Minimising the expectation value of the procurement cost in electricity markets based on the prediction error of energy consumption

TL;DR

This paper proposes a method to minimize the expected procurement cost of electricity in day-ahead and intra-day markets by optimizing two parameters based on known price expectations and prediction error distributions, without requiring demand forecasts.

Contribution

It introduces a novel approach to determine optimal procurement parameters using only statistical information, reducing reliance on demand predictions.

Findings

Numerical results show small variance in total electricity cost using the method.

The approach effectively reduces procurement costs in actual market data.

The method is applicable even with limited demand forecast information.

Abstract

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.