Multi-time scale optimal scheduling method of cmies considering carbon-green certificate transaction and combined operation of ccs-p2g

Abstract

The present application relates to a kind of CMIES multi-time scale optimization scheduling method considering carbon-green certificate transaction and CCS-P2G combined operation, belong to mining area energy comprehensive utilization technical field.The present application comprehensively considers the energy characteristics of CMIES, the market characteristics of carbon-green certificate transaction mechanism and the low-carbon characteristics of CCS, two-stage P2G, and proposes a CMIES low-carbon economic scheduling method considering carbon-green certificate transaction and CCS-P2G combined operation under multi-time scale.The economic target is to minimize the energy purchase cost, energy abandonment penalty cost, carbon-green certificate transaction cost, equipment operation cost, carbon sequestration cost and load calling cost, and a CMIES day-ahead and day-ahead rolling optimization scheduling model based on MPC is established.The present application can better cope with the load and mining area multi-energy output fluctuation under different time dimensions, and can be adjusted according to the flexibility of CMIES internal equipment and the multi-time scale characteristics of demand response resources, which can effectively improve the wind, light, associated energy consumption capacity, CMIES low carbon and economy.

Description

Technical Field

[0001] The present invention relates to a CMIES multi-time scale optimization scheduling method taking into account carbon-green certificate trading and CCS-P2G joint operation, belonging to the technical field of comprehensive utilization of mining area energy. Background Art

[0002] With the dramatic increase in global greenhouse gas emissions, promoting low-carbon energy transformation and promoting carbon reduction and emission reduction in society have become top priorities. The coal mining industry, a large, energy-intensive industry, uses a variety of energy sources. The mining process also produces associated energy sources such as gas, ventilation air, and water. Focusing solely on mining efficiency while ignoring the utilization of these associated energy sources will result in serious environmental pollution and resource waste. According to statistics, the greenhouse effect caused by gas and ventilation air in my country is equivalent to approximately 200 million tons of CO2 annually. Furthermore, approximately 4.5 billion tons of mine water is generated during coal mining. Therefore, developing a coalmine integrated energy system (CMIES) to coordinate and optimize the mutual conversion of various energy sources and the cascaded utilization of energy in mining areas is of great practical significance. Summary of the Invention

[0003] This paper provides a multi-timescale optimization scheduling method for CMIES that takes into account the combined operation of carbon-green certificate trading and CCS-P2G. Taking into account the energy characteristics of CMIES, the market characteristics of the carbon-green certificate trading mechanism, and the low-carbon characteristics of CCS and two-stage P2G, a low-carbon economic scheduling method for CMIES that takes into account carbon-green certificate trading and CCS-P2G is proposed over multiple timescales. With the economic objectives of minimizing energy purchase costs, energy abandonment penalty costs, carbon-green certificate trading costs, equipment operation and maintenance costs, carbon sequestration costs, and load deployment costs, a MPC-based CMIES day-ahead and intraday rolling optimization scheduling model is established.

[0004] The technical solution of the present invention is: a CMIES multi-timescale optimization scheduling method taking into account the combined operation of carbon-green certificate trading and CCS-P2G, comprising:

[0005] Step 1: Based on the energy consumption characteristics of the mining area, a mining area associated energy utilization model including gas, ventilation air, and water inflow is constructed, and the CCS-P2G coupling system is introduced to form a CMIES structure that includes associated energy utilization and CCS-P2G coupling;

[0006] Step 2: Comprehensively consider the carbon trading mechanism and the green certificate trading mechanism, analyze the response characteristics of the mining area load at different time scales, and build a multi-time scale scheduling framework;

[0007] Step 3. Establish an MPC-based CMIES multi-time-scale optimization scheduling model, including a day-ahead scheduling model and an MPC-based intraday rolling optimization scheduling model. Solve the day-ahead optimization scheduling model, select the operating status of energy coupling equipment and the call volume of Class A IDR as the determining parameters and substitute them into the intraday rolling optimization scheduling model. Use MPC to perform rolling optimization on the intraday CMIES equipment and determine the call plan of each energy coupling equipment, energy storage equipment and Class B IDR.

[0008] The multi-timescale scheduling framework is divided into two stages: day-ahead scheduling and intraday scheduling. The day-ahead scheduling plan is formulated 24 hours in advance with a time scale of 1 hour. In the day-ahead stage, the working plan of the coupling equipment and the Class A IDR load call plan are determined and substituted as determined quantities in the intraday rolling scheduling stage. The intraday rolling scheduling plan has a time step of 15 minutes and an execution cycle of 4 hours. The output plan of each energy coupling equipment, the distributed generation output plan, and the Class B IDR load call plan are formulated.

[0009] The objective function of the day-ahead optimization scheduling model is as follows:

[0010]

[0011] Where: F1 is the objective function of the CMIES day-ahead optimization model, F e 、F q 、F ce-gc 、F oper 、 F load They are energy purchase cost, energy abandonment penalty cost, carbon-green certificate transaction cost, equipment operation and maintenance cost, carbon storage cost, and load call cost, which are expressed as follows:

[0012]

[0013] Where: T is the 24-hour optimization cycle; p, h, c are electricity, heat, and cold energy respectively; P grid,t 、P gas,t are the electricity and natural gas purchased by CMIES from the power grid and natural gas grid at time t respectively; The electricity purchase price and gas purchase price at time t respectively; P cwind,t 、P cpv,t The wind power and solar power abandoned at time t respectively; α w , α pv are the penalty coefficients for wind and solar curtailment, respectively; λ1, λ2, λ3 are the penalty coefficients for gas curtailment, wind curtailment, and water inrush, respectively; w,t 、F f,t 、F y,t are the power of gas abandonment, wind shortage and water inflow at time t respectively; F CE,t 、F GC,tare respectively the transaction costs of carbon and green certificates; α i 、P i,t are the unit maintenance cost and output power of equipment i at time t respectively; is the unit price of carbon sequestration; are the incentive IDR costs of Class A electricity, heating and cooling loads at time t, respectively.

[0014] The constraints of the day-ahead optimization scheduling model include:

[0015] (1) Electric power balance

[0016]

[0017] Where: P wind,t is the wind power actually consumed by the system at time t, P pv,t is the photovoltaic power actually consumed by the system at time t, P CHP,t is the electric power output by CHP at time t, P HFC,t is the electric power output by HFC at time t, P GT,t is the electric power output of the gas turbine at time t, are the charging and discharging power of the storage device at time t, is the electric power input to the electric refrigerator at time t, P DR,t is the total electrical load, P EL,t is the hydrogen power output by EL at time t, P CCS,t The electric power consumed by the CCS device to capture CO2 at time t;

[0018] (2) Thermal power balance

[0019]

[0020] Where: H CHP,t is the thermal power output of CHP at time t, H GB,t is the thermal power output of the gas boiler at time t, H HFC,t is the thermal power output of HFC at time t, is the thermal power output of the waste heat boiler at time t, H WSHP,t is the thermal power output of the water source heat pump at time t, H DR,t is the total heat load; are the charging and discharging power of the heat storage device at time t respectively;

[0021] (3) Cooling power balance

[0022] C EC,t +C AC,t =C DR,t

[0023] Where: C EC,tis the power of the electric refrigerator, C AC,t is the power of the absorption chiller, C DR,t is the total cooling load;

[0024] (4) Demand response constraints

[0025]

[0026] Where n∈{p,h,c}; They are the load amount increased by Class A IDR for each type of load and the upper limit of the load amount increased by Class A IDR for each type of load; They are the load reduction amount of Class A IDR for each type of load and the upper limit of the load reduction amount of Class A IDR for each type of load.

[0027] The MPC-based intraday rolling optimization scheduling model uses grey prediction to predict the energy output and load of the mining area:

[0028] (1) Input original wind, solar, associated energy and electric heating and cooling power sequence

[0029]

[0030] Where: k is the observed data j represents the time series of wind power, photovoltaic power, gas, wind power, water inrush and electric heating and cooling loads;

[0031] (2) Accumulate the predicted sequence and establish a linear differential equation to obtain the approximate solution of the differential equation:

[0032]

[0033] Where, is the new cumulative sequence, d j is the gray action, c j is the development coefficient;

[0034] Calculate wind, solar, associated energy, and electric heating and cooling power:

[0035]

[0036] In the formula Provide ultra-short-term wind, solar, associated energy, and electric heating and cooling power forecasts;

[0037] (3) Take σ = 1, 2, ..., k for the distribution to obtain the fitting sequence of the original sequence Where δ=1,2,…,k;

[0038] (4) Take σ=1+k,2+k,…,m+k for the distribution to obtain the predicted value sequence after the original sequence Where δ = 1, 2,…, m.

[0039] The objective function of the MPC-based intraday rolling optimization scheduling model is:

[0040]

[0041]

[0042] Where F2 is the objective function of the CMIES intraday rolling optimization model; are the incentive IDR costs of Class B electricity, heating and cooling loads at time t, respectively.

[0043] The demand response constraint in the constraint conditions of the MPC-based intraday rolling optimization scheduling model is:

[0044]

[0045] Where, They are the load amount increased by Class B IDR for each type of load and the upper limit of the load amount increased by Class B IDR for each type of load; They are the load reduction amount of Class B IDR for each type of load and the upper limit of the load reduction amount of Class B IDR for each type of load.

[0046] The beneficial effects of the present invention are:

[0047] This paper proposes a CMIES multi-timescale low-carbon optimization scheduling strategy that considers carbon-green certificate trading and CCS-P2G joint operation. It establishes a CMIES framework for the comprehensive utilization of multiple associated energy sources such as gas, ventilation air, and gushing water, as well as a CCS-P2G coupled operation model. The specific effects are as follows:

[0048] 1) The participation of the carbon-green certificate trading mechanism in the formulation of scheduling plans can improve the absorption of clean energy such as wind and light in mining areas and reduce the carbon emissions and total cost of CMIES.

[0049] 2) The introduction of CCS and two-stage P2G equipment in the mining area can capture CO2 and generate natural gas, which not only reduces the carbon emissions and total operating costs of CMIES, but also improves the utilization efficiency of associated energy.

[0050] 3) The multi-time-scale optimization operation mode based on MPC can better cope with the load under different time dimensions and the fluctuation of multi-energy output in the mining area, and perform hierarchical adjustment according to the flexibility of CMIES internal equipment and the multi-time-scale characteristics of demand response resources, which can effectively improve the wind, solar and associated energy absorption capacity, the low-carbon nature and economy of CMIES. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1is a CMIES structure diagram in the embodiment of the present application;

[0052] Figure 2 is a multi-time scale scheduling framework diagram in the embodiment of the present application;

[0053] Figure 3 is a partial device comparison diagram of scenario 4 day-ahead-intra-day in the embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be clearly and completely described below with reference to the drawings. All other embodiments obtained by those skilled in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other in any manner without conflict.

[0055] The embodiments such as Figures 1-3 As shown in the figure, the present embodiment takes a large coal mine in Yunnan as an example, provides a CMIES multi-time scale optimal scheduling method considering carbon-green certificate transaction and CCS-P2G combined operation, considers that a wind power plant and a photovoltaic power station are built around the mine area, first establishes a CMIES coupling structure as Figure 1As shown. It includes energy supply, utilization, conversion and storage. Specifically, energy supply includes: external power grid, natural gas grid, wind and solar renewable energy and associated energy in mining areas. Energy utilization includes: electricity, heat, cooling and other mining area loads and CCS. Energy coupling equipment includes: two-stage P2G, gas boiler (GB), combined heat and power (CHP), water source heat pump (WSHP), gas turbine (GT), regenerative thermal oxidation (RTO), electric chiller (EC), waste heat boilers (WHB), absorption chiller (AC). Energy storage includes: electricity storage (ES) and heat storage (HS). The two-stage P2G system consists of an electrolyzer (EL), a methane reactor (MR), a hydrogen fuel cell (HFC), and a hydrogen energy storage (HES). CCS is used to capture CO2 produced by carbon-emitting equipment to reduce carbon emissions in mining areas.

[0056] Based on the characteristics of associated energy, gas is supplied to GT for power generation, while exhaust air and gushing water are supplied to RTO and WSHP for heating respectively. WHB and AC recycle the high-temperature flue gas generated by GT and RTO, realizing the coupling, interconnection and coordinated conversion of various heterogeneous energy sources such as electricity, heat and cooling in the mining area.

[0057] 1) Gas turbine

[0058]

[0059] Where: P GT,t is the electric power output of the gas turbine at time t; η GT is the gas turbine electric power conversion coefficient; L gas is the lower heating value of methane; F GT,t , β GT,t are the gas flow rate and methane concentration in the gas turbine at time t; H GT,t is the thermal power output of the gas turbine at time t; η loss is the heat loss coefficient of the gas turbine; P GT,max is the maximum output power of the gas turbine; They are the upper and lower limits of the gas turbine climbing power respectively.

[0060] 2) Thermal storage oxidation device

[0061]

[0062] Where: H RTO,t is the thermal power output of the thermal storage oxidation device at time t; η RTO is the thermal power conversion coefficient of the thermal storage oxidation device; F RTO,t , β RTO,t is the exhaust air flow and methane concentration in the thermal storage oxidation device at time t; H RTO,max is the maximum output power of the thermal storage oxidation device; They are the upper and lower limits of the climbing power of the thermal storage oxidation device respectively.

[0063] 3) Water source heat pump

[0064]

[0065] Where: H WSHP,t is the thermal power output of the water source heat pump at time t; F WSHP,t is the flow rate of water in the water source heat pump at time t; P1 and P2 are the fitting coefficients of the water source heat pump in the heating state; H WSHP,max is the maximum output power of the water source heat pump; They are the upper and lower limits of the water source heat pump climbing power respectively.

[0066] 4) Wind power operation model

[0067] 0≤P wind,t ≤P pwind,t

[0068] Where: P wind,t is the wind power actually consumed by the system at time t; P pwind,t is the predicted wind power output at time t.

[0069] 5) Photovoltaic operation model

[0070] 0≤P pv,t ≤P ppv,t

[0071] Where: P pv,t is the photovoltaic power actually consumed by the system at time t; P ppv,t is the predicted photovoltaic output at time t.

[0072] 6) CHP model

[0073]

[0074] Where: PCHP,t , H CHP,t are the electric and thermal power output by the CHP at time t, respectively; are the CHP electric and thermal power conversion factors, respectively; is the natural gas power input to the CHP at time t; CHP,max is the maximum output power of the CHP; are the upper and lower limits of the CHP ramping power, respectively; are the upper and lower limits of the CHP heat-to-power ratio, respectively.

[0075] 7) Gas boiler model

[0076]

[0077] where: H GB,t , are the thermal power output by the gas boiler at time t and the natural gas power input to the gas boiler, respectively; η GB is the thermal power conversion factor of the gas boiler; H GB,max is the maximum output power of the gas boiler; are the upper and lower limits of the gas boiler ramping power, respectively.

[0078] 8) Electric chiller model

[0079]

[0080] where: P EC,t , are the cold power output by the electric chiller at time t and the electric power input to the electric chiller, respectively; η EC is the cold power conversion factor of the electric chiller; P EC,max is the maximum output power of the electric chiller; are the upper and lower limits of the electric chiller ramping power, respectively.

[0081] 9) Waste heat boiler

[0082]

[0083] where: are the waste heat power input to the waste heat boiler at time t and the thermal power output by the waste heat boiler, respectively; η WHB is the thermal power conversion factor of the waste heat boiler; H WHB,max is the maximum output power of the waste heat boiler; are the upper and lower limits of the waste heat boiler ramping power, respectively.

[0084] 10) Absorption chiller

[0085]

[0086] where: HAC,t 、P AC,t The waste heat power input and cooling power output of the absorption chiller at time t respectively; η AC is the cooling power conversion coefficient of the absorption chiller; P AC,max is the maximum output power of the absorption chiller; They are the upper and lower limits of the climbing power of the absorption chiller respectively.

[0087] 11) Energy storage device model

[0088]

[0089] Where: x∈{ES,HS,HES}; S x,t is the capacity of energy storage device x at time t; α x is the energy self-loss coefficient of energy storage device x; are the charging and discharging power of energy storage device x at time t respectively; are the x charging and discharging efficiencies of the energy storage device, respectively; are the maximum charging and discharging power of the energy storage device x respectively; It is a 0-1 variable, which is used to limit the charging and discharging from happening at the same time; S x,max 、S x,min are the upper and lower limits of the energy storage device x capacity respectively; S x,T 、S x,0 They represent the energy storage at the end and start of scheduling of energy storage device x respectively.

[0090] The equipment parameters are shown in Table 3. The time-of-use electricity price is shown in Table 1. The penalty coefficients for curtailing wind, solar, and associated energy are shown in Table 2.

[0091] Table 1 Time-of-use electricity prices

[0092]

[0093] Table 2 Penalty coefficients

[0094]

[0095] Table 3 System parameters

[0096]

[0097] The CCS-P2G coupled system includes a two-stage P2G and carbon capture process. The following is a modeling of the energy conversion characteristics of each process:

[0098] Two-stage P2G link

[0099] If significant amounts of wind and solar power are still curtailed, even if wind and solar output meet system requirements, electrolyzers can utilize excess electricity to electrolyze water and produce hydrogen. A portion of this hydrogen is fed into a methane reactor for methanation with CO to produce natural gas, which is then supplied to the CHP and GB systems. The remaining hydrogen is directly fed into hydrogen fuel cells for conversion into electricity and heat, or stored in hydrogen tanks.

[0100] 1) EL equipment

[0101]

[0102] Where: P EL,t 、 are the hydrogen power output and electric power input of EL at time t respectively; η EL is the hydrogen power conversion coefficient of EL; P EL,max is the maximum output power of EL; They are the upper and lower limits of EL climbing power respectively.

[0103] 2) MR equipment

[0104]

[0105] Where: P MR,t is the natural gas produced by MR at time t; H MR,t is the hydrogen power consumed by MR at time t; η MR is the natural gas conversion coefficient of MR; C MR,t is the amount of CO2 required for the methanation reaction during period t; χ is the calculation coefficient of the amount of CO2; P MR,max is the maximum output power of MR

[0106] 3) HFC equipment

[0107]

[0108] Where: P HFC,t 、H HFC,t are the electrical power and thermal power output of HFC at time t, respectively; are the HFC electric power and thermal power conversion coefficients, respectively; is the hydrogen power input to HFC at time t; P HFC,max is the maximum output power of HFC; They are the upper and lower limits of HFC climbing power respectively; They are the upper and lower limits of HFC heat-to-power ratio respectively.

[0109] Carbon capture process

[0110] Carbon capture technologies are primarily categorized as pre-combustion, post-combustion, and oxygen-enriched combustion. This article discusses post-combustion capture only. Specifically, during the capture process, CO2 generated by gas equipment is captured, a portion of which is stored and processed, while the remainder is transported to a P2G facility as methanogen feedstock for methanation. The mathematical model for this is as follows:

[0111]

[0112] Where: P CCS,t The electrical power consumed by the CCS device to capture CO2 at time t; are the fixed energy consumption and operating energy consumption of CCS at time t, respectively, where the fixed energy consumption is negligible relative to the operating energy consumption; λ c The energy consumption required to capture a unit of CO2; is the total amount of CO2 captured by the CCS equipment at time t; E MR,t 、E F,t are the CO2 consumed by methanogenesis and the amount of CO2 stored at time t, respectively; is the amount of CO2 emitted by the unit at time t; η CCS is the carbon capture rate of CCS.

[0113] Next, we comprehensively consider the carbon trading mechanism and the green certificate trading mechanism, pay attention to the response characteristics of the mining area load at different time scales, construct a multi-time scale scheduling framework, and establish a multi-time scale optimization scheduling model of the mining area comprehensive energy system based on model predictive control by optimizing various energy equipment.

[0114] For carbon trading, this embodiment uses a free quota method, assuming that all electricity purchased from the upper grid comes from coal-fired power plants. Carbon emissions in CMIES mainly come from power purchased from the grid, CHP, GB, gas turbines, and thermal storage oxidation devices. The free carbon quota obtained by the system is determined by the consumed electricity, thermal energy, and renewable energy generation. The mathematical model is as follows:

[0115]

[0116] Where: E P,t is the total amount of free carbon quota in the system at time t; are the carbon quotas of system electricity, thermal energy and renewable energy power generation at time t; are the carbon emission quota coefficients for generating unit electricity and thermal power, is the carbon quota coefficient per unit of renewable energy electricity consumed; considering that CCS will capture CO2 from gas equipment in the system, the actual carbon emission model of CMIES can be expressed as:

[0117]

[0118]

[0119] Where: E r,t 、E CMIES are the actual carbon emissions and total carbon emissions of CMIES respectively; The actual carbon emission coefficients for power purchase from the grid, CHP, GT, GB, and RTO are as follows:

[0120]

[0121] Where, F CE,t is the step-by-step carbon transaction cost at time t; CE is the carbon trading base price; n1, h, and m1 are the reward coefficient, carbon emission interval length, and penalty coefficient respectively.

[0122] Regarding green certificate trading, the green certificate trading model of this embodiment is similar to the carbon trading model, and its green certificates mainly come from the electricity generated by the distributed wind and light of the system.

[0123]

[0124] Where C p,t is the green certificate quota target of CMIES at time t; It is the green certificate quota coefficient.

[0125] C r,t =(P wind,t +P pv,t ) / 1000

[0126] Where C r,t is the actual number of CMIES green certificates at time t. The transaction cost of CMIES green certificates is as follows:

[0127]

[0128] Where, F GC,t is the cost of the green certificate for the reward and punishment ladder, λ GC , n2, l, and m2 are the green certificate transaction price, reward coefficient, green certificate interval length, and penalty coefficient respectively.

[0129] The CMIES includes five types of energy, such as electricity, gas, heat, cold and hydrogen. However, due to the energy consumption characteristics of the mining area, gas and hydrogen energy are ultimately converted into electric energy and heat energy. Therefore, in this embodiment, only the demand response (DR) resource management of the electricity, heat and cold loads is performed by using the demand response characteristics. The demand response resources are divided into two types, i.e., price-based demand response (PDR) and incentive-based demand response (IDR), according to different response modes. The PDR guides users to perform reasonable power consumption behaviors by changing the electricity price, so as to adjust the power consumption plan. The IDR mainly includes direct load control, interruptible load, demand side bidding and emergency demand response. In this application, the electricity and gas prices are both in a day-ahead pricing mode. Therefore, the PDR is not considered in the optimization, and only the IDR needs to be considered. According to the response speed of the CMIES, the following types are specifically divided:

[0130] 1) A type of IDR, the response time is greater than 1 hour, and the advance notice is 1 day;

[0131] 2) B type of IDR, the response time is 5-15 minutes, and the advance notice is 0.25-4 hours.

[0132] In this embodiment, a multi-time scale scheduling framework as shown in Figure 2 is constructed. The day-ahead scheduling plan is made in advance for 24 hours, and the time scale is 1 hour. In the day-ahead stage, the working plan of the coupled device and the A type of IDR load calling plan need to be determined and used as the determined quantity in the day-ahead scheduling stage. The time step of the day-ahead rolling scheduling plan is 15 minutes, and the execution cycle is 4 hours. In this stage, the output plan of each energy coupled device, the distributed power generation output plan and the B type of IDR load calling plan need to be made, and the purpose is to correct the deviation between the day-ahead scheduling plan and the day-ahead working condition.

[0133] Based on the above analysis, a CMIES multi-time scale optimization scheduling model based on MPC is established, including a day-ahead scheduling model and an intra-day rolling optimization scheduling model based on model prediction control. The specific contents are as follows:

[0134] Day-ahead optimization scheduling model

[0135] In the day-ahead scheduling stage, the sum of the energy purchase cost, the energy abandonment penalty cost, the carbon-green certificate transaction cost, the device operation and maintenance cost, the carbon sequestration cost and the load calling cost is minimized as the optimization objective, and the objective function is as follows:

[0136]

[0137] In the formula, F1 is the objective function of the CMIES day-ahead optimization model, F e、F q 、F ce-gc 、F oper 、 F load They are energy purchase cost, energy abandonment penalty cost (including abandonment of wind, solar and associated energy), carbon-green certificate transaction cost, equipment operation and maintenance cost, carbon storage cost, and load call cost, which are expressed as follows:

[0138]

[0139] Where: T is the 24-hour optimization cycle; p, h, c are electricity, heat, and cold energy respectively; P grid,t 、P gas,t are the electricity and natural gas purchased by CMIES from the power grid and natural gas grid at time t respectively; The electricity purchase price and gas purchase price at time t respectively; P cwind,t 、P cpv,t The wind power and solar power abandoned at time t respectively; α w , α pv are the penalty coefficients for wind and solar curtailment, respectively; λ1, λ2, λ3 are the penalty coefficients for gas curtailment, wind curtailment, and water inrush, respectively; w,t 、F f,t 、F y,t are the power of gas abandonment, wind shortage and water inflow at time t respectively; F CE,t 、F GC,t are the transaction costs of carbon and green certificates respectively; α i 、P i,t are the unit maintenance cost and output power of equipment i at time t respectively; is the unit price of carbon sequestration; are the incentive IDR costs of Class A electricity, heating and cooling loads at time t, respectively.

[0140] The constraints are as follows:

[0141] 1) Electric power balance

[0142]

[0143] Where: P DR,t is the total electrical load; are the charging and discharging powers of the energy storage device at time t respectively.

[0144] 2) Thermal power balance

[0145]

[0146] Where: H DR,t is the total heat load; are the charging and discharging power of the heat storage device at time t respectively.

[0147] 3) Cold power balance

[0148] C EC,t +C AC,t =C DR,t

[0149] Where: C DR,t is the total cooling load.

[0150] 4) Demand response constraints

[0151]

[0152] Where n∈{p,h,c}; They are the load amount increased by Class A IDR for each type of load and the upper limit of the load amount increased by Class A IDR for each type of load; They are the load reduction amount of Class A IDR for each type of load and the upper limit of the load reduction amount of Class A IDR for each type of load.

[0153] The day-ahead optimization scheduling model is solved, and the operating status of energy coupling equipment and the call volume of Class A IDR are selected as the determining parameters and substituted into the intraday rolling optimization model.

[0154] Intraday rolling optimization scheduling model based on MPC

[0155] MPC primarily consists of a prediction model, rolling optimization, and feedback correction. Its basic concept is to predict the wind, solar, and associated energy output and load data of the CMIES within the prediction horizon based on the prediction model and measured output. At each sampling moment, an optimization algorithm is used to calculate the control input sequence for a future control horizon M based on the predicted CMIES energy output and load. However, at the first sampling moment, only the first value of the optimization result is applied to the controlled object. At the second sampling moment, the entire optimization process is repeated using the new output measurement value, i.e., rolling optimization. The performance indicators of rolling optimization only cover a limited time within the prediction horizon starting from the current moment. At the next moment, the optimization period advances. During this rolling optimization process, the prediction information is continuously updated. Through feedback correction, historical information is simultaneously used to promptly and effectively correct prediction errors and deviations in the optimization scheduling results caused by random factors, effectively improving the accuracy of optimized control.

[0156] In order to obtain more accurate data on the energy output and load of the mining area, this embodiment uses the grey prediction method to predict it. The specific steps of the method are as follows:

[0157] 1) Input original wind, solar, associated energy and electric heating and cooling power sequence

[0158]

[0159] where k is the time series of observation data j represents wind power, photovoltaic power, gas power, wind curtailment, water inflow and electric heating and cooling load.

[0160] 2) Accumulate the predicted sequence and establish a linear differential equation to obtain an approximate solution of the differential equation:

[0161]

[0162] where, is a new accumulated sequence, d j is the grey action, c j is the development coefficient.

[0163] Calculate the wind, light, associated energy and electric heating and cooling power:

[0164]

[0165] where is the ultra-short-term wind, light, associated energy and electric heating and cooling power prediction.

[0166] 3) Take σ = 1, 2, …, k in the formula to obtain the fitting sequence of the original sequence where δ = 1, 2, …, k;

[0167] 4) Take σ = 1+k, 2+k, …, m+k in the formula to obtain the predicted value sequence after the original sequence where δ = 1, 2, …, m.

[0168] Due to the characteristics of CMIES, the predicted output of MPC cannot be exactly the same as the actual controlled process, thereby generating a prediction error. Therefore, a prediction error feedback link is introduced to correct the actual measured output information, and then a new round of optimization is performed. In this way, the feedback information is utilized to form a closed-loop optimization.

[0169] In the aforementioned step 3), the fitting sequence of the original sequence is obtained and compared with the original load sequence, the root mean square relative error index is used to evaluate the prediction result:

[0170]

[0171] If the fitting accuracy is high, the predicted value sequence obtained in the aforementioned step 4) is continued If the fitting accuracy is poor, a residual sequence is constructed:

[0172]

[0173] When j ≥ σ0, i.e., j = σ0, σ0+1, …, k, the residual sequence The sign of the residual sequence is consistent with the sign of the residual sequence The predicted value is obtained by using the above prediction model:

[0174] The predicted error vector is obtained by using the above prediction error vector The next prediction sequence is corrected by using the weighted prediction model, and the corrected predicted vector is obtained:

[0175]

[0176]

[0177] The value of "±" is consistent with the sign of the residual sequence .

[0178] In this embodiment, the fitting parameters are: average prediction error 10%, root mean square relative error index not more than 5%.

[0179] The target function of the day-ahead scheduling model is the same as that of the day-ahead target function, and the only change in the day-ahead scheduling model is the calling cost of the IDR type load. Specifically, the A type IDR is determined, and therefore only the calling cost of the B type IDR needs to be considered. The day-ahead rolling stage takes 15 minutes as the time scale, and the target function of the CMIES day-ahead rolling optimization scheduling is as follows:

[0180]

[0181]

[0182] In the formula, F2 is the target function of the CMIES day-ahead rolling optimization model; respectively, are the incentive IDR costs of the B type electric, heat and cold loads at t time.

[0183] Similar to the day-ahead scheduling stage, the output constraints of the energy coupling device, the distributed power generation constraints and the energy storage device constraints in the day-ahead rolling stage are similar to those in the day-ahead scheduling stage, and therefore will not be described again.

[0184] It should be pointed out that the calling amount of the B type IDR load will be considered in this stage, and therefore the demand response constraints are different from those in the day-ahead stage, and are as shown in the following formula:

[0185]

[0186] In the formula, respectively, are the increased load amount of the B type IDR of each type of load and the upper limit of the increased load amount of the B type IDR of each type of load; They are the load reduction amount of Class B IDR for each type of load and the upper limit of the load reduction amount of Class B IDR for each type of load.

[0187] Based on the parameters determined by the day-ahead optimization, the intraday rolling optimization will ultimately determine: the call plan for each energy coupling device, energy storage device and Class B IDR.

[0188] To verify the economic and environmental benefits of considering carbon-green certificate trading and P2G-CCS coordinated operation in CMIES, the following four scenarios are set for analysis.

[0189] Scenario 1: excluding carbon-green certificate trading, CCS and P2G;

[0190] Scenario 2: Only carbon-green certificate trading is considered;

[0191] Scenario 3: Considering CCS and two-stage P2G;

[0192] Scenario 4: Considering carbon-green certificate trading, including CCS and two-stage P2G;

[0193] Analysis of day-ahead optimized scheduling results

[0194] As mentioned above, the four scenarios are simulated and analyzed, and the day-ahead optimization scheduling results are shown in Table 4.

[0195] Table 4 Optimization scheduling results of various scenarios in the early stage

[0196]

[0197] Table 4 shows that during the day-ahead optimization scheduling phase, Scenario 2 incorporates carbon-green certificate trading compared to Scenario 1. Although the cost of curtailing wind power increases, the carbon-green certificate trading mechanism promotes the utilization of wind, solar power, and gas, reducing the CO2 generated by wind power, thereby reducing CMIES carbon emissions. As shown in Table 1, Scenario 2 generates 1,165.89 yuan in carbon-green certificate revenue compared to Scenario 1. Compared to Scenario 2, Scenario 3 further introduces CCS and P2G equipment within the mine. The CO2 captured at the mine site and hydrogen are methanized in a methane reactor to produce natural gas, which is ultimately supplied to the mine's gas equipment. Therefore, the costs of curtailing wind, solar power, and gas in Scenario 3 are higher than in Scenario 2. Furthermore, the use of carbon capture equipment increases the utilization rate of wind power, ultimately reducing carbon emissions in Scenario 3. Compared to Scenario 3, Scenario 4 incorporates a carbon-green certificate trading mechanism alongside CCS and two-stage P2G. Consequently, it achieves the lowest total carbon emissions and CMIES costs, decreasing by 28.59% and 38.70%, respectively. This demonstrates that the combined operation of the carbon-green certificate trading mechanism with CCS and P2G increases the environmental and economic benefits of CMIES.

[0198] Analysis of intraday rolling optimization scheduling results

[0199] The operation status plan of the mine area coupling equipment and the Class A IDR call plan determined in the day-ahead scheduling stage are substituted into the intraday rolling optimization scheduling stage for optimization, and the results are shown in Table 5 below.

[0200] Table 5 Optimization scheduling results for each scenario in the intraday stage

[0201]

[0202] Table 5 shows that compared to Scenario 1, Scenario 2's carbon emissions and total CMIES costs decreased by 19.67% and 5.58%, respectively, remaining largely consistent with the day-ahead period. Compared to Scenario 2, Scenario 3's curtailment penalty costs (the sum of wind, solar, gas, wind power, and water curtailment), energy purchase costs, and carbon emissions decreased by 7.24%, 17.23%, and 48.08%, respectively. However, equipment operation and maintenance costs, carbon sequestration costs, Class A and B IDR costs, and total CMIES costs increased. This is because the use of CCS and P2G equipment reduces the use of high-carbon-emitting equipment but increases their operation and maintenance costs. Overall, Scenario 3 demonstrates the effectiveness of CCS and P2G equipment in reducing carbon emissions. Scenario 4, after adopting the carbon-green certificate trading mechanism, generated a profit of 7,353.31 yuan. Compared with Scenario 3, Scenario 4 further reduced carbon emissions and total CMIES costs by 30.12% and 36.62%, respectively, resulting in superior environmental and economic benefits. This demonstrates the positive impact of carbon-green certificate trading and CCS-P2G combined operations on the economic and environmental performance of CMIES.

[0203] Taking scenario 4 as an example, the day-ahead and day-intraday output comparison of some units in CMIES is as follows: Figure 3 As shown in the figure, under the conditions of intraday forecasted load fluctuations, the operating status of each unit after intraday rolling optimization is consistent with the day-ahead scheduling phase. Specifically, due to the fluctuations in the output of associated energy resources and the electricity, heating, and cooling loads in the mining area, the units undergo certain adjustments during the intraday rolling optimization phase, but the overall operating status remains consistent with the day-ahead scheduling phase. Compared to the day-ahead phase, CHP units experience some load reduction during the intraday periods of 7:00–12:00 and 18:00–21:00, while GB units experience some load reduction during the 1:00–5:00 and 22:00–24:00 periods, and EC units experience some load reduction throughout the day. The analysis of the load demand response in Scenario 4 reveals that load reductions during these periods reduce unit output, while the CHP unit output is reversed during the 4:00–5:00 and 21:00–22:00 periods.

[0204] The specific embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

Claims

1. The CMIES multi-timescale optimization scheduling method taking into account the carbon-green certificate trading and CCS-P2G joint operation is characterized by: include: Step 1: Based on the energy consumption characteristics of the mining area, a mining area associated energy utilization model including gas, ventilation air, and water inflow is constructed, and the CCS-P2G coupling system is introduced to form a mining area integrated energy system CMIES structure that includes associated energy utilization and CCS-P2G coupling; Step 2: Comprehensively consider the carbon trading mechanism and the green certificate trading mechanism, analyze the response characteristics of the mining area load at different time scales, and build a multi-time scale scheduling framework; Step 3. Establish an MPC-based CMIES multi-timescale optimization scheduling model, including a day-ahead scheduling model and an MPC-based intraday rolling optimization scheduling model. Solve the day-ahead optimization scheduling model, select the operating status of energy coupling equipment and the call volume of Class A IDRs as determining parameters and substitute them into the intraday rolling optimization scheduling model. Use MPC to perform rolling optimization on the intraday CMIES equipment and determine the call plan for each energy coupling equipment, energy storage equipment, and Class B IDR. For Class A IDR, the response time is > 1 hour and needs to be notified 1 day in advance; The response time for Class B IDR is 5 to 15 minutes, and notification is required 0.25 to 4 hours in advance. The objective function of the day-ahead optimization scheduling model is as follows: Where: F1 is the objective function of the CMIES day-ahead optimization model, F e 、F q 、F ce-gc 、F oper 、 F load They are energy purchase cost, energy abandonment penalty cost, carbon-green certificate transaction cost, equipment operation and maintenance cost, carbon storage cost, and load call cost, which are expressed as follows: Where: T is the 24-hour optimization cycle; p, h, c are electricity, heat, and cold energy respectively; P grid,t 、P gas,t are the electricity and natural gas purchased by CMIES from the power grid and natural gas grid at time t respectively; The electricity purchase price and gas purchase price at time t respectively; P cwind,t 、P cpv,t The wind power and solar power abandoned at time t respectively; α w , α pv are the penalty coefficients for wind and solar curtailment, respectively; λ1, λ2, λ3 are the penalty coefficients for gas curtailment, wind curtailment, and water inrush, respectively; w,t 、F f,t 、F y,t are the power of gas abandonment, wind shortage and water inflow at time t respectively; F CE,t 、F GC,t are the transaction costs of carbon and green certificates respectively; α i 、P i,t are the unit maintenance cost and output power of equipment i at time t respectively; is the unit price of carbon sequestration; are the incentive IDR costs of Class A electric, heating, and cooling loads at time t, respectively; The objective function of the MPC-based intraday rolling optimization scheduling model is: Where F2 is the objective function of the CMIES intraday rolling optimization model; are the incentive IDR costs of Class B electricity, heating and cooling loads at time t, respectively.

2. The CMIES multi-timescale optimization scheduling method considering carbon-green certificate trading and CCS-P2G joint operation according to claim 1 is characterized in that: The multi-timescale scheduling framework is divided into two stages: day-ahead scheduling and intraday scheduling. The day-ahead scheduling plan is formulated 24 hours in advance with a time scale of 1 hour. In the day-ahead stage, the working plan of the coupling equipment and the Class A IDR load call plan are determined and substituted as determined quantities in the intraday rolling scheduling stage. The intraday rolling scheduling plan has a time step of 15 minutes and an execution cycle of 4 hours. The output plan of each energy coupling equipment, the distributed generation output plan, and the Class B IDR load call plan are formulated.

3. The CMIES multi-timescale optimization scheduling method considering carbon-green certificate trading and CCS-P2G joint operation according to claim 1 is characterized in that: The constraints of the day-ahead optimization scheduling model include: (1) Electric power balance Where: P wind,t is the wind power actually consumed by the system at time t, P pv,t is the photovoltaic power actually consumed by the system at time t, P CHP,t is the electric power output by CHP at time t, P HFC,t is the electric power output by HFC at time t, P GT,t is the electric power output of the gas turbine at time t, are the charging and discharging power of the storage device at time t, is the electric power input to the electric refrigerator at time t, P DR,t is the total electrical load, P EL,t is the hydrogen power output by EL at time t, P CCS,t The electrical power consumed by the CCS device to capture CO2 at time t; (2) Thermal power balance Where: H CHP,t is the thermal power output of CHP at time t, H GB,t is the thermal power output of the gas boiler at time t, H HFC,t is the thermal power output of HFC at time t, is the thermal power output of the waste heat boiler at time t, H WSHP,t is the thermal power output of the water source heat pump at time t, H DR,t is the total heat load; are the charging and discharging power of the heat storage device at time t respectively; (3) Cooling power balance C EC,t +C AC,t =C DR,t Where: C EC,t is the power of the electric refrigerator, C AC,t is the power of the absorption chiller, C DR,t is the total cooling load; (4) Demand response constraints Where n∈{p,h,c}; They are the load amount increased by Class A IDR for each type of load and the upper limit of the load amount increased by Class A IDR for each type of load; They are the load reduction amount of Class A IDR for each type of load and the upper limit of the load reduction amount of Class A IDR for each type of load.

4. The CMIES multi-timescale optimization scheduling method considering carbon-green certificate trading and CCS-P2G joint operation according to claim 1 is characterized in that: The MPC-based intraday rolling optimization scheduling model uses grey prediction to predict the energy output and load of the mining area: (1) Input original wind, solar, associated energy and electric heating and cooling power sequence Where: k is the observed data j represents the time series of wind power, photovoltaic power, gas, wind power, water inrush and electric heating and cooling loads; (2) Accumulate the predicted sequence and establish a linear differential equation to obtain the approximate solution of the differential equation: Where, is the new cumulative sequence, d j is the gray action, c j is the development coefficient; Calculate wind, solar, associated energy, and electric heating and cooling power: In the formula Provide ultra-short-term wind, solar, associated energy, and electric heating and cooling power forecasts; (3) Take σ = 1, 2, ..., k for the distribution to obtain the fitting sequence of the original sequence Where δ=1,2,…,k; (4) Take σ=1+k,2+k,…,m+k for the distribution to obtain the predicted value sequence after the original sequence Where δ = 1, 2,…, m.

5. The CMIES multi-timescale optimization scheduling method considering carbon-green certificate trading and CCS-P2G joint operation according to claim 1 is characterized in that: The demand response constraint in the constraint conditions of the MPC-based intraday rolling optimization scheduling model is: Where, They are the load amount increased by Class B IDR for each type of load and the upper limit of the load amount increased by Class B IDR for each type of load; They are the load reduction amount of Class B IDR for each type of load and the upper limit of the load reduction amount of Class B IDR for each type of load.

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