Chance-constrained dynamic thermal line rating of power system for enhancing stochastic renewable penetration

Abstract

The escalating electricity demand, accompanied by the growing prevalence of renewable energy sources—often situated in remote regions distant from load centers—is pushing power lines to function near their transfer capacity margins. This line congestion brings about suboptimal system operation, overloading risks, and renewable energy spillage. Nevertheless, the thermal capacity of power lines is inherently dynamic, denoted as dynamic thermal line rating (DTLR), and fluctuates with prevailing weather conditions. This study investigates the impact of DTLR, acting as remedial capacity, on bolstering renewable energy penetration, emphasizing its economic, environmental, and energy losses effects. By leveraging DTLR under real-time thermal balance equations, the untapped line loadability within safe operating thresholds is realized. A chance-constrained programming framework is applied to help cope with uncertainties and associated risks across varying confidence levels. The outcomes underscore the substantial benefits of DTLR for congestion relief, evidenced by a marked increase in renewable penetration from 30.7 % to 44.7 %. Additionally, operational expenses decline from $53,051 to $43,123 and emissions drop from 690.71 tCO2 to 554.08 tCO2 (a nearly 20 % reduction). This research provides critical insights for researchers owing to its thorough analysis and for grid operators striving to employ DTLR over conservative ratings.