The significant integration of variable energy resources in power systems requires the consideration of greater operational details in capacity expansion planning processes. In hydrothermal systems, this motivates a more thorough assessment of the flexibility that hydroelectric reservoirs may provide to cope with variability. This work proposes a stochastic programming model for capacity expansion planning that considers representative days with hourly resolution and uncertainty in yearly water inflows. This allows capturing high resolution operational details, such as load and renewable profile chronologies, ramping constraints, and optimal reservoir management. In addition, long-term scenarios in the multi-year scale are included to obtain investment plans that yield reliable operations under extreme conditions, such as water inflow reduction due to climate change. The Progressive Hedging Algorithm is applied to decompose the problem on a long-term scenario basis. Computational experiments on an actual power system show that the use of representative days significantly outperforms traditional load blocks to assess the flexibility that reservoir hydroelectric plants provide to the system, enabling an economic and reliable integration of variable resources. The results also illustrate the impacts of considering extreme long-term scenarios in the obtained investment plans.
|Número de páginas||8|
|Publicación||International Journal of Electrical Power and Energy Systems|
|Estado||Publicada - dic. 2018|
|Publicado de forma externa||Sí|