论文标题
能源市场的竞标和日程安排：我们需要哪种概率预测？
Bidding and Scheduling in Energy Markets: Which Probabilistic Forecast Do We Need?
论文作者
论文摘要
概率预测与随机编程结合使用是处理未来能源系统中日益确定的不确定性的关键工具。源自能源市场最佳计划和竞标的一般随机编程公式，我们研究了几个常见的特殊实例，其中包含不确定的负载，能源价格和可变的可再生能源。我们对每种设置进行分析，无论是否仅一个预期值预测，边缘或双变量预测分布，还是需要完整的联合预测分布。为了优化市场计划，我们发现几乎所有情况下的预期价格预测都足够，而经常需要可再生能源生产和需求的边际分布。为了优化竞标曲线，除了特定情况外，需要成对或完整的联合分布。这项工作可帮助从业者选择最简单的预测类型，这些预测仍然可以为他们的问题带来最好的理论上可能的结果，研究人员将专注于最相关的实例。
Probabilistic forecasting in combination with stochastic programming is a key tool for handling the growing uncertainties in future energy systems. Derived from a general stochastic programming formulation for the optimal scheduling and bidding in energy markets we examine several common special instances containing uncertain loads, energy prices, and variable renewable energies. We analyze for each setup whether only an expected value forecast, marginal or bivariate predictive distributions, or the full joint predictive distribution is required. For market schedule optimization, we find that expected price forecasts are sufficient in almost all cases, while the marginal distributions of renewable energy production and demand are often required. For bidding curve optimization, pairwise or full joint distributions are necessary except for specific cases. This work helps practitioners choose the simplest type of forecast that can still achieve the best theoretically possible result for their problem and researchers to focus on the most relevant instances.
