Abstract
The growing integration of renewable energy in power systems demands for adequate planning of generation systems not only to meet long-term capacity requirements hut also to cope with sudden capacity shortages that can occur during system operation. As a matter of fact, system operators must schedule an adequate amount of operational reserve to avoid capacity deficits which can be caused by, for instance, overestimating the wind power that will be available. The framework proposed for the long-term assessment of operational reserve relies on the Nave forecasting method to produce wind power forecasts for the next hour. This forecasting model is simple and widely used to obtain short-term forecasts. However, it has been shown that regression models, such as the Autoregressive Integrated Moving Average (ARIMA) model, can outperform the Naive model even for forecasting horizons of up to 1 hour. This paper investigates the differences in the risk indices obtained for the long-term operational reserve when using the Naive and the ARIMA forecasting models. The objective is to assess the impact of the forecasting error in the long-term operational reserve risk indices. Experiments using the Sequential Monte Carlo Simulation (SMCS) method were carried out on a modified version of the IEEE RTS 79 test system that includes wind and hydro power variability. A sensitivity analysis was also performed taking into account several wind power integration scenarios and two different merit orders for scheduling generating units.

Abstract
In this paper, a number of possibilities are presented and discussed for the ownership of distributed storage devices (DSD) in a Smart Grid environment. The cases in which the distribution system operator (DSO) has either full control (grid owned storage) or no control whatsoever over the operation of the DSD (independently owned storage) will be differentiated. For each ownership possibility, the technical and regulatory implications are discussed, with analysis and validation of the results being performed on real MV distribution networks, both rural and urban. In order to evaluate each ownership possibility, a number of multi-period optimization models are presented, corresponding to different assumptions in regards to the operation of the DSD. The resulting daily operation strategies are subsequently used as a basis for carrying out distribution reinforcement planning.

Abstract
The emergence of self-consumption in low voltage consumers has technical and regulatory consequences, due to the changes in the network flows. In this paper these impacts are analysed through a small case study where different production/storage arrangements are considered. The technical analysis shows the voltage profiles' variation and identifies possible overvoltages and the effect of mitigation measures like storage. The regulatory analysis shows that, in order to maintain the remuneration of the Distribution System Operator (DSO) and Transmission System Operator (TSO), network tariffs parameters would change dramatically for the ordinary consumers, unless new rules are defined for the contribution of LV consumers.

Abstract
This paper presents a new methodology to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demand, using the artificial immune algorithm Copt-aiNet (Artificial Immune Network for Combinatorial Optimization). This algorithm is an optimization technique inspired by immune network theory (aiNet). The reconfiguration problem with variable demand is a complex problem of a combinatorial nature. The goal is to identify the best radial topology for an EDS in order to minimize the cost of energy losses in a given operation period. A specialized sweep load flow for radial systems was used to evaluate the feasibility of the topology with respect to the operational constraints of the EDS and to calculate the active power losses for each demand level. The algorithm was implemented in C++ and was evaluated using test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes. The obtained results were compared with those in the literature in order to validate and prove the efficiency of the proposed algorithm.

Abstract
This paper describes the application of the Opt-aiNet algorithm to the reconfiguration problem of distribution systems considering variable demand levels. The Opt-aiNet algorithm is an optimization technique inspired in the immunologic bio system and it aims at reproducing the main properties and functions of this system. The reconfiguration problem of distribution networks with variable demands is a complex problem that aims at identifying the most adequate radial topology of the network that complies with all technical constraints in every demand level while minimizing the cost of power losses along an extended operation period. This work includes results of the application of the Opt-aiNet algorithm to distribution systems with 33, 84, 136 and 417 buses. These results demonstrate the robustness and efficiency of the proposed approach.

Abstract
Transmission Expansion Planning (TEP) is an optimization problem that has a non-convex and combinatorial search space so that several solution algorithms may converge to local optima. Therefore, many works have been proposed to solve the TEP problem considering its relaxation or reducing its search space. In any case, relaxation and reduction approaches should not compromise the quality of the final solution. This paper aims at analyzing the performance of a search space technique using a Constructive Heuristic Algorithm (CHA) admitting that the TEP problem is then solved using a Discreet Evolutionary Particle Swarm Optimization (DEPSO). On one hand the reduction quality is performed by analyzing whether the optimal expansion routes are included in the CHA constrained set and, on the other hand, the relaxation quality of the DC model is analyzed by checking if the optimal solution obtained with it violates any constraint using the AC model. The simulations were performed using three different test systems. The results suggest that the proposed CHA provides very good results in reducing the TEP search space and that the adoption of the DC model originates several violations if the full AC model is used to model the operation of the power system.