Transmission expansion planning

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

A decomposition procedure based on approximate newton directions

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

Complementarity, not optimization, is the language of markets

Each market agent (producer or consumer) in a power market pursues its own objective, typically to maximize its own profit. As such, the specific behavior of each agent in the market is conveniently formulated as a bi-level optimization problem whose upper-level problem represents the profit seeking behavior of the agent and whose lower-level problem represents the clearing of the market. The objective function and the constraints of this bi-level problem depend on the agent's own decision variables and on those of other agents as well. Understanding the outcomes of the market requires considering and solving jointly the interrelated bi-level problems of all market agents, which is beyond the purview of optimization. Solving jointly a set of bi-level (or single-level) optimization problems that are interrelated is the purview of complementarity. In this paper and in the context of power markets, we review complementarity using a tutorial approach

A decomposition methodology applied to the multiarea optimal power flow problem

The original publication is available at www.springerlink.comThis 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.Publicad

A decomposition procedure based on approximate Newton directions

The original publication is available at www.springerlink.comThe 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.Publicad

A new decomposition method applied to optimization problems arising in power systems: Local and global behavior

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

Multimarket optimal bidding for a power producer

This paper considers a profit-maximizing thermal producer that participates in a sequence of spot markets, namely, day-ahead, automatic generation control (AGC), and balancing markets. The producer behaves as a price-taker in both the day-ahead market and the AGC market but as a potential price-maker in the volatile balancing market. The paper provides a stochastic programming methodology to determine the optimal bidding strategies for the day-ahead market. Uncertainty sources include prices for the day-ahead and AGC markets and balancing market linear price variations with the production of the thermal producer. Results from a realistic case study are reported and analyzed. Conclusions are duly drawn.The work of A. J. Conejo was supported in part by the Ministry of Science and Education of Spain under CICYT Project DPI2003-01362 and in part by Junta de Comunidades de Castilla-La Mancha under Project GC-02-006.Publicad