Pedro Pereira Barbeiro

Abstract
Frequency stability in inverter-based renewable energy sources (RES)-dominated, low-inertia, power systems is a timely challenge. This paper employs a systematic approach, utilizing an artificial neural network (ANN) and dynamic simulation, to infer two key frequency stability indicators: nadir and rate of change of frequency (RoCoF). By reformulating the ANN mathematical model, these indicators are then integrated as mixed-integer non-linear constraints into a classical AC security-constrained optimal power flow (AC SCOPF), resulting in the proposed AC-F-SCOPF problem. The results of the proposed AC-F-SCOPF on the IEEE 39-bus system show that the problem identifies accurately the synchronous condensers which must run to ensure the frequency stability.

Abstract

Abstract
The increasing connection of new assets in LV networks will surely require a better monitoring of these systems in a real-time manner. In order to meet this purpose, a Distribution State Estimator (DSE) module clearly appears as the most cost-effective and possibly the only reliable option available. In this sense, in the scope of the evolvDSO project, a DSE tool exploiting the concept of ELM-AE was developed and tested in two distinct real LV distribution networks. In this paper the main results achieved with the proposed DSE tool are presented and discussed.

Abstract
The LV SCADA project aimed at the development of advanced technical, commercial and regulatory solutions to contribute for an effective smart grid implementation. One of the biggest challenges of the project was related with the lack of characterization that usually exists in LV networks, together with the almost non-existing observability. In order to overcome these issues, a LV management system integrating a state estimation tool based on artificial intelligence techniques was developed. The tool is currently installed in one pilot demonstration site that aggregates 2 MV/LV substations. In this paper the performance of tool in real environment is evaluated and the results gathered from the pilot site are analyzed.

Abstract
In the European Union (EU), the greenhouse emissions from the transportation sector increased around 36% since 1990, which degraded the environmental quality. This sector, owing to its oil dependency, is responsible for around a quarter of EU greenhouse emissions, and the road transportation represents about 20% of the total CO[[inf]]2[[/inf]] emissions in EU. Moreover, concerns such as the dependency on oil supply and the foreseen prices increase during this century have motivated a wide range of policy and technological measures for the transportation sector. A large part of these measures were to incentivize the electric vehicle (EV) adoption, which is one element with great potential to decarbonize the transportation sector and decrease its oil dependency. This chapter describes relevant methodologies to actively manage EV charging/discharging (as distributed storage devices) to achieve different goals, such as avoid grid congestion, the EV participation in primary frequency control, and the coordination of EV charging with renewable generation. To contextualize the methodologies described, a brief state of the art in active management functionalities for EV is provided. Some of the results obtained with the described approaches are also presented to demonstrate their overall performance. © 2016 John Wiley & Sons, Ltd. All rights reserved.