Target-oriented robust optimization of polygeneration systems under uncertainty

• Published in: Energy (Oxford) Vol. 116; pp. 1334 - 1347
• Main Authors: Sy, Charlle L., Aviso, Kathleen B., Ubando, Aristotle T., Tan, Raymond R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2016
• Subjects: byproducts; case studies; energy; Energy efficiency; markets; mathematical models; Polygeneration; processing technology; Target-oriented robust optimization; Techno-economic risk; thermodynamics; Uncertainty; Target-oriented robust optimization; Uncertainty; Energy efficiency; Polygeneration; Techno-economic risk
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2016.06.057

Production of clean, low-carbon energy and by-products is possible through the use of highly integrated, efficient systems such as polygeneration plants. Mathematical programming methods have proven to be valuable for the optimal synthesis of such systems. However, in practice, numerical parameters used in optimization models may be subject to uncertainties. Examples include cost coefficients in volatile markets, and thermodynamic coefficients in new process technologies. In such cases, it is necessary for the uncertainties to be incorporated into the optimization procedure. This paper presents a target-oriented robust optimization (TORO) approach for the synthesis of polygeneration systems. The use of this methodology leads to the development of a mathematical model that maximizes robustness against uncertainty, subject to the achievement of system targets. Its properties allow us to preserve computational tractability and obtain solutions to realistic-sized problems. The methodology is demonstrated for the synthesis of polygeneration systems using TORO with an illustrative case study. •Investment risks hinder widespread use of polygeneration systems.•Target-oriented robust optimization (TORO) applied for synthesis of polygeneration plants.•TORO approach accounts for long-term investment risks from price and demand volatility.•An illustrative case study is solved to demonstrate the model.