Automation Workflow for Pumped Hydro Storage Reservoir Management

Pumped hydro storage is a capital-intensive, long-duration asset where value is captured by buying low-cost power to pump water uphill and selling high-cost power during generation cycles. Manual scheduling is suboptimal, leaving millions in annual revenue uncaptured. A custom workflow automates this by ingesting day-ahead and real-time price forecasts from ISOs, modeling reservoir hydrology and environmental constraints, and calculating the optimal sequence of operations to maximize arbitrage revenue while preserving asset integrity and complying with water rights.

Implementation integrates with market data APIs (e.g., CAISO, PJM), SCADA systems for dam control, and hydrological models. The orchestration engine, built on frameworks like LangGraph, runs continuous simulations to evaluate schedule NPV. Critical controls include approval gates for major operational shifts, real-time telemetry validation against commands, and exception routing for sensor faults or extreme weather alerts. This architecture transforms a manually intensive, reactive operation into a proactive, margin-optimizing system, directly improving the asset's return on investment by capturing more price spreads and reducing operational risk.

This workflow automates the complex, multi-day optimization of pumped hydro facilities, a critical long-duration storage asset. It eliminates manual analysis of volatile power markets, water levels, and environmental constraints. The operational upside comes from capturing higher price spreads, reducing water spillage, and improving asset utilization. The architecture integrates price forecasting agents, hydrological models, and a central orchestrator that issues commands to the plant's SCADA or Distributed Control System (DCS), translating forecasts into executable dispatch schedules.

Implementation requires deploying specialized agents for data ingestion, market simulation, and constraint validation, orchestrated via a framework like LangGraph. The orchestrator validates each proposed schedule against operational guardrails before submission. Critical controls include human-in-the-loop approval gates for major deviations, continuous reconciliation of actual vs. planned water usage, and comprehensive audit trails for regulatory compliance. This custom build shifts reservoir management from a reactive, daily task to a proactive, profit-optimizing operation.

Phase 1 establishes the data foundation, integrating SCADA, market price APIs, and hydrological models into a unified data lake. We deploy initial forecasting agents for day-ahead price and reservoir inflow predictions, validating accuracy against historical baselines. This phase delivers immediate value through enhanced situational awareness and manual decision support, de-risking the core data pipelines before layering on automated control.

Phase 2 builds and rigorously backtests the optimization engine, using linear programming and reinforcement learning to model pump/generate cycles against multi-day price curves and water constraints. We run millions of simulations to stress-test logic under extreme hydrological and market scenarios. Phase 3 integrates with the dam's PLC/control system via a secure, rate-limited API layer, beginning with a human-in-the-loop pilot where operators approve all automated setpoints before progressing to full, guarded autonomy with continuous observability.

Automating pump/generate cycles for revenue maximization introduces significant operational risk. The workflow must enforce hard safety constraints on reservoir levels, dam integrity, and environmental permits. A custom orchestration layer integrates long-horizon price forecasts from day-ahead markets with real-time SCADA data from dam control systems, but every dispatch command must pass through a multi-stage approval gate. The business case hinges on capturing arbitrage while eliminating human error in complex, time-sensitive calculations.

Implementation requires a state-aware orchestrator, likely built on LangGraph, that maintains a digital twin of reservoir volumes and equipment status. Control signals are routed through a hardened approval gateway that validates against maximum/minimum water levels, equipment maintenance schedules, and environmental flow requirements. Full observability is non-negotiable; every decision, its rationale, and overrides must be logged to a tamper-evident audit trail for regulators and internal governance. This architecture turns a high-risk manual process into a scalable, defensible operating model.