Journal
JOURNAL OF CLEANER PRODUCTION
Volume 112, Issue -, Pages 3285-3293Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2015.09.034
Keywords
Forest-based biomass; Uncertainty; Mixed integer programming model; Supply chain optimization; Multi-stage stochastic model; Robust optimization
Categories
Funding
- Natural Sciences and Engineering Council of Canada (NSERC Discovery Research Grant) [RGPIN 249986-09]
- power plant
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Electricity generated from forest-based biomass is an attractive source of renewable energy. However, the cost of generating heat and/or electricity from it is relatively high due to the low energy density of wood, high moisture content and variations in its quality and availability. Models have been developed to optimize the supply chain and reduce the cost per kilowatt hour generated. This paper focuses on incorporating uncertainty in the supply chain of such a model. The model considers the tactical supply chain planning of a power plant over a one-year time horizon with monthly time steps. Uncertain parameters which impact the net profit of the power plant include 'biomass quality,' namely moisture content and higher heating value, and 'monthly available biomass' from different suppliers. Robust optimization is used to model uncertainty in the quality of biomass. Then a hybrid, multi-stage, 'stochastic programming-robust optimization' model is presented in order to simultaneously include uncertainty in biomass quality and biomass availability. It is demonstrated that the hybrid model takes advantage of both modelling approaches to balance the profit estimates and the tractability to various circumstances. The model provides solution considering all instances of the uncertain parameters within the defined sets and scenario tree. The results revealed a major trade-off between profit and range of biomass quality. Profit decreased by up to 23% when there was +/- 13% variation in moisture content and +/- 5% change in higher heating value. The model achieved a biomass purchase cost that was lower than the current commercial costs at the power plant. Implementing the model could prevent production curtailment and undesirable fluctuation in storage levels which occurred in the past due to variations in biomass availability and quality. (C) 2015 Elsevier Ltd. All rights reserved.
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