Microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon transaction

Abstract

The invention discloses a micro-grid capacity optimal configuration method based on CVaR-IGDT and green certificate-carbon transaction, and relates to the technical field of micro-grids, comprising the following steps: establishing a hydrogen-containing energy storage micro-grid mathematical model; establishing a green certificate-carbon transaction fusion interaction model; establishing a micro-grid capacity low-carbon optimization configuration model in a deterministic scene; a micro-grid capacity low-carbon optimization configuration model based on the CVaR-IGDT is established; and verifying and evaluating the micro-grid capacity low-carbon optimization configuration model. According to the technical scheme of the invention, the CVaR and the IGDT are used for processing the long-short uncertainty of the source load, and the uncertainty of the wind and light output and the uncertainty of the load fluctuation are converted into the quantitative combination of the risk cost and the fluctuation amplitude with the total cost of the micro-grid planning. According to the method, a micro-grid planning investor can effectively balance two uncertainties of source and load according to actual requirements, a reasonable and effective micro-grid capacity optimization configuration method considering economy and robustness is obtained, and reference is provided for lean configuration of the capacity of the hydrogen-containing energy storage micro-grid.

Description

Technical Field

[0001] The present invention relates to the field of microgrid technology, and in particular to a microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading. Background Art

[0002] With the proposal of my country's dual carbon goals, the development and utilization of renewable energy has become a key way to achieve this goal. As an important form of renewable energy access and consumption, the optimization of microgrid capacity is a key issue that needs to be solved urgently. The reasonable configuration of microgrid capacity can directly affect the economic efficiency of its planning and the utilization rate of renewable energy units. Hydrogen energy has good development prospects due to its clean, low-carbon and high calorific value characteristics. Introducing hydrogen as energy storage in the optimization of microgrid capacity plays an important role in energy transformation. At the same time, the access of a high proportion of new energy in microgrids and the uncertainty of load fluctuations have a negative impact on the lean configuration of microgrid capacity. It is of great significance to consider the uncertainty of source and load in the process of optimizing microgrid capacity. In addition, under the requirements of low-carbon electricity, carbon emissions are particularly important. Therefore, comprehensive consideration of the above factors plays an important role in the lean configuration of microgrid capacity.

[0003] In this regard, how to propose a method for optimizing the capacity of a hydrogen energy storage microgrid that takes into account source-load uncertainty and the integration and interaction of green certificates and step-by-step carbon trading is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0004] The technical solution of the present invention to solve the above technical problems is to provide a microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading, comprising the following steps:

[0005] Step 1: Establish a mathematical model of a hydrogen energy storage microgrid:

[0006] Construct the basic structure of a hydrogen energy storage microgrid including wind turbines, photovoltaic arrays, diesel generators, hydrogen energy storage systems and loads;

[0007] The hydrogen energy storage system is composed of an electrolyzer, a fuel cell and a hydrogen storage tank, and is used to balance the mismatch between renewable energy generation and load demand;

[0008] The amount of hydrogen produced by the electrolyzer through the residual electricity is calculated by the electrolyzer mathematical model;

[0009] Calculate the output power of the fuel through the fuel cell mathematical model;

[0010] Calculate the hydrogen storage capacity of the hydrogen storage tank through the hydrogen storage tank mathematical model;

[0011] Step 2: Establish a green certificate-carbon trading integration interaction model:

[0012] Establish a GCT model to allocate quotas based on renewable energy power generation and calculate the transaction costs of green certificates;

[0013] Establish a CET model to control carbon emissions on the power supply side in the form of carbon emission quotas and calculate the carbon emission trading costs;

[0014] Combining the GCT model and the CET model, a GCT-CET fusion interaction model is established to calculate the free carbon quota obtained when obtaining green certificates and the carbon emission trading amount of microgrids;

[0015] Step 3: Establish a low-carbon optimization configuration model for microgrid capacity under deterministic scenarios:

[0016] Taking the economic efficiency of microgrid capacity configuration results as the optimization goal, the green certificate-carbon trading fusion interaction model is considered;

[0017] Set the objective function, including the annual construction cost and annual operation cost of the microgrid;

[0018] Set constraints, including power balance constraints, distributed power output constraints, hydrogen energy storage system constraints, power purchase and sales constraints, demand response constraints, and wind and solar power abandonment rate constraints;

[0019] Step 4: Establish a low-carbon optimization configuration model for microgrid capacity based on CVaR-IGDT:

[0020] Use CVaR theory to quantify the uncertainty of wind power and photovoltaic output and set the risk preference coefficient;

[0021] Use IGDT to handle the uncertainty of load fluctuations and set risk avoidance strategies;

[0022] Combining CVaR and IGDT, a low-carbon optimization configuration model of microgrid power capacity based on CVaR-IGDT is constructed. The model includes the expected value of annual operating cost, CVaR value and the fluctuation range of uncertain variables.

[0023] Furthermore, in step 1,

[0024] The electrolyzer mathematical model:

[0025] The calculation formula for the amount of hydrogen produced by the surplus electricity through the electrolyzer is:

[0026]

[0027] Where: V el (t) is the volume of hydrogen produced by the electrolytic cell at time t; η el The working efficiency of the electrolyzer; P is the volume of hydrogen produced by electrolyzing water per unit of electricity; el (t) is the input power of the electrolytic cell at time t; Δt is the time change;

[0028] The fuel cell mathematical model:

[0029] The formula for calculating the output power of fuel is;

[0030]

[0031] Where: P fc (t) is the output power of the fuel cell at time t; η fc For the efficiency of fuel cells; V is the electrical power generated by the fuel cell when a unit volume of hydrogen is input; fc (t) is the volume of hydrogen consumed by the fuel cell at time t;

[0032] The mathematical model of the hydrogen storage tank:

[0033] The hydrogen storage tank can provide hydrogen for the fuel cell, and can also store the hydrogen produced by electrolysis in the electrolyzer; the calculation formula for the hydrogen storage capacity of the hydrogen storage tank is;

[0034] E s (t) = E s (t-1)+[V s,ch (t)η ch -V s,dis (t) / η dis )],

[0035] Where: E s (t) is the total volume of hydrogen contained in the hydrogen storage tank at time t; V s,ch (t), V s,dis (t) are the hydrogen storage and release volumes of the hydrogen storage tank at time t; η ch , η dis They are the hydrogen storage and release efficiency of the hydrogen storage tank respectively.

[0036] Further, in step (2),

[0037] The GCT model:

[0038] Based on the quota of renewable energy power generation, the green certificate transaction cost is calculated using the formula:

[0039]

[0040] Where: F GCTis the green certificate transaction cost; λ is the green certificate transaction unit price; N is the number of days in the settlement cycle; ρ is the green certificate quota coefficient that the microgrid operator needs to hold; σ is the quantitative coefficient of renewable energy power generation converted into green certificates; P load,T (t), P pv,T (t), P wt,T (t) are load power, photovoltaic power and wind power at time t in the T day, respectively;

[0041] A quantity competition Cournot trading model is established to consider the green certificate trading price with market fluctuations and normal influences. The specific model is:

[0042]

[0043] Where: c GCT 、e GCT is the positive parameter of the inverse price function of the Cournot model; M is the number of green certificates sold; λ0 is the base price of green certificates; r GCT It is the green certificate transaction price ratio derived from market and policy historical data;

[0044] The CET model:

[0045] The carbon emissions on the power supply side are controlled in the form of carbon emission quotas; the carbon emission trading cost is calculated using a stepped carbon trading model, and the calculation formula is:

[0046]

[0047] Where: is the carbon emission transaction cost; ξ is the transaction price per unit of carbon emission; l is the carbon emission step length; γ is the growth rate of the transaction price per unit of carbon emission; E G is the carbon emission trading amount;

[0048] Establish a multilateral carbon trading price model that is consistent with market fluctuations and policy changes. In the model, one buyer is allowed to match transactions with multiple sellers, and the transaction order and volume are formed based on the prices declared by multiple parties. The specific model is:

[0049]

[0050] Where: I i,buy (x), I i,sell (x) are the bid prices of the microgrid operators and buyers participating in the bidding in the xth round of transactions; I i,min ,I i,max are the reward and penalty prices obtained by the i-th microgrid operator respectively; i (x) is the correction factor of the price declared by the i-th microgrid operator in the x-th round; I ap ,I aware the assessment penalty and reward prices of the market respectively; X is the maximum number of corrections; υ 0,j is the willingness of the jth seller, and its value is [-1,1]; w i , H i are the carbon trading volume that has been completed and the carbon trading volume required by the i-th microgrid operator respectively;

[0051] The GCT-CET fusion interaction model:

[0052] You will get free carbon quotas when you get green certificates. The calculation formula for free carbon quotas is:

[0053]

[0054] Where: E green is the carbon emission quota obtained by converting green certificates for renewable energy power generation; ε is the conversion coefficient of renewable energy power generation into carbon emission quota;

[0055] Microgrid carbon emission trading amount E G The calculation formula is:

[0056] E G =E m -E green -E a ,

[0057] Where: E m E is the carbon emissions generated by the system; a Free carbon emission quotas;

[0058] E m 、E a The calculation formula is:

[0059]

[0060] Where: de , χ buy P is the carbon emission intensity of diesel generator sets and electricity purchased from the upper power grid per unit of power generation; de,T (t), P buy,T (t) are the power purchased by the diesel generator set and the upper power grid at time t in the Tth day; E de 、E buy They are the free carbon emission quotas for diesel generator sets per unit of power generation and electricity purchased from the superior power grid.

[0061] Further, in step (3),

[0062] Taking the economic efficiency of microgrid capacity configuration results as the optimization goal, the green certificate-carbon trading fusion interaction model is considered; the objective function is set:

[0063]

[0064] Where: F is the annual construction cost of the microgrid; F inv is the investment cost of equal annual value; F op is the annual operating cost;

[0065] F inv Including the equal annual value investment cost and annual maintenance cost of the equipment, the calculation formula is;

[0066]

[0067] Where: k is the equipment type, k = 1, 2, ..., 6 represent wind turbines, photovoltaic arrays, diesel generators, electrolyzers, fuel cells and hydrogen storage tanks respectively; F k,inv is the unit investment cost of the kth type of equipment; F k,om is the annual maintenance cost of the k-th type of equipment; τ k is the installed capacity of the kth type of equipment; Y is the equal annual value operator; ζ is the discount rate; L is the planning period of the microgrid;

[0068] The annual operating cost of the microgrid is:

[0069]

[0070] Where: F de is the annual fuel cost of diesel generator; C fuel is the unit price of diesel; Ω D It is a collection of typical days, including three typical days in spring, autumn, summer and winter; is the number of days that a typical day d occupies in a year; are the power generation and maximum power of the diesel generator at a typical day; F dr is the demand response cost; C dr is the demand response cost per unit of electricity; is the demand response power at time t; C buy (t), C sell They are the purchase and sale prices of electricity per unit of electricity at the moment; are the power purchased and sold at time t; C waste The penalty cost for curtailing wind and solar power per unit of electricity; They are wind power abandonment and solar power abandonment at time t respectively;

[0071] in, The calculation formula is:

[0072]

[0073] Where: are the power that can be generated by the wind turbine and the photovoltaic array at time t; are the actual output power of wind turbine and photovoltaic array at each moment;

[0074] Constraints:

[0075] a. Power balance constraints: The microgrid system power balance and hydrogen balance constraints are as follows:

[0076]

[0077] V el (t)+V s,dis (t) = V fc (t)+V s,ch (t),

[0078] Where: The fuel cell power, load power, electrolyzer power and demand response load transfer power at time t respectively;

[0079] b. Distributed power output constraints:

[0080] The scale of distributed power generation construction is limited by geographical conditions, as shown in the following formula:

[0081]

[0082] Where: are the maximum output power of wind turbine, photovoltaic array and diesel generator respectively;

[0083] c. Hydrogen energy storage system constraints:

[0084] The constraints of the hydrogen energy storage system are as follows:

[0085]

[0086] Where: are the maximum output power of electrolyzer and fuel cell respectively; is the hydrogen storage capacity of the hydrogen storage tank at time t; are the minimum and maximum hydrogen storage capacity of the hydrogen storage tank respectively; is the hydrogen charge state of the hydrogen storage tank at time t within a typical day d; They are the minimum and maximum hydrogen charge states of the hydrogen storage tank respectively; are the start and end times of the scheduling cycle within a typical day d respectively;

[0087] d. Constraints on power purchase and sales:

[0088] Excessive interaction power between the microgrid and the upper power grid will affect the upper power grid; the power consumption constraints are as follows:

[0089]

[0090] Where: are the maximum power purchase and sales power allowed for the interaction between the microgrid and the upper grid;

[0091] e. Power purchase and sales constraints:

[0092] Considering the flexible transferable load in the microgrid, the power consumption characteristics at time t are constrained as follows:

[0093]

[0094] Where: They represent the minimum and maximum power of the load that can be shifted in time and participate in demand response at time t respectively;

[0095] f. Constraints on wind and solar power abandonment rates:

[0096] In order to ensure the utilization rate of renewable energy power generation, the wind and solar power abandonment rate constraint should satisfy the formula:

[0097]

[0098] Where: are the maximum wind and solar power abandonment rates, respectively.

[0099] Further, in step (4),

[0100] CVaR measures the risk value of a microgrid by evaluating the probability and magnitude of losses incurred in the microgrid;

[0101] The annual operating cost of the microgrid of CVaR consists of two parts: the first part is the expected value of the operating cost of the microgrid under different scenarios; the second part is the product of β and CVaR; β is defined as the risk preference coefficient; when β is less than 0.05, the planning investor is risk-seeking; when β is greater than 0.1, the planning investor is considered to be risk-averse; the CVaR model is shown in the following formula;

[0102]

[0103] Where: F o ' p To consider the annual operating cost of CVaR; F op,CVaR are the expected annual operating cost and CVaR value respectively; ω wt ,ω pv are the number of wind power and photovoltaic output scenarios generated by the scenarios; ω p ,ω q They are wind power and photovoltaic output scene sets {ω p ,p=1,2,...,ω wt}、{ω q,q=1,2,...,ω pv};π(ω p ),π(ω q ) are wind power output scenarios ω p 、Photovoltaic output scenario q The probability of op (ω p ,ω q ) is the microgrid in the scenario (ω p ,ω q ) Minimum operating costs; F op,CVaR is the CVaR value; δ VaR is the VaR value, which is the decision variable introduced to calculate CVaR; θ CVaR is the confidence factor; the expression [x] + =max{x,0};

[0104] The cost optimization of IGDT is shown in the following formula:

[0105]

[0106] Where: f is the objective function of the cost optimization problem; v is the decision variable; u is the uncertain variable; h is the equality constraint about v and u; g is the inequality constraint about v and u; U is the set of uncertain variables, as shown in the following formula;

[0107]

[0108] Where: is the predicted value of the uncertain variable; α is the fluctuation range of the uncertain variable;

[0109] The risk avoidance strategy of IGDT is as follows:

[0110]

[0111] Where: α RA is the fluctuation range of uncertain variables under the risk aversion strategy; μ RA is the risk aversion coefficient, i.e., the investor's ability to accept additional investment; F0 is the benchmark value of the objective function, i.e., the investment cost under a deterministic scenario; are the actual load power and predicted power at time t in a typical day d, respectively;

[0112] make The low-carbon optimization configuration model of microgrid power capacity based on CVaR-IGDT is as follows:

[0113]

[0114] Furthermore, the microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading also includes:

[0115] Step 5: Verify and evaluate the microgrid capacity low-carbon optimization configuration model.

[0116] Aiming at the problem of low-carbon optimal configuration of capacity in hydrogen energy storage microgrid considering source-load uncertainty, the present invention establishes a CVaR-IGDT optimal configuration model of hydrogen energy storage microgrid capacity considering the fusion interaction of green certificate and carbon trading, which has the following advantages:

[0117] 1) The present invention uses CVaR and IGDT to deal with the long-short uncertainty of source and load, converts the uncertainty of wind and solar power output and the uncertainty of load fluctuation into risk cost and fluctuation amplitude, and quantitatively combines them with the total cost of microgrid planning. It enables microgrid planning investors to effectively balance the two uncertainties of source and load according to actual needs, and obtains a reasonable and effective microgrid capacity optimization configuration method that takes into account both economy and robustness, providing a reference for the lean configuration of hydrogen energy storage microgrid capacity.

[0118] 2) In terms of carbon emission reduction in microgrids, the green certificate-carbon trading integrated interactive model proposed in the present invention can effectively guide microgrids to reduce carbon emissions, improve renewable energy utilization and installed capacity. Compared with the single green certificate-carbon trading model, the integrated interactive green certificate-carbon trading model can further reduce the total construction cost and carbon emissions of microgrids, making the optimal configuration method of microgrid capacity more economical and environmentally friendly. BRIEF DESCRIPTION OF THE DRAWINGS

[0119] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0120] Figure 1 It is a flowchart of the steps of the microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading according to the present invention;

[0121] Figure 2 This is a structural diagram of a hydrogen energy storage microgrid of the present invention;

[0122] Figure 3 The typical daily wind power output characteristics of the present invention;

[0123] Figure 4 The typical daily photovoltaic output characteristics of the present invention;

[0124] Figure 5 is the typical daily load of the present invention;

[0125] Figure 6 The influence of different risk preference coefficients on the optimization results of the present invention;

[0126] Figure 7 This is the operating condition when the risk preference coefficient of the present invention is 0.1;

[0127] Figure 8 This is the operating condition when the risk preference coefficient of the present invention is 0.3;

[0128] Fig. 9 The influence of different risk aversion coefficients on the optimization results of the present invention;

[0129] Fig.10 The impact of the green certificate-carbon trading linkage price on carbon emissions in the present invention;

[0130] Fig.11 This is the impact of the green certificate-carbon trading linkage price on the total cost of the present invention. DETAILED DESCRIPTION

[0131] The present invention proposes a microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading, aiming to propose a hydrogen energy storage microgrid capacity optimization configuration method that considers source-load uncertainty and the fusion interaction of green certificate-ladder carbon trading.

[0132] The microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading proposed by the present invention will be described below in a specific embodiment:

[0133] A microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading, such as Figure 1 As shown, the following steps are included:

[0134] Step 1: Establish a mathematical model of a hydrogen energy storage microgrid:

[0135] Due to certain limitations on the transmission power between the microgrid and the upper power grid and to meet the power balance requirements of the upper power grid when the microgrid is off-grid, a diesel generator set is introduced into the microgrid structure. The basic structure of the hydrogen energy storage microgrid mainly includes wind turbines, photovoltaic arrays, diesel generators, hydrogen energy storage systems and loads. Each power generation system is connected to the AC bus through different converters. The hydrogen energy storage system consists of an electrolyzer, a fuel cell and a hydrogen storage tank.

[0136] When the renewable energy power generation from wind power and photovoltaic power generation is greater than the power required by the load, the electrolyzer in the hydrogen energy storage system will use the excess electricity to produce hydrogen by electrolyzing water, thereby consuming the excess electricity; when the renewable energy power generation from wind power and photovoltaic power generation is less than the power required by the load, the microgrid purchases electricity from the superior power grid, generates electricity from diesel generators and supplies electricity to the fuel cells in the hydrogen energy storage system, thereby ensuring the reliability of power supply to the load.

[0137] In the hydrogen energy storage system, when the wind and solar power generation exceeds the load demand, the electrolyzer uses the remaining electricity to electrolyze water and stores the generated hydrogen in the hydrogen storage tank. When the wind and solar power generation is not enough to meet the load demand, the fuel cell uses the hydrogen in the hydrogen storage tank as fuel to generate electricity to meet the load demand.

[0138] ① Mathematical model of electrolytic cell:

[0139] The amount of hydrogen produced by the excess electricity through the electrolyzer is shown in the following formula:

[0140]

[0141] Where: V el (t) is the volume of hydrogen produced by the electrolytic cell at time t; η el The working efficiency of the electrolyzer; P is the volume of hydrogen produced by electrolyzing water per unit of electricity; el (t) is the input power of the electrolytic cell at time t; Δt is the time change.

[0142] ② Fuel cell mathematical model:

[0143] The output power of the fuel is shown as follows:

[0144]

[0145] Where: P fc (t) is the output power of the fuel cell at time t; η fc For the efficiency of fuel cells; V is the electrical power generated by the fuel cell when a unit volume of hydrogen is input; fc (t) is the volume of hydrogen consumed by the fuel cell at time t.

[0146] ③Mathematical model of hydrogen storage tank:

[0147] The hydrogen storage tank can provide hydrogen for the fuel cell, and can also store the hydrogen produced by electrolysis in the electrolyzer. The hydrogen storage capacity of the hydrogen storage tank is shown in the following formula:

[0148] E s (t) = E s (t-1)+[Vs,ch (t)η ch -V s,dis (t) / η dis )],

[0149] Where: E s (t) is the total volume of hydrogen contained in the hydrogen storage tank at time t; V s,ch (t), V s,dis (t) are the hydrogen storage and release volumes of the hydrogen storage tank at time t; η ch , η dis They are the hydrogen storage and release efficiency of the hydrogen storage tank respectively.

[0150] Step 2: Establish a green certificate-carbon trading integration interaction model:

[0151] The renewable energy quota system refers to a mandatory policy measure adopted by the government to cultivate the renewable energy market and ensure that the renewable energy power generation reaches a guaranteed minimum level. The carbon emissions trade (CET) and green certificate trade (GCT) mechanisms are important manifestations of the renewable energy quota system and are also effective ways to reduce carbon emissions.

[0152] ①GCT model:

[0153] The principle of GCT is to allocate quotas based on renewable energy generation. If the renewable energy generation in the system is less than the quota value, green certificates need to be purchased to fill the quota gap; if the renewable energy generation in the system is greater than the quota value, green certificates can be sold to obtain profits. The transaction cost of green certificates can be calculated using the following formula:

[0154]

[0155] Where: F GCT is the green certificate transaction cost; λ is the green certificate transaction unit price; N is the number of days in the settlement cycle; ρ is the green certificate quota coefficient that the microgrid operator needs to hold; σ is the quantitative coefficient of renewable energy power generation converted into green certificates; P load,T (t), P pv,T (t), P wt,T (t) are the load power, photovoltaic power and wind power at time t within the Tth day.

[0156] Due to market fluctuations and policy influences, the price of green certificates will fluctuate with changes in quantity. The transaction price of green certificates considering market fluctuations and normal influences is established through the Cournot trading model of quantity competition. The specific model is shown in the following formula:

[0157]

[0158] Where: cGCT 、e GCT is the positive parameter of the inverse price function of the Cournot model; M is the number of green certificates sold; λ0 is the base price of the green certificate; r GCT It is the green certificate trading price ratio derived from market and policy historical data.

[0159] ②CET model

[0160] The CET model controls the carbon emissions on the power supply side in the form of carbon emission quotas. When the carbon emissions in the microgrid system are less than its quota, the microgrid can sell the excess carbon quotas in the market to gain benefits; when the carbon emissions of the microgrid exceed the quota, the excess needs to purchase quotas from the carbon market to avoid fines. This application adopts a stepped carbon trading model, and the carbon emission trading cost can be calculated using the following formula:

[0161]

[0162] Where: is the carbon emission transaction cost; ξ is the transaction price per unit of carbon emission; l is the carbon emission step length; γ is the growth rate of the transaction price per unit of carbon emission; E G The carbon emission trading amount.

[0163] Taking into account the impact of market fluctuations and policies, carbon trading prices will also change accordingly. A multilateral carbon trading price model that conforms to market fluctuations and policy changes is established. In this model, one buyer is allowed to match transactions with multiple sellers, and the transaction order and transaction volume are formed based on the prices declared by multiple parties. The specific model is shown in the following formula:

[0164]

[0165] Where: I i,buy (x), I i,sell (x) are the bid prices of the microgrid operators and buyers participating in the bidding in the xth round of transactions; I i,min ,I i,max are the reward and penalty prices obtained by the i-th microgrid operator respectively; i (x) is the correction factor of the price declared by the i-th microgrid operator in the x-th round; I ap ,I aw are the assessment penalty and reward prices of the market respectively; X is the maximum number of corrections; υ 0,j is the willingness of the jth seller, and its value is [-1,1]; w i , H i are the carbon trading volume that has been completed and the carbon trading volume required by the i-th microgrid operator respectively.

[0166] ③GCT-CET fusion interaction model:

[0167] Microgrid operators can obtain additional free carbon quotas from the government while obtaining green certificates. The specific free carbon quotas can be calculated using the following formula.

[0168]

[0169] Where: E green It is the carbon emission quota obtained by converting green certificates from renewable energy power generation; ε is the conversion coefficient from renewable energy power generation to carbon emission quota.

[0170] Microgrid carbon emission trading amount E G The following formula can be used for calculation:

[0171] E G =E m -E green -E a ,

[0172] Where: E m E is the carbon emissions generated by the system; a Free carbon emission quota.

[0173] E m 、E a The following formula can be used for calculation:

[0174]

[0175] Where: de , χ buy P is the carbon emission intensity of diesel generator sets and electricity purchased from the upper power grid per unit of power generation; de,T (t), P buy,T (t) are the power purchased by the diesel generator set and the upper power grid at time t in the Tth day; E de 、E buy They are the free carbon emission quotas for diesel generator sets per unit of power generation and electricity purchased from the superior power grid.

[0176] Step 3: Establish a low-carbon optimization configuration model for microgrid capacity under deterministic scenarios:

[0177] Taking the economy of microgrid capacity configuration results as the optimization goal, considering the green certificate-carbon trading fusion interaction model, under a deterministic scenario, a hydrogen energy storage microgrid capacity optimization configuration model based on the green certificate-carbon trading fusion interaction is established.

[0178] ①Objective function:

[0179] The objective function of the optimization configuration model established by the present invention is shown in the following formula:

[0180]

[0181] Where: F is the annual construction cost of the microgrid; F inv is the investment cost of equal annual value; F op The annual operating cost.

[0182] F inv Including the annual investment cost and annual maintenance cost of each equipment, the calculation formula is as follows:

[0183]

[0184] Where: k is the equipment type, k = 1, 2, ..., 6 represent wind turbines, photovoltaic arrays, diesel generators, electrolyzers, fuel cells and hydrogen storage tanks respectively; F k,inv is the unit investment cost of the kth type of equipment; F k,om is the annual maintenance cost of the k-th type of equipment; τ k is the installed capacity of the kth type of equipment; Y is the equal annual value operator; ζ is the discount rate; L is the planning period of the microgrid.

[0185] The annual operating cost of a microgrid is as follows:

[0186]

[0187] Where: F de is the annual fuel cost of diesel generator; C fuel is the unit price of diesel; Ω D It is a collection of typical days, including three typical days in spring, autumn, summer and winter; is the number of days that a typical day d occupies in a year; are the power generation and maximum power of the diesel generator at a typical day; F dr is the demand response cost; C dr is the demand response cost per unit of electricity; is the demand response power at time t; C buy (t), C sell They are the purchase and sale prices of electricity per unit of electricity at the moment; are the power purchased and sold at time t; C waste The penalty cost for curtailing wind and solar power per unit of electricity; are the wind power and solar power abandoned at time t respectively.

[0188] in, The following formula can be used for calculation:

[0189]

[0190] Where: are the power that can be generated by the wind turbine and photovoltaic array at time t respectively; are the actual output powers of wind turbines and photovoltaic arrays at each moment.

[0191] ②Constraints:

[0192] a. Power balance constraints:

[0193] The electric power balance and hydrogen balance constraints of the microgrid system are shown as follows:

[0194]

[0195] V el (t)+V s,dis (t) = V fc (t)+V s,ch (t),

[0196] Where: Fuel cell power, load power, electrolyzer power and demand response load transfer power at time t respectively.

[0197] b. Distributed power output constraints:

[0198] The scale of distributed power generation construction is limited by geographical conditions, and the constraints are shown in the following formula:

[0199]

[0200] Where: are the maximum output powers of wind turbines, photovoltaic arrays and diesel generators respectively.

[0201] c. Hydrogen energy storage system constraints:

[0202] The constraints of the hydrogen energy storage system are as follows:

[0203]

[0204] Where: are the maximum output power of electrolyzer and fuel cell respectively; is the hydrogen storage capacity of the hydrogen storage tank at time t; are the minimum and maximum hydrogen storage capacity of the hydrogen storage tank respectively; is the hydrogen charge state of the hydrogen storage tank at time t within a typical day d; They are the minimum and maximum hydrogen charge states of the hydrogen storage tank respectively; They are the start and end times of the scheduling cycle within a typical day.

[0205] d. Constraints on power purchase and sales:

[0206] Excessive interaction power between the microgrid and the upper power grid will affect the upper power grid. The power constraints for purchasing and selling electricity are shown in the following formula.

[0207]

[0208] Where: They are the maximum power purchase and sales allowed for the interaction between the microgrid and the upper-level power grid.

[0209] e. Power purchase and sales constraints:

[0210] Considering the flexible transferable load in the microgrid, the power consumption characteristic constraint at time t is as follows:

[0211]

[0212] Where: They represent the minimum and maximum power of the load that can be time-shifted in demand response at time t respectively.

[0213] f. Constraints on wind and solar power abandonment rates:

[0214] In order to ensure the utilization rate of renewable energy power generation, the wind and solar power abandonment rate constraints should satisfy the following formula:

[0215]

[0216] Where: are the maximum wind and solar power abandonment rates, respectively.

[0217] Step 4: Establish a low-carbon optimization configuration model for microgrid capacity based on CVaR-IGDT:

[0218] The uncertainties faced in the process of optimizing the capacity of microgrids mainly include the short-term uncertainty of wind power and photovoltaic output and the long-term uncertainty of load fluctuation. The present invention adopts CVaR theory to quantitatively analyze the uncertainty of wind power and photovoltaic output; and adopts IGDT to deal with the uncertainty of load fluctuation.

[0219] As a commonly used risk measurement method, CVaR theory measures the risk value of microgrids by evaluating the probability and size of losses in microgrids.

[0220] The annual operating cost of a microgrid considering CVaR consists of two parts: the first part is the expected value of the operating cost of the microgrid under different scenarios; the second part is the product of β and CVaR. Define β as the risk preference coefficient. It is generally believed that when the value of β is less than 0.05, the planning investor is risk-seeking and hopes to exchange high risk for low cost; when the value of β is greater than 0.1, the planning investor is considered to be risk-averse and hopes to exchange high cost for low risk. The CVaR model is shown as follows:

[0221]

[0222] Where: F o 'p To consider the annual operating cost of CVaR; F op,CVaR are the expected annual operating cost and CVaR value respectively; ω wt ,ω pv are the number of wind power and photovoltaic output scenarios generated by the scenarios; ω p ,ω q They are wind power and photovoltaic output scene sets {ω p ,p=1,2,...,ω wt}、{ω q ,q=1,2,...,ω pv};π(ω p ),π(ω q ) are wind power output scenarios ω p 、Photovoltaic output scenario q The probability of op (ω p ,ω q ) is the microgrid in the scenario (ω p ,ω q ) Minimum operating costs; F op,CVaR is the CVaR value; δ VaR is the VaR value, which is the decision variable introduced to calculate CVaR; θ CVaR is the confidence factor; the expression [x] + =max{x,0}.

[0223] IGDT is a mathematical optimization method for parameter uncertainty models, which can maximize the fluctuation range of uncertain variables when the probability distribution and fluctuation range of uncertain variables are unknown. The cost optimization problem of IGDT is as follows:

[0224]

[0225] Where: f is the objective function of the cost optimization problem; v is the decision variable; u is the uncertain variable; h is the equality constraint about v and u; g is the inequality constraint about v and u; U is the set of uncertain variables, as shown in the following formula:

[0226]

[0227] Where: is the predicted value of the uncertain variable; α is the fluctuation range of the uncertain variable.

[0228] Specifically, there are two strategies: risk aversion and opportunity seeking. Consider the risk aversion strategy of IGDT.

[0229] In the risk avoidance strategy, the deviation between the actual value and the predicted value is considered a dangerous phenomenon, so the microgrid operator maximizes the fluctuation range of the uncertain variable to cope with the impact of the uncertain variable on the system. The specific model is shown in the following formula:

[0230]

[0231] Where: α RA is the fluctuation range of uncertain variables under the risk aversion strategy; μ RA is the risk aversion coefficient, i.e., the investor's ability to accept additional investment; F0 is the benchmark value of the objective function, i.e., the investment cost under a deterministic scenario; are the actual power and predicted power of the load at time t in a typical day d, respectively.

[0232] make The low-carbon optimization configuration model of microgrid power capacity based on CVaR-IGDT is as follows:

[0233]

[0234] Step 5: Verify and evaluate the microgrid capacity low-carbon optimization configuration model:

[0235] 1) Example background and parameter setting:

[0236] Due to certain limitations on the transmission power between the microgrid and the upper power grid and in order to meet the power balance requirements of the microgrid when the upper power grid is unstable and the microgrid is off-grid, a diesel generator set is introduced into the microgrid structure. Figure 2 This is a structural diagram of a hydrogen energy storage microgrid, which mainly includes wind turbines, photovoltaic arrays, diesel generators, hydrogen energy storage systems and loads. Each power generation system is connected to the AC bus through different converters. The hydrogen energy storage system consists of an electrolyzer, a fuel cell and a hydrogen storage tank. When the renewable energy power generated by wind power and photovoltaic power generation is greater than the power required by the load, the electrolyzer in the hydrogen energy storage system will use the excess electricity to produce hydrogen by electrolyzing water, thereby consuming excess electricity; when the wind power and photovoltaic renewable energy power generation is less than the power required by the load, the microgrid purchases electricity from the superior power grid, generates electricity from diesel generators and supplies electricity to the fuel cell in the hydrogen energy storage system, thereby ensuring the reliability of the load power supply. The equipment investment and operating parameter values ​​in the microgrid are shown in Table 1.

[0237] Table 1 Main parameters of microgrid

[0238]

[0239]

[0240] Three typical days are set for transition season, summer and winter, namely 185 days in spring and autumn, 90 days in summer and 90 days in winter. Figure 3 , Figure 4 , Figure 5 The wind power and photovoltaic output characteristic curves and load curves in spring, autumn, summer and winter are obtained by processing the source and load data throughout the year through the K-means clustering algorithm. The time value of the wind and photovoltaic output characteristic curve of each typical day is taken as the mean, and 0.2 times the mean is taken as the variance. The Latin hypercube sampling method is used to generate 1,000 wind and photovoltaic output scenarios for each typical day according to the normal distribution. At the same time, in order to reduce the burden of solving, the fast backward reduction method is used to reduce the wind power and photovoltaic output scenarios on each typical day to 5.

[0241] 2) Solve the low-carbon optimization configuration model of microgrid capacity based on CVaR-IGDT:

[0242] A low-carbon optimization configuration model for microgrid capacity based on CVaR-IGDT was constructed. Since the objective function and constraints in the model can be linearly represented and the model contains integer variables, the model is a typical mixed integer linear programming problem. Therefore, the overall calculation process is solved on MATLAB R2022a with the help of CPLEX solver.

[0243] The following five microgrid optimization configuration methods are set up for comparison.

[0244] Method 1: Does not consider the uncertainty of green certificates, carbon trading models and source loads.

[0245] Method 2: Based on Method 1, the uncertainty of load fluctuation is considered.

[0246] Method 3: Based on Method 2, the uncertainty of wind power and photovoltaic output is considered.

[0247] Method 4: Consider the green certificate-carbon trading model based on Method 3, without considering the integration and interaction between the two.

[0248] Method 5: Based on method 4, the integrated interactive model of green certificate and carbon trading is considered, which is the model proposed in the present invention.

[0249] The microgrid optimization configuration results of different methods are shown in Table 2 and Table 3.

[0250] Table 2 Capacity optimization configuration results of different schemes

[0251]

[0252]

[0253] Table 3 Calculation results of costs and carbon emissions of different schemes

[0254]

[0255] ① The impact of uncertainty on optimization results

[0256] a. Comparative analysis of method 1 and method 2

[0257] As shown in Table 3, the total planning cost of Method 2 is 397,400 yuan higher than that of Method 1. This is because Method 1 optimizes the configuration according to the predicted value of the load and does not consider the impact of the uncertainty of load fluctuation on the planning cost. Method 2 uses IGDT to deal with the uncertainty of load fluctuation and calculates the fluctuation range α of the uncertainty parameter. RA It is 8.71%, which means that the maximum load fluctuation range that can be accepted by Method 2 within the planning period of the microgrid is 8.71%. At this time, the microgrid planning investor needs to bear an additional 10% investment budget to cope with the impact of the uncertainty of load fluctuation.

[0258] b. Comparative analysis of method 2 and method 3

[0259] The calculated fluctuation range of the uncertainty parameters of Method 3 is 2.59% lower than that of Method 2. This is because Method 3 takes into account the uncertainty of wind power and photovoltaic output on the basis of Method 2 while keeping the total planning cost unchanged. This uncertainty is converted into risk cost and included in the total planning cost, which leads to a relative reduction in the cost of coping with load fluctuation uncertainty and reduces its fluctuation range.

[0260] ②Benefit analysis of the green certificate-carbon trading integrated interactive model:

[0261] Taking into account the uncertainty of source load, by comparing Method 3, Method 4 and Method 5, a comparison of the benefits before and after the introduction of the green certificate-carbon trading fusion interaction model is carried out, as shown in Table 4.

[0262] Table 4 Comparison of benefits before and after the introduction of the green certificate-carbon trading fusion interaction model

[0263]

[0264] As shown in Table 4, by comparing Method 3 and Method 4, Method 4 takes into account the green certificate-carbon trading model on the basis of Method 3, and the carbon emissions are reduced by 68.47 tons, and the total cost increases by 212,600 yuan. This is because after the introduction of the green certificate-carbon trading model, the renewable energy consumption and carbon emissions are converted into green certificate costs and carbon trading costs, which increases the installed capacity of renewable energy units with relatively high costs, thereby increasing the total cost.

[0265] By comparing Method 4 and Method 5, it can be seen that Method 5 takes into account the fusion interaction model of green certificate-carbon trading on the basis of Method 4. As shown in Table 3, the carbon trading cost is reduced by 14,500 yuan, and the carbon emissions are reduced by 21.97 tons. This is because in the fusion interaction model of green certificate-carbon trading, the microgrid can obtain an additional part of free carbon quota while absorbing the power generated by renewable energy, thereby reducing the carbon trading cost and carbon emissions.

[0266] Although the introduction of the green certificate-carbon trading model makes the economic efficiency of the optimization configuration result not optimal, the environmental benefits obtained in reducing carbon emissions far outweigh the total cost of the increase. It is calculated that compared with method 3, method 4 reduces carbon emissions by 20.72%, and the total cost only increases by 4.86%. Compared with method 3, method 5 reduces carbon emissions by 27.37%, and the total cost only increases by 4.41%. At the same time, the operating costs of method 4 and method 5 are reduced by 50,200 yuan and 52,200 yuan respectively compared with method 3. This shows that the green certificate-carbon trading fusion interaction model is effective in reducing carbon emissions and operating costs.

[0267] In addition, as can be seen from Table 4, the renewable energy utilization rate of Method 4 and Method 5 is increased by 6.73% and 15.72% compared with Method 3, and the installed capacity of renewable energy units is increased by 71kW and 94kW. It can be seen that the introduction of the green certificate-carbon trading fusion interaction model plays an important role in the consumption of renewable energy.

[0268] ③Sensitivity analysis of risk preference coefficient:

[0269] The larger the risk preference coefficient β is, the stronger the risk aversion awareness of microgrid planning investors is. Taking method 3 as an example, the risk aversion coefficient β is 0.1, μ RA Start from 0 and increase in intervals of 0.05 to 0.3. Figure 5 is the fluctuation range of the uncertain parameters corresponding to different risk preference coefficients.

[0270] Depend on Figure 6 It can be seen that as the risk preference coefficient increases, the robustness level of the microgrid in dealing with the uncertainty of load fluctuations decreases from 8.71% to 4.90%. This is because as the risk preference coefficient increases, the uncertainty of wind power and photovoltaic output increases the risk of microgrid optimization configuration. Microgrid planning investors will adopt the purchase of electricity from the upper power grid and diesel generator sets, which are more stable than wind power and photovoltaic output, resulting in an increase in operating costs. Taking a typical winter day as an example, Figure 6 , Figure 7 For microgrid in μ RA =0.1, β=0.1 and 0.3.

[0271] Depend on Figure 7 , Figure 8 It can be seen that when the risk preference coefficient β = 0.3, the microgrid purchases electricity from the upper power grid at two more times than when β = 0.1, and the amount of electricity purchased and the power generation of the diesel generator set increase, which leads to an increase in the operating cost of the microgrid when the risk preference coefficient is β = 0.3 compared to when β = 0.1. When the total cost of microgrid planning is constant, the operating cost increases when β = 0.3, so the investment cost of dealing with the uncertainty of load fluctuations when β = 0.3 is reduced compared to when β = 0.1, thereby reducing the allowable load fluctuation range.

[0272] In summary, microgrid planning investors can adjust the proportion of the two types of uncertainty impacts according to their own investment budget and risk preference level for uncertainty factors, and make an effective trade-off between the two uncertainty factors to obtain the best optimization configuration method.

[0273] ④Sensitivity analysis of risk preference coefficient:

[0274] Risk aversion coefficient μ RA is the proportion coefficient that microgrid planning investors can accept beyond the cost of the optimal configuration method under the deterministic scenario. Taking method 5 as an example, the risk preference coefficient β is kept unchanged at 0.2, μ RA Start from 0.05 and increase to 0.3 in intervals of 0.05. Figure 8 is the corresponding load fluctuation amplitude α RA and planning total investment costs.

[0275] Depend on Fig. 9 It can be seen that when the risk aversion coefficient μ RA When the risk aversion coefficient increases from 5% to 30%, the robustness level of the microgrid to cope with the uncertainty of long-term load fluctuations increases from 1.84% to 24.20%, and the total investment cost of the microgrid increases from 4.3578 million yuan to 5.3953 million yuan. This is because the greater the risk aversion coefficient, the greater the total investment cost of the microgrid, in order to cope with the long-term uncertainty of load fluctuations.

[0276] ⑤ Sensitivity analysis of green certificate-carbon trading linkage price:

[0277] The green certificate price and carbon trading base price will affect the renewable energy generation and carbon emissions in the microgrid system, and thus affect the total cost of system capacity configuration. Therefore, in order to explore the impact of the green certificate-carbon trading linkage price on the lean configuration of microgrid capacity, based on method 5, a line graph of the impact of the green certificate-carbon trading linkage price on the system carbon emissions and total configuration cost is drawn. Fig.10 , Fig.11 This is a line chart showing the impact of the green certificate-carbon trading linkage price on the system carbon emissions and total configuration costs.

[0278] Depend on Fig.10 , Fig.11 It is known that when the carbon trading base price is low, the carbon emissions of the microgrid are higher and the total cost is lower. This is because when the carbon trading base price is low, the carbon emission cost is lower, and the microgrid operator is more inclined to purchase electricity from the upper power grid and diesel generator sets with lower costs but higher carbon emissions than newly built renewable energy units to meet the load demand. Therefore, the carbon emissions are higher and the total cost is lower; with the increase of the carbon trading base price, in order to avoid excessive carbon trading costs and green certificate costs, the microgrid operator is more inclined to purchase electricity from the upper power grid and diesel generator sets with lower carbon emissions but higher costs to meet the load demand, so the carbon emissions of the system have decreased significantly and the total cost has increased; in addition, when the carbon trading base price remains unchanged, the green certificate price is increased, and the carbon emissions of the system are reduced. This is because when the green certificate price increases, it means that the system can absorb more renewable energy to obtain benefits, but the installed capacity of renewable energy units needs to be increased to meet this requirement, which leads to an increase in total cost.

[0279] Therefore, when formulating green certificate-carbon trading prices based on policies and markets, it is necessary to carefully balance the linkage prices of the two in order to optimize the low-carbon and economic efficiency of the microgrid system capacity configuration.

[0280] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading, characterized in that: The following steps are involved: Step 1: Establish a mathematical model of a hydrogen energy storage microgrid: Construct the basic structure of a hydrogen energy storage microgrid including wind turbines, photovoltaic arrays, diesel generators, hydrogen energy storage systems and loads; The hydrogen energy storage system is composed of an electrolyzer, a fuel cell and a hydrogen storage tank, and is used to balance the mismatch between renewable energy generation and load demand; The amount of hydrogen produced by the electrolyzer through the residual electricity is calculated by the electrolyzer mathematical model; Calculate the output power of the fuel through the fuel cell mathematical model; Calculate the hydrogen storage capacity of the hydrogen storage tank through the hydrogen storage tank mathematical model; Step 2: Establish a green certificate-carbon trading integration interaction model: Establish a GCT model to allocate quotas based on renewable energy power generation and calculate the transaction costs of green certificates; Establish a CET model to control carbon emissions on the power supply side in the form of carbon emission quotas and calculate the carbon emission trading costs; Combining the GCT model and the CET model, a GCT-CET fusion interaction model is established to calculate the free carbon quota obtained when obtaining green certificates and the carbon emission trading amount of microgrids; Step 3: Establish a low-carbon optimization configuration model for microgrid capacity under deterministic scenarios: Taking the economic efficiency of microgrid capacity configuration results as the optimization goal, the green certificate-carbon trading fusion interaction model is considered; Set the objective function, including the annual construction cost and annual operation cost of the microgrid; Set constraints, including power balance constraints, distributed power output constraints, hydrogen energy storage system constraints, power purchase and sales constraints, demand response constraints, and wind and solar power abandonment rate constraints; Step 4: Establish a low-carbon optimization configuration model for microgrid capacity based on CVaR-IGDT: Use CVaR theory to quantify the uncertainty of wind power and photovoltaic output and set the risk preference coefficient; Use IGDT to handle the uncertainty of load fluctuations and set risk avoidance strategies; Combining CVaR and IGDT, a low-carbon optimization configuration model of microgrid power capacity based on CVaR-IGDT is constructed. The model includes the expected value of annual operating cost, CVaR value and the fluctuation range of uncertain variables.

2. The microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading according to claim 1 is characterized in that: In step 1, The electrolyzer mathematical model: The calculation formula for the amount of hydrogen produced by the surplus electricity through the electrolyzer is: Where: V el (t) is the volume of hydrogen produced by the electrolytic cell at time t; η el The working efficiency of the electrolyzer; P is the volume of hydrogen produced by electrolyzing water per unit of electricity; el (t) is the input power of the electrolytic cell at time t; Δt is the time change; The fuel cell mathematical model: The formula for calculating the output power of fuel is; Where: P fc (t) is the output power of the fuel cell at time t; η fc For the efficiency of fuel cells; V is the electrical power generated by the fuel cell when a unit volume of hydrogen is input; fc (t) is the volume of hydrogen consumed by the fuel cell at time t; The mathematical model of the hydrogen storage tank: The hydrogen storage tank can provide hydrogen for the fuel cell, and can also store the hydrogen produced by electrolysis in the electrolyzer; the calculation formula for the hydrogen storage capacity of the hydrogen storage tank is; E s (t)=E s (t-1)+[V s,ch (t)η ch -V s,dis (t) / η dis )], Where: E s (t) is the total volume of hydrogen contained in the hydrogen storage tank at time t; V s,ch (t), V s,dis (t) are the hydrogen storage and release volumes of the hydrogen storage tank at time t; η ch , η dis They are the hydrogen storage and release efficiency of the hydrogen storage tank respectively.

3. The microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading according to claim 1 is characterized in that: In step (2), The GCT model: Based on the quota of renewable energy power generation, the green certificate transaction cost is calculated using the formula: Where: F GCT is the green certificate transaction cost; λ is the green certificate transaction unit price; N is the number of days in the settlement cycle; ρ is the green certificate quota coefficient that the microgrid operator needs to hold; σ is the quantitative coefficient of renewable energy power generation converted into green certificates; P load,T (t), P pv,T (t), P wt,T (t) are load power, photovoltaic power and wind power at time t in the T day, respectively; A quantity competition Cournot trading model is established to consider the green certificate trading price with market fluctuations and normal influences. The specific model is: Where: c GCT 、e GCT is the positive parameter of the inverse price function of the Cournot model; M is the number of green certificates sold; λ0 is the base price of the green certificate; r GCT It is the green certificate transaction price ratio derived from market and policy historical data; The CET model: The carbon emissions on the power supply side are controlled in the form of carbon emission quotas; the carbon emission trading cost is calculated using a stepped carbon trading model, and the calculation formula is: Where: is the carbon emission transaction cost; ξ is the transaction price per unit of carbon emission; l is the carbon emission step length; γ is the growth rate of the transaction price per unit of carbon emission; E G is the carbon emission trading amount; Establish a multilateral carbon trading price model that is consistent with market fluctuations and policy changes. In the model, one buyer is allowed to match transactions with multiple sellers, and the transaction order and volume are formed based on the prices declared by multiple parties. The specific model is: Where: I i,buy (x), I i,sell (x) are the bid prices of the microgrid operators and buyers participating in the bidding in the xth round of transactions; I i,min ,I i,max are the reward and penalty prices obtained by the i-th microgrid operator respectively; i (x) is the correction factor of the price declared by the i-th microgrid operator in the x-th round; I ap ,I aw are the assessment penalty and reward prices of the market respectively; X is the maximum number of corrections; υ 0,j is the willingness of the jth seller, and its value is [-1,1]; w i , H i are the carbon trading volume that has been completed and the carbon trading volume required by the i-th microgrid operator respectively; The GCT-CET fusion interaction model: You will get free carbon quotas when you get green certificates. The calculation formula for free carbon quotas is: Where: E green is the carbon emission quota obtained by converting green certificates for renewable energy power generation; ε is the conversion coefficient of renewable energy power generation into carbon emission quota; Microgrid carbon emission trading amount E G The calculation formula is: AND G =And m -AND green -AND a , Where: E m E is the carbon emissions generated by the system; a Free carbon emission quotas; E m 、E a The calculation formula is: Where: de , χ buy P is the carbon emission intensity of diesel generator sets and electricity purchased from the upper power grid per unit of power generation; de,T (t), P buy,T (t) are the power purchased by the diesel generator set and the upper power grid at time t in the Tth day; E de 、E buy They are the free carbon emission quotas for diesel generator sets per unit of power generation and electricity purchased from the superior power grid.

4. The microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading according to claim 1 is characterized in that: In step (3), Taking the economic efficiency of microgrid capacity configuration results as the optimization goal, the green certificate-carbon trading fusion interaction model is considered; the objective function is set: Where: F is the annual construction cost of the microgrid; F inv is the investment cost of equal annual value; F op is the annual operating cost; F inv Including the equal annual value investment cost and annual maintenance cost of the equipment, the calculation formula is; Where: k is the equipment type, k = 1, 2, ..., 6 represent wind turbines, photovoltaic arrays, diesel generators, electrolyzers, fuel cells and hydrogen storage tanks respectively; F k,inv is the unit investment cost of the kth type of equipment; F k,om is the annual maintenance cost of the kth type of equipment; τ k is the installed capacity of the kth type of equipment; Y is the equal annual value operator; ζ is the discount rate; L is the planning period of the microgrid; The annual operating cost of the microgrid is: Where: F de is the annual fuel cost of diesel generator; C fuel is the unit price of diesel; Ω D It is a collection of typical days, including three typical days in spring, autumn, summer and winter; is the number of days that a typical day d occupies in a year; are the power generation and maximum power of the diesel generator at a typical day; F dr is the demand response cost; C dr is the demand response cost per unit of electricity; is the demand response power at time t; C buy (t), C sell They are the purchase and sale prices of electricity per unit of electricity at the moment; are the power purchased and sold at time t; C waste The penalty cost for curtailing wind and solar power per unit of electricity; They are wind power abandonment and solar power abandonment at time t respectively; in, The calculation formula is: Where: are the power that can be generated by the wind turbine and the photovoltaic array at time t; are the actual output power of wind turbine and photovoltaic array at each moment; Constraints: a. Power balance constraints: The microgrid system power balance and hydrogen balance constraints are as follows: V el (t)+V s,dis (t)=V fc (t)+V s,ch (t), Where: The fuel cell power, load power, electrolyzer power and demand response load transfer power at time t respectively; b. Distributed power output constraints: The scale of distributed power generation construction is limited by geographical conditions, as shown in the following formula: Where: are the maximum output power of wind turbine, photovoltaic array and diesel generator respectively; c. Hydrogen energy storage system constraints: The constraints of the hydrogen energy storage system are as follows: Where: are the maximum output power of electrolyzer and fuel cell respectively; is the hydrogen storage capacity of the hydrogen storage tank at time t; are the minimum and maximum hydrogen storage capacity of the hydrogen storage tank respectively; is the hydrogen charge state of the hydrogen storage tank at time t within a typical day d; They are the minimum and maximum hydrogen charge states of the hydrogen storage tank respectively; are the start and end times of the scheduling cycle within a typical day d respectively; d. Constraints on power purchase and sales: Excessive interaction power between the microgrid and the upper power grid will affect the upper power grid; the power consumption constraints are as follows: Where: are the maximum power purchase and sales power allowed for the interaction between the microgrid and the upper grid; e. Power purchase and sales constraints: Considering the flexible transferable load in the microgrid, the power consumption characteristics at time t are constrained as follows: Where: They represent the minimum and maximum power of the load that can be shifted in time and participate in demand response at time t respectively; f. Constraints on wind and solar power abandonment rates: In order to ensure the utilization rate of renewable energy power generation, the wind and solar power abandonment rate constraint should satisfy the formula: Where: are the maximum wind and solar power abandonment rates, respectively.

5. The microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading according to claim 1 is characterized in that: In step (4), CVaR measures the risk value of a microgrid by evaluating the probability and magnitude of losses incurred in the microgrid; The annual operating cost of the microgrid of CVaR consists of two parts: the first part is the expected value of the operating cost of the microgrid under different scenarios; the second part is the product of β and CVaR; β is defined as the risk preference coefficient; when β is less than 0.05, the planning investor is risk-seeking; when β is greater than 0.1, the planning investor is considered to be risk-averse; the CVaR model is shown in the following formula; Where: F o ' p To consider the annual operating cost of CVaR; F op,CVaR are the expected annual operating cost and CVaR value respectively; ω wt ,ω pv are the number of wind power and photovoltaic output scenarios generated by the scenarios; ω p ,ω q They are wind power and photovoltaic output scene sets {ω p ,p=1,2,...,ω wt }、{ω q ,q=1,2,...,ω pv };π(ω p ),π(ω q ) are wind power output scenarios ω p 、Photovoltaic output scenario q The probability of op (ω p ,ω q ) is the microgrid in the scenario (ω p ,ω q ) Minimum operating costs; F op,CVaR is the CVaR value; δ VaR is the VaR value, which is the decision variable introduced to calculate CVaR; θ CVaR is the confidence factor; the expression [x] + =max{x,0}; The cost optimization of IGDT is shown in the following formula: Where: f is the objective function of the cost optimization problem; v is the decision variable; u is the uncertain variable; h is the equality constraint about v and u; g is the inequality constraint about v and u; U is the set of uncertain variables, as shown in the following formula; Where: is the predicted value of the uncertain variable; α is the fluctuation range of the uncertain variable; The risk avoidance strategy of IGDT is as follows: Where: α RA is the fluctuation range of uncertain variables under the risk aversion strategy; μ RA is the risk aversion coefficient, i.e., the investor's ability to accept additional investment; F0 is the benchmark value of the objective function, i.e., the investment cost under a deterministic scenario; are the actual load power and predicted power at time t in a typical day d, respectively; make The low-carbon optimization configuration model of microgrid power capacity based on CVaR-IGDT is as follows:

6. The microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading according to any one of claims 1 to 5, characterized in that: The microgrid capacity optimization configuration method based on CVaR-IGDT and green certificate-carbon trading also includes: Step 5: Verify and evaluate the microgrid capacity low-carbon optimization configuration model.

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