Methodology for Analyzing the Relationships Between Physical Parameters and Price Variables in Regional Demand for Wind Electricity (As Applied to the Latvian Aggregator)

Abstract

The present paper presents the methodology for the complex analysis of correlation and regression dependences of the relationship between the electricity price and the wind power generation in an electricity market of Latvia. The methodology is based on the adaptation of classical mathematical models in relation to the analysis of dependencies using the examples of official statistical data: average hourly consumption and the price of one MWh of electricity for the period 2014-2019; average hourly consumption and price per MWh of wind power in 2019 and similar indicators for peak consumption hours in 2019. The calculated data showed that the polynomial regression models are not suitable for the purposes of the analysis. The use of the sinusoidal dependence of wind power indicators on the number of the month of the year as a basic model for a correlation study is substantiated. A method has been developed for modifying this model for the case when the maximum and minimum outliers of the studied data do not have the same average deviations from the total average. The quantitative analysis has been chosen for assessment of research results. The developed models are recommended to be used as a real-time analytical tool for understanding and evaluating the business model of a regional electricity aggregator in response to supply and demand. The development of the functionality of this aggregator will increase the efficiency of the use of Latvian wind power and increase the level of its energy security. In 2021, the European Commission proposed a strengthening of the EU Energy Efficiency Directive to reduce net greenhouse gas emissions by at least 55% compared to 1990. Russia’s invasion of Ukraine exposed the vulnerability in European energy system and accelerated the need to increase its resilience and independence from Russian fossil fuels.