发表机构
Pontifical Catholic University of Rio de Janeiro; Stanford University; PSR（里约热内卢天主教大学; 斯坦福大学; PSR）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究巴西水电系统中乐观入流预测偏差如何导致水库水位降低、热电机组延迟调度、现货价格尖峰、可靠性风险增加及运行成本上升，并削弱水电生产商的签约意愿。
AI 中文摘要
集中式水热规划模型根据入流预测确定发电计划和电力现货价格，这在审计成本电力系统中很常见，例如拉丁美洲普遍存在的系统，并为水电主导的竞争性电力市场提供运行基准和决策支持。因此，有偏的预测可以直接传播到运行决策和市场结果中。本文研究了持续乐观的入流预测偏差如何通过巴西水热电力系统和市场传播。对于一个典型的水热模型，我们分析表明，相对于无偏最优，乐观偏差会降低水价值并增加第一阶段的水电放水量，从而降低水库蓄水量并推迟火电机组启动。利用巴西官方规划和运行数据，我们提供了与这一机制一致的经验证据。然后，我们进行了一个受控的SDDP实验，比较在偏差和偏差校正的入流预测过程下训练的策略，并在相同的偏差校正入流情景下评估两者。在偏差预测下训练的策略产生更低的水库水位、延迟的旱季火电调度、更尖锐的现货价格峰值、更高的可靠性风险和更高的预期运行成本。最后，我们表明这些扭曲增加了水电生产商的价格-数量风险，并降低了他们的签约意愿。结果表明，入流预测偏差不仅仅是一个统计预测问题，而是水电主导电力系统中运行效率低下、可靠性风险和市场激励扭曲的根源。我们认为，本文得出的见解和政策含义可能不仅适用于巴西，也适用于其他水电主导的系统以及日益依赖储能的电力市场。
英文摘要
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.
