摘要
随着天然气需求量的日益增加,保证天然气管网系统的安全运行和供气稳定是首要要求,但现有的设施和调控政策往往不能满足城市用气高峰的需求,对生产、生活造成一定影响。实施天然气需求响应能够引导用户在天然气需求高峰时期削减或转移用能,以达到降低用能峰值、缓解系统调峰压力、减少天然气切负荷和延长设备扩建计划的目的。为分析天然气需求响应对天然气管网系统的影响,以价格弹性理论为基础,采用K-means聚类算法对天然气峰、平、谷时段进行划分,融入最优化方法,建立以系统峰谷差最小和用户满意度最大的需求响应多目标运行优化模型,使用原始内点法求解。以郑州市某日天然气需求量为例,制定出合理的天然气需求响应方案。算例验证了实施天然气需求响应措施能够均衡天然气管网系统需求,达到削峰填谷目的。
Given the increasing demand for natural gas,ensuring the safe operation of the system and the reliability of gas supply is a primary requirement.Existing facilities and regulation policies often fail to meet the demand during urban gas consumption peaks,causing certain impact on gas production and social life.The implementation of demand response measures can guide users to cut or shift energy use during peak demand periods,in order to reduce peak energy use,relieve system peaking pressure,reduce load-cutting demand and extend facilities expansion plans.Based on the price elasticity theory,this paper uses the K-means clustering algorithm to divide peak and valley periods,incorporates optimization methods,and establishes a multi-objective optimization model for demand response with minimum system peak-to-valley differences and maximum customer satisfaction.This is solved using the primal interior point method to develop a reasonable natural gas demand response plan.The Pareto optimal solution set is obtained by taking the natural gas demand of a certain day in Zhengzhou city as an example,and it can be seen that the implementation of natural gas demand response can balance the system demand and achieve the purpose of peak load shifting.
作者
周军
李帅帅
梁光川
蒙恬
ZHOU Jun;LI Shuaishuai;LIANG Guangchuan;MENG Tian(Petroleum Engineering School,Southwest Petroleum University,Chengdu,Sichuan,610050,China;HSE and Technical Supervision Research Institute of PetroChina Southwest Oil&Gasfield Company,Chengdu,Sichuan,610041,China)
出处
《天然气与石油》
2022年第3期130-137,共8页
Natural Gas and Oil
基金
国家自然科学基金青年科学基金资助项目(51704253)。
关键词
需求响应
分时定价
天然气
价格弹性
多目标优化
聚类算法
Demand response
Time-of-use pricing
Natural gas
Price elasticity
Multi-objective optimization
Clustering algorithm
