Abstract

Abstract
Self-Regulation and retail problem solving using Multi-Agent Systems are considered two promising areas but relatively little explored. The regulation in these environments must be able to handle with the implicit dynamism and variation in a complex area such as retail systems with a minimum human interference. Agents must be able to change their behaviour based on rules previously formatted, using an autonomic process that does the maintenance of a knowledge base which is well defined and consistent, to be possible to meet all the objectives and to provide support on the need of change. © 2013, Springer Science+Business Media Dordrecht.

Abstract
This paper describes the development and evaluation process of a user interface for a scheduling system. It is intended to provide the user with a graphical and interactive way in order to define a scheduling problem as well as an interactive way to visualize and adapt a scheduling plan. The realization of these goals was achieved through a modular prototype whose development was based on a methodology focused on the usability evaluation: the star life cycle. In order to evaluate the usability prototype an evaluation session was made, allowing not only the ease of use evaluation, but also observing the different interaction forms provided by each participant.

Abstract
This paper describes the development and evaluation process of a user interface for a scheduling system. It is intended to provide the user with a graphical and interactive way in order to define a scheduling problem as well as an interactive way to visualize and adapt a scheduling plan. The realization of these goals was achieved through a modular prototype whose development was based on a methodology focused on the usability evaluation: the star life cycle. In order to evaluate the usability prototype an evaluation session was made, allowing not only the ease of use evaluation, butalso observing the different interaction forms provided by each participant.

Abstract
Power systems have been through deep changes in recent years, namely due to the operation of competitive electricity markets in the scope the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities that have emerged. Virtual Power Players (VPPs) are a new type of player that allows aggregating a diversity of players (Distributed Generation (DG), Storage Agents (SA), Electrical Vehicles (V2G) and consumers) to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players' benefits. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. This paper proposes a model to implement fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in the scope of electricity markets.

Abstract
The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.