 Data granularity and the optimal planning of distributed generationL. Kools and F. Phillipson Energy, 2016, vol. 112, issue C, 342-352 Abstract: Research regarding the optimal planning of distributed generation is often based on coarse energy use and generation data, which does not accurately reflect real variations in energy profiles. This paper investigates the impact of this lack of temporal variation on the optimal planning of distributed generation. The problem of loss minimization in the residential setting is used as a guideline. The outcomes of a stochastic optimization model for energy profiles defined on different time aggregation levels are compared. At first glance, modeling on a finer time scale seems to affect optimal planning solutions, with a shift from variable stochastic sources to sources that provide constant generation. However, it turns out that the gains of using these new optimal solutions in terms of reducing energy losses are limited. The results suggest that for optimization purposes it is not necessary to use data at a resolution smaller than hourly time steps. If energy profiles are defined on time steps smaller than one hour it is important that the full range of the stochastic fluctuations is taken into account, rather than evaluating a couple of scenarios. Keywords: Stochastic programming; Distributed generation; Capacity planning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:112:y:2016:i:c:p:342-352 DOI: 10.1016/j.energy.2016.06.089 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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