An optimization methodology for identifying robust process integration investments under uncertainty
Journal article, 2009

Uncertainties in future energy prices and policies strongly affect decisions on investments in process integration measures in industry. In this paper, we present a five-step methodology for the identification of robust investment alternatives incorporating explicitly such uncertainties in the optimization model. Methods for optimization under uncertainty (or, stochastic programming) are thus combined with a deep understanding of process integration and process technology in order to achieve a framework for decision-making concerning the investment planning of process integration measures under uncertainty. The proposed methodology enables the optimization of investments in energy efficiency with respect to their net present value or an environmental objective. In particular, as a result of the optimization approach, complex investment alternatives, allowing for combinations of energy efficiency measures, can be analyzed. Uncertainties as well as time-dependent parameters, such as energy prices and policies, are modelled using a scenario-based approach, enabling the identification of robust investment solutions. The methodology is primarily an aid for decision-makers in industry, but it will also provide insight for policy-makers into how uncertainties regarding future price levels and policy instruments affect the decisions on investments in energy efficiency measures.

stochastic programming

process integration

investment planning


Elin Svensson

Industrial Energy Systems and Technologies

Thore Berntsson

Industrial Energy Systems and Technologies

Ann-Brith Strömberg

Fraunhofer-Chalmers Centre

Michael Patriksson

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematics

Energy Policy

0301-4215 (ISSN)

Vol. 37 2 680-685

Subject Categories

Other Mechanical Engineering

Energy Engineering

Computational Mathematics

Driving Forces

Sustainable development

Areas of Advance




More information

Latest update