• Heinz-Uwe Lewe
A successful transition towards a 100% sustainable energy system requires investments into renewable energy technologies. Public support schemes proved to be successful in stimulating such investments, but the financial burden grows and it puts economic pressure on renewables. To ensure the expansion of sustainable energy technologies, improved energy planning methods and better investment decisions are necessary. Therefore, potential benefits from combining two tools from different fields of studies are explored. EnergyPRO for energy system analysis is combined with Monte-Carlo simulation (MCS) which is usually applied for quantitative risk analysis in finance studies. Possible value creation through this synthesis is explored by using the future district heating system of Aalborg, Denmark’s fourth biggest city, as an example. The system was modelled in energyPRO and scenarios were developed to represent different technological investment options. The tool synthesis requires a new definition of input variables for energy system analysis in the form of probability density functions. A method is presented on how to generate these. The results show that the combination of energyPRO and MCS generates output data that is only marginally more valuable. Scenario and sensitivity analysis could reproduce all main results from the previous MCS. The remaining benefit of this tool synthesis are statements about probabilities, which for obvious reasons cannot be generated from sensitivity or scenario analysis. A major drawback is the additional computation time that is caused by the large amount of repetitive calculations in energyPRO. After all, using MCS with energyPRO should only be considered if regular scenario or sensitivity analysis does not generate the required output quality for investment decisions.
SpecialisationSustainable Energy Planning and Management
Publication date1 Jun 2016
Number of pages100
External collaboratorAalborg Forsyning, Varme
Lars Boye Mortensen l.mortensen@aalborg.dk
Place of Internship
ID: 234563955