Title 
Competitive Bidding and Stability Analysis in Electricity Markets Using Control Theory 
Author

Giabardo, Paolo Zugno, Marco Pinson, Pierre (Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK2800 Kgs. Lyngby, Denmark)

Supervisor

Madsen, Henrik (Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK2800 Kgs. Lyngby, Denmark)

Institution 
Technical University of Denmark, DTU, DK2800 Kgs. Lyngby, Denmark 
Thesis level 
Master's thesis 
Year 
2008 
Abstract 
The process of deregulation that has involved electricity markets in the recent years has opened the way for several interesting research topics. This thesis addresses one of the most fascinating ones among them: the study of strategic bidding and the analysis of its consequences in terms of market stability. The problem faced is twofold. From the generators' point of view, it is of interest to develop bidding strategies aimed to optimize the individual profits, given their costs of production, the evolution of energy demand and the response of the other players in the market. On the other hand, the point of view of the society is addressed by analyzing the behavior and the stability of the market when these strategies are applied. In this thesis, two competition models are considered in analyzing electricity markets: the Cournot and the Linear Supply Function (LSF) models. In the former one, the supply bid is assumed to be in the form of a quantity representing the amount of energy that each generator is going to dispatch to the market. In the latter framework, instead, the bid is in the form of a linear function relating the quantity to the relative price the producers are willing to sell the energy at. In both the cases, the problem is tackled by means of optimal control theory and the approach used is the same. First, a dynamic closed loop system is built in order to model electricity markets' competition, in which each generator aims to optimize its profits in the next bidding round. Then, an optimal strategy is developed with the goal of maximizing the individual profits over a longer horizon. This multi step strategy is derived analytically in the Cournot framework and numerically in the LSF competition model, namely through the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The simulations performed show that a generator can increase its profits by employing the multi step strategy; both in the Cournot and the LSF frameworks. On the other hand, the analysis of the social consequences of strategic bidding gives different results in the two competition models. In the Cournot framework, the society benefits when the generators become more strategic. In the LSF competition model, instead, the social welfare decreases when players bid more strategically. Furthermore, sensitivity analysis are performed in order to evaluate the effects of changes in the market demand on both the individual generators and on the society. Besides these analysis in a deterministic framework, the work is aimed to develop stochastic versions of these models. The closed loop dynamic systems are modified in order to account for wind power generation, which brings uncertainty into the system. Then, optimal strategies are developed with the aim of maximizing the expectation of the profits over more days. Again, the convenience of switching to the multi step strategy is shown in both the competition models for the generators, while a benefit for the society is verified, again, only in the Cournot framework. The stochastic models allow also the assessment of the consequences of the introduction of wind power in electricity generation markets. The results obtained in both the Cournot and the LSF frameworks show that switching to wind power generation is convenient both for the generators and for the society. 
Series 
IMMM.Sc.200866 
Fulltext 

Admin 
Creation date: 20080717
Update date: 20080717
Source: dtu
ID: 221858
Original MXD
