A mine integrated energy system information gap decision robust scheduling method and system

Abstract

The application discloses a kind of mine comprehensive energy system information gap decision robust scheduling method and system, the method includes: using information gap decision theory to establish considering associated resource experience proportion disturbance mine comprehensive energy system information gap decision robust scheduling model;Model includes the uncertainty constraint of associated resource and load demand and associated resource experience proportion disturbance constraint;The double-layer structure model of mine comprehensive energy system information gap decision robust scheduling model is converted into single-layer structure model;Linear conversion is carried out to relevant nonlinear constraint in the mine comprehensive energy system information gap decision robust scheduling model converted into single-layer structure, and mixed integer linear programming model is established;Through the mixed integer linear programming model established, calling solver is solved.The uncertainty of coal associated resource is fully quantified, and the optimization scheduling scheme prepared ensures that mine comprehensive energy system can still reliably operate under the worst scenario possibly existing.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization operation technology, and particularly relates to a robust scheduling method and system for information gap decision-making in a mining integrated energy system. Background Technology

[0002] Coal remains my country's primary energy source, and its dominant position will not change for a considerable period. During coal mining, associated resources such as coalbed methane, exhaust gas, and water inrush are generated. According to data from the National Bureau of Statistics, my country is rich in associated coal resources, with coalbed methane production reaching 10 billion cubic meters in 2021 alone. 3 Water output reaches up to 8 billion cubic meters. 3 The production of associated resources continues to grow, and how to leverage their potential, reduce pollution from direct emissions, and promote the clean transformation of coal mining are issues of common concern. Integrated mine energy systems, as an effective means of integrating the diverse spatial and temporal distribution of coal resources, can achieve coordinated complementarity of various heterogeneous resources in coal mines, meet the diverse energy needs of coal mines, and improve the overall energy utilization rate, economic and environmental benefits of coal mines. This represents an important development direction for achieving the clean transformation of coal mines.

[0003] During coal mining, factors such as coal seam thickness distribution, random aquifers, and gas distribution all affect the recovery and utilization of associated coal resources, resulting in significant uncertainty in associated coal resource production. Furthermore, coal mine management of associated resources is often rudimentary, and the complex underground terrain makes data collection and recording difficult, hindering the discovery of patterns in historical information. This further complicates the prediction of uncertainties related to associated coal resources. Moreover, when dealing with the uncertainties of multiple associated resources, multiple uncertain variables are typically normalized to a single uncertain quantity based on empirical proportions. However, associated resource production is significantly influenced by geological conditions, making it difficult to accurately quantify empirical proportions, and the proportion coefficients may fluctuate. Therefore, the uncertainty of associated resources and the disturbances caused by empirical proportions must be fully considered during the optimized scheduling of the mine's integrated energy system.

[0004] Currently, most integrated energy systems for mines only consider common uncertainties such as wind and solar loads, rarely addressing the uncertainties of associated resources. Furthermore, robust and stochastic optimization methods are often used to handle uncertainties in mines; however, robust optimization yields overly conservative results, and stochastic optimization requires extensive scenario data. For integrated energy systems for mines, there is a lack of application of information gap decision theory to address the uncertainties of coal-associated resources, and no consideration has been given to the perturbation of empirical proportion coefficients for associated resources.

[0005] Therefore, there is an urgent need for a robust scheduling method for information gap decision-making in integrated mine energy systems that takes into account the disturbance of the empirical proportion of associated resources, in order to solve the problems of uncertainty of coal associated resources and disturbance of empirical proportion coefficients, so as to improve the operational reliability of integrated mine energy systems. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a robust scheduling method and system for information gap decision-making in a mine integrated energy system.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0008] This invention provides a robust scheduling method for information gap decision-making in a mine integrated energy system, the method comprising:

[0009] A robust scheduling model for mine integrated energy systems, considering the perturbation of associated resource empirical proportions, is established using information gap decision theory. The model has a two-layer structure: the objective function of the upper layer is to maximize the comprehensive uncertainty of associated resources, and the objective function of the lower layer is to maximize the total system scheduling cost. The model includes uncertainty constraints on associated resources and load demand, as well as perturbation constraints on associated resource empirical proportions.

[0010] The two-layer structure model of the information gap decision-making robust scheduling model of the integrated energy system in the mine is transformed into a single-layer structure model;

[0011] Linearization transformation is performed on the relevant nonlinear constraints in the information gap decision-making robust scheduling model of the integrated energy system of a mine transformed into a single-layer structure, and a mixed-integer linear programming model is established.

[0012] The mixed-integer linear programming model is established and solved by calling the solver; the solution results include the uncertainty of coal-associated resources, system scheduling cost, and unit output plan.

[0013] In one implementation, the uncertainty constraint of the associated resources and load demand is:

[0014]

[0015]

[0016]

[0017]

[0018] In the formula, i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush; α i Let be the uncertainty of the yield of the i-th associated resource; These are the predicted and actual values ​​of the i-th associated resource yield at time t, respectively. Let represent the range of uncertainty fluctuations for the i-th associated resource; k represents the type of load demand; B represents the set of load demand types, including electrical load, heat load, and cooling load demand; l k Let be the uncertainty of the k-th type of load demand; These are the predicted and actual values ​​of the k-th type of load demand at time t, respectively. The maximum uncertainty of the production of the i-th associated resource; This represents the maximum uncertainty of the k-th type of load demand; Let represent the range of uncertainty fluctuations in the k-th type of load demand.

[0019] In one implementation, the associated resource experience ratio perturbation constraint is addressed by introducing a weight correction coefficient during robust scheduling to process the associated resource experience ratio perturbation, as follows:

[0020]

[0021]

[0022]

[0023] In the formula, ψ represents the overall uncertainty; i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush; ε i The empirical weighting coefficient of the i-th associated resource; α i Let be the uncertainty of the i-th associated resource; For the empirical weight correction coefficient of the i-th associated resource; These are the lower and upper limits of the weight correction coefficient for associated resources, respectively.

[0024] In one implementation, the robust scheduling model for mine integrated energy systems based on information gap decision theory, which considers the perturbation of associated resource experience proportions, is as follows:

[0025]

[0026] In the formula, ψ represents the overall uncertainty; X represents the decision variable; V represents the uncertainty variable; and C represents the overall uncertainty. C The maximum expected value that decision-makers can accept; β R α represents the deviation factor; C0 represents the optimal solution of the deterministic model; i represents the type of associated resource, including coalbed methane, exhaust gas, and water inrush; α i Let be the uncertainty of the i-th associated resource; These are the predicted and actual values ​​of the i-th associated resource yield at time t, respectively. Let be the fluctuation range of the i-th associated resource; k represents the type of load demand, including electricity load demand, cooling load demand, and heating load demand; l k Let be the uncertainty of the k-th type of load demand; These are the predicted and actual values ​​of the k-th type of load demand at time t, respectively. Let be the fluctuation range of the k-th type of load demand; G(X,V) = 0 represents the weight correction coefficient for the i-th associated resource; G(X,V) = 0 represents other related equality constraints; K(X,V) ≤ 0 represents other related inequality constraints.

[0027] In one implementation, the process of transforming the two-layer model of the information gap decision-making robust scheduling model of the integrated energy system in a mine into a single-layer model is as follows:

[0028] (1) Satisfy the boundary condition that maximizes the scheduling cost of the lower layer.

[0029] In the lower-level model, the system scheduling cost is maximized when the output of coal-associated resources is minimized and the load demand is maximized.

[0030]

[0031]

[0032] (2) Transformed single-layer model

[0033]

[0034] At this point, for any objective function C(X,V), the value is less than the maximum expected value C set by the decision-maker. C .

[0035] In one implementation, the linearization transformation of relevant nonlinear constraints in the information gap decision-making robust scheduling model of the integrated mine energy system, which is transformed into a single-layer structure, is specifically as follows:

[0036] (1) Variable transformation when multiplying two continuous variables

[0037]

[0038] In the formula, Let be the weighting adjustment coefficient for the i-th type of coal-associated resource; m is a constant coefficient; n is the number of 0 / 1 variables; u j For the j-th 0 / 1 variable; α i Let be the uncertainty of the i-th associated resource;

[0039] (2) Linearization transformation of multiplication of 0 / 1 variables with continuous variables

[0040]

[0041] In the formula, R is u j α i The alternative term is equivalent to u. j α i ; Let be the upper limit of uncertainty for the i-th associated resource.

[0042] In one implementation, after linearization, a mixed-integer linear programming model is established, the compact form of which is:

[0043]

[0044] In the formula, ψ represents the overall uncertainty; X represents the decision variable; V represents the uncertainty variable; and β represents the overall uncertainty. R G(X,V) = 0 represents the deviation factor; G0 represents the optimal solution of the deterministic model; G(X,V) = 0 represents other relevant equality constraints; and K(X,V) ≤ 0 represents other relevant inequality constraints.

[0045] In one implementation, before establishing a robust scheduling model for a mine integrated energy system that considers the perturbation of associated resource experience proportions using information gap decision theory, the method further includes:

[0046] The high-concentration methane converted from abandoned wind and solar power through power-to-gas conversion is blended with coalbed methane containing low-concentration methane. At the same time, idle underground spaces in the mine are transformed into underground gas storage facilities to cooperate with power-to-gas conversion for methane blending, forming a methane blending and utilization mode of different concentrations that is coupled with power-to-gas conversion, coalbed methane, and underground gas storage. Based on the composition and parameters of the mine's integrated energy system and the methane blending and utilization mode of different concentrations, an optimized scheduling model for the mine's integrated energy system of power-to-gas conversion and coalbed methane blending is established.

[0047] The integrated energy system for the mine comprises: a wind turbine power generation unit, a photovoltaic power generation unit, a power grid supply unit, a coalbed methane utilization unit, a waste air utilization unit, a water inflow utilization unit, an electricity-to-gas conversion unit, a gas blending unit, a thermal storage oxidation unit, a water source heat pump unit, a gas turbine unit, a waste heat boiler unit, an absorption chiller unit, an underground gas storage unit, a thermal storage device unit, an electrical load unit, a heat load unit, and a cooling load unit. The parameters include: electrical load, heat load, cooling load, wind turbine output, photovoltaic output, predicted values ​​of coalbed methane, waste air, and water inflow, system equipment composition, equipment operating parameters, and the initial value of the current gas storage capacity in the underground gas storage.

[0048] In one embodiment, the optimal scheduling model for the integrated mine energy system of electricity-to-gas and coalbed methane is as follows: the objective function is to minimize the operating cost of the integrated mine energy system within one scheduling cycle, and multiple constraints are also considered; the objective function for minimizing the operating cost within one scheduling cycle is:

[0049] minC = C1 + C2 + C3 + C4

[0050]

[0051]

[0052]

[0053]

[0054] In the formula, C, C1, C2, C3, and C4 represent the total operating cost of the system, the cost of purchasing electricity, the cost of equipment operation and maintenance, the cost of curtailment of wind and solar power, and the cost of purchasing natural gas for the initial capacity in the underground gas storage, respectively; T represents the total number of time periods in a complete scheduling cycle. The system's power purchase at time t; Let μ be the electricity price at time t; PV μ WT μ GT μ WHB μ UGS μ P2G μ WSHP μ RTO μ AC μ HSD These are the unit maintenance costs for photovoltaic systems, wind turbines, gas turbines, waste heat boilers, underground gas storage facilities, electricity-to-gas conversion systems, water source heat pumps, thermal storage oxidation devices, absorption chillers, and thermal storage devices, respectively. The output power of photovoltaic, wind turbine, gas turbine, waste heat boiler, electric to gas converter, thermal storage oxidation device, and absorption chiller at time t are respectively. These represent the gas release and injection rates in the underground gas storage facility at time t, respectively. Let be the outflow at time t; The thermal storage power and thermal storage power of the thermal storage device at time t are respectively; k PV k WT These are the penalty coefficients for abandoning solar power and wind power, respectively. These are the predicted output power values ​​of photovoltaic and wind turbines at time t, respectively. p represents the initial capacity of the underground gas storage facility. G The price of natural gas;

[0055] The constraints include: operating constraints of thermal regenerative oxidation devices, operating constraints of gas turbines, operating constraints of power-to-gas conversion, operating constraints of underground gas storage facilities, operating constraints of water source heat pumps, operating constraints of absorption chillers, operating constraints of waste heat boilers, operating constraints of thermal storage devices, system power balance constraints, and balance constraints of coalbed methane mixed with high-concentration methane.

[0056] The operational constraints of the underground gas storage facility are as follows:

[0057]

[0058]

[0059]

[0060]

[0061] In the formula, These represent the gas storage capacity in the underground gas storage facility at times t and t-1, respectively. These represent the gas injection and release rates of the underground gas storage facility at time t, respectively. These represent the minimum and maximum injection flow rates for underground gas storage facilities, respectively. These represent the minimum and maximum release flows of the underground gas storage facility, respectively. These are 0 / 1 variables, representing the state of gas injection and release in the underground gas storage facility at time t, respectively. 0 indicates closed and 1 indicates open.

[0062] The equilibrium constraint for the coalbed methane mixed with high-concentration methane is:

[0063]

[0064]

[0065]

[0066]

[0067] In the formula, Let be the volume of the mixed gas at time t; The flow rate of high-concentration methane produced by the electro-gas conversion at time t is mixed with the methane. Let t be the gas release flow rate of the underground gas storage facility; Let t be the flow rate of coalbed methane extracted at time t; Let t be the gas flow rate injected into the gas turbine;

[0068] Let t be the concentration of methane injected into the gas turbine at time t; Let be the concentration of methane produced by the electro-gas conversion at time t; Let t be the concentration of methane in the coalbed methane extracted at time t; These are the minimum and maximum methane concentrations required to ensure normal combustion and power generation in a gas turbine, respectively.

[0069] This invention also provides a robust scheduling system for information gap decision-making in a mining integrated energy system, comprising: a robust scheduling model module for information gap decision-making in a mining integrated energy system, a structure transformation module, a linear programming model module, and a solution module;

[0070] The robust scheduling model module for information gap decision-making in the integrated mining energy system is used to establish a robust scheduling model for information gap decision-making in the integrated mining energy system that considers the disturbance of the empirical proportion of associated resources using information gap decision theory. The model has a two-layer structure, with the objective function of the upper layer being to maximize the comprehensive uncertainty of associated resources and the objective function of the lower layer being to maximize the total system scheduling cost. The model includes uncertainty constraints on associated resources and load demand and disturbance constraints on the empirical proportion of associated resources.

[0071] The structural transformation module is used to transform the two-layer structural model of the information gap decision-making robust scheduling model of the integrated energy system in a mine into a single-layer structural model.

[0072] The linear programming model module is used to linearize the relevant nonlinear constraints in the information gap decision-making robust scheduling model of the integrated energy system for mines, which is transformed into a single-layer structure, and to establish a mixed-integer linear programming model.

[0073] The solution module is used to call the solver to solve the established mixed-integer linear programming model; the solution results include the uncertainty of coal-associated resources, system scheduling costs, and unit output plans.

[0074] The beneficial effects of this invention are:

[0075] This invention presents a robust scheduling method for mine integrated energy systems that considers the perturbation of associated resource empirical proportions. Based on a selected mine integrated energy system, it fully considers the operational constraints of each system component. Using information gap decision theory, it establishes a robust scheduling model for the mine integrated energy system that considers the perturbation of associated resource empirical proportions. Through the linearization transformation of nonlinear constraints, it calls relevant mathematical solvers to solve the model, obtaining an optimized scheduling scheme for the mine integrated energy system. This model comprehensively considers the uncertainty of coal-associated resources and the perturbation of empirical proportion coefficients, fully quantifying the uncertainty of coal-associated resources. The formulated optimized scheduling scheme can ensure the reliable operation of the mine integrated energy system even under the worst-case scenario. Attached Figure Description

[0076] The accompanying drawings, as part of this invention, are provided to further illustrate the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation thereof. Clearly, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0077] Figure 1 A flowchart of a robust scheduling method for information gap decision-making in a mine integrated energy system is provided in an embodiment of the present invention.

[0078] Figure 2 This is a diagram of a mine integrated energy system architecture provided in an embodiment of the present invention;

[0079] Figure 3 The graph shows the changes in uncertainty and scheduling cost of different combinations of coexisting resources as a function of cost deviation factor, without considering empirical proportional disturbances.

[0080] Figure 4 The figure shows how the associated resource uncertainty and scheduling cost vary with the cost deviation factor when using the method provided by this invention, taking into account empirical proportional disturbances.

[0081] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation

[0082] The following detailed description, in conjunction with embodiments and accompanying drawings, illustrates the proposed method for optimizing and scheduling integrated energy systems in mines, which considers the uncertainty of associated coal resources and the disturbance of empirical proportional coefficients.

[0083] This invention, based on the operational constraints of each link in a mine integrated energy system, and according to the general form of a robust model of information gap decision theory, establishes a robust scheduling model for mine integrated energy systems that considers the perturbation of the empirical proportion of associated resources. This model comprehensively considers the uncertainty of coal associated resources and the perturbation of the empirical proportion coefficient, fully quantifies the uncertainty of coal associated resources, and the optimized scheduling scheme formulated can ensure that the mine integrated energy system can still operate reliably under the worst possible scenario. Figure 1 A robust scheduling method for information gap decision-making in a mine integrated energy system is provided as an example, such as Figure 1 As shown, the specific steps include the following:

[0084] Step S100: Establish a robust scheduling model for the mine's integrated energy system based on information gap decision theory, considering the perturbation of associated resource experience ratios; the model has a two-layer structure, with the upper layer's objective function being to maximize the comprehensive uncertainty of associated resources, and the lower layer's objective function being to maximize the total system scheduling cost; the model includes uncertainty constraints on associated resources and load demand, and perturbation constraints on associated resource experience ratios.

[0085] In this embodiment of the application, the information gap decision theory is expressed as:

[0086]

[0087]

[0088] F C =(1+β)F0 (3)

[0089] 0≤β≤1 (4)

[0090]

[0091] In the formula, F is the objective function; s is the decision variable; w is the uncertainty variable; G(s,w)=0 indicates equality constraint; K(s,w)≤0 indicates inequality constraint; Uncertainty represents the magnitude of fluctuation of an uncertain variable. These are the predicted values ​​for uncertain variables; F0 represents the fluctuation range of the uncertain variable; F0 is the value when... At that time, the optimal solution of the system is the optimal solution of the deterministic model; β deviation factor is the degree of deviation between the expected robust optimization objective value and the optimal solution of the deterministic model; F C This represents the maximum expected value acceptable to the decision-maker. The information gap decision robust optimization model is a two-layer model: the upper layer solves for the maximum value of uncertainty, and the lower layer solves for the maximum value of the objective function.

[0092] The objective function of the upper layer is to maximize the comprehensive uncertainty of associated resources:

[0093] maxψ(6)

[0094]

[0095] In the formula, ψ represents the overall uncertainty; i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush; ε i The empirical weighting coefficient of the i-th associated resource; α i Let be the uncertainty of the yield of the i-th associated resource; is the weighting correction coefficient for the i-th coal-associated resource.

[0096] The objective function of the lower level is to maximize the total system scheduling cost:

[0097] maxC = C1 + C2 + C3 + C48)

[0098]

[0099]

[0100]

[0101]

[0102] In the formula, C, C1, C2, C3, and C4 represent the total operating cost of the system, the cost of purchasing electricity, the cost of equipment operation and maintenance, the cost of curtailment of wind and solar power, and the cost of purchasing natural gas for the initial capacity in the underground gas storage, respectively; T represents the total number of time periods in a complete scheduling cycle. The system's power purchase at time t; Let μ be the electricity price at time t; PV μ WT μ GT μ WHB μ UGS μ P2G μ WSHP μ RTO μ AC μ HSD These are the unit maintenance costs for photovoltaic systems, wind turbines, gas turbines, waste heat boilers, underground gas storage facilities, electricity-to-gas conversion systems, water source heat pumps, thermal storage oxidation devices, absorption chillers, and thermal storage devices, respectively. The output power of photovoltaic, wind turbine, gas turbine, waste heat boiler, electric to gas converter, thermal storage oxidation device, and absorption chiller at time t are respectively. These represent the gas release and injection power in the underground gas storage facility at time t, respectively. Let be the outflow at time t; The thermal storage power and thermal storage power of the thermal storage device at time t are respectively; k PV k WT These are the penalty coefficients for abandoning solar power and wind power, respectively. These are the predicted output power values ​​of photovoltaic and wind turbines at time t, respectively. p represents the initial capacity of the underground gas storage facility. G The price of natural gas.

[0103] Furthermore, the uncertainty constraints of associated resources and load demand are as follows:

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush; α i Let be the uncertainty of the yield of the i-th associated resource; These are the predicted and actual values ​​of the i-th associated resource yield at time t, respectively. Let represent the range of uncertainty fluctuations for the i-th associated resource; k represents the type of load demand; B represents the set of load demand types, including electrical load, heat load, and cooling load demand; l k Let be the uncertainty of the k-th type of load demand; These are the predicted and actual values ​​of the k-th type of load demand at time t, respectively. The maximum uncertainty of the production of the i-th associated resource; This represents the maximum uncertainty of the k-th type of load demand; Let represent the range of uncertainty fluctuations in the k-th type of load demand.

[0109] Furthermore, regarding the associated resource experience ratio disturbance constraint, a weight correction coefficient is introduced during robust scheduling to handle the associated resource experience ratio disturbance, expressed as:

[0110]

[0111]

[0112]

[0113] In the formula, ψ represents the overall uncertainty; i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush; ε i The empirical weighting coefficient of the i-th associated resource; α i Let be the uncertainty of the i-th associated resource; For the empirical weight correction coefficient of the i-th associated resource; These are the lower and upper limits of the weight correction coefficient for associated resources, respectively.

[0114] Furthermore, using information gap decision theory, a robust scheduling model for mine integrated energy systems considering the perturbation of associated resource experience proportions is established as follows:

[0115]

[0116] In the formula, ψ represents the overall uncertainty; X represents the decision variable; V represents the uncertainty variable; and C represents the overall uncertainty. CThe maximum expected value that decision-makers can accept; β R α represents the deviation factor; C0 represents the optimal solution of the deterministic model; i represents the type of associated resource, including coalbed methane, exhaust gas, and water inrush; α i Let be the uncertainty of the i-th associated resource; These are the predicted and actual values ​​of the i-th associated resource yield at time t, respectively. Let be the fluctuation range of the i-th associated resource; k represents the type of load demand, including electricity load demand, cooling load demand, and heating load demand; l k Let be the uncertainty of the k-th type of load demand; These are the predicted and actual values ​​of the k-th type of load demand at time t, respectively. Let be the fluctuation range of the k-th type of load demand; G(X,V) = 0 represents the weight correction coefficient for the i-th associated resource; G(X,V) = 0 represents other related equality constraints; K(X,V) ≤ 0 represents other related inequality constraints.

[0117] Step S200: Transform the two-layer structure model of the information gap decision-making robust scheduling model of the integrated energy system in the mine into a single-layer structure model;

[0118] In this embodiment of the application, the specific process of transforming the two-layer model of the information gap decision-making robust scheduling model of the integrated energy system in the mine into a single-layer model is as follows:

[0119] (1) Satisfy the boundary condition that maximizes the scheduling cost of the lower layer.

[0120] In the lower-level model, the system scheduling cost is maximized when the output of coal-associated resources is minimized and the load demand is maximized.

[0121]

[0122]

[0123] (2) Transformed single-layer model

[0124]

[0125] At this point, for any objective function C(X,V), the value is less than the maximum expected value C set by the decision-maker. C .

[0126] Step S300: Linearize the relevant nonlinear constraints in the information gap decision-making robust scheduling model of the integrated energy system of the mine into a single-layer structure, and establish a mixed integer linear programming model;

[0127] Specifically, the nonlinear constraints in the robust scheduling model for information gap decision-making in the integrated energy system of the mine, which considers the perturbation of the empirical proportion of associated resources, obtained in step S200, are linearized. A mixed-integer linear programming model is established and the relevant solver is called to solve it. For the product terms of two continuous variables in equations (7), (17), (20), and (23), one of the continuous variables is decomposed into a constant multiplied by an integer, and then auxiliary variables and constraints are introduced to linearize these nonlinear terms. After linearization, this problem is transformed into a mixed-integer linear programming problem.

[0128] In this application, the nonlinear constraints are linearized, and the specific process is as follows:

[0129] (1) Variable transformation when multiplying two continuous variables

[0130]

[0131] In the formula, Let be the weighting adjustment coefficient for the i-th type of coal-associated resource; m is a constant coefficient; n is the number of 0 / 1 variables; u j For the j-th 0 / 1 variable; α i Let be the uncertainty of the i-th associated resource;

[0132] (2) Linearization transformation of multiplication of 0 / 1 variables with continuous variables

[0133]

[0134] In the formula, R is u j α i The alternative term is equivalent to u. j α i ; Let be the upper limit of uncertainty for the i-th associated resource.

[0135] Furthermore, after linearization, a mixed-integer linear programming model is established, the compact form of which is:

[0136]

[0137] In the formula, ψ represents the overall uncertainty; X represents the decision variable; V represents the uncertainty variable; and β represents the overall uncertainty. R C0 is the deviation factor; G(X,V) = 0 is other relevant equality constraints; K(X,V) ≤ 0 is other relevant inequality constraints.

[0138] In an optional embodiment, before establishing a robust scheduling model for a mine integrated energy system that considers the perturbation of associated resource empirical proportions using information gap decision theory, the method further includes:

[0139] The high-concentration methane converted from abandoned wind and solar power through power-to-gas conversion is blended with coalbed methane containing low-concentration methane. At the same time, idle underground spaces in the mine are transformed into underground gas storage facilities to cooperate with power-to-gas conversion for methane blending, forming a methane blending and utilization mode of different concentrations that is coupled with power-to-gas conversion, coalbed methane, and underground gas storage. Based on the composition and parameters of the mine's integrated energy system and the methane blending and utilization mode of different concentrations, an optimized scheduling model for the mine's integrated energy system of power-to-gas conversion and coalbed methane blending is established.

[0140] The integrated energy system of the mine consists of: a wind turbine power generation unit, a photovoltaic power generation unit, a power grid supply unit, a coalbed methane utilization unit, a waste air utilization unit, a water inflow utilization unit, an electricity-to-gas conversion unit, a gas blending unit, a thermal storage oxidation unit, a water source heat pump unit, a gas turbine unit, a waste heat boiler unit, an absorption chiller unit, an underground gas storage unit, a thermal storage device unit, an electrical load unit, a heat load unit, and a cooling load unit; parameters include: electrical load, heat load, cooling load, wind turbine output, photovoltaic output, predicted values ​​of coalbed methane, waste air, and water inflow, system equipment composition, equipment operating parameters, and the initial value of the current gas storage capacity of the underground gas storage.

[0141] Specifically, in this embodiment, the predicted values ​​of electrical load, heat load, cooling load, fan output, photovoltaic output, coalbed methane, exhaust air, and water inrush for one scheduling cycle of the system are first input; then, the initial values ​​of variables or parameters such as system equipment composition, equipment operating parameters, and the current gas storage capacity of the underground gas storage facility are input. The power demand is met by fans, photovoltaics, the power grid, and gas turbines; the heat load demand is met by waste heat boilers and thermal storage devices; the cooling load demand is met by absorption chillers and water source heat pumps; the thermal oxidation device converts the chemical energy in the exhaust air into heat energy through catalytic oxidation; the power-to-gas conversion device consumes electricity to perform a methanation reaction, generating high-concentration gas, a portion of which is mixed with coalbed methane, and the surplus is injected into the underground gas storage facility; the underground gas storage facility provides a source of high-concentration gas during the power-to-gas conversion shutdown phase; the gas turbine generates electricity by burning the mixed gas, and the waste heat generated is absorbed by the waste heat boiler / absorption chiller for heating / cooling. The multi-resource recycling architecture of the integrated mine energy system is as follows: Figure 2 As shown, detailed parameters of the system equipment are shown in Table 1.

[0142] Table 1 System Composition and Parameters

[0143]

[0144]

[0145] Furthermore, the optimal scheduling model for the integrated energy system of a mine that combines electricity and gas with coalbed methane is as follows: the objective function is to minimize the operating cost of the integrated energy system within one scheduling cycle, and multiple constraints are also included.

[0146] The objective function for minimizing the operating cost within a scheduling cycle is:

[0147] minC=C1+C2+C3+C4 (27)

[0148]

[0149]

[0150]

[0151]

[0152] In the formula, C, C1, C2, C3, and C4 represent the total operating cost of the system, the cost of purchasing electricity, the cost of equipment operation and maintenance, the cost of curtailment of wind and solar power, and the cost of purchasing natural gas for the initial capacity in the underground gas storage, respectively; T represents the total number of time periods in a complete scheduling cycle. The system's power purchase at time t; Let μ be the electricity price at time t; PV μ WT μ GT μ WHB μ IGS μ P2G μ WSHP μ RTO μ AC μ HSD These are the unit maintenance costs for photovoltaic systems, wind turbines, gas turbines, waste heat boilers, underground gas storage facilities, electricity-to-gas conversion systems, water source heat pumps, thermal storage oxidation devices, absorption chillers, and thermal storage devices, respectively. The output power of photovoltaic, wind turbine, gas turbine, waste heat boiler, electric to gas converter, thermal storage oxidation device, and absorption chiller at time t are respectively. These represent the gas release and injection rates in the underground gas storage facility at time t, respectively. Let be the outflow at time t; The thermal storage power and thermal storage power of the thermal storage device at time t are respectively; k PV k WT These are the penalty coefficients for abandoning solar power and wind power, respectively. These are the predicted output power values ​​of photovoltaic and wind turbines at time t, respectively. p represents the initial capacity of the underground gas storage facility. G The price of natural gas.

[0153] Multiple constraints include: operating constraints of thermal regenerative oxidation devices, operating constraints of gas turbines, operating constraints of power-to-gas conversion, operating constraints of underground gas storage facilities, operating constraints of water source heat pumps, operating constraints of absorption chillers, operating constraints of waste heat boilers, operating constraints of thermal storage devices, system power balance constraints, and balance constraints of coalbed methane blended with high-concentration methane.

[0154] Furthermore, the operational constraints of underground gas storage facilities are expressed as follows:

[0155]

[0156]

[0157]

[0158]

[0159] In the formula, These represent the gas storage capacity in the underground gas storage facility at times t and t-1, respectively. These represent the gas injection and release rates of the underground gas storage facility at time t, respectively. These represent the minimum and maximum injection flow rates for underground gas storage facilities, respectively. These represent the minimum and maximum release flows of the underground gas storage facility, respectively. The variables are 0 and 1, representing the state of gas injection and release in the underground gas storage facility at time t, respectively. 0 indicates closed and 1 indicates open.

[0160] Furthermore, the equilibrium constraint for coalbed methane blending with high-concentration methane is:

[0161]

[0162]

[0163]

[0164]

[0165] In the formula, Let be the volume of the mixed gas at time t; The flow rate of high-concentration methane produced by the electro-gas conversion at time t is mixed with the methane. Let t be the gas release flow rate of the underground gas storage facility; Let t be the flow rate of coalbed methane extracted at time t; Let t be the gas flow rate injected into the gas turbine; Let t be the concentration of methane injected into the gas turbine at time t; Let be the concentration of methane produced by the electro-gas conversion at time t; Let t be the concentration of methane in the coalbed methane extracted at time t; These are the minimum and maximum methane concentrations required to ensure normal combustion and power generation in a gas turbine, respectively.

[0166] In one possible implementation, multiple operational constraints also include: operational constraints for the thermal regenerative oxidation unit, operational constraints for the gas turbine, operational constraints for the power-to-gas conversion unit, operational constraints for the water source heat pump, operational constraints for the absorption chiller, operational constraints for the waste heat boiler, operational constraints for the thermal storage unit, and system power balance constraints.

[0167] Furthermore, the operating constraints of the regenerative thermal oxidation unit are:

[0168]

[0169]

[0170]

[0171] In the formula, The output thermal power of the regenerative oxidation device at times t and t-1 are respectively; η RTO The thermal conversion efficiency of the regenerative oxidation device; C represents the CH4 concentration in VAM at time t. CH4 The lower heating value of gas; The exhaust air flow rate input to the regenerative thermal oxidizer at time t; This refers to the maximum output thermal power of the regenerative oxidation device, the same below; These are the upper and lower limits of the ramp power for the regenerative thermal oxidation device, respectively, and the same applies below.

[0172] Furthermore, the operating constraints of the gas turbine are:

[0173]

[0174]

[0175]

[0176]

[0177] In the formula, The output electrical power of the gas turbine at times t and t-1 are respectively; η GT The power generation efficiency of the gas turbine; Let t be the concentration of methane input into the gas turbine. Let t be the gas flow rate input into the gas turbine; Let t be the output waste heat power of the gas turbine; This is the heat loss coefficient of the gas turbine.

[0178] Furthermore, the constraints for electricity-to-gas operation are:

[0179]

[0180]

[0181] In the formula, η is the power of the gas produced by the electro-gas conversion at time t; P2G The conversion efficiency of electricity to gas; Let t be the electrical power input to the electro-gas converter.

[0182] Furthermore, the operational constraints for underground gas storage facilities are as follows:

[0183]

[0184]

[0185]

[0186]

[0187] In the formula, These represent the gas storage capacity in the underground gas storage facility at times t and t-1, respectively. These represent the gas injection and release rates of the underground gas storage facility at time t, respectively. These represent the minimum and maximum injection flow rates for underground gas storage facilities, respectively. These represent the minimum and maximum release flows of the underground gas storage facility, respectively. The variables are 0 and 1, representing the state of gas injection and release in the underground gas storage facility at time t, respectively. 0 indicates closed and 1 indicates open.

[0188] Furthermore, the operating constraints of the water source heat pump are:

[0189]

[0190]

[0191]

[0192] In the formula, The cooling power of the water source heat pump at times t and t-1 are respectively; η WSHP The refrigeration correlation coefficient of the water source heat pump; Let be the water inflow rate of the system at time t.

[0193] Furthermore, the operating constraints of the absorption chiller are:

[0194]

[0195]

[0196]

[0197] In the formula, , respectively, represent the cooling power of the absorption chiller at times t and t-1; The coefficient of performance (COP) of an absorption chiller; Let t be the waste heat input power of the absorption chiller at time t.

[0198] Furthermore, the operating constraints for the waste heat boiler are:

[0199]

[0200]

[0201]

[0202] In the formula, These represent the heat production power of the waste heat boiler at times t and t-1, respectively. The heating efficiency coefficient of the waste heat boiler; Let t be the waste heat input power of the waste heat boiler.

[0203] Furthermore, the operating constraints of the thermal storage device are:

[0204]

[0205]

[0206]

[0207]

[0208] In the formula, These represent the heat storage capacity of the heat storage device at times t and t-1, respectively. These are the self-loss rate, heat storage efficiency, and heat release efficiency of the heat storage device, respectively. These represent the heat storage and heat release power of the heat storage device at time t; The variables are 0 and 1, representing the on / off state of the heat storage device at time t, respectively.

[0209] Furthermore, the system power balance constraint is as follows:

[0210]

[0211]

[0212]

[0213]

[0214] In the formula, These represent the photovoltaic and wind turbine power absorbed by the system at time t, respectively. Purchase electricity for the system at time t; These represent the system's electrical, thermal, and cooling load demands at time t, respectively.

[0215] Step S400: Solve the mixed-integer linear programming model by calling the solver; the solution results include the uncertainty of coal-associated resources, system scheduling cost, and unit output plan.

[0216] Furthermore, the solver is the GUROBI solver.

[0217] This invention establishes a robust scheduling method for information gap decision-making in a mine integrated energy system that considers the disturbance of associated resource empirical proportions. After linearization, a correlation solver is used to solve the system operation scheme within the scheduling period.

[0218] In one embodiment, a robust scheduling system for information gap decision-making in a mining integrated energy system is proposed. The system includes: a robust scheduling model module for information gap decision-making in a mining integrated energy system, a structural transformation module, a linear programming model module, and a solution module.

[0219] The robust scheduling model module for information gap decision-making in the integrated mining energy system is used to establish a robust scheduling model for information gap decision-making in the integrated mining energy system that considers the disturbance of the empirical proportion of associated resources using information gap decision theory. The model has a two-layer structure, with the objective function of the upper layer being to maximize the comprehensive uncertainty of associated resources and the objective function of the lower layer being to maximize the total system scheduling cost. The model includes uncertainty constraints on associated resources and load demand and disturbance constraints on the empirical proportion of associated resources.

[0220] The structural transformation module is used to transform the two-layer structural model of the information gap decision-making robust scheduling model of the integrated energy system in a mine into a single-layer structural model.

[0221] The linear programming model module is used to linearize the relevant nonlinear constraints in the information gap decision-making robust scheduling model of the integrated energy system for mines, which is transformed into a single-layer structure, and to establish a mixed-integer linear programming model.

[0222] The solution module is used to call the solver to solve the established mixed-integer linear programming model; the solution results include the uncertainty of coal-associated resources, system scheduling costs, and unit output plans.

[0223] It should be noted that the robust scheduling system for information gap decision-making in the integrated mining energy system provided in the above embodiments is only illustrated by the division of the above functional modules when executing a robust scheduling method for information gap decision-making in an integrated mining energy system. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the robust scheduling system for information gap decision-making in the integrated mining energy system and the embodiment of the robust scheduling method for information gap decision-making in an integrated mining energy system provided in the above embodiments belong to the same concept. The implementation process is detailed in the embodiment of the robust scheduling system for information gap decision-making in an integrated mining energy system, and will not be repeated here.

[0224] The following presents two sets of comparative examples. Comparison 1: Without considering empirical proportional disturbances, the impact of uncertainties on system operation of different coexisting resource combinations is compared. Combination 1 only considers the uncertainty of coalbed methane, combination 2 only considers the uncertainty of exhaust gas, combination 3 only considers the uncertainty of water inflow, and combination 4 simultaneously considers the uncertainties of coalbed methane, exhaust gas, and water inflow. The operating results are as follows. Figure 3 .

[0225] Comparison Example 2: Considering the empirical proportion disturbance, the impact of the disturbance on the uncertainty of associated resources and the operating results is analyzed. The changes in the weighting coefficients under the empirical proportion disturbance are shown in Table 2, and the operating results are as follows. Figure 4 .

[0226] The computer hardware environment for performing the optimization calculations was an Intel(R) Core(TM) i7-11370H with a clock speed of 3.30GHz and 16GB of memory; the software environment was a Windows 11 operating system.

[0227] Analysis of Case Comparison 1: From Figure 3 It is evident that as the cost deviation factor increases, the uncertainty of each associated resource also increases. The sensitivity of the uncertainty of coal-associated resources to changes in the cost deviation factor, from highest to lowest, is: coalbed methane, water inrush, and exhaust ventilation. Once the system reaches its maximum robustness, the constraints related to associated resources become more extensive. Although the expected dispatch costs acceptable to decision-makers continue to increase, the robustness of the mine's integrated energy system does not further improve. Therefore, after the system reaches its maximum robustness, continuing to increase cost reserves is uneconomical.

[0228] Analysis of Case Study 2: Table 2 shows that in the worst-case scenario, the weighting coefficients of coalbed methane and exhaust gas increase relatively, while the weighting of water inflow decreases relatively. This is because coalbed methane, exhaust gas, and system equipment are tightly coupled, while water inflow only converts cold energy through a water source heat pump, making the coupling relatively simple; the tighter the coupling, the higher the scheduling cost. Figure 4 It can be seen that, considering the perturbation of the empirical ratio, the uncertainty of water inflow is compressed to zero due to the relatively increased weight coefficients of coalbed methane and exhaust ventilation, while the uncertainty of other associated resources remains relatively stable, thus reducing the overall uncertainty of associated resources. Therefore, changes in the weight coefficients of associated resources will affect information gap decision-making. The cost reserve considering the fluctuation of the empirical ratio is reduced by 9.8% compared to the case without considering the fluctuation of the empirical ratio, which improves the operational economy of the system to a certain extent. At the same time, when the fluctuation of the actual value of coalbed methane and exhaust ventilation relative to the predicted value is within 20%, and the fluctuation of the actual value of water inflow relative to the predicted value is 0, the perturbation of the empirical ratio of coal associated resources can be effectively avoided. In this case, the total scheduling cost of the mine's integrated energy system will not exceed 46,412.6 yuan.

[0229] Table 2 shows the changes in weighting coefficients under empirical proportional perturbation.

[0230]

[0231] The above analysis shows that the information gap decision robust model effectively quantifies the uncertainty of coal-associated resources in the integrated energy system of mines. For the expected cost given by the decision-maker, there is a corresponding uncertainty fluctuation range, which can realize optimized scheduling under different risks and enhance the adaptability of decision to uncertainty fluctuations. By introducing a weight correction amount to correct the disturbance of empirical weight coefficients in the process of normalizing the uncertainty of multiple coal-associated resources, the information gap decision robust scheduling scheme is more practical and has strong adaptability to the disturbance of the empirical proportion of associated resources.

[0232] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0233] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features found in other embodiments but not others, combinations of features from different embodiments are also within the scope of protection of this invention and form different embodiments. For example, in the embodiments described above, those skilled in the art can use them in combination based on known technical solutions and the technical problems to be solved by this application.

[0234] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-described technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A robust scheduling method for information gap decision-making in a mine integrated energy system, characterized in that, The method includes: A robust scheduling model for mine integrated energy systems, considering the perturbation of associated resource empirical proportions, is established using information gap decision theory. The model has a two-layer structure: the objective function of the upper layer is to maximize the comprehensive uncertainty of associated resources, and the objective function of the lower layer is to maximize the total system scheduling cost. The model includes uncertainty constraints on associated resources and load demand, as well as perturbation constraints on associated resource empirical proportions. The uncertainty constraints on associated resources and load demand are as follows: In the formula, i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush. Let be the uncertainty of the yield of the i-th associated resource; , These are the predicted and actual values ​​of the i-th associated resource yield at time t, respectively. Let represent the range of uncertainty fluctuations for the i-th associated resource; k represents the type of load demand; and B represents the set of load demand types, including electrical load, heat load, and cooling load demand. Let be the uncertainty of the k-th type of load demand; , These are the predicted and actual values ​​of the k-th type of load demand at time t, respectively. The maximum uncertainty of the production of the i-th associated resource; This represents the maximum uncertainty of the k-th type of load demand; The range of uncertainty fluctuations in the k-th type of load demand; The associated resource experience ratio disturbance constraint is addressed by introducing a weight correction coefficient during robust scheduling to process the associated resource experience ratio disturbance, expressed as follows: In the formula, The overall uncertainty is represented by ; i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush. The empirical weighting coefficient of the i-th associated resource; Let be the uncertainty of the i-th associated resource; For the empirical weight correction coefficient of the i-th associated resource; , These are the lower and upper limits of the associated resource weight correction coefficient, respectively; The robust scheduling model for mine integrated energy system based on information gap decision theory, considering the perturbation of associated resource experience ratios, is as follows: In the formula, To account for the overall uncertainty; For decision variables; It is an uncertain variable; The maximum expected value that decision-makers can accept; This is the deviation factor; is the optimal solution of the deterministic model; i represents the type of associated resource, including coalbed methane, exhaust gas, and water inrush. Let be the uncertainty of the i-th associated resource; , These are the predicted and actual values ​​of the i-th associated resource yield at time t, respectively. Let be the fluctuation range of the i-th associated resource; k represents the type of load demand, including electrical load demand, cooling load demand, and heating load demand. Let be the uncertainty of the k-th type of load demand; , These are the predicted and actual values ​​of the k-th type of load demand at time t, respectively. Let be the fluctuation range of the k-th type of load demand; is the weight correction coefficient for the i-th associated resource; For other related equality constraints; For other related inequality constraints; The two-layer structure model of the information gap decision-making robust scheduling model of the integrated energy system in the mine is transformed into a single-layer structure model; Linearization transformation is performed on the relevant nonlinear constraints in the information gap decision-making robust scheduling model of the integrated energy system of a mine transformed into a single-layer structure, and a mixed-integer linear programming model is established. The mixed-integer linear programming model is established and solved by calling the solver; the solution results include the uncertainty of coal-associated resources, system scheduling cost, and unit output plan.

2. The robust scheduling method for information gap decision-making in a mine integrated energy system according to claim 1, characterized in that, The process of transforming the two-layer model of the information gap decision-making robust scheduling model of the integrated energy system in the mine into a single-layer model is as follows: (1) Satisfy the boundary condition that maximizes the scheduling cost of the lower layer. In the lower-level model, the system scheduling cost is maximized when the output of coal-associated resources is minimized and the load demand is maximized. (2) Transformed single-layer model At this point, for any objective function All were less than the maximum expected value set by the decision-makers. .

3. The robust scheduling method for information gap decision-making in a mine integrated energy system according to claim 1, characterized in that, The linearization transformation of relevant nonlinear constraints in the information gap decision-making robust scheduling model of the integrated energy system for mines, which has been converted into a single-layer structure, is specifically as follows: (1) Variable transformation when multiplying two continuous variables In the formula, is the weighting correction coefficient for the i-th type of coal-associated resource; The coefficient is constant. The number of 0 / 1 variables; It is the j-th 0 / 1 variable; Let be the uncertainty of the i-th associated resource; (2) Linearization transformation of multiplication of 0 / 1 variables with continuous variables In the formula, for The alternative is equivalent to ; Let be the upper limit of uncertainty for the i-th associated resource.

4. The robust scheduling method for information gap decision-making in a mine integrated energy system according to claim 3, characterized in that, After linearization, a mixed-integer linear programming model is established, the compact form of which is: In the formula, To account for the overall uncertainty; For decision variables; It is an uncertain variable; This is the deviation factor; This is the optimal solution for the deterministic model; For other related equality constraints; For other related inequality constraints.

5. The robust scheduling method for information gap decision-making in a mine integrated energy system according to claim 1, characterized in that, Before establishing a robust scheduling model for mine integrated energy systems that considers the perturbation of associated resource empirical proportions using information gap decision theory, the following steps are also included: The high-concentration methane converted from abandoned wind and solar power through power-to-gas conversion is blended with coalbed methane containing low-concentration methane. At the same time, idle underground spaces in the mine are transformed into underground gas storage facilities to cooperate with power-to-gas conversion for methane blending, forming a methane blending and utilization mode of different concentrations that is coupled with power-to-gas conversion, coalbed methane, and underground gas storage. Based on the composition and parameters of the mine's integrated energy system and the methane blending and utilization mode of different concentrations, an optimized scheduling model for the mine's integrated energy system of power-to-gas conversion and coalbed methane blending is established. The integrated energy system for the mine comprises: a wind turbine power generation unit, a photovoltaic power generation unit, a power grid supply unit, a coalbed methane utilization unit, a waste air utilization unit, a water inflow utilization unit, an electricity-to-gas conversion unit, a gas blending unit, a thermal storage oxidation unit, a water source heat pump unit, a gas turbine unit, a waste heat boiler unit, an absorption chiller unit, an underground gas storage unit, a thermal storage device unit, an electrical load unit, a heat load unit, and a cooling load unit. The parameters include: electrical load, heat load, cooling load, wind turbine output, photovoltaic output, predicted values ​​of coalbed methane, waste air, and water inflow, system equipment composition, equipment operating parameters, and the initial value of the current gas storage capacity in the underground gas storage.

6. The robust scheduling method for information gap decision-making in a mine integrated energy system according to claim 5, characterized in that, The optimal scheduling model for an integrated energy system in a mine that combines electricity and gas with coalbed methane is as follows: the objective function and multiple constraints are to minimize the operating cost of the integrated energy system in one scheduling cycle. The objective function for minimizing the operating cost within a scheduling cycle can be: In the formula, C, C1, C2, C3, and C4 represent the total operating cost of the system, the cost of purchasing electricity, the cost of equipment operation and maintenance, the cost of curtailment of wind and solar power, and the cost of purchasing natural gas for the initial capacity in the underground gas storage facility, respectively. T represents the total number of time periods in a complete scheduling cycle; The system's power purchase at time t; Let be the electricity price at time t; , , , , , , , , , These are the unit maintenance costs for photovoltaic systems, wind turbines, gas turbines, waste heat boilers, underground gas storage facilities, electricity-to-gas conversion systems, water source heat pumps, thermal storage oxidation devices, absorption chillers, and thermal storage devices, respectively. , , , , , , The output power of photovoltaic, wind turbine, gas turbine, waste heat boiler, electric to gas converter, thermal storage oxidation device, and absorption chiller at time t are respectively. , These represent the gas release and injection rates in the underground gas storage facility at time t, respectively. Let be the outflow at time t; , These represent the heat release and heat storage power of the heat storage device at time t, respectively. , These are the penalty coefficients for abandoning solar power and wind power, respectively. , These are the predicted output power values ​​of photovoltaic and wind turbines at time t, respectively. This represents the initial capacity of the underground gas storage facility. The price of natural gas; The constraints include: operating constraints of thermal regenerative oxidation devices, operating constraints of gas turbines, operating constraints of power-to-gas conversion, operating constraints of underground gas storage facilities, operating constraints of water source heat pumps, operating constraints of absorption chillers, operating constraints of waste heat boilers, operating constraints of thermal storage devices, system power balance constraints, and balance constraints of coalbed methane mixed with high-concentration methane. The operational constraints of the underground gas storage facility are as follows: In the formula, , These represent the gas storage capacity in the underground gas storage facility at times t and t-1, respectively. , These represent the gas injection and release rates of the underground gas storage facility at time t, respectively. , These represent the minimum and maximum injection flow rates for underground gas storage facilities, respectively. , These represent the minimum and maximum release flows of the underground gas storage facility, respectively. , These are 0 / 1 variables, representing the state of gas injection and release in the underground gas storage facility at time t, respectively. 0 indicates closed and 1 indicates open. The equilibrium constraint for the coalbed methane mixed with high-concentration methane is: In the formula, Let be the volume of the mixed gas at time t; The flow rate of high-concentration methane produced by the electro-gas conversion at time t is mixed with the methane. Let t be the gas release flow rate of the underground gas storage facility; Let t be the flow rate of coalbed methane extracted at time t; Let t be the gas flow rate injected into the gas turbine; Let t be the concentration of methane injected into the gas turbine at time t; Let be the concentration of methane produced by the electro-gas conversion at time t; Let t be the concentration of methane in the coalbed methane extracted at time t; , These are the minimum and maximum methane concentrations required to ensure normal combustion and power generation in a gas turbine, respectively.

7. A robust scheduling system for information gap decision-making in a mine integrated energy system, characterized in that, It includes a robust scheduling model module for information gap decision-making in integrated energy systems for mines, a structural transformation module, a linear programming model module, and a solution module; The robust scheduling model module for information gap decision-making in the integrated mining energy system is used to establish a robust scheduling model for information gap decision-making in the integrated mining energy system that considers the disturbance of the empirical proportion of associated resources using information gap decision theory. The model has a two-layer structure, with the objective function of the upper layer being to maximize the comprehensive uncertainty of associated resources and the objective function of the lower layer being to maximize the total system scheduling cost. The model includes uncertainty constraints on associated resources and load demand and disturbance constraints on the empirical proportion of associated resources. The uncertainty constraints on associated resources and load demand are as follows: In the formula, i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush. Let be the uncertainty of the yield of the i-th associated resource; , These are the predicted and actual values ​​of the i-th associated resource yield at time t, respectively. Let represent the range of uncertainty fluctuations for the i-th associated resource; k represents the type of load demand. B represents the set of load demand types, including electrical load, heating load, and cooling load demand; Let be the uncertainty of the k-th type of load demand; , These are the predicted and actual values ​​of the k-th type of load demand at time t, respectively. The maximum uncertainty of the production of the i-th associated resource; This represents the maximum uncertainty of the k-th type of load demand; The range of uncertainty fluctuations in the k-th type of load demand; The associated resource experience ratio disturbance constraint is addressed by introducing a weight correction coefficient during robust scheduling to process the associated resource experience ratio disturbance, expressed as follows: In the formula, Let represent the overall uncertainty; i represents the type of associated resource; S represents the set of associated resource types, including coalbed methane, exhaust gas, and water inrush. The empirical weighting coefficient of the i-th associated resource; Let be the uncertainty of the i-th associated resource; For the empirical weight correction coefficient of the i-th associated resource; , These are the lower and upper limits of the associated resource weight correction coefficient, respectively; The robust scheduling model for mine integrated energy system based on information gap decision theory, considering the perturbation of associated resource experience ratios, is as follows: In the formula, To account for the overall uncertainty; For decision variables; It is an uncertain variable; The maximum expected value that decision-makers can accept; This is the deviation factor; is the optimal solution of the deterministic model; i represents the type of associated resource, including coalbed methane, exhaust gas, and water inrush. Let be the uncertainty of the i-th associated resource; , These are the predicted and actual values ​​of the i-th associated resource yield at time t, respectively. Let be the fluctuation range of the i-th associated resource; k represents the type of load demand, including electrical load demand, cooling load demand, and heating load demand. Let be the uncertainty of the k-th type of load demand; , These are the predicted and actual values ​​of the k-th type of load demand at time t, respectively. Let be the fluctuation range of the k-th type of load demand; is the weight correction coefficient for the i-th associated resource; For other related equality constraints; For other related inequality constraints; The structural transformation module is used to transform the two-layer structural model of the information gap decision-making robust scheduling model of the integrated energy system in a mine into a single-layer structural model. The linear programming model module is used to linearize the relevant nonlinear constraints in the information gap decision-making robust scheduling model of the integrated energy system for mines, which is transformed into a single-layer structure, and to establish a mixed-integer linear programming model. The solution module is used to call the solver to solve the established mixed-integer linear programming model; the solution results include the uncertainty of coal-associated resources, system scheduling costs, and unit output plans.

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