Abstract
This paper describes a genetic algorithm approach to the optimal multistage planning of distribution networks. The authors describe a mathematical and algorithmic model that they have developed and experimented with success. The paper also presents application examples, with real size systems. The advantages of adopting this new approach are discussed in the planning context, namely in conjunction with the adoption of multicriteria decision making methods.

Abstract
This paper reports a new approach in defining preventive control measures to assure transient stability relatively to one or several contingencies that may occur separately in a power system. Generation dispatch is driven not only by economic functions but also with the derivatives of the transient energy margin value; these derivatives are obtained directly from a trained Artificial Neural Network (ANN), using ri:al time monitorable system values. Results obtained from computer simulations, for several contingencies in the CIGRE test system, confirm the validity of the developed approach.

Abstract
This paper proposes a methodology to consider the effects of the integration of DG on planning. Since DG has potential to defer investments in networks, the impact of DG on grid capacity is evaluated. A multi-objective optimization tool based on the meta-heuristic MEPSO is used, supporting an alternative approach to exploiting the Pareto front features. Tests were performed in distinct conditions with two well-known distribution networks: IEEE-34 and IEEE-123. The results combined minimization and maximization in order to produce different Pareto fronts and determine the extent of the impact caused by DG. The analysis provides useful information, such as the identification of futures that should be considered in planning. A future means a set of realizations of all uncertainties. MEPSO also presented a satisfactory performance in obtaining the Pareto fronts. © 2011 IEEE.

Abstract
The development of techniques under geographical information system (GIS) platforms, such as geocomputational modeling, which increase the capabilities of GIS, allowing the systems to adapt to optimal distributed generation (DG) planning studies, is discussed. Using adequate software under the GIS platform, users can obtain useful information on the economic or technical viability of any distributed power generation facilities. GIS offers a variety of structured data models suitable for the storage, manipulation, and analysis of the information needed in DG planning. GIS can also be used in DG planning to study negotiation processes among different energy actors to look for geographical planning solutions.

Abstract
This paper presents an overview of a simulation platform for studying the behavior of energy retail markets where multiple energies enter in competition. This platform is based on autonomous agent techniques. The simulations include agents representing Residential, Commercial and Industrial Consumer Groups, Electricity, Gas, Heat Retail Suppliers and Energy Deliverers, Regulators, Market Operators, Economy and Information Environment. Each pursues its own interests and from their interaction a complex collective behavior emerges. Agents formulate their strategies namely by inner complex simulation process that try to guess other agent move s and define optimum decisions in energy purchasing, price fixing, market share wining, investing and capturing new consumers, among other. The process works on a FIPA complying platform being able to run in a parallel cluster machines.

Abstract
This paper presents the structure of a negotiation aid system (NAS) to select the best locations for new DG facilities, using sophisticated spatial techniques [based on geographical information systems (GISs)] and decision aid methodologies for negotiation, based on consensus among groups that may have conflicting interests. This system helps to overcome the problems posed by initially opposing positions stemming from diverse technological, economic, environmental and/or social interests. The NAS use is illustrated with results from a negotiation process between two groups to select locations for new wind farms in the region of La Rioja, Spain.