Why benders decomposition is used to handle uncertainty in power system?
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Abstract
Decision-making for operations, maintenance, and investment planning of electric power systems must handle a great deal of uncertainty. In the work described here, the enhanced risk index is used to describe these uncertainties, and the Benders decomposition algorithm plays the role of integrating three components of the decision making problem: economy, reliability, and risk. A decomposed security-constrained optimal power flow is developed to demonstrate the significant speed enhancement of the chosen algorithm. The risk-based optimal power flow, risk-based unit commitment problem, risk-based transmission line expansion, and risk-based Var resource allocation are formulated and demonstrated. A general Benders decomposition structure is developed to cover most of the decision making problems encountered in everyday use within the power industry. In order to facilitate this algorithm, a service oriented architecture (SOA) is introduced and a Benders decomposition and SOA based computation platform is designed.