Long-term uncertainties in generation expansion planning: Implications for electricity market modelling and policy
| Autor: | Ian J. Scott, Carlos A. Silva, Pedro Carvalho, Audun Botterud |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Process (engineering) 020209 energy Mechanical Engineering 02 engineering and technology Building and Construction Investment (macroeconomics) Pollution Industrial and Manufacturing Engineering Energy policy Term (time) General Energy 020401 chemical engineering Work (electrical) 0202 electrical engineering electronic engineering information engineering Econometrics Range (statistics) Electricity market Scenario analysis 0204 chemical engineering Electrical and Electronic Engineering Civil and Structural Engineering |
| Zdroj: | Energy. 227:120371 |
| ISSN: | 0360-5442 |
| DOI: | 10.1016/j.energy.2021.120371 |
| Popis: | Investment decision making in the energy sector is a complex process due to the inherent long-term uncertainty. This work investigates the importance of representing a wide range of economic and physical sources of uncertainty in the modelling of the electricity market, both for investment decision making and descriptive market modelling. The results demonstrate that the difference between a deterministic and stochastic solution increases non-linearly when uncertainties across multiple inputs are combined and is 109% higher than when uncertainties across individual inputs are superimposed. Further, combining uncertainty sources by adding a limited number of scenarios to multiple sources of uncertainty outperforms adding additional scenarios to any individual source of uncertainty. Additionally, for the purpose of market modelling, the generation mix found by the stochastic optimisation solution differs significantly from the average solution found by looking at scenarios individually, emphasising the importance of the approach chosen to represent uncertainty. Finally, modelling scenarios individually underestimates the range of price outcomes and overestimates the range of potential carbon dioxide emission outcomes, given uncertainty. |
| Databáze: | OpenAIRE |
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