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El sector energético desempeña un papel fundamental en una economía al proveer tanto un insumo para empresas y comercios como un bien de consumo final para las familias. Por tanto, la garantía de suministro de energía al menor coste posible es esencial tanto para la competitividad de cualquier economía como para el bienestar de sus ciudadanos. Desde finales de los años 90, las actividades de suministro de energía en España, y en particular de electricidad, se realizan en régimen de libre competencia con la excepción de ciertas actividades como el transporte, que están reguladas. La magnitud de las inversiones necesarias para construir la red de transporte de energía hace que ésta constituya un monopolio natural que debe ser regulado. Esta regulación adopta varias formas, una de las cuales es la planificación de la propia red de transporte. La planificación de las infraestructuras de transporte de energía tiene como objetivo primordial garantizar el suministro eléctrico en situaciones de máxima demanda al menor coste posible, para lo cual es necesario prever la evolución de la demanda de energía en el horizonte de planificación contemplado. Por este motivo, la planificación de infraestructuras de transporte tiene tanto una parte indicativa, que recoge la previsión de la evolución de la demanda energética española, como una parte vinculante, que recoge las necesidades de inversión en nuevas instalaciones de transporte. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.

To a large extent, electricity markets worldwide still rely on deterministic procedures for clearing energy and reserve auctions. However, larger and larger shares of the production mix consist of renewable sources whose nature is stochastic and non-dispatchable, as their output is not known with certainty and cannot be controlled by the operators of the production units. Stochastic programming models for the joint determination of the day-ahead energy and reserve dispatch, necessary for coping with the real-time output deviations from these sources, have been proposed in the literature. In this work, we take an alternative approach and cast the problem as an adaptive robust optimization problem. The day-ahead and reserve schedules determined in this fashion yield the minimum system cost, accounting for the cost of the redispatching decisions at the balancing stage, in the worst-case realization of the stochastic production within a specified uncertainty set. In a case-study based on a 24-node system, we assess the degree of suboptimality of the robust solution with respect to the optimal dispatch obtained with a stochastic programming approach, and compare their worst-case cost. Furthermore, we discuss the robustness of these two alternative approaches with respect to changes in the distribution of the uncertainty, as well as their computational properties.

The original publication is available at www.springerlink.com This paper describes a decomposition methodology applied to the multi-area optimal power fiow problem in the context of an electric energy system. The proposed procedure is simple and efficient, and presents sorne advantages with respect to other common decomposition techniques such as Lagrangian relaxation and augmented Lagrangian decomposition. The application to the multi-area optimal power fiow problem allows the computation of an optimal coordinated but decentralized solution. The proposed method is appropriate for an Independent System Operator in charge of the electric energy system technical operation. Convergence properties of the proposed decomposition algorithm are described and related to the physical coupling between the areas. Theoretical and numerical results show that the proposed decentralized methodology has a lower computational cost than other decomposition techniques, and in large large-scale cases even lower than a centralized approach. Research supported by Spanish grants PB98-0728 and BEC 2000-0167. Research partly supported by Ministerio de Ciencia y Tecnología of Spain, project CICYT DPI-2000- 0654. Publicado

The efficient solution of large-scale linear and nonlinear optimization problems may require exploiting any special structure in them in an efficient manner. We describe and analyze some cases in which this special structure can be used with very little cost to obtain search directions from decomposed subproblems. We also study how to correct these directions using (decomposable) preconditioned conjugate gradient methods to ensure local convergence in all cases. The choice of appropriate preconditioners results in a natural manner from the structure in the problem. Finally, we conduct computational experiments to compare the resulting procedures with direct methods, as well as to study the impact of different preconditioner choices.

The efficient solution of large-scale linear and nonlinear optimization problems may require exploiting any special structure in them in an efficient manner. We describe and analyze some cases in which this special structure can be used with very little cost to obtain search directions from decomposed subproblems. We also study how to correct these directions using (decomposable) preconditioned conjugate gradient methods to ensure local convergence in all cases. The choice of appropriate preconditioners results in a natural manner from the structure in the problem. Finally, we conduct computational experiments to compare the resulting procedures with direct methods, as well as to study the impact of different preconditioner choices.

In this report a new decomposition methodology for optimization problems is presented. The proposed procedure is general, simple and efficient. It avoids most disadvantages of other common decomposition techniques, such as Lagrangian Relaxation or Augmented Lagrangian Relaxation. The new methodology is applied to a problem coming from interconnected power systems. The application of the new method to this problem allows the computation of an optimal coordinated but decentralized solution. Local and global convergence properties of the proposed decomposition algorithm are described. Numerical results show that the new decentralized methodology has a lower computational cost than other decomposition techniques, and in large-scale cases even lower than a centralized approach.

Decision making in electricity markets under uncertainty has worldwide gained attention due to an increasing number of uncertain parameters associated to technology developments and market evolution. Hence, the market operator faces new challenges pertaining to technical and economic aspects of electricity markets. To tackle these challenges, appropriate models are necessary. This dissertation aims to analyze some of the challenges pertaining to the management in electricity markets under uncertainty and to provide the market operator with the models that enable it to make informed decisions in such uncertain market environments. In the context above, we categorize market operation problems into the following four groups. With the aim of obtaining informed day-ahead decisions in the presence of a number of intra-day markets and high renewable production, we propose a multi-stage stochastic clearing model, where the first stage represents the day-ahead market, n stages model n intra-day markets, and a final stage represents real-time operation. The proposed multi-stage clearing model considers not only different realizations of renewable power output, but also how these realizations evolve from day-ahead forecasts into real-time values, and allows flexibility for the contribution of renewable production in both the day-ahead and intra-day markets in form of scheduled productions and their adjustments. This improves the market outcomes and integration of renewable generation. With the purpose of obtaining marginal prices with cost-recovery features, we develop novel pricing methodologies in the presence of non-convexities and uncertainty in the market. These models minimize the duality gap of a stochastic non-convex clearing model and the dual problem of a relaxed version of this original model subject to primal constraints, dual constraints, cost-recovery constraints, and integrity constraints. The prices obtained deviate in the least possible manner from conventional marginal prices. This implies that a minimum deviation from the optimal value of social welfare is also guaranteed. Moreover, the new prices preserve the short-term economic efficiency and long-term cost recovery properties of marginal prices, while eliminating a need for uplifts. We provide insightful analyses on the impact of demand flexibility on the operational and economic aspects of the power system operation. We investigate how market prices are affected by flexible demands and what economic consequences are observed. For this purpose, we consider a system with high renewable production and a number of comparatively expensive fast-ramping units, which are flexible to react to the uncertainty pertaining to renewable power production. We investigate the role of flexible demands froman economic viewpoint, particularly the impact of flexible demands on demand revenues. Lastly, we develop a risk-neutral two-stage stochastic clearing model and a risk-averse one for reserve markets. We particularly focus on the Swiss reserve market, which consists of a weekly market with a gate closure one week ahead of real-time operation and a daily market with a gate closure two days ahead of real time. The decision-making problem consists of determining which amount of reserves to procure in the weekly market and which one in the daily market. If a risk-averse instance of this two-stage model is also proposed.