Mixed hydrogen-electricity combined system coordinated scheduling method and device and electronic equipment

Abstract

The invention discloses a coordinated scheduling method and device for a mixed hydrogen-electricity combined system, and electronic equipment, and belongs to the technical field of electrical engineering, and the method comprises the steps: reconstructing a dynamic response process model of a hydrogen-mixed natural gas network into a linearization matrix adaptive to optimal scheduling; the response conditions of an electric power system and a natural gas system under the action of various flexible resources such as an electric hydrogen production device, a hydrogen-doped gas turbine and pipeline gas storage after a disturbance event can be accurately reflected; taking the linearization matrix as a hydrogen-mixed natural gas network security constraint, and constructing a double-layer optimization model considering different benefit subjects in the joint system; reconstructing the double-layer optimization model into a single-layer optimization problem and solving the single-layer optimization problem to realize scheduling; coordinated operation of an electric power system operator and a natural gas system operator is guided through two price signals, excitation compatibility and high dispatching efficiency are achieved on the economic benefit level, wind and light abandoning is reduced on the operation safety level, power load supply is guaranteed, and meanwhile gas network blocking is relieved.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering technology, and more specifically, relates to a method, device and electronic equipment for coordinated scheduling of a hybrid hydrogen-electricity system. Background Art

[0002] With the intensification of global environmental pollution and energy shortages, the development of a low-carbon energy system has become an inevitable requirement for achieving sustainable development. Hydrogen energy, as a clean energy, has the characteristics of low carbon, high efficiency, and long-term storage. Using renewable energy to electrolyze water to produce hydrogen, and mixing hydrogen with natural gas to form mixed hydrogen natural gas, the coupling of the power grid and the natural gas grid is realized, which can realize the cross-provincial transportation and utilization of hydrogen energy, reduce the use of natural gas and combustion pollutant emissions, and effectively promote the consumption of renewable energy. Research shows that in the mixed hydrogen-electricity system, the integration of hydrogen not only brings changes in the energy conversion process and operation mode, but also brings a series of challenges.

[0003] On the one hand, the hybrid hydrogen-electricity system involves different physical systems and stakeholders. The traditional single-objective overall coordinated scheduling cannot meet individual interests, resulting in the natural gas system's lack of willingness to use pipeline gas storage capacity to assist in the absorption of renewable energy during coordinated operation.

[0004] On the other hand, hydrogen blending causes changes in the hydraulic conditions and gas composition in the natural gas system, significantly increasing the operating risk of the natural gas system. However, existing research lacks a quantitative method for the impact of the risks brought by hydrogen blending on the profits of natural gas operators. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a method, device and electronic equipment for coordinated scheduling of a hybrid hydrogen-electricity system, which aims to solve the technical problem that traditional single-objective overall coordinated scheduling cannot meet the efficient scheduling requirements of the hybrid hydrogen-electricity system and has high operating risks.

[0006] To achieve the above object, according to one aspect of the present invention, a method for coordinated scheduling of a hybrid hydrogen-electricity system is provided, comprising:

[0007] S1: Construct a dynamic energy flow model of the hybrid hydrogen natural gas network, simplify and reconstruct the dynamic energy flow model of the hybrid hydrogen natural gas network into a linearized matrix suitable for solving the optimization scheduling problem;

[0008] S2: Construct two types of price signals, namely, the electricity price subsidy provided by the power system and the node gas price provided by the hybrid hydrogen natural gas system, to calculate the revenue of the power system operator and the hybrid hydrogen natural gas system; take maximizing the revenue of the power system operator and maximizing the revenue of the hybrid hydrogen natural gas system operator as the optimization objectives, take the linearized matrix as the hybrid hydrogen natural gas network security constraint, and construct a two-level optimization model that considers different stakeholders in the joint system; the first objective function of the two-level optimization model is:

[0009] The second objective function of the two-level optimization model is: Among them, x E is the decision-making quantity of the power system; x C is the decision quantity of the energy conversion unit; C It is the electricity price subsidy provided by the power system to the energy conversion unit; e is the serial number; ε CFU is the collection of conventional coal-fired units; WF is the wind farm collection; ε EL It is a collection of power loads; is the operating cost of conventional coal-fired units in the power system; Penalty fees for wind farms that abandon wind in the power system; Including the benefits of supplying power load and the penalties for load shedding, is the original electricity price at the node where the hydrogen-blended gas turbine is located; A tariff subsidy for hydrogen-blended gas turbines provided to power systems; To provide power for hydrogen-blended gas turbines; is the original electricity price at the node where hydrogen production is located; A subsidy for the electricity price provided to the power system for consuming electricity for hydrogen production; Provide power for the electric hydrogen production unit in the coupling link; The operating costs of the hydrogen-blended natural gas system corresponding to the operation of the hydrogen-blended gas turbine; Operating costs of the hydrogen-mixed natural gas system corresponding to the operation of the electric hydrogen production device;

[0010] S3: Reconstruct the two-layer optimization model into a single-layer optimization problem and solve it to obtain a real-time scheduling plan; execute the real-time scheduling plan to schedule the hybrid hydrogen-electricity system.

[0011] In one embodiment, the dynamic energy flow model of mixed hydrogen natural gas includes control equations and boundary conditions; the control equations include momentum conservation equations, continuity equations and hydrogen component transmission equations; the boundary conditions include source node operating pressure and gas composition, intersection node gas uniform mixing and material conservation, and load node energy demand; the differential forms corresponding to the momentum conservation equation, continuity equation and hydrogen component transmission equation in the control equations are as follows:

[0012]

[0013] Where Δx and Δt represent the spatial step and time step, respectively; the subscripts El and Fl represent the end and the beginning of the pipeline, respectively; c is the speed of sound in the hydrogen-mixed natural gas, in m / s; γ is the adiabatic index; f is the friction coefficient; A is the cross-sectional area of ​​the pipeline, in m 2 ; d is the pipe diameter, in m; is the reference flow rate, the unit is m / s; p, M, m correspond to three types of gas network status quantities: pressure, mixed gas mass flow rate, and hydrogen mass flow rate, respectively.

[0014] In one embodiment, the boundary conditions include the source node operating pressure and gas composition, the intersection node gas uniform mixing, gas composition conservation, and the load node energy demand, respectively, as follows:

[0015]

[0016] in, are the source node, intersection node, and load node set respectively; the subscripts Fl and El represent the outflow and inflow to a certain node respectively; is the pressure at source node i at time t; is the mass fraction of hydrogen at source node i at time t; the uniform mixing constraint means that the mass flow rate of hydrogen in the outflowing gas is proportional to the flow rate of the mixed gas; are the mass flow rate of hydrogen and the reference flow rate of the mixed gas in the pipeline k flowing out of a certain intersection node at time t, respectively; is the mass flow rate of hydrogen-mixed natural gas injected at node i; is the mass flow rate of hydrogen injected into the hydrogen-mixed natural gas at node i; is the mass flow requirement when the fuel of the load node is natural gas; The mass flow demand of the load node after considering the change in calorific value caused by hydrogen blending; m i,t is the hydrogen mass flow rate at load node i; q h ,q g are the calorific values ​​of hydrogen and natural gas, respectively.

[0017] In one embodiment, the dynamic energy flow model of the hybrid hydrogen natural gas network is simplified and reconstructed into a linearized matrix suitable for solving the optimization scheduling problem, including:

[0018] The dynamic energy flow of the single-period hydrogen-mixed natural gas network is reconstructed as: T t ·z t =T t ' -1 ·z t-1 +u t ; T tis the coefficient matrix of the state variables at time t in the state equation of the single-period hydrogen-mixed natural gas system; T t ' -1 is the coefficient matrix of the state variables at time t-1 in a single period; z t is the vector of state variables at time t; u t is the boundary condition at time t.

[0019] The multi-period power flow state quantity is related to the initial state quantity and the boundary conditions at each time. The multi-period power flow equation in the linearization matrix is ​​expressed as:

[0020]

[0021] The multi-period power flow equation can be constructed as a function of the system initial state and boundary conditions:

[0022] Π G =S G ×u G +S G,0 ·T G,0 × G,0 and Π H =S H ×u H +S H,0 ·T H,0 × H,0 ;

[0023] Among them, T G,t is the coefficient of the gas network state quantity at time t in the state equation at time t; T' G,t is the coefficient of the gas network state quantity at time t in the state equation at time t+1; t is the state vector of the gas network pressure, flow rate, and hydrogen composition at time t; u t is the boundary condition at time t; z G,0 is the pressure-flow distribution in the initial state; T G,0 is the coefficient corresponding to the initial state quantity in the state equation; Π G is the state vector composed of the mixed gas pressure and mass flow rate at all times; H is the state vector composed of the mass flow rate of hydrogen components during the whole period; S G is the sensitivity matrix corresponding to the pressure-flow relationship; S H is the sensitivity matrix corresponding to the transmission of hydrogen components; u G is the boundary condition vector; u H is the boundary condition vector; z H,0 is the hydrogen component distribution in the initial state.

[0024] The node load and branch power flow sensitivity matrix S GThe part related to the decision variables is decomposed by columns to obtain the gas flow sensitivity matrix of hydrogen-blended gas turbine Sensitivity matrix of inlet flow rate of hydrogen production unit Constant term relationship matrix Reconstruction of gas network dynamic flow constraints:

[0025]

[0026] in, Π G Corresponding upper and lower limits of pressure and flow; Π H Corresponding to the upper and lower limits of hydrogen mass flow rate; M GFU M is the gas mass flow consumed by the hydrogen-blended gas turbine; P2H The mass flow rate of hydrogen injected into the electric hydrogen production device; b G is the constant term vector in the pressure-flow equation of the gas network; b H is the constant term vector in the hydrogen component transport equation.

[0027] In one embodiment, the subsidized electricity price is expressed as:

[0028] The node gas price is expressed as:

[0029]

[0030] in, The original electricity price for hydrogen-blended gas turbines and electric hydrogen production units; Subsidized electricity prices for hydrogen-blended gas turbines and electric hydrogen production units; Provide electricity price subsidies for hydrogen-blended gas turbines and electric hydrogen production devices to the power system. and The original gas price at the hydrogen production unit and hydrogen-blended gas turbine; is the blocking component corresponding to the upper and lower limit constraints of gas pressure and flow rate at the electric hydrogen production node; It is the blocking component corresponding to the upper and lower limit constraints of the hydrogen composition at the electric hydrogen production node.

[0031] In one embodiment, the price signal corresponding to the electricity price subsidy and the node gas price adjusts the operating state of the equipment in the system through the following action conditions:

[0032]

[0033] in, The original electricity price for hydrogen production and hydrogen blending gas turbines; Electricity price subsidies for power systems to provide hydrogen production and hydrogen-blended gas turbines; is the critical action electricity price related to the node gas price; It is the node gas price at the electric hydrogen production device and hydrogen-blended gas turbine; is the electricity-to-hydrogen conversion efficiency of the electric hydrogen production device, The gas-to-electricity conversion efficiency when natural gas is the fuel.

[0034] In one embodiment, S3 includes: using the Carlo-Kuhn-Tucker condition to convert the two-level optimization problem into a single-level optimization problem; then using the big M method and the complementary relaxation condition to linearize the nonlinear constraints and nonlinear objective functions in the KKT optimal conditions, respectively, so as to convert the nonlinear programming problem corresponding to the two-level optimization model into a mixed integer quadratic programming problem.

[0035] According to another aspect of the present invention, a scheduling method for a hybrid hydrogen-electricity system is provided, comprising:

[0036] The first building block is used to construct a dynamic energy flow model of a mixed hydrogen natural gas network, simplifying and reconstructing the dynamic energy flow model of a mixed hydrogen natural gas network into a linearized matrix suitable for solving the optimization scheduling problem; the dynamic energy flow model of a mixed hydrogen natural gas network includes control equations and boundary conditions; the control equations include momentum conservation equations, continuity equations, and hydrogen component transmission equations; the boundary conditions include operating pressure and gas composition at source nodes, uniform gas mixing and material conservation at intersection nodes, and energy requirements at load nodes;

[0037] The second construction module is used to construct two types of price signals, namely, the electricity price subsidy provided by the power system and the node gas price provided by the hybrid hydrogen natural gas system, to calculate the revenue of the power system operator and the hybrid hydrogen natural gas system; taking maximizing the revenue of the power system operator and maximizing the revenue of the hybrid hydrogen natural gas system operator as the optimization objectives, and taking the linearized matrix as the hybrid hydrogen natural gas network security constraint, a two-layer optimization model considering different stakeholders in the joint system is constructed; the first objective function of the two-layer optimization model is:

[0038] The second objective function of the two-level optimization model is: Among them, x E is the decision-making quantity of the power system; x C is the decision quantity of the energy conversion unit; C It is the electricity price subsidy provided by the power system to the energy conversion unit; e is the serial number; ε CFU is the collection of conventional coal-fired units; WF is the wind farm collection; ε EL It is a collection of power loads; is the operating cost of conventional coal-fired units in the power system; Penalty fees for wind farms that abandon wind in the power system; Includes benefits from supplying electrical loads and penalties for load shedding; is the original electricity price at the node where the hydrogen-blended gas turbine is located; A tariff subsidy for hydrogen-blended gas turbines provided to power systems; To provide power for hydrogen-blended gas turbines; is the original electricity price at the node where hydrogen production is located; A subsidy for the electricity price provided to the power system for consuming electricity for hydrogen production; Provide power for the electric hydrogen production unit in the coupling link; The operating costs of the hydrogen-blended natural gas system corresponding to the operation of the hydrogen-blended gas turbine; Operating costs of the hydrogen-mixed natural gas system corresponding to the operation of the electric hydrogen production device;

[0039] The solution scheduling module is used to reconstruct the double-layer optimization model into a single-layer optimization problem and solve it to obtain a real-time scheduling plan; the real-time scheduling plan is executed to schedule the hybrid hydrogen-electricity system.

[0040] 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.

[0041] 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.

[0042] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0043] (1) The present invention provides a method for coordinated scheduling of a hybrid hydrogen-gas-electricity system, which reconstructs the model corresponding to the dynamic response process of the established hybrid hydrogen natural gas network into a linearized matrix form that is suitable for optimized scheduling, and can accurately reflect the response of the power system and the natural gas system under the action of various flexible resources such as electric hydrogen production devices, hydrogen-blended gas turbines, and pipeline gas storage after a disturbance event, especially the blockage that may occur after the natural gas network participates in the regulation; the linearized matrix is ​​used as the security constraint of the hybrid hydrogen natural gas network, and a two-layer optimization model considering different stakeholders in the joint system is constructed; the two-layer optimization model is reconstructed into a single-layer optimization problem and solved, thereby realizing scheduling; the two price signals are used to guide the coordinated operation of the power system operator and the natural gas system operator, so as to achieve incentive compatibility at the economic benefit level, high scheduling efficiency, reduce wind and solar power abandonment at the operational safety level, ensure power load supply, and alleviate gas network congestion.

[0044] (2) This scheme takes into account the dynamic transmission process of hydrogen components in the gas network and designs a linear discretized hydrogen component transmission equation. The advantage is that compared with the existing model, it reflects the dynamic change characteristics of hydrogen component distribution over time and space, which helps to capture the state quantity exceeding the limit in the mixed hydrogen network and generate price signals reflecting the congestion status of the gas network.

[0045] (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.

[0046] (4) This scheme takes into account that although the hydrogen mass flow distribution is coupled with the pressure-flow equation in the power flow equation, the pressure-flow and hydrogen mass flow can still be solved separately by dividing the original sparse power flow matrix into blocks. Dividing a large coefficient matrix into small blocks helps improve computational efficiency and reduce computational time. Furthermore, decomposing the sensitivity matrix related to the decision variables has the advantage of intuitively and quantitatively characterizing the impact of the output of the energy coupling link on the gas grid power flow and simplifying the solution process of the price signal.

[0047] (5) This scheme designs two types of price signals for dispatching. The advantage is that the electricity price subsidy can compensate for the risks and economic losses caused by hydrogen blending, including decreased energy transmission capacity, hydrogen embrittlement, leakage, and deviation from the original optimal operation plan; the node gas price signal reflects the operating status of the gas grid system and provides a reference for power system dispatching.

[0048] (6) This scheme designs a price signal for coordinated scheduling. The advantage is that even if no power scheduling command is issued and only the price is transmitted, the energy conversion link can make output decisions based on arbitrage maximization, thereby promoting the coordinated operation of the two systems.

[0049] (7) The two-level optimization model for joint system scheduling proposed in this scheme is a mathematical optimization problem with equilibrium constraints and cannot be solved directly. Therefore, a simplified solution method of KKT optimal condition linearization and objective function convexification is designed. The advantage of this method is that it converts the lower-level optimization problem into the constraints of the upper-level optimization problem, and can convert the two-level optimization problem into a single-level optimization problem for direct solution without the need for traditional iterative solution methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flow chart of a coordinated scheduling method for a hybrid hydrogen-electricity system provided in Example 1 of the present invention;

[0051] Figure 2 A schematic diagram of an economic dispatch method for a hybrid hydrogen-electricity system provided in Example 1 of the present invention;

[0052] Figure 3 A network structure diagram of a hybrid hydrogen-electricity system provided in Example 1 of the present invention;

[0053] Figure 4 and Figure 5 The subsidized electricity price and the node gas price when gas pressure blockage occurs at the natural gas network node 9 in Example 1 of the present invention;

[0054] Figure 6 When the pressure of the natural gas network node 9 exceeds the limit in Example 1 of the present invention, the gas pressure values ​​before and after adjustment are guided by prices;

[0055] Figure 7 When the pressure and hydrogen composition of the natural gas network node 8 exceed the limit in the embodiment of the present invention, the gas pressure and hydrogen integral values ​​before and after adjustment are guided by prices. DETAILED DESCRIPTION

[0056] 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.

[0057] Figure 1 This is a flow chart of the coordinated dispatching method of the hybrid hydrogen-electricity system proposed by the present invention; Figure 1 , combined with Figure 2 , the scheduling method in this embodiment is described in detail, including operations S1 to S3.

[0058] S1. Based on the dynamic transmission characteristics of the hybrid hydrogen natural gas network, a dynamic energy flow model of the hybrid hydrogen natural gas network is constructed, and it is simplified and reconstructed into a linearized matrix form suitable for solving the optimization scheduling problem.

[0059] S2. Analyze the impact of electric hydrogen production and hydrogen blending on different stakeholders, and construct two types of price signals: electricity price subsidies provided by the power system and node electricity prices provided by the hydrogen-blended natural gas system, to incentivize the coordinated operation of the system. Consider establishing a two-level optimization model with different stakeholders in the joint system, whose optimization objectives are to maximize the benefits of the power system operator and the hydrogen-blended natural gas system operator, respectively. Constraints may include grid constraints, hydrogen-blended natural gas network security constraints, electricity-gas energy conversion constraints, and operation constraints of various equipment in the system.

[0060] S3. Reconstruct the two-layer optimization model into a single-layer optimization problem. Through online pre-decision-making, solve the real-time value of the price signal and the executable economic dispatch plan, so as to promote the consumption of renewable energy while ensuring the safety of system operation.

[0061] Furthermore, in operation S1, the dynamic energy flow model of the hybrid hydrogen natural gas network and its linearized matrix form for solving the optimal scheduling problem are as follows:

[0062] The dynamic energy flow model of mixed hydrogen natural gas includes control equations and boundary conditions. The control equations include momentum conservation equations, continuity equations and hydrogen component transport equations, and their differential forms are constructed as follows:

[0063]

[0064]

[0065] Where Δx and Δt represent the spatial step and time step, respectively; the subscripts El and Fl represent the end and the beginning of the pipeline, respectively; c is the speed of sound in the hydrogen-mixed natural gas, in m / s; γ is the adiabatic index; f is the friction coefficient; A is the cross-sectional area of ​​the pipeline, in m 2 ; d is the pipe diameter, in m; is the reference flow rate, the unit is m / s; p, M, m correspond to three types of gas network status quantities: pressure, mixed gas mass flow rate, and hydrogen mass flow rate, respectively.

[0066] The boundary conditions include the operating pressure and gas composition of the source node, uniform gas mixing and material conservation at the intersection node, and energy demand at the load node. The corresponding constraints are constructed as follows:

[0067]

[0068] in, are the source node, intersection node, and load node set respectively; the subscripts Fl and El represent the outflow and inflow to a certain node respectively; is the pressure at source node i at time t; is the mass fraction of hydrogen at source node i at time t; the uniform mixing constraint means that the mass flow rate of hydrogen in the outflowing gas is proportional to the flow rate of the mixed gas; are the mass flow rate of hydrogen and the reference flow rate of the mixed gas in the pipeline k flowing out of a certain intersection node at time t, respectively; is the mass flow rate of hydrogen-mixed natural gas injected at node i at time t; is the mass flow rate of hydrogen injected into the hydrogen-mixed natural gas at node i at time t; is the mass flow demand at load node i at time t when the fuel is natural gas; is the mass flow demand of load node i after considering the calorific value change caused by hydrogen blending at time t; m i,t is the hydrogen mass flow rate at load node i at time t; q h ,q g are the calorific values ​​of hydrogen and natural gas, respectively.

[0069] In order to adapt to the solution of the optimal scheduling problem, the dynamic energy flow of the single-period hydrogen-mixed natural gas network can be reconstructed into the following linearized matrix form:

[0070] T t ·z t =T t ' -1 ·z t-1 +u t (9)

[0071]

[0072] Among them, T t is the coefficient matrix of the state variables at time t in the state equation of the single-period hydrogen-mixed natural gas system; T t ' -1 is the coefficient matrix of the state variables at time t-1 in a single period; z t is the vector of state variables at time t; u t is the boundary condition at time t.

[0073] The linearized single-period state equation can be decoupled, that is, T t is a sparse matrix, and the coefficient matrix corresponding to the pressure-mass distribution relationship and the hydrogen component distribution can be solved in blocks:

[0074]

[0075] Among them, S t is the sensitivity matrix, that is, the coefficient matrix T t The inverse matrix of G,t is the pressure-flow distribution sensitivity matrix; S H,t Corresponding hydrogen component distribution; u G,t is the pressure-flow state vector, u H,t is the hydrogen mass flow state vector; G,t is the result of the power flow equation; G,t is the part of the output vector of the power flow equation related to pressure and mass flow; H,t is the part of the power flow equation output vector related to the hydrogen mass flow rate. Equation (16) describes u H,t with u G,t Since the hydrogen content in the gas is u H,t Boundary conditions affecting load nodes This affects u G,t , so there is a functional relationship between the two sets of state quantities. However, due to u G,t with u H,t The dimensions are different, and zero vectors are needed to pad the matrix operations to achieve matrix dimension matching.

[0076] The multi-period tidal flow state quantity is related to the initial state quantity and the boundary conditions at each time. Taking the pressure-flow equation as an example, according to the relationship described in (9), it is constructed as follows:

[0077]

[0078] The multi-period power flow equation can be constructed as a function of the system initial state and boundary conditions:

[0079] Π G =S G ×u G +S G,0 ·T G,0 × G,0 (19)

[0080] Π H =S H ×u H +S H,0 ·T H,0 × H,0 (20)

[0081] Among them, G , Π H is the multi-period power flow state variable; S G , S H All are multi-period sensitivity matrices; u G ,u H is the corresponding boundary condition; z G,0 , z H,0 This is the initial state of the system.

[0082] The electric hydrogen production device and hydrogen-blended gas turbine are special gas sources and load nodes. The corresponding boundary conditions are related to the optimal power flow of the power grid and the optimal energy flow of the hydrogen-blended natural gas grid. The node load in the boundary condition is related to the branch power flow sensitivity matrix S G Decompose the gas flow sensitivity matrix of hydrogen-blended gas turbine by column Sensitivity matrix of inlet flow rate of hydrogen production unit Relationship matrix of other constant terms (gas network fixed load, source point gas pressure, zero term, etc.) The dynamic flow constraints of the gas network are obtained as follows:

[0083]

[0084] in, Π G Corresponding upper and lower limits of pressure and flow; Π H Corresponding to the upper and lower limits of hydrogen mass flow rate; M GFUM is the gas mass flow consumed by the hydrogen-blended gas turbine; P2H The mass flow rate of hydrogen injected into the electric hydrogen production device; b G is the constant term vector in the pressure-flow equation of the gas network; b H is the constant term vector in the hydrogen component transport equation;

[0085]

[0086] in, is the corresponding part of the mass flow in the power flow vector; It is the part of the hydrogen mass flow rate of the hydrogen-blended gas turbine node in the air intake volume and branch flow sensitivity matrix of the hydrogen production device.

[0087] Furthermore, the two types of price signals constructed in operation S2 for incentivizing the coordinated operation of the system are as follows:

[0088] The first type of price signal is the electricity price subsidy provided by the power system. When hydrogen is mixed into the natural gas system, changes in hydraulic conditions may cause the gas pressure to exceed the limit, and exceeding the limit of hydrogen mass fraction may lead to risks such as hydrogen embrittlement and leakage. In addition, the introduction of high-cost green hydrogen may cause the natural gas system to deviate from its original optimal operation plan. Therefore, in order to utilize the storage and consumption of renewable energy in the natural gas pipeline network, the power system needs to provide certain subsidies to compensate for the risks and economic losses caused by hydrogen mixing. The subsidized electricity price is as follows:

[0089]

[0090] in, The basic electricity price for hydrogen-blended gas turbines and electric hydrogen production units; Subsidized electricity prices for hydrogen-blended gas turbines and electric hydrogen production units; The power price subsidy provided by the power system to the hydrogen production plant will reduce the purchase price of electricity for the hydrogen production plant, so as to encourage the hydrogen production plant to use renewable energy to produce hydrogen; The power system provides electricity price subsidies to hydrogen-blended gas turbines, increases the electricity sales price of hydrogen-blended gas turbines, and encourages hydrogen-blended gas turbines to consume hydrogen-containing fuel.

[0091] The second type of price signal is the node gas price signal provided by the hydrogen-mixed natural gas system. For the natural gas system, the pressure, flow, and gas composition need to be monitored in real time. However, for the sake of data privacy protection, it is difficult for power system operators to exchange operating data with natural gas system operators in real time. Therefore, price signals are used to reflect the system operation and provide a reference for power system dispatch.

[0092] Similar to the node marginal electricity price of the power system, the node gas price of hydrogen-blended natural gas is composed of constraints and their corresponding dual multipliers:

[0093]

[0094]

[0095] in, and The original gas price at the hydrogen production unit and hydrogen-blended gas turbine; is the blocking component corresponding to the upper and lower limit constraints of gas pressure and flow rate at the electric hydrogen production node, It is the blocking component corresponding to the upper and lower limit constraints of the hydrogen components at the electric hydrogen production node. When the state quantities in the gas network do not reach the upper and lower limits, the corresponding blocking component is 0.

[0096] The price signal regulates the operating status of the equipment in the system through the following action conditions:

[0097]

[0098] This condition reflects the conditions that each price signal meets when the electric hydrogen production device and the hydrogen-blended gas turbine are in operation. The original electricity price for hydrogen production and hydrogen blending gas turbines; Electricity price subsidies for power systems to provide hydrogen production and hydrogen-blended gas turbines; is the critical action electricity price related to the node gas price, which means that the operating status of the hydrogen production and gas units is about to change, that is, or The following critical action conditions should be met:

[0099]

[0100] Furthermore, in operation S2, a two-level optimization model considering different stakeholders in the joint system is constructed as follows:

[0101] The upper level considers maximizing the net profit of the power system operator, which includes the operating costs of conventional coal-fired units, penalty costs for wind curtailment, net income from electricity sales, coal consumption of coal-fired units, the cost of purchasing electricity from hydrogen-blended gas units, and income from selling electricity to hydrogen production devices.

[0102]

[0103] The grid operation constraints in the system include: upper and lower limit constraints on the output of conventional coal-fired units, wind curtailment constraints, load shedding constraints, power balance constraints, line transmission capacity constraints, unit ramping constraints, and electricity price subsidy constraints.

[0104]

[0105] in, is the operating cost of conventional coal-fired units; is the operating cost coefficient of conventional coal-fired units; Penalty fees for wind farms curtailing wind power; is the penalty cost coefficient for wind farm wind curtailment; Includes benefits from supplying electrical loads and load penalties; Represents the electricity purchase price of the power load; is the penalty cost coefficient for cutting power load; the last two terms in the objective function represent the cost of purchasing electricity from the hydrogen-blended gas unit and the income from selling electricity to the electric hydrogen production device.

[0106] The lower layer considers maximizing the net profit of the natural gas system operator, which includes the operating costs of the hydrogen-blended gas turbine, the operating costs of the hydrogen-electric production unit, the revenue from selling electricity from the hydrogen-blended gas turbine to the power system, and the cost of purchasing electricity from the hydrogen-electric production unit from the power system.

[0107]

[0108] in, The operating cost of hydrogen-blended gas turbines; is the operating cost of the hydrogen production unit; the last two terms of the objective function represent the electricity sales revenue of the hydrogen-blended gas unit and the electricity purchase cost of the hydrogen production unit; Q g is the quadratic cost coefficient; is the gas consumption cost coefficient of the hydrogen-blended gas generator set; is the gas production profit coefficient of the electric hydrogen production device; the last two terms in the objective function represent the profit from the sale of electricity from the hydrogen-blended gas unit and the cost of purchasing electricity from the electric hydrogen production device.

[0109] The gas network operation constraints in the system include: gas network dynamic flow constraints, pressure and flow upper and lower limit constraints, hydrogen composition constraints, and gas-electricity energy conversion constraints.

[0110] Furthermore, the two types of price signals and dispatching schemes described in operation S3 are solved as follows:

[0111] In the two-layer model, the upper and lower layer decisions affect each other and cannot be solved directly. Therefore, the KKT optimal condition is used to transform the two-layer optimization problem into a single-layer optimization problem; the large M method and complementary relaxation conditions are then used to linearize the nonlinear constraints and nonlinear objective functions in the KKT optimal condition, respectively, and the nonlinear programming problem is transformed into an MIQP problem.

[0112] The lower-level optimization problem is reconstructed into equilibrium constraints using KKT optimal conditions in the following form:

[0113]

[0114] Among them, L lower is the Lagrangian function of the lower optimization problem, μ and π are the dual multipliers of the corresponding constraints.

[0115] For the complementary relaxation conditions in the equilibrium constraints, the large M method can be used for linearization. Taking the upper limit constraint (55) of the operating power of the hydrogen-blended gas turbine as an example, the constraint is reconstructed into the following form:

[0116]

[0117]

[0118] The objective function contains the product of two decision variables, namely, electricity price subsidy and power. Through the linearization of stability conditions and complementary relaxation conditions, the simplified objective function form is as follows:

[0119]

[0120] The present invention adopts a modified 6-node power system and a 12-node natural gas system to form a hybrid hydrogen-electricity system. The system includes 1 wind farm, 1 conventional coal-fired unit, 1 hydrogen-blended gas turbine, and 2 electric hydrogen production stations. The present invention is implemented through the MATLAB platform, and the Gurobi solver is used to solve the MIQP problem. The installed capacity of wind power is 800MW, the installed capacity of hydrogen-blended gas turbine is 250MW, the capacity of conventional coal-fired units is 500MW, the power load capacity is 1GW, and the installed capacity of electric hydrogen production is 300MW. The operating pressure level of the natural gas source is 6.4MPa. The upper limit of the pressure of node 9 is 5.7MPa, the lower limit of the pressure of node 8 is 5.3MPa, and the upper limit of the adaptability of the hydrogen gas integral fraction is 15%.

[0121] Figure 3 The network structure diagram of the hybrid hydrogen-electricity combined system provided in Example 1 of the present invention; Based on this example, the present invention sets up three sets of comparison scenarios: 1) non-cooperative dispatch mode, that is, the power grid and the gas grid are economically dispatched separately; 2) traditional forced dispatch, that is, the economic dispatch objective function is to maximize the total revenue of the combined system; 3) price-guided two-layer collaborative dispatch method, that is, the dispatch method proposed in the present invention. The present invention compares the revenue and operation of scenarios 1-3 (Table 1), and illustrates that the proposed coordinated dispatch scheme guided by price signals can promote the consumption of renewable energy, ensure the supply of power load, and realize the distribution of revenue between power system operators and natural gas system operators, so as to achieve incentive compatibility. Figure 4 and Figure 5 The subsidized electricity price and node gas price when gas pressure blockage occurs at the natural gas network node 9 in the embodiment of the present invention are adjusted by two types of price signals, and the node pressure is always within a safe range ( Figure 6 ). Similarly, Figure 7This is the regulation process when gas pressure blockage and hydrogen component exceeding the limit occur at natural gas network node 8.

[0122] Table 1 Benefits and operation status under different scenarios

[0123]

[0124] The present invention promotes the spatiotemporal coupling of the electricity-gas-hydrogen hybrid system through price signal guidance, improves the flexibility and overall economy of the system operation, and provides a theoretical basis for the application of hydrogen energy in the integrated energy system.

[0125] Example 2

[0126] This embodiment provides a scheduling method for a hybrid hydrogen-electricity system, comprising the following steps.

[0127] The first building block is used to construct a dynamic energy flow model of a mixed hydrogen natural gas network, simplifying and reconstructing the dynamic energy flow model of a mixed hydrogen natural gas network into a linearized matrix suitable for solving the optimization scheduling problem; the dynamic energy flow model of a mixed hydrogen natural gas network includes control equations and boundary conditions; the control equations include momentum conservation equations, continuity equations, and hydrogen component transmission equations; the boundary conditions include operating pressure and gas composition at source nodes, uniform gas mixing and material conservation at intersection nodes, and energy requirements at load nodes;

[0128] The second construction module is used to construct two types of price signals, namely, the electricity price subsidy provided by the power system and the node gas price provided by the hybrid hydrogen natural gas system, to calculate the power system operator's income and the hybrid hydrogen natural gas system's income; taking maximizing the power system operator's income and maximizing the hybrid hydrogen natural gas system operator's income as the optimization objectives, and taking the linearization matrix as the hybrid hydrogen natural gas network security constraint, a two-level optimization model considering different stakeholders in the joint system is constructed; the first objective function of the two-level optimization model is:

[0129] The second objective function of the two-level optimization model is: Among them, x E is the decision-making quantity of the power system; x C is the decision quantity of the energy conversion unit; C It is the electricity price subsidy provided by the power system to the energy conversion unit; e is the serial number; ε CFU is the collection of conventional coal-fired units; WF is the wind farm collection; ε EL It is a collection of power loads; is the operating cost of conventional coal-fired units in the power system; Penalty fees for wind farms that abandon wind in the power system; Includes benefits from supplying electrical loads and penalties for load shedding; is the original electricity price at the node where the hydrogen-blended gas turbine is located; A tariff subsidy for hydrogen-blended gas turbines provided to power systems; To contribute; is the original electricity price at the node where hydrogen production is located; A subsidy for the electricity price provided to the power system for consuming electricity for hydrogen production; Provide power for the electric hydrogen production unit in the coupling link; The operating costs of the hydrogen-blended natural gas system corresponding to the operation of the hydrogen-blended gas turbine; Operating costs of the hydrogen-mixed natural gas system corresponding to the operation of the electric hydrogen production device;

[0130] The solution scheduling module is used to reconstruct the double-layer optimization model into a single-layer optimization problem and solve it to obtain a real-time scheduling plan; the real-time scheduling plan is executed to schedule the hybrid hydrogen-electricity system.

[0131] Example 3

[0132] 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.

[0133] Example 4

[0134] 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.

[0135] 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 method for coordinated dispatching of a hybrid hydrogen-electricity system, characterized in that: include: S1: constructing a dynamic energy flow model of a mixed hydrogen and natural gas network, simplifying and reconstructing the dynamic energy flow model of a mixed hydrogen and natural gas network into a linearized matrix suitable for solving an optimization scheduling problem; S2: Construct two types of price signals, namely, the electricity price subsidy provided by the power system and the node gas price provided by the hybrid hydrogen natural gas system, to calculate the revenue of the power system operator and the hybrid hydrogen natural gas system; take maximizing the revenue of the power system operator and maximizing the revenue of the hybrid hydrogen natural gas system operator as the optimization objectives, take the linearized matrix as the hybrid hydrogen natural gas network security constraint, and construct a two-level optimization model that considers different stakeholders in the joint system; the first objective function of the two-level optimization model is: The second objective function of the two-level optimization model is: in, xE is the decision-making quantity of the power system; x C is the decision quantity of the energy conversion unit; C It is the electricity price subsidy provided by the power system to the energy conversion unit; e is the serial number; ε CFU is the collection of conventional coal-fired units; WF is the wind farm collection; ε EL It is a collection of power loads; is the operating cost of conventional coal-fired units in the power system; Penalty fees for wind farms that abandon wind in the power system; Includes benefits from supplying electrical loads and penalties for load shedding; is the original electricity price at the node where the hydrogen-blended gas turbine is located; A tariff subsidy for hydrogen-blended gas turbines provided to power systems; To provide power for hydrogen-blended gas turbines; is the original electricity price at the node where hydrogen production is located; Subsidy for electricity prices provided to power systems for consuming electricity for hydrogen production; Provide power for the electric hydrogen production unit in the coupling link; The operating costs of the hydrogen-blended natural gas system corresponding to the operation of the hydrogen-blended gas turbine; Operating costs of the hydrogen-mixed natural gas system corresponding to the operation of the electric hydrogen production device; S3: Reconstruct the two-layer optimization model into a single-layer optimization problem and solve it to obtain a real-time scheduling solution; The real-time scheduling scheme is executed to schedule the hybrid hydrogen-electricity system.

2. The scheduling method of the hybrid hydrogen-electricity system according to claim 1, characterized in that: The dynamic energy flow model of mixed hydrogen natural gas includes control equations and boundary conditions; the control equations include momentum conservation equations, continuity equations and hydrogen component transmission equations; the boundary conditions include source node operating pressure and gas composition, intersection node gas uniform mixing and material conservation, and load node energy demand; the differential forms corresponding to the momentum conservation equation, continuity equation and hydrogen component transmission equation in the control equations are as follows: Where Δx and Δt represent the spatial step and time step, respectively; the subscripts El and Fl represent the end and the beginning of the pipeline, respectively; c is the speed of sound in the hydrogen-mixed natural gas, in m / s; γ is the adiabatic index; f is the friction coefficient; A is the cross-sectional area of ​​the pipeline, in m 2 ; d is the pipe diameter, in m; is the reference flow rate, the unit is m / s; p, M, m correspond to three types of gas network status quantities: pressure, mixed gas mass flow rate, and hydrogen mass flow rate, respectively.

3. The scheduling method of the hybrid hydrogen-electricity system according to claim 2, characterized in that: The boundary conditions include the source node operating pressure and gas composition, the uniform mixing of gas at the intersection node, the conservation of gas composition, and the energy demand at the load node, respectively, as follows: in, are the source node, intersection node, and load node set respectively; the subscripts Fl and El represent the outflow and inflow to a certain node respectively; is the pressure at source node i at time t; is the mass fraction of hydrogen at source node i at time t; the uniform mixing constraint means that the mass flow rate of hydrogen in the outflowing gas is proportional to the flow rate of the mixed gas; are the mass flow rate of hydrogen and the reference flow rate of the mixed gas in the pipeline k flowing out of a certain intersection node at time t, respectively; is the mass flow rate of hydrogen-mixed natural gas injected at node i at time t; is the mass flow rate of hydrogen injected into the hydrogen-mixed natural gas at node i at time t; is the mass flow demand at load node i at time t when the fuel is natural gas; is the mass flow demand of load node i after considering the calorific value change caused by hydrogen blending at time t; m i,t is the hydrogen mass flow rate at load node i at time t; q h ,q g are the calorific values ​​of hydrogen and natural gas, respectively.

4. The scheduling method of the hybrid hydrogen-electricity system according to claim 1, characterized in that: The method of simplifying and reconstructing the dynamic energy flow model of the mixed hydrogen natural gas network into a linearized matrix suitable for solving the optimization scheduling problem includes: The dynamic energy flow of the single-period hydrogen-mixed natural gas network is reconstructed as: T t ·z t =T t ' -1 ·z t-1 +u t ; T t is the coefficient matrix of the state variables at time t in the state equation of the single-period hydrogen-mixed natural gas system; T t ' -1 is the coefficient matrix of the state variables at time t-1 in a single period; z t is the vector of state variables at time t; u t is the boundary condition at time t; The multi-period power flow state quantity is related to the initial state quantity and the boundary conditions at each time. The multi-period power flow equation in the linearization matrix is ​​expressed as: The multi-period power flow equation can be constructed as a function of the system initial state and boundary conditions: P G =S G ×u G +S G,0 ·T G,0 ×z G,0 and P H =S H ×u H +S H,0 ·T H,0 ×z H,0 ; Among them, T G,t is the coefficient of the gas network state quantity at time t in the state equation at time t; T′ G,t is the coefficient of the gas network state quantity at time t in the state equation at time t+1; t is the state vector of the gas network pressure, flow rate, and hydrogen composition at time t, u t is the boundary condition at time t; z G,0 is the pressure-flow distribution in the initial state; T G,0 is the coefficient corresponding to the initial state quantity in the state equation; Π G is the state vector composed of the mixed gas pressure and mass flow rate at all times; H is the state vector composed of the mass flow rate of hydrogen components during the whole period; S G is the sensitivity matrix corresponding to the pressure-flow relationship; S H is the sensitivity matrix corresponding to the transmission of hydrogen components; u G is the boundary condition vector; u H is the boundary condition vector; z H,0 is the hydrogen component distribution in the initial state; The node load and branch power flow sensitivity matrix S G The part related to the decision variables is decomposed by columns to obtain the gas flow sensitivity matrix of hydrogen-blended gas turbine Sensitivity matrix of inlet flow rate of hydrogen production unit Constant term relationship matrix Reconstruction of gas network dynamic flow constraints: in, Π G Corresponding upper and lower limits of pressure and flow; Π H Corresponding to the upper and lower limits of hydrogen mass flow rate; M GFU M is the gas mass flow consumed by the hydrogen-blended gas turbine; P2H The mass flow rate of hydrogen injected into the electric hydrogen production device; b G is the constant term vector in the pressure-flow equation of the gas network; b H is the constant term vector in the hydrogen component transport equation.

5. The scheduling method of the hybrid hydrogen-electricity system according to claim 1, characterized in that: The subsidized electricity price is expressed as: The node gas price is expressed as: in, The original electricity price for hydrogen-blended gas turbines and electric hydrogen production units; Subsidized electricity prices for hydrogen-blended gas turbines and electric hydrogen production units; Subsidy for electricity prices provided to power systems for hydrogen-blended gas turbines and electric hydrogen production plants; and The original gas price at the hydrogen production unit and hydrogen-blended gas turbine; is the blocking component corresponding to the upper and lower limit constraints of gas pressure and flow rate at the electric hydrogen production node; It is the blocking component corresponding to the upper and lower limit constraints of the hydrogen composition at the electric hydrogen production node.

6. The scheduling method of the hybrid hydrogen-electricity system according to claim 1, characterized in that: The price signals corresponding to the electricity price subsidy and the node gas price regulate the operating status of the equipment in the system through the following action conditions: in, The original electricity price at the power-to-hydrogen and hydrogen-blended gas turbines; Electricity price subsidies for power systems to provide hydrogen production and hydrogen-blended gas turbines; is the critical action electricity price related to the node gas price; It is the node gas price at the electric hydrogen production device and hydrogen-blended gas turbine; The electricity-to-hydrogen conversion efficiency of the electric hydrogen production device; The gas-to-electricity conversion efficiency when natural gas is the fuel.

7. The scheduling method of the hybrid hydrogen-electricity system according to any one of claims 1 to 6, characterized in that: The S3 includes: The Carlo-Kuhn-Tucker condition is used to transform the bi-level optimization problem into a single-level optimization problem. The big M method and complementary relaxation condition are then used to linearize the nonlinear constraints and nonlinear objective functions in the KKT optimal conditions, respectively, so as to transform the nonlinear programming problem corresponding to the bi-level optimization model into a mixed integer quadratic programming problem.

8. A method for dispatching a hybrid hydrogen-electricity system, characterized in that: include: The first construction module is used to construct a dynamic energy flow model of a mixed hydrogen natural gas network, simplifying and reconstructing the dynamic energy flow model of the mixed hydrogen natural gas network into a linearized matrix suitable for solving the optimization scheduling problem; the dynamic energy flow model of the mixed hydrogen natural gas includes control equations and boundary conditions; the control equations include momentum conservation equations, continuity equations and hydrogen component transmission equations; the boundary conditions include source node operating pressure and gas composition, intersection node gas uniform mixing and material conservation, and load node energy demand; The second construction module is used to construct two types of price signals, namely, the electricity price subsidy provided by the power system and the node gas price provided by the hybrid hydrogen natural gas system, to calculate the power system operator's income and the hybrid hydrogen natural gas system's income; taking maximizing the power system operator's income and maximizing the hybrid hydrogen natural gas system operator's income as the optimization objectives, and taking the linearized matrix as the hybrid hydrogen natural gas network security constraint, a two-level optimization model considering different stakeholders in the joint system is constructed; the first objective function of the two-level optimization model is: The second objective function of the two-level optimization model is: Among them, x E is the decision-making quantity of the power system; x C is the decision quantity of the energy conversion unit; C It is the electricity price subsidy provided by the power system to the energy conversion unit; e is the serial number; ε CFU is the collection of conventional coal-fired units; WF is the wind farm collection; ε EL It is a collection of power loads; is the operating cost of conventional coal-fired units in the power system; Penalty fees for wind farms that abandon wind in the power system; Includes benefits from supplying electrical loads and penalties for load shedding; is the original electricity price at the node where the hydrogen-blended gas turbine is located; A tariff subsidy for hydrogen-blended gas turbines provided to power systems; To provide power for hydrogen-blended gas turbines; is the original electricity price at the node where hydrogen production is located; Subsidy for electricity prices provided to power systems for consuming electricity for hydrogen production; Provide power for the electric hydrogen production unit in the coupling link; The operating costs of the hydrogen-blended natural gas system corresponding to the operation of the hydrogen-blended gas turbine; Operating costs of the hydrogen-mixed natural gas system corresponding to the operation of the electric hydrogen production device; The scheduling solution module is used to reconstruct the double-layer optimization model into a single-layer optimization problem and solve it to obtain a real-time scheduling solution; and execute the real-time scheduling solution to schedule the hybrid hydrogen-electricity 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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