Abstract
The Horizon 2020 Storage ENabled SustaInable energy for BuiLdings and communitiEs (SENSIBLE) project is currently looking at the integration of small-scale storage technologies in buildings and distribution networks. In the demonstration site of the SENSIBLE project, EDP has already installed an experimental storage system supplying a university campus in MV. It was mainly designed to increase service quality to the university by providing backup power in the event of MV grid failure, but it can also control voltage profile and conduct peakshaving. In parallel, small-scale storage is being also installed at the LV level by SENSIBLE. For these new grid assets, the SENSIBLE project is implementing a use case for centralized control approach that guarantees a coordinated operation of MV and LV storage. Furthermore, adding a MV switchgear, the resulting system will be able to isolate from the main grid thus effectively working as a microgrid with MV and LV Storage, PV generation and residential/commercial loads. This paper presents an overview of the technologies and software that will enable new grid support functions from small-scale storage.

Abstract
The smart grid concept increases the observability and controllability of the distribution system, which creates conditions for bi-directional control of Distributed Energy Resources (DER). The high penetration of Renewable Energy Resources (RES) in the distribution grid may create technical problems (e.g., voltage problems, branch congestion) in both transmission and distribution systems. The flexibility from DER can be explored to minimize RES curtailment and increase its hosting capacity. This paper explores the use of the Monte Carlo Simulation to estimate the flexibility range of active and reactive power at the boundary nodes between transmission and distribution systems, considering the available flexibility at the distribution grid level (e.g., demand response, on-load tap changer transformers). The obtained results suggest the formulation of an optimization problem in order to overcome the limitations of the Monte Carlo Simulation, increasing the capability to find extreme points of the flexibility map and reducing the computational effort.

Abstract
This paper presents two contributions developed in the framework of evolvDSO Project to support TSO-DSO cooperation. The Interval Constrained Interval Power Flow (ICPF) tool estimates the flexibility range at primary substations by aggregating the distribution network flexibility. The Sequential Optimal Power Flow (SOPF) tool defines a set of control actions that keep the active and reactive power flow within pre-agreed limits at primary substations level, by integrating different types of flexibility levers. Several study test cases were simulated using data of four real distribution networks from France and Portugal, with different demand/generation profiles and several degrees of flexibility.

Abstract
The current literature in wind power forecast is focused in generating accurate uncertainty forecasts and communicating this information to the end-user. Multi-temporal decision-making problems require information about the temporal trajectory of wind power for the next hours. Presently, this information is provided through a set of temporal trajectories (or scenarios). This paper aims at contributing with an alternative approach for communicating this information through simultaneous prediction intervals. These intervals include the temporal dependency of forecast errors since they provide information about the probability of having the observed wind power trajectory fully inside the quantiles forming the interval. First, a learning sample of temporal trajectories are generated with the Gaussian copula method and using the marginal prediction intervals. Then, two methods proposed in the literature are used to construct the simultaneous intervals. The quality of these intervals is evaluated for three real wind farms.

Abstract
This paper presents the approach followed under project SENSIBLE to prove, in field-test scenarios, the benefits of integrating and coordinating small-scale storage devices to: (i) reduce the impact of Distributed Renewable Energy Sources in the Low Voltage grid and (ii) support the transition and the operation in islanding mode in the demonstration grid. The functional and ICT architecture developed for the Portuguese Demonstrator of Évora is presented, focusing in the use cases defined to test and validate the tools developed to enable the active management of the LV grid during both normal and islanded modes.

Abstract
Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k-fold cross-validation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead.