An interconnected park integrated energy system optimization method considering carbon trading

Abstract

The application discloses an interconnected park comprehensive energy system optimization method considering carbon trading. Firstly, the energy transaction mode of each park system and the superior energy network is determined, and the internal structure diagram of the park system is constructed; then, the internal equipment of each park system is modeled, and the operation cost of the independently operated park system is established; finally, the cooperation game optimization model of the interconnected park system for energy transaction is constructed, and the model is solved. The application has the advantages of reducing carbon emission, promoting new energy consumption, reducing the operation cost of each park system and reducing the energy supply pressure.

Description

Technical Field

[0001] This invention relates to a method for optimizing an integrated energy system in an interconnected industrial park that takes carbon trading into account. Background Technology

[0002] With the continued consumption of fossil fuels, carbon emissions are increasing daily, making carbon reduction a global concern. The energy sector has long been a major contributor to carbon emissions, and its transformation can further contribute to achieving dual-carbon goals. The shift from traditional coal-fired power systems to integrated energy systems can reduce carbon dioxide emissions. Currently, game theory plays a crucial role in resolving conflicting interests among multiple stakeholders in market operations and promoting informed decision-making. Carbon trading mechanisms are considered an effective method for reducing carbon emissions in energy market transactions. However, research combining both approaches across multiple integrated energy system parks is relatively limited. The introduction of game theory can make energy market participants more active and stabilize energy supply and demand. Carbon trading mechanisms can reduce carbon emissions across various integrated energy system parks, thus responding to national dual-carbon goals. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies and to achieve the goals of reducing carbon emissions and promoting energy trading among various park systems, this invention proposes an interconnected park integrated energy system scheduling optimization method that considers carbon trading. This method has advantages such as reducing carbon emissions, promoting the consumption of new energy sources, reducing the operating costs of various park systems, and reducing the pressure on energy supply.

[0004] The present invention can be implemented through the following technical solutions:

[0005] An optimization method for an integrated energy system in an interconnected industrial park that considers carbon trading includes the following steps:

[0006] S1: Configure corresponding energy supply and storage equipment for multiple park systems, construct the integrated energy system and internal structure of each park, and clarify the way each park obtains electricity from the outside, that is, the way electricity is exchanged between the upper-level distribution network and each park.

[0007] S2: Model the equipment included in the internal structure of the integrated energy system of each park;

[0008] S3: Model the costs of each park system when it is running independently, and construct the corresponding constraints for each device;

[0009] S4: Establish a game-theoretic cooperative optimization scheduling model for energy interaction among multiple parks, solve for the optimal solution of the game-theoretic optimization model of the interconnected park integrated energy system, and obtain the optimal energy scheduling scheme for the integrated energy system of each park.

[0010] Furthermore, in step S1, determining the specific power supply device and interaction method includes the following process:

[0011] S1-1. Each integrated energy system in a park participating in interconnection transactions includes distributed renewable energy power supply, energy storage equipment, user loads, gas turbine units, and loads;

[0012] S1-2. The specific methods by which each park obtains electricity from external sources are as follows: These park systems can exchange electricity with the upper-level distribution network, and can also purchase natural gas from the natural gas network to maintain the operation of their own gas turbines. In addition, each park entity can engage in energy trading with other parks in the event of energy shortages or surpluses.

[0013] Furthermore, in step S2, the equipment of the integrated energy system in each park is modeled, including the following process:

[0014] S2-1. The gas turbine model is shown below:

[0015]

[0016] in, Let i be the power generation capacity of the gas turbine at time t. Let L be the natural gas flow rate consumed by the gas turbine at time t. L is the calorific value of the natural gas, taken as 9.7 kW·h / m³. 3 η i,t This refers to the efficiency of a gas turbine in converting gas into electricity.

[0017] S2-2. The energy storage device is modeled as follows:

[0018]

[0019]

[0020] in, and These represent the natural gas flow rate and filling / discharging volume of the gas storage tank at time t; η ch,GS and η dch,GS The efficiency of filling and venting the gas storage tank device; η represents the battery capacity at time t and the battery's charging and discharging power, respectively; ch,ES and η dch,ES For the charging and discharging efficiency of the battery;

[0021] S2-3. The modeling of distributed renewable energy in relation to the upper-level energy grid's power purchase and sales model is as follows:

[0022]

[0023]

[0024]

[0025] in, These refer to the purchase of electricity from the upper-level power grid and the upper limit of the purchase amount, respectively. This refers to the maximum amount of electricity sold to the upper-level power grid. This indicates the amount of gas purchased from the superior gas distribution network and the upper limit of the purchased amount.

[0026] S2-4. Modeling Uncertainties in Wind and Solar Power Output

[0027]

[0028] It is an uncertain parameter, representing the net output power of wind turbines and photovoltaics; Forecast power output values ​​for wind turbines and photovoltaic systems; ζ i This represents the uncertainty in predicting the output of wind turbines and solar power, and its range is between 0 and 1.

[0029] S2-5. Modeling Carbon Trading

[0030] The tiered carbon pricing model is as follows:

[0031]

[0032] The actual carbon emission models for each park system are as follows:

[0033]

[0034]

[0035]

[0036] 0≤E m ≤α (12)

[0037] 1≤m≤M+1 (13)

[0038] Where, β em,0 The benchmark value for carbon prices; E i E represents the actual total carbon emissions within the system. p For carbon quotas; m is the number of allocation intervals; α is the interval length of carbon emissions; σ is the carbon price increment; E p For carbon quotas; E m This represents the net carbon emissions in the m-th segment.

[0039] Furthermore, in step S3, a cost model and corresponding constraints are constructed for each integrated park system when it operates independently. The process is as follows:

[0040] S3-1. Constructing a cost model for purchasing and selling energy to a higher-level energy network.

[0041]

[0042] and These represent the prices at which the park system purchases electricity from the upper-level distribution network, sells surplus electricity, and purchases natural gas in time slot t, respectively.

[0043] Consumption costs during the use of energy storage equipment:

[0044]

[0045]

[0046] C sele C sgas These represent parameters for battery charging and discharging losses and amortization costs of gas storage tanks during use.

[0047] S3-2. Carbon Trading Cost Function:

[0048]

[0049] In the formula, E0 is the reference baseline value for the carbon emissions of the park system;

[0050] S3-3. The total operating cost of a single integrated energy system in a single park when operating independently is:

[0051]

[0052] S3-4. The corresponding constraints of the equipment described above are as follows:

[0053] 1) Gas turbine constraints

[0054]

[0055]

[0056] In the formula, Let i be the upper and lower limits of the output of the i-th gas turbine. These are the maximum uphill and downhill power limits for gas turbine i;

[0057] 2) Constraints of energy storage devices

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] In the formula, These are the maximum and minimum values ​​of the capacity of the i-th gas storage tank, respectively; These are the maximum limits for inflated and deflated gas storage tanks, respectively. These are the upper and lower limits of the total capacity of battery i, respectively. These are the maximum values ​​of the battery's charging and discharging capacity, respectively. and It has only two possible values, 0 or 1, which are used to indicate that the charging and discharging phases and the charging and discharging phases cannot be performed simultaneously.

[0067] 3) Constraints on wind turbines and photovoltaic equipment

[0068]

[0069] In the formula, These represent the net power generation and power generation efficiency of the wind turbine and photovoltaic system at time t, respectively. The total power generation of wind turbines and photovoltaic units in the park system at time t.

[0070] 4) Electrical network constraints

[0071] The electrical network constraints within the park system are as follows:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] φ(j), Let P be the set of nodes at the beginning and end of a branch with node j as the starting and ending nodes, respectively; ij,t P jk,tand Q ij,t Q jk,t Let p represent the active and reactive power of segments ij and jk in the power network at time t, respectively; j,t q j,t These represent the active and reactive power of node j at time t, respectively. These represent the line current in segment ij, the voltage values ​​at node i and node j, respectively; r ij x ij This represents the resistance and reactance in the line through which electrical energy is transmitted in segment ij;

[0079] Each node satisfies power balance, and the corresponding expression is as follows:

[0080]

[0081]

[0082] In the formula, and These represent the active and reactive power injections of the gas turbine, wind turbine or photovoltaic system, upstream distribution network interconnection, battery, and load node in the i-th park system at time t, respectively.

[0083] The gas network constraints within the park system are as follows:

[0084] (Gf mn,t ) 2 ≤(C mn φ m,t ) 2 -(C mn φ n,t ) 2 (38)

[0085] 0≤Gf mn,t ≤Gf mn,max (39)

[0086]

[0087] p out =β pre p in (41)

[0088] In the formula, Gf mn,t and Gf mn,max C represents the amount of natural gas at time t within the pipeline through which natural gas flows, and its maximum value. mn Parameters for natural gas pipeline transmission; φ m,t φ n,t Let be the air pressures at nodes m and n at time t, respectively. and p represents the lower and upper limits of gas pressure at node m in a natural gas network;out p in This indicates the pressure at the natural gas outlet and inlet of the compressor; β pre This refers to the compression ratio;

[0089] The equation that the flow balance at the gas node satisfies is as follows:

[0090]

[0091] In the formula, A w A l A gc A GT This is the association matrix between gas sources, natural gas pipelines, compressors, gas turbines, and gas network nodes in the gas network; GComp k,t , Let t be the gas consumption of the compressor, gas turbine, and gas load at node k.

[0092] In step S4, a game-theoretic cooperative optimization scheduling model for energy interaction among multiple parks is established and solved, including the following steps:

[0093] S4-1. When the integrated energy systems of the park are interconnected, the energy trading volume must be added to the power network nodes of each integrated energy system in the park. Then the injected power of each node becomes:

[0094]

[0095]

[0096] S4-2. During the transmission of electrical energy, transaction costs with other parks and network access costs should be considered. The corresponding expressions are as follows:

[0097]

[0098]

[0099] Among them, the energy trading volume and energy payment volume between the park's integrated energy systems were respectively Pe i and Qe i , These represent the sum of active and reactive power of the node transactions in the i-electric network of the park's integrated energy system, respectively.

[0100] S4-3. When energy trading takes place in interconnected parks, the total cost of integrated energy for each park is [not specified]. for:

[0101] S4-4. The cooperative game model for energy trading between interconnected park systems can be expressed as follows:

[0102]

[0103]

[0104] In the formula, in It is the sum of costs associated with purchasing and selling energy, carbon trading, and energy storage when participating in energy trading. N is the set of park systems participating in energy trading; Payment benefits obtained by the park system for energy trading through cooperative operation;

[0105] S4-5. The parks maximize their own interests through cooperative game theory. The solution process can be divided into solving two sub-problems: maximizing social welfare and the distribution of benefits.

[0106] The problem of minimizing social operating costs is a common objective of the park systems participating in energy trading cooperation. A specific model that can be expressed as follows:

[0107]

[0108] In the formula,

[0109] The problem of maximizing payment benefits is to fairly distribute the benefits obtained from participating in energy trading to each participating industrial park system. The specific model is as follows:

[0110]

[0111] In the formula, yes The optimal value.

[0112] The social welfare maximization problem is solved using the ADMM distributed algorithm, which first introduces an auxiliary variable. It equals the trading volume. Then, we construct an augmented Lagrange function expression, which is as follows:

[0113]

[0114] ρ1 is the penalty parameter; As dual variables;

[0115] The solution to this expression is divided into two levels: the lower level problem is as follows:

[0116]

[0117] The trading volume Pe is solved by fixing the dual variables and auxiliary variables. ij (k+1);

[0118] The expression for the lower-level problem is as follows:

[0119]

[0120] Use the calculated transaction volume Substitute these variables into the lower-level problem to solve for the auxiliary variables. Based on these two variables, a new dual variable is derived, which is expressed as:

[0121]

[0122] The problem of maximizing payment benefits is also solved using the ADMM algorithm. The solution process is similar, but auxiliary variables are introduced. and dual variable y = {y ij Solve the problem.

[0123] The advantages of this invention compared to previous inventions are as follows: This invention considers the coupling relationship between electrical systems, and the constructed model is consistent with reality; it considers the environmental friendliness of the park system, which is in line with the current goal of carbon peaking and carbon neutrality; and it introduces game theory to resolve the contradictions in the participation of various park systems in the energy market. Attached Figure Description

[0124] Figure 1 This is a schematic diagram of the energy trading system of the interconnected park's integrated energy system.

[0125] Figure 2 This is the electrical load diagram for each park system.

[0126] Figure 3 This is the electricity purchase map for the industrial park.

[0127] Figure 4 This is a map showing the gas purchase process within the industrial park.

[0128] Figure 5 This is an interactive power consumption chart for the park.

[0129] Figure 6 This is a flowchart of the solution method of the present invention. Detailed Implementation

[0130] The invention will now be further described with reference to the accompanying drawings.

[0131] Reference Figures 1-6 An optimization method for an integrated energy system in an interconnected industrial park considering carbon trading; a schematic diagram of energy trading in the integrated energy system of an interconnected industrial park, as described in the invention research. Figure 1The demonstration showcased how each park system can obtain electricity and natural gas from the upstream power distribution network and gas distribution network to meet its energy needs. When a park has surplus electricity, it can sell it back to the upstream power distribution network, thus generating profit and avoiding waste of distributed energy resources, bringing benefits to itself. Parks can also trade electricity with each other, which brings two advantages. First, since the transactions involve renewable energy, carbon emissions can be reduced; second, inter-park energy trading can also promote the use of renewable energy within each park, achieving the goal of generating revenue. Each park system participates in energy market transactions with the goal of minimizing its own costs.

[0132] An optimization method for an integrated energy system in an interconnected industrial park that considers carbon trading includes the following steps:

[0133] S1: Configure corresponding energy supply and storage equipment for multiple park systems, construct the integrated energy system and internal structure of each park, and clarify the way each park obtains electricity from the outside, that is, the way electricity is exchanged between the upper-level distribution network and each park.

[0134] In step S1, determining the specific power supply device and interaction method includes the following process:

[0135] S1-1. Each integrated energy system in a park participating in interconnection transactions includes distributed renewable energy power supply, energy storage equipment, user loads, gas turbine units, and loads;

[0136] S1-2. The specific methods by which each park obtains electricity from external sources are as follows: These park systems can exchange electricity with the upper-level distribution network, and can also purchase natural gas from the natural gas network to maintain the operation of their own gas turbines. In addition, each park entity can engage in energy trading with other parks in the event of energy shortages or surpluses.

[0137] S2: Model the equipment included in the internal structure of the integrated energy system of each park;

[0138] In step S2, the equipment of the integrated energy system in each park is modeled, including the following process:

[0139] S2-1. The gas turbine model is shown below:

[0140]

[0141] in, Let i be the power generation capacity of the gas turbine at time t. Let L be the natural gas flow rate consumed by the gas turbine at time t. L is the calorific value of the natural gas, taken as 9.7 kW·h / m³. 3 η i,t This refers to the efficiency of a gas turbine in converting gas into electricity.

[0142] S2-2. The energy storage device is modeled as follows:

[0143]

[0144]

[0145] in, and These represent the natural gas flow rate and filling / discharging volume of the gas storage tank at time t; η ch,GS and η dch,GS The efficiency of filling and venting the gas storage tank device; η represents the battery capacity at time t and the battery's charging and discharging power, respectively; ch,ES and η dch,ES For the charging and discharging efficiency of the battery;

[0146] S2-3. The modeling of distributed renewable energy in relation to the upper-level energy grid's power purchase and sales model is as follows:

[0147]

[0148]

[0149]

[0150] in, These refer to the purchase of electricity from the upper-level power grid and the upper limit of the purchase amount, respectively. This refers to the maximum amount of electricity sold to the upper-level power grid. This indicates the amount of gas purchased from the superior gas distribution network and the upper limit of the purchased amount.

[0151] S2-4. Modeling Uncertainties in Wind and Solar Power Output

[0152]

[0153] It is an uncertain parameter, representing the net output power of wind turbines and photovoltaics; Forecast power output values ​​for wind turbines and photovoltaic systems; ζ i This represents the uncertainty in predicting the output of wind turbines and solar power, and its range is between 0 and 1.

[0154] S2-5. Modeling Carbon Trading

[0155] The tiered carbon pricing model is as follows:

[0156]

[0157] The actual carbon emission models for each park system are as follows:

[0158]

[0159]

[0160]

[0161] 0≤E m ≤α (12)

[0162] 1≤m≤M+1 (13)

[0163] Where, β em,0 The benchmark value for carbon prices; E i E represents the actual total carbon emissions within the system. p For carbon quotas; m is the number of allocation intervals; α is the interval length of carbon emissions; σ is the carbon price increment; E p For carbon quotas; E m This represents the net carbon emissions in the m-th segment.

[0164] In step S3, the cost construction model and corresponding constraints for each integrated park system operating independently are as follows:

[0165] S3-1. Constructing a cost model for purchasing and selling energy to a higher-level energy network.

[0166]

[0167] and These represent the prices at which the park system purchases electricity from the upper-level distribution network, sells surplus electricity, and purchases natural gas in time slot t, respectively.

[0168] Consumption costs during the use of energy storage equipment:

[0169]

[0170]

[0171] C sele C sgas These represent parameters for battery charging and discharging losses and amortization costs of gas storage tanks during use.

[0172] S3-2. Carbon Trading Cost Function:

[0173]

[0174] In the formula, E0 is the reference baseline value for the carbon emissions of the park system;

[0175] S3-3. The total operating cost of a single integrated energy system in a single park when operating independently is:

[0176]

[0177] S3-4. The corresponding constraints of the equipment described above are as follows:

[0178] 1) Gas turbine constraints

[0179]

[0180]

[0181] In the formula, Let i be the upper and lower limits of the output of the i-th gas turbine. These are the maximum uphill and downhill power limits for gas turbine i;

[0182] 2) Constraints of energy storage devices

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191] In the formula, These are the maximum and minimum values ​​of the capacity of the i-th gas storage tank, respectively; These are the maximum limits for inflated and deflated gas storage tanks, respectively. These are the upper and lower limits of the total capacity of battery i, respectively. These are the maximum values ​​of the battery's charging and discharging capacity, respectively. and It has only two possible values, 0 or 1, which are used to indicate that the charging and discharging phases and the charging and discharging phases cannot be performed simultaneously.

[0192] 3) Constraints on wind turbines and photovoltaic equipment

[0193]

[0194] In the formula, These represent the net power generation and power generation efficiency of the wind turbine and photovoltaic system at time t, respectively. This represents the total power generation of wind turbines and photovoltaic units in the park system at time t.

[0195] 4) Electrical network constraints

[0196] The electrical network constraints within the park system are as follows:

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203] φ(j), Let P be the set of nodes at the beginning and end of a branch with node j as the starting and ending nodes, respectively; ij,t P jk,t and Q ij,t Q jk,t Let p represent the active and reactive power of segments ij and jk in the power network at time t, respectively; j,t q j,t These represent the active and reactive power of node j at time t, respectively. These represent the line current in segment ij, the voltage values ​​at node i and node j, respectively; r ij x ij This represents the resistance and reactance in the line through which electrical energy is transmitted in segment ij;

[0204] Each node satisfies power balance, and the corresponding expression is as follows:

[0205]

[0206]

[0207] In the formula, and These represent the active and reactive power injections of the gas turbine, wind turbine or photovoltaic system, upstream distribution network connection, battery, and load node in the i-th park system at time t, respectively.

[0208] The gas network constraints within the park system are as follows:

[0209] (Gf mn,t ) 2 ≤(C mn φ m,t) 2 -(C mn φ n,t ) 2 (38)

[0210] 0≤Gf mn,t ≤Gf mn,max (39)

[0211]

[0212] p out =β pre p in (41)

[0213] In the formula, Gf mn,t and Gf mn,max C represents the amount of natural gas at time t within the pipeline through which natural gas flows, and its maximum value. mn Parameters for natural gas pipeline transmission; φ m,t φ n,t Let be the air pressures at nodes m and n at time t, respectively. and p represents the lower and upper limits of gas pressure at node m in a natural gas network; out p in This indicates the pressure at the natural gas outlet and inlet of the compressor; β pre This represents the compression ratio.

[0214] The equation that the flow balance at the gas node satisfies is as follows:

[0215]

[0216] In the formula, A w A l A gc A GT This is the association matrix between gas sources, natural gas pipelines, compressors, gas turbines, and gas network nodes in the gas network; GComp k,t , Let t be the gas consumption of the compressor, gas turbine, and gas load at node k.

[0217] S4: Establish a game-theoretic cooperative optimization scheduling model for energy interaction among multiple parks, solve for the optimal solution of the game-theoretic optimization model of the interconnected park integrated energy system, and obtain the optimal energy scheduling scheme for the integrated energy system of each park.

[0218] In step S4, establishing and solving a game-theoretic cooperative optimization scheduling model for energy interaction among multiple parks includes the following steps:

[0219] S4-1. When the integrated energy systems of the park are interconnected, the energy trading volume must be added to the power network nodes of each integrated energy system in the park. Then the injected power of each node becomes:

[0220]

[0221]

[0222] S4-2. During the transmission of electrical energy, transaction costs with other parks and network access costs should be considered. The corresponding expressions are as follows:

[0223]

[0224]

[0225] Among them, the energy trading volume and energy payment volume between the park's integrated energy systems were respectively Pe i and Qe i . These represent the sum of active and reactive power of the node transactions in the i-electric network of the park's integrated energy system, respectively.

[0226] S4-3. When energy trading takes place in interconnected parks, the total cost of integrated energy for each park is [not specified]. for:

[0227]

[0228] S4-4. The cooperative game model for energy trading between interconnected park systems can be expressed as follows:

[0229]

[0230]

[0231] In the formula, in It is the sum of costs associated with purchasing and selling energy, carbon trading, and energy storage when participating in energy trading. N is the set of park systems participating in energy trading; Payment benefits obtained by the park system for energy trading through cooperative operation;

[0232] S4-5. The parks maximize their own interests through cooperative game theory. The solution process can be divided into solving two sub-problems: maximizing social welfare and the distribution of benefits.

[0233] The problem of minimizing social operating costs is a common objective of the park systems participating in energy trading cooperation. A specific model that can be expressed as follows:

[0234]

[0235] In the formula,

[0236] The problem of maximizing payment benefits is to fairly distribute the benefits obtained from participating in energy trading to each participating industrial park system. The specific model is as follows:

[0237]

[0238] In the formula, yes The optimal value.

[0239] The social welfare maximization problem is solved using the ADMM distributed algorithm, which first introduces an auxiliary variable. It equals the trading volume. Then, we construct an augmented Lagrange function expression, which is as follows:

[0240]

[0241] ρ1 is the penalty parameter; As dual variables;

[0242] The solution to this expression is divided into two levels: the lower level problem is as follows:

[0243]

[0244] The trading volume Pe is solved by fixing the dual variables and auxiliary variables. ij (k+1).

[0245] The expression for the lower-level problem is as follows:

[0246]

[0247] Use the calculated transaction volume Substitute these variables into the lower-level problem to solve for the auxiliary variables. Based on these two variables, a new dual variable is derived, which can be expressed as:

[0248]

[0249] The problem of maximizing payment benefits is also solved using the ADMM algorithm. The solution process is similar, but auxiliary variables are introduced. and dual variable y = {y ij Solve the problem.

[0250] Example: The code is written in MATLAB, and the model is solved using the yalmip, cplex, and ipopt solvers.

[0251] Park System 1's new energy supply equipment is wind turbines, which have a high output at night. Park Systems 2 and 3's energy supply equipment is photovoltaic (PV). The battery discharge capacities are respectively... The charging capacity is The charging and discharging efficiencies are equal, η ch,ES =η dch,ES =0.95. Maximum capacity of the gas turbine. minimum capacity The filling and releasing volumes of the gas storage tank are equal. The graph shows the predicted output of new energy sources. Figure 2 As shown.

[0252] Figure 3 This is a graph showing the electricity purchases of System 1 in the industrial park. Taking the time intervals of 1:00-5:00 and 19:00-24:00 as examples, there is a surplus of wind turbine output in Industrial Park 1, and the excess wind power will be traded with other industrial parks.

[0253] Figure 4 This is a gas purchase volume chart for System 1 in the industrial park. Taking the period from 20:00 to 22:00 as an example, the gas price is relatively low. In order to sell more electricity to Systems 2 and 3, System 1 will increase the output of its gas turbines, and the corresponding gas purchase volume will also increase.

[0254] Figure 5 This is a graph showing the electricity exchange between systems within the park. Taking the time period from 9:00 to 13:00 as an example, Park 2 and Park 3 have surplus photovoltaic power, which they will trade with Park 1. Electricity trading is actively taking place at all times.

Claims

1. A method for optimizing an integrated energy system in an interconnected industrial park that considers carbon trading, characterized in that, The method includes the following steps: S1: Configure corresponding energy supply and storage equipment for multiple park systems, construct the integrated energy system and internal structure of each park, and clarify the way each park obtains electricity from the outside, that is, the way electricity is exchanged between the upper-level distribution network and each park. S2: Model the equipment included in the internal structure of the integrated energy system of each park; S3: Model the costs of each park system when it operates independently, and construct the corresponding constraints for each device. The costs of independent operation include the cost of purchasing energy from the upper-level energy grid, the consumption cost during the use of energy storage equipment, and the carbon trading cost. S4: Establish a game-theoretic cooperative optimization scheduling model for energy interaction among multiple parks, solve for the optimal solution of the game-theoretic optimization model of the interconnected park integrated energy system, and obtain the optimal energy scheduling scheme for the integrated energy system of each park. In step S2, the equipment of the integrated energy system in each park is modeled, including the following process: S2-1. The gas turbine model is shown below: (1)； in, Let i be the power generation capacity of the gas turbine at time t. Let L be the natural gas flow rate consumed by the gas turbine at time t, and L be the calorific value of the natural gas, taken as 9.7 kW·h / m³. 3 η i,t This refers to the efficiency of a gas turbine in converting gas into electricity. S2-2. The energy storage device is modeled as follows: (2)； (3)； in, , and These represent the natural gas flow rate and filling / discharging volume of the gas storage tank at time t; η ch,GS and η dch,GS The efficiency of filling and venting the gas storage tank device; , , η represents the battery capacity at time t and the battery's charging and discharging power, respectively; ch,ES and η dch,ES For the charging and discharging efficiency of the battery; S2-3. Modeling of Electricity Purchase and Sales with the Higher-Level Energy Grid The modeling of distributed renewable energy is as follows: (4)； (5)； (6)； in, , These refer to the purchase of electricity from the upper-level power grid and the upper limit of the purchase amount, respectively. , This refers to the maximum amount of electricity sold to the upper-level power grid. , This indicates the amount of gas purchased from the superior gas distribution network and the upper limit of the purchased amount. S2-4. Modeling Uncertainties in Wind and Solar Power Output (7)； It is an uncertain parameter, representing the net output power of wind turbines and photovoltaics; Forecast power output values ​​for wind turbines and photovoltaic systems; ζ i This represents the uncertainty in predicting the output of wind turbines and solar power, and its range is between 0 and 1. S2-5. Modeling Carbon Trading The tiered carbon pricing model is as follows: (8)； The actual carbon emission models for each park system are as follows: (9)； (10)； (11)； (12)； (13)； Where, β em,0 The benchmark value for carbon prices; E i E represents the actual total carbon emissions within the system. p For carbon quotas; m is the number of allocation intervals; α is the interval length of carbon emissions; σ is the carbon price increment; E p For carbon quotas; E m This represents the net carbon emissions in the m-th interval.

2. The method for optimizing an integrated energy system in an interconnected industrial park considering carbon trading as described in claim 1, characterized in that, In step S1, determining the specific power supply device and interaction method includes the following process: S1-1. Each integrated energy system in a park participating in interconnection transactions includes distributed renewable energy power supply, energy storage equipment, user loads, gas turbine units, and loads; S1-2. The specific ways in which each park obtains electricity from the outside are as follows: These park systems can exchange electricity with the upper-level distribution network, and can also purchase natural gas from the natural gas network to maintain the operation of their own gas turbines. In addition, each park entity can conduct energy transactions with other parks in the event of energy shortage or surplus.

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