# A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform
## 7 Conclusions  
This paper presents an innovative methodology for reducing the risks associated with operation and decision-making in a VPP. The UT method accurately predicts the generation and demand uncertainties of a VPP.  
The UT is an alternative application for this forecasting, presenting a response very close to the desired response, while presenting a higher computational efficiency compared to other existing techniques. It has adequate performance for real-time operations and error reduction compared to the traditional forecasting method. The UT was used to estimate predictions from the stochastic model of generations and demands of a VPP. The results of the UT confirmed the expected benefits in terms of performance, the possibility of real-time applications, and risk-reduction for new business models.  
The validation of the method was performed by simulations and comparisons of its results with actual measurements from a real-world VPP. Compared with the persistence method, the UT method presented similar accuracy to the actual data, and the smallest errors for all generators and loads. This proves the applicability and quality of the proposed methodology. Furthermore, this paper shows the fundamental importance of considering  
Figure 12: Persistence applied on demand forecasting plus error UT.  
and knowing the variability of future generation and demand for a VPP aggregator from an economic point of view.  
An important fact that can be verified is that all simulation models introduce some kind of intrinsic prediction error. Such error cannot yet be adequately modeled using the UT. However, future works can be conducted to address this issue. Another direction for future work includes the integration of energy storage systems.  
Conceptualization, L.F.R. and L.R.A.X.d.M.; methodology, L.F.R., L.R.A.X.d.M. and J.C.d.P.; software, L.F.R.; validation, L.F.R., L.N.C., J.C.d.P. and L.R.A.X.d.M.; formal analysis, L.F.R.; investigation, L.F.R.; resources, L.F.R. and L.N.C.; data curation, L.F.R. and J.C.d.P.; writing--original draft preparation, L.F.R., L.N.C., J.C.d.P. and L.R.A.X.d.M.; writing--review and editing, L.F.R. and J.C.d.P.; visualization, L.F.R. and J.C.d.P.; supervision, L.F.R. and L.N.C.; project administration, L.F.R. and L.N.C.; funding acquisition, L.N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the Federal University of Rondonia and support from Federal University of Santa Maria, Santa Maria--RS, Brazil, funding from CNPq PQ 1-D 310761/2018-2, CNPq process 465640/2014-1, CAPES process no. 23038.000776/2017-54 and FAPERS 17/2551-0000517-1, support from CNPq, Brazilian National Council for Scientific and Technological Development--Brazil. This study was also financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES/PROEX)--Finance Code 001.  
The authors declare no conflict of interest and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.