An application of mine area light storage and charging intelligent energy system and dynamic scheduling method

Abstract

The application discloses a kind of applied to mine area light storage fills wisdom energy system and dynamic scheduling method, installs photovoltaic subsystem on the warehouse and shed of mining area, and energy storage equipment and charging equipment are connected with photovoltaic subsystem respectively by cable;Charging equipment is equipped with full-liquid cooling device and at least two charging guns, which is adapted to multiple charging interfaces on electric mine card used in mining area, and full-liquid cooling device carries out cooling treatment to charging equipment;Intelligent management subsystem carries out data communication with charging equipment and each electric mine card respectively, real-time power of charging equipment, residual capacity of electric mine card and transport task are monitored in real time, and the dynamic charging time of each electric mine card is preset according to residual capacity and transport task, and the charging equipment is instructed to control each electric mine card and charge in order.The application fully utilizes mining area building facilities to convert solar energy into electrical energy, and makes vehicle charging orderly through intelligent management, to ensure the reasonable allocation and efficient use of charging resources.

Description

Technical Field

[0001] This invention relates to the fields of new energy and smart mining technology, specifically to a smart energy system and dynamic scheduling method for photovoltaic-storage-charging systems applied in mining areas. Background Technology

[0002] With increasing global emphasis on environmental protection and sustainable development, the application of electric mining trucks in mining areas is gradually increasing. However, mining areas are typically located in remote areas with weak power supply infrastructure, leading to power supply difficulties. Traditional charging methods are insufficient to meet the fast charging needs of electric mining trucks and have low energy efficiency. Furthermore, how to rationally arrange the charging sequence, ensure charging safety, and improve the driver's waiting experience are also urgent issues to be addressed.

[0003] Traditional mining areas face the following technical challenges in energy supply:

[0004] Power supply constraints: The power grid capacity in the mining area is insufficient, diesel generators are expensive (increasing the transportation cost of each ton of goods by ¥15-20), and carbon emissions exceed the standards;

[0005] Low charging efficiency: Existing charging equipment has low power density (≤250kW), and the charging time for a single gun is >2 hours, which affects transportation turnover rate;

[0006] Energy waste: The utilization rate of building roof area in the mining area is less than 5%, and the potential photovoltaic installed capacity of ≥10MW remains undeveloped;

[0007] Inefficient scheduling: Vehicle charging sequence lacks intelligent planning, and queuing time accounts for more than 30% of the total operation time.

[0008] In addition, the transportation routes in the mining area are fixed but have strong dynamic scheduling requirements, so there is an urgent need for a comprehensive solution that integrates high-efficiency power generation, energy storage buffer, rapid energy replenishment and intelligent management. Summary of the Invention

[0009] To address the aforementioned problems and shortcomings, this invention provides a smart energy system and dynamic scheduling method for photovoltaic-storage-charging systems in mining areas. It makes full use of mining area buildings and facilities, uses a photovoltaic subsystem to convert solar energy into electrical energy, improves charging speed, and ensures orderly vehicle charging through intelligent management, thereby guaranteeing the rational allocation and efficient utilization of charging resources.

[0010] The present invention adopts the following technical solution:

[0011] On one hand, this invention provides a smart energy system for photovoltaic-storage-charging applications in mining areas. The system includes a photovoltaic subsystem, energy storage equipment, charging equipment, and an intelligent management subsystem. The photovoltaic subsystem is installed on the material silos and sheds in the mining area. The energy storage equipment and charging equipment are connected to the photovoltaic subsystem via cables. The charging equipment is equipped with a liquid-cooled cooling device and at least two charging guns, which are compatible with multiple charging interfaces on electric mining trucks used in the mining area. The liquid-cooled cooling device cools the charging equipment. The intelligent management subsystem communicates with the charging equipment and each electric mining truck, monitors the real-time power of the charging equipment, the remaining power of the electric mining trucks, and the transportation tasks in real time, calculates the dynamic preset charging time for each electric mining truck based on the remaining power, transportation tasks, and charging costs, generates a charging sequence, and instructs the charging equipment to charge each electric mining truck in a sequential manner.

[0012] Furthermore, the DC power generated by the photovoltaic subsystem is converted into AC power by the energy storage inverter and connected to the charging device, or the generated DC power is converted into DC power of a suitable voltage level and input into the energy storage device.

[0013] Preferably, the energy storage device is a lithium battery pack, which is equipped with a battery management module for collecting power consumption data of the charging device and power generation data of the light-emitting subsystem. When the power generation of the photovoltaic subsystem is less than the power consumption of the charging device, the battery management module controls the energy storage device to discharge to the charging device; when the power generation of the photovoltaic subsystem is greater than the power consumption of the charging device, the battery management module controls the photovoltaic subsystem to charge the energy storage device.

[0014] Furthermore, the system also includes a safety assurance subsystem, which comprises an intelligent temperature control subsystem and a safety management subsystem. The intelligent temperature control subsystem collects the temperature of the charging equipment, energy storage equipment, and electric mining truck charging interface in real time through temperature sensors, and evaluates the collected temperature parameters in real time through evaluation module I. The intelligent temperature control subsystem is connected to the heat dissipation devices in the charging equipment and energy storage equipment. The safety management subsystem is connected to the power switches of the charging equipment and energy storage equipment. The safety management subsystem collects the voltage and current parameters of the charging equipment, energy storage equipment, and electric mining truck during charging in real time, and evaluates the collected voltage and current parameters and the battery health status of the energy storage equipment in real time through evaluation module II.

[0015] On the other hand, the present invention also provides a dynamic scheduling method for a smart energy system for photovoltaic, energy storage, and charging in mining areas. The method is characterized by an intelligent management subsystem collecting the location information, remaining power, and transportation tasks of each electric mining truck, as well as the real-time power information of the charging equipment; calculating the dynamic preset charging time for each electric mining truck based on the remaining power and transportation tasks, and generating a charging sequence; sending the charging sequence of each electric mining truck to the charging equipment for sorted charging; the electric mining trucks arriving at the charging equipment according to the preset charging time, and the charging equipment charging according to the preset charging parameters of the electric mining trucks.

[0016] Furthermore, the intelligent management subsystem automatically generates a charging sequence based on the urgency of the electric mining truck's transportation task and its remaining battery power, and sends it to the charging equipment. The specific method is as follows:

[0017] The intelligent management subsystem prioritizes charging time for electric mining trucks with urgent transportation tasks and low remaining battery power.

[0018] The intelligent management subsystem is designed for electric mining trucks with relatively relaxed transportation tasks. It schedules charging times for the electric mining trucks based on the peak-valley electricity price difference and the power allocation of the charging equipment.

[0019] More preferably, the intelligent management subsystem uses an improved genetic algorithm to generate charging sequences, and the dynamic update cycle of the charging sequences is 5 minutes. The dynamic scheduling algorithm used is:

[0020] F = α·E + β·S + γ·P

[0021] Among them, the F-value is a comprehensive indicator for measuring the charging priority of vehicles;

[0022] α, β, and γ are the weight values, and α+β+γ takes the value 1;

[0023] E represents the urgency of the transport, and its value usually ranges from 0 to 1. The larger the value, the more urgent the task.

[0024] S is the SOC decay rate, which represents the rate at which the remaining battery power decreases. The larger the value, the more the battery needs to be charged first.

[0025] P represents the peak-valley electricity price difference, which is the difference between the current electricity price and the off-peak electricity price. The larger the value, the higher the charging cost during the current period.

[0026] Furthermore, the dynamic update cycle of the charging sequence is 5 minutes. It adjusts the weight values ​​of α, β and γ in combination with the real-time status of peak transportation period, battery temperature or SOC decay rate and peak-valley difference in electricity price. During peak transportation period, the weight of α increases, the weight of β increases when the battery is at high temperature or low SOC decay rate to protect the equipment, and the weight of γ increases when the peak-valley difference in electricity price widens.

[0027] Preferably, the specific method for dynamically determining the weight values ​​of α, β, and γ is as follows:

[0028] Establish an objective function based on minimizing transportation delay costs, battery damage costs, and electricity costs;

[0029] Based on vehicle dispatch records, battery SOC decay curves, and historical operational data of the mining area with electricity price fluctuations, the impact weights of each data point on the total system cost are fitted using linear regression or machine learning models.

[0030] By combining the objective function, the optimal weight combination can be obtained by iteratively solving the problem using a genetic algorithm or a particle swarm optimization algorithm.

[0031] More preferably, the intelligent management subsystem controls the charging equipment to allocate charging current according to the transportation urgency command, and allocates a larger charging current to electric mining trucks with high transportation urgency.

[0032] Furthermore, the intelligent temperature control subsystem in the safety assurance subsystem collects the temperature at the charging equipment, energy storage equipment, and electric mining truck charging interface. The evaluation module I evaluates the collected temperature data. When the collected temperature data exceeds the set threshold, the heat dissipation device in the corresponding equipment is activated or the charging strategy is adjusted.

[0033] Furthermore, the safety management subsystem within the safety assurance subsystem collects the voltage and current parameters of the charging equipment, energy storage equipment, and electric mining truck during charging in real time. The evaluation module II evaluates the collected voltage and current data. When overvoltage, overcurrent, or leakage is detected, the power switch of the corresponding equipment is automatically cut off, and an early warning is issued.

[0034] The present invention has the following advantages over the prior art:

[0035] A. This invention, the photovoltaic-storage-charging smart energy system, utilizes existing facilities in the mining area to install a photovoltaic subsystem, achieving efficient utilization of solar energy and providing clean, sustainable energy for electric mining trucks. This reduces reliance on the traditional power grid and lowers energy costs. Simultaneously, the inclusion of energy storage equipment effectively solves the intermittent power generation problem of the photovoltaic subsystem, improving energy utilization efficiency, ensuring continuous and stable charging, and reducing damage to vehicle batteries caused by power fluctuations. Furthermore, the charging equipment employs dual-gun or multi-gun charging technology combined with a fully liquid-cooled cooling device, achieving megawatt-level charging rates. This significantly shortens single-vehicle charging time, improves the transportation efficiency of vehicles in the mining area, meets the high-intensity production demands of the mining area, and the intelligent management subsystem enables intelligent sequencing and optimized management of vehicle charging, improving the utilization efficiency of charging resources, reducing vehicle waiting time, and further enhancing the overall transportation efficiency of the mining area.

[0036] B. This invention incorporates a safety protection subsystem within the system. By monitoring parameters such as temperature, current, and voltage at the charging interface of the charging equipment, energy storage equipment, and electric mining truck in real time, and by evaluating the charging temperature, current, and voltage through corresponding assessment modules, it promptly detects safety issues such as excessively high temperatures, overcurrent, overvoltage, and leakage. This provides comprehensive safety protection for the charging process of the electric mining truck, effectively reducing safety risks during charging and ensuring the safety of personnel and equipment.

[0037] C. Based on the fixed characteristics of mining vehicle routes, the charging plan is optimized by adopting a dynamic scheduling method that combines the transportation tasks and remaining power of each electric mining truck to dynamically set the pre-charging time for each truck and send the preset charging sequence to the charging equipment. The charging equipment charges the electric mining trucks according to the charging sequence. The intelligent management subsystem can plan the charging plan in advance based on the vehicle's transportation route and time arrangement. When the vehicle is about to return to the charging station, the system automatically adjusts the working status of the charging equipment and energy storage equipment to ensure that the vehicle can be charged quickly, reduce waiting time, improve transportation efficiency, and achieve synergistic optimization of energy and transportation.

[0038] D. This invention integrates electric mining trucks with intelligent technology, enabling functions such as remote monitoring and autonomous driving, improving the automation and intelligence level of mining area transportation, enhancing transportation efficiency and management accuracy, and reducing labor costs and human error. Attached Figure Description

[0039] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0040] Figure 1 The system control principle diagram provided for this invention;

[0041] Figure 2 Thermodynamic model of the all-liquid cooling device provided by the present invention;

[0042] Figure 3 The logic diagram of the dynamic scheduling algorithm provided by this invention;

[0043] Figure 4 A schematic diagram of the security management subsystem provided for this invention;

[0044] Figure 5 The schematic diagram of the photovoltaic-storage-charging smart energy system provided by this invention;

[0045] Figure 6The photovoltaic integrated layout diagram of the material shed provided by this invention;

[0046] Figure 7 This is a partial top view of the building-integrated photovoltaics system provided by the present invention;

[0047] Figure 8 for Figure 7 The main view shown.

[0048] The diagram is labeled as follows:

[0049] 1-Carport; 2-Photovoltaic modules; 21-Cables; 3-Roof panels; 4-Purlins. Detailed Implementation

[0050] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0052] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0053] like Figure 1 As shown, this invention provides a smart energy system for photovoltaic, energy storage, and charging in mining areas. The system includes a photovoltaic subsystem, energy storage equipment, charging equipment, and an intelligent management subsystem. The photovoltaic subsystem is installed in the material silos and sheds (such as coal sheds in coal washing plants) of the mining area. The energy storage equipment and charging equipment are connected to the photovoltaic subsystem via cables. The photovoltaic subsystem is installed using a building-integrated photovoltaics (BIPV) approach, such as... Figure 6 , Figure 7 and Figure 8 As shown, a photovoltaic shed for mining trucks is preferably constructed at the transit and rest area of ​​electric mining trucks. Photovoltaic modules 2 are installed on the roof panels 3 of the shed 1, which are mounted on purlins 4. The photovoltaic modules 2 are fixed to the roof panels 3 by adhesive bonding, and the photovoltaic modules 2 are connected to each other by cables 21. The DC power generated by the photovoltaic modules 2 is transmitted to an energy storage inverter, which then converts it into AC power, connecting it to the power supply network of the charging equipment or directly converting it into DC power of a suitable voltage level to charge the energy storage equipment. The photovoltaic subsystem converts solar energy into electrical energy, providing green energy for charging electric mining trucks. The photovoltaic shed not only charges the vehicles but also provides sun protection, extending the service life of the vehicles.

[0054] This invention utilizes existing facilities in the mining area to install a photovoltaic subsystem, achieving efficient use of solar energy, providing clean and sustainable energy for electric mining trucks, reducing dependence on the traditional power grid, and lowering energy costs.

[0055] like Figure 6 As shown, the building-integrated photovoltaic (BIPV) design adopted in this invention uses curved surface-mounted roof panels and frameless modules on the roof of the carport 1, with a module efficiency of ≥21%, achieving the dual functions of power generation and material shading; the photovoltaic installed capacity optimization formula is: PPV=A·G·η PV ·K t

[0056] Where: A is the usable area (m²) 2 G represents the local irradiance (kWh / m²). 2 / day), η PV For component efficiency, K t For system efficiency.

[0057] This invention equips the provided charging equipment with a full liquid cooling device and at least two charging guns, which are compatible with multiple charging interfaces on electric mining trucks used in mining areas. The full liquid cooling device cools the charging equipment. The electric mining trucks use dual-gun or multi-gun charging technology, and the charging equipment employs full liquid cooling technology. This design enables megawatt-level charging rates, keeping the charging time for a single vehicle within 30 minutes, significantly shortening the charging time and greatly improving charging efficiency to meet the high-intensity transportation needs of mining vehicles. The full liquid cooling technology also effectively reduces the operating temperature of the charging equipment, improving its reliability and service life. The full liquid cooling device used in this invention utilizes a three-stage temperature control strategy, as detailed below:

[0058] Normal operation: coolant flow rate v = 2.5 m / s, ΔT ≤ 8℃;

[0059] Overload protection: When I > 1.2I_rated, the auxiliary pump is started, and the flow rate is increased to v = 4m / s;

[0060] Fault isolation: Automatically cut off the liquid circuit when leakage is detected (flow difference ΔQ > 15%).

[0061] like Figure 2 As shown, this invention employs dual leak detection and a pressure relief valve in its safety protection section. The dual leak detection system boasts high-precision (±0.5 ml / min) detection capability, determining leaks by monitoring changes in coolant flow rate. A leak is identified when the detected flow difference ΔQ > 15%. This is the first line of defense for the liquid cooling system's safety, providing accurate data for timely detection of coolant leaks. The pressure relief valve opens when the system pressure reaches 1.5 MPa to release excessive pressure within the system, preventing damage to cooling system components due to excessive pressure; this is the second line of defense for safety.

[0062] The cooling system employs a main circulation pipeline, redundant pump sets, and plate heat exchangers.

[0063] The main circulation pipe has a diameter of Φ25mm and is the main channel for coolant circulation, providing a pathway for coolant to flow within the system.

[0064] The flow rate range of the redundant pump set is 2.5-4 m³ / h. 3 The auxiliary pump provides power for coolant circulation. Under normal operation, the coolant flow rate is v = 2.5 m / s; when an overload occurs (I > 1.2I_rated), the auxiliary pump starts, increasing the flow rate to v = 4 m / s to enhance heat dissipation.

[0065] Plate heat exchangers limit temperature variation ΔT ≤ 8℃, dissipating the heat carried by the coolant through heat exchange to ensure the coolant temperature remains within a suitable range and maintain the normal operating temperature of the equipment.

[0066] The power module section includes a silicon carbide MOSFET array and a multilayer busbar design. The silicon carbide MOSFET array, as the core component for power conversion, generates heat during charging, requiring a cooling system to dissipate heat promptly and ensure efficient and stable operation. The multilayer busbar design has an inductance of ≤15nH. This design reduces stray inductance, lowers power loss and heat generation, and, together with the cooling system, further guarantees equipment performance.

[0067] The all-liquid cooling device works in concert with the above components and combines a three-level temperature control strategy to achieve efficient heat dissipation for the charging equipment, ensuring its reliability and stability under high power operation, and achieving megawatt-level charging speed and related performance improvements.

[0068] like Figure 4 As shown, the battery compartment is equipped with a VOCs sensor and a perfluorohexanone fire extinguishing system, achieving millisecond-level fault response. It features a monitoring network, control center, and actuators.

[0069] The monitoring network includes VOC gas sensors and distributed fiber optic temperature measurement.

[0070] The VOC gas sensor inside the battery compartment has a detection limit of 1 ppm and can monitor the concentration of volatile organic compounds (VOCs) in the compartment in real time. When a malfunction occurs in the battery compartment (such as a trace amount of harmful gas leakage in the early stage of battery thermal runaway), the VOC concentration will change. The sensor quickly captures this change and transmits the detection signal to the control center.

[0071] Distributed fiber optic temperature measurement achieves an accuracy of ±0.5℃. By detecting temperature changes within the battery compartment, it can identify whether the battery is overheating. Abnormally high temperatures are also a significant indicator of battery malfunction, and the detected temperature signal will be transmitted to the control center.

[0072] A triple-redundant PLC system (Safety Integrity Level 3) is employed in the control center: serving as the control hub, it receives signals transmitted from the monitoring network (VOC gas sensors and distributed fiber optic temperature measurement). The triple redundancy design improves system reliability, and the SIL3 safety integrity level ensures accurate and reliable signal processing. The system analyzes and judges the received signals, and when a fault is detected in the battery compartment (such as excessive VOC concentration or abnormal temperature rise reaching a preset threshold), it quickly issues control commands.

[0073] The actuators used include a perfluorohexanone fire extinguishing device and a millisecond-level circuit breaker. Upon receiving a command from the control center, the perfluorohexanone fire extinguishing device rapidly releases the extinguishing agent at a spray rate of 200 g / s to suppress any potential fires in the battery compartment. Upon receiving a command, the millisecond-level circuit breaker can cut off the circuit within an action time of ≤3 ms to prevent the fault from escalating further and avoid more serious accidents caused by electrical short circuits or other problems.

[0074] The present invention preferably sets up the charging equipment in the rest area and catering area, providing drivers waiting for charging with a comfortable rest environment and catering services, reducing and alleviating drivers' anxiety while waiting, improving drivers' work experience, indirectly increasing drivers' work enthusiasm and efficiency, and also reflecting humanistic care.

[0075] The charging equipment's workflow: After the electric mining truck enters the charging station, the driver inserts a dual- or multi-gun charging plug into the vehicle's charging port. Once the charging equipment detects a normal connection, it communicates with the vehicle's battery management system to obtain battery information and charging requirements. Based on the vehicle's needs, the charging equipment adjusts its output voltage and current to charge the vehicle at megawatt-level charging rates. During charging, a full liquid cooling system continuously operates, using coolant circulation to remove heat generated by the charging equipment, ensuring the charging station's operating temperature remains within the normal range.

[0076] like Figure 5 As shown, the energy storage device is preferably a lithium battery pack, equipped with a battery management module that collects power consumption data from the charging device and power generation data from the photovoltaic subsystem. When the power generation of the photovoltaic subsystem is less than the power consumption of the charging device, the battery management module controls the energy storage device to discharge to the charging device; when the power generation of the photovoltaic subsystem is greater than the power consumption of the charging device, the battery management module controls the photovoltaic subsystem to charge the energy storage device. In other words, when the electric truck is not charging, excess energy generated by the photovoltaic subsystem can be stored in the battery of the energy storage device. When the photovoltaic subsystem's power generation is insufficient or during peak charging demand, the energy storage device releases electrical energy to power the charging device, improving energy utilization efficiency and ensuring the continuity and stability of charging. Alternatively, the photovoltaic subsystem can be integrated with the mains power grid, using the mains power grid as a backup power source. The mains power grid mode is only used when the energy storage device and photovoltaic power are insufficient; otherwise, the photovoltaic subsystem is preferred for charging.

[0077] The intelligent management subsystem communicates with both the charging equipment and each electric mining truck. Through sensors and communication modules installed on the vehicles, it acquires real-time information such as vehicle location, remaining battery power, and transportation tasks. It also monitors the real-time power of the charging equipment and presets dynamic charging times for each truck based on remaining battery power and transportation tasks. The intelligent management subsystem automatically generates charging sequence instructions based on the preset charging plan, the actual remaining battery power of the vehicles, and the urgency of the transportation tasks. These instructions are then sent to the charging equipment, which in turn controls the charging equipment to prioritize and charge each truck, achieving intelligent sequencing of vehicle charging. For example, trucks 01-10 can be scheduled for charging at 8:00 AM, and trucks 11-20 at 9:00 AM, ensuring the rational allocation and efficient utilization of charging resources.

[0078] For vehicles with urgent transportation tasks and low remaining battery power, the intelligent management subsystem prioritizes charging. For vehicles with less urgent transportation tasks, the system can rationally schedule charging times based on peak-valley electricity price differences and the power allocation of charging equipment to reduce charging costs and improve energy efficiency. When a vehicle arrives at the charging equipment, the equipment charges the vehicle according to instructions. Simultaneously, the intelligent management subsystem can dynamically adjust the charging plan based on real-time charging data and vehicle status, further improving charging efficiency and energy utilization.

[0079] The system also includes a safety subsystem, comprising an intelligent temperature control subsystem and a safety management subsystem. The intelligent temperature control subsystem uses temperature sensors to collect real-time temperatures at the charging interfaces of the charging equipment, energy storage devices, and electric mining trucks. Evaluation module I performs real-time assessments of the collected temperature parameters. This subsystem is connected to the heat dissipation devices in the charging and energy storage devices; when the temperature exceeds a set threshold, it automatically activates the built-in heat dissipation devices or adjusts the charging / discharging strategy (e.g., reducing charging current or voltage). The safety management subsystem collects real-time voltage and current parameters during charging of the charging equipment, energy storage devices, and electric mining trucks. Evaluation module II performs real-time assessments of these parameters. This subsystem is connected to the power switches of the charging and energy storage devices. For example, if overvoltage, overcurrent, or leakage is detected in the energy storage device, the connection between the energy storage device and the charging circuit is immediately disconnected, and an alarm signal is issued to notify personnel for handling. Simultaneously, evaluation module II also performs real-time assessments of the battery health status of the energy storage device, predicting potential failure risks and taking proactive maintenance measures to ensure reliable operation of the energy storage device.

[0080] This invention also provides a dynamic scheduling method for a smart energy system for photovoltaic, energy storage, and charging in mining areas. The intelligent management subsystem collects the location information, remaining power, and transportation tasks of each electric mining truck, as well as the real-time power information of the charging equipment. Based on the remaining power and transportation tasks, it calculates the dynamic preset charging time for each electric mining truck and automatically generates a charging sequence. The charging sequence of each electric mining truck is sent to the charging equipment for sorted charging. The electric mining trucks arrive at the charging equipment according to the preset charging time, and the charging equipment charges according to the preset charging parameters of the electric mining trucks.

[0081] The charging equipment employs dynamic power pooling technology, and the established charging power optimization model is as follows:

[0082]

[0083] Among them: S0C target The target state of charge (SOC) refers to the desired level of charge the battery will achieve, typically expressed as a percentage of the battery's remaining capacity relative to its total capacity. For example, if you want the battery to be charged to 80%, the SOC is... target That is 0.8

[0084] SOC i It represents the current state of charge of the i-th battery (or charging unit), also measured as the percentage of remaining capacity relative to the total capacity, reflecting the proportion of the battery's current stored charge.

[0085] P iIt is the charging power of the i-th battery (or charging unit), usually measured in watts (W), which represents the electrical energy consumed by the battery per unit time during the charging process.

[0086] η E2E It is the energy-to-energy conversion efficiency from the grid to the battery. It is a coefficient between 0 and 1 used to measure the efficiency of converting electrical energy into battery chemical energy during the charging process. For example, \(\eta_{E2E}\)=0.9 means that if 100 joules of electrical energy are input, 90 joules of energy are actually stored in the battery.

[0087] t charge It is the charging time, usually expressed in seconds (s), minutes (min), or hours (h), representing the duration of charging the battery.

[0088] Constraints: ΣP i ≤P_{total}(720kW), t charge The charging time is ≤30 minutes; the charging equipment supports dynamic power distribution for 12 charging terminals, with each charging gun adjustable steplessly from 50-600A. The maximum total power is 720kW, determined based on a comprehensive assessment of the charging equipment's power capacity, load-bearing capacity, and actual charging needs in the mining area. This maximum is determined through statistical analysis of the charging power demand of electric mining trucks in the mining area, combined with the rated power of the charging equipment, to ensure stable operation. The maximum charging time of 30 minutes is designed to meet the requirements of high-intensity transportation tasks in the mining area. Actual testing and theoretical calculations show that completing charging within this timeframe maximizes vehicle turnover. This invention provides support for dynamic power distribution for 12 charging terminals, with each charging gun adjustable steplessly from 50-600A. This adjustment range can be flexibly adjusted based on the vehicle's battery type, remaining charge, and real-time power distribution within the charging equipment. When multiple vehicles are charging simultaneously, the system prioritizes vehicles with urgent transportation tasks to receive a larger charging current, shortening the overall charging wait time and improving charging resource utilization efficiency. However, in practical applications, the adjustment range may be limited by factors such as line resistance and the heat dissipation capacity of the charging equipment, requiring optimization based on specific circumstances.

[0089] The intelligent management subsystem automatically generates a charging sequence based on the urgency of the electric mining truck's transportation task and its remaining power, and sends it to the charging equipment. Specifically, the intelligent management subsystem prioritizes charging time for electric mining trucks with urgent transportation tasks and low remaining power; for electric mining trucks with relatively relaxed transportation tasks, the intelligent management subsystem schedules charging time based on the peak-valley electricity price difference and the power allocation of the charging equipment.

[0090] The intelligent management subsystem in this invention uses an improved genetic algorithm to generate charging sequences, and the dynamic update cycle of the charging sequences is 5 minutes. The dynamic scheduling algorithm used is:

[0091] F = α·E + β·S + γ·P

[0092] in:

[0093] The F-value is a comprehensive indicator that measures the charging priority of a vehicle. It integrates three factors: the urgency of the transportation task, the health of the battery, and the economic cost. The higher the F-value, the more urgent the charging need of the vehicle is, and the more priority it should be given to charging.

[0094] α, β, and γ are the weight values, and α+β+γ takes the value 1;

[0095] E represents the urgency of the transport, and its value usually ranges from 0 to 1. The larger the value, the more urgent the task.

[0096] S is the SOC decay rate, which represents the rate at which the remaining battery power decreases. The larger the value, the more the battery needs to be charged first.

[0097] P represents the peak-valley electricity price difference, which is the difference between the current electricity price and the off-peak electricity price. The larger the value, the higher the charging cost during the current period.

[0098] The dynamic update cycle of the charging sequence is 5 minutes. It combines the real-time status of peak transportation period, battery temperature or SOC decay rate and electricity price peak-valley difference to adjust the weight values ​​of α, β and γ. During peak transportation period, the weight of α increases, the weight of β increases when the battery is at high temperature or low SOC decay rate to protect the equipment, and the weight of γ increases when the electricity price peak-valley difference widens.

[0099] The weights of α, β, and γ need to balance three objectives: transportation efficiency, battery life, and economics. Their values ​​are typically determined through the following steps:

[0100] Historical data regression analysis: Based on historical operational data of the mining area (such as vehicle dispatch records, battery SOC decay curves, and electricity price fluctuations), the influence weights of each parameter on the total system cost are fitted through linear regression or machine learning models (such as support vector machines and random forests).

[0101] Constraint optimization: Combining the objective function (e.g., minimizing transportation delay costs + battery wear costs + electricity costs), a genetic algorithm or particle swarm optimization algorithm is used to iteratively solve for the optimal weight combination. For example:

[0102] α (transportation urgency) is strongly correlated with vehicle task priority and can be inferred from the cost of task delay penalties.

[0103] β (SOC degradation rate) should be referenced from battery health models (such as lithium-ion battery aging formulas) to avoid overcharging / over-discharging;

[0104] γ (peak-valley electricity price difference) is directly related to the economics of time-of-use electricity pricing and is quantified through the electricity price curve.

[0105] This invention employs a real-time dynamic adjustment mechanism. The weights are not fixed values. Within a dynamic update cycle of 5 minutes, the system adjusts according to the real-time status: during peak transportation periods (such as shift handover times), the α weight increases; when the battery is at high temperature or low SOC, the β weight increases to protect the equipment; and when the peak-valley difference in electricity prices widens, the γ weight increases.

[0106] For example, in the following typical scenario 1:

[0107] When transportation urgency is high (e.g., emergency vehicles) + high SOC decay rate (battery power is about to be depleted) + during a period of low electricity prices (P is negative or small), the F value increases significantly, and the vehicle is charged immediately.

[0108] In the following typical scenario II:

[0109] If the transportation task is not urgent (such as daily material transportation), the SOC decay rate is low (battery power is sufficient), and it is during the peak electricity price period (P is positive and relatively large), the F value is relatively small. The vehicle is postponed to the off-peak period for charging, and vehicles with larger F values ​​are prioritized for dispatch.

[0110] The setting of weight values ​​(α, β, γ) needs to be combined with the actual needs of the mining area, and multi-objective balance is achieved through "experience judgment + data optimization + dynamic adjustment"; the F value serves as a comprehensive priority indicator, which guides the rational allocation of charging resources through dynamic calculation, and ultimately improves the operating efficiency, economy and reliability of the photovoltaic-storage-charging system in the mining area.

[0111] like Figure 3 As shown, the SOC sensor and vehicle GPS are installed on each electric mining truck. The intelligent management subsystem has an input layer, a decision layer and an output layer. The input layer takes in the detected SOC attenuation rate and vehicle GPS positioning data, and then calculates the charging sequence and power allocation matrix through the improved genetic algorithm and multi-objective optimization function in the decision layer, and then outputs the data from the output layer.

[0112] The intelligent management subsystem enables intelligent sequencing and optimized management of vehicle charging, improving the utilization efficiency of charging resources, reducing vehicle waiting time, and further enhancing the overall transportation efficiency of the mining area. Intelligent temperature control and safety management provide comprehensive safety assurance for the charging process, effectively reducing safety risks and ensuring the safety of personnel and equipment. Intelligent management achieves synergistic optimization of energy and transportation.

[0113] In terms of economics: For investors, the levelized cost of electricity (LCOE) over the entire life cycle is reduced to ¥0.35, with an investment payback period of less than 5 years; For operating units, although the initial purchase cost of electric mining trucks is higher, in the long run, their energy consumption costs are lower. The energy consumption of electric mining trucks is 12 kWh / km, while that of traditional diesel mining trucks is 3.4 L / km. In addition, electric trucks have fewer parts and are simpler to maintain, which can save a lot of maintenance costs. Under the same operating conditions, the economic benefits are obvious.

[0114] In terms of safety: the electric mining truck operates with low noise, which can reduce driver fatigue; at the same time, there is no diesel exhaust emission, which improves the air quality in the confined space of the mining area, reduces the risk of drivers being exposed to harmful gases and particulate matter, and enhances the safety of the working environment.

[0115] In terms of environmental protection: exhaust emissions from traditional diesel mining trucks are one of the main sources of pollution in mining areas, while electric mining trucks produce zero exhaust emissions, which can significantly reduce greenhouse gas emissions, reduce air pollution, and help mining areas achieve green and low-carbon development, meeting environmental policy requirements. For example, after using electric mining trucks, the Benxi open-pit iron mine effectively reduced carbon emissions, becoming a model for green mine transformation.

[0116] In terms of intelligence: electric mining trucks are easier to integrate with intelligent technologies, enabling functions such as remote monitoring and autonomous driving, improving the automation and intelligence level of mining area transportation, enhancing transportation efficiency and management accuracy, and reducing labor costs and human error.

[0117] Any aspects not described in this invention are applicable to existing technologies.

[0118] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A dynamic scheduling method applied to a mine area light storage and charging intelligent energy system, characterized in that, The intelligent management subsystem collects position information, residual power and transportation tasks of each electric mine truck, and real-time power information of the charging device; calculates the dynamic preset charging time of each electric mine truck according to the residual power, transportation task and charging cost, and generates a charging sequence; sends the charging sequence of each electric mine truck to the charging device for sorting charging; the electric mine truck reaches the charging device according to the preset charging time, and the charging device charges according to the preset charging parameters of the electric mine truck; The intelligent management subsystem automatically generates a charging sequence according to the emergency degree of the transportation task and the residual power of the electric mine truck, and sends it to the charging device, and the specific method is: the intelligent management subsystem arranges the charging time of the electric mine truck with urgent transportation task and low residual power first; the intelligent management subsystem arranges the charging time of the electric mine truck with relatively loose transportation task according to the peak-valley electricity price difference and the power distribution of the charging device; The intelligent management subsystem uses an improved genetic algorithm to generate a charging sequence, and the dynamic update period of the charging sequence is 5 minutes, and the dynamic scheduling algorithm used is: F = α·E + β·S + γ·P Wherein: F value is a comprehensive index for measuring the priority of vehicle charging; α, β and γ are weight values, and α + β + γ is 1; E is the transportation emergency degree, usually in the range of 0-1, and the larger the value, the more urgent the task; S is the SOC decay rate, which represents the speed of battery residual power decline, and the larger the value, the more the battery needs to be charged; P is the peak-valley electricity price difference, which is the difference between the current period price and the low valley price, and the larger the value, the higher the current period charging cost; The dynamic update period of the charging sequence is 5 minutes, which adjusts the weight values of α, β and γ in combination with the real-time state of the transportation peak period, battery temperature or SOC decay rate, and the peak-valley difference of electricity price, the α weight increases in the transportation peak period, the β weight increases when the battery temperature is high or the SOC decay rate is low to protect the equipment, and the γ weight increases when the peak-valley difference of electricity price expands; The specific method of dynamically determining the weight values of α, β and γ is: establishing a target function based on minimizing the transportation delay cost, battery damage cost and electricity cost; based on the vehicle scheduling record, battery SOC decay curve and electricity price fluctuation mine historical operation data, the influence weight of each data on the total cost is fitted through linear regression or machine learning model; combined with the target function, the genetic algorithm or particle swarm algorithm is used for iterative solution to obtain the optimal weight combination.

2. The dynamic scheduling method applied to the mine area light storage and charging smart energy system according to claim 1, characterized in that, The intelligent management subsystem controls the charging current distribution of the charging device according to the transportation emergency degree instruction, and allocates a larger charging current to the electric mine truck with a larger transportation emergency degree. 3.The dynamic scheduling method applied to the mine area light storage and charging smart energy system according to claim 1, characterized in that, The intelligent temperature control subsystem in the safety guarantee subsystem collects the temperature at the charging interface of the charging device, energy storage device and electric mine truck, and makes an evaluation on the collected temperature data through the evaluation module I, and when the collected temperature data is greater than the set threshold, the cooling device in the corresponding device is started or the charging strategy is adjusted. 4.The dynamic scheduling method applied to the mine area light storage and charging smart energy system according to claim 1, characterized in that, The safety management subsystem in the security guarantee subsystem collects the voltage and current parameters of the charging device, the energy storage device and the electric mine truck in real time, evaluates the collected voltage and current data through the evaluation module II, and automatically cuts off the power switch of the corresponding device and sends an early warning to the outside when overvoltage, overcurrent and electric leakage exist.

5. A mine area light storage charging intelligent energy system, characterized in that, The application discloses a dynamic scheduling method applied to a mine area light storage and charging intelligent energy system.

6. The mine area light storage charging intelligent energy system according to claim 5, characterized in that, The direct current generated by the photovoltaic subsystem is converted into alternating current through an energy storage inverter and is connected to the charging device, or the direct current is converted into direct current with a proper voltage level and is input into the energy storage device.

7. The mine area light storage charging wisdom energy system according to claim 6, characterized in that, The energy storage device is a lithium battery pack which is provided with a battery management module for collecting the power consumption data of the charging device and the power generation data of the photovoltaic subsystem.

8. The mine area light storage charging intelligent energy system according to claim 5, characterized in that, The system is also provided with a security guarantee subsystem which comprises an intelligent temperature control subsystem and a safety management subsystem, the intelligent temperature control subsystem collects the temperature of the charging device, the energy storage device and the charging interface of the electric mine truck in real time through a temperature sensor and evaluates the collected temperature parameters in real time through an evaluation module I, the intelligent temperature control subsystem is connected with the heat dissipation devices in the charging device and the energy storage device, the safety management subsystem is connected with the power switches of the charging device and the energy storage device, the safety management subsystem collects the voltage and current parameters of the charging device, the energy storage device and the electric mine truck in real time, and evaluates the collected voltage and current parameters and the battery health state of the energy storage device in real time through an evaluation module II.

Images