A methodology to evaluate the uncertainties used to perform security assessment for branch overloads. (November 2019)

• Record Type: Journal Article
• Authors: Vasconcelos, M.H.; Gonçalves, C.; Meirinhos, J.; Omont, N.; Pitto, A.; Ceresa, G.
• Is Part Of: International journal of electrical power & energy systems. Volume 112(2019)
• Page Start: 169
• Page End: 177
• Publication Date: 2019-11
• Subjects: Forecast uncertainty -- Online security assessment -- Performance evaluation -- Probabilistic forecasting -- Power system operation; CRPS Continuous Ranked Probability Score -- DACF Day-Ahead Congestion Forecasts -- DF Deterministic Forecast -- ES Energy Score -- FA False Alarm -- MA Missed Alarm -- MCLA Monte Carlo Like Approach -- Qp marginal Quantile with probability p -- SM Security Margin -- SN Snapshot -- TSO Transmission System Operator; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.04.043
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A framework to evaluate and compare uncertainty forecasts of power system state. Comparing alternative uncertainty models for branch load security assessment. Assessing the benefits of using probabilistic over deterministic forecasts. The quality and scalability of the framework is applied on the French EHV/HV system. The added values of uncertainty forecasts are illustrated for a N-2 contingency. Abstract: This paper presents a generic framework to evaluate and compare the quality of the uncertainties provided by probabilistic forecasts of power system state when used to perform security assessment for branch overloads. Besides exploiting advanced univariate and multivariate metrics that are traditionally used in weather prediction, the evaluation is complemented by assessing the benefits from exploiting probabilistic forecasts over the current practices of using deterministic forecasts of the system operating conditions. Another important feature of this framework is the provision of parameters tuning when applying flow probabilistic forecasts to perform security assessment for branch overloads. The quality and scalability of this framework is demonstrated and validated on recent historical data of the French transmission system. Although being developed to address branch overload problems, with proper adaptations, this work can be extended to other power system security problems.