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Generation expansion and retirement planning based on the stochastic programming

Tohidi, Y ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.epsr.2013.06.014
  3. Abstract:
  4. This paper develops a mathematical model based on the stochastic programming for simultaneous generation expansion and retirement planning. Retirement decision of the existing generating units is accommodated since these units are aging more and more. The proposed method is formulated as an optimization problem in which the objective function is to minimize the expected total cost consisting of the investment required for commissioning new units, operation and maintenance costs, the retirement salvage cost, and the system risk cost. The problem is subjected to a set of generating unit and system physical and operational constraints. The modeling of energy limited units is also devised in a probabilistic manner as an underlying requirement in practical studies. The Monte Carlo simulation approach is used to consider the component random outages. A large number of scenarios are simulated and the scenario reduction technique is applied to tailor the computational effort within a tractable range, which is essential for large-scale problems. Numerical studies are conducted on the IEEE-RTS79 and the performance of the proposed model is investigated. As expected, the retirement option could be beneficial particularly when the contribution of aged units in the system unreliability becomes more severe
  5. Keywords:
  6. Generation expansion planning (GEP) ; Mixed-integer programming (MIP) ; Monte- carlo simulations ; Operation and maintenance ; Operational constraints ; Scenario reduction techniques ; Simultaneous generation ; Uncertainty unit retirement ; Costs ; Integer programming ; Investments ; Mathematical models ; Monte Carlo methods ; Stochastic programming
  7. Source: Electric Power Systems Research ; Volume 104 , November , 2013 , Pages 138-145 ; 03787796 (ISSN)
  8. URL: http://www.sciencedirect.com/science/article/pii/S0378779613001648