| Autor: |
Tuomas Rintamaki, Fabricio Oliveira, Afzal S. Siddiqui, Ahti Salo |
| Přispěvatelé: |
Department of Mathematics and Systems Analysis, Aalto-yliopisto, Aalto University |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Popis: |
Stochastic adaptive robust optimization is capable of handling short-term uncertainties in demand and variable renewable-energy sources that affect investment in generation and transmission capacity. We build on this setting by considering a multi-year investment horizon for finding the optimal plan for generation and transmission capacity expansion while reducing greenhouse gas emissions. In addition, we incorporate multiple hours in power-system operations to capture hydropower operations and flexibility requirements for utilizing variable renewable-energy sources such as wind and solar power. To improve the computational performance of existing exact methods for this problem, we employ Benders decomposition and solve a mixed-integer quadratic programming problem to avoid computationally expensive big-M linearizations. The results for a realistic case study for the Nordic and Baltic region indicate which investments in transmission, wind power, and flexible generation capacity are required for reducing greenhouse gas emissions. Through out-of-sample experiments, we show that the stochastic adaptive robust model leads to lower expected costs than a stochastic programming model under increasingly stringent environmental considerations. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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