Master-slave game optimization scheduling method for mining area integrated energy system in electricity-carbon-green certificate combined market

Abstract

The invention discloses a master-slave game optimization scheduling method for a mining area integrated energy system in an electricity-carbon-green certificate combined market. The method comprises the following steps: establishing a transaction architecture; establishing a mining area energy utilization model according to the transaction architecture; establishing a mining area integrated energy system stepped carbon transaction cost model and a mining area integrated energy system stepped green certificate transaction cost model; establishing an upper-layer energy operator income model comprising a first objective function and a first constraint condition by taking energy operator income maximization as a target; taking the minimum operation cost of the mining area integrated energy system as a target, and establishing a lower-layer mining area integrated energy system cost model comprising a second target function and a second constraint condition; according to an upper-layer energy operator income model and a lower-layer mining area integrated energy system cost model, a master-slave game framework with an upper-layer leader as ESO and a lower-layer follower as CMIES is established; and based on a master-slave game framework, establishing a Stackelberg game model and carrying out solving. Simulation results show that the model provided by the invention can realize optimal economy under game interaction of both parties.

Description

Technical Field

[0001] The present invention relates to a master-slave game optimization dispatching method for a comprehensive energy system in a mining area under an electricity-carbon-green certificate joint market, and belongs to the technical field of power system operation and control. 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. As an important carrier of energy transformation, the integrated energy system (IES) can coordinate and optimize the mutual conversion of various energy sources in the mining area and realize the cascade utilization of energy. However, when there are conflicts in the interests of the main participants in the mining area, how to ensure the optimal energy use and low-carbon energy use of the main participants in the mining area through market mechanisms and scheduling strategies is of great practical significance.

[0003] From the perspective of integrated energy system modeling and scheduling, a wealth of research results have been achieved. However, research on the integrated mining energy system (CMIES) is still in its infancy. Reference [1] points out that coalbed methane in coal mines can be used to generate electricity or heat using equipment including gas turbines, internal combustion engines, boilers or steam turbines. Reference [2] uses water source heat pumps to use the water from underground coal mines to supply renewable heat to the heat load of buildings in the surrounding area. Reference [3] constructs a comprehensive energy system model containing coal gangue, coalbed methane and water and a multi-objective two-level optimization model for the configuration and operation of the integrated energy system in the mining area, which promotes the cascade utilization of coal resources. Reference [4] couples coal associated resources with flexible loads in the mining area, comprehensively considers the economic cost of the system, carbon trading costs and user dissatisfaction, and constructs a CMIES scheduling model based on flexible loads. The above research provides a way to utilize associated energy in mining areas, but does not involve the impact of multiple market trading mechanisms on the low-carbon economic scheduling of CMIES.

[0004] At present, the coordinated development of the electricity market, carbon market, and green certificate market is expected to promote the low-carbon transformation of energy. Based on the blockchain distributed market framework, reference [4] established the CMIES carbon emission trading and electricity trading mechanism. Reference [5] established an equilibrium model of the carbon trading market and the green certificate market, which promoted the development of renewable energy and reduced carbon emissions by coordinating the relationship between carbon emissions and renewable energy. So far, most of the research on CMIES has focused on optimizing operation, and has failed to consider the impact of the coupling of the electricity-carbon-green certificate market on CMIES and the conflict of interests between the main parties in its trading process.

[0005] In view of this, the present invention is hereby proposed.

[0006] The references are described as follows:

[0007] [1] MAHDEVARI S. Coal mine methane: control, utilization, and abatement [C] / / HIRSCHI J. Advances in Productive, Safe, and Responsible Coal Mining. Duxford: Woodhead Publishing, 2019: 179 - 198.

[0008] [2] J, A, [2] J, A, J M, et al. Feasibility analysis of using mine water from abandoned coal mines in Spain for heating and cooling of buildings [J]. Renewable Energy, 2020(146): 1166 - 1176.

[0009] [3] HUANG Hongxu, LIANG Rui, ZHANG Xiaotong, et al. Two - stage multi - objective deployment optimization of coal mine integrated energy system considering carbon emission constraints [J]. Power System Technology, 2022, 46(05): 1731 - 1742.

[0010] [4] HU H J, SUN X Y, ZENG B, et al. Enhanced evolutionary multi - objective optimization - based dispatch of coal mine integrated energy system with flexible load [J]. Applied Energy, 2022, 307: 118130.

[0011] [5]Ge Wang, Qi Zhang, Bin Su, Bo Shen, Yan Li, Zheng jun Li, Coordination of tradable carbon emission permits market and renewable electricity certificates market in China, Energy Economics, Volume 93, 2021.

[0012] [6] Arild Heimvik, Eirik S. Amundsen, Prices vs. percentages: Use of tradable green certificates as an instrument of greenhouse gas mitigation, Energy Economics, Volume 99, 2021. Summary of the invention

[0013] The present invention provides a master-slave game optimization scheduling method for the comprehensive energy system of a mining area under the electricity-carbon-green certificate joint market. On the one hand, a reward and punishment ladder carbon trading mechanism and a reward and punishment ladder green certificate trading mechanism are introduced to achieve the economy and low-carbon nature of CMIES; on the other hand, considering the conflict of CMIES interest subjects, an ESO-CMIES master-slave game model is established to coordinate the low-carbon economic operation of the mining area subjects.

[0014] The technical solution of the present invention is:

[0015] According to a first aspect of the present invention, a master-slave game optimization scheduling method for a mining area integrated energy system under an electricity-carbon-green certificate joint market is provided, comprising the following steps: S1, establishing a trading framework with the energy supply side, energy spot market, energy operator ESO, electricity-carbon-green certificate trading market and mining area integrated energy system CMIES as participants; S2, establishing a mining area energy utilization model based on the trading framework; the mining area energy utilization model includes a wind power operation model, a photovoltaic operation model, an MT model, a gas boiler model, an electric refrigerator model, a waste heat boiler model, an absorption refrigerator model, an energy storage device model, and an associated energy utilization model; the associated energy utilization model includes a gas turbine model, a thermal storage oxidation device model, a water Source heat pump model; S3, establish a ladder carbon transaction cost model for the integrated energy system in the mining area and a ladder green certificate transaction cost model for the integrated energy system in the mining area; S4, with the goal of maximizing the benefits of energy operators, establish an upper-level energy operator benefit model including the first objective function and the first constraint condition; with the goal of minimizing the operating cost of the integrated energy system in the mining area, establish a lower-level integrated energy system cost model for the mining area including the second objective function and the second constraint condition; S5, based on the upper-level energy operator benefit model and the lower-level integrated energy system cost model for the mining area, establish a master-slave game framework with the upper-level leader as ESO and the lower-level follower as CMIES; based on the master-slave game framework, establish a Stackelberg game model and solve it.

[0016] Furthermore, the S3 specifically includes: determining the initial carbon emission quota of the mining area comprehensive energy system based on the CO2 generated by the gas turbine and thermal oxidation device in the purchased energy and associated energy of the mining area comprehensive energy system; determining the actual carbon emissions of the mining area comprehensive energy system based on the mining area energy utilization model; adopting a reward-and-punishment ladder carbon trading mechanism based on the initial carbon emission quota and actual carbon emissions of the mining area comprehensive energy system to establish a ladder carbon trading cost model for the mining area comprehensive energy system; calculating the green certificate assessment amount of the mining area comprehensive energy system based on the mining area energy utilization model, the amount of purchased green electricity, and the green certificate assessment coefficient; calculating the actual green certificate of the mining area comprehensive energy system based on the mining area energy utilization model and the amount of purchased green electricity; adopting a reward-and-punishment ladder green certificate trading mechanism based on the green certificate assessment amount and actual green certificates of the mining area comprehensive energy system to establish a ladder green certificate trading cost model for the mining area comprehensive energy system.

[0017] Furthermore, for energy operators ESO, their main revenue and costs come from the energy supply side, spot market and CMIES, so the first objective function is established as:

[0018] maxF ESO =f cmies +f sm -f e,b

[0019] Where: FESO is the total benefit of ESO; f cmies is the electricity sales revenue of ESO to CMIES, f sm is the income from ESO and spot market transactions, f e,b The energy purchase cost of ESO on the energy supply side;

[0020] The first constraint conditions include price constraints, transmission power constraints, upper and lower power limit constraints on the energy supply side, and power balance constraints.

[0021] Furthermore, the electricity sales revenue of the ESO to CMIES is expressed as:

[0022]

[0023] Where: are the thermal power price, green power price and gas price of ESO to CMIES at time t respectively; G u,t They are the system's purchased thermal power, purchased green power, and purchased gas at time t.

[0024] The profit obtained from the ESO and spot market transactions is expressed as:

[0025]

[0026] In the formula, They are ESO’s thermal power trading revenue, green power trading revenue and gas trading revenue in the spot market;

[0027] The energy purchase cost of the ESO on the energy supply side is expressed as:

[0028] f e,b =f tp +f gp +f gas

[0029] Where: f tp 、f gp 、f gas They are respectively the ESO's thermal power purchasing cost, green electricity purchasing cost, and gas purchasing cost on the energy supply side.

[0030] Furthermore, the second objective function is expressed as:

[0031] f CMIES =f cmies +f c +f ope (30)

[0032] In the formula, F CMIESis the operating cost of the integrated energy system in the mining area, f cmies is the electricity sales revenue of ESO to CMIES, f c 、f ope They are electricity-carbon-green certificate incentive costs and equipment operation and maintenance costs;

[0033] The second constraint conditions include: energy transmission constraint, electric power balance constraint, thermal power balance constraint, and cold power balance constraint.

[0034] Furthermore, the income obtained from the ESO and spot market transactions is expressed as:

[0035]

[0036] In the formula, They are ESO’s thermal power trading revenue, green power trading revenue and gas trading revenue in the spot market;

[0037] The electricity-carbon-green certificate incentive cost is expressed as:

[0038]

[0039] In the formula, f CE,t 、f GC,t The tiered carbon trading costs and tiered green certificate trading costs of the integrated energy system in mining areas are respectively;

[0040] The equipment operation and maintenance cost is expressed as:

[0041]

[0042] In the formula, α i , P i,t are the unit maintenance cost and output power of equipment i at time t respectively; equipment i refers to the energy conversion equipment and energy storage equipment in the comprehensive energy system of the mining area.

[0043] In solving the ESO-CMIES master-slave game two-layer model, the upper-layer energy operator profit model uses a genetic algorithm to give the energy purchase price of the mining area comprehensive energy system, while the lower-layer mining area comprehensive energy system cost model uses CPLEX to solve its energy demand; repeat the above genetic algorithm steps, and iterate in a cycle until a balance is reached.

[0044] Furthermore, in the solution, the upper-level energy operator revenue model uses genetic algorithms to give the energy purchase price of the integrated energy system in the mining area, while the lower-level integrated energy system cost model of the mining area uses CPLEX to solve its energy demand.

[0045] According to a second aspect of the present invention, a master-slave game optimization scheduling system for a mining area integrated energy system in an electricity-carbon-green certificate joint market is provided, comprising a module for executing a master-slave game optimization scheduling method for a mining area integrated energy system in an electricity-carbon-green certificate joint market as described in any one of the above.

[0046] The beneficial effects of the present invention are as follows: based on the reward-penalty ladder carbon trading mechanism and the reward-penalty ladder green certificate trading mechanism, the present invention establishes a ladder carbon trading cost model for the integrated energy system in the mining area and a ladder green certificate trading cost model for the integrated energy system in the mining area, thereby realizing the economy and low carbon of CMIES; further, considering the utilization of associated energy and the conflict of CMIES stakeholders, a master-slave game model is established, and the energy use strategy of CMIES is guided by reasonable price information. The simulation results show that the proposed model can achieve the optimal economy under the game interaction between the two parties. In addition, by analyzing the key parameters of the reward-penalty ladder carbon trading mechanism and the reward-penalty ladder green certificate trading mechanism, a reference is provided for CMIES to select the appropriate interval width and reward-penalty coefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a transaction structure diagram;

[0048] Figure 2 It is a master-slave game framework diagram;

[0049] Figure 3 It is a wind, solar and associated energy prediction diagram provided according to an embodiment of the present invention;

[0050] Figure 4 It is the energy purchase result of the energy supply side provided according to the embodiment of the present invention;

[0051] Figure 5 is a spot market transaction result provided according to an embodiment of the present invention; wherein 5(a) represents an electric energy transaction; 5(b) represents a gas transaction;

[0052] Figure 6 is an ESO pricing result provided according to an embodiment of the present invention;

[0053] Figure 7 7(a) represents the power optimization result of electric energy; 7(b) represents the thermal energy optimization result; 7(c) represents the cooling energy optimization result;

[0054] Figure 8 It is the change of the linkage between electricity, carbon and green certificate provided in accordance with the embodiment of the present invention;

[0055] Fig. 9 It is the influence of the interval width on CMIES provided in the embodiment of the present invention;

[0056] Fig.10 This is the influence of the reward and punishment coefficient provided in the embodiment of the present invention on CMIES. DETAILED DESCRIPTION

[0057] 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.

[0058] Example 1: Figure 1-10 As shown, according to a first aspect of an embodiment of the present invention, a master-slave game optimization scheduling method for a mining area integrated energy system in an electricity-carbon-green certificate joint market is provided, comprising the following steps:

[0059] S1. Establish a trading framework with energy supply side, energy spot market, energy operator ESO, electricity-carbon-green certificate trading market and mining area integrated energy system CMIES as participants, such as Figure 1 As shown; it should be noted that in the mining area comprehensive energy system CMIES, there are electric loads, cold loads, heat loads, energy conversion equipment, and energy storage equipment. The energy conversion equipment includes: micro gas turbines (MT), gas boilers (GB), electric refrigerators (EC), waste heat boilers (WHB), absorption refrigerators (AC), water source heat pumps (WSHP), gas turbines (GT), thermal storage oxidizers (RTO); energy storage equipment includes: batteries (ES) and heat storage tanks (HS). For CMIES, it uses photovoltaic PV, wind power WT, and associated energy such as gas, exhaust air, and gushing water to participate in the energy supply of electricity, heat, and cold loads (gas is supplied to GT for power generation, and exhaust air and gushing water are supplied to RTO and WSHP for heating respectively). Considering the supply capacity problem between associated energy and load demand, CMIES purchases electricity, gas and other energy from energy operators to meet load demand.

[0060] S2. Establish a mining area energy utilization model based on the transaction framework; the mining area energy utilization model includes a wind power operation model, a photovoltaic operation model, an MT model, a gas boiler model, an electric refrigerator model, a waste heat boiler model, an absorption refrigerator model, an energy storage device model, and an associated energy utilization model; the associated energy utilization model includes a gas turbine model, a thermal storage oxidation device model, and a water source heat pump model;

[0061] The wind power operation model is:

[0062] 0≤P wind,t ≤P pwind,t (1)

[0063] 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.

[0064] The photovoltaic operation model is:

[0065] 0≤P pv,t ≤P ppv,t (2)

[0066] 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.

[0067] The MT model is:

[0068]

[0069] Where: P MT,t , H MT,t are the electrical power and thermal power output by MT at time t, respectively; are the MT electrical power and thermal power conversion coefficients, respectively; is the natural gas power input to MT at time t; P MT,max is the maximum output power of MT; They are the upper and lower limits of MT climbing power respectively; They are the upper and lower limits of MT thermoelectric ratio respectively.

[0070] The gas boiler model is:

[0071]

[0072] 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.

[0073] The electric refrigerator model is:

[0074]

[0075] 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,maxis the maximum output power of the electric refrigerator; They are the upper and lower limits of the climbing power of the electric refrigerator respectively.

[0076] The waste heat boiler model is:

[0077]

[0078] 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.

[0079] The absorption refrigerator model is:

[0080]

[0081] Where: H AC,t , P AC,t are the waste heat power input and cooling power output of the absorption refrigerator at time t; 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.

[0082] The energy storage device model is:

[0083]

[0084] Where: x∈{ES,HS}; 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.

[0085] The gas turbine model is:

[0086]

[0087] 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.

[0088] The thermal storage oxidation device model is:

[0089]

[0090] Where: H RTO,t is the thermal power output of the thermal storage oxidation device at time t; L gas is the lower heating value of methane; 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.

[0091] The water source heat pump model is:

[0092]

[0093] 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.

[0094] S3. Determine the initial carbon emission quota of the integrated energy system of the mining area based on the CO2 generated by the gas turbine and thermal oxidation device in the purchased energy and associated energy of the integrated energy system of the mining area; determine the actual carbon emissions of the integrated energy system of the mining area based on the energy utilization model of the mining area; adopt a reward-and-punishment ladder carbon trading mechanism based on the initial carbon emission quota and actual carbon emissions of the integrated energy system of the mining area, establish a ladder carbon trading cost model for the integrated energy system of the mining area, and realize the linkage between the electricity market and the carbon market; calculate the green certificate assessment amount of the integrated energy system of the mining area based on the energy utilization model of the mining area, the purchased green electricity, and the green certificate assessment coefficient; calculate the actual green certificate of the integrated energy system of the mining area based on the energy utilization model of the mining area and the purchased green electricity; adopt a reward-and-punishment ladder green certificate trading mechanism based on the green certificate assessment amount and actual green certificate of the integrated energy system of the mining area, establish a ladder green certificate trading cost model for the integrated energy system of the mining area, and realize the linkage between the electricity market and the green certificate market; specifically, it includes two parts:

[0095] Part I: Establishing a ladder carbon trading cost model for the integrated energy system in mining areas:

[0096] The initial carbon emission quota of the integrated energy system in the mining area is determined by the CO2 generated by the system's purchased energy, gas turbines in the associated energy, and the wind-lack thermal storage oxidation device, as shown below:

[0097]

[0098] Where: E p,t 、E b,t 、E as,t They are initial carbon emission quota, purchased energy quota, and associated energy quota; G u,t They are the system's purchased thermal power, purchased green power, and purchased gas. GT,t , H RTO,t They are the power generation of gas turbines and the heat generation of thermal storage oxidation device in the comprehensive energy system of the mining area respectively; κ1 is the carbon emission quota coefficient of purchased thermal power, κ2 is the carbon emission quota coefficient of purchased green electricity, κ3 is the carbon emission quota coefficient of purchased gas, κ4 is the carbon emission quota coefficient of gas turbine power generation, and κ5 is the carbon emission quota coefficient of heat generation of thermal storage oxidation device.

[0099] The actual carbon emissions calculation formula of the system is as follows:

[0100]

[0101] Where: E r,t , E' b,t , E' as,t They are the actual carbon emissions of the system, the carbon emissions generated by purchased energy, and the carbon emissions generated by associated energy; P is the amount of thermal power purchased by the system; MT,t is the electric power output by MT at time t; H GB,t P is the thermal power output of the gas boiler at time t; GT,t , H RTO,t They are respectively the power generation of gas turbines and the heat generation of thermal storage oxidation devices in the integrated energy system of the mining area; κ mt , κ gb , κ w , κ v They are the system electricity carbon emission coefficient, MT carbon emission coefficient, GB carbon emission coefficient, GT carbon emission coefficient and RTO carbon emission coefficient.

[0102] In CMIES, in order to motivate the system's carbon reduction and emission reduction capabilities, the reward-and-punishment ladder carbon trading mechanism shown in the following formula is adopted. When the system's actual carbon emissions are less than the initial carbon emission quota, a certain reward will be given. Otherwise, the system needs to purchase a certain amount of carbon emission rights.

[0103]

[0104] In the formula, F CE,t is the step-wise carbon transaction cost of the integrated energy system in the mining area at time t; CE is the carbon trading base price; n1, h, and m1 are the reward coefficient, carbon emission range width, and penalty coefficient respectively;

[0105] Part II: Establishing a ladder green certificate transaction cost model for the integrated energy system in mining areas:

[0106]

[0107] Among them, C p,t is the system green certificate assessment quantity, κ6 is the green certificate assessment coefficient; Purchase green electricity for the system; 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;

[0108]

[0109] Where: C r,t It is the actual green certificate obtained by CMIES when it purchases green electricity from energy operators and consumes renewable energy electricity itself.

[0110] For green certificates, the transaction cost is similar to the carbon transaction cost, and a reward and punishment ladder green certificate trading mechanism is adopted, as shown below.

[0111]

[0112] In the formula, FGC,t is the transaction cost of the step-by-step green certificate for the integrated energy system of the mining area at time t, GC , n2, l, and m2 are the green certificate transaction price, reward coefficient, green certificate interval width, and penalty coefficient respectively.

[0113] S4. Taking the maximum benefit of energy operators as the goal, establish an upper-level energy operator benefit model including the first objective function and the first constraint condition; specifically including:

[0114] For energy operators (ESOs), their main revenue and costs come from the energy supply side, the spot market, and CMIES. The first objective function is:

[0115] maxF ESO =f cmies +f sm -f e,b (18)

[0116] Where: F ESO is the total benefit of ESO; f cmies is the electricity sales revenue of ESO to CMIES, f sm is the income from ESO and spot market transactions, f e,b is the energy purchase cost of ESO on the energy supply side.

[0117]

[0118] Where: are the thermal power price, green power price and gas price of ESO to CMIES at time t respectively; G u,t They are the amount of thermal power, green power and gas purchased by the system at time t. The system chooses to purchase thermal power, green power and gas in each period based on the ESO quotation.

[0119]

[0120] In the formula, They are ESO’s thermal power trading revenue, green power trading revenue and gas trading revenue in the spot market; They are the thermal power price, green power price and gas price of ESO in the spot market at time t respectively; They are the thermal power sold / purchased by ESO in the spot market at time t, green power sold / purchased, and gas power sold / purchased;

[0121] f e,b =f tp +f gp +f gas (twenty two)

[0122]

[0123] Where: f tp 、f gp 、f gas They are ESO’s thermal power purchase cost, green power purchase cost, and gas purchase cost on the energy supply side; They are the thermal power purchase price, green power purchase price, and gas purchase price of ESO on the energy supply side; They are respectively the thermal power output by the thermal power units on the energy supply side at time t, the green power output by the green power units, and the gas power output by the gas pipeline network.

[0124] The first constraint condition includes:

[0125] 1) Price constraints

[0126]

[0127] Where: N T is the total number of trading sessions, are the upper and lower limits of the operator’s pricing for thermal power, green power, and gas in CMIES at time t; and They are the upper limits of the daily average prices of thermal power, green electricity and natural gas respectively.

[0128] 2) Transmission power constraints

[0129]

[0130] Where: z tp 、z gp 、z gas are 0-1 variables for thermal power, green power, and gas, respectively, which restrict energy operators from buying or selling thermal power, green power, and gas at the same time in the spot market at time t; It indicates the upper limit of thermal power generation capacity of thermal power units on the energy supply side, the upper limit of green power generation capacity of new energy units, and the upper limit of gas power generation capacity of the gas grid (thermal power units are GT+coal-fired units); The power caps for thermal power, green power and gas input to energy operators by the spot market at time t.

[0131] 3) Power upper and lower limit constraints on the energy supply side

[0132]

[0133] Where: It is the thermal power, green power and gas power output by thermal power units, new energy units and gas pipeline network on the energy supply side to ESO at time t. They respectively represent the lower limit of thermal power output, green power output and gas power output of thermal power units, new energy units and gas grid on the energy supply side.

[0134] 4) Power balance constraints

[0135]

[0136] Equations (27)-(29) are the power balance constraints of ESO.

[0137] With the goal of minimizing the operating cost of the integrated energy system in the mining area, a cost model of the lower-level integrated energy system in the mining area is established, which includes the second objective function and the second constraint conditions. Specifically, it includes:

[0138] For CMIES, the second objective function is to minimize its own operating cost. It is shown as follows:

[0139] f CMIES =f cmies +f c +f ope (30)

[0140] In the formula, F CMIES is the operating cost of the integrated energy system in the mining area, f c 、f ope They are electricity-carbon-green certificate incentive cost and equipment operation and maintenance cost respectively.

[0141]

[0142] In the formula, f CE,t 、f GC,t The tiered carbon trading costs and tiered green certificate trading costs of the integrated energy system in mining areas are respectively.

[0143]

[0144] In the formula, α i , P i,t are the unit maintenance cost and output power of equipment i at time t, respectively; equipment i refers to gas boiler (GB), micro gas turbine (MT), water source heat pump (WSHP), gas turbine (GT), thermal storage oxidizer (RTO), electric refrigerator (EC), waste heat boiler (WHB), absorption refrigerator (AC), battery (ES) or heat storage tank (HS); the output power of each equipment involved in the calculation corresponds to the corresponding relationship in the mining area energy utilization model: gas boiler (GB) output power - H GB,t 、Micro gas turbine (MT)-P MT,t 、Water source heat pump (WSHP) output power - H WSHP,t , Gas turbine (GT) output power - P GT,t、Regenerative Thermal Oxidation Device (RTO) Output Power - H RTO,t , Electric Refrigerator (EC) Output Power - P EC,t , Waste Heat Boiler (WHB) output power - Absorption Refrigerator (AC) Output Power-P AC,t , Battery (ES) output power - S ES,t , heat storage tank (HS) output power - S HS,t .

[0145] The second constraint condition includes:

[0146] 1) Energy transmission constraints

[0147]

[0148] Where: They are the upper and lower limits of CMIES purchased electricity and gas; They are the gas power consumed by the MT unit and the gas boiler respectively.

[0149] 2) Electric power balance constraints

[0150]

[0151] Where: They are the system's purchased thermal power and purchased green power respectively; 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 MT,t is the electric power output by MT at time t; P GT,t is the electric power output of the gas turbine at time t; P pl,t is the electric load demand at time t; are the charging and discharging power of the energy storage device at time t respectively.

[0152] 3) Thermal power balance constraints

[0153]

[0154] Where: H GB,t is the thermal power output of the gas boiler at time t; H WSHP,t is the thermal power output of the water source heat pump at time t; is the thermal power output of the waste heat boiler at time t; H pl,t is the heat load demand at time t; are the charging and discharging powers of the heat storage tank at time t respectively.

[0155] 4) Cold power balance constraints

[0156] P AC,t+P EC,t =C pl,t (36)

[0157] Where: P AC,t is the cooling power output of the absorption refrigerator at time t; P EC,t is the cooling power output by the electric refrigerator at time t; C pl,t is the cooling load demand at time t.

[0158] According to the above-mentioned upper energy operator revenue model and the lower mining area integrated energy system cost model, a master-slave game framework is established, with the upper leader being ESO and the lower follower being CMIES. The specific framework is shown in Figure 2 .Depend on Figure 2 It can be seen that the master-slave game scheduling under the electricity-carbon-green certificate market linkage mechanism proposed in the present invention is divided into two layers: the upper layer is for ESO to make CMIES pricing decisions, and according to the supply and demand relationship and the electricity price of the upper power market, by formulating prices and optimal energy purchase strategies, the ESO's own interests are maximized. The lower layer is for CMIES to make quantitative decisions, and according to the pricing strategy formulated by ESO, it makes purchase and sale decisions on thermal power, green electricity, gas, etc. under the premise of minimizing costs. Specifically:

[0159] S5. Based on the upper energy operator revenue model and the lower mining area comprehensive energy system cost model, a master-slave game framework is established with the upper leader as ESO and the lower follower as CMIES. Based on the master-slave game framework, a Stackelberg game model is established. The Stackelberg game model can be expressed as:

[0160]

[0161] Where: N is 2 game participants; are the strategies of the leader ESO and the follower CMIES respectively; F ESO The optimization target in the upper energy operator profit model - the total profit of ESO; F CMIES The optimization target of the cost model for the integrated energy system in the lower mining area is the operating cost of CMIES.

[0162] When the leader accepts the optimal response feedback from all followers and does not change its own strategy, the game reaches the Stackelberg equilibrium. For the Stackelberg model proposed in this invention, the equilibrium solution must satisfy:

[0163]

[0164] In the formula, They are the optimal strategies of ESO and CMIES respectively.

[0165] For the Stackelberg game model, the following three conditions must be met to prove that its equilibrium solution exists and is unique: 1) The strategy spaces of the leader and the follower are both non-empty compact convex sets; 2) For a given leader strategy set, the optimal solution of all followers exists and is unique; 3) For a given follower strategy set, the leader's optimal solution exists and is unique. The proof is as follows:

[0166] According to the Stackelberg model, the optimal strategy of the leader ESO must satisfy constraints (24)-(29), and the optimal strategy of the follower CMIES must satisfy constraints (33)-(36). Therefore, the strategy set of each participant is a non-empty compact convex set.

[0167] Find the objective function (30) of CMIES about G u,t The second-order partial derivative of :

[0168]

[0169] From the above formula, we can see that the CMIES objective function is about its strategy set G u,t A linear function of , according to the definition of concavity and convexity, a linear function is both a concave function and a convex function.

[0170] The optimal solution of CMIES Substitute F ESO , and for F ESO Ask about The second-order partial derivative of :

[0171]

[0172] Therefore, the master-slave game model described in the present invention has a unique Stackelberg equilibrium, and the proof is complete.

[0173] In solving the ESO-CMIES master-slave game two-layer model, the upper energy operator profit model uses genetic algorithms to give the purchase price of the integrated energy system in the mining area. The integrated energy system cost model of the lower mining area uses CPLEX to solve its energy demand G u,t. First, set the initial parameters of the genetic algorithm. Second, use the algorithm to randomly generate the energy purchase price of group U, pass the parameters to the lower-level mining area comprehensive energy system cost model, use the CPLEX solver to solve its energy demand, calculate the operating cost of retaining the current CMIES, and return the energy demand to the upper-level energy operator profit model. It solves the optimal energy purchase decision on the energy supply side and the spot market based on the returned energy demand, retains the current ESO total revenue and the optimal system energy purchase price. Finally, repeat the above genetic algorithm steps, iterate in this cycle until a balance is reached.

[0174] In order to verify the low-carbon and economical nature of the ESO and CMIES master-slave game model in the electricity-carbon-green certificate trading market proposed in this invention, a mining area in Yunnan is taken as an example. Figure 1 The gas, wind exhaustion, water inflow and concentration, as well as wind and solar output power under a typical day in the mining area are shown in Figure 3 As shown. Take the carbon trading price λ CE The transaction price of green certificate is 0.12 yuan / kg. GC It is 100 yuan per ticket.

[0175] Considering the energy supply side (thermal power units, new energy units, gas grid), the electricity-carbon-green certificate trading market, the energy spot market, the ESO and CMIES as participants, a simulation analysis is conducted on the trading market with a trading cycle of one day, that is, T=24.

[0176] In order to highlight the linkage transaction of electricity-carbon-green certificate market and consider the different impacts of reward-penalty ladder carbon trading and reward-penalty ladder green certificate trading on the overall economic benefits and environmental benefits of the master-slave game model, the following schemes are designed for comparison. The comparison results are shown in Table 1:

[0177] Scenario 1: The linkage between the electricity market, carbon market and green certificate market is not considered.

[0178] Scenario 2: Only consider the linkage between the electricity market and the carbon market.

[0179] Scenario three: The linkage between the electricity market, carbon market and green certificate market based on reward and punishment ladder carbon trading and reward and punishment ladder green certificate trading is not considered.

[0180] Scenario 4: Consider the linkage between the electricity market, carbon market and green certificate market based on reward and punishment ladder carbon trading and reward and punishment ladder green certificate trading, that is, the method of the present invention.

[0181] Table 1 Operation results in different scenarios

[0182]

[0183] As can be seen from Table 1, in scenario 1, when the linkage between electricity, carbon and green certificate markets is not considered, the total revenue of ESO is higher than that of scenario 2 and scenario 3. At this time, the total cost of CMIES is higher than that of the other three scenarios. For scenario 2, after adopting the electricity-carbon linkage trading mechanism, although the total cost of CMIES has been reduced, some of the interests of ESO have been sacrificed. When the electricity-carbon-green certificate trading mechanism is adopted in scenario 3, the total revenue of ESO and the total cost of CMIES have changed, and the carbon emissions at this time are reduced by 10456.141kg and 6664.861kg respectively compared with scenarios 1 and 2. It can be seen that it is difficult to promote load carbon emission reduction only through the electricity-carbon linkage trading mechanism. For scenario four, after adopting the electricity-carbon-green certificate linkage mechanism based on reward and punishment ladder carbon trading and reward and punishment ladder green certificate trading, ESO has the highest total benefit among the four scenarios and the lowest total CMIES cost; in this scenario, the cost is reduced by a total of 2,464.761 yuan through electricity-carbon-green certificate incentives, and compared with the other three scenarios, the carbon emissions are reduced by 16,625.829 kg, 12,834.549 kg, and 6,169.688 kg respectively. At the same time, the number of green certificates in scenario four is the highest; therefore, the electricity-carbon-green certificate linkage mechanism based on reward and punishment ladder carbon trading and green certificate trading can further reduce carbon emissions in mining areas.

[0184] Furthermore, combined with the optimization results of scenario 4, the impact of ESO trading results on CMIES operation is analyzed. Figure 4 , Figure 5 shown.

[0185] from Figure 4 It can be clearly seen that when ESO purchases energy on the energy supply side, the purchasing trends of green electricity, thermal power and gas are basically the same, with peaks roughly distributed in the 10:00-12:00 and 18:00-19:00 periods, and valleys distributed in the 1:00-5:00 and 22:00-24:00 periods. During peak periods, the energy purchased by ESO is mainly used for CMIES demand, while during valley periods, CMIES demand decreases, so the above scenario occurs. In the model proposed in this invention, gas is used for CMIES electricity and heat load demand, from Figure 4 It can be seen that the ESO gas purchase power is the lowest during the period of 1:00-5:00, which is due to the low heat load demand of CMIES at this time.

[0186] ESO's purchase and sale of energy in the spot market Figure 5 As shown in formula (25), when ESO conducts energy transactions in the spot market, it cannot buy or sell the same type of energy at the same time, mainly to avoid undermining the fairness of the market. Figure 5In the fourth scenario, the energy sales power is higher than the energy purchase power. The main reason is that the energy price on the energy supply side is cheaper than the spot market, so ESO purchases all kinds of energy to the maximum extent on the energy supply side. Figure 5 China Energy sells more power than it buys. Figure 5 In the process, the sales power of green electricity is gradually decreasing, while the sales power of thermal power is increasing. The reason is that after the adoption of carbon trading mechanism and green certificate trading mechanism, the green electricity consumption potential of CMIES has increased. It should be pointed out that due to power constraints, ESO cannot completely purchase energy on the energy supply side to meet its own needs and CMIES needs. Therefore, it is necessary to purchase energy in the spot market. On the basis of balancing the interests of both parties, ESO finally makes the following decision: Figure 5 The energy purchasing decision shown.

[0187] Figure 6 The ESO pricing result for CMIES. For the transaction electricity price, it can be seen that the green electricity price is significantly higher than the thermal power price, because the operation and maintenance cost of green electricity is higher than that of thermal power. In the 8:00-13:00 period and the 19:00-23:00 period, the thermal power price and the green electricity price increased significantly. This is because the overall energy demand of the power grid increased at this time. Therefore, the ESO price for CMIES also increased. In the 1:00-8:00 period, the energy demand of the power grid was low, so its transaction electricity price was also low. In the 13:00-19:00 period, due to the addition of new energy units and the reduction in load demand, the prices of both thermal power and green electricity decreased. Similarly, the analysis of gas prices is similar to the analysis of electricity prices. The trend of gas prices is roughly the same as the actual thermal load trend of CMIES.

[0188] Figure 7 The CMIES electricity, heat, and cooling optimization dispatch results obtained after the master-slave game (the green power purchase power and thermal power purchase power in the figure correspond to Battery discharge power corresponding Battery charging power correspondence Wind power corresponds to P wind,t ; Photovoltaic power corresponds to P pv,t MT electric power corresponds to P MT,t ;EC power consumption corresponding Electrical load corresponding to P pl,t ; GT electric power corresponds to P GT,t ; Heat load corresponding to H pl,t ;WHB heat power corresponding WSHP thermal power corresponding to H WSHP,t ; GB thermal power corresponds to H GB,t ; The charging power of the heat storage tank corresponds to Heat storage tank heat dissipation power corresponding AC cooling power corresponding to P AC,t;EC cooling power corresponds to P EC,t ; Cooling load corresponds to C pl,t ). In scenario 4, for the results of power optimization, considering the economy and low carbon, CMIES consumes more green electricity; in the period of 1:00-7:00, the price of green electricity is relatively low, and due to the introduction of the reward and punishment ladder green certificate trading mechanism, the green electricity consumption potential in this period is improved; in the period of 8:00-12:00, due to the influence of energy demand, the electricity price has increased significantly. Therefore, in this period, CMIES mainly consumes thermal power; in the period of 13:00-24:00, the analysis of thermal power and green power is similar to the above, so it will not be repeated.

[0189] For CMIES, in addition to purchased electricity and MT power generation, it can also generate electricity using gas in the mine. In the present invention, the electricity from GT is used for electrical load demand, and the heat energy generated can be used for AC cooling. Figure 7 It can be seen from the power optimization results of each scenario that the power generated by photovoltaic, wind power and GT is distributed throughout the day, while ES is only discharged during peak power consumption hours. Figure 6 It can be seen that the main reason is that during these periods, energy prices are high, so ES is used for discharge. For heat load, the heat load demand of CMIES is low during 1:00-7:00, and WHB and WSHP are mainly used to meet the heat load demand. During 9:00-12:00 and 21:00-24:00, the system heat load demand is high, so GB and HS are used to balance the heat power. For cooling load, it is mainly met by AC and EC, such as Figure 7 The cooling energy optimization results are shown.

[0190] Furthermore, based on the optimization results of each scenario, combined with scenarios 1 to 4, the impact of the electricity-carbon-green certificate market linkage on CMIES is analyzed. The changes in electricity load, total carbon emissions and green certificates under the four scenarios are as follows: Figure 8 shown.

[0191] Scenario 1 fails to consider the linkage between the electricity-carbon-green certificate market, so the carbon emissions are relatively high. Scenario 2 considers the linkage between the electricity market and the carbon market, so the carbon emissions of CMIES are reduced; and after the electricity-carbon-green certificate linkage mechanism is adopted in Scenario 3, the green electricity consumption capacity is significantly improved, and at the same time, carbon emissions are significantly reduced. When Scenario 4 adopts the electricity-carbon-green certificate linkage mechanism based on the reward and punishment ladder carbon trading and reward and punishment ladder green certificate trading mechanism, the carbon emissions are the lowest.

[0192] Table 2 Carbon trading and green certificate trading results under different scenarios

[0193] Scenario <![CDATA[Carbon quota E p,t / kg]]> <![CDATA[Green certificate assessment quantity C p,t / this]]> <![CDATA[Carbon trading cost F CE,t / yuan]]> <![CDATA[Green certificate trading cost F GC,t / yuan]]> Scene 1 — — — — Scene 2 36485.64 — -600.89 — Scene 3 30513.93 33.07 -684.07 -826.64 Scene 4 24975.09 42.86 -1193.55 -1271.22

[0194] Table 2 shows the carbon trading and green certificate trading results of CMIES under different scenarios. Except for scenario 1, from scenario 2 to scenario 4, the carbon quota of the system is gradually reduced, while the green certificate assessment is the opposite, and the final carbon trading cost and green certificate trading cost are significantly reduced; it can be seen that under the mechanism proposed by the present invention, the economy of the game subject can be guaranteed and carbon emissions can be further reduced.

[0195] In scenario 4, the parameters of the reward-penalty ladder carbon trading mechanism and the reward-penalty ladder green certificate trading mechanism will have an impact on the total operating cost of CMIES. Due to limited space, only the interval length, reward coefficient, and penalty coefficient of the ladder carbon trading and green certificate trading are analyzed as follows:

[0196] exist Fig. 9 It can be seen that the length of carbon trading and green certificate trading intervals has a greater impact on the total cost of the system. When the carbon trading interval length is 50-150kg and the green certificate trading range is 0.1-0.3 / billion, the total operating cost of CMIES is relatively small. As the length of the carbon trading interval and the width of the green certificate interval increase, the cost of the system also increases.

[0197] exist Fig.10 In the present invention, under the premise that the reward and punishment coefficients of carbon trading and green certificate trading are the same, as the reward coefficient continues to increase, the cost of CMIES gradually decreases, and finally when the reward coefficient and the penalty coefficient are 0.6, the system cost is the lowest; in the present invention, when a certain reward coefficient is fixed, as the penalty coefficient increases, the total system cost changes little.

[0198] In general, when adopting the reward and punishment ladder carbon trading and reward and punishment green certificate trading mechanism, changes in the interval width, reward coefficient, and penalty coefficient will cause changes in the total cost of system operation.

[0199] According to a second aspect of an embodiment of the present invention, a master-slave game optimization scheduling system for a mining area integrated energy system under an electricity-carbon-green certificate joint market is provided, comprising the following steps: a first module is used to establish a trading framework with energy supply side, energy spot market, energy operator ESO, electricity-carbon-green certificate trading market and mining area integrated energy system CMIES as participants; a second module is used to establish a mining area energy utilization model based on the trading framework; the mining area energy utilization model includes a wind power operation model, a photovoltaic operation model, an MT model, a gas boiler model, an electric refrigerator model, a waste heat boiler model, an absorption refrigerator model, an energy storage equipment model, and an associated energy utilization model; the associated energy utilization model includes a gas turbine model, a thermal storage oxidation device model, a water Source heat pump model; the third module is used to establish a ladder carbon transaction cost model for the integrated energy system in the mining area and a ladder green certificate transaction cost model for the integrated energy system in the mining area; the fourth module is used to establish an upper energy operator revenue model including a first objective function and a first constraint condition with the goal of maximizing the revenue of energy operators; with the goal of minimizing the operating cost of the integrated energy system in the mining area, a lower integrated energy system cost model including a second objective function and a second constraint condition is established; the fifth module is used to establish a master-slave game framework with the upper leader as ESO and the lower follower as CMIES based on the upper energy operator revenue model and the lower integrated energy system cost model; based on the master-slave game framework, a Stackelberg game model is established and solved. The term "module" as used above can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the above embodiment is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived. For parts that are not described in detail in each module, please refer to the relevant description of other embodiments.

[0200] 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. A master-slave game optimization scheduling method for the integrated energy system of a mining area under the electricity-carbon-green certificate joint market, characterized in that: The following steps are involved: S1. Establish a trading framework with energy supply side, energy spot market, energy operator ESO, electricity-carbon-green certificate trading market and mining area integrated energy system CMIES as participants; S2. Establish a mining area energy utilization model based on the transaction framework; the mining area energy utilization model includes a wind power operation model, a photovoltaic operation model, an MT model, a gas boiler model, an electric refrigerator model, a waste heat boiler model, an absorption refrigerator model, an energy storage device model, and an associated energy utilization model; the associated energy utilization model includes a gas turbine model, a thermal storage oxidation device model, and a water source heat pump model; S3. Establish a ladder carbon transaction cost model for the integrated energy system in mining areas and a ladder green certificate transaction cost model for the integrated energy system in mining areas; S4. With the goal of maximizing the benefits of energy operators, an upper-level energy operator benefit model including a first objective function and a first constraint condition is established; with the goal of minimizing the operating cost of the mining area comprehensive energy system, a lower-level mining area comprehensive energy system cost model including a second objective function and a second constraint condition is established; S5. Based on the upper-level energy operator revenue model and the lower-level mining area comprehensive energy system cost model, a master-slave game framework is established with the upper-level leader as ESO and the lower-level follower as CMIES; based on the master-slave game framework, a Stackelberg game model is established and solved.

2. The master-slave game optimization scheduling method for the integrated energy system of a mining area under the electricity-carbon-green certificate joint market according to claim 1 is characterized in that: The S3 specifically includes: Determine the initial carbon emission quota of the integrated energy system of the mining area based on the CO2 produced by the gas turbine and thermal oxidation device in the purchased energy and associated energy of the integrated energy system of the mining area; determine the actual carbon emissions of the integrated energy system of the mining area based on the energy utilization model of the mining area; adopt a reward-and-punishment tiered carbon trading mechanism based on the initial carbon emission quota and actual carbon emissions of the integrated energy system of the mining area, and establish a tiered carbon trading cost model for the integrated energy system of the mining area; The green certificate assessment amount of the mining area's comprehensive energy system is calculated based on the mining area's energy utilization model, the amount of green electricity purchased, and the green certificate assessment coefficient. The actual green certificates of the mining area's comprehensive energy system are calculated based on the mining area's energy utilization model and the amount of green electricity purchased. Based on the green certificate assessment amount and actual green certificates of the mining area's comprehensive energy system, a reward and punishment tiered green certificate trading mechanism is adopted to establish a tiered green certificate trading cost model for the mining area's comprehensive energy system.

3. The master-slave game optimization scheduling method for the integrated energy system of a mining area under the electricity-carbon-green certificate joint market according to claim 1 is characterized in that: For energy operators (ESOs), their main revenue and costs come from the energy supply side, the spot market, and CMIES. Based on this, the first objective function is established as follows: max F ESO =f cmies +f sm -f e,b Where: F ESO is the total benefit of ESO; f cmies is the electricity sales revenue of ESO to CMIES, f sm is the income from ESO and spot market transactions, f e,b The energy purchase cost of ESO on the energy supply side; The first constraint conditions include price constraints, transmission power constraints, upper and lower power limit constraints on the energy supply side, and power balance constraints.

4. The master-slave game optimization scheduling method for the integrated energy system of a mining area under the electricity-carbon-green certificate joint market according to claim 3 is characterized in that: The ESO's electricity sales revenue to CMIES is expressed as: Where: are the thermal power price, green power price and gas price of ESO to CMIES at time t respectively; G u,t They are the system's purchased thermal power, purchased green power, and purchased gas at time t. The profit obtained from the ESO and spot market transactions is expressed as: In the formula, They are ESO’s thermal power trading revenue, green power trading revenue and gas trading revenue in the spot market; The energy purchase cost of the ESO on the energy supply side is expressed as: f e,b =f tp +f gp +f gas Where: f tp 、f gp 、f gas They are respectively the thermal power purchasing cost, green electricity purchasing cost and gas purchasing cost of ESO on the energy supply side.

5. The master-slave game optimization scheduling method for the integrated energy system of a mining area under the electricity-carbon-green certificate joint market according to claim 1 is characterized in that: The second objective function is expressed as: minF CMIES =f cmies +f c +f ope In the formula, F CMIES is the operating cost of the integrated energy system in the mining area, f cmies is the electricity sales revenue of ESO to CMIES, f c 、f ope They are electricity-carbon-green certificate incentive costs and equipment operation and maintenance costs; The second constraint conditions include: energy transmission constraint, electric power balance constraint, thermal power balance constraint, and cold power balance constraint.

6. The master-slave game optimization scheduling method for the integrated energy system of the mining area under the electricity-carbon-green certificate joint market according to claim 5 is characterized in that: The profit obtained from the ESO and spot market transactions is expressed as: In the formula, They are ESO’s thermal power trading revenue, green power trading revenue and gas trading revenue in the spot market; The electricity-carbon-green certificate incentive cost is expressed as: In the formula, f CE,t 、f GC,t The tiered carbon trading costs and tiered green certificate trading costs of the integrated energy system in mining areas are respectively; The equipment operation and maintenance cost is expressed as: In the formula, α i , P i,t are the unit maintenance cost and output power of equipment i at time t respectively; equipment i refers to the energy conversion equipment and energy storage equipment in the comprehensive energy system of the mining area.

7. The master-slave game optimization scheduling method for the integrated energy system of a mining area under the electricity-carbon-green certificate joint market according to claim 1 is characterized in that: In the solution, the upper-level energy operator revenue model uses genetic algorithms to give the energy purchase price of the mining area integrated energy system, while the lower-level mining area integrated energy system cost model uses CPLEX to solve its energy demand.

8. A master-slave game optimization scheduling system for the integrated energy system of mining areas under the electricity-carbon-green certificate joint market, characterized in that: It includes a module for executing the master-slave game optimization scheduling method for the integrated energy system of a mining area under the electricity-carbon-green certificate joint market as described in any one of claims 1 to 7.

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