Abstract:

The formulation of inventory optimization strategies for biomass power plants is the basis for ensuring regional power supply. However, the seasonality and demand uncertainty of biofuels have brought great challenges to inventory optimization. In order to reduce the impact of multiple uncertain factors on inventory optimization, a stochastic-robust inventory optimization model for biomass power plants based on multiple uncertainties was proposed. First, the uncertainties of the price and quality level of biomass fuels are described using ellipsoidal uncertainty sets. And use the scenario tree method to construct typical biomass availability scenarios to reduce the impact of fuel supply seasonality on inventory optimization strategies. Secondly, considering the randomness and ambiguity of the error, three kinds of user load curves are simulated by using the ecological cloud generator, which improves the accuracy of demand curve fitting. Finally, a stochastic-robust optimization model of biomass power plant inventory taking multiple uncertainties into account is developed with the objective of minimizing the total inventory cost. The validity of the model is verified by comparing the optimization results of the deterministic, stochastic and stochastic-robust optimization models through examples. The results show that the total cost of biomass power plant inventory in the stochastic-robust optimization model is the lowest, which is 2.6915 million yuan. Compared with the stochastic optimization model, the total inventory cost of the proposed strategy is reduced by 34.59%, which can improve the economy and reliability of the inventory optimization strategy.

Key words: stochastic-robust optimization, ellipsoidal uncertain set, normal cloud model, biomass power plant, inventory optimization