WIT Press

Evaluation Of Coefficients For An Energy Security Indicators System


Free (open access)





Page Range

101 - 112




717 kb

Paper DOI



WIT Press


J. Augutis, R. Krikštolaitis, A. E. Lutynska, S. Pečiulytė & I. Žutautaitė


A sufficient security level of energy supply is vital to the functioning of modern economy since reliable supply is necessary to ensure industrial activities and satisfy population needs. In our previous papers (Lithuanian energy security level assessment based on indicator dependence (2011), Dynamic model for energy security level assessment (2012)) there was constructed a system of energy security indicators for the investigation of the Lithuanian energy security level. A security indicator is a special index which provides numerical values to important issues for the security of energy sector. Further, a dynamic indicator model, which includes interdependencies between indicators, was created for assessment of the energy security level. This model enables us to forecast the Lithuanian energy security level according to different development scenarios, such as a building of liquefied natural gas (LNG) terminal, electricity connection between Sweden and Lithuania (NordBalt), etc. Since technical parameters of new objects are not exactly known; their influence on indicators are expressed as random variables with known probabilistic distributions. A security indicators model based on historic data is adjusted by a probabilistic model of LNG or NordBalt influence on indicators using the Bayesian approach. The purpose of this paper is to evaluate the coefficients in a dynamic model of indicators for energy security level assessment. These coefficients are calculated in two ways: using algebraic and least square methods. This paper presents the dynamic indicator model, calculation methods of coefficients of indicators equations system and pilot calculations. Keywords: energy supply security, differential equations, Bayesian approach.


energy supply security, differential equations, Bayesian approach