Abstract
Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level a. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Abstract
This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level a is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Abstract
Electricity markets based on simple bids provide a very high degree of transparency and simplicity. However, simple bids fail to capture many well-known characteristics of generating units and, therefore, the responsibility for obtaining feasible schedules is transferred to market participants. The purpose of this paper is to help the generating utility to automatically analyze the last energy program cleared in the market and, in case this program is technically unfeasible, to provide an alternative schedule by redispatching the generating units. This is achieved by formulating an optimization problem where the objective is to find the cheapest and feasible instantaneous power trajectory of each generator, trying to minimize the differences between its hourly average values and the last energy program. As the objectives of the utility can vary during the day, three different models are presented. Two of them are formulated as a joint energy and reserve dispatch in order to take into account possible commitments acquired in the ancillary services market of AGC regulation. In this sense, a novel approach for considering discontinuous ancillary regulation curves is proposed. Some numerical examples are included to illustrate the essential features of the models.

Abstract
The main objective of this paper is to describe a strategy to deal with the voltage/reactive power problem for a MV distribution network integrating microgrids. The global problem, concerning all voltage levels, is detailed here and will imply the optimization of operating conditions by using the control capabilities of power electronic interfaces from DG sources, OLTCs and microgrids, through the application of an EPSO optimization algorithm.

Abstract
Voltage stability has been of the major concern in power system operation. To prevent these problems, technical staff evaluates frequently the distance of the operation state to the voltage collapse point. This distance normally is calculated with power flow equations. This classic technique is very slow for electric power systems with large dimension. In abnormal exploration situations it may introduce serious limitation in the voltage stability analysis process. So, the application of a fast and reliable evaluation technique is very important to diminish the evaluation time. This paper presents a study of the application of artificial neural network (ANN) to the evaluation of this distance to the voltage collapse point. To detection the point of collapse the new method FSQV was used.

Abstract
The main purpose of this work is the development of an optimization model for the design of the grounding grid in electrical substations. The problem is formulated as a mixedinteger linear programming problem, in terms of the constructive characteristics and the peculiar requirements to construct and to install the grounding grid. The model incorporates the variables that define the grid characteristics of all possible configurations including the grid geometry and the depth and conductor size. The optimization problem is subject to safety constraints related with the maximum allowed touching and step voltages. It also includes the equivalent impedance of the transmission line connected to the substation where it will be located the grounding grid to be designed. The methodology allows selecting the optimum grid of the possible configurations, so that is a very useful tool for the engineering design. The formulation and specifications used is based in IEEE Std. 80-2000.