Research on the Optimization Model and Method of Natural Gas Sales Based on Stepped Gas Prices

doi:10.12422/j.issn.1006-396X.2025.01.006

Abstract

Abstract:

With the rapid growth of natural gas demand, there is an urgent need to consider the production, supply, storage and marketing structure of natural gas pipeline network, optimize the natural gas sub-customer sales scheme based on system analysis and decision-making theory, and maximize the profitability and economic benefits. With the objective of maximizing the annual total benefit of the natural gas sales pipeline network of the provincial company, taking the gas supply volume from the gas source, the pipeline transmission volume, and the sales volume of the customers as the decision-making variables, and taking into account the node flow balance constraints, the upper and lower limits of the gas volume of the production, supply, storage, and marketing links, the total sales volume of the sub-province constraints, etc., we optimize the gas procurement costs, pipeline costs, storage and transfer costs of storage reservoirs and LNG receiving stations and the customer sales revenues of the gas supply network of the industry chain in the upstream, midstream, and downstream segments. The customer sales revenue adopts the step pricing method, establishes the mathematical model of natural gas customer sales volume optimization, and selects GUROBI solver as the optimization calculation tool to solve the model. The model is tested on the basis of basic data of natural gas pipeline network of a province in the next 10 years to maximize the benefits while meeting the customers' natural gas demand, which verifies the reasonableness and accuracy of the model, and provides advanced analytical tools and technical methods for improving the quality and increasing the efficiency of the natural gas sales business.

Key words: Natural gas, Maximum benefit, Ladder price, Sales optimization, Improve quality and efficiency

摘要：

随着天然气需求的快速增长，亟需考虑天然气管网产供储销结构。基于系统分析与决策理论，可优化天然气分客户销售方案，实现盈利能力和经济效益的最大化。以某省某公司天然气销售管网年总效益最大为目标，以气源供气量、管道输送量、客户销售量为决策变量，综合考虑节点流量平衡约束、产供储销各环节气量上下限约束、分省总销量约束等，对产业链上中下游各个环节中涉及到的气源采购成本、管输成本、储气库及LNG接收站储转成本以及客户销售收入采用阶梯计价方式，建立天然气客户销量优化数学模型，并选择GUROBI求解器作为优化计算工具对模型求解。以某省天然气管网未来10年的数据为基础进行测试，在满足客户天然气需求量的同时使效益达到最大，验证了该模型的合理性与准确性，为天然气销售业务提质增效提供了先进的分析工具和方法。

关键词: 天然气, 效益最大, 阶梯价格, 销量优化, 提质增效

CLC Number:

F426.22

Haoyuan HOU, Dingzhi LIU, Xi ZHANG, Sirui ZHAO, Yuantao ZHANG, Lili ZUO. Research on the Optimization Model and Method of Natural Gas Sales Based on Stepped Gas Prices[J]. Journal of Petrochemical Universities, 2025, 38(1): 42-48.

侯浩远, 刘定智, 张曦, 赵思睿, 张元涛, 左丽丽. 基于阶梯气价的天然气销量优化模型及方法研究[J]. 石油化工高等学校学报, 2025, 38(1): 42-48.

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URL: https://journal.lnpu.edu.cn/syhg/EN/10.12422/j.issn.1006-396X.2025.01.006

https://journal.lnpu.edu.cn/syhg/EN/Y2025/V38/I1/42

Figures/Tables 6

Fig.1 Example of natural gas tiered pricing

Fig.2 Technical roadmap for optimizing customer sales

Fig.3 Schematic diagram of topological structure of a natural gas pipeline network

Table 1 Input data type and content

类型	内容
拓扑参数	各管段、储气罐、储气库、中间元件、气源在管网中的拓扑连接关系，各组成部分所属区域和所属管网系统等
工艺参数	各管道长度，中间元件输气能力，各储气罐接卸能力和罐容，各储气库注采能力和储气能力以及罐容，固定自耗气，各元件的节点序号
成本价格参数	边际成本分段数，管段固定成本和边际成本，储气库固定成本和边际成本，各气源固定成本以及边际价格，各天然气客户固定销售价格和边际价格，各中间元件固定成本和边际成本
资源参数	气源供应量上下限，用户销售量上下限

Table 2 Partial customer sales optimization results

客户编号	销量/（亿m³ $⋅$ a^-1）			客户编号	销量/（亿m³ $⋅$ a^-1）
客户编号	优化前	优化后	上限	客户编号	优化前	优化后	上限
3	1.096	1.096	1.096	56	0.043	0.043	0.043
4	0.251	0.251	0.251	57	0.075	0.065	0.075
5	0.030	0.008	0.030	58	0.133	0.133	0.133
11	3.136	1.917	3.136	59	0.120	0.120	0.120
13	0.352	0.352	0.352	60	0.000	0.000	0.000
18	10.736	8.110	10.736	61	0.019	0.001	0.019
20	2.433	1.904	2.433	62	0.656	0.656	0.656

Table 2 Partial customer sales optimization results

客户编号	销量/（亿m³ $⋅$ a^-1）			客户编号	销量/（亿m³ $⋅$ a^-1）
客户编号	优化前	优化后	上限	客户编号	优化前	优化后	上限
3	1.096	1.096	1.096	56	0.043	0.043	0.043
4	0.251	0.251	0.251	57	0.075	0.065	0.075
5	0.030	0.008	0.030	58	0.133	0.133	0.133
11	3.136	1.917	3.136	59	0.120	0.120	0.120
13	0.352	0.352	0.352	60	0.000	0.000	0.000
18	10.736	8.110	10.736	61	0.019	0.001	0.019
20	2.433	1.904	2.433	62	0.656	0.656	0.656

Fig.4 Comparison of benefits before and after optimization

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