Abstract
This paper presents a novel time-adaptive quantile-copula estimator for kernel density forecast and a discussion of how to select the adequate kernels for modeling the different variables of the problem. Results are presented for different case-studies and compared with splines quantile regression (QR). The datasets used are from NREL's Eastern Wind Integration and Transmission Study, and from a real wind farm located in the Midwest region of the United States. The new probabilistic prediction model is elegant and simple and yet displays advantages over the traditional QR approach. Especially notable is the quality of the results achieved with the time-adaptive version, namely when evaluated in terms of prediction calibration, which is a characteristic that is advantageous for both system operators and wind power producers.

Abstract
This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a probabilistic wind power forecast, whereas uncertainties in DA and RT prices are assumed to be Gaussian. Utility theory and conditional value at risk (CVAR) are used to represent the risk preferences of the wind power producers. The model is tested on real-world data from a large-scale wind farm in the United States. Optimal DA bids are derived under different assumptions for risk preferences and deviation penalty schemes. The results show that in the absence of a deviation penalty, the optimal bidding strategy is largely driven by price expectations. A deviation penalty brings the bid closer to the expected wind power forecast. Furthermore, the results illustrate that the proposed model can effectively control the trade-off between risk and return for wind power producers operating in volatile electricity markets.

Abstract
This paper presents an application of Monte Carlo chronological simulation to evaluate the reserve requirements of generating systems, considering renewable energy sources. The idea is to investigate the behavior of reliability indices, including those from the well-being analysis, when the major portion of the energy sources is renewable. Renewable in this work comprises hydroelectric, mini-hydroelectric, and wind power sources. Case studies on a configuration of the Portuguese Generating System are presented and discussed. Copyright (c) 2007 John Wiley & Sons, Ltd.

Abstract
This paper describes a system for estimating load curves at low-voltage (LV) substations. The system is built by the aggregation of individual fuzzy inference systems of the Takagi-Sugeno type. The model was developed from actual measurements forming a base of raw data of consumer behavior. This database allowed one to build large test and,training sets of simulated LV substations, which led to the development of the fuzzy system. The results are compared in terms of accuracy with the ones obtained with a previous artificial neural network approach, with better performance.

Abstract
A major goal for managers of electric power networks is maximum asset performance. Minimal life cycle cost and maintenance optimization becomes crucial in reaching this goal, while meeting demands from customers and regulators. This necessitates the determination of the optimal balance between preventive and corrective maintenance in order to obtain the lowest total cost. The approach of this paper is to study the problem of balance between preventive and corrective maintenance as a multiobjective optimization problem, with customer interruptions on one hand and the maintenance budget of the network operator on the other. The problem is solved with meta-heuristics developed for the specific problem, in conjunction with an evolutionary particle swarm optimization algorithm. The maintenance optimization is applied in a case study to an urban distribution system in Stockholm, Sweden. Despite a general decreased level of maintenance (lower total maintenance cost), better network performance can be offered to the customers. This is achieved by focusing the preventive maintenance on components with a high potential for improvements. Besides this, this paper displays the value of introducing more maintenance alternatives for every component and choosing the right level of maintenance for the components with respect to network performance.

Abstract
This paper demonstrates that a classical stochastic optimization is, in many cases, not convenient for power system planning. Instead, a risk analysis approach is proposed. In a comparison of both planning paradigms, the probabilistic approach is in occasions not adequate, is half blind to compromise solutions and leads, in numerous, cases to riskier decisions. The technical discussion is illustrated with a distribution planning example.