How optimistic inflow forecasts distort dispatch, prices, and contracts in hydro-dominated power systems: evidence from Brazil

2026-07-01

AI Summary

This study addresses the systemic consequences of optimistic inflow forecasts in hydro-dominated power systems, demonstrating how such biases distort operational scheduling, amplify price volatility, heighten contract risk, reduce efficiency, and weaken market incentives. Integrating theoretical analysis, official Brazilian operational and planning data, and stochastic dual dynamic programming (SDDP) experiments with bias-corrected inflow scenarios, the research reveals that forecast errors are not merely statistical inaccuracies but pivotal drivers of suboptimal reservoir energy storage, delayed thermal unit commitment, exacerbated spot price spikes, and elevated operating costs. Furthermore, the findings indicate that these biases significantly dampen hydropower producers’ willingness to enter into forward contracts, offering broadly applicable policy insights for other hydro-rich electricity markets.

Abstract

Centralized hydrothermal planning models determine generation schedules and electricity spot prices based on inflow forecasts in audited-cost power systems, such as those prevalent in Latin America, and provide operational benchmarks and decision support in hydro-dominated competitive electricity markets. Consequently, biased forecasts can propagate directly into both operational decisions and market outcomes. This paper studies how persistent optimistic inflow-forecast bias propagates through the Brazilian hydrothermal power system and market. For a stylized hydrothermal model, we show analytically that optimistic bias weakly reduces water values and weakly increases first-stage hydro discharge relative to the unbiased optimum, thereby lowering reservoir storage and postponing thermal commitment. Using official Brazilian planning and operational data, we provide empirical evidence consistent with this mechanism. We then conduct a controlled SDDP experiment to compare policies trained under biased and bias-corrected inflow-forecast processes, evaluating both under the same bias-corrected inflow scenarios. The policy trained under biased forecasts produces lower reservoir levels, delayed dry-season thermal dispatch, sharper spot-price peaks, higher reliability risk, and higher expected operating costs. Finally, we show that these distortions increase the price-quantity risk for hydropower producers and reduce their willingness to contract. The results indicate that inflow-forecast bias is not merely a statistical forecasting problem, but can be a source of operational inefficiency, reliability risk, and distorted market incentives in hydro-dominated power systems. We argue that the insights and policy implications drawn in this paper may be relevant beyond Brazil to other hydro-dominated systems and electricity markets that are increasingly reliant on energy storage.

Problem

• inflow forecast bias
• hydro-dominated power systems
• dispatch distortion
• spot prices
• contracting behavior

Innovation

• inflow forecast bias
• hydrothermal scheduling
• stochastic dual dynamic programming (SDDP)
• electricity market distortion
• hydropower contracting risk