The increasing interest in demand-side management (DSM) as part of the energy cost optimization calls for effective methods to determine representative electricity prices for energy optimization and scheduling investigations. We propose a practical method to construct price profiles of day-ahead (DA) and intraday (ID) electricity spot markets. We construct single-day and single-week price profiles based on historical market time series to provide ready-to-use price data sets. Our method accounts for dominant mechanisms in price variation to preserve critical statistical features (e.g., mean and standard deviation) and transient patterns in the constructed profiles. Unlike common scenario generation approaches, the method is deterministic, with few degrees of freedom and minimal application effort. Our method ensures consistency between ID and DA price profiles when both are considered and introduces profile scaling to enable multiple scenario generation. Finally, we compare the constructed profiles to clustering techniques in a DSM case study, noting similar cost results.

翻译：作为能源成本优化中需求侧管理日益受到关注的一部分，确定具有代表性的电价以用于能源优化与调度研究需要有效的方法。我们提出了一种构建日前与日内电力现货市场价格曲线的实用方法。基于历史市场时间序列，我们构建了单日与单周价格曲线，以提供可直接使用的价格数据集。我们的方法考虑了价格波动的主导机制，以保留所构建曲线中的关键统计特征（如均值与标准差）和瞬态模式。与常见的场景生成方法不同，该方法具有确定性，自由度少且应用工作量极小。当同时考虑日内与日前价格曲线时，我们的方法确保了两者间的一致性，并引入了曲线缩放以实现多场景生成。最后，我们在一个需求侧管理案例研究中将所构建的曲线与聚类技术进行了比较，并指出了相似的成本结果。