Abstract
The Transmission Expansion Planning (TEP) problem aims at identifying a list of equipment, among transmission lines, cables and transformers, that will be installed on the grid over a predefined planning horizon. TEP must also identify the respective periods of installations of the selected pieces-of-equipment to expand the grid. TEP is a mixed-integer non-linear and non-convex problem that requires a huge computational effort to be solved and, for this reason, many authors have been proposing relaxed TEP versions to reduce the mentioned computational burden. In this direction, this paper presents a broad comparison between the relaxed static TEP approach and the complete dynamic TEP. Numerical simulations are conducted using the Garver-6-bus and the RTS-24-bus test systems. The problem is handled by a hybrid tool composed by the Evolutionary Particle Swarm Optimization (EPSO) algorithm and a version of Hill-Climbing (HC), besides an AC-optimal power flow model is used to get more realistic operation conditions of the network. Even though dynamic TEP approaches present a higher computational effort, the results show that with this solution approach it is possible to obtain relevant investment savings when compared with the static TEP approach.

Abstract
The Portuguese generation system includes a large amount of wind parks plus a rapidly increasing capacity in PV units. These units as well as some other technologies are classified as Special Regime Generation, SRG, and are paid feed in tariffs. The operation planning for the next day is based on estimates of these injections made in the morning of the day before operation. Then on the operation day, the wind and PV generations will likely be different originating the activation of reserves, namely automatic Frequency Restoration Reserve, aFRR, and Replacement Reserve, RR. Therefore, the main purpose of this paper is to report the research that was conducted to check whether the large amount of wind and PV capacities explain the amounts of contracted and mobilized aFRR and RR reserves. To do this we used data for 2016 publicly available on the web page of the Portuguese TSO. The clarification of this issue is relevant because the amount of money required to pay reserves has been increasing in recent years and this amount is internalized in the Access Tariffs paid by all Portuguese consumers.

Abstract
This work describes an analysis of the impact of distributed generation, focusing on the generation from renewable sources, in the technical losses of electrical energy distribution systems. To do so, the OPENDSS software was used to simulate the operation of two distribution feeders, considering: (a) different generation penetration levels; (b) generation with different power factors; (c) variations in the location of distributed sources in the distribution system. The results show that these factors can both interfere positively (decrease) or negatively (increase) in the distribution network technical losses levels, and therefore, there may be distortions in the recognized electrical losses by regulatory agencies, mainly due to the generation unpredictability of renewable sources.

Abstract
This paper presents a long-term impact evaluation algorithm to assess advanced under frequency load shedding (UFLS) schemes on distribution systems with intentional islanding of distributed generation (DG). The algorithm is based on a combined discrete-continuous simulation model which is utilized to verify the effect of the schemes on reliability indices such as the system average interruption frequency index, system average interruption duration index, and energy not supplied. Moreover, a polynomial neural network-based approach to advanced load shedding is implemented to support DG islanding in order to illustrate the applicability of the evaluation. Simulation results highlight the long-term effect of employing UFLS to support intentional islanding of DG using an actual network from the South of Brazil.

Abstract
Smart Transformers (ST) are power-electronic based apparatuses that bring new opportunities for defining innovative operating strategies for Medium Voltage (MV) and Low Voltage (LV) distribution grids in future scenarios with increased shares of small-scale generation units. A distinctive advantage of ST is the possibility of connecting a storage device in the DC link when considering a three-stage configuration (AC/DC/AC). In this paper, ST is exploited within the context of Multi-Microgrids (MMG) in order to enhance the possibility of islanding operation through the identification of new control functionalities. The identification of robust control strategies to coordinate the flexibility of all the available resources (distributed resources at the LV and MV grids and storage units connected to the ST) is required to guarantee the successful operation of the MMG in the islanded mode. In order to address the need of specific control requirements, two different configurations are considered, being the proposed control strategies properly described: (1) ST with an energy storage unit in the DC link which fully decouples all the control possibilities for the MV and LV grid sides; (2) ST without an energy storage unit, requiring proper coordination between the MV and LV grid levels.

Abstract
Home energy management systems (HEMSs) are important platforms to allow consumers the use of flexibility in their consumption to optimise the total energy cost. The optimisation procedure embedded in these systems takes advantage of the nature of the existing loads and the generation equipment while complying with user preferences such as air temperature comfort configurations. The complexity in finding the best schedule for the appliances within an acceptable execution time for practical applications is leading not only to the development of different formulations for this optimisation problem, but also to the exploitation of non-deterministic optimisation methods as an alternative to traditional deterministic solvers. This study proposes the use of genetic algorithms (GAs) and the cross-entropy method (CEM) in low-power HEMS to solve a conventional mixed-integer linear programming formulation to optimise the total energy cost. Different scenarios for different countries are considered as well as different types of devices to assess the HEMS operation performance, namely, in terms of outputting fast and feasible schedules for the existing devices and systems. Simulation results in low-power HEMS show that GAs and the CEM can produce comparable solutions with the traditional deterministic solver requiring considerably less execution time.