IEEE Power & Energy Society General Meeting - IEEE PES GM 2016, Boston (United States of America). 17-21 July 2016
Summary:
This paper presents a new dynamic and stochastic decision supporting model for distributed generation investment planning (DGIP). The model is formulated as a mixed integer linear programming (MILP) optimization problem that simultaneously minimizes emission, operation and maintenance, as well as reliability costs. One of the salient features of the model is that it is based on a two-period planning horizon: a short-term planning period that requires robust decisions to be made and a medium to long-term one involving exploratory or flexible investment decisions. Each period has multiple decision stages. The operational variability introduced by intermittent generation sources and electricity demand are accounted for via probabilistic methods. To ensure computational tractability, the associated operational states are reduced via a clustering technique. Moreover, uncertainties related to emission price, demand growth and the unpredictability of intermittent generation sources are taken into account stochastically. A real-life distribution network system is used as a case study, and the results of our analyses generally show the efficacy of the proposed model.
Keywords: Distributed generation; DG investment planning; distribution network systems; stochastic programming; uncertainty
DOI: 
https://doi.org/10.1109/PESGM.2016.7741093
Published in IEEE PES GM 2016, pp: 1-5, ISBN: 978-1-5090-4169-5
Publication date: 2016-07-17.
Citation:
D. Fitiwi, S. F. Santos, A. W. Bizuayehu, M. Shafie-khah, J.P.S. Catalão, A new dynamic and stochastic distributed generation investment planning model with recourse, IEEE Power & Energy Society General Meeting - IEEE PES GM 2016, Boston (United States of America). 17-21 July 2016. In: IEEE PES GM 2016: Conference proceedings, ISBN: 978-1-5090-4169-5
