Abstract
The global push for environmental sustainability is driving substantial changes in power systems, prompting extensive grid upgrades. Policies and initiatives worldwide aim to reduce CO2 emissions, with a focus on increasing reliance on Renewable Energy Sources (RESs) and electrifying transportation. However, the geographical variability and uncertainties of RESs directly impact power generation and distribution, necessitating adjustments in transmission system planning and operation. This paper presents a Transmission Expansion Planning (TEP) model using the 2021 Texas snowstorm as a benchmark scenario, incorporating wind and solar energy penetration while addressing associated uncertainties. Climate Change (CC) and Extreme Weather Events (EWE) are integrated into the set of scenarios aiming at evaluating the proposed method's effectiveness. Comparisons in extreme operative conditions highlight the importance of network reliability and security, emphasizing the significance of merged grids. All simulations are conducted using the ACTIVSg2000 synthetic test system, which emulates the ERCOT grid, with comparisons made between TEP scenarios considering and disregarding CC and EWEs, supporting the concept of umbrella protection.

Abstract
This research study presents an optimized approach for charging electric vehicles (EVs) in existing residential multi dwelling buildings. The proposed solution tackles the problem in two distinct, but complementary ways. First it takes advantage, in a novel way, of the existing electrical infrastructure by taping directly into the main feeder of the building, second it distributes the power in real time by leveraging in an optimized methodology. The aim of this methodology is to minimize the discrepancy between the desired and final state of charge (SOC) of EVs by the end of each charging session. To achieve this, the method leverages on commuting and charging preferences of EV owners, as well as the electrical infrastructure of residential buildings. To dynamically adjust the charging power for each EV in real-time, an optimized charging management system is employed. This system solves a non-linear minimization optimization problem that considers various parameters, including the initial SOC of each EV, the desired final SOC, the available charging time, and the available charging power. To assess the effectiveness of the proposed methodology, comparative analysis was conducted against a baseline methodology commonly used in practice. The results show that the optimized approach significantly outperforms the non-optimized methods, particularly in high demand scenarios. In these scenarios, the optimized methodology allows for a 200% increase in the supplied energy to the buildings' EV fleet, as well as more than doubling the range made available to users when compared to traditional approaches. In conclusion, this research work offers a robust and effective solution for charging EVs in residential buildings.

Abstract
The decarbonization of the economy and the increasing integration of renewable energy sources into the generation mix are bringing new challenges, requiring novel technological solutions in the topic of smart grids, which include smart transformers and energy storage systems. Additionally, power quality is a vital concern for the future smart grids; therefore, the continuous development of power electronics solutions to overcome power quality problems is of the utmost importance. In this context, this article proposes a novel three-phase multiobjective unified power quality conditioner (MO-UPQC), considering interfaces for solar PV panels and for energy storage in batteries. The MO-UPQC is capable of compensating power quality problems in the voltages (at the load side) and in the currents (at the power grid side), while it enables injecting power into the grid (from the PV panels or batteries) or charging the batteries (from the PV panels or from the grid). Experimental results were obtained with a three-phase four-wire laboratory prototype, demonstrating the feasibility and the large range of applications of the proposed MO-UPQC.

Abstract
Currently, the transmission system operators (TSOs) from Portugal and Spain do not procure a frequency containment reserve (FCR) through market mechanisms. In this context, a virtual power plant (VPP) that aggregates sources, such as wind and solar power and hydrogen electrolyzers (HEs), would benefit from future participation in this ancillary service market. The methodology proposed in this paper allows for quantifying the revenues of a VPP that aggregates wind and solar power and HEs, considering the opportunity costs of these units when reserving power for FCR participation. The results were produced using real data from past FCR market sessions. Using market data from 2022, a VPP that aggregates half of the HEs and is expected to be connected in the country by 2025 will have revenues over EUR 800k, of which EUR 90k will be HEs revenues.

Abstract
Frequency Containment Reserve (FCR) Cooperation is a European effort to integrate several countries in an integrated international electricity market platform for FCR procurement. In this market, Balancing Service Providers (BSPs) are on the supply side and Transmission System Operators (TSOs) on the demand side. This paper proposes a novel settlement scheme for sharing costs among TSOs; it proposes no changes to existing market clearing rules or to the existing settlement of the BSPs' revenues. It is shown that the current TSO settlement scheme is an inequitable mechanism that originates negative costs for some TSOs in specific conditions, which are extensively discussed. The proposed TSO settlement scheme overcomes these inequities. In the proposed scheme, TSOs begin paying the local BSPs for the cleared bids needed locally, and the remaining imports are calculated in a subsequent step. Doing so avoids using the so-called import/export costs, which are demonstrated to be the source of the inequities in the current scheme. It is shown that if the proposed pricing scheme had been adopted from July 2019 to December 2022, all TSOs would have been affected. Specifically, the most negatively impacted TSO would have its accumulated costs increased by 16% and the most positively impacted TSO would have its accumulated cost decreased by 32%. The inequities of the current mechanism amount to more than 50 Me or 7.4% of the total accumulated costs. Although the proposed mechanism is tested here under the FCR Cooperation, it can be applied to other markets where the rules allow different local settlement prices.

Abstract
The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic Algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.