An optimization scheduling method for an integrated energy system including an electricity-hydrogen-electricity energy hub

Abstract

The present invention discloses an optimization dispatching method for an integrated energy system including an electricity-hydrogen-electricity energy hub, relates to the technical field of energy conversion efficiency analysis, and solves the problem that the existing integrated energy system dispatching method including an electricity-hydrogen-electricity energy hub is difficult to ensure the balance of power grid supply and demand. The method comprises: constructing an objective function of an optimization dispatching model for an integrated energy system with the goal of maximizing the total operating profit of an integrated energy system including an electricity-hydrogen-electricity conversion hub; constructing constraint conditions for an optimization dispatching model for an integrated energy system with power balance constraints of an electricity-hydrogen coupling system, power constraints of electric hydrogen production, upper and lower power limits, flexible load demand response constraints, and hydrogen energy flow balance constraints as constraints; solving the optimization dispatching model for an integrated energy system to obtain an optimization dispatching strategy for an integrated energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy conversion efficiency analysis, and in particular to an optimization scheduling method for an integrated energy system including an electricity-hydrogen-electricity energy hub. Background Art

[0002] As energy consumption increases, environmental problems become increasingly prominent. Accelerating the transformation of energy structure and taking the path of low-carbon development is the only way to achieve sustainable development.

[0003] At present, carbon emissions from the power industry account for more than 40% of the total carbon emissions in the country. Under the background of carbon peak and carbon neutrality, the development of low-carbon electricity is of great significance to the implementation and development of a low-carbon economy. Hydrogen energy has the advantages of clean carbon-free, high energy density, and strong flexibility. It is deeply coupled with the power system in terms of power generation, transmission, and power consumption, which can effectively promote the efficient consumption of new energy and improve the safe and economic operation level of the power system. Therefore, the electric-hydrogen coupling system is considered to be a key paradigm for achieving deep decarbonization of the power energy system. The technology of hydrogen production by electrolysis of water is relatively mature and can efficiently convert electrical energy into hydrogen energy. At the same time, fuel cell technology is also developing continuously, which can convert hydrogen energy into electrical energy and has the advantages of high efficiency and low emissions. In the field of power systems, the output power of new energy units is used to realize the conversion of electricity to hydrogen, and hydrogen energy is converted into electricity according to the power demand of the power grid. That is, in the electricity-hydrogen-electricity energy conversion technology, hydrogen energy can be used as an energy storage medium to adjust the supply and demand balance of the power grid and improve the stability and reliability of the power grid. Therefore, in the operation of the integrated energy system including the electricity-hydrogen-electricity energy hub, how to optimize the scheduling of various power supply modes to meet the supply and demand balance requirements of the power grid is a technical problem that urgently needs to be solved. Summary of the invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide an optimization scheduling method for an integrated energy system including an electricity-hydrogen-electricity energy hub, so as to solve the problem that the existing scheduling method of an integrated energy system including an electricity-hydrogen-electricity energy hub is difficult to ensure the balance of supply and demand in the power grid.

[0005] The present invention discloses an optimization scheduling method for a comprehensive energy system including an electricity-hydrogen-electricity energy hub, the method comprising:

[0006] The objective function of the optimal dispatch model of the integrated energy system is constructed with the goal of maximizing the total operating profit of the integrated energy system containing the electricity-hydrogen-electricity conversion hub;

[0007] The constraints of the comprehensive energy system optimization scheduling model are constructed based on the power balance constraints of the electric-hydrogen coupling system, the power constraints of electric hydrogen production, the upper and lower power limits, the flexible load demand response constraints, and the hydrogen energy flow balance constraints;

[0008] Solve the optimal dispatch model of the integrated energy system to obtain the optimal dispatch strategy of the integrated energy system.

[0009] On the basis of the above scheme, the present invention also makes the following improvements:

[0010] Furthermore, the objective function of the integrated energy system optimization dispatching model constructed with the goal of maximizing the total operating profit of the integrated energy system containing the electricity-hydrogen-electricity conversion hub is expressed as:

[0011] maxI=(R1+R2+R3+R4)-(C1+C2+C3+C4) (1)

[0012] Among them, I represents the total operating profit of the integrated energy system including the electricity-hydrogen-electricity conversion hub; R1, R2, R3, and R4 represent the income obtained from the energy market and the hydrogen energy market, the income obtained from the capacity market, the income obtained by deploying AGC to regulate the frequency of power generation units, and the peak-shaving income, respectively; C1, C2, C3, and C4 represent the operating cost of energy units, the operating cost of the electricity-hydrogen conversion device, the opportunity cost, and the cost of compensating for the reducible load, respectively.

[0013] Furthermore, the power balance constraint of the electric-hydrogen coupling system is expressed as:

[0014]

[0015] Where: P1 t , Respectively represent the output of new energy units and thermal power units in period t; P5 t is the output of hydrogen energy storage in period t; is the electric load of load node d in period t; D is the total number of load nodes; P3 t is the hydrogen production power of the electrolyzer in period t;

[0016] The power constraint of hydrogen production is expressed as:

[0017]

[0018] Where: P1 i,t , P3 is the output power, load power provided and power abandonment of new energy station i in period t. i,t is the input power of the electrolyzer in the new energy station i during period t.

[0019] Furthermore, the power upper and lower limit constraints are expressed as:

[0020] P2 min ≤P2 t ≤P2 max (5)

[0021] 0≤P1 t ≤P1 max (6)

[0022] Where: Respectively represent the minimum output and maximum output of the thermal power unit; P1 max Indicates the predicted maximum output of the new energy unit;

[0023] The flexible load demand response constraint is expressed as:

[0024]

[0025] Where: is the original load power before the reduction in period t, is the load power reduced during period t, ε max is the maximum load reduction rate per unit time, E max is the maximum total load reduction rate within the dispatch time.

[0026] Further, the hydrogen energy flow balance constraint is expressed as:

[0027] Q a,t +Q b,t =Q c,t +Q d,t +Q f,t (9)

[0028] Where: Q a,t It represents the mass of hydrogen produced by electrolyzing water in the electrolyzer during the period t, Q b,t It represents the hydrogen output of the hydrogen storage tank in period t, Q c,t represents the hydrogen consumption of the fuel cell during period t, Q d,t represents the hydrogen input into the hydrogen storage tank during period t, Q f,t Represents the amount of hydrogen sold during period t.

[0029] Furthermore, the income R1 obtained from the energy market and the hydrogen market is expressed as:

[0030]

[0031] In the formula, represents the electricity price injected into the grid during period t, They represent the predicted power generation values ​​of the new energy units and thermal power units in period t respectively; represents the price of hydrogen sold during period t, Q f,t represents the hydrogen sales volume in period t; T represents the optimization period, and Δt represents the time scale;

[0032] The revenue R2 from the capacity market is expressed as:

[0033]

[0034] In the formula, They represent the price per unit capacity increase of fuel cells and electrolyzers in period t respectively; They represent the rising capacity of the fuel cell and electrolyzer in period t respectively; They represent the price reduction per unit capacity of the fuel cell and electrolyzer in period t respectively; They represent the decreased capacity of the fuel cell and electrolyzer in period t respectively.

[0035] Furthermore, the revenue R3 obtained by deploying AGC to regulate the frequency of the generator set is expressed as:

[0036]

[0037] Where N s represents the total number of scenes that occur during the frequency adjustment process, θ s Indicates the probability of scene s appearing; FR + (t,s) Indicates the frequency-up regulation signal of scene s in period t, FR - (t,s) Indicates the frequency reduction signal of scene s occurring in period t;

[0038] Peak load income R4 is expressed as:

[0039]

[0040] p t =λ t ·p (14)

[0041] In the formula, p is the basic electricity price, p t is the electricity price in period t, λ t is the peak-to-valley coefficient of period t, and U3(t) represents the amount of electricity used for peak regulation in the electricity that needs to be regulated during period t.

[0042] Furthermore, the operating cost C1 of the energy unit is expressed as:

[0043] C1=C 11 +C 12 +C 13 (15)

[0044] In the formula, C 11 represents the initial investment cost, C 12 represents the power generation cost of the new energy unit, C 13 represents the power generation cost of thermal power units;

[0045]

[0046] In the formula, c1 is the operating cost per unit of electricity generated by the new energy unit, and c2 is the operating cost per unit of electricity generated by the thermal power unit;

[0047] The operating cost C2 of the electric-hydrogen conversion device is calculated as follows:

[0048]

[0049] Where c3 is the operating cost of the electrolyzer per unit hydrogen production, is the input power of the electrolyzer in period t; c4 is the operating cost of the fuel cell per unit power generation, is the electric power output of the fuel cell in period t; c5 is the unit charging and discharging cost of the hydrogen storage tank, Q b,t It represents the hydrogen output of the hydrogen storage tank in period t, Q d,t Represents the amount of hydrogen input into the hydrogen storage tank during period t.

[0050] Furthermore, the opportunity cost C3 is expressed as:

[0051]

[0052] In the formula, The unit income obtained by adjusting the electric energy for hydrogen storage is U1(t) which represents the output of the new energy unit in period t, and U2(t) which represents the load in period t;

[0053] The reducible load compensation cost C4 is expressed as:

[0054]

[0055] In the formula, c e is the compensation price per unit power load transfer, is the load power reduced during period t.

[0056] Furthermore, the comprehensive energy system optimization scheduling model is solved in the following way:

[0057] Initialize the parameters of the bat population; set the bat position matrix;

[0058] Randomly generate the initial bat position matrix of each bat population as the first generation bat position matrix;

[0059] In the first generation, the objective function corresponding to the corresponding bat is calculated according to the bat position matrix of each bat in the first generation bat population, and the result of taking the negation of the objective function corresponding to the corresponding bat is used as the fitness of the corresponding bat in the first generation bat population; and the bat with the smallest fitness is selected as the optimal bat of the first generation;

[0060] In the nth generation, n≥1, the pulse frequency of the n+1th generation, as well as the flight speed and bat position matrix of each bat in the n+1th generation bat population are updated; and the fitness of each bat in the n+1th generation bat population is calculated;

[0061] Determine whether each bat of the n+1th generation performs local search; if so, update the bat position matrix and fitness of the corresponding bat in the n+1th generation bat population; otherwise, do not perform local search; then, obtain the optimal bat of the n+1th generation;

[0062] Determine whether the termination condition is met. If the maximum number of iterations is reached, stop the iteration and output the bat position matrix corresponding to the bat with the smallest fitness as the optimization scheduling result of the integrated energy system optimization scheduling model; otherwise, take n=n+1, and then jump to the step of updating the pulse frequency of the n+1th generation.

[0063] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0064] The optimization scheduling method of the integrated energy system containing the electricity-hydrogen-electricity energy hub provided by the present invention constructs the objective function of the optimization scheduling model of the integrated energy system by taking the maximization of the total profit of the operation of the integrated energy system containing the electricity-hydrogen-electricity conversion hub as the goal, and takes the power balance constraint of the electric-hydrogen coupling system, the power constraint of the electric hydrogen production, the upper and lower power constraints, the flexible load demand response constraint, and the hydrogen energy flow balance constraint as the constraint to construct the constraint conditions of the optimization scheduling model of the integrated energy system; it can well model the optimization scheduling model of the integrated energy system, and the model integrates the overall scheduling process of the integrated energy system. Afterwards, by solving the optimization scheduling model of the integrated energy system, the optimization scheduling strategy of the integrated energy system can be obtained, thereby realizing the optimization scheduling of the integrated energy system containing the electricity-hydrogen-electricity energy hub, and achieving the effect of balancing the supply and demand of the power grid.

[0065] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0067] Figure 1 A schematic diagram of the structure of a comprehensive energy system including an electricity-hydrogen-electricity energy hub provided in an embodiment of the present invention;

[0068] Figure 2 A flow chart of a method for optimizing the scheduling of an integrated energy system including an electricity-hydrogen-electricity energy hub provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0070] A specific embodiment of the present invention discloses an optimization scheduling method for an integrated energy system including an electricity-hydrogen-electricity energy hub. In this embodiment, the structural schematic diagram of the integrated energy system including an electricity-hydrogen-electricity energy hub is as follows: Figure 1 As shown, in Figure 1 In the embodiment, thermal power generating sets and new energy generating sets (including wind generating sets and photovoltaic generating sets) form energy generating sets, and both can be input into the AC bus; the generator supplies power to the AC bus via the AGC. At the same time, due to the randomness, volatility, intermittency and other characteristics of the power generation process of the new energy generating set, in order to fully improve the utilization rate of electric energy, this embodiment uses the excess power output by the new energy generating set as the power source of the electric hydrogen conversion device. A plurality of new energy stations are arranged in the new energy generating set, and each new energy station is connected to the electrolyzer in the electric hydrogen conversion device one by one. Inside the electric hydrogen conversion device, electricity is converted into hydrogen through the electrolyzer, and hydrogen is converted into electricity through the hydrogen fuel cell. Part of the hydrogen produced by the electrolyzer is stored in the hydrogen storage tank, and then hydrogen is injected into the hydrogen fuel cell through the hydrogen injection machine; part of it is converted into electric energy through the hydrogen fuel cell, and the electric energy generated by the hydrogen fuel cell is input into the AC bus.

[0071] This embodiment uses Figure 1 The comprehensive energy system containing the electricity-hydrogen-electricity energy hub shown in the figure is taken as the research object, and the following optimization scheduling method for the comprehensive energy system containing the electricity-hydrogen-electricity energy hub is formed. The flow chart is as follows Figure 2 The method comprises the following steps:

[0072] Step S1: constructing an objective function of an integrated energy system optimization scheduling model with the goal of maximizing the total operating profit of an integrated energy system containing an electricity-hydrogen-electricity conversion hub;

[0073] Step S2: Constructing the constraints of the comprehensive energy system optimization scheduling model based on the power balance constraint of the electric-hydrogen coupling system, the electric-hydrogen production power constraint, the power upper and lower limit constraints, the flexible load demand response constraint, and the hydrogen energy flow balance constraint;

[0074] Step S3: Solve the integrated energy system optimization scheduling model to obtain the integrated energy system optimization scheduling strategy.

[0075] In this embodiment, the objective function of constructing an integrated energy system optimization scheduling model with the goal of maximizing the total operating profit of the integrated energy system containing the electricity-hydrogen-electricity conversion hub is expressed as:

[0076] maxI=(R1+R2+R3+R4)-(C1+C2+C3+C4) (1)

[0077] Among them, I represents the total operating profit of the integrated energy system including the electricity-hydrogen-electricity conversion hub; R1, R2, R3, and R4 represent the income obtained from the energy market and the hydrogen energy market, the income obtained from the capacity market, the income obtained by deploying AGC to regulate the frequency of power generation units, and the peak-shaving income, respectively; C1, C2, C3, and C4 represent the operating cost of energy units, the operating cost of the electricity-hydrogen conversion device, the opportunity cost, and the cost of compensating for the reducible load, respectively.

[0078] The components of the objective function are described as follows:

[0079] (1) Revenue from the energy market and hydrogen market

[0080] In this embodiment, the income obtained from the energy market includes the sales income of the power generation of thermal power units and new energy units. The income obtained from the hydrogen energy market refers to the income from the sale of hydrogen.

[0081] Specifically, the income R1 obtained from the energy market and the hydrogen market is expressed as:

[0082]

[0083] In the formula, represents the electricity price injected into the grid during period t, They represent the predicted power generation values ​​of the new energy units and thermal power units in period t respectively; represents the price of hydrogen sold during period t, Q f,t represents the amount of hydrogen sold in period t; T represents the optimization period, and Δt represents the time scale.

[0084] (2) Revenue from the capacity market

[0085] The revenue from the capacity market refers to the revenue from the up / down capacity provided by fuel cells and electrolyzers due to fluctuations in capacity prices. The revenue R2 from the capacity market is expressed as:

[0086]

[0087] In the formula, They represent the price per unit capacity increase of fuel cells and electrolyzers in period t respectively; They represent the rising capacity of the fuel cell and electrolyzer in period t respectively; They represent the price reduction per unit capacity of the fuel cell and electrolyzer in period t respectively; They represent the decreased capacity of the fuel cell and electrolyzer in period t respectively.

[0088] (3) Revenue from frequency regulation through deployment of AGC

[0089] AGC (Automatic Generation Control) is an automatic control system used to adjust the output power of generator sets. Deploying AGC for frequency regulation means that the generator sets in the power system automatically adjust the upper and lower limit parameters of the output power of the generator sets according to the changes in the grid frequency during the automatic generation control process.

[0090] The revenue R3 obtained by deploying AGC to regulate the frequency of the generator set is expressed as:

[0091]

[0092] Where N s represents the total number of scenes that occur during the frequency adjustment process, θ s Indicates the probability of scene s appearing; FR + (t,s) Indicates the frequency-up regulation signal of scene s in period t, FR - (t,s) Indicates the frequency reduction signal of scene s occurring during period t.

[0093] (4) Peak load shaving income

[0094] Peak load income R4 is expressed as:

[0095]

[0096] p t =λ t ·p (6)

[0097] In the formula, p is the basic electricity price, p t is the electricity price in period t, λ t is the peak-to-valley coefficient of period t, and U3(t) represents the amount of electricity used for peak regulation in the electricity that needs to be regulated during period t.

[0098] (5) Operating costs of energy units

[0099] The energy units in this embodiment include new energy units and thermal power units.

[0100] The operating cost C1 of the energy unit is expressed as:

[0101] C1=C 11 +C12 +C 13 (7)

[0102] In the formula, C 11 represents the initial investment cost, C 12 represents the power generation cost of the new energy unit, C 13 Represents the power generation cost of thermal power units.

[0103]

[0104] In the formula, c1 is the operating cost of the new energy unit per unit of power generation, and c2 is the operating cost of the thermal power unit per unit of power generation.

[0105] (6) Operating costs of the electric-hydrogen conversion device

[0106] The operating cost C2 of the electric-hydrogen conversion device is calculated as follows:

[0107]

[0108] Where c3 is the operating cost of the electrolyzer per unit hydrogen production, is the input power of the electrolyzer in period t; c4 is the operating cost of the fuel cell per unit power generation, is the electric power output of the fuel cell in period t; c5 is the unit charging and discharging cost of the hydrogen storage tank, Q b,t It represents the hydrogen output of the hydrogen storage tank in period t, Q d,t Represents the amount of hydrogen input into the hydrogen storage tank during period t.

[0109] (7) Opportunity Cost

[0110] The opportunity cost C3 is expressed as:

[0111]

[0112] In the formula, In order to adjust the unit profit obtained by using electric energy for hydrogen storage, U1(t) represents the output of the new energy unit in period t, and U2(t) represents the load in period t.

[0113] (8) Can reduce load compensation costs

[0114] The reducible load compensation cost C4 is expressed as:

[0115]

[0116] In the formula, c e is the compensation price per unit power load transfer, is the load power reduced during period t.

[0117] In this embodiment, the power balance constraints of the electric-hydrogen coupling system, the electric-hydrogen power constraints, the upper and lower power constraints, the flexible load demand response constraints, and the hydrogen energy flow balance constraints are used as constraints to construct the constraints for optimizing the scheduling model of the comprehensive energy system.

[0118] (1) Power balance constraints of the electric-hydrogen coupling system.

[0119]

[0120] Where: P1 t , Respectively represent the output of new energy units and thermal power units in period t; P5 t is the output of hydrogen energy storage in period t; is the electric load of load node d in period t; D is the total number of load nodes; P3 t is the hydrogen production power of the electrolyzer during period t.

[0121] (2) Power constraints of hydrogen production from electricity.

[0122] Since hydrogen is produced using excess electricity from new energy units, the power of hydrogen production is subject to the following constraints:

[0123]

[0124] Where: P1 i,t , P3 is the output power, load power provided and abandoned power of the new energy station i in period t. i,t is the input power of the electrolyzer in the new energy station i during period t.

[0125] (3) Power upper and lower limit constraints.

[0126] P2 min ≤P2 t ≤P2 max (16)

[0127] 0≤P1 t ≤P1 max (17)

[0128] Where: Respectively represent the minimum output and maximum output of the thermal power unit; P1 max Indicates the predicted maximum output of the new energy unit.

[0129] (4) Flexible load demand response constraints.

[0130] In order to ensure normal energy consumption of load users, the load reduction for load users shall not be greater than the maximum possible reduction.

[0131]

[0132] Where: is the original load power before the reduction in period t, is the load power reduced during period t, ε max is the maximum load reduction rate per unit time, E max is the maximum total load reduction rate within the dispatch time.

[0133] (5) Hydrogen energy flow balance constraints

[0134] In the hydrogen power generation system, the amount of hydrogen produced by the electrolyzer, the amount of hydrogen consumed by the fuel cell, the input and output of hydrogen storage tanks, and the amount of hydrogen purchased and sold need to be balanced.

[0135] Q a,t +Q b,t =Q c,t +Q d,t +Q f,t (20)

[0136] Where: Q a,t It represents the mass of hydrogen produced by electrolyzing water in the electrolyzer during the period t, Q b,t It represents the hydrogen output of the hydrogen storage tank in period t, Q c,t represents the hydrogen consumption of the fuel cell during period t, Q d,t represents the hydrogen input into the hydrogen storage tank during period t, Q f,t Represents the amount of hydrogen sold during period t.

[0137] In the integrated energy system optimization dispatch model, the decision variables include Q f,t , During the specific implementation process, an adaptive bat algorithm can be used to solve the above-mentioned integrated energy system optimization scheduling model. The specific process is described as follows.

[0138] Step S31: Initialize the parameters of the bat population; set the bat position matrix.

[0139] Specifically, the parameters of the bat population are initialized, including: the number of bat populations, the number of iterations G, the pulse loudness reduction coefficient α, the pulse emission frequency increase coefficient γ, the pulse frequency f, the pulse loudness A, the initial pulse emission frequency r o wait.

[0140] The bat position matrix is ​​set as a multidimensional vector to represent the decision variables in the integrated energy system optimization scheduling model in an encoded form.

[0141] Step S32: randomly generate an initial bat position matrix for each bat population as the first generation bat position matrix.

[0142] Step S33: In the first generation, the objective function corresponding to the corresponding bat is calculated according to the bat position matrix of each bat in the first generation bat population, and the negative result of the objective function corresponding to the corresponding bat is used as the fitness of the corresponding bat in the first generation bat population; and the bat with the smallest fitness is selected as the optimal bat of the first generation.

[0143] Step S34: In the nth generation, n≥1, update the pulse frequency of the n+1th generation, as well as the flight speed and bat position matrix of each bat in the n+1th generation bat population; and calculate the fitness of each bat in the n+1th generation bat population.

[0144] Specifically, the pulse frequency f of the n+1th generation n+1 It is expressed as:

[0145] f n+1 =rand(f min ,f max ) (twenty one)

[0146] In the formula, f min 、f max are the lower and upper limits of the pulse frequency, respectively, rand(f min ,f max ) indicates that in f min 、f max Pick a random number within the range.

[0147] The flight speed of bat e in the n+1 generation bat population Bat position matrix Respectively expressed as:

[0148]

[0149] In the formula, is the flying speed of bat e in the nth generation bat population, is the bat position matrix of bat e in the nth generation bat population, is the bat position matrix of the optimal bat of the nth generation.

[0150] Specifically, in the n+1th generation, the objective function corresponding to the corresponding bat is calculated according to the bat position matrix of each bat in the n+1th generation bat population, and the negative result of the objective function corresponding to the corresponding bat is used as the fitness of the corresponding bat in the n+1th generation bat population; and the bat with the smallest fitness is selected as the optimal bat of the n+1th generation.

[0151] Step S35: Determine whether each bat of the n+1th generation performs local search; if so, update the bat position matrix and fitness of the corresponding bats in the n+1th generation bat population; otherwise, do not perform local search; then, obtain the optimal bat of the n+1th generation.

[0152] If the random number between 0 and 1 selected by bat e is greater than the bat's pulse emission frequency r in the nth generation n , then perform a local search and update the bat position matrix of bat e in the n+1th generation bat population as follows:

[0153]

[0154] In the formula, ε is a random number between 0 and 1, A n is the echo loudness of the bat in the nth generation.

[0155] If the random number between 0 and 1 selected by bat e is less than the bat pulse loudness of the bat in the nth generation, and the fitness of bat e in the n+1th generation bat population is less than the fitness of the best bat in the nth generation bat population, no local search is performed and the newly obtained bat is accepted. The pulse loudness and pulse emission frequency are updated according to the following formula:

[0156] A n+1 =αA n (25)

[0157] r n+1 =r0[1-e -γ(n+1) ] (26)

[0158] Where α is the pulse loudness attenuation coefficient, r n is the pulse emission frequency of the bats in the nth generation.

[0159] Step S36: Determine whether the termination condition is met. If the maximum number of iterations is reached, stop the iteration and output the bat position matrix corresponding to the bat with the smallest fitness as the optimization scheduling result of the integrated energy system optimization scheduling model; otherwise, take n=n+1, and then jump to step S34.

[0160] Afterwards, the output power dispatch results of each device in the integrated energy system containing the electricity-hydrogen-electricity conversion hub can be sent to the control center, which will dispatch each device.

[0161] In summary, the optimization scheduling method of the integrated energy system containing the electricity-hydrogen-electricity energy hub provided by the embodiment of the present invention constructs the objective function of the optimization scheduling model of the integrated energy system by taking the maximization of the total operating profit of the integrated energy system containing the electricity-hydrogen-electricity conversion hub as the goal, and takes the power balance constraint of the electric-hydrogen coupling system, the power constraint of electric hydrogen production, the upper and lower power constraints, the flexible load demand response constraints, and the hydrogen energy flow balance constraints as the constraints to construct the constraint conditions of the optimization scheduling model of the integrated energy system; it can well model the optimization scheduling model of the integrated energy system, which integrates the overall scheduling process of the integrated energy system. Afterwards, by solving the optimization scheduling model of the integrated energy system, the optimization scheduling strategy of the integrated energy system can be obtained, thereby realizing the optimization scheduling of the integrated energy system containing the electricity-hydrogen-electricity energy hub, and achieving the effect of balancing the supply and demand of the power grid.

[0162] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0163] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. An optimization scheduling method for an integrated energy system including an electricity-hydrogen-electricity energy hub, characterized in that: The method comprises: The objective function of the optimization dispatch model of the integrated energy system is constructed with the goal of maximizing the total profit of the operation of the integrated energy system containing the electricity-hydrogen-electricity conversion hub; The constraints of the comprehensive energy system optimization scheduling model are constructed based on the power balance constraints of the electric-hydrogen coupling system, the power constraints of electric hydrogen production, the upper and lower power limits, the flexible load demand response constraints, and the hydrogen energy flow balance constraints; Solving the optimal dispatching model of the integrated energy system to obtain an optimal dispatching strategy for the integrated energy system; The objective function of the integrated energy system optimization dispatch model constructed with the goal of maximizing the total profit of the integrated energy system containing the electricity-hydrogen-electricity conversion hub is expressed as: max I=(R1+R2+R3+R4)-(C1+C2+C3+C4) (1) Among them, I represents the total operating profit of the integrated energy system including the electricity-hydrogen-electricity conversion hub; R1, R2, R3, and R4 represent the income obtained from the energy market and the hydrogen energy market, the income obtained from the capacity market, the income obtained by deploying AGC to adjust the frequency of the generator set, and the peak-shaving income; C1, C2, C3, and C4 represent the operating cost of the energy unit, the operating cost of the electricity-hydrogen conversion device, the opportunity cost, and the cost of compensating for the load that can be reduced; AGC is an automatic control system used to adjust the output power of the generator set; The power balance constraint of the electric-hydrogen coupling system is expressed as: Where: P1 t , They represent the output of new energy units and thermal power units in period t respectively; is the output of hydrogen energy storage in period t; is the electric load of load node d in period t; D is the total number of load nodes; P3 t is the hydrogen production power of the electrolyzer in period t; The power constraint of hydrogen production by electricity is expressed as: Where: P1 i,t , They are the output power, provided load power and abandoned power of the new energy station i in period t respectively; is the input power of the electrolyzer in the new energy station i during period t; The power upper and lower constraints are expressed as: Where: Respectively represent the minimum output and maximum output of the thermal power unit, P1 max Indicates the predicted maximum output of the new energy unit; The flexible load demand response constraint is expressed as: Where: is the original load power before the reduction in period t, is the load power reduced during period t, ε max is the maximum load reduction rate per unit time, E max is the maximum total load reduction rate within the dispatch time; T represents the optimization period; The hydrogen energy flow balance constraint is expressed as: Q a,t +Q b,t =Q c,t +Q d,t +Q f,t (9) Where: Q a,t It represents the mass of hydrogen produced by electrolyzing water in the electrolyzer during the period t, Q b,t represents the hydrogen output of the hydrogen storage tank in period t, Q c,t represents the hydrogen consumption of the fuel cell during period t, Q d,t represents the hydrogen input into the hydrogen storage tank during period t, Q f,t represents the amount of hydrogen sold during period t; The income R1 obtained from the energy market and the hydrogen market is expressed as: In the formula, represents the electricity price injected into the grid during period t, They represent the predicted power generation values ​​of the new energy units and thermal power units in period t respectively; represents the price of hydrogen sold during period t, Q f,t represents the amount of hydrogen sold in period t; Δt represents the time scale; The revenue R2 from the capacity market is expressed as: In the formula, They represent the price per unit capacity increase of fuel cells and electrolyzers in period t respectively; They represent the rising capacity of the fuel cell and electrolyzer in period t respectively; They represent the price reduction per unit capacity of the fuel cell and electrolyzer in period t respectively; They represent the decreased capacity of the fuel cell and electrolyzer in period t respectively; The revenue R3 obtained by deploying AGC to regulate the frequency of the generator set is expressed as: Where N s represents the total number of scenes that occur during the frequency adjustment process, θ s Indicates the probability of scene s appearing; FR + (t,s) Indicates the frequency-up regulation signal of scene s in period t, FR - (t,s) Indicates the frequency reduction signal of scene s occurring in period t; Peak load income R4 is expressed as: p t =λ t ·p (14) In the formula, p is the basic electricity price, p t is the electricity price in period t, λ t is the peak-valley coefficient of period t, and U3(t) represents the amount of electricity used for peak regulation in the electricity required to be regulated in period t; The operating cost C1 of the energy unit is expressed as: C1=C 11 +C 12 +C 13 (15) In the formula, C 11 represents the initial investment cost, C 12 represents the power generation cost of the new energy unit, C 13 represents the power generation cost of thermal power units; In the formula, c1 is the operating cost per unit of electricity generated by the new energy unit, and c2 is the operating cost per unit of electricity generated by the thermal power unit; The operating cost C2 of the electric-hydrogen conversion device is calculated as follows: Where c3 is the operating cost of the electrolyzer per unit hydrogen production, is the input power of the electrolyzer in period t; c4 is the operating cost of the fuel cell per unit power generation, is the electric power output of the fuel cell in period t; c5 is the unit charging and discharging cost of the hydrogen storage tank, Q b,t represents the hydrogen output of the hydrogen storage tank in period t, Q d,t represents the hydrogen input into the hydrogen storage tank during period t; The opportunity cost C3 is expressed as: In the formula, The unit income obtained by adjusting the electric energy for hydrogen storage is U1(t) which represents the output of the new energy unit in period t, and U2(t) which represents the load in period t; The reducible load compensation cost C4 is expressed as: In the formula, c e is the compensation price per unit power load transfer, is the load power reduced during period t; The comprehensive energy system optimization dispatch model is solved in the following way: Initialize the parameters of the bat population; set the bat position matrix; Randomly generate the initial bat position matrix of each bat population as the first generation bat position matrix; In the first generation, the objective function corresponding to the corresponding bat is calculated according to the bat position matrix of each bat in the first generation bat population, and the result of taking the negation of the objective function corresponding to the corresponding bat is used as the fitness of the corresponding bat in the first generation bat population; and the bat with the smallest fitness is selected as the optimal bat of the first generation; In the nth generation, n≥1, update the pulse frequency of the n+1th generation, as well as the flight speed and bat position matrix of each bat in the n+1th generation bat population; and calculate the fitness of each bat in the n+1th generation bat population; Determine whether each bat of the n+1th generation performs local search; if so, update the bat position matrix and fitness of the corresponding bat in the n+1th generation bat population; otherwise, do not perform local search; then, obtain the optimal bat of the n+1th generation; Determine whether the termination condition is met. If the maximum number of iterations is reached, stop the iteration and output the bat position matrix corresponding to the bat with the smallest fitness as the optimization scheduling result of the integrated energy system optimization scheduling model; otherwise, take n=n+1, and then jump to the step of updating the pulse frequency of the n+1th generation.

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