Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning. (15th June 2023)

• Record Type: Journal Article
• Authors: Wen, Xin; Jaxa-Rozen, Marc; Trutnevyte, Evelina
• Is Part Of: Applied energy. Volume 340(2023)
• Publication Date: 2023-06-15
• Subjects: Electricity system models -- Hindcasting -- Retrospective modeling -- Ex-post modeling -- Endogenous electricity demand -- Endogenous technology learning -- Model evaluation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2023.121035
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Hindcasting to inform electricity system transition models in 31 European countries. Testing of model versions with endogenous demand ED and technology learning TL. ED functionality captures real-world transition with well estimated elasticity factor. TL boosts earlier and larger uptake of renewable technologies in some countries. TL can introduce inaccuracies as compared to the real-world transition. Abstract: Bottom-up, technology-rich electricity system models are commonly used to generate scenarios for policy support at a national or global level. In literature, previous hindcasting studies (also called retrospective modeling or ex-post modeling) evaluated existing models rather than sought to inform model development from the beginning. In this study, we present a hindcasting exercise with D-EXPANSE model for national electricity systems in 31 European countries over the 1990–2019 period. We develop several model versions with or without elastic electricity demand and with or without endogenous technology learning, and use hindcasting to choose the most accurate configuration of the bottom-up model. The hindcasting results show that a model with endogenous elastic demand can capture well the real-world evolution of electricity demand, if elasticity factor is chosen well and if the countries did not undergo severe structural changes. Endogenous technology learning, however, increases the uptake of new emerging technologies in cost-optimal scenarios, but still cannot fully capture the real-world dynamics and at times even introduces further inaccuracies.