A Carryover Storage Valuation Framework for Medium-Term Cascaded Hydropower Planning: A Portland General Electric System Study
Xianbang Chen, Yikui Liu, Zhiming Zhong, Neng Fan, Zhechong Zhao, Lei Wu
TL;DR
The paper addresses medium-term cascaded hydropower planning by quantifying the future value of carryover storage and embedding it into MILP-based planning. It introduces a three-step framework: (i) a future-period model to maximize generation given carryover storage and water inflows, (ii) a partition-then-extract mp-MILP algorithm to derive region-specific Locational Marginal Water Values (LMWV), and (iii) analytical if-then rules that express the future value as a linear combination of carryover storage deviations, enabling easy integration into planning models. The framework is demonstrated on Portland General Electric's CHP system, showing favorable results such as improved total hydropower generation (e.g., ~9% over a baseline) and interpretable, hydrology-adaptive future-value surfaces that highlight how upstream storage and dry-season conditions drive value. The work provides a practical, visualization-friendly tool for operators to balance immediate generation with robust future reliability, with potential extensions to stochastic inflows and larger, more complex CHPs.
Abstract
Medium-term planning of cascaded hydropower (CHP) determines appropriate carryover storage levels in reservoirs to optimize the usage of available water resources. This optimization seeks to maximize 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 medium-term CHP planning, properly quantifying the future value deposited in carryover storage is essential to achieve a balanced trade-off 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 model 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 model; 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 medium-term CHP planning models as tractable mixed-integer linear constraints to quantify the future value properly, and can be easily visualized to offer valuable insights for CHP operators. Finally, numerical results on a CHP system of Portland General Electric demonstrate the effectiveness of the presented framework in determining proper carryover storage values to facilitate medium-term CHP planning.