Quantifying flexibility of industrial steam systems for ancillary services: a case study of an integrated pulp and paper mill

Due to the increasing use of intermittent renewable generation, the power grid requires more flexible resources to balance supply and demand of electricity. Steam systems with turbine-generators, which are widely used in industries, can be operated flexibly to support the power grid. Yet, the available amount of flexibility of industrial steam systems is still not clearly quantified. This study presents the method to quantify electricity generation flexibility of a typical industrial steam system with a steam turbine-generator and process heat demands. The proposed method is introduced based on a real case of an integrated pulp and paper mill in Austria. An integrated mathematical model representing the combined electricity and steam system is developed to simulate the behaviour of the on-site energy system to quantify the potential flexibility provision. Flexibility is represented as the maximum upward and downward changes in the imported electricity from the public power grid. The results demonstrate that it is possible to aggregate the flexibility of the industrial facility as a lookup table. Also, the results reflect key factors that limit the flexibility at different operating points of the turbine-generator.

[1]  Jianzhong Wu,et al.  Enhanced Frequency Response From Industrial Heating Loads for Electric Power Systems , 2019, IEEE Transactions on Industrial Informatics.

[2]  Wina Crijns-Graus,et al.  Technical demand response potentials of the integrated steelmaking site of Tata Steel in IJmuiden , 2018 .

[3]  Xin Lu,et al.  Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing , 2017 .

[4]  Russell McKenna,et al.  Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry , 2019, Applied Energy.

[5]  Victor M. Zavala,et al.  Economic opportunities for industrial systems from frequency regulation markets , 2017, Comput. Chem. Eng..

[6]  Ignacio E. Grossmann,et al.  Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty , 2015, Comput. Chem. Eng..

[7]  Eric Martinot,et al.  Grid Integration of Renewable Energy: Flexibility, Innovation, and Experience , 2016 .

[8]  Jianzhong Wu,et al.  Benefits of using virtual energy storage system for power system frequency response , 2017 .

[9]  Jinye Zhao,et al.  A Unified Framework for Defining and Measuring Flexibility in Power System , 2016, IEEE Transactions on Power Systems.

[10]  Henry Leung,et al.  Data-driven based model for flow prediction of steam system in steel industry , 2012, Inf. Sci..

[11]  Xianglong Luo,et al.  Modeling and optimization of a utility system containing multiple extractions steam turbines , 2011 .

[12]  Ignacio E. Grossmann,et al.  Optimal scheduling of industrial combined heat and power plants under time-sensitive electricity prices , 2013 .

[13]  Francois Bouffard,et al.  Flexibility Envelopes for Power System Operational Planning , 2014, IEEE Transactions on Sustainable Energy.

[14]  John G. Brisson,et al.  Targeting the optimum steam system for power generation with increased flexibility in the steam powe , 2011 .

[15]  E. Lannoye,et al.  Evaluation of Power System Flexibility , 2012, IEEE Transactions on Power Systems.

[16]  Feng Qian,et al.  Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach , 2020 .

[17]  M Naqvi,et al.  Bio-refinery system of DME or CH4 production from black liquor gasification in pulp mills. , 2010, Bioresource technology.

[18]  F. Qian,et al.  Modeling and Optimization of a Large-Scale Ethylene Plant Energy System with Energy Structure Analysis and Management , 2019, Industrial & Engineering Chemistry Research.

[19]  Liang Zhao,et al.  Operational optimization of industrial steam systems under uncertainty using data‐ D riven adaptive robust optimization , 2018, AIChE Journal.

[20]  Miadreza Shafie-Khah,et al.  Assessing Increased Flexibility of Energy Storage and Demand Response to Accommodate a High Penetration of Renewable Energy Sources , 2019, IEEE Transactions on Sustainable Energy.

[21]  Nadia Maïzi,et al.  Optimizing industries’ power generation assets on the electricity markets , 2017 .

[22]  Gabriela Hug,et al.  Demand Response of Ancillary Service From Industrial Loads Coordinated With Energy Storage , 2018, IEEE Transactions on Power Systems.