(514h) Capacity Planning of Industrial Gas Plants with Rational Markets Under Demand Uncertainty
Capacity planning for industrial gas producers is a challenging task because expansion plans are highly dependent on market behavior and the future demands . A suboptimal expansion strategy may lead to unnecessary investments or loss of market share. To mitigate the risk associated with uncertain demands and to account for market preferences in the face of competition, a stochastic bi-level MILP optimization model is proposed .
The stochastic bilevel MILP optimization model involves an upper level that maximizes the expected Net Present Value (NPV) of an industrial producer, and a lower level that minimizes the expected cost paid by the markets over different demand scenarios. The upper level determines capacity planning decisions, which are modeled with discrete variables; whereas the lower level is a Linear Program (LP) which assigns market demands to competing producers. Our formulation finds a Stackelberg equilibrium between the leading producer and the markets. The producer makes the first move by deciding on the expansion plan, considering the rational responses of the markets in each scenario; then, the markets respond according to their cost minimization criterion. The strong Stackelberg equilibrium is found from the solution of the bilevel optimization problem. In order to solve the capacity planning problem, we reformulate it as a single-level MILP optimization problem. This reformulation leverages the strong duality property of the lower-level LP [2,3]. Uncertainty is considered in scenarios describing different realizations of demand. In this way, the upper level of the bi-level program maximizes the expected NPV obtained from the capacity expansion plan, while the lower level minimizes the cost paid by the markets in different scenarios. The model also accommodates the possibility to distribute capacity among different products and has the flexibility to constrain their production ratios.
The proposed capacity planning model is first implemented on a small illustrative example. The results demonstrate the advantages over its equivalent deterministic formulation. In particular, in the face of large uncertainties in demands, the proposed stochastic model is more conservative in the investments but resulting in a higher expected NPV compared to implementing the deterministic model under conditions of uncertainly. An effective domain reduction strategy developed to solve large-scale problems is demonstrated with an industrial size example. A stochastic bi-level model for capacity planning with non-uniform time periods is also proposed to better handle demand forecasting and to reduce the problem size of the resulting MILP.
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