Abstract
We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs). The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.

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Abstract
A fully operational Multi-Terminal DC (MTDC) grid will play a key role for the creation of AC systems interconnection and to integrate offshore wind farms. Disturbances (at both AC and DC side) may culminate in the sudden disconnection of onshore HVDC-VSC (High Voltage Direct Current - Voltage Source Converter). To continue operating the DC grid under these conditions, the development of control functionalities is required. A communication-free advanced control scheme is proposed to be used as a supplementary local control acting at VSC level and aiming on providing fast active power accommodation in the DC grid, culminating on the mitigation of the resulting DC overvoltage. The implementation of the proposed control mechanisms exploits a set of coordinated local control rules at the converter stations and at wind turbines (WT) level. The performance of the proposed strategies is discussed and assessed through numerical simulation in the paper.

Abstract
Power systems with high wind penetration experience increased variability and uncertainty, such that determination of the required additional operating reserve is attracting a significant amount of attention and research. This paper presents methods used in recent wind integration analyses and operating practice, with key results that compare different methods or data. Wind integration analysis over the past several years has shown that wind variability need not be seen as a contingency event. The impact of wind will be seen in the reserves for non-event operation (normal operation dealing with deviations from schedules). Wind power will also result in some events of larger variability and large forecast errors that could be categorized as slow events. The level of operating reserve that is induced by wind is not constant during all hours of the year, so that dynamic allocation of reserves will reduce the amount of reserves needed in the system for most hours. The paper concludes with recent emerging trends.

Abstract
In this paper is presented a methodology to identify and evaluate the influence of the external elements on the Transmission System Operator's (TSO) responsibility area. The algorithm is based on the influence factor approach and is described for this purpose. This algorithm offer a concrete support in the determination of the observability area, which at the end remains in the responsibility of a single TSO. The influence factor is a numerical value used to quantify the greatest effect of the outage of an external network component on any internal network branch. The proposed methodology was applied to identify and to assess the influence of the external elements on the Portuguese transmission network. All simulations of the Portuguese and Spanish systems were performed using the computational software package PSS/E of Siemens/PTI. The results obtained with the proposed methodology are compared with the solutions produced by the horizontal network methodology.