Abstract
This work presents the formulation of a two-stage stochastic mixed-integer linear programming (MILP) model to include uncertainty in the design of renewable-based utility plants. The model is based on a superstructure that integrates technologies to process biomass, waste, solar radiation, and wind and considers uncertainty in availability of the renewable resources and on the utility demands. The uncertain parameter space is calculated based on a monthly probability density function for each uncertain parameter and discretized into different levels. It is shown that as uncertainty is considered in the model formulation, design flexibility improves with respect to the deterministic-based designs, although the flexibility is achieved at the expense of higher underused facilities and therefore unused investment cost.
Highlights
Renewable energy resources, such as solar and wind energy, are being considered with special interest due to their contribution toward the development of a sustainable energy industry
This section of the plant is modeled based on a surrogate model that consists of three steps: (a) computation of mass and energy balances for the digestor using the yield data of each waste reported by León and Martín (2016); (b) simulation and sensitivity analysis of the biogas turbine using Aspen plus; and (c) development of an optimization subroutine to design the heat recovery steam generator (HRSG)
Section Design of a Renewable-Based Utility Plant Under Uncertainty shows the optimal design of the utility plant that includes uncertainty, along with an analysis of the effect of reducing the discretization level of the uncertainty on the optimal solution
Summary
Renewable energy resources, such as solar and wind energy, are being considered with special interest due to their contribution toward the development of a sustainable energy industry. The model considered the integration of wind, solar radiation, biomass, and waste to produce steam and electricity, and was used to study the effect of time discretization on the optimal design. This section of the plant is modeled based on a surrogate model that consists of three steps: (a) computation of mass and energy balances for the digestor using the yield data of each waste reported by León and Martín (2016); (b) simulation and sensitivity analysis (power produced vs compression ratio) of the biogas turbine using Aspen plus; and (c) development of an optimization subroutine to design the HRSG. The number of scenarios that are generated for each period of time, SCt, are calculated They are given by the Cartesian product of the levels selected to discretize the probability function of each uncertain parameter. A condition to meet is that the summation of the probabilities of each scenario generated in the period of time t has to be equal to one
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