Abstract
This paper, which is part one of a two-part series, presents a general methodology for reducing system complexity by calculating the electrical and reliability equivalent models of low and medium voltage distribution networks. These equivalent models help to reduce calculation times while preserving the accuracy assessment of power system reliability performance. The analysis is applied to typical UK distribution systems, which supply four generic load sectors with different networks and demand compositions (residential, commercial and industrial). This approach allows for a direct correlation between reliability performance and network characteristics, while assessing the most representative aggregate values of failure rates and repair times of power components at each load sector. These are used in the Part 2 paper for assessing the potential benefits of energy storage and demand-side resources on the reliability performance of different generic distribution networks. © 2013 IEEE.

Abstract
This paper, which is the second part of a two-part series, considers the influence of distributed energy resource functionalities on reliability performance of active networks. The reliability and network equivalent models defined in the Part 1 paper are used to assess the potential improvements that different demand-side management and energy storage schemes will have on the frequency and duration of customer interruptions. Particular attention is given to energy-related reliability indices which measure the energy and power not supplied to residential and commercial customers. A new theoretical interruption model is also introduced for a more accurate correlation between the different low-voltage and medium-voltage demand profiles and the time when both long and short interruptions are more likely to occur. © 2013 IEEE.

Abstract
This paper presents the proof of concept for a new solution to the problem of recomposing missing information at the SCADA of energy/distribution management systems (EMS/DMS), through the use of offline trained autoencoders. These are neural networks with a special architecture, which allows them to store knowledge about a system in a nonlinear manifold characterized by their weights. Suitable algorithms may then recompose missing inputs (measurements). The paper shows that, trained with adequate information, autoencoders perform well in recomposing missing voltage and power values, and focuses on the particularly important application of inferring the topology of the network when information about switch status is absent. Examples with the IEEE RTS 24-bus network are presented to illustrate the concept and technique.

Abstract
This paper reports the application of a new kernel density estimation model based on the Nadaraya-Watson estimator, for the problem of wind power uncertainty forecasting. The new model is described, including the use of kernels specific to the wind power problem. A novel time-adaptive approach is presented. The quality of the new model is benchmarked against a splines quantile regression model currently in use in the industry. The case studies refer to two distinct wind farms in the United States and show that the new model produces better results, evaluated with suitable quality metrics such as calibration, sharpness, and skill score.

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.