CN118036987A - Hybrid mining card low-energy-consumption scheduling method based on WPT technology - Google Patents
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Abstract
The invention provides a mixed mining card low-energy-consumption scheduling method based on a WPT technology, and relates to the technical field of wireless power transmission. The method comprises the steps of firstly setting working conditions, working modes and vehicle states of a hybrid power mine card; then constructing an objective function of an ore card optimal scheduling model to minimize the energy consumption of the whole mining area; constraint conditions of an ore card optimizing and scheduling model are set, and feasibility and efficiency of scheduling of the strip mine mixed moving ore cards are ensured; and finally, designing a mixed mining card low-energy-consumption scheduling linear programming algorithm based on the WPT technology, solving a mining card optimal scheduling model, and optimizing a low-energy-consumption scheduling path of the mixed mining card in the strip mine. According to the method, efficient transportation and charging strategies are formulated by calculating the energy consumption among all paths, a new energy supply mode is provided for the mixed mining card, dependence on traditional fuel is reduced, and the energy consumption is further reduced.
Description
Technical Field
The invention relates to the technical field of wireless power transmission, in particular to a hybrid mining card low-energy-consumption scheduling method based on WPT (Wireless Power Transmission) technology.
Background
Strip mines are an important way of developing global mineral resources, and how to more reasonably and effectively dispatch mixed mining cards so as to further reduce energy consumption and improve operation efficiency is an important subject in strip mine operation. The transportation system of strip mines has unique transportation requirements and constraints. In actual dispatching, the influence factors are quite numerous, including road conditions, distribution of loading and unloading points, energy states of mine cards and the like. Furthermore, since open-pit mining areas are often provided with complex and variable terrain conditions, such as uphill slopes, downhill slopes and varying degrees of tortuous path, these can affect the energy consumption and energy recovery efficiency of the mining truck. Particularly in downhill sections, the energy consumption of the mine truck can be significantly reduced by effective energy recovery. Traditional mine card scheduling modes are often based on fixed transportation routes and schedules, lacking response and optimization to real-time conditions, resulting in wasted energy and low operating efficiency. In addition, in the operation of the strip mine, energy supply and management are also important factors influencing the operation efficiency and cost, and the traditional wired power transmission mode has the problems of difficult wiring, high maintenance cost, influence on the transportation efficiency of the mining area and the like. In order to achieve the aims of reducing energy consumption and improving operation efficiency. The invention designs a mixed mining card low-energy-consumption scheduling method based on a WPT technology.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a mixed mining card low-energy-consumption scheduling method based on WPT technology, and a high-efficiency transportation and charging strategy is formulated by calculating energy consumption among paths, so that a new energy supply mode is provided for the mixed mining card; through setting up the stake of charging, realize the ore deposit card and charge in the operation in-process, reduce the dependence to traditional fuel, further reduce the energy consumption.
In order to solve the technical problems, the invention adopts the following technical scheme: a mixed mining card low-energy-consumption scheduling method based on WPT technology comprises the following steps:
Step 1: setting working conditions, working modes and vehicle states of the hybrid power mine card;
Step 2: constructing an objective function of an ore card optimization scheduling model to minimize the energy consumption of the whole mining area;
In the research of mixed mining card low-energy-consumption scheduling technology of strip mines, the aim is to minimize the energy consumption of the whole mining area so as to ensure the operation efficiency and the economy;
the calculation of the energy consumption depends on the driving distance between loading and unloading points and the loading state of the mixed mining card; considering energy recovery during downhill driving, the distance between loading and unloading points is divided into the distance between an ascending slope and a flat road And distance to downhill/>Because the energy recovery efficiency is related to the loading state of the mining card, the energy consumption is calculated by taking the energy consumption Q and the energy recovery efficiency eta of each kilometer of the mixed mining card when the mining card is fully loaded and unloaded into consideration; the energy consumption E ij of the ore card from the loading point i to the unloading point j and the energy consumption from the unloading point j to the loading point i are calculated according to the following formula:
thus, the total energy consumption E min to minimize transport of the mining cards between the m loading points and the n unloading points throughout the entire operating cycle is:
Where i denotes a loading point of the ore card, j denotes an unloading point of the ore card, E ij denotes the total energy consumption of the ore card from the loading point to the unloading point, E ji denotes the total energy consumption of the ore card from the unloading point to the loading point, Indicating the uphill and level road distance from the loading point to the unloading point,Representing the downhill distance from the loading point to the unloading point,/>Representing the distance of the load from the unloading point to the loading point on the uphill slope,/>Representing the downhill distance from the unloading point to the loading point, wherein Q f represents the energy consumed per kilometer when the ore card is fully loaded, Q e represents the energy consumed per kilometer when the ore card is empty, eta f represents the full-load energy recovery rate of the ore card, and eta e represents the empty-load energy recovery efficiency of the ore card;
Minimizing the energy consumption E min of the transportation of the ore cards between loading and unloading points as an objective function of an ore card optimal scheduling model, and optimizing efficiency and cost while ensuring low energy consumption;
step 3: setting constraint conditions of an ore card optimizing and scheduling model, and ensuring feasibility and efficiency of mixed ore card scheduling of the strip mine;
The ore card optimization scheduling model comprises the following constraint conditions:
a) The total transportation amount of the ore cards to each unloading point cannot be larger than the maximum capacity of the unloading point;
b) The total amount of the ore cards transported at the loading point cannot exceed the total storage amount of the loading point;
c) The loading times of the loading points are smaller than the loading times of all loading and unloading points in one shift;
d) The total yield of the loading point is greater than the minimum yield requirement;
e) The transportation task of the mine truck is completed within a set working time T;
f) The residual capacity of the mine card must meet the minimum requirements from the current position to the next target point or charging station;
Step 4: designing a mixed mining card low-energy-consumption scheduling linear programming algorithm based on the WPT technology, solving a mining card optimal scheduling model, and optimizing a low-energy-consumption scheduling path of the mixed mining card in the strip mine;
firstly, determining key factors influencing energy consumption; then, the factors are integrated into a linear programming model, and a mine card optimization scheduling model comprising an objective function and a series of constraint conditions is formed; the objective function reflects the need to minimize the total energy consumption, while the constraints ensure that the solution of the model can be implemented in actual operation;
Optimizing the calculation complexity of an algorithm, reducing the scale of the problem through an algorithm preprocessing step, and carrying out model solving by utilizing MATLAB to ensure that a feasible scheduling scheme can be obtained in reasonable time;
the concrete method for solving the ore card optimizing and scheduling model by adopting the mixed-motion ore card low-energy-consumption scheduling linear programming algorithm based on the WPT technology comprises the following steps:
step1, setting an initial state of a mine card and calculating energy consumption;
Setting an initial electric quantity SOC of the mine card and defining all constraint conditions of an optimal dispatching model of the mine card; calculating the energy recovery condition of the mixed mining truck during downhill braking under different road conditions by using map information, and solving the energy consumption E ij among loading and unloading points;
Step2, selecting a mine card destination, and calculating residual energy consumption;
according to the current position of the ore card, evaluating the energy consumption of the ore card reaching each possible destination, and selecting a station with the lowest energy consumption as a next destination station; calculating the expected residual capacity of the ore card after reaching the destination;
Step3, judging the feasibility of the electric quantity;
Evaluating whether the mine card has enough power to reach the next planned destination; if the charge is sufficient, the mine card will go to the destination; if not, turning to Step5;
Step4, checking the satisfaction of constraint conditions;
after reaching the destination, checking whether the mine card meets preset constraint conditions; if the constraint condition is satisfied, ending the scheduling task; otherwise, returning to Step2 to continue optimizing the path selection;
step5, judging and executing a charging strategy;
if the mine card cannot reach the next destination, judging whether the mine card has enough electric quantity to reach the nearest WPT charging pile; if the charging is available, the charging is carried out; if not, diesel is adopted to provide power to go to the charging pile; after the charging or power generation is completed, the path and destination optimization process is restarted, and the process returns to Step2.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in: the mixed mining card low-energy scheduling method based on the WPT technology provided by the invention has the advantages that the transportation route of the mining card is planned in detail, and the total amount of loaded ores, the total consumed electric quantity and fuel quantity, and the total time and total cost of the whole transportation process are accurately calculated. The optimization process ensures that the aim of minimizing energy consumption and cost is fulfilled while meeting the operation requirements of the mine site through iterative computation.
Drawings
Fig. 1 is a flowchart of a WPT technology-based hybrid mining card low-energy-consumption scheduling linear programming algorithm provided by an embodiment of the present invention;
FIG. 2 is a diagram of an exemplary operation of ore card dispatching provided in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a full-load operation cycle of a hybrid mining truck provided by an embodiment of the invention;
fig. 4 is a graph of a WPT-based hybrid mining card scheduling energy consumption change provided by an embodiment of the present invention;
FIG. 5 is a graph comparing results of different scheduling techniques according to an embodiment of the present invention;
Fig. 6 is a graph of comparison of energy consumption of three mine cards according to an embodiment of the present invention, wherein (a) is THMT energy consumption, (b) is DMT energy consumption, and (c) is LPWHT energy consumption.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In the embodiment, a mixed mining card low-energy consumption scheduling method based on a WPT technology is provided, firstly, the assumption condition aims at simplifying complex factors possibly encountered in actual operation, and a clear analysis framework is provided for subsequent model construction; secondly, taking the energy consumption of the whole mining area as an objective function; setting a series of constraint conditions for ensuring the feasibility and efficiency of the mixed mining card dispatching of the strip mine; finally, a mixed mining card low-energy-consumption scheduling linear programming algorithm (Linear Programming for WPT-Assisted Hybrid Trucks, LPWHT) based on the WPT technology is designed and solved. The LPWHT algorithm utilizes the characteristic that the mixed ore card can recover energy during downhill braking, and selects as many downhill paths as possible during algorithm solving so as to realize low energy consumption. In the algorithm, the mine card starts diesel power only when the residual electric quantity does not reach the charging pile, so that the dependence on the traditional fuel is reduced, and the aim of further reducing the energy consumption is fulfilled.
The method specifically comprises the following steps:
Step 1: setting working conditions, working modes and vehicle states of the hybrid power mine card;
In order to simplify the complex factors that may be encountered in the actual operation, and provide an explicit analysis framework for the subsequent model construction, the following assumption conditions are proposed in this embodiment:
(1) The amount of ore extracted from each loading point during an operating period is fixed and does not change during an operating period.
(2) The initial electric quantity of the hybrid power mine card in one shift is full, and when the electric quantity is insufficient to complete the next transportation task, the hybrid power mine card can go to a charging station for charging. When the electric quantity is insufficient when the electric quantity is sent to the charging station, the diesel engine is adopted for generating electricity.
(3) The desired loading duration of the mine card at the loading point and the desired unloading duration at the unloading point are fixed and unaffected by other factors.
(4) The speed of the mine truck in full load or no load in the transportation process is constant and is not influenced by external factors such as traffic, weather and the like.
(5) The maximum unloading capacity of each unloading point is fixed and does not change during an operating period.
(6) The energy consumption and recovery efficiency of the mine truck are determined by its loading state and road surface conditions.
(7) The charging speed of the charging station is fixed and is not affected by other factors.
(8) The location of the loading point, unloading point and charging station is fixed and does not change during an operating period.
(9) After the ore card is loaded, any unloading point can be selected for unloading, but only one unloading point can be selected at a time.
(10) The mine cards do not fail or otherwise require additional processing time during transportation and charging.
The above assumption aims at simplifying the complex factors possibly encountered in actual operation, and provides an explicit analysis framework for subsequent model construction.
Step 2: constructing an objective function of an ore card optimizing and scheduling model, and minimizing the energy consumption of an ore area;
In this embodiment, in order to minimize the energy consumption and cost of the mining card operation cycle, the mining card operation cycle including only one loading point, one unloading point and two-way paths is taken as one mining card unit, and meanwhile, the optimization of transportation time is considered, the operation efficiency and economy are ensured, and the objective function is constructed. The energy consumption calculation is related to the load state of the mixed mining card and the distance between the loading point and the unloading point; the energy recovery efficiency of downhill travel is related to the load status of the mine truck.
The energy consumption E ij of the ore card from the loading point i to the unloading point j and the energy consumption from the unloading point j to the loading point i are calculated according to the following formula:
The total energy consumption of the transportation of the mine card unit between the loading point and the unloading point throughout the mine operation period is:
For a clearer definition in describing the objective function, table 1 gives the sign meaning of the individual parameters used in the objective function:
Table 1 description of parameters in objective function
The energy consumption of the mine card unit is used as an objective function, the optimization of efficiency and cost is realized while the low energy consumption is ensured, and decision support is provided for mixed mine card scheduling.
Step 3: setting constraint conditions of an ore card optimizing and scheduling model, and ensuring feasibility and efficiency of mixed ore card scheduling of the strip mine;
In this embodiment, the ore card optimization scheduling model includes the following constraint conditions:
a) The total amount of transportation of the ore cards to each unloading point cannot be greater than the maximum capacity of the unloading point:
b) The total amount of ore card transported at loading point i cannot exceed the total storage capacity of loading point i:
c) The loading times of the loading points i are smaller than the loading times of all loading points in one shift:
d) The total yield of the loading point is greater than the minimum yield requirement:
e) The working time T of the mine card is fixed to eight hours, and all transportation tasks must be completed in this time:
f) The remaining power of the mine card must meet the minimum requirements from the current location to the next target point or charging station:
Ecurrent-Erequired≥0 (9)
In this embodiment, table 2 shows the symbolic meaning of each parameter used for the constraint condition in the ore card optimization scheduling model.
Table 2 description of parameters in the constraints
Step 4: designing a mixed mining card low-energy-consumption scheduling algorithm based on the WPT technology, solving a mining card optimal scheduling model, and optimizing a low-energy-consumption scheduling path of the mixed mining card in the strip mine;
The invention provides an algorithm based on a linear programming theory, optimizes a low-energy consumption scheduling path of the mixed mining truck in the strip mine, minimizes the energy consumption of the mining truck, and simultaneously meets actual operation requirements such as transportation time, electric quantity limitation, capacity constraint of loading and unloading points and the like. In the design of the algorithm, key factors influencing energy consumption, such as heavy load and no-load running paths of a mining truck, energy consumption difference between uphill and downhill running, charging efficiency of the WPT technology and the like, are determined first. These factors are then incorporated into the linear programming model to form a mathematical model containing the objective function and constraints. In order to ensure that the linear programming algorithm can be efficiently executed, the computational complexity of the algorithm is optimized, and the scale of processing problems is reduced.
In this embodiment, a flow of a mixed mining card low-energy consumption scheduling algorithm based on WPT technology is shown in fig. 1. The flow chart is the core of the algorithm, clearly describing the logic path from the initial state to the destination state.
The algorithm can realize minimum energy consumption and highest transportation efficiency by calculating the residual electric quantity of the mixed mining card and optimizing the path. The implementation flow of the embodiment ensures the definition and the execution effectiveness of the scheduling algorithm, and provides a feasible solution for the low-energy-consumption scheduling of the strip mine mixed mining card.
In this embodiment, the specific steps for solving the ore card optimizing and scheduling module by adopting LPWHT algorithm are as follows:
Step S1: parameter initialization: setting an initial electric quantity state of the mine card, calibrating the positions of a loading point, an unloading point and a charging station on a mine map, and initializing the demand quantity of the points and various parameters of the mine card to serve as a dispatching basis.
The linear programming is used for quantitatively calculating the optimal transportation path and the dispatching strategy of the mixed mining cards, and the energy consumption is ensured to be the lowest possible under the premise of not violating any constraint condition by accurately adjusting the operation route and the transportation task of each mining card.
In this embodiment, an example of the ore card dispatch operation is shown in fig. 2.
In the initial stage of constructing a strip mine mixed mining card low energy consumption scheduling algorithm, the embodiment firstly establishes a basic operation framework in a mining area, and relates to six loading points A, B, C, D, E, F, three unloading points a, b and c and a Charging station in a centralized position. Ore is transported from the loading point to the unloading point in one shift and is cycled between loading and unloading points.
In the charging strategy part, the charging rate parameter of the charging pile is set so as to effectively charge the mine card in the operation process, ensure that the mine card can continuously operate and maximize the operation efficiency. The throughput of each loading point and the maximum throughput of the unloading point are also well defined, and these data are critical to the ensuing assurance that the transportation campaign is respecting the capacity constraints of the mining area.
In this example, the information of the unloading point and the charging pile in the strip mine is shown in table 3.
Table 3 strip mine base data
| Name of the name | Description of the invention |
| Loading point | A、B、C、D、E、F |
| Unloading point | a、b、c |
| Charging station | Charging station |
| Minimum traffic volume in one working period | 9000t |
| Ore yield at loading point a | 2000t |
| Ore yield at loading point B | 1000t |
| Ore withdrawal from loading point C | 2200t |
| Ore withdrawal from loading point D | 1400t |
| Ore withdrawal from loading point E | 1200t |
| Ore yield at loading point F | 1200t |
| Maximum unloading capacity of unloading point a | 3000 Ton |
| Maximum unloading capacity of unloading point b | 3000 Ton |
| Maximum unloading capacity of unloading point c | 3000 Ton |
| Loading time and unloading time | 5 Min, 3 min |
| Charging speed of charging pile | 5kWh/min |
In this embodiment, a schematic diagram of the mine truck empty fully loaded operation cycle is shown in fig. 3.
The parameters of the hybrid mining card used in this embodiment, including the vehicle size, total weight, cargo box volume, and battery capacity, are shown in table 4, and especially the energy consumption characteristics of the vehicle under different loading conditions, such as the energy recovery efficiency when the vehicle is in a full-empty downhill slope, are also accurately calculated and incorporated into the algorithm to optimize the mining card travel path selection.
Table 4 mixing mining card basic parameters
Furthermore, the distance from the loading point to the unloading point and its corresponding energy consumption calculation are also fully considered in the algorithm, including the distance of uphill, downhill and level roads. The accuracy of these data directly affects the reliability of the energy consumption calculation, thereby significantly affecting the scheduling efficiency. In this embodiment, the distances between the loading and unloading points and the charging piles are shown in tables 5 and 6 below.
Table 5 distance between loading point A, B, C and unloading point and charging station on uphill and downhill level road (km)
Table 6 distance between loading point D, E, F and unloading point and charging station on uphill and downhill level road (km)
Step S2: calculating energy consumption and selecting paths; and (3) energy consumption calculation is carried out on all possible paths in the mining area, a next destination is selected according to the calculation result of the energy consumption, and the path with the lowest energy consumption is preferentially considered. The required electric quantity between each point calculated in this embodiment is shown in table 7 and table 8.
TABLE 7 electric quantity (kWh) required for charging pile from loading point to unloading point
| a | b | c | Charging | |
| A | 12.6092 | 33.15685 | 19.37055 | 12.5483 |
| B | 21.42875 | 38.56275 | 45.4725 | 15.8115 |
| C | 25.089075 | 31.601625 | 35.022675 | 18.9393 |
| D | 37.8923 | 37.0273 | 31.601625 | 23.2569 |
| E | 36.94175 | 39.51975 | 32.78105 | 24.5835 |
| F | 42.70835 | 20.358475 | 47.7386 | 29.6504 |
TABLE 8 electric quantity (kWh) required for discharging point to loading point and charging pile
| A | B | C | D | E | F | Charging | |
| a | 10.5096 | 23.6684 | 40.2318 | 20.307 | 4.1944 | 18.2156 | 29.65 |
| b | -7.9132 | 18.3744 | -6.063 | 26.639 | 23.937 | 24.1482 | 3.026 |
| c | 5.1484 | 29.7228 | 14.0778 | 11.75 | 2.1616 | 16.8848 | 31.45 |
Step S3: checking electric quantity and making a charging decision;
before proceeding to the next destination, it is checked whether the current charge of the mine card is sufficient to support reaching the destination. If the electric quantity is insufficient, the algorithm can automatically determine to supplement the electric quantity to the charging station, and when the electric quantity is insufficient to go to the charging pile, the diesel engine is used for generating electricity, so that the smooth proceeding of the transportation task is ensured.
Step S4: performing transportation and update status:
The mine cards are transported according to the selected paths, and the positions and the electric quantity states of the mine cards are updated in real time. After the loading and unloading operation is completed, the residual demand of the loading and unloading points is synchronously updated, and preparation is carried out for the next round of transportation tasks.
Step S5: constraint inspection and transportation adjustment:
In the transportation process, whether the current state of the ore card meets the transportation requirement and other key constraint conditions is continuously checked. If necessary, the route selection or charging strategy is adjusted in time to optimize the transport efficiency.
Step S6: iterative optimization:
steps S2 to S5 are repeated until an optimal transportation plan satisfying all constraints is found. And carrying out cost and efficiency evaluation, and continuing iteration until a preset optimization target is met.
The algorithm can realize energy consumption minimization by precisely controlling the electric quantity of the mixed mining card and optimizing route selection. The algorithm flow ensures the definition and execution effectiveness of the scheduling algorithm, and provides a feasible solution for the low-energy-consumption scheduling of the strip mine mixed mining card.
The algorithm provided by the invention plans the transportation route of the ore card in detail, and accurately calculates the total amount of loaded ore, the total consumed electric quantity and fuel quantity, and the total time and total cost of the whole transportation process. The optimization process ensures that the aim of minimizing energy consumption and cost is fulfilled while meeting the operation requirements of the mine site through iterative computation. The pseudo code of the algorithm is shown in Table 9:
Table 9 pseudo code of mixed mining card low energy consumption scheduling algorithm based on WPT technology
With an exhaustive presentation of the solving steps, the algorithm design of the present embodiment is fully presented. Each step is to ensure the accuracy and the high efficiency of the algorithm, and the double requirements of energy consumption optimization and operation continuity are considered. The initialized parameter settings provide a solid basis for transportation scheduling, and the strategies of path selection and power management ensure the smoothness and economy of the transportation process. By means of real-time monitoring and adjustment of the loading and unloading points, the algorithm can flexibly cope with variables possibly occurring in the transportation process. The steps are not only the key for realizing low energy consumption of mining area transportation, but also provide a stable starting point for subsequent invention research, especially in the aspect of further optimization of strip mine scheduling.
In this embodiment, the WPT-based hybrid mining card scheduling energy consumption change is shown in fig. 4. From fig. 4 it can be observed that the total energy consumption of the mine card, the amount of electricity remaining during operation of the mine card, i.e. the current energy in fig. 4. This period of time before the charging stake is reached is highlighted in particular in fig. 4, where the remaining amount of the mine card is insufficient to support it to the charging station, thus starting the diesel engine to generate electricity, i.e. the range extender. The rated operating mode fuel consumption rate of the range extender of the mixed mining card is 4.11kWh/L, so that the energy consumption of the mining card in the period of time can be clearly seen to be obviously increased.
In addition, the up-and-down fluctuation exhibited by the two curves reflects the energy recovery process of the mine truck on the downhill road section. In some cases, the energy recovery of the downhill segment even exceeds the total energy consumption of the segment, resulting in a decrease in total energy consumption and an increase in the amount of remaining power. When the mine card arrives at the charging station to start charging, the total energy consumption appears as a horizontal straight line, which indicates that no energy consumption is generated during charging; at the same time, the electric quantity steadily rises by a sloping line until the initial 422kWh is restored. And the mine truck continues to run after the charging is completed until the working period is finished.
In this embodiment, under the same working conditions, no matter the working time length or the completed traffic, the energy consumption and cost difference between the three different types of ore cards (WPT-based mixed ore card, traditional mixed ore card, diesel ore card) are shown in fig. 5. As is obvious from comparison, LPWHT proposed by the present study is excellent in effectively reducing the operation cost and energy consumption of the strip mine. Specifically, the operation cost of LPWHT is 511.26 yuan for the same traffic volume, compared to 714.98 yuan for the traditional mixed mining cards (Traditional Hybrid Mining Truck, THMT), and up to 2696.96 yuan for the diesel mining cards (DIESEL MINING Truck, DMT). Likewise, the cost of LPWHT is 511.26 yuan, significantly lower than 1052.98 yuan for THMT and 3577.98 yuan for DMT, for the same operating time.
In addition, since LPWHT includes a charging time during the scheduling, this results in a difference in cost from the same traffic in the same operating time. In terms of energy consumption, LPWHT had an energy consumption of 748.88kWh, THMT 913.61kWh and DMT 2065.68kWh for the same operating time. This data reflects the significant impact of WPT technology and hybrid mining card energy recovery technology in mining card scheduling. The scheduling strategy provided in the embodiment not only optimizes energy management but also significantly reduces energy consumption by implementing the WPT technology, thereby achieving positive effects in both economic benefits and environmental impact.
In this embodiment, the energy consumption changes of the three mine cards in the same working period are compared with those of fig. 6, and the three energy consumption graphs are placed in the same view, and it is obvious that LPWHT and THMT equipped with the energy recovery technology show obvious fluctuation on the energy consumption changes, and this phenomenon is due to the action of the energy recovery system and the use of electric power as main energy sources, so that the energy consumption of the two mixed mine cards is significantly lower than that of the diesel mine cards.
Specifically, the difference between LPWHT and THMT is mainly represented by charging by using WPT charging piles in the scheduling scheme of LPWHT, and THMT is turned to generate electricity by using a diesel engine after the electric energy is exhausted. This strategy results in a total energy consumption of THMT significantly higher than LPWHT. By this comparison, the obvious advantage of WPT technology and hybrid mining card energy recovery technology for reducing energy consumption can be clearly seen. The application of the WPT technology not only optimizes the energy utilization efficiency of the mine card, but also reduces the dependence on fuel oil and further reduces the energy consumption through the arrangement of the charging pile. The application of this technology presents great potential to promote sustainable development and efficiency enhancement in a strip mine environment.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions, which are defined by the scope of the appended claims.
Claims (5)
1. A mixed mining card low-energy-consumption scheduling method based on WPT technology is characterized by comprising the following steps: the method comprises the following steps:
Step 1: setting working conditions, working modes and vehicle states of the hybrid power mine card;
Step 2: constructing an objective function of an ore card optimization scheduling model to minimize the energy consumption of the whole mining area;
step 3: setting constraint conditions of an ore card optimizing and scheduling model, and ensuring feasibility and efficiency of mixed ore card scheduling of the strip mine;
step 4: and designing a mixed mining card low-energy-consumption scheduling linear programming algorithm based on the WPT technology, solving a mining card optimal scheduling model, and optimizing a low-energy-consumption scheduling path of the mixed mining card in the strip mine.
2. The WPT technology-based hybrid mining card low-energy consumption scheduling method of claim 1, characterized by comprising the following steps: the specific method for constructing the objective function of the ore card optimal scheduling model in the step 2 is as follows:
The calculation of the energy consumption of the mining area depends on the running distance between loading and unloading points and the loading state of the mixed mining card; considering energy recovery during downhill driving, the distance between loading and unloading points is divided into the distance between an ascending slope and a flat road And distance to downhill/>Because the energy recovery efficiency is related to the loading state of the mining card, the energy consumption is calculated by taking the energy consumption Q and the energy recovery efficiency eta of each kilometer of the mixed mining card when the mining card is fully loaded and unloaded into consideration; the energy consumption E ij of the ore card from the loading point i to the unloading point j and the energy consumption from the unloading point j to the loading point i are calculated according to the following formula:
thus, the total energy consumption E min to minimize transport of the mining cards between the m loading points and the n unloading points throughout the entire operating cycle is:
Where i denotes a loading point of the ore card, j denotes an unloading point of the ore card, E ij denotes the total energy consumption of the ore card from the loading point to the unloading point, E ji denotes the total energy consumption of the ore card from the unloading point to the loading point, Representing the distance of the loading point to the unloading point on the uphill slope and level road,/>Representing the downhill distance from the loading point to the unloading point,/>Representing the distance of the load from the unloading point to the loading point on the uphill slope,/>Representing the downhill distance from the unloading point to the loading point, wherein Q f represents the energy consumed per kilometer when the ore card is fully loaded, Q e represents the energy consumed per kilometer when the ore card is empty, eta f represents the full-load energy recovery rate of the ore card, and eta e represents the empty-load energy recovery efficiency of the ore card;
The energy consumption E min for transporting the ore cards between loading and unloading points is minimized as an objective function of an ore card optimizing and dispatching model, and the optimization of efficiency and cost is realized while the low energy consumption is ensured.
3. The WPT technology-based hybrid mining card low-energy consumption scheduling method of claim 2, characterized by comprising the following steps: the ore card optimization scheduling model in the step 3 comprises the following constraint conditions:
a) The total transportation amount of the ore cards to each unloading point cannot be larger than the maximum capacity of the unloading point;
b) The total amount of the ore cards transported at the loading point cannot exceed the total storage amount of the loading point;
c) The loading times of the loading points are smaller than the loading times of all loading and unloading points in one shift;
d) The total yield of the loading point is greater than the minimum yield requirement;
e) The transportation task of the mine truck is completed within a set working time T;
f) The remaining power of the mine card must meet minimum requirements from the current location to the next target point or charging station.
4. The WPT technology-based hybrid mining card low-energy consumption scheduling method of claim 3, wherein the method is characterized by comprising the following steps: step 4, firstly determining key factors influencing energy consumption; then, the factors are integrated into a linear programming model, and a mine card optimization scheduling model comprising an objective function and a series of constraint conditions is formed; the objective function reflects the need to minimize the total energy consumption, while the constraints ensure that the solution of the model can be implemented in actual operation;
Optimizing the calculation complexity of the algorithm, reducing the scale of the problem through an algorithm preprocessing step, and solving a model by utilizing MATLAB, so that a feasible scheduling scheme can be obtained in reasonable time.
5. The WPT technology-based hybrid mining card low-energy consumption scheduling method of claim 3, wherein the method is characterized by comprising the following steps: the specific method for solving the ore card optimizing and scheduling model by adopting the mixed-motion ore card low-energy-consumption scheduling linear programming algorithm based on the WPT technology in the step 3 is as follows:
step1, setting an initial state of a mine card and calculating energy consumption;
Setting an initial electric quantity SOC of the mine card and defining all constraint conditions of an optimal dispatching model of the mine card; calculating the energy recovery condition of the mixed mining truck during downhill braking under different road conditions by using map information, and solving the energy consumption E ij among loading and unloading points;
Step2, selecting a mine card destination, and calculating residual energy consumption;
according to the current position of the ore card, evaluating the energy consumption of the ore card reaching each possible destination, and selecting a station with the lowest energy consumption as a next destination station; calculating the expected residual capacity of the ore card after reaching the destination;
Step3, judging the feasibility of the electric quantity;
Evaluating whether the mine card has enough power to reach the next planned destination; if the charge is sufficient, the mine card will go to the destination; if not, turning to Step5;
Step4, checking the satisfaction of constraint conditions;
after reaching the destination, checking whether the mine card meets preset constraint conditions; if the constraint condition is satisfied, ending the scheduling task; otherwise, returning to Step2 to continue optimizing the path selection;
step5, judging and executing a charging strategy;
if the mine card cannot reach the next destination, judging whether the mine card has enough electric quantity to reach the nearest WPT charging pile; if the charging is available, the charging is carried out; if not, diesel is adopted to provide power to go to the charging pile; after the charging or power generation is completed, the path and destination optimization process is restarted, and the process returns to Step2.
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