Model for Identifying the Impacts of Climate Change on the Operation of Hydroelectric Power Plants: Learning from Ant Colony Behaviour

Abstract

Brazilian hydropower plants have been facing increasing challenges during periods of hydrological instability associated with climate change, making adaptation imperative. One promising approach to achieving adaptation is to learn from nature, given its inherent capacity for adaptability and resilience to environmental change. Accordingly, this study proposes an integrated framework that combines Animal Decision-Making (ADM) theory with a successful example of natural adaptation, namely Amazon rainforest ant colonies. The proposed methodology comprises the following stages: (1) identification; (2) definition; (3) alternative generation; (4) solution selection; and (5) implementation and testing. A simulated case study and a real-world case study involving the Ponte de Pedra Hydropower Plant, located in the state of Mato Grosso, Brazil, were investigated. Human operators and ant colonies exhibited similar probabilities of shifting towards adaptation (human operators, pR = 0.7088; ant colonies, pR = 0.7091). However, the adaptation strategy adopted by the ant colonies proved to be more focused, concentrating on a smaller number of performance areas. Human operators identified Operation and Maintenance (O&M), Finance, and Plant as the most affected performance areas, with similar levels of importance (12%, 11%, and 11%, respectively). In contrast, the ant colonies prioritised O&M, Plant, and Environmental Impact, with corresponding importance levels of 18%, 13%, and 10%, respectively. Furthermore, the ant colonies demonstrated a dynamic and integrated decision-making strategy that prioritised different activities according to local environmental conditions. Future research should focus on evaluating, calibrating, and validating the proposed framework using historical data.