Energy scheduling method, device and equipment, storage medium and computer program product

Abstract

The invention relates to the technical field of comprehensive energy system optimization, and discloses an energy scheduling method, device and equipment, a storage medium and a computer program product, and the method comprises the steps: obtaining a compression coefficient of hydrogen-rich compressed natural gas, and carrying out the optimization of an initial hydrogen-rich compressed natural gas network model based on the compression coefficient, generating a target hydrogen-rich compressed natural gas network model; according to the target hydrogen-rich compressed natural gas network model, all links of the hydrogen-rich compressed natural gas supply chain are integrated, and a hydrogen-rich compressed natural gas optimal scheduling model is constructed; the hydrogen-rich compressed natural gas optimal scheduling model is converted into a mixed integer second-order quadratic programming problem through continuous linear programming, and an optimal scheduling strategy is solved based on a target iterative algorithm; and scheduling based on the optimal scheduling strategy. The influence of hydrogen-rich compressed natural gas on gas network dynamics is considered, an optimal scheduling model of the comprehensive energy system of the supply chain is provided, and the performance of the comprehensive energy system is improved.

Description

Technical Field

[0001] This application relates to the field of integrated energy system optimization technology, and in particular to an energy dispatching method, apparatus, equipment, storage medium, and computer program product. Background Technology

[0002] Hydrogen-rich compressed natural gas (HCNG) holds immense potential for renewable energy and hydrogen utilization. However, injecting hydrogen into natural gas networks alters the original fluid dynamics, complicates the physical properties of the compressed gas, and impacts the operating conditions of natural gas infrastructure. Summary of the Invention

[0003] The main objective of this application is to provide an energy dispatching method, apparatus, equipment, storage medium, and computer program product, aiming to address the enormous potential of hydrogen-rich compressed natural gas in renewable energy and hydrogen utilization. However, injecting hydrogen into the natural gas network alters the original fluid dynamics, complicates the physical properties of the compressed gas, and affects the technical problems of the operating conditions of the natural gas infrastructure.

[0004] To achieve the above objectives, this application proposes an energy dispatching method, which includes:

[0005] Obtain the compressibility coefficient of hydrogen-rich compressed natural gas, and optimize the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate the target hydrogen-rich compressed natural gas network model.

[0006] Based on the target hydrogen-rich compressed natural gas network model, various links in the hydrogen-rich compressed natural gas supply chain are integrated to construct an optimal scheduling model for hydrogen-rich compressed natural gas.

[0007] The optimal scheduling model for hydrogen-rich compressed natural gas is transformed into a mixed-integer second-order quadratic programming problem through continuous linear programming, and the optimal scheduling strategy is obtained by solving the problem based on the objective iterative algorithm.

[0008] Scheduling is performed based on the optimal scheduling strategy.

[0009] Optionally, the step of integrating various links in the hydrogen-rich compressed natural gas supply chain based on the target hydrogen-rich compressed natural gas network model to construct an optimal scheduling model for hydrogen-rich compressed natural gas includes:

[0010] Obtain the scheduling target and cost information at each stage of the hydrogen-rich compressed natural gas supply chain, and construct the target cost function of the hydrogen-rich compressed natural gas supply chain based on the cost information and the scheduling target;

[0011] Based on the target hydrogen-rich compressed natural gas network model, establish the constraints of the target cost function under grid constraints and multi-energy flow balance;

[0012] The optimal scheduling model for hydrogen-rich compressed natural gas is obtained based on the objective cost function and the constraints.

[0013] Optionally, the step of obtaining the compressibility coefficient of hydrogen-rich compressed natural gas and optimizing the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate the target hydrogen-rich compressed natural gas network model includes:

[0014] Obtain an initial hydrogen-rich compressed natural gas network model and the volume fraction of the hydrogen-rich compressed natural gas, wherein the volume fraction represents the proportion of hydrogen in the hydrogen-rich compressed natural gas;

[0015] The pressure level of the hydrogen-rich compressed natural gas is obtained, and the compressibility coefficient is calculated linearly based on the pressure level and relevant parameters in the volume fraction.

[0016] The initial hydrogen-rich compressed natural gas network model is transformed based on the compression coefficient, and the transformed model is integrated over the pipeline data of the hydrogen-rich compressed natural gas to obtain the target constraint equation.

[0017] The target hydrogen-rich compressed natural gas network model is obtained based on the target constraint equation and the preset hydrogen-rich compressed natural gas network constraint information.

[0018] Optionally, after the step of obtaining the target hydrogen-rich compressed natural gas network model based on the target constraint equation and the preset hydrogen-rich compressed natural gas network constraint information, the method further includes:

[0019] Obtain a general hydrogen-rich compressed natural gas network model, wherein the compressibility coefficient of the general hydrogen-rich compressed natural gas network model is a constant;

[0020] Calculate the optimization results of the general hydrogen-rich compressed natural gas network model and the target hydrogen-rich compressed natural gas network model in minimizing the daily cost of hydrogen-rich compressed natural gas;

[0021] The optimization results of the target hydrogen-rich compressed natural gas network model are used as reference values. The deviation of the optimization results of the general hydrogen-rich compressed natural gas network model is calculated and displayed to the target user based on the deviation.

[0022] Optionally, the step of transforming the optimal scheduling model of hydrogen-rich compressed natural gas into a mixed-integer second-order quadratic programming problem through continuous linear programming, and solving for the optimal scheduling strategy based on an objective iterative algorithm, includes:

[0023] Identify the nonlinear terms and nonlinear constraints of the optimal scheduling model for hydrogen-rich compressed natural gas;

[0024] The nonlinear terms are converted into linear terms by successive linearization algorithms, and the nonlinear constraints are approximated as linear constraints by applying McCormick envelopes, thus obtaining the mixed integer second-order quadratic programming problem.

[0025] The optimal scheduling strategy is obtained by solving the mixed-integer second-order quadratic programming problem based on the objective iterative algorithm.

[0026] Optionally, the step of solving the mixed-integer second-order quadratic programming problem based on the objective iterative algorithm to obtain the optimal scheduling strategy includes:

[0027] Initialize the algorithm parameters of the target iterative algorithm, wherein the algorithm parameters include a preset number of iterations and a tolerance error value;

[0028] The mixed-integer second-order quadratic programming problem is solved using an optimization solver to obtain an initial solution, which is then used as the starting point for the target iterative algorithm to perform iterations.

[0029] After each iteration, if the number of iterations reaches the preset number of iterations and the obtained target solution is within the tolerance error value, the iteration ends and the target solution is taken as the final solution.

[0030] The optimal scheduling strategy is determined based on the final solution.

[0031] Furthermore, to achieve the above objectives, this application also proposes an energy dispatching device, which includes:

[0032] The network model construction module is used to obtain the compressibility coefficient of hydrogen-rich compressed natural gas and optimize the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate the target hydrogen-rich compressed natural gas network model.

[0033] The scheduling model construction module is used to integrate various links in the hydrogen-rich compressed natural gas supply chain based on the target hydrogen-rich compressed natural gas network model, and construct the optimal scheduling model for hydrogen-rich compressed natural gas.

[0034] The scheduling strategy solution module is used to transform the optimal scheduling model of hydrogen-rich compressed natural gas into a mixed integer second-order quadratic programming problem through continuous linear programming, and to obtain the optimal scheduling strategy based on the objective iterative algorithm.

[0035] An integrated energy scheduling module is used for scheduling based on the optimal scheduling strategy.

[0036] In addition, to achieve the above objectives, this application also proposes an energy dispatching device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the energy dispatching method as described above.

[0037] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the energy scheduling method described above.

[0038] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the energy scheduling method described above.

[0039] This application discloses an energy dispatching method, apparatus, equipment, storage medium, and computer program product, comprising: obtaining the compressibility coefficient of hydrogen-rich compressed natural gas (CNG), optimizing an initial CNG network model based on the compressibility coefficient to generate a target CNG network model; integrating various links of the CNG supply chain according to the target CNG network model to construct an optimal CNG dispatching model; transforming the optimal CNG dispatching model into a mixed-integer second-order quadratic programming problem through continuous linear programming, and solving it using a target iterative algorithm to obtain the optimal dispatching strategy; and performing dispatching based on the optimal dispatching strategy. The impact of CNG on gas network dynamics is studied, and by considering the optimal dispatching model of the CNG supply chain's production, storage, mixing, transmission, and utilization links, and using an iterative algorithm, the optimal dispatching solution is obtained, thereby improving the performance of the integrated energy system. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the first embodiment of the energy dispatching method of this application;

[0043] Figure 2 This is a flowchart illustrating the second embodiment of the energy dispatching method of this application;

[0044] Figure 3 The linear regression result for the compression coefficient of this application;

[0045] Figure 4 A schematic diagram illustrating the deviation of the optimization results for the objective function;

[0046] Figure 5 This is a flowchart illustrating the third embodiment of the energy dispatching method of this application;

[0047] Figure 6 This is a schematic diagram of the hydrogen-rich compressed natural gas supply chain structure for this application;

[0048] Figure 7 This is a flowchart illustrating the fourth embodiment of the energy dispatching method of this application;

[0049] Figure 8 This is a schematic diagram of the successive linearization of this application;

[0050] Figure 9 This is a schematic diagram of the module structure of the energy dispatching device according to an embodiment of this application;

[0051] Figure 10 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the energy dispatching method in this application embodiment.

[0052] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0054] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solution of this application embodiment is as follows: obtain the compressibility coefficient of hydrogen-rich compressed natural gas, and optimize the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate a target hydrogen-rich compressed natural gas network model; integrate the various links of the hydrogen-rich compressed natural gas supply chain according to the target hydrogen-rich compressed natural gas network model to construct an optimal scheduling model for hydrogen-rich compressed natural gas; transform the optimal scheduling model for hydrogen-rich compressed natural gas into a mixed integer second-order quadratic programming problem through continuous linear programming, and solve it based on a target iterative algorithm to obtain the optimal scheduling strategy; and perform scheduling based on the optimal scheduling strategy.

[0056] Existing technologies that mix hydrogen with natural gas and inject HCNG (Hydrogen-Compressed Natural Gas) into existing natural gas pipeline networks facilitate the inter-regional transportation and utilization of hydrogen, breaking the "chicken and egg" dilemma and effectively promoting the large-scale consumption of renewable energy. To better adapt to renewable energy and achieve efficient coupling and conversion of hydrogen, electricity, and natural gas, it is urgent to explore implementation methods for HCNG applications. Due to the differences in the physicochemical properties between hydrogen and natural gas, HCNG will inevitably affect the operating conditions of natural gas infrastructure. Whether the natural gas network equations are still applicable to HCNG networks with complex gas compositions remains to be verified.

[0057] Therefore, this application provides an energy dispatching method for an electric-compressed natural gas integrated energy system, which enables more accurate modeling of the HCNG network under centralized hydrogen mixing mode, so as to realize a low-carbon and efficient operation method for E-HCNG-IES (Electricity-Hydrogen-Compressed Natural Gas Integrated Energy System) and improve the application prospects of hydrogen.

[0058] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following description uses an integrated energy management system as an example to illustrate this embodiment and the subsequent embodiments.

[0059] Based on this, the embodiments of this application provide an energy dispatching method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the energy dispatching method of this application.

[0060] In this embodiment, the energy dispatching method includes:

[0061] Step S10: Obtain the compressibility coefficient of hydrogen-rich compressed natural gas, and optimize the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate the target hydrogen-rich compressed natural gas network model.

[0062] It should be noted that hydrogen-rich compressed natural gas (CNG) refers to compressed natural gas containing a high proportion of hydrogen. This gas is typically obtained by mixing hydrogen and natural gas through a specific process and then compressing them. The compressibility factor refers to the proportion by which a gas's volume decreases when compressed under certain conditions. For hydrogen-rich CNG, the compressibility factor is affected by various factors such as hydrogen content, temperature, and pressure. The initial hydrogen-rich CNG network model is a mathematical model describing hydrogen-rich CNG in the initial stage of network planning or design. The target hydrogen-rich CNG network model is the gas network model obtained after optimization based on the initial model, taking into account the influence of key parameters such as the compressibility factor.

[0063] Understandably, the improved HCNG network model considers the impact of mixed hydrogen on the pressure drop equation and coil equation, thus more accurately simulating the flow characteristics of hydrogen in natural gas pipelines and adapting to different hydrogen ratios and operating conditions. The compressibility coefficient of hydrogen-rich compressed natural gas can be obtained by compressing the gas using experimental equipment and measuring the volume change before and after compression, or by theoretical calculations based on the ideal gas law or the actual gas law, combined with parameters such as the composition, temperature, and pressure of the hydrogen-rich compressed natural gas.

[0064] Step S20: Based on the target hydrogen-rich compressed natural gas network model, integrate all links of the hydrogen-rich compressed natural gas supply chain to construct an optimal scheduling model for hydrogen-rich compressed natural gas.

[0065] It's important to understand that the optimal scheduling model for hydrogen-rich compressed natural gas (CNG) is a comprehensive mathematical model or simulation system that integrates all aspects of the CNG supply chain, including gas supply, compression and storage, pipeline transportation, allocation and scheduling, and user demand. Furthermore, through optimization algorithms and technical means, it's possible to achieve efficient, economical, safe, and environmentally friendly scheduling of CNG within the supply chain.

[0066] Understandably, in this model, each component is abstracted into mathematical variables or modules, which are interconnected through specific logical relationships and data flows. The model's objective function is typically related to key indicators such as cost, efficiency, reliability, or environmental benefits, while constraints may include limitations on gas flow rate, pressure, and temperature, as well as performance and capacity limitations of compressors, pipelines, and storage facilities.

[0067] Step S30: The optimal scheduling model of hydrogen-rich compressed natural gas is transformed into a mixed integer second-order quadratic programming problem through continuous linear programming, and the optimal scheduling strategy is obtained by solving the problem based on the objective iterative algorithm.

[0068] It should be noted that continuous linear programming is a mathematical optimization method used to find the maximum or minimum value of a linear objective function under given linear equality or inequality constraints. Objective iteration algorithms are iterative methods for solving complex optimization problems, starting from an initial solution and then gradually approximating the optimal solution through a series of iterative steps.

[0069] It should be understood that solving the optimal scheduling model for hydrogen-rich compressed natural gas involves a large amount of computation and low scheduling efficiency. It can be converted into a mixed integer second-order quadratic programming problem that is easier to solve to obtain the optimal solution.

[0070] Step S40: Perform scheduling based on the optimal scheduling strategy.

[0071] It is understandable that when scheduling based on the optimal scheduling strategy, the scheduling system also needs to monitor the operating status of each link in the supply chain in real time, and issue alarms and take measures to deal with abnormal situations in a timely manner.

[0072] In this embodiment, the compressibility coefficient of hydrogen-rich compressed natural gas (CNG) is obtained, and the initial CNG network model is optimized based on the compressibility coefficient to generate a target CNG network model. The CNG supply chain is integrated according to the target CNG network model to construct an optimal CNG scheduling model. The optimal scheduling model is transformed into a mixed-integer second-order quadratic programming problem using continuous linear programming, and the optimal scheduling strategy is obtained by solving the problem using a target iterative algorithm. Scheduling is then performed based on the optimal scheduling strategy. The impact of CNG on gas network dynamics is studied, and by considering the optimal scheduling model of the production, storage, mixing, transmission, and utilization links of the CNG supply chain, an iterative algorithm is used to obtain the optimal scheduling solution, thereby improving the performance of the integrated energy system.

[0073] Reference Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the energy dispatching method of this application. Based on the first embodiment described above, a second embodiment of the energy dispatching method of this application is proposed.

[0074] In the second embodiment, step S10 includes:

[0075] Step S101: Obtain the initial hydrogen-rich compressed natural gas network model and the volume fraction of the hydrogen-rich compressed natural gas, wherein the volume fraction represents the proportion of hydrogen in the hydrogen-rich compressed natural gas.

[0076] It is understandable that the volume fraction of hydrogen-rich compressed natural gas can be estimated by measuring the proportion of hydrogen in a sample of hydrogen-rich compressed natural gas, calculated based on the production data of hydrogen-rich compressed natural gas, or by consulting scientific literature or industry standards to understand the typical value or range of the hydrogen component in hydrogen-rich compressed natural gas.

[0077] In one example, neglecting convection and inertia terms, the equations and transformations describing steady-state isothermal gas flow are as follows:

[0078]

[0079] Where ρ and v are the gas density and velocity, respectively, x is the pipe length (x-coordinate), D is the pipe diameter, and θ is the convection angle. t represents time, g is the time acceleration, λ represents the gas friction factor, and p represents the gas pressure.

[0080] When considering the volume fraction to describe the proportion of hydrogen in HCNG, the equation of state and density equation for the actual gas are as follows, where M represents the molar mass of the gas, Z represents the gas compressibility factor, R represents the gas constant, A represents the cross-sectional area of ​​the pipe section, m and q represent the gas mass flow rate and volume flow rate, respectively, and T represents the temperature.

[0081]

[0082] The formula describing steady-state isothermal gas flow can be transformed using the above formula, as follows:

[0083]

[0084] Among them, p,T em Here, M represents the gas pressure and temperature, R represents the molar mass of the gas, R represents the gas constant, and A represents the cross-sectional area of ​​the pipe. This represents the flow rate in the pipeline.

[0085] For containing N m A mixture of gases, the average molecular mass of gas M can be expressed as:

[0086]

[0087] Where, r HCNG M is the integral number of hydrogen gas. c This represents the molar mass of gaseous component c.

[0088] Step S102: Obtain the pressure level of the hydrogen-rich compressed natural gas, and calculate the compressibility coefficient using a linear formula based on the pressure level and relevant parameters in the volume fraction.

[0089] Understandably, pressure levels can be obtained using pressure sensors or gauges during measurements. These devices can be installed at various points in the supply chain, such as compressor outlets, storage facilities, and pipeline networks, to monitor natural gas pressure in real time.

[0090] In one example, the compressibility factor Z of HCNG differs from that of natural gas; it is no longer a constant. Under isothermal conditions, its value is related to the integral r of hydrogen gas. HCNG It is related to the pressure level p. When the hydrogen volume fraction is less than 50%, the relationship between Z and p can be expressed by the following linear formula.

[0091] Z = αp + β

[0092] Where α and β are r HCNG Relevant parameters.

[0093] Step S103: Transform the initial hydrogen-rich compressed natural gas network model according to the compression coefficient, and integrate the transformed model with the pipeline data of the hydrogen-rich compressed natural gas to obtain the target constraint equation.

[0094] It should be noted that the target constraint equations are a series of equations established based on physical laws, network characteristics, and actual needs when optimizing or designing hydrogen-rich compressed natural gas networks. These equations are used to describe the relationships between various variables (such as pressure, flow rate, temperature, etc.) in the network.

[0095] It should be understood that in hydrogen-rich compressed natural gas networks, the flow and energy equations, typically used to describe gas flow and energy conversion, need to be adjusted based on the compressibility coefficient to reflect the actual gas behavior. During integration, an appropriate integration method, such as the trapezoidal rule or Simpson's rule, needs to be selected based on the characteristics of the pipeline data and the required accuracy to approximate the integral value. Based on the integration results and the constraints of the network model (such as flow balance and pressure limits), the objective constraint equation is constructed.

[0096] Step S104: Obtain the target hydrogen-rich compressed natural gas network model based on the target constraint equation and the preset hydrogen-rich compressed natural gas network constraint information.

[0097] It should be noted that the preset hydrogen-rich compressed natural gas network constraint information is a series of constraints set before constructing or optimizing the hydrogen-rich compressed natural gas network, based on factors such as network design standards, safety requirements, and economic considerations. These constraints may include pressure range, flow limit, temperature limit, pipeline capacity, compressor performance, etc.

[0098] For ease of understanding, the following examples are provided, but they do not limit this application. In one example, refer to... Figure 3 , Figure 3This is the linear regression result of the compression coefficient of this application. In the figure, gray dots represent the actual values ​​of Z, while gray shading represents the deviation between the approximate and actual values, showing T. em =288.15K and r HCNG The linear regression results for Z at 20% yielded α and β values ​​of -8.44 × 10⁻⁶. -9 The values ​​are 0.99337, with an average deviation of approximately 0.29%, which is within an acceptable range. Based on this, the steady-state isothermal gas flow formula can also be transformed into:

[0099]

[0100] Integrating over the upstream side (x = 0, p = pi) and downstream side (x = Lij, p = pj) of pipe ij, we obtain:

[0101]

[0102] In the formula p i,t ,p j,t Let L be the gas pressure at node i / j. ij Let be the length of the pipe between nodes i and j. Let i be the average gas flow rate in the pipe between nodes i and j. The inflow / outflow rate of pipe ij. The logarithmic term in the above formula is in the interval [p]. min ,p max Piecewise linearization. Transform the bilinear terms within the interval... Linearization is performed using a special ordered set method. Where p min ,p max Minimum / maximum node pressure, This represents the minimum / maximum pipe flow rate.

[0103] At the same time, the HCNG network must adhere to node energy conservation, and the node energy conservation formula is as follows:

[0104] HCNG networks must adhere to node energy conservation standards, which are denoted as:

[0105]

[0106] In the formula, LHV HCNG =r HCNG LHV H +(1-r HCNG LHV NG LHV H,NG,HCNG Indicates the lower calorific value of hydrogen / natural gas / HCNG, q w,t and This indicates gas injection at the source node and gas injection through the upstream pipeline connected to node i. and q gt,t This indicates that the gas flowing out of node i flows to its downstream pipeline and gas turbine; The gas load is expressed in MW; the arrow symbol indicates that the index node is connected to node i.

[0107] The gas flow rate at the source node and the pressure at each node are constrained by upper and lower limits, as shown below:

[0108]

[0109] Where, q w,min q w,max Describes the upper / lower limits of the gas flow rate at the source node, p i,min p i,max Describe the upper / lower limits of the pressure at each node.

[0110] Gas inertia enables pipelines to store energy, a process known as pipeline packing or pipeline bagging. The type of pipeline packing is determined by the pipeline's physical parameters and average pressure, as shown below:

[0111]

[0112] In the formula Let p be the average line envelope of pipe ij. n ,T n These represent standard atmospheric pressure and temperature, respectively. ij Let L be the cross-sectional area of ​​pipe ij. i,t The gas load is expressed in MW, and Δt represents the time variation.

[0113] When using HCNG as the gas medium, Z can be replaced with The conversion is as follows:

[0114]

[0115] Where, p i p j This represents the pressure at points i and j. Furthermore, the total number of packets in the HCNG network should be periodically restored to its initial value to facilitate periodic scheduling.

[0116]

[0117] Where Ω pipe This is the index set of pipe ij in the HCNG network.

[0118] By combining the above formulas, an improved HCNG network model can be obtained.

[0119] Of course, in order to illustrate the impact of hydrogen-rich compressed natural gas network model on gas network scheduling at different pressure levels by comparing the target hydrogen-rich compressed natural gas network model with a general process model, and to provide data for user decision-making, in the second embodiment, after step S104, the following is also included:

[0120] Obtain a general hydrogen-rich compressed natural gas (CNG) network model, wherein the compression coefficient of the CNG network model is constant; calculate the optimization results of the general CNG network model and the target CNG network model in minimizing the daily cost of CNG; use the optimization result of the target CNG network model as a reference value, calculate the deviation of the optimization result of the general CNG network model, and display the deviation to the target user.

[0121] Under normal circumstances, when determining the volume fraction of an HCNG network under high pressure, the fluctuation in compressibility coefficient caused by pressure changes is not negligible. If a general flow model is still used for HCNG network optimization and scheduling under high pressure, a significant deviation will occur. This impact can be demonstrated by calculating the deviation of the compressibility coefficient under different pressures, providing users with reference data.

[0122] For ease of understanding, the following examples are provided, but they do not limit this application. In one example, refer to... Figure 4 , Figure 4 This diagram illustrates the deviation of the optimization results for the objective function. To illustrate the impact of hydrogen-natural gas mixing on gas network scheduling under different pressure levels, a 37-node gas network and a 6-node gas network are used as examples. A comparison is made between scheduling models using a general flow model (M0) and an improved HCNG network model (M1). Both models aim to minimize the daily HCNG cost, but they differ in operational constraints. Specifically, the two topologies r... HCNG The values ​​vary between 0 and 20%, with a step size of 1%. The figure shows the optimization results of the objective function under two models, where the doped circular line represents the 2-bar scenario and the doped triangular line represents the 60-bar scenario. Using the optimization results obtained from the improved HCNG network model as a reference, the deviation of the optimization results obtained from the general flow model relative to it is obtained. Specifically, the average deviation in the 2-bar scenario is approximately 0.008%, and the average deviation in the 60-bar scenario is approximately 4.81%. The latter is 601.25 times that of the former, indicating that the hydrogen-natural gas mixture has a much greater impact on the high-pressure gas network than on the low-pressure gas network.

[0123] In this embodiment, an initial hydrogen-rich compressed natural gas (HCNG) network model and the volume fraction of the HCNG are obtained, where the volume fraction represents the proportion of hydrogen in the HCNG. The pressure level of the HCNG is obtained, and the compressibility coefficient is calculated linearly based on the relevant parameters in the pressure level and the volume fraction. The initial HCNG network model is transformed according to the compressibility coefficient, and the transformed model is integrated over the pipeline data of the HCNG to obtain the target constraint equation. The target HCNG network model is obtained based on the target constraint equation and the preset HCNG network constraint information. The improved HCNG network model considers the influence of mixed hydrogen on the pressure drop equation and the coil equation, thereby more accurately simulating the flow characteristics of hydrogen in the natural gas pipeline network. It can adapt to different hydrogen ratios and operating conditions, providing greater flexibility for the operation of the power-HCNG integrated energy system.

[0124] Reference Figure 5 , Figure 5 This is a flowchart illustrating the third embodiment of the energy dispatching method of this application. Based on the second embodiment described above, a third embodiment of the energy dispatching method of this application is proposed.

[0125] In the third embodiment, step S20 includes:

[0126] Step S201: Obtain the scheduling target and cost information at each stage of the hydrogen-rich compressed natural gas supply chain, and construct the target cost function of the hydrogen-rich compressed natural gas supply chain based on the cost information and the scheduling target.

[0127] It is important to understand that scheduling objectives typically refer to achieving specific economic or operational goals through optimizing resource allocation and process arrangements, such as maximizing profits, minimizing costs, and ensuring supply stability.

[0128] It should be understood that cost information at each stage of the hydrogen-rich compressed natural gas supply chain can include raw material acquisition costs (including the procurement costs of raw materials such as natural gas and coal, as well as the transportation and storage costs of raw materials), hydrogen production process costs, compression processing costs (compressing the produced hydrogen for storage and transportation), and storage and transportation costs (the storage and transportation costs of hydrogen-rich compressed natural gas include the purchase and maintenance costs of storage equipment, the rental and maintenance costs of transportation vehicles, and energy consumption during transportation).

[0129] In one example, the goal of E-HCNG-IES scheduling is to minimize the daily operating cost, and the target cost function is as follows:

[0130]

[0131] In the formula CNG,t The cost of purchasing natural gas from long-distance transmission lines, and These are penalties for carbon emissions and renewable energy generation, respectively. om,t For equipment maintenance costs, C wheel,t The refueling fee paid to the public grid when transmitting electricity from a hydrogen station to a multi-energy zone. For the unit procurement cost of natural gas, For natural gas purchase volume, Fines will be imposed on entities that emit carbon dioxide. Fines per unit for wind / solar power curtailment, P gt / wt / pv,t For GT / WT / PV output electrical power, P elz,t For the input power of ELZ, For the predicted WT / PV output power, For the unit maintenance cost of MR / HS / MZ / GT, For the unit maintenance cost of WT / PV / ELZ / CP, The flow rate of natural gas produced by MR using ELZ / HS as the hydrogen source. For the hydrogen injected from ELZ / HS into mr, For hydrogen injected from ELZ / HS into mz, Hydrogen gas is injected into / exited from the HS. For public power grid units to charge fees on a rotating basis, P grid,t Power flow transmitted through the power grid.

[0132] Step S202: Based on the target hydrogen-rich compressed natural gas network model, establish the constraints of the target cost function on grid constraints and multi-energy flow balance.

[0133] Understandably, grid constraints primarily concern the physical characteristics and operational rules of the power network. At each grid node, the input and output power must be equal to maintain stable grid operation; each line in the grid has its maximum transmission capacity, which cannot be exceeded to avoid overload and faults; voltage and frequency must be maintained within certain ranges. Multi-energy flow balance refers to the need to maintain a balance in the conversion and transmission of various energy sources in a network containing multiple energy forms (such as electricity, natural gas, hydrogen, etc.).

[0134] In one example, the power grid is modeled using DC power flow, as follows:

[0135]

[0136] Among them, F nl,t Let θ be the capacity of line nl. n / l,t P is the phase angle of line n / l. gt,tX is the output electrical power of GT. nl Let nl be the length of the line. For the total DC charge, F nl,max This is the maximum capacity.

[0137] It is important to note that the electrical energy P transmitted from the gas station to the multi-energy zone... grid,t The following constraints must be met:

[0138]

[0139] in, This refers to the line capacity of the public power grid.

[0140] Gas stations need to maintain an electrical balance, and the electrical balance model is as follows:

[0141]

[0142] Where, γ cp / mr The CP / MR power consumption, natural gas balance constraints, and purchase limits are as follows:

[0143]

[0144] The total amount of hydrogen injected into MZ is used This means that the gaseous components in MZ should satisfy the integral number requirement for hydrogen gas and the law of conservation of energy, as shown below:

[0145]

[0146]

[0147] in, This represents the minimum total natural gas consumption. For the amount of natural gas purchased, To purchase the maximum amount of natural gas, This represents the minimum amount of hydrogen gas to be injected.

[0148] Step S203: Obtain the optimal scheduling model for hydrogen-rich compressed natural gas based on the objective cost function and the constraints.

[0149] It should be understood that by combining the above objective cost function and the constraints, the optimal scheduling model for hydrogen-rich compressed natural gas can be obtained, and the optimal scheduling strategy can be obtained by solving the model.

[0150] For ease of understanding, the following examples are provided, but they do not limit this application. In one example, refer to... Figure 6 , Figure 6This is a schematic diagram of the hydrogen-rich compressed natural gas (HCNG) supply chain structure for this application. The centralized hydrogen refueling station integrates renewable energy power generation, hydrogen production, compression, storage, methanation, and blending functions. Abundant renewable energy sources near the HCNG station, including wind and solar power, serve two purposes: first, to supply power to downstream electricity users via the public grid; and second, to power the equipment within the HCNG station, including the electrolyzer (ELZ), compressor (CP), and methanation reactor (MR). Hydrogen produced by water electrolysis, after compression by the CP, can be stored in a hydrogen storage (HS) facility, injected into the MR to convert into synthetic natural gas, or directly injected into the blending zone (MZ) to produce HCNG. When hydrogen demand reaches its peak, hydrogen can be extracted from the HS to supply the MR and MZ. It is important to note that upstream long-distance pipelines remain the primary gas source for the refueling station. HCNG flowing out of the blending zone is injected into the multi-energy zone and transported by the HCNG network to meet the gas and electricity loads of industrial, commercial, and residential users. Furthermore, the HCNG and power grid in the multi-energy zone are coupled through a gas-electric system. Among these, P... elz,t P is the input power of ELZ. grid,t Power flow transmitted through the power grid. For the hydrogen injected from ELZ / HS into mr, Hydrogen gas is injected into HS. Let i be the average gas flow rate in the pipe between nodes i and j. For natural gas purchase volume, This represents the amount of natural gas mixed in. q g,t q represents the total flow rate delivered to the gas turbine at time t. g,t F represents the total flow rate in the pipe. nl,t The diagram shows the capacity of line nl. It also includes gas turbines (GT1 and GT2), multiple nodes (N1 to N6), and pipes P1-P5.

[0151] In this embodiment, the scheduling objective and cost information at each stage of the hydrogen-rich compressed natural gas (HCNG) supply chain are obtained. Based on the cost information and the scheduling objective, a target cost function for the HCNG supply chain is constructed. Constraints on the target cost function under grid constraints and multi-energy flow balance are established based on the target HCNG network model. The optimal scheduling model for the HCNG is obtained according to the target cost function and the constraints. The "production-storage-blending-transmission-utilization" link of the HCNG supply chain is considered, and the goal of minimizing daily operating costs, including natural gas procurement costs, carbon emission penalties, renewable energy generation penalties, and equipment maintenance costs, is determined. This maximizes the economic benefits of the entire system and promotes the application of hydrogen.

[0152] Reference Figure 7 , Figure 7This is a flowchart illustrating the fourth embodiment of the energy dispatching method of this application. Based on the third embodiment described above, the fourth embodiment of the energy dispatching method of this application is proposed.

[0153] In the fourth embodiment, step S30 includes:

[0154] Step S301: Identify the nonlinear terms and nonlinear constraints of the optimal scheduling model for hydrogen-rich compressed natural gas.

[0155] It should be noted that nonlinear terms are those that do not satisfy a linear relationship; that is, the rate of change of these terms is not constant, or the relationship between them is not linear. Nonlinear constraints refer to restrictions imposed on the range of values ​​or relationships of decision variables that do not satisfy a linear relationship.

[0156] It should be understood that identifying nonlinear terms and constraints can be done by carefully examining all equations and expressions in the model. This includes the objective function, constraints, and any auxiliary equations used to describe the system's behavior. Identifying nonlinear terms and constraints can also be done by analyzing the physical processes in the model, such as the flow, compression, storage, and conversion of gases. These physical processes often involve nonlinear relationships.

[0157] Step S302: The nonlinear term is converted into a linear term by successive linearization algorithm, and the McCormick envelope is applied to approximate the nonlinear constraint as a linear constraint to obtain the mixed integer second-order quadratic programming problem.

[0158] It's important to understand that successive linearization is an iterative method used to approximate a nonlinear optimization problem as a series of linear optimization problems. In each iteration, the algorithm linearizes the nonlinear terms based on the current solution. The McCormick envelope is a technique for approximating nonlinear constraints as linear constraints, constructing a set of linear inequalities containing the original nonlinear constraints based on the convexity or concavity of the upper and lower bound functions of the variables.

[0159] For ease of understanding, the following example is provided, but it does not limit this application. In one example, due to the inaccuracy of the height of the solution space edges in early iterations, the actually valid solution may be cut off by pressure constraints. Therefore, the bilinear terms are transformed into the following formula:

[0160]

[0161] in and and As an auxiliary variable; ζ is to ensure The parameter is introduced because it is close to p in magnitude, thus avoiding numerical problems. For pipeline flow rate, This represents the pipe segment's storage capacity. p is the pressure. To reduce the error caused by successive linearization, a McCormick envelope is introduced, as follows:

[0162]

[0163] in, Indicates the maximum / minimum value of the pipe section's inventory. p represents the maximum / minimum flow rate of the pipeline. max / min This represents the maximum / minimum pressure. and As auxiliary variables. The equation is further transformed into:

[0164]

[0165] Step S303: Solve the mixed integer second-order quadratic programming problem based on the objective iterative algorithm to obtain the optimal scheduling strategy.

[0166] It's important to understand that an iterative goal-oriented algorithm is an iterative method for solving optimization problems. Its key characteristic is that in each iteration, the algorithm attempts to find a solution that satisfies a specific objective (or "sub-objective"). This objective can be based on a performance metric, constraints, or other aspects of the optimization problem. By continuously iterating and adjusting the solution, the algorithm gradually approaches the global optimum.

[0167] Of course, in order to continuously approximate and refine the solution through iterative algorithms, a solution that satisfies all constraints and has a high optimization objective value is ultimately obtained. Step S303 may include:

[0168] The algorithm parameters of the target iterative algorithm are initialized, wherein the algorithm parameters include a preset number of iterations and a tolerance error value; the mixed integer second-order quadratic programming problem is solved using an optimization solver to obtain an initial solution, and the initial solution is used as the starting point of the target iterative algorithm for iteration; after each iteration, if the number of iterations reaches the preset number of iterations and the obtained target solution is within the tolerance error value, the iteration ends and the target solution is used as the final solution; the optimal scheduling strategy is determined based on the final solution.

[0169] It should be understood that the preset iteration count is an upper limit set before the target iterative algorithm begins, used to control the algorithm's running time and prevent it from running in an infinite loop. The tolerance error value is a threshold used during iteration to determine whether the current solution is sufficiently close to the optimal solution. When the change in the solution found by the algorithm is less than this threshold, the algorithm can be considered to have converged to a sufficiently good solution and can stop iterating. The initial solution is the starting point when the algorithm begins iteration. In each iteration, the algorithm attempts to find a better solution; this solution is called the target solution. When the algorithm meets the stopping condition (such as reaching the preset iteration count and the target solution being within the tolerance error value), the solution found by the algorithm is called the final solution.

[0170] For ease of understanding, the following examples are provided, but they do not limit this application. In one example, refer to... Figure 8 , Figure 8 This is a schematic diagram illustrating the successive linearization of the logarithmic term ln p. When the number of segments is fixed, the accuracy of the successive linearization of the logarithmic term ln p is affected by the segment interval. Assuming the number of segments is 1, for a point (p*, ln(p*)), the segment interval... The linearization accuracy within is significantly higher than To improve the linearization accuracy within the objective function, an iterative solution algorithm is proposed that considers a penalty term in the objective function to obtain a local optimum. A tight-bound method is employed to further enhance the linearization accuracy of the logarithmic term.

[0171]

[0172] Where ε (k) The control parameter for constraint tightening is a positive constant less than 1, which monotonically decreases as the number of iterations k increases. The stopping criterion for constraint tightening is the maximum deviation between the actual and approximate value of p.

[0173] Taking the first non-convex constraint as an example, it can be regarded as a linear function. With convex functions The difference, such as: The relaxed form can be written as:

[0174]

[0175] Thus, the penalty model minC is obtained. tot +κ (k) s sum In the formula κ (k) Let s be the penalty coefficient for the k-th iteration. sum The expression is:

[0176]

[0177] Among them, C totLet s1, s2, and s3 be the total cost, and s be the tolerance error. sum Let be the total error. The objective function converges when the stopping criterion is met through iterative solutions.

[0178]

[0179] Where δ1 and δ2 are the tolerance errors of GAP1 and GAP2. GAP1 and GAP2 represent the optimality gap.

[0180] In this embodiment, the nonlinear terms and nonlinear constraints of the optimal scheduling model for hydrogen-rich compressed natural gas are identified; the nonlinear terms are converted into linear terms using a successive linearization algorithm, and the nonlinear constraints are approximated as linear constraints using the McCormick envelope, thus obtaining the mixed-integer second-order quadratic programming problem; the mixed-integer second-order quadratic programming problem is solved based on an objective iteration algorithm to obtain the optimal scheduling strategy. By employing continuous linear programming, the original complex scheduling model is transformed into a problem solvable by existing optimization algorithms, making the solution process more efficient and accurate.

[0181] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the energy dispatching method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0182] This application also provides an energy dispatching device, please refer to... Figure 9 The energy dispatching device includes:

[0183] The network model construction module 10 is used to obtain the compressibility coefficient of hydrogen-rich compressed natural gas and optimize the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate the target hydrogen-rich compressed natural gas network model.

[0184] The scheduling model construction module 20 is used to integrate the various links of the hydrogen-rich compressed natural gas supply chain according to the target hydrogen-rich compressed natural gas network model, and construct the optimal scheduling model for hydrogen-rich compressed natural gas.

[0185] The scheduling strategy solution module 30 is used to transform the optimal scheduling model of hydrogen-rich compressed natural gas into a mixed integer second-order quadratic programming problem through continuous linear programming, and to obtain the optimal scheduling strategy based on the objective iterative algorithm.

[0186] The integrated energy scheduling module 40 is used for scheduling based on the optimal scheduling strategy.

[0187] The energy dispatching device provided in this application, employing the energy dispatching method described in the above embodiments, can address the significant potential of hydrogen-rich compressed natural gas renewable energy and hydrogen utilization. However, injecting hydrogen into the natural gas network alters the original fluid dynamics, complicates the physical properties of the compressed gas, and affects the operating conditions of the natural gas infrastructure—a technical problem. Compared to the prior art, the beneficial effects of the energy dispatching device provided in this application are the same as those of the energy dispatching method provided in the above embodiments, and other technical features of the energy dispatching device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0188] This application provides an energy dispatching device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the energy dispatching method in the above embodiment 1.

[0189] The following is for reference. Figure 10 The diagram illustrates a structural schematic of an energy dispatching device suitable for implementing embodiments of this application. The energy dispatching device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 10 The energy dispatching device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0190] like Figure 10As shown, the energy dispatching device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the energy dispatching device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the energy dispatching equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows energy dispatching equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0191] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0192] The energy dispatching equipment provided in this application, employing the energy dispatching method described in the above embodiments, can address the significant potential of hydrogen-rich compressed natural gas in renewable energy and hydrogen utilization. However, injecting hydrogen into the natural gas network alters the original fluid dynamics, complicates the physical properties of the compressed gas, and affects the operating conditions of the natural gas infrastructure—a technical problem. Compared to the prior art, the beneficial effects of the energy dispatching equipment provided in this application are the same as those of the energy dispatching method provided in the above embodiments, and other technical features of this energy dispatching equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0193] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0194] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0195] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the energy dispatching method in the above embodiments.

[0196] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0197] The aforementioned computer-readable storage medium may be included in the energy dispatching equipment; or it may exist independently and not be assembled into the energy dispatching equipment.

[0198] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the energy dispatching device, enable the energy dispatching device to implement the energy dispatching method described above.

[0199] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0200] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0201] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0202] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described energy dispatching method, which can address the significant potential of hydrogen-rich compressed natural gas in renewable energy and hydrogen utilization. However, injecting hydrogen into the natural gas network alters the original hydrodynamics, complicates the physical properties of the compressed gas, and affects the operating conditions of the natural gas infrastructure. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the energy dispatching method provided in the above embodiments, and will not be repeated here.

[0203] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the energy scheduling method described above.

[0204] The computer program product provided in this application addresses the significant potential of hydrogen-rich compressed natural gas in renewable energy and hydrogen utilization. However, injecting hydrogen into the natural gas network alters the original hydrodynamics, complicates the physical properties of the compressed gas, and impacts the operating conditions of the natural gas infrastructure. Compared to existing technologies, the beneficial effects of the computer program product provided in this application are the same as those of the energy dispatching methods provided in the above embodiments, and will not be elaborated upon here.

[0205] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An energy dispatching method, characterized in that, The energy dispatching method includes: Obtain the compressibility coefficient of hydrogen-rich compressed natural gas, and optimize the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate the target hydrogen-rich compressed natural gas network model. Based on the target hydrogen-rich compressed natural gas network model, various links in the hydrogen-rich compressed natural gas supply chain are integrated to construct an optimal scheduling model for hydrogen-rich compressed natural gas. The optimal scheduling model for hydrogen-rich compressed natural gas is transformed into a mixed-integer second-order quadratic programming problem through continuous linear programming, and the optimal scheduling strategy is obtained by solving the problem based on the objective iterative algorithm. Scheduling is performed based on the optimal scheduling strategy.

2. The energy dispatching method as described in claim 1, characterized in that, The step of integrating various links in the hydrogen-rich compressed natural gas supply chain based on the target hydrogen-rich compressed natural gas network model to construct an optimal scheduling model for hydrogen-rich compressed natural gas includes: Obtain the scheduling target and cost information at each stage of the hydrogen-rich compressed natural gas supply chain, and construct the target cost function of the hydrogen-rich compressed natural gas supply chain based on the cost information and the scheduling target; Based on the target hydrogen-rich compressed natural gas network model, establish the constraints of the target cost function under grid constraints and multi-energy flow balance; The optimal scheduling model for hydrogen-rich compressed natural gas is obtained based on the objective cost function and the constraints.

3. The energy dispatching method as described in claim 1, characterized in that, The steps of obtaining the compressibility coefficient of hydrogen-rich compressed natural gas and optimizing the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate the target hydrogen-rich compressed natural gas network model include: Obtain an initial hydrogen-rich compressed natural gas network model and the volume fraction of the hydrogen-rich compressed natural gas, wherein the volume fraction represents the proportion of hydrogen in the hydrogen-rich compressed natural gas; The pressure level of the hydrogen-rich compressed natural gas is obtained, and the compressibility coefficient is calculated linearly based on the pressure level and relevant parameters in the volume fraction. The initial hydrogen-rich compressed natural gas network model is transformed based on the compression coefficient, and the transformed model is integrated over the pipeline data of the hydrogen-rich compressed natural gas to obtain the target constraint equation. The target hydrogen-rich compressed natural gas network model is obtained based on the target constraint equation and the preset hydrogen-rich compressed natural gas network constraint information.

4. The energy dispatching method as described in claim 3, characterized in that, After the step of obtaining the target hydrogen-rich compressed natural gas network model based on the target constraint equation and the preset hydrogen-rich compressed natural gas network constraint information, the method further includes: Obtain a general hydrogen-rich compressed natural gas network model, wherein the compressibility coefficient of the general hydrogen-rich compressed natural gas network model is a constant; Calculate the optimization results of the general hydrogen-rich compressed natural gas network model and the target hydrogen-rich compressed natural gas network model in minimizing the daily cost of hydrogen-rich compressed natural gas; The optimization results of the target hydrogen-rich compressed natural gas network model are used as reference values. The deviation of the optimization results of the general hydrogen-rich compressed natural gas network model is calculated and displayed to the target user based on the deviation.

5. The energy dispatching method as described in claim 1, characterized in that, The step of transforming the optimal scheduling model of hydrogen-rich compressed natural gas into a mixed-integer second-order quadratic programming problem through continuous linear programming, and solving for the optimal scheduling strategy based on the objective iterative algorithm, includes: Identify the nonlinear terms and nonlinear constraints of the optimal scheduling model for hydrogen-rich compressed natural gas; The nonlinear terms are converted into linear terms by successive linearization algorithms, and the nonlinear constraints are approximated as linear constraints by applying McCormick envelopes, thus obtaining the mixed integer second-order quadratic programming problem. The optimal scheduling strategy is obtained by solving the mixed-integer second-order quadratic programming problem based on the objective iterative algorithm.

6. The energy dispatching method as described in claim 5, characterized in that, The steps for solving the mixed-integer second-order quadratic programming problem based on the objective iterative algorithm to obtain the optimal scheduling strategy include: Initialize the algorithm parameters of the target iterative algorithm, wherein the algorithm parameters include a preset number of iterations and a tolerance error value; The mixed-integer second-order quadratic programming problem is solved using an optimization solver to obtain an initial solution, which is then used as the starting point for the target iterative algorithm to perform iterations. After each iteration, if the number of iterations reaches the preset number of iterations and the obtained target solution is within the tolerance error value, the iteration ends and the target solution is taken as the final solution. The optimal scheduling strategy is determined based on the final solution.

7. An energy dispatching device, characterized in that, The device includes: The network model construction module is used to obtain the compressibility coefficient of hydrogen-rich compressed natural gas and optimize the initial hydrogen-rich compressed natural gas network model based on the compressibility coefficient to generate the target hydrogen-rich compressed natural gas network model. The scheduling model construction module is used to integrate various links in the hydrogen-rich compressed natural gas supply chain based on the target hydrogen-rich compressed natural gas network model, and construct the optimal scheduling model for hydrogen-rich compressed natural gas. The scheduling strategy solution module is used to transform the optimal scheduling model of hydrogen-rich compressed natural gas into a mixed integer second-order quadratic programming problem through continuous linear programming, and to obtain the optimal scheduling strategy based on the objective iterative algorithm. An integrated energy scheduling module is used for scheduling based on the optimal scheduling strategy.

8. An energy dispatching device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the energy dispatching method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the energy scheduling method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the energy dispatching method as described in any one of claims 1 to 6.

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