Abstract

Abstract
The expansion and development of the electricity distribution grid is a complex multicriteria decision problem. The planning definition should take into consideration the investment benefits on the security of supply, quality of service, losses, as well as in other network features. Given the variety of assets and their context-dependent effects, estimating their global impact is very challenging. An additional difficulty is the combination of different types of benefits into a simple and clear portrayal of the planning alternatives. This paper proposes a methodology to estimate the benefits of distribution investments, in terms of five features: security of supply, quality of service, network losses, operational efficiency and new services. The approach is based on the adoption of objective and measurable indicators for each feature. The approach was tested with real data of Portuguese distribution grids and the results support the adopted approach and are being used as a decision-aid tool for grid planning. © 2020 IEEE.

Abstract
This paper reports the main results that were obtained in the scope of a consultancy study that was developed for EDP Distribuição, the main Portuguese distribution company, to evaluate the impact of a number of changes to be introduced in the Tariff System. These changes were proposed by ERSE, the Portuguese Regulatory Agency for the Energy Services, and included the redesign of the tariff periods and the possible introduction of a geographic differentiation on the Access Tariff to reflect different daily and yearly demand and flow patterns along the country. This work involved the development of a Cost Benefit Analysis, CBA, as well as a Pilot Project that included 82 MV and HV consumers to evaluate several Key Performance Indices, KPI, used to characterize the proposed changes on the tariff system. © 2020 IEEE.

Abstract
The rapid growth of wind generation in northeast Brazil has led to multiple benefits to many different stakeholders of energy industry, especially because the wind is a renewable resource - an abundant and ubiquitous power source present in almost every state in the northeast region of Brazil. Despite the several benefits of wind power, forecasting the wind speed becomes a challenging task in practice, as it is highly volatile over time, especially when one has to deal with long-term predictions. Therefore, this paper focuses on applying different Machine Learning strategies such as Random Forest, Neural Networks and Gradient Boosting to perform regression on wind data for long periods of time. Three wind farms in the northeast Brazil have been investigated, whose data sets were constructed from the wind farms data collections and the National Institute of Meteorology (INMET). Statistical analyses of the wind data and the optimization of the trained predictors were conducted, as well as several quantitative assessments of the obtained forecast results.

Abstract
This study introduces a robust orthogonal implementation for a new class of information theory-based state estimation algorithms that rely on the maximum correntropy criterion (MCC). They are attractive due to their capability to suppress bad data. In practice, applying the MCC concept amounts to solving a matrix equation similar to the weighted least-squares normal equation, with difference that measurement weights change as a function of iteratively adjusted observation window widths. Since widely distinct measurement weights are a source of numerical ill-conditioning, the proposed orthogonal implementation is beneficial to impart numerical robustness to the MCC solution. Furthermore, the row-processing nature of the proposed solver greatly facilitates bad data removal as soon as outliers are identified by the MCC algorithm. Another desirable feature of the orthogonal MCC estimator is that it avoids the need of post-processing stages for bad data treatment. The performance of the proposed scheme is assessed through tests conducted on the IEEE 14-bus, 30-bus, 57-bus and 118-bus test systems. Simulation results indicate that the MCC orthogonal implementation exhibits superior bad data suppression capability as compared with conventional methods. It is also advantageous in terms of computational effort, particularly as the number of simultaneous bad data grows.

Abstract
Residential consumers have been adopting distributed energy resources (DER) like photovoltaics (PV), electric vehicles (EV) as well as electric heating, ventilation and air conditioning devices (HVAC) in recent years - thus substantially reshaping power systems. This study is dedicated to the analysis of such adopters in continental Portugal, using both spatial analysis tools and census data with information theoretic criteria. Results suggest that the current uptake of EV, PV, and HVAC is characterised by spatially auto-correlated adoption patterns. The analysis of census variables, on the other hand, reveals that Portuguese EV, PV, and HVAC adopters exhibit a few surprising, unrecorded characteristics compared with previous studies. Comparing different dataset resolutions, EV and HVAC adopters are found to be most similar across all three aggregation levels considered. Results further show that fewer adopter groups tend to own both EV-HVAC and PV-HVAC, reducing per se synergy potentials that may arise behind the metre. One of the main outcomes from this work is that studies describing energy technology adopters using census variables might receive very unstable results across different data aggregation levels. This may lead to adverse effects on studies' conclusiveness and energy policy design choices.