CN-122026372-A - Port system energy scheduling method, device, equipment, storage medium and product

CN122026372ACN 122026372 ACN122026372 ACN 122026372ACN-122026372-A

Abstract

The application discloses a port system energy scheduling method, a device, equipment, a storage medium and a product, and relates to the technical field of power distribution network optimization scheduling. And selecting an optimal compromise solution in the target solution set through an evaluation algorithm, and carrying out energy scheduling on the port multi-energy integrated system according to the optimal compromise solution. The port data is input into the target port system optimization model, the total carbon emission amount and economic benefit of the port in a preset time period can be accurately predicted and optimized by utilizing the historical data and the real-time information, the optimal compromise solution is selected from the target solution set through the evaluation algorithm, the optimal balance among a plurality of optimization targets can be ensured, the port multi-energy integrated system is subjected to energy scheduling according to the optimal compromise solution, and the efficient utilization of the port energy resource is realized.

Inventors

WANG FAN
YU HAI
Yan Zuanhong
Du Gaibi
WANG SHUAI
GUO LIDONG

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: 20260512
Application Date: 20241108

Claims (10)

1. A method for scheduling energy in a port system, said method comprising: the port data are input into a target port system optimization model, a target solution set is obtained through an optimization algorithm, and the optimization algorithm is used for optimizing the total carbon emission amount and the economic benefit of the port in a preset time period; selecting an optimal compromise solution in the target solution set through an evaluation algorithm; And carrying out energy scheduling on the port multi-energy integrated system according to the optimal compromise solution.
2. The method of claim 1, wherein the target port system optimization model includes a first objective function for minimizing a total amount of carbon emissions of the port over a preset time period, a second objective function for maximizing an economic benefit of the port over the preset time period, and a preset constraint condition including a device power constraint, a device capacity constraint, and a device status constraint, the step of inputting the port data into the target port system optimization model to find a target solution set by an optimization algorithm includes: Performing function conversion on the first objective function, the second objective function, the equipment power constraint, the equipment capacity constraint and the equipment state constraint to obtain a multi-objective optimization solution model, wherein the function conversion step is used for converting a multi-objective optimization problem into an optimization problem matched with a non-dominant genetic algorithm; And solving a target solution set through the non-dominant genetic algorithm based on the multi-target optimization solution model.
3. The method of claim 2, wherein the step of solving for the target solution set by a non-dominant genetic algorithm based on the multi-target optimization solution model comprises: acquiring an initial solution set based on the multi-objective optimization solution model; Evaluating each solution in the initial solution set according to the constraint condition pair to obtain an intermediate solution set; Dividing the intermediate solution set into different grades through non-dominant sorting, and defining the intermediate solution set with the grade in a preset grade range as a non-dominant solution set; updating the non-dominant solution set through crossover and mutation to generate a new solution set; And evaluating and updating the new solution set until the new solution set converges on the pareto optimal front, and outputting a target solution set containing non-inferior solutions.
4. A method according to claim 3, wherein the step of selecting the optimal compromise solution in the target solution set by an evaluation algorithm comprises: Acquiring an optimal boundary of the target solution set, wherein the optimal boundary is a solution positioned at the optimal front edge of pareto in the target solution set; Based on the optimal boundary, evaluating the comprehensive satisfaction degree of a decision maker on each non-inferior solution in the target solution set by using a fuzzy membership function; and selecting the solution with the highest comprehensive membership as the optimal compromised solution based on the comprehensive satisfaction degree of each non-inferior solution.
5. The method of any one of claims 1 to 4, wherein prior to the step of inputting the port data into a target port system optimization model, comprising: Constructing an initial harbor district system optimization model; Constructing constraint conditions based on the device power data; And updating the initial harbor system optimization model according to the constraint condition to obtain a target harbor system optimization model.
6. The method of claim 5, wherein the port data includes equipment energy data, electricity price data, and electricity quantity data, and the step of constructing an initial port system optimization model comprises: constructing a carbon flow model based on the equipment energy data, and acquiring carbon flow data of target energy equipment in different running states; establishing a first objective function according to the carbon flow data, wherein the first objective function is used for minimizing the total carbon emission of a harbor district in a preset time period; establishing a second objective function based on the equipment energy data, the electricity price data and the electric quantity data, wherein the second objective function is used for maximizing the economic benefit of the harbor district in a preset time period; and constructing an initial harbor system optimization model according to the first objective function and the second objective function.
7. A port system energy scheduling apparatus, the apparatus comprising: the solution set acquisition module is used for inputting port data into a target port system optimization model, and obtaining a target solution set through an optimization algorithm, wherein the optimization algorithm is used for optimizing the total carbon emission amount and economic benefit of the port in a preset time period; The solution set evaluation module is used for selecting the optimal compromise solution in the target solution set through an evaluation algorithm; and the energy scheduling module is used for performing energy scheduling on the port multi-energy integrated system according to the optimal compromise solution.
8. Port system 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 port system 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, implements the steps of the port system 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 being executed by a processor, implements the steps of the port system energy scheduling method according to any one of claims 1 to 6.

Description

Port system energy scheduling method, device, equipment, storage medium and product Technical Field The application relates to the technical field of power distribution network optimal scheduling, in particular to a port system energy scheduling method, device, equipment, storage medium and product. Background The port is used as a logistics hub, and the internal load mainly comprises loading and unloading equipment such as a wharf container crane, a bridge (portal) crane and the like. During operation of these devices, feedback energy is injected into the grid due to the large power impact, frequent start-up and braking, which presents challenges to the stability and efficiency of the grid. With the gradual maturity of new energy technology, more environment-friendly and sustainable energy selection is provided for ports, however, the current energy management and scheduling system faces higher requirements from the long-term point of view of promoting port multi-energy complementation and realizing low-carbon economy. At present, aiming at the characteristics of complex and changeable energy demands and supply of ports, the existing system energy scheduling scheme is insufficient, and the dual targets of carbon emission control and economic benefit are difficult to precisely balance, so that the potential release of ports on low-carbon economic roads is limited. Therefore, how to improve the accuracy of the port system energy scheduling is a problem that needs to be solved at present for carbon emission and economic benefit. Disclosure of Invention The application mainly aims to provide a port system energy scheduling method, device, equipment, storage medium and product, and aims to solve the technical problem that the port system energy scheduling is difficult to accurately balance carbon emission control and economic benefit. In order to achieve the above object, the present application provides a port system energy scheduling method, which includes: the port data are input into a target port system optimization model, a target solution set is obtained through an optimization algorithm, and the optimization algorithm is used for optimizing the total carbon emission amount and the economic benefit of the port in a preset time period; selecting an optimal compromise solution in the target solution set through an evaluation algorithm; And carrying out energy scheduling on the port multi-energy integrated system according to the optimal compromise solution. In an embodiment, the target port system optimization model includes a first objective function, a second objective function and a preset constraint condition, the first objective function is used for minimizing the total carbon emission of the port in a preset time period, the second objective function is used for maximizing the economic benefit of the port in the preset time period, the preset constraint condition includes a device power constraint, a device capacity constraint and a device state constraint, the port data is input into the target port system optimization model, and the step of obtaining a target solution set through an optimization algorithm includes: Performing function conversion on the first objective function, the second objective function, the equipment power constraint, the equipment capacity constraint and the equipment state constraint to obtain a multi-objective optimization solution model, wherein the function conversion step is used for converting a multi-objective optimization problem into an optimization problem matched with a non-dominant genetic algorithm; And solving a target solution set through the non-dominant genetic algorithm based on the multi-target optimization solution model. In one embodiment, the step of solving the target solution set by a non-dominant genetic algorithm based on the multi-target optimization solution model includes: acquiring an initial solution set based on the multi-objective optimization solution model; Evaluating each solution in the initial solution set according to the constraint condition pair to obtain an intermediate solution set; Dividing the intermediate solution set into different grades through non-dominant sorting, and defining the intermediate solution set with the grade in a preset grade range as a non-dominant solution set; updating the non-dominant solution set through crossover and mutation to generate a new solution set; And evaluating and updating the new solution set until the new solution set converges on the pareto optimal front, and outputting a target solution set containing non-inferior solutions. In an embodiment, the step of selecting the optimal compromise solution in the target solution set by an evaluation algorithm includes: Acquiring an optimal boundary of the target solution set, wherein the optimal boundary is a solution positioned at the optimal front edge of pareto in the target solution set; Based on the optimal boundary, evaluating the comprehensive s