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摘要
The existing optimization methods to solve the uncertainty of steam demand in steam power system include stochastic programming and robust optimization. However, these methods can not trade off the stability and economy at the same time. This paper proposes a two-stage stochastic programming based on Markov chain to solve this problem. In the first stage, uncertain variables are divided based on spatial distance expression and divided into different working conditions by clustering algorithm. In the second stage, the Markov chain is constructed based on the state transition probability, and the demand value of steam is predicted by the method of scenario generation and reduction. The steam power system of a coal-to-gas enterprise is taken as an example to establish the corresponding optimization model, and the predicted steam value is brought into the optimization model to solve. The optimal operation scheme obtained is compared and analyzed with stochastic programming and robust optimization. The results show that the proposed optimization method combines the advantages of high economy of stochastic programming and high stability of robust optimization, both stability and economy are intermediate between stochastic programming and robust optimization, and provides a new idea for solving uncertain optimization problems of steam power system. ? 2023 Chemical Industry Press.
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