A robust framework for waste-to-energy technology selection: A case study in Nova Scotia, Canada

Mostafa Mostafavi Sani,Hamid Afshari,Ahmed Saif

Energy Conversion and Management(2023)

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摘要
With recent advances in waste-to-energy technologies, the integration of municipal solid waste in energy recovery systems is becoming a promising alternative. However, it is still unclear how these technologies can be optimally combined, especially when the future price of energy and the quantity of waste are uncertain. Additionally, it remains an open question whether these uncertainties exert a notable impact on the optimal configuration and trade-off between costs and emissions of energy recovery systems. This paper studies a multi-carrier energy infrastructure to generate energy from municipal solid waste, aiming to optimize the selection and size of waste-to-energy technologies. To account for uncertainty in heat, electricity, and hydrogen prices, as well as waste supply in the future, a two-stage robust optimization model is proposed to minimize the total annual cost (including an environmental penalty) of the waste-to-energy facility. On a real test case in Nova Scotia, Canada, the solution obtained from the robust model led to a 19.9% decrease in emissions compared to that of the deterministic model, albeit with an increase in total cost under the current prices. Plasma arc gasification is selected as the optimal technology in the deterministic case, but pyrolysis outperforms it when the cost of hydrogen sufficiently decreases. Moreover, the viability of hydrogen production hinges upon a 36% reduction in its cost or a 90% decrease in energy operating costs. The outcomes of this investigation furnish compelling proof regarding the influence of parameter uncertainties on both optimal system design and performance. They also showcase the efficacy of the proposed framework for selecting waste-to-energy technologies, and highlight the circumstances that prompt a change in the optimal technology mix.
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关键词
Waste-to-energy,Adaptive robust optimization,Uncertainty,Municipal solid waste,Hydrogen generation,Renewable energy
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