Capacity planning with uncertain endogenous technology learning
نویسندگان
چکیده
Optimal capacity expansion requires complex decision-making, often influenced by technology learning, which represents the reduction in cost due to factors such as cumulative installed capacity. However, having perfect foresight over is highly unlikely. In this work, we develop a multistage stochastic programming framework model planning problems with endogenous uncertainty learning. To assess benefit of proposed deterministic optimization, apply shrinking-horizon approach compute value solution. Further, decomposition scheme based on column generation developed solve large instances. Results from our computational experiments indicate substantial potential savings and effectiveness algorithm solving instances numbers scenarios. Lastly, power case study presented, highlighting optimization’s ability anticipate significantly different production decisions low- high-learning
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ژورنال
عنوان ژورنال: Computers & Chemical Engineering
سال: 2022
ISSN: ['1873-4375', '0098-1354']
DOI: https://doi.org/10.1016/j.compchemeng.2022.107868