CN-121998271-A - Energy scheduling method, device, equipment, storage medium and computer program product

CN121998271ACN 121998271 ACN121998271 ACN 121998271ACN-121998271-A

Abstract

The application relates to the technical field of comprehensive energy system optimization and discloses an energy scheduling method, device, equipment, storage medium and computer program product, which comprise the steps of obtaining a compression coefficient of hydrogen-rich compressed natural gas, optimizing an initial hydrogen-rich compressed natural gas network model based on the compression coefficient, and generating a target hydrogen-rich compressed natural gas network model; the method comprises the steps of integrating each link of a hydrogen-rich compressed natural gas supply chain according to a target hydrogen-rich compressed natural gas network model, constructing an optimal hydrogen-rich compressed natural gas scheduling model, converting the optimal hydrogen-rich compressed natural gas scheduling model into a mixed integer second-order quadratic programming problem through continuous linear programming, solving the problem based on a target iterative algorithm to obtain an optimal scheduling strategy, and scheduling based on the optimal scheduling strategy. The influence of the 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.

Inventors

Yan Zuanhong
Du Gaibi
WANG SHUAI
WANG FAN
YU HAI
PENG YI

Assignees

中石油深圳新能源研究院有限公司
中国石油天然气股份有限公司

Dates

Publication Date: 20260508
Application Date: 20241106

Claims (10)

1. An energy scheduling method is characterized by comprising the following steps: Acquiring a compression coefficient of the hydrogen-rich compressed natural gas, optimizing an initial hydrogen-rich compressed natural gas network model based on the compression coefficient, and generating a target hydrogen-rich compressed natural gas network model; Integrating all links of a hydrogen-rich compressed natural gas supply chain according to the target hydrogen-rich compressed natural gas network model, and constructing an optimal scheduling model of the hydrogen-rich compressed natural gas; Converting the optimal scheduling model of the hydrogen-rich compressed natural gas into a mixed integer second-order quadratic programming problem through continuous linear programming, and solving based on a target iterative algorithm to obtain an optimal scheduling strategy; And scheduling based on the optimal scheduling strategy.
2. The energy scheduling method of claim 1, wherein the step of integrating each link 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 of the hydrogen-rich compressed natural gas comprises the steps of: acquiring a scheduling target of the hydrogen-rich compressed natural gas supply chain and cost information on each link, and constructing a target cost function of the hydrogen-rich compressed natural gas supply chain based on the cost information and the scheduling target; Establishing constraint conditions of the target cost function on grid constraint and multi-energy flow balance based on the target hydrogen-rich compressed natural gas network model; And obtaining the optimal dispatching model of the hydrogen-rich compressed natural gas according to the target cost function and the constraint condition.
3. The energy scheduling method of claim 1, wherein the step of obtaining the compression coefficient of the hydrogen-enriched compressed natural gas, optimizing the initial hydrogen-enriched compressed natural gas network model based on the compression coefficient, and generating the target hydrogen-enriched compressed natural gas network model comprises: Acquiring an initial hydrogen-rich compressed natural gas network model and a 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; Acquiring the pressure level of the hydrogen-rich compressed natural gas, and calculating the compression coefficient by adopting a linear mode based on the pressure level and related parameters in the volume fraction; converting the initial hydrogen-rich compressed natural gas network model according to the compression coefficient, and integrating the converted model with pipeline data of the hydrogen-rich compressed natural gas to obtain a target constraint equation; And obtaining a target hydrogen-rich compressed natural gas network model according to the target constraint equation and preset hydrogen-rich compressed natural gas network constraint information.
4. The energy scheduling method of claim 3, further comprising, after the step of obtaining the target hydrogen-enriched compressed natural gas network model according to the target constraint equation and the preset hydrogen-enriched compressed natural gas network constraint information: Obtaining a general hydrogen-rich compressed natural gas network model, wherein the compression coefficient of the general hydrogen-rich compressed natural gas network model is a constant; Calculating an optimization result of the general hydrogen-rich compressed natural gas network model and the target hydrogen-rich compressed natural gas network model in terms of minimizing daily hydrogen-rich compressed natural gas cost; And taking the optimization result of the target hydrogen-rich compressed natural gas network model as a reference value, counting the deviation of the optimization result of the general hydrogen-rich compressed natural gas network model, and displaying the deviation to a target user.
5. The energy scheduling method according to claim 1, wherein the step of converting the hydrogen-rich compressed natural gas optimal scheduling model into a mixed integer second order quadratic programming problem through continuous linear programming and solving to obtain an optimal scheduling strategy based on a target iterative algorithm comprises the following steps: Identifying nonlinear terms and nonlinear constraints of the optimal hydrogen-rich compressed natural gas scheduling model; Converting the nonlinear term into a linear term through a successive linearization algorithm, and approximating the nonlinear constraint into a linear constraint by using an McCormick envelope to obtain the mixed integer second-order quadratic programming problem; And solving the mixed integer second-order quadratic programming problem based on a target iterative algorithm to obtain an optimal scheduling strategy.
6. The energy scheduling method of claim 5, wherein the step of solving the mixed integer second order quadratic programming problem based on the target iterative algorithm to obtain an optimal scheduling policy comprises: initializing algorithm parameters of the target iterative algorithm, wherein the algorithm parameters comprise preset iteration times and tolerance error values; solving the mixed integer second-order quadratic programming problem by using an optimization solver to obtain an initial solution, and iterating by taking the initial solution as a starting point of the target iterative algorithm; After each iteration, if the iteration number reaches the preset iteration number and the obtained target solution is within the tolerance error value, ending the iteration and taking the target solution as a final solution; And determining the optimal scheduling strategy based on the final solution.
7. An energy scheduling apparatus, the apparatus comprising: The network model construction module is used for obtaining the compression coefficient of the hydrogen-enriched compressed natural gas, optimizing the initial hydrogen-enriched compressed natural gas network model based on the compression coefficient and generating a target hydrogen-enriched compressed natural gas network model; The scheduling model construction module is used for integrating all links of a hydrogen-rich compressed natural gas supply chain according to the target hydrogen-rich compressed natural gas network model to construct an optimal scheduling model of the hydrogen-rich compressed natural gas; The scheduling strategy solving module is used for converting the optimal scheduling model of the hydrogen-rich compressed natural gas into a mixed integer second-order quadratic programming problem through continuous linear programming and solving the optimal scheduling strategy based on a target iterative algorithm; And the integrated energy scheduling module is used for scheduling based on the optimal scheduling strategy.
8. An energy scheduling device, characterized in that the device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the energy scheduling method according to any one of claims 1 to 6.
9. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the energy scheduling method according to any one of claims 1 to 6.
10. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the steps of the energy scheduling method according to any one of claims 1 to 6.

Description

Energy scheduling method, device, equipment, storage medium and computer program product Technical Field The present application relates to the field of integrated energy system optimization technology, and in particular, to an energy scheduling method, apparatus, device, storage medium, and computer program product. Background Hydrogen-rich compressed natural gas (Hydrogen Compressed Natural Gas, HCNG) has great potential in renewable energy and hydrogen utilization. However, injecting hydrogen into the natural gas network will change the original fluid dynamics, complicating the physical properties of the compressed gas, affecting the operating conditions of the natural gas infrastructure. Disclosure of Invention The application mainly aims to provide an energy scheduling method, an energy scheduling device, energy scheduling equipment, an energy storage medium and a computer program product, and aims to solve the problem that hydrogen-rich compressed natural gas has great potential in renewable energy and hydrogen utilization. However, injecting hydrogen into a natural gas network will change the original fluid dynamics, complicating the physical properties of the compressed gas, and affecting the technical problem of the operating conditions of the natural gas infrastructure. In order to achieve the above object, the present application provides an energy scheduling method, which includes: Acquiring a compression coefficient of the hydrogen-rich compressed natural gas, optimizing an initial hydrogen-rich compressed natural gas network model based on the compression coefficient, and generating a target hydrogen-rich compressed natural gas network model; Integrating all links of a hydrogen-rich compressed natural gas supply chain according to the target hydrogen-rich compressed natural gas network model, and constructing an optimal scheduling model of the hydrogen-rich compressed natural gas; Converting the optimal scheduling model of the hydrogen-rich compressed natural gas into a mixed integer second-order quadratic programming problem through continuous linear programming, and solving based on a target iterative algorithm to obtain an optimal scheduling strategy; And scheduling based on the optimal scheduling strategy. Optionally, the step of integrating each link of the hydrogen-enriched compressed natural gas supply chain according to the target hydrogen-enriched compressed natural gas network model to construct an optimal scheduling model of the hydrogen-enriched compressed natural gas includes: acquiring a scheduling target of the hydrogen-rich compressed natural gas supply chain and cost information on each link, and constructing a target cost function of the hydrogen-rich compressed natural gas supply chain based on the cost information and the scheduling target; Establishing constraint conditions of the target cost function on grid constraint and multi-energy flow balance based on the target hydrogen-rich compressed natural gas network model; And obtaining the optimal dispatching model of the hydrogen-rich compressed natural gas according to the target cost function and the constraint condition. Optionally, the step of obtaining the compression coefficient of the hydrogen-enriched compressed natural gas, optimizing the initial hydrogen-enriched compressed natural gas network model based on the compression coefficient, and generating the target hydrogen-enriched compressed natural gas network model includes: Acquiring an initial hydrogen-rich compressed natural gas network model and a 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; Acquiring the pressure level of the hydrogen-rich compressed natural gas, and calculating the compression coefficient by adopting a linear mode based on the pressure level and related parameters in the volume fraction; converting the initial hydrogen-rich compressed natural gas network model according to the compression coefficient, and integrating the converted model with pipeline data of the hydrogen-rich compressed natural gas to obtain a target constraint equation; And obtaining a target hydrogen-rich compressed natural gas network model according to the target constraint equation and preset hydrogen-rich compressed natural gas network constraint information. Optionally, after the step of obtaining the target hydrogen-enriched compressed natural gas network model according to the target constraint equation and the preset hydrogen-enriched compressed natural gas network constraint information, the method further includes: Obtaining a general hydrogen-rich compressed natural gas network model, wherein the compression coefficient of the general hydrogen-rich compressed natural gas network model is a constant; Calculating an optimization result of the general hydrogen-rich compressed natural gas network model and the target hydrogen-rich compresse