Leontina Pinto

Load Modeling and Forecast based on a Hilbert Space Decomposition

Many energy markets have experienced extreme changes in load dynamics - due, for instance, to energy shortages or changes in market rules. Under these situations, new history may correspond to a mere few years, leaving insufficient amount of data to be explored by classic models, from statistical to neural networks. This paper addresses this problem - modeling under lack of data - and proposes a new method based on functional analysis, applied as a sequential iterative procedure. A real case- study enlightens the approach advantages.

Risk Management - beyond Risk Analysis

Most portfolio models target the risk analysis problem - that is, optimize a set of products or contracts and evaluate the risk associated to the optimum solution. This paper proposes a novel concept for the optimum portfolio problem: evaluate the optimum solution associated to a desired risk level n other words, risks levels are here taken as constraints to be obeyed, not consequences of a decision. The resulting non-linear, integer problem is solved by an efficient framework, based on the real-options concept, minimizing costs and maximizing flexibility. A case study with a real contract portfolio (ranging from months to several years ahead) illustrates the model potential.

A Novel Risk Management Model Based on the Real Options Concept

This paper proposes a novel solution to the risk management problem based on the real options concept. Global and scenario-dependent variables are mixed together and optimized in order to achieve global optimum according to companys needs and targets. A special constraint set - maximum admissible risk levels- ensures the risk management environment. The resulting model corresponds to a stochastic non-linear integer programming problem and is solved by a customized algorithm, designed for efficiency and reliability. Possible extensions (targeting special markets customization) are straightforward and may be easily taken into account.

Long term wind production modelling and forecast

This work introduces a comprehensive approach for the long term wind farm production analysis and forecast. Initially, a customized model overcomes the usually limited set of measurements by recreating a long, “virtual” history based on available climatological records. This local “virtual” history series is then analyzed and decomposed by associated “explaining” physical variables. These “explanations” are finally used to extend the resulting series, yielding a prediction (next twelve months) and a set of future scenario forecasts (at least 20 years ahead). A real case study with an application to the Northeastern Brazil is presented and discussed.

Overcoming renewable energy climatological risks: the hydroeolic pool

This paper analyzes the systems and markets risks associated to renewables climatological uncertainty. It will be shown that complementary eolic/hydrological dynamics allow the design of a customized regulatory framework based on a pool scheme. Each participant will “cede” resources surplus and receive “coverage” during severe shortages. The result is a strong risk mitigation, stabilizing investors results and consumers supply. The potentiality of the proposed approach is discussed through a case study with the Brazilian system and market.

Climatological Analysis: A Fast Non-linear Signal Filtering Approach

This paper proposes a new approach to detect, model and analyze climatological signals solely based on the largest eigenvalue of a signal derived matrix, used as a measure of innovation. The resulting process offers a substantial reduction in computational complexity with respect to presently applied methods. Furthermore, the approach yields much precise and detailed result. A case study illustrates the application of the proposed approach to the identification and analysis of a Nino event, tracing the interactions between different regions in the planet and pointing out perturbation paths otherwise unknown, allowing the prediction of the phenomena months before the climatological development.

Building the Optimal Contract Portfolio under Non-Probabilistic Uncertainties

This paper proposes an integrated solution to the optimum portfolio building considering price and demand uncertainties. More than simply assessing risks, the proposed approach opens the possibility of a real and effective risk management, including maximum risk levels as optimization constraints. The resulting model corresponds to a stochastic non-linear integer programming problem and is solved by a customized algorithm, designed for efficiency and reliability. Possible extensions (targeting special markets customization) are straightforward and may be easily taken into account.

A renewable generation portfolio: Maximizing delivery, minimizing risks

This paper presents a model for the optimal generation portfolio, specially designed to accommodate the characteristics of renewable generators and mitigate associated risks. The mathematical model is flexible enough to adjust to planners desired goals - from economy to sustainability - and provide regulatory solutions to induce such adjustments. A realistic case study with the Brazilian system illustrates the potentiality of the proposed approach and paves the way to an efficient and fair regulatory framework.

Signal processing and climate understanding

Clean energy, as generated by wind and sun, is a must nowadays. It has, however, to be competitive with other sources, offering acceptable costs and production risks. This work faces the usual lack of a long and reliable historic of climato-logical data and consequent uncertainty about expected plant production. Customized machine learning and signal processing techniques are used to map regional satellite measurements into local information, creating a virtual historical series of energy production. Two illustrative cases are presented for eolic and photovoltaic energy, where neural network inputs and operations are described.

An optimal, less vulnerable renewable portfolio

This paper proposes a model and methodology to mitigate some important risks associated to renewable energy: the climatological uncertainties and system failures. The basic idea is to combine the complementarities of the availabilities of the different sources in order to ensure a more stable, reliable output. Additional risk management constraints, generation/transmission reliability, are also included. A realistic application to the Brazilian long-term expansion planning highlights its advantages and suggests future extensions.