Optimization of process integration investments under energy market uncertainties
The increased climate concern in society puts pressure on industries to decrease their energy use in different ways, and a number of studies show that there is a large potential for improved energy efficiency in the energy-intensive industry, for example through process integration. Uncertainties about future energy prices and policy instruments make it, however, difficult to evaluate and compare different kinds of energy-saving measures with respect to net present value as well as reduction of CO2 emissions.
This thesis presents a systematic methodology for optimization of investments in process integration under energy market uncertainty. The methodology, which also allows the timing of investments to be studied, is based on the assumption that investment decisions must be made before the outcome of uncertain parameters is known. In this way, the uncertainties are explicitly incorporated in the optimization model in a stochastic programming approach, and an investment plan that is robust to changes in the energy market can be obtained.
The uncertain parameters focused on in this thesis are electricity, wood fuel, and district heating prices, which are also indirectly affected by policy instruments such as the price of CO2 emission permits and green electricity certificates. These uncertain parameters are modelled in a scenario-based approach where probabilities for the different scenarios have to be estimated. For the case study presented in the thesis, the probability distribution could, however, be varied substantially without altering the optimal solution.