Abstract
Electric Vehicles (EVs) are increasing the interdependence of transportation policies and the electricity market dimension. In this paper, an Electricity Market Model with Electric Vehicles (EMMEV) was developed, exploiting an agent-based model that analyzes how carbon reduction policy in transportation may increase the number of Electric Vehicles and how that would influence electricity price. Agents are Energy Service Providers (ESCOs) which can distribute fuels and their objective is to maximize their profit. In this paper, the EMMEV is used to analyze the impacts of the Low-Carbon Fuel Standard (LCFS), a performance-based policy instrument, on electricity prices and EV sales volume. The agents in EMMEV are regulated parties in LCFS should meet a certain Carbon Intensity (CI) target for their distributed fuel. In case they cannot meet the target, they should buy credits to compensate for their shortfall and if they exceed it, they can sell their excess. The results, considering the assumptions and limitations of the model, show that the banking strategy of the agents contributing in the LCFS might have negative impact on penetration of EVs, unless there is a regular Credit Clearance to trade credits. It is also shown that the electricity price, as a result of implementing the LCFS and increasing number of EVs, has increased between 2% and 3% depending on banking strategy.

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.

Abstract
This work consists in assessing a real islanded power system from a dynamic security point of view, to support the planned installation, in the near future, of a hydro power plant with pumped storage, aiming to increase the integration of renewable energy. The analysed hydro power plant will include a Pelton turbine as it is a high-head hydro facility. Due to economic reasons, the adopted water pumping technology consists in fixed speed pumps coupled to induction motors with direct grid connection. It was possible to verify through detailed simulations of this power system's time domain behaviour that, even though the expected installation of this new power plant will bring additional frequency stability constraints, a robust technical solution may be found dealing with the new constraints without increasing the complexity of operation in this islanded power system. The conclusions obtained from this specific case are also valid for similar isolated power systems, namely when hydro pumping stations are being considered to increase time-variable renewable generation penetration.

Abstract