Energy system optimization method based on mine compressed air energy storage

Abstract

This invention relates to the field of energy engineering technology, specifically to an energy system optimization method based on compressed air energy storage in mines, comprising the following steps: S1, Multi-energy flow network construction: Combining the entire life cycle of a mine, a multi-energy flow network is constructed, including renewable energy power generation in abandoned mines, compressed air energy storage, and cogeneration and ORC devices in mines, forming a waste-mining coupled energy topology with coordinated power supply of electricity, heat, cooling, and gas; S2, Multi-energy flow system modeling: Establishing equipment models on the mining side and models of compressors, turbine power generation, gas storage, and thermal storage tanks on the abandoned mine side; S3, Optimization scheduling modeling: Introducing the CVaR index, constructing an optimization objective function including energy purchase, operation and maintenance, and abandonment costs, setting system operation and equipment constraints, and forming a CMIES optimization scheduling model that takes risk into account. This invention achieves reduced operating costs of mining area energy systems, efficient consumption of renewable energy, and coordinated utilization of resources throughout the entire life cycle of the mine.

Description

Technical Field

[0001] This invention relates to the field of energy engineering technology, and in particular to an energy system optimization method based on mine compressed air energy storage. Background Technology

[0002] As an energy-intensive industry, the mining industry is facing the major challenge of accelerating its low-carbon transformation. With the continuous operation of mines and the gradual withdrawal of some mines, the mining area presents a situation where mining operations and abandoned mines coexist. On the one hand, the load of mining equipment fluctuates drastically during operation, and the energy supply system is under significant pressure. On the other hand, a large number of abandoned mines have formed usable underground space resources, which have good potential for the synergistic application of energy storage and renewable energy.

[0003] In existing technologies, underground energy storage pathways mainly focus on compressed air energy storage and pumped hydro storage systems, while mining operations rely on waste heat recovery and the cascade utilization of associated energy sources to improve energy efficiency. However, these solutions generally suffer from problems such as fragmented system design and lack of coordination in spatiotemporal regulation. They fail to effectively integrate the energy system characteristics of abandoned mines and those in mining operations, and in particular, they lack source-storage-load integrated modeling and optimization strategies for dynamic loads. At the same time, cogeneration systems are constrained by the principle of heat-driven power generation, making it difficult to coordinate the fluctuating output of new energy sources with the cogeneration system. Demand-side regulation capabilities are also not fully utilized, resulting in limited overall system regulation margin and new energy utilization efficiency. Summary of the Invention

[0004] This invention provides an energy system optimization method based on mine compressed air energy storage, which enhances the system's ability to adapt to uncertainties in new energy sources and improves its operational robustness, thereby helping the integrated energy system in mining areas to develop towards low-carbon, high-efficiency, and collaborative directions.

[0005] An energy system optimization method based on mine compressed air energy storage includes the following steps:

[0006] S1, Multi-energy flow network construction: Combining the characteristics of the entire life cycle of a mine, a multi-energy flow coupling network is constructed, including renewable energy power generation in abandoned mines, compressed air energy storage systems, and cogeneration equipment and organic Rankine cycle (ORC) devices in mining mines, to establish a waste-mining coupled integrated energy topology structure that realizes coordinated power supply of electricity, heat, cold and gas.

[0007] S2, Multi-energy flow system modeling: Establish equipment models on the mining side, and construct compressor models, turbine power generation models, underground compressed air storage models, and thermal storage tank models of the compressed air energy storage system on the abandoned mine side to realize the modeling of the dynamic energy conversion process between heat, electricity, and cooling in the multi-energy flow system;

[0008] S3, Optimized Scheduling Modeling: Based on the established source-storage-load system model, the Conditional Value at Risk (CVaR) index is introduced to construct an economic optimization objective function that includes external energy purchase costs, equipment operation and maintenance costs, and wind and solar curtailment costs. A series of operational constraints such as system operation, equipment, and network are set to form an optimized scheduling model for the waste-mining coupled integrated energy system (CMIES) that takes into account operational risk factors.

[0009] Optionally, the multi-energy flow system modeling in S2 includes:

[0010] S21, Modeling of equipment on the mining side: Establishing mathematical models for energy equipment in the mine, including the CHP model based on ORC waste heat power generation, the waste wind thermal storage oxidation device model, the water source heat pump model, the electric chiller model, and the absorption chiller model.

[0011] S22, Mathematical Model of Integrated Energy System in Abandoned Mines: Construct an energy model for abandoned mines based on Compressed Air Energy Storage System (CAES), including a compressor model, a turbine power generation model, an underground compressed air storage tank model, and a thermal storage tank model. This model characterizes the physical state changes and energy flow of air during compression, storage, expansion, and thermal energy management, and establishes a systematic expression of mine space resources and multi-energy synergy.

[0012] Optionally, the modeling of the mining hillside equipment in S21 includes:

[0013] S211, CHP Model Based on ORC Waste Heat Power Generation: A combined heat and power (CHP) unit generates electricity through a gas turbine and recovers waste heat to achieve combined power and heat supply. After introducing an ORC waste heat power generation system, the recovered heat energy is distributed to a waste heat boiler (WHB) for heating or ORC for power generation, thus decoupling the rigid thermoelectric coupling. A multi-parameter simultaneous model is established, including CHP power generation, heat generation, and ORC conversion efficiency, expressed as:

[0014]

[0015] Among them, P CHP,t H CHP,t The power generation and heat production of the CHP unit at time t are respectively, H ORC,t Let H be the thermal power of the ORC waste heat recovery in the CHP unit at time t. WHB,t Let P be the thermal power output of WHB at time t. ORC,t Let be the power generation capacity of ORC waste heat recovery at time t. The power generation efficiency and heat production efficiency of the CHP unit at time t are respectively, η ORC For the electrothermal conversion efficiency of ORC, H respectively ORC,t Maximum and minimum values;

[0016] S212, Exhaust Air Regenerative Oxidation Unit Model: The exhaust air regenerative oxidation unit is used to treat low-concentration methane gas in mine exhaust air. It converts methane into carbon dioxide and water through a regenerative oxidation reaction, recovering the heat from the reaction for heating. The exhaust air regenerative oxidation unit includes a regenerative bed and an oxidation chamber. Methane burns and releases heat in the oxidation chamber. The heat is stored in the regenerative body and then released periodically. The output equivalent thermal power is expressed as:

[0017]

[0018] Among them, H VOD,t Q generates heat for the exhaust gas oxidation unit fw,t Let d be the flow rate of the mixed gas at time t. fw,t Let t be the concentration of the mixed gas in the exhaust gas. η is the calorific value of methane. fw,t Let be the methane oxidation efficiency at time t;

[0019] S213, Water Source Heat Pump Model: A water source heat pump uses groundwater or mine water as a heat source or heat sink. Based on a reverse Carnot cycle, it provides heating in winter and cooling in summer. In heating mode, it absorbs heat from the water source and supplies heat to the mining area; in cooling mode, it absorbs heat from the mining area and discharges heat to the water source. This is represented as:

[0020]

[0021] Among them, Q WSHP,t Let t be the cooling power output of the water source heat pump. Let be the electrical power input to the water source heat pump during cooling at time t. H is the coefficient of performance (COP) of a water source heat pump. WSHP,t Let t be the output heat power of the water source heat pump. Let be the electrical power input of the water source heat pump when it is heating at time t. T1 is the cooling energy efficiency coefficient of the water source heat pump, T2 is the temperature of the low-temperature heat source, and T1 is the temperature of the high-temperature heat source.

[0022] S214, Electric Refrigeration Unit Model: The electric refrigeration unit uses electricity to drive a compressor for cooling. The refrigerant evaporates and absorbs heat to lower the water or air temperature, achieving a rapid response to the cooling load in the mining area. The relationship between the cooling power output and the electrical power consumed by the electric refrigeration unit is expressed as follows:

[0023] Q EC,t =P EC,t η EC ;

[0024] Among them, Q EC,t Let P be the cooling power of the electric chiller at time t. EC,t Let η be the power consumption of the electric chiller at time t. EC The refrigeration efficiency of the electric chiller;

[0025] S215, Absorption Refrigeration Unit Model: An absorption refrigerator uses a heat source to drive a refrigeration cycle, converting heat energy into cold energy to reduce electrical energy consumption. The relationship between the cooling power output and the heat power consumed by the absorption refrigerator is expressed as:

[0026] Q AV,t =H AV,t η AV ;

[0027] Among them, Q AV,t Let H be the refrigeration power of the absorption chiller at time t. AV,t Let η be the heat power consumed by the absorption chiller at time t. AV This refers to the heat-to-cold conversion efficiency of an absorption chiller.

[0028] Optionally, the mathematical model for the integrated energy system of the abandoned mine in S22 includes:

[0029] S221, Compressor Modeling: Construct a compressor model for the compressed air energy storage system, establish the relationship between the electrical power consumption and inlet / outlet temperature changes of each stage of the compressor under different operating conditions, considering the number of compression stages, adiabatic efficiency, air mass flow rate, and compression ratio, and constrain the upper and lower limits of power and temperature rise characteristics under compression conditions, expressed as:

[0030]

[0031] Among them, P c,t N is the sum of the power consumption of each stage of the compressor at time t. c η represents the total number of stages in the compressor. c κ represents the adiabatic efficiency of the compression unit, q represents the air adiabatic index, and q represents the adiabatic efficiency of the compression unit. c,m,t Let R be the air mass flow rate at time t. g The gas constant is Let β be the inlet and outlet temperatures of the i-th stage compressor at time t. c,i m is the rated compression ratio of the i-th stage compressor. c,t This is a binary variable used to represent the CAES compression status; 1 indicates the working state, and 0 indicates the stopped state. These represent the upper and lower limits of the power consumption of the compressor at time t;

[0032] S222, Turbine Power Generation Model: A turbine power generation model is established to describe the energy conversion relationship during air expansion, including turbine adiabatic efficiency, air mass flow rate, inlet and outlet temperatures, and expansion ratio. This model constrains the power generation range under turbine operating conditions and reflects the energy release behavior of compressed air, expressed as:

[0033]

[0034] Among them, P g,t η is the total power output of the turbine during time period t. g For the adiabatic efficiency of the turbine, q g,m,t Let be the mass flow rate of the air entering the turbine at time t. These are the inlet and outlet temperatures of the turbine during time period t, respectively, where γ is the turbine expansion ratio, and m... g,t This is a binary variable representing the CAES power generation status; 1 indicates the operating state, and 0 indicates the stopped state. These represent the upper and lower limits of the turbine's output power at time t;

[0035] S223, Underground Compressed Air Storage Model: This model constructs an underground compressed air storage facility. Based on the gas state equation, it describes the dynamic relationship between storage pressure and storage volume at different times, clarifies the upper and lower limits of storage volume, temperature, and pressure, and realizes a stable storage and regulation mechanism for high-pressure gas. This is represented as:

[0036]

[0037] Where, p st,t Let T be the pressure of the underground compressed air storage tank at time t, and V be the volume of the underground compressed air storage tank. st,t The temperature of the underground compressed air storage facility. These are the upper and lower limits of the gas storage pressure, respectively.

[0038] S224, Thermal Storage Tank Model: Construct a thermal storage tank model in a compressed air energy storage system, covering the heat exchange, heat storage, and external heat supply processes during compression. Define the calculation relationship between thermal storage power and heat loss, set thermal boundary conditions for the thermal storage tank, and ensure the continuity and controllability of the heat supply required for the expansion power generation process.

[0039] Optionally, the thermal storage tank model in S224 includes:

[0040] S2241, Constructing a heat storage power model for the compression process: During the operation of the compression unit, the heat released by the compressed air enters the heat storage system through the heat exchanger. A calculation model for the heat storage power of each stage of the compression unit is constructed, expressed as follows:

[0041]

[0042] Where, m c,i,t c is the mass flow rate of the compressed gas. p,a The specific heat capacity of air. For the heat storage power of the i-th stage compression process, The total heat storage capacity of the compressed air energy storage system;

[0043] S2242, Constructing the preheating power model for the expansion process: In the expansion power generation stage, the expanding gas is preheated, and the preheating power model of the expansion unit is expressed as follows:

[0044]

[0045] Where, m g,i,t The mass flow rate of the expanding gas. For the heat storage power of the i-th stage compression process, The total heat storage capacity of the compressed air energy storage system;

[0046] S2243, Constructing a heat balance model for the thermal storage tank: The change in heat in the thermal storage tank over time is determined by the initial heat and the heat input and output at each stage, expressed as:

[0047]

[0048] Among them, H st,t Let t be the thermal energy output power of the thermal storage tank to the outside. The internal heat at the initial moment, Let k be the heat inside the thermal storage tank at time t. st The heat loss coefficient is... To account for losses and the actual heat energy output, H stmax This represents the upper limit of the output thermal power.

[0049] S2244, Set upper and lower limit constraints for thermal storage: Set the upper and lower limits of the heat inside the thermal storage tank, expressed as:

[0050]

[0051] Optionally, the optimized scheduling modeling in S3 includes:

[0052] S31, Scheduling optimization objective modeling: Construct a scheduling optimization objective function with the goal of minimizing the total operating cost of the mine's integrated energy system. The cost items include external energy purchase cost, equipment operation and maintenance cost, and wind and solar curtailment penalty cost.

[0053] S32, Definition of Operational Constraints: Defines various operational constraints to be satisfied during the optimization scheduling process, including balance constraints of electric power, thermal power, and cooling power, power purchase limits of the power grid and gas pipeline network, and upper and lower limits of power for the operation of various energy equipment. At the same time, it introduces power output limits of new energy sources and load matching boundary conditions.

[0054] S33, Risk Perception Objective Function Modeling: Based on the scheduling optimization objective function, the Conditional Value at Risk (CVaR) mechanism is introduced to construct an objective function that includes a weighted combination of expected cost and risk cost, in order to cope with the operational fluctuation risk caused by the uncertainty of new energy output.

[0055] Optionally, the scheduling optimization objective function in S31, which aims to minimize the total operating cost of the integrated energy system of the mine, is expressed as follows:

[0056]

[0057] in, The electricity purchase price during time period t is the grid purchase price. P is the gas purchase price for time period t. t e P represents the power purchased during time period t. t g C1 represents the gas purchase power during time period t, C2 represents the sum of the operation and maintenance costs of wind turbines and photovoltaic and other new energy equipment, exhaust wind thermal storage oxidation and water source heat pump and other derivative energy utilization equipment, and refrigeration equipment, and C3 represents the sum of the operation and maintenance costs of underground compressed air storage and thermal storage tanks. GS2,r For the unit maintenance cost of underground compressed air storage facilities, c HS,r q represents the unit maintenance cost of the thermal storage tank. m,comp2 The mass flow rate of air entering the compressed air storage tank. Let P be the predicted wind power at time t. WT,t Let t be the actual grid-connected power of wind power. Let P be the predicted photovoltaic power at time t. PV,t Let C be the actual grid-connected photovoltaic power at time t. WT C PV This represents the penalty coefficient for wind and solar power curtailment.

[0058] Optionally, the operational constraint definitions in S32 include:

[0059] Electric power balance constraints:

[0060] Among them, P wt,t P represents the actual output power of the wind turbine at time t. pv,t Let P be the output power of the photovoltaic cell at time t. l,t The mine's electrical load during time period t;

[0061] Thermal power balance constraint:

[0062] Among them, H l,t The mine's heat load during time period t;

[0063] Cold power balance constraint: Q EC,t +Q AV,t =Q l,t ;

[0064] Tie line power balance constraints:

[0065] Constraints on new energy output:

[0066] Equipment operating constraints:

[0067] Optionally, the risk perception objective function modeling in S33 includes:

[0068] S331, Constructing the Risk Perception Objective Function: Introducing the Conditional Value at Risk (CVaR) mechanism, a risk perception objective function is constructed with the weighted sum of expected cost and CVaR as the objective, expressed as:

[0069] minf RIES =βE(F1)+(1-β)V CVaR,α (F1);

[0070] Where α is the confidence level, E(F1) is the expected total cost, β is the trade-off coefficient between expected operating cost and operating cost volatility risk, and V CVaR,α (F1) represents the conditional value-of-risk cost when the confidence level is α;

[0071] S332, Objective Function Transformation: The risk perception objective function is transformed into an equivalent optimization model that incorporates auxiliary variables ζ and a scenario-based structure, expressed as:

[0072]

[0073] Where n represents the nth scene, N is the set of all scenes, and μ n Let ζ and κ represent the probability of the nth scenario occurring. n For intermediate parameters;

[0074] S333, Set scene constraints: Define ζ and κ for each scene. n The relationship between these factors forms the constraints of the risk perception objective function, which are expressed as:

[0075]

[0076] The beneficial effects of this invention are:

[0077] This invention constructs a waste-mining coupled integrated energy system for mines, enabling the coordinated operation of compressed air energy storage systems in abandoned mines with multi-source equipment such as cogeneration and organic Rankine cycles in mining operations. It builds a multi-energy complementary energy supply network covering electricity, heat, cooling, and gas, breaking through the limitations of the traditional heat-driven power generation model. By introducing an ORC system to effectively recover low-grade heat energy, and combining it with compressed air energy storage and diversified cooling and heating devices, it enhances the system's regulation capacity while meeting the load demand of the mining area, improves the utilization rate of renewable energy, and mitigates the phenomenon of wind and solar curtailment.

[0078] This invention introduces a scheduling optimization mechanism that takes into account Conditional Value at Risk (CVaR) to incorporate the uncertainty of new energy output into the cost control scope, achieving a dynamic balance between expected cost and operational risk, and improving the economy and robustness of system operation. This method combines the concept of mine life cycle design to extend the service life of mining equipment and improve the space utilization rate of abandoned mines. Implementation results show that it can effectively reduce the total life cycle cost of mining areas, external energy purchase expenditures, and energy abandonment costs, providing support for the clean energy transformation of mining areas. Attached Figure Description

[0079] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0080] Figure 1 This is a schematic diagram of the optimization method flow according to an embodiment of the present invention;

[0081] Figure 2 This is a schematic diagram of the waste-mining coupled mine power-heat-cooling-gas coordinated energy supply topology according to an embodiment of the present invention;

[0082] Figure 3 This is a schematic diagram of the power dispatching results in scenario 1 of this embodiment of the invention;

[0083] Figure 4 This is a schematic diagram of the power dispatching results in scenario 2 of this embodiment of the invention;

[0084] Figure 5 This is a schematic diagram of the thermal energy scheduling results in scenario 1 of this embodiment of the invention;

[0085] Figure 6 This is a schematic diagram of the thermal energy scheduling results in scenario 2 of this embodiment of the invention;

[0086] Figure 7 This is a schematic diagram of the cold energy scheduling results in scenario 1 of this embodiment of the invention;

[0087] Figure 8 This is a schematic diagram of the cold energy scheduling results in scenario 2 of this embodiment of the invention;

[0088] Figure 9 This is a schematic diagram of renewable energy consumption in scenario 1 of this embodiment of the invention;

[0089] Figure 10 This is a schematic diagram of renewable energy consumption in scenario 2 of this embodiment of the invention;

[0090] Figure 11This diagram illustrates the relationship between the total cost and risk coefficient of the CMIES optimization method in this embodiment of the invention. Detailed Implementation

[0091] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0092] like Figures 1-11 As shown, the energy system optimization method based on mine compressed air energy storage includes the following steps:

[0093] Step 1: Construction and Topology Design of Multi-Energy Flow Coupled Network in Waste-Mining Coupled Mines:

[0094] Combining the characteristics of the entire life cycle of a mine, a multi-energy flow coupling network is constructed, including renewable energy power generation in abandoned mines, compressed air energy storage, cogeneration in mining mines, and organic Rankine cycle (ORC) devices, to establish a coordinated energy supply topology of "electricity-heat-cooling-gas" in abandoned-mining coupled mines.

[0095] Step 2: Modeling of multi-energy flow systems in waste-mining coupled mines:

[0096] 2.1 Mathematical Model of Typical Equipment in a Mining Module:

[0097] 2.1.1 CHP Model Based on ORC Waste Heat Power Generation:

[0098] Combined heat and power (CHP) units generate electricity through gas turbines and recover waste heat to achieve combined power and heat supply. However, constrained by the "heat-driven power generation" mechanism, reverse fluctuations in electricity and heat loads can easily lead to wind curtailment. Introducing an ORC waste heat power generation system allows for flexible allocation of heat energy to either a waste heat boiler (WHB) for heating or ORC for power generation, thereby decoupling the rigid coupling of heat and power and improving energy supply flexibility. The relationship between CHP unit power generation, heat production, and gas consumption power is as follows:

[0099]

[0100] In the formula: P CHP,t H CHP,t These represent the power generation and heat production of the CHP unit at time t, respectively; H ORC,t H represents the thermal power of ORC waste heat recovery in the CHP unit at time t; WHB,t P represents the thermal power output of WHB at time t. ORC,t Let t be the power generation capacity of ORC waste heat recovery; These represent the power generation efficiency and heat production efficiency of the CHP unit at time t, respectively; η ORC The electrothermal conversion efficiency of ORC; H respectively ORC,t The maximum and minimum values.

[0101] 2.1.2 Model of Exhaust Air Thermal Oxidation Unit:

[0102] The waste gas regenerative oxidation unit treats low-concentration methane (<1%) in mine waste gas, converting it into CO2 and H2O through regenerative oxidation technology. The heat from the reaction is recovered for heating the mine area. The workflow involves waste gas entering the regenerative bed for preheating, methane oxidation in the oxidation chamber releasing heat, which is stored in the regenerator and released periodically. The equivalent thermal power generated by the waste gas regenerative oxidation unit after being processed by the exhaust gas is:

[0103]

[0104] Where: H VOD,t Q generates heat for the exhaust gas oxidation unit fw,t Let d be the flow rate of the mixed gas at time t. fw,t Let t be the concentration of the mixed gas in the exhaust gas. η is the calorific value of methane. fw,t Let be the methane oxidation efficiency at time t.

[0105] 2.1.3 Water Source Heat Pump Model:

[0106] Ground source heat pumps utilize groundwater or mine water as a heat source / sink, achieving heating (in winter) or cooling (in summer) through a reverse Carnot cycle. Heating mode: heat is absorbed from the water source and released to the mining area. Cooling mode: heat is absorbed from the mining area and released to the water source. A typical cooling (heating) formula for a ground source heat pump is:

[0107]

[0108] In the formula: Q WSHP,t Let t be the cooling power output of the water source heat pump. Let be the electrical power input to the water source heat pump during cooling at time t. H is the coefficient of performance (COP) of a water source heat pump. WSHP,t Let t be the output heat power of the water source heat pump. Let be the electrical power input of the water source heat pump when it is heating at time t. T1 represents the coefficient of performance (COP) of the water source heat pump, T2 represents the temperature of the low-temperature heat source, and T1 represents the temperature of the high-temperature heat source.

[0109] 2.1.4 Electric Refrigeration Unit Model:

[0110] The system utilizes electricity to drive a compressor for refrigeration, rapidly responding to the cooling load demands of the mining area. Its workflow is as follows: electricity drives the compressor to compress the refrigerant; the refrigerant evaporates and absorbs heat, lowering the water / air temperature. The relationship between the cooling power output of the electric chiller and the electrical power consumed is as follows:

[0111] Q EC,t =P EC,t η EC (4)

[0112] In the formula: Q EC,t Let P be the cooling power of the electric chiller at time t. EC,t Let η be the power consumption of the electric chiller at time t. EC This refers to the refrigeration efficiency of the electric chiller.

[0113] 2.1.5 Absorption Refrigeration Unit Model:

[0114] Absorption chillers utilize a heat source (such as waste heat from CHP or VOD recovery heat) to drive a refrigeration cycle, achieving the conversion of heat energy into cold energy and reducing electricity consumption. The working process involves the heat source heating the absorbent solution, releasing refrigerant vapor, and the refrigerant evaporating and absorbing heat, thus completing the refrigeration cycle. The relationship between the cooling power output and the heat power consumed by an absorption chiller is as follows:

[0115] Q AV,t =H AV,t η AV (5)

[0116] In the formula: Q AV,t Let H be the refrigeration power of the absorption chiller at time t. AV,t Let η be the heat power consumed by the absorption chiller at time t. AV The heat-to-cold conversion efficiency of an absorption chiller;

[0117] 2.2 Mathematical Model of Integrated Energy System for Abandoned Mines:

[0118] 2.2.1 Compressed Air Energy Storage Modeling:

[0119] Compressor model:

[0120] The power consumption of the r-th stage compressor at time t is shown in equation (1), where equation (1) represents the total electrical power consumed by each stage compressor; equation (1) represents the upper and lower limits of the compression power; and equation (1) represents the relationship between the inlet and outlet air temperatures of the compressor.

[0121]

[0122] In the formula: P c,t N is the sum of the power consumption of each stage of the compressor at time t. c η represents the total number of stages in the compressor. c κ represents the adiabatic efficiency of the compression unit, q represents the air adiabatic index, and q represents the adiabatic efficiency of the compression unit. c,m,t Let R be the air mass flow rate at time t. g The gas constant is Let β be the inlet and outlet temperatures of the i-th stage compressor at time t. c,im is the rated compression ratio of the i-th stage compressor. c,t This is a binary variable used to represent the CAES compression status; 1 indicates the working state, and 0 indicates the stopped state. These represent the upper and lower limits of the power consumption of the compressor at time t;

[0123] 2.2.2 Turbine Power Generation Model:

[0124]

[0125] In the formula: P g,t η is the total power output of the turbine during time period t. g For the adiabatic efficiency of the turbine, q g,m,t Let be the mass flow rate of the air entering the turbine at time t. These are the inlet and outlet temperatures of the turbine during time period t, respectively, where γ is the turbine expansion ratio, and m... g,t This is a binary variable representing the CAES power generation status; 1 indicates the operating state, and 0 indicates the stopped state. These represent the upper and lower limits of the turbine's output power at time t.

[0126] 2.2.3. Underground Compressed Air Storage Model:

[0127] A mathematical model for establishing an underground compressed air storage facility in an abandoned underground mine tunnel is provided. The high-pressure exhaust gas entering the underground compressed air storage facility after being pressurized by a secondary compressor is mainly composed of N2. The pressure of the high-pressure gas in the storage facility is represented by equation (14), and equation (17) represents the upper and lower limits of the pressure in the storage facility.

[0128]

[0129] In the formula: p st,t Let T be the pressure of the underground compressed air storage tank at time t, and V be the volume of the underground compressed air storage tank. st,t The temperature of the underground compressed air storage facility. These represent the upper and lower limits of the gas storage pressure.

[0130] 2.2.4 Thermal storage tank model:

[0131] The heat flow inside an adiabatic compressed air energy storage device consists of three main stages: heat exchange, heat storage, and heat release; the heat energy is circulated and recycled through heat exchangers and storage tanks. When the i-th stage compression unit is put into operation, the released heat is absorbed by the i-th stage heat exchanger, and then the airflow continues into the (i+1)-th stage compression unit. Its compression heat storage power can be expressed as:

[0132]

[0133] Where: m c,i,tc is the mass flow rate of the compressed gas. p,a The specific heat capacity of air (taken as 1.003 kJ / (kg·K)); For the heat storage power of the i-th stage compression process, This refers to the total heat storage capacity of the compressed air energy storage system.

[0134] The process of an expander releasing electrical energy is similar, and the heat power is:

[0135]

[0136] Where: m g,i,t The mass flow rate of the expanding gas. For the heat storage power of the i-th stage compression process,

[0137] This refers to the total heat storage capacity of the compressed air energy storage system.

[0138] The heat supply process from the thermal storage tank of a compressed air energy storage device is similar to the internal compression / expansion heat exchange process. The remaining heat inside the thermal storage tank at a certain moment can be expressed as:

[0139]

[0140] Where: H st,t Let t be the thermal energy output power of the thermal storage tank to the outside. The internal heat at the initial moment, Let k be the heat inside the thermal storage tank at time t. st The heat loss coefficient is... To account for losses and the actual heat energy output, H stmax This is the upper limit of the output thermal power.

[0141] For compressed air energy storage equipment to supply heat externally, the prerequisite is that the remaining heat in the storage tank is sufficient to meet the preheating needs for expansion and power generation over a subsequent period. Once the stored heat in the tank reaches its upper limit, it cannot continue storing heat; if compression charging occurs at this point, this heat will be dissipated and wasted. Therefore, upper and lower limits are set for the actual heat capacity within the storage tank.

[0142]

[0143] Step 3: Optimization scheduling model for CVaR-coupled waste-sampling type CMIES:

[0144] 3.1 Objective Function:

[0145]

[0146] 1) External energy purchase costs:

[0147]

[0148] In the formula: The electricity purchase price during time period t is the grid purchase price. P is the gas purchase price for time period t. t e P represents the power purchased during time period t. t g The gas purchase power during time period t.

[0149] 2) Equipment operation and maintenance costs:

[0150]

[0151] In the formula: C1 is the sum of the operation and maintenance costs of new energy equipment such as wind turbines and photovoltaics, derivative energy utilization equipment such as exhaust wind thermal storage oxidation and water source heat pumps, and refrigeration equipment; C2 is the sum of the operation and maintenance costs of underground compressed air storage and thermal storage tanks; c GS2,r For the unit maintenance cost of underground compressed air storage facilities, c HS,r q represents the unit maintenance cost of the thermal storage tank. m,comp2 The mass flow rate of air entering the compressed air storage tank is expressed in kg / s.

[0152] 3) Costs of wind and solar power curtailment:

[0153]

[0154] In the formula: Let P be the predicted wind power at time t. WT,t Let t be the actual grid-connected power of wind power. Let P be the predicted photovoltaic power at time t. PV,t Let C be the actual grid-connected photovoltaic power at time t. WT C PV This represents the penalty coefficient for wind and solar power curtailment.

[0155] 3.2 Constraints:

[0156] In addition to equipment and network constraints, the following constraints should also be followed.

[0157] 3.2.1 System operation constraints:

[0158] 1) Power balance constraint:

[0159]

[0160] In the formula: P wt,t P represents the actual output power of the wind turbine at time t. pv,t Let P be the output power of the photovoltaic cell at time t. l,t Let t be the mine's electrical load during time period t.

[0161] 2) Thermal power balance constraint:

[0162]

[0163] Where: H l,t The heat load of the mine during time period t.

[0164] 3) Cold power balance constraint:

[0165] Q EC,t +Q AV,t =Q l,t (19)

[0166] 4) Tie line power balance constraints:

[0167] The system's power and gas purchase capacity must not exceed the transmission capacity limits of the power grid interconnection lines and natural gas pipeline network.

[0168]

[0169] 3.2.2 Other constraints:

[0170] Constraints on new energy output:

[0171]

[0172] 3.2.3 Equipment operating constraints:

[0173]

[0174] Considering the impact of source and load uncertainties on system operation, this paper introduces a conditional risk metric into the conventional scheduling model to mitigate economic losses caused by uncertainties. The IES low-carbon economic scheduling objective function considering CVaR is as follows:

[0175] minf RIES =βE(F1)+(1-β)V CVaR,α (F1) (23)

[0176] In the formula: α is the confidence level, E(F1) is the expected total cost, and β is the trade-off coefficient between expected operating cost and operating cost volatility risk, taking 0 ≤ β ≤ 1; V CVaR,α (F1) represents the value-at-risk cost of condition when the confidence level is α.

[0177] Equation (23) represents the conditional risk measures β and 1-β assigned by the user when the confidence level is α, and the expected operating cost E(F1). Equation (23) can be converted into

[0178]

[0179] In the formula: n represents the nth scene, N is the set of all scenes, and μ n Let ζ and κ represent the probability of the nth scenario occurring. n These are intermediate parameters.

[0180] Step 4: Case study analysis of the waste-sampling coupled CMIES optimization scheduling model:

[0181] To verify the effectiveness of the proposed waste-mining coupled CMIES optimized scheduling model, the following two scenarios are set up for comparison.

[0182] Scenario 1: Ignoring the energy storage portion of abandoned mines, i.e., the traditional mine integrated energy system scheduling model.

[0183] Scenario 2: Considering the CAES and ORC devices in abandoned mines, a multi-energy flow model of abandoned-mining coupled mines.

[0184] Comparing Scenario 1 and Scenario 2, the table shows that in terms of total cost, the total cost, energy purchase cost, and energy curtailment cost of Scenario 2 decreased by 9.54%, 40.92%, and 93.92% respectively compared to Scenario 1. The operation and maintenance cost of Scenario 2 increased by 9.00% compared to Scenario 1 due to the increased output of the equipment. Scenario 2 offers significantly improved flexibility in renewable energy consumption compared to Scenario 1. The CAES device is in charging mode during off-peak electricity periods such as 04:00-07:00, 10:00-17:00, and 23:00-24:00, improving clean energy consumption. During periods of high electricity prices or high load, such as 08:00-09:00 and 18:00-22:00, it releases electricity. A small amount of wind and solar energy is curtailed at 11:00, a period when large-scale electricity storage is not possible and CAES capacity is limited. By introducing compressed air energy storage, the abundant heat can be stored in the heat storage tank, which can then be used to generate electricity for the ORC device, achieving electro-thermal coupling and increasing the system's flexibility.

[0185] The results of the example are shown below:

[0186] Table 1. Scene Comparison Results

[0187]

[0188] The impact of risk factor on IES scheduling:

[0189] The range of values ​​for the risk coefficient β characterizes the decision-maker's inclination towards system operational risk. To study the impact of the risk coefficient β on optimal system operation, this section discusses the relationship between β and the total cost of CMIES. This section presents six values ​​for β, illustrating the relationship between the risk coefficient β and the total system cost. A higher β indicates a lower system operational risk, while β = 1 indicates that the impact of uncertainty factors is completely ignored, i.e., complete risk aversion.

[0190] As β increases, the total cost of CMIES also increases, while the conditional risk cost CVaR corresponding to the total cost decreases with increasing risk coefficient β. When β = 1, it indicates that the decision-maker does not bear operational risk; the system is in a state of complete risk aversion, with the lowest conditional risk cost of IES and the highest total cost. When the risk coefficient 0.7 ≤ β < 1, it indicates that the decision-maker can bear some operational risk, and the total cost of IES decreases slightly as the risk coefficient decreases. When the risk coefficient 0.1 ≤ β < 0.7, it indicates that the decision-maker has a good attitude towards system risk and is willing to bear greater operational risk costs in pursuit of lower total costs.

[0191] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0192] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An energy system optimization method based on mine compressed air energy storage, characterized in that, Includes the following steps: S1, Multi-energy flow network construction: Combining the characteristics of the entire life cycle of a mine, a multi-energy flow coupling network is constructed, including renewable energy power generation in abandoned mines, compressed air energy storage systems, and cogeneration equipment and organic Rankine cycle devices in mining mines, to establish a waste-mining coupled integrated energy topology structure that realizes coordinated power supply of electricity, heat, cold and gas. S2, Multi-energy Flow System Modeling: This involves establishing equipment models on the mining side and constructing compressor models, turbine power generation models, underground compressed air storage models, and thermal storage tank models for the compressed air energy storage system on the abandoned mine side. This achieves modeling of the dynamic energy conversion process between heat, electricity, and cooling in the multi-energy flow system; specifically including: S21, Modeling of equipment on the mining side: Establishing mathematical models for energy equipment in the mine, including the CHP model based on ORC waste heat power generation, the waste wind thermal storage oxidation device model, the water source heat pump model, the electric chiller model, and the absorption chiller model. S22, Mathematical Model of Integrated Energy System in Abandoned Mines: This model constructs an energy system for abandoned mines based on compressed air energy storage, including a compressor model, a turbine power generation model, an underground compressed air storage tank model, and a thermal storage tank model. It characterizes the physical state changes and energy flow of air during compression, storage, expansion, and thermal energy management, establishing a systematic expression of mine space resources and multi-energy synergy. Specifically, it includes: S221, Compressor Modeling: Construct a compressor model for the compressed air energy storage system, establish the relationship between the electrical power consumption and inlet / outlet temperature changes of each stage of the compressor under different operating conditions, considering the number of compression stages, adiabatic efficiency, air mass flow rate, and compression ratio, and constrain the upper and lower limits of power and temperature rise characteristics under compression conditions, expressed as: ； in, Let be the sum of the power consumption of each stage of the compressor at time t. This represents the total number of stages in the compressor. For the adiabatic efficiency of the compression unit, The air insulation index. Let be the air mass flow rate at time t. The gas constant is... , Let be the inlet temperature and outlet temperature of the i-th stage compressor at time t, respectively. The rated compression ratio of the i-th stage compressor. This is a binary variable used to represent the CAES compression status; 1 indicates the working state, and 0 indicates the stopped state. , These represent the upper and lower limits of the power consumption of the compressor at time t; S222, Turbine Power Generation Model: A turbine power generation model is established to describe the energy conversion relationship during air expansion, including turbine adiabatic efficiency, air mass flow rate, inlet and outlet temperatures, and expansion ratio. This model constrains the power generation range under turbine operating conditions and reflects the energy release behavior of compressed air, expressed as: ； in, for Total power output of the turbine during the time period For the turbine's insulation efficiency, Let be the mass flow rate of the air entering the turbine at time t. , They are respectively The inlet and outlet temperatures of the time-lapse turbine. The turbine expansion ratio, This is a binary variable representing the CAES power generation status; 1 indicates the operating state, and 0 indicates the stopped state. , These represent the upper and lower limits of the turbine's output power at time t; S223, Underground Compressed Air Storage Model: This model constructs an underground compressed air storage facility. Based on the gas state equation, it describes the dynamic relationship between storage pressure and storage volume at different times, clarifies the upper and lower limits of storage volume, temperature, and pressure, and realizes a stable storage and regulation mechanism for high-pressure gas. This is represented as: ； in, for The pressure of the underground compressed air storage tank at all times. The volume of the underground compressed air storage tank. Temperature of the underground compressed air storage facility. , These are the upper and lower limits of the gas storage pressure, respectively. S224, Thermal Storage Tank Model: Construct a thermal storage tank model in a compressed air energy storage system, covering the heat exchange, heat storage and external heat supply process during compression, defining the calculation relationship between thermal storage power and heat loss, setting thermal boundary conditions for the thermal storage tank, and ensuring the continuity and controllability of the heat supply required for the expansion power generation process. S3, Optimized Scheduling Modeling: Based on the established source-storage-load system model, the conditional risk value index is introduced to construct an economic optimization objective function that includes external energy purchase costs, equipment operation and maintenance costs, and wind and solar curtailment costs. System operation, equipment and network operation constraints are set to form an optimized scheduling model for the waste-mining coupled integrated energy system of mines that takes into account operational risk factors.

2. The energy system optimization method based on mine compressed air energy storage according to claim 1, characterized in that, The modeling of the mining hillside equipment in S21 includes: S211, CHP Model Based on ORC Waste Heat Power Generation: A combined heat and power (CHP) unit generates electricity through a gas turbine and recovers waste heat to achieve combined power and heat supply. After introducing an ORC waste heat power generation system, the recovered heat energy is distributed to the waste heat boiler for heating or to the ORC for power generation, thus decoupling the rigid coupling of heat and electricity. A multi-parameter simultaneous model is established, including CHP power generation, heat generation, and ORC conversion efficiency, expressed as: ； in, , Let be the power generation and heat production of the CHP unit at time t, respectively. Let be the thermal power of ORC waste heat recovery in the CHP unit at time t. Let WHB be the thermal power output at time t. Let be the power generation capacity of ORC waste heat recovery at time t. , The power generation efficiency and heat production efficiency of the CHP unit at time t are respectively. For the electrothermal conversion efficiency of ORC, , They are respectively Maximum and minimum values; S212, Exhaust Air Regenerative Oxidation Unit Model: The exhaust air regenerative oxidation unit is used to treat low-concentration methane gas in mine exhaust air. It converts methane into carbon dioxide and water through a regenerative oxidation reaction, recovering the heat from the reaction for heating. The exhaust air regenerative oxidation unit includes a regenerative bed and an oxidation chamber. Methane burns and releases heat in the oxidation chamber. The heat is stored in the regenerative body and then released periodically. The output equivalent thermal power is expressed as: ； in, To generate heat for the exhaust gas oxidation unit, Let t be the flow rate of the mixed gas in the exhaust gas. Let t be the concentration of the mixed gas in the exhaust gas. The calorific value of methane. Let be the methane oxidation efficiency at time t; S213, Water Source Heat Pump Model: A water source heat pump uses groundwater or mine water as a heat source or heat sink. Based on a reverse Carnot cycle, it provides heating in winter and cooling in summer. In heating mode, it absorbs heat from the water source and supplies heat to the mining area; in cooling mode, it absorbs heat from the mining area and discharges heat to the water source. This is represented as: ； in, Let t be the cooling power output of the water source heat pump. Let be the electrical power input to the water source heat pump during cooling at time t. The coefficient of performance (COP) for water source heat pump cooling. Let t be the output heat power of the water source heat pump. Let be the electrical power input of the water source heat pump when it is heating at time t. The coefficient of performance (COP) for water source heat pump cooling. The temperature of the low-temperature heat source. The temperature of the high-temperature heat source; S214, Electric Refrigeration Unit Model: The electric refrigeration unit uses electricity to drive a compressor for cooling. The refrigerant evaporates and absorbs heat to lower the water or air temperature, achieving a rapid response to the cooling load in the mining area. The relationship between the cooling power output and the electrical power consumed by the electric refrigeration unit is expressed as follows: ； in, Let t be the cooling power of the electric chiller. Let t be the power consumption of the electric chiller. The refrigeration efficiency of the electric chiller; S215, Absorption Refrigeration Unit Model: An absorption refrigerator uses a heat source to drive a refrigeration cycle, converting heat energy into cold energy to reduce electrical energy consumption. The relationship between the cooling power output and the heat power consumed by the absorption refrigerator is expressed as: ； in, Let t be the cooling power of the absorption chiller. Let t be the heat consumption power of the absorption chiller. This refers to the heat-to-cold conversion efficiency of an absorption chiller.

3. The energy system optimization method based on mine compressed air energy storage according to claim 1, characterized in that, The thermal storage tank model in S224 includes: S2241, Constructing a heat storage power model for the compression process: During the operation of the compression unit, the heat released by the compressed air enters the heat storage system through the heat exchanger. A calculation model for the heat storage power of each stage of the compression unit is constructed, expressed as follows: ； in, The mass flow rate of the compressed gas. The specific heat capacity of air. For the heat storage power of the i-th stage compression process, The total heat storage capacity of the compressed air energy storage system; S2242, Constructing the preheating power model for the expansion process: In the expansion power generation stage, the expanding gas is preheated, and the preheating power model of the expansion unit is expressed as follows: ； in, The mass flow rate of the expanding gas. For the heat storage power of the i-th stage compression process, The total heat storage capacity of the compressed air energy storage system; S2243, Constructing a heat balance model for the thermal storage tank: The change in heat in the thermal storage tank over time is determined by the initial heat and the heat input and output at each stage, expressed as: ； in, Let t be the thermal energy output power of the thermal storage tank to the outside. The internal heat at the initial moment, Let be the heat inside the thermal storage tank at time t. The heat loss coefficient is... To account for the actual heat energy output after losses, This represents the upper limit of the output thermal power. S2244, Set upper and lower limit constraints for thermal storage: Set the upper and lower limits of the heat inside the thermal storage tank, expressed as: 。 4. The energy system optimization method based on mine compressed air energy storage according to claim 3, characterized in that, The optimized scheduling modeling in S3 includes: S31, Scheduling optimization objective modeling: Construct a scheduling optimization objective function with the goal of minimizing the total operating cost of the mine's integrated energy system. The cost items include external energy purchase cost, equipment operation and maintenance cost, and wind and solar curtailment penalty cost. S32, Definition of Operational Constraints: Defines various operational constraints to be satisfied during the optimization scheduling process, including balance constraints of electric power, thermal power, and cooling power, power purchase limits of the power grid and gas pipeline network, and upper and lower limits of power for the operation of various energy equipment. At the same time, it introduces power output limits of new energy sources and load matching boundary conditions. S33, Risk Perception Objective Function Modeling: Based on the scheduling optimization objective function, a conditional risk value mechanism is introduced to construct an objective function that includes a weighted combination of expected cost and risk cost, in order to cope with the operational fluctuation risk caused by the uncertainty of new energy output.

5. The energy system optimization method based on mine compressed air energy storage according to claim 4, characterized in that, The scheduling optimization objective function in S31, which aims to minimize the total operating cost of the integrated energy system of the mine, is expressed as follows: ； ； ； ； in, for Time-of-use electricity purchase price for Gas purchase price during specific time periods for Power purchased during specific time periods for Gas purchase power during specific time periods This is the sum of the operation and maintenance costs of wind turbines and photovoltaic equipment, exhaust wind thermal storage oxidation and water source heat pumps, and refrigeration equipment. This is the sum of the operation and maintenance costs of underground compressed air storage facilities and thermal storage tanks. The unit maintenance cost of underground compressed air storage facilities. The unit maintenance cost of the thermal storage tank. The mass flow rate of air entering the compressed air storage tank. Let t be the predicted wind power. Let t be the actual grid-connected power of wind power. Let be the predicted photovoltaic power at time t. Let t be the actual grid-connected photovoltaic power. , This represents the penalty coefficient for wind and solar power curtailment.

6. The energy system optimization method based on mine compressed air energy storage according to claim 5, characterized in that, The operational constraint definitions in S32 include: Electric power balance constraints: ; in, This represents the actual output power of the wind turbine at time t. Let be the output power of the photovoltaic cell at time t. for Mine electrical load during specific time periods; Thermal power balance constraint: ; in, The mine's heat load during time period t; Cold power balance constraints: ; Tie line power balance constraints: ; Constraints on new energy output: ; Equipment operating constraints: .

7. The energy system optimization method based on mine compressed air energy storage according to claim 6, characterized in that, The risk perception objective function modeling in S33 includes: S331, Constructing the Risk Perception Objective Function: Introducing the Conditional Value at Risk (CVaR) mechanism, a risk perception objective function is constructed, which aims to achieve a weighted sum of expected cost and CVaR, expressed as: ； Where α is the confidence level. Let β be the expected total cost, and let β be the trade-off coefficient between expected operating cost and the risk of operating cost fluctuation. The conditional value-of-risk cost when the confidence level is α; S332, Objective Function Transformation: The risk perception objective function is transformed into an equivalent optimization model that incorporates auxiliary variables ζ and a scenario-based structure, expressed as: ； Where n represents the nth scene, N is the set of all scenes, and μ n Let ζ and ζ represent the probability of the nth scenario occurring. For intermediate parameters; S333, Set scene constraints: Define ζ and ζ for each scene. The relationship between these factors forms the constraints of the risk perception objective function, which are expressed as: 。

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