Dies master-slave game optimization scheduling method considering carbon emission tax and demand response

Abstract

The application discloses a DIES master-slave game optimization scheduling method considering carbon emission tax and demand response, and belongs to the field of optimization scheduling. Firstly, a refined DIES architecture is constructed, on the basis of which, a master-slave game model of a carbon emission tax mechanism is analyzed, and the energy use characteristics of the user side are considered. Secondly, a master-slave game optimization scheduling model considering carbon emission tax and DR is constructed with the sum of tax cost and production cost being taken as an objective function. Then, the model is linearized by using KKT optimal conditions, and Yamilp / Cplex is used for solving. Finally, example analysis is carried out, and the result shows that the strategy can effectively take into account the low carbon nature and the economy of system operation.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of optimal scheduling, and particularly relates to a DIES master-slave game optimal scheduling method considering carbon emission tax and demand response. BACKGROUND

[0002] Under the background of "carbon peak and carbon neutral", solving the problem of low-carbon economic operation in a district integrated energy system (DIES) and giving full play to the complementary advantages of multi-energy flow are effective ways to achieve low-carbon operation.

[0003] In recent years, with the gradual adjustment of the national energy trading market, building a low-carbon economy and a safe and efficient new generation of smart grid has become a top priority of current research. The district integrated energy system (DIES) centered on combined cooling heating and power (CCHP) is an important link to improve the energy cascade utilization rate and sustainable development of green economy, and has become one of the mainstream new models of energy efficient utilization in the world.

[0004] At present, there have been a large number of studies on the low-carbon operation of DIES, which can be divided into two categories in general. One is to directly tax the carbon emissions generated by the system, and the other is to trade the carbon emissions generated by the system under the current carbon trading market system. Although the proposed carbon trading mechanism directly targets carbon emissions, as a kind of artificially designed and controlled market, it has expensive management costs and potential moral risks. Compared with carbon trading, the carbon emission tax proposed in this paper has relatively low management and operation costs; moreover, as one of the sources of government tax revenue, it is relatively stable and convenient for related enterprises to do a good job in emission reduction, and in addition, it can also increase government revenue, so as to be used to invest in the development of new emission reduction technologies.

[0005] Based on this, in order to further deepen the reform of the energy market and promote the consumption of new energy, the DIES is flexibly regulated and controlled through user-side demand response (DR) on the basis of considering carbon emission tax. The single electric load DR model is extended to cold and heat load, realizing flexible operation of the system with multi-energy complementation. The proposed scheduling method combining carbon emission with demand response can improve the energy cascade utilization rate and effectively limit the carbon emissions, but it does not solve the problem of conflicting interests of system subjects and the requirement of distributed scheduling strategy.

[0006] The reform of energy market provides conditions for the influx of a large number of emerging subjects, and the idea of game theory can well deal with the problem of conflict of interests of each subject. A RIES multi-agent master-slave game optimization based on carbon trading mechanism is proposed, but the interaction between the user side energy characteristics and the energy market is not considered. In view of the above, the combination of carbon emission tax, master-slave game and energy market has not been reported, therefore, the establishment of DIES optimal scheduling strategy which can play the complementary advantages of the three is the key to be solved in current research.

[0007] Therefore, a master-slave game optimization scheduling strategy considering carbon emission tax and DR is proposed. Firstly, a refined DIES multi-energy complementary architecture is constructed, on the basis of which, the master-slave game model of carbon emission tax mechanism is analyzed, and the energy characteristics of the user side are considered. Secondly, a master-slave game optimization scheduling model considering carbon emission tax and DR is established with the sum of tax cost and production cost as the objective function. Then, the model is linearized by using KKT optimal condition, and is solved by using target cascade analysis method combined with Cplex toolbox. Finally, the low-carbon and economic nature of the proposed strategy is verified by example results. SUMMARY

[0008] In view of the above technical problems existing in the prior art, the present application proposes a DIES master-slave game optimization scheduling method considering carbon emission tax and demand response, which is reasonable in design, overcomes the shortcomings of the prior art, and has good effects.

[0009] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0010] A DIES master-slave game optimization scheduling method considering carbon emission tax and demand response, comprising the following steps:

[0011] Step 1: Constructing a regional integrated energy system architecture, taking the direct tax of carbon emissions of government agencies as the upper leader, and taking the operation plan of integrated energy suppliers based on carbon emission tax and user side DR as the lower follower, a double-layer optimization master-slave game model based on carbon emission tax mechanism is established;

[0012] Step 2: Constructing a master-slave game optimization scheduling model considering carbon emission tax and user side demand response with the sum of tax cost and production cost as the objective function;

[0013] Step 3: Linearizing the master-slave game optimization scheduling model by using KKT (Karush-Kuhn-Tucker, optimal condition), and solving the model by using Yamilp / Cplex (mathematical double-layer programming solution tool);

[0014] Step 4: Example analysis.

[0015] Preferably, characterized in that: the regional integrated energy system comprises an input side unit, a coupling point unit and an output side unit;

[0016] The input side unit comprises a power grid and a gas grid;

[0017] The coupling point unit comprises a wind turbine, a P2G device, a CCHP combined unit, a coupling device and an energy storage device;

[0018] The wind turbine is configured to convert excess wind energy into electrical energy;

[0019] The P2G device is configured to realize the bidirectional coupling between electricity and gas, and enhance the coupling level between the power grid and the natural gas grid;

[0020] The CCHP combined unit comprises a gas turbine, a waste heat boiler and a gas boiler;

[0021] The coupling device comprises a cold storage air conditioner and an absorption chiller;

[0022] The energy storage device comprises a battery and a heat storage device;

[0023] The output side unit comprises a cold load, a heat load and an electrical load;

[0024] The input side unit and the wind turbine serve as the energy supply side to supply electricity and gas; the coupling device, the CCHP combined unit and the P2G device serve to convert energy and improve the cascade utilization rate of energy; the output side unit serves as the energy consumption side to meet the multi-energy demand of the user cluster; the energy storage device stores energy during the energy consumption valley period and releases energy during the energy consumption peak period, thereby playing a positive role in "peak load shifting"; the input side unit and the output side unit are connected through an energy tie line, and the CCHP combined unit, the coupling device, the P2G device and the energy storage device are connected to the energy tie line for energy conversion, thereby realizing the coupling between multi-energy flows.

[0025] Preferably, the mathematical model of the P2G device is:

[0026]

[0027] In the formula: P P2G (t), H P2G (t), P H2 (t), F P2G (t) are the electrical energy input, the thermal energy output, the hydrogen energy output and the natural gas flow of the P2G device, respectively; η EL , η' EL are the input and output energy conversion efficiencies, respectively; R LHV is the low heat value of natural gas.

[0028] Preferably, the mathematical model of the gas turbine of the CCHP cogeneration unit is:

[0029]

[0030] wherein a, b, c are the fuel consumption coefficient and the GT start-stop cost coefficient respectively; P GT (t) is the electrical energy output by the GT; G GT (t) is the gas energy consumed by the GT; H GT (t) is the thermal energy output by the GT; η GT is the energy conversion efficiency;

[0031] The mathematical model of the waste heat boiler of the CCHP cogeneration unit is:

[0032]

[0033] wherein H WHB (t) is the thermal energy output by the WHB; η h , η WHB are the heat loss rate and the heat recovery rate;

[0034] The mathematical model of the gas boiler of the CCHP cogeneration unit is:

[0035] H GB (t) is the thermal energy output by the GB; G GB (t) is the gas energy input by the GB; η GB (5).

[0036] wherein H GB (t), G GB (t) are the thermal energy output by the GB and the gas energy input by the GB respectively; η GB is the heat production efficiency.

[0037] Preferably, the mathematical model of the cold storage air conditioner and the absorption refrigeration machine is:

[0038]

[0039] wherein Q AC (t) is the cold energy output by the AC; P AC (t) is the electrical energy input by the AC; Q AR (t) is the cold energy output by the AR; H AR (t) is the thermal energy input by the AR; η AC , η AR are the refrigeration efficiency;

[0040] The mathematical model of the battery is:

[0041]

[0042] wherein SOC tis the state of charge of the BT; Δt is the duration; P BT,ch (t), P BT,dis (t) is the charge-discharge energy of the BT; η BT,ch , η BT,dis is the charge-discharge efficiency.

[0043] Preferably, in step 1, the carbon emission tax mechanism is a mechanism in which a government agency directly pays tax according to the carbon emission amount, with the purpose of limiting the carbon emission amount of a certain DIES within a certain range while ensuring the maximum benefit; therefore, the single leader-single follower game provides a reasonable modeling and analysis method for describing the energy transaction between the government department and the DIES; since the government agency and the comprehensive energy seller are relatively independent and mutually restrictive, a double-layer optimization leader-follower game model is established, in which the government agency is the upper leader and the comprehensive energy seller is the lower follower, and the user-side DR behavior is considered;

[0044] The double-layer optimization leader-follower game model includes an upper leader model, a lower follower model and a user-side DR model;

[0045] The upper leader model;

[0046] The government agency formulates a reasonable carbon emission tax rate for the carbon emission amount, so as to achieve the goal of controlling the carbon emission amount within the allowable range at the minimum tax cost, and the objective function is:

[0047]

[0048] In the formula, S i is the capacity of unit i; α i is the carbon emission tax rate formulated by the government agency;

[0049] In order to avoid the increase of the total carbon emission amount due to the decrease of the carbon emission limit, it is also necessary to constrain it, which is expressed as:

[0050]

[0051] In the formula, E P is the total allowable carbon emission amount; i is a unit; j is a load section; ΔT j is the duration of the jth load section; e i is the carbon emission coefficient; P ij is the power generation amount of the comprehensive energy seller;

[0052] The lower follower model;

[0053] The comprehensive energy seller formulates a power selling plan on the basis of the government agency and the user side, with the minimum carbon emission production cost as the objective function, which is expressed as:

[0054]

[0055] P (t) is the power output of the equipment at time t; P ij ∈argmin is the optimal solution of the lower-level optimization problem; b i is the equivalent generation cost of unit i;

[0056] In the lower-level Follower model, the seller and the power supply or heat supply company obtain the energy selling priority of the user side through bidding, thereby forming an internal energy price optimization result. To avoid direct transactions between the user side and the power supply or heat supply company, the selling price of the seller needs to be slightly lower than the market price. Therefore, the constraint condition of the lower-level optimization problem is:

[0057]

[0058] P (t) is the power output of the equipment at time t; P i m P (t) is the power output of the equipment at time t; P i n P (t) is the power output of the equipment at time t; P μ j is the dual variable of the scheduling problem; D j is the power demand; c g,b (t) and c g,s (t) are the grid-connected and power selling prices at time t; c e,b (t) and c e,s (t) are the prices of selling to the user side and purchasing from the upper-level power grid at time t; c h,b (t) and c h,s (t) are the prices of selling to the user side and purchasing from the upper-level heat grid at time t; c h,min (t) is the lowest price limit of thermal energy; c h,max (t) is the highest price limit of thermal energy;

[0059] In addition, the upper-level power grid power purchase constraint, the upper-level heat grid heat purchase constraint, the upper and lower limits of the power output of each unit, and the energy selling constraint are:

[0060]

[0061] P (t) is the power output of the equipment at time t; P e (t) is the power purchase amount of the seller from the upper-level power grid; P h (t) is the heat purchase amount of the seller from the upper-level heat grid; P d (t) is the power output of the equipment at time t; P is the selling price of k type energy; P e,max (t) is the upper limit of the power purchase amount from the upper-level power grid; P h,max (t) is the upper limit of the heat purchase amount from the upper-level heat grid; P d,min (t) and P d,max (t) are the upper and lower limits of the power output of the equipment; upper limit of the electricity price for the seller; upper limit of the heat price for the seller;

[0062] user-side DR model;

[0063] The actual load of electricity, heat, and cold energy of the user-side DR is:

[0064]

[0065] L (t) = L (t) + L (t) + L (t) (1) j,e,R L (t), L (t), L (t) are the actual electricity, heat, and cold load at time t, respectively; j,h,R L (t), L (t), L (t) are the actual electricity, heat, and cold load at time t, respectively; j,c,R L (t), L (t), L (t) are the basic electricity, heat, and cold load, respectively; j,E L (t), L (t), L (t) are the basic electricity, heat, and cold load, respectively; j,H L (t), L (t), L (t) are the basic electricity, heat, and cold load, respectively; j,C L (t), L (t), L (t) are the basic electricity, heat, and cold load, respectively; j,E,DR L (t), L (t), L (t) are the electricity, heat, and cold demand response load, respectively; j,h L (t), L (t), L (t) are the electricity, heat, and cold demand response load, respectively; j,c L (t), L (t), L (t) are the electricity, heat, and cold demand response load, respectively;

[0066] The user-side optimizes the load demand of electricity, heat, and cold flexibly on the basis of the price given by the seller; the maximum consumer surplus is taken as the objective function, which is expressed as:

[0067]

[0068]

[0069]

[0070] DI (t) = L (t) - L (t) (17) j,k DI (t) = L (t) - L (t) (17) j,k,R DI (t) = L (t) - L (t) (17) j,k,B DI (t) = L (t) - L (t) (17)

[0071] F (t) = L (t) - L (t) (17) j,user F (t) = L (t) - L (t) (17) j,k,R F (t) = L (t) - L (t) (17) j,k,B F (t) = L (t) - L (t) (17) k F (t) = L (t) - L (t) (17) j,user F (t) = L (t) - L (t) (17) j,waste F (t) = L (t) - L (t) (17) j,k F (t) = L (t) - L (t) (17) F (t) = L (t) - L (t) (17) j,k F (t) = L (t) - L (t) (17) j,k F (t) = L (t) - L (t) (17) j,k F (t) = L (t) - L (t) (17) K = {e, h, c}, where e, h, and c are the electricity, heat, and cold load.

[0072] Preferably, in step 2, the master-slave game optimization scheduling model includes a carbon emission tax direct tax model of a government agency, i.e., an upper leader, a carbon emission amount based operation plan model of a comprehensive energy seller, i.e., a lower follower, and a user side DR model;

[0073] The process of modeling the optimization scheduling model specifically includes the following steps:

[0074] Step 2.1: creating a target function;

[0075] The sum of the tax cost and the production cost is minimized as the target function, which is expressed as:

[0076] min F=F1+F2-F3 (18);

[0077]

[0078]

[0079]

[0080] In the formula, F, F1, F2 and F3 respectively represent the total cost, the tax cost, the production cost and the compensation cost of the user side DR load reduction; C DR is the unit compensation price; P k (t) is the k type load of the user side after DR; L k (t) is the k type load demand without DR;

[0081] Step 2.2: setting the operation constraint condition of the regional comprehensive energy system;

[0082] The operation constraint of the regional comprehensive energy system includes the energy balance constraint, the power upper and lower limit constraint, the unit ramp constraint and the energy storage device constraint;

[0083] The energy balance constraint includes the electric energy balance constraint, the thermal energy balance constraint and the cold energy balance constraint;

[0084] The power upper and lower limit constraint includes the electric power constraint, the thermal power constraint and the cold power constraint;

[0085] The expression form of the electric energy balance constraint is shown in formula (22):

[0086]

[0087] The expression form of the thermal energy balance constraint is shown in formula (23):

[0088]

[0089] The representation of the cold energy balance constraint is shown in equation (24):

[0090] Q AC (t) + Q AR (t) = L j,c,R (t) (24).

[0091] The representation of the electrical power constraint is shown in equation (25):

[0092]

[0093] wherein: P GT,max (t) is the upper limit of GT output electrical power; P AC,max (t) is the upper limit of AC input electrical power; P e,max (t), P e,min (t) are the upper and lower limits of electrical power exchanged with the grid, respectively; P BT,max (t), P BT,min (t) are the upper and lower limits of BT charge-discharge power, respectively; P P2G,max (t), P P2G,min (t) are the upper and lower limits of P2G device input electrical power, respectively;

[0094] The representation of the thermal power constraint is shown in equation (26):

[0095]

[0096] wherein: H GB,max (t) is the upper limit of GB output thermal power; H WHB,max (t) is the upper limit of WHB output thermal power; P HSD,max (t), P HSD,min (t) are the upper and lower limits of HSD charge-discharge thermal power, respectively;

[0097] The representation of the cold power constraint is shown in equation (27):

[0098]

[0099] wherein: Q AC,max (t) is the upper limit of AC output cold power; Q AR,max (t), Q AR,min (t) are the upper and lower limits of AR storage-release cold power, respectively;

[0100] The representation of the unit ramp constraint is shown in equation (28)

[0101]

[0102] wherein: R h,ui , R h,direspectively are the maximum up and down ramp rates of CCHP cogeneration unit i; R ui di respectively are the maximum and minimum ramp rates of thermal power unit i; respectively are the upper and lower limits of P2G device ramping constraints;

[0103] The expression of energy storage device constraints is shown in formula (29)

[0104]

[0105] In the formula: respectively are the upper and lower limits of BT state of charge; SOC T respectively are the initial and final state of charge of BT in the dispatching period; store,max store,min respectively are the upper and lower limits of HSD heat storage capacity; respectively are the initial and final heat storage capacity of HSD in the dispatching period; store,max store,min respectively are the upper and lower limits of AR cold storage capacity; respectively are the initial and final cold storage capacity of AR in the dispatching period.

[0106] Preferably, in step 3, the model is linearized by using KKT optimal conditions, specifically:

[0107] The Latin hypercube scenario method is used to process wind power uncertainty;

[0108] It is assumed that the wind power follows a normal distribution N(μ,δ 2 ), μ is the expected predicted value, and δ is the volatility rate; Specifically, the following steps are included:

[0109] Step S1: Latin hypercube scenario is used to generate wind power output scenarios;

[0110] Step S2: Kantorovich distance scenario reduction method is used for processing;

[0111] Step S3: Induction of reduced scenarios with the same probability.

[0112] Preferably, in S1, the Latin hypercube scenario method includes the following steps:

[0113] Let the number of scenarios generated by Latin hypercube sampling be N, and the number of reduced scenarios be n;

[0114] Step S11: Let the initial reduced scenario number be n*=N; the wind power output prediction probability is P k =1 / N;

[0115] ​​​Step S12: Calculate the Kantorovich distance X of each reduction scenario k (s i ,s j );

[0116] Step S13: Select the shortest scenario S k from the scenarios S r , and calculate the Kantorovich distance product P k (s i ,s j ) = X k (s i ,s j ) ε r ; ε r represents the shortest distance;

[0117] Step S14: The selected shortest scenario is recorded as y and deleted; the scenario r is updated as P r = P r + P y ;

[0118] Step S15: Determine whether the scenario needs to be updated;

[0119] If the scenario needs to be updated, execute Step S16;

[0120] If the scenario does not need to be updated, execute Step S12;

[0121] Step S16: Let n* = n.

[0122] Preferably, in Step 3, Yamilp / Cplex is used for solving, specifically including:

[0123] Step 3.1: Upper and lower sub-model solving;

[0124] The lower Follower model is a linear problem, which is replaced by the KKT optimal condition, so as to convert the principal-agent game into a nonlinear problem; the KKT condition of the lower scheduling model is expressed as:

[0125]

[0126] At the optimal solution of the above optimization problem, there are:

[0127]

[0128] Therefore, the objective function is written as:

[0129]

[0130] wherein, and For the complementary slackness constraint in KKT optimality condition, since this constraint does not satisfy Mangasarian-Fromovitz constraint qualification nor Slater regularity condition, it cannot be directly solved by nonlinear programming algorithm, so we introduce Boolean variable The complementary slackness constraint in formula (30) is converted into the following linear inequality constraint:

[0131]

[0132] In the formula: M is a large enough positive number; is the additional 0-1 variable introduced by linearized complementary slackness condition;

[0133] In summary, the master-slave game problem formula (8) ~ (12) is equivalent to the following mixed integer linear programming:

[0134]

[0135]

[0136] Step 3.2: User side model solving;

[0137] Since the user side model does not contain coupling constraints, each user optimization problem belongs to an independent convex programming problem, which can be solved independently without global information, and the KKT optimal condition of user side solving is:

[0138]

[0139] In the formula: is the KKT condition of user j optimization problem; τ j is the dual variable; is the user j purchased from the seller at t time; RTP t is the seller's offer at t time; Q j,all is the total energy demand; a D , b D are the intercept and slope of the energy supply company offer curve respectively; D t represents the duration of t period.

[0140] Step 3.3: solving the unique optimal solution of game;

[0141] In the game model, the government agency is the upper Leader, whose strategy set is the carbon emission tax rate {α i} and has been given, and its objective function is a continuous convex function according to formula (8); the seller is the lower Follower, whose strategy set is the power generation {P i m ,P in};By KKT condition transformation, the lower optimization problem becomes a strictly convex optimization problem, at this time, the system after transformation by KKT optimal condition and the system after Nash game are equivalent, but there are many optimal solutions that meet the KKT system, in order to make the lower optimization problem have a unique optimal solution, the alldifferent command provided by Yamilp toolbox is used to make the power generation cost coefficient b i +α i e i Different, so that the lower optimization problem has a unique optimal solution; since at the equilibrium, the interests of government agencies, sellers and users are simultaneously optimal, any party cannot benefit by changing the strategy unilaterally, so when the lower optimization problem has only one optimal solution, the entire game model has a unique optimal solution.

[0142] The beneficial technical effects brought by the application are as follows:

[0143] Based on Stackelberg game theory, the application proposes a DIES master-slave game optimization strategy considering carbon emission tax mechanism and user side DR, takes government agencies as leaders and comprehensive energy sellers as followers, obtains game equilibrium solution thereof, and realizes collaborative optimization operation of the system.

[0144] (1) The master-slave game model based on the carbon emission tax mechanism can well reflect the interaction relationship between policy makers (government agencies) and dispatch participants (comprehensive energy sellers); the model obtains an optimal solution through linearization, that is, corresponds to Stackelberg game equilibrium, so the model can take into account optimal tax rate setting of government agencies, optimal dispatch scheme of comprehensive energy sellers and optimal energy purchasing plan of users.

[0145] (2) The user side DR is introduced in the model, and the relationship between user side energy purchasing demand and energy price is considered in detail, so that the energy use characteristics and dispatchable characteristics of users in daily life can be more accurately described, and the flexibility of user side DR can be fully utilized.

[0146] (3) Unlike traditional economic dispatch means that cannot directly realize quantitative control on control targets, the strategy gives a limit of actual carbon emission of the system, and analyzes the influence of five carbon tax rates on DIES dispatch results; simulation results show that the strategy can not only effectively reduce system carbon emission, but also take into account the interests of system subjects, and realizes win-win of low carbon and economy of DIES. BRIEF DESCRIPTION OF DRAWINGS

[0147] Figure 1 It is a structural diagram of a regional comprehensive energy system;

[0148] Figure 2 Structure diagram of master-slave game model

[0149] Figure 3 Schematic diagram of DR peak shaving

[0150] Figure 4 Flow chart of Latin hypercube scenario method

[0151] Figure 5 Schematic diagram of various power load prediction curves

[0152] Figure 6 Schematic diagram of carbon emission tax system setting results

[0153] Figure 6 (a) in which is a schematic diagram of carbon emission level setting under different tax rates Figure 6 (b) in which is a carbon emission control effect diagram Figure 6 (c) in which is a generator set output diagram

[0154] Figure 7 Load curve diagram before and after demand response

[0155] Figure 7 (a) in which is an electrical load curve diagram before and after demand response Figure 7 (b) in which is a thermal load curve diagram before and after demand response Figure 7 (c) in which is a cold load curve diagram before and after demand response

[0156] Figure 8 Profit curve diagram of master-slave game

[0157] Figure 8 (a) in which is a profit curve diagram of upper layer Leader Figure 8 (b) in which is a profit curve diagram of lower layer Follower Figure 8 (c) in which is a profit curve diagram of user side

[0158] Figure 9 Internal electricity and heat price optimization result diagram

[0159] Figure 9 (a) in which is an internal electricity price optimization result diagram Figure 9 (b) in which is an internal heat price optimization result diagram

[0160] Figure 10 Optimization result diagram of energy balance of each device

[0161] Figure 10 (a) in which is an electrical energy balance optimization result diagram Figure 10 (b) in which is a thermal energy balance optimization result diagram Figure 10 (c) in which is a cold energy balance optimization result diagram. Detailed Implementation

[0162] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0163] This paper proposes a master-slave game-theoretic optimal scheduling strategy that considers carbon emission tax and energy consumption reduction (DR). First, a refined DIES multi-energy complementary architecture is constructed. Based on this, the master-slave game model of the carbon emission tax mechanism is analyzed, taking into account the energy consumption characteristics of the user side. Second, a master-slave game-theoretic optimal scheduling model considering carbon emission tax and DR is established with the objective function of minimizing the sum of tax costs and production costs. Then, the model is linearized using KKT optimal conditions, and the objective cascade analysis method combined with the Cplex toolbox is used for solution. Finally, numerical examples demonstrate the low-carbon and economical nature of the proposed strategy.

[0164] 1DIES architecture

[0165] DIES architecture, such as Figure 1 As shown, the input side includes the power grid and gas grid; the coupling points include wind turbine (WT), P2G unit and CCHP cogeneration unit. The core unit of this DIES mainly includes gas turbine (GT), waste heat boiler (WHB), absorption refrigerator (AR), gas boiler (GB), storage air-conditioners (AC), battery (BT) and heat storage device (HSD); the output side includes cold, heat and electricity loads.

[0166] 1.1 Wind turbine

[0167] To improve the utilization rate of renewable energy, wind turbines are used to convert excess wind energy into electricity, achieving the goals of green energy conservation and emission reduction. The mathematical model is as follows:

[0168]

[0169] In the formula: P WT 、P' WT These are the output power and rated power of WT, respectively; V, V i V0, V f These are the predicted speed, cut-in speed, cut-off speed, and rated speed, respectively.

[0170] 1.2P2G device

[0171] P2G devices consume electrical energy, but unlike traditional unidirectional electro-gas coupling centered on gas turbines, they achieve bidirectional coupling between electricity and gas, enhancing the coupling level between the power grid and the natural gas grid. This plays a crucial role in improving the absorption of renewable energy. Its mathematical model is as follows:

[0172]

[0173] In the formula: P P2G (t), H P2G (t), P H2 (t), F P2G (t) represents the input electrical energy, output thermal energy, hydrogen energy, and natural gas flow rate of the P2G unit, respectively; η EL η' EL These represent the input and output energy conversion efficiencies, respectively; R LHV It has a low calorific value for natural gas, typically 9.7 kM·h / m³. 3 .

[0174] 1.3CCHP cogeneration unit

[0175] (1) The mathematical model of the gas turbine is:

[0176]

[0177] In the formula: a, b, and c are the fuel consumption coefficient and GT start-stop cost coefficient, respectively; P GT (t), G GT (t), H GT (t) represents the electrical energy output by GT, the gas energy consumed, and the heat energy output, respectively; η GT Energy conversion efficiency.

[0178] (2) The mathematical model of the waste heat boiler is:

[0179]

[0180] Where: H WHB (t) represents the thermal energy output by WHB; η h η WHB For heat loss rate and heat recovery rate.

[0181] (3) The mathematical model of the gas-fired boiler is:

[0182] H GB (t)=G GB (t)η GB (5);

[0183] Where: H GB (t), G GB (t) represents the thermal energy output and the gas energy input from GB, respectively; η GBFor heat generation efficiency.

[0184] 1.4 Coupling Device

[0185] The mathematical models for cold storage air conditioners and absorption chillers are as follows:

[0186]

[0187] In the formula: Q AC (t), P AC (t) represents the cooling energy output from the AC and the electrical energy input, respectively; Q AR (t), H AR (t) represents the cold energy output and the heat energy input by the AR, respectively; η AC η AR For cooling efficiency.

[0188] 1.5 Energy Storage Device

[0189] Since the three energy storage processes are similar, this article will only take the battery as an example, and the heat storage and cold storage devices will not be described in detail. Their mathematical models are as follows:

[0190]

[0191] Where: SOC t The charge state of BT is Δt; the duration is P. BT,ch (t), P BT,dis (t) represents the charging and discharging energy of BT; η BT,ch η BT,dis This refers to the charge / discharge efficiency.

[0192] 2. Master-Slave Game Theory Based on Carbon Emission Tax Mechanism

[0193] The carbon emission tax mechanism is a system where government agencies directly pay taxes based on carbon emissions. Its purpose is to maximize profits while limiting the carbon emissions of a particular energy-distributing industry (DIES) within a certain range. Therefore, a single-leader / single-follower game theory model provides a reasonable method for modeling and analyzing energy transactions between government agencies and DIES. Since government agencies and integrated energy distributors are both relatively independent and mutually constraining, a leader-follower game model is established, with the government agency as the upper-level leader and the integrated energy distributor as the lower-level follower, while also considering user-side energy consumption (DR) behavior. Figure 2 As shown.

[0194] 2.1 Upper-level leader model

[0195] Government agencies aim to control carbon emissions within permissible limits with minimal tax costs by setting reasonable carbon emission tax rates. The objective function is as follows:

[0196]

[0197] S = ∑i∈I αi i αi is the capacity of unit i; α is the carbon emission tax rate set by government agency. i αi is the capacity of unit i; α is the carbon emission tax rate set by government agency.

[0198] To avoid the total carbon emission increasing due to the decrease of carbon emission limit, it also needs to be constrained, which can be expressed as:

[0199]

[0200] E = ∑i∈I αi P E is the total allowable carbon emission; i is the unit; j is the load segment; ΔT is the time interval. j Tj is the duration of the jth load segment; e is the carbon emission coefficient; P is the power demand. i Tj is the duration of the jth load segment; e is the carbon emission coefficient; P is the power demand. ij P is the power generation of integrated energy seller.

[0201] 2.2 Lower Follower Model

[0202] The integrated energy seller formulates the energy sale plan based on the government agency and user side, with the minimum carbon emission production cost as the objective function, which can be expressed as:

[0203]

[0204] P ∈ argmin is the optimal solution of lower optimization problem; b is the equivalent generation cost of unit i. ij P ∈ argmin is the optimal solution of lower optimization problem; b is the equivalent generation cost of unit i. i P ∈ argmin is the optimal solution of lower optimization problem; b is the equivalent generation cost of unit i.

[0205] In the lower Follower model, the seller and the power (heat) company obtain the user side's energy sale priority through bidding, thus forming an internal energy price optimization result. To avoid the user side directly trading with the power (heat) company, the seller's sale price needs to be slightly lower than the market price, so the constraint condition of lower optimization problem is:

[0206]

[0207] P ∈ argmin is the optimal solution of lower optimization problem; b is the equivalent generation cost of unit i. i m P ∈ argmin is the optimal solution of lower optimization problem; b is the equivalent generation cost of unit i. i n Pmin and Pmax are the minimum and maximum output power of unit, respectively; μ is the dual variable of scheduling problem; D is the power demand; c j μ is the dual variable of scheduling problem; D is the power demand; c j μ is the dual variable of scheduling problem; D is the power demand; c g,b P(t) and c g,s P(t) and c e,b P(t) and c e,s(t) is the electricity price sold to the user side and purchased from the superior grid at time t; c h,b (t) and c h,s (t) is the heat price sold to the user side and purchased from the superior grid at time t; c h,min (t) and c h,max (t) is the lowest (highest) price limit of heat energy.

[0208] In addition, the electricity (heat) purchase constraint from the superior grid, the upper and lower output constraints of each unit, and the energy sale constraint are:

[0209]

[0210] In the formula: P e (t) and P h (t) is the electricity (heat) purchase amount of the seller from the superior grid; P d (t) is the output of the device; is the sale price of k type energy; P e,max (t) and P h,max (t) is the upper limit of the electricity (heat) purchase amount from the superior grid; P d,min (t) and P d,max (t) is the upper and lower limit of the output of the device; and are the upper and lower limits of the electricity (heat) sale price of the seller.

[0211] 2.3 User side DR model

[0212] In this paper, the user side DR behavior is considered based on the master-slave game model based on the carbon emission tax mechanism. Since there is an economic agreement between the user side and the seller, and the user side can flexibly choose different energy supply modes according to its own needs during the energy use period, this paper only considers the interruptible, translatable and reducible load, and the basic load does not participate in DR. The DR reduction energy peak price is shown as Figure 3

[0213] From Figure 3 It can be seen that the benefit of DR is very obvious, DR has positive externality, and government agencies or third-party sellers can participate in it. The actual load of electricity, heat and cold energy is:

[0214]

[0215] In the formula: L j,e,R (t), L j,h,R (t), L j,c,R (t) are the actual electricity, heat and cold load at time t; L j,E (t), L j,H (t), L j,C ​(t) are the basic electricity, heat, and cooling loads, respectively; L j,E,DR (t), H j,h (t), C j,c (t) are the electricity, heat, and cooling demand response loads, respectively.

[0216] The user side optimizes the load demand of electricity, heat, and cooling flexibly based on the selling price given by the seller. The maximum consumer surplus is taken as the objective function, which can be expressed as:

[0217]

[0218]

[0219]

[0220] DI j,k (t) = L j,k,R (t) - L j,k,B (t) (17).

[0221] In the formula: F j,user is the comprehensive benefit of the seller; L j,k,R (t), L j,k,B (t), ζ k (t) are the actual load, baseline load, and price of k type energy at time t, respectively; A j,user (t), B j,waste (t) are the user energy benefit and loss, respectively; ω j,k and is the energy use preference constant; λ j,k and δ j,k are the energy use loss parameters of k type energy; DI j,k (t) is the actual loss amount of energy use at time t. K is a set, K = {e, h, c}, where e, h, and c are the electricity, heat, and cooling loads.

[0222] 3. DIES master-slave game optimization scheduling model considering carbon emission tax and DR

[0223] Considering that the carbon emission tax mechanism in DIES and the energy use characteristics of the user side can both be used as resources to improve the flexible operation of the system, this paper constructs a DIES master-slave game optimization scheduling model considering carbon emission tax and DR.

[0224] 3.1 Objective function

[0225] The DIES operation optimization scheduling in this paper takes the sum of tax cost and production cost as the objective function, which can be expressed as:

[0226] min F = F1 + F2 - F3 (18).

[0227]

[0228]

[0229]

[0230] In the formula: F, F1, F2, and F3 represent the total cost, tax cost, production cost, and compensation cost for load reduction during user-side DR, respectively; C DR The unit compensation price; P k (t) represents the user-side load type k after DR; L k (t) represents the load demand of type k without DR.

[0231] 3.2 System Operating Constraints

[0232] 3.2.1 Energy Balance Constraints

[0233] (1) The energy balance constraint is:

[0234]

[0235] (2) The thermal energy balance constraint is:

[0236]

[0237] (3) The cold energy balance constraint is:

[0238] Q AC (t)+Q AR (t)=L j,c,R (t)(24);

[0239] 3.2.2 Power Upper and Lower Limit Constraints

[0240] (1) The power constraint is:

[0241]

[0242] In the formula: P GT,max (t) represents the upper limit of the GT's output power; P AC,max (t) represents the upper limit of AC input power; P e,max (t), P e,min (t) represents the upper and lower limits of the power exchanged with the power grid; P BT,max (t), P BT,min (t) represents the upper and lower limits of BT's charging and discharging power; P P2G,max (t), P P2G,min (t) represents the upper and lower limits of the input electrical power of the P2G device.

[0243] (2) The thermal power constraint is:

[0244]

[0245] H GB,max (t) is the upper limit of the WHB output thermal power; P WHB,max (t) is the upper limit of the WHB output thermal power; P HSD,max (t), P HSD,min (t) is the upper and lower limits of the HSD charge and discharge thermal power.

[0246] (3) The cold power constraint is:

[0247]

[0248] Q AC,max (t) is the upper limit of the AC output cold power; Q AR,max (t), Q AR,min (t) is the upper and lower limits of the AR charge and discharge cold power.

[0249] 3.2.3 Unit ramping constraint

[0250]

[0251] R h,ui , R h,di is the maximum upward (downward) ramping rate of the CCHP cogeneration unit i; R ui , R di is the maximum (minimum) ramping rate of the thermal power unit i; is the upper and lower limits of the P2G device ramping constraint.

[0252] 3.2.4 Energy storage device constraint

[0253]

[0254] SOC is the upper and lower limits of the BT state of charge; SOC T is the initial and final state of charge of the BT in the scheduling period; H store,max , H store,min is the upper limit of the HSD heat storage capacity; is the initial and final heat storage amount of the HSD in the scheduling period; C store,max , C store,min is the upper limit of the AR cold storage capacity; is the initial and final cold storage amount of the AR in the scheduling period.

[0255] 4 Model processing and solving

[0256] 4.1 Wind power uncertainty processing

[0257] The Latin hypercube scenario method is used to process the wind power uncertainty. It is assumed that the wind power is subject to a normal distribution N(μ,δ2 ), μ is the expected prediction value, and δ is its volatility. First, the Latin hypercube sampling is used to generate the scenarios of wind power output, and then the scenario reduction method based on Kantorovich distance is used. Finally, the reduced scenarios with the same probability are summarized.

[0258] Let the number of scenarios generated by Latin hypercube sampling be N, and the number of reduced scenarios be n. The specific steps are as shown in Figure 4

[0259] 4.2 Master-slave game model solution

[0260] The optimal solution of the following optimization problem can be obtained by means of Yamilp / Cplex toolbox.

[0261] 4.2.1 Upper and lower sub-model solution

[0262] First, the lower Follower model is a linear problem, which is replaced by the KKT optimal condition, so that the master-slave game is converted into a traditional nonlinear problem. The KKT condition of the lower scheduling model can be expressed as:

[0263]

[0264] At the optimal solution of the above optimization problem, we have:

[0265]

[0266] Therefore, the objective function formula (8) can be written as:

[0267]

[0268] where and are the complementary slack constraints in the KKT optimal condition. Since this constraint does not satisfy the Mangasarian-Fromovitz constraint specification, nor does it satisfy the Slater regularization condition, it cannot be directly solved by a nonlinear programming algorithm, so a Boolean variable is introduced to convert the complementary slack constraint in formula (30) into the following linear inequality constraint:

[0269]

[0270] In the formula: M is a large enough positive number; is an additional 0-1 variable introduced by the linearized complementary slack condition.

[0271] In summary, the master-slave game problem formula (8)~(12) is equivalent to the following mixed integer linear programming:

[0272] ​

[0273]

[0274] 4.2.2 User-side model solution

[0275] Since the user-side model does not contain coupling constraints, each user optimization problem is an independent convex programming problem, which can be solved independently without global information. The KKT optimal condition of user-side solution is:

[0276]

[0277] In the formula: is the KKT condition of user j optimization problem; τ j is the dual variable; is the energy purchased by user j from the seller (energy supplier) at time t; RTP t is the price offered by the seller at time t; Q j,all is the total energy demand; a D , b D are the intercept and slope of the energy supplier price curve, respectively; D t represents the duration of the t period.

[0278] 4.2.3 Game unique optimal solution

[0279] In the Stackelberg game model of the present application, the government agency is the upper Leader, and its strategy set is the carbon emission tax rate {α i} and is given. As can be seen from formula (8), its objective function is a continuous convex function; the seller is the lower Follower, and its strategy set is the power generation {P i m ,P i n}. After transformation through the KKT condition, the lower optimization problem becomes a strictly convex optimization problem. At this time, the KKT system and the Nash game are equivalent, but there are many optimal solutions that satisfy the KKT system. In order to make the lower optimization problem have a unique optimal solution, the alldifferent command provided by the Yamilp toolbox is used to make the power generation cost coefficient b i +α i e i different, so that the lower optimization problem has a unique optimal solution. Since at the equilibrium, the interests of the government agency, the seller and the user side are simultaneously optimized, and any party cannot benefit by changing the strategy unilaterally, when the lower optimization problem has only one optimal solution, the entire game model has a unique optimal solution.

[0280] 5 Example analysis

[0281] 5.1 Basic data

[0282] The model and algorithm constructed in DIES are verified by using a 10-unit system. The parameters of each generator unit and the DIES equipment parameters are shown in Tables 1-2. Since the electricity and heat prices are volatile, the DIES energy transaction parameters are shown in Table 3. The winter typical day power load prediction curve is shown in Figure 5 The dispatch cycle is set to 24 hours a day, and 1 hour is set as a period in the dispatch cycle.

[0283] Table 1 Generator parameters of each unit

[0284]

[0285] Table 2 Equipment parameters

[0286]

[0287]

[0288] Table 3 Energy transaction parameters

[0289]

[0290] As can be seen from Table 1, due to the intensive effect, the larger the capacity of the unit, the lower the generation cost b i , but other costs are often higher than that of small-capacity units. As can be seen from Tables 3 and Figure 5 , the electricity load peaks appear from 10:00 to 13:00 and from 16:00 to 20:00, the heat load peaks appear from 20:00 to 23:00, and the wind curtailment is serious in the evening.

[0291] 5.2 Analysis of optimization results of carbon emission tax system

[0292] According to the data in Table 1, the economic benefit results obtained from formulas (10)-(17) are shown in Table 4. The carbon emission results obtained from formula (9) are shown in Table 5. According to the results, five different tax rate scenarios are set, and the setting of the carbon emission level coefficient of the 10 units, the carbon emission control effect, and the generator output under different tax rates are shown in Figure 6 .

[0293] Table 4 Solution results of economic benefit problem

[0294]

[0295] Table 5 Solution results of carbon emission problem

[0296]

[0297]

[0298] As can be seen from Tables 4 and 5, the minimum cost C required to complete the electricity production task without considering carbon emission taxes is... min It is 16.625 billion yuan, corresponding to a maximum carbon emission E max 11.087 million tons; the minimum carbon emissions required to complete the electricity production task without considering production costs. min The figure is 10.619 million tons, corresponding to a maximum production cost C. max The figure is 18.941 billion yuan. Taxes and production costs increase with the increase in tax rates. Due to the strict control of carbon emissions by government agencies, the carbon emission tax levied on energy-saving and environmentally friendly units is lower. Therefore, DIES will give priority to energy-saving and environmentally friendly units, while keeping the increase in the total production cost of the system within a reasonable range, thereby promoting the enthusiasm for implementing low-carbon scheduling of DIES.

[0299] Depend on Figure 6 It can be seen that, in order to reduce carbon emissions, generating units with higher carbon emission levels are usually subject to higher carbon emission taxes, and the two are positively correlated. The actual carbon emissions of each unit are always lower than expected, which indicates that the master-slave game model in this paper endows the carbon emission tax mechanism with an effective ability to regulate carbon emissions.

[0300] 5.3 Analysis of Demand Response Optimization Results

[0301] Based on the data simulation analysis in Table 3, the optimized DR preload and postload curves can be obtained as follows: Figure 7 As shown. By introducing load optimization (DR) between the user side and the vendor, the energy supply and consumption sides are incentivized to participate in load adjustment. Figure 7 (a) Taking this as an example, the change in the electrical load curve before and after DR can be clearly seen in the figure below. During the peak load periods of 10:00-13:00 and 16:00-20:00, the curve shifts backward, which plays a role in "peak shaving and valley filling" and smoothing load fluctuations. The analysis of heat and cold loads is similar to that of electrical loads.

[0302] 5.4 Analysis of Master-Slave Game Optimization Results

[0303] Based on the data in Tables 2 and 3, the proposed model is solved using the ATC algorithm. The payoff curve after game-theoretic optimization, the internal electricity (heat) price optimization results, and the scheduling optimization results are as follows: Figures 8-10 As shown. By Figure 8 It can be seen that the upper and lower layer models and the user-side revenue curve converged after 28 iterations. With the increase in the number of iterations, the target revenue continuously increases, verifying the good economic efficiency of the proposed strategy. Figure 10(a) For example, 10:00-13:00 and 16:00-20:00 time periods are power consumption peak periods, and power needs to be purchased from the upper power grid to supplement the insufficient power in addition to the power supplied by the GT, WT and BT; 7:00-10:00, 13:00-16:00, 20:00-23:00 time periods are stable periods of power consumption, and power is mainly supplied by the GT, WT and BT; and 23:00-7:00 time periods are power consumption valley periods, and the power demand is small, and the GT and WT supply the required power, and the remaining part is stored by the BT to be released in the power consumption peak period to improve the energy utilization rate. The analysis of heat and cold energy is similar to that of power.

[0304] Of course, the above description is not a limitation of the present application, and the present application is not limited to the above examples. Changes, modifications, additions or replacements made by those skilled in the art within the scope of the present application should also be within the protection scope of the present application.

Claims

1. A DIES master-slave game optimization scheduling method considering carbon emission tax and demand response, characterized in that: The method comprises the following steps: Step 1: constructing a regional integrated energy system architecture, taking direct taxation of carbon emissions by government agencies as an upper leader, and taking carbon emission tax and user-side DR as the basis for the integrated energy seller to develop an operation plan, taking the integrated energy seller as a lower follower, and establishing a double-layer optimization master-slave game model based on a carbon emission tax mechanism; Step 2: constructing a master-slave game optimization scheduling model considering carbon emission tax and user-side demand response, taking the sum of tax cost and production cost as the objective function; In step 2, the master-slave game optimization scheduling model comprises a direct taxation model of carbon emission tax by government agencies as an upper leader, an operation plan model of carbon emission amount by an integrated energy seller as a lower follower, and a user-side DR model; The process of modeling the optimization scheduling model specifically comprises the following steps: Step 2.1: creating an objective function; taking the sum of tax cost and production cost as the objective function, and being expressed as: min F = F1 + F2 - F3 (18); In the formula: F, F1, F2, F3 represent total cost, tax cost, production cost and compensation cost of user side load reduction during DR; C DR P is unit compensation price; k (t) is k type load of user side after DR; k (t) is k type load demand without DR; Step 2.2: setting regional integrated energy system operation constraints; the regional integrated energy system operation constraints comprise energy balance constraints, power upper and lower limit constraints, unit ramping constraints and energy storage device constraints; the energy balance constraints comprise electric energy balance constraints, thermal energy balance constraints and cold energy balance constraints; the power upper and lower limit constraints comprise electric power constraints, thermal power constraints and cold power constraints; the expression form of the electric energy balance constraints is shown in formula (22): the expression form of the thermal energy balance constraints is shown in formula (23): the expression form of the cold energy balance constraints is shown in formula (24): Q AC (t)+Q AR (t) = L j,c,R (t)(24) the expression form of the electric power constraints is shown in formula (25): In the formula, P GT,max (t) is the upper limit of the GT output electric power; P AC,max (t) is an AC input electrical power upper limit; P e,max (t), P e,min (t) are respectively the upper and lower limits of the electrical power exchanged with the grid; P BT,max (t), P BT,min (t) are respectively the upper and lower limits of the BT charging and discharging power; P P2G,max (t), P P2G,min (t) are respectively the upper and lower limits of the P2G device input electrical power; the expression form of the thermal power constraints is shown in formula (26): where: H GB,max (t) is the upper limit of the GB output thermal power; H WHB,max (t) is the upper limit of the WHB output thermal power; P HSD,max (t), P HSD,min (t) are the upper and lower limits of the HSD power, respectively. the expression form of the cold power constraints is shown in formula (27): where: Q AC,max (t) is the AC output cold power upper limit; Q AR,max (t), Q AR,min (t) are the AR storage and release cold power upper and lower limits, respectively; the expression form of the unit ramping constraints is shown in formula (28) wherein: R h,ui , R h,di are the maximum up and down ramp rates of the CCHP cogeneration unit i, respectively; ui , R di are the maximum and minimum ramp rates of the thermal power unit i, respectively; are the P2G device ramping constraints upper and lower limits, respectively; the expression form of the energy storage device constraints is shown in formula (29) wherein: BT SoC upper and lower limits, respectively; SOC T BT SoC at the beginning and end of the dispatch period, respectively; H store,max , H store,min HSD heat storage capacity upper and lower limits, respectively; HSD heat storage at the beginning and end of the dispatch period, respectively; C store,max , C store,min AR cold storage capacity upper and lower limits, respectively; AR cold storage at the beginning and end of the dispatch period, respectively; Step 3: linearizing the master-slave game optimization scheduling model by using KKT optimal conditions, and solving the model by using a mathematical double-layer planning solution tool; Step 4: performing example analysis.

2. The DIES master-slave game optimization scheduling method considering carbon emission tax and demand response according to claim 1, wherein: the regional integrated energy system comprises an input side unit, a coupling point unit and an output side unit; the input side unit comprises an electric grid and a gas grid; the coupling point unit comprises a wind turbine, a P2G device, a CCHP cogeneration unit, a coupling device and an energy storage device; the wind turbine is configured to convert excess wind energy into electric energy; the P2G device is configured to realize bidirectional coupling between electricity and gas, and enhance the coupling level between the electric grid and the natural gas grid; the CCHP cogeneration unit comprises a gas turbine, a waste heat boiler and a gas boiler; the coupling device comprises a cold storage air conditioner and an absorption refrigeration machine; the energy storage device comprises a battery and a heat storage device; the output side unit comprises a cold load, a heat load and an electric load. The input side unit and the wind turbine are used as the energy supply side to supply electricity and gas; the coupling device, the CCHP combined unit and the P2G device are used to convert energy and improve the cascade utilization rate of energy; the energy storage device stores energy in the energy consumption valley period and releases energy in the energy consumption peak period, thereby playing a positive role of "peak load shifting"; the output side unit is used as the energy consumption side to meet the multi-energy demand of the user cluster; the input side unit and the output side unit are connected through the energy tie line; the CCHP combined unit, the coupling device, the P2G device and the energy storage device are connected with the energy tie line to convert energy, so as to realize the coupling among the multi-energy flows.

3. The DIES master-slave game optimization scheduling method considering carbon emission tax and demand response according to claim 2, characterized in that: The mathematical model of the P2G device is: where: P P2G (t), H P2G (t), P H2 (t), F P2G (t) are the electrical energy input, the thermal energy output, the hydrogen energy and the natural gas flow rate of the P2G plant, respectively η EL , η' EL are the input and output energy conversion efficiencies, respectively; R LHV is the lower heating value of natural gas.

4. The DIES master-slave game optimization scheduling method considering carbon tax and demand response according to claim 2, characterized in that: The mathematical model of the gas turbine of the CCHP combined unit is: Where: a, b, c are the fuel consumption coefficient and GT start-stop cost coefficient respectively; P GT (t), G GT (t), H GT (t) are the GT output electric energy, consumed gas energy and output heat energy respectively η GT η is the energy conversion efficiency; The mathematical model of the waste heat boiler of the CCHP combined unit is: where: H WHB (t) is the thermal energy output by the WHB; η h , η WHB is the heat loss rate and heat recovery rate; The mathematical model of the gas boiler of the CCHP combined unit is: H GB (t) = G GB (t) η GB (5); wherein: H GB (t), G GB (t) is the thermal energy output by the GB and the gas energy input, respectively; η GB η is the thermal efficiency of the heat engine.

5. The DIES master-slave game optimization scheduling method considering carbon tax and demand response according to claim 2, characterized in that: The mathematical model of the cold storage air conditioner and the absorption refrigerating machine is: wherein: Q AC (t), P AC (t) is the cold energy output and the electric energy input, respectively; Q AR (t), H AR (t) is the cold energy output and the heat energy input, respectively; η AC , η AR is the refrigeration efficiency; The mathematical model of the battery is: wherein: SOC t is the state of charge of the BT; Δt is the duration; P BT,ch (t), P BT,dis (t) is the charge-discharge energy of the BT; η BT,ch , η BT,dis is the charge-discharge efficiency.

6. The DIES master-slave game optimization scheduling method considering carbon tax and demand response according to claim 2, characterized in that: In step 1, the carbon emission tax mechanism is a mechanism in which the government agency directly pays tax according to the carbon emission amount, and the purpose is to limit the carbon emission amount of a certain DIES within a certain range while ensuring the maximum benefit; therefore, the single master single slave game provides a reasonable modeling and analysis method for describing the energy transaction between the government department and the DIES; since the government agency and the integrated energy seller are both relatively independent and mutually restrictive, a double-layer optimization master-slave game model is established, in which the government agency is the upper leader and the integrated energy seller is the lower follower, and the user side DR behavior is considered; The double-layer optimization master-slave game model includes an upper leader model, a lower follower model and a user side DR model; The upper leader model; The government agency formulates a reasonable carbon emission tax rate for the carbon emission amount to achieve the goal of controlling the carbon emission amount within the allowable range at the minimum tax cost, and the objective function is: where: S i Ci is the capacity of the unit i; a i is the carbon emission tax rate set by the government agency; In order to avoid the increase of the total carbon emission amount due to the decrease of the carbon emission limit, it also needs to be constrained, which is expressed as: wherein: E P is the total amount of carbon emissions allowed; i is the unit; j is the load segment; ΔT j is the duration of the jth load segment; e i is the carbon emission factor; P ij is the power generation of the integrated energy seller; The lower follower model; The integrated energy seller formulates the energy selling plan based on the government agency and the user side, and takes the minimum carbon emission production cost as the objective function, which is expressed as: where: P ij ∈ argmin is the optimal solution of the lower-level optimization problem; b i is the equivalent generation cost of unit i; In the lower follower model, the seller and the power supply or heat supply company obtain the energy selling priority of the user side through bidding, thereby forming an internal energy price optimization result; in order to avoid the direct transaction between the user side and the power supply or heat supply company, it is necessary to ensure that the selling price of the seller is slightly lower than the market price, so the constraint condition of the lower optimization problem is: In the formula: P i m , P i n are the minimum and maximum output power of the unit, respectively; μ j is the dual variable of the scheduling problem; D j is the power demand; c g,b (t) and c g,s (t) are the on-grid and selling electricity price at time t, respectively; c e,b (t) and c e,s (t) are the selling price to the user side and the purchasing price of the superior power grid at time t, respectively; c h,b (t) and c h,s (t) are the selling price to the user side and the purchasing price of the superior heat grid at time t, respectively; c h,min (t) is the lowest price limit of thermal energy; c h,max (t) is the highest price limit of thermal energy; In addition, the power purchase quantity constraint from the upper power grid, the heat purchase quantity constraint from the upper heat grid, the output upper and lower limit constraints of each unit and the energy selling constraint are: where: P e (t) is the amount of electricity purchased by the vendor from the superior grid; P h (t) is the amount of heat purchased by the vendor from the superior heat grid; P d (t) is the output of the equipment; is the selling price of the kth energy; P e,max (t) is the upper limit of the amount of electricity purchased from the superior grid; P h,max (t) is the upper limit of the amount of heat purchased from the superior heat grid; P d,min (t) and P d,max (t) is the upper and lower limit of the output of the equipment; is the upper and lower limit of the electricity selling price of the vendor; is the upper and lower limit of the heat selling price of the vendor; The user side DR model; The actual load amount of electricity, heat and cold energy of the user side DR is: wherein: L j,e,R (t), L j,h,R (t), L j,c,R (t) are actual electric, thermal, cooling load at time t, respectively; L j,E (t), L j,H (t), L j,C (t) are basic electric, thermal, cooling load, respectively; L j,E,DR (t), H j,h (t), C j,c (t) are electric, thermal, cooling demand response load, respectively; The user side optimizes the load demand of electricity, heat and cold energy based on the selling price given by the seller; and takes the maximum consumer surplus as the objective function, which is expressed as: DI j,k (t) = L j,k,R (t) - L j,k,B (t) (17); wherein: F j,user is the overall benefit of the vendor; L j,k,R (t), L j,k,B (t), ζ k (t) is the actual load of the kth energy at time t, the baseline load and the price, respectively; A j,user (t), B j,waste (t) is the benefit and loss of the user's energy consumption, respectively; ω j,k and is the constant coefficient of energy consumption preference; λ j,k and δ j,k is the energy consumption loss parameter of the kth energy; DI j,k (t) is the actual loss of energy consumption at time t; K is a set, K = {e, h, c}, wherein e, h and c are electric, heat and cold loads.

7. The DIES master-slave game optimization scheduling method considering carbon tax and demand response according to claim 1, characterized in that: In step 3, the model is linearized by using the KKT optimal condition, which is specifically: Latin hypercube scenario method is used to deal with wind power uncertainty; Assume that the wind power obeys normal distribution N(μ,δ 2 ), μ is the expected predicted value, and δ is the volatility rate thereof; specifically comprising the following steps: Step S1: Latin hypercube scenario method is used to generate wind power output scenarios; Step S2: Scenario reduction method based on Kantorovich distance is used to deal with; Step S3: scenarios with the same probability are summarized.

8. The DIES master-slave game optimization scheduling method considering carbon tax and demand response according to claim 4, characterized in that: In S1, the Latin hypercube scenario method includes the following steps: Let the number of scenarios generated by Latin hypercube sampling be N, and the number of reduced scenarios be n; Step S11: Set the initial reduction scenario number as n* = N; the wind power output prediction probability as P k = 1 / N; Step S12: Calculate the Kantorovich distance X of each reduction scenario k (s i ,s j ); Step S13: Selecting the scene S k Shortest scene S r , calculating the Kantorovich distance with the scene product P k (s i ,s j ) = X k (s i ,s j ) ε r ; ε r represents the shortest distance; Step S14: the selected shortest scene is noted as y and deleted; scene r is updated as P r = P r + P y ; Step S15: determine whether the scenario needs to be updated; If the scenario needs to be updated, step S16 is executed; If the scenario does not need to be updated, step S12 is executed; Step S16: n*=n.

9. The DIES master-slave game optimization scheduling method considering carbon tax and demand response according to claim 1, characterized in that: In step 3, Yamilp / Cplex is used for solving, which specifically includes: Step 3.1: upper and lower sub-model solving; The lower Follower model is a linear problem, which is replaced by KKT optimal condition, so that the master-slave game is converted into a nonlinear problem; the KKT condition of the lower scheduling model is expressed as: At the optimal solution of the above optimization problem, we have: Therefore, the objective function is written as: where, and is the complement slackness constraint in KKT optimality conditions, which cannot be directly solved by nonlinear programming algorithms because it does not satisfy the Mangasarian-Fromovitz constraint qualification or the Slater regularity condition. Therefore, a Boolean variable is introduced to convert the complement slackness constraint in formula (30) into the following linear inequality constraints: where M is a sufficiently large positive number; is an additional 0-1 variable introduced by linearizing the complementary slackness condition In summary, the master-slave game problem (8)~(12) is equivalent to the following mixed integer linear programming: Step 3.2: user side model solving; Since the user side model does not contain coupling constraints, each user optimization problem belongs to an independent convex programming problem, which can be solved independently without global information, and the KKT optimal condition of user side solving is: where: KKT conditions for the optimization problem of user j; τ j Dual variable; RTP t Q j,all Total energy demand; a D , b D Intercept, slope of supply company offer curve; D t Duration of time period t; Step 3.3: solving the unique optimal solution of the game; In the game model, the government agency is the upper Leader, whose strategy set is carbon emission tax rate {α i} and has been given, and its objective function is a continuous convex function according to formula (8); the seller is the lower Follower, whose strategy set is power generation {P i m ,P i n}; After transformation by KKT condition, the lower optimization problem becomes a strictly convex optimization problem, at this time the system after transformation by KKT optimal condition and the system after Nash game are equivalent, but there are many optimal solutions satisfying KKT system, in order to make the lower optimization problem have a unique optimal solution, the alldifferent command provided by Yamilp toolbox is used to make the generation cost coefficient b i +α i e i be different, and thus the lower optimization problem has a unique optimal solution; since at equilibrium, the interests of government agencies, sellers and users are simultaneously optimal, any party cannot benefit by changing the strategy unilaterally, so when the lower optimization problem has only one optimal solution, the entire game model has a unique optimal solution.

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