scholarly journals Fuzzy Byproduct Gas Scheduling in the Steel Plant Considering Uncertainty and Risk Analysis

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2727
Author(s):  
Xueying Sun ◽  
Zhuo Wang ◽  
Jingtao Hu
Keyword(s):  
Confidence Level ◽  
Optimal Scheduling ◽  
Iron And Steel ◽  
Fuzzy Variables ◽  
Confidence Levels ◽  
Scheduling Model ◽  
Byproduct Gas ◽  
Gas System ◽  
Risk Cost ◽  
Operation Cost

In the iron and steel enterprises, efficient utilization of byproduct gas is of great significance for energy conservation and emission reduction. This work presents a fuzzy optimal scheduling model for byproduct gas system. Compared with previous work, uncertainties in byproduct gas systems are taken into consideration. In our model, uncertain factors in byproduct systems are described by fuzzy variables and gasholder level constraints are formulated as fuzzy chance constraints. The economy and reliability of byproduct gas system scheduling are sensitive to different confidence levels. To provide a reference for operators to determine a proper confidence level, the risk cost is defined to quantify the risk of byproduct gas shortage and emission during the scheduling process. The best confidence level is determined through the trade-off between operation cost and risk cost. The experiment results demonstrated that the proposed method can reduce the risk and give a more reasonable optimal scheduling scheme compared with deterministic optimal scheduling.

GAP-Like Scheduling Model of Byproduct Gas in Iron and Steel Process

2012 ◽  
Vol 217-219 ◽  
pp. 505-510
Author(s):  
Yong Liang Zhou
Keyword(s):  
Dynamic Scheduling ◽  
Energy Management System ◽  
External Energy ◽  
Iron And Steel ◽  
Intelligent Optimization ◽  
Scheduling Model ◽  
Byproduct Gas ◽  
Production And Consumption ◽  
Intelligent Optimization Algorithm ◽  
Gas System

Gas is a key byproduct of the iron and steel process, and the scheduling of gas is the most valuable one in Energy Management System. The production and consumption of the byproduct gas will be related to many sub-processes and tends to encounter imbalance problems. One GAP-like optimization model of gas scheduling is setup, where there are 3 key objectives, minimization of emission, external energy purchasing and instability of the byproduct gas system. The model is NP-Hard and can be find the solution by using intelligent optimization algorithm to realize the static and dynamic scheduling.

Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing

2017 ◽  
Vol 195 ◽  
pp. 100-113 ◽  
Author(s):  
Xiancong Zhao ◽  
Hao Bai ◽  
Qi Shi ◽  
Xin Lu ◽  
Zhihui Zhang
Keyword(s):  
Steel Plant ◽  
Optimal Scheduling ◽  
Electricity Pricing ◽  
Byproduct Gas ◽  
Gas System

scholarly journals Prediction Interval Construction for Byproduct Gas Flow Forecasting Using Optimized Twin Extreme Learning Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xueying Sun ◽  
Zhuo Wang ◽  
Jingtao Hu
Keyword(s):  
Extreme Learning Machine ◽  
Gas Flow ◽  
Prediction Interval ◽  
Prediction Intervals ◽  
Second Step ◽  
Iron And Steel ◽  
Byproduct Gas ◽  
Gas System ◽  
Benchmark Datasets ◽  
Learning Machine

Prediction of byproduct gas flow is of great significance to gas system scheduling in iron and steel plants. To quantify the associated prediction uncertainty, a two-step approach based on optimized twin extreme learning machine (ELM) is proposed to construct prediction intervals (PIs). In the first step, the connection weights of the twin ELM are pretrained using a pair of symmetric weighted objective functions. In the second step, output weights of the twin ELM are further optimized by particle swarm optimization (PSO). The objective function is designed to comprehensively evaluate PIs based on their coverage probability, width, and deviation. The capability of the proposed method is validated using four benchmark datasets and two real-world byproduct gas datasets. The results demonstrate that the proposed approach constructs higher quality prediction intervals than the other three conventional methods.

A Decomposition Algorithm for Byproduct Gases Scheduling in Iron and Steel Enterprises

2013 ◽  
Vol 448-453 ◽  
pp. 4609-4614
Author(s):  
Can Tao Shi ◽  
Zi Sheng Liu ◽  
Yu Zhuo Liu ◽  
Yong Liang Zhou
Keyword(s):  
Linear Programming ◽  
Integer Programming ◽  
Programming Problem ◽  
Decomposition Algorithm ◽  
Integer Programming Problem ◽  
Iron And Steel ◽  
Byproduct Gas ◽  
Proposed Model ◽  
Gas System ◽  
Pipeline Networks

Concentrating on byproduct gas system with multiple pipeline networks, this paper established a mathematical model for byproduct gas scheduling (BGS) problem in iron and steel enterprises with consideration of supply priorities of consumption units. A decomposition algorithm is employed to transform the original model into an integer programming and a linear programming. The genetic algorithm is introduced as the solution mainframe to solve the integer programming problem and the linear programming problem iteratively. The result of practical-data-based experiment indicates that the proposed model and algorithm are feasible and effective.

scholarly journals Low-Carbon Economical Dispacth of Electricity-Heat Integrated Energy System Considering Concentrating Solar Power and Heat Loss in Heat Pipe Network

2020 ◽  
Author(s):  
Nenghan Zhang ◽  
Yufeng Wang ◽  
Xiyan Jian ◽  
Yibo Ding
Keyword(s):  
Heat Loss ◽  
Solar Power ◽  
Energy Coupling ◽  
Energy System ◽  
Optimal Scheduling ◽  
Low Carbon ◽  
Concentrating Solar Power ◽  
Scheduling Model ◽  
Integrated Energy System ◽  
Operation Cost

With the development of energy internet, integrated energy system can effectively reduce carbon emissions and improve the utilization of renewable energy. In this paper, a low-carbon optimal scheduling model of integrated energy system considering heat loss of heat network pipeline is proposed. Based on the study of concentrating solar power (CSP) plant and heat storage tank (HS), an optimal scheduling model is established, which takes system operation cost, environmental pollution and penalty cost of abandoning wind and solar energy as objectives. Through the analysis of example results, it is proved that the model proposed in this paper can achieve the goal of reliable, low-carbon and economic operation of the system. At the same time, it shows that CSP unit can reduce the operation cost of system and increase energy coupling and utilization.

scholarly journals Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2539
Author(s):  
Zhengjie Li ◽  
Zhisheng Zhang
Keyword(s):  
Wind Power ◽  
Demand Response ◽  
Solar Power ◽  
Load Forecasting ◽  
Energy System ◽  
Optimal Scheduling ◽  
Scheduling Scheme ◽  
Load Power ◽  
Integrated Energy System ◽  
Operation Cost

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

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