Συντάχθηκε 20-11-2015 09:48
από Esthir Gelasaki
Email συντάκτη: egelasaki<στο>tuc.gr
Ενημερώθηκε:
-
Ιδιότητα: υπάλληλος.
ΠΟΛΥΤΕΧΝΕΙΟ ΚΡΗΤΗΣ
Σχολή Ηλεκτρονικών Μηχανικών και Μηχανικών Υπολογιστών
Πρόγραμμα Μεταπτυχιακών Σπουδών
ΠΑΡΟΥΣΙΑΣΗ ΜΕΤΑΠΤΥΧΙΑΚΗΣ ΕΡΓΑΣΙΑΣ
ΑΓΓΕΛΟΥ ΑΓΓΕΛΙΔΑΚΗ
με θέμα
Παραγοντικές Στοχαστικές Διαδικασίες Markov
για Βέλτιστες Αποφάσεις Αγοραπωλησίας Ενέργειας
στο Έξυπνο Δίκτυο Ηλεκτροδότησης
Factored MDPs for Optimal Prosumer Decision Making
in the Smart Grid
Εξεταστική Επιτροπή
Επίκουρος Καθηγητής Γεώργιος Χαλκιαδάκης (επιβλέπων)
Επίκουρος Καθηγητής Ευτύχιος Κουτρούλης
Αναπληρωτής Καθηγητής Μιχαήλ Λαγουδάκης
Abstract
Tackling the decision-making problem faced by a prosumer (i.e., a producer that
is simultaneously a consumer) when selling and buying energy in the emerging
smart electricity grid, is of utmost importance for the economic profitability of
such a business entity. In this thesis, we model, for the first time, this problem
as a factored Markov Decision process (MDP). Our model successfully captures
the main aspects of the business decisions of a prosumer corresponding to a com-
munity microgrid of any size. Moreover, it includes appropriate sub-models for
prosumer production and consumption prediction.
Employing this model, we are able to represent the problem compactly, and
to provide an exact optimal solution via dynamic programming—notwithstanding
its large size. In addition, we show how to use approximate MDP solution meth-
ods for taking decisions in this domain, without the need of discretizing the state
space. Specifically, we employ fitted value iteration, a sampling-based approxi-
mation method that is known to be well behaved. By so doing, we generalize our
factored MDP solution method to continuous state spaces.
Our experimental simulations verify the effectiveness of our approach. They
show that our exact value iteration solution matches that of a state-of-the-art
method for stochastic planning in very large environments, while outperforming it
in terms of computation time. Furthermore, we evaluate our approximate solution
method via using a variety of basis functions over different state sample sizes,
and comparing its performance to that of our exact value iteration algorithm. Our
approximation method is shown to exhibit stable performance in terms of accu-
mulated reward, which for certain basis functions reaches 90% of that gathered by
the exact algorithm.
Τόπος: Λ - Κτίριο Επιστημών/ΗΜΜΥ, Αίθουσα Intelligence Lab, Κτίριο Επιστημών, Πολυτεχνειούπολη
Έναρξη: 25/11/2015 11:00
Λήξη: 25/11/2015 12:30
