This paper explores the impact of CO 2 emission trading on capacity planning of electric power transmission systems. Two different models for annual emission costs are assumed. The CO 2 emission price is modeled as a probability density function in the transmission network planning problem.The Monte Carlo technique is deployed to simulate the CO 2 emission price volatility. The transmission network planning problem is formulated as a mixed-integer optimization whose objective is to minimize the sum of annual generator operating costs and annuitized transmission investment costs over different demand levels subject to N-1 network security constraints as well as operating limits on system components. The overall problem is formulated within the framework of a linear dc optimal power flow incorporating binary decision variables to model the lumpy nature of transmission investment. A linear model of losses is also proposed and included in the dc power flow model. The proposed approach can be used to determine the most probable optimal transmission capacity. The methodology is demonstrated through case studies simulated on the IEEE 24-bus network.

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This paper addresses the de-risking of real-time thermal ratings (RTTRs) for overhead lines to help build DNO business confidence in the adoption of the technology. Through the use of thermal state estimation with integrated sensors and graceful degradation algorithms, a costeffective RTTR system is being implemented across the 132kV network in North Wales. The system unlocks significant energy yields (up to 14,358 MWh in November 2012 for a single overhead line circuit) whilst minimising the risk of line temperature profile exceedence to a suitably low value.