CMIES multi-time scale optimization scheduling method considering carbon-green certificate transaction and CCS-P2G joint operation

Abstract

The invention relates to a CMIES multi-time scale optimization scheduling method considering carbon-green certificate transaction and CCS-P2G joint operation, and belongs to the technical field of mining area energy comprehensive utilization. According to the CMIES low-carbon economic dispatching method, the energy characteristics of the CMIES, the market characteristics of a carbon-green certificate transaction mechanism and the low-carbon characteristics of the CCS and the two-stage P2G are comprehensively considered, and the CMIES low-carbon economic dispatching method considering carbon-green certificate transaction and CCS-P2G combined operation under the multi-time scale is provided. And establishing a CMIES day-ahead and intra-day rolling optimization scheduling model based on MPC by taking minimization of energy purchasing cost, energy abandoning punishment cost, carbon-green certificate transaction cost, equipment operation and maintenance cost, carbon sequestration cost and load calling cost as economic targets. According to the method, loads under different time dimensions and mining area multi-energy output fluctuation can be well coped with, graded adjustment is carried out according to the flexibility of CMIES internal equipment and the multi-time scale characteristic of demand response resources, and the wind, light and associated energy consumption capacity, the CMIES low-carbon property and the CMIES economical efficiency can be effectively improved.

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 sharp increase of global greenhouse gases, promoting low-carbon transformation of energy and promoting social carbon reduction and emission reduction have become top priorities. As a large energy-intensive industry, the coal mining industry uses energy in various forms, and the mining process will produce associated energy such as gas, wind exhaustion, and water. If we only focus on mining efficiency and ignore the utilization of this associated energy, it will cause serious environmental pollution and waste of resources. According to statistics, the greenhouse effect caused by gas and wind exhaustion in my country is equivalent to about 200 million tons of CO2 each year. In addition, the mine water generated in the coal mining process is about 4.5 billion tons. Therefore, how to build a coalmine integrated energy system (CMIES) to coordinate and optimize the mutual conversion of various energy sources in the mining area and the cascade utilization of energy has great practical significance. Summary of the invention

[0003] The present invention provides a CMIES multi-time scale optimization scheduling method taking into account carbon-green certificate trading and CCS-P2G joint operation, comprehensively considering 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, and proposes a CMIES low-carbon economic scheduling method taking into account carbon-green certificate trading and CCS-P2G joint operation under multiple time scales. Taking the minimum energy purchase cost, abandoned energy penalty cost, carbon-green certificate trading cost, equipment operation and maintenance cost, carbon sequestration cost, and load call cost as the economic goal, a CMIES day-ahead and intraday rolling optimization scheduling model based on MPC is established.

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

[0005] Step 1. According to the energy consumption characteristics of the mining area, a mining area associated energy utilization model including gas, wind exhaustion, and water inflow is constructed, and the CCS-P2G coupling system is introduced to form a CMIES structure including associated energy utilization and CCS-P2G coupling;

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

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

[0008] The multi-time scale 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 into the intraday rolling scheduling stage. The time step of the intraday rolling scheduling plan is 15 minutes and the execution cycle is 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 unit wind and solar power abandonment, respectively; λ1, λ2, λ3 are the penalty coefficients for unit gas abandonment, wind shortage, and water inflow, respectively; w,t 、F f,t 、F y,t are the power of abandoned gas, wind shortage and water inflow at time t respectively; F CE,t 、F GC,tare 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.

[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 of CHP at time t, P HFC,t is the electric power output of HFC at time t, P GT,t is the electrical 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;

[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) Cold 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 refrigerator, 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 of each type of load and the upper limit of the load amount increased by Class A IDR of 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 the 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 exhaustion, 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] In the formula, 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 For ultra-short-term wind, solar, associated energy, and electric heating and cooling power forecasts;

[0037] (3) Take σ = 1, 2, ..., k for the distribution and get 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, and get the predicted value sequence after the original sequence Where δ=1,2,…,m.

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

[0044]

[0045] In the formula, They are the load amount increased by Class B IDR of each type of load and the upper limit of the load amount increased by Class B 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.

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

[0047] This paper proposes a CMIES multi-time scale low-carbon optimization scheduling strategy considering the joint operation of carbon-green certificate trading and CCS-P2G, and establishes a CMIES framework for the comprehensive utilization of multiple associated energy sources such as gas, wind exhaustion, and water, as well as a CCS-P2G coupling operation model. The specific effects are as follows:

[0048] 1) The carbon-green certificate trading mechanism participates in the formulation of scheduling plans, which can improve the absorption of clean energy 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 make graded adjustments 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 and associated energy absorption capacity, CMIES low carbon and economy. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0053] Figure 3 This is a comparison diagram of some devices within 4 days before the scene in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0055] Implementation example Figure 1-3 As shown, this embodiment takes a large coal mine in Yunnan as an example to provide a CMIES multi-time scale optimization scheduling method taking into account carbon-green certificate trading and CCS-P2G joint operation. Considering that there are wind power plants and photovoltaic power stations built around the mining area, the CMIES coupling structure is first established as follows 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, cold 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 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 emission equipment to reduce carbon emissions in mining areas.

[0056] According to the characteristics of associated energy, gas is supplied to GT for power generation, and wind exhaustion and gushing water are supplied to RTO and WSHP for heating respectively. Among them, WHB and AC recycle the high-temperature flue gas generated by GT and RTO, realizing the coupling, interconnection and coordinated transformation of various heterogeneous energy sources such as electricity, heat and cold 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 electrical power conversion coefficient; L gas is the lower heating value of methane; F GT,t , β GT,t are the gas flow 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 respectively the upper and lower limits of the climbing power of the thermal storage oxidation device.

[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 climbing power of the water source heat pump 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 electrical power and thermal power output of CHP at time t respectively; are the CHP electric power and thermal power conversion coefficients, respectively; is the natural gas power input to CHP at time t; P CHP,max is the maximum output power of CHP; They are the upper and lower limits of CHP climbing power respectively; They are the upper and lower limits of the CHP thermal-to-electric ratio respectively.

[0075] 7) Gas boiler model

[0076]

[0077] Where: H GB,t , are the thermal power output of the gas boiler and the natural gas power input at time t; η GB is the thermal power conversion coefficient of the gas boiler; H GB,max is the maximum output power of the gas boiler; They are the upper and lower limits of the gas boiler climbing power respectively.

[0078] 8) Electric Refrigerator Model

[0079]

[0080] Where: P EC,t , are the cooling power output and electrical power input of the electric refrigerator at time t respectively; η EC P is the cooling power conversion coefficient of the electric refrigerator; EC,max is the maximum output power of the electric refrigerator; They are the upper and lower limits of the climbing power of the electric refrigerator respectively.

[0081] 9) Waste heat boiler

[0082]

[0083] Where: are the waste heat power input and the heat power output of the waste heat boiler at time t respectively; η WHB is the heat power conversion coefficient of the waste heat boiler; H WHB,max is the maximum output power of the waste heat boiler; They are the upper and lower limits of the waste heat boiler climbing power respectively.

[0084] 10) Absorption Refrigeration Machine

[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 refrigerator; 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 equipment 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; They are the x charging and discharging efficiency of the energy storage device respectively; They are the maximum charging and discharging power of energy storage device x respectively; It is a 0-1 variable, which is used to limit the simultaneous charging and discharging. x,max , S x,min are the upper and lower limits of the capacity of the energy storage device x; S x,T , S x,0 They respectively represent the energy storage at the end and start of scheduling of energy storage device x.

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

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

[0092]

[0093] Table 2 Penalty coefficients

[0094]

[0095] Table 3 System parameters

[0096]

[0097] The CCS-P2G coupling system includes two-stage P2G and carbon capture. The energy conversion characteristics of each link are modeled as follows:

[0098] Two-stage P2G link

[0099] Under the premise that the wind and solar output meet the system requirements, if there is still a large amount of wind and solar power abandonment, the electrolyzer can use the surplus electricity to electrolyze water to produce H2. Among them, part of the H2 is sent to the methane reactor for methanation with CO2 to produce natural gas, which is supplied to CHP and GB; the remaining H2 is directly sent to the hydrogen fuel cell to be converted into electricity and heat energy, or stored in a hydrogen storage tank.

[0100] 1) EL equipment

[0101]

[0102] Where: P EL,t , are the hydrogen power output and electrical 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 methanogenic reaction during the 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 electrical 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-electricity ratio respectively.

[0109] Carbon capture process

[0110] Carbon capture technology is mainly divided into three types: pre-combustion capture, post-combustion capture and oxygen-enriched combustion capture. This article only discusses post-combustion capture. Specifically, during the capture process, the CO2 generated by the gas equipment will be captured, part of which will be used for storage and the rest will be transported to the P2G equipment as a methanogenic raw material for methanogenesis. Its mathematical model 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 compared to the operating energy consumption; c The energy consumption required to capture a unit of CO2; is the total amount of CO2 captured by the CCS device 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, build 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 adopts a free quota method, assuming that the electricity purchased from the superior power grid comes from coal-fired power plants. The carbon emissions in CMIES mainly come from power grid purchases, 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 new energy power 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 generation at time t; are the carbon emission quota coefficients for generating unit electricity and thermal power, is the carbon quota coefficient for consuming unit renewable energy electricity; 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 They are the actual carbon emissions and total carbon emissions of CMIES; They are the actual carbon emission coefficients of power purchase from the power grid, CHP, GT, GB, and RTO. The CMIES carbon trading costs are as follows:

[0120]

[0121] In the formula, F CE,t is the step-wise carbon transaction cost at time t; CE is the carbon trading base price; n1, h, 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] In the formula, 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] In the formula, 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] In the formula, 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] CMIES includes five types of energy, including electricity, gas, heat, cold, and hydrogen. However, due to the energy consumption characteristics of mining areas, gas and hydrogen are eventually converted into electricity and heat. Therefore, this embodiment only uses the demand response characteristics to manage demand response (DR) resources for the three types of loads, electricity, heat, and cold. Among them, demand response resources are divided into two types according to different response methods: price-based demand response (PDR) and incentive-based demand response (IDR). PDR guides users to perform reasonable electricity consumption behaviors by changing electricity prices, thereby adjusting electricity consumption plans. As for IDR, it mainly includes direct load control, interruptible loads, demand-side bidding, and emergency demand response. In this application, the proposed electricity and gas prices all adopt the day-ahead pricing model, so PDR is not within the scope of optimization considerations, and only IDR needs to be considered. According to the different response speeds of CMIES, it is specifically divided into the following categories:

[0130] 1) Class A IDR, response time> 1h, requires 1 day advance notice;

[0131] 2) Class B IDR: The response time is 5 to 15 minutes and notification is required 0.25 to 4 hours in advance.

[0132] In this embodiment, the Figure 2 In the multi-time scale scheduling framework shown in the figure, 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 need to be determined and substituted into the intraday rolling scheduling stage as determined quantities. The time step of the intraday rolling scheduling plan is 15 minutes and the execution cycle is 4 hours. In this stage, it is necessary to formulate the output plan of each energy coupling equipment, the output plan of distributed generation and the Class B IDR load call plan, the purpose of which is to correct the deviation between the day-ahead scheduling plan and the intraday working conditions.

[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 intraday rolling optimization scheduling model based on model predictive control. The details are as follows:

[0134] Day-ahead Optimal Scheduling Model

[0135] The optimization goal of the day-ahead dispatch stage is to minimize the sum of energy purchase cost, energy abandonment penalty cost, carbon-green certificate transaction cost, equipment operation and maintenance cost, carbon storage cost, and load call cost. The objective function is as follows:

[0136]

[0137] 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 (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 unit wind and solar power abandonment, respectively; λ1, λ2, λ3 are the penalty coefficients for unit gas abandonment, wind shortage, and water inflow, respectively; w,t 、F f,t 、F y,t are the power of abandoned gas, 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 power 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 of each type of load and the upper limit of the load amount increased by Class A IDR of 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 determination parameters and substituted into the intraday rolling optimization model.

[0154] Intraday rolling optimization scheduling model based on MPC

[0155] MPC is mainly composed of prediction model, rolling optimization and feedback correction. Its basic idea is to predict the wind, light, associated energy output and load data of CMIES in the prediction time domain based on the prediction model and combined with the measured output; at each sampling moment, based on the prediction of CMIES energy output and load, the control input sequence in a certain control time domain M in the future is calculated through the optimization algorithm, but at the first sampling moment, only the first value of the optimization result is added to the controlled object, and at the second sampling moment, the new output measurement value is used to repeat the above optimization steps, that is, rolling optimization. The performance index of rolling optimization only involves a limited time in the prediction time domain starting from the current moment, and at the next moment, the optimization period moves forward. In this rolling optimization process, the prediction information is constantly updated, and through feedback correction, historical information is used to timely and effectively correct the prediction error and the optimization scheduling result deviation caused by random factors, effectively improving the accuracy of optimization control.

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

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

[0158]

[0159] Where: k is the observed data j represents the time series of wind power, photovoltaic power, gas, wind exhaustion, water inrush and electric heating and cooling loads.

[0160] 2) Accumulate the predicted sequence, establish a linear differential equation, and solve the approximate solution of the differential equation:

[0161]

[0162] In the formula, is the new cumulative sequence, d j is the gray action, c j is the development coefficient.

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

[0164]

[0165] In the formula Provide ultra-short-term forecasts for wind, solar, associated energy, and electric heating and cooling power.

[0166] 3) Take σ=1,2,…,k for the distribution in the formula to get the fitting sequence of the original sequence Where δ=1,2,…,k;

[0167] 4) Take σ=1+k,2+k,…,m+k for the distribution in the formula to get 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, which leads to prediction errors. Therefore, a prediction error feedback link is introduced to correct the actual measured output information, and then a new round of optimization is carried out. The feedback information is used to form a closed-loop optimization.

[0169] In the above step 3), the fitting sequence of the original sequence is obtained And compare it with the original load series, and use the root mean square relative error index to evaluate the prediction results:

[0170]

[0171] If the fitting accuracy is high, continue with the above step 4) to obtain the predicted value sequence If the fitting accuracy is poor, construct a residual sequence:

[0172]

[0173] When j ≥ σ0, that is, j = σ0, σ0 + 1, ..., k, the residual sequence The signs are consistent, then for the residual subsequence Use the above prediction model to find the predicted value:

[0174] Using the above prediction error vector The weighted prediction model is then used to correct the next prediction sequence, and the corrected The prediction vector is:

[0175]

[0176]

[0177] The value of “±” and the residual sequence The signs are consistent.

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

[0179] The objective function of the intraday rolling optimization scheduling stage is basically the same as that of the day-ahead scheduling model, and its objective function is also to minimize the total cost of CMIES. Compared with the day-ahead scheduling model, the only thing that changes in the intraday rolling optimization scheduling model is the call cost of IDR class loads; specifically, Class A IDR has been determined, so only Class B IDR call cost needs to be considered. The intraday rolling stage uses 15 minutes as the time scale, and its objective function of CMIES intraday rolling optimization scheduling is as follows:

[0180]

[0181]

[0182] 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.

[0183] Similar to the day-ahead dispatching stage, the output constraints of each energy coupling device, distributed generation constraints, and energy storage device constraints in the intraday rolling stage are similar to those in the day-ahead dispatching stage, so they will not be repeated here.

[0184] It should be noted that this stage will consider the call volume of Class B IDR load, so the demand response constraints are different from those in the day-ahead stage, as shown in the following formula:

[0185]

[0186] In the formula, They are the load amount increased by Class B IDR of each type of load and the upper limit of the load amount increased by Class B 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] The intraday rolling optimization will ultimately determine the calling plan of each energy coupling device, energy storage device and Class B IDR based on the parameters determined by the day-ahead optimization.

[0188] In order 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: No carbon-green certificate trading, no CCS and P2G;

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

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

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

[0193] Analysis of day-ahead optimization 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 previous stage

[0196]

[0197] As shown in Table 4, in the day-ahead optimization scheduling stage, compared with scenario 1, scenario 2 considers carbon-green certificate trading. Although the cost of wind abandonment increases, the carbon-green certificate trading mechanism promotes the consumption of wind, light, and gas, and reduces the CO2 generated by wind abandonment. Therefore, the carbon emissions of CMIES are reduced. In Table 1, scenario 2 obtains a carbon-green certificate income of 1,165.89 yuan compared with scenario 1. In scenarios 2 and 3, compared with scenario 2, scenario 3 further introduces CCS and P2G equipment in the mining area. The CO2 and hydrogen captured in the mining area can be methanized in the methane reactor to produce natural gas, which is finally supplied to the mining area gas equipment. Therefore, the cost of wind abandonment, light abandonment, and gas abandonment in scenario 3 is higher than that in scenario 2, and due to the presence of carbon capture equipment, the utilization rate of wind abandonment is improved, which ultimately further reduces the carbon emissions in scenario 3. Compared with scenario 3, scenario 4 adds a carbon-green certificate trading mechanism on the basis of considering CCS and two-stage P2G. Therefore, the total cost of carbon emissions and CMIES is the lowest, decreasing by 28.59% and 38.70% respectively. It can be seen that after adopting the carbon-green certificate trading mechanism and operating it in conjunction with CCS and P2G, the environmental and economic benefits of CMIES are increased.

[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 intra-day stage

[0201]

[0202] As shown in Table 5, compared with scenario 1, the carbon emissions and total CMIES costs of scenario 2 decreased by 19.67% and 5.58%, respectively, which are basically consistent with the day-ahead stage. Compared with scenario 2, the penalty cost for energy abandonment (the sum of wind abandonment, solar abandonment, gas abandonment, wind abandonment, and water abandonment), energy purchase cost, and carbon emissions of scenario 3 decreased by 7.24%, 17.23%, and 48.08%, respectively, while the equipment operation and maintenance cost, carbon sequestration cost, Class A and B IDR cost, and total CMIES cost increased. The reason is that the use of CCS and P2G equipment will reduce the use of high-carbon emission equipment, but will increase the operation and maintenance costs of CCS and P2G equipment. In general, scenario 3 verifies the effectiveness of CCS and P2G equipment in reducing carbon emissions. After adopting the carbon-green certificate trading mechanism, scenario 4 obtained a profit of 7,353.31 yuan. Compared with scenario 3, the carbon emissions and total CMIES costs of scenario 4 further decreased by 30.12% and 36.62%, respectively, bringing better environmental and economic benefits. This reflects the positive role of carbon-green certificate trading and CCS-P2G joint operation in 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 case of intraday predicted load changes, the operating status of each unit after intraday rolling optimization is consistent with the day-ahead scheduling stage. Specifically, due to the volatility of the associated energy output and the electricity, heat and cooling loads in the mining area, the units will make certain adjustments during the intraday rolling optimization stage, but the overall operating status is consistent with the day-ahead scheduling stage. Compared with the day-ahead stage, the CHP units have made certain reductions in the intraday periods of 7:00-12:00 and 18:00-21:00, while GB has made certain load reductions in the periods of 1:00-5:00 and 22:00-24:00, and EC has made certain load reductions almost throughout the day. In scenario 4, the response to each load demand is analyzed. The reason is that in these periods, the load in the intraday stage has been reduced, thereby reducing the output of the unit, while the output of the CHP unit is the opposite in the periods of 4:00-5:00 and 21:00-22:00.

[0204] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. The CMIES multi-time scale optimization scheduling method considering carbon-green certificate trading and CCS-P2G joint operation is characterized by: include: Step 1. According to the energy consumption characteristics of the mining area, a mining area associated energy utilization model including gas, wind exhaustion, and water inflow is constructed, and the CCS-P2G coupling system is introduced to form a CMIES structure including associated energy utilization and CCS-P2G coupling; Step 2: Comprehensively consider the carbon trading mechanism and green certificate trading mechanism, analyze the response characteristics of the mine load at different time scales, and build a multi-time scale scheduling framework; Step 3. Establish a CMIES multi-time scale optimization scheduling model based on MPC, including a day-ahead scheduling model and an intraday rolling optimization scheduling model based on MPC; solve the day-ahead optimization scheduling model, select the operating status of the energy coupling equipment and the call volume of Class A IDR as the determination parameters and substitute them into the intraday rolling optimization scheduling model, perform rolling optimization on the intraday CMIES equipment through MPC, and determine the call plan of each energy coupling equipment, energy storage equipment and Class B IDR.

2. The CMIES multi-time scale optimization scheduling method considering carbon-green certificate trading and CCS-P2G joint operation according to claim 1 is characterized in that: The multi-time scale 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 into the intraday rolling scheduling stage. The time step of the intraday rolling scheduling plan is 15 minutes and the execution cycle is 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-time scale optimization scheduling method considering carbon-green certificate trading and CCS-P2G joint operation according to claim 1 is characterized in that: 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 unit wind and solar abandonment, respectively; λ1, λ2, λ3 are the penalty coefficients for unit gas abandonment, wind shortage, and water inflow, 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.

4. The CMIES multi-time scale 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 of CHP at time t, P HFC,t is the electric power output of HFC at time t, P GT,t is the electrical 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) Cold 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 refrigerator, 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 of each type of load and the upper limit of the load amount increased by Class A IDR of each type of load; They are the load reduction of Class A IDR for each type of load and the upper limit of the load reduction of Class A IDR for each type of load.

5. The CMIES multi-time scale 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 the 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 exhaustion, 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: In the formula, 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 For ultra-short-term wind, solar, associated energy, and electric heating and cooling power forecasts; (3) Take σ = 1, 2, ..., k for the distribution and get the fitting sequence of the original sequence Where δ=1,2,…,k; (4) Take σ = 1 + k, 2 + k, ..., m + k for the distribution, and get the predicted value sequence after the original sequence Where δ=1,2,…,m.

6. The CMIES multi-time scale optimization scheduling method considering carbon-green certificate trading and CCS-P2G joint operation according to claim 1 is characterized in that: The objective function of the intraday rolling optimization scheduling model based on MPC 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.

7. The CMIES multi-time scale 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 intraday rolling optimization scheduling model based on MPC is: In the formula, They are the load amount increased by Class B IDR of each type of load and the upper limit of the load amount increased by Class B 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.

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