Abstract
Variable-speed pump power storage is an innovative large-scale technology that is being deployed across the world. In addition to price arbitrage and provision of downward replacement reserve, its operational flexibility enables the provision of frequency restoration reserve (FRR) both in turbine and pump modes. This work proposes a bidding optimization strategy for the participation in the FRR market. The proposed framework encompasses a medium-term module to optimally allocate the yearly natural water inflows, representation of electrical and hydraulic losses in the water inflow/power curves, as well as forecasting techniques to predict market prices and natural water inflows. Moreover, it does not assume prior knowledge of the amount of activated FRR capacity band. An evaluation module is also proposed to replicate the real operation of the power plant and enables an accurate calculation of the revenue. A comparison between fixed and variable-speed Pump storage power (PSP) units participating in the Iberian electricity market presented an increase in revenue of almost 12%. Due to the low liquidity of the FRR market in Portugal, and the considerable capacity of the PSP unit, under some specific situations, it might be necessary to cap the size of the FRR bid to decrease the difference between the expected and realized revenue.

Abstract
In the last two decades, renewable energy forecasting progressed toward the development of advanced physical and statistical algorithms aiming at improving point and probabilistic forecast skill. This paper describes a forecasting framework to explore information from a grid of numerical weather predictions (NWP) applied to both wind and solar energy. The methodology combines the gradient boosting trees algorithm with feature engineering techniques that extract the maximum information from the NWP grid. Compared to a model that only considers one NWP point for a specific location, the results show an average point forecast improvement (in terms of mean absolute error) of 16.09% and 12.85% for solar and wind power, respectively. The probabilistic forecast improvement, in terms of continuous ranked probabilistic score, was 13.11% and 12.06%, respectively.

Abstract
The technological revolution in the electric power system sector is producing large volumes of data with pertinent impact in the business and functional processes of system operators, generation companies, and grid users. Big data techniques can be applied to state estimation, forecasting, and control problems, as well as to support the participation of market agents in the electricity market. This chapter presents a revision of the application of data mining techniques to these problems. Trends like feature extraction/reduction and distributed learning are identified and discussed. The knowledge extracted from power system and market data has a significant impact in key performance indicators, like operational efficiency (e.g., operating expenses), investment deferral, and quality of supply. Furthermore, business models related to big data processing and mining are emerging and boosting new energy services.

Abstract
Multi-temporal decision-making problems require information about the potential temporal trajectories of wind generation for a given time horizon. Typically, the Gaussian copula is used for modelling the dependency between probabilistic forecasts from different lead-times. This paper explores the vine copula framework as a benchmark model since it captures complex multivariate dependence structures with mixed types of dependencies. The results show that a Gaussian copula with a suitable covariance matrix suffice to generate high quality temporal trajectories.

Abstract

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.