Method for constructing material flow-energy flow coupled coal mine transportation network model and optimization control method

Abstract

The application discloses a kind of material flow-energy flow coupling coal mine transport network model construction methods, comprising the following steps: step 1: with mine power flow and coal carbon transport material flow as research object, construct the node topology structure of inverse material flow mine power supply system;Step 2: based on the coupling relationship between material flow and distribution network flow, combined with the coal flow transport safety of coal face, belt conveyor and underground coal bunker and distribution network flow constraint, construct the material flow-energy flow coupling coal mine transport network model.The application also discloses a kind of coal material flow-energy flow coupling coal mine transport network model optimization control method, with mine power supply system energy cost and carbon emission penalty cost minimum as target, with Distflow branch flow constraint, node voltage constraint, upper and lower limit constraint of distributed power, mine production safety constraint, material flow-energy flow coupling relationship as constraint condition, obtain the optimization operation scheme of coal flow transport and mine grid low-carbon scheduling.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of application of power supply control systems, and particularly relates to a coal mine transportation network model construction method and an optimization control method based on material flow-energy flow coupling. BACKGROUND

[0002] In the process of rapidly improving the coal mining progress, coal mining enterprises are not good at coordinating and managing the coal production, transportation process and its power demand, forming an extensive operation and management mode, which increases the production energy consumption and energy consumption economic cost of the mine. At the same time, with the increase of the proportion of new energy access, the energy consumption of the mine is gradually clean, and the establishment of the carbon trading market makes the mine also face the problem of carbon reduction of energy consumption. The transportation energy consumption of coal is large, and the variation of the load power deeply affects the operation and dispatching of the mine power distribution network. Therefore, how to regulate and control the coal flow transportation of multiple processes to realize the economic and low-carbon operation of the mine power supply system is a key problem to be studied at present.

[0003] The energy consumption of the coal flow transportation system mainly comes from the belt conveyor. Some existing researches mainly focus on the speed or variable working condition energy consumption optimization control of a single belt conveyor, however, in the actual field, the coal mine transportation system is large in scale and the coupling of different links is complex. The existing research has not established a transportation network model among multiple coal mining faces, belt conveyors and coal storage silos, and lacks consideration of the correlation between the belt speed, transportation capacity and the coal mining capacity and the coal storage capacity in the transportation network. At the same time, how to realize the collaborative optimization of coal flow and distribution network power flow under the premise of ensuring voltage safety and stability still needs further research.

[0004] Based on the operation requirements of the coal flow transportation system, the collaborative optimization of coal flow and power flow is of great significance to the energy saving and consumption reduction of mine operation. The transportation optimization of coal flow needs the collaborative coupling of energy scheduling among different links; the load of the mine is mainly the energy demand of the production and transportation equipment, which makes the mine distribution network power flow change sharply following the coal flow transportation scheduling, and the energy consumption of coal flow transportation also deeply affects the power supply safety of the coal mine. The existing research only optimizes and dispatches the mine as a centralized energy station, does not involve the constraints of the multi-process flow of mine transportation and the influence of coal flow optimization on the operation of mine power supply system, and still lacks a coal flow-power flow collaborative operation optimization model considering the transportation safety constraints.

[0005] In addition, higher requirements are put forward for the clean energy consumption of the coal mine energy system, therefore, it is also necessary to reduce the carbon emissions generated in the energy consumption links while reducing the energy consumption economic cost of the mine energy system. Although some existing researches have quantitatively analyzed the carbon emissions of the energy system, however, in the coal industry, a time-segmented carbon measurement method considering the characteristics of coal production and transportation processes has not been established, the carbon emission measurement of the operation of the mine power supply system cannot be accurately completed, and the low-carbon optimization and dispatching of the collaborative operation of the material flow-energy flow of the mine cannot be realized. SUMMARY

[0006] Therefore, the present application aims to provide a coal substance flow-energy flow coupled coal mine transportation network model construction method and an optimization control method, which can not only realize the collaborative optimization of coal flow and distribution network power flow under the premise of ensuring voltage safety and stability, but also realize low-carbon optimization scheduling, i.e., effectively improve the energy efficiency level of the mine, reduce the energy consumption per ton of coal, and reduce carbon emissions.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions.

[0008] The present application first proposes a substance flow-energy flow coupled coal mine transportation network model construction method, which comprises the following steps:

[0009] Step 1: Taking the mine power flow and coal carbon transportation substance flow as the research object, the node topology structure of the mine power supply system in the reverse substance flow is constructed.

[0010] Step 2: Based on the coupling relationship between the substance flow and the distribution network power flow, combined with the coal flow transportation safety of the coal mining face, the belt conveyor and the coal bunker at the bottom of the well, and the distribution network power flow constraint, a substance flow-energy flow coupled coal mine transportation network model is constructed.

[0011] Further, in the step 2, the operation constraint of the coal mining face is:

[0012] The relationship between the coal mining amount and the power of the coal mining machine is:

[0013] E t =λP c,t △tε t

[0014] In the formula: E t is the coal mining amount in the t period; P c,t is the average power value of the coal mining machine in the t period; λ is the power correlation coefficient of the coal mining amount and the load node of the coal mining face; Δt is the unit time interval; ε t is the coal mine working condition coefficient;

[0015] The relationship between the coal mining amount and the coal transportation amount in a period is:

[0016] W in,t =E t τ t

[0017]

[0018] In the formula: W in,t is the coal amount stored in the bunker in the t period; τ t is the coal transportation working condition coefficient; T is the scheduling period.

[0019] Further, in step 2, the operation constraints of the belt conveyor are:

[0020] The mathematical relationship between the power of the belt conveyor and the belt speed and the amount of coal is:

[0021] P bc,t = 1 / (η d η m )*(μ1v t W t 2 + μ2v t + μ3W t 2 / v t + μ4W t +v t 2 W t / 3.6)

[0022] In the formula: P bc,t is the power of the belt conveyor in the t period; μ1, μ2, μ3, μ4 are four parameters related to the structure of the belt conveyor; η d , η m are the efficiencies of the motor and the drive system, respectively; W t is the amount of coal transported by the belt conveyor in the t period, i.e. the amount of coal transported by the belt conveyor per unit of time; v t is the belt speed of the belt conveyor;

[0023] In order to prevent coal overflow caused by low belt speed and large coal flow, the belt speed and the amount of coal transported by the belt conveyor are subject to the following constraints:

[0024] M t = W t / (3.6*v t )

[0025] 0 ≤ M t ≤ M max

[0026] 0 ≤ v t ≤ v max

[0027] In the formula: M t is the mass per unit length of the belt conveyor in the t period; M max is the upper limit of the mass per unit length of the belt conveyor; v max is the upper limit of the belt speed of the belt conveyor.

[0028] Further, in step 2, the operation constraints of the belt conveyor are:

[0029] R t+1 = Rt +W in,t -W out,t

[0030] 0.2R N ≤R t ≤0.9R N

[0031] wherein: R t is the amount of coal in the coal bunker at the t period, R N is the capacity of the coal bunker, W in,t is the amount of coal stored in the coal bunker at the t period, W out,t is the amount of coal transported out of the coal bunker at the t period.

[0032] The application also provides a coal mine transportation network model optimization control method of material flow-energy flow coupling, comprising the following steps:

[0033] Step one: a coal mine transportation network model of material flow-energy flow coupling is constructed by the method described above;

[0034] Step two: a mine energy supply side accurate carbon metering model is constructed, taking the minimum of mine power supply system energy consumption cost and carbon emission penalty cost as the target, taking Distflow branch flow constraint, node voltage constraint, distributed power upper and lower limit constraint, mine production safety constraint, material flow-energy flow coupling relationship as the constraint condition, and obtaining the optimization operation control scheme of coal flow transportation and mine power grid low-carbon scheduling based on the coal mine transportation network model of material flow-energy flow coupling.

[0035] Further, in the step two, the construction method of the mine energy supply side accurate carbon metering model comprises the following steps:

[0036] S1: mine electric energy component analysis

[0037] The electric energy component of a large power grid in a unit period is expressed as:

[0038]

[0039]

[0040] wherein: i is the energy generation type, including five types of thermal power, nuclear power, hydropower, photovoltaic and wind power; P t i is the average power of the i-th energy generation type in the t period; P t e is the average power of the mine power purchase in the t period; ε t i is the proportion of the i-th energy generation output in the mine external power purchase in the t period.

[0041] The electric energy component of the overall energy consumption of the mine is expressed as:

[0042]

[0043]

[0044] In the formula, ε' t i is the proportion of the power generation output of the i-th energy source in the t-th period in the overall power consumption of the mine; P t ci is the average output of the i-th power source in the t-th period in the mine; P t WT is the average output of the wind power configured in the t-th period in the mine; P t PV is the average output of the photovoltaic configured in the t-th period in the mine;

[0045] S2: Time-sharing carbon metering model

[0046] In order to describe the carbon emission intensity of energy consumption in the t-th period, the comprehensive carbon emission factor is defined as:

[0047]

[0048]

[0049] In the formula, α i is the carbon emission factor of the i-th type of energy generation in the t-th period; P is the comprehensive carbon emission factor of the purchased power outside the mine in the t-th period; P is the comprehensive carbon emission factor of the overall power consumption of the mine in the t-th period; P

[0050] S3: Construction of a precise carbon metering model for the supply side of the mine energy

[0051] The carbon emission of the energy consumption of the mine per unit period and the carbon emission generated by the energy consumption per ton of coal production are related to the comprehensive carbon emission factor, that is:

[0052]

[0053]

[0054] In the formula, C t is the carbon emission generated by the energy consumption of the mine in the t-th period; E t is the coal production of the mine in the t-th period; CM is the carbon emission generated by the energy consumption per ton of coal production in the dispatching period of the mine; and t is the time interval.

[0055] Further, in the second step, the minimum target of the energy consumption cost and the carbon emission penalty cost of the power supply system of the mine is:

[0056] min F=F1+F2

[0057]

[0058]

[0059] C rate =P t e *r e +(P t WT +P t PV )*r DG

[0060] Where: F is the total objective function of the transportation system and power supply system scheduling optimization model; F1 is the energy cost; F2 is the carbon penalty cost; Cp t P is the electricity purchase price per unit time period; t WT The average output of wind power configured for the mine in the tth period; C WT Operation and maintenance costs of configuring wind power for mines; P t PV is the average output of photovoltaic power configured for the mine in the tth period; C PV is the operation and maintenance cost of photovoltaic power configured in the mine; δ is the carbon market transaction price on a certain day, C rate For a given carbon emission quota, r e and r DG They are the carbon emission quotas for the large power grid and new energy power generation respectively.

[0061] Furthermore, in step 2, the Distflow branch flow constraint is:

[0062]

[0063]

[0064]

[0065]

[0066] Where: P ij,t , Q ij,t is the active and reactive power flowing through the head end of branch ij in period t, I ij,t is the current of line ij in period t, r ij 、x ij are the resistance and reactance of line ij, U i,t is the voltage of node i in period t; P j,t , Q j,tThe active and reactive power supplied to the node j in the t period; h represents the downstream node of node j; Ω b represents the set of grid branches.

[0067] Further, since there is a quadratic term in the Distflow branch power flow constraint, convex transformation is performed by using second-order cone relaxation, and the method is:

[0068] (1) Introducing variables α i,t and β ij,t :

[0069]

[0070]

[0071]

[0072] In the formula: U min , U max are the lower limit and upper limit of the node voltage respectively;

[0073] (2) The Distflow branch power flow constraint is transformed into:

[0074]

[0075]

[0076]

[0077]

[0078] (3) The second-order cone relaxation is performed on to obtain:

[0079]

[0080] After transformation and rewriting, it is transformed into a standard second-order cone form:

[0081]

[0082] After convex transformation, the constraint range is relaxed, and there will be a certain error in the calculation result, which is represented as:

[0083]

[0084] In the formula: err ij,t represents the error of the second-order cone relaxation.

[0085] Further, in the step two, the node voltage constraint is:

[0086] U min ≤ Ui,t ≤U max

[0087] wherein: U min , U max are the lower and upper limits of the node voltage, respectively;

[0088] The mine power grid topology presents the characteristics of single-end radiation, long line, and large end load. Therefore, a reactive power compensation device is configured in the power supply grid to solve the problem of unqualified node voltage. The unit time period reactive power compensation amount is limited by the configured capacity, which is expressed as:

[0089]

[0090] wherein: is the reactive power compensation amount of the reactive power compensation device at node i in the t time period; are the minimum and maximum values of the device reactive power compensation amount, respectively;

[0091] The upper and lower limit constraints of the distributed power supply are:

[0092] When the i node accesses the distributed power supply, the output of which satisfies the constraint:

[0093]

[0094]

[0095] wherein: is the output of the distributed photovoltaic at node i in the t time period; is the maximum output of the distributed photovoltaic; is the output of the distributed wind power at node i in the t time period; is the maximum output of the distributed wind power. The mine production safety constraint is:

[0096] It includes the coal flow operation constraint and the maintenance time constraint. The coal flow operation constraint includes the coal flow transportation safety of the coal mining face, the belt conveyor, and the underground coal bunker, and the distribution network power flow constraint. The maintenance time constraint is:

[0097]

[0098]

[0099]

[0100] wherein: k t is the state variable of the important equipment of the coal mining and coal transportation type in the t time period; t1 and t2 are the start time and end time of the maintenance period, respectively;

[0101] The material flow-energy flow coupling relationship constraint is:

[0102] The coal mining amount of the coal mining face determines the power demand of the coal mining face, and the power value of the coal mining face determines the load value of the coal mining face node in the mine power distribution network; the carrying capacity and belt speed of the belt conveyor in the coal flow determine the power value of the belt conveyor, and the power value of the belt conveyor determines the load value of the belt conveyor node in the mine power distribution network; that is, the material flow-energy flow coupling relationship is:

[0103]

[0104]

[0105] In the formula, P j,t , Q j,t are active and reactive power supplied by the node j in the t period; P coal,t , Q coal,t are average active and reactive power of the coal mining face in the t period; Ω c is a set of load nodes of the coal mining face; Ω bc is a set of load nodes of the belt conveyor; P bc,t , Q bc,t represent active and reactive power obtained by the belt conveyor from the node of the coal flow; and j represents a node of the power distribution network.

[0106] The present application has the following advantages:

[0107] In view of the problems of low energy efficiency, high energy consumption per ton of coal and high carbon characteristics in mines, the coal mine transportation network model construction method based on material flow-energy flow coupling takes mine power flow and coal flow as the research object, abstracts the topological structure, mines the energy consumption characteristics of the coal flow system based on the mine production process, studies the coupling relationship between the coal flow and the power flow, and establishes the coal mine transportation network model based on material flow-energy flow coupling. The coal mine transportation network model optimization control method based on material flow-energy flow coupling of the present application establishes a precise carbon metering model of the energy supply side of the mine, takes the minimum economic cost and the minimum carbon emission penalty cost as the target, considers the production safety constraint under the cooperation of the material flow and the energy flow of the mine, meters the carbon emission of the real-time electric energy component of the power grid electricity purchase, takes the minimum economic cost and the minimum carbon emission penalty cost as the target, obtains the optimization operation scheme of the coal mine transportation network model based on material flow-energy flow coupling, and under the premise of ensuring voltage safety and stability, not only can realize the collaborative optimization of the coal flow and the power flow of the distribution network, but also can realize low-carbon optimal dispatching, that is, can effectively improve the energy efficiency of the mine, reduce the energy consumption per ton of coal and reduce carbon emissions. BRIEF DESCRIPTION OF DRAWINGS

[0108] In order to make the purpose, technical scheme and beneficial effects of the present application clearer, the present application provides the following drawings for illustration:

[0109] In order to make the purpose, technical scheme and beneficial effects of the present application clearer, the present application provides the following drawings for illustration:Figure 1 The node topology diagram of the mine power supply system for reverse coal flow;

[0110] Figure 2 The material flow-energy flow coupling relationship diagram for a mine;

[0111] Figure 3 The power component composition diagram for a mine from the power grid;

[0112] Figure 4 The day-ahead prediction output curve of the distributed power supply;

[0113] Figure 5 The relaxation error diagram of each branch at each time period;

[0114] Figure 6 The speed comparison curve of the belt conveyor under the same coal mining amount in the coal flow;

[0115] Figure 7 The belt conveyor capacity, belt speed and power curve of scheme 2;

[0116] Figure 8 The coal amount change in the coal bunker of scheme 1;

[0117] Figure 9 The coal amount change in the coal bunker of scheme 2;

[0118] Figure 10 The power consumption power comparison diagram under whether considering flexible maintenance period;

[0119] Figure 11 The time-of-use electricity price and comprehensive carbon emission factor curve of external power purchase;

[0120] Figure 12 The unit time period power consumption of different operation schemes;

[0121] Figure 13 The unit time period carbon emission under different operation schemes. DETAILED DESCRIPTION

[0122] The present application will be further described below in conjunction with the drawings and specific examples, so that those skilled in the art can better understand the present application and implement it, but the examples are not limiting to the present application.

[0123] The material flow-energy flow coupling coal mine transportation network model optimization control method of the present embodiment includes the following steps:

[0124] Step one: build a material flow-energy flow coupling coal mine transportation network model

[0125] Step 1: Take the mine power flow and coal carbon transportation material flow as the research object, and build the node topology structure of the mine power supply system for reverse material flow.

[0126] The mine power supply system involves many devices, many line branches, many voltage levels, and a radial structure. According to the mine power supply system diagram, the embodiment simplifies and abstracts a 34-node mine power supply system topology through load arrangement and topology drawing, as shown in Figure 1 .

[0127] The mine power supply system topology is composed of key nodes such as coal mining, transportation, ventilation, and gas extraction, and contains 5 voltage levels of 35 kV, 10 kV, 1.14 kV, 0.69 kV, and 0.4 kV, a total of 34 nodes and 33 branches. In order to ensure the safety and reliability of power supply, the mine adopts a double-circuit power supply mode. The main source of power supply in the mine area is the large power grid. In the constructed mine power grid, photovoltaic (PV) units are connected to nodes 3, 8, and 11, and wind turbines (WT) are connected to node 2. The process flow of coal flow transportation in the mine is as follows: raw coal in the working face is mined by a coal mining machine, transported by a scraper conveyor from the mining face, then transported by a belt conveyor to a belt conveyor, temporarily stored in a coal bunker, and transported and lifted by a belt conveyor to a ground coal bunker for centralized storage. The coal flow direction is from underground to the ground, and the production load is distributed underground, while the power supply is on the ground, and the power flow direction is from the ground to the underground, as shown in Figure 1 . In order to meet the demand for electricity in the mine, the power flow direction is opposite to the coal flow direction.

[0128] Step 2: Based on the coupling relationship between material flow and distribution network power flow, combined with the coal flow transportation safety of the coal mining working face, belt conveyor, and underground coal bunker and the distribution network power flow constraints, a coal mine transportation network model coupled with material flow and energy flow is constructed.

[0129] From coal mining to coal transportation to the underground coal bunker, and then to the ground coal bunker, the physical quantities of each link of the coal flow have a clear order correlation. The amount of coal mining determines the amount of coal transportation, the speed of the belt conveyor, and the amount of coal in the coal bunker, and further determines the power situation of each link, as shown in Figure 2 . In a unit time period, the longer the working time of the mine working face equipment, the greater the cumulative power consumption, and the more coal is mined. Because the start and stop time of the equipment and the no-load time are relatively short, generally on the order of minutes, expert experience shows that their influence on the average power within an hour can be ignored. The embodiment selects one hour as the scheduling time interval.

[0130] 2.1, Operation constraints of the coal mining working face

[0131] Excluding the influence of factors such as equipment working condition, coal seam geology, underground environment, etc., the coal mining quantity and the equipment power of the coal mining face are in a linear correlation relationship. The equipment power of the coal mining face is the load value of the nodes 7, 24, 25 and 34 of the power grid, which is the power value of the coal mining machine, the scraper conveyor, the transfer machine and the crusher.

[0132] Specifically, the relationship between the coal mining quantity and the power of the coal mining machine is as follows:

[0133] E t = λP c,t △tε t

[0134] In the formula, E t is the coal mining quantity in the t period, in tons; P c,t is the average power value of the coal mining machine in the t period, in kW; λ is the correlation coefficient of the coal mining quantity and the power of the load node of the coal mining face, in tons / kWh; Δt is the unit time interval, in h; ε t is the coal mine working condition coefficient, which is related to factors such as coal seam geology, underground environment, equipment working condition, etc.

[0135] Due to the narrow space in the mine, the coal mined is transported to the ground except for the buffer storage in the coal bunker at the bottom of the mine. Therefore, the coal mining quantity in each period determines the coal conveying quantity of the belt conveyor of the mining and excavation face in the period. The coal conveying quantity and the belt speed jointly determine the power consumption of the belt conveyor of the mining and excavation face, and the power value of the belt conveyor of the mining and excavation face is the load value of the power supply system 22 node. The coal mining quantity and the coal conveying quantity are equal in a scheduling period, and the relationship between the coal mining quantity and the coal conveying quantity is as follows:

[0136] W in,t = E t τ t

[0137]

[0138] In the formula, W in,t is the coal quantity stored in the coal bunker in the t period, in tons; τ t is the coal conveying working condition coefficient, and τ t ∈(0.98, 1.02), which is related to factors such as equipment working condition, underground environment, coal density, etc.; T is the scheduling period, and the value in the embodiment is 24 h.

[0139] 2.2 Running constraints of the belt conveyor

[0140] Based on the standards and specifications such as ISO 5048, DIN 22101 and JIS B 8805, the mathematical relationship between the power of the belt conveyor and the belt speed and the conveying quantity is established as follows:

[0141] P bc,t=1 / (η d η m )*(μ1v t W t 2 +μ2v t +μ3W t 2 / v t +μ4W t +v t 2 W t / 3.6)

[0142] Where: P bc,t is the power of the belt conveyor in the tth period, in kW; the four parameters μ1, μ2, μ3, and μ4 are coefficients related to the structure of the belt conveyor; η d ,η m are the efficiencies of the motor and drive system respectively; W t is the transport volume of the belt conveyor in the tth period, that is, the amount of coal transported by the belt conveyor per unit time, in tons / h; v t It is the belt conveyor speed in m / s.

[0143] During the coal transportation process on a belt conveyor, to prevent coal overflow due to a low belt speed and a large coal flow rate, the following constraints are imposed on the belt conveyor speed and coal transport capacity:

[0144] M t =W t / (3.6*v t )

[0145] 0≤M t ≤M max

[0146] 0≤v t ≤v max

[0147] Where: M t M is the mass per unit length of the belt conveyor in the t period, in kg / m; max v is the upper limit of mass per unit length of the belt conveyor; max It is the upper limit of belt conveyor speed, in m / s.

[0148] 2.3 Operational constraints of the bottom coal bunker

[0149] The effective capacity of the coal bunker at the bottom of the well is generally calculated based on the coal lifting capacity of the transportation and lifting equipment for 0.5-1 hours for medium-sized mines and 1-2 hours for large mines. If the coal storage in the coal bunker is too much, there is a risk of overflow, and if the coal storage is too little, it will cause the belt conveyor to be empty, so generally 90% of the capacity of the coal bunker is set as the upper limit and 20% is set as the lower limit. The capacity of the coal bunker is limited to:

[0150] R t+1 = R t + W in,t - W out,t

[0151] 0.2R N ≤ R t ≤ 0.9R N

[0152] In the formula: R t is the amount of coal in the coal bunker at the t period, R N is the capacity of the coal bunker, W in,t is the amount of coal stored in the coal bunker at the t period, and W out,t is the amount of coal transported from the coal bunker at the t period, all in tons.

[0153] Step 2: Construct a precise carbon measurement model for the energy supply side of the mine, with the goal of minimizing the energy cost and carbon emission penalty cost of the mine power supply system, and with the constraints of Distflow branch flow, node voltage, distributed power upper and lower limits, mine production safety, and material flow-energy flow coupling relationship. Based on the coal flow transportation and mine power grid low-carbon scheduling optimization operation scheme obtained from the material flow-energy flow coupling coal mine transportation network model.

[0154] 3.1, the construction method of the precise carbon measurement model for the energy supply side of the mine includes the following steps:

[0155] S1: Mine electric energy component analysis

[0156] The electric energy component represents the proportion of different types of energy generated by the used electricity. The mine electric energy comes from the large power grid and the distributed power source configured inside the mine. The main sources of electric energy from the large power grid include thermal power, nuclear power, hydroelectric power, photovoltaic power, and wind power. With one hour as the time interval, the power of different sources of electric energy sent to the large power grid is different, and the proportion is also different. In order to estimate and analyze the electric energy component of the mine, the electric energy component of the mine from the large power grid is counted, which can accurately measure the carbon emissions of the mine energy consumption. With the development of carbon tracking, carbon measurement, and carbon flow analysis technologies, more accurate data will support the analysis of the electric energy component of the mine in the future. As shown in Figure 3 , it is the electric energy component composition diagram of a mine purchasing electricity from the large power grid. Specifically, the electric energy component of the large power grid in a unit period is represented as:

[0157]

[0158]

[0159] where i is the type of energy generation, including thermal power, nuclear power, hydropower, photovoltaic power, and wind power; P t i is the average power of the i-th type of energy generation in the t-th period, in kW; P t e is the average power of the mine's electricity purchase in the t-th period, in kW; ε t i is the proportion of the i-th type of energy generation in the t-th period in the mine's electricity purchase.

[0160] Considering that the mine is equipped with a certain capacity of distributed power, including wind power and photovoltaic power, the proportion of photovoltaic and wind power in the total energy consumption of the mine increases, while the proportion of thermal power, hydropower, and nuclear power decreases, compared to the composition of the large grid power. The composition of the total energy consumption of the mine is represented as:

[0161]

[0162]

[0163] where ε' t i is the proportion of the i-th type of energy generation in the t-th period in the total electricity consumption of the mine; P t ci is the average output of the i-th type of power source in the t-th period of the mine, in kW; P t WT is the average output of the wind power configured in the t-th period of the mine, in kW; P t PV is the average output of the photovoltaic power configured in the t-th period of the mine, in kW.

[0164] S2: Time-sharing carbon measurement model

[0165] The time-sharing carbon measurement of the mine refers to measuring the carbon emissions generated by the energy consumption of the mine by the hour, and obtaining the carbon emissions generated by the energy consumption of one ton of coal production, from the time and coal production level. The carbon emission factors of different types of energy generation are different, as shown in Table 1.

[0166] Table 1 Carbon emission factors of different types of energy generation

[0167]

[0168] To describe the carbon emission intensity of energy consumption in the t-th period, the comprehensive carbon emission factor is defined as:

[0169]

[0170]

[0171] wherein: a i is the carbon emission factor of the i-th type of energy generation in the t-th time interval; is the comprehensive carbon emission factor of the purchased electricity from outside in the t-th time interval; is the comprehensive carbon emission factor of the total electricity consumption in the t-th time interval.

[0172] S3: Constructing a precise carbon accounting model for the supply side of mine energy

[0173] The carbon emissions of energy consumption per time interval of the mine and the carbon emissions generated by energy consumption per ton of coal production are related to the comprehensive carbon emission factor, i.e.:

[0174]

[0175]

[0176] wherein: C t is the carbon emissions generated by energy consumption of the mine in the t-th time interval, in kg; E t is the coal production of the mine in the t-th time interval, in tons; CM is the carbon emissions generated by energy consumption per ton of coal production in the dispatching cycle of the mine, in kg CO2 / ton of coal; and t is the time interval.

[0177] 3.2, Objective function

[0178] The mine power supply system operation model in coordination with coal flow and tidal flow takes the minimum of the energy consumption cost and the carbon emission penalty cost of the mine power supply system as the objective, and takes the Distflow branch tidal flow constraint, the node voltage constraint, the upper and lower limit constraint of the distributed power supply, the mine production safety constraint, and the material flow-energy flow coupling relationship as the constraint conditions.

[0179] Specifically, the capacity of the distributed power supply in the mine scene is small and can achieve complete consumption, so the cost of abandoned wind and light is not considered; the reliability of energy supply for underground loads is related to production safety, the load level is high, and important underground loads cannot be reduced; the coal mining capacity is evenly distributed to each working day according to the approved production, and considering the production situation of the coal mine, it is known that except for the maintenance period and the fault period, the time interval is always in full production. In summary, the objective function of the mine power supply system includes the energy consumption cost F1 and the carbon penalty cost F2. The mine energy system operation model takes the minimum of the economic optimization and the daily carbon emission penalty cost as the objective, and is expressed as:

[0180] min F = F1 + F2

[0181]

[0182]

[0183] C rate = P t e * r e + P t WT + P t PV * r DG

[0184] In the formula: F is the total objective function of the scheduling optimization model of the transportation system and the power supply system; F1 is the energy cost objective function of the mine power supply system; F2 represents the carbon emission penalty cost objective function; Cp t is the electricity purchase price per unit period, with a unit of yuan; P t WT is the average power of the wind power configured by the mine in the t period; C WT is the operation and maintenance cost of the wind power configured by the mine, with a unit of yuan; P t PV is the average power of the photovoltaic configured by the mine in the t period; C PV is the operation and maintenance cost of the photovoltaic configured by the mine, with a unit of yuan; δ is the carbon market transaction price of a certain day, C rate is the given carbon emission quota, r e and r DG are the carbon emission quotas of the large power grid and the new energy power generation, respectively, with a unit of kg / kWh.

[0185] 3.2.1, Distflow branch power flow constraint

[0186]

[0187]

[0188]

[0189]

[0190] In the formula: P ij,t , Q ij,t are the active and reactive power flowing through the head of the branch ij in the t period, I ij,t is the current of the line ij in the t period, r ij , x ij are the resistance and reactance of the line ij, respectively, U i,t is the voltage of node i in the t period; P j,t , Q j,t are the active and reactive power supplied by node j to the load in the t period; h represents the downstream node of node j; Ω bThe power grid branch set is represented.

[0191] Since there is a quadratic term in the Distflow branch power flow constraint, convex transformation is performed by using second-order cone relaxation, and the method is as follows:

[0192] (1) Introducing variables i,t and ij,t :

[0193]

[0194]

[0195]

[0196] In the formula: U min , U max are the lower limit and the upper limit of the node voltage respectively;

[0197] (2) The Distflow branch power flow constraint is transformed into:

[0198]

[0199]

[0200]

[0201]

[0202] (3) The second-order cone relaxation is performed on to obtain:

[0203]

[0204] After transformation and rewriting, the standard second-order cone form is obtained:

[0205]

[0206] After convex transformation, the constraint range is relaxed, and there will be a certain error in the calculation result, which is expressed as:

[0207]

[0208] In the formula: err ij,t represents the error of the second-order cone relaxation.

[0209] 3.2.2, node voltage constraint

[0210] U min ≤ U i,t ≤ U max

[0211] In the formula: Umin , U max are the lower and upper limits of the node voltage, respectively;

[0212] The mine power grid topology is single-ended and radiating, with long lines and large end loads. Therefore, the embodiment is configured with a reactive power compensation device at nodes 4 and 6 of the power supply grid to solve the problem of unqualified node voltage. The reactive power compensation amount per unit period is limited by the configured capacity, and is represented as:

[0213]

[0214] In the formula:Qi(t) is the reactive power compensation amount of the reactive power compensation device at node i within period t; Qi,min and Qi,max are the minimum and maximum values of the device reactive power compensation amount, respectively. 3.2.3, upper and lower limit constraints of distributed power supply

[0215] When the i node accesses a distributed power supply, the output of which satisfies the constraint:

[0216]

[0217]

[0218]

[0219] In the formula:Pi(t) is the output of the distributed photovoltaic at node i within period t;Pi,max is the maximum output of the distributed photovoltaic;Pi(t) is the output of the distributed wind power at node i within period t;Pi,max is the maximum output of the distributed wind power. 3.2.4, mine production safety constraints The mine production safety constraints include coal flow operation constraints and maintenance time constraints. The coal flow operation constraints include coal flow transportation safety and distribution network power flow constraints of the coal mining face, belt conveyor and underground coal bunker, as described in sections 2.1-2.3 above. To ensure mine production safety, important equipment such as coal mining machines and belt conveyors must be regularly maintained every day, and the maintenance period does not produce coal, so the coal mining load value is 0, and other non-maintenance loads such as ventilation are still in operation. The maintenance period is generally continuous for 6 hours. The maintenance time constraint is:

[0220]

[0221]

[0222]

[0223]

[0224]

[0225] In the formula: kt is the state variable of important coal mining and coal transportation equipment in period t, and the load value during the maintenance period is 0. In one scheduling cycle, the total maintenance period is 6 hours, and the coal mining period is 18 hours. t1 and t2 are the start and end times of the maintenance period, respectively. The maintenance period is a continuous 6 hours, so the state variable changes twice in one scheduling cycle.

[0226] 3.2.5 Constraints on the coupling relationship between material flow and energy flow

[0227] The coal mining volume of the coal mining face determines the power demand of the coal mining face, and the power value of the coal mining face determines the load value of the coal mining face node in the mine distribution network; the transport volume and belt speed of the belt conveyor in the coal flow determine the power value of the belt conveyor, and the power value of the belt conveyor determines the load value of the belt conveyor node in the mine distribution network, such as Figure 2 As shown. That is, the material flow-energy flow coupling relationship is:

[0228]

[0229]

[0230] Where: P j,t , Q j,t The active and reactive power supplied to the load by node j in time period t; P coal,t , Q coal,t is the average active and reactive power of the coal mining face in the tth period; Ω c is the load node set of the coal mining face; Ω bc is the belt conveyor load node set; P bc,t , Q bc,t represents the active and reactive power obtained from the node by the belt conveyor of the coal flow; j represents the distribution network node. In this embodiment, the coal mining face load node includes the coal mining machine, scraper conveyor, transfer machine, and crusher load of the coal mining face.

[0231] Case Study

[0232] The specific implementation of this embodiment is described and verified below with reference to specific cases.

[0233] The mine selected in this embodiment has an annual coal production of 1.2-1.5 million tons and an average coal output of about 160 tons / hour. Its maintenance period is from 11:00 to 16:00, during which no coal is mined. During the mining period, coal output is generally stable.

[0234] The mine power supply system adopts a time-of-use electricity price mechanism. The four nodes of the mine power supply network are connected to distributed power sources of different capacities, including wind turbines and photovoltaic power generation. Their locations and capacities are shown in Table 2. The output forecast is as follows: Figure 4 shown.

[0235] Table 2 Nodes and capacities of the power supply system with distributed power access

[0236]

[0237] The scheduling period of the simulation case is 24 h, and the scheduling time interval is 1 h. The power and speed variables involved are the average values within 1 h. The software used to execute the simulation case is MATLAB_R2018a configured with the YALMIP toolbox, and the problem is solved by calling the Gurobi 9.1 solver. The simulation platform processor used is AMD Ryzen 5 5500U, the memory is 16 GB, and the operating system is 64-bit Windows 10.

[0238] To verify the effectiveness of the proposed method in improving the energy consumption level and carbon reduction benefits of the mine energy system operation, four simulation scenarios were performed, with the existing operation scheme not considering coal flow adjustment, adjusting the maintenance period, and carbon emission measurement under time-of-use carbon emission measurement being set as the control. The operation schemes are shown in Table 3. The coal flow transportation speed and volume are adjustable, and the maintenance period adjustment and carbon emission penalty cost target considered in the operation scheme can also be achieved through mine management.

[0239] The error of the method used in this embodiment in the mine energy system operation scheduling is shown in Figure 5 , and the maximum order of magnitude of the branch power relaxation error is 10e-7, indicating that it is feasible to perform second-order cone relaxation without losing accuracy.

[0240] Table 3 Operation schemes under different conditions

[0241]

[0242] Under the independent operation of coal flow, the average speed of the belt conveyor per unit period is constant; under the coordination of coal flow and power flow, the belt conveyor speed is as shown in Figure 6 . It can be seen that the belt conveyor speed is negatively correlated with the coal production, and when the coal production is large, the belt conveyor speed is slower, and the power in the corresponding time period is reduced, so that the overall load of the power flow can be reduced as much as possible under the condition of ensuring safe operation. The belt conveyor volume, belt speed, and power obtained by scheme 2 are shown in Figure 7 . The larger the belt speed and volume, the greater the power of the belt conveyor. The coal volume change of the coal bunker in schemes 1 and 2 is shown in Figure 8 , 9 . It can be seen that compared with scheme 1, the coal volume fluctuation in scheme 2 is large, and the buffer capacity of the coal bunker is fully utilized, which reduces the overall energy consumption of the mine energy system.

[0243] The mine maintenance period is originally fixed at 11:00-16:00 time period, most of the time is in the flat section of electricity price; and scheme 3 takes the minimum economic cost as the goal, so that the 17:00-22:00 time period is the maintenance period, and the maintenance coverage is in the peak section of electricity price most of the time, as shown in the figure, under the condition of ensuring the unchanged running time of the mine, the system running cost is significantly reduced. Figure 10

[0244] Considering the lowest running cost and the minimum carbon emission cost, in the running optimization result of the mine energy system of scheme 4, the maintenance period is the same as scheme 3, which is 17:00-22:00 time period. As shown in the figure, Figure 11 It can be seen that the 17:00-22:00 time period has the characteristics of high electricity price and high carbon emission factor, and the maintenance period set in this time period can reduce the system electricity purchase cost and reduce the carbon emission amount.

[0245] The unit time period power consumption and carbon emission amount of the mine under the four schemes are respectively as shown in the figures, Figure 12 Figure 13 Compared with scheme 1, scheme 2 reduces the system energy consumption and the carbon emission amount per ton of coal because it realizes the coordination of coal flow and power flow in most time periods; compared with scheme 2, scheme 3 reduces the total energy consumption and the carbon emission amount per ton of coal of the system because the maintenance period of scheme 3 is adjustable, and the mine chooses to use more energy when the electricity price is lower and less energy when the electricity price is higher; compared with scheme 3, scheme 4 increases the minimum total carbon emission penalty target, which guides the system to use more energy when the comprehensive carbon emission factor is low and less energy when the comprehensive carbon emission factor is high, at the cost of increasing the small power consumption, effectively reducing the carbon emission amount per ton of coal of the system.

[0246] Based on the four running schemes, the running optimization analysis of the mine energy system is carried out, and the running results are shown in Table 4. The daily coal output of scheme 1 and scheme 2 is 3853.45 tons, and the daily coal output of scheme 3 and scheme 4 is different from scheme 1 and scheme 2 because the maintenance period is different. As shown in Table 4, the method proposed in the embodiment can reduce the power consumption per ton of coal by 3.49%, reduce the power consumption cost per ton of coal by 8.28%, and reduce the carbon emission amount per ton of coal by 5.22%; if the clean energy wind power and photovoltaic power are not considered, the carbon emission amount is 64973.83 kg, and the carbon reduction benefit of clean energy can realize carbon reduction by 25.25%.

[0247] Table 4 Comparison of results of different running schemes

[0248]

[0249] ​​In summary, in view of the problems of low energy efficiency, high energy consumption per ton of coal, and high carbon characteristics in mines, the coal mine transportation network model construction method based on material flow-energy flow coupling takes mine flow and coal flow as the research object, abstracts the topological structure, excavates the energy consumption characteristics of the coal flow system based on the mine production process, studies the coupling relationship between coal flow and mine flow, and establishes a coal mine transportation network model based on material flow-energy flow coupling. The coal mine transportation network model optimization control method based on material flow-energy flow coupling of coal, by establishing a precise carbon metering model of the supply side of mine energy, taking the minimum economic cost and the minimum carbon emission penalty cost as the target, considering the production safety constraint under the coordination of mine material flow-energy flow, metering the carbon emissions of real-time electric energy components of grid power purchase, taking the minimum economic cost and the minimum carbon emission penalty cost as the target, obtaining the optimization operation scheme of the coal mine transportation network model based on material flow-energy flow coupling, under the premise of ensuring voltage safety and stability, not only can realize the collaborative optimization of coal flow and distribution network flow, but also can realize low-carbon optimal scheduling, that is, can effectively improve the energy efficiency of mine, reduce the energy consumption per ton of coal and reduce carbon emissions. Through case simulation comparison, the method has the following advantages:

[0250] 1) Flexible and economical: the mine energy system operation model based on material flow-energy flow coordination guides the flexible adjustment of coal flow in multiple production links, and the flexible and elastic characteristics of the silo and belt speed of the belt conveyor in the coal flow transportation link are utilized, and the economic operation and dispatching of the mine power supply system are realized based on the coordination of coal flow and mine flow. The method can reduce the daily production power consumption by 3916.80kWh, and the energy consumption reduction ratio per ton of coal reaches 3.49%; the power consumption cost is reduced by 4628.50 yuan, and the energy saving ratio of mine energy consumption reaches 8.85%.

[0251] 2) Clean and low carbon: the time-sharing carbon metering model provides a carbon reduction strategy for the mine, promotes energy reduction in high-carbon period and energy increase in low-carbon period, and realizes a 5.22% reduction in carbon emissions per ton of coal production; considering the carbon reduction benefit of clean energy, the carbon reduction ratio can reach 25.25%.

[0252] The above-described embodiments are only preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Equivalent substitutions or transformations made by those skilled in the art based on the present application are within the protection scope of the present application. The protection scope of the present application is subject to the claims.

Claims

1. A method for constructing a material flow-energy flow coupled coal mine transportation network model, characterized by: The steps include: Step 1: Taking the mine power flow and coal transportation material flow as the research objects, construct the mine power supply system node topology with reverse material flow; Step 2: Based on the coupling relationship between material flow and distribution network tidal current, and combining the coal flow transportation safety and distribution network tidal current constraints of the coal mining face, belt conveyor, and bottom coal bunker, a material flow-energy flow coupled coal mine transportation network model is constructed. The operating constraints of the coal mining face are: The relationship between coal mining volume and shearer power is: Where: for Coal production during the period; for Average power value of the coal mining machine during the period; is the power correlation coefficient between coal mining volume and load node of coal mining face; is the unit time interval; is the coal mine operating condition coefficient; The coal production and transportation volume in a cycle are equal, so the relationship between the coal production and transportation volume is: Where: For the The amount of coal stored in the coal bunker during the period; is the coal transport operating coefficient; is the scheduling period; The material flow-energy flow coupling constraint is: The coal mining volume of the coal mining face determines the power demand of the coal mining face, and the power value of the coal mining face determines the load value of the coal mining face node in the mine distribution network. The transport volume and belt speed of the belt conveyor in the coal flow determine the belt conveyor power value, and the belt conveyor power value determines the load value of the belt conveyor node in the mine distribution network. In other words, the material flow-energy flow coupling relationship is: Where: 、 For the Time period node Active and reactive power supplied to the load; 、 For coal mining face Average active and reactive power during the period; is the coal mining face load node set; is the belt conveyor load node set; 、 The active and reactive power drawn from the node by the belt conveyor representing the coal flow; Represents a distribution network node.

2. The method for constructing a material flow-energy flow coupled coal mine transportation network model according to claim 1, characterized in that: In step 2, the operation constraints of the belt conveyor are: The mathematical relationship between the power, belt speed and transport capacity of a belt conveyor is: Where: For belt conveyor Power during the time period; 、 、 、 The four parameters are coefficients related to the belt conveyor structure; 、 are the efficiencies of the motor and drive system, respectively; For belt conveyor The transport volume within the time period, that is, the amount of coal transported by the belt conveyor per unit time; is the belt conveyor belt speed; To prevent coal overflow due to low belt speed and high coal flow, the belt conveyor speed and coal transport capacity are subject to the following constraints: Where: For the The mass per unit length of the period belt conveyor; The upper limit of mass per unit length that the belt conveyor can bear; It is the upper limit of belt conveyor speed.

3. The method for constructing a material flow-energy flow coupled coal mine transportation network model according to claim 1, characterized in that: In step 2, the operation constraints of the bottom coal bunker are: Where: For the The amount of coal in the coal bunker during the period, is the capacity of the coal bunker, For the The amount of coal stored in the coal bunker during the period, For the t The amount of coal shipped out from the coal bunker during a period of time.

4. A material flow-energy flow coupled coal mine transportation network model optimization control method, characterized by: The steps include: Step 1: constructing a material flow-energy flow coupled coal mine transportation network model using the method described in any one of claims 1 to 3; Step 2: Construct a precise carbon metering model for the mine energy supply side, with the goal of minimizing the energy consumption cost and carbon emission penalty cost of the mine power supply system. With Distflow branch flow constraints, node voltage constraints, upper and lower limit constraints of distributed power supply, mine production safety constraints, and material flow-energy flow coupling relationships as constraints, an optimized operation control scheme for coal flow transportation and low-carbon scheduling of mine power grids is obtained based on the material flow-energy flow coupling coal mine transportation network model.

5. The material flow-energy flow coupled coal mine transportation network model optimization control method according to claim 4 is characterized by: In step 2, the method for constructing a precise carbon measurement model for the mine energy supply side includes the following steps: S1: Analysis of mine power composition The electric energy composition of the large power grid per unit time period is expressed as: Where: Energy generation type, including thermal power, nuclear power, hydropower, photovoltaic power and wind power; For the During the period Average power of each energy generation type; For the The average power of electricity purchased by the mine during the period; For the During the period The proportion of the power generation output of this energy source in the mine's purchased electricity; The electrical energy component of the mine's overall energy consumption is expressed as: Where: For the Time period The proportion of power generation output of this energy source in the total electricity consumption of the mine; For the Period of time in the mine The average output of the power source; Wind power configured for mines Average output during the period; The photovoltaic power plant equipped for the mine is Average output during the period; S2: Time-sharing carbon measurement model To describe the The carbon emission intensity of energy consumption during a period of time is defined as the comprehensive carbon emission factor: Where: For the Time period Carbon emission factors for electricity generation by type of energy; Purchasing electricity for the mine Comprehensive carbon emission factor for the period; The total electricity consumption of the mine is Comprehensive carbon emission factor for the period; S3: Building a precise carbon measurement model for the mine energy supply side The carbon emissions from energy consumption per unit time period of a mine and the carbon emissions from energy consumption per ton of coal produced are related to the comprehensive carbon emission factor, namely: Where: Energy for mines Carbon emissions generated during the period; For the mine Coal production during the period; The carbon emissions generated by the energy consumption per ton of coal produced during the mine scheduling cycle; is the time interval.

6. The material flow-energy flow coupled coal mine transportation network model optimization control method according to claim 4 is characterized by: In step 2, the minimum target for energy consumption cost and carbon emission penalty cost of the mine power supply system is: Where: The overall objective function of the transportation system and power supply system scheduling optimization model; Energy cost; Penalty costs for carbon; The electricity purchase price per unit time period; Wind power configured for mines Average output during the period; The operation and maintenance costs of deploying wind power for mines; The photovoltaic power plant equipped for the mine is Average output during the period; The operation and maintenance costs of photovoltaic power generation for mines; is the carbon market transaction price on a certain day, For a given carbon emission quota, and They are the carbon emission quotas for the large power grid and new energy power generation respectively.

7. The material flow-energy flow coupled coal mine transportation network model optimization control method according to claim 4 is characterized by: In step 2, the Distflow branch flow constraint is: Where: 、 For the Period flow through branch Active and reactive power at the head end, For the Time Route The current, 、 Line The resistance and reactance, For the Time period node voltage; 、 For the Time period node j Active and reactive power supplied to the load; Representation node downstream nodes; Represents a collection of power grid branches.

8. The material flow-energy flow coupled coal mine transportation network model optimization control method according to claim 7 is characterized by: Since there are quadratic terms in the Distflow branch power flow constraints, a convex transformation is performed using second-order cone relaxation. The method is: (1) Introducing variables and : Where: 、 are the lower and upper limits of the node voltage respectively; (2) Convert the Distflow branch flow constraint into: (3) Yes Performing second-order cone relaxation, we get: After transformation, it is rewritten into the standard second-order cone form: After the convex transformation, the constraint range is relaxed, and the calculation results will have a certain error, which can be expressed as: Where: represents the error of the second-order cone relaxation.

9. The material flow-energy flow coupled coal mine transportation network model optimization control method according to claim 4, characterized in that: In step 2, the node voltage constraint is: Where: 、 are the lower and upper limits of the node voltage respectively; The mine power grid topology is characterized by a single-ended radial structure, long lines, and heavy end loads. Therefore, reactive power compensation devices are configured within the power grid to address the problem of substandard node voltages. The amount of reactive power compensation per unit time period is limited by the configured capacity and is expressed as: Where: For the node The reactive power compensation device at Reactive power compensation amount within the time period; 、 are the minimum and maximum values ​​of the reactive compensation amount of the device respectively; The upper and lower limit constraints of distributed power generation are: when The node is connected to a distributed power source, and its output satisfies the following constraints: Where: For the node Distributed photovoltaic Output during the time period; is the maximum output of distributed photovoltaics; For the node Distributed wind power in Output during the time period; is the maximum output of distributed wind power; the mine production safety constraint is: It includes coal flow operation constraints and maintenance time constraints. The coal flow operation constraints include coal flow transportation safety and distribution network flow constraints of the coal mining working face, belt conveyor and bottom coal bunker. The maintenance time constraint is: Where: For the State variables of important equipment for coal mining and transportation during a certain period; 、 The start time and end time of the maintenance period respectively; The material flow-energy flow coupling constraint is: The coal mining volume of the coal mining face determines the power demand of the coal mining face, and the power value of the coal mining face determines the load value of the coal mining face node in the mine distribution network. The transport volume and belt speed of the belt conveyor in the coal flow determine the belt conveyor power value, and the belt conveyor power value determines the load value of the belt conveyor node in the mine distribution network. In other words, the material flow-energy flow coupling relationship is: Where: 、 For the Time period node Active and reactive power supplied to the load; 、 For coal mining face Average active and reactive power during the period; is the coal mining face load node set; is the belt conveyor load node set; 、 The active and reactive power drawn from the node by the belt conveyor representing the coal flow; Represents a distribution network node.

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