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Dynamic line rating (DLR) models the transmission capacity of overhead lines as a function of ambient conditions. It takes advantage of the physical thermal property of overhead line conductors, thus making DLR less conservative compared to the traditional worst-case oriented nominal line rating (NLR). Employing DLR brings potential benefits for grid integration of variable Renewable Energy Sources (RES), such as wind and solar energy. In this paper, we reproduce weather conditions from renewable feed-ins and local temperature records, and calculate DLR in accordance with the RES feed-in and load demand data step. Simulations with high time resolution, using a predictive dispatch optimization and the Power Node modeling framework, of a six-node benchmark power system loosely based on the German power system are performed for the current situation, using actual wind and PV feed-in data. The integration capability of DLR under high RES production shares is inspected through simulations with scaled-up RES profiles and reduced dispatchable generation capacity. The simulation result demonstrates a comparison between DLR and NLR in terms of reductions in RES generation curtailments and load shedding, while discussions on the practicality of adopting DLR in the current power system is given in the end.

This paper presents a new and efficient method based on evolutionary algorithms to address the multi-stage (dynamic) planning of open loop structured distribution networks under uncertainty taking into account distributed generation connected to distribution system. The proposed model can cope with important features implicit in planning studies such as time-phased representation and uncertainty in loads, distributed generation and prices. As European distribution system in urban and suburban areas are operated as open loops, besides using modern reliability indexes as optimization attributes, the traditional approach of contingency coverage perspective in the horizon of study period is introduced as one of the constraints in the model of the problem. Besides optimal radial layout along several stages in time, the algorithm can determine the optimal locations of reserve feeders that achieve the best network reliability with the lowest expansion and operational costs. The uncertainty is modelled using fuzzy numbers. The model and evolutionary algorithms have been applied intensively to real life power distribution systems showing its potential applicability to large scale systems. Results have illustrated the significant influence of the uncertainties in the optimal distribution network planning mainly in terms of topology and supply capacity of the resulting optimal distribution system.