Conference Paper

A Hybrid Method for Multi-Area Generation Expansion using Tabu-search and Dynamic Programming

Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX
DOI: 10.1109/ICPST.2006.321417 Conference: Power System Technology, 2006. PowerCon 2006. International Conference on
Source: IEEE Xplore

ABSTRACT This paper combines Tabu search with an optimization technique using dynamic programming for the solution of generation expansion and placement considering reliability in multi-area power systems. Instead of random selection, initial solution for Tabu search is obtained from optimizing a simplified problem utilizing dynamic programming and reliability assessment technique called global decomposition. The comparison between random initial solutions and the proposed method is made. The method is implemented for an actual 12-area power system.

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    ABSTRACT: This dissertation aims to address two optimization problems involving power system reliabilty analysis, namely multi-area power system adequacy planning and transformer maintenance optimization. A new simulation method for power system reliability evaluation is proposed. The proposed method provides reliability indexes and distributions which can be used for risk assessment. Several solution methods for the planning problem are also proposed. The first method employs sensitivity analysis with Monte Carlo simulation. The procedure is simple yet effective and can be used as a guideline to quantify effectiveness of additional capacity. The second method applies scenario analysis with a state-space decomposition approach called global decomposition. The algorithm requires less memory usage and converges with fewer stages of decomposition. A system reliability equation is derived that leads to the development of the third method using dynamic programming. The main contribution of the third method is the approximation of reliability equation. The fourth method is the stochastic programming framework. This method offers modeling flexibility. The implementation of the solution techniques is presented and discussed. Finally, a probabilistic maintenance model of the transformer is proposed where mathematical equations relating maintenance practice and equipment lifetime and cost are derived. The closed-form expressions insightfully explain how the transformer parameters relate to reliability. This mathematical model facilitates an optimum, cost-effective maintenance scheme for the transformer.
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