Abstract
The European Union (EU) is adopting proactive strategies toward global decarbonization, proposing ambitious climate objectives to the international community, and adopting by itself ambitious energy and climate change objectives, as can be checked in its strategic packages for 2020, 2030 and 2050. As can be seen in these packages and in global climate and energy agreements, renewable generation is a key aspect to reach the global decarbonizing objectives. This chapter starts with a review of the very basic concepts of greenhouse gases emissions and its impact on climate change and summarizes the main objectives of the strategic EU energy packages and of the Paris agreement. It then focuses on methodologies for estimating the impact of renewable energies on greenhouse gases emissions reduction and reviews the reported EU related achievements. It also proposes a classification of EU countries in terms of Tapio decoupling states by analyzing and classifying the countries emissions intensities and its variation for a considered time period and analyzes the impact renewable energies had in that greenhouse gases emissions variations, in relation with other possibly relevant factors. The chapter ends with a summary of the expected evolution of renewable energies in the EU, and with the final conclusions.

Abstract
This work combines a detailed model of the electricity sector with a general equilibrium model for Spain, to analyze the effects of new investments and technological evolution in the electricity sector, as well as their impact in global aspects of the economy. A reference scenario with high prices for CO2 emissions together with insufficient investments in renewable energy was simulated, showing an expected negative economic impact. This scenario was then combined with five potential policies of economic reactivation. The most positive one was related to the reduction of the cost of access to capital, leading to improvements in capital income and GDP, thus mitigating the impact of the electricity price increase. This policy also leads to a migration of the labour from the production to the service sectors and suggests that a transition towards a cleaner electricity sector with minor economic impacts is possible, when energy policies are combined with adequate fiscal policies.

Abstract
Power systems of most EU countries will face substantial changes driven mainly by the EU Clean Energy Package and the expected increasing role of customers making use of demand response and distributed self-generation and self-storage resources (self-DG&S). This work aims to analyze the impact of these two factors on the long term evolution of both utility scale centralized generation and storage (CG&S), and self-DG&S. An hourly resolution generation expansion model based on cost minimization for CG&S and expenses' minimization for self-DG&S is used. Four case studies covering the period 2018-2047 for a real sized system (the Spanish one) are presented. Results highlight a strong synergy between battery costs and solar photovoltaic investments, while wind power seems to be better complemented by peaker thermal units.

Abstract
Transport systems and buildings are among the bigger energy users inside cities. Abundant research has been developed about these systems (facilities and transport). However, synergies among them are commonly overlooked, not taking advantage of the possible benefits of their joint coordination and management. This paper presents a linear programming model to find the optimal operation and planning of distributed energy resources (DER) in a residential district, while considering electric private and public transport systems, in particular electric vehicles and metro. Hence, the main contribution of this paper is the analysis of synergies of such an interconnected scheme. It has been assumed that part of the metro regenerative braking energy can be stored into electric vehicles' (EVs') batteries, so that it can be used later for other trains or for the EV itself. Several case studies have been proposed using data from a residential district and a metro line in Madrid. The obtained results show important cost savings in the overall system, especially a significant power cost reduction for the metro system.

Abstract
In order to avoid voltage problems derived from the connection of large amounts of renewable-based energy generation to distribution networks, new advanced tools need to be developed that are able to exploit the presence of Distributed Energy Resources (DER). This paper describes the approach proposed for a predictive voltage control algorithm to be used in LV distribution networks in order to make use of available flexibilities from domestic consumers via their Home Energy Management System (HEMS) and more traditional Distribution System Operator's (DSO's) resources, such as transformers with On-Load Tap Changer (OLTC) and storage devices. The proposed algorithm for the Low Voltage Control (LVC) is detailed in this paper. The algorithm was then tested through simulation using a representative Portuguese Low Voltage (LV) network in order to assess its performance in several future scenarios with different operating conditions. The future prospects for field-trial validation in a Portuguese smart grids pilot are also discussed.

Abstract
In this work a new trading framework for demand response (DR) aggregators is proposed using a non-probabilistic model. In this model, DR is acquired from consumers to sell it to the purchasers by aggregators. Two programs, i.e., time-of-use (TOU) and reward-based DR program, are implemented to obtain DR from consumers. Then, the obtained DR is sold to buyers via two considered agreements, i.e., fixed DR contracts and DR options. The information-gap decision theory is also employed to consider the uncertainties for risk-averse aggregators. Consumer's participation behavior is considered as an uncertain parameter. A robustness function is proposed to examine the immunity of the model against adverse variations of uncertain parameters. The feasibility of the proposed model is studied on the real-world data.