Optimal configuration of polygeneration plants under process failure, supply chain uncertainties, and emissions policies

Comput Ind Eng. 2022 Oct;172:108637. doi: 10.1016/j.cie.2022.108637. Epub 2022 Sep 9.

ABSTRACT

The COVID-19 pandemic exacerbated the erratic demand, supply, and prices of energy. It is unlikely that these effects would subside post-pandemic, especially with the pre-existing climate change crisis that also needs to be addressed. Emissions policies aimed at mitigating climate change place economic pressures on already disrupted energy systems, which could worsen energy insecurity. Configuring disrupted energy systems to build robustness to supply chain-related uncertainties and economic pressures of emissions policies are desired to simultaneously address these problems. To this end, this study introduces a robust mixed-integer linear program that simultaneously incorporates the abovementioned needs for configuring energy production systems. The proposed model is tested through a demonstrative case study that deals with a biomass-based polygeneration plant design problem. The scenario analysis and sensitivity test on the model concerning the case under consideration yields the following results: (1) setting ambitious target profits reduces the probability of the resulting plant configuration to achieving the set targets in the presence of supply chain-related uncertainties, while conservative targets promote the opposite; (2) the inoperability of the plant’s process units reduces the robustness of optimal process configurations, and drastic configurations may be required to achieve targets despite the inoperability of process units; (3) a hybrid cap-and-trade and emissions tax policy yields approximately similar implications to the robustness of the resulting optimal plant configurations compared to a pure cap-and-trade policy, but the rate of decrease in robustness with respect to the initial emissions cap is lesser in the hybrid policy than in the pure cap-and-trade policy.

PMID:36105864 | PMC:PMC9461240 | DOI:10.1016/j.cie.2022.108637

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