Aliabadi D.E., Kaya M., Şahin G. (2017). An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms, Energy Policy 100:191-205.
Aliabadi, Danial Esmaeili (2016). Analysis of collusion and competition in electricity markets using an agent-based approach (Supervisors: Güvenç Şahin and Murat Kaya).
Preventing Tacit Collusion In Deregulated Electricity Markets
The goal of deregulated electricity markets is to provide consumers with affordable
electricity prices by sustain competition among electricity generators. Although market-
clearing mechanisms are to attain perfect competition, electricity generators exploit the
deficiencies of such mechanism in order to decrease the level of competition in the
market against public welfare. Independent system manager and/or operator are
responsible for administering the market; in the interest of public welfare, they aim to
prevent tacit collusion among generators that may decrease the level of competition in
the market and even create an oligopolistic environment. In this project, we aim to study
analytical optimization models to suggest changes in the market parameters so that
system manager and system operator prevent tacit collusion.
In the scope of this study, decision processes of system manager, system operator and
electricity generators are considered in an integrated manner. In order to determine the
existence of tacit collusion, strategic behavior of generators should be analyzed. We
assume that generators’ actions in the day-ahead market are a reflection of their
strategies. System operator takes into account the actions of generators to clear the
market and determine electricity prices. System manager determines the parameters
and conditions of the market under which generators determine their bids.
In the first phase of our study, we study the problem to determine the existence of tacit
collusion in the market. The market’s decision process where generators determine their
bids in order to maximize their profit while the system operator allocates power and
determine locational electricity prices is represented as a bi-level optimization problem.
The resulting optimization problem is a bi-level multi-criteria problem with non-linear
terms. In the second phase of our study, decisions of the system manager to prevent
collusion among generators are integrated as the top level decision process. As a result,
we obtain a tri- level optimization problem with a bi-level sub-problem which is already
complex and difficult to solve.
The ultimate goal of our study is to develop an algorithm with reasonable computational
complexity to solve the tri-level optimization problem in which the system manager aims
to prevent tacit collusion. Although our primary goal is to solve the problem to optimality,
we may also study heuristic methods for this problem. Our methodological approach
requires firstly solving the bi-level problem in the first phase of our study. In this respect,
we plan to develop an exact solution approach for the first-phase problem.
“SML Lab aims at facilitating and stimulating collaboration among faculty,
students, and practitioners to foster continuous learning and translating
knowledge into innovative solutions for making transport and logistics
more efficient, smarter, greener, and safer.”
Transport and logistics planning is extremely important in particular
for Turkey. Current development plans highlight the need for modern and
reliable transportation systems, and aim at transforming Turkey to a global
logistic hub, both for materials and energy flows, at the crossroads of
three continents. Moreover, the traffic volume in all sorts of transport
modes has increased rapidly, particularly in metropolitan areas like Istanbul.
There have been a lot of investments to improve the infrastructure. Thus,
the efficient use and planning of the resources is an important and challenging
task. Moreover, developing effective plans is essentially a national priority
due to the high likelihood of serious natural disasters.
Smart Mobility and Logistics Lab (SML) in Sabancı University focuses on
transport logistics and mobility planning including urban transport, first-mile,
long-distance and last-mile pickup/delivery operations, humanitarian logistics,
electro-mobility, and sustainable logistics chains. SML team is equipped
with extensive domain knowledge in logistics and transportation research
and experienced in addressing multifaceted problems through systematic
modeling approaches and effective solution methods using operations research
tools and techniques. The Lab conducts research projects particularly on
urban mobility, humanitarian logistics, and sustainable transport planning
with a special emphasis on route optimization, electrification of logistics
vehicles, battery performance analysis.