Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise can lead to complex operational models...

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Vydané v:IEEE transactions on sustainable energy Ročník 10; číslo 1; s. 459 - 469
Hlavní autori: Fatouros, Panagiotis, Konstantelos, Ioannis, Papadaskalopoulos, Dimitrios, Strbac, Goran
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Aggregator
Autoregressive models
Computational modeling
Computer applications
Computer simulation
Computing time
distributed energy resources
Dynamic programming
Energy resources
Energy sources
Monte Carlo simulation
multidimensional uncertainty
Real time
Real-time systems
Schedules
stochastic dual dynamic programming
Stochastic models
Stochastic processes
Uncertainty
vector autoregressive modeling
Wind power generation
ISSN:1949-3029, 1949-3037
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Shrnutí:The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a p-order vector autoregressive model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2017.2764065