Turbine investment optimisation for energy recovery plants by utilising historic steam flow profiles

• Published in: Energy (Oxford) Vol. 155; pp. 668 - 677
• Main Authors: Venter, Philip van Zyl, Terblanche, Stephanus Esias, van Eldik, Martin
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.07.2018; Elsevier BV
• Subjects: Cogeneration; Decision making; econometric models; Electricity generation; energy; Energy recovery; Energy sources; Flow profiles; Gas flow; Gas turbine engines; Gas turbines; Historic steam profiles; Integer programming; Investments; linear programming; Mixed integer; Mixed integer linear programming; Natural gas; Natural gas industry; Net present value; Optimal investment; Optimization; Power generation; Procurement; profits and margins; steam; Steam electric power generation; Steam flow; Steam power; turbines; Energy recovery; Optimal investment; Mixed integer linear programming; Net present value; Historic steam profiles; Power generation
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.04.186

Burnable off-gases generated in engineering process plants are regularly utilised as energy sources. A common use is for steam production, where excess steam is allocated to power generation turbines. Fluctuating off-gas productions may, however, result in power generation losses from turbine trips, due to insufficient steam. Numerous power co-generation investment models exist, which are typically based on cost minimisations or meeting energy demands. These models do not, however, incorporate plant-specific historic steam profiles and typically use average-based patterns for decision making. This paper presents a novel stochastic mixed integer linear programming model that utilises historic steam profiles to determine optimal turbine investments in terms of the net present value. A further advantage is the ability to investigate the investment and procurement of a, typically very expensive, supplementary energy resource to assist during low off-gas flow periods. The proposed model is solved to optimise over 10 years for an engineering factory seeking to invest into an energy recovery plant. Optimal results demonstrate how natural gas in a fluctuating off-gas environment can increase power generation profits and should be invested in, together with a 30 MW turbine. Furthermore, an average-based approach yields sub-optimal investments and overestimates the net present value beyond 22%. •A novel power generation stochastic MILP formulation for an energy recovery plant.•The model incorporates plant-specific signature steam profiles.•Investment optimisation for turbines combined with a supplementary energy resource.•Results show how average-based steam profiles yield sub-optimal investments.