Multi-Objective Decision-Making on Peak Shaving of West-East Gas Pipeline

2008 ◽  
Author(s):  
Lili Zuo ◽  
Changchun Wu ◽  
Hongwei Zheng ◽  
Fukun Zhang
Keyword(s):  
Decision Making ◽  
Flow Rate ◽  
Gas Pipeline ◽  
Inlet Pressure ◽  
Pipeline System ◽  
Peak Shaving ◽  
Multi Objective ◽  
Operation Cost ◽  
Gas Supply ◽  
Supply Reliability

This paper addresses the peak shaving of West-East Gas Pipeline. For a gas pipeline system, the decision-making on the peak shaving scenarios usually involves a delicate balance between low operation cost and high gas supply reliability. In order to select an acceptable peak shaving scenario from these two angles for West-East Gas Pipeline, the idea of multi-objective decision-making has been introduced. Based on design flow-rate, eight typical peak shaving scenarios have been evaluated, analyzed and optimized with the help of TGNET. During the simulation, in order to make the peak shaving process more approach to the actual operation of the pipeline system, 51 days of forecasted data are used to establish the system starting state for the study period. On the other hand, in order to reduce the effect of different peak shaving process to the subsequent operation, the study period is extended by 7 days to fully account for operating costs and conditions subsequent to the study period, which make different peak shaving scenarios comparable. According to multi-objective optimization criteria including operation cost, gas supply reliability and operation stability, different pareto peak shaving scenarios have been obtained. These scenarios show that from the object of minimizing operation cost, inlet pressure of Shanghai terminal should approach to contractual pressure, from the object of gas supply reliability and operation stability, inlet pressure of Shanghai terminal should maintain a higher value. Operators can adopt different peak shaving scenarios according to different optimization object. Furthermore, with mathematical statistics knowledge, control value of inlet pressure of Shanghai terminal is recommended. When the change of withdrawal flow-rate from underground gas storage is more frequent, the fluctuation of inlet flow-rate is smaller. When withdrawal flow-rate from underground natural gas storage increases, it will not only increase the inventory of whole pipeline and end segment, but also decrease the inlet flow-rate.

Gas Supply Reliability Analysis of a Natural Gas Pipeline System Considering the Effects of Demand Side Management

2021 ◽  
Vol 143 (4) ◽  
Author(s):  
Yichen Li ◽  
Jing Gong ◽  
Weichao Yu ◽  
Weihe Huang ◽  
Kai Wen
Keyword(s):  
Natural Gas ◽  
Demand Side Management ◽  
Gas Pipeline ◽  
Classification Rule ◽  
Pipeline System ◽  
Demand Side ◽  
User Classification ◽  
Natural Gas Pipeline ◽  
Gas Supply ◽  
Supply Reliability

Abstract At present, China has a developing natural gas market, and ensuring the security of gas supply is an issue of high concern. Gas supply reliability, the natural gas pipeline system's ability to satisfy the market demand, is determined by both supply side and demand side and is usually adopted by the researches to measure the security of gas supply. In the previous study, the demand side is usually simplified by using load duration curve (LDC) to describe the demand, which neglects the effect of demand side management. The simplification leads to the inaccurate and unreasonable assessment of the gas supply reliability, especially in high-demand situation. To overcome this deficiency and achieve a more reasonable result of gas supply reliability, this paper extends the previous study on demand side by proposing a novel method of management on natural gas demand side, and the effects of demand side management on gas supply reliability is analyzed. The management includes natural gas prediction models for different types of users, the user classification rule, and the demand adjustment model based on user classification. First, an autoregressive integrated moving average (ARIMA) model and a support vector machine (SVM) model are applied to predict the natural gas demand for different types of users, such as urban gas distributor (including residential customer, commercial customer, small industrial customer), power plant, large industrial customer, and compressed natural gas (CNG) station. Then, the user classification rule is built based on users' attribute and impact of supplied gas's interruption or reduction. Natural gas users are classified into four levels. (1) demand fully satisfied, (2) demand slightly reduced, (3) demand reduced, and (4) demand interrupted. The user classification rule also provides the demand reduction range of different users. Moreover, the optimization model of demand adjustment is built, and the objective of the model is to maximize the amount of gas supplied to each user based on the classification rule. The constraints of the model are determined by the classification rule, including the demand reduction range of different users. Finally, the improved method of gas supply reliability assessment is developed and is applied to the case study of our previous study derived from a realistic natural gas pipeline system operated by PetroChina to analyze the effects of demand side management on natural gas pipeline system's gas supply reliability.

Gas supply reliability analysis of a natural gas pipeline system considering the effects of underground gas storages

2019 ◽  
Vol 252 ◽  
pp. 113418 ◽  
Author(s):  
Weichao Yu ◽  
Jing Gong ◽  
Shangfei Song ◽  
Weihe Huang ◽  
Yichen Li ◽  
...  
Keyword(s):  
Natural Gas ◽  
Reliability Analysis ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Natural Gas Pipeline ◽  
Gas Supply ◽  
Supply Reliability

Multi-objective transient peak shaving optimization of a gas pipeline system under demand uncertainty

2021 ◽  
Vol 147 ◽  
pp. 107260
Author(s):  
Qian Chen ◽  
Changchun Wu ◽  
Lili Zuo ◽  
Mahdi Mehrtash ◽  
Yixiu Wang ◽  
...  
Keyword(s):  
Demand Uncertainty ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Peak Shaving ◽  
Multi Objective

Gas Supply Reliability Analysis of a Natural Gas Pipeline System Considering the Effects of Demand Side Management

2020 ◽  
Author(s):  
Yichen Li ◽  
Jing Gong ◽  
Weichao Yu ◽  
Weihe Huang ◽  
Kai Wen
Keyword(s):  
Natural Gas ◽  
Demand Side Management ◽  
Gas Pipeline ◽  
Classification Rule ◽  
Pipeline System ◽  
Demand Side ◽  
User Classification ◽  
Natural Gas Pipeline ◽  
Gas Supply ◽  
Supply Reliability

Abstract At present, China has a developing natural gas market, and ensuring the security of gas supply is an issue of high concern. Gas supply reliability, the natural gas pipeline system’s ability to satisfy the market demand, is determined by both supply side and demand side, and is usually adopted by the researches to measure the security of gas supply. In the previous study, the demand side is usually simplified by using load duration curve (LDC) to describe the demand, which neglects the effect of demand side management. The simplification leads to the inaccurate and unreasonable assessment of the gas supply reliability, especially in high demand situation. To overcome this deficiency and achieve a more reasonable result of gas supply reliability, this paper extends the previous study on demand side by proposing a novel method of management on natural gas demand side, and the effects of demand side management on gas supply reliability is analyzed. The management includes natural gas prediction models for different types of users, the user classification rule, and the demand adjustment model based on user classification. Firstly, An autoregressive integrated moving average (ARIMA) model and a support vector machine (SVM) model are applied to predict the natural gas demand for different types of users, such as urban gas distributor (including residential customer, commercial customer, small industrial customer), power plant, large industrial customer, and Compressed Natural Gas (CNG) station. Then, the user classification rule is built based on users’ attribute and impact of supplied gas’s interruption or reduction. Natural gas users are classified into four levels. (1) Demand Fully Satisfied; (2) Demand Slightly Reduced; (3) Demand Reduced; (4) Demand Interrupted. The user classification rule also provides the demand reduction range of different users. Moreover, the optimization model of demand adjustment is built, and the objective of the model is to maximize the amount of gas supply for each user based on the classification rule. The constraints of the model are determined by the classification rule, including the demand reduction range of different users. Finally, the improved method of gas supply reliability assessment is developed, and is applied to the case study of our previous study derived from a realistic natural gas pipeline system operated by PetroChina to analyze the effects of demand side management on natural gas pipeline system’s gas supply reliability.

Optimization of Short-Term Peak Shaving Scenarios for a Gas Pipeline Network

2008 ◽  
Author(s):  
Lili Zuo ◽  
Changchun Wu ◽  
Li Fan ◽  
Meng Wang
Keyword(s):  
Initial Conditions ◽  
Optimal Operation ◽  
Simulation Software ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Short Term ◽  
Peak Shaving ◽  
Flow Rates ◽  
Pipeline Network ◽  
Gas Supply

PetroChina owns and operates the largest gas pipeline network in China of more than 10000 km in length, which includes the famous West-East gas pipeline, the first Shannxi-Beijing gas pipeline and the second Shannxi-Beijing gas pipeline etc. As an outstanding feature of the network, its two circuits of pipelines increases the flexibility of gas transmission and the guarantee of gas supply through the network. On the other hand, these two circuits complicate the topological structure, so that it is a challenge to work out an optimal operation scenario for the network. A steady and transient simulation model of the network has been built based on the gas pipeline network simulation software TGNET, and has been tuned by the historical operation data. By means of the model, several winter operation scenarios in 2007 have been simulated. The steady simulations of the network were carried out for the two planed daily flow-rates of West-East gas pipeline respectively, 41 MMSCMD and 45 MMSCMD. Given the steady operation scenarios determined by the steady simulations as the initial conditions, 4 typical short-term peak shaving scenarios in winter high load week have been analyzed, evaluated and optimized with transient simulations. The main difference of those peak shaving scenarios is the flow-rates of West-East gas pipeline and the regulating mode of underground gas storage named Dagang connected to Shanxi-Beijing gas pipeline system. The technologically and economically optimal peak shaving scenario and the optimal control pressure of end stations have been obtained. The research results shows that the actual control pressure of end stations are higher than the optimization results, indicating that the network has the potential of saving energy and reducing spending. These results not only guarantee the safety of gas supply but also reduce the spending of the gas pipeline network, offering an important value of direction for actual operation.

A Systematic Methodology to Assess Hydraulic Reliability and Gas Supply Reliability of the Natural Gas Pipeline Network

2021 ◽  
Vol 143 (4) ◽  
Author(s):  
Weichao Yu ◽  
Jing Gong ◽  
Weihe Huang ◽  
Hongfei Liu ◽  
Fuhua Dang ◽  
...  
Keyword(s):  
Natural Gas ◽  
Indicator System ◽  
Gas Pipeline ◽  
Hydraulic Characteristic ◽  
Market Demand ◽  
Pipeline Network ◽  
Natural Gas Pipeline ◽  
Gas Supply ◽  
Hydraulic Reliability ◽  
Supply Reliability

Abstract Reliability of the natural gas pipeline network is related to security of gas supply directly. According to the different required functions of the natural gas pipeline network, its reliability is divided into three aspects, namely mechanical reliability, hydraulic reliability, and gas supply reliability. However, most of the previous studies confused the definitions of the hydraulic reliability and gas supply reliability. Moreover, the uncertainty in the process of supplying natural gas to the targeted market and the hydraulic characteristic of the natural gas pipeline network are often ignored. Therefore, a methodology to assess hydraulic reliability and gas supply reliability of the natural gas pipeline network is developed in the study, and the uncertainty and hydraulic characteristic of the natural gas pipeline network are both considered. The methodology consists of four parts: establishment of the indicator system, calculation of the gas supply, prediction of the market demand, and assessment of the hydraulic reliability and gas supply reliability. Moreover, a case study is applied to confirm the feasibility of the methodology, and the reliability evaluation results provide a comprehensive picture about the abilities of the natural gas pipeline network to perform the specified gas supply function and satisfy consumers' demand, respectively. Furthermore, a comparison between these two types of reliability is presented. The results indicate that the natural gas pipeline network may not be able to meet the market demand even if the system completes the required gas supply tasks due to the impact of the market demand uncertainty.

scholarly journals An Empirical Study for Adopting Machine Learning Approaches for Gas Pipeline Flow Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Guoliang Shen ◽  
Mufan Li ◽  
Jiale Lin ◽  
Jie Bao ◽  
Tao He
Keyword(s):  
Machine Learning ◽  
Flow Rate ◽  
Learning Algorithms ◽  
Gas Pipeline ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Pipeline System ◽  
Learning Models ◽  
Industrial Control ◽  
Machine Learning Models

As industrial control technology continues to develop, modern industrial control is undergoing a transformation from manual control to automatic control. In this paper, we show how to evaluate and build machine learning models to predict the flow rate of the gas pipeline accurately. Compared with traditional practice by experts or rules, machine learning models rely little on the expertise of special fields and extensive physical mechanism analysis. Specifically, we devised a method that can automate the process of choosing suitable machine learning algorithms and their hyperparameters by automatically testing different machine learning algorithms on given data. Our proposed methods are used in choosing the appropriate learning algorithm and hyperparameters to build the model of the flow rate of the gas pipeline. Based on this, the model can be further used for control of the gas pipeline system. The experiments conducted on real industrial data show the feasibility of building accurate models with machine learning algorithms. The merits of our approach include (1) little dependence on the expertise of special fields and domain knowledge-based analysis; (2) easy to implement than physical models; (3) more robust to environment changes; (4) requiring much fewer computation resources when it is compared with physical models that call for complex equation solving. Moreover, our experiments also show that some simple yet powerful learning algorithms may outperform industrial control problems than those complex algorithms.

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