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.

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“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.