A Carryover Storage Quantification Framework for Mid-Term Cascaded Hydropower Planning: A Portland General Electric System Study
<|reference_start|>A Carryover Storage Quantification Framework for Mid-Term Cascaded Hydropower Planning: A Portland General Electric System Study: Mid-term planning of cascaded hydropower systems (CHSs) determines appropriate carryover storage levels in reservoirs to optimize the usage of available water resources, i.e., maximizing the hydropower generated in the current period (i.e., immediate benefit) plus the potential hydropower generation in the future period (i.e., future value). Thus, in the mid-term CHS planning, properly quantifying the future value deposited in carryover storage is essential to achieve a good balance between immediate benefit and future value. To this end, this paper presents a framework to quantify the future value of carryover storage, which consists of three major steps: i) constructing a module to calculate the maximum possible hydropower generation that a given level of carryover storage can deliver in the future period; ii) extracting the implicit locational marginal water value (LMWV) of carryover storage for each reservoir by applying a partition-then-extract algorithm to the constructed module; and iii) developing a set of analytical rules based on the extracted LMWV to effectively calculate the future value. These rules can be seamlessly integrated into mid-term CHS planning models as tractable mixed-integer linear constraints to quantify the future value properly, and can be easily visualized to offer valuable insights for CHS operators. Finally, numerical results on a CHS of Portland General Electric demonstrate the effectiveness of the presented framework in determining proper carryover storage values to facilitate mid-term CHS planning.<|reference_end|>
@article{chen2024a,
    title={A Carryover Storage Quantification Framework for Mid-Term Cascaded
  Hydropower Planning: A Portland General Electric System Study},
    author={Xianbang Chen, Yikui Liu, Zhiming Zhong, Neng Fan, Zhechong Zhao, Lei
  Wu},
    journal={arXiv preprint arXiv:2409.09876},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09876},
    primaryClass={eess.SY cs.SY}
}