An heuristic approach for optimal location of gas supply units in transportation system

The best places to locate the Gas Supply Units (GSUs) on a natural gas systems and their optimal allocation to loads are the key factors to organize an efficient upstream gas infrastructure. The number of GSUs and their optimal location in a gas network is a decision problem that can be formulated as a linear programming problem. Our emphasis is on the formulation and use of a suitable location model, reflecting real-world operations and constraints of a natural gas system. This paper presents a heuristic model, based on lagrangean approach, developed for finding the optimal GSUs location on a natural gas network, minimizing expenses and maximizing throughput and security of supply.The location model is applied to the Iberian high pressure natural gas network, a system modelised with 65 demand nodes. These nodes are linked by physical and virtual pipelines – road trucks with gas in liquefied form. The location model result shows the best places to locate, with the optimal demand allocation and the most economical gas transport mode: by pipeline or by road truck.

Object transportation by multiple mobile robots controlled by attractor dynamics: theory and implementation

Dynamical systems theory is used as a theoretical language and tool to design a distributed control architecture for teams of mobile robots, that must transport a large object and simultaneously avoid collisions with (either static or dynamic) obstacles. Here we demonstrate in simulations and implementations in real robots that it is possible to simplify the architectures presented in previous work and to extend the approach to teams of n robots. The robots have no prior knowledge of the environment. The motion of each robot is controlled by a time series of asymptotical stable states. The attractor dynamics permits the integration of information from various sources in a graded manner. As a result, the robots show a strikingly smooth an stable team behaviour.

Virtual Power Players Internal Negotiation and Management in MASCEM

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in very helpful sophisticated tools. This paper presents a new methodology for the management of coalitions in electricity markets. This approach is tested using the multi-agent market simulator MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), taking advantage of its ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market and internally, with their members in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. A case study using real data from the Iberian Electricity Market is performed to validate and illustrate the proposed approach.