Multi-mine integrated energy system double-layer game optimization method based on flexible link

Abstract

The invention discloses a multi-mine integrated energy system double-layer game optimization method based on a flexible link. The method comprises the following steps: S1, constructing a flexible link model; s2, forming a mine comprehensive energy system on the basis of the flexible link model; s3, establishing a single-mining-area double-layer game model with an aggregation service provider as an upper layer and an energy supplier and a user as a lower layer; s4, selecting a production mine and an infrastructure mine which are close to each other in geographic position, and constructing a multi-mine-area collaborative low-carbon economic optimization dispatching model; and S5, according to the multi-mine-area collaborative low-carbon economic optimization scheduling model, constructing an objective function and constraint conditions of mine upper-layer income maximization and lower-layer energy consumption cost minimization, and determining an optimal economic low-carbon scheduling scheme of the multi-mine integrated energy system. According to the method, the limitation that a traditional scheduling method cannot meet the requirements of multiple benefit subjects can be overcome, and the multi-mining-area new energy and associated energy consumption capacity and the flexible resource utilization efficiency are improved.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization and scheduling technology, specifically involving a two-level game optimization method for multi-mine integrated energy systems based on flexible links. Background Technology

[0002] As a crucial supply base for traditional energy and key mineral resources, mines are increasingly facing challenges in energy consumption and carbon emissions. Some mines in my country contain abundant renewable energy resources, but the mining process also generates associated resources such as coalbed methane, gas, and water inrush. Therefore, establishing a multi-energy complementary coalmine integrated energy system (CMIES) that incorporates the utilization of associated energy resources is of paramount importance.

[0003] In the production process of a mine's integrated energy system, with the input of materials and energy in multiple stages such as mining, transportation, ventilation, drainage, and gas extraction, associated energy sources such as coalbed methane, gas, and water inflow are released. Simultaneously, there are flexible aspects in the production process. During mining and transportation, the coal bunker at the bottom of the mine can temporarily store a portion of the coal to reduce mine energy consumption and carbon emissions. Similarly, during water inflow utilization, water bunkers can temporarily store a portion of the inflow, improving the system's economic efficiency and low-carbon operation. The potential of these flexible resources in the production process has not yet been fully explored.

[0004] Existing research considers dividing integrated energy systems into entities such as power system operators, power suppliers, and users, and employs game theory to improve overall profitability among these entities. However, it fails to consider how to further enhance the potential for flexible resource scheduling and achieve economic and low-carbon systems in mine integrated energy systems with flexible components. Furthermore, due to the geographical distribution of mines, they often exist in clusters; however, current research is mostly limited to single CMIES (Combined Energy Systems of Mines), with little consideration given to cluster scenarios. Therefore, how to improve the interaction between multiple mine integrated energy systems with flexible components, reduce the peak-to-valley load difference, improve the utilization efficiency of associated and new energy sources, and reduce system operating costs and carbon emissions is a pressing issue that needs to be addressed in the industry. Summary of the Invention

[0005] The purpose of this invention is to provide a two-layer game optimization method for multi-mine integrated energy systems based on flexible links. This method can realize the adjustment of coal mine load peak and valley periods, overcome the limitations of traditional scheduling methods that cannot meet the needs of multiple stakeholders, improve the absorption capacity of new energy and associated energy in multi-mine areas and the efficiency of flexible resource utilization, increase the overall revenue of mining areas, and improve the economic efficiency and low-carbon nature of mining area clusters.

[0006] To achieve the above objectives, this invention provides a two-level game optimization method for multi-mine integrated energy systems based on flexible links, comprising the following steps:

[0007] S1. Based on the characteristics of the utilization of derived energy resources in the mining area, analyze the energy input and output in the core production process of the mine, identify the flexible and adjustable loads in the mining, ventilation, and drainage production processes, and construct a flexible process model.

[0008] S2. Based on the flexible link model, analyze the material flow and energy output relationship of the mine integrated energy system, and construct a mine integrated energy system consisting of an external energy supply system, a distributed renewable energy system, derivative energy utilization equipment, energy conversion equipment, and different types of energy loads.

[0009] S3. Based on the energy consumption characteristics of production and daily life in the mining area, the aggregated service provider is regarded as the decision-maker to manage the output of the equipment units of the mine's integrated energy system and receive the energy demand of users. The energy suppliers and users are regarded as the implementers to determine the energy supply and consumption strategies based on price information and feed them back to the upper-level aggregated service provider. A single mining area two-level game model is established with the aggregated service provider as the upper level and the energy suppliers and users as the lower level.

[0010] S4. Based on the geographical distribution and development of the mining area clusters, select production mines and infrastructure mines that are geographically close to each other to construct a low-carbon economic optimization scheduling model for multi-mining area collaboration.

[0011] S5. Based on the low-carbon economic optimization scheduling model of multi-mining area collaboration, construct the objective function and constraints for maximizing the upper-level revenue and minimizing the lower-level energy cost of the mine, and determine the optimal economic low-carbon scheduling scheme for the multi-mining integrated energy system.

[0012] As a further aspect of the present invention: the flexible link model in S1 includes:

[0013] The mathematical model of the water reservoir is as follows:

[0014]

[0015] In the formula: V sp.t V represents the water volume stored in the reservoir at time t; sp_in,t V sp_out,t V represents the inflow and outflow of water from the reservoir at time t; sp_max This represents the maximum capacity of the water tank.

[0016] Waste heat utilization from mine inflow: A water source heat pump recovers waste heat from mine inflow for mine heating; the mathematical model for waste heat utilization from mine inflow is as follows:

[0017]

[0018] In the formula: D is the head; ρ gwλ1 represents the density of the mine inflow water; g represents the acceleration due to gravity; λ1 and λ2 are unit conversion factors. denoted as t, where c is the specific heat capacity of water; and ΔT is the temperature difference before and after heat extraction from the mine water. The energy efficiency ratio (EER) of a water source heat pump; P t WSHP Let t be the operating power of the water source heat pump at time t; Let t be the maximum operating power of the water source heat pump.

[0019] The mathematical model of a coal mining machine is as follows:

[0020]

[0021] In the formula: E t P represents the amount of coal mined at time t; c,t Let t be the power of the coal mining machine; ω represents the maximum power of the coal mining machine at time t; β is the correlation coefficient between the coal mining volume and the power of the coal mining machine; t Δt represents the working condition coefficient at time t, and its value is related to the geological factors of the coal seam; Δt is the scheduling time interval.

[0022] The mathematical model of the coal bunker is as follows:

[0023]

[0024] In the formula: R t Let W be the amount of coal stored in the coal bunker at time t; in,t Let W be the amount of coal transported into the coal bunker at time t; out,t Let R be the amount of coal removed from the coal bunker at time t. max This represents the maximum coal storage capacity of the coal bunker.

[0025] The mathematical model of a storage battery is as follows:

[0026]

[0027] In the formula: and The variable is 0-1, representing the charging and discharging state of the energy storage device during time period t; and These are the charging and discharging power of the energy storage device; δ represents the energy stored in the energy storage device during time period t; ESS This is the energy loss coefficient; and For the charging and discharging efficiency of the equipment; and These are the upper and lower limits of the charging and discharging power, respectively. and These are the upper and lower limits of energy storage, respectively;

[0028] The mathematical model of a thermal storage tank is as follows:

[0029]

[0030] In the formula: The heat storage capacity of the thermal storage tank during time period t; The heat loss rate of the thermal storage tank; and Let t be the heat storage and heat release power of the thermal storage tank at time t; and These are the heat storage and heat release efficiencies of the thermal storage tank, respectively. These are the state variables for the thermal storage tank during heat charging and discharging; and These are the upper limits of the input and output thermal power of the thermal storage device, respectively. and These are the upper and lower limits of the thermal storage capacity of the thermal storage device, respectively. and These represent the heat storage capacity of the thermal storage device at 0:00 and 24:00, respectively.

[0031] As a further aspect of the present invention: the integrated energy system model for mines in S2 includes:

[0032] A gas turbine, which uses coalbed methane as fuel to drive a gas turbine to generate electricity and utilizes waste heat for heating, has the following mathematical model:

[0033]

[0034] In the formula: and These are the volume and calorific value of methane, respectively. and These represent the electrical efficiency and thermal efficiency of a micro gas turbine, respectively.

[0035] The power output of the ethylene glycol heat pump, which utilizes ethylene glycol as a medium in a waste air oxidation heat storage device, is determined by its heat output. Its energy conversion characteristics are as follows:

[0036]

[0037] In the formula: The heat pump outputs heat power; and These represent the power and ventilation rate of the fan, respectively. For thermoelectric ratio; H sd It is wind pressure; η s Refers to the efficiency of the ventilation fan; This is the necessary heat loss generated to maintain the oxidation process in the exhaust air oxidation heat storage device;

[0038] The mathematical model for the air compressor and its residual heat recovery is as follows:

[0039]

[0040] In the formula: P represents the waste heat power output by the air compressor. t acm The electrical power it consumes; η acmf and η acmh These are the air compressor load rate and waste heat recovery efficiency, respectively.

[0041] The mathematical model of an electric boiler is as follows:

[0042]

[0043] In the formula: P is the thermal power output of the electric boiler. t EB The electrical power it consumes; η EB The energy conversion efficiency of an electric boiler.

[0044] As a further aspect of the present invention: the two-layer game model for a single mining area in S3 is specifically represented as follows:

[0045]

[0046] The single-mine two-level game model includes participants, strategies, and a set of game utilities; the participants include the leader (ASP), followers (ES), and users (USER); the strategies include the ASP's purchase and sale prices of energy, c. ASP The output W of all devices in ES ES The user's adjustable electrical and thermal load L USER and the power interaction between mining areas P send The set of game utility is the objective function maxE of each stakeholder. ASP maxE ES and maxE USER .

[0047] As a further aspect of the present invention: the objective function in S5 includes:

[0048] Objective function of the aggregation service provider:

[0049]

[0050] In the formula: t is the time segment, and T represents the total 24-hour time period; The revenue generated from energy sales to users by ASP; and These are the interaction costs between ASP, ES, and the power grid, respectively. The penalty cost for heating interruption; For transaction costs within the mining area; Carbon trading costs for purchased electricity; ε1 and These are the weighting coefficients for ASP to bear user carbon compensation and the user's carbon compensation function, respectively.

[0051] Energy supplier objective function:

[0052]

[0053] In the formula: For ES equipment capacity revenue; The revenue generated by energy storage systems through peak-valley arbitrage; Indicates equipment maintenance costs; F t ES ε1 represents the carbon trading cost of ES; ε2 represents the weighting coefficient of ES in bearing the user's carbon compensation.

[0054] User objective function:

[0055]

[0056] In the formula: Indicates user satisfaction; This represents the user's energy purchase cost.

[0057] As a further aspect of the present invention, the solution method for the low-carbon economic optimization scheduling model of multi-mining area collaboration includes:

[0058] A. Initialize the population, number of iterations, and maximum number of iterations. The aggregation service provider generates an initial price plan and passes it to the lower-level energy suppliers and users. The energy suppliers and users call the CPLEX solver to solve the problem according to the price signal from the upper level and pass the optimization results to the aggregation service provider. The aggregation service provider calculates its own benefits and forms a mining area electricity exchange plan.

[0059] B. The upper layer uses an adaptive difference algorithm to update the population, and crossover mutations form a new population. The lower layer energy suppliers and users call the CPLEX solver again to solve the problem, and the aggregated service provider generates its own corresponding revenue.

[0060] C. Compare the results. If the reward increases, continue updating the population until the maximum reward is found for this number of iterations. If the reward no longer increases, proceed to the next iteration until the maximum number of iterations is reached.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] This invention establishes a multi-mine integrated energy system with multiple flexible links including coal bunkers, water bunkers, and energy storage devices. It can effectively achieve energy supply for various loads, and takes into account the sequential nature of each production link, giving full play to the temporary storage advantages of equipment such as coal bunkers and water bunkers, so as to fully tap the potential of flexible resources, while improving the economic efficiency and low carbon emissions of the system operation.

[0063] This invention employs a two-layer game model for a single mining area, with aggregated service providers at the top and energy suppliers and users at the bottom. This model significantly improves the benefits for each entity, enabling the full allocation of flexible resources and maximizing the overall interests of the mining area.

[0064] This invention takes into account the production characteristics and geographical distribution of mining areas, and constructs a low-carbon economic optimization scheduling model based on flexible links and multi-mining area collaboration. The upper layer of the model is solved by an adaptive differential algorithm, and the lower layer is solved by CPLEX. The upper layer acts as the decision-maker to set energy transaction prices between entities within the mining area and between mining areas, while the lower layer acts as the executor to determine energy supply and consumption strategies based on price information and feeds them back to the upper layer. Thus, the benefits are maximized under this model. This solution method is accurate and fast. Attached Figure Description

[0065] Figure 1 This is a structural diagram of the multi-mine integrated energy system based on flexible links according to the present invention;

[0066] Figure 2 This invention provides a low-carbon economic optimization scheduling model for multi-mining area collaboration.

[0067] Figure 3 This is a flowchart illustrating the solution process for the low-carbon economic optimization scheduling model for multi-mining area collaboration in this invention. Detailed Implementation

[0068] The invention will now be further described with reference to the accompanying drawings.

[0069] like Figure 1 and Figure 2 As shown, the two-level game optimization method for multi-mine integrated energy systems based on flexible links includes the following steps:

[0070] S1. Based on the characteristics of the utilization of derived energy resources in the mining area, analyze the energy input and output in the core production process of the mine, identify the flexible and adjustable loads in the mining, ventilation, and drainage production processes, and construct a flexible process model.

[0071] Furthermore, the flexible link model includes:

[0072] A water reservoir's capacity is generally determined by the inflow rate. The amount of water stored in the reservoir at a given moment is related to the inflow and outflow rates at that moment. Its mathematical model is as follows:

[0073]

[0074] In the formula: V sp.t V represents the water volume stored in the reservoir at time t; sp_in,t V sp_out,t V represents the inflow and outflow of water from the reservoir at time t; sp_max This represents the maximum capacity of the water tank.

[0075] Waste heat utilization from mine inflow: A water source heat pump recovers waste heat from mine inflow for mine heating; the mathematical model for waste heat utilization from mine inflow is as follows:

[0076]

[0077] In the formula: D is the head; ρ gw λ1 represents the density of the mine inflow water; g represents the acceleration due to gravity; λ1 and λ2 are unit conversion factors. denoted as t, where c is the specific heat capacity of water; and ΔT is the temperature difference before and after heat extraction from the mine water. The energy efficiency ratio (EER) of a water source heat pump; P t WSHP Let t be the operating power of the water source heat pump at time t; Let t be the maximum operating power of the water source heat pump.

[0078] Coal mining machines, in the process of underground coal mining, have the following mathematical model:

[0079]

[0080] In the formula: E t P represents the amount of coal mined at time t; c,t Let t be the power of the coal mining machine; ω represents the maximum power of the coal mining machine at time t; β is the correlation coefficient between the coal mining volume and the power of the coal mining machine; t Δt represents the working condition coefficient at time t, and its value is related to the geological factors of the coal seam; Δt is the scheduling time interval.

[0081] The mathematical model of the coal bunker is as follows:

[0082]

[0083] In the formula: R t Let W be the amount of coal stored in the coal bunker at time t; in,t Let W be the amount of coal transported into the coal bunker at time t; out,t Let R be the amount of coal removed from the coal bunker at time t. max This represents the maximum coal storage capacity of the coal bunker.

[0084] Specifically:

[0085]

[0086] In the formula: W in,t Let τ be the amount of coal transported into the coal bunker at time t; t P represents the coal transportation condition coefficient at time t; κ1 and κ2 are the fitting parameters between the power of the belt conveyor and the belt speed and transport capacity, respectively; bc,t V t W t These represent the power, belt speed, and carrying capacity of the belt conveyor at time t.

[0087]

[0088] In the formula: M is the mass borne by the belt conveyor per unit length; V max This is the upper limit of belt speed; M max This is the upper limit of the weight that the belt conveyor can bear;

[0089] The mathematical model of a storage battery is as follows:

[0090]

[0091] In the formula: and The variable is 0-1, representing the charging and discharging state of the energy storage device during time period t; and These are the charging and discharging power of the energy storage device; δ represents the energy stored in the energy storage device during time period t; ESS This is the energy loss coefficient; and For the charging and discharging efficiency of the equipment; and These are the upper and lower limits of the charging and discharging power, respectively. and These are the upper and lower limits of energy storage, respectively;

[0092] The mathematical model of a thermal storage tank is as follows:

[0093]

[0094] In the formula: The heat storage capacity of the thermal storage tank during time period t; The heat loss rate of the thermal storage tank; and Let t be the heat storage and heat release power of the thermal storage tank at time t; and These are the heat storage and heat release efficiencies of the thermal storage tank, respectively. These are the state variables for the thermal storage tank during heat charging and discharging; and These are the upper limits of the input and output thermal power of the thermal storage device, respectively. and These are the upper and lower limits of the thermal storage capacity of the thermal storage device, respectively. and These represent the heat storage capacity of the thermal storage device at 0:00 and 24:00, respectively.

[0095] S2. Based on the flexible link model, analyze the material flow and energy output relationship of the mine integrated energy system, and construct a mine integrated energy system consisting of an external energy supply system, a distributed renewable energy system, derivative energy utilization equipment, energy conversion equipment, and different types of energy loads.

[0096] Furthermore, the integrated energy system model for mines includes:

[0097] Gas turbines use coalbed methane as fuel to drive power generation and utilize waste heat for heating, achieving the dual goals of energy recovery and emission reduction. Their mathematical model is as follows:

[0098]

[0099] In the formula: V t gas and These are the volume and calorific value of methane, respectively. and These represent the electrical efficiency and thermal efficiency of a micro gas turbine, respectively.

[0100] Mine exhaust air is low-temperature waste gas discharged from the mine ventilation system. It has the characteristics of constant temperature, large flow and continuous flow. By using an air source heat pump to recover its low-grade waste heat, it can be efficiently converted into hot water or hot air above 45℃ to provide winter heating for buildings in the mining area. It saves more than 50% energy compared with traditional electric heating, and at the same time alleviates the thermal pollution of mine exhaust air.

[0101] The power output of the ethylene glycol heat pump, which utilizes ethylene glycol as a medium in a waste air oxidation heat storage device, is determined by its heat output. Its energy conversion characteristics are as follows:

[0102]

[0103] In the formula: The heat pump outputs heat power; P t Vent and These represent the power and ventilation rate of the fan, respectively. For thermoelectric ratio; H sd It is wind pressure; η s Refers to the efficiency of the ventilation fan; This is the necessary heat loss generated to maintain the oxidation process in the exhaust air oxidation heat storage device;

[0104] An air compressor is a power device that converts mechanical energy into compressed air energy. Air compressors have a relatively high waste heat grade and relatively stable heat output. The mathematical model for waste heat recovery is as follows:

[0105]

[0106] In the formula: P represents the waste heat power output by the air compressor. t acm The electrical power it consumes; η acmf and η acmh These are the air compressor load rate and waste heat recovery efficiency, respectively.

[0107] Electric boilers, as highly efficient electro-thermal conversion devices, generate heat through electromagnetic induction technology. Due to their zero emissions and high energy conversion rate, they can replace traditional coal-fired boilers. Their mathematical model is as follows:

[0108]

[0109] In the formula: The thermal power output of the electric boiler; The electrical power it consumes; η EB The energy conversion efficiency of an electric boiler.

[0110] S3. Based on the energy consumption characteristics of production and daily life in the mining area, the aggregated service provider is regarded as the decision-maker to manage the output of the equipment units of the mine's integrated energy system and receive the energy demand of users. The energy suppliers and users are regarded as the implementers to determine the energy supply and consumption strategies based on price information and feed them back to the upper-level aggregated service provider. A single mining area two-level game model is established with the aggregated service provider as the upper level and the energy suppliers and users as the lower level.

[0111] Furthermore, the two-layer game model for a single mining area is specifically expressed as follows:

[0112]

[0113] The single-mine two-level game model includes participants, strategies, and a set of game utilities; the participants include the leader (ASP), followers (ES), and users (USER); the strategies include the ASP's purchase and sale prices of energy, c. ASP The output W of all devices in ES ES The user's adjustable electrical and thermal load L USER and the power interaction between mining areas P send The set of game utility is the objective function maxE of each stakeholder. ASP maxE ES and maxE USER .

[0114] In a game, when none of the parties can gain more benefit by unilaterally changing their own strategies, that is, when the following conditions are met simultaneously:

[0115]

[0116] This indicates that the game has reached a Stackelberg equilibrium. Equilibrium for the Stackelberg game.

[0117] S4. Based on the geographical distribution and development status of the mining area cluster, select production mines and infrastructure mines that are geographically close, including Mine Energy System 1 (CMIES1) and Mine Energy System 2 (CMIES2). Mine Energy System 1 is a relatively mature mine with large output of new energy sources and stable electricity and heat consumption, with a peak electricity load of 15.5MW. Mine Energy System 2 is an expanding mine with large electricity and heat consumption, and a peak electricity load of 25MW. Construct a low-carbon economic optimization scheduling model for multi-mining area collaboration.

[0118] S5. Based on the low-carbon economic optimization scheduling model of multi-mining area collaboration, construct the objective function and constraints for maximizing the upper-level revenue and minimizing the lower-level energy cost of the mine, and determine the optimal economic low-carbon scheduling scheme for the multi-mining integrated energy system.

[0119] Furthermore, the objective function includes:

[0120] Objective function of the aggregation service provider:

[0121]

[0122] In the formula: t is the time segment, and T represents the total 24-hour time period; The revenue generated from energy sales to users by ASP; and These are the interaction costs between ASP, ES, and the power grid, respectively. The penalty cost for heating interruption; For transaction costs within the mining area; Carbon trading costs for purchased electricity; ε1 and These are the weighting coefficients for ASP to bear user carbon compensation and the user's carbon compensation function, respectively.

[0123] Specifically:

[0124]

[0125] In the formula: and These are the ASP prices for electricity and heat sales, respectively. and These are the actual electrical and thermal loads on the user side, respectively; P t e The power that ASP purchases from the grid; The interaction power between mining areas; and These are the electricity and heat purchase prices that ASP pays from ES, respectively. t eb and These represent the electricity and heat purchased by ASP from ES, respectively, P t es and These represent the electricity and heat sales capacity from ASP to ES, respectively. It is the price that ASP pays for electricity from the grid. It is the ASP (Average Selling Price) for surplus electricity fed into the grid; This is the penalty coefficient for heating interruption; The interaction price between mining areas, η e λ is the transmission loss coefficient; c is the carbon trading price; λ e and λ b These represent the carbon allowance per unit of electricity and per unit of heat, respectively.

[0126] To ensure ASP's leading role in the transaction and to satisfy the interests of all parties, the electricity and heat exchange price is constrained by the following conditions:

[0127]

[0128] In the formula: and These represent the upper and lower limits of the heat price.

[0129] ASP's constraints on purchasing / selling electricity from external grids:

[0130]

[0131]

[0132] In the formula: and These represent the amount of electricity that the ASP purchases / sells from the external power grid; and These are all binary variables, representing the electricity purchase and sale status of the ASP; and These are the upper limits for purchasing / selling electricity, respectively.

[0133] Energy supplier objective function:

[0134]

[0135] In the formula: For ES equipment capacity revenue; The revenue generated by energy storage systems through peak-valley arbitrage; Indicates equipment operation and maintenance costs; F t ESε1 represents the carbon trading cost of ES; ε2 represents the weighting coefficient of ES in bearing the user's carbon compensation.

[0136] Specifically:

[0137]

[0138] In the formula: and These represent the charging cost, energy release revenue, and operation and maintenance cost of the energy storage system, respectively; m x These represent the unit maintenance costs for each piece of equipment.

[0139] The constraints of the energy supplier's objective function include:

[0140] Power balance constraints:

[0141] P t eb +P t sd =P t PV +P t WT +P t GT -(P t RTO +P t EB )-(P t WSHP +P t acm )

[0142]

[0143] Equipment output constraints:

[0144] x∈{PV,WT,GT,RTO,EB,WSHP,ACM}

[0145]

[0146] In the formula: and These represent the upper and lower limits of the equipment's output, respectively.

[0147] User objective function:

[0148]

[0149] In the formula: Indicates user satisfaction; This represents the user's energy purchase cost.

[0150] Specifically:

[0151]

[0152] In the formula: v e u e v h u h These are the user's preference coefficients for consuming electricity and heat, respectively.

[0153] Furthermore, such as Figure 3 As shown, the solution methods for the low-carbon economic optimization scheduling model of multi-mining area collaboration include:

[0154] A. Initialize the population, number of iterations, and maximum number of iterations. The aggregation service provider generates an initial price plan and passes it to the lower-level energy suppliers and users. The energy suppliers and users call the CPLEX solver to solve the problem according to the price signal from the upper level and pass the optimization results to the aggregation service provider. The aggregation service provider calculates its own benefits and forms a mining area electricity exchange plan.

[0155] B. The upper layer uses an adaptive difference algorithm to update the population, and crossover mutations form a new population. The lower layer energy suppliers and users call the CPLEX solver again to solve the problem, and the aggregated service provider generates its own corresponding revenue.

[0156] C. Compare the results. If the reward increases, continue updating the population until the maximum reward is found for this number of iterations. If the reward no longer increases, proceed to the next iteration until the maximum number of iterations is reached.

Claims

1. A two-level game optimization method for multi-mine integrated energy systems based on flexible links, characterized in that, Includes the following steps: S1. Based on the characteristics of the utilization of derived energy resources in the mining area, analyze the energy input and output in the core production process of the mine, identify the flexible and adjustable loads in the mining, ventilation, and drainage production processes, and construct a flexible process model. S2. Based on the flexible link model, analyze the material flow and energy output relationship of the mine integrated energy system, and construct a mine integrated energy system consisting of an external energy supply system, a distributed renewable energy system, derivative energy utilization equipment, energy conversion equipment, and different types of energy loads. S3. Based on the energy consumption characteristics of production and daily life in the mining area, the aggregated service provider is regarded as the decision-maker to manage the output of the equipment units of the mine's integrated energy system and receive the energy demand of users. The energy suppliers and users are regarded as the implementers to determine the energy supply and consumption strategies based on price information and feed them back to the upper-level aggregated service provider. A single mining area two-level game model is established with the aggregated service provider as the upper level and the energy suppliers and users as the lower level. S4. Based on the geographical distribution and development of the mining area clusters, select production mines and infrastructure mines that are geographically close to each other to construct a low-carbon economic optimization scheduling model for multi-mining area collaboration. S5. Based on the low-carbon economic optimization scheduling model of multi-mining area collaboration, construct the objective function and constraints for maximizing the upper-level revenue and minimizing the lower-level energy cost of the mine, and determine the optimal economic low-carbon scheduling scheme for the multi-mining integrated energy system.

2. The two-level game optimization method for multi-mine integrated energy systems based on flexible links according to claim 1, characterized in that, The flexible link model in S1 includes: The mathematical model of the water reservoir is as follows: In the formula: V sp.t V represents the water volume stored in the reservoir at time t; sp_in,t V sp_out,t V represents the inflow and outflow of water from the reservoir at time t; sp_max This represents the maximum capacity of the water tank. Waste heat utilization from mine inflow: A water source heat pump recovers waste heat from mine inflow for mine heating; the mathematical model for waste heat utilization from mine inflow is as follows: In the formula: D is the head; ρ gw λ1 represents the density of the mine inflow water; g represents the acceleration due to gravity; λ1 and λ2 are unit conversion factors. denoted as t, where c is the specific heat capacity of water; and ΔT is the temperature difference before and after heat extraction from the mine water. The energy efficiency ratio (EER) of a water source heat pump; P t WSHP Let t be the operating power of the water source heat pump at time t; Let t be the maximum operating power of the water source heat pump. The mathematical model of a coal mining machine is as follows: In the formula: E t P represents the amount of coal mined at time t; c,t Let t be the power of the coal mining machine; ω represents the maximum power of the coal mining machine at time t; β is the correlation coefficient between the coal mining volume and the power of the coal mining machine; t Δt represents the working condition coefficient at time t, and its value is related to the geological factors of the coal seam; Δt is the scheduling time interval. The mathematical model of the coal bunker is as follows: In the formula: R t Let W be the amount of coal stored in the coal bunker at time t; in,t Let W be the amount of coal transported into the coal bunker at time t; out,t Let R be the amount of coal removed from the coal bunker at time t. max This represents the maximum coal storage capacity of the coal bunker. The mathematical model of a storage battery is as follows: In the formula: and The variable is 0-1, representing the charging and discharging state of the energy storage device during time period t; and These are the charging and discharging power of the energy storage device; δ represents the energy stored in the energy storage device during time period t; ESS This is the energy loss coefficient; and For the charging and discharging efficiency of the equipment; and These are the upper and lower limits of the charging and discharging power, respectively. and These are the upper and lower limits of energy storage, respectively; The mathematical model of a thermal storage tank is as follows: In the formula: The heat storage capacity of the thermal storage tank during time period t; The heat loss rate of the thermal storage tank; and Let t be the heat storage and heat release power of the thermal storage tank at time t; and These are the heat storage and heat release efficiencies of the thermal storage tank, respectively. These are the state variables for the thermal storage tank during heat charging and discharging; and These are the upper limits of the input and output thermal power of the thermal storage device, respectively. and These are the upper and lower limits of the thermal storage capacity of the thermal storage device, respectively. and These represent the heat storage capacity of the thermal storage device at 0:00 and 24:00, respectively.

3. The two-level game optimization method for multi-mine integrated energy systems based on flexible links according to claim 1, characterized in that, The integrated energy system model for mines in S2 includes: A gas turbine, which uses coalbed methane as fuel to drive a gas turbine to generate electricity and utilizes waste heat for heating, has the following mathematical model: In the formula: V t gas and These are the volume and calorific value of methane, respectively. and These represent the electrical efficiency and thermal efficiency of a micro gas turbine, respectively. The power output of the ethylene glycol heat pump, which utilizes ethylene glycol as a medium in a waste air oxidation heat storage device, is determined by its heat output. Its energy conversion characteristics are as follows: In the formula: The heat pump outputs heat power; P t Vent and Q t Vent These represent the power and ventilation rate of the fan, respectively. For thermoelectric ratio; H sd It is wind pressure; η s Refers to the efficiency of the ventilation fan; This is the necessary heat loss generated to maintain the oxidation process in the exhaust air oxidation heat storage device; The mathematical model for the air compressor and its residual heat recovery is as follows: In the formula: P represents the waste heat power output by the air compressor. t acm The electrical power it consumes; η acmf and η acmh These are the air compressor load rate and waste heat recovery efficiency, respectively. The mathematical model of an electric boiler is as follows: In the formula: P is the thermal power output of the electric boiler. t EB The electrical power it consumes; η EB The energy conversion efficiency of an electric boiler.

4. The two-level game optimization method for multi-mine integrated energy systems based on flexible links according to claim 1, characterized in that, The specific representation of the two-layer game model for a single mining area in S3 is as follows: The single-mine two-level game model includes participants, strategies, and a set of game utilities; the participants include the leader (ASP), followers (ES), and users (USER); the strategies include the ASP's purchase and sale prices of energy, c. ASP The output W of all devices in ES ES The user's adjustable electrical and thermal load L USER and the power interaction between mining areas P send The set of game utility is the objective function maxE of each stakeholder. ASP maxE ES and maxE USER .

5. The two-layer game optimization method for multi-mine integrated energy systems based on flexible links according to claim 4, characterized in that, The objective function in S5 includes: Objective function of the aggregation service provider: In the formula: t is the time segment, and T represents the total 24-hour time period; The revenue generated from energy sales to users by ASP; and These are the interaction costs between ASP, ES, and the power grid, respectively. The penalty cost for heating interruption; For the transaction cost within the mining area; F t ASP Carbon trading costs for purchased electricity; ε1 and These are the weighting coefficients for ASP to bear user carbon compensation and the user's carbon compensation function, respectively. Energy supplier objective function: In the formula: For ES equipment capacity revenue; The revenue generated by energy storage systems through peak-valley arbitrage; Indicates equipment maintenance costs; F t ES ε1 represents the carbon trading cost of ES; ε2 represents the weighting coefficient of ES in bearing the user's carbon compensation. User objective function: In the formula: Indicates user satisfaction; This represents the user's energy purchase cost.

6. The two-level game optimization method for multi-mine integrated energy systems based on flexible links according to claim 1, characterized in that, The solution methods for the low-carbon economic optimization scheduling model of multi-mining area collaboration include: A. Initialize the population, number of iterations, and maximum number of iterations. The aggregation service provider generates an initial price plan and passes it to the lower-level energy suppliers and users. The energy suppliers and users call the CPLEX solver to solve the problem according to the price signal from the upper level and pass the optimization results to the aggregation service provider. The aggregation service provider calculates its own benefits and forms a mining area electricity exchange plan. B. The upper layer uses an adaptive difference algorithm to update the population, and crossover mutations form a new population. The lower layer energy suppliers and users call the CPLEX solver again to solve the problem, and the aggregated service provider generates its own corresponding revenue. C. Compare the results. If the reward increases, continue updating the population until the maximum reward is found for this number of iterations. If the reward no longer increases, proceed to the next iteration until the maximum number of iterations is reached.

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