Energy system optimization method based on mine compressed air energy storage

Abstract

The invention relates to the technical field of energy engineering, in particular to an energy system optimization method based on mine compressed air energy storage, and the method comprises the following steps: S1, constructing a multi-energy-flow network: constructing the multi-energy-flow network comprising waste mine renewable energy power generation, compressed air energy storage, mining mountain combined heat and power generation and ORC devices in combination with the full life cycle of a mine, a waste-recovery coupling energy topology of electricity-heat-cold-gas cooperative energy supply is formed; s2, multi-energy flow system modeling: establishing a mining mountain side equipment model and an abandoned mine side compressor, turbine power generation, gas storage and heat storage tank model; and S3, optimization scheduling modeling: introducing a CVaR index, constructing an optimization objective function containing energy purchase, operation and maintenance and energy abandoning costs, setting system operation and equipment constraints, and forming a risk-considered CMIES optimization scheduling model. According to the invention, the operation cost of the energy system in the mining area is reduced, renewable energy sources are efficiently consumed, and resources in the whole life cycle of the mine are cooperatively utilized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy engineering, and in particular to an energy system optimization method based on mine compressed air energy storage. BACKGROUND

[0002] As an energy-intensive industry, the mining industry is facing the major challenge of accelerating low-carbon transformation. With the continuous operation of mines and the gradual withdrawal of some mines, the mining area presents a situation of coexistence of mining and abandoned mines. On the one hand, the load fluctuation of mining equipment during operation is severe, and the energy supply system is under obvious pressure. On the other hand, a large number of abandoned mines form available underground space resources, which have good potential for energy storage and renewable energy collaborative application.

[0003] In the prior art, the underground space energy storage path mainly focuses on compressed air energy storage and pumped storage systems, and the mining side relies on waste heat recovery and associated energy cascade utilization to improve energy efficiency. However, these schemes generally have problems such as fragmented system design, non-synchronization of time and space regulation, and the like, and fail to effectively integrate the energy system characteristics of abandoned mines and mines in operation. In particular, there is a lack of source-storage-load unified modeling and optimization strategies for dynamic load. At the same time, the combined heat and power system is constrained by the heat-to-power ratio, and it is difficult to coordinate between fluctuating output of new energy and the combined heat and power system. The demand side regulation capability is also not fully stimulated, resulting in limited overall regulation margin and new energy utilization efficiency of the system. SUMMARY

[0004] The present application provides an energy system optimization method based on mine compressed air energy storage, which improves the regulation capability and operation robustness of the system under the uncertainty of new energy, and helps the comprehensive energy system in the mining area to develop towards low carbon, high efficiency and coordination.

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

[0006] S1, multi-energy flow network construction: a multi-energy flow coupling network including renewable energy power generation in abandoned mines, compressed air energy storage system, combined heat and power equipment in mines in operation and organic Rankine cycle device (ORC) is constructed in combination with the whole life cycle characteristics of mines, and a waste-mine coupled comprehensive energy topology structure realizing coordinated energy supply of electricity, heat, cold and gas is established;

[0007] S2, multi-energy flow system modeling: a device model on the side of mines in operation is established, and a compressor model, a turbine power generation model, an underground compressed air storage tank model and a heat storage tank model of the compressed air energy storage system on the side of abandoned mines are constructed, so as to realize modeling of the dynamic energy conversion process among heat, electricity and cold of 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, the model of the ventilation air thermal storage and oxidation device: the ventilation air thermal storage and oxidation device is used for treating low-concentration methane gas in mine ventilation air, converting the methane into carbon dioxide and water through a thermal storage and oxidation reaction, and recovering reaction heat for heating. The ventilation air thermal storage and oxidation device comprises a thermal storage bed and an oxidation chamber. Methane is combusted to release heat in the oxidation chamber, the heat is stored in the thermal storage body and then released periodically, and the output equivalent heat power is represented as:

[0017]

[0018] wherein H VOD,t is the heat output of the ventilation air oxidation device, Q fw,t is the ventilation air mixing gas flow at t, d fw,t is the ventilation air mixing gas concentration at t, is the methane heat value, η fw,t is the methane oxidation efficiency at t;

[0019] S213, the model of the water source heat pump: the water source heat pump takes groundwater or mine water as a heat source or a heat sink, and realizes heating in winter and cooling in summer based on the reverse Carnot cycle. In the heating mode, heat is absorbed from the water source and supplied to the mine area, and in the cooling mode, heat is absorbed from the mine area and discharged to the water source, and is represented as:

[0020]

[0021] wherein Q WSHP,t is the output cold power of the water source heat pump at t, is the input electric power of the water source heat pump when cooling at t, is the cooling energy efficiency coefficient of the water source heat pump, H WSHP,t is the output heat power of the water source heat pump at t, is the input electric power of the water source heat pump when heating at t, is the cooling energy efficiency coefficient of the water source heat pump, T1 is the low-temperature heat source temperature, and T2 is the high-temperature heat source temperature;

[0022] S214, the model of the electric refrigerator: the electric refrigerator drives a compressor to cool through electric energy, and the evaporation of the refrigerant absorbs heat to reduce the water temperature or the air temperature, thereby realizing rapid response to the cold load of the mine area. The relationship between the output cold power and the consumed electric power of the electric refrigerator is represented as:

[0023] Q EC,t = P EC,t η EC ;

[0024] wherein Q EC,t is the refrigeration power of the electric refrigerator at t, P EC,t is the electric power consumed by the electric refrigerator at t, and η EC is the refrigeration efficiency of the electric refrigerator;

[0025] S215, absorption refrigeration machine model: the absorption refrigeration machine utilizes a heat source to drive a refrigeration cycle to realize conversion of heat energy to cold energy to reduce power consumption, and the relationship between the cold power output by the absorption refrigeration machine and the heat power consumed is represented as:

[0026] Q AV,t = H AV,t η AV ;

[0027] wherein Q AV,t is the refrigeration power of the absorption refrigeration machine at time t, H AV,t is the heat consumption power of the absorption refrigeration machine at time t, and η AV is the heat-to-cold conversion efficiency of the absorption refrigeration machine.

[0028] Optionally, the waste mine comprehensive energy system mathematical model in S22 comprises:

[0029] S221, compressor modeling: a compressor model in the compressed air energy storage system is constructed, an electric power consumption and an inlet and outlet temperature change relationship of each stage of compressor under different working conditions are established, the number of compression stages, adiabatic efficiency, air mass flow and compression ratio are considered, the power upper and lower limits and temperature rise characteristics under compression working conditions are constrained, and are represented as:

[0030]

[0031] wherein P c,t is the sum of the power consumed by each stage of compressor at time t, N c is the total number of stages of compressor, η c is the adiabatic efficiency of the compression unit, κ is the air adiabatic index, q c,m,t is the air mass flow at time t, R g is the gas constant, are the inlet temperature and outlet temperature of the i-th stage of compressor at time t, respectively, β c,i is the rated compression ratio of the i-th stage of compressor, m c,t is a binary variable for indicating the CAES compression working condition, 1 indicating a working state and 0 indicating a stop state, are the upper and lower limits of the power consumed by the compressor at time t, respectively;

[0032] S222, turbine power generation model: a turbine power generation model is established to describe the conversion relationship of energy released by air in the expansion process, including turbine adiabatic efficiency, air mass flow, inlet and outlet temperature and expansion ratio, the power generation power range under turbine working conditions is constrained, the compressed air energy release behavior is reflected, and is represented as:

[0033]

[0034] wherein P g,t is the total power output of the turbine at time t, η g is the isentropic efficiency of the turbine, q g,m,t is the mass flow rate of air entering the turbine at time t, are the inlet and outlet temperatures of the turbine at time t, respectively, γ is the expansion ratio of the turbine, m g,t is a binary variable indicating the CAES power generation condition, 1 indicates the working state, and 0 indicates the stop state, are the upper and lower limits of the turbine power output at time t, respectively;

[0035] S223, underground compressed air storage model: an underground compressed air storage model is constructed, based on the gas state equation, to describe the dynamic relationship between the storage pressure and the storage volume at different times, to clearly define the upper and lower limit constraints of the storage volume, temperature, and pressure, to realize the stable storage adjustment mechanism of high-pressure gas, and to be represented as:

[0036]

[0037] wherein p st,t is the pressure of the underground compressed air storage at time t, V is the volume of the underground compressed air storage, T st,t is the temperature of the underground compressed air storage, are the upper and lower limits of the storage pressure, respectively;

[0038] S224, heat storage tank model: a heat storage tank model in the compressed air energy storage system is constructed, covering the heat exchange, heat storage, and external heat supply process during the compression process, defining the calculation relationship of the heat storage power and heat loss, setting the heat boundary conditions of the heat storage tank, and guaranteeing the continuity and controllability of the heat supply required for the expansion power generation process.

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

[0040] S2241, constructing a compression process heat storage power model: when the compression unit is running, the heat released by the compressed air enters the heat storage system through the heat exchanger, a heat storage power calculation model for each stage of the compression unit is constructed, and is represented as:

[0041]

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

[0043] S2242, build the preheating power model of the expansion process: in the expansion power generation stage, the expansion gas is preheated, and the preheating power model of the expansion unit is built, which is expressed as:

[0044]

[0045] Wherein, m g,i,t is the mass flow of the expansion gas, is the heat storage power of the i-th stage compression process, is the total heat storage power of the compressed air energy storage system;

[0046] S2243, build the heat balance model of the heat storage tank: the heat of the heat storage tank changes with time, which is determined by the initial heat and the heat input and output in each stage, and is expressed as:

[0047]

[0048] Wherein, H st,t is the heat energy power output by the heat storage tank to the outside at time t, is the internal heat at the initial time, is the heat in the heat storage tank at time t, k st is the heat energy loss coefficient, is the actual heat energy power output to the outside after considering the loss, H stmax is the upper limit value of the output heat power;

[0049] S2244, set the upper and lower limit constraints of heat storage: set the upper and lower limits of the internal heat of the heat storage tank, which is expressed as:

[0050]

[0051] Optionally, the optimization scheduling modeling in S3 comprises:

[0052] S31, scheduling optimization target modeling: build a scheduling optimization target function with the goal of minimizing the total cost of the mine integrated energy system operation, including external energy purchase cost, equipment operation and maintenance cost, and wind and light punishment cost;

[0053] S32, operation constraint definition: define various operation constraint conditions met in the optimization scheduling process, including balance constraints of electric power, heat power and cold power, power grid and gas pipeline purchase power limits, and power upper and lower limit constraints of various energy equipment operation, while introducing new energy power output limit and load matching boundary condition;

[0054] S33, risk perception target function modeling: based on the scheduling optimization target function, introduce the conditional value at risk (CVaR) mechanism, and build a target function including the weighted combination of expected cost and risk cost to cope with the operation fluctuation risk brought by new energy output uncertainty.

[0055] Optionally, the scheduling optimization objective function in S31 for minimizing the total cost of the mine integrated energy system operation is represented as:

[0056]

[0057] wherein, is the grid electricity purchase price at time t, is the gas purchase price at time t, P t e is the electricity purchase power at time t, P t g is the gas purchase power at time t, C1 is the sum of operation and maintenance costs of new energy equipment such as wind turbines and photovoltaic cells, derivative energy utilization equipment such as spent wind heat oxidation and water source heat pump, and refrigeration equipment, C2 is the sum of operation and maintenance costs of underground compressed air storage and heat storage tank, GS2,r is the unit maintenance cost of underground compressed air storage, c HS,r is the unit maintenance cost of heat storage tank, q m,comp2 is the air mass flow into the compressed air storage, is the wind power prediction power at time t, P WT,t is the actual wind power into the grid at time t, is the photovoltaic prediction power at time t, P PV,t is the actual photovoltaic power into the grid at time t, C WT , C PV is the penalty coefficient of abandoned wind and light.

[0058] Optionally, the operation constraints in S32 include:

[0059] Electric power balance constraint:

[0060] wherein, P wt,t is the actual output power of the wind turbine at time t, P pv,t is the output power of the photovoltaic cell at time t, P l,t is the mine electrical load at time t;

[0061] Thermal power balance constraint:

[0062] wherein, H l,t is the mine thermal load at time t;

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

[0064] Interconnection line electric power balance constraint:

[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] The application, by introducing a scheduling optimization mechanism considering conditional value at risk (CVaR), incorporates new energy output uncertainty into the cost control range, realizes dynamic balance of expected cost and operation risk, improves the economy and robustness of system operation, combines the whole life cycle design concept of the mine, prolongs the service cycle of the mining equipment, improves the space utilization rate of the abandoned mine, and the implementation results show that the total life cycle cost of the mining area, the external energy purchase expenditure and the abandoned energy cost can be effectively reduced, which provides support for the clean energy transformation of the mining area. BRIEF DESCRIPTION OF DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only illustrate the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0080] Figure 1 The optimization method flowchart of the embodiment of the application;

[0081] Figure 2 The waste-mining coupled mine electric-thermal-cold-gas coordinated energy supply topology schematic diagram of the embodiment of the application;

[0082] Figure 3 The electric energy scheduling result schematic diagram of scenario 1 of the embodiment of the application;

[0083] Figure 4 The electric energy scheduling result schematic diagram of scenario 2 of the embodiment of the application;

[0084] Figure 5 The thermal energy scheduling result schematic diagram of scenario 1 of the embodiment of the application;

[0085] Figure 6 The thermal energy scheduling result schematic diagram of scenario 2 of the embodiment of the application;

[0086] Figure 7 The cold energy scheduling result schematic diagram of scenario 1 of the embodiment of the application;

[0087] Figure 8 The cold energy scheduling result schematic diagram of scenario 2 of the embodiment of the application;

[0088] Figure 9 The renewable energy consumption schematic diagram of scenario 1 of the embodiment of the application;

[0089] Figure 10 The renewable energy consumption schematic diagram of scenario 2 of the embodiment of the application;

[0090] Figure 11A schematic diagram of the relationship between the total cost and the risk coefficient of the optimization method CMIES of the embodiments of the present application. DETAILED DESCRIPTION

[0091] The present application will be described in detail below with reference to the drawings and specific embodiments. For some known technologies, other alternative ways can also be implemented by those skilled in the art; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the present application.

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

[0093] Step 1, construction and topological design of waste-mining coupled mine multi-energy flow coupling network:

[0094] Combined with the whole life cycle characteristics of the mine, a waste mine renewable energy power generation, compressed air energy storage and mining mine combined heat and power, organic Rankine cycle device (ORC) and other multi-energy flow coupling network are constructed, and a waste-mining coupled mine "electricity-heat-cold-gas" collaborative energy supply topology is established.

[0095] Step 2, modeling of waste-mining coupled mine multi-energy flow system:

[0096] 2.1, mathematical model of typical equipment in mining mine module:

[0097] 2.1.1, CHP model based on ORC waste heat power generation:

[0098] The combined heat and power (CHP) unit generates electricity through the gas turbine and recovers waste heat to realize electricity and heat supply, but is restricted by the "heat determines electricity" mechanism, and the reverse fluctuation of electricity and heat load easily leads to wind curtailment. After introducing the ORC waste heat power generation system, thermal energy can be flexibly distributed to the waste heat boiler (WHB) for heating or ORC power generation, thereby decoupling the rigid coupling of heat and electricity and improving the energy supply flexibility. The relationship between the power generation and heat production of the CHP unit and the gas consumption power is as follows:

[0099]

[0100] In the formula, P CHP,t , H CHP,t are the power generation and heat production of the CHP unit at time t, respectively; H ORC,t is the heat power of the ORC waste heat recovery in the CHP unit at time t; H WHB,t is the heat power output by the WHB at time t; P ORC,t is the power generation of the ORC waste heat recovery at time t; , and η ORC is the electricity-heat conversion efficiency of the ORC; ​H ORC,t maximum, minimum value.

[0101] 2.1.2, the model of ventilation air thermal storage oxidation device:

[0102] The ventilation air thermal storage oxidation device processes low concentration methane (concentration <1%) in mine ventilation air, converts it into CO2 and H2O by thermal storage oxidation technology, and recovers reaction heat for mine heating. The working process is that the ventilation air enters the preheating bed, the methane oxidation in the oxidation chamber releases heat, and the heat is stored in the thermal storage body and released periodically. The equivalent heat power generated by the ventilation air thermal storage oxidation device for the extracted ventilation air gas is:

[0103]

[0104] In the formula: H VOD,t is the heat output of the ventilation air oxidation device, Q fw,t is the ventilation air mixing gas flow at t, d fw,t is the ventilation air mixing gas concentration at t, is the methane heat value, η fw,t is the methane oxidation efficiency at t.

[0105] 2.1.3, water source heat pump model:

[0106] The water source heat pump uses groundwater or mine water as heat source / sink, realizes heating (winter) or refrigeration (summer) through reverse Carnot cycle. Heating mode: absorbs heat from the water source and releases heat to the mine area. Refrigeration mode: absorbs heat from the mine area and releases cold to the water source. The general refrigeration (heating) formula of the water source heat pump is:

[0107]

[0108] In the formula: Q WSHP,t is the water source heat pump output cold power at t, is the water source heat pump input electric power at t, is the water source heat pump refrigeration energy efficiency coefficient, H WSHP,t is the water source heat pump output heat power at t, is the water source heat pump input electric power at t, is the water source heat pump refrigeration energy efficiency coefficient, T1 is the low temperature heat source temperature, and T2 is the high temperature heat source temperature.

[0109] 2.1.4, electric refrigerator model:

[0110] It quickly responds to the cooling load demand of the mine area by consuming electric energy to drive the compressor to refrigerate. Its working process is that the electric energy drives the compressor to compress the refrigerant, the refrigerant evaporates and absorbs heat, and the water temperature / air temperature is reduced. The relationship between the cold power output by the electric refrigerator and the consumed electric power is:

[0111] Q EC,t = P EC,t η EC (4)

[0112] wherein Q EC,t is the refrigeration power of the electric refrigerator at time t, P EC,t is the power consumption of the electric refrigerator at time t, and η EC is the refrigeration efficiency of the electric refrigerator.

[0113] 2.1.5, Absorption Refrigerator Model:

[0114] The absorption refrigerator uses a heat source (such as CHP waste heat or VOD recovered heat) to drive the refrigeration cycle, realizing heat energy to cold energy conversion and reducing power consumption. The working process is that the heat source heats the absorbent solution, releases the refrigerant vapor, the refrigerant evaporates and absorbs heat, and the refrigeration cycle is completed. The relationship between the cold power output and the heat power consumption of the absorption refrigerator is:

[0115] Q AV,t = H AV,t η AV (5)

[0116] wherein Q AV,t is the refrigeration power of the absorption refrigerator at time t, H AV,t is the heat consumption of the absorption refrigerator at time t, and η AV is the heat-to-cold conversion efficiency of the absorption refrigerator.

[0117] 2.2, Mathematical Model of Waste Mine Comprehensive Energy System:

[0118] 2.2.1, Modeling of Compressed Air Energy Storage:

[0119] Compressor Model:

[0120] The power consumption of the rth stage compressor at time t is shown in equation (1), and the total power consumption of all stages of compressors is shown in equation (2). The upper and lower limits of the compression power are shown in equation (3). The relationship between the inlet and outlet air temperatures of the compressor is shown in equation (4):

[0121]

[0122] wherein P c,t is the sum of the power consumption of all stages of compressors at time t, N c is the total number of stages of compressors, η c is the adiabatic efficiency of the compression unit, κ is the air adiabatic index, q c,m,t is the air mass flow rate at time t, and R g is the gas constant, are the inlet temperature and outlet temperature of the ith stage compressor at time t, respectively, and β c,iThe rated compression ratio of the i-th compressor, m c,t A binary variable, indicating the CAES compression operation, 1 for working state and 0 for stop state, The upper and lower limits of the compressor power consumption at time t, respectively;

[0123] 2.2.2, Turbine power generation model:

[0124]

[0125] P = m * g * h g,t The total turbine power at time t, η g The adiabatic efficiency of the turbine, q g,m,t The air mass flow rate entering the turbine at time t, The turbine inlet and outlet temperatures at time t, respectively, and γ is the turbine expansion ratio, m g,t A binary variable, indicating the CAES power generation operation, 1 for working state and 0 for stop state, The upper and lower limits of the turbine power at time t, respectively.

[0126] 2.2.3, Underground compressed air storage tank model:

[0127] The mathematical model of the underground compressed air storage tank established in the abandoned mine tunnel, the high-pressure exhaust gas after the secondary compressor enters the underground compressed air storage tank mainly consists of N2, the pressure of the high-pressure gas in the storage tank is represented by formula (14), and the upper and lower limits of the pressure of the storage tank are represented by formula (17):

[0128]

[0129] P = m * g * h st,t The pressure of the underground compressed air storage tank at time t, V is the volume of the underground compressed air storage tank, T st,t The temperature of the underground compressed air storage tank, The upper and lower limits of the pressure of the storage tank, respectively.

[0130] 2.2.4, Heat storage tank model:

[0131] The heat flow in the adiabatic compressed air energy storage device is divided into three main links: heat exchange-heat storage-heat release; heat energy is circulated through the heat exchanger and the heat storage tank. When the i-th compression unit is put into operation, the released heat will be absorbed by the i-th heat exchanger, and then the air flow continues to enter the i+1-th compression unit. Its compression and heat storage power can be represented as:

[0132]

[0133] P = m * g * h c,i,tThe mass flow rate of the compressed gas, c p,a The specific heat capacity of air (1.003 kJ / (kg·K)); The regenerative power of the i-th compression process, The total regenerative power of the compressed air energy storage system.

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

[0135]

[0136] In the formula: m g,i,t The mass flow rate of the expanded gas, The regenerative power of the i-th compression process,

[0137] The total regenerative power of the compressed air energy storage system.

[0138] The heat supply process of the heat storage tank of the compressed air energy storage device to the outside is similar to the internal compression / expansion heat exchange process. The residual heat in the heat storage tank at a certain time can be represented as:

[0139]

[0140] In the formula: H st,t The thermal energy power output by the heat storage tank to the outside at time t, The internal heat at the initial time, The heat in the heat storage tank at time t, k st The thermal energy loss coefficient, The actual thermal energy power output to the outside after considering the loss, H stmax The upper limit value of the output thermal power.

[0141] The prerequisite for the compressed air energy storage device to supply heat to the outside is that the residual heat in the heat storage tank is sufficient to meet the preheating heat required for expansion power generation in the following period of time. The heat storage tank cannot continue to store heat after the upper limit of the stored heat is reached. At this time, if compression charging occurs, this part of the heat will be wasted. Accordingly, the upper and lower limits of the actual heat in the gas tank are set:

[0142]

[0143] Step 3, optimization scheduling model of waste-extraction coupled CMIES considering CVaR:

[0144] 3.1, Objective function:

[0145]

[0146] 1) External energy purchase cost:

[0147]

[0148] wherein: is the electricity purchase price of the power grid at time t, is the gas purchase price at time t, P t e is the electricity purchase power at time t, P t g is the gas purchase power at time t.

[0149] 2) Equipment operation and maintenance cost:

[0150]

[0151] wherein: C1 is the operation and maintenance cost of new energy equipment such as wind turbines and photovoltaic cells, derivative energy utilization equipment such as spent wind heat storage oxidation and water source heat pump, and refrigeration equipment, C2 is the operation and maintenance cost of underground compressed air storage and heat storage tank, c GS2,r is the unit maintenance cost of underground compressed air storage, c HS,r is the unit maintenance cost of heat storage tank, q m,comp2 is the air mass flow entering the compressed air storage, with a unit of kg / s.

[0152] 3) Wind and light abandonment cost:

[0153]

[0154] wherein: is the wind power prediction power at time t, P WT,t is the actual wind power into the grid at time t, is the photovoltaic prediction power at time t, P PV,t is the actual photovoltaic power into the grid at time t, C WT , C PV is the wind and light abandonment penalty coefficient.

[0155] 3.2, constraint condition:

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

[0157] 3.2.1, system operation constraint:

[0158] 1) Electric power balance constraint:

[0159]

[0160] wherein: P wt,t is the actual output power of the wind turbine at time t, P pv,t is the output power of the photovoltaic cell at time t, P l,t is the mine electrical load at time t.

[0161] 2) Thermal power balance constraint:

[0162]

[0163] where H l,t is the thermal load of the mine at time t.

[0164] 3) Cold power balance constraint:

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

[0166] 4) Tie-line electric power balance constraint:

[0167] The system purchased electric and gas power cannot exceed the transmission power upper limit of the grid tie-line and natural gas pipeline:

[0168]

[0169] 3.2.2, Other constraints:

[0170] New energy output constraint:

[0171]

[0172] 3.2.3, Equipment operation constraint:

[0173]

[0174] Considering the impact of source and load uncertainty on system operation, this paper introduces a conditional risk measurement term in the conventional scheduling model to alleviate the economic loss caused by uncertain factors. The IES low-carbon economic dispatch objective function considering CVaR is as follows:

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

[0176] where α is the confidence level, E(F1) is the expected total cost, β is the trade-off coefficient between the expected operation cost and the operation cost fluctuation risk, and 0≤β≤1; V CVaR,α (F1) is the conditional risk value cost when the confidence level is α.

[0177] Equation (23) indicates that when the confidence level is α, the conditional risk measurement term is β and 1-β, and the expected operation cost is E(F1). Equation (23) can be converted to

[0178]

[0179] where n represents the nth scenario, N is the set of all scenarios, μ n is the probability of the nth scenario, ζ and κ n are intermediate parameters.

[0180] Step 4, Example analysis of waste-mining coupled CMIES optimization scheduling model:

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

[0182] Scenario 1: Without considering the waste mine energy storage part, i.e. the traditional mine integrated energy system scheduling model.

[0183] Scenario 2: Considering the waste mine CAES and ORC devices, waste-mining coupled mine multi-energy flow model.

[0184] Comparing scenario 1 and scenario 2, as shown in the table, in terms of total cost, the total cost, energy purchase cost, and energy abandonment 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. The flexibility of renewable energy consumption in scenario 2 is greatly improved compared to scenario 1. The CAES device is in charging state during the flat valley period of electricity price, such as 04:00-07:00, 10:00-17:00, and 23:00-24:00, which improves the consumption of clean energy. During the high electricity price or high load period, such as 08:00-09:00 and 18:00-22:00, it releases power. There is a little wind and light abandoned during the period of 11:00 when electricity cannot be stored on a large scale and the CAES capacity is limited. After the introduction of compressed air energy storage, the heat can be stored in the heat storage tank, which can be used for ORC device power generation, realizing electric-thermal coupling and increasing the flexibility of the system.

[0185] The calculation results are as follows:

[0186] Table 1 Comparison of scenarios

[0187]

[0188] Influence of risk coefficient on IES scheduling:

[0189] The value range of risk coefficient β represents the degree of decision maker's inclination to system operation risk. In order to study the influence of risk coefficient β on system optimization operation, this section discusses the relationship between β and the total cost of CMIES. This section takes six values of β, i.e. the relationship between risk coefficient β and system total cost. The higher β is, the lower the system operation risk is. β = 1 represents completely ignoring the influence of uncertainty factors, i.e. complete risk aversion.

[0190] With the increase of β, the total cost of CMIES also increases, while the total cost corresponding to the conditional risk cost CVaR decreases with the increase of risk coefficient β. When β = 1, it indicates that the decision maker does not bear the operation risk, at this time the system is in complete risk aversion, the conditional risk cost of IES is the lowest, and the total cost is the highest. When risk coefficient 0.7≤β<1, it indicates that the decision maker can bear certain operation risk, and the total cost of IES slightly decreases with the decrease of risk coefficient. When risk coefficient 0.1≤β<0.7, it indicates that the decision maker has a better attitude towards system risk and is willing to bear greater operation risk cost to pursue lower total cost.

[0191] The present application encompasses any alternatives, modifications, equivalent methods and solutions made to the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0192] The above is only the preferred embodiment of the present application, it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for optimizing an energy system based on mine compressed air energy storage, characterized in that, The method comprises the following steps: S1, multi-energy flow network construction: combined with the characteristics of the whole life cycle of the mine, a multi-energy flow coupling network including renewable energy power generation in the abandoned mine, compressed air energy storage system, and combined heat and power equipment and organic Rankine cycle device in the mining mountain is constructed, and an abandoned-mining coupled mine comprehensive energy topology structure realizing coordinated energy supply of electricity, heat, cold and gas is established; S2, multi-energy flow system modeling: a device model on the mining mountain side is established, a compressor model, a turbine power generation model, an underground compressed air storage tank model and a heat storage tank model of the compressed air energy storage system on the abandoned mine side are constructed, and the dynamic energy conversion process modeling among heat, electricity and cold of the multi-energy flow system is realized; S3, optimization scheduling modeling: on the basis of establishing the source-storage-load system model, a conditional risk value index is introduced, an economic optimization objective function including external energy purchase cost, equipment operation and maintenance cost and abandoned wind and light cost is constructed, and a series of operation constraints of system operation, equipment and network are set, and an abandoned-mining coupled mine comprehensive energy system optimization scheduling model considering operation risk factors is formed.

2. The energy system optimization method based on mine compressed air energy storage according to claim 1, characterized in that, The multi-energy flow system modeling in S2 comprises: S21, mining mountain side device modeling: a mathematical model of energy equipment in the mining mountain is established, including a CHP model based on ORC waste heat power generation, a spent air heat storage oxidation device model, a water source heat pump model, an electric refrigerator model and an absorption refrigerator model; S22, abandoned mine comprehensive energy system mathematical model: an abandoned mine energy model based on the compressed air energy storage system is constructed, including a compressor model, a turbine power generation model, an underground compressed air storage tank model and a heat storage tank model, representing the physical state change and energy flow conversion of air in the compression, storage, expansion and heat energy management process, and establishing a systematic expression mode of mine space resources and multi-energy coordination.

3. The energy system optimization method based on mine compressed air energy storage according to claim 2, characterized in that, The mining mountain side device modeling in S21 comprises: S211, CHP model based on ORC waste heat power generation: the combined heat and power unit generates electricity through the gas turbine and recovers waste heat to realize electric heat supply, after introducing the ORC waste heat power generation system, the recovered heat energy is distributed to the waste heat boiler for heating or the ORC power generation, thereby decoupling the rigid coupling of heat and electricity, a multi-parameter simultaneous model including CHP power generation, heat generation and ORC conversion efficiency is established, and is expressed as: where P CHP,t , H CHP,t are the power and heat output of the CHP unit at time t, H ORC,t is the heat power of the ORC waste heat recovery at time t, H WHB,t is the heat power output of the WHB at time t, P ORC,t is the power output of the ORC waste heat recovery at time t, are the power and heat efficiency of the CHP unit at time t, η ORC is the power-to-heat conversion efficiency of the ORC, are the maximum and minimum values of H ORC,t . S212, spent air heat storage oxidation device model: the spent air heat storage oxidation device is used for treating low-concentration methane gas in the mine spent air, the methane is converted into carbon dioxide and water through the heat storage oxidation reaction, and the reaction heat is recovered for heating, the spent air heat storage oxidation device includes a heat storage bed and an oxidation chamber, the methane burns and releases heat in the oxidation chamber, the heat is stored in the heat storage body and then released periodically, and the output equivalent heat power is expressed as: wherein H VOD,t is the heat production of the lean air oxidation device, Q fw,t is the flow rate of the lean air mixing gas at time t, d fw,t is the concentration of the lean air mixing gas at time t, is the methane heat value, η fw,t is the methane oxidation efficiency at time t; S213, water source heat pump model: the water source heat pump takes underground water or mine water as a heat source or a heat sink, realizes heating in winter and refrigeration in summer based on the reverse Carnot cycle, absorbs heat from the water source in the heating mode and supplies heat to the mine area, absorbs heat from the mine area in the refrigeration mode and releases heat to the water source, and is expressed as: wherein Q WSHP,t is the water source heat pump output cold power at time t, is the water source heat pump input electric power when refrigerating at time t, is the water source heat pump refrigeration energy efficiency coefficient, H WSHP,t is the water source heat pump output heat power at time t, is the water source heat pump input electric power when heating at time t, is the water source heat pump refrigeration energy efficiency coefficient, T1 is the low-temperature heat source temperature, and T2 is the high-temperature heat source temperature. S214, electric refrigerator model: the electric refrigerator drives the compressor to refrigerate by electric energy, the refrigerant evaporates to absorb heat to reduce the water temperature or air temperature, realizes the quick response to the mine cold load, and the relationship between the cold power output by the electric refrigerator and the consumed electric power is represented as: Q EC,t = P EC,t η EC ; wherein Q EC,t is the refrigeration power of the electric refrigerator at time t, P EC,t is the power consumption of the electric refrigerator at time t, η EC is the refrigeration efficiency of the electric refrigerator; S215, absorption refrigerator model: the absorption refrigerator drives the refrigeration cycle by using a heat source to realize the conversion of heat energy to cold energy to reduce the electric energy consumption, and the relationship between the cold power output by the absorption refrigerator and the consumed heat power is represented as: Q AV,t = H AV,t η AV ; wherein Q AV,t is the absorption chiller refrigeration power at time t, H AV,t is the absorption chiller heat consumption power at time t, and η AV is the absorption chiller heat-to-cold conversion efficiency.

4. The energy system optimization method based on mine compressed air energy storage according to claim 3, characterized in that, The waste mine comprehensive energy system mathematical model in S22 comprises: S221, compressor modeling: a compressor model in a compressed air energy storage system is constructed, an electric power consumption and an inlet and outlet temperature change relationship of each stage compressor under different working conditions are established, a compression series, an adiabatic efficiency, an air mass flow and a compression ratio are considered, a power upper and lower limit and a temperature rise characteristic under the compression working condition are constrained, and the following is represented: where P c,t is the sum of the power consumption of each stage of the compressor at time t, N c is the total number of compressor stages, η c is the adiabatic efficiency of the compression unit, κ is the air adiabatic index, q c,m,t is the air mass flow at time t, R g is the gas constant, are the inlet and outlet temperatures of the i-th stage of the compressor at time t, respectively, β c,i is the rated compression ratio of the i-th stage of the compressor, m c,t is a binary variable used to represent the CAES compression operating condition, 1 indicates the working state, and 0 indicates the stop state, are the upper and lower limits of the power consumption of the compressor at time t, respectively; S222, turbine power generation model: a turbine power generation model is established, a conversion relationship of energy released by air in an expansion process is described, including a turbine adiabatic efficiency, an air mass flow, an inlet and outlet temperature and an expansion ratio, a power generation power range under the turbine working condition is constrained, a compressed air energy release behavior is reflected, and the following is represented: where P g,t is the total power output of the turbine at time t, η g is the isentropic efficiency of the turbine, q g,m,t is the mass flow rate of air entering the turbine at time t, are the inlet and outlet temperatures of the turbine at time t, respectively, γ is the expansion ratio of the turbine, m g,t is a binary variable indicating the CAES power generation condition, 1 indicates the working state, and 0 indicates the stop state, are the upper and lower limits of the turbine power output at time t, respectively; S223, underground compressed air storage model: an underground compressed air storage model is constructed, a dynamic relationship between a storage tank gas pressure and a storage capacity at different times is described based on a gas state equation, a storage tank volume, a temperature and a pressure upper and lower limit constraint are defined, a stable storage adjustment mechanism of high-pressure gas is realized, and the following is represented: wherein p st,t is the pressure of the underground compressed air reservoir at time t, V is the volume of the underground compressed air reservoir, T st,t is the temperature of the underground compressed air reservoir, are, respectively, the upper and lower bounds of the reservoir pressure; S224, heat storage tank model: a heat storage tank model in a compressed air energy storage system is constructed, a heat exchange, heat storage and external heat supply process of heat in the compression process are covered, a calculation relationship of a heat storage power and a heat loss is defined, a heat boundary condition of the heat storage tank is set, and the continuity and controllability of the heat supply required by the expansion power generation process are guaranteed.

5. The energy system optimization method based on mine compressed air energy storage according to claim 4, characterized in that, The heat storage tank model in S224 comprises: S2241, constructing a compression process heat storage power model: when the compression unit is running, the heat released by the compressed air enters the heat storage system through the heat exchanger, a heat storage power calculation model of each stage compression unit is constructed, and the following is represented: wherein, m c,i,t is the mass flow rate of compressed gas, c p,a is the specific heat capacity of air, is the regenerative power of the i-th stage of compression process, is the total regenerative power of the compressed air energy storage system; S2242, constructing an expansion process preheating power model: in the expansion power generation stage, the expansion gas is preheated, a preheating power model of the expansion unit is constructed, and the following is represented: wherein m g,i,t is the mass flow rate of the expanded gas, is the regenerative power of the i-th compression process, is the total regenerative power of the compressed air energy storage system; S2243, constructing a heat storage tank heat balance model: the change of the heat storage tank heat with time is determined by the initial heat and the heat input and output at each stage, and the following is represented: H st,t is the heat energy power outputted by the heat storage tank to the outside at time t, is the internal heat at the initial moment, is the heat in the heat storage tank at time t, k st is the heat energy loss coefficient, is the actual heat energy power outputted to the outside after considering the loss, H stmax is the upper limit value of the output heat power; S2244, setting a heat storage upper and lower limit constraint condition: the upper and lower limits of the heat inside the heat storage tank are set, and the following is represented:

6. The energy system optimization method based on mine compressed air energy storage according to claim 5, characterized in that, The optimization scheduling modeling in S3 comprises: S31, scheduling optimization target modeling: a scheduling optimization target function with the minimum total cost of the mine comprehensive energy system as the target is constructed, the cost items include external energy purchase cost, equipment operation and maintenance cost and wind and light curtailment penalty cost; S32, operation constraint definition: define various operation constraint conditions met in the optimization scheduling process, including balance constraints of electric power, thermal power and cold power, purchase power limits of power grid and gas pipeline network, and power upper and lower limit constraints of various energy equipment operation, while introducing new energy power output limit and load matching boundary condition; S33, risk perception objective function modeling: based on the scheduling optimization objective function, introduce the conditional value at risk mechanism, and construct an objective function including the weighted combination of expected cost and risk cost to cope with the operation fluctuation risk brought by new energy output uncertainty.

7. The energy system optimization method based on mine compressed air energy storage according to claim 6, characterized in that, The scheduling optimization objective function in S31 with the goal of minimizing the total cost of the mine comprehensive energy system operation is represented as: wherein, is the electricity purchase price of the power grid for the t period, is the gas purchase price for the t period, P t e is the electricity purchase power for the t period, P t g is the gas purchase power for the t period, C1is the sum of the operation and maintenance costs of new energy equipment such as wind turbines and photovoltaic devices, derivative energy utilization equipment such as windless heat storage oxidation and water source heat pump, and refrigeration equipment, C2is the sum of the operation and maintenance costs of underground compressed air storage and heat storage tank, GS2,r is the unit maintenance cost of the underground compressed air storage, c HS,r is the unit maintenance cost of the heat storage tank, q m,comp2 is the air mass flow into the compressed air storage, is the wind power prediction power at t moment, P WT,t is the actual wind power into the grid at t moment, is the photovoltaic prediction power at t moment, P PV,t is the actual photovoltaic power into the grid at t moment, C WT , C PV is the penalty coefficient of abandoned wind and light.

8. The energy system optimization method based on mine compressed air energy storage of claim 7, wherein, The operation constraint definition in S32 includes: Electrical power balance constraint: Wherein, P wt,t is the actual output power of the fan at time t, P pv,t is the output power of the photovoltaic cell at time t, P l,t is the mine electrical load in the t period; Thermal power balance constraint: where H l,t is the thermal load of the mine for the period t; Cold power balance constraint: Q EC,t + Q AV,t = Q l,t ; Interconnection line electric power balance constraints: New energy output constraints: Device operation constraints:

9. The energy system optimization method based on mine compressed air energy storage of claim 8, wherein, The risk perception objective function modeling in S33 includes: S331, construction of risk perception objective function: introduce the conditional value at risk mechanism, and construct a risk perception objective function with the goal of weighted sum of expected cost and CVaR, represented as: minf RIES = βE(F1) + (1 - β)V CVaR,α (F1); wherein a is the confidence level, E(F1) is the expected total cost, β is a trade-off coefficient between the expected operating cost and the operating cost fluctuation risk, and V CVaR,α (F1) is the conditional value at risk cost when the confidence level is a. S332, objective function transformation: transform the risk perception objective function into an equivalent optimization model introducing auxiliary variables ζ and a scenario structure, represented as: where n denotes the nth scene, N is the set of all scenes, μ n is the probability of the nth scene occurring, ζ and κ n are intermediate parameters; S333, set scenario constraints: define ζ and κ for each scenario n The relationship of ζ and κ, which forms the constraint of the risk perception objective function, is expressed as:

Images