Transmission Expansion Planning: A Methodology to Include Security Criteria and Uncertainties Using Optimization Techniques

Armando M. Leite da Silva (), Leandro S. Rezende () and Luiz Antônio F. Manso ()
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Armando M. Leite da Silva: Federal University of Itajubá – UNIFEI
Leandro S. Rezende: Federal University of Itajubá – UNIFEI
Luiz Antônio F. Manso: Federal University of São João Del Rei – UFSJ

A chapter in Innovations in Power Systems Reliability, 2011, pp 191-220 from Springer

Abstract: Abstract Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the forecasted demand and the security criteria along the planning horizon, while minimizing investment, operational, and interruption costs. Optimization techniques based on metaheuristics have demonstrated the potential to find high-quality solutions. Numerous advantages can be linked to these tools: the software complexity is acceptable; they are able to mix integer and non-integer variables; and also present relatively faster computational times. Their success is related to the ability to avoid local optima by exploring the basic structure of each problem. However, owing to today’s power network dimensions, random behavior of transmission and generation equipments, load growth uncertainties, etc., the TEP problem has become combinatorial, stochastic, and highly complex. When uncertainties and chronological aspects are added to these problems, the optimal solution becomes almost inaccessible, even when using metaheuristics. This chapter proposes a methodology to solve the multi-stage TEP problem considering security criteria and the treatment of external uncertainties, as load/generation growth. In addition, a discussion about how to include security criteria using deterministic and probabilistic approaches is presented through a case study on a small test system. A real transmission network is used as an illustration of the application of the proposed methodology.

Keywords: Particle Swarm Optimization; Reliability Index; Planning Horizon; Greedy Randomize Adaptive Search Procedure; Artificial Immune System (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/978-0-85729-088-5_6

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