Penalized sample average approximation methods for stochastic programs in economic and secure dispatch of a power system

Tong, Xiaojiao, Xu, Huifu, Wu, Felix and Zhao, Z. (2016) Penalized sample average approximation methods for stochastic programs in economic and secure dispatch of a power system. Computational Management Science, 1-30. (doi:10.1007/s10287-016-0251-8).

Abstract

In this paper, we develop a stochastic programming model for economic dispatch of a power system with operational reliability and risk control constraints. By defining a severity-index function, we propose to use conditional value-at-risk (CVaR) for measuring the reliability and risk control of the system. The economic dispatch is subsequently formulated as a stochastic program with CVaR constraint. To solve the stochastic optimization model, we propose a penalized sample average approximation (SAA) scheme which incorporates specific features of smoothing technique and level function method. Under some moderate conditions, we demonstrate that with probability approaching to 1 at an exponential rate with the increase of sample size, the optimal solution of the smoothing SAA problem converges to its true counterpart. Numerical tests have been carried out for a standard IEEE-30 DC power system.

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Contributors

Author: Huifu Xu

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