Combining energyPRO and Monte-Carlo simulation - an approach towards sustainable energy planning
Author
Lewe, Heinz-Uwe
Term
4. Term
Publication year
2016
Submitted on
2016-06-01
Pages
100
Abstract
En fuld overgang til bæredygtig energi kræver investeringer i vedvarende teknologier. Offentlige støtteordninger har hjulpet, men omkostningerne stiger og lægger økonomisk pres på området. For at forbedre energiplanlægning og investeringsbeslutninger afprøver dette speciale en kombination af to velkendte værktøjer: energyPRO, der bruges til at modellere og analysere energisystemer, og Monte Carlo-simulering (MCS), en metode fra finans til at kvantificere risiko ved at køre mange tilfældige simuleringer. Eksemplet er det planlagte fjernvarmesystem i Aalborg, Danmarks fjerdestørste by. Systemet blev modelleret i energyPRO, og der blev udviklet scenarier, som repræsenterer forskellige teknologiske investeringsvalg. For at anvende MCS blev centrale inputvariabler til energimodellen defineret som sandsynlighedstæthedsfunktioner, og der præsenteres en praktisk metode til at skabe dem. Hovedresultatet er, at den kombinerede tilgang kun giver en smule mere værdifuld information end almindelige scenarie- og følsomhedsanalyser. Den særlige fordel er, at den kan angive sandsynligheder for udfald—noget scenarie- eller følsomhedsanalyser ikke kan. Den væsentlige ulempe er markant længere beregningstid på grund af mange gentagne kørsler i energyPRO. Samlet set bør MCS sammen med energyPRO kun overvejes, hvis almindelige scenarie- eller følsomhedsanalyser ikke leverer den nødvendige beslutningskvalitet til investeringer.
A full transition to sustainable energy depends on investing in renewable technologies. Public support schemes have helped, but their cost is rising, which puts economic pressure on renewables. To improve energy planning and investment decisions, this thesis tests combining two established tools: energyPRO, used to model and analyze energy systems, and Monte Carlo simulation (MCS), a finance-origin method for quantifying risk by running many randomized simulations. The case study is the planned district heating system in Aalborg, Denmark’s fourth-largest city. The system was modeled in energyPRO, and scenarios were created to reflect different technology investment choices. To apply MCS, key input variables for the energy model were defined as probability density functions, and a practical way to generate these is presented. The main finding is that the combined approach produces results only slightly more informative than standard scenario and sensitivity analyses. Its distinctive advantage is the ability to state probabilities for outcomes—information that scenarios or sensitivity tests cannot provide. The major drawback is substantially longer computation time due to repeated runs in energyPRO. Overall, using MCS together with energyPRO should be considered only when conventional scenario or sensitivity analysis cannot deliver the decision quality needed for investments.
[This abstract was generated with the help of AI]
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