IIASA RepositoryA Stochastic Version of the Dynamic Linear Programming Model MESSAGE IIIA.GolodnikovauthorA.GritsevskiiauthorS.MessnerauthorThe ECS Project at IIASA is using a dynamic linear programming model, MESSAGE III, for the analysis of long-term energy strategies to mitigate climate change. These model analyses utilize information on potential future technology characteristics, energy services demands and resource availability to investigate paths into a sustainable future energy system during the next century. One major shortcoming of conventional energy optimization models is the requirement to use point estimates for the technology characteristics and other important system parameters. This paper introduces a new approach to overcome this problem by introducing distribution functions for technology parameters into model formulation. The stochastic version of MESSAGE captures the risk of underestimating future technology costs. Computational overhead for applying the approach is very low compared to the original model; for the runs investigated here, no increase in CPU time was detected. Another problem often encountered with deterministic models, in particular linear programming models, is their sensitivity to input parameters. Linear programming models are, by definition, worst in this respect because they tend to favor single solutions and extreme developments instead of mixing various technologies or strategies. In such cases modelers develop robust scenarios by parameter variations and delimiting the solution space to the area that yields acceptable and robust results. Clearly, this approach is rather labor intensive and requires experience on the modeler's side. The important parameters for variation have to be found, and model runs with combinations of such parameter variations have to be performed. Such investigations can result in a large number of model runs or, more probably, in model outcomes that are not robust with respect to small changes in model parameters. In any case additional constraints introduced to stabilize model results reflect experts expectations, which are always subjective to a certain degree, representing a certain risk of over- or underestimating the parameters. The stochastic approach presented here helps to overcome these problems by explicitly introducing the uncertainties concerning expert's opinions of future investment costs of technologies. The data used for the distribution function of investment costs were derived from the greenhouse gas mitigation technology inventory, called C02DB, developed by ECS over the last five years. The strategies derived with the stochastic approach possess the required technological diversity without exogenous flexibility constraints. They also have a more robust structure with respect to present uncertainties concerning future parameters. Thirdly, the strategies derived with the stochastic model extension are less costly than strategies obtained on the basis of a purely deterministic model.1995-09WP-95-094Monograph

For work being deposited by its own author: In self-archiving this collection of files and associated bibliographic metadata, I grant IIASA Repository the right to store them and to make them permanently available publicly for free on-line. I declare that this material is my own intellectual property and I understand that IIASA Repository does not assume any responsibility if there is any breach of copyright in distributing these files or metadata. (All authors are urged to prominently assert their copyright on the title page of their work.)

For work being deposited by someone other than its author: I hereby declare that the collection of files and associated bibliographic metadata that I am archiving at IIASA Repository) is in the public domain. If this is not the case, I accept full responsibility for any breach of copyright that distributing these files or metadata may entail.

Clicking on the deposit button indicates your agreement to these terms.