Abstract

Abstract
The consolidation of smart grids is inevitably related with the development and actual implementation of different functionalities envisioned for future electric grids. This paper presents the major implementations of smart grid projects in Portugal, which resulted from a close collaboration between academia and industry. An overview of the entire development process is presented culminating with the real implementation of the developed concepts. The architectures and functional models are presented as the initial step in defining the management and control functionalities for future smart distribution networks. The intermediate step consists in validating the advances introduced by smart grids. Simulation tools are emphasized considering both electrical and communications aspects. Finally, a laboratory infrastructure implemented to be used as a real test bed and a pilot deployed in a large city are presented in the end. The associated learning has provided relevant information for future developments.

Abstract
This work proposes a novel primary frequency control technique with electric vehicles (EV), the combination of inertial emulation and droop control, for isolated systems. Being EV dispersed along the grids, the impacts of possible delayed actions are assessed. Islanded systems have reduced inertia and so load/generation imbalance situations may lead to large frequency deviations. Therefore, this paper focuses essentially on the EV contribution for primary reserves provision, in order to allow a safe integration of further intermittent Renewable Energy Sources (RES). An avant-garde generation dispatch was adopted for the test system used in this work, fully reliant on RES, mainly conventional hydro units and some wind generation. The studied disturbances include a rapid shortfall on wind power production and a sequence of consecutive events caused by the variability of the wind resource in an ordinary situation.

Abstract
The three-phase state estimation algorithms developed for distribution systems (DS) are based on traditional approaches, requiring components modeling and the complete knowledge of grid parameters. These algorithms are capable of dealing with the particular characteristics of DS but cannot be used in cases where grid topology and parameters are unknown, which is the most common situation in existing low voltage grids. This paper presents a novel three-phase state estimator for DS that enables the explicit estimation of voltage magnitudes and phase angles in all phases, neutral, and ground wires even when grid topology and parameters are unknown. The proposed approach is based on the use of auto-associative neural networks, the autoencoders (AE), which only require an historical database and few quasi-real-time measurements to perform an effective state estimation. Two test cases were used to evaluate the algorithm's performance: a low and a medium voltage grid. Results show that the algorithm provides accurate results even without information about grid topology and parameters. Several tests were performed to evaluate the best AE configuration. It was found that training an AE for each network feeder leads generally to better results than having a single AE for the entire system. The same happened when different AE were trained for each network phase in comparison with a single AE for the three phases.

Abstract
Demand aggregators are new market players that represent a group of consumers in the electricity market. This paper proposes an aggregator model responsible for gathering residential and commercial consumers, which has the role of managing their flexible consumption in the day-ahead electricity market. A methodology to optimize the aggregator's bids is also presented. It optimizes the scheduling of the flexible loads taking simultaneously into account the consumers' preferences and temporal trajectories of forecasted outdoor temperatures and electricity prices. The proposed methodology was tested using a case study with 200 residential and commercial consumers from the Iberian market.

Abstract
Stationary batteries are currently seen as an interesting solution to deal with the variability of the renewable energy sources. In the same way as other types of storage, e.g. pumped-hydro units, this new type of storage equipment can improve the use of Renewable Energy Sources (RES). Additionally, the stationary batteries location in the grid is not as physically constrained as other storage systems and can be optimally selected to maximize its overall benefits. This paper proposes a new methodology to represent the unique stochastic behavior of stationary batteries while integrated into an electrical power system. This methodology includes not only the technical restrictions of this type of storage system but also how its operation strategy affects its lifetime. The methodology was tested on a small test system, which is based on the IEEE-RTS 79, using sequential Monte Carlo simulation as its core to accurately reproduce the chronology of events of stationary batteries. The results of the simulation are focused on the potential impacts of these storage devices not only in terms of renewable energy used but also in the adequacy of supply.