Abstract
A considerable number of classic and modern control applications in distribution systems are developed aiming at make the most of the existing infrastructure. Under this perspective, investments postponement in grid upgrade appears as a good expectable result. The advent of smart grids enhances this expectation since it promises ubiquitous system monitoring, secure and trustworthy communication technologies and advanced control schemes. These features will allow the implementation of innovative algorithms for better operation and management of system assets. New possibilities arise for DSM, microgeneration control, electric vehicle charging and intelligent operation of energy storage devices. The scientific literature has countless examples that illustrate the benefits of such tools. A frequent outcome of these studies highlights the advantage of investments deferral in network reinforcement. This paper analyzes the combined effects of these control actions with the investments required for network upgrade, namely in lines and transformers reinforcement. Contrary to other research papers, our results demonstrate that investments deferral could lead frequently to higher costs in a long-term perspective.

Abstract
The purpose of this paper is to present the main results of the ongoing analysis of applying dynamic network access tariffs in Portugal. For the 2015-2017 regulatory period, the Portuguese National Regulatory Authority, ERSE, required the three main Portuguese DSOs to submit, until the end of June 2016, plans for the implementation of network dynamic tariff schemes targeting Medium, High and Extra High Voltage customers, as well as the respective cost-benefit analysis. EDP Distribuição, the main Portuguese DSO, is preparing a report regarding the implementation of pilot projects on a sample of these segments of customers, which are due to be on the field during 2017. These pilots should help electrical energy stakeholders understand how the Electric System can benefit from the use of dynamic tariffs focused on networks, allowing for the quantification of benefits in a more accurate way. The level of demand response that results from price signals is a key issue that both the regulator and EDP Distribuição will quantify. Other important issue to assess in this study is the efficiency of cost recovery under a dynamic tariff scheme. In conclusion, this paper will present some results obtained from the cost-benefit analysis regarding the implementation of a Critical Peak Pricing scheme, as well as the key learnings supporting the introduction of dynamic schemes in the future, not only for EHV, HV and MV customers but also eventually extending it to LV ones.

Abstract
In this paper, a novel hybrid single-objective metaheuristic, the so called C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed and tested. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO, or a swarm optimization method with selection and self-adaptiveness properties proper from DE. A case study on the problem of optimal control for reactive sources in energy production by Wind Power Plants (WPP), solved by means of Optimal Power Flow (OPF-like), is used to test the new hybrid algorithm and to evaluate its performance. C-DEEPSO is compared to the baseline algorithm, DEEPSO, and to a reference algorithm, Mean-Variance Mapping Optimization (MVMO). The experiments indicate that the proposed algorithm is efficient and competitive, capable to tackle this large-scale problem. The results also show that the new approach exhibits better results, when compared to MVMO.

Abstract
The Unit Commitment (UC) problem consists on the day-ahead scheduling of thermal generation units. The scheduling process is based on a forecast for the demand, which adds uncertainty to the decision of starting or shutting down units. With the increasing penetration of renewable energy sources, namely wind power, the level of uncertainty is such that deterministic UC approaches that rely uniquely on point forecasts are no longer appropriate. The UC approach reported in this paper considers a stochastic formulation and includes constraints for the technical limits of thermal generation units, like ramp-rates and minimum and maximum power output, and also for the power flow equations by integrating the DC model in the optimization process. The objective is to assess the ability of the stochastic UC approach to decrease the expected value of load shedding and wind power loss when compared to the deterministic UC approach. A case study based on IEEE-RTS 79 system, which has 24 buses and 32 thermal generation units, for two different penetrations of wind power and a 24-hour horizon is carried out. The computational performance of the methodology proposed is also discussed to show that considerable performance gains without compromising the robustness of the stochastic UC approach can be achieved.

Abstract
State estimation (SE) has been considered the fulcrum of advanced computer-aided tools used to monitor, control, and optimise the performance of power grids. It is destined for the provision of a consistent real-time dataset, free of compromising errors. To the SE eye, observability is the faculty of seeing the actual system operating state. As such, it is vital to evaluate this faculty, especially in quantitative terms. Drawing a parallel between the financial market (in which investment grades - intended to signal the suitability of an investment - are assigned by credit rating agencies) and SE arena, this study proposes the establishment of observation grades. With a view to performing a reliable SE, these are defined as ratings capable of indicating that a measurement system (devoted to observing the state of a power grid under many different conditions), has a seal of approval, i.e. relatively low risk of being unsuccessful. The methodology proposed to express observation grades is based on the Monte Carlo simulation approach. The availability of measurement units and grid branches are adequately considered. Numerical results of a proof of concept study performed on the 24- and 118-bus benchmark systems illustrate the application and expected benefits of the proposed methodology.

Abstract
In order to reduce the curtailment of renewable generation in periods of low load, operators can limit the import net transfer capacity (NTC) of interconnections. This paper presents a probabilistic approach to support the operator in setting the maximum import NTC value in a way that the risk of curtailment remains below a pre-specified threshold. Main inputs are the probabilistic forecasts of wind power and solar PV generation, and special care is taken regarding the tails of the global margin distribution (all generation all loads and pumping), since the accepted thresholds are generally very low. Two techniques are used for this purpose: interpolation with exponential functions and nonparametric estimation of extreme conditional quantiles using extreme value theory. The methodology is applied to five representative days, where situations ranging from high maximum NTC values to NTC=0 are addressed. Comparison of the two techniques for modeling tails is also comprised.