TY - JOUR
T1 - Expansion planning under uncertainty for hydrothermal systems with variable resources
AU - Maluenda, Benjamin
AU - Negrete-Pincetic, Matias
AU - Olivares, Daniel E.
AU - Lorca, Álvaro
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/12
Y1 - 2018/12
N2 - 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.
AB - 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.
KW - Hydrothermal power systems
KW - Long-term uncertainty
KW - Power system expansion planning
KW - Progressive hedging algorithm
KW - Stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=85048937830&partnerID=8YFLogxK
U2 - 10.1016/j.ijepes.2018.06.008
DO - 10.1016/j.ijepes.2018.06.008
M3 - Article
AN - SCOPUS:85048937830
SN - 0142-0615
VL - 103
SP - 644
EP - 651
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
ER -