Abstract
This study explores models for synthetic data generation of time series. In order to improve the achieved results, i.e., the data generated, new ways of improvement are explored and different models of synthetic data generation are compared. The model addressed in this work is the Generative Adversarial Networks (GANs), known for generating data similar to the original basis data through the training of a generator. The GANs are applied using the datasets of Quinta de Santa Barbara and the Pinhao region, with the main variables being the Average temperature, Wind direction, Average wind speed, Maximum instantaneous wind speed and Solar radiation. The model allowed to generate missing data in a given period and, in turn, enables to analyze the results and compare them with those of a multiple linear regression method, being able to evaluate the effectiveness of the generated data. In this way, through the study and analysis of the GANs we can see if the model presents effectiveness and accuracy in the synthetic generation of meteorological data. With the proper conclusions of the results, this information can be used in order to improve the search for different models and the ability to generate synthetic time series data, which is representative of the real, original, data.

Abstract
Over the last decades, district heating has been under development, especially the technologies like heat pumps, solar thermal and cogeneration. However, there is still a long way to go regarding regulation, legislation and market liberalization, which varies across countries and regions. The objective of this work is to investigate the potential benefits of decentralized district heating systems in residential areas. By studying a case study of EnergyLab Nordhavn, a residential area in Copenhagen, Denmark, the paper compares the market outcomes of decentralized systems such as community markets to the centralized pool market currently in practice, under the EMB3Rs platform. The study focuses on key market outputs such as dispatched production, revenues, and daily consumption patterns. Additionally, the paper examines the impact of advanced features such as flexible heat consumption and network awareness in the market. The results of this research suggest that decentralized district heating systems have the potential to improve market outcomes and increase energy efficiency in residential areas.

Abstract
Recently, the uptake of renewable energy has surged in distribution networks, particularly due to the costeffectiveness and modular nature of photovoltaic systems. This has paved the way to a new era of user engagement, embodied by individual and collective self-consumption, and promoted by the EU Directive 2018/ 2001, which advocates for the establishment of Renewable Energy Communities. However, the transposition of this directive varies across Member States, resulting in specific rules for each country. In this work, the impact that different energy sharing models have on the same community is quantitatively assessed. The policy analysis focuses on the regulation of two countries, Italy and Portugal, chosen for the specular ways in which their models operate, respectively virtually and physically. The analysis is supported by a suite of tools which includes two optimization problems for community's operations, one for each analysed regulation, and a set of consumer protection mechanisms, to ensure no member is losing money while in community. Results demonstrate that the sharing model impacts community's optimal operations, optimal battery size and configuration, and members' benefit. As these models are sensitive to different variables, personalized interventions at national level are required.

Abstract
A notable shift from an internal combustion engine vehicles (ICEVs) fleet to an electric vehicles (EVs) fleet is expected in the medium term due to increasing environmental concerns and technological breakthroughs. In this context, this paper conducts a systematic literature review on life cycle assessment (LCA) research of EVs compared to ICEVs based on highly impactful articles. Several essential aspects and characteristics were identified and discussed, such as the assumed EV types, scales, models, storage technologies, boundaries, lifetime, electricity consumption, driving cycles, combustion fuels, locations, impact assessment methods, and functional units. Furthermore, LCA results in seven environmental impact categories were gathered and evaluated in detail. The research indicates that, on average, battery electric vehicles are superior to ICEVs in terms of greenhouse gas (GHG) emissions (182.9 g CO2-eq/km versus 258.5 g CO2-eq/km), cumulative energy demand (3.2 MJ/km versus 4.1 MJ/km), fossil depletion (49.7 g oil-eq/km versus 84.4 g oil-eq/km), and photochemical oxidant formation (0.47 g NMVOC-eq/km versus 0.61 g NMVOC-eq/km) but are worse than ICEVs in terms of human toxicity (198.1 g 1,4-DCB-eq/km versus 64.8 g 1,4-DCB-eq/km), particulate matter formation (0.32 g PM10-eq/km versus 0.26 g PM10-eq/km), and metal depletion (69.3 g Fe-eq/km versus 19.0 g Fe-eq/km). Emerging technological developments are expected to tip the balance in favor of EVs further. Based on the conducted research, we propose to organize the factors that influence the vehicle life cycle into four groups: user specifications, vehicle specifications, local specifications, and multigroup specifications. Then, a set of improvement opportunities is provided for each of these groups. Therefore, the present paper can contribute to future research and be valuable for decision-makers, such as policymakers.

Abstract
Regulatory changes associated with distributed generation have occurred in several countries (e.g., the USA, Germany, the UK, and Australia). However, there is a lack of robust and holistic analytical models that can be used to implement the best regulatory framework among possible options. In this context, the present paper proposes a cutting-edge regulatory framework for distributed generation based on multi-objective optimization, taking into account socioeconomic (socioeconomic welfare created by the regulated electricity market and electricity tariff affordability) and environmental (global warming potential) indicators. Such indicators are modeled primarily based on the optimized tariff model (socioeconomic regulated electricity market model), Bass diffusion model (forecasting model of distributed generation deployment), and life cycle assessment (environmental impact assessment method). The design variables are assumed to be the regulated electricity tariff and remuneration of the electricity injected into the grid over the years. First, the proposed methodology is applied to fifteen large-scale Brazilian concession areas with a significant deployment of distributed generation assuming two approaches, a multi-compensation scenario, where the compensation is set individually for each concession area, and a single-compensation scenario, where the compensation is set equally for all concession areas. Then, the optimal solutions are compared to Ordinary Law 14300, which is a recently implemented regulatory framework for distributed generation in Brazil. Results demonstrate that Ordinary Law 14300 is a dominated or non-optimal solution since it is not located on the optimal Pareto frontiers for any of the assessed concession areas. Assuming the Euclidian knee points, benefits averaging 33% and 15% were achieved in terms of electricity tariff affordability for the multi and single-compensation scenarios, respectively, with small losses of 8% and 3% in terms of socioeconomic welfare and global warming potential. Though the proposed methodology is applied in the Brazilian context, it can also be applied to other countries with regulated electricity markets; thus, it is expected to be valuable for researchers, government institutions, and regulatory agencies worldwide.

Abstract
Widespread adoption of distributed energy resources led to changes in low -voltage power grids, turning prosumers into active members of distribution networks. This incentivized the development of consumercentric energy markets. These markets enable trades between peers without third -party involvement. However, violations in network technical constraints during trades challenges integration of market and grid. The methodology used in this work employs batteries to prevent network violations and improve social welfare in communities. The method uses sequential simulations of market optimization and distribution network power flows, installing batteries if violations are identified. Simulation solves nonlinear deterministic optimization for market trades and results are used in power flow analysis. The main contribution is assessing battery participation in energy markets to solve distribution network violations. Case studies use realistic data from distribution grids in Costa Rica neighborhoods. Results indicate potential gains in social welfare when using batteries, and case -by -case analysis for prevention of network violations.