Electricity-hydrogen-heat island micro-grid low-carbon planning method and system considering market coupling

Abstract

The invention relates to an electricity-hydrogen-heat island micro-grid low-carbon planning method and system considering market coupling. The method comprises the following steps: establishing an electricity-hydrogen-heat island micro-grid model considering an electricity-carbon market coupling characteristic; based on the electricity-hydrogen-heat island micro-grid model, a low-carbon planning mathematical model of the electricity-hydrogen-heat island micro-grid is established by considering typical inter-day energy connection and biomass energy and carbon capture and storage coupled negative carbon emission technology; and solving the low-carbon planning mathematical model by adopting a mathematical optimization method, and outputting a low-carbon planning scheme. Compared with the prior art, the method has the advantages that the problem of long-time-scale power imbalance in the island micro-grid can be effectively solved, adjustment of the power on the long-time scale is achieved, and the energy utilization efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system planning, and in particular to a low-carbon planning method and system for an electric-hydrogen-heat island microgrid considering market coupling. Background Art

[0002] Under the strategic goal of "dual carbon", the traditional energy system dominated by fossil energy urgently needs to transform to a sustainable, clean and low-carbon energy structure. The integrated energy system is one of the important ways to achieve the clean and low-carbon transformation of the energy system. Among them, hydrogen energy can effectively promote the consumption of renewable energy and improve the reliability of power system operation. At the same time, hydrogen energy also plays an important role in reducing carbon emissions. By converting surplus renewable energy into hydrogen and storing it, it not only solves the volatility caused by the grid connection of renewable energy, but also provides a low-carbon solution for energy storage and transportation. Therefore, the deep coupling of electricity, hydrogen and heat will become an important form of clean and low-carbon transformation of the integrated energy system in the future.

[0003] The technology of producing hydrogen by electrolysis of water utilizes excess wind and solar energy to produce hydrogen. While increasing the utilization rate of renewable energy, it also helps the traditional chemical industry that is difficult to electrify to deeply decarbonize. It is an important means to indirectly achieve carbon emission reduction in the energy system. At the same time, hydrogen production by electricity also shows important value in smoothing the long-term fluctuations in the output of renewable energy. However, the modeling methods described in existing studies cannot accurately simulate the energy storage level characteristics of long-term energy storage of hydrogen production that continuously changes across days. In addition, carbon capture and storage technology is a carbon emission reduction technology that directly reduces carbon emissions from the source. Negative carbon emission technology is a low-carbon planning method for electric-hydrogen-heat island microgrids that considers the coupling of electricity and carbon markets. It is an innovative way to meet the net zero goal and aims to reduce the concentration of carbon dioxide in the atmosphere. Coupling biomass energy with carbon capture is currently one of the key technologies to achieve negative carbon emissions.

[0004] However, existing methods cannot effectively cope with the volatility of renewable energy such as wind and solar power on different time scales while meeting users' electricity and heat needs and ensuring the low-carbon operation characteristics of the system. Summary of the invention

[0005] The purpose of the present invention is to provide a low-carbon planning method and system for an electric-hydrogen-heat island microgrid that takes market coupling into consideration in order to achieve the goal of low-carbon operation.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling includes the following steps:

[0008] Establish an electricity-hydrogen-heat island microgrid model considering the coupling characteristics of electricity and carbon markets;

[0009] Based on the electric-hydrogen-heat island microgrid model, a low-carbon planning mathematical model of the electric-hydrogen-heat island microgrid is established by considering the energy connection between typical days and the negative carbon emission technology of coupling biomass energy with carbon capture and storage;

[0010] The mathematical optimization method is used to solve the low-carbon planning mathematical model and output a low-carbon planning solution.

[0011] Furthermore, the electric-hydrogen-heat island microgrid model includes photovoltaic units, wind turbines, biomass units, batteries, hydrogen storage systems, fuel cells, electrolyzers, electric boilers, carbon capture equipment and heat storage tanks. The photovoltaic units, wind turbines and biomass units are used to meet the electricity demand of flexible loads. The batteries and hydrogen storage systems constitute hybrid energy storage devices to cope with power fluctuations on different time scales. The waste heat of the electric boilers and electrolyzers is recovered to provide thermal energy. The heat storage tank is used as a thermal energy storage device to store excess heat to achieve thermal energy regulation and balance when heat demand and heat supply are inconsistent.

[0012] Furthermore, the low-carbon planning mathematical model includes an objective function and corresponding constraints. The objective function aims to minimize the total investment and operation cost of the electricity-hydrogen-heat island microgrid. The expression of the objective function is:

[0013]

[0014] In the formula, C is the objective function, C inv is the investment cost, C m is the maintenance cost, C BU is the fuel cost of the biomass power generator, is carbon storage and transaction costs, C E Profits from electricity market transactions.

[0015] Furthermore, the expressions of the investment cost, maintenance cost and fuel cost of the biomass power generation unit are respectively:

[0016]

[0017] C m =αC inv

[0018]

[0019] In the formula, r represents the discount rate; β represents the life of the equipment; c SHS,e 、c SHS,pThey represent the SHS unit capacity investment cost and unit power investment cost respectively; E SHS 、E Bat Respectively represent the investment capacity of SHS and Bat; c Bat,e 、c Bat,p represents Bat unit capacity investment cost and unit power investment cost respectively; c TES,e 、c TES,p They represent the TES unit capacity investment cost and unit power investment cost respectively; c EB,p 、c BU,p 、c CCS,p 、c SST,e are the unit investment costs of EB, BU, CCS, and SST respectively; α represents the maintenance cost coefficient; D represents the total number of typical days; W d Indicates the weights of different typical days; D p is the total number of days in a year; C BU The fuel cost per unit output of the biomass unit; is the output power of the biomass energy unit at time t.

[0020] Furthermore, the expression of carbon storage and transaction cost is:

[0021]

[0022] Where D p is the total number of days in a year; D is the total number of typical days; W d Indicates the weights of different typical days; C sto Unit: CO 2 storage costs; is the CO captured by CCS at time t 2 quantity; The income obtained by the system from participating in the carbon market; W d Indicates the weights of different typical days; The revenue from carbon emissions trading at the hourly level; Indicates the actual trading share of the IES-BECCS system in the carbon trading market; Respectively represent the free carbon emission quota and actual carbon emission of IES-BECCS system; Represents the carbon emission quota corresponding to the unit power of external electricity purchase; Indicates the carbon emission quota corresponding to the unit electric power of BU; The carbon emissions corresponding to the unit power of external electricity purchase; is the output power of BU at time t, is the electricity purchase amount of IES-BECCS at time t; is the CO generated by BU at time t2 Quantity; cet is the carbon price; θ is the carbon price growth coefficient; M is the length of the carbon emission interval, and d is the dth day.

[0023] Furthermore, the expression of the power market transaction revenue is:

[0024]

[0025] Where D p is the total number of days in a year; W d Indicates the weights of different typical days; Respectively represent the unit electricity selling price and electricity purchasing price; They represent the power sold and purchased at time t respectively, and D represents the total number of typical days.

[0026] Furthermore, the constraints include:

[0027] Consider the energy connection between typical days and the constraints of the hydrogen storage device model coordinated with the battery:

[0028]

[0029] In the formula, represents the capacity of Bat at time t+1; is the self-consumption rate of Bat; η bat,char and η bat,dis Respectively represent the charging and discharging efficiency of Bat; represents the charging and discharging power of Bat at time t; Indicates the start and stop state variable of Bat; P bat,max is the upper limit of Bat's charging and discharging power; E bat,min is the lower limit of Bat capacity; E bat,max is the upper limit of Bat’s capacity, and d is the dth day;

[0030] Carbon capture and storage and carbon emission management model constraints:

[0031]

[0032] In the formula, is the CO generated by BU at time t 2 Quality, The unit carbon emission of biomass energy units; Indicates the fuel consumption coefficient of BU; is the output power of BU at time t; P BU,max The upper limit of the output power of the BU; is the CO generated by BU at time t 2 quantity; is the mass of the solution temporarily stored in SST; η CCS For the operational efficiency of CCS; Directly captured CO 2 quality; The carbon emissions corresponding to the unit power of external electricity purchase; The CO extracted from SST 2 quality; Indicates CO 2 Solubility in monoethanolamine water; σ SST is the maximum proportion of solvent in SST, Cap SST,max is the maximum capacity of the SST; is the output power of CCS at time t; is the fixed energy consumption of CCS equipment, λ CCS CO captured per unit for CCS 2 Energy consumption; P CCS,max is the output limit of CCS;

[0033] Battery model constraints:

[0034]

[0035] In the formula, represents the capacity of Bat at time t+1; is the self-consumption rate of Bat; η bat,char and η bat,dis Respectively represent the charging and discharging efficiency of Bat; represents the charging and discharging power of Bat at time t; Indicates the start and stop state variable of Bat; P bat,max is the upper limit of Bat's charging and discharging power; E bat,min is the lower limit of Bat capacity; E bat,max The upper limit of Bat capacity;

[0036] Electric boiler and biomass energy unit constraints:

[0037]

[0038] In the formula, is the electric power consumed by EB at time t; P EB,max is the upper power limit; is the output power of BU at time t; P BU,max The upper limit of biomass unit output;

[0039] Upper and lower limits for purchasing and selling electricity:

[0040]

[0041] In the formula, For the purchase of electricity; P is the electricity sales; e,max The upper limit of electricity purchase and sale;

[0042] Power balance constraints:

[0043]

[0044] In the formula, Respectively represent the electric power output of the photovoltaic unit per hour, the electric power output of the wind turbine unit, the electric power consumed by the carbon capture unit, the abandoned power and the electric power used;

[0045] Thermal storage equipment model constraints:

[0046]

[0047]

[0048] In the formula, represents the capacity of TES at time, represents the charging and discharging heat power of TES at time t; η TES,char , η TES,dis Indicates the charging and discharging efficiency of TES; represents the self-consumption efficiency of TES; H TES,max It is the upper limit of TES charging and discharging heat power; and They are TES charging and discharging heat marks; Q TES,max Indicates the maximum capacity of TES;

[0049] Model constraints for waste heat recovery:

[0050]

[0051] In the formula, and They represent the waste heat recovered by ELZ and FC at time t respectively;

[0052] Electric boiler model constraints:

[0053]

[0054] In the formula, is the heat generated by EB at time t, η EB is the electrothermal conversion efficiency of EB;

[0055] Thermal energy balance constraints:

[0056]

[0057] In the formula, Represents the heat load demand.

[0058] Furthermore, the mathematical optimization method is a mixed integer linear programming method.

[0059] Furthermore, a mixed integer linear programming method is used to solve the problem of solving the low-carbon planning mathematical model to a mixed integer linear constraint problem. The expression solved by the mixed integer linear programming method is:

[0060] y=a·x

[0061]

[0062] In the formula, a is a binary variable, x is a continuous variable, and M is a set number. When the value of a is 1, the value of y is equal to x; when the value of a is 0, the value of y is equal to 0.

[0063] The present invention also provides a low-carbon planning system for an electricity-hydrogen-heat island microgrid taking market coupling into consideration, comprising:

[0064] Island microgrid model building module: used to build an electric-hydrogen-heat island microgrid model that takes into account the coupling characteristics of the electricity-carbon market;

[0065] A low-carbon planning mathematical model building module is used to establish a low-carbon planning mathematical model for the electric-hydrogen-heat island microgrid based on the electric-hydrogen-heat island microgrid model, taking into account the energy connection between typical days and the negative carbon emission technology of coupling biomass energy with carbon capture and storage;

[0066] Solution module: used to solve the low-carbon planning mathematical model using a mathematical optimization method and output a low-carbon planning solution.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] (1) The present invention participates in the electricity-carbon coupling market and establishes an electricity-hydrogen-heat island microgrid model, which helps to achieve the best balance between economic benefits and carbon emission reduction. At the same time, the low-carbon planning mathematical model of the electricity-hydrogen-heat island microgrid is established by considering the energy connection on a long time scale as well as biomass energy and carbon capture and storage technology. This not only verifies the important role of biomass energy and carbon capture and storage technology in carbon emission reduction, but also enables the island microgrid to significantly reduce carbon emissions while meeting energy needs, thus achieving the low-carbon operation goal of the island microgrid.

[0069] (2) The present invention takes into account the energy connection between typical days and the model constraints of the hydrogen storage equipment coordinated with the battery, which can effectively deal with the long-term power imbalance problem in the island microgrid, realize the regulation of power on a longer time scale, and significantly improve the energy utilization efficiency.

[0070] (3) The low-carbon planning method of the present invention can effectively take into account the economic and low-carbon nature of the integrated energy system, achieve dual optimization of economic benefits and carbon emission reduction, and provide new research ideas and technical support for the planning and operation of future low-carbon energy systems. Compared with traditional planning methods, the total cost of the method of the present invention is reduced by 19.6%, and the actual carbon emissions are reduced by 85.41%. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0072] Figure 2 This is a schematic diagram of the structure of an island microgrid in an embodiment of the present invention;

[0073] Figure 3 This is a representative periodic SOC variation diagram in an embodiment of the present invention;

[0074] Figure 4 This is a structural diagram of an improved CCS based on SSTs in an embodiment of the present invention;

[0075] Figure 5 This is the electric power balance diagram of scenario 1 in the embodiment of the present invention;

[0076] Figure 6 This is the electric power balance diagram of scenario 2 in the embodiment of the present invention;

[0077] Figure 7 This is the electric power balance diagram of scenario 3 in the embodiment of the present invention;

[0078] Figure 8 This is a thermal power balance diagram of scenario 3 in an embodiment of the present invention;

[0079] Fig. 9 The long-term hydrogen storage device of scenario 3 in the embodiment of the present invention works across days. DETAILED DESCRIPTION

[0080] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0081] Example 1

[0082] This embodiment provides a low-carbon planning method for an electric-hydrogen-heat island microgrid considering market coupling, which considers the influence of long-term energy storage of electric hydrogen production and coupling of the electricity-carbon market. First, in view of the continuous change characteristics of the energy of long-term energy storage of electric hydrogen production across days, a hydrogen storage equipment model suitable for system planning and considering the energy connection between typical days is proposed, and it is combined with the battery to solve the problem of multi-time scale power and electricity balance under the output of new energy; secondly, the negative carbon emission technology of biomass energy and carbon capture and storage is introduced, and the operation of the electricity-carbon market is participated in at the same time, and the low-carbon planning mathematical model of the electric-hydrogen-heat island microgrid considering the coupling of biomass energy and carbon capture and storage technology and the electricity-carbon market is constructed. Finally, the simulation results show that the planning mathematical model proposed by the present invention can effectively take into account the economy and low carbon of the comprehensive energy system in the environment of the electricity-carbon coupling market. Compared with the traditional planning mathematical model, the total cost of the proposed method is reduced by 19.6%, and the actual carbon emissions are reduced by 85.41%.

[0083] like Figure 1 As shown, the method comprises the following steps:

[0084] 1 Electricity-hydrogen-heat island microgrid model considering electricity-carbon market coupling

[0085] The coupling characteristics are mainly reflected in the following aspects: First, the price signal of the carbon market will prompt the system to pay more attention to the carbon emission cost in the process of electricity production, thereby optimizing its production mix and reducing carbon emissions. Secondly, the coupling of the electricity market and the carbon market makes the optimization process not only focus on traditional cost minimization, but also considers the limitation of carbon emissions, ensuring that the emission reduction target is achieved while achieving economic benefits. Through the carbon market trading mechanism, the system can obtain more carbon emission quotas, so that the system can optimize economic efficiency while complying with carbon emission constraints. The electricity-carbon market coupling model can achieve dynamic response to market changes. For example, changes in carbon prices will directly affect the choice of electricity production and the optimal scheduling of energy storage systems, thereby enhancing the flexibility and adaptability of the system.

[0086] Island microgrids usually rely on local renewable energy and traditional fossil fuel power generation, but due to the volatility of these renewable energy sources, the power system faces the instability of energy supply. By introducing IES-BECCS (Integrated Energy System with Bioenergy with Carbon Capture and Storage), biomass energy can be combined with carbon capture and storage technology to reduce carbon emissions and provide a stable power supply. Biomass energy provides a relatively stable source of renewable energy for island microgrids, and effectively reduces carbon emissions through carbon capture technology, thereby achieving low-carbon and efficient energy production. At the same time, through the electric-hydrogen-heat island microgrid model, excess renewable electricity can be converted into hydrogen storage, providing regulation capabilities when wind and solar output fluctuates, thereby further enhancing the stability and reliability of the microgrid. In addition, island systems usually face the dual challenges of energy supply and environmental protection, especially in terms of carbon emission control. By combining BECCS technology, island microgrids can use biomass energy to generate electricity and capture and store carbon dioxide in it, significantly reducing carbon emissions. This provides the possibility of carbon emission reduction for island microgrids, especially when it is not possible to rely entirely on wind or solar energy. In general, the combination of IES-BECCS technology and island microgrids provides a feasible solution for energy structure optimization, carbon emission reduction, energy security, etc., which can help islands achieve low-carbon and sustainable energy development while improving the stability and reliability of the system.

[0087] The IES-BECCS system framework proposed in this embodiment is as follows Figure 2 As shown. The photovoltaic unit (PV), wind turbine generator (WT) and biomass energy unit (BU) in the system are used to meet the electricity demand of conventional electric loads and flexible loads such as carbon capture system (CCS) and electric boiler (EB). The hybrid energy storage device composed of battery (Bat) and hydrogen storage system (SHS) mainly deals with the problem of power fluctuations on different time scales. The waste heat recovery of EB, fuel cell (FC) and electrolyzer (ELZ) provides thermal energy for users. The thermal energy storage (TES) is a thermal energy storage device used to store excess heat to achieve thermal energy regulation and balance when the heat demand and heat supply are inconsistent.

[0088] SHS is mainly composed of ELZ, FC and hydrogen storage tank (HST). It can realize power regulation across long time scales by converting excess electricity into hydrogen and storing it, and then converting hydrogen into electricity when needed. This feature of SHS makes it particularly suitable for dealing with power imbalance problems on long time scales, and can cooperate with Bat to jointly solve power supply and demand fluctuations on multiple time scales. At the same time, CCS cooperates with solvent storage tank (SST) to make the carbon capture process more flexible. SST realizes the decoupled operation between power generation output and carbon capture. By temporarily storing solvent during peak load periods, the power conflict between the CCS system and power generation equipment is avoided, so that the carbon capture process can be flexibly adjusted under different load conditions.

[0089] In addition, the entire IES-BECCS system achieves the coordinated optimization of power dispatch and carbon emissions in the thermal island microgrid by participating in the electricity-carbon coupling market. The system can reasonably dispatch power generation and energy storage equipment according to the real-time electricity price in the electricity market and the price signal of the carbon market, while reducing carbon emissions through CCS technology and selling the remaining carbon quota to obtain revenue. Through this market-oriented mechanism, the system can find the best balance between electricity and carbon emissions, maximize economic benefits and achieve carbon reduction goals. Therefore, the entire system can not only effectively cope with the volatility of renewable energy such as wind and solar power on different time scales while meeting the user's electricity and heat needs, but also ensure the low-carbon operation characteristics of the system, and truly achieve a win-win situation of economic benefits and environmental protection goals.

[0090] 2 Mathematical model for low-carbon planning of electric-hydrogen-heat island microgrids considering energy connection between typical days and negative carbon emission technology coupled with biomass energy and carbon capture and storage

[0091] 2.1 Objective Function

[0092] This embodiment aims to minimize the total investment and operating cost of the electricity-hydrogen-heat island microgrid and optimizes the equipment capacity in IES-BECCS. The specific objective function is shown in the formula:

[0093]

[0094] In the formula, C is the system objective function, C inv is the system investment cost, C m is the system maintenance cost, C BU is the fuel cost of the biomass power generator, is carbon storage and transaction costs, C E Profits from electricity market transactions.

[0095] 2.1.1 Investment, Maintenance, and Fuel Costs

[0096]

[0097] C m = αC inv (3)

[0098]

[0099] In the formula, r represents the discount rate; β represents the lifespan of the equipment; c SHS,e , c SHS,p represent the investment cost per unit capacity and the investment cost per unit power of the SHS respectively; c Bat,e , c Bat,p represent the investment cost per unit capacity and the investment cost per unit power of the Bat respectively; c TES,e , c TES,p represent the investment cost per unit capacity and the investment cost per unit power of the TES respectively; c EB,p , c BU,p , c CCS,p , c SST,e are the unit investment costs of EB, BU, CCS, and SST respectively; α represents the maintenance cost coefficient; D represents the total number of typical days; W d represents the weight of different typical days; D p is the total number of days in a year; C BU is the fuel cost per unit output of the biomass energy unit; is the output power of the biomass energy unit at time t.

[0100] 2.1.2 Carbon Storage and Transaction Costs

[0101]

[0102]

[0103] In the formula, C sto represents the storage cost per unit of CO 2 ; is the amount of CO 2 captured by CCS at time t; represents the trading share of the IES-BECCS system actually participating in the carbon trading market; represent the free carbon emission quota and the actual carbon emissions of the IES-BECCS system respectively; represents the carbon emission quota corresponding to the unit power of externally purchased electricity; represents the carbon emission quota corresponding to the unit electric power of BU. Since BU is usually regarded as carbon-neutral, with the government's increasing emphasis on carbon emission control, BU usually can be allocated more carbon quotas; is the carbon emission corresponding to the unit power of external power purchase; is the output power of the BU at time t, is the power purchase quantity of IES-BECCS at time t; is the amount of CO 2 produced by the BU at time t; λ cet is the carbon price; θ is the carbon price growth coefficient; M is the length of the carbon emission interval.

[0104] 2.1.3 Electricity market trading revenue

[0105]

[0106] In the formula, respectively represent the system's unit power selling price and power purchase price; respectively represent the system's power selling power and power purchase power at time t.

[0107] 2.2 IES-BECCS system equipment constraints

[0108] 2.2.1 Hydrogen storage equipment model

[0109] In IES-BECCS, the SHS, as a long-term energy storage device, is mainly used to regulate the imbalance between power supply and demand on a long time scale. Different from the Bat, the SHS can not only stabilize the power supply on a long time scale, but also realizes the cross-period scheduling and exchange of typical daily energy, ensuring the energy balance of the system within different time cycles. Therefore, this paper's SHS considers the continuity problem of SOC between different typical days.

[0110] As Figure 3 shown, the same typical day of consecutive n days is regarded as a cycle, and the charge and discharge states of different cycles are distinguished by color blocks. For simplicity of analysis, it is assumed that within the same typical day, the charge and discharge state of the SHS remains consistent, which ensures the monotonicity of the state change of the SHS within each cycle. Therefore, the change of the SOC of the SHS mainly occurs at the initial moment of each cycle. Therefore, as long as the SOC at the initial moment of the cycle is between the upper and lower bounds of the allowable power, the SOC of each hour within the entire cycle can meet the limit conditions. The SHS model proposed in this paper mainly constrains the initial SOC of each cycle and the charge and discharge power within the cycle.

[0111]

[0112] In the formula, represents the energy state at the end of the dth day; represents the energy state at the beginning of the dth day; represents the self-loss of the SHS, η SHS,char and η SHS,dis represent the charge and discharge efficiency of the SHS; represents the charge and discharge power of the electrolyzer and fuel cell at time t; P el,max , P fc,max Indicates the upper power limit of electrolyzers and fuel cells; Indicates the start and stop state variables of the electrolyzer and fuel cell; E SHS,max Indicates the upper limit of SHS capacity.

[0113] 2.2.2 Carbon capture and storage and carbon emission management model

[0114] CO2 that can be captured in BECCS 2 Generated by BU, most CO 2 Directly captured by CCS. However, the traditional CCS system has the problem of excessive power consumption during peak load periods, that is, a large amount of electricity is consumed in the process of capturing CO2, which brings additional burden to the system. In order to avoid the conflict between the power consumption of CCS and the power supply pressure of the system during peak load periods, this paper improves the structure of CCS by installing a solvent storage tank (SST). CCS with SST Figure 4 shown.

[0115]

[0116]

[0117] In the formula, is the CO generated by BU at time t 2 Quality, The unit carbon emission of biomass energy units; Indicates the fuel consumption coefficient of BU. η CCS For the operational efficiency of CCS; Directly captured CO 2 quality; is the mass of the solution temporarily stored in SST; The carbon emissions corresponding to the unit power of external electricity purchase; The CO extracted from SST 2 quality; Indicates CO 2 Solubility in monoethanolamine water; σ SST is the maximum proportion of solvent in SST, Cap SST,max is the maximum capacity of the SST; is the fixed energy consumption of CCS equipment, λ CCS Capture units CO for CCS 2 Energy consumption; P CCS,max is the upper limit of CCS output.

[0118] 2.2.3 Battery Model

[0119]

[0120] In the formula, represents the capacity of Bat at time t; is the self-consumption rate of Bat; η bat,char and η bat,dis Respectively represent the charging and discharging efficiency of Bat; represents the charging and discharging power of Bat at time t; Indicates the start and stop state variable of Bat; P bat,max is the upper limit of Bat's charging and discharging power; E bat,min is the lower limit of Bat capacity; E bat,max The upper limit of Bat capacity.

[0121] 2.2.4 Electric boilers and biomass energy units

[0122]

[0123] In the formula, is the electric power consumed by EB at time t; P EB,max is the upper power limit; is the output power of BU at time t; P BU,max It is the upper limit of biomass energy unit output.

[0124] 2.2.5 Upper and lower limits on electricity purchase and sale

[0125]

[0126]

[0127] In the formula, The amount of electricity purchased by the system; P is the system's electricity sales; e,max The upper limit for purchasing and selling electricity.

[0128] 2.2.6 Power balance constraints

[0129]

[0130] In the formula, They respectively represent the electric power output by the photovoltaic unit per hour, the electric power output by the wind turbine unit, the electric power consumed by the carbon capture unit, the abandoned power and the electric power used.

[0131] 2.2.7 Thermal storage equipment model

[0132]

[0133] In the formula, represents the capacity of TES at time, represents the charging and discharging heat power of TES at time t; η TES,char , η TES,dis Indicates the charging and discharging efficiency of TES; represents the self-consumption efficiency of TES; H TES,max It is the upper limit of TES charging and discharging heat power; and They are TES charging and discharging heat marks. TES,max Indicates the maximum capacity of TES.

[0134] 2.2.8 Waste heat recovery model

[0135]

[0136] In the formula, and They represent the waste heat recovered by ELZ and FC at time t respectively.

[0137] 2.2.9 Electric boiler model

[0138]

[0139] In the formula, is the heat generated by EB at time t, η EB is the electrothermal conversion efficiency of EB.

[0140] 2.2.10 Thermal energy balance constraints

[0141]

[0142] In the formula, Represents the heat load demand.

[0143] 3. Solving by Mixed Integer Linear Programming Method

[0144] For the nonlinear constraints of the product of binary variables and continuous variables in the above planning model, the nonlinear constraints are equivalently converted into mixed integer linear constraints by using the big M method. The conversion principle of the big M method is as follows:

[0145] y=a·x (48)

[0146] In the formula, a is a binary variable and x is a continuous variable. The constraints of the large M transformation are as follows:

[0147]

[0148] Where M is a sufficiently large number. When the value of a is 1, the value of y is equal to x; when the value of a is 0, the value of y is equal to 0. The original nonlinear equality constraint is converted into a mixed integer linear constraint.

[0149] Thus, the original nonlinear equality constraint is converted into a mixed integer linear constraint. This embodiment takes the Bat charge and discharge constraint formula as an example for introduction, and other nonlinear parts of this embodiment are also converted using the same method.

[0150]

[0151] When solving the above low-carbon planning mathematical model, we can further explore the impact of changes in the growth ratio θ in the carbon trading mechanism on the economy and equipment configuration of the system.

[0152] 4 Example Analysis

[0153] 4.1 Basic data and scenario design

[0154] This embodiment selects a certain industrial park as the research object for case analysis to verify the effectiveness of the proposed planning scheme. The annual wind and solar power generation forecast data and load forecast data used in the example are from the existing data set. The K-means clustering method is used to obtain 12 typical days for the annual data. The optimal planning mathematical model is constructed using YALMIP in MATLAB R2018b, and solved by the commercial solver CPLEX 10.2.1. The gap threshold of the MILP solver CPLEX is set to 0.001. The relevant parameters of each device are shown in Table 1.

[0155] In order to verify the effectiveness of the IES-BECCS formulated in this embodiment in terms of environmental protection and economy, this embodiment sets up three scenarios for comparative analysis. Scenario 1: Without considering BECCS technology and SHS, traditional diesel generators, Bat and EB are used in conjunction with WT and PV to supply local electric heating loads. Scenario 2: Considering BECCS technology but not SHS, BECCS, Bat, EB are combined with WT and PV to supply local electric heating loads. Scenario 3: Considering both BECCS technology and SHS, and Bat, EB, TES are combined with WT and PV to supply local electric heating loads.

[0156] Table 1 Parameters of each device

[0157]

[0158] 4.2 Analysis of planning mathematical model results

[0159] Table 2 Costs, benefits and actual carbon emissions of various scenarios

[0160]

[0161] This section first compares the planning costs under different scenarios to illustrate the economic benefits of IES-BECCS participating in the electricity-carbon coupling market. The costs, benefits, and actual carbon emissions of various scenarios are shown in Table 2. Compared with scenarios 1 and 2, the total cost of scenario 3 decreased by 19.6% and 15%, respectively. In addition, from scenario 1 to scenario 3, with the introduction of BECCS equipment and SHS, the actual carbon emissions of the system decreased by 52.6% and 85.41%, respectively.

[0162] Compared with Scenario 1, Scenario 2 uses BECCS technology to replace the traditional diesel generator sets in Scenario 1. Although the introduction of BECCS technology has led to an increase in the investment cost and maintenance cost of Scenario 2, the carbon-related cost in Scenario 2 has dropped by 101.62% compared with Scenario 1, which shows that the introduction of BECCS technology has significantly reduced the carbon emissions of the system. In Scenario 1, the carbon emissions generated by diesel generators are relatively high, so more carbon emission quotas need to be purchased, resulting in higher carbon costs. In Scenario 2, due to the use of BECCS technology, biomass energy itself is regarded as carbon neutral, and combined with CCS, carbon emissions are further reduced, and there are excess carbon emission quotas for sale within the system. This means that under the same power generation conditions, BECCS technology can effectively utilize its carbon emission advantages, increase the system's revenue in the carbon market, and thus optimize the economic benefits of the entire system.

[0163] Scenario 3 adds SHS as a long-term energy storage device on the basis of Scenario 2. The fuel cost in Scenario 3 is reduced by 32% compared with Scenario 2. This shows that SHS considering energy fluctuations between typical days can better cope with energy fluctuations across days, making the energy demand changes of the system on a longer time scale more effectively regulated and reducing fuel costs. Scenario 3 optimizes energy scheduling and balances supply and demand to the greatest extent by utilizing the synergy of long-term and short-term energy storage, making the overall operation of the system more efficient and economical. At the same time, unlike traditional diesel generator sets, in addition to BU power generation, the system also needs to supply energy for CCS in BECCS technology. Therefore, the electricity cost in Scenario 2 increased by 69.32% compared with Scenario 1, while the electricity cost in Scenario 3 decreased by 41.82% compared with Scenario 2. This shows that SHS can reduce the system's dependence on the external power market through its long-term energy storage characteristics. SHS stores energy during periods of low electricity demand and provides stable power output for a long time during peak demand periods. Such characteristics make SHS more suitable for balancing long-term supply and demand imbalances, thereby greatly reducing the system's long-term dependence on external systems.

[0164] Table 3 gives the planning capacity results of each device under different scenarios. The capacity of Bat in Scenario 2 increased by 74.07% compared with Scenario 1. This shows that after considering BECCS technology, due to its more stable low-carbon power generation characteristics, the system is more inclined to configure a larger Bat capacity to make full use of the low-carbon advantages of BECCS and reduce the system's dependence on the external power grid. Compared with Scenario 2, the Bat capacity of Scenario 3 decreased by 55.56%. The main reason is that SHS can handle larger-scale energy storage and release tasks as long-term energy storage, especially in dealing with long-term imbalances in electricity supply and demand. With the introduction of SHS, the system no longer relies on Bat to handle large-scale energy storage needs. The role of Bat is more focused on the regulation of intraday power fluctuations, mainly responsible for rapid response to short-time scale load fluctuations. Therefore, the capacity demand of Bat is significantly reduced. At the same time, Scenario 3 recovers waste heat from ELZ and FC in SHS. This part of the recovered heat energy can be better coped with thermal load fluctuations through TES peak shifting and valley filling, thereby reducing the configuration capacity of EB. The results in Table 3 show that the EB configuration capacity in Scenario 3 is lower than that in Scenario 2 and Scenario 1.

[0165] Table 3 Planning capacity results of each device under different scenarios

[0166]

[0167] In Scenario 2, Bat cannot effectively deal with the problem of inter-day power imbalance, and CCS cannot operate stably and continuously, resulting in its carbon capture capacity being limited. Therefore, although CCS has been configured in Scenario 2, the overall system scheduling is not flexible enough, resulting in relatively small SST capacity and carbon capture. In Scenario 3, through the coordination of long-term and short-term energy storage, the system can operate power generation equipment more stably, allowing CCS equipment to continue to work efficiently. The configuration scale of CCS equipment and SST capacity in Scenario 3 is increased, so that more CO can be captured. 2 .

[0168] 4.3 Analysis of IES-BECCS operation results

[0169] In order to further demonstrate the system's performance in coping with wind and solar power generation fluctuations, flexible scheduling, and carbon emission control under different scenarios, this example analyzes the operating results of the IES-BECCS system in detail. Figure 5-Figure 9 The results of the system’s electric power dispatch within 12 days under three scenarios, the results of the thermal power dispatch considering waste heat recovery in scenario 3, and the performance of SHS in inter-day energy dispatch are shown respectively. Figure 5The operation results show that in scenario 1, when there is a shortage of wind and solar power generation, the system mainly relies on diesel generators and external power purchases to meet the electric and thermal load demand. In this case, due to the high carbon emission characteristics of diesel generators and the carbon emissions of external power sources, the system's carbon emissions are relatively high. Figure 6 The operation results show that BECCS can be combined with Bat and external power purchases to meet the electric and thermal loads when there is a shortage of wind and solar power generation. Bat's charging and discharging behavior is more obvious in scenario 2, indicating that the system has higher flexibility in dealing with fluctuations in renewable energy.

[0170] Figure 7 The synergy of SHS and Bat long-term and short-term energy storage is demonstrated, which is mainly reflected in the fact that SHS can release energy over a long period of time, so that the system can better adapt to energy fluctuations on a longer time scale. Bat in scenario 3 is mainly used to handle intraday fluctuations and balance short-term supply and demand differences. BECCS can capture carbon and reduce carbon emissions while providing electricity. At the same time, SHS can supplement the shortage of intermittent renewable energy through long-term storage of energy to ensure the continuous operation of BECCS.

[0171] according to Figure 7 and Figure 8 The results show that in scenario 1 and scenario 2, when there is no SHS and waste heat recovery cannot be considered, the system can only rely on EB to meet the system heat load. When the heat load demand is high in autumn and winter, the high-power operation of EB significantly increases the power demand, limits the system's ability to flexibly dispatch, and leads to an increase in system costs. When SHS waste heat recovery is considered, the power of EB as an electrical load is significantly reduced, especially during periods of insufficient power supply. The waste heat of SHS can be used to balance the heat load, while improving the overall efficiency of the system and reducing the system's dependence on EB. Figure 8 The operation results show that the charging and discharging of SHS is not a strict intra-day balance, but a connection of energy across days. This shows that SHS can allocate energy between multiple days, store a large amount of excess electricity, and gradually release it in the following days to balance the periodically fluctuating energy demand. Through a flexible energy scheduling mechanism, SHS balances the instability caused by fluctuations in wind and solar power generation over a longer time scale. On a long time scale, especially in cross-day scheduling, SHS can effectively store excess electricity and release it during subsequent high-demand periods. This energy balance mechanism reduces the system's dependence on external electricity and reduces the risk of unstable power supply caused by fluctuations in renewable energy.

[0172] Fig. 9The operation results show that the charging and discharging of SHS is not a strict intra-day balance, but a connection of energy across days. This shows that SHS can allocate energy between multiple days, store a large amount of excess electricity, and gradually release it in the following days to balance the periodically fluctuating energy demand. Through a flexible energy scheduling mechanism, SHS balances the instability caused by fluctuations in wind and solar power generation over a longer time scale. On a long time scale, especially in cross-day scheduling, SHS can effectively store excess electricity and release it during subsequent high-demand periods. This energy balance mechanism reduces the system's dependence on external electricity and reduces the risk of unstable power supply caused by fluctuations in renewable energy.

[0173] 4.4 Impact of changes in carbon trading growth ratio on IES-BECCS synergy benefits

[0174] Based on scenario 3, the impact of the change in the growth ratio θ in the carbon trading mechanism on the economy and equipment configuration of the system is further explored, and the main results are shown in Table 4. The data in Table 4 show the specific change trends of key indicators under different growth ratios.

[0175] When the growth ratio changes from -50% to +50%, the total cost gradually decreases, especially when the growth ratio changes to +50%, the total cost is the lowest. When θ increases, the system will face higher carbon trading costs under high carbon emissions. This high cost prompts the system to capture CO more actively. 2 , thus relying more on CCS and SHS, which directly leads to an increase in CCS power configuration, thereby reducing total carbon emissions and increasing carbon trading revenue. And as θ increases, P el,max and P fc,max The increase in CCS power configuration is particularly evident in the high θ scenario, when the cost and benefit of carbon trading increase faster, and the system is more inclined to increase CCS capacity to reduce overall operating costs.

[0176] Table 4 Specific trends of key indicators under different growth rates

[0177]

[0178] The change in the growth rate of carbon trading has prompted the system to adjust its power generation capacity and CCS capacity, making the system perform better in terms of carbon emissions and total costs. This shows that in the carbon market, the impact of carbon trading prices on system operation strategies and costs is very significant, and high θ encourages cleaner energy use and more active carbon capture behavior.

[0179] In summary, this embodiment proposes a low-carbon planning method for an electric-hydrogen-heat island microgrid based on long-term energy storage of electric hydrogen production and coupling of electricity and carbon markets. First, a hydrogen storage model that considers long-term energy fluctuations is established based on the problem of energy connection between typical days; secondly, a mathematical model for low-carbon planning of an electric-hydrogen-heat island microgrid that considers biomass energy and carbon capture and storage technology (BECCS) and coupling of electricity and carbon markets is constructed; finally, through the implementation of the planning mathematical model, the system operates in a coordinated manner in the electricity market and the carbon market, realizing the optimal planning of electricity-carbon coupling. The simulation example verifies the superiority of the proposed method and draws the following conclusions:

[0180] 1) The proposed hydrogen storage model with long-term energy fluctuations can effectively deal with the long-term power imbalance problem in the system, realize the regulation of electricity on a longer time scale, and significantly improve the energy utilization efficiency of the system.

[0181] 2) By introducing BECCS technology into the mathematical model of low-carbon planning, the important role of biomass energy and carbon capture and storage technology in carbon emission reduction is verified. The system can significantly reduce carbon emissions while meeting energy demand, achieving the low-carbon operation goal of the system.

[0182] 3) By participating in the electricity-carbon coupling market, the system can achieve the best balance between economic benefits and carbon emission reduction. While greatly improving the economic efficiency of the system, the carbon market trading mechanism effectively promotes the realization of carbon emission reduction targets.

[0183] In summary, the low-carbon planning method of the electricity-hydrogen-heat island microgrid proposed in this embodiment can effectively achieve the dual optimization of economic benefits and carbon emission reduction in the context of the electricity-carbon coupling market, and provides new research ideas and technical support for the planning and operation of future low-carbon energy systems.

[0184] Example 2

[0185] This embodiment 2 provides a low-carbon planning system for an electricity-hydrogen-heat island microgrid considering market coupling, including:

[0186] Island microgrid model building module: used to build an electric-hydrogen-heat island microgrid model that takes into account the coupling characteristics of the electricity-carbon market;

[0187] A low-carbon planning mathematical model building module is used to establish a low-carbon planning mathematical model for the electric-hydrogen-heat island microgrid based on the electric-hydrogen-heat island microgrid model, taking into account the energy connection between typical days and the negative carbon emission technology of coupling biomass energy with carbon capture and storage;

[0188] Solution module: used to solve the low-carbon planning mathematical model using a mathematical optimization method and output a low-carbon planning solution.

[0189] The rest is the same as in Example 1.

[0190] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0191] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0192] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0195] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0196] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling, characterized in that: The following steps are involved: Establish an electricity-hydrogen-heat island microgrid model considering the coupling characteristics of electricity and carbon markets; Based on the electric-hydrogen-heat island microgrid model, a low-carbon planning mathematical model of the electric-hydrogen-heat island microgrid is established by considering the energy connection between typical days and the negative carbon emission technology of coupling biomass energy with carbon capture and storage; The mathematical optimization method is used to solve the low-carbon planning mathematical model and output a low-carbon planning solution.

2. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling according to claim 1, characterized in that: The electric-hydrogen-heat island microgrid model includes photovoltaic units, wind turbines, biomass units, batteries, hydrogen storage systems, fuel cells, electrolyzers, electric boilers, carbon capture equipment and heat storage tanks. The photovoltaic units, wind turbines and biomass units are used to meet the electricity demand of flexible loads. The batteries and hydrogen storage systems constitute hybrid energy storage devices to cope with power fluctuations on different time scales. The waste heat of the electric boilers and electrolyzers is recovered to provide thermal energy. The heat storage tank is used as a thermal energy storage device to store excess heat to achieve thermal energy regulation and balance when heat demand and heat supply are inconsistent.

3. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling according to claim 1, characterized in that: The low-carbon planning mathematical model includes an objective function and corresponding constraints. The objective function aims to minimize the total investment and operation cost of the electricity-hydrogen-heat island microgrid. The expression of the objective function is: In the formula, C is the objective function, C inv is the investment cost, C m is the maintenance cost, C BU is the fuel cost of the biomass power generator, is carbon storage and transaction costs, C E Profits from electricity market transactions.

4. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling according to claim 3, characterized in that: The expressions for the investment cost, maintenance cost and fuel cost of the biomass power generation unit are: C m =αC inv In the formula, r represents the discount rate; β represents the life of the equipment; c SHS,e 、c SHS,p They represent the SHS unit capacity investment cost and unit power investment cost respectively; E SHS 、E Bat Respectively represent the investment capacity of SHS and Bat; c Bat,e 、c Bat,p Bat represents the unit capacity investment cost and unit power investment cost respectively; c TES,e 、c TES,p They represent the TES unit capacity investment cost and unit power investment cost respectively; c EB,p 、c BU,p 、c CCS,p 、c SST,e are the unit investment costs of EB, BU, CCS and SST respectively; α represents the maintenance cost coefficient; D represents the total number of typical days; W d Indicates the weights of different typical days; D p is the total number of days in a year; C BU The fuel cost per unit output of the biomass unit; is the output power of the biomass energy unit at time t.

5. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling according to claim 3, characterized in that: The expression of carbon storage and transaction cost is: Where D p is the total number of days in a year; D is the total number of typical days; W d Indicates the weights of different typical days; C sto Indicates the storage cost per unit of CO2; is the amount of CO2 captured by CCS at time t; The income obtained by the system from participating in the carbon market; W d Indicates the weights of different typical days; The revenue from carbon emissions trading at the hourly level; Indicates the actual trading share of the IES-BECCS system in the carbon trading market; Respectively represent the free carbon emission quota and actual carbon emission of IES-BECCS system; Represents the carbon emission quota corresponding to the unit power of external electricity purchase; Indicates the carbon emission quota corresponding to the unit electric power of BU; The carbon emissions corresponding to the unit power of external electricity purchase; is the output power of BU at time t, is the electricity purchase amount of IES-BECCS at time t; is the amount of CO2 produced by BU at time t; cet is the carbon price; θ is the carbon price growth coefficient; M is the length of the carbon emission interval, and d is the dth day.

6. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling according to claim 3, characterized in that: The expression of the power market transaction revenue is: Where D p is the total number of days in a year; W d Indicates the weights of different typical days; Respectively represent the unit electricity selling price and electricity purchasing price; They represent the power sold and purchased at time t respectively, and D represents the total number of typical days.

7. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling according to claim 3, characterized in that: The constraints include: Consider the energy connection between typical days and the constraints of the hydrogen storage device model coordinated with the battery: In the formula, represents the capacity of Bat at time t+1; is the self-consumption rate of Bat; η bat,char and η bat,dis Respectively represent the charging and discharging efficiency of Bat; represents the charging and discharging power of Bat at time t; Indicates the start and stop state variable of Bat; P bat,max is the upper limit of Bat's charging and discharging power; E bat,min is the lower limit of Bat capacity; E bat,max is the upper limit of Bat’s capacity, and d is the dth day; Carbon capture and storage and carbon emission management model constraints: In the formula, is the mass of CO2 produced by BU at time t, The unit carbon emission of biomass energy units; Indicates the fuel consumption coefficient of BU; is the output power of BU at time t; P BU,max The upper limit of the output power of the BU; is the amount of CO2 produced by BU at time t; is the mass of the solution temporarily stored in SST; η CCS For the operational efficiency of CCS; is the mass of CO2 directly captured; The carbon emissions corresponding to the unit power of external electricity purchase; It represents the mass of CO2 extracted from SST; Indicates the solubility of CO2 in monoethanolamine water; σ SST is the maximum proportion of solvent in SST, Cap SST,max is the maximum capacity of the SST; is the output power of CCS at time t; is the fixed energy consumption of CCS equipment, λ CCS P is the energy consumption of CCS to capture unit CO2; CCS,max is the output limit of CCS; Battery model constraints: In the formula, represents the capacity of Bat at time t+1; is the self-consumption rate of Bat; η bat,char and η bat,dis Respectively represent the charging and discharging efficiency of Bat; represents the charging and discharging power of Bat at time t; Indicates the start and stop state variable of Bat; P bat,max is the upper limit of Bat's charging and discharging power; E bat,min is the lower limit of Bat capacity; E bat,max The upper limit of Bat capacity; Electric boiler and biomass energy unit constraints: In the formula, is the electric power consumed by EB at time t; P EB,max is the upper power limit; is the output power of BU at time t; P BU,max The upper limit of biomass unit output; Upper and lower limits for purchasing and selling electricity: In the formula, For the purchase of electricity; P is the electricity sales volume; e,max The upper limit of electricity purchase and sale; Power balance constraints: In the formula, Respectively represent the electric power output of the photovoltaic unit per hour, the electric power output of the wind turbine unit, the electric power consumed by the carbon capture unit, the abandoned power and the electric power used; Thermal storage equipment model constraints: In the formula, represents the capacity of TES at time, represents the charging and discharging heat power of TES at time t; η TES,char , η TES,dis Indicates the charging and discharging efficiency of TES; represents the self-consumption efficiency of TES; H TES,max It is the upper limit of TES charging and discharging heat power; and They are TES charging and discharging heat marks; Q TES,max Indicates the maximum capacity of TES; Model constraints for waste heat recovery: In the formula, and They represent the waste heat recovered by ELZ and FC at time t respectively; Electric boiler model constraints: In the formula, is the heat generated by EB at time t, η EB is the electrothermal conversion efficiency of EB; Thermal energy balance constraints: In the formula, Represents the heat load demand.

8. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling according to claim 1, characterized in that: The mathematical optimization method is a mixed integer linear programming method.

9. A low-carbon planning method for an electricity-hydrogen-heat island microgrid considering market coupling according to claim 8, characterized in that: A mixed integer linear programming method is used to solve the problem of solving the low-carbon planning mathematical model, so as to convert the problem of solving the low-carbon planning mathematical model into a problem of solving mixed integer linear constraints. The expression solved by the mixed integer linear programming method is: y=a·x In the formula, a is a binary variable, x is a continuous variable, and M is a set number. When the value of a is 1, the value of y is equal to x; when the value of a is 0, the value of y is equal to 0.

10. A low-carbon planning system for an electricity-hydrogen-heat island microgrid considering market coupling, characterized in that: include: Island microgrid model building module: used to build an electric-hydrogen-heat island microgrid model that takes into account the coupling characteristics of the electricity-carbon market; A low-carbon planning mathematical model building module is used to establish a low-carbon planning mathematical model for the electric-hydrogen-heat island microgrid based on the electric-hydrogen-heat island microgrid model, taking into account the energy connection between typical days and the negative carbon emission technology of coupling biomass energy with carbon capture and storage; Solution module: used to solve the low-carbon planning mathematical model using a mathematical optimization method and output a low-carbon planning solution.

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