Mine load multistage flexible load shedding collaborative optimization method and device

Abstract

The invention provides a mine load multistage flexible load shedding collaborative optimization method and device, and the method comprises the steps: extracting mining and preparation equipment load characteristics, productivity influence and load shedding cost parameters through a data processing module, and laying a data foundation for a strategy generation module; the strategy generation module divides multi-gear priority load shedding strategies from low influence and low cost to high influence and high cost according to the multi-gear priority load shedding strategies, and differentiated scheduling is achieved; the model building module builds an optimization model for minimizing the total operation cost based on the strategy, and outputs an optimal scheduling scheme through an optimization solver in combination with real-time operation data of the mine; and subsequently, through a capacity supplement and dynamic scheduling module, inventory early warning and new energy fluctuation are respectively responded, and a scheduling scheme is accurately adjusted. The whole process is progressive layer by layer, productivity waste caused by disordered load shedding is avoided through differential load shedding, the optimal cost is guaranteed through an optimization model, meanwhile, the anti-interference capacity of the system is improved through dynamic adjustment, and finally the effects of mine energy supply and demand balance, production stability and the lowest operation cost are achieved.

Description

Technical Field

[0001] This application relates to the field of mine energy system optimization and scheduling technology, and more specifically, to a method and apparatus for multi-level flexible load reduction and collaborative optimization of mine load. Background Technology

[0002] In existing technologies, two main solutions are used when mines face power shortages. The first is an indiscriminate forced load reduction scheme: This involves randomly shutting down some production equipment without considering the impact of different equipment in mining and mineral processing on production capacity, thereby reducing the power load and achieving a balance between power supply and demand. The second is an over-configuration of backup power: This involves increasing the capacity of backup power sources such as gas turbines, activating backup power to supplement power supply when renewable energy output is insufficient, ensuring the continuous operation of all production equipment and avoiding the impact of load reduction operations on production capacity.

[0003] In addition, existing general energy system optimization methods mostly adopt a two-layer architecture of "upper-level equipment configuration lumped planning and lower-level equipment operation optimization". With the help of mature optimization algorithms such as mathematical optimization and intelligent heuristic algorithms, a general optimization framework for source-load energy balance is constructed, with the core focus on the macro-balance of "equipment-cost".

[0004] The aforementioned existing technologies have significant drawbacks: the load shedding strategy lacks hierarchical prioritization, which can easily lead to capacity loss or excessive costs; it fails to achieve effective synergy between new energy sources, energy storage, backup power sources and load shedding strategies, resulting in low new energy absorption rates; it over-reliance on backup equipment or disorderly load shedding leads to persistently high costs; and the lack of a capacity replenishment mechanism makes it difficult to ensure production continuity, and it cannot balance power balance, capacity guarantee and cost optimization. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for multi-level flexible load reduction and coordinated optimization in mines, so as to achieve the effects of energy supply and demand balance, stable production and lowest operating cost in mines.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a multi-level flexible load reduction and collaborative optimization method for mine loads, including: The data processing module inputs mining and mineral processing equipment parameters to obtain equipment load characteristic information, parameters on the impact of load reduction on production capacity of each piece of equipment, and load reduction cost parameters; the equipment load characteristic information covers the load reduction range and interruptibility characteristics of mining equipment, and the non-interruption characteristics of core mineral processing equipment. Based on the equipment load characteristic information, the impact parameters, and the load reduction cost parameters, the strategy generation module divides the load reduction strategies into multiple levels according to the degree of impact on production capacity and the load reduction cost; the multiple load reduction strategies are sorted in order of priority from high to low. The model building module is based on a multi-level load reduction strategy to build an optimization model with the goal of minimizing the total operating cost; The optimization solver takes the optimization model and real-time mine operation data as input and obtains the optimal load reduction strategy, energy storage charging and discharging plan and backup power operation scheme. The capacity replenishment module performs capacity replenishment operations based on real-time inventory data from the raw material warehouse. The dynamic scheduling module adjusts the optimal load reduction strategy, energy storage charging and discharging plan, and backup power operation scheme based on real-time power output data of new energy sources.

[0007] Secondly, embodiments of this application provide a multi-level flexible load reduction and collaborative optimization device for mine loads, including: a data processing module, a strategy generation module, a model building module, an optimization solver, a capacity replenishment module, and a dynamic scheduling module; The data processing module is used to input mining and mineral processing equipment parameters to obtain equipment load characteristic information, parameters on the impact of load reduction on production capacity of each piece of equipment, and load reduction cost parameters; the equipment load characteristic information covers the load reduction range and interruptibility characteristics of mining equipment and the non-interruption characteristics of core mineral processing equipment. The strategy generation module is used to obtain multiple load reduction strategies based on the equipment load characteristic information, the impact parameters, and the load reduction cost parameters, according to the degree of impact on production capacity and the load reduction cost; the multiple load reduction strategies are sorted in order of priority from high to low. The model building module is used to build an optimization model based on multi-level load reduction strategies, with the goal of minimizing the total operating cost. The optimization solver is used to input the optimization model and real-time mine operation data to obtain the optimal load reduction strategy, energy storage charging and discharging plan and backup power operation scheme. The capacity replenishment module is used to perform capacity replenishment operations based on real-time inventory data of the raw material warehouse; The dynamic scheduling module is used to adjust the optimal load reduction strategy, energy storage charging and discharging plan, and backup power operation scheme based on real-time power output data of new energy sources.

[0008] Compared to existing technologies, the multi-level flexible load reduction collaborative optimization method and apparatus for mines provided in this application extracts load characteristics, capacity impact, and load reduction cost parameters of mining and ore dressing equipment through a data processing module, laying the data foundation for the strategy generation module. Based on this, the strategy generation module divides load reduction strategies into multiple priority levels, from low-impact and low-cost to high-impact and high-cost, to achieve differentiated scheduling. The model building module builds an optimization model that minimizes the total operating cost based on this strategy, and, combined with real-time mine operating data, outputs the optimal scheduling scheme through an optimization solver. Subsequently, the capacity replenishment and dynamic scheduling modules respond to inventory warnings and new energy fluctuations, respectively, and precisely adjust the scheduling scheme. The entire process is progressive, avoiding the waste of capacity from disorderly load reduction through differentiated load reduction, ensuring optimal cost through an optimized model, and improving the system's anti-interference capability through dynamic adjustment, ultimately achieving the effect of mine energy supply and demand balance, stable production, and minimum operating costs.

[0009] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the architecture of a mining energy system; Figure 2 A flowchart illustrating a multi-level flexible load reduction and collaborative optimization method for mine loads provided in this application embodiment; Figure 3 A flowchart illustrating another method for collaborative optimization of multi-level flexible load reduction in mines, provided in an embodiment of this application; Figure 4 A flowchart illustrating another method for collaborative optimization of multi-level flexible load reduction in mines, provided in an embodiment of this application; Figure 5 A flowchart illustrating another method for collaborative optimization of multi-level flexible load reduction in mines, provided in an embodiment of this application; Figure 6 A flowchart illustrating another method for collaborative optimization of multi-level flexible load reduction in mines, provided in an embodiment of this application; Figure 7 A flowchart illustrating another method for collaborative optimization of multi-level flexible load reduction in mines, provided in an embodiment of this application; Figure 8 This is a schematic diagram of the architecture of a multi-level flexible load reduction and collaborative optimization device for mines, provided in an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0013] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0014] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0015] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0016] In the description of this application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set" and "connection" 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 connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0017] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0018] In the operation of mining energy systems, when faced with power shortages due to fluctuations in the output of renewable energy sources such as photovoltaics and wind power, existing technologies mainly employ two types of solutions. The first is the indiscriminate forced load shedding scheme: without distinguishing the degree of impact of different equipment in mining and mineral processing on production capacity, some production equipment is randomly and directly shut down to reduce the power load, achieving a balance between power supply and demand by reducing the total load. The second is the over-configuration of backup power sources: by increasing the configuration capacity of backup power sources such as gas turbines, backup power sources are activated to supplement power supply when renewable energy output is insufficient, ensuring the continuous operation of all production equipment and avoiding the impact of load shedding operations on production capacity.

[0019] In addition, existing general energy system optimization methods mostly adopt a two-layer architecture of "upper-level equipment configuration lumped planning + lower-level equipment operation optimization". With the help of mature optimization algorithms such as mathematical optimization and intelligent heuristic algorithms, a general optimization framework for source-load energy balance is constructed. Under the premise of meeting the load energy demand, the operation optimization without affecting the production capacity is achieved through the coordinated cooperation of various power sources and energy storage devices. The core focus is on the macro-balance of "equipment-cost".

[0020] Existing technologies suffer from significant technical deficiencies, making it difficult to adapt to the differentiated process characteristics of mining and beneficiation processes in mines and the actual needs of fluctuating renewable energy output: The load shedding strategy lacks scientific rigor: There is no hierarchical priority design, and the core and auxiliary equipment affecting production capacity are not differentiated. Forced load shedding can easily lead to core capacity loss, or increase shedding costs by blindly retaining non-core equipment; Insufficient multi-equipment coordination: An effective coordination mechanism between renewable energy, energy storage, backup power, and load shedding strategies has not been established. During peak renewable energy output, excess energy cannot be fully stored, leading to severe wind and solar curtailment. During off-peak output, there is over-reliance on backup power or disorderly load shedding, resulting in low backup power operating efficiency; Weak cost control: Over-configuration of backup power leads to redundant equipment investment, and operating costs remain high when relying solely on backup power for power supply. Disorderly load shedding leads to capacity loss, indirectly increasing economic costs, and neither can achieve optimal life-cycle cost; Insufficient production continuity assurance: There is a lack of capacity recovery mechanisms. After load shedding, lost capacity cannot be effectively restored, or production stability is affected by sudden changes in equipment operating status during the recovery process, making it difficult to simultaneously address the three core needs of power supply and demand balance, capacity assurance, and cost optimization.

[0021] For example, Figure 1 This is a schematic diagram of the architecture of a mining energy system. (See attached diagram) Figure 1The system includes: wind turbine, fuel generator set, photovoltaic equipment, storage battery, electrical load, and load reduction mechanism.

[0022] Among them, wind turbines have the following core characteristics: they are new energy power generation equipment that uses wind energy as energy and converts wind energy into electrical energy; their output is intermittent and fluctuates (affected by wind speed), and the output range is usually 0-300MW (suitable for the large-scale electricity demand of mines); they have low operating costs, are clean and pollution-free, consume no fuel, and only require daily operation and maintenance.

[0023] Corresponding function: to provide basic power supply for mining energy systems, and to form a new energy power supply core in conjunction with photovoltaic equipment, thereby reducing dependence on traditional fossil energy.

[0024] Fuel generator sets are characterized by the following: traditional backup power equipment that uses coal, natural gas, etc. as fuel to generate electricity through combustion; stable output and strong controllability, and can be quickly started, stopped and adjusted according to demand (output range 0-70MW); higher operating costs (including fuel and maintenance costs), but strong anti-interference ability and unaffected by the natural environment.

[0025] Corresponding function: As an emergency power supply device when the output of new energy sources is insufficient or fluctuates, it ensures continuous power supply to critical loads in the mine and avoids production interruptions due to a shortage of new energy sources.

[0026] Photovoltaic equipment is characterized by the following: it is a new energy power generation equipment that converts solar energy into electrical energy; its output is significantly affected by the intensity of sunlight and the time of day (such as peak output in the afternoon and no output at night), with an output range of 0-400MW; similar to wind turbines, it is clean, low-cost, and has fluctuating output, and the two can complement each other (wind power may be stronger at night, while photovoltaic output is dominant during the day).

[0027] Corresponding function: To form a new energy power supply matrix in conjunction with wind turbines, increase the proportion of clean energy in the mine energy system, and provide a low-cost power source for the optimization model's goal of "minimizing total operating costs".

[0028] Batteries, with their core characteristics, are: energy storage devices used to store excess electrical energy and release it when needed; they have bidirectional charging and discharging regulation capabilities, with a charging and discharging power range of 0-150MW and a safe remaining charge (SOC) range of 20%-80%; they have fast response speeds and can quickly smooth out fluctuations in new energy output, but they also suffer from charging and discharging losses.

[0029] Corresponding functions: As an energy regulation hub, it absorbs redundant new energy (peak shaving), fills the gap in new energy shortage (valley filling), provides a buffer for the implementation of load reduction strategies, and reduces the load reduction magnitude and cost.

[0030] Electrical load, its core characteristics are: the carrier of electricity demand in mines, including the total load of various electrical equipment such as mining, mineral processing, and auxiliary facilities; the value range is 300-600MW, which changes dynamically with the production process, but has a certain rigidity (such as some mineral processing equipment cannot be interrupted); it is the core service object of the energy system's power supply, and its stable supply directly affects the continuity of production.

[0031] Corresponding function: As the core constraint object of scheduling optimization, all power supply, load reduction, and energy storage scheduling strategies revolve around "meeting the electricity load demand" to ensure the normal progress of production.

[0032] It can reduce electrical load, and its core characteristics are: the part of the electrical load that can temporarily reduce power or interrupt operation, corresponding to electric wheel mining trucks and excavators in mining processes, and flotation cells in mineral processing processes; it has multiple load reduction capabilities (such as low / medium / high impact load reduction), and the impact of load reduction on production capacity and cost varies for different equipment (unit load reduction cost 0.5-5.0 yuan / kWh).

[0033] Corresponding function: It is the core execution carrier of the multi-level flexible load reduction strategy. By precisely reducing the load on it, the output of new energy and the load demand are balanced, so as to achieve the dual goals of "supply and demand balance and optimal cost".

[0034] In the embodiments of this application, the Figure 1 The components shown are organically integrated around the core design concept of "multi-level flexible load reduction and coordinated optimization in mines" (dynamically balancing supply and demand, minimizing total operating costs, and ensuring stable production). The supporting roles of each component are as follows: 1) New energy components (wind turbines and photovoltaic equipment): laying the foundation for low-cost optimization.

[0035] As core power sources, both provide low-cost, clean electricity, directly supporting the optimization model's goal of "minimizing total operating costs." Meanwhile, their output volatility (such as afternoon degradation of photovoltaic power and sudden changes in wind turbine speed) is a key trigger for dynamic scheduling and load shedding strategies. It is precisely because of the instability of renewable energy output that subsequent energy storage regulation, backup power replenishment, and load shedding are necessary to form an optimization logic of "fluctuation → regulation → balance."

[0036] 2) Regulation components (batteries and fuel generator sets): Construct an emergency and buffer system.

[0037] Both are core regulatory tools for addressing fluctuations in new energy sources and supply-demand imbalances, complementing the load reduction strategy: Batteries: They have a fast response speed and can quickly smooth out small fluctuations (such as a slight decrease in new energy output within 15 minutes), reduce the frequency of load reduction operations, and reduce capacity loss; their charging and discharging plans are a core component of the optimal scheduling scheme, working in conjunction with load reduction strategies to achieve supply and demand balance.

[0038] Fuel cell generator sets: As the ultimate emergency power replenishment equipment, they can be activated to supplement energy when renewable energy fluctuates significantly (exceeding the 20% threshold) and energy storage discharge is insufficient to fill the gap. This reduces the probability of activation during high-impact load shedding, ensuring stable production. Figure 7 Emergency logic of "dynamically adjusting the optimal scheduling scheme".

[0039] 3) Load components (electrical load and reducible electrical load): Clearly define the core optimization and execution carrier.

[0040] Electrical load is the core constraint for optimization (its stable supply must be prioritized), while reducible electrical load is the core of optimization execution. The multi-level flexible load shedding strategy is essentially the precise scheduling of reducible electrical load. Based on the characteristics of equipment that can reduce electrical load (such as the range of load reduction power, unit load reduction cost, and uninterrupted time), the load reduction levels are divided into low / medium / high impact levels to provide decision data support for the optimization model. By implementing tiered load reduction for reduceable electrical loads, the goal of balancing the output gap of new energy sources while meeting basic electrical load requirements, and achieving the objective of "optimal cost and minimal capacity loss," is the core execution target of the example below.

[0041] Optionally, Figure 1 The mining energy system shown constructs a hardware foundation adapted to multi-level flexible load shedding optimization through a collaborative model of "new energy power supply, energy storage regulation, backup power emergency response, and flexible load shedding." Each component has a clear division of labor: new energy provides low-cost electricity, energy storage and backup power cope with fluctuations, and load shedding can be implemented by reducing electrical load. Ultimately, based on the core design concept, it achieves the optimization goals of "supply and demand balance, optimal cost, and stable production," providing solid hardware support and a closed-loop operational logic for the optimization process illustrated below.

[0042] Based on the aforementioned problems with existing technologies, the core design concept of this application is to deeply explore the flexible characteristics of mining processes—which can be reduced or interrupted, while mineral processing processes cannot be interrupted—and construct a complete technical system encompassing "tiered load reduction strategies, optimization models, multi-equipment collaboration, and closed-loop capacity replenishment." This system quantifies the impact and cost of load reduction on capacity, clarifies load reduction priorities, and integrates the synergistic effects of new energy sources, energy storage, backup power supplies, and load reduction strategies to achieve a dynamic balance between power supply and demand, capacity assurance, and cost optimization. Through a complete process of "characteristic quantification - strategy formulation - model building - solution output - replenishment adjustment," flexible load reduction and multi-equipment collaborative optimization in mines are achieved, balancing power supply and demand, capacity assurance, and cost control, and adapting to mine energy systems with new energy power supply.

[0043] Optionally, Figure 2A flowchart illustrating a multi-level flexible load reduction and collaborative optimization method for mine loads provided in this application is shown below. Figure 2 The method includes: Step 100: The data processing module inputs the mining and beneficiation process equipment parameters to obtain equipment load characteristic information, the impact parameters of load reduction on production capacity of each piece of equipment, and load reduction cost parameters.

[0044] The equipment load characteristic information includes the range of load reduction for mining equipment, its interruptibility characteristics, and the non-interruption characteristics of core mineral processing equipment.

[0045] Step 101: The strategy generation module generates multiple load reduction strategies based on equipment load characteristic information, influencing parameters, and load reduction cost parameters, according to the degree of impact on production capacity and the cost of load reduction.

[0046] The multiple load reduction strategies are prioritized from highest to lowest, corresponding to low impact and low cost, no production capacity impact with minor losses, and high impact and high cost, thus achieving differentiated load reduction scheduling. The equipment load characteristic information refers to the inherent attributes of each piece of equipment in the mining and mineral processing processes, such as operating power requirements, operational continuity requirements, and load reduction range. Mining equipment has the characteristics of being able to reduce load and be interrupted, while core mineral processing equipment has the characteristic of being uninterruptible. The impact parameter refers to the quantified value of the degree of impact of load reduction on mine production capacity indicators such as ore output and concentrate output per unit time for a single piece or type of equipment. The load reduction cost parameter refers to the quantified value of the sum of direct and indirect economic losses generated by load reduction per unit time for a single piece or type of equipment. Step 102: The model building module builds an optimization model based on a multi-level load reduction strategy, with the goal of minimizing the total operating cost.

[0047] The optimization model is a mathematical programming model with the objective function of minimizing total operating cost and including constraints such as power balance and equipment capacity.

[0048] Step 103: Optimize the solver by inputting the optimization model and real-time mine operation data to obtain the optimal load reduction strategy, energy storage charging and discharging plan, and backup power operation scheme.

[0049] The real-time operational data of the mine may include: real-time output data of new energy sources, real-time load demand data of the mine, equipment operating status data, and real-time inventory data of raw material warehouses.

[0050] Step 104: The capacity replenishment module performs capacity replenishment operations based on real-time inventory data from the raw material warehouse.

[0051] Step 105: The dynamic scheduling module adjusts the optimal load reduction strategy, energy storage charging and discharging plan, and backup power operation scheme based on the real-time output data of new energy sources.

[0052] The multi-level flexible load reduction and collaborative optimization method for mine load provided in this invention extracts load characteristics, capacity impact, and load reduction cost parameters of mining and ore dressing equipment through a data processing module, laying the data foundation for the strategy generation module. Based on this, the strategy generation module divides load reduction strategies into multiple priority levels, from low-impact and low-cost to high-impact and high-cost, to achieve differentiated scheduling. The model building module constructs an optimization model that minimizes the total operating cost based on this strategy, and, combined with real-time mine operating data, outputs the optimal scheduling scheme through an optimization solver. Subsequently, the capacity replenishment and dynamic scheduling modules respond to inventory warnings and new energy fluctuations, respectively, and precisely adjust the scheduling scheme. The entire process is progressive, avoiding the waste of capacity from disorderly load reduction through differentiated load reduction, ensuring optimal cost through an optimized model, and improving the system's anti-interference capability through dynamic adjustment, ultimately achieving a synergistic effect of mine energy supply and demand balance, stable production, and minimum operating costs.

[0053] Optionally, taking an off-grid mine as an application example, the mine's load range is 200-500MW. The power supply system includes 700MW of photovoltaic power, 600MW of wind turbines, energy storage equipment, and a gas turbine backup power supply. The original configuration included large-capacity energy storage and a 280MW gas turbine, resulting in extremely high investment and operating costs. For the above example, the specific parameters for its low, medium, and high load reduction strategies are shown in Table 1 below: Table 1

[0054] Optionally, to accurately distinguish the load characteristics of mining and mineral processing equipment, and to provide precise and detailed data support for subsequent graded load reduction strategies, thus avoiding unreasonable load reduction strategies due to generalized data, a possible implementation method is provided below. Specifically, in Figure 2 On this basis, Figure 3 A flowchart illustrating another multi-level flexible load reduction and collaborative optimization method for mine loads provided in this application embodiment is shown below. Figure 3 Step 100 includes: Step 100-1: The data processing module analyzes the operating information of mining equipment to obtain the load reduction parameters of mining equipment.

[0055] Among them, mining process equipment represents all kinds of equipment used in the mining process, including mining auxiliary equipment; mining equipment operation information represents the inherent operation attributes of mining process equipment; mining equipment load reduction parameters represent quantitative data related to the load reduction of mining auxiliary equipment, including the range of load reduction of mining equipment, the impact parameters of mining equipment, and the cost parameters of mining equipment load reduction; mining equipment impact parameters represent the quantitative value of the impact of mining auxiliary equipment load reduction on production capacity per unit time; and mining equipment load reduction cost parameters represent the quantitative value of the economic loss generated by the load reduction of mining auxiliary equipment per unit time. Step 100-2: The data processing module analyzes the operating information of the mineral processing equipment to obtain the load reduction parameters of the mineral processing equipment.

[0056] Among them, mineral processing equipment represents all kinds of equipment used in the mineral processing stage of mining, including core mineral processing equipment; mineral processing equipment operation information represents the inherent operating attributes of mineral processing equipment; mineral processing equipment load reduction parameters represent quantitative data related to the load reduction of core mineral processing equipment, including the uninterrupted characteristics of mineral processing equipment, the impact parameters of mineral processing equipment, and the cost parameters of mineral processing equipment load reduction; mineral processing equipment impact parameters represent the quantitative value of the impact of core mineral processing equipment load reduction on production capacity per unit time; and mineral processing equipment load reduction cost parameters represent the quantitative value of the economic loss generated by the load reduction of core mineral processing equipment per unit time. Step 100-3: The data processing module combines the load reduction parameters of the mining equipment and the load reduction parameters of the mineral processing equipment to form equipment load characteristic information, influencing parameters, and load reduction cost parameters.

[0057] Optionally, taking a copper mine as an example, the data processing module executes step 100-1: Analyze auxiliary equipment in the mining process such as electric wheel mining trucks, excavators, and crushers, and quantify the mining equipment load reduction parameters of each piece of equipment, as shown in Table 2 below: Table 2

[0058] Table 2 quantifies the “load reduction capacity (power range), direct impact of load reduction on mining capacity, and basic load reduction cost” for mining equipment such as electric wheel mining trucks and excavators, addressing the questions of “how much can be reduced in the mining process, how much capacity is lost due to reduction, and how much cost is incurred”.

[0059] Similarly, proceed to step 100-2: Analyze the core equipment in the mineral processing process, such as flotation cells, mixers, and thickeners, and quantify the load reduction parameters of each piece of equipment, as shown in Table 3 below: Table 3

[0060] Table 3 quantifies “load reduction constraints (uninterrupted time), capacity loss due to forced load reduction, and high-cost load reduction” for mineral processing equipment such as flotation cells and mixers, addressing the questions of “when can load reduction be implemented in the mineral processing stage, how great are the risks of forced load reduction, and how much does load reduction cost under high constraints”.

[0061] The two types of parameters shown in Tables 2 and 3 above work together to support the hierarchical design of multi-level load reduction strategies, clarifying the load reduction priority and operational boundaries in different scenarios: Cost-related factors: Table 2 shows a unit load reduction cost of 0.5 yuan / kWh (low cost), while Table 3 shows 5.0 yuan / kWh (high cost), forming a load reduction priority gradient of "low cost → high cost". That is, low-impact load reduction prioritizes mining equipment (such as stripping equipment, which does not affect mining volume and has low cost), while medium / high-impact load reduction considers ore dressing equipment (which requires bearing high costs and capacity loss). Capacity constraints are linked: Reducing the load of mining equipment (Table 2) will reduce the supply of raw materials for mineral processing, while mineral processing equipment has an "uninterrupted time threshold" (Table 3, such as flotation cells cannot be interrupted for 2 hours). Therefore, when formulating strategies, the two must be linked. If the mineral processing equipment is in an uninterrupted period, the reduction in the load of mining equipment must be controlled to avoid insufficient raw material supply that would force the mineral processing to be interrupted. Conversely, if the reduction in the load of mining leads to a shortage of raw materials, priority should be given to ensuring the operation of core mineral processing equipment (such as thickeners, which cannot be interrupted for 3 hours) to avoid greater losses in downstream capacity.

[0062] The aforementioned loading reduction parameters for mining equipment and loading reduction parameters for mineral processing equipment together constitute the influencing parameters and loading reduction cost parameters required for step 100, providing data support for the subsequent formulation of precise multi-level loading reduction strategies.

[0063] Optionally, based on quantified loading reduction parameters for mining and mineral processing equipment, differentiated loading reduction levels are defined according to the degree of impact on production capacity and loading reduction costs. The core operating indicators for each level are clearly defined, providing clear strategic inputs for subsequent optimization model building. This achieves flexible loading reduction scheduling that prioritizes low-impact and cautiously selects high-impact options, balancing production capacity assurance and cost control, and avoiding production waste or cost surges caused by disorderly loading reduction. These multiple loading reduction strategies can include: low-impact, medium-impact, and high-impact strategies. Low-impact strategies correspond to loading reduction parameters for mining equipment, while high-impact strategies correspond to loading reduction parameters for mineral processing equipment. Figure 2 On this basis, Figure 4 A flowchart illustrating another multi-level flexible load reduction and collaborative optimization method for mine loads provided in this application embodiment is shown below. Figure 4 Step 101 includes: Step 101-1: The strategy generation module divides the mining auxiliary equipment load reduction operation to obtain the low-impact load reduction strategy and the corresponding first core parameters.

[0064] Among them, the low-impact load reduction strategy represents the load reduction scheme for mining auxiliary equipment; the first core parameter represents the key operating indicators of the low-impact load reduction strategy, including the load reduction power range, unit load reduction cost and the maximum annual operating time; the load reduction power range represents the load reduction power range that the mining auxiliary equipment cluster can achieve; the unit load reduction cost represents the economic loss per unit of energy consumption for the load reduction of mining auxiliary equipment; and the maximum annual operating time represents the annual cumulative operating time that constrains the load reduction of mining auxiliary equipment.

[0065] Step 101-2: The strategy generation module divides the load reduction operations of non-production auxiliary facilities in the mine and obtains the load reduction strategies with medium impact and the corresponding second core parameters.

[0066] Among them, the medium-influence load reduction strategy characterizes the load reduction scheme for non-production auxiliary facilities in the mine; the second core parameter characterizes the key operational indicators of the medium-influence load reduction strategy, including the load reduction power range, unit load reduction cost, and maximum annual operating time; the load reduction power range characterizes the load reduction power range that the non-production auxiliary facility cluster can achieve; the unit load reduction cost characterizes the economic loss per unit of energy consumption for load reduction of non-production auxiliary facilities; and the maximum annual operating time characterizes the annual cumulative operating time that constrains the load reduction of non-production auxiliary facilities. Step 101-3: The strategy generation module divides the load reduction operation of the core equipment in mineral processing and obtains the high-impact load reduction strategy and the corresponding third core parameters.

[0067] Among them, the high-impact load reduction strategy represents the emergency load reduction plan for the core equipment of mineral processing; the third core parameter represents the key operating indicators of the high-impact load reduction strategy, including the load reduction power range, unit load reduction cost and emergency annual operating limit; the load reduction power range represents the load reduction power range that the core equipment cluster of mineral processing can achieve; the unit load reduction cost represents the economic loss per unit of energy consumption for the load reduction of the core equipment of mineral processing; the emergency annual operating limit represents the annual cumulative operating time that constrains the load reduction of the core equipment of mineral processing, and can only be activated in emergency scenarios.

[0068] Optionally, taking a copper mine off-grid (load range 200-500MW, including 700MW photovoltaic and 600MW wind turbines) as an example, the strategy generation module executes the mining equipment load reduction parameters (see Table 1) and ore dressing equipment load reduction parameters (see Table 2) quantified in step 100. Figure 4 The process generates three load reduction strategies, and the core parameter values ​​and data definitions for each strategy are as follows: 1) Data definition description: The power reduction range is the power range that the corresponding equipment cluster can stably reduce the load under a single gear. The value is calculated based on the power reduction range of a single device (see Table 1 and Table 2) and the number of devices in the cluster.

[0069] The unit load reduction cost is the economic loss incurred by reducing 1 kWh of electricity in a single load, including equipment downtime losses, indirect losses of production capacity, etc. The value is based on the weighted average of the unit load reduction cost of a single piece of equipment.

[0070] The maximum annual operating time is the cumulative annual operating time allowed for low / medium impact load reduction strategies, based on equipment fatigue life and capacity planning.

[0071] The annual operating time limit for this emergency measure is the cumulative annual operating time allowed for the high-impact load reduction strategy. It is only activated for sudden power shortages (such as a sudden drop of more than 50% in renewable energy output or backup power failure), and the operating time is strictly controlled to avoid loss of core production capacity.

[0072] Therefore, the strategies and core parameter values ​​for each gear are shown in Table 4 below: Table 4

[0073] When the real-time output of new energy in the mine drops to 300MW (180MW of photovoltaic power + 120MW of wind power) and the real-time load is 400MW, resulting in a 100MW power supply gap, the strategy generation module prioritizes matching the low-impact load reduction strategy, enabling the mining auxiliary equipment cluster to reduce the load by 100MW (within the range of 38,000-86,000kW), with a unit load reduction cost of 0.5 yuan / kWh. The annual cumulative running time is included in the maximum annual running time of the low-impact strategy, which not only fills the power supply gap but also avoids production capacity loss and high cost expenditures.

[0074] Through the mechanism provided above, three load reduction strategies are defined and core parameters are clarified to achieve precise and differentiated load reduction scheduling: low-impact strategies are prioritized to control capacity loss, medium-impact strategies serve as transitional support, and high-impact strategies are activated in emergencies to address sudden shortages; the parameter values ​​for each level are matched with equipment characteristics and capacity plans, providing clear and quantifiable strategy inputs for subsequent optimization models, ensuring that the optimized scheduling scheme is both feasible and economical, while reducing the negative impact of load reduction operations on mine production.

[0075] By refining the quantitative process of mining and mineral processing equipment characteristics, the obtained data becomes more targeted and accurate, ensuring that parameters such as load reduction power, cost, and duration at each stage match the actual characteristics of the equipment, and providing a reliable basis for load reduction priority allocation. For example, this is based on the fact that stripping equipment in mining equipment does not suffer direct production capacity loss.

[0076] Optionally, based on the multi-level load reduction strategies generated in the above examples, an optimization model is constructed with the minimization of total operating cost as its core. By clearly defining the objective function and multi-dimensional constraints, the coordinated scheduling optimization of load reduction strategies, energy storage, and backup power is achieved. The constraints consider power balance, equipment safety, production continuity, and strategy rationality, ensuring that the scheduling scheme solved by the model not only meets the economic optimization objective but also adapts to the actual operating needs of the mine, avoiding safety risks or production disruptions caused by a single cost-oriented approach. Optionally, this optimization model may include: an objective function and multi-dimensional constraints. The multi-dimensional constraints include: power supply and demand balance constraints, equipment rated capacity operation constraints, usage time constraints for each load reduction strategy level, raw material warehouse inventory threshold constraints, and the uniqueness constraint of a single-period load reduction strategy; Figure 2On this basis, Figure 5 A flowchart illustrating another multi-level flexible load reduction and collaborative optimization method for mine loads provided in this application embodiment is shown below. Figure 5 Step 102 includes: Step 102-1: Set the objective function of the optimization model in the model building module.

[0077] Optionally, the objective function is to minimize the total operating cost.

[0078] Among them, total operating cost represents the sum of all kinds of economic costs generated during the operation of the mine energy system, including load reduction operation cost, load increase and replenishment cost, backup power operation cost, and energy storage charging and discharging cost; load reduction operation cost represents the economic loss caused by activating load reduction strategy, corresponding to the unit load reduction cost of multiple load reduction strategies; load increase and replenishment cost represents the additional energy consumption cost of equipment when capacity is replenished; backup power operation cost represents the various consumption costs generated by backup power operation; and energy storage charging and discharging cost represents the cost of energy storage equipment wear and tear and charging and discharging energy consumption loss.

[0079] Step 102-2: Set multi-dimensional constraints for the optimization model in the model building module.

[0080] The multi-dimensional constraints include any or a combination of the following: power supply and demand balance constraints, which represent the restrictions that ensure the balance between power supply and demand at any given time; equipment rated capacity operation constraints, which represent the restrictions that require the operating power of various equipment to be within the safe rated range; usage time constraints for each load reduction strategy, which represent the restrictions that constrain the cumulative running time of each load reduction strategy within a single year, corresponding to the longest running time limit of each load reduction strategy; raw material inventory threshold constraints, which represent the restrictions that limit the raw material inventory to a reasonable range; raw material inventory threshold constraints include a lower inventory threshold and an upper inventory threshold; the lower inventory threshold is used to trigger capacity replenishment operations, and the upper inventory threshold is used to constrain mining load to avoid inventory backlog; and the uniqueness constraint of a single-time load reduction strategy, which represents the restriction that only one load reduction strategy is allowed to be used at the same time.

[0081] Alternatively, the expression for minimizing the total operating cost "Ctotal" is as follows: Total cost (Ctotal) = C1 (load reduction operation cost) + C2 (load expansion and replenishment cost) + C3 (standby power supply operation cost) + C4 (energy storage charging and discharging cost) Among them, C1: the economic loss caused by the activation of the load reduction strategy, which is directly linked to the unit load reduction cost of the corresponding level; C2: the additional energy consumption cost of the equipment during the capacity replenishment process; C3: various consumption costs (including energy consumption, maintenance, etc.) generated during the operation of the backup power supply; C4: the cost of energy storage equipment's own losses and energy consumption loss during the charging and discharging process.

[0082] Optionally, the above multi-dimensional constraints can be activated individually or in combination, as follows: Power supply and demand balance constraint: The total power supply and total demand are kept in real time balance during any period; Equipment rated capacity operation constraint: The operating power of various equipment must be controlled within the safe rated range; Time constraint for use of each load reduction strategy: The cumulative running time of each load reduction strategy within a single year shall not exceed the set upper limit; Raw material inventory threshold constraint: Raw material inventory must be maintained within a reasonable range, including the lower limit of inventory (to trigger capacity replenishment operation) and the upper limit of inventory (to constrain mining load and avoid inventory backlog); Uniqueness constraint of load reduction strategy in a single period: Only one load reduction strategy is allowed to be activated within the same scheduling period.

[0083] The following example illustrates the minimization of total operating costs and multi-dimensional constraints. First, assume the following: Scheduling period: 1 hour / segment, 8760 scheduling periods annually; Power supply: Total output of new energy sources (photovoltaics + wind power), energy storage discharge, and backup power, in MW; Power demand: Total power load of mine production (mining + ore dressing) and auxiliary facilities, in MW; Inventory: Ore storage in the raw material warehouse, in 10,000 tons; Activation flag: Binary activation flags corresponding to the three load reduction strategies (low / medium / high impact), designated as X1 (low impact), X2 (medium impact), and X3 (high impact), with values ​​of only 0 or 1 (0 = not activated, 1 = activated); The core function is to constrain the uniqueness of the load reduction strategy within a single time period, meaning that within the same time period, only one of X1, X2, and X3 can be 1, and the rest 0, avoiding excessive capacity loss and cost waste caused by multiple overlapping strategies.

[0084] The corresponding values ​​of the objective function are shown in Table 5 below: Table 5

[0085] Here is an example of enabling the identification application: 1) Low-impact load reduction activation (accounting for 70% of the annual load reduction time, approximately 420 hours): At this time, X1=1, X2=0, X3=0. The C1 cost for this period is calculated at 0.4 yuan / kWh. The cost per period = load reduction power × 0.4 yuan / kWh × 1h.

[0086] 2) The load reduction is activated during the period (accounting for 25% of the annual load reduction time, approximately 150 hours): At this time, X2=1, X1=0, X3=0. The C1 cost for this period is calculated at 1.2 yuan / kWh. The cost per period = load reduction power × 1.2 yuan / kWh × 1h.

[0087] 3) High-impact load reduction activation (accounting for 5% of the annual load reduction time, approximately 30 hours, emergency scenario): At this time, X3=1, X1=0, X2=0. The C1 cost for this period is calculated at 4.8 yuan / kWh. The cost per period = load reduction power × 4.8 yuan / kWh × 1h.

[0088] The annual total cost of C1 is calculated as follows: (420h × corresponding period load reduction power × 0.4 yuan / kWh) + (150h × corresponding period load reduction power × 1.2 yuan / kWh) + (30h × corresponding period load reduction power × 4.8 yuan / kWh). The final estimate is 1 million yuan, which is consistent with the example cost.

[0089] Correspondingly, examples of the values ​​for the above multi-dimensional constraints are shown in Table 6 below. Table 6

[0090] Optionally, this application also provides a collaborative control mechanism, which is implemented as follows; The collaborative control module establishes a collaborative mechanism between new energy sources, energy storage, backup power supplies, and multi-level load reduction strategies.

[0091] Among them, the coordination mechanism represents the linkage scheduling rules established based on the operating characteristics of each device and the power output conditions of new energy sources. The core includes: the coordination rules for peak power output conditions of new energy sources, off-peak power output conditions of new energy sources, linkage of energy storage power thresholds, and emergency scenarios.

[0092] 1) Peak output of new energy indicates that the total output of new energy exceeds the load demand. At this time, the energy storage is controlled to charge first, and the surplus output is absorbed through a low-impact load reduction strategy.

[0093] 2) The low output of new energy sources indicates that the total output of new energy sources cannot meet the load demand. At this time, the energy storage is controlled to discharge first to replenish energy, and the load reduction strategy is activated for the insufficient part.

[0094] 3) The energy storage power threshold linkage rule represents the rule that triggers different scheduling operations based on the high and low energy storage power thresholds.

[0095] 4) Emergency scenario coordination rules characterize the coordinated dispatch rules for responding to sudden power supply gaps.

[0096] In summary, this collaborative mechanism is used to improve the capacity for renewable energy absorption and reduce the configuration and operation time of backup power supplies.

[0097] Optionally, based on the optimization model in the above example, the problem of the disconnect between the static model and dynamic operating conditions is solved by collecting key operational data of the mine's energy system in real time. Relying on integer linear programming algorithms for rapid solution, the optimal scheduling scheme adapted to the current renewable energy output, load demand, and equipment status is output, making the coordinated scheduling of load shedding strategies, energy storage, and backup power more precise. This satisfies both the economic optimization objective and ensures real-time balance between power supply and demand and stable production. Specifically, the real-time operational data of this mine includes: real-time renewable energy output data, real-time mine load demand data, equipment operating status data, and real-time raw material warehouse inventory data. Figure 2 On this basis, Figure 6 A flowchart illustrating another multi-level flexible load reduction and collaborative optimization method for mine loads provided in this application embodiment is shown below. Figure 6 Step 103 includes: Step 103-1: Optimize the solver to collect real-time mine operation data according to the set data acquisition cycle.

[0098] Among them, the data acquisition cycle represents the time interval for the optimization solver to collect real-time operating data; the real-time output data of new energy represents the actual output power of new energy equipment at the current moment; the real-time load demand data of the mine represents the total power demand of the mine at the current moment; the equipment operation status data represents the current operating parameters and status of various equipment, including energy storage capacity, backup power start-stop status and operating power of each production equipment; and the real-time inventory data of the raw material warehouse represents the current ore storage volume of the raw material warehouse.

[0099] Step 103-2: The optimization solver substitutes the real-time operation data of the mine into the optimization model, calculates the optimal scheduling scheme for each time period through integer linear programming algorithms, and outputs the optimal scheduling scheme for each time period.

[0100] Among them, integer linear programming algorithms represent optimization algorithms applicable to discrete decision variables. The optimal scheduling scheme includes the optimal load shedding strategy, energy storage charging and discharging power plan, and backup power output scheme. The optimal load shedding strategy is selected from multiple load shedding strategies to satisfy the objective function and constraints of the optimization model.

[0101] Optionally, steps 103-1 and 103-2 are illustrated below, assuming the following: data collection cycle: 15 minutes / time (4 collection points per hour, 35,040 collection points per year); new energy output (Psolar, Pwind): photovoltaic range 0-400MW, wind power range 0-300MW; mine real-time load (Pload): range 300-600MW, dynamically changing with production processes; equipment status: energy storage remaining power (SOC) safe range 20%-80%, charging and discharging power 0-150MW; standby power supply (gas turbine) start / stop status 0=stop / 1=run, output range 0-70MW during operation; raw material warehouse inventory (S): range 30,000-220,000 tons (matching...). Figure 5 Inventory constraint threshold); optimization algorithm: integer linear programming algorithm, solution period ≤ 5 minutes (to ensure real-time scheduling).

[0102] Furthermore, examples of real-time data collection (4 collection points within a 1-hour period) are shown in Table 7 below: Table 7

[0103] The data items in Table 7 above provide "dynamic input data that closely matches actual operating conditions" for the optimization solution, simulating the real operational fluctuations of the mine within one hour. This not only verifies the adaptability of the optimization model to dynamic data but also creates reasonable operating condition contradictions (such as supply-demand gaps caused by decreased renewable energy output and increased load) for the derivation of subsequent scheduling schemes, highlighting the necessity and practicality of scheduling decisions. At the same time, the values ​​strictly match the constraints mentioned above, laying a data foundation for the rationality of the subsequent scheme outputs.

[0104] Among them, the output of new energy sources (P-solar and P-wind) shows a gradual downward trend (solar power from 380 to 250MW and wind power from 250 to 180MW), simulating the natural law of solar power decay and wind power weakening in the afternoon, laying the groundwork for subsequent supply and demand imbalance. Real-time mine load (P_load): shows a gradual upward trend (500 to 580MW), simulating load fluctuations caused by the advancement of mine production processes (such as the concentrated start-up of mining equipment), which inversely changes with the output of new energy sources, amplifying the supply-demand gap; Energy storage SOC: Initially 75% (higher end of the safe range), decreasing slightly over time (75% to 65%), reserving charging and discharging adjustment space, which not only meets the daily operation of energy storage, but also supports subsequent discharge and replenishment needs; Gas turbine status: shutdown in the first two periods (0), operation in the last two periods (1), corresponding to the change in operating conditions from sufficient to insufficient new energy, realizing the dynamic start-stop logic of backup power supply; Raw material inventory: 150,000 to 142,000 tons (slight decrease), remaining within the constraint range of 30,000 to 220,000 tons. There is no need to trigger capacity replenishment operations. The focus is on optimizing the scheduling of power supply and demand to avoid multiple variables interfering with the core logic.

[0105] Similarly, examples of the optimal scheduling scheme output are shown in Table 8 below:

[0106] The data items in Table 8 clearly demonstrate how, after real-time data input, an integer linear programming algorithm is used to derive a scheduling decision that meets the constraints and is economically optimal. Furthermore, specific values ​​are used to quantify the scheduling effect, reflecting the closed-loop logic of "data to decision to cost," making the optimization process implementable and verifiable.

[0107] The scheduling scheme's values ​​strictly correspond to the changes in the real-time data mentioned earlier, and are designed around the objective of "minimizing total operating cost" and multiple constraints. The specific logic is as follows: Optimal load shedding strategy: No load shedding in the first two periods (X1=X2=X3=0), because the output of new energy sources is sufficient and the load is low, so there is no need to balance supply and demand through load shedding; Low-impact load shedding is activated in the latter two periods (X1=1), and the load shedding power is gradually increased (20 to 30MW), which not only matches the gap between the decline in new energy output and the increase in load, but also prioritizes the low-cost load shedding level (low impact 0.4 yuan / kWh) to control the cost of load shedding; Energy storage dispatch plan: charging in the first two periods (30 to 10MW), due to the overcapacity of new energy output, energy storage is used to absorb redundant electrical energy (SOC rises to 78% and 73%); discharging in the latter two periods (40 to 60MW), releasing electrical energy to replenish energy, alleviate the supply and demand gap, and keeping the SOC above 60% (safe range) to meet equipment constraints. Gas turbine output plan: The turbine will be shut down for the first two periods (0MW output) because the load demand can be met by new energy sources and energy storage, avoiding the high cost of backup power supply operation; the turbine will gradually increase its output (30 to 60MW) in the last two periods to make up for the remaining gap after the energy storage discharge, and the output will not exceed the rated capacity constraint of 70MW. Time-period cost: gradually increases with the complexity of the scheduling strategy (from 8250 to 21300 yuan), which is consistent with the cost increase logic of "no load reduction to low impact load reduction + energy storage discharge + gas turbine operation". The cumulative cost of 54050 yuan per hour is consistent with the annual cost estimate in the example table 5 above, which verifies the achievement effect of the objective function.

[0108] In summary, the above optimal scheduling schemes all meet the constraints of power supply and demand balance, equipment capacity, and inventory threshold, with a cumulative cost of 54,050 yuan per hour, achieving a synergy between economic optimization and operational stability.

[0109] Optionally, based on the optimal scheduling scheme described in the foregoing example, the focus is on two core needs: "ensuring production continuity" and "coping with new energy fluctuations." By real-time monitoring of inventory and new energy output, targeted capacity replenishment operations are executed (to avoid production interruptions due to raw material shortages), and the scheduling scheme is dynamically adjusted (to address supply-demand imbalances caused by significant fluctuations in new energy output), forming a closed loop from "scheduling execution to anomaly monitoring to emergency adjustment." This further enhances the stability and anti-interference capability of the mine's energy system, while ensuring that the adjusted scheme still meets the objective function and constraints of the optimization model. Specifically, in Figure 2 On this basis, Figure 7 A flowchart illustrating another multi-level flexible load reduction and collaborative optimization method for mine loads provided in this application embodiment is shown below. Figure 7 Step 104 includes: Step 104-1: The capacity replenishment module monitors the real-time inventory data of the raw material warehouse. When the inventory is lower than the lower limit threshold, the capacity replenishment operation is executed.

[0110] Among them, the mining equipment load limit threshold represents the maximum allowable load power limit of mining equipment.

[0111] The corresponding step 105 includes: Step 105-1: The dynamic scheduling module monitors the real-time output data of new energy sources in real time. When the total output fluctuation of new energy sources exceeds the new energy output fluctuation threshold within the set data acquisition period, the optimal scheduling scheme is adjusted.

[0112] Among them, the new energy output fluctuation threshold represents the critical value for determining significant fluctuations in new energy output. Adjusting the optimal scheduling scheme represents the recalculation based on the optimization model, adjusting the energy storage charging and discharging plan, the backup power operation scheme, and the optimal load reduction strategy.

[0113] The following is a specific example for illustration, assuming the following data is relevant: real-time inventory data of raw material warehouse: lower limit of 30,000 tons, average daily consumption of raw material warehouse is 5,000 tons, and the benchmark output of mining equipment is 200MW (corresponding to daily output of 5,000 tons); load increase related: mining equipment load increase limit threshold is 50MW (maximum output is 250MW), and the unit energy consumption cost of load increase is 1.0 yuan / kWh; new energy fluctuation related: data collection cycle is 15 minutes, new energy output fluctuation threshold is 20%, and the fluctuation calculation method is = (total output of the current cycle - total output of the previous cycle) / total output of the previous cycle × 100%; adjustment constraints: the adjusted scheduling scheme must meet the original constraints such as equipment capacity, energy storage SOC (20%-80%), and the uniqueness of the load reduction strategy.

[0114] The following table 9 shows an example of real-time monitoring values ​​(6 data collection points within a 2-hour period): Table 9

[0115] Furthermore, when the inventory was monitored to be 28,000 tons (below the lower limit of 30,000 tons) between 15:30 and 15:45, a capacity replenishment operation was triggered. Specific examples of the capacity replenishment operation are shown in Table 10 below: Table 10

[0116] Among them, the increased power was controlled within the increased power limit threshold (50MW) to avoid equipment overload; after replenishment, the inventory rose to 32,000 tons, and the capacity replenishment trigger status was lifted.

[0117] Furthermore, when the total output of new energy sources was monitored to be 230MW during the period from 16:00 to 16:15, which was -30.3% (exceeding the 20% threshold) compared to the previous cycle (330MW), the dispatching scheme was adjusted. For an example of dynamic dispatching scheme adjustment, the comparison of the scheme before and after the adjustment is shown in Table 11 below: Table 11

[0118] The aforementioned capacity replenishment operations and dynamic scheduling adjustments all met conditions such as inventory constraints, equipment load limitations, and the safe range of energy storage SOC. Capacity replenishment quickly restored inventory to a safe level, avoiding production interruptions; after adjustments to new energy supply and demand, the supply-demand gap was completely filled, and costs were controlled within a reasonable range, validating the... Figure 7 The effectiveness of the "anomaly monitoring → emergency handling" process forms a closed-loop collaboration with the aforementioned scheduling process.

[0119] Optionally, this application provides a status monitoring mechanism, which may be implemented as follows: It monitors multi-dimensional operating status in real time, records operating data, and feeds it back to the optimization solver.

[0120] The multi-dimensional operational status includes the operational status of each piece of equipment, the execution progress of the optimal load reduction strategy, the effect of capacity replenishment, and the power supply and demand balance. The operational status of each piece of equipment represents the current operating parameters and fault conditions of various types of equipment. The execution progress of the optimal load reduction strategy represents the running time of the current load reduction strategy and the annual cumulative operating percentage. The effect of capacity replenishment represents the improvement of raw material inventory after the execution of capacity replenishment operations. The power supply and demand balance represents the matching of real-time power supply and power demand. The operational data represents various real-time parameters obtained from monitoring, which are statistically archived periodically to provide data support for subsequent scheduling scheme adjustments and model parameter optimization.

[0121] To perform the steps and corresponding technical effects of the above examples, this application also provides a possible implementation of a multi-level flexible load reduction and collaborative optimization device for mine loads. Specifically, Figure 8This is a schematic diagram of the architecture of a multi-level flexible load reduction and collaborative optimization device for mines provided in an embodiment of the present invention. (See attached diagram.) Figure 8 The device 20 includes: a data processing module 200, a strategy generation module 201, a model building module 202, an optimization solver 203, a capacity replenishment module 204, and a dynamic scheduling module 205.

[0122] The data processing module 200 is used to input the parameters of mining and mineral processing equipment to obtain equipment load characteristic information, the impact parameters of load reduction on production capacity of each piece of equipment, and load reduction cost parameters. The equipment load characteristic information covers the load reduction range and interruptibility characteristics of mining equipment, as well as the non-interruption characteristics of core mineral processing equipment.

[0123] The strategy generation module 201 is used to generate multiple load reduction strategies based on equipment load characteristic information, influencing parameters and load reduction cost parameters, according to the degree of impact on production capacity and the cost of load reduction; the multiple load reduction strategies are sorted from high to low priority.

[0124] Model building module 202 is used to build an optimization model based on multi-level load reduction strategies with the goal of minimizing total operating cost.

[0125] The optimization solver 203 is used to input the optimization model and real-time mine operation data to obtain the optimal load reduction strategy, energy storage charging and discharging plan and backup power operation scheme.

[0126] The capacity replenishment module 204 is used to perform capacity replenishment operations based on real-time inventory data from the raw material warehouse.

[0127] The dynamic scheduling module 205 is used to adjust the optimal load reduction strategy, energy storage charging and discharging plan and backup power operation scheme based on the real-time output data of new energy sources.

[0128] Optionally, the data processing module 200 is specifically used to analyze the operating information of mining process equipment to obtain mining equipment load reduction parameters; wherein, the mining equipment operating information represents the inherent operating attributes of the mining process equipment; the mining equipment influence parameters represent the quantitative value of the impact of mining auxiliary equipment load reduction on production capacity per unit time; the mining equipment load reduction cost parameters represent the quantitative value of the economic loss generated by mining auxiliary equipment load reduction per unit time; and analyze the operating information of mineral processing process equipment to obtain mineral processing equipment load reduction parameters; wherein, the mineral processing process equipment operating information represents the inherent operating attributes of the mineral processing process equipment; the mineral processing equipment load reduction parameters represent the quantitative data related to the load reduction of the core mineral processing equipment; the mineral processing equipment influence parameters represent the quantitative value of the impact of the core mineral processing equipment load reduction on production capacity per unit time; and the mineral processing equipment load reduction cost parameters represent the quantitative value of the economic loss generated by the core mineral processing equipment load reduction per unit time; and the mining equipment load reduction parameters and the mineral processing equipment load reduction parameters constitute equipment load characteristic information, influence parameters, and load reduction cost parameters.

[0129] Optionally, the multi-level load reduction strategies include low-impact load reduction strategies, medium-impact load reduction strategies, and high-impact load reduction strategies. Low-impact load reduction strategies correspond to load reduction parameters for mining equipment, while high-impact load reduction strategies correspond to load reduction parameters for mineral processing equipment. The strategy generation module 201 is specifically used for: classifying load reduction operations for mining auxiliary equipment to obtain low-impact load reduction strategies and their corresponding first core parameters; wherein, the low-impact load reduction strategy represents a load reduction scheme for mining auxiliary equipment; classifying load reduction operations for non-production auxiliary facilities in the mine to obtain medium-impact load reduction strategies and their corresponding second core parameters; wherein, the medium-impact load reduction strategy represents a load reduction scheme for non-production auxiliary facilities in the mine; the second core parameters represent key operational indicators of the medium-impact load reduction strategy; and classifying load reduction operations for core mineral processing equipment to obtain high-impact load reduction strategies and their corresponding third core parameters; wherein, the high-impact load reduction strategy represents an emergency load reduction scheme for core mineral processing equipment; the third core parameters represent key operational indicators of the high-impact load reduction strategy.

[0130] Optionally, the optimization model includes an objective function and multi-dimensional constraints. These multi-dimensional constraints include power supply and demand balance constraints, equipment rated capacity operation constraints, time constraints for each load reduction strategy, raw material warehouse inventory threshold constraints, and the uniqueness constraint of a single-period load reduction strategy. The model construction module 202 is specifically used for: setting the objective function of the optimization model, which minimizes the total operating cost; where the total operating cost represents the sum of various economic costs generated during the operation of the mine energy system; and setting the multi-dimensional constraints of the optimization model; where the multi-dimensional constraints include any or a combination of the following: power supply and demand balance constraints represent ensuring that power supply and demand remain balanced at any given time period. The constraints include: Balanced load reduction measures; Equipment rated capacity operation constraints, which define the requirement that the operating power of various equipment must be within the safe rated range; Time constraints for each load reduction strategy, which define the cumulative running time of each load reduction strategy within a single year, corresponding to the longest running time limit for each load reduction strategy; Raw material inventory threshold constraints, which define the requirement that raw material inventory be within a reasonable range; Raw material inventory threshold constraints include a lower inventory threshold and an upper inventory threshold; The lower inventory threshold is used to trigger capacity replenishment operations, and the upper inventory threshold is used to constrain mining load to avoid inventory backlog; Uniqueness constraints for single-period load reduction strategies, which define the requirement that only one load reduction strategy can be used at the same time period.

[0131] Optionally, the real-time mine operation data includes real-time output data of new energy sources, real-time load demand data of the mine, equipment operation status data, and real-time inventory data of raw material warehouses; the optimization solver 203 is specifically used to: collect real-time mine operation data according to a set data collection cycle; substitute the real-time mine operation data into the optimization model, calculate using an integer linear programming algorithm, and output the optimal scheduling scheme corresponding to each time period; wherein, the integer linear programming algorithm represents an optimization algorithm applicable to discrete decision variables.

[0132] Optionally, the capacity replenishment module 204 is specifically used for: real-time monitoring of raw material warehouse inventory data, and performing capacity replenishment operation when the inventory is lower than the lower inventory limit threshold; wherein, the mining equipment load limit threshold represents the maximum allowable load power limit of the mining equipment; The dynamic scheduling module 205 is specifically used for: real-time monitoring of real-time power output data of new energy sources; and adjusting the optimal scheduling scheme when the total power output fluctuation of new energy sources exceeds the power output fluctuation threshold of new energy sources within a set data acquisition period. The power output fluctuation threshold of new energy sources represents the critical value for judging large fluctuations in power output of new energy sources.

[0133] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0134] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0135] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0136] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0137] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A multi-level flexible load reduction and coordinated optimization method for mine loads, characterized in that, include: The data processing module takes in the parameters of mining and mineral processing equipment and obtains equipment load characteristics, parameters on the impact of load reduction on production capacity of each piece of equipment, and parameters on the cost of load reduction. The equipment load characteristic information covers the load reduction range and interruptibility characteristics of mining equipment, as well as the non-interruption characteristics of core mineral processing equipment. Based on the equipment load characteristic information, the impact parameters, and the load reduction cost parameters, the strategy generation module divides the load reduction strategies into multiple levels according to the degree of impact on production capacity and the load reduction cost; the multiple load reduction strategies are sorted in order of priority from high to low. The model building module is based on a multi-level load reduction strategy to build an optimization model with the goal of minimizing the total operating cost; The optimization solver takes the optimization model and real-time mine operation data as input and obtains the optimal load reduction strategy, energy storage charging and discharging plan and backup power operation scheme. The capacity replenishment module performs capacity replenishment operations based on real-time inventory data from the raw material warehouse. The dynamic scheduling module adjusts the optimal load reduction strategy, energy storage charging and discharging plan, and backup power operation scheme based on real-time power output data of new energy sources.

2. The method as described in claim 1, characterized in that, The data processing module takes into account the parameters of mining and mineral processing equipment to obtain equipment load characteristic information, the impact parameters of load reduction on production capacity, and load reduction cost parameters. The steps include: The data processing module analyzes the operating information of mining equipment to obtain the load reduction parameters of the mining equipment. The mining equipment operation information represents the inherent operating attributes of the mining process equipment; the mining equipment impact parameters represent the quantitative value of the impact of mining auxiliary equipment load reduction on production capacity per unit time; and the mining equipment load reduction cost parameters represent the quantitative value of the economic loss generated by mining auxiliary equipment load reduction per unit time. The data processing module analyzes the operating information of the mineral processing equipment to obtain the load reduction parameters of the mineral processing equipment. Among them, the mineral processing equipment operation information represents the inherent operating attributes of the mineral processing equipment; the mineral processing equipment load reduction parameters represent the quantitative data related to the load reduction of the core mineral processing equipment; the mineral processing equipment impact parameters represent the quantitative value of the impact of the load reduction of the core mineral processing equipment on the production capacity per unit time; and the mineral processing equipment load reduction cost parameters represent the quantitative value of the economic loss generated by the load reduction of the core mineral processing equipment per unit time. The data processing module combines the load reduction parameters of the mining equipment and the load reduction parameters of the ore dressing equipment to form the equipment load characteristic information, the influencing parameters, and the load reduction cost parameters.

3. The method as described in claim 2, characterized in that, The multi-level load reduction strategy includes a low-impact load reduction strategy, a medium-impact load reduction strategy, and a high-impact load reduction strategy. The low-impact load reduction strategy corresponds to the load reduction parameters of the mining equipment, and the high-impact load reduction strategy corresponds to the load reduction parameters of the ore dressing equipment. The step of the strategy generation module obtaining the multi-level load reduction strategy based on the equipment load characteristic information, the impact parameters, and the load reduction cost parameters, according to the degree of impact on production capacity and the load reduction cost, includes: The strategy generation module divides the unloading operation of mining auxiliary equipment to obtain low-impact unloading strategies and corresponding first core parameters. The low-impact load reduction strategy refers to a load reduction scheme for mining auxiliary equipment. The strategy generation module divides the load reduction operations of non-production auxiliary facilities in the mine into medium-impact load reduction strategies and corresponding second core parameters. Among them, the load reduction strategy is characterized by the load reduction scheme for non-production auxiliary facilities in the mine; the second core parameter is characterized by the key operating indicators that affect the load reduction strategy. The strategy generation module divides the load reduction operation of the core equipment in mineral processing, obtains the high-impact load reduction strategy and matches it with the corresponding third core parameter. Among them, the high-impact load reduction strategy represents an emergency load reduction plan for core mineral processing equipment; the third core parameter represents the key operating indicators of the high-impact load reduction strategy.

4. The method as described in claim 1, characterized in that, The optimization model includes an objective function and multi-dimensional constraints. These constraints include power supply and demand balance constraints, equipment rated capacity operation constraints, time constraints for each load reduction strategy, raw material warehouse inventory threshold constraints, and the uniqueness constraint of a single-period load reduction strategy. The model construction module, based on multi-level load reduction strategies, builds an optimization model with the objective of minimizing total operating cost, including the following steps: The model building module sets the objective function for optimizing the model, which is to minimize the total operating cost. The total operating cost represents the sum of all economic costs generated during the operation of the mine energy system; The model building module sets multi-dimensional constraints for optimizing the model; The multi-dimensional constraints include any or a combination of the following: the power supply and demand balance constraint represents the restriction condition that ensures the power supply and demand remain balanced at any given time; the rated capacity operation constraint represents the restriction condition that the operating power of various types of equipment must be within the safe rated range; the usage time constraint of each load reduction strategy represents the restriction condition that constrains the cumulative running time of each load reduction strategy within a single year, corresponding to the longest running time limit of each load reduction strategy; the raw material inventory threshold constraint represents the restriction condition that limits the raw material inventory to a reasonable range; the raw material inventory threshold constraint includes a lower inventory threshold and an upper inventory threshold; the lower inventory threshold is used to trigger capacity replenishment operations, and the upper inventory threshold is used to constrain mining load to avoid inventory backlog; the uniqueness constraint of a single-time-period load reduction strategy represents the restriction condition that only one load reduction strategy is allowed to be activated at the same time period.

5. The method as described in claim 1, characterized in that, The real-time mine operation data includes real-time renewable energy output data, real-time mine load demand data, equipment operating status data, and real-time raw material warehouse inventory data; the optimization solver, inputting the optimization model and the real-time mine operation data, obtains the optimal load reduction strategy, energy storage charging and discharging plan, and backup power operation plan through the following steps: The optimization solver collects real-time operational data of the mine according to a set data acquisition cycle; The optimization solver substitutes real-time mine operation data into the optimization model, calculates the optimal scheduling scheme for each time period using integer linear programming algorithms, and outputs the optimal scheduling scheme for each time period. Integer linear programming algorithms are optimization algorithms applicable to discrete decision variables.

6. The method as described in claim 1, characterized in that, The capacity replenishment module performs capacity replenishment operations based on real-time inventory data from the raw material warehouse, including the following steps: The capacity replenishment module monitors the real-time inventory data of the raw material warehouse and performs a capacity replenishment operation when the inventory is lower than the lower limit threshold. The mining equipment load limit threshold represents the maximum allowable load power limit of the mining equipment. The dynamic scheduling module monitors the real-time output data of the new energy source in real time. When the total output fluctuation of the new energy source exceeds the new energy output fluctuation threshold within the set data collection period, the optimal scheduling scheme is adjusted. The new energy output fluctuation threshold represents the critical value for determining a large fluctuation in new energy output.

7. A multi-level flexible load reduction and collaborative optimization device for mine loads, characterized in that, include: The system includes a data processing module, a strategy generation module, a model building module, an optimization solver, a capacity replenishment module, and a dynamic scheduling module. The data processing module is used to input mining and mineral processing equipment parameters to obtain equipment load characteristic information, parameters on the impact of load reduction on production capacity of each piece of equipment, and load reduction cost parameters. The equipment load characteristic information covers the load reduction range and interruptibility characteristics of mining equipment, as well as the non-interruption characteristics of core mineral processing equipment. The strategy generation module is used to obtain multiple load reduction strategies based on the equipment load characteristic information, the impact parameters, and the load reduction cost parameters, according to the degree of impact on production capacity and the load reduction cost; the multiple load reduction strategies are sorted in order of priority from high to low. The model building module is used to build an optimization model based on multi-level load reduction strategies, with the goal of minimizing the total operating cost. The optimization solver is used to input the optimization model and real-time mine operation data to obtain the optimal load reduction strategy, energy storage charging and discharging plan and backup power operation scheme. The capacity replenishment module is used to perform capacity replenishment operations based on real-time inventory data of the raw material warehouse; The dynamic scheduling module is used to adjust the optimal load reduction strategy, energy storage charging and discharging plan, and backup power operation scheme based on real-time power output data of new energy sources.

8. The apparatus as claimed in claim 7, characterized in that, The data processing module is specifically used to analyze the operating information of mining equipment to obtain mining equipment load reduction parameters. The mining equipment operating information represents the inherent operating attributes of the mining equipment; the mining equipment impact parameters represent the quantitative value of the impact of mining auxiliary equipment load reduction on production capacity per unit time; and the mining equipment load reduction cost parameters represent the quantitative value of the economic loss generated by mining auxiliary equipment load reduction per unit time. The module also analyzes the operating information of mineral processing equipment to obtain mineral processing equipment load reduction parameters. The mineral processing equipment operating information represents the inherent operating attributes of the mineral processing equipment; the mineral processing equipment load reduction parameters represent the quantitative data related to the load reduction of core mineral processing equipment; the mineral processing equipment impact parameters represent the quantitative value of the impact of core mineral processing equipment load reduction on production capacity per unit time; and the mineral processing equipment load reduction cost parameters represent the quantitative value of the economic loss generated by core mineral processing equipment load reduction per unit time. The mining equipment load reduction parameters and the mineral processing equipment load reduction parameters constitute the equipment load characteristic information, the impact parameters, and the load reduction cost parameters.

9. The apparatus as claimed in claim 8, characterized in that, The multi-level load reduction strategies include low-impact load reduction strategies, medium-impact load reduction strategies, and high-impact load reduction strategies. The low-impact load reduction strategies correspond to the load reduction parameters of the mining equipment, and the high-impact load reduction strategies correspond to the load reduction parameters of the mineral processing equipment. The strategy generation module is specifically used for: classifying the load reduction operations of mining auxiliary equipment to obtain low-impact load reduction strategies and corresponding first core parameters; wherein, the low-impact load reduction strategy represents a load reduction scheme for mining auxiliary equipment; classifying the load reduction operations of non-production auxiliary facilities in the mine to obtain medium-impact load reduction strategies and corresponding second core parameters; wherein, the medium-impact load reduction strategy represents a load reduction scheme for non-production auxiliary facilities in the mine; the second core parameters represent key operating indicators of the medium-impact load reduction strategy; classifying the load reduction operations of mineral processing core equipment to obtain high-impact load reduction strategies and corresponding third core parameters; wherein, the high-impact load reduction strategy represents an emergency load reduction scheme for mineral processing core equipment; the third core parameters represent key operating indicators of the high-impact load reduction strategy.

10. The apparatus as claimed in claim 7, characterized in that, The optimization model includes an objective function and multi-dimensional constraints. These constraints include power supply and demand balance constraints, equipment rated capacity operation constraints, time constraints for each load reduction strategy, raw material warehouse inventory threshold constraints, and the uniqueness constraint of a single-period load reduction strategy. The model construction module is specifically used for: setting the objective function of the optimization model, which is to minimize the total operating cost; wherein the total operating cost represents the sum of various economic costs generated during the operation of the mine energy system; and setting the multi-dimensional constraints of the optimization model; wherein the multi-dimensional constraints include any or a combination of the following: the power supply and demand balance constraint represents the limit for ensuring that power supply and demand remain balanced at any given time period. The constraints include: the rated capacity operation constraint of the equipment, which represents the restriction that the operating power of various equipment must be within the safe rated range; the usage time constraint of each load reduction strategy, which represents the restriction that the cumulative running time of each load reduction strategy within a single year is limited, corresponding to the longest running time limit of each load reduction strategy; the raw material warehouse inventory threshold constraint, which represents the restriction that the raw material warehouse inventory is limited to a reasonable range; the raw material warehouse inventory threshold constraint includes a lower inventory threshold and an upper inventory threshold; the lower inventory threshold is used to trigger capacity replenishment operations, and the upper inventory threshold is used to constrain mining load to avoid inventory backlog; and the uniqueness constraint of the single-period load reduction strategy, which represents the restriction that only one load reduction strategy is allowed to be used in the same period.

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