Low-carbon economic dispatching method of combined cooling, heating, power and hydrogen supply type comprehensive energy system

Abstract

A low-carbon economic dispatching method for a combined cooling, heating, power and hydrogen supply type comprehensive energy system comprises the steps that firstly, a combined cooling, heating, power and hydrogen supply type comprehensive energy system model is established in a simulation platform, then a comprehensive energy multi-target optimization dispatching model based on stepped carbon transaction is established, and finally an optimization dispatching solving method for the combined cooling, heating, power and hydrogen supply type comprehensive energy system is established. According to the invention, a high-efficiency and low-carbon comprehensive energy system transaction mechanism is realized, a hydrogen energy multi-element utilization structure is introduced to form a combined cooling, heating, power and hydrogen supply type comprehensive energy system, and the reliability and flexibility of the system are improved; a stepped carbon transaction mechanism is introduced, so that the enthusiasm of a subject to participate in the carbon market and the decarburization and emission reduction level of the system are improved; according to the method, the solution of the particle swarm algorithm is improved, the solution efficiency of the algorithm is improved, and a more flexible and quicker optimization strategy is provided for real-time scheduling of the system.

Description

Technical Field

[0001] The present invention relates to the field of economic dispatch of power systems, and in particular to a low-carbon economic dispatch method applied to a combined cooling, heating, electricity and hydrogen energy system. Background Art

[0002] Under the premise of ensuring energy supply, the optimal scheduling of the integrated energy system plays a vital role in rationally allocating the power generation capacity of thermal power units to reduce fuel costs. However, it is well known that thermal power units are a type of power generation equipment that completely relies on the combustion of fossil fuels - coal to generate energy. They have low energy utilization rates and emit a large amount of carbon dioxide during their combustion process, which increases the greenhouse effect in the atmosphere and aggravates the problem of global climate change.

[0003] In order to comprehensively consider the economic and environmental benefits of optimal scheduling, multi-objective optimal scheduling has become one of the research hotspots in the field of optimal scheduling of integrated energy systems. Among them, hydrogen energy, as an ideal clean energy, has received widespread attention.

[0004] Hydrogen energy has the characteristics of abundant resources, high calorific value and no pollution, and is widely regarded as one of the ideal clean energy sources in the future. In the development and utilization of hydrogen energy, green hydrogen is considered to have good development prospects. The so-called green hydrogen refers to the use of renewable energy as raw materials to produce hydrogen energy, and hydrogen energy is easier to store on a large scale for a long time than electricity.

[0005] There have been many studies on the optimization and scheduling of hydrogen energy systems under existing technologies, such as:

[0006] 1) Using renewable energy to produce hydrogen with electrolyzers (EL) to achieve flexible consumption of wind and solar power;

[0007] 2) The power to gas (P2G) device is subdivided into an electrolyzer and a methane reactor (MR) and operated in conjunction with a hydrogen fuel cell (HFC), solving the problem of imbalance between source and load supply and demand;

[0008] 3) The natural gas market was introduced, and a coal-fired power plant with carbon capture equipment and a P2G collaborative operation system were built;

[0009] 4) On the hydrogen energy side, hydrogen-powered vehicles and the chemical industry have been introduced, expanding the ways of consuming hydrogen energy.

[0010] However, the studies 1) to 4) above all focus on the collaborative operation framework of thermal power units and power-to-gas in the electricity-gas interconnection system, and fail to consider the coupling of multiple energy sources such as electricity, cooling, heat, and hydrogen.

[0011] 5) A unified carbon trading mechanism was introduced, and the carbon emission reduction effects on both the source and the load were comprehensively considered;

[0012] 6) On the basis of the carbon trading mechanism, introduce low-carbon means of solar thermal power stations to increase the system's carbon emission reduction and renewable energy consumption rate;

[0013] However, the above studies 5) to 6) mainly focus on the unified carbon trading model and fail to fully explore the potential low-carbon capacity in the system.

[0014] It can be seen that the research on the above-mentioned existing technologies lacks consideration of multiple energy supply and lacks further exploration of low-carbon trading. Therefore, in recent years, researchers have begun to pay attention to the ladder carbon trading model and have carried out relevant research. They have proposed a virtual power plant optimization scheduling model based on P2G and ladder carbon trading mechanism, which further constrains carbon emissions. They have conducted a comparative study on the two carbon trading mechanisms of ladder carbon trading and unified carbon trading in the integrated energy system, as well as further analyzed the ladder carbon trading mechanism, and studied the impact of carbon price and coefficient selection on optimal scheduling.

[0015] Optimization scheduling can no longer focus on a single goal (such as minimizing operating costs), but needs to pay attention to carbon emissions at the same time. Traditional single-goal optimization scheduling under existing technologies can no longer meet the requirements in this new context and has many defects, such as:

[0016] 1) For the combined cooling, heating and power microgrid, an objective function of comprehensive operating costs and environmental penalty factors was established, and the fuzzy geometric weighted method was used to clarify the weights of each objective function. Then, multiple objective functions were fuzzified through fuzzy logic operations and finally converted into a single objective function.

[0017] In the study of comprehensive energy optimization scheduling, the above scheme usually adopts the method of simplifying the multi-objective problem into a single-objective problem. However, this transformation involves the decision-maker's preference for multiple objectives, especially the subjective factors in determining the weight ratio of each objective, which is difficult.

[0018] 2) Construct an optimal dispatching scheme for virtual power plants with operation economic benefits, user comfort and carbon emissions as optimization objectives, and use an improved genetic algorithm to solve the optimal dispatching model for virtual power plants;

[0019] 3) Construct a multi-objective optimization scheduling model based on the combined cooling, heating and power microgrid. The objective function of the model covers multiple aspects such as scheduling cost, load curve variance and renewable energy curtailment, and the particle swarm algorithm is used to solve it;

[0020] 4) Construct an optimal dispatching model for the solar thermal-wind power combined system and use the particle swarm algorithm to solve it, solve the peak-valley difference of the power grid load and reduce the peak-shaving cost of thermal power.

[0021] However, the above solutions all use the traditional particle swarm algorithm, which will lead to the problems of convergence difficulty and slow solution speed, thus limiting the flexibility of real-time scheduling of the system to some extent.

[0022] In summary, the defects of existing technologies and research are summarized as follows:

[0023] The energy utilization rate is low; there is no consideration of coupling multiple energy sources such as electricity, cold, heat, hydrogen, etc., coordinating and complementing to meet demand and achieve optimal energy utilization; the carbon trading mechanism is imperfect: the unified carbon trading model is difficult to fully stimulate the enthusiasm and activity of the market; the solution algorithm performance is poor: the model solution is difficult to converge and the solution speed is slow.

[0024] Therefore, there is an urgent need for a new type of economic dispatch method for high-efficiency, low-carbon integrated energy systems. Summary of the invention

[0025] In order to solve the above problems, the present invention provides a low-carbon economic scheduling method for a combined energy system of heat, electricity and hydrogen. The present invention can realize a high-efficiency, low-carbon trading mechanism for the combined energy system, and also introduces a multi-utilization structure of hydrogen energy to form a combined energy system of heat, electricity and hydrogen, thereby improving the reliability and flexibility of the system. The present invention introduces a step-by-step carbon trading mechanism, thereby increasing the enthusiasm of the subjects to participate in the carbon market and the level of decarbonization and emission reduction of the system. In addition, the present invention improves the particle swarm algorithm solution, thereby improving the algorithm solution efficiency, and providing a more flexible and faster optimization strategy for the real-time scheduling of the system.

[0026] A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system of the present invention specifically comprises the following steps:

[0027] 1) Establish a comprehensive energy system model of combined cooling, heating, electricity and hydrogen in the simulation platform, including: building an interactive architecture of the comprehensive energy system, a thermal power unit model taking into account carbon capture, a hydrogen energy multi-utilization structure model, a gas boiler model and a refrigeration system model.

[0028] 2) Establish a comprehensive energy multi-objective optimization scheduling model based on ladder carbon trading, including: establishing objective functions and establishing constraints.

[0029] 3) Based on the above steps 2) and 3), a solution method for optimizing the scheduling of a combined energy system of heat, electricity and hydrogen is established, including a particle swarm algorithm with a linear decreasing constraint and a particle swarm algorithm flow with a linear decreasing constraint.

[0030] A low-carbon economic dispatch method for a combined energy system of cooling, heating, electricity and hydrogen according to the present invention is characterized in that the interactive architecture of constructing the combined energy system in the step 1) is specifically composed of a fan, a two-stage power-to-gas, a hydrogen fuel cell, a hydrogen storage tank, a carbon capture device, a gas boiler, a thermal power unit, an electric refrigerator and an absorption refrigerator, and a heat storage tank, and the load includes an electric load, a heat load and a cold load, wherein the electric load is borne by the output of the fan, the thermal power unit and the hydrogen fuel cell, the heat load is heated by the gas boiler and the heat storage tank, and the cold load is cooled by the electric refrigerator and the absorption refrigerator.

[0031] In the integrated energy system of combined cooling, heating, electricity and hydrogen, the two-stage power-to-gas and energy storage devices are used as flexible resources to give full play to their energy time-shifting characteristics. By predicting the output and load of renewable energy, the output of each device is coordinated to smooth the output fluctuation of renewable energy. The system is committed to obtaining the best economic benefits while ensuring that the load demand is met and safety is guaranteed.

[0032] A low-carbon economic dispatch method for a combined cooling, heating, electricity and hydrogen energy system according to the present invention is characterized in that the thermal power unit model taking carbon capture into account in step 1) is specifically:

[0033] Energy consumption of carbon capture equipment in thermal power units taking carbon capture into account P CCS Provided by thermal power units, this energy consumption is determined by the basic energy consumption P B And operating energy consumption P o Composition, among which, the basic energy consumption P B Does not change with the state of the carbon capture equipment, the operating energy consumption P o It is related to the amount of carbon dioxide captured, and the specific formula is:

[0034]

[0035] Where: α c is the operating energy consumption factor, which is the energy consumption required to capture a unit of carbon dioxide;

[0036] Q c2 (t) is the amount of carbon dioxide processed by the carbon capture equipment during period t.

[0037] Carbon capture technology provides technical support for thermal power units to achieve low-carbon goals, and helps to balance the relationship between energy security, environmental protection and economic benefits. Carbon capture equipment captures carbon dioxide produced by thermal power units, a part of which is used for oil recovery and storage, and the other part is used as the carbon source required in the two-stage power-to-gas methanation reaction process.

[0038] According to a low-carbon economic dispatching method for a combined energy system of cold, heat, electricity and hydrogen supply of the present invention, it is characterized in that the hydrogen energy multi-utilization structural model in step 1) includes a two-stage power-to-gas model, a hydrogen fuel cell model and a hydrogen storage tank model, as follows:

[0039] 1) The two-stage power-to-gas system is divided into an electrolyzer and a methane reactor, and a hydrogen storage tank is set in the middle link. In the electrolyzer, an external power supply applies current to decompose water into hydrogen and oxygen to form a hydrogen production link, wherein the generated hydrogen is used in the methane reactor to react with carbon dioxide to generate methane, and the hydrogen storage tank is used as an intermediate link to store excess wind power in the form of hydrogen, which is converted into electrical energy and thermal energy through hydrogen fuel cells when the load demand is high, providing a flexible energy supply for the system. The electrolyzer model and methane reactor model of the two-stage power-to-gas model are as follows:

[0040] 1.1) Electrolyzer model:

[0041] Hydrogen production power of electrolyzer during period t With its operating power P EL_e The relationship between (t) is:

[0042]

[0043] Where: η EL is the energy conversion efficiency of the electrolyzer;

[0044] 1.2) Methane reactor model:

[0045] Hydrogen power consumption of methane reactor during period t Its gas production power Q MR_g The relationship between (t) is:

[0046]

[0047] Where: η MR is the energy conversion efficiency of the methane reactor;

[0048] From the electrochemical reaction process of the methane reactor, it can be seen that the volume of methane produced is V MR_g (t) and the volume V of carbon dioxide consumed MR_c (t) Same:

[0049]

[0050] Where: e-h is the thermoelectric conversion coefficient; L g is the calorific value of natural gas combustion; Q MR_C (t) is the amount of carbon dioxide required for methanogenesis during period t;

[0051] When the carbon capture power plant is operated in conjunction with power-to-gas, the CO2 required for P2G to produce methane does not need to be purchased additionally, and is satisfied by the carbon capture and utilization of the thermal power units, that is:

[0052] Q c2 (t) = Q MR_C (t)+Q storage (t) (7)

[0053] Where: Q storage (t) is the carbon storage amount during period t;

[0054] Power-to-gas reduces the gas purchase cost of the natural gas system and can also be transmitted to hydrogen fuel cells for power generation.

[0055] 2) Hydrogen fuel cell model:

[0056]

[0057] Where: is the input hydrogen energy of HFC during period t;

[0058] P HFC_e (t), P HFC_h (t) are the electrical energy and thermal energy output of HFC in period t, respectively;

[0059] η HFC_e The total efficiency of HFC conversion to thermal power;

[0060] λ min ,λ max are the minimum and maximum values ​​of HFC thermoelectric efficiency, respectively;

[0061] Hydrogen fuel cells use hydrogen produced in electrolyzers to provide electricity and heat. In an integrated energy system, scheduling hydrogen fuel cells can reduce system operating costs and carbon emissions to achieve low-carbon economic operation of the system.

[0062] 3) Hydrogen storage tank model:

[0063]

[0064] Where: P ch_n (t), P dis_n (t) are the charging and discharging power of hydrogen storage tank n during period t;

[0065] P ESS is the maximum charging and discharging power of the hydrogen storage tank;

[0066] S ESS_n (t), S ESS_n (t-1) are the storage capacities of hydrogen storage tank n in periods t and t-1 respectively;

[0067] are the maximum and minimum values ​​of hydrogen storage capacity respectively;

[0068] η ch , η dis They are the charging and discharging efficiency of the hydrogen storage tank respectively;

[0069] E ESS_n is the rated capacity of hydrogen storage tank n.

[0070] The hydrogen storage tank can release the stored hydrogen during peak energy production or other situations where high energy demand is required to meet the increase in electricity demand. Conversely, when there is an oversupply of energy, the hydrogen storage tank can convert the remaining electrical energy into hydrogen and store the hydrogen for subsequent use, thereby achieving energy balance and reserve. And the thermal storage tank model is similar to the hydrogen storage tank model.

[0071] According to a low-carbon economic dispatching method for a combined energy system of cooling, heating, electricity and hydrogen supply of the present invention, it is characterized in that the gas boiler model in step 1) is as follows: GB_h (t) and its natural gas power consumption Q GB_g The relationship between (t) is:

[0072]

[0073] Where: It is the heating efficiency of gas boiler.

[0074] A low-carbon economic dispatch method for a combined energy system of cooling, heating, electricity and hydrogen according to the present invention is characterized in that the refrigeration system model in step 1) includes an electric refrigerator model and an absorption refrigerator model, which are respectively as follows:

[0075] 1) Electric refrigerator model:

[0076] Refrigeration power P of electric refrigerator during period t EC_c (t) and its input power P EC_e The relationship between (t) is:

[0077]

[0078] Where: is the cooling coefficient of the electric refrigerator;

[0079] 2) Absorption Refrigeration Machine Model:

[0080] Refrigeration power P of absorption chiller during period t AC_c (t) and its input heat power P AC_h The relationship between (t) is:

[0081]

[0082] Where: is the refrigeration coefficient of the absorption chiller.

[0083] According to a low-carbon economic dispatching method for a combined energy system of cold, heat, electricity and hydrogen cogeneration type of the present invention, it is characterized in that the step 2) establishes an objective function in the comprehensive energy multi-objective optimization dispatching model based on step carbon trading, and the objective function f min as follows:

[0084] f min =(f e ,f CET ) (14)

[0085] Where: f e For economic purposes;

[0086] f CET For carbon trading, the two objectives have equal weight and constrain and promote each other;

[0087] Among them, f CET Carbon trading is a ladder carbon trading mechanism, including carbon emission quota model, actual carbon emission model and ladder carbon trading model, which are as follows:

[0088] 1) Carbon emission quota model:

[0089] The carbon emission sources in the combined energy system of cooling, heating, electricity and hydrogen mainly include upstream power purchase, thermal power units and gas boilers, and their free carbon quotas are as follows:

[0090]

[0091] Where: C VPP Free carbon quota; C G is the carbon quota of thermal power units; C CB is the carbon quota for gas boilers; e , χ g They are the quotas corresponding to the carbon dioxide emissions per unit of electricity generated by thermal power units and the quotas corresponding to the carbon dioxide emissions per unit of natural gas consumed by gas boilers;

[0092] 2) Actual carbon emission model:

[0093]

[0094] Where: C VPP,a is the actual carbon emissions; C buy,a The actual carbon emissions of electricity purchased from the grid; C G,a is the actual carbon emission of thermal power units; C GB,a is the actual carbon emission of the gas boiler; CMR,a The amount of carbon dioxide absorbed by the methanogenization plant;

[0095] 3) Tiered carbon trading model:

[0096] C VPP,t =C VPP,a -C VPP (17)

[0097] Where: C VPP,t is the carbon emission trading amount;

[0098] By comparing actual carbon emissions with carbon quotas, the actual transaction amount of the carbon trading market can be obtained. If there are remaining carbon quotas, these quotas can be considered for sale to obtain economic returns. On the contrary, if the carbon quota allocated is exceeded, additional carbon quotas need to be purchased.

[0099] Finally, we get the tiered carbon trading cost f CET for:

[0100]

[0101] In the formula: λ is the initial price of carbon trading; l is the length of the step interval; α is the price growth rate.

[0102] 4) Economic objective function:

[0103] The economic objective function comprehensively considers the operating cost of the integrated energy system. om , energy purchase cost buy and carbon sequestration costs storage ,as follows:

[0104] f e =f om +f buy +f storage (19)

[0105] Running costs:

[0106]

[0107] Where: j P is the operating cost per unit power output of device j; j (t) is the output power of device j in time period t;

[0108] Energy purchase cost:

[0109]

[0110] Where: P g,buy (t) is the gas purchase volume in period t; α t , βt are the electricity price and gas price in period t respectively;

[0111] Carbon storage costs:

[0112]

[0113] Where: γ t The cost for storing unit CO2 is 40 yuan / t.

[0114] As an important means of systemic carbon emission reduction, the tiered carbon trading mechanism, with the help of the mechanism of the carbon emission rights trading market, encourages power generation companies to adjust their production and carbon emission plans according to their carbon emission quotas, thereby effectively achieving carbon emission reduction targets.

[0115] According to a low-carbon economic dispatching method for a combined energy system of cold, heat, electricity and hydrogen supply type of the present invention, it is characterized in that the step 2) establishes the establishment of constraints in the comprehensive energy multi-objective optimization dispatching model based on ladder carbon trading, and the constraints are power balance constraints, wind power output constraints and equipment operation constraints, which are as follows:

[0116] 1) Power balance constraints:

[0117] The electricity, heat, natural gas, and hydrogen within the virtual power plant must meet the following power balance constraints:

[0118]

[0119] 2) Wind power output constraints:

[0120]

[0121] Where: P w (t) is the wind power output power in period t; is the maximum wind power output power;

[0122] 3) Equipment operation constraints:

[0123] The equipment includes thermal power units, two-stage power-to-gas, hydrogen fuel cells, gas boilers and refrigeration systems, all of which must meet equipment operation constraints, including equipment capacity constraints and ramp constraints:

[0124]

[0125] Where: P i min , P i max are the upper and lower limits of the input power of device i; ΔP i min , ΔP i maxare the upper and lower limits of the climbing speed of device i respectively.

[0126] According to a low-carbon economic dispatch method for a combined energy system of cooling, heating, electricity and hydrogen supply type of the present invention, it is characterized in that the particle swarm algorithm with linear decreasing constraints introduced in step 3) is specifically as follows:

[0127] In the particle swarm algorithm, the particle position and speed as follows:

[0128]

[0129] Where: i = 1, 2, ..., m, m is the size of the particle swarm;

[0130] j=1,2,...,J, J is the dimension of the particle swarm;

[0131] ω is the inertia parameter;

[0132] c1, c2 are learning factors, both are constants;

[0133] r1, r2 are random numbers in [0,1];

[0134] G is the global optimal solution, P i is the individual optimal solution of the i-th particle;

[0135] x ij , v ij , P gj , P ij X i , U i , G, P i The j-dimensional component of

[0136] is the j-dimensional component of the maximum velocity;

[0137] A linear decreasing constraint is introduced into the above particle swarm algorithm as follows:

[0138] 1) Set a larger inertia parameter and learning factor and start the algorithm iteration process;

[0139] 2) In each iteration, these parameters are adjusted in real time to gradually decrease;

[0140] 3) Complete the entire iteration process with smaller inertia parameters and learning factors;

[0141] In this step, the processing method for inertia weight and learning factor is as follows:

[0142]

[0143] Where: t is the number of current iterations; t max is the total number of iterations; ω max ,ω min Take 0.9 and 0.2 respectively; c max 、c min Take 1.5 and 1 respectively;

[0144] The linear decreasing parameter setting method helps to flexibly adjust the parameters and comprehensively consider the individual characteristics of different generations of particles in the particle swarm. By introducing this improved strategy, the present invention can improve the solution rate and convergence of the particle swarm algorithm. By dynamically adjusting the inertia parameter and learning factor, the search space can be explored more quickly in the early iteration, and converged to the optimal solution more carefully in the later iteration.

[0145] A multi-objective particle swarm algorithm with linear decreasing constraints is introduced, and a fuzzy decision method is used to extract the final optimal scheduling scheme of the integrated energy system from the non-inferior solution set, as shown in the following formula:

[0146]

[0147] Where: F i is the value of the multi-objective function after fuzzification; f i is the objective function value of the i-th non-inferior solution in the non-inferior solution set; f max 、f min are the upper and lower limits of the objective function value of the i-th non-inferior solution in the non-inferior solution set respectively;

[0148] When solving multi-objective optimization problems through particle swarm optimization, multiple non-inferior solutions are usually generated, that is, a set of solutions that are not significantly inferior to other solutions in the objective space. The non-inferior solutions are finally stored in an external archive. The external archive refers to the set of non-inferior solutions in each time period of the multi-objective particle swarm algorithm improvement scheme.

[0149] (f e 、f CET ) as the factor set, and make a single factor evaluation on the non-inferior solution set to form a fuzzy evaluation matrix. For the convenience of analysis, the evaluation factor weight vector is taken as [0.5, 0.5]. Finally, through the fuzzy comprehensive evaluation, the optimal solution is selected as the final comprehensive energy system optimization scheduling plan.

[0150] The particle swarm algorithm is based on the principle of simulating social group behavior, and represents the potential solutions in the search space as particles. These particles adjust their positions and speeds through update rules according to their individual current positions and speeds, as well as the best performing positions in the entire group in history (global optimal solutions). Through continuous iteration, the particle swarm algorithm can find the optimal solution or a solution set close to the optimal solution in the search space. However, the traditional particle swarm algorithm is significantly affected by the inertia parameter and learning factor settings in terms of optimization performance. In each iteration process, although the spatial position and speed are updated, the individual differences between particles of different generations are often ignored. In order to solve this problem, step 3) of the present invention proposes an improvement strategy, which introduces linear decreasing constraints on the inertia parameter and learning factor of the particle swarm algorithm to improve the performance of the optimization algorithm.

[0151] According to a low-carbon economic dispatch method for a combined energy system of cooling, heating, electricity and hydrogen supply type of the present invention, it is characterized in that the establishment of the step 3) introduces a particle swarm algorithm process with a linear decreasing constraint, which is specifically as follows:

[0152] 1) Given the initial position and velocity of the particles, determine the upper and lower limits of the velocity and the maximum number of iterations of the corresponding particle swarm according to the model requirements of each device;

[0153] 2) Input the data of the combined cooling, heating, electricity and hydrogen energy system for initial calculation, and record the optimal value of the particle and the optimal value of the particle group;

[0154] 3) In each iteration, the inertia parameters and learning factors are dynamically updated, the optimization conditions of the particles are determined, and the corresponding calculations are performed. In the iteration process, the optimal solution set of the non-dominated solution is recorded, which saves the best non-dominated solution obtained in the iteration process;

[0155] 4) The system stops searching for the optimal value after iterating for a set number of times. When searching for the optimal value stops, the set of historical optimal values ​​recorded at this iteration is recorded.

[0156] The following is obtained by using a low-carbon economic dispatching method for a cold, heat, electricity and hydrogen combined energy system of the present invention:

[0157] Beneficial effects:

[0158] 1. The low-carbon economic dispatching method of the integrated energy system of combined cooling, heating, electricity and hydrogen of the present invention can realize a high-efficiency, low-carbon integrated energy system trading mechanism;

[0159] 2. The low-carbon economic dispatching method of a combined energy system of cold, heat, electricity and hydrogen of the present invention introduces a multi-utilization structure of hydrogen energy to form a combined energy system of cold, heat, electricity and hydrogen, thereby improving the reliability and flexibility of the system;

[0160] 3. The low-carbon economic dispatching method of the integrated energy system of combined cooling, heating, electricity and hydrogen of the present invention introduces a stepped carbon trading mechanism, which improves the enthusiasm of the subject to participate in the carbon market and the decarbonization and emission reduction level of the system;

[0161] 4. The low-carbon economic dispatching method of the combined heat, electricity and hydrogen energy system of the present invention improves the particle swarm algorithm solution, improves the algorithm solution efficiency, and provides a more flexible and faster optimization strategy for the real-time dispatch of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0162] Figure 1 A schematic diagram of the interactive architecture of a cold, heat, electricity and hydrogen combined energy system model of a low-carbon economic dispatch method for a cold, heat, electricity and hydrogen combined energy system of the present invention;

[0163] Figure 2 The present invention is a flowchart of a solution algorithm for a low-carbon economic dispatch method of a combined energy system of cooling, heating, electricity and hydrogen in a specific implementation.

[0164] Note: Figure 2 The Pareto optimal solution in = Pareto optimal solution, that is, the non-dominated optimal solution. DETAILED DESCRIPTION

[0165] The following further describes the technical means, creative features, objectives and effects achieved by the low-carbon economic scheduling method of a cold, heat, electricity and hydrogen combined energy system of the present invention in combination with the accompanying drawings and embodiments.

[0166] A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system, comprising the following steps:

[0167] 1) Establish a comprehensive energy system model of combined cooling, heating, electricity and hydrogen in the simulation platform, including: building an interactive architecture of the comprehensive energy system, a thermal power unit model taking into account carbon capture, a hydrogen energy multi-utilization structure model, a gas boiler model and a refrigeration system model.

[0168] 2) Establish a comprehensive energy multi-objective optimization scheduling model based on ladder carbon trading, including: establishing objective functions and establishing constraints.

[0169] 3) Based on the above steps 2) and 3), a solution method for optimizing the scheduling of a combined energy system of heat, electricity and hydrogen is established, including a particle swarm algorithm with a linear decreasing constraint and a particle swarm algorithm flow with a linear decreasing constraint.

[0170] like Figure 1As shown, the interactive architecture of building a comprehensive energy system in step 1) is specifically composed of a fan, a two-stage power-to-gas, a hydrogen fuel cell, a hydrogen storage tank, a carbon capture device, a gas boiler, a thermal power unit, an electric refrigerator and an absorption refrigerator, and a heat storage tank. The load includes an electric load, a heat load and a cold load. Among them, the electric load is borne by the output of the fan, the thermal power unit and the hydrogen fuel cell, the heat load is heated by the gas boiler and the heat storage tank, and the cold load is cooled by the electric refrigerator and the absorption refrigerator.

[0171] The thermal power unit model taking carbon capture into account in step 1) is specifically:

[0172] Energy consumption of carbon capture equipment in thermal power units taking carbon capture into account P CCS Provided by thermal power units, this energy consumption is determined by the basic energy consumption P B And operating energy consumption P o Composition, among which, the basic energy consumption P B Does not change with the state of the carbon capture equipment, the operating energy consumption P o It is related to the amount of carbon dioxide captured, and the specific formula is:

[0173]

[0174] Where: α c is the operating energy consumption factor, which is the energy consumption required to capture a unit of carbon dioxide;

[0175] Q c2 (t) is the amount of carbon dioxide processed by the carbon capture equipment during period t.

[0176] The hydrogen energy multi-utilization structural model in step 1) includes a two-stage power-to-gas model, a hydrogen fuel cell model, and a hydrogen storage tank model, as follows:

[0177] 1) The two-stage power-to-gas system is divided into an electrolyzer and a methane reactor, and a hydrogen storage tank is set in the middle link. In the electrolyzer, an external power supply applies current to decompose water into hydrogen and oxygen to form a hydrogen production link, wherein the generated hydrogen is used in the methane reactor to react with carbon dioxide to generate methane, and the hydrogen storage tank is used as an intermediate link to store excess wind power in the form of hydrogen, which is converted into electrical energy and thermal energy through hydrogen fuel cells when the load demand is high, providing a flexible energy supply for the system. The electrolyzer model and methane reactor model of the two-stage power-to-gas model are as follows:

[0178] 1.1) Electrolyzer model:

[0179] Hydrogen production power of electrolyzer during period t With its operating power P EL_e The relationship between (t) is:

[0180]

[0181] Where: η EL is the energy conversion efficiency of the electrolyzer;

[0182] 1.2) Methane reactor model:

[0183] Hydrogen power consumption of methane reactor during period t Its gas production power Q MR_g The relationship between (t) is:

[0184]

[0185] Where: η MR is the energy conversion efficiency of the methane reactor;

[0186] From the electrochemical reaction process of the methane reactor, it can be seen that the volume of methane produced is V MR_g (t) and the volume V of carbon dioxide consumed MR_c (t) Same:

[0187]

[0188] Where: e-h is the thermoelectric conversion coefficient; L g is the calorific value of natural gas combustion; Q MR_C (t) is the amount of carbon dioxide required for methanogenesis during period t;

[0189] When the carbon capture power plant is operated in conjunction with power-to-gas, the CO2 required for P2G to produce methane does not need to be purchased additionally, and is satisfied by the carbon capture and utilization of the thermal power units, that is:

[0190] Q c2 (t) = Q MR_C (t)+Q storage (t) (7)

[0191] Where: Q storage (t) is the carbon storage amount during period t;

[0192] 2) Hydrogen fuel cell model:

[0193]

[0194] Where: is the input hydrogen energy of HFC during period t;

[0195] P HFC_e (t), P HFC_h (t) are the electrical energy and thermal energy output of HFC in period t, respectively;

[0196] η HFC_eThe total efficiency of HFC conversion to thermal power;

[0197] λ min ,λ max are the minimum and maximum values ​​of HFC thermoelectric efficiency, respectively;

[0198] 3) Hydrogen storage tank model:

[0199]

[0200] Where: P ch_n (t), P dis_n (t) are the charging and discharging power of hydrogen storage tank n during period t;

[0201] P ESS is the maximum charging and discharging power of the hydrogen storage tank;

[0202] S ESS_n (t), S ESS_n (t-1) are the storage capacities of hydrogen storage tank n in periods t and t-1 respectively;

[0203] are the maximum and minimum values ​​of hydrogen storage capacity respectively;

[0204] η ch , η dis They are the charging and discharging efficiency of the hydrogen storage tank respectively;

[0205] E ESS_n is the rated capacity of hydrogen storage tank n.

[0206] The gas boiler model in step 1), where the heat generation power P of the gas boiler during period t is GB_h (t) and its natural gas power consumption Q GB_g The relationship between (t) is:

[0207]

[0208] Where: It is the heating efficiency of gas boiler.

[0209] The refrigeration system model in step 1) includes an electric refrigerator model and an absorption refrigerator model, which are as follows:

[0210] 1) Electric refrigerator model:

[0211] Refrigeration power P of electric refrigerator during period t EC_c (t) and its input power P EC_e The relationship between (t) is:

[0212]

[0213] Where: is the cooling coefficient of the electric refrigerator;

[0214] 2) Absorption Refrigeration Machine Model:

[0215] Refrigeration power P of absorption chiller during period t AC_c (t) and its input heat power P AC_h The relationship between (t) is:

[0216]

[0217] Where: is the refrigeration coefficient of the absorption chiller.

[0218] Step 2) Establishment of the objective function in the comprehensive energy multi-objective optimization scheduling model based on ladder carbon trading. The objective function f min as follows:

[0219] f min =(f e ,f CET ) (14)

[0220] Where: f e For economic purposes;

[0221] f CET For carbon trading, the two objectives have equal weight and constrain and promote each other;

[0222] Among them, f CET Carbon trading is a ladder carbon trading mechanism, including carbon emission quota model, actual carbon emission model and ladder carbon trading model, which are as follows:

[0223] 1) Carbon emission quota model:

[0224] The carbon emission sources in the combined energy system of cooling, heating, electricity and hydrogen mainly include upstream power purchase, thermal power units and gas boilers, and their free carbon quotas are as follows:

[0225]

[0226] Where: C VPP Free carbon quota; C G is the carbon quota of thermal power units; C CB is the carbon quota for gas boilers; e , χ g They are the quotas corresponding to the carbon dioxide emissions per unit of electricity generated by thermal power units and the quotas corresponding to the carbon dioxide emissions per unit of natural gas consumed by gas boilers;

[0227] 2) Actual carbon emission model:

[0228]

[0229] Where: C VPP,a is the actual carbon emissions; C buy,a The actual carbon emissions of electricity purchased from the grid; C G,a is the actual carbon emission of thermal power units; C GB,a is the actual carbon emission of the gas boiler; C MR,a The amount of carbon dioxide absorbed by the methanogenization plant;

[0230] 3) Tiered carbon trading model:

[0231] C VPP,t =C VPP,a -C VPP (17)

[0232] Where: C VPP,t is the carbon emission trading amount;

[0233] Finally, we get the tiered carbon trading cost f CET for:

[0234]

[0235] In the formula: λ is the initial price of carbon trading; l is the length of the step interval; α is the price growth rate.

[0236] 4) Economic objective function:

[0237] The economic objective function comprehensively considers the operating cost of the integrated energy system. om , energy purchase cost buy and carbon sequestration costs storage ,as follows:

[0238] f e =f om +f buy +f storage (19)

[0239] Running costs:

[0240]

[0241] Where: j P is the operating cost per unit power output of device j; j (t) is the output power of device j in time period t;

[0242] Energy purchase cost:

[0243]

[0244] Where: P g,buy(t) is the gas purchase volume in period t; α t , β t are the electricity price and gas price in period t respectively;

[0245] Carbon storage costs:

[0246]

[0247] Where: γ t The cost for storing unit CO2 is 40 yuan / t.

[0248] Step 2) Establishment of constraints in the comprehensive energy multi-objective optimization scheduling model based on ladder carbon trading. The constraints are power balance constraints, wind power output constraints and equipment operation constraints, which are as follows:

[0249] 1) Power balance constraints:

[0250] The electricity, heat, natural gas, and hydrogen within the virtual power plant must meet the following power balance constraints:

[0251]

[0252] 2) Wind power output constraints:

[0253]

[0254] Where: P w (t) is the wind power output power in period t; is the maximum wind power output power;

[0255] 3) Equipment operation constraints:

[0256] The equipment includes thermal power units, two-stage power-to-gas, hydrogen fuel cells, gas boilers and refrigeration systems, all of which must meet equipment operation constraints, including equipment capacity constraints and ramp constraints:

[0257]

[0258] Where: P i min , P i max are the upper and lower limits of the input power of device i; ΔP i min , ΔP i max are the upper and lower limits of the climbing speed of device i respectively.

[0259] The particle swarm algorithm with linear decreasing constraints introduced in step 3) is as follows:

[0260] In the particle swarm algorithm, the particle position and speed as follows:

[0261]

[0262] Where: i = 1, 2, ..., m, m is the size of the particle swarm;

[0263] j=1,2,...,J, J is the dimension of the particle swarm;

[0264] ω is the inertia parameter;

[0265] c1, c2 are learning factors, both are constants;

[0266] r1, r2 are random numbers in [0,1];

[0267] G is the global optimal solution, P i is the individual optimal solution of the i-th particle;

[0268] x ij , v ij , P gj , P ij X i , U i , G, P i The j-dimensional component of

[0269] is the j-dimensional component of the maximum velocity;

[0270] A linear decreasing constraint is introduced into the above particle swarm algorithm as follows:

[0271] 1) Set a larger inertia parameter and learning factor and start the algorithm iteration process;

[0272] 2) In each iteration, these parameters are adjusted in real time to gradually decrease;

[0273] 3) Complete the entire iteration process with smaller inertia parameters and learning factors;

[0274] In this step, the processing method for inertia weight and learning factor is as follows:

[0275]

[0276] Where: t is the number of current iterations; t max is the total number of iterations; ω max ,ω min Take 0.9 and 0.2 respectively; c max 、c min Take 1.5 and 1 respectively;

[0277] A multi-objective particle swarm algorithm with linear decreasing constraints is introduced, and a fuzzy decision-making method is used to extract the final optimal scheduling scheme of the integrated energy system from the non-inferior solution set, as shown in the following formula:

[0278]

[0279] Where: F i is the value of the multi-objective function after fuzzification; f i is the objective function value of the i-th non-inferior solution in the non-inferior solution set; f max 、f min are the upper and lower limits of the objective function value of the i-th non-inferior solution in the non-inferior solution set respectively;

[0280] (f e 、f CET ) as the factor set, and make a single factor evaluation on the non-inferior solution set to form a fuzzy evaluation matrix. For the convenience of analysis, the evaluation factor weight vector is taken as [0.5, 0.5]. Finally, through the fuzzy comprehensive evaluation, the optimal solution is selected as the final comprehensive energy system optimization scheduling plan.

[0281] Step 3) establishes the particle swarm algorithm process with the introduction of linear decreasing constraints, which is specifically as follows:

[0282] 1) Given the initial position and velocity of the particles, determine the upper and lower limits of the velocity and the maximum number of iterations of the corresponding particle swarm according to the model requirements of each device;

[0283] 2) Input the data of the combined cooling, heating, electricity and hydrogen energy system for initial calculation, and record the optimal value of the particle and the optimal value of the particle group;

[0284] 3) In each iteration, the inertia parameters and learning factors are dynamically updated, the optimization conditions of the particles are determined, and the corresponding calculations are performed. In the iteration process, the optimal solution set of the non-dominated solution is recorded, which saves the best non-dominated solution obtained in the iteration process;

[0285] 4) The system stops searching for the optimal value after iterating for a set number of times. When searching for the optimal value stops, the set of historical optimal values ​​recorded at this iteration is recorded.

[0286] Example

[0287] like Figure 2 As shown, a low-carbon economic dispatching method for a combined energy system of cooling, heating, electricity and hydrogen of the present invention is specifically implemented as follows:

[0288] 1) Create a blank interface in the simulation platform to prepare for inputting parameters and building models;

[0289] 2) Collect the calculation parameters required for resource modeling in the integrated energy system and read them in the simulation platform in an interactive way with external files;

[0290] 3) According to formula (1) to formula (13) and formula (15) to formula (22), the calculation formulas involved in modeling various types of resources are input into the simulation platform. Among them, the multi-utilization of hydrogen energy including carbon capture and power-to-gas devices refers to formula (1) to formula (10), the gas boiler model refers to formula (11), the refrigeration system model refers to formula (12) to formula (13), the step-by-step carbon trading model refers to formula (15) to formula (18), and the comprehensive energy system cost model refers to formula (19) to formula (22). In this embodiment, it should be noted that if other energy conversion equipment needs to be added, the calculation formula can be directly added at the corresponding position;

[0291] 4) According to formula (14), the objective function is input into the simulation platform to make multi-objective decisions on the economy and carbon emissions of the integrated energy system of combined cooling, heating, electricity and hydrogen;

[0292] 5) Input constraint conditions in the simulation platform according to equations (23) to (25). In this embodiment, it should be noted that the calculation formulas of various resources should also be included in the constraint conditions for restriction;

[0293] 6) Given the initial position and velocity of the particle and the upper and lower limits of the velocity of the corresponding particle swarm and the maximum number of iterations, the particle swarm algorithm with a linear decreasing constraint is used to solve the problem;

[0294] 7) Determine whether the convergence conditions are met. If so, update the learning factor and inertia parameter, record the optimal solution and optimal value of the particle swarm, if not, directly output the fuzzy decision. It should be noted that the convergence conditions can be formulated by yourself.

[0295] It can be seen from the embodiments that the low-carbon economic dispatching method of a combined heat, electricity and hydrogen energy system of the present invention can realize a high-efficiency, low-carbon combined energy system trading mechanism; and the present invention introduces a hydrogen energy multi-utilization structure to form a combined heat, electricity and hydrogen energy system, thereby improving the reliability and flexibility of the system; the introduction of a step-by-step carbon trading mechanism increases the enthusiasm of the subjects to participate in the carbon market and the level of decarbonization and emission reduction of the system; the present invention also improves the particle swarm algorithm solution, thereby improving the algorithm solution efficiency, and providing a more flexible and faster optimization strategy for the real-time scheduling of the system.

[0296] The present invention is applicable to the field of economic dispatch of power systems of integrated energy systems combining cooling, heating, electricity and hydrogen.

[0297] In addition, those skilled in the art should recognize that the above embodiments are only used to illustrate the present application, and are not used to limit the present application. As long as they are within the spirit of the present application, any changes or modifications to the above embodiments will fall within the scope of the claims of the present application.

Claims

1. A low-carbon economic dispatching method for a combined energy system of cooling, heating, electricity and hydrogen, comprising the following steps: 1) Establish a comprehensive energy system model of combined cooling, heating, electricity and hydrogen in the simulation platform, including: building an interactive architecture of the comprehensive energy system, a thermal power unit model taking into account carbon capture, a hydrogen energy multi-utilization structure model, a gas boiler model and a refrigeration system model. 2) Establish a comprehensive energy multi-objective optimization scheduling model based on ladder carbon trading, including: establishing objective functions and establishing constraints. 3) Based on the above steps 2) and 3), a solution method for optimizing the scheduling of a combined energy system of heat, electricity and hydrogen is established, including a particle swarm algorithm with a linear decreasing constraint and a particle swarm algorithm flow with a linear decreasing constraint.

2. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system as claimed in claim 1, characterized in that: The interactive architecture of constructing the integrated energy system in the step 1) is specifically composed of a fan, a two-stage power-to-gas, a hydrogen fuel cell, a hydrogen storage tank, a carbon capture device, a gas boiler, a thermal power unit, an electric refrigerator and an absorption refrigerator, and a heat storage tank. The load includes an electric load, a heat load and a cold load, wherein the electric load is borne by the output of the fan, the thermal power unit and the hydrogen fuel cell, the heat load is jointly heated by the gas boiler and the heat storage tank, and the cold load is jointly cooled by the electric refrigerator and the absorption refrigerator.

3. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system as claimed in claim 1, characterized in that: The thermal power unit model taking carbon capture into account in step 1) is specifically: Energy consumption of carbon capture equipment in thermal power units taking carbon capture into account P CCS Provided by thermal power units, this energy consumption is determined by the basic energy consumption P B And operating energy consumption P o Composition, among which, basic energy consumption P B Does not change with the state of the carbon capture equipment, the operating energy consumption P o It is related to the amount of carbon dioxide captured, and the specific formula is: Where: α c is the operating energy consumption factor, which is the energy consumption required to capture a unit of carbon dioxide; Q c2 (t) is the amount of carbon dioxide processed by the carbon capture equipment during period t.

4. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system as claimed in claim 1, characterized in that: The hydrogen energy multi-utilization structural model in step 1) includes a two-stage power-to-gas model, a hydrogen fuel cell model and a hydrogen storage tank model, as follows: 1) The two-stage power-to-gas system is divided into an electrolyzer and a methane reactor, and a hydrogen storage tank is set in the middle link. In the electrolyzer, an external power supply applies current to decompose water into hydrogen and oxygen to form a hydrogen production link, wherein the generated hydrogen is used in the methane reactor to react with carbon dioxide to generate methane, and the hydrogen storage tank is used as an intermediate link to store excess wind power in the form of hydrogen, which is converted into electrical energy and thermal energy through hydrogen fuel cells when the load demand is high, providing a flexible energy supply for the system. The electrolyzer model and methane reactor model of the two-stage power-to-gas model are as follows: 1.1) Electrolyzer model: Hydrogen production power of electrolyzer during period t With its operating power P EL_e The relationship between (t) is: Where: η EL is the energy conversion efficiency of the electrolyzer; 1.2) Methane reactor model: Hydrogen power consumption of methane reactor during period t Its gas production power Q MR_g The relationship between (t) is: Where: η MR is the energy conversion efficiency of the methane reactor; From the electrochemical reaction process of the methane reactor, it can be seen that the volume of methane produced is V MR_g (t) and the volume V of carbon dioxide consumed MR_c (t) Same: Where: e-h is the thermoelectric conversion coefficient; L g is the calorific value of natural gas combustion; Q MR_C (t) is the amount of carbon dioxide required for methanogenesis during period t; When the carbon capture power plant is operated in conjunction with power-to-gas, the CO2 required for P2G to produce methane does not need to be purchased additionally, and is satisfied by the carbon capture and utilization of the thermal power units, that is: Q c2 (t)=Q MR_C (t)+Q storage (t) (7) Where: Q storage (t) is the carbon storage amount during period t; 2) Hydrogen fuel cell model: Where: is the input hydrogen energy of HFC during period t; P HFC_e (t), P HFC_h (t) are the electrical energy and thermal energy output of HFC in period t, respectively; η HFC_e The total efficiency of HFC conversion to thermal power; λ min ,λ max are the minimum and maximum values ​​of HFC thermoelectric efficiency, respectively; 3) Hydrogen storage tank model: Where: P ch_n (t), P dis_n (t) are the charging and discharging power of hydrogen storage tank n during period t; P ESS is the maximum charging and discharging power of the hydrogen storage tank; S ESS_n (t), S ESS_n (t-1) are the storage capacities of hydrogen storage tank n in periods t and t-1 respectively; are the maximum and minimum values ​​of hydrogen storage capacity respectively; η ch , η dis are the charging and discharging efficiencies of the hydrogen storage tank respectively; E ESS_n is the rated capacity of hydrogen storage tank n.

5. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system as claimed in claim 1, characterized in that: The gas boiler model in step 1) is as follows: the heat generation power P of the gas boiler in period t GB_h (t) and its natural gas power consumption Q GB_g The relationship between (t) is: Where: It is the heating efficiency of gas boiler.

6. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system as claimed in claim 1, characterized in that: The refrigeration system model in step 1) includes an electric refrigerator model and an absorption refrigerator model, which are respectively as follows: 1) Electric refrigerator model: Refrigeration power P of electric refrigerator during period t EC_c (t) and its input power P EC_e The relationship between (t) is: Where: is the cooling coefficient of the electric refrigerator; 2) Absorption Refrigeration Machine Model: Refrigeration power P of absorption chiller during period t AC_c (t) and its input heat power P AC_h The relationship between (t) is: Where: is the refrigeration coefficient of the absorption chiller.

7. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system as claimed in claim 1, characterized in that: The step 2) of establishing the objective function in the comprehensive energy multi-objective optimization scheduling model based on the ladder carbon trading is as follows: min as follows: f min =(f e ,f CET ) (14) Where: f e For economic purposes; f CET For carbon trading, the two objectives have equal weight and constrain and promote each other; Among them, f CET Carbon trading is a ladder carbon trading mechanism, including carbon emission quota model, actual carbon emission model and ladder carbon trading model, which are as follows: 1) Carbon emission quota model: The carbon emission sources in the combined energy system of cooling, heating, electricity and hydrogen mainly include upstream power purchase, thermal power units and gas boilers, and their free carbon quotas are as follows: Where: C VPP Free carbon quota; C G is the carbon quota of thermal power units; C CB is the carbon quota for gas boilers; e , χ g They are the quotas corresponding to the carbon dioxide emissions per unit of electricity generated by thermal power units and the quotas corresponding to the carbon dioxide emissions per unit of natural gas consumed by gas boilers; 2) Actual carbon emission model: Where: C VPP,a is the actual carbon emissions; C buy,a The actual carbon emissions of electricity purchased from the grid; C G,a is the actual carbon emission of thermal power units; C GB,a is the actual carbon emission of the gas boiler; C MR,a The amount of carbon dioxide absorbed by the methanogenization plant; 3) Tiered carbon trading model: C VPP,t =C VPP,a -C VPP (17) Where: C VPP,t is the carbon emission trading amount; Finally, we get the tiered carbon trading cost f CET for: In the formula: λ is the initial price of carbon trading; l is the length of the step interval; α is the price growth rate. 4) Economic objective function: The economic objective function comprehensively considers the operating cost of the integrated energy system. om , energy purchase cost buy and carbon sequestration costs storage ,as follows: f e =f om +f buy +f storage (19) Running costs: Where: j is the operating cost per unit power output of device j; P j (t) is the output power of device j in time period t; Energy purchase cost: Where: P g,buy (t) is the gas purchase volume in period t; α t , β t are the electricity price and gas price in period t respectively; Carbon storage costs: Where: γ t The cost for storing unit CO2 is 40 yuan / t.

8. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system as claimed in claim 1, characterized in that: The step 2) establishes the constraints in the comprehensive energy multi-objective optimization scheduling model based on the ladder carbon trading, and the constraints are power balance constraints, wind power output constraints and equipment operation constraints, which are as follows: 1) Power balance constraints: The electricity, heat, natural gas, and hydrogen within the virtual power plant must meet the following power balance constraints: 2) Wind power output constraints: Where: P w (t) is the wind power output power in period t; is the maximum output power of wind power; 3) Equipment operation constraints: The equipment includes thermal power units, two-stage power-to-gas, hydrogen fuel cells, gas boilers and refrigeration systems, all of which must meet equipment operation constraints, including equipment capacity constraints and ramp constraints: Where: P i min , P i max are the upper and lower limits of the input power of device i; ΔP i min , ΔP i max are the upper and lower limits of the climbing speed of device i respectively.

9. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system as claimed in claim 1, characterized in that: The particle swarm algorithm with linear decreasing constraints introduced in step 3) is specifically as follows: In the particle swarm algorithm, the particle position and speed as follows: Where: i = 1, 2, ..., m, m is the size of the particle swarm; j=1,2,...,J, J is the dimension of the particle swarm; ω is the inertia parameter; c1, c2 are learning factors, both are constants; r1, r2 are random numbers in [0,1]; G is the global optimal solution, P i is the individual optimal solution of the i-th particle; x ij , v ij , P gj , P ij X i , U i , G, P i The j-dimensional component of is the j-dimensional component of the maximum velocity; A linear decreasing constraint is introduced into the above particle swarm algorithm as follows: 1) Set a larger inertia parameter and learning factor and start the algorithm iteration process; 2) In each iteration, these parameters are adjusted in real time to gradually decrease; 3) Complete the entire iteration process with smaller inertia parameters and learning factors; In this step, the processing method for inertia weight and learning factor is as follows: Where: t is the number of current iterations; t max is the total number of iterations; ω max ,ω min Take 0.9, 0.2 respectively; c max 、c min Take 1.5 and 1 respectively; A multi-objective particle swarm algorithm with linear decreasing constraints is introduced, and a fuzzy decision method is used to extract the final optimal scheduling scheme of the integrated energy system from the non-inferior solution set, as shown in the following formula: Where: F i is the value of the multi-objective function after fuzzification; f i is the objective function value of the i-th non-inferior solution in the non-inferior solution set; f max 、f min are the upper and lower limits of the objective function value of the i-th non-inferior solution in the non-inferior solution set respectively; (f e 、f CET ) as the factor set, and make a single factor evaluation on the non-inferior solution set to form a fuzzy evaluation matrix. For the convenience of analysis, the evaluation factor weight vector is taken as [0.5, 0.5]. Finally, through the fuzzy comprehensive evaluation, the optimal solution is selected as the final comprehensive energy system optimization scheduling plan.

10. A low-carbon economic dispatching method for a combined cooling, heating, electricity and hydrogen energy system according to claim 1, characterized in that: The establishment of the particle swarm algorithm process with the introduction of linear decreasing constraints in step 3) is specifically as follows: 1) Given the initial position and velocity of the particles, determine the upper and lower limits of the velocity and the maximum number of iterations of the corresponding particle swarm according to the model requirements of each device; 2) Input the data of the combined cooling, heating, electricity and hydrogen energy system for initial calculation, and record the optimal value of the particle and the optimal value of the particle group; 3) In each iteration, the inertia parameters and learning factors are dynamically updated, the optimization conditions of the particles are determined, and the corresponding calculations are performed. In the iteration process, the optimal solution set of the non-dominated solution is recorded, which saves the best non-dominated solution obtained in the iteration process; 4) The system stops searching for the optimal value after iterating for a set number of times. When searching for the optimal value stops, the set of historical optimal values ​​recorded at this iteration is recorded.

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