Abstract
This letter is an enhancement to our previous paper that quantifies additional reinforcement costs (ARCs) for low-voltage assets under moderate degree of three-phase imbalance. The original formulas cause an overestimation of the ARCs under severe imbalance. This letter first quantifies the threshold of the severe degree of imbalance (DIB), below which the original formulas are applicable. Then, the ARC formulas are extended to account for the whole range of DIB. Case studies demonstrate that when the asset loading level is below 33.3% (50%) for a feeder (a transformer), the DIB never exceeds the threshold and the original ARC formulas are applicable; otherwise, the DIB can exceed the threshold and the extended formulas yield correct ARCs.

Abstract
In this paper, typical strengths, fault levels, and source impedances are thoroughly analyzed and calculated for the study of quality of supply in 230/400 V 50 Hz distribution systems. Considering all the disparity in distribution network design, this study is based on a comprehensive database containing typical arrangements and equipment in U.K./European systems, as well as on fully documented generic network models supplying four residential load subsectors in the U.K., i.e., from metropolitan to rural areas. Thus, this paper proposes an alternative method for determining reference values of network supply impedances and short-circuit fault levels at different points and locations of the medium-to-low voltage distribution system. The aim of this study is to provide a wider range of benchmark values than those stipulated in the IEC 60725 Standard, which only defines a single-reference threshold of public supply impedances for all types of distribution systems and residential customers. In order to assist network operators in the planning and design of their distribution systems, these values are further disaggregated and classified in this paper according to network/demand type.

Abstract

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.