Abstract
Distribution lines are one of the most critical assets in distribution networks. In fact, overhead lines and subterranean cables are subjected to numerous internal and external factors that can cause failures and degradation over time. To prevent customer disconnection and to ensure continuous electricity delivery, the Distribution System Operator (DSO) strives to minimize the number of distribution line failures by carrying out inspection and preventive maintenance actions. Typically, HV and MV networks cover wide areas of the territory and comprise many lines of different types (overhead & subterranean) and equipment (e.g. conductor, isolator, poles), which makes it difficult to predict when failures will occur, which distribution line will fail and its location. The latter information is especially relevant since some distribution lines can cover a considerable distance. Motivated by a real-world application, this work presents a methodology to predict and locate future HV and MV distribution line failures. The methodology encompasses clustering techniques to group lines sharing similar characteristics, identifying the most relevant factors on the lines' degradation. In addition, historical records are leveraged by a prediction algorithm to estimate the number of failures and the failed section of the line. The approach was validated using data from the Portuguese HV and MV DSO (E-Redes). The results highlight the advantages of the proposed method compared with benchmark approaches. © 2021 The Institution of Engineering and Technology.

Abstract
In many industries, the employed maintenance policies contributed to the concentration of asset replacements in a short period of time. Thus, the number of O&M activities increases, leading to rising operational costs that are not compatible with the available resources. Moreover, these assets encompass multiple failure modes, which reduce asset availability and influence its longevity. Because asset degradation is stochastic, a considerable amount of uncertainty is associated with this problem. The recent technological advances in monitoring technology may foster a reduction in degradation uncertainty but the extra effort regarding the investment plan must be carefully planned. Bearing this in mind, we propose a methodology to determine the investments in the installation of monitoring equipment accounting for the impact in maintenance budget for O&M activities for a resource-dependent asset portfolio with multiple failure modes. The budget is shared between multiple assets and must be determined, a priori, and managed throughout an established time horizon. Since investing in monitoring equipment requires substantial capital due to the system size, DMs have to define which and when a given asset monitoring technology will be installed. Hence, not every asset may have the same monitoring technology and, consequently, the same degradation uncertainty. We formulate the problem as a stochastic optimization problem to capture the dynamic uncertainty in the assets’ condition. Due to its inherent complexity, we employ a meta-heuristic based on a co-evolutionary genetic algorithm to achieve high-quality solutions under reasonable computational time for real world-sized systems. The approach is validated in a case study in the electricity distribution in which a system operator has to manage a portfolio of power transformers operating under different operational conditions. © ESREL 2021. Published by Research Publishing, Singapore.

Abstract
Service operation vessels are becoming the dominant mode for the maintenance of most offshore wind farms. To minimize turbine downtime, it is essential to bring the right components to the wind farm, while budget and volume constraints prohibit having excess inventories on board. This setting can be interpreted as a repair kit problem, which seeks to define a set of components that may be necessary for on-site maintenance operations in a given time period during which emergency resupply is costly. Current repair kit problem approaches however, do not cater sufficiently for some of the characteristics of offshore wind farm maintenance, including weather-dependent deterioration and the possibility to perform emergency resupplies. We propose mixed-integer programming models both to determine (tactical model) and validate (operational model) repair kits when maintenance operations are performed under different weather conditions. The models are flexible enough to be used with real world data considering multiple turbines composed of different deteriorating components, service operation vessels characteristics (speed and volumetric capacity), different weather conditions, and emergency resupplies. An important feature of this approach is its ability to consider detailed maintenance and vessel routing operations to test and validate repair kits in realistic wind farm environments. We provide valuable insights on the composition of repair kits and on relevant business indicators for a set of different scenarios. The practical implications are that repair kits should be adapted depending on weather forecasts and that considerable downtime reductions can be achieved by allowing emergency resupplies. © 2021 The Author(s)

Abstract
Based in the current growth rate of metropolitan areas, providing infrastructures and services to allow the safe, quick and sustainable mobility of people and goods, is increasingly challenging. The European Union has been promoting diverse initiatives towards sustainable transport development and environment protection by setting targets for changes in the sector, as those proposed in the 2011 White Paper on transport. Under this context, this study aims at evaluating the environmental performance of the transport sector in the 28 European Union countries, from 2015 to 2017, towards the policy agenda established in strategic documents. The assessment of the transport environmental performance was made through the aggregation of seven sub-indicators into a composite indicator using a Data Envelopment Analysis approach. The model used to determine the weights to aggregate the sub-indicators is based on a variant of the Benefit of the Doubt model with virtual proportional weights restrictions. The results indicate that, overall, the European Union countries had almost no variation on its transport environmental performance during the time span under analysis. The inefficient countries can improve the transport sustainability mainly by drastically reducing the greenhouse gas emissions from fossil fuels combustion, increasing the share of freight transport that uses rail and waterways and also the share of transport energy from renewable sources. © 2021, Springer Nature Switzerland AG.

Abstract
Governments all over the world have been promoting electric mobility as an effort to reduce the transport sector’s greenhouse emissions and fossil fuel dependency. This work analyses the deployment of electric vehicles in the European Union countries, between 2015 and 2019, and the variables that may influence it, using a panel data methodology. The present work focuses on the deployment of battery and plug-in hybrid electric vehicles, individually and jointly. Nine explanatory variables were included in the model: density of recharging points, gross domestic product per capita, cumulative number of policies on electromobility, share of renewable energy in transport, total greenhouse gas emissions per capita, tertiary education attainment, electricity price, employment rate and new registrations of passenger cars per capita. The results showed that the indicators influence differently the deployment of the different types of electric vehicles. The most significant factor driving the battery electric vehicles deployment was the density of recharging points, while for plug-in hybrid electric vehicles was the share of renewable energy. Policy makers should focus on adjusting actions to the demand for the different types of electric vehicles.

Abstract