{"id":1014686,"date":"2026-07-01T08:56:16","date_gmt":"2026-07-01T11:56:16","guid":{"rendered":"https:\/\/www.psr-inc.com\/analytics-report\/post\/assessing-the-capability-of-agentic-ai-in-a-stochastic-hydrothermal-scheduling-case-study\/"},"modified":"2026-07-10T11:25:59","modified_gmt":"2026-07-10T14:25:59","slug":"assessing-the-capability-of-agentic-ai-in-a-stochastic-hydrothermal-scheduling-case-study","status":"publish","type":"analytics_post","link":"https:\/\/www.psr-inc.com\/en\/analytics-report\/post\/assessing-the-capability-of-agentic-ai-in-a-stochastic-hydrothermal-scheduling-case-study\/","title":{"rendered":"Assessing the capability of agentic AI in a Stochastic Hydrothermal Scheduling case study"},"content":{"rendered":"\n
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Abstract<\/h2>\n\n

Agentic artificial intelligence is emerging as a paradigm in which reasoning-capable models interact with external tools, simulations, and data sources to perform multi-step analytical tasks. For energy-system applications, this raises the question of whether AI agents can operate effectively within structured analytical environments using domain-specific capabilities to explore complex decision problems, rather than merely as wrappers around existing solvers.<\/span><\/p>\n\n

This paper investigates this question through a stochastic hydrothermal scheduling case study, which combines uncertainty, intertemporal coupling, physical constraints, and economically meaningful operational trade-offs while offering reliable optimization benchmarks such as stochastic dual dynamic programming (SDDP). We propose a capability-driven agentic architecture in which a reasoning agent interacts with the hydrothermal model only through controlled, typed domain capabilities, without access to pre-programmed dynamic programming methods, precomputed water values, dual variables, or internal solver information.<\/p>\n\n

The architecture is evaluated on a simplified but structurally realistic representation of the Brazilian Interconnected Power System. Across ten independent sessions, the agent inferred water-value logic from simulation feedback, constructed approximate future-cost representations, and deployed storage-preserving policies that substantially improved upon myopic behavior. Relative to an independently computed SDDP benchmark, the best session achieved a 1.7% mean cost gap, while the ten-session average gap was approximately 6.0%.<\/p>\n\n

These results indicate that structured capability orchestration can enable reasoning agents to function as analytical layers embedded in energy-system modeling environments, most valuably for problems where analytical formulations are incomplete, difficult to specify, or insufficient to capture the full solution-exploration process.<\/p>\n\n

Keywords:<\/span><\/i> agentic AI, hydrothermal scheduling, stochastic optimization, water value, SDDP<\/span><\/p>\n\n