Comprehensive energy system and optimized operation method thereof

Abstract

The invention discloses an integrated energy system and an optimized operation method thereof. The optimized operation method comprises the following specific steps: constructing a first objective function based on an upper-layer energy supply optimization model and determining constraint conditions; constructing a second objective function based on the lower-layer consumer remaining optimization model and determining constraint conditions; solving the first objective function and the second objective function based on an improved particle swarm algorithm and constraint conditions; and determining an optimized operation strategy based on a solving result. According to the invention, the comprehensive energy system is divided into an upper-layer energy supply total cost module and a lower-layer consumer remaining module, and the conditional risk value is introduced for the uncertainty of the scene to quantify the market risk; and the target functions of the upper layer and the lower layer are optimized by adopting an improved multi-target particle swarm optimization algorithm, and CPLEX is called for solving to obtain an operation strategy, so that the carbon emission generated by the operation of the integrated energy system is reduced.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy systems, and in particular to an integrated energy system and an optimized operation method thereof. Background Art

[0002] An integrated energy system is a system that coordinates and optimizes multiple energy forms such as electricity, natural gas, and thermal energy. It can improve energy efficiency and flexibility and achieve the goal of low-carbon development. The carbon trading mechanism is an energy market mechanism that takes into account the cost and impact of carbon emissions. It can motivate the supply and demand sides in the integrated energy system to redistribute carbon emission rights and reduce emissions. At present, the optimization operation technology of the integrated energy system mainly focuses on the optimization scheduling method on the energy supply side, and simplifies the energy demand side to fixed electricity and heat load requirements, without considering the active emission reduction behavior on the demand side. Therefore, this paper proposes a new low-carbon operation framework for an integrated energy system to study the interactive impact between the supply and consumption sides of the carbon trading mechanism. The supply and consumption sides are associated through the node marginal electricity price that takes into account the carbon emission cost. Summary of the invention

[0003] The technical solution adopted by the present invention is:

[0004] An integrated energy system, comprising: an upper layer energy supply total cost module, used to transmit node marginal price to the consumer surplus module; and a lower layer consumer surplus module, used to update energy consumption structure to the energy supply total cost module;

[0005] Among them, the total energy supply cost module includes: an energy scheduling module, which is used to adjust the energy supply structure; and a carbon market trading module, which is used to plan and trade carbon quotas; the consumer surplus module includes: an energy consumption module, which is used to record the consumption of end users; and a photovoltaic power generation module, which is used to reduce dependence on the upper supply layer; the energy scheduling module purchases carbon quotas from and sells carbon quotas to the carbon market trading module, the energy scheduling module supplies power to the energy consumption module, and the photovoltaic power generation module sells carbon quotas to the carbon market trading module.

[0006] Another technical solution adopted by the present invention is:

[0007] An optimized operation method of a comprehensive energy system comprises the following specific steps:

[0008] S1. Construct the first objective function based on the upper energy supply optimization model and determine the constraints;

[0009] S2. Constructing the second objective function based on the lower-level consumer surplus optimization model and determining the constraints;

[0010] S3, solving the first objective function and the second objective function based on the improved particle swarm algorithm and the constraint conditions;

[0011] S4. Determine an optimization operation strategy based on the solution results.

[0012] 6. Further, the step S1 specifically includes:

[0013] The first objective function is as follows:

[0014]

[0015] In formula (1), Represents a set of energy types; (Ele, Gas) are associated with different energy forms of power generation and gas suppliers, where power generation is divided into three types according to fuel: coal, gas, and wind; C Ele (P Git ), C Gas (g Gi,t ) represent the operating cost functions of electricity and gas energy respectively; P Gi,t , g Gi,t They represent the output of power generation i and natural gas supplier i at time t respectively; λ represents the carbon price in the carbon trading market; represents the carbon quota for power generation i, and its value corresponds to Y; E Gi.t represents the calculated carbon emissions of power generation i at time t; T refers to the operation interval time; Y represents the total scheduling time.

[0016] The upper energy supply optimization model includes an operating cost mathematical model of the energy scheduling module and a carbon emission flow mathematical model of the carbon market trading module;

[0017] The mathematical model of the operating cost of the energy scheduling module is:

[0018]

[0019]

[0020] In formulas (2) to (5), g Gi,t They represent the power generation of the i-th generation at time t associated with different power generation types of coal, gas and wind, respectively, and the same symbols have the same meaning in the following text; Indicates the electricity output of wind power generation; μ Wind represents the capital recovery factor associated with the investment in wind power generation; represents the cost coefficient of energy;

[0021] The carbon emission flow mathematical model of the carbon market trading module is:

[0022]

[0023] In formulas (6) to (7), e i,t represents the node carbon intensity of bus i in time; ρ ij,t represents the branch carbon intensity of busbar i in time; Represents the generator set that injects energy into the bus; Represents the power unit flowing into bus i; represents the power flow between busbars i and j; e Gk Indicates the carbon intensity of the generator set, which is a parameter related to the power supply characteristics and depends on the state of the power supply unit; P Gk,t represents the injected power of generator k per unit time;

[0024] Introducing the conditional value at risk (CVaR) analysis, the first objective function is rewritten as:

[0025]

[0026] In formula (8), θ k represents the scenario set; f2 represents the objective function of CVaR; Pr k represents the probability of scenario k; f 1,k represents the value of the objective function in scenario k; represents the weighting factor of risk; β represents the confidence level indicating risk preference; ζ represents an auxiliary variable with continuous non-negative values, which is defined as the maximum value between zero and the difference between the cost of each scenario minus VaR; VaR represents the value at risk;

[0027] Determine the constraints of the rewritten first objective function:

[0028] 1) Supply and demand balance constraints:

[0029]

[0030] In formula (9), θ D represents the set of microgrids, which can be regarded as demand from the perspective of the main grid; θ P2G Represents the set of P2G facilities; P Di,t , They represent the pure power demand of bus i at time t and the power consumed by the heat load respectively; i represents the planned PV quantity of bus i; represents the photovoltaic output power of bus i at time t;

[0031] 2) Power generation related constraints:

[0032]

[0033] In formulas (10) to (11), and Indicates the rising and falling limits of power generation i;

[0034] 3) Electrical node balance constraints:

[0035]

[0036] In formulas (12) to (13), B ij represents the admittance between bus i and bus j; θ i,t ,θ j,t They represent the voltage angles of bus i and bus j at time t respectively;

[0037] 4) System constraints of the power grid, including power output boundaries and ramp limits:

[0038]

[0039] 5) Gas node balance constraints:

[0040]

[0041] In formula (15), represents the gas sold by P2G to bus i at time t; represents the gas supplied to the gas generator at bus i at time t; g Di,t represents the gas demand of bus i at time t;

[0042] 6) Energy demand constraints of heat load:

[0043]

[0044] In formulas (16) to (17), τ Di,t represents the heat demand of bus i at time t; η Gas,heat , η Power,heat Energy conversion coefficient expressing the unit correlation constant:

[0045] 7) Natural gas supply restrictions:

[0046]

[0047] 8) The ratio of electricity and natural gas for the heat load, which is limited by the dual fuel ratio:

[0048]

[0049] 9)CVaR-related constraints:

[0050] VaR-f1≤ζ (20)

[0051] ζ ≥ 0 (21)

[0052] Furthermore, the step S2 specifically includes:

[0053] The second objective function is as follows:

[0054]

[0055] In formula (22), represents the set of P2G facilities; E con,t The amount of electricity consumed by the end user at time t; δ i,t The price of the P2G facility providing ancillary services; i represents the carbon price in the carbon trading market; represents the carbon quota of bus i; represents the carbon emissions associated with natural gas purchased from P2G by bus i at time t; E Di,t represents the calculated carbon emission quota of bus i at time t; μ PV represents the capital recovery income of photovoltaic panels;

[0056] Determine the constraints of the lower-level consumer surplus optimization model:

[0057] 1) Limit the number of planned PV installations on bus i:

[0058]

[0059] 2) Introducing photovoltaic power output:

[0060]

[0061] In formula (24), P MPP represents the maximum output power of the photovoltaic panel; h1 represents the power output function of the photovoltaic panel, which can be obtained through the single PV panel parameter table; γ i,t represents solar radiation;

[0062] 3) Explain the carbon emission quotas that come with gas purchased from P2G:

[0063]

[0064] In formulas (25) to (26), Indicates the natural gas sold. represents the natural gas produced by P2G at bus i at time t, h2 can be regarded as a linear function of a and b, where is a constant; T represents time; Θ P2G Represents a set of P2G facilities;

[0065] 4) Natural gas export boundary of natural gas supply:

[0066]

[0067] In formula (27), The natural gas output boundaries represent the natural gas supply, respectively;

[0068] 5) Energy conversion from electricity to natural gas in P2G facilities:

[0069]

[0070] In formula (28), η P2G It represents the energy conversion factor from electricity to natural gas of the P2G facility; Indicates electrical power;

[0071] 6) The gas balance after selling gas in P2G facilities is stipulated as follows:

[0072]

[0073] In formula (29), represents the gas stored in the gas storage of bus i at time t; represents the gas consumed by bus i at time t;

[0074] Wherein, the lower-layer consumer surplus optimization model includes a mathematical model of an energy consumption module and a mathematical model of a photovoltaic power generation module;

[0075] The mathematical model of the energy consumption module is:

[0076]

[0077] In formula (30), U Ele , U Gas represents the utility function, which is defined to describe the satisfaction degree of energy consumption; P Di,t represents the power demand of bus i at time t; represents the power consumed by the heat load of bus i at time t; i represents the number of planned PV installations on bus i; represents the photovoltaic output power of bus i at time t; g Di,t represents the natural gas demand and supply of bus i at time t; represents the natural gas sold by P2G at bus i at time t; represents the natural gas price sold by P2G to bus i at time t;

[0078] The mathematical model of the photovoltaic power generation module is:

[0079]

[0080] In formulas (31) to (32), Vi,oc represents the open circuit voltage of the photovoltaic cell (V); γ i,t Indicates light intensity; A i Represents the effective area of ​​photovoltaic cells; represents the carbon quota sold by PV to bus i at time t.

[0081] Furthermore, the step S3 specifically includes:

[0082] 1) Input the power network topology, gas network topology and installed capacity of various generators;

[0083] 2) Sampling wind speed, load, carbon price, and solar radiation to generate different scenarios;

[0084] 3) Calculate the total cost of energy supply taking into account carbon emission costs in the upper energy supply optimization model;

[0085] 4) Obtain information such as upper-level node electricity prices and node carbon flows;

[0086] 5) The node electricity price information obtained based on the lower-level consumer surplus optimization model is used to optimize the energy consumption structure of the consumer layer;

[0087] 6) Use improved particle swarm algorithm to solve, set loop termination threshold, position and speed of all particles, constraint check and fitness calculation;

[0088] 7) Whether the maximum number of iterations or the best fitness value is reached, if yes, proceed to the next step, otherwise proceed to step 3);

[0089] 8) Output the results of low-carbon operation under the sampling scenario.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] 1) The present invention divides the integrated energy system into an upper-layer total energy supply cost module and a lower-layer consumer surplus module, and introduces conditional risk value to quantify market risks in view of the uncertainty of the scenario;

[0092] 2) The improved multi-objective particle swarm optimization algorithm is used to optimize the objective functions of the upper and lower layers, and CPLEX is called to solve the operation strategy to reduce the carbon emissions generated by the operation of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0094] Figure 1 It is a schematic flow chart of an optimization operation method of a comprehensive energy system of the present invention;

[0095] Figure 2It is a schematic diagram of a framework of a comprehensive energy system of the present invention;

[0096] Figure 3 It is a schematic diagram of the flow of solving the upper and lower objective functions of an optimization operation method of a comprehensive energy system of the present invention;

[0097] Figure 4 is the node electricity price under the carbon constraint scenario in Example 2;

[0098] Figure 5 is the total cost probability distribution in this embodiment 2;

[0099] Figure 6 This is the 24-hour operation result of P2G in this embodiment 2. DETAILED DESCRIPTION

[0100] Example 1

[0101] like Figure 2 As shown, a comprehensive energy system includes an upper energy supply total cost module 1, which is used to transmit node marginal prices to the consumer surplus module 2; and a lower consumer surplus module 2, which is used to update the energy consumption structure to the energy supply total cost module 1;

[0102] Among them, the total energy supply cost module 1 includes an energy scheduling module 101, which is used to adjust the energy supply structure; and a carbon market trading module 102, which is used to plan and trade carbon quotas; the consumer surplus module 2 includes an energy consumption module 201, which is used to record the consumption of end users; and a photovoltaic power generation module 202, which is used to reduce dependence on the upper supply layer; the energy scheduling module 101 purchases and sells carbon quotas to the carbon market trading module 102, the energy scheduling module 201 supplies power to the energy consumption module 101, and the photovoltaic power generation module 202 sells carbon quotas to the carbon market trading module 102.

[0103] Example 2

[0104] like Figure 1 As shown, a method for optimizing the operation of a comprehensive energy system includes the following specific steps:

[0105] S1. Construct the first objective function based on the upper energy supply optimization model and determine the constraints;

[0106] 7. Specifically, the step S1 includes:

[0107] The first objective function is as follows:

[0108]

[0109] In formula (1), Represents a set of energy types; (Ele, Gas) are associated with different energy forms of power generation and gas suppliers, where power generation is divided into three types according to fuel: coal, gas, and wind; C Ele (P Git ), C Gas (g Gi,t ) represent the operating cost functions of electricity and gas energy respectively; P Gi,t , g Gi,t They represent the output of power generation i and natural gas supplier i at time t respectively; λ represents the carbon price in the carbon trading market; represents the carbon quota for power generation i, and its value corresponds to Y; E Gi.t represents the calculated carbon emissions of power generation i at time t; T refers to the operation interval time; Y represents the total scheduling time.

[0110] The upper energy supply optimization model includes an operating cost mathematical model of the energy scheduling module and a carbon emission flow mathematical model of the carbon market trading module;

[0111] The mathematical model of the operating cost of the energy scheduling module is:

[0112]

[0113] In formulas (2) to (5), g Gi,t They represent the power generation of the i-th generation at time t associated with different power generation types of coal, gas and wind, respectively, and the same symbols have the same meaning in the following text; Indicates the electricity output of wind power generation; μ Wind represents the capital recovery factor associated with the investment in wind power generation; represents the cost coefficient of energy;

[0114] The carbon emission flow mathematical model of the carbon market trading module is:

[0115]

[0116] In formulas (6) to (7), e i,t represents the node carbon intensity of bus i in time; ρ ij,t represents the branch carbon intensity of busbar i in time; Represents the generator set that injects energy into the bus; Represents the power unit flowing into bus i; represents the power flow between busbars i and j; e Gk Indicates the carbon intensity of the generator set, which is a parameter related to the power supply characteristics and depends on the state of the power supply unit; P Gk,t represents the injected power of generator k per unit time;

[0117] Introducing the conditional value at risk (CVaR) analysis, the first objective function is rewritten as:

[0118]

[0119] In formula (8), θ k represents the scenario set; f2 represents the objective function of CVaR; Pr k represents the probability of scenario k; f 1,k represents the value of the objective function in scenario k; represents the weighting factor of risk; β represents the confidence level indicating risk preference; ζ represents an auxiliary variable with continuous non-negative values, which is defined as the maximum value between zero and the difference between the cost of each scenario minus VaR; VaR represents the value at risk;

[0120] Determine the constraints of the rewritten first objective function:

[0121] 1) Supply and demand balance constraints:

[0122]

[0123] In formula (9), θ D represents the set of microgrids, which can be regarded as demand from the perspective of the main grid; θ P2G Represents the set of P2G facilities; P Di,t , They represent the pure power demand of bus i at time t and the power consumed by the heat load respectively; i represents the planned PV quantity of bus i; represents the photovoltaic output power of bus i at time t;

[0124] 2) Power generation related constraints:

[0125]

[0126] In formulas (10) to (11), and Indicates the rising and falling limits of power generation i;

[0127] 3) Electrical node balance constraints:

[0128]

[0129] In formulas (12) to (13), B ij represents the admittance between bus i and bus j; θ i,t ,θ j,t They represent the voltage angles of bus i and bus j at time t respectively;

[0130] 4) System constraints of the power grid, including power output boundaries and ramp limits:

[0131]

[0132] 5) Gas node balance constraints:

[0133]

[0134] In formula (15), represents the gas sold by P2G to bus i at time t; represents the gas supplied to the gas generator at bus i at time t; g Di,t represents the gas demand of bus i at time t;

[0135] 6) Energy demand constraints of heat load:

[0136]

[0137] In formulas (16) to (17), τ Di,t represents the heat demand of bus i at time t; η Gas,heat , η Power,heat Energy conversion coefficient expressing the unit correlation constant:

[0138] 7) Natural gas supply restrictions:

[0139]

[0140] 8) The ratio of electricity and natural gas for the heat load, which is limited by the dual fuel ratio:

[0141]

[0142] 9)CVaR-related constraints:

[0143] VaR-f1≤ζ (20)

[0144] ζ ≥ 0 (21)

[0145] S2. Constructing the second objective function based on the lower-level consumer surplus optimization model and determining the constraints;

[0146] Specifically, the second objective function is as follows:

[0147]

[0148] In formula (22), represents the set of P2G facilities; E con,t The amount of electricity consumed by the end user at time t; δ i,t The price of the P2G facility providing ancillary services; irepresents the carbon price in the carbon trading market; represents the carbon quota of bus i; represents the carbon emissions associated with natural gas purchased from P2G by bus i at time t; E Di,t represents the calculated carbon emission quota of bus i at time t; μ PV Represents the capital recovery income of photovoltaic panels.

[0149] Determine the constraints of the lower-level consumer surplus optimization model:

[0150] 1) Limit the number of planned PV installations on bus i:

[0151]

[0152] 2) Introducing photovoltaic power output:

[0153]

[0154] In formula (24), P MPP represents the maximum output power of the photovoltaic panel; h1 represents the power output function of the photovoltaic panel, which can be obtained through the single PV panel parameter table; γ i,t represents solar radiation;

[0155] 3) Explain the carbon emission quotas that come with gas purchased from P2G:

[0156]

[0157] In formulas (25) to (26), Indicates the natural gas sold. represents the natural gas produced by P2G at bus i at time t, h2 can be regarded as a linear function of a and b, where is a constant; T represents time; Θ P2G Represents a set of P2G facilities;

[0158] 4) Natural gas export boundary of natural gas supply:

[0159]

[0160] In formula (27), The natural gas output boundaries represent the natural gas supply, respectively;

[0161] 5) Energy conversion from electricity to natural gas in P2G facilities:

[0162]

[0163] In formula (28), η P2G It represents the energy conversion factor from electricity to natural gas of the P2G facility; Indicates electrical power;

[0164] 6) The gas balance after selling gas in P2G facilities is stipulated as follows:

[0165]

[0166] In formula (29), represents the gas stored in the gas storage of bus i at time t; represents the gas consumed by bus i at time t;

[0167] Wherein, the lower-layer consumer surplus optimization model includes a mathematical model of an energy consumption module and a mathematical model of a photovoltaic power generation module;

[0168] The mathematical model of the energy consumption module is:

[0169]

[0170] In formula (30), U Ele , U Gas represents the utility function, which is defined to describe the satisfaction degree of energy consumption; P Di,t represents the power demand of bus i at time t; represents the power consumed by the heat load of bus i at time t; i represents the number of planned PV installations on bus i; represents the photovoltaic output power of bus i at time t; g Di,t represents the natural gas demand and supply of bus i at time t; represents the natural gas sold by P2G at bus i at time t; represents the natural gas price sold by P2G to bus i at time t;

[0171] The mathematical model of the photovoltaic power generation module is:

[0172]

[0173] In formulas (31) to (32), V i,oc represents the open circuit voltage of the photovoltaic cell (V); γ i,t Indicates light intensity; A i Represents the effective area of ​​photovoltaic cells; represents the carbon quota sold by PV to bus i at time t;

[0174] S3, solving the first objective function and the second objective function based on the improved particle swarm algorithm and the constraint conditions;

[0175] Specifically, Figure 2As shown, the improved particle swarm algorithm is used to call CPLEX to solve the first objective function and the second objective function, including the following steps:

[0176] 1) Input the power network topology, gas network topology and installed capacity of various generators;

[0177] 2) Sampling wind speed, load, carbon price, and solar radiation to generate different scenarios;

[0178] 3) Calculate the total cost of energy supply taking into account carbon emission costs in the upper energy supply optimization model;

[0179] 4) Obtain information such as upper-level node electricity prices and node carbon flows;

[0180] 5) The node electricity price information obtained based on the lower-level consumer surplus optimization model is used to optimize the energy consumption structure of the consumer layer;

[0181] 6) Use improved particle swarm algorithm to solve, set loop termination threshold, position and speed of all particles, constraint check and fitness calculation;

[0182] 7) Whether the maximum number of iterations or the best fitness value is reached, if yes, proceed to the next step, otherwise proceed to step 3);

[0183] 8) Output the results of low-carbon operation under the sampling scenario;

[0184] S4. Determine an optimization operation strategy based on the solution results.

[0185] like Figure 3 As shown, the integrated energy system is composed of an IEEE-24-node power system coupled with a 19-node gas system, where each power load node is coupled with a certain heat load, which is connected to the pipeline in the gas system;

[0186] The detailed information of the modified IEEE-24 node power system is shown in Table 1; the distributed photovoltaic parameters in the model proposed in this invention are shown in Table 2; photovoltaic panels are installed on all load buses, and the number of installations is limited by the site area. Some main parameters used in the model are shown in Table 3. Based on the given confidence level and weighting factor, the conditional risk value CvaR in the case is set to the expected value of the 20% minimum cost scenario.

[0187] Table 1 Improved IEEE 24-node system parameters

[0188]

[0189] Table 2 Parameters of photovoltaic panels

[0190]

[0191] Table 3 Main simulation parameter settings

[0192]

[0193]

[0194] like Figure 4 As shown in the figure, the node electricity price in the hierarchical model case when the emission reduction target gradually increases from 50% to 90% carbon constraint scenario. As can be seen from the figure, the node electricity price shows a clear growth trend. The average electricity price of the node with 90% carbon constraint, that is, the lowest carbon emission quota, is the highest; conversely, the average electricity price of the node with 50% carbon constraint, that is, the highest carbon emission quota, is the lowest. At the same time, for example, 13:00, the node electricity price of 90% carbon constraint is about twice that of 50% carbon constraint. The simulation results effectively show that the node electricity price is highly sensitive to carbon constraints.

[0195] After the introduction of the carbon emissions trading mechanism, the integrated energy system has generated approximately 1,250 tons of emission reduction. Under the node marginal electricity price market mechanism that takes into account the value of carbon quotas, the demand-side energy structure is more inclined to renewable energy. Compared with the traditional integrated energy system, which does not participate in carbon emissions trading, with the adoption of power-to-gas technology and distributed photovoltaic on-site construction, another 2,100 tons of carbon emissions will be reduced. Therefore, the hierarchical model is conducive to reducing expected costs and risks, where the probability distribution of total costs is as follows Figure 5 shown.

[0196] like Figure 6 As shown, it shows the 24-hour dispatch result of the P2G facility connected to the power network node 14. When the heat load demand is large, such as at 3:00 or the gas power plant has a large demand for natural gas, such as at 13:00, the P2G facility will sell natural gas to the integrated energy system. When the wind power output in the system is large and the node electricity price is low, the P2G facility will purchase electricity to electrolyze water to generate natural gas.

[0197] The present invention considers the coupling relationship between the power system, gas system and thermal system, as well as the impact of distributed photovoltaic and power-to-gas technology; the optimization operation method of the present invention achieves cost minimization and risk control of the integrated energy system by establishing a conditional risk value objective function, verifying the effectiveness and feasibility of the method. Simulation results show that the optimization operation method of the present invention can effectively reduce the operating cost and carbon emissions of the integrated energy system, improve the utilization rate of renewable energy, and promote the low-carbon development of the integrated energy system.

[0198] The embodiments described above are merely descriptions of preferred implementation modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the principles and essence of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the scope of protection determined by the claims of the present invention.

Claims

1. An integrated energy system, characterized in that: include: The upper layer total energy supply cost module (1) is used to transmit the node marginal price to the consumer surplus module (2); and A lower layer consumer surplus module (2) is used to update the energy consumption structure to the energy supply total cost module (1); The energy supply total cost module (1) includes: An energy scheduling module (101), used for adjusting the energy supply structure; and A carbon market trading module (102), used for planning and trading carbon quotas; The consumer surplus module (2) comprises: An energy consumption module (201) for recording the consumption of end users; and Photovoltaic power generation module (202) for reducing dependence on upper supply layers; The energy scheduling module (101) purchases carbon quotas and sells carbon quotas to the carbon market trading module (102), the energy scheduling module (201) supplies power to the energy consumption module (101), and the photovoltaic power generation module (202) sells carbon quotas to the carbon market trading module (102).

2. A method for optimizing the operation of a comprehensive energy system, characterized in that: The specific steps include: S1. Construct the first objective function based on the upper energy supply optimization model and determine the constraints; S2. Constructing the second objective function based on the lower-level consumer surplus optimization model and determining the constraints; S3, solving the first objective function and the second objective function based on the improved particle swarm algorithm and the constraint conditions; S4. Determine an optimization operation strategy based on the solution results.

3. The method for optimizing operation of a comprehensive energy system according to claim 2, characterized in that: The step S1 specifically includes: The first objective function is as follows: In formula (1), Represents a set of energy types; (Ele, Gas) are associated with different energy forms of power generation and gas suppliers, where power generation is divided into three types according to fuel: coal, gas, and wind; C Ele (P Git ), C Gas (g Gi,t ) represent the operating cost functions of electricity and gas energy respectively; P Gi,t , g Gi,t They represent the output of power generation i and natural gas supplier i at time t respectively; λ represents the carbon price in the carbon trading market; represents the carbon quota for power generation i, and its value corresponds to Y; E Gi.t represents the calculated carbon emissions of power generation i at time t; T refers to the operation interval time; Y represents the total scheduling time. The upper energy supply optimization model includes an operating cost mathematical model of the energy scheduling module and a carbon emission flow mathematical model of the carbon market trading module; The mathematical model of the operating cost of the energy scheduling module is: In formulas (2) to (5), g Gi,t They represent the power generation of the i-th generation at time t associated with different power generation types: coal, gas, and wind, respectively, and the same symbols have the same meaning in the following text; Indicates the electricity output of wind power; m Wind represents the capital recovery factor associated with the investment in wind power generation; represents the cost coefficient of energy; The carbon emission flow mathematical model of the carbon market trading module is: In formulas (6) to (7), e i,t represents the node carbon intensity of bus i in time; r ij,t represents the branch carbon intensity of busbar i in time; Represents the generator set that injects energy into the bus; Represents the power unit flowing into bus i; represents the power flow between busbars i and j; e Gk Indicates the carbon intensity of the generator set, which is a parameter related to the power supply characteristics and depends on the state of the power supply unit; P Gk,t represents the injected power of generator k per unit time; Introducing the conditional value at risk (CVaR) analysis, the first objective function is rewritten as: In formula (8), q k represents the scenario set; f2 represents the objective function of CVaR; Pr k represents the probability of scenario k; f 1,k represents the value of the objective function in scenario k; represents the weighting factor of risk; b represents the confidence level indicating the risk preference; z represents an auxiliary variable with continuous non-negative values, which is defined as the maximum value between zero and the difference between the cost of each scenario minus VaR; VaR represents the value at risk; Determine the constraints of the rewritten first objective function: 1) Supply and demand balance constraints: In formula (9), q D represents the collection of microgrids, which can be regarded as demand from the perspective of the main grid; q P2G Represents the set of P2G facilities; P Di,t , They represent the pure power demand of bus i at time t and the power consumed by the heat load respectively; c i represents the planned PV quantity of bus i; represents the photovoltaic output power of bus i at time t; 2) Power generation related constraints: In formulas (10) to (11), and Indicates the rising and falling limits of power generation i; 3) Electrical node balance constraints: In formulas (12) to (13), B ij represents the admittance between bus i and bus j; q i,t ,q j,t They represent the voltage angles of bus i and bus j at time t respectively; 4) System constraints of the power grid, including power output boundaries and ramp limits: 5) Gas node balance constraints: In formula (15), represents the gas sold by P2G to bus i at time t; represents the gas supplied to the gas generator at bus i at time t; g Di,t represents the gas demand of bus i at time t; 6) Energy demand constraints of heat load: In formulas (16) to (17), t Di,t represents the heat demand of bus i at time t; h Gas,heat 、h Power,heat Energy conversion coefficient expressing the unit correlation constant: 7) Natural gas supply restrictions: 8) The ratio of electricity and natural gas for the heat load, which is limited by the dual fuel ratio: 9)CVaR-related constraints: VaR-f1￡z(20) z 30(21)。 4. The method for optimizing operation of a comprehensive energy system according to claim 2, characterized in that: The step S2 specifically includes: The second objective function is as follows: In formula (22), represents the set of P2G facilities; E con,t The amount of electricity consumed by the end user at time t; The price of P2G facilities providing ancillary services; i represents the carbon price in the carbon trading market; represents the carbon quota of bus i; represents the carbon emissions associated with natural gas purchased from P2G by bus i at time t; E Di,t represents the calculated carbon emission quota of bus i at time t; m PV Represents the capital recovery income of photovoltaic panels. Determine the constraints of the lower-level consumer surplus optimization model: 1) Limit the number of planned PV installations on bus i: 2) Introducing photovoltaic power output: In formula (24), P MPP represents the maximum output power of the photovoltaic panel; h1 represents the power output function of the photovoltaic panel, which can be obtained from the single PV panel parameter table; g i,t represents solar radiation; 3) Explain the carbon emission quotas that come with gas purchased from P2G: In formulas (25) to (26), Indicates the natural gas sold. represents the natural gas produced by P2G at bus i at time t, h2 can be regarded as a linear function of a and b, where is a constant; T represents time; Q P2G Represents a set of P2G facilities; 4) Natural gas export boundary of natural gas supply: In formula (27), The natural gas output boundaries represent the natural gas supply, respectively; 5) Energy conversion from electricity to natural gas in P2G facilities: In formula (28), h P2G It represents the energy conversion factor from electricity to natural gas of the P2G facility; Indicates electrical power; 6) The gas balance after selling gas in P2G facilities is stipulated as follows: In formula (29), represents the gas stored in the gas storage of bus i at time t; represents the gas consumed by bus i at time t; Wherein, the lower-layer consumer surplus optimization model includes a mathematical model of an energy consumption module and a mathematical model of a photovoltaic power generation module; The mathematical model of the energy consumption module is: In formula (30), U Ele , U Gas represents the utility function, which is defined to describe the satisfaction degree of energy consumption; P Di,t represents the power demand of bus i at time t; represents the power consumed by the heat load of bus i at time t; c i represents the number of planned PV installations on bus i; represents the photovoltaic output power of bus i at time t; g Di,t represents the natural gas demand and supply of bus i at time t; represents the natural gas sold by P2G at bus i at time t; represents the natural gas price sold by P2G to bus i at time t; The mathematical model of the photovoltaic power generation module is: In formulas (31) to (32), V i,oc represents the open circuit voltage of the photovoltaic cell (V); γ i,t Indicates light intensity; A i Represents the effective area of ​​photovoltaic cells; represents the carbon quota sold by PV to bus i at time t.

5. The method for optimizing operation of a comprehensive energy system according to claim 2, characterized in that: The step S3 specifically includes: 1) Input the power network topology, gas network topology and installed capacity of various generators; 2) Sampling wind speed, load, carbon price, and solar radiation to generate different scenarios; 3) Calculate the total cost of energy supply taking into account carbon emission costs in the upper energy supply optimization model; 4) Obtain information such as upper-level node electricity prices and node carbon flows; 5) The node electricity price information obtained based on the lower-level consumer surplus optimization model is used to optimize the energy consumption structure of the consumer layer; 6) Use improved particle swarm algorithm to solve, set loop termination threshold, position and speed of all particles, constraint check and fitness calculation; 7) Whether the maximum number of iterations or the best fitness value is reached, if yes, proceed to the next step, otherwise proceed to step 3); 8) Output the results of low-carbon operation under the sampling scenario.

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