Abstract
In this paper, we present and compare two approaches to model supply and demand curves of a sealed-bid auction market. Both the Linear Hinges model and the Sigmo model are able to extract the relevant information from these bidding curves, without losing significant market information. We discuss their main similarities and important differences using an unified framework, to highlight their main strengths and weakness. A practical comparative study based on real supply functions from the Californian electricity market has been included to derive practical conclusions. © 2001 IEEE.

Abstract
This paper describes SGO, a management information system for bidding in deregulated electricity markets, developed for the Spanish case. SGO has a client-server architecture and consists of a set of integrated cooperative and flexible software tools for assisting the users during the whole bidding process: resources identification, bids generation, market performance characterisation, bidding strategy analysis and optimisation, generation of markets results reports and automatic performance monitoring for suggesting on-line corrective actions. © 2001 IEEE.

Abstract
This paper proposes a method to quickly and accurately predict the dynamic response of a power system during an under-frequency load shedding scenario. Emergency actions in a power system due to loss of generation typically calls for under-frequency load shedding measures to avoid potential collapse due to the lack of time in which to correct the imbalance via other means. Due to the slow and repetitious use of dynamic simulators the need for a fast and accurate procedure is evident when calculating optimal bad-shedding strategies A neural network (NN) seems to he an ideal solution for a quick and accurate way to replace standard dynamic simulations. The steps taken to produce a viable NN and corresponding results will he discussed.

Abstract
This text describes a real-world DMS environment in which intelligent tools and techniques such as neural networks, fuzzy sets and meta-heuristics (like evolutionary computing and simulated annealing) have given a strong positive contribution.

Abstract
Forecasting electric demand and its geographical distribution is a prerequisite to generate expansion planning scenarios for distribution planning. This paper presents a comprehensive methodology that uses a fuzzy inference model over a GIS support, to capture the behavior of influence factors on load growth patterns and map the potential for development. The load growth is spread over maps with cellular automate. The interaction with a scenario generator inputs data into a graph generator, which will serve as a basis for more classic network planning tools.

Abstract
This paper describes a Load Allocation model to be used in a DMS environment. A process of rough allocation is initiated, based on information on actual measurements and on data about installed capacity and power and energy consumption at LV substations. This process generates a fuzzy load allocation, which is then corrected by a fuzzy state estimator procedure in order to generate a crisp power flow compatible set of load allocations, coherent with available real time measurements recorded in the SCADA.