Characterizing the Effect of Ensemble Streamflow Forecasts on the Operation of Coupled Water-Energy Systems
Abstract
Multi-sector modelling frameworks are fundamental platforms for exploring the complex interactions between the water and energy sectors. In a coupled water-energy system, there lie at least two sources of uncertainty. First, release decisions from hydropower reservoirs are often based on a forecast of water availability, which is directly influenced by hydro-meteorological variables. The inherent uncertainty of such forecasts could easily be propagated into the grid, especially if the power system is highly reliant on hydropower. Second, existing frameworks generally rely on the coupling between hydrologic models and power system models representing the broad spectrum of decisions made at the grid scale. Such coupling is typically unidirectional, meaning that it captures the dependence of electricity supply on water availability. But, by doing so, models neglect the feedback mechanisms between power and water systems: failing to do so may add uncertainty to vulnerability assessments and misguide the design of water-energy management strategies. To characterize the effect of such uncertainties, we developed a novel modelling approach that (i) hard-couples a reservoir system model and a power system model, and (ii) is subject to reservoir inflow forecasts with different levels of accuracy. We evaluate the framework on a real-world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance of both systems in terms of power production costs, CO2 emissions, and the amount of curtailed hydropower. Through this framework, we demonstrate that identifying and characterizing uncertainty within a coupled natural-human system allows us to better understand the complex interactions among the system components and facilitates a more efficient operation of the overall system.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMGC42M0873K