Abstract

Abstract
The 2015-2030 agenda framed Sustainable Development as a Universal venture. This decision has a great historic significance as it encompasses building a better future for the whole of humanity, enrolling the millions who have been denied the chance to live a decent, dignified and fulfilling life and to achieve their potential. For the first time, an entire generation will have the chance to succeed in ending poverty while being the last to have a chance of saving the planet. The world will be a better place in 2030 if humanity succeeds in this journey. However, there is hovering skepticism around the feasibility of this accomplishment. The article aims to empirically test the (in)compatibilities among these objectives even for developed economies such as the European Union countries, demonstrating that unless solid bridges are built promoting innovative networks at a transnational level, welfare and prosperity among those ecosystems will be compromised. The results show that the Sustainable Development Goals (SDG) pillars have heterogeneous determinants, which are to some extent incompatible. Moreover, policy makers need to further reinforce multi-country compensations if the environment is to be preserved.

Abstract
Forecasts of residual demand curves (RDCs) are valuable information for price-maker market agents since it enables an assessment of their bidding strategy in the market-clearing price. This paper describes the application of deep learning techniques, namely long short-term memory (LSTM) network that combines past RDCs and exogenous variables (e.g., renewable energy forecasts). The main contribution is to build up on the idea of transforming the temporal sequence of RDCs into a sequence of images, avoiding any feature reduction and exploiting the capability of LSTM in handling image data. The proposed method was tested with data from the Iberian day-ahead electricity market and outperformed machine learning models with an improvement of above 35% in both root mean square error and Frechet distance.

Abstract
The multiplicity of stakeholders in urban contexts can greatly increase the complexity of transportation systems. Since all stakeholders depend, to varying degrees, on the same data to get the information for their mobility, this work considers that an integrated information system, focused on their different needs, will significantly improve the efficiency of transportation systems. A stakeholder-focused system makes the provided information more relevant, while an integrated system fosters the sharing of the data that generates this information. To build such a system, a conceptual framework focused on stakeholders and their decision processes was developed. This new framework takes advantage of existing ones, such as the Zachman framework, the Enterprise Architecture Design, and the Multilevel Service Design. The proposed multidisciplinary approach, putting together information systems (IS) and service design concepts, has considerable potential in ensuring that the right information reaches each stakeholder at the right time.

Abstract
In this paper, a post-event restoration framework for distribution system in response to high impact low probable events is presented. The mobile emergency generators and mobile energy storage systems are considered for grid-support services in order to boost the system performance by supplying load demands, weighted by their priority. The restoration strategy of repair crew is dispatched in a deterministic way and the coordinated operation is performed with mobile power sources for picking up lost loads as soon as possible. Also, the transportation system constraints are integrated into the restoration tools for obtaining more realistic results while directing mobile power sources from staging locations to the damaged areas. A mixed integer linear programing (MILP)based scheme is performed on a modified version of the 10-bus test system with different case studies for illustrating the capabilities of the model implemented in the PuLP 1.6.8 open source library of Python 2.7 version, solved by CPLEX solver.

Abstract