Microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas but also in urban communities. Since microgrids are expected to coexist with traditional power grids (such as district heating does with traditional heating systems), their planning process must be addressed to economic feasibility, as a long-term stability guarantee. Planning a microgrid is a complex process due to existing alternatives, goals, constraints and uncertainties. Usually planning goals conflict each other and, as a consequence, different optimization problems appear along the planning process. In this context, technical literature about optimization techniques applied to microgrid planning have been reviewed and the guidelines for innovative planning methodologies focused on economic feasibility can be defined. Finally, some trending techniques and new microgrid planning approaches are pointed out.

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The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.

Abstract. Industrial equipment is evolving towards data gathering and communication, following the Internet of things approach. During last years, different management systems and related standards have been developed, in order to raise the performance of manufacturing processes. But a complete optimization of a manufacturing process requires a holistic approach, and adopting microgrid architectures can actually empower the optimization of a manufacturing process. In this paper, synergies about microgrids and manufacturing processes planning will be pointed out. Thus, a suitable approach to MG planning for manufacturing companies from a Knowledge Discovery in Databases (KDD) point of view is described.

The large increase in renewable energy sources (RES-E) penetration in the European Union (EU) has raised the concern of policy makers about the costs of public promotion of RES-E, in spite of the commitment of the European Commission to the deployment of RES-E in the last years. This paper is focused on wind energy, because it is the renewable technology with the highest contribution to electricity mix in Europe (excluding hydro). An economic analysis of wind energy contribution should not only take into account the policy costs of the deployment, which are finally paid by electricity consumers, but also its benefits, particularly those related to climate change mitigation and the reduction of fossil fuel dependence distributed among citizens. The aim of this paper is to fill this gap and evaluate the policy costs and the most relevant benefits of wind energy deployment in the EU28 (28 Member States) using 2013 data. For this purpose, an innovated methodology internationally validated has been used. The results show that the benefits considered are relevant and should be taken into account when support costs are assessed and in the future development of energy policies in Europe.