在电力市场中基于能源消费预测误差最小化采购成本的期望值
Minimising the expectation value of the procurement cost in electricity markets based on the prediction error of energy consumption
AI总结：
本文研究了在日前和日内两个主流现货市场中，基于已知的单位价格期望值和两个市场中电力需求交易预测误差分布，最小化电力采购成本的期望值问题，通过优化两个参数来最小化总电力成本的期望值，并通过实际数据验证了所提采购方法的实用性。
摘要：
在本文中，我们提出了一种方法，用于在日前提前和日内两个主流现货市场中，基于已知的单位价格期望值和两个市场中电力需求交易预测误差分布，最小化电力采购成本的期望值。总电力成本的期望值通过优化两个参数来最小化，这两个参数仅取决于电力的期望单位价格和两个市场中电力需求交易预测误差分布。也就是说，即使不知道电力需求的预测，也可以确定两个参数的值，以最小化日前提前和日内两个主流现货市场中电力采购成本的期望值。我们通过数值分析证明，估计两个参数通常会导致总电力成本的方差较小，并通过实际数据的分析展示了所提采购方法的实用性。
英文摘要：
In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in two markets are known. The expectation value of the total electricity cost is minimised over two parameters that change the amounts of electricity. Two parameters depend only on the expected unit prices of electricity and the distributions of prediction errors for the electricity demand traded in two markets. That is, even if we do not know the predictions for the electricity demand, we can determine the values of two parameters that minimise the expectation value of the procurement cost of electricity in two popular spot markets. We demonstrate numerically that the estimate of two parameters often results in a small variance of the total electricity cost, and illustrate the usefulness of the proposed procurement method through the analysis of actual data.
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