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Learning rate matters: Reexamining optimal power expansion planning with endogenized technological experience curves

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  • Kosugi, Takanobu

Abstract

The solution of an optimization-type power generation expansion planning model that endogenizes technology costs through the application of experience curves is highly dependent on the assumed values of the learning rate. Practical numerical models of this type derive a solution in which early diffusion of emerging technology, such as solar photovoltaic systems, leads to an intertemporal minimization of total electricity generation costs if the technology's assumed learning rate is above a particular boundary value; however, the principle behind this derivation is not well understood. This study aims to clarify the principle using a simple analytical model represented as an optimal control problem in addition to a numerical model. It identifies the factors influencing the solution as the discount rate and deployment rate at an initial time relative to the potential market share of the emerging technology. Analysis shows: (1) the higher the values of these two factors, the stricter the condition of learning rate required for the early diffusion of the technology to be optimal, holding other conditions constant; (2) the two factors have interaction effects on the optimal diffusion condition. Also, the study verifies the consistency of the results obtained from the analytical and numerical models.

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  • Kosugi, Takanobu, 2023. "Learning rate matters: Reexamining optimal power expansion planning with endogenized technological experience curves," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s036054422302443x
    DOI: 10.1016/j.energy.2023.129049
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