Abstract

Abstract
The motivation for this work is the study and prediction of wind ramp events occurring in a large-scale wind farm located in the US Midwest. In this paper we introduce the SHRED framework, a stream-based model that continuously learns a discrete HMM model from wind power and wind speed measurements. We use a supervised learning algorithm to learn HMM parameters from discretized data, where ramp events are HMM states and discretized wind speed data are HMM observations. The discretization of the historical data is obtained by running the SAX algorithm over the first order variations in the original signal. SHRED updates the HMM using the most recent historical data and includes a forgetting mechanism to model natural time dependence in wind patterns. To forecast ramp events we use recent wind speed forecasts and the Viterbi algorithm, that incrementally finds the most probable ramp event to occur. We compare SHRED framework against Persistence baseline in predicting ramp events occurring in short-time horizons, ranging from 30 minutes to 90 minutes. SHRED consistently exhibits more accurate and cost-effective results than the baseline. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
This chapter presents EPSO (Evolutionary Particle Swarm Optimization), as an evolutionary meta-heuristic that implements a scheme of self-adaptive recombination, borrowing the movement rule from PSO (Particle Swarm Optimization). Besides the basic model, it discusses a Stochastic Star topology for the communication among particles and presents a variant called differential EPSO or dEPSO. The chapter presents results in a didactic Unit Commitment/Generator Scheduling Power System problem and results of a competition among algorithms in an intelligent agent platform for Energy Retail Market simulation where EPSO comes out as the winner algorithm. © 2007 Springer-Verlag Berlin Heidelberg.

Abstract
This paper describes a new concept of a Negotiation Aid System, developed over a GIS (Geographic Information System) and designed to facilitate reaching compromises among agents such as investors, environmental groups and governmental agencies, when deciding the location and sizing of new renewable energy sources in a region. The core model of an Actor is similar to a Fuzzy Inference System of the Takagi-Sugeno type, built from a definition of preferences and levels of acceptability. An outranking method is employed to define geographical places of less conflict among the several Actors negotiating. An application to the region of La Rioja, in Spain, is described.

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