Multi-energy micro-grid optimization scheduling method considering electricity-hydrogen coupling flexibility margin

Abstract

The invention discloses a multi-energy micro-grid optimization scheduling method considering an electricity-hydrogen coupling flexibility margin. The method comprises the following steps: constructing each equipment model of a multi-energy micro-grid; a flexibility margin evaluation model is established based on the flexible adjustment characteristic of electro-hydrogen coupling, and flexibility margins and flexible adjustment capabilities of different devices and different coupling nodes are analyzed; a multi-energy micro-grid day-ahead optimization scheduling model considering electricity-hydrogen coupling is established, maximum system life cycle total profit and adjustment flexibility are taken as objective functions, and under constraint conditions of stable operation of photovoltaic wind power and electric hydrogen production equipment, capacity of hydrogen storage equipment, micro-grid system flexibility and the like, the objective functions are solved by adopting an improved particle swarm optimization algorithm, so that the optimal multi-energy micro-grid day-ahead optimization scheduling model is established. And obtaining a day-ahead optimization scheduling result. Meanwhile, the characteristic that the flexibility demand is uncertain due to the fluctuation of renewable energy sources and loads is considered, historical data are processed by using a Monte Carlo method, and the flexibility demand of the microgrid is quantitatively analyzed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power distribution technology, and in particular to a multi-energy microgrid optimization scheduling method considering the flexibility margin of electric-hydrogen coupling. Background Art

[0002] Traditional power systems usually only consider the balance of power and electricity, and generally do not have flexibility requirements. Load growth and redundancy requirements can be met mainly by adding units. With the reform of the power system, a large number of microgrids connected to distributed renewable energy will have a higher demand for flexibility resources than conventional power grids. Hydrogen energy has the characteristics of zero carbon, clean, storable, and bidirectional conversion with electricity. It has outstanding advantages in new energy consumption, energy conversion and storage, and flexible and stable regulation. Therefore, exploring the flexible adjustment margin of electric-hydrogen coupling is a key link in exploring the flexibility of multi-energy microgrids. Summary of the invention

[0003] In view of the above-mentioned background technology, the present invention provides a multi-energy microgrid day-ahead optimization scheduling method taking into account the flexibility margin of electric-hydrogen coupling. By exploring the flexible adjustment margin of the electric-hydrogen coupling technology in the three links of "production-use-storage" and based on the controllable adjustment characteristics of hydrogen energy, the adjustment flexibility of the multi-energy microgrid is improved to balance the disturbance of renewable energy to the regional distribution network and improve the consumption of renewable energy.

[0004] To achieve the above object, the present invention is implemented through the following technical solutions:

[0005] A multi-energy microgrid optimization scheduling method considering the flexibility margin of electric-hydrogen coupling includes the following steps:

[0006] Establish the equipment model and system power balance model of the microgrid system; analyze the energy conversion characteristics of the electric-hydrogen coupling in the three links of production, storage and use, and establish the equipment flexibility margin model; use the Monte Carlo method to establish the system flexibility margin model; establish the evaluation index of the power system flexibility margin; take the maximum total profit and adjustment flexibility of the system throughout its life cycle as the objective function; use the operation constraints of power generation equipment and electric hydrogen production equipment, the capacity constraints of hydrogen storage equipment, and the flexibility balance constraints as constraints; use the improved particle swarm algorithm for solution; and perform multi-energy microgrid day-ahead optimization scheduling based on the optimal solution obtained.

[0007] Furthermore, the establishment of the equipment model and system power balance model of the microgrid system specifically includes:

[0008] First, an electric-hydrogen coupling model is established, including hydrogen production, storage, transportation, and use. Then a power balance model is established, including electric, hydrogen, and thermal power balance, to provide a theoretical basis for the subsequent establishment of a flexibility margin model.

[0009] Furthermore, the analysis of the energy conversion characteristics of the electric-hydrogen coupling in the three links of production, storage and use, and the establishment of a flexibility margin model specifically include:

[0010] During the process of hydrogen production by electrolyzer, the flexibility margin of renewable energy consumption is mainly affected by factors such as input power and power adjustable range. A flexibility margin model is established based on the equipment model of the electrolyzer.

[0011] During the power generation process of the hydrogen-to-electricity equipment, the flexible adjustment margin is mainly affected by the fuel quantity and battery voltage. A flexible adjustment margin model is established based on the equipment model of the hydrogen-to-electricity equipment.

[0012] Furthermore, the Monte Carlo method specifically includes:

[0013] To explore the flexibility issue on a long time scale, it is necessary to probabilize the flexibility issue. Therefore, it is necessary to use the Monte Carlo non-sequential production method to output the equipment output probability of the microgrid in the form of probability, convert the output probability into the flexibility supply probability, and then add the flexibility supply of different equipment by the roll sum method to obtain the up / down flexibility supply margin of the microgrid.

[0014] Furthermore, the Monte Carlo method is used to establish a system flexibility margin model, which specifically includes:

[0015] The principle of considering system flexibility in an "envelope" mode using traditional power sources to track net load and reserve backup is shown in the following equation.

[0016]

[0017] In the formula: represents the peak-shaving capacity of unit i; represents the total peak-shaving demand of net load.

[0018] Flexibility Balance Model:

[0019]

[0020] Where: is the total supply of flexibility to adjust up / down at time t; ω + (t),ω - (t) is the total flexibility demand for upward / downward adjustment at time t; is the predicted positive / negative photovoltaic fluctuation at time t; is the predicted fluctuation of positive / negative wind power at time t; YL is the predicted fluctuation of positive / negative load at time t; + (t), YL - (t) is the up / down adjustment system flexibility margin at time t.

[0021] However, in the micro-energy grid with electric-hydrogen coupling, the uncertainty of renewable energy and load must be considered when analyzing long-term situations. The introduction of probabilistic analysis can better analyze the flexibility margin of the system. Therefore, the supply and demand of flexibility are random variables that obey a specific distribution. By introducing probability density for description, the criterion for sufficient flexibility is: the probability that the system's flexibility resource supply capacity is less than the flexibility demand, that is, the probability of insufficient flexibility must be lower than a given threshold to indicate that the flexibility is sufficient.

[0022] Define G and X as random variables of total supply and total demand of system flexibility, and Z = XY as flexibility margin variable. Then the deterministic criterion of flexibility balance is:

[0023] Pr(Z≤0)=Pr(X≤Y)=Pr(∑ i∈S X i ≤∑ i∈D Y i )≤θ (8)

[0024] Where: θ represents the level of abundance; S represents the set of flexible supply sources; G i represents the supply of the i-th source; D represents the set of flexibility requirements; X i represents the i-th demand.

[0025] The meaning of the above formula is that when Z≤0, the flexibility margin is insufficient, that is, the flexibility supply is less than the flexibility demand, and the probability sum of this part is made less than the abundance level θ, that is, the system is in flexibility balance.

[0026] Considering the characteristics of flexibility, the general form of the above formula is:

[0027]

[0028] In the formula, + and - do not represent the size but the direction of flexibility adjustment. + means flexibility is increased and - means flexibility is decreased. Flexibility is related to system state C i About (C i can be defined as load level); S C A collection of enumerable states.

[0029] According to the basic principle of probabilistic convolution operation, the addition and subtraction operations of flexible random variables are represented by convolution sum and convolution difference respectively:

[0030]

[0031] Where: The probability density function representing the system flexibility margin; Represent the probability density functions of flexible supply X and demand Y respectively; They are volume sum / volume difference operations respectively.

[0032] Furthermore, the evaluation index for establishing the flexibility margin of the power system specifically includes:

[0033] Existing system flexibility indicators focus on measuring the scale of flexible power sources on the supply side, but cannot measure the consequences of an imbalance in the binary relationship between flexibility supply and demand. The present invention proposes a set of flexibility margin evaluation indicators including flexibility margin distribution, flexibility insufficiency probability, and flexibility margin expectation.

[0034] Furthermore, the total profit and adjustment flexibility of the system throughout its life cycle are maximized as the objective function, which specifically includes:

[0035] (1) System life cycle total cost calculation model

[0036] It includes the system initial equipment investment cost calculation model, the system equipment operation and maintenance cost calculation model, and the system full life cycle total operating cost mathematical model.

[0037] (2) Mathematical calculation model of total economic benefits of the system throughout its life cycle

[0038] The economic benefits of this system include the revenue from the sale of electricity from renewable energy grid-connected to the grid, the revenue from the sale of hydrogen, and the revenue from the grid-connected power generation of controllable power generation equipment.

[0039] (3) Net profit calculation model for the system’s entire life cycle

[0040] The net profit of the system is equal to the difference between the total economic benefits and the total costs of the system throughout its life cycle. The total income also includes the conversion of the residual value of each device during the entire life cycle.

[0041] Furthermore, the constraints of the operation constraints of power generation equipment and electric hydrogen production equipment, the capacity constraints of hydrogen storage equipment, and the flexibility balance constraints are specifically as follows:

[0042] Stable operation constraints of wind turbines and photovoltaic generators:

[0043] P wmin ≤P W ≤P wmax (12)

[0044] P vmin ≤P v ≤P vmax (13)

[0045] Where: P wmin , P wmax is the minimum and maximum output of the fan; P vmin , P vmax It is the minimum and maximum output of photovoltaic.

[0046] Constraints for stable operation of electric hydrogen production equipment:

[0047] V min ≤V≤V max (14)

[0048] δ min P max ≤P≤δ max P max (15)

[0049] Where: V min 、V max are the minimum and maximum hydrogen production rates;

[0050] Hydrogen storage equipment capacity constraints:

[0051] V out ≤V H (16)

[0052] V in ≤V HT -V H (17)

[0053] Flexibility Balance:

[0054]

[0055] Where: θ is the level of inflexibility.

[0056] Furthermore, the particle swarm algorithm specifically includes:

[0057] In particle swarm optimization, the solution to the optimization problem is considered as a bird in the search space, called a "particle". The i-th particle has a velocity of and location The update equations are:

[0058]

[0059] Where k is the number of iterations. c1 and c2 are both learning factors, c1 represents the individual cognition of the particle, and c2 represents the social cognition of the particle; w is the inertia weight; r1 and r2 are random numbers, which are uniformly distributed in the range of [0, 1].

[0060] When the PSO algorithm is running, each particle in the particle swarm will continuously update its own speed and position during iteration, thereby iterating the optimal solution in the particle swarm. The optimal solution mainly consists of two types: the group optimal solution Gbest and the individual optimal solution Pbest. The relationship between the two is the subordinate relationship between the individual and the group.

[0061] Furthermore, the improved particle swarm algorithm is used to solve the problem, specifically including:

[0062] (1) Initialize the particle swarm and set the speed threshold and over-limit response mechanism of the corresponding particle swarm according to the model requirements of each device, perform the initial calculation, and record the historical optimal and global optimal positions of the particle swarm.

[0063] (2) In each iteration, the inertia parameters and learning factors are updated, the particle adaptive optimization conditions are determined and corresponding calculations are performed, and the iterative Pareto optimal solution set is recorded.

[0064] (3) After the system has iterated to a set number of times, the optimization search is stopped, and the historical optimal value set recorded at this time is recorded and output.

[0065] Starting from the perspective of the three links of "production-use-storage" of electric-hydrogen coupling, the present invention analyzes the flexible adjustment mechanism of electric-hydrogen coupling technology, constructs a day-ahead optimization scheduling model for a microgrid system that takes into account the flexibility margin of electric-hydrogen coupling, and improves the adjustment flexibility of multi-energy microgrids to balance the disturbance of renewable energy to the regional distribution network. In addition to its role in flexibility, the microgrid system is equipped with hydrogen production equipment such as electrolyzers, which can produce hydrogen sustainably and bring additional economic benefits. At the same time, the waste heat generated by hydrogen production and use equipment can also supply the demand for heat loads and reduce the cost of purchasing energy. In general, the microgrid system that considers electric-hydrogen coupling is superior to traditional microgrids in terms of adjustment flexibility and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of the day-ahead dispatch optimization of a microgrid system taking into account the flexibility margin of electric-hydrogen coupling according to the present invention.

[0067] Figure 2 The flexibility demand of a typical day for the multi-energy microgrid system constructed for this embodiment.

[0068] Figure 3 It is the upper / lower flexibility supply margin for typical day scenario 1 of the multi-energy microgrid system in this embodiment.

[0069] Figure 4 It is the upper / lower flexibility supply margin for typical day scenario 2 of the multi-energy microgrid system in this embodiment.

[0070] Figure 5 It is the flexibility margin of the typical day scenario 1 of the multi-energy microgrid system in this embodiment.

[0071] Figure 6 It is the flexibility margin of typical day scenario 2 of the multi-energy microgrid system in this embodiment. DETAILED DESCRIPTION

[0072] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0073] The technical solution of the present invention to solve the above technical problems is:

[0074] Establish the equipment model of the microgrid system and the system power balance model; analyze the energy conversion characteristics of the electric-hydrogen coupling in the three links of production, storage and use; use the Monte Carlo method to establish the system flexibility margin model; take the maximum total profit and adjustment flexibility of the system over the entire life cycle as the objective function; use the operation constraints of power generation equipment and electric hydrogen production equipment, hydrogen storage equipment capacity constraints, and flexibility balance constraints as constraints; use the improved particle swarm algorithm to solve, and perform multi-energy microgrid day-ahead optimization scheduling based on the optimal solution obtained.

[0075] The equipment model and system power balance model of the microgrid system are established in detail including:

[0076] (1) Electricity-hydrogen coupling model

[0077] 1) Hydrogen production

[0078] Electrolyzer hydrogen production H dj for:

[0079]

[0080] In the formula, H dj is the hydrogen production of the electrolyzer, P dj is the electrolytic cell load, U is the electrolytic cell voltage;

[0081] 2) Storage and transportation

[0082] Hydrogen storage tank model:

[0083]

[0084] Where: P H (t) is the internal pressure of the hydrogen storage tank at time t; ρ H is the hydrogen density; k = {-1, 0, 1}, representing dehydrogenation, steady state and storage of hydrogen respectively; ΔV H (t) is the change in hydrogen volume in the hydrogen storage tank at time t; R H is the molar gas constant; T H is the internal temperature of the hydrogen storage tank; M H is the molar mass of hydrogen; V HT is the volume of the hydrogen storage tank; V H (t) is the amount of hydrogen in the hydrogen storage tank at time t; V H0 (t) is the amount of hydrogen in the hydrogen storage tank at the initial moment;

[0085] 3) Hydrogen use

[0086] The fuel cell efficiency can be expressed as follows:

[0087]

[0088] Where: U is the fuel cell stack voltage; μ is the fuel utilization efficiency; H H2 It is the higher calorific value of hydrogen;

[0089] The total power input to the fuel cell is P Z , the output power is P, and the remaining power is P1, which can be expressed as:

[0090]

[0091] Waste heat recovery model:

[0092]

[0093] Q Zr =η r Q Z (7)

[0094] Where: Q Z is the output thermal efficiency of the equipment; η d is the heat loss coefficient; η r The efficiency of waste heat recovery;

[0095] Because the amount of waste heat recovered is large and volatile, the system needs a heat storage tank to store the heat. The model of the heat storage tank is as follows:

[0096] E xr (t+1)=E xr (t)+(μ1P xrx -P xrc / μ2)Δt (8)

[0097] Where: E xr (t) is the heat stored in the heat storage tank at time t; μ1 and μ2 are the heat absorption and release efficiency of the heat storage tank; P xrx , P xrc is the heat absorption and release power of the heat storage tank;

[0098] (2) Power balance model

[0099] 1) Electric power balance

[0100] P pv +P w +P ou +P ie =P f +P ih +P a (9)

[0101] Where: P ou Purchase electricity from external power grid; P ie Output of hydrogen power generation equipment, i = {dc, ice, GT}; P ih is the electric power load of the electric hydrogen production equipment, i={dj,bc}; P f is the electrical load; P a To abandon wind and electricity;

[0102] 2) Hydrogen power balance

[0103] H in +H dj =H out +H ie +H rh (10)

[0104] Where: H in , H ou They are respectively purchasing hydrogen from the outside and selling hydrogen; H dj is the hydrogen production of the electric hydrogen production equipment; H ie is the hydrogen consumption of hydrogen power generation equipment, i = {dc, ice, GT};

[0105] 3) Thermal power balance

[0106] Q dj +Q in +Q ie +Q rh =Q f (11)

[0107] Where: Q dj Q is the waste heat recovery amount of the electric hydrogen production equipment; in To purchase heat from the external power grid; Q ie is the waste heat recovery amount of hydrogen power generation equipment, i = {dc, ice, GT}; Q f For heat load.

[0108] Analyze the energy conversion characteristics of the electric-hydrogen coupling in the three links of production, storage and use, and establish the equipment flexibility margin model, which specifically includes:

[0109] (1) Flexible adjustment margin model of electric hydrogen production equipment

[0110]

[0111] P min =P i *δ min (15)

[0112]

[0113] Where: It is the flexibility margin of the electrolyzer to absorb energy; + and - represent upward and downward adjustment; is the electrolytic cell load; i represents four electrolytic cells, i = {awe, pem, aem, soe}; is the maximum load of the electrolyzer; is the minimum load of the electrolyzer; is the climbing amount of the electrolytic cell; is the climbing rate; P i is the rated load of the electrolytic cell; δ max ,δ min are the upper and lower limits of the electrolytic cell load rate;

[0114] As a power storage device, the flexibility margin of batteries is different from that of electrolyzers and is mainly affected by the real-time power status;

[0115]

[0116] In the formula, C is the battery capacity, SOC(t) is the state of charge at that moment;

[0117] (2) Modeling of flexible adjustment margin of hydrogen power generation equipment

[0118]

[0119] Where: is the flexibility margin of hydrogen power generation equipment; + and - represent upward and downward adjustments; P ie is the output power of hydrogen power generation equipment; i represents three types of hydrogen power generation equipment, i = {dc, ice, GT}; is the maximum output power of the hydrogen power generation equipment; is the minimum output power of the hydrogen power generation equipment; It is the climbing amount of hydrogen power generation equipment; is the climbing rate.

[0120] The Monte Carlo method specifically includes:

[0121] To explore the flexibility issue on a long time scale, it is necessary to probabilize the flexibility issue. Therefore, it is necessary to use the Monte Carlo non-sequential production method to output the equipment output probability of the microgrid in the form of probability, convert the output probability into the flexibility supply probability, and then add the flexibility supply of different equipment by the roll sum method to obtain the up / down flexibility supply margin of the microgrid.

[0122] The Monte Carlo method is used to establish the system flexibility margin model, which includes:

[0123] The principle of considering system flexibility by adopting the "envelope" mode of using traditional power sources to track net load and reserve backup is shown in the following formula:

[0124]

[0125] Where: P max,i represents the peak load capacity of unit i; P nL represents the total peak load demand of the net load;

[0126] Flexibility Balance Model:

[0127]

[0128] Where: is the total supply of flexibility to adjust up / down at time t; ω + (t),ω - (t) is the total flexibility demand for upward / downward adjustment at time t; is the predicted positive / negative photovoltaic fluctuation at time t; is the predicted fluctuation of positive / negative wind power at time t; YL is the predicted fluctuation of positive / negative load at time t; + (t), YL - (t) is the flexibility margin of the system for up / down adjustment at time t;

[0129] Since the uncertainty of renewable energy and load must be considered in the analysis of long-term conditions in the electric-hydrogen coupled micro-energy grid, the introduction of probabilistic analysis can better analyze the flexibility margin of the system; therefore, the supply and demand of flexibility are both random variables that obey a specific distribution; the introduction of probability density for description, the criterion for sufficient flexibility is: the probability that the system's flexibility resource supply capacity is less than the flexibility demand, that is, the probability of insufficient flexibility must be lower than a given threshold to indicate that the flexibility is sufficient;

[0130] Define G and X as random variables of total supply and total demand of system flexibility, and Z = XY as flexibility margin variable. Then the deterministic criterion of flexibility balance is:

[0131] Pr(Z≤0)=Pr(X≤Y)=Pr(∑ i∈S X i ≤∑ i∈D Y i )≤θ (29)

[0132] Where: θ represents the level of abundance; S represents the set of flexible supply sources; G i represents the supply of the i-th source; D represents the set of flexibility requirements; X i represents the i-th demand;

[0133] The meaning of the above formula is that when Z≤0, the flexibility margin is insufficient, that is, when the flexibility supply is less than the flexibility demand, let the probability sum of this part be less than the abundance level θ, that is, the system is in flexibility balance;

[0134] Considering the characteristics of flexibility, the general form of the above formula is:

[0135]

[0136] In the formula, + and - do not represent the size but the direction of flexibility adjustment. + means flexibility is increased and - means flexibility is decreased. Flexibility is related to system state C i About (C i can be defined as load level); S C is a collection of enumerable states;

[0137] According to the basic principle of probabilistic convolution operation, the addition and subtraction operations of flexible random variables are represented by convolution sum and convolution difference respectively:

[0138]

[0139] Where: The probability density function representing the system flexibility margin; Represent the probability density functions of flexible supply X and demand Y respectively; They are volume sum / volume difference operations respectively.

[0140] Establish evaluation indicators for power system flexibility margin, including:

[0141] (1) Distribution of flexibility margin

[0142]

[0143] Where: The probability density function representing the system flexibility margin; Represent the probability density functions of flexible supply X and demand Y respectively; They are volume sum / volume difference operations respectively;

[0144] (2) Probability of insufficient flexibility

[0145]

[0146] Where: FNS is the probability of insufficient flexibility, The probability density function representing the system flexibility margin;

[0147] (3) Flexibility margin expectations

[0148]

[0149] Where: EFZ is the expected flexibility margin, The probability density function representing the system flexibility margin.

[0150] The objective function is to maximize the total profit and adjustment flexibility of the system throughout its life cycle, including:

[0151] (1) System life cycle total cost calculation model

[0152] System initial equipment investment cost calculation model:

[0153] The total one-time investment cost of the equipment is F0, which mainly includes the cost of solar photovoltaic panels F pv , wind turbine cost F w 、Hydrogen production device F H 、Electricity storage device F e 、Hydrogen storage device F HC , controllable power generation device F Hd and heat generating device F He , ignoring the equipment installation and commissioning fees, which are added to the equipment cost;

[0154] F0=F pv +F w +F H +F e +F HC +F Hd +F He (36)

[0155] The relationship between the initial total investment of each equipment and its equipment scale is as follows:

[0156] F i =m i P i (37)

[0157] Where: P i Rated planned installed capacity of the equipment; m i is the unit capacity cost of the equipment; i represents the equipment in the system, i = {pv, w, H, e, HC, Hd, He};

[0158] System equipment operation and maintenance cost calculation model:

[0159] F r =F pvr +F wr +F Hr +F er +F HCr +F Hdr +F Her (38)

[0160] Where: F pvrF is the operation and maintenance cost of photovoltaic power generation equipment throughout its life cycle; wr F is the operation and maintenance cost of wind power equipment throughout its life cycle; Hr F is the operation and maintenance cost of the hydrogen production equipment throughout its life cycle; er F is the operation and maintenance cost of the energy storage equipment throughout its life cycle; HCr F is the operation and maintenance cost of the hydrogen storage equipment throughout its life cycle; Hdr F is the operation and maintenance cost of the controllable power generation equipment over its entire life cycle; Her The operation and maintenance costs of the heat generating device over its entire life cycle;

[0161] The cost of each part can be estimated by its percentage of the initial investment of the project; as follows:

[0162]

[0163] Where: M represents each device in the system; j is the equipment operation and maintenance coefficient; i is the discount rate, which is generally 5%; n is the full life cycle of the system;

[0164] Mathematical model of total operating cost of the system throughout its life cycle:

[0165]

[0166] Where: F total is the total cumulative operating cost in the tth year, F0 is the total one-time investment cost of the equipment, and F r The operating and maintenance costs of system equipment;

[0167] (2) Mathematical calculation model of total economic benefits of the system throughout its life cycle

[0168] Economic benefits include the income from the sale of electricity from renewable energy grid-connected power plants, the income from the sale of hydrogen, and the income from the grid-connected power generation equipment. The total economic benefits are E total :

[0169] E total =E R +E k +E H +E e -E e0 -E R0 (41)

[0170] Where: E k Revenue from grid-connected electricity sales from renewable energy; H E is the income from hydrogen sales; e The income from the controllable power generation equipment connected to the grid; E R E is the revenue from hot sales; e0 Expenditure on electricity purchase; E R0 Expenditure on purchasing heat;

[0171] The specific calculation formula for each part of the income is as follows:

[0172]

[0173] Where: m k,t is the grid-connected power generation of renewable energy in year t; C k is the grid-connected electricity price of renewable energy; k is the annual average operating time of renewable energy; t H is the average annual operating time of the electric hydrogen production equipment; C H is the unit price of hydrogen; V e is the amount of hydrogen input to the hydrogen production equipment; μ is the power generation per standard cubic meter of hydrogen fuel; t k C is the annual average operating time of hydrogen power generation equipment e The grid-connected electricity price for hydrogen power generation equipment; Q t Provide heat to the system; C r is the heating grid connection unit price; C ou Time-of-use electricity price;

[0174] (3) System life cycle net profit calculation model

[0175] The net profit of the system is equal to the difference between the total economic benefits and the total cost of the system throughout its life cycle; the total income also includes the conversion of the residual value of each device during the entire life cycle; so the net profit F S :

[0176]

[0177] Where: F total is the cumulative total operating cost in year t, E total is the total economic benefit of the system, L t is the residual value of each part of the system equipment in the tth year, calculated at 5% of fixed assets; r1 is the discount rate, calculated at 10% of the fixed value.

[0178] The constraints are the operation constraints of power generation equipment and electric hydrogen production equipment, the capacity constraints of hydrogen storage equipment, and the flexibility balance constraints, which specifically include:

[0179] Stable operation constraints of wind turbines and photovoltaic generators:

[0180] P wmin ≤P W ≤P wmax (49)

[0181] P vmin ≤P v ≤P vmax (50)

[0182] Where: Pwmin , P wmax is the minimum and maximum output of the fan; P vmin , P vmax is the minimum and maximum output of photovoltaic power;

[0183] To ensure the safe and stable operation of all parts of the system, the proportion of renewable power generation capacity used for water electrolysis must meet the following requirements:

[0184] 0.5≤f≤1 (51)

[0185] Where f is the proportion of renewable power generation capacity used for water electrolysis;

[0186] Constraints for stable operation of electric hydrogen production equipment:

[0187] V min ≤V≤V max (52)

[0188] δ min P max ≤P≤δ max P max (53)

[0189] Where: V min 、V max is the minimum and maximum hydrogen production rate; P is the operating power of the electric hydrogen production equipment, P max is the maximum power of the electric hydrogen production equipment, δ min is the minimum power factor, δ max is the maximum power factor;

[0190] Hydrogen storage equipment capacity constraints:

[0191] V out ≤V H (54)

[0192] V in ≤V HT -V H (55)

[0193] Where V out is the output hydrogen volume of the hydrogen storage device, V H is the hydrogen storage capacity of the hydrogen storage device, V in is the amount of hydrogen input to the hydrogen storage device, V HT is the total hydrogen capacity of the hydrogen storage device at high temperature;

[0194] Flexibility Balance:

[0195]

[0196] Where: EFNS is the expected value of insufficient flexibility, is the probability density function of inflexibility deficiency, where z is the variable of inflexibility deficiency, θ is the level of inflexibility deficiency, and FZ is the cumulative distribution function of inflexibility deficiency.

[0197] The particle swarm algorithm specifically includes:

[0198] In particle swarm optimization, the solution to the optimization problem is considered as a bird in the search space, called a "particle"; the i-th particle has a velocity of and location The update equations are:

[0199]

[0200]

[0201] In the formula, k is the number of iterations; is the velocity of the ith particle at the kth iteration, is the position of the ith particle at the kth iteration, P best is the individual optimal solution, G best is the optimal solution for the group, c1 and c2 are both learning factors, c1 represents the individual cognition of the particle, and c2 represents the social cognition of the particle; w is the inertia weight; r1 and r2 are random numbers, which are uniformly distributed in the range of [0, 1];

[0202] When the PSO algorithm is running, each particle in the particle swarm will continuously update its own speed and position during iteration, thereby iterating the optimal solution in the particle swarm; the optimal solution consists of two types: the group optimal solution Gbest and the individual optimal solution Pbest, and the relationship between the two is the subordinate relationship between the individual and the group.

[0203] The improved particle swarm algorithm is used to solve the problem, including:

[0204] (1) Initialize the particle swarm, set the speed threshold and over-limit response mechanism of the corresponding particle swarm according to the model requirements of each device, perform the initial calculation, and record the historical optimal and global optimal positions of the particle swarm;

[0205] (2) In each iteration, the inertia parameters and learning factors are updated, the particle adaptive optimization conditions are determined and corresponding calculations are performed, and the iterative Pareto optimal solution set is recorded;

[0206] (3) After the system has iterated to a set number of times, the optimization search is stopped, and the historical optimal value set recorded at this time is recorded and output.

[0207] Here are some specific implementation examples:

[0208] This embodiment relates to a method for optimizing the day-ahead scheduling of a microgrid system taking into account the flexibility margin of electricity-hydrogen coupling. A multi-energy microgrid system taking into account the flexibility margin of electricity-hydrogen coupling is now constructed to illustrate the specific implementation method.

[0209] This example uses a park in the north as a research case, sets the time scale to 1 hour, and the time period to 8760 hours in the target year. The park is equipped with 10MW distributed photovoltaic and 4 5MW wind turbines. The average level of renewable energy supply is 5MW, and the maximum load demand is 8.7MW. Due to the volatility of renewable energy, there is a large amount of wind and power abandonment in daily operation.

[0210] In this embodiment, in order to reflect the regulation and optimization capability of the electric-hydrogen coupling participating in the microgrid, two scenarios are set:

[0211] Scenario 1: Multi-energy microgrid system considering traditional power storage.

[0212] Scenario 2: Consider a multi-energy microgrid system in which alkaline electrolyzers and batteries work together.

[0213] Furthermore, economic parameters, equipment operation cost and maintenance coefficients and equipment parameters are set.

[0214] Economic parameter setting:

[0215] The whole life cycle of the whole system is set to 15 years, the grid connection price of wind power generation is 0.43 yuan / kWh, and the grid connection price of photovoltaic power generation is 0.45 yuan / kWh, so the grid connection price of all clean energy power generation is unified to 0.043 million yuan / MWh, the selling price of hydrogen is 0.03 million yuan / MWh, and the unit price of heat is 0.02 million yuan / MWh. The purchase of electricity from the power grid adopts the mode of time-of-use electricity price pricing: the valley section is divided into 0:00-7:00; the flat section is 7:00-8:00 and 11:00-18:00; the peak section is 10:00-11:00 and 19:00-21:00; the peak section is 8:00-10:00, 18:00-19:00, 21:00-23:00. Among them, the valley electricity price is 0.381 yuan / kWh, the flat electricity price is 0.643 yuan / kWh, the peak electricity price is 1.0058 yuan / kWh, and the peak electricity price is 0.917 yuan / kWh.

[0216] Operation and maintenance coefficient of equipment operating cost:

[0217] The operation and maintenance coefficient of wind turbines is 1%, the operation and maintenance coefficient of photovoltaic generators is 1%, the operation and maintenance coefficient of electrolyzers is 2%, the operation and maintenance coefficient of batteries is 1%, the operation and maintenance coefficient of hydrogen storage tanks is 1%, the operation and maintenance coefficient of fuel cells is 0.5%, and the operation and maintenance coefficient of electric boilers is 1%

[0218] See Table 1 for details of the equipment parameters for scenario 1 and scenario 2.

[0219] Table 1 Equipment capacity of scenario 1 and scenario 2

[0220]

[0221] In this embodiment, Figure 2 is the flexibility demand of the microgrid system. Due to the volatility of renewable energy and load, the flexibility demand is uncertain. In order to quantitatively analyze flexibility, the Monte Carlo method is used to process a large amount of historical data to obtain the fluctuations of wind power, photovoltaic power and load based on typical daily load. The sum of the three is the flexibility demand of the microgrid system.

[0222] To meet the flexibility requirements of the microgrid, the flexibility equipment in the microgrid provides the adjustment amount of the flexibility requirements. The upper / lower flexibility supply margin in scenario 1 is as follows: Figure 3 As shown. The only flexible device in scenario 1 is the battery. The adjustment amount of the battery is different from that of conventional devices. It is determined by the real-time storage state of the battery. When the flexibility demand is positive, it means that the battery needs to accept more electricity. At this time, the flexibility supply margin is the difference between the maximum capacity of the battery and the real-time storage state. When the flexibility demand is negative, it means that there is not much renewable energy or load demand. At this time, the flexibility supply margin is the difference between the real-time storage state and the minimum capacity of the battery.

[0223] Scenario 1 Microgrid flexibility margin Figure 5 As shown in the figure, the flexibility margin of the microgrid is the difference between the flexibility supply and the flexibility demand. This means that after considering the historical fluctuations of renewable energy and load, the flexibility adjustment amount of the microgrid is the real adjustment capacity of the microgrid. Since the supply of renewable energy is sufficient most of the time, the battery needs to be continuously discharged only in the period of 21:00-23:00. The battery tank is always in a charging state from 1:00 to 4:00, and the battery is fully charged at 4:00. There is no need for the battery to discharge from 4:00 to 21:00. In terms of flexibility, the flexibility adjustment from 4:00 to 21:00 is 0, and no adjustment amount can be provided. The flexibility margin during this period is negative. On the 24h time scale, the flexibility margin of scenario 1 is expected to be -6.8MW.

[0224] The upper / lower flexibility supply margin and flexibility margin of scenario 2 are as follows Figure 4 , 6 As shown in the figure, scenario 2 has more flexible equipment, including electrolyzers, batteries and fuel cells. The flexibility margin of scenario 2 is expected to be 1.745MW, which is much higher than -6.8MW of scenario 1. This means that the flexibility supply margin and flexibility margin of scenario 2 are much greater than those of scenario 1.

[0225] In addition to the role of flexibility, scenario 2 will also bring economic benefits due to the continuous production of hydrogen energy by the electrolyzer. At the same time, since there are more devices, a large amount of waste heat can be generated. When it is insufficient, the hydrogen in the hydrogen storage tank can be used to generate heat, which is fully sufficient to meet the demand for heat load and save the energy expenditure of buying heat from the heat network. Therefore, the microgrid system considering the coupling of electricity and hydrogen is superior to the traditional microgrid in terms of adjustment flexibility and economy. The comparison data of the operating benefits of the two scenarios are shown in Table 2.

[0226] Table 2 Comparison of operating benefits between scenarios 1 and 2

[0227]

[0228] In addition, this embodiment performs a long-term optimization on the microgrid system considering the flexibility margin of the electric-hydrogen coupling with a time scale of one year, and the optimized equipment capacity is shown in Table 3:

[0229] Table 3 Equipment capacity after optimization in scenario 1 and scenario 2

[0230]

[0231] During the operation cycle of the target year, Scenario 1 has more power load demand supplied by fuel cells than Scenario 2. Therefore, during the long-term operation of the system, when renewable energy and energy storage are insufficient, Scenario 1 can only choose to purchase electricity from the external power grid. However, the electrolyzer in Scenario 2 is in full load operation most of the time, and is overloaded for a small part of the time. The power generation plan can be adjusted according to the real-time status of the hydrogen storage tank. Therefore, both the system flexibility and economy are far better than Scenario 1. The flexibility parameters of optimized Scenario 1 and Scenario 2 are detailed in Table 4, and the economic data comparison is detailed in Table 5.

[0232] Table 4 Comparison of flexibility indicators between scenario 1 and scenario 2

[0233]

[0234] The amount of wind power curtailment in scenario 1 is 19,567 MW, and the amount of wind power curtailment in scenario 2 is 3,201 MW. The new energy absorption capacity of electric-hydrogen coupling is much stronger than traditional power storage. The probability of insufficient flexibility in scenario 1 is 47.57%, and the expected flexibility margin is -9.74 MW; the probability of insufficient flexibility in scenario 2 is 5.98%, and the expected flexibility margin is 15.28 MW, which is equivalent to the flexibility optimization capacity of electric-hydrogen coupling being 15.28-(-9.74)=25.02 MW, and the optimization effect is significant.

[0235] Table 5 Economic comparison between scenario 1 and scenario 2

[0236]

[0237] The total profit of the whole life cycle of scenario 2 is much greater than that of scenario 1. When the input of renewable energy is large, the battery will be in a state of frequent charging and discharging. In order to improve the renewable energy absorption capacity, the capacity needs to be appropriately increased, which leads to high cost of the battery in the whole life cycle of 15 years. The electric hydrogen coupling of scenario 2 can sell electricity to the power grid and use waste heat to heat the thermal network. From the perspective of the whole year, the use of hydrogen is still relatively tight, so when hydrogen is insufficient, it is still necessary to purchase electricity from the external power grid.

[0238] The accompanying drawings are only used for illustrative purposes and cannot be understood as limiting the present invention; the above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling, characterized in that: The following steps are involved: Step (1), establishing a device model and a system power balance model of the microgrid system; Step (2), analyzing the energy conversion characteristics of the electricity-hydrogen coupling in the three links of production, storage and use, and establishing an equipment flexibility margin model; Step (3), using the Monte Carlo method to establish a system flexibility margin model; Step (4), establishing an evaluation index for the flexibility margin of the power system; Step (5), taking the maximum total profit and adjustment flexibility of the system throughout its life cycle as the objective function; Step (6), using the operation constraints of power generation equipment and electric hydrogen production equipment, the capacity constraints of hydrogen storage equipment, and the flexibility balance constraints as constraint conditions; Step (7), using the improved particle swarm algorithm to solve; performing day-ahead optimization scheduling of the multi-energy microgrid based on the optimal solution obtained.

2. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 1, characterized in that: The device model and system power balance model of the microgrid system are established specifically including: (1) Electricity-hydrogen coupling model 1) Hydrogen production Electrolyzer hydrogen production H dj for: In the formula, H dj is the hydrogen production of the electrolyzer, P dj is the electrolytic cell load, U is the electrolytic cell voltage; 2) Storage and transportation Hydrogen storage tank model: Where: P H (t) is the internal pressure of the hydrogen storage tank at time t; ρ H is the hydrogen density; k = {-1, 0, 1}, representing dehydrogenation, steady state and storage of hydrogen respectively; ΔV H (t) is the change in hydrogen volume in the hydrogen storage tank at time t; R H is the molar gas constant; T H is the internal temperature of the hydrogen storage tank; M H is the molar mass of hydrogen; V HT is the volume of the hydrogen storage tank; V H (t) is the amount of hydrogen in the hydrogen storage tank at time t; V H0 (t) is the amount of hydrogen in the hydrogen storage tank at the initial moment; 3) Hydrogen use The fuel cell efficiency can be expressed as follows: Where: U is the fuel cell stack voltage; μ is the fuel utilization efficiency; H H2 It is the higher calorific value of hydrogen; The total power input to the fuel cell is P Z , the output power is P, and the remaining power is P1, which can be expressed as: Waste heat recovery model: Q Zr =η r Q Z (7) Where: Q Z is the output thermal efficiency of the equipment; η d is the heat loss coefficient; η r The efficiency of waste heat recovery; Because the amount of waste heat recovered is large and volatile, the system needs a heat storage tank to store the heat. The model of the heat storage tank is as follows: E xr (t+1)=E xr (t)+(μ1P xrx -P xrc / μ2)Δt (8) Where: E xr (t) is the heat stored in the heat storage tank at time t; μ1 and μ2 are the heat absorption and release efficiency of the heat storage tank; P xrx , P xrc is the heat absorption and release power of the heat storage tank; (2) Power balance model 1) Electric power balance P pv +P w +P ou +P ie =P f +P ih +P a (9) Where: P ou Purchase electricity from external power grid; P ie Output of hydrogen power generation equipment, i = {dc, ice, GT}; P ih is the electric power load of the electric hydrogen production equipment, i={dj,bc}; P f is the electrical load; P a To abandon wind and electricity; 2) Hydrogen power balance H in +H dj =H out +H ie +H rh (10) Where: H in , H ou They are respectively purchasing hydrogen from the outside and selling hydrogen; H dj is the hydrogen production of the electric hydrogen production equipment; H ie is the hydrogen consumption of hydrogen power generation equipment, i = {dc, ice, GT}; 3) Thermal power balance Q dj +Q in +Q ie +Q rh =Q f (11) Where: Q dj Q is the waste heat recovery amount of the electric hydrogen production equipment; in To purchase heat from the external power grid; Q ie is the waste heat recovery amount of hydrogen power generation equipment, i = {dc, ice, GT}; Q f For heat load.

3. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 1, characterized in that: The analysis of the energy conversion characteristics of the electric-hydrogen coupling in the three links of production, storage and use and the establishment of the equipment flexibility margin model specifically include: (1) Flexible adjustment margin model of electric hydrogen production equipment P min =P i *d min (15) Where: It is the flexibility margin of the electrolyzer to absorb energy; + and - represent upward and downward adjustment; is the electrolytic cell load; i represents four electrolytic cells, i = {awe, pem, aem, soe}; is the maximum load of the electrolyzer; is the minimum load of the electrolyzer; is the climbing amount of the electrolytic cell; is the climbing rate; P i is the rated load of the electrolytic cell; δ max , δ min is the upper and lower limits of the electrolytic cell load rate; As a power storage device, the flexibility margin of batteries is different from that of electrolyzers and is mainly affected by the real-time power status; In the formula, C is the battery capacity, SOC(t) is the state of charge at that moment; (2) Modeling of flexible adjustment margin of hydrogen power generation equipment Where: is the flexibility margin of hydrogen power generation equipment; + and - represent upward and downward adjustments; P ie is the output power of hydrogen power generation equipment; i represents three types of hydrogen power generation equipment, i = {dc, ice, GT}; is the maximum output power of the hydrogen power generation equipment; is the minimum output power of the hydrogen power generation equipment; It is the climbing amount of hydrogen power generation equipment; is the climbing rate.

4. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 1, characterized in that: The Monte Carlo method specifically includes: To explore the flexibility issue on a long time scale, it is necessary to probabilize the flexibility issue. Therefore, it is necessary to use the Monte Carlo non-sequential production method to output the equipment output probability of the microgrid in the form of probability, convert the output probability into the flexibility supply probability, and then add the flexibility supply of different equipment by the roll sum method to obtain the up / down flexibility supply margin of the microgrid.

5. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 1, characterized in that: The Monte Carlo method is used to establish a system flexibility margin model, which specifically includes: The principle of considering system flexibility by using the "envelope" mode of traditional power supply tracking net load and reserving backup is as follows: Where: P max,i represents the peak load capacity of unit i; P nL represents the total peak load demand of the net load; Flexibility Balance Model: Where: is the total supply of flexibility to adjust up / down at time t; ω + (t),ω - (t) is the total flexibility demand for upward / downward adjustment at time t; is the predicted positive / negative photovoltaic fluctuation at time t; is the predicted fluctuation of positive / negative wind power at time t; YL is the predicted fluctuation of positive / negative load at time t; + (t), YL - (t) is the flexibility margin of the system for up / down adjustment at time t; Since the uncertainty of renewable energy and load must be considered in the analysis of long-term conditions in the electric-hydrogen coupled micro-energy grid, the introduction of probabilistic analysis can better analyze the flexibility margin of the system; therefore, the supply and demand of flexibility are both random variables that obey a specific distribution; the introduction of probability density for description, the criterion for sufficient flexibility is: the probability that the system's flexibility resource supply capacity is less than the flexibility demand, that is, the probability of insufficient flexibility must be lower than a given threshold to indicate that the flexibility is sufficient; Define G and X as random variables of total supply and total demand of system flexibility, and Z = XY as flexibility margin variable. Then the deterministic criterion of flexibility balance is: Pr(Z≤0)=Pr(X≤Y)=Pr(∑ i∈S X i i∈D Y i )≤θ (29)​ Where: θ represents the level of abundance; S represents the set of flexible supply sources; G i represents the supply of the i-th source; D represents the set of flexibility requirements; X i represents the i-th demand; The meaning of the above formula is that when Z≤0, the flexibility margin is insufficient, that is, when the flexibility supply is less than the flexibility demand, let the probability sum of this part be less than the abundance level θ, that is, the system is in flexibility balance; Considering the characteristics of flexibility, the general form of the above formula is: In the formula, + and - do not represent the size but the direction of flexibility adjustment. + means flexibility is increased and - means flexibility is decreased. Flexibility is related to system state C i About (C i can be defined as load level); S C is a collection of enumerable states; According to the basic principle of probabilistic convolution operation, the addition and subtraction operations of flexible random variables are represented by convolution sum and convolution difference respectively: Where: The probability density function representing the system flexibility margin; Represent the probability density functions of flexible supply X and demand Y respectively; ⊕ / They are volume sum / volume difference operations respectively.

6. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 1, characterized in that: The evaluation index for establishing the flexibility margin of the power system specifically includes: (1) Distribution of flexibility margin Where: The probability density function representing the system flexibility margin; Represent the probability density functions of flexible supply X and demand Y respectively; ⊕ / They are volume sum / volume difference operations respectively; (2) Probability of insufficient flexibility Where: FNS is the probability of insufficient flexibility, The probability density function representing the system flexibility margin; (3) Flexibility margin expectations Where: EFZ is the expected flexibility margin, The probability density function representing the system flexibility margin.

7. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 1, characterized in that: The objective function is to maximize the total profit and adjustment flexibility of the system throughout its life cycle, and specifically includes: (1) System life cycle total cost calculation model System initial equipment investment cost calculation model: The total one-time investment cost of the equipment is F0, which mainly includes the cost of solar photovoltaic panels F pv , wind turbine cost F w 、Hydrogen production device F H 、Electricity storage device F e 、Hydrogen storage device F HC , controllable power generation device F Hd and heat generating device F He , ignoring the equipment installation and commissioning fees, which are added to the equipment cost; F0=F pv +F w +F H +F e +F HC +F Hd +F He (36) The relationship between the initial total investment of each equipment and its equipment scale is as follows: F i =m i P i (37) Where: P i Rated planned installed capacity of the equipment; m i is the unit capacity cost of the equipment; i represents the equipment in the system, i = {pv, w, H, e, HC, Hd, He}; System equipment operation and maintenance cost calculation model: F r =F pvr +F wr +F Hr +F er +F HCr +F Hdr +F Her (38) Where: F pvr F is the operation and maintenance cost of photovoltaic power generation equipment throughout its life cycle; wr F is the operation and maintenance cost of wind power equipment throughout its life cycle; Hr F is the operation and maintenance cost of the hydrogen production equipment throughout its life cycle; er F is the operation and maintenance cost of the energy storage equipment throughout its life cycle; HCr F is the operation and maintenance cost of the hydrogen storage equipment throughout its life cycle; Hdr F is the operation and maintenance cost of the controllable power generation equipment over its entire life cycle; Her The operation and maintenance costs of the heat generating device over its entire life cycle; The cost of each part can be estimated by its percentage of the initial investment of the project; as follows: Where: M represents each device in the system; j is the equipment operation and maintenance coefficient; i is the discount rate, which is generally 5%; n is the full life cycle of the system; Mathematical model of total operating cost of the system throughout its life cycle: Where: F total is the total cumulative operating cost in the tth year, F0 is the total one-time investment cost of the equipment, and F r The operating and maintenance costs of system equipment; (2) Mathematical calculation model of total economic benefits of the system throughout its life cycle Economic benefits include the income from the sale of electricity from renewable energy grid-connected power plants, the income from the sale of hydrogen, and the income from the grid-connected power generation equipment. The total economic benefits are E total : AND total =And R +E k +E H +E e -AND e0 -AND R0 (41) Where: E k Revenue from electricity sales from renewable energy grid; E H E is the income from hydrogen sales; e The income from the controllable power generation equipment connected to the grid; E R E is the revenue from hot sales; e0 Expenditure on electricity purchase; E R0 Expenditure on purchasing heat; The specific calculation formula for each part of the income is as follows: Where: m k,t is the grid-connected power generation of renewable energy in year t; C k is the grid-connected electricity price of renewable energy; k is the annual average operating time of renewable energy; t H is the average annual operating time of the hydrogen production equipment; C H is the unit price of hydrogen; V e is the amount of hydrogen input to the hydrogen production equipment; μ is the power generation per standard cubic meter of hydrogen fuel; t k C is the annual average operating time of hydrogen power generation equipment e The grid-connected electricity price for hydrogen power generation equipment; Q t Provide heat to the system; C r is the heating grid connection unit price; C ou Time-of-use electricity price; (3) System life cycle net profit calculation model The net profit of the system is equal to the difference between the total economic benefits and the total cost of the system throughout its life cycle; the total income also includes the conversion of the residual value of each device during the entire life cycle; so the net profit F S : Where: F total is the cumulative total operating cost in year t, E total is the total economic benefit of the system, L t is the residual value of each part of the system equipment in the tth year, calculated at 5% of fixed assets; r1 is the discount rate, calculated at 10% of the fixed value.

8. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 7, characterized in that: The constraints of power generation equipment and electric hydrogen production equipment operation constraints, hydrogen storage equipment capacity constraints, and flexibility balance constraints are specifically: Stable operation constraints of wind turbines and photovoltaic generators: P wmin ≤P W ≤P wmax (49) P vmin ≤P v ≤P vmax (50) Where: P wmin , P wmax is the minimum and maximum output of the fan; P vmin , P vmax is the minimum and maximum output of photovoltaic power; To ensure the safe and stable operation of all parts of the system, the proportion of renewable power generation capacity used for water electrolysis must meet the following requirements: 0.5≤f≤1 (51) Where f is the proportion of renewable power generation capacity used for water electrolysis; Constraints for stable operation of electric hydrogen production equipment: In min ≤V≤V max (52) d min P max ≤P≤δ max P max (53) Where: V min 、V max is the minimum and maximum hydrogen production rate; P is the operating power of the electric hydrogen production equipment, P max is the maximum power of the electric hydrogen production equipment, δ min is the minimum power factor, δ max is the maximum power factor; Hydrogen storage equipment capacity constraints: In out ≤V H (54) V in ≤V HT -V H (55) Where V out is the output hydrogen volume of the hydrogen storage device, V H is the hydrogen storage capacity of the hydrogen storage device, V in is the amount of hydrogen input to the hydrogen storage device, V HT is the total hydrogen capacity of the hydrogen storage device at high temperature; Flexibility Balance: Where: EFNS is the expected value of insufficient flexibility, is the probability density function of inflexibility deficiency, where z is the variable of inflexibility deficiency, θ is the level of inflexibility deficiency, and FZ is the cumulative distribution function of inflexibility deficiency.

9. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 1, characterized in that: The particle swarm algorithm specifically includes: In particle swarm optimization, the solution to the optimization problem is considered as a bird in the search space, called a "particle"; the i-th particle has a velocity of and location The update equations are: In the formula, k is the number of iterations; is the velocity of the ith particle at the kth iteration, is the position of the i-th particle at the k-th iteration, P best is the individual optimal solution, G best is the optimal solution for the group, c1 and c2 are both learning factors, c1 represents the individual cognition of the particle, and c2 represents the social cognition of the particle; w is the inertia weight; r1 and r2 are random numbers, which are uniformly distributed in the range of [0, 1]; When the PSO algorithm is running, each particle in the particle swarm will continuously update its own speed and position during iteration, thereby iterating the optimal solution in the particle swarm; the optimal solution consists of two types: the group optimal solution Gbest and the individual optimal solution Pbest, and the relationship between the two is the subordinate relationship between the individual and the group.

10. A multi-energy microgrid optimization scheduling method considering the flexibility margin of electricity-hydrogen coupling according to claim 9, characterized in that: The improved particle swarm algorithm is used to solve the problem, specifically including: (1) Initialize the particle swarm, set the speed threshold and over-limit response mechanism of the corresponding particle swarm according to the model requirements of each device, perform the initial calculation, and record the historical optimal and global optimal positions of the particle swarm; (2) In each iteration, the inertia parameters and learning factors are updated, the particle adaptive optimization conditions are determined and corresponding calculations are performed, and the iterative Pareto optimal solution set is recorded; (3) After the system has iterated to a set number of times, the optimization search is stopped, and the historical optimal value set recorded at this time is recorded and output.

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