Abstract
Cross-border energy markets are becoming more relevant and line congestions may cause significant modifications in their underlined bidding strategies, increasing the market power. This environment requires the development of new tools to measure biased behaviors, and also to try to mitigate them. The related literature makes use of techniques to simulate the bidding strategies, mostly based in optimization models, with or without equilibrium constraints, or mixed complementary problems, and embedded in ad-hoc iterative procedures. This paper presents a model for a two areas system, assuming a conjectural equilibrium. A Mixed Integer Programming method has been implemented to solve the equilibrium conditions, where a binary variable is used to represent the line's power flow status (congested or not) between both two areas. Instead of an ad-hoc algorithm, the Branch and Bound algorithm is used for the resolution, which can assure convergence in specific situations where previous algorithms may diverge. © 2014 IEEE.

Abstract
The technical and economic importance of electricity ancillary services, responsible for guaranteeing the reliability and security of the power systems, is growing due to the continuous penetration of intermittent technologies. Indeed, changes in the net demand are forcing more expensive thermal generation to provide reserves while reducing their energy production. Therefore generation companies' are more and more concerned with reserve markets and their impact on the electricity market forecasting models. This paper analyses historical data from the Spanish electricity market to better understand and forecast secondary reserve requirements and real-time secondary reserve usage. It focuses on the relationship between secondary reserve requirements by the System Operator and the secondary reserve finally cleared, the long term evolution of secondary reserve requirements with respect to the intermittent generation growing, their annual and daily seasonalities, the real-time secondary reserve usage and its annual and daily seasonalities, and some additional hints for the requirements forecasting. Several interesting and not always intuitive conclusions are drawn from the analyzed data. © 2014 IEEE.

Abstract
Supply Function Equilibrium (SFE) and Conjectured Supply Function Equilibrium (CSFE) are some of the approaches most used to model electricity markets in the medium and long term. SFE represents the generators' strategies with functions that link prices and quantities, but leads to systems of differential equations hard to solve, unless linearity is assumed (Linear Supply Function Equilibrium, LSFE). CSFE also assumes linearity of the supply functions but only around the equilibrium point, also avoiding the system of differential equations. This paper analyzes the existence and uniqueness of G-CSFE (a CSFE previously proposed by the Authors) for both elastic and inelastic demands. In addition, it also proves that the iterative algorithm proposed to compute G-CSFE has a fixed point structure and is convergent, and that LSFE is a particular case of G-CSFE when demand and marginal costs are linear. Selected examples show the performance of G-CSFE and how it can be applied to market power analysis with meaningful results.

Abstract
The continuous penetration of intermittent technologies is gradually reinforcing the technical and economic importance of electricity ancillary services, which are responsible for guaranteeing the reliability and security of the power systems. Generation companies', regulating entities, system operators and other institutions (such as researchers on these fields) are more and more concerned on using market models to forecast most relevant outcomes for particular markets (such as energy and reserves cleared quantities and prices), under different simulation scenarios (such as costs or demand) and under different markets structures (such as more competitive or more oligopolistic). This paper reviews most energy and reserve markets implementations (mainly focusing on reserve types and dispatching methods), and discusses different approaches to model them. A theoretical equilibrium model for energy and reserve markets is also proposed.

Abstract
This paper attempts to propose a fair solution in generation scheduling problem in the presence of inherent uncertainties in short-term power system operation. The proposed methodology incorporates probabilistic methodology in the uncertainties representation section, while harmony search algorithm is adopted as a fast and reliable soft computing algorithm to solve the proposed nonlinear, non-convex, large-scaled and combinatorial problem. As an indispensable step towards a more economical power system operation, the optimal generation scheduling strategy in the presence of mixed hydro-thermal generation mix, deemed to be the most techno-economically efficient scheme, comes to the play and is profoundly taken under concentration in this study. This paper devises a comprehensive hybrid optimisation approach by which all the crucial aspects of great influence in the generation scheduling process can be accounted for. Two-point estimation method is also adopted probabilistically approaching the involved uncertain criteria. In the light of the proposed methodology being implemented on an adopted test system, the anticipated efficiency of the proposed method is well verified. © 2014 Taylor & Francis.

Abstract
This paper presents a new adaptive scheme for doubly fed induction generators (DFIGs) in order to augment their flexibility confronting with unpredicted operational conditions. Due to large fluctuations in the wind velocity, the proposed scheme would handle system unreliable operational conditions. In such system, which has multi-input, multi-output and is also represented as a nonlinear control system, the uncertain parameters would affect the operational conditions. So, in order to have a robust controlling scheme, the mentioned characteristics should be considered in the proposed method. The adaptive control scheme proposed in this paper satisfies the expected constraints and could also be implemented in real-world platforms, especially in large-scale wind farms with DFIG turbines. © 2014 Taylor & Francis.