0068] S7, Monte Carlo Simulation and Statistics Repeat steps 1-6 for a total of Each time, a random number is generated independently.

[0069] Statistical results: Daily electricity consumption Expected value = 1880 kWh, standard deviation = 45 kWh, 95% confidence interval kWh.

[0070] Daily peak load Expected value = 117.1kW, standard deviation = 2.5kW, 95% confidence interval kW.

[0071] Peak-valley difference Expected value = 84.1kW, Standard deviation = 3.2kW, 95% confidence interval kW.

[0072] Comparison of the impact of different numbers of electric vehicles on the daily load curve: Under the same basic traffic flow, the annual average daily basic number of electric vehicles was set separately. The average daily traffic volume was calculated using Monte Carlo simulations with values ​​of 0, 125, 250, and 375 vehicles per day (corresponding to 0, 162.5, 325, and 487.5 vehicles per day after the July adjustment factor of 1.3). The average daily load curve was obtained as follows: Figure 3 As shown, Figure 3 The horizontal axis represents hours (0-23), and the vertical axis represents the total power of tunnel lighting (kW). The graph shows four curves, each corresponding to the average daily number of electric vehicle bases per year. The average daily load curves are for 0 vehicles / day (no electric vehicles), 125 vehicles / day, 250 vehicles / day, and 375 vehicles / day. (From...) Figure 3 It is evident that with the increase in the number of electric vehicles, the lighting power during morning and evening peak hours (7-9 am and 5-7 pm) rises significantly, while the changes are not significant at night and at noon. This result indicates that the concentrated effect of electric vehicle travel significantly increases the peak load and peak-valley difference of tunnel lighting, providing a quantitative basis for energy-saving retrofitting of tunnel lighting.

[0073] Example 2 This embodiment considers the random influence of weather factors on natural light intensity. The difference between this embodiment and Embodiment 1 is: The basic natural light intensity factor is calculated in S3. Subsequently, to reflect the random attenuation effect of cloud cover on natural light, a random perturbation term was introduced. The final natural light intensity factor for: In the formula, and Here are the truncation functions, which take the minimum and maximum values, respectively. It is an independent and identically distributed normal random variable used to simulate the random modulation of natural light intensity by weather changes such as sunny, cloudy, and overcast days.

[0074] S4 The value ranges from 0.4 to 1. The value is 1 at night and less than 1 during the day, with a minimum value of 0.4.

[0075] The rest is the same as in Example 1.

[0076] Example 3 This embodiment introduces a coupling analysis of electric vehicle charging load and lighting load. The difference between this embodiment and Embodiment 1 is that the calculation in step S6 of Embodiment 1... Total power of tunnel lighting at all times And set the average charging power of a single electric vehicle as The unit is kW, and a probability distribution of 24-hour charging time is established. ,satisfy In this embodiment, The values ​​for each time period between 19:00 and 24:00 are as follows: The remaining time periods are 0, simulating the behavior pattern of electric vehicles charging at home after the evening rush hour.

[0077] Total charging load at any given time for: In the formula, This represents the total number of electric vehicles on that day. Adding the charging load to the lighting load (total lighting power) yields... Total power of the time system : In hours The x-axis is the calculated value. Using the vertical axis as the ordinate, connect sequentially The daily tunnel lighting load curve is obtained by taking discrete points of the total tunnel lighting power at each time point. Based on the load curve, the following three characteristic quantities are calculated: Calculate daily electricity consumption The formula is: Calculate daily peak load The formula is: Calculate peak-valley difference The formula is: Electricity consumption of the day The unit is kWh; daily peak load Peak-valley difference The units are all kW; for .

[0078] This embodiment can quantify the combined impact of "lighting load + charging load" on the power grid within the same evaluation framework.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load, characterized in that: Includes the following steps: S1. Set the annual average daily number of electric vehicles in the tunnel to be evaluated and the seasonal adjustment factor for each month. Calculate the average daily number of electric vehicles in any month and use the Poisson distribution to generate the total number of electric vehicles on that day. S2. Establish a 24-hour travel time probability mass function, and use the total number of electric vehicles generated in S1 as the total number of trials, and the probability mass function corresponding to each hour as the probability vector, and use a multinomial distribution to generate the number of new electric vehicle trips per hour on that day. S3. Calculate the normalized natural light intensity outside the tunnel, and calculate the natural light intensity factor based on the normalized natural light intensity outside the tunnel; S4. Divide the tunnel to be evaluated into three sections: the entrance section, the transition section, and the exit section. Set the baseline threshold for the minimum traffic flow required to turn on the enhanced lighting at night and the coefficient for each section. Calculate the dynamic threshold for lighting control in each section. S5. Set the base traffic flow when there are no electric vehicles in the tunnel, add the total number of electric vehicles obtained in S2, and calculate the total traffic flow in the tunnel. Use the ratio of the total traffic flow to the dynamic threshold for lighting control obtained in S4 as the traffic flow ratio, and make a dimming level decision based on the traffic flow ratio. S6. Set the total power of basic tunnel lighting and calculate the power of enhanced lighting in each section. Add the power of enhanced lighting in each section to the total power of basic lighting to obtain the total power of tunnel lighting. Combine the total power of tunnel lighting for each hour to form a load curve and calculate the daily electricity consumption, daily peak load and peak-valley difference. S7. Repeat steps S1-S6, and use Monte Carlo simulation to collect and statistically analyze the daily electricity consumption, daily peak load and peak-valley difference samples of the tunnel. Calculate the sample mean, standard deviation and 95% confidence interval to achieve a probabilistic assessment of the impact of electric vehicles on tunnel lighting load.

2. The Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load according to claim 1, characterized in that: The specific process for S1 is as follows: Set the average daily number of electric vehicle bases for the tunnel to be evaluated. The unit is vehicles per day, and seasonal adjustment factors are set for each month. , ; Calculate the average number of electric vehicles per day in any given month : ； The total number of electric vehicles for the day is generated using a Poisson distribution. : 。 3. The Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load according to claim 2, characterized in that: The specific process of S2 is as follows: Establish the 24-hour travel time probability mass function. The formula is: ； In the formula, A time index for the day. ; For unnormalized weights, higher weights are assigned to the morning rush hour (7-9 am) and the evening rush hour (17-19 pm). This is the sum of weights; The normalized probability mass function value; The following constraints must be satisfied: ； Generated by S1 The total number of trials, with Using a probability vector, the number of new electric vehicle trips per hour on a given day is generated using a multinomial distribution. The formula is: ； In the formula, The unit is vehicles per hour, indicating the number of vehicles per hour. The number of times the hour appears.

4. The Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load according to claim 3, characterized in that: In S2, The values ​​for 7-9 hours are 0.3, 0.4, and 0.3 respectively, the values ​​for 17-19 hours are 0.25, 0.5, and 0.25 respectively, and the value for the rest of the time is 0.

5. The Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load according to claim 4, characterized in that: The specific process for S3 is as follows: Calculation Normalized natural light intensity outside the tunnel at any moment : ； Only It generates a positive value during certain time periods and is 0 during other time periods; Based on the calculation of normalized natural light intensity outside the tunnel, the natural light intensity factor is used. : ； To reflect the random attenuation effect of cloud cover on natural light, a random perturbation term is introduced. The final natural light intensity factor for: ； In the formula, and Here are the truncation functions, which take the minimum and maximum values ​​respectively. It is an independent and identically distributed normal random variable used to simulate the random modulation of natural light intensity by weather changes, including sunny, cloudy, and overcast days.

6. The Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load according to claim 5, characterized in that: The specific process of S4 is as follows: the tunnel to be evaluated is divided into three sections: the entrance section, the transition section, and the exit section. The entrance section is the section at the tunnel entrance that adapts to changes in external brightness. The transition section is the section that connects to the entrance section and whose brightness gradually decreases. The exit section is the section at the tunnel exit that adapts to changes in external brightness. Set a baseline threshold for the minimum traffic volume required to activate enhanced nighttime lighting. The unit is pcu / h; set the section coefficient for each section. , The introduction section, transition section, and exit section are respectively represented as... , and : ； Each section in The dynamic threshold for lighting control at any given time is The calculation formula is: ； The unit is This indicates the traffic flow threshold that triggers enhanced lighting for each hour and each section. , and The corresponding calculated dynamic thresholds for lighting control are as follows: , and ; for or , The value range is 0.4-1.

7. The Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load according to claim 6, characterized in that: The specific process of S5 is as follows: Assume the hourly traffic flow at the tunnel foundation when there are no electric vehicles. If the unit is pcu / h, then Total traffic volume at any time for: In the formula, The number of new electric vehicle trips per hour calculated for S2; Calculate the traffic flow ratio The formula is: ； , and The corresponding traffic flow ratios are as follows: , and ; according to Make dimming level decisions: ； Dimming level 0 indicates that the enhanced lighting is off; dimming level 0.5 indicates that the enhanced lighting is operating at 50% of its rated power; dimming level 1 indicates that the enhanced lighting is fully on at 100% of its rated power. pass , and Calculated They are respectively , and .

8. The Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load according to claim 7, characterized in that: The specific process for S6 is as follows: Set the total power of basic lighting. The unit is kW; the rated power of the enhanced lighting in each section is set. , These correspond to the inlet section, transition section, and outlet section, respectively, with units of kW. The total power of the tunnel lighting at all times is: The average charging power of a single electric vehicle is set as follows: Establish a 24-hour charging time probability distribution, with units in kW. ,satisfy Simulates the behavior pattern of electric vehicles charging at home after the evening rush hour: Total charging load at any given time for: ； In the formula, This represents the total number of electric vehicles on that day. Adding the total charging load to the lighting load yields... Total power of the time system for: ； In hours The x-axis is the calculated value. Using the vertical axis as the ordinate, connect sequentially The daily tunnel lighting load curve is obtained by taking discrete points of the total tunnel lighting power at each time point. Based on the load curve, the following three characteristic quantities are calculated: Calculate daily electricity consumption The formula is: ； Calculate daily peak load The formula is: ； Calculate peak-valley difference The formula is: ； Electricity consumption of the day The unit is kWh; daily peak load Peak-valley difference The units are all kW; for or .

9. The Monte Carlo simulation method for the influence of electric vehicle tunnel lighting load according to claim 8, characterized in that: The specific process of S7 is as follows: Repeat S1-S6 for a total of Second-rate, ,collect Daily electricity consumption obtained from the simulation Daily peak load Peak-valley difference For the sample, calculate the following statistics: Calculate the sample mean The formula is: ； In the formula, For simulating round index; For the first The index values ​​obtained from the simulation; Calculate the standard deviation The formula is: ； The formula for calculating the 95% confidence interval is: ； Three statistics were used to conduct a probabilistic assessment of the impact of electric vehicles on tunnel lighting load.

10. A Monte Carlo simulation system for the influence of tunnel lighting load on electric vehicles implementing any one of the methods described in 1-9 above, characterized in that: It includes a module for generating the number of electric vehicles, a module for allocating travel time, a module for calculating natural light, a module for calculating lighting thresholds, a module for deciding dimming levels, and a module for compiling and statistically analyzing loads. The electric vehicle quantity generation module is used to execute step S1, the travel time allocation module is used to execute step S2, the natural light calculation module is used to execute step S3, the lighting threshold calculation module is used to execute step S4, the dimming level decision module is used to execute step S5, and the load synthesis and statistics module is used to execute steps S6 and S7.

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