Low-carbon economic dispatching method and device for mixed hydrogen-electricity combined system

Abstract

The invention discloses a low-carbon economic dispatching method and device for a mixed hydrogen-electricity combined system, and belongs to the technical field of electrical control engineering.According to the method, the lowest system operation cost under the condition of considering carbon tax is taken as an optimization target, a discretization linear dynamic energy flow equation is taken as a mixed hydrogen net operation constraint, and a low-carbon economic dispatching result is obtained; a mixed hydrogen-electricity combined system low-carbon economic dispatching model is constructed, the dynamic characteristics in the mixed hydrogen natural gas transportation process can be described, the optimization problem solving requirement is met, and the change of gas components in the transportation process can be tracked to accurately reflect the running state of the mixed hydrogen natural gas system; the calculation cost for solving the nonlinear dynamic energy flow model in real time is reduced while the model characteristics are reserved; the low-carbon economic dispatching model of the mixed hydrogen-electricity combined system is solved for dispatching, it is guaranteed that the combined system always runs within the safety boundary, the renewable energy consumption capacity of the system is improved, and the running cost and carbon emission of the system are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical control engineering, and more specifically, relates to a low-carbon economic dispatching method and device for a hybrid hydrogen-electricity system. Background Art

[0002] With the maturity of electric hydrogen production technology and the development of comprehensive utilization of hydrogen energy, hydrogen energy, as a clean energy, plays an increasingly important role in new power systems. Through energy conversion hubs such as electric hydrogen production and hydrogen-blended gas turbines, heterogeneous energy sources such as electricity, hydrogen, and gas are integrated to build a hybrid hydrogen-electricity system. This can utilize the differences in the characteristics of different types of energy, improve the utilization rate of energy infrastructure, enhance the overall self-healing ability of the energy supply system, and ensure the reliability of energy supply. At the same time, it provides a valuable opportunity for the traditional oil and gas industry to participate in the integrated energy industry, obtain efficiency growth, and effectively promote the low-carbon transformation of the energy system. Studies have shown that due to the differences in the physical and chemical properties of hydrogen and natural gas, when natural gas and hydrogen are mixed using existing infrastructure, the operating conditions such as pressure and flow rate change significantly, which has a significant impact on the gas quality and transmission safety, and also introduces difficulties for the operation of the combined system. Therefore, a low-carbon economic dispatch method for a hybrid hydrogen-electricity system that considers gas composition tracking is urgently needed to provide a theoretical basis for guiding the optimal operation, planning and design, and market mechanism of the combined system.

[0003] At present, steady-state energy flow models are mostly used in research related to the optimization and scheduling of hybrid hydrogen-electricity systems. Such models ignore the transition time of the hydrogen-blended gas network, which is as long as tens of hours, and lack quantitative characterization of the differences in gas components at different times and spaces, resulting in a significant difference between the operating state obtained by solving and the actual state. The optimized scheduling scheme is not the actual optimal solution, or even has no feasible solution. In addition, the steady-state model cannot take into account the pipeline storage capacity, and cannot fully tap the flexibility of pipeline storage and use it to improve the reliability of system operation. The dedicated gas pipeline network simulation software uses numerical simulation, and the model contains a large number of highly nonlinear partial differential equations, which are difficult to coordinate with other forms of energy flow in the integrated energy system. Due to the above reasons, the scheduling method of the hybrid hydrogen-electricity system often has low scheduling efficiency. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a low-carbon economic scheduling method and device for a hybrid hydrogen-electricity system, which aims to solve the technical problem that the scheduling model of the existing hybrid hydrogen-electricity system scheduling method cannot characterize the dynamic characteristics of the hybrid hydrogen natural gas transportation process, resulting in reduced scheduling scheme efficiency.

[0005] To achieve the above object, according to one aspect of the present invention, a low-carbon economic dispatching method for a hybrid hydrogen-electricity system is provided, comprising:

[0006] S1: Considering the dynamic transmission process of mixed gas and the effect of gas parameter distribution, a group of partial differential equations corresponding to the dynamic energy flow of the pipeline network is constructed;

[0007] S2: replacing the corresponding nonlinear terms in the partial differential equation group corresponding to the pipeline network dynamic energy flow with the speed reference value, the flow reference value and the hydrogen mass flow rate, so as to reconstruct the partial differential equation group into a discretized linear dynamic energy flow equation suitable for solving the optimization scheduling problem;

[0008] S3: Taking the lowest system operating cost under carbon tax as the optimization goal and the discretized linear dynamic energy flow equation as the operation constraint of the hybrid hydrogen network, a low-carbon economic dispatch model of the hybrid hydrogen-electricity system is constructed. Y is the total operating cost, including the operating cost of coal-fired units C CFU , renewable energy reduction costs C RCUR , power load reduction cost C LCUR , gas source purchase cost Carbon Emissions Fees

[0009] S4: Solve the low-carbon economic dispatch model of the hybrid hydrogen-electricity system and obtain an executable joint system dispatch plan;

[0010] S5: Execute the combined system dispatching plan to dispatch the hybrid hydrogen-electricity combined system.

[0011] In one embodiment, in S1: the partial differential equations corresponding to the constructed single-pipeline dynamic energy flow include: momentum conservation of the mixed gas, continuity equation and hydrogen component transmission equation, which are as follows:

[0012]

[0013] Where t and x represent the axial distance and time along the pipeline respectively; ρ is the density of the mixed gas in kg / m 3 ; u is the mixed gas flow rate, unit is m / s; p is the pressure, unit is Pa; λ is the hydraulic friction coefficient, dimensionless; d is the pipe diameter, unit is m; τ k is the mass fraction of the kth component in the mixed gas, that is, the mass fraction of hydrogen.

[0014] In one embodiment, the constraints corresponding to the gas density, pressure, and composition of the source node of the pipeline network are:

[0015]

[0016] Among them, ρ i,t represents the gas density at node i at time t, ρ src,0 Indicates the gas density of the gas source, p i,trepresents the air pressure at node i at time t, p src,0 Indicates the air pressure of the air source, τ k i,t represents the hydrogen mass fraction at node i at time t, τ k src,0 Indicates the hydrogen mass fraction of the gas source, T N Indicates the total number of scheduling time periods;

[0017] Load node flow constraints at the end of the pipe network:

[0018]

[0019] Among them, M i,t is the mass flow rate at node i at time t, is the mass flow demand of mixed hydrogen natural gas at load node i at time t, is the natural gas mass flow demand at node i at time t without considering hydrogen blending, τ k i,t is the mass fraction of hydrogen in the hydrogen-mixed natural gas at node i at time t, q g ,q h represent the calorific values ​​of natural gas and hydrogen respectively;

[0020] Gas flow balance constraints:

[0021]

[0022] Uniform mixing constraints: Among them, N pipe represents the number of pipes connected to node i; is the mass flow rate flowing into / out of node i from the jth pipe connected to node i at time t; is the mass flow rate of hydrogen-mixed natural gas injected at node i at time t; is the mass flow rate of hydrogen in the hydrogen-mixed natural gas injected at node i; is the hydrogen mass fraction of the gas flowing into / out of node i through the j-th pipe connected to node i at time t; is the mass fraction of hydrogen at the load node at node i; is the mass flow rate of hydrogen-mixed natural gas required by the load at node i; is the mass flow rate of hydrogen injected into the hydrogen-mixed natural gas at node i.

[0023] In one embodiment, the linearized and reconstructed partial differential equation of the single-pipe flow is as follows:

[0024]

[0025] Where M is the mass flow rate of the mixed gas, in kg / s; A is the cross-sectional area of ​​the pipe, in m 2 ; Z is the compressibility factor; R is the gas constant of the mixed gas, in J / (kg·K); T is the thermodynamic temperature, in K; u b is the gas velocity reference value, in m / s; m k is the mass flow rate of hydrogen, in kg / s; a is the coefficient related to the mass flow rate of the mixed gas;

[0026] The reconstructed gas flow balance constraint and uniform mixing constraint are:

[0027]

[0028] in, is the hydrogen mass flow rate of the gas flowing into / out of node i through the j-th pipeline connected to node i at time t; is the hydrogen mass flow rate at the load node at node i; It is the reference value of the gas flow rate out of node i from the j-th pipe connected to node i at time t.

[0029] In one embodiment, before S2, it also includes: based on the stability condition in the numerical analysis, reasonably dividing the numerical calculation difference grid, and then pre-solving the nonlinear pipeline network dynamic energy flow through the sequential algorithm to obtain the velocity reference value and the flow reference value; the step size of the difference grid of the energy flow equation meets the following conditions:

[0030]

[0031] Among them, u i,t is the gas flow rate at node i at time t; u i-1,t is the gas flow rate at node i-1 at time t, Δt is the time step, and Δx is the space step.

[0032] In one embodiment, pre-solving the nonlinear pipeline network dynamic energy flow by a sequential algorithm to obtain a velocity reference value and a flow reference value includes:

[0033] 1) Solve the steady-state energy flow equation by Newton iteration method as the initial condition for dynamic energy flow calculation;

[0034] 2) Solve the pressure-flow equation in the nonlinear pipeline network dynamic energy flow through Newton iteration method to obtain the pressure p t and mass flow rate M t Then, the flow rate, gas parameters and load flow requirements are solved.

[0035] 3) Use the flow velocity u obtained in step 2) tSolve the distribution of mixed gas components at time t k,t ;

[0036] 4) Use the state variable p at time t t 、M t Update the gas parameters and go to step 3) to perform energy flow calculation at time t+1, and finally obtain the speed reference value and flow reference value in the linearization process.

[0037] In one embodiment, S4 includes:

[0038] S41: record the number of iterations r=1;

[0039] S42: Relax the gas grid operation constraints and solve the optimal energy flow of the combined system to obtain the output of hydrogen production and hydrogen-blended gas turbines

[0040] S43: Converted into node hydrogen injection amount Gas demand at load nodes As the boundary condition for the numerical simulation of the hydrogen-mixed natural gas network;

[0041] S44: Obtaining the benchmark flow rate u through numerical simulation of the hydrogen-mixed natural gas network b,r , parameter a, gas parameter Z Rλ, used to construct the linear dynamic energy flow model of the mixed hydrogen natural gas network;

[0042] S45: Call the solver to solve the low-carbon economic dispatch problem of the hybrid hydrogen-electricity system, and convert the required electricity-to-hydrogen and hydrogen-blended gas turbine output into Convert to Boundary Conditions The numerical simulation of the hydrogen-mixed natural gas network was carried out again to solve the flow velocity distribution u r ;

[0043] S46: Using flow velocity distribution u r Calculate the error between the actual flow rate and the reference flow rate ε=||u r -u b,r ||;

[0044] S47: Determine whether the convergence condition ε≤ε0 is satisfied: if satisfied, the calculation is terminated; if not satisfied, the boundary conditions are updated r=r+1, return to S43 to enter the next round of iteration.

[0045] According to another aspect of the present invention, a low-carbon economic dispatching method for a hybrid hydrogen-electricity system is provided, comprising:

[0046] The first building block is used to take into account the dynamic transmission process of the mixed gas and the gas parameter distribution effect, and to construct a group of partial differential equations corresponding to the dynamic energy flow of the pipeline network;

[0047] A reconstruction module, used to replace the corresponding nonlinear terms in the partial differential equation group corresponding to the dynamic energy flow of the pipeline network with the speed reference value, the flow reference value and the hydrogen mass flow rate, so as to reconstruct the partial differential equation group into a discretized linear dynamic energy flow equation suitable for solving the optimization scheduling problem;

[0048] The second building block is used to build a low-carbon economic dispatch model for the hybrid hydrogen-electricity system with the lowest system operating cost under the consideration of carbon tax as the optimization goal and the discretized linear dynamic energy flow equation as the operating constraint of the hybrid hydrogen network. Y is the total operating cost, including the operating cost of coal-fired units C CFU , renewable energy reduction costs C RCUR , power load reduction cost C LCUR , gas source purchase cost Carbon Emissions Fees

[0049] The solution module is used to solve the low-carbon economic dispatch model of the hybrid hydrogen-electricity system and obtain an executable joint system dispatch plan;

[0050] The scheduling module is used to execute the joint system scheduling plan to schedule the hybrid hydrogen-electricity system.

[0051] According to another aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0052] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0053] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0054] (1) The present invention provides a low-carbon economic dispatching method for a hybrid hydrogen-electricity system, which takes the lowest system operating cost under the condition of carbon tax as the optimization goal, takes the discretized linear dynamic energy flow equation as the hybrid hydrogen network operation constraint, and constructs a low-carbon economic dispatching model for the hybrid hydrogen-electricity system. The method can not only characterize the dynamic characteristics of the hybrid hydrogen natural gas transportation process, but also meet the needs of solving optimization problems, and can track the changes in gas composition during the transportation process, and more accurately reflect the operating status of the hybrid hydrogen natural gas system; while retaining the model characteristics to the maximum extent, it reduces the computational cost of solving the nonlinear dynamic energy flow model in real time, and ensures the efficiency and economy of the combined system dispatch; solves the low-carbon economic dispatching model of the hybrid hydrogen-electricity system for dispatching, and ensures that the combined system always operates within the safety boundary, improves the renewable energy absorption capacity of the system, and reduces the operating cost and carbon emissions of the system.

[0055] (2) This scheme introduces the hydrogen component transmission equation in the single pipeline dynamic energy flow for gas composition tracking, which is conducive to reflecting the dynamic spatiotemporal distribution characteristics of heterogeneous gas components and further constructing the operation constraints of the mixed hydrogen network.

[0056] (3) This scheme takes into account the gas quality inhomogeneity and calorific value changes caused by hydrogen mixing, and designs uniform mixing constraints, gas component conservation constraints and load node boundary conditions that consider calorific value changes. The advantage is that it can reflect the changes in the operating state of the gas network caused by the heterogeneous gas mixed with hydrogen.

[0057] (4) This scheme introduces a reference flow rate and a hydrogen mass flow rate to process the bilinear terms in the constraints and reconstruct the linearized dynamic energy flow of the pipeline network, which significantly improves the mathematical properties of the pipeline network energy flow model and makes the model suitable for large-scale optimization problems.

[0058] (5) This scheme provides the stability conditions that the time-space difference grid step size division should meet. The partial differential equation is discretized based on this condition, which can avoid numerical oscillations in the gas grid simulation in the scheduling model, or even the situation where there is no feasible solution.

[0059] (6) The sequential algorithm in this scheme solves the pressure-flow and mixed gas component distribution parts of the unlinearized pipeline network energy flow equation in sequence, thereby obtaining velocity and flow reference values ​​for linearization. The advantage is that the accuracy of the gas network linear approximation is improved through iterative solution.

[0060] (7) The scheduling solution algorithm proposed in this scheme improves the solution accuracy of the mixed hydrogen network through iteration, and also makes the joint scheduling optimization result close to the actual optimal value. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flow chart of a low-carbon economic dispatching method for a hybrid hydrogen-electricity system provided in Example 1 of the present invention;

[0062] Figure 2 The process of the sequential algorithm for solving the linearization reference value provided in Example 1 of the present invention;

[0063] Figure 3 The iterative process for solving the optimization scheduling model provided in Example 1 of the present invention;

[0064] Figure 4 This is a schematic diagram of a non-branched gas network of a single electric hydrogen production device in Example 1 of the present invention;

[0065] Figure 5 A schematic diagram of a hybrid hydrogen-electricity system consisting of a 6-node power system and a 12-node natural gas system in Example 1 of the present invention;

[0066] Figure 6 and Figure 7 They are respectively the calculation results of the hydrogen mass fraction and the source node mass flow rate of the branchless hydrogen-mixed natural gas network in Example 1 of the present invention using the steady-state energy flow and the proposed linear dynamic energy flow model;

[0067] Figure 8 A schematic structural diagram of a low-carbon economic dispatching device suitable for a hybrid hydrogen-electricity system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0069] Example 1

[0070] Figure 1 A flowchart of a low-carbon economic dispatch method for a hybrid hydrogen-electricity system provided in this embodiment; see Figure 1 , a low-carbon economic dispatching method for a hybrid hydrogen-electricity system in this embodiment is described in detail, and the method includes the following steps.

[0071] S1, obtain the conventional technical parameters of the natural gas pipeline network, including pipeline network topology, operating pressure level, temperature, pipeline inner diameter, absolute roughness of pipeline inner wall, gas well pressure and gas load data group; obtain power system parameters, including power grid topology, unit parameters, renewable energy forecast output data group and power load data group, and obtain gas-electricity coupling link parameters, including electric hydrogen production device parameters and hydrogen-blended gas turbine parameters. Considering the dynamic transmission process of mixed gas and the distribution effect of gas parameters, establish the partial differential equation of mixed hydrogen natural gas pipeline flow that can reflect the gas transmission characteristics in a single pipeline, and establish the pipeline network dynamic energy flow equation according to the pipeline network topology and node type.

[0072] S2, reconstruct the nonlinear partial differential dynamic energy flow equation into a discretized linear dynamic energy flow equation suitable for solving the optimization scheduling problem; specifically, the speed reference value and flow reference value can be determined first, and then the speed reference value, flow reference value and hydrogen mass flow rate can be used to replace the corresponding nonlinear terms in the partial differential equation group corresponding to the dynamic energy flow of the pipeline network. The process of determining the speed reference value and flow reference value is as follows: based on the stability conditions in the numerical analysis, the numerical calculation grid is reasonably divided to save computing resources, improve computing efficiency and suppress numerical oscillations; the speed reference value and flow reference value in the linearization processing are obtained by pre-solving through the sequential algorithm.

[0073] S3, establish a low-carbon economic dispatch model for the hybrid hydrogen-electricity system. Its optimization goal is to minimize the system operating cost under the condition of carbon tax. The constraints include the operation constraints of equipment within the system, the operation constraints of the power grid, the operation constraints of the hybrid hydrogen network, the electricity-gas energy conversion constraints, and the carbon quota constraints.

[0074] S4, in the scenario of renewable energy output fluctuations and changes in hydrogen doping ratio at the hydrogen injection point, solve the optimization scheduling model through online pre-decision and obtain an executable joint system scheduling plan.

[0075] S5: Execute the joint system dispatching plan to dispatch the hybrid hydrogen-electricity system and make full use of the flexibility resources within the system.

[0076] Furthermore, the pipe flow equation reflecting the transmission characteristics of mixed hydrogen and natural gas in a single pipeline and the pipeline network dynamic energy flow equation considering gas composition tracking are constructed as follows:

[0077] The pipe flow equations reflecting the transmission characteristics of hydrogen-mixed natural gas in a single pipeline include the momentum conservation and continuity equations of the mixed gas, as well as the hydrogen component transmission equations reflecting the hydrogen diffusion process:

[0078]

[0079] Where t and x represent the axial distance and time along the pipeline respectively; ρ is the density of the mixed gas in kg / m 3 ; u is the mixed gas flow rate, unit is m / s; p is the pressure, unit is Pa; λ is the hydraulic friction coefficient, dimensionless; d is the pipe diameter, unit is m; τ k is the mass fraction of hydrogen in the mixed gas; m k is the mass flow rate of hydrogen in the mixed gas, in kg / s; M is the mass flow rate of the mixed gas, in kg / s.

[0080] The dynamic energy flow equation of the pipeline network considering gas composition tracking, in addition to the single-pipe flow equation, also includes the given operating conditions, pipeline topology and component parameters:

[0081] The starting station is responsible for receiving purified natural gas from the mine purification plant or other gas sources, and serves as the source node of the pipeline network. The gas pressure, density and composition characteristics of this link remain basically constant:

[0082]

[0083] Among them, ρ i,t represents the gas density at node i at time t, ρ src,0 Indicates the gas density of the gas source, p i,t represents the air pressure at node i at time t, p src,0 Indicates the air pressure of the air source, τ ki,t represents the hydrogen mass fraction at node i at time t, τ k src,0 Indicates the hydrogen mass fraction of the gas source, T N Indicates the total number of scheduling time periods.

[0084] The last section of the pipeline network is regarded as a load node, and the pressure is mainly affected by the flow required by the gas distribution station:

[0085]

[0086] Among them, M i,t is the mass flow rate at node i at time t; is the mass flow demand of mixed hydrogen natural gas at load node i at time t; is the natural gas mass flow demand at node i at time t without considering hydrogen blending; τ k i,t is the mass fraction of hydrogen in the hydrogen-mixed natural gas at node i at time t; q g ,q h Represent the calorific value of natural gas and hydrogen respectively.

[0087] The mass of each gas component at the intersection of the pipeline is conserved, and the gas flow balance constraint is constructed as shown in equations (7)-(8). At the same time, the gases with different hydrogen contents are fully mixed at the intersection, and the uniform mixing constraint (9) is constructed:

[0088]

[0089]

[0090] Among them, N pipe represents the number of pipes connected to node i; is the mass flow rate flowing into / out of node i from the jth pipe connected to node i at time t; is the mass flow rate of hydrogen-mixed natural gas injected at node i at time t; is the mass flow rate of hydrogen in the hydrogen-mixed natural gas injected at node i; is the hydrogen mass fraction of the gas flowing into / out of node i through the j-th pipe connected to node i at time t; is the mass fraction of hydrogen at the load node at node i; is the mass flow rate of hydrogen-mixed natural gas required by the load at node i; is the mass flow rate of hydrogen injected into the hydrogen-mixed natural gas at node i.

[0091] In the dynamic energy flow equation of the mixed hydrogen natural gas network, the parameter distribution effect caused by different gas compositions is as follows:

[0092] The hydrogen blending ratio affects the equation of state, gas constant, compressibility factor and specific heat at constant pressure:

[0093] R = τ k R h +(1-τ k )R g (10)

[0094]

[0095] T R,subs =T subs / T subs,cr subs=NG,H2 (15)

[0096]

[0097] k subs =0.480+1.574ω subs -0.176ω subs 2 (17)

[0098] Among them, R, R g ,R h is the gas constant of mixed gas / natural gas / hydrogen, J / (kg·K); ρ NG is the density of natural gas / hydrogen; c v is the hydrogen gas integral, with the superscript NG y represents the yth component in natural gas; is the critical temperature and critical pressure of hydrogen, ω subs is the eccentricity factor, reflecting the molecular polarity; A Z ,B Z ,T R,subs ,k subs are dimensionless constants.

[0099] In the calculation of the energy flow of the mixed hydrogen natural gas network, the gas parameters need to be updated synchronously, that is, the parameter equation and the dynamic energy flow equation should be solved simultaneously. The relationship between the pressure and density of the mixed gas is expressed by the state equation (18):

[0100] p=ρZ(τ,p,T)R(τ)T (18)

[0101] The hydrogen mixing ratio affects the gas viscosity, which in turn affects the hydraulic friction coefficient in the pipe flow equation:

[0102]

[0103] Where, λ is the hydraulic friction coefficient; ε and D are the pipe roughness and diameter, respectively; Re is the Reynolds number; μ is the dynamic viscosity coefficient, Pa·s; M is the gas molar mass, g / mol.

[0104] The above-mentioned parameter equations take into account the dynamic energy flow equations of the pipeline network for gas composition tracking and together constitute a low-carbon economic dispatch model for the mixed hydrogen-electricity system, which can more accurately reflect the dynamic process of the system after hydrogen mixing, including changes in state quantities and gas parameters such as gas pressure, flow, and gas composition.

[0105] Furthermore, the low-carbon economic dispatch model of the hybrid hydrogen-electricity system applies the following linearization method:

[0106] At low hydrogen blending ratio, the hydraulic conditions of natural gas pipelines will not change significantly, and the reference flow rate is used to approximately replace the quadratic term:

[0107]

[0108] When using mass fraction to describe the variation of gas composition over time and space, the conservation equation (8) of hydrogen composition contains a large number of bilinear terms τ k M, it is difficult to solve this constraint directly in the optimization problem, so the hydrogen mass flow rate m is used k Equivalent replacement of τ k M, and replace the k Constraints. The partial differential equations for single-pipe flow include the pressure-flow equations (22)-(23) and the linearized reconstruction of the hydrogen component transport equation (24) as follows:

[0109]

[0110] Hydrogen mass fraction τ k The constraints (8)-(9) are reconstructed as follows:

[0111]

[0112] Where, the reference flow rate u b , gas parameter Z Rλ, coefficient a reference steady-state value, or use base value correction iteration method of dynamic energy flow calculation, the latter calculation accuracy is generally better than the former. In the present invention, the numerical simulation method is used for base value correction iteration, and a finite number of iterations are performed in the process of solving the optimization problem, and the obtained reference flow rate value can better meet the calculation accuracy requirements.

[0113] In the low-carbon economic dispatch model of the hybrid hydrogen-electricity system, the differential form is constructed as follows:

[0114]

[0115] In the formula, Δt and Δx are the time and space difference steps respectively. After the difference processing, in a mixed hydrogen natural gas network with M pipelines and N nodes (including p source nodes and q load nodes), T N There are (N+4M)×T in each time period N state variables, which include N×T N Node pressure, 2M×T N Mass flow rate, 2M×T N The control equations and boundary conditions are (N+4M)×T N , including M×T N continuity equation, M×T N momentum equation, (Npq)×T N The material conservation equation, M×T N component transport equation, (Npq)×T N The hydrogen component conservation equation is (M-N+q)×T N The uniform mixing equation at each intersection, p×T N Source node pressure condition, q×T N Load mass flow condition, p×T N The hydrogen doping ratio conditions of the source nodes are calculated, and the number of equations is equal to the number of state quantities, which can be solved.

[0116] Differential meshing needs to meet the stability conditions of numerical analysis. The CFL condition is used to determine whether the finite element calculation results converge. In a one-dimensional pipeline, the Courant number calculation method for the space-time grid is as follows:

[0117]

[0118] Among them, the Courant number γ cfl It indicates the number of grids that the fluid particles pass through in the time step. When the speed of time advancement is greater than the speed of physical disturbance propagation, it can ensure that all disturbances are captured. A large Courant number means a large time step, a small space step, fast convergence, low stability, and oscillations may occur in the steady-state segment; a small Courant number means a small time step, a large space step, slow convergence, high stability, and oscillations may occur in the unsteady-state segment. For the explicit upwind difference method of the one-dimensional linear convection equation, C is usually taken as 1.

[0119] In addition, the condition that no numerical oscillation occurs in the hydrogen component transport equation is constructed as follows:

[0120]

[0121] (K+1)τ k i,t +(1-K)τ k i-1,t -τ ki,t-1 -τ k i-1,t-1 =0 (30)

[0122] (K+1)(τ k i,t -τ k i,t-1 )+(1-K)(τ k i-1,t -τ k i-1,t-1 )+K(τ k i,t-1 -τ k i-1,t-1 )=0 (31)

[0123]

[0124] Among them, satisfying K-1>0 can avoid numerical oscillation. More specifically, when the hydrogen doping ratio is disturbed at time t, the system is in a steady state before the disturbance, τ k i-1,t-1 =τ k i,t-1 , that is, the gas composition at the beginning and end of the pipeline is uniform. When K>1, τ k i,t -τ k i,t-1 With τ k i-1,t -τ k i-1,t-1 With the same sign, the change trend of the gas composition at the end of the pipeline is consistent with that at the head end, which conforms to the laws of physics. Otherwise, numerical oscillation will occur.

[0125] According to the above criteria, the difference step size of the dynamic energy flow equation should meet the following conditions:

[0126]

[0127] Furthermore, Figure 2 The process of the sequential algorithm for solving the linearized reference value provided in Example 1 of the present invention; in the numerical simulation for solving the linearized reference value, the sequential algorithm is constructed as follows:

[0128] In the dynamic model of the pipeline network, the mass flow demand of the load node and hydrogen mass fraction τ k Related, reference flow rate u b Related to the state variables such as pressure p and mass flow rate M, gas parameters such as Z, R, λ and hydrogen mass fraction τ kThe linearized equation still contains the above parameters, which leads to the mutual coupling between the constraints and makes it difficult to solve the optimization problem. The present invention proposes a sequential algorithm to solve the original nonlinear energy flow equation in a certain order and update the parameters in the linear pipeline network dynamic model. More specifically:

[0129] 1) The steady-state energy flow equation is solved by Newton iteration method as the initial condition for dynamic energy flow calculation.

[0130] 2) Solve the pressure-flow equation in the nonlinear pipeline network dynamic energy flow through Newton iteration method; obtain the pressure p t and mass flow rate M t Then, we can solve the flow velocity u t ; Gas parameters such as Z, R, λ and load flow requirements The hydrogen composition distribution τ at the previous moment is used k,t-1 calculate.

[0131] 3) Use the flow velocity u obtained in step 2) t Solve the distribution of mixed gas components at time t k,t ;

[0132] 4) Use the state variable p at time t t 、M t Update Z, R, λ, and go to step 3) to calculate the energy flow at time t+1.

[0133] There is a certain error between the state quantities obtained by the sequential algorithm and the direct joint results, because the pressure and flow distribution at the current moment is calculated using the last moment τ. However, if the time step is not too large, the error is within an acceptable range.

[0134] Furthermore, the objective function and constraints of the joint system low-carbon economic dispatch model are constructed as follows:

[0135]

[0136] Where Y is the total operating cost, including the operating cost of the coal-fired unit C CFU , renewable energy reduction costs C RCUR , power load reduction cost C LCUR , gas source purchase cost Carbon Emissions Fees Among them, the low-carbon economic operation constraints of the hybrid hydrogen-electricity system include: dynamic energy flow constraints of the hybrid hydrogen natural gas network on the gas grid side, pressure flow constraints, and equipment hydrogen blending adaptability constraints; DC power flow constraints, power balance constraints, branch transmission capacity constraints, unit output upper and lower limit constraints, and unit climbing constraints on the power grid side; electricity-gas energy conversion constraints, including electricity-to-hydrogen production and hybrid hydrogen gas turbine operation constraints; and carbon quota constraints.

[0137] More specifically, the solution process of the low-carbon economic dispatch problem of the joint system is as follows:

[0138] 1) Record the number of iterations r = 1;

[0139] 2) Relax the gas grid operation constraints and solve the optimal energy flow of the joint system to obtain the output of hydrogen production and hydrogen-blended gas turbines

[0140] 3) Converted into node hydrogen injection amount Gas demand at load nodes As the boundary condition for the numerical simulation of the hydrogen-mixed natural gas network;

[0141] 4) Obtain the benchmark flow rate u through numerical simulation of the hydrogen-mixed natural gas network b,r , parameter a, gas parameter ZRλ, used to construct the linear dynamic energy flow model of the mixed hydrogen natural gas network;

[0142] 5) Call the solver to solve the low-carbon economic dispatch problem of the hybrid hydrogen-electricity system, and calculate the output of the required electricity-to-hydrogen and hydrogen-blended gas turbines. Convert to Boundary Conditions The numerical simulation of the hydrogen-mixed natural gas network was performed again to solve the flow rate u r distributed;

[0143] 6) Calculate the error between the actual flow rate and the reference flow rate, ε=||u r -u b,r ||;

[0144] 7) Determine whether the convergence condition ε≤ε0 is met: if it is met, end the calculation; if not, update the boundary conditions r=r+1, return to step 3) to enter the next round of iteration.

[0145] For example, the low-carbon economic dispatch model of the hybrid hydrogen-electricity system is constructed as follows:

[0146] The carbon quota is allocated using the benchmark method. The carbon emission quotas for conventional coal-fired units and gas-fired units can be expressed as follows: In the formula, i is the unit number; is the carbon emission quota of unit i during period t; is the carbon quota allocation benchmark value of unit i, in tCO2 / MWh; P i,t Represents the active power of thermal power unit i during period t.

[0147] The carbon emissions of conventional coal-fired units and gas-fired units are related to their output and can be expressed as follows: In the formula, is the carbon emission of thermal power unit i in period t; is the carbon emission intensity of thermal power unit i, in tCO2 / MWh.

[0148] The carbon emission cost can be expressed as: In the formula, Emi a is the allowable value of carbon quota trading volume; τ C is the carbon emission cost price coefficient.

[0149] According to the above example data, the objective function of the low-carbon economic dispatch model of the hybrid hydrogen-electricity system is:

[0150]

[0151] Where, T N is the total number of optimized time periods; Δt is the scheduling time interval; N g ,N w ,N load ,N src They are the number of coal-fired units, wind farms, power loads, and natural gas sources; The fuel cost corresponding to the unit power generation of the i-th coal-fired unit at time t; is the penalty fee for wind curtailment of the i-th wind farm at time t; is the load shedding cost of the ith power load at time t; is the gas purchase price at the i-th gas source node at time t.

[0152] The low-carbon economic dispatch model of the hybrid hydrogen-electricity system needs to meet the equipment operation constraints, power grid operation constraints, hybrid hydrogen network operation constraints, and carbon emission quota constraints within the system.

[0153] Specifically, the operating constraints of the equipment in the system include:

[0154] Conventional coal-fired unit operation constraints, including output upper and lower limit constraints, up / down ramp constraints: Operation constraints of electric hydrogen production equipment, including energy conversion constraints and output upper and lower limit constraints: Gas unit operation constraints, including energy conversion constraints, output upper and lower limit constraints: Grid operation constraints, including power balance constraints, spinning reserve constraints, and branch transmission capacity constraints:

[0155]

[0156] The operation constraints of the mixed hydrogen network include pressure and flow constraints and equipment hydrogen blending adaptability constraints. Among them, the mass flow rate of the source node gas flow should be greater than 0, the pressure and flow should be within the safe operating range, and the equipment and hydrogen mass fraction in the gas network should meet the upper and lower limit constraints:

[0157]

[0158] in, p i,t and They are the minimum and maximum operating pressures allowed at node i at time t after hydrogen addition; the upper and lower limits of hydrogen mass fraction are selected mainly considering the safety constraints of pipeline equipment operation. For pipelines, the possibility of hydrogen embrittlement, leakage, and seepage should be considered. For compressors, the aerodynamic performance and surge margin should be normal. For end users, the gas interchangeability constraint should be met, that is, the calorific value and Wobbe index are within the safe range. Considering the carbon trading policy, the joint system should follow the carbon emission quota constraint:

[0159]

[0160] Operation S4, in the scenario of renewable energy output fluctuations and hydrogen injection point hydrogen mixing ratio changes, through online pre-decision, according to Figure 3 The iterative process shown solves the optimal scheduling model and obtains an executable joint system scheduling plan, which fully utilizes the flexibility resources in the system.

[0161] S5: Execute the joint system dispatching plan to dispatch the hybrid hydrogen-electricity system and make full use of the flexibility resources within the system.

[0162] In order to further illustrate the low-carbon economic dispatch method of the hybrid hydrogen-electricity system provided in this embodiment, Figure 4 and Figure 5 The system verification method shown is effective, and the main steps are as follows:

[0163] 1) Using a single electric hydrogen production device without a branched gas network to verify the effectiveness of the dynamic energy flow model of the present invention;

[0164] 2) A hybrid hydrogen-electricity system consisting of a Garver 6-node power system and a 12-node natural gas system is used as a simulation analysis example to verify the effectiveness of the low-carbon economic dispatch method of the combined system of the present invention.

[0165] The calculation results of hydrogen mass fraction and source node mass flow rate of the branchless hydrogen-mixed natural gas network using steady-state energy flow and linear dynamic energy flow models are as follows: Figure 6 and Figure 7 As shown in the figure, the steady-state values ​​calculated by the two types of models are the same, but the method can better reflect the transition process caused by the fluctuation of hydrogen injection flow rate and more accurately reflect the dynamic response process of the gas network after the disturbance occurs.

[0166] Table 1 Scheduling results of the scheduling model considering the dynamic process of mixed transportation

[0167]

[0168] Combined with the simulation results shown in Table 1, it can be seen that the low-carbon economic dispatch method of the hybrid hydrogen-electricity system can be used for the optimized operation of the hybrid hydrogen-electricity system under multiple gas source access scenarios with different components. On the premise of ensuring that the gas quality meets the specifications and the hydrogen concentration at each location is within a safe range, the output of each link is optimized, and the use of water electrolysis to produce hydrogen and natural gas pipeline hydrogen storage to assist the power system in absorbing renewable energy is maximized, so as to achieve overall safe, economical and low-carbon operation of the system. It has broad application prospects in the integrated energy system containing electricity, gas and hydrogen.

[0169] This embodiment also provides an electronic device suitable for low-carbon economic dispatch of a hybrid hydrogen-electricity system, such as Figure 8 As shown, it includes parameter acquisition and model building module, model solving and execution module. During the scheduling process, the system parameters collected in real time are input, and the above two modules are called to implement the following Figure 1 The scheduling function shown in the figure solves the optimal scheduling solution and issues corresponding scheduling instructions.

[0170] Example 2

[0171] This embodiment provides a low-carbon economic dispatch method for a hybrid hydrogen-electricity system, including the following modules.

[0172] The first building block is used to take into account the dynamic transmission process of the mixed gas and the gas parameter distribution effect, and to construct a group of partial differential equations corresponding to the dynamic energy flow of the pipeline network;

[0173] A reconstruction module, used to replace the corresponding nonlinear terms in the partial differential equation group corresponding to the dynamic energy flow of the pipeline network with the speed reference value, the flow reference value and the hydrogen mass flow rate, so as to reconstruct the partial differential equation group into a discretized linear dynamic energy flow equation suitable for solving the optimization scheduling problem;

[0174] The second building block is used to build a low-carbon economic dispatch model for the hybrid hydrogen-electricity system with the lowest system operating cost under the consideration of carbon tax as the optimization goal and the discretized linear dynamic energy flow equation as the operating constraint of the hybrid hydrogen network. Y is the total operating cost, including the operating cost of coal-fired units C CFU , renewable energy reduction costs C RCUR , power load reduction cost C LCUR , gas source purchase cost Carbon Emissions Fees

[0175] The solution module is used to solve the low-carbon economic dispatch model of the hybrid hydrogen-electricity system and obtain an executable joint system dispatch plan;

[0176] The scheduling module is used to execute the joint system scheduling plan to schedule the hybrid hydrogen-electricity system.

[0177] Example 3

[0178] This embodiment provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0179] Example 4

[0180] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0181] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A low-carbon economic dispatching method for a hybrid hydrogen-electricity system, characterized in that: include: S1: Considering the dynamic transmission process of mixed gas and the effect of gas parameter distribution, a group of partial differential equations corresponding to the dynamic energy flow of the pipeline network is constructed; S2: replacing the corresponding nonlinear terms in the partial differential equation group corresponding to the pipeline network dynamic energy flow with the speed reference value, the flow reference value and the hydrogen mass flow rate, so as to reconstruct the partial differential equation group into a discretized linear dynamic energy flow equation suitable for solving the optimization scheduling problem; S3: Taking the lowest system operating cost under carbon tax as the optimization goal and the discretized linear dynamic energy flow equation as the operation constraint of the hybrid hydrogen network, a low-carbon economic dispatch model of the hybrid hydrogen-electricity system is constructed. Y is the total operating cost, including the operating cost of coal-fired units C CFU , renewable energy reduction costs C RCUR , power load reduction cost C LCUR , gas source purchase cost Carbon Emissions Fees S4: solving the low-carbon economic dispatch model of the hybrid hydrogen-electricity combined system to obtain an executable combined system dispatch plan; S5: Execute the combined system scheduling plan to schedule the hybrid hydrogen-electricity combined system.

2. The low-carbon economic dispatching method of the hybrid hydrogen-electricity system according to claim 1, characterized in that: In S1: The partial differential equations corresponding to the constructed single-pipeline dynamic energy flow include: momentum conservation of the mixed gas, continuity equation and hydrogen component transmission equation, which are as follows: Where t and x represent the axial distance and time along the pipeline respectively; ρ is the density of the mixed gas in kg / m 3 ; u is the mixed gas flow rate, in m / s; p is the pressure, in Pa; λ is the hydraulic friction coefficient, dimensionless; d is the pipe diameter; τ k is the mass fraction of the kth component in the mixed gas.

3. The low-carbon economic dispatching method of the hybrid hydrogen-electricity system according to claim 2, characterized in that: Constraints corresponding to gas density, pressure, and composition at source nodes of the network: Among them, ρ i,t represents the gas density at node i at time t, ρ src,0 Indicates the gas density of the gas source, p i,t represents the air pressure at node i at time t, p src,0 Indicates the air pressure of the air source, τ ki,t represents the hydrogen mass fraction at node i at time t, τ ksrc,0 Indicates the hydrogen mass fraction of the gas source, T N Indicates the total number of scheduling time periods; Load node flow constraints at the end of the pipe network: Among them, M i,t is the mass flow rate at node i at time t, is the mass flow demand of hydrogen-mixed natural gas at load node i at time t, is the natural gas mass flow demand at node i at time t without considering hydrogen blending, τ ki,t is the mass fraction of hydrogen in the hydrogen-mixed natural gas at node i at time t, q g ,q h represent the calorific values ​​of natural gas and hydrogen respectively; Gas flow balance constraints: and Uniform mixing constraints: Among them, N pipe represents the number of pipes connected to node i; is the mass flow rate flowing into / out of node i from the jth pipe connected to node i at time t; is the mass flow rate of hydrogen-mixed natural gas injected at node i at time t; is the mass flow rate of hydrogen in the hydrogen-mixed natural gas injected at node i; is the hydrogen mass fraction of the gas flowing into / out of node i through the j-th pipe connected to node i at time t; is the mass fraction of hydrogen at the load node at node i; is the mass flow rate of hydrogen-mixed natural gas required by the load at node i; is the mass flow rate of hydrogen injected into the hydrogen-mixed natural gas at node i.

4. The low-carbon economic dispatching method of the hybrid hydrogen-electricity system according to claim 3, characterized in that: The linearized and reconstructed partial differential equation of single-pipe flow is as follows: Where M is the mass flow rate of the mixed gas; A is the cross-sectional area of ​​the pipe; Z is the compressibility factor; R is the gas constant of the mixed gas, in J / (kg·K); T is the thermodynamic temperature, in K; u b is the reference value of gas flow rate; m k is the mass flow rate of hydrogen; a is the coefficient related to the mass flow rate of the mixed gas; The reconstructed gas flow balance constraint and uniform mixing constraint are: and in, is the hydrogen mass flow rate of the gas flowing into / out of node i through the j-th pipeline connected to node i at time t; is the hydrogen mass flow rate at the load node at node i; It is the reference value of the gas flow rate out of node i from the j-th pipe connected to node i at time t.

5. The low-carbon economic dispatching method of the hybrid hydrogen-electricity system according to claim 1, characterized in that: The S2 also includes: Based on the stability conditions in numerical analysis, the numerical calculation difference grid is reasonably divided, and then the nonlinear pipeline network dynamic energy flow is pre-solved by sequential algorithm to obtain the velocity reference value and flow reference value; the step size of the difference grid of the energy flow equation meets the following conditions: Among them, u i,t is the gas flow rate at node i at time t; u i-1,t is the gas flow rate at node i-1 at time t, Δt is the time step, and Δx is the space step.

6. The low-carbon economic dispatching method of the hybrid hydrogen-electricity system according to claim 5, characterized in that: The method of pre-solving the nonlinear pipeline network dynamic energy flow by sequential algorithm to obtain the velocity reference value and the flow reference value includes: 1) Solve the steady-state energy flow equation by Newton iteration method as the initial condition for dynamic energy flow calculation; 2) Solve the pressure-flow equation in the nonlinear pipeline network dynamic energy flow through Newton iteration method to obtain the pressure p t and mass flow rate M t Then, the flow rate, gas parameters and load flow requirements are solved. 3) Use the flow velocity u obtained in step 2) t Solve the distribution of mixed gas components at time t k,t ; 4) Use the state variable p at time t t 、M t Update the gas parameters and go to step 3) to perform energy flow calculation at time t+1, and finally obtain the speed reference value and flow reference value in the linearization process.

7. The low-carbon economic dispatching method of the hybrid hydrogen-electricity system according to claim 1, characterized in that: The S4 includes: S41: record the number of iterations r=1; S42: Relax the gas grid operation constraints and solve the optimal energy flow of the combined system to obtain the output of hydrogen production and hydrogen-blended gas turbines S43: Converted into node hydrogen injection amount Gas demand at load nodes As the boundary condition for the numerical simulation of the hydrogen-mixed natural gas network; S44: Obtaining the benchmark flow rate u through numerical simulation of the hydrogen-mixed natural gas network b,r , parameter a, gas parameter Z Rλ, used to construct the linear dynamic energy flow model of the mixed hydrogen natural gas network; S45: Call the solver to solve the low-carbon economic dispatch problem of the hybrid hydrogen-electricity system, and convert the required electricity-to-hydrogen and hydrogen-blended gas turbine output into Convert to Boundary Conditions The numerical simulation of the hydrogen-mixed natural gas network was carried out again to solve the flow velocity distribution u r ; S46: Using flow velocity distribution u r Calculate the error between the actual flow rate and the reference flow rate ε=||u r -u b,r ||; S47: Determine whether the convergence condition ε≤ε0 is satisfied: if satisfied, the calculation is terminated; if not satisfied, the boundary conditions are updated r=r+1, return to S43 to enter the next round of iteration.

8. A low-carbon economic dispatching method for a hybrid hydrogen-electricity system, characterized in that: include: The first building block is used to take into account the dynamic transmission process of the mixed gas and the gas parameter distribution effect, and to construct a group of partial differential equations corresponding to the dynamic energy flow of the pipeline network; A reconstruction module, used to replace the corresponding nonlinear terms in the partial differential equation group corresponding to the dynamic energy flow of the pipeline network with the speed reference value, the flow reference value and the hydrogen mass flow rate, so as to reconstruct the partial differential equation group into a discretized linear dynamic energy flow equation suitable for solving the optimization scheduling problem; The second building block is used to build a low-carbon economic dispatch model for the hybrid hydrogen-electricity system with the lowest system operating cost under the consideration of carbon tax as the optimization goal and the discretized linear dynamic energy flow equation as the operating constraint of the hybrid hydrogen network. Y is the total operating cost, including the operating cost of coal-fired units C CFU , renewable energy reduction costs C RCUR , power load reduction cost C LCUR , gas source purchase cost Carbon Emissions Fees A solution module, used to solve the low-carbon economic dispatch model of the hybrid hydrogen-electricity combined system to obtain an executable combined system dispatch plan; The scheduling module is used to execute the combined system scheduling plan to schedule the hybrid hydrogen-electricity combined system.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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