Multi-agent reinforcement learning for reservoir cascade release scheduling: a climate-analytics evaluation of downstream low-flow equity under forecast uncertainty
Year: 2026
Type: article
Abstract: Cascade reservoir operators must release water under uncertain inflow forecasts while satisfying competing demands for hydropower, irrigation and downstream environmental flows. The framework is formalised as a constrained Markov game with a lexicographic objective: maximise energy revenue subject to a probabilistic low-flow equity constraint at downstream reaches. A decomposed soft actor-critic architecture with a centralised equity critic is proposed, and a formal analysis establishes conditions under which the learned joint policy satisfies the equity constraint with high probability. The framework is evaluated conceptually against rule-based and single-agent benchmarks using a stylised three-reservoir cascade, with forecast uncertainty represented by ensemble inflow scenarios. The analysis identifies a structural trade-off between revenue and equity that widens as forecast uncertainty increases, and shows that equity-aware credit assignment reduces the frequency of severe low-flow violations relative to revenue-only learning. The contribution is a formalisation and algorithmic design for equity-aware MARL in cascade scheduling, together with a set of testable propositions about the conditions under which decentralised learning can deliver equitable low-flow outcomes.
Institution: Addis Ababa University
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223307669