Energy Trading and Asset Optimization
in Turkish Market
Project Student
Advisor
Gizem Akkan
Prof. Dr. Vedat Akgiray
Energy Trading and Asset Optimization in Turkish Market
01
02
Electricity Price Modeling
Real Asset Valuation &
Asset valuation has been ad
Energies 2013, 6, 5897-5920; doi:10.3390/en6115897
OPEN ACCESS
energies
ISSN 1996-1073
www.mdpi.com/journal/energies
Article
Price Forecasting in the Day-Ahead Energy Market by an Iterative
Method with Separate Normal Price and Price Spike Frameworks
Serg
Consulting Case
BPC dnya apnda bir strateji danmanl firmasdr. Trkiyedeki
nemli projelerinden birini, e-ticaret firmas oyster.com iin
yrtmektedir.
Anlk Satn Alm Davran:
E-ticaret & Oyster.com
Oyster.com moda e-ticaretinde faaliyet gsteren bir irkettir. Sat
Asset Optimisation and Trading
Harald von Heyden
Head of Business Division Asset Optimisation and Trading
Stockholm, 21 September 2011
Todays focus
Organisation and key figures
AOT comprises four business activities
AOTs role within Vattenfall
Hedge s
www.preplounge.com
McBurger
Topic
Difficulty
Market sizing
Beginner
Profitability analysis
Style
Interviewer-led (McKinsey
style)
A large fast food chain has hired us to improve the companys profitability
by cutting costs. Before engaging in the initial b
CASE STUDY 1:
Porsche, Volkswagen, and CSX: Cars, Trains, and Derivatives
Gizem Akkan
Basically, the reason behind the increase in share price of Volkswagen by 5 times after Porsche's
disclosure is short squeeze meaning quick increase in the price of an a
Sermaye Varlklar Fiyatlama
Modeli
The Capital Asset Pricing
Model (CAPM)
INTRODUCTION TO CORPORATE FINANCE (Laurence
Booth ve W. Sean Cleary) kitab iin hazrlanm ingilizce
slaytlarn trkesi
Ulalabilir Portfy Kombinasyonlar
Sermaye Varlklar Fiyatlama Modeli
Digital Receipt
T his receipt acknowledges that T urnitin received your paper. Below you will f ind the receipt
inf ormation regarding your submission.
T he f irst page of your submissions is displayed below.
Submission author:
Gizem Akkan
Assignment titl
Designing the Biggest Ethanol Pipeline in the World
Challenges faced
Actions taken
Benefits achieved
Poor logistical infrastructure and
uncertainties on execution capability
Development of supply centers in
remote areas, far from demand
centers
D
Client XXX
Project XXX Pricing Strategy for
XXX Brand Yoghurt
Draft
April 26th,
2016
Strategic Pricing Approach for the New Product (Yoghurt)
We have identified 6 major steps for determining the price of your new product;
i.e. Yoghurt which reduces the ri
Lab: Box-Jenkins Methodology Test Data Set 1
In this lab we explore the Box-Jenkins methodology by applying it to a test timeseries data set comprising100 observations as set out in the worksheet Test data 1
worksheet (see chart below).
Time Series and Fo
M. Gray Gler
Bogazici University
IE 413 TERM PROJECT
PROJECT & REPORT TUTORIAL
PART 2
This tutorial describes how to do and report the second part of the term project.
The phrases with green background are the menus, like:
InventoryItemsOrganizational Ite
Vehicle Routing Problems
Taner Bilgi
c
Boazii University
g c
Department of Industrial Engineering
Bebek 34342 Istanbul TURKEY
[email protected]
Dec 2010
TB ()
Vehicle Routing Problems
Dec 2010
1 / 19
Introduction
The aim is to provide service, in a given
Vehicle Routing Problems: A Solution
Method
Taner Bilgic
[email protected]
Bogazici University
Department of Industrial Engineering
Bebek 34342 Istanbul TURKEY
Taner Bilgic Bogazici University IE413 SCM p. 1/16
Characteristics
CHARACTERISTICS
POSSIBLE
Vehicle Routing Problems
Taner Bilgic
[email protected]
Bogazici University
Department of Industrial Engineering
Bebek 34342 Istanbul TURKEY
Taner Bilgic Bogazici University IE413 SCM p. 1/10
VRP and m/c Scheduling
Consider the following extension of
Optimality of Schedules and Single
Machine Problems
Taner Bilgic
[email protected]
Bogazici University
Department of Industrial Engineering
Bebek 34342 Istanbul TURKEY
Taner Bilgic Bogazici University IE413 SCM p. 1/24
Research Questions
For any parti
22.11.2009
IE 413 Supply Chain Management
Scheduling: Introduction
Taner Bilgi
Department of Industrial Engineering
Boazii University
Short range
Mid range
Long range
CRP & MPC
Resource
Planning
Sales and operations
Planning
Rough-cut
capacity planning
Ma
19.11.2009
IE 413 Supply Chain Management
Probabilistic Inventory Models
Taner Bilgi
Department of Industrial Engineering
Boazii University
Outline
Continuous review lot size reorder point
systems
t
Derivation of policy parameters
Derivation under norm
IE 413 SCM
Prof. Taner Bilgi
M. Gray Gler
Term Project
PART II
Following are the weekly forecasts for January and February, 2011:
03.01
Arm1
Arm2
Arm3
10.01
17.01
24.01
31.02
07.02
14.02
21.02
120
100
80
120
100
80
120
100
80
120
100
80
120
100
80
120
100
Flow Shop Scheduling
Taner Bilgic
[email protected]
Bogazici University
Department of Industrial Engineering
Bebek 34342 Istanbul TURKEY
Taner Bilgic Bogazici University IE413 SCM p. 1/11
Introduction
The machines are setup in series and the jobs pass
Job Shop Scheduling
Taner Bilgic
[email protected]
Bogazici University
Department of Industrial Engineering
Bebek 34342 Istanbul TURKEY
Taner Bilgic Bogazici University IE413 SCM p. 1/16
Introduction
The jobs now can visit machines in any order, omit
4
Maximization of expected utility
Revised:March 17, 2004
Denition 4.1 Let X = cfw_x1 , x2 , , xr be a nite set of possible prizes, let
= p1 , x1 ; p2 , x2 ; ; pr , xr
be a simple lottery where pi 0 is the probability of winning xi , i = 1, 2, , r and r=
3
Subjective probability
Revised:February 24, 2004
3.1
Interpretations of probability
The interpretation of what probability means is still a subject of intense debate. One major division is between objective and epistemological understandings of what P r
6
Inference in Bayesian Networks
Revised:March 17, 2004
6.1
Representation
A Probabilistic Network (aka causal graph, Bayesian belief network, etc.) is a graphical
representation of a joint probability function.
Denition 6.1 G = cfw_N, A is a directed gra
8
Approximate Inference in BNs
Revised:April 1, 2004
Since exact inference in belief networks is NP-hard we do not expect to nd an ecient
algorithm to solve the exact inference problem unless P N P .
In this case we can think of an approximation algorithm
5
Dependency Models
Revised:March 16, 2004
Denition 5.1 A dependency model, M over a nite set of elements U is any subset of
triplets (X, Y, Z ) where X, Y, Z are disjoint subsets of U . The triplets in M represent independencies, i.e., (X, Y, Z ) M asser
7
Complexity of Exact Inference in BNs
Revised:April 1, 2004
We follow Cooper (1990) and reduce a decision problem version of the inference in BN
problem to a well known NP-complete problem 3SAT (three satisability).
We start by dening the 3SAT problem. C
2
Measurement Theory
Revised:February 24, 2004
The theory of measurement deals with representing qualitative structures with numerical
ones . The aim is to assign numbers to the elements of the qualitative structure such that
the properties of the qualita
Lecture Notes for IE544 Decision Analysis
Taner Bilgi
c
Department of Industrial Engineering
Boazii University
gc
34342 Bebek Istanbul, Turkey
[email protected]
June 1998
Revised February 24, 2004
Contents
1 Introduction
1.1 Newcombs problem . . . . . . .
ANATOMICAL POSITIONS, DIRECTIONS AND PLANES
ABDUCTION - Movement away laterally from the central axis of the body (median plane: midsagital plane).
ADDUCTION - Movement toward the central axis of the body (median plane: midsagital plane)
ANATOMICAL POSITI