Abstract
This paper proposes a dynamic line rating (DLR) technology application as an alternative to improve the operational reliability of interconnected electrical islands. Transmission system interconnection represents the main asset to identify the border between electrical areas, and they are essential not only for energy market interchanges but also for power assistance among distinct electrical areas. To introduce DLR technology as an option to multi-area systems reliability evaluation, this paper exploits the multi-variate requirements associated with DLR methods, discussing how this technology can be viewed as an operational alternative that can reveal hidden capacity of transmission lines. Therefore, the paper proposes a probabilistic framework to calculate the impact of DLR technology into multi-area systems operation reliability assessment, by means of distinct operative and market agreements. Numerical results are provided for the IEEE-RTS 96 HW along with a brief discussion of its impact in the Iberian Peninsula interconnected power system.

Abstract
Isolated power systems with high shares of renewables can require additional inertia as a complementary resource to assure the system operation in a dynamic safe region. This paper presents a methodology for the day-ahead Unit Commitment/ Economic Dispatch (UC/ED) for low-inertia power systems including dynamic security constraints for key frequency indicators computed by an Artificial Neural-Network (ANN)-supported Dynamic Security Assessment (DSA) tool. The ANN-supported DSA tool infers the system dynamic performance with respect to key frequency indicators following critical disturbances and computes the additional synchronous inertia that brings the system back to its dynamic security region, by dispatching Synchronous Condensers (SC) if required. The results demonstrate the effectiveness of the methodology proposed by enabling the system operation within safe frequency margins for a set of high relevance fault type contingencies while minimizing the additional costs associated with the SC operation.

Abstract
In this paper, we investigate the representation of wind power forecasting (WPF) uncertainty in the unit commitment (UC) problem. While deterministic approaches use a point forecast of wind power output, WPF uncertainty in the stochastic UC alternative is captured by a number of scenarios that include cross-temporal dependency. A comparison among a diversity of UC strategies (based on a set of realistic experiments) is presented. The results indicate that representing WPF uncertainty with wind power scenarios that rely on stochastic UC has advantages over deterministic approaches that mimic the classical models. Moreover, the stochastic model provides a rational and adaptive way to provide adequate spinning reserves at every hour, as opposed to increasing reserves to predefined, fixed margins that cannot account either for the system's costs or its assumed risks.

Abstract
This paper presents a sequential Monte Carlo simulation algorithm that can simultaneously assess composite system adequacy and detect wind power curtailment events. A simple procedure at the end of the state evaluation stage is proposed to categorize wind power curtailment events according to their cause. Furthermore, the dual variables of the DC optimal power flow procedure are used to identify which transmission circuits are restricting the use of the total wind power available. In the first set of experiments, the composite system adequacy is assessed, incorporating different generation technologies. This is conducted to clarify the usual comparisons made between wind and thermal technologies which, in fact, depend on the performance measure selected. A second set of experiments considering several wind penetration scenarios is also performed to determine the operational rules or system components responsible for the largest amount of wind energy curtailed. The experiments are carried out on configurations of the IEEE-RTS 79 power system.

Abstract
This paper presents an application of evolutionary particle swarm optimization (EPSO)-based methods to evaluate power system reliability. Population-based (PB) methods appear as competitors to the traditional Monte Carlo simulation (MCS), because they are computationally efficient in estimating a variety of reliability indices. The work reported in this paper demonstrates that EPSO variants can focus the search in the region of the state space where contributions to the formation of a reliability index may be found, instead of conducting a blind sampling of the space. The results obtained with EPSO are compared to MCS and with other PB methods.

Abstract
A natural movement towards artificial intelligence (AI) techniques took place in the last years in power system analysis. Many research works have used AI topics like search techniques, knowledge representation, reasoning and learning systems, as well as heuristic tools to address power system problems. This paper focuses the discussion on power system reliability evaluation and this natural transition from AI topics to a more sophisticated software design, known as intelligent agent (IA) technology. Instead of applying AI techniques to improve a single stage of the Monte Carlo Simulation (MCS), the IA architecture explores new ways to support AI topics. However, this natural movement needs to be managed through the proposal of a modern framework of power system tools, where several different techniques have to be combined in order to maximize each one's benefits and advantages. © 2010 IEEE.