Generation scheduling of autonomous power plant in energy intensive enterprises with unknown load demand distribution

Short-term generation scheduling for autonomous power plants (APP) in energy intensive enterprises (EIE) is a typical problem in production scheduling. The problem is generally formulated as a stochastic one since the load demand is uncertain. The full distribution information of the load demand is usually required in previous literature but it is very hard to be obtained in practical applications. In this paper, a short-term generation scheduling method which requires only limited distribution information of the load demand is presented for APP in EIE to get the minimum expected total cost of electricity consumption. Since the probability density function (PDF) is unavailable, the objective function corresponding to the expected cost is unknown. Due to this, Polynomial interpolation is adopted to get a good approximation of the objective utilizing the prediction mean and interval of the load demand. Numerical tests are performed with the actual data of a large iron and steel enterprise. The results obtained by our approach based on limited distribution information are quite closer to the real optimal ones, which demonstrate the proposed approach is practicable and effective. The method can be extended to solve other related generation scheduling problems with unknown demands.